diff --git a/notebooks/2015-11-23-2d-tie-contact.ipynb b/notebooks/2015-11-23-2d-tie-contact.ipynb
index 69761a0..77c932a 100644
--- a/notebooks/2015-11-23-2d-tie-contact.ipynb
+++ b/notebooks/2015-11-23-2d-tie-contact.ipynb
@@ -867,7 +867,7 @@
     "using JuliaFEM.Preprocess: parse_aster_med_file\n",
     "using JuliaFEM.Core: PlaneStressLinearElasticityProblem, DirichletProblem,\n",
     "                     get_connectivity, Quad4, Tri3, Seg2, LinearSolver,\n",
-    "                     update!, get_elements, BiorthogonalBasis"
+    "                     update!, get_elements, BiorthogonalBasis, StandardBasis"
    ]
   },
   {
@@ -919,6 +919,7 @@
    ],
    "source": [
     "field_problem = PlaneStressLinearElasticityProblem()\n",
+    "basis = StandardBasis\n",
     "\n",
     "# field problems\n",
     "joo = Dict(:QU4 => Quad4, :TR3 => Tri3)\n",
@@ -942,7 +943,7 @@
     "end\n",
     "\n",
     "# boundary conditions\n",
-    "boundary_problem = DirichletProblem(\"displacement\", 2; basis=BiorthogonalBasis)\n",
+    "boundary_problem = DirichletProblem(\"displacement\", 2; basis=basis)\n",
     "\n",
     "for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
     "    eltype == :SE2 || continue\n",
@@ -974,8 +975,8 @@
     }
    ],
    "source": [
-    "using JuliaFEM.Core: ContactProblem, SmallSlidingContact, Element,\n",
-    "                     calculate_normal_tangential_coordinates!\n",
+    "using JuliaFEM.Core: ContactProblem, SmallSlidingContact, Element, BoundaryProblem,\n",
+    "                     calculate_normal_tangential_coordinates!, BoundaryAssembly\n",
     "\n",
     "mortar_surface = :UPPER_TO_LOWER\n",
     "slave_surface = :LOWER_TO_UPPER\n",
@@ -991,6 +992,24 @@
     "\n",
     "contact_problem = ContactProblem(\"slider between two half-beams\", \"displacement\", 2;\n",
     "                                 contact_type=SmallSlidingContact)\n",
+    "\n",
+    "import JuliaFEM.Core: postprocess_assembly!\n",
+    "\n",
+    "function JuliaFEM.Core.postprocess_assembly!{T}(assembly::BoundaryAssembly, problem::BoundaryProblem{ContactProblem{T}}, time::Real)\n",
+    "    info(\"postprocess contact assembly: remove stiffness in x direction on boundary.\")\n",
+    "    C1 = sparse(assembly.C1)\n",
+    "    C2 = sparse(assembly.C2)\n",
+    "    dim = size(C1)\n",
+    "    info(\"dim = $dim\")\n",
+    "    for i=1:2:dim[1]\n",
+    "        C1[i,:] = 0\n",
+    "        C2[i,:] = 0\n",
+    "    end\n",
+    "    assembly.C1 = C1\n",
+    "    assembly.C2 = C2\n",
+    "    info(\"postprocess contact assembly: done.\")\n",
+    "end\n",
+    "\n",
     "for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
     "    eltype == :SE2 || continue\n",
     "    elset == slave_surface || continue\n",
@@ -1036,669 +1055,12 @@
       "INFO: Assembling boundary problems...\n",
       "INFO: Assembling boundary 1: dirichlet boundary\n",
       "INFO: Assembling boundary 2: slider between two half-beams\n",
-      "INFO: slave dofs of element: [65,66,61,62]\n",
-      "INFO: normal dofs: [65]\n",
-      "INFO: tangent dofs: [61]\n",
-      "INFO: normal dofs: [66]\n",
-      "INFO: tangent dofs: [62]\n",
-      "INFO: normal dofs: [65]\n",
-      "INFO: tangent dofs: [61]\n",
-      "INFO: normal dofs: [66]\n",
-      "INFO: tangent dofs: [62]\n",
-      "INFO: normal dofs: [65]\n",
-      "INFO: tangent dofs: [61]\n",
-      "INFO: normal dofs: [66]\n",
-      "INFO: tangent dofs: [62]\n",
-      "INFO: normal dofs: [65]\n",
-      "INFO: tangent dofs: [61]\n",
-      "INFO: normal dofs: [66]\n",
-      "INFO: tangent dofs: [62]\n",
-      "INFO: normal dofs: [65]\n",
-      "INFO: tangent dofs: [61]\n",
-      "INFO: normal dofs: [66]\n",
-      "INFO: tangent dofs: [62]\n",
-      "INFO: slave dofs of element: [97,98,99,100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: normal dofs: [97]\n",
-      "INFO: tangent dofs: [99]\n",
-      "INFO: normal dofs: [98]\n",
-      "INFO: tangent dofs: [100]\n",
-      "INFO: slave dofs of element: [99,100,101,102]\n",
-      "INFO: normal dofs: [99]\n",
-      "INFO: tangent dofs: [101]\n",
-      "INFO: normal dofs: [100]\n",
-      "INFO: tangent dofs: [102]\n",
-      "INFO: normal dofs: [99]\n",
-      "INFO: tangent dofs: [101]\n",
-      "INFO: normal dofs: [100]\n",
-      "INFO: tangent dofs: [102]\n",
-      "INFO: normal dofs: [99]\n",
-      "INFO: tangent dofs: [101]\n",
-      "INFO: normal dofs: [100]\n",
-      "INFO: tangent dofs: [102]\n",
-      "INFO: normal dofs: [99]\n",
-      "INFO: tangent dofs: [101]\n",
-      "INFO: normal dofs: [100]\n",
-      "INFO: tangent dofs: [102]\n",
-      "INFO: normal dofs: [99]\n",
-      "INFO: tangent dofs: [101]\n",
-      "INFO: normal dofs: [100]\n",
-      "INFO: tangent dofs: [102]\n",
-      "INFO: slave dofs of element: [45,46,83,84]\n",
-      "INFO: normal dofs: [45]\n",
-      "INFO: tangent dofs: [83]\n",
-      "INFO: normal dofs: [46]\n",
-      "INFO: tangent dofs: [84]\n",
-      "INFO: normal dofs: [45]\n",
-      "INFO: tangent dofs: [83]\n",
-      "INFO: normal dofs: [46]\n",
-      "INFO: tangent dofs: [84]\n",
-      "INFO: normal dofs: [45]\n",
-      "INFO: tangent dofs: [83]\n",
-      "INFO: normal dofs: [46]\n",
-      "INFO: tangent dofs: [84]\n",
-      "INFO: normal dofs: [45]\n",
-      "INFO: tangent dofs: [83]\n",
-      "INFO: normal dofs: [46]\n",
-      "INFO: tangent dofs: [84]\n",
-      "INFO: normal dofs: [45]\n",
-      "INFO: tangent dofs: [83]\n",
-      "INFO: normal dofs: [46]\n",
-      "INFO: tangent dofs: [84]\n",
-      "INFO: slave dofs of element: [73,74,69,70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: normal dofs: [73]\n",
-      "INFO: tangent dofs: [69]\n",
-      "INFO: normal dofs: [74]\n",
-      "INFO: tangent dofs: [70]\n",
-      "INFO: slave dofs of element: [87,88,89,90]\n",
-      "INFO: normal dofs: [87]\n",
-      "INFO: tangent dofs: [89]\n",
-      "INFO: normal dofs: [88]\n",
-      "INFO: tangent dofs: [90]\n",
-      "INFO: normal dofs: [87]\n",
-      "INFO: tangent dofs: [89]\n",
-      "INFO: normal dofs: [88]\n",
-      "INFO: tangent dofs: [90]\n",
-      "INFO: normal dofs: [87]\n",
-      "INFO: tangent dofs: [89]\n",
-      "INFO: normal dofs: [88]\n",
-      "INFO: tangent dofs: [90]\n",
-      "INFO: normal dofs: [87]\n",
-      "INFO: tangent dofs: [89]\n",
-      "INFO: normal dofs: [88]\n",
-      "INFO: tangent dofs: [90]\n",
-      "INFO: normal dofs: [87]\n",
-      "INFO: tangent dofs: [89]\n",
-      "INFO: normal dofs: [88]\n",
-      "INFO: tangent dofs: [90]\n",
-      "INFO: slave dofs of element: [93,94,95,96]\n",
-      "INFO: normal dofs: [93]\n",
-      "INFO: tangent dofs: [95]\n",
-      "INFO: normal dofs: [94]\n",
-      "INFO: tangent dofs: [96]\n",
-      "INFO: normal dofs: [93]\n",
-      "INFO: tangent dofs: [95]\n",
-      "INFO: normal dofs: [94]\n",
-      "INFO: tangent dofs: [96]\n",
-      "INFO: normal dofs: [93]\n",
-      "INFO: tangent dofs: [95]\n",
-      "INFO: normal dofs: [94]\n",
-      "INFO: tangent dofs: [96]\n",
-      "INFO: normal dofs: [93]\n",
-      "INFO: tangent dofs: [95]\n",
-      "INFO: normal dofs: [94]\n",
-      "INFO: tangent dofs: [96]\n",
-      "INFO: normal dofs: [93]\n",
-      "INFO: tangent dofs: [95]\n",
-      "INFO: normal dofs: [94]\n",
-      "INFO: tangent dofs: [96]\n",
-      "INFO: slave dofs of element: [85,86,87,88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: normal dofs: [85]\n",
-      "INFO: tangent dofs: [87]\n",
-      "INFO: normal dofs: [86]\n",
-      "INFO: tangent dofs: [88]\n",
-      "INFO: slave dofs of element: [95,96,97,98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: normal dofs: [95]\n",
-      "INFO: tangent dofs: [97]\n",
-      "INFO: normal dofs: [96]\n",
-      "INFO: tangent dofs: [98]\n",
-      "INFO: slave dofs of element: [101,102,103,104]\n",
-      "INFO: normal dofs: [101]\n",
-      "INFO: tangent dofs: [103]\n",
-      "INFO: normal dofs: [102]\n",
-      "INFO: tangent dofs: [104]\n",
-      "INFO: normal dofs: [101]\n",
-      "INFO: tangent dofs: [103]\n",
-      "INFO: normal dofs: [102]\n",
-      "INFO: tangent dofs: [104]\n",
-      "INFO: normal dofs: [101]\n",
-      "INFO: tangent dofs: [103]\n",
-      "INFO: normal dofs: [102]\n",
-      "INFO: tangent dofs: [104]\n",
-      "INFO: normal dofs: [101]\n",
-      "INFO: tangent dofs: [103]\n",
-      "INFO: normal dofs: [102]\n",
-      "INFO: tangent dofs: [104]\n",
-      "INFO: normal dofs: [101]\n",
-      "INFO: tangent dofs: [103]\n",
-      "INFO: normal dofs: [102]\n",
-      "INFO: tangent dofs: [104]\n",
-      "INFO: slave dofs of element: [77,78,73,74]\n",
-      "INFO: normal dofs: [77]\n",
-      "INFO: tangent dofs: [73]\n",
-      "INFO: normal dofs: [78]\n",
-      "INFO: tangent dofs: [74]\n",
-      "INFO: normal dofs: [77]\n",
-      "INFO: tangent dofs: [73]\n",
-      "INFO: normal dofs: [78]\n",
-      "INFO: tangent dofs: [74]\n",
-      "INFO: normal dofs: [77]\n",
-      "INFO: tangent dofs: [73]\n",
-      "INFO: normal dofs: [78]\n",
-      "INFO: tangent dofs: [74]\n",
-      "INFO: normal dofs: [77]\n",
-      "INFO: tangent dofs: [73]\n",
-      "INFO: normal dofs: [78]\n",
-      "INFO: tangent dofs: [74]\n",
-      "INFO: normal dofs: [77]\n",
-      "INFO: tangent dofs: [73]\n",
-      "INFO: normal dofs: [78]\n",
-      "INFO: tangent dofs: [74]\n",
-      "INFO: slave dofs of element: [53,54,49,50]\n",
-      "INFO: normal dofs: [53]\n",
-      "INFO: tangent dofs: [49]\n",
-      "INFO: normal dofs: [54]\n",
-      "INFO: tangent dofs: [50]\n",
-      "INFO: normal dofs: [53]\n",
-      "INFO: tangent dofs: [49]\n",
-      "INFO: normal dofs: [54]\n",
-      "INFO: tangent dofs: [50]\n",
-      "INFO: normal dofs: [53]\n",
-      "INFO: tangent dofs: [49]\n",
-      "INFO: normal dofs: [54]\n",
-      "INFO: tangent dofs: [50]\n",
-      "INFO: normal dofs: [53]\n",
-      "INFO: tangent dofs: [49]\n",
-      "INFO: normal dofs: [54]\n",
-      "INFO: tangent dofs: [50]\n",
-      "INFO: normal dofs: [53]\n",
-      "INFO: tangent dofs: [49]\n",
-      "INFO: normal dofs: [54]\n",
-      "INFO: tangent dofs: [50]\n",
-      "INFO: slave dofs of element: [91,92,93,94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: normal dofs: [91]\n",
-      "INFO: tangent dofs: [93]\n",
-      "INFO: normal dofs: [92]\n",
-      "INFO: tangent dofs: [94]\n",
-      "INFO: slave dofs of element: [83,84,85,86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: normal dofs: [83]\n",
-      "INFO: tangent dofs: [85]\n",
-      "INFO: normal dofs: [84]\n",
-      "INFO: tangent dofs: [86]\n",
-      "INFO: slave dofs of element: [61,62,57,58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: normal dofs: [61]\n",
-      "INFO: tangent dofs: [57]\n",
-      "INFO: normal dofs: [62]\n",
-      "INFO: tangent dofs: [58]\n",
-      "INFO: slave dofs of element: [57,58,53,54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: normal dofs: [57]\n",
-      "INFO: tangent dofs: [53]\n",
-      "INFO: normal dofs: [58]\n",
-      "INFO: tangent dofs: [54]\n",
-      "INFO: slave dofs of element: [103,104,81,82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: normal dofs: [103]\n",
-      "INFO: tangent dofs: [81]\n",
-      "INFO: normal dofs: [104]\n",
-      "INFO: tangent dofs: [82]\n",
-      "INFO: slave dofs of element: [89,90,91,92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: normal dofs: [89]\n",
-      "INFO: tangent dofs: [91]\n",
-      "INFO: normal dofs: [90]\n",
-      "INFO: tangent dofs: [92]\n",
-      "INFO: slave dofs of element: [81,82,77,78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: normal dofs: [81]\n",
-      "INFO: tangent dofs: [77]\n",
-      "INFO: normal dofs: [82]\n",
-      "INFO: tangent dofs: [78]\n",
-      "INFO: slave dofs of element: [69,70,65,66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
-      "INFO: normal dofs: [69]\n",
-      "INFO: tangent dofs: [65]\n",
-      "INFO: normal dofs: [70]\n",
-      "INFO: tangent dofs: [66]\n",
+      "INFO: assemble: doing postprocess for problem JuliaFEM.Core.BoundaryProblem{JuliaFEM.Core.ContactProblem{JuliaFEM.Core.SmallSlidingContact}} assembly\n",
+      "INFO: postprocess contact assembly: remove stiffness in x direction on boundary.\n",
+      "INFO: dim = (104,156)\n",
+      "INFO: postprocess contact assembly: done.\n",
       "INFO: Solving system\n",
-      "INFO: UMFPACK: solved in 0.33138203620910645 seconds. norm = 104.1324449731285\n",
-      "INFO: timing info for iteration:\n"
+      "INFO: UMFPACK: solved in 0.3429999351501465 seconds. norm = 313.6918010692049\n"
      ]
     },
     {
@@ -1715,13 +1077,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: boundary assembly       : 0.9452250003814697\n",
-      "INFO: field assembly          : 1.2981817722320557\n",
-      "INFO: dump matrices to disk   : 9.5367431640625e-7\n",
-      "INFO: solve problem           : 0.45281195640563965\n",
-      "INFO: update element data     : 0.022320985794067383\n",
-      "INFO: non-linear iteration    : 2.7185611724853516\n",
-      "INFO: solver finished in 2.850562810897827 seconds.\n"
+      "INFO: timing info for iteration:\n",
+      "INFO: boundary assembly       : 1.373000144958496\n",
+      "INFO: field assembly          : 1.434999942779541\n",
+      "INFO: dump matrices to disk   : 0.0\n",
+      "INFO: solve problem           : 0.4829998016357422\n",
+      "INFO: update element data     : 0.016000032424926758\n",
+      "INFO: non-linear iteration    : 3.306999921798706\n",
+      "INFO: solver finished in 3.4630000591278076 seconds.\n"
      ]
     }
    ],
@@ -1746,22 +1109,22 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "data": {
+      "image/png": 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4OHDQoEG8deuWnUpqXRcuXGCXLl2IP64zQUFBvHr1qtkyBoOBAwcO1I6psLAwGgwGO5XYeoxGI+fOnctq1app+yMgIIBbt241W+7WrVv08PCgTqfj/v377VRa68rOzuaiRYsYEBBAvV6v3YMCAgK4ePFiZmdna8s+ek05f/48hwwZYnYdatOmTa79+CTZtWsXnZ2dCYBTpkzR3r979y5btWpFAKxTpw6vXbtmx1JaT3Z2NufOncvatWtr506NGjUYGxtrdq0wHSvr16/n9OnT6erqSgCsXLky169fb8dvYD8Pnz9r1qyht7c3AbBcuXKcOXMmFUWxdxGt4urVqwwKCtKOl/bt2zM1NZWk+T7Zt28fn3nmGe1+HBkZybt379q59Lb36HWWJHfv3s1evXpp11oArFKlCqOiopiWlmbH0lpXixYtCIBLly7N8/cXLlzgjBkz2LJlS+0aA4DOzs5s2rQpJ0+erB1rTyJTGzkpKcneRRHCLmxxDlg1kPbgwQP269ePzs7O/PLLL/NdLjo6mm5ubrx8+bL23jfffEOdTseVK1fm+7knJpBGMisri0uXLmX37t1ZoUIF7YKv0+lYu3Ztvvzyy9yzZ0/uytT9++S//kW++y7ZtSuvN2rELwGmdetGzptHbt9OHjjAB507c23fvhzq7s6dAD8HOLtKFe6eMYPcsoVctoxcuZJ86Oacy5kz5KxZZNOm5gE4Pz+yYUOyRQty6FBy48b813H7NpmRQd64YfEAXGpqKqtUqUIAbNOmDbOysnItk5mZSX9/fwJg69atn5hAgMFg4KxZs1izZk3t2KlWrRqnTZuW537IqzJmcvfuXcbGxtLX11dbV/ny5RkREfFEVcpGjx6t7aeCAmTbt29n3bp1tfMxODg4V5DpcZWRkcHevXtrwdP27dvz3LlzBX7m9OnTbNiwIQHQ1dWVixcvtlFprSstLY19+/alo6Oj9t1efvnlAoMeKSkpdHR0pLOzM8+fP2/D0lrP3bt3OW/ePDZq1Eg7LhwcHNi6dWuuWLGCRqMxz88VdE3ZsmULW7Vqpa3P3d2dERERuYLXj7OpU6dqjbSdO3fmuczEiRMLXeZxtH//fj733HN0cHDQHsL069ePp0+fznP5R48Vo9HI8ePHa0HI6tWrc8uWLbb8CnaX1/mzdOlSrT7o7u7OuLi4JyaglpGRwT59+mjXhBYtWvDkyZNmy+S1T3bt2qXVTRwdHTlq1Kgnph5XFAVdZ0ny0KFDHDBgAMuXL6/V37y8vDhkyJBc+/dxZqq/hYeHF/kzKSkpnDRpEps0aUInJydt/7i6urJVq1acOXMmL168aMVS25YE0sTT7rEPpE2YMIE6nY79+vXj+vXrc71MLl++TB8fHz7zzDP84IMPGBcXx0qVKrFFixa8f/9+vut/nANp2dnZXLt2LXv37q09eTQ11KtVq8aBAwdy69ataqVJUcjffst7RTk55Pr15I4d5ObN3D5/fu6b7MKFZK9e5B+VjeP//Ce3VK/OfQATAPZ2dmZEeLhZIDPfbWVnk2fPknv3kps2kR98QMbGkhMmkK++Sk6fTiYm5r+OjRvJ115TA3B165KNGpGtWpGdO6tlHD6cjIsj09OLtT8XL15MvV5PnU7HWbNmFbisoigcPHiwVsEoLHBQlm3bto1t27bVskVcXV3Zv39/njp1qsDPFVYZM7l27Rqjo6Pp4+NjFqCbOHHiY52d1bdvXwJgw4YNi1wJ37t3Lxs1aqSdp127dn1sj52srCyGh4drx02zZs2KfaNZu3Yt3d3dtSyb/DKOyzJTJma9evW047tu3bpcsWJFkRuse/bsoU6nY4UKFZiZmWnlEltHVlYWY2Nj6e/vrzVsHR0d2b59e65Zs6ZI+6Io15SsrCxOmTJFe+ABgH5+flywYEG+AbqyLjs7mx07diQA1qxZs9Age2JiohYwmjhxoo1KaXm3bt3i2LFjWalSJe1v2ahRI/7jH/8o9HjJ71gxGAwcPXq0FsyuXbv2ExVwLEh++0RRFC5YsEDLpi9fvvxj/fDi9u3bHDhwoHbvady4cb73joKuKVu3btUyxp2cnBgTE/PYXkOKo6h1N1LthREREUEvLy+zh6L9+/c379HymFm/fj0B0N/fv1SB5ePHj/O1116jv7+/9hDAFLTu0KED4+LimF7MtkhZIoE08bR77ANp3bp104Ibj770er3ZsmfOnGGvXr3o7u5OLy8vDh06lDdu3Chw/Y9TIM1oNHLLli0cMGCAWZchAPTx8WGfPn24fv16ZhsM5LlzavfJuXPJmBj15z//WaTt5LrJ5uSQ4eHktGm5ljXs28ek5s15yNGR8wDW+uPGtHLlSus99czIULuELlpEvv02OX48OWwYOWAA+fzzZO/e5KBBZH5ZIPfukXPmqMu3aUOlc2ceqVSJmwCudXLitchI8qOPyCJ0tYqPj6dOp6OjoyO3bdtm4S9qPefOnWN4eLgWxNDpdGzRogU3btxY5L9bcSpjJqdOneKgQYO07QJg/fr1OX/+fLMuXmWZ0WhkmzZtCIBdunQp0XF+7NgxtmzZUtsH7dq1e2ye9GZnZ3PMmDFaQ7VevXrct29fiddnNBo5atQorVH0/PPP8/bt25YrsJWkp6dz6NChLFeunBY0Cg4OZkpKSonWt2LFCgKgr6/vY9OYu337NqdPn24WRHRycmKnTp2KdS0xKe41JSkpib169dIyAxwcHNitW7fHqoGXkpKiNVL79OlT5H127do1rftjq1atHpsuaoqicP369WzWrJkWcK1QoQJHjx5drAZnYcfK3bt3OWzYMK1xW69ePbNhGp5Ehe0TRVEYGxurXbO8vLy4atUqG5ey5O7evcvIyEjtb/rMM88Ueu8pyjUlISFBC8y7uLhw6tSpT0zWXl5KUncj1YzrqKgos4cYbm5ufOGFF7h7924rldbyUlJS6ODgQDc3N4vWNRRF4b59+zhs2DD6+vpqdRpT8DEwMJCLFy9+rB6WSSBNPO0e+0CatZXlQJqiKNy1axcjIiJYq1YtrdIJgBUrVmRQUBBXrFjBuzdvkqmpalbZa6+pgaSuXdXXgAHkG2+Qhw6Rly4Vabu5brI//0zOnEkmJ+dXUHL3bl6KieFaPz/2/+Pm4eLiwtDQ0LKXcZOdTR49Ss6bx4zevbnD0ZFfAzzt6UmlWTPS35/s0IEcNSr/dZw+Ta5bR4aGMr1HD67S6zkP4M6uXdUg3LZtZEqKGvQrI+O/GQwGzp4922zsmapVq3LKlCklurGXtDJmsmfPHnbr1k1rBOv1erZu3ZoJCQllthJ79+5dLWgwaNCgUq/v9OnTWiYKoI6jdfjwYQuU1PIUReG0adPo4uKidZ2yZPD44sWLWoDS0dGxzI7pk5iYyObNm5udQ7Nnz7ZI8GvSpEkEwE6dOlmgpNaRkZHBN954g3Xq1NH2gYuLC7t27fpnBnQJlfSaoigKly1bpnW5NwUJxo4dW6azXteuXUsHBwfqdDrGx8cX+/OKonDQoEEEQE9PzzLd2ElLS+PAgQO1cYb0ej07derEPXv2lGh9RT1WMjMzOXjwYK1R26hRozJ7jS2tou4TRVE4depU7VpepUqVEt/HbcFgMHDkyJHawxtfX1/u2rWrSJ8tzjVl5cqVWlC7XLlyjI2NLZP3oNIqbd2NVHsBjRs3zmwoEBcXFwYGBnLLli1ldr8ZDAZWqlSJOp3O6hnwiqIwMTGRYWFhrFGjhlkbrlKlSuzZsydXrVpVprsVSyBNPO0kkFaIshZIO3jwIKOioli3bl2zpxmenp7s0qULFy5cmPsJytmz6rhjU6aQY8eqmVp79hS7a6NJrpvsnj3k/PmFB4RycsiTJ2lMSuLcuXPNbrB+fn5cvHhxmbq5zpo1S8tszNXNwWgkCwoupaWp48L16EG2a8f7jRrxJwcH/g/gdWdn5lSpQlaurHY7rVNHHfuta1c1U+7119Wx5zZtUoOTVs4+SUxMZPv27bXjycXFhSEhIYV23SyMJSpjpFrZWLNmDVu0aKFVNJydnRkUFFSqTCdLS09PZ+XKlQmA48ePt+i6z58/zx49emjf39/fv8QNTGuYP3++lkXo5eXFNWvWWG1biYmJWmPGx8enTDzpzsrK4sSJE1mxYkUtg7NTp05WyXwydRkeOnSoxdddUlevXmVMTIzZNb1cuXIMCgpiYkHd8IvJEteUa9euMSoqymx8n6ZNm3LdunVl6v4zbNgwrQtQaRt0K1as0DL3Fy1aZKESll5RJw4oieIeKxkZGQwJCdGusQEBAUzO7+HgY6q4+8RoNDImJkbrJlyjRo0yNZGH0WjkuHHjtIdtJSlfSa4pixYt0q4fHh4eXLBgQZm6dpSWpepuJhkZGZw6dao2DqwpM7lDhw5F7tZvK6aHdfbo2mw0Grlp0yb269fPLKsPUCdJCQkJ4caNG8tURroE0sTTTgJphdACacuX22X7ycnJHDt2LBs0aKA9bTOlS3du04YfjR7NjIQEtZvmDz/kvZLsbHUA/pwci5TJ7Cabk0O+917BY5YV4NSpUwwODtYqQk5OTuzTp0++AwjbQlZWlnYzrVKlisVm3FFOneLHderwW4DJDg78/fnnyQYN1GDb8OFknz5kx45qcM00yUKTJmo2YX5ycsibN4ud1Xb+/HlGRESYdd0MCAjg+vXrLVapsXRljFSfFs6ZM8esQubp6cnw8HC7zoyUmpqq7cv58+dbbTvXrl1jSEiIFvT09fW1a8Nm1apV2vhF7u7uJcqaKQlT9pvpmti+fXu7TM5w9OhRdunSRft7lC9fnjExMXlOvmEpiqKwWbNmBMA5c+ZYbTuFuXDhAseMGWM2jICbmxt79+7Nb775xirbtPQ1Zffu3Xz22WfNHiL079+/xN1vLeH27dvaRBuNGze2WDef06dPa4He4OBguzZeiztxQEmU9Fi5evUqe/bsqQXU2rRpY9fjwZJKuk8MBgNHjRqlXW/r1KlT5Iwva7Bkxlxpslzj4uK0+37FihXzndnxcWONuptJVlYWZ8+ezYYNG5pNNNOyZUsuWbLErkN4jBs3jgAYGhpqtzI8zGAw8B//+AdfeOEFszHodDodq1evzsGDB3P79u12vZZLIE087SSQVgjTDrJVWntaWhrfeOMNNmvWTHsKCICuzs4MbdiQG198kb+MG0e+/DLZvbuaxWQas+vMGZuU0ewme/OmOp7Y//5XqnUqv/3GT8eNo/9D3YFq1qzJuLg4mz592bt3rza9d//+/Ut/gzIayYMHyago9W/Vpw93du/OOn80Hs5HRZHBwdokDZqsLHWct8TE3L972N27ZLduZL16ubPa4uLIDRu0AGteT/+rVKnCyZMnW2VMBmtWxkg1A2zcuHFaFpjp+8TExDAjI8Mq28zLoUOH6OzsTJ1OZzbBiTXdvn2b4eHhWkO0WrVqXLt2rU22TZKfffaZFkBxcXHhjBkz7FKZu3XrFnv06KF1BRszZozVy2E0Gjl37lyzAFKzZs1sGtA0GAzaE+tNmzbZbLvnzp1jVFSU2Tnn4eHBkJAQHjx40Orbt9Y1xdSt/eGMuho1anDWrFk27VZz+PBh7f4TFRVl8fUbDAY+++yzWtdrW84eV5qJA0qitMfKhQsXGBgYqJW1U6dOj/2suaXdJ4+OQVa/fn2bZoXnNYbbypUrS7XO0u4TRVE4c+ZMrUuyt7e3Te/F1mDtupuJwWDgwoULGRAQoD3M0Ov1bNy4MePi4mw6ruPmzZsJqBMBlaUMuYdlZmZyyZIl7Nq1q1lGtV6vZ506dTh06FDu3bvXpuWXQJp42kkgrRDWDqRdvnyZb731Flu3bq3diE2ZWY0bN+b48eN5fuVKdTKA/v3VoIlpYP+1a8mkJHVwfBvKdZO9d08dB600fvhBDcgtWMBrixYx7MUXtaeNjo6ODAoK4vHjx0tf+ALExMRo21u3bp1lVnrkiDp+3F/+Qn7+uZodSHUmTCcnJ9YHeLFePXWm0ZK4f5/89FP1eMgnq+1SUBA7duxolnXRt2/fP7sqdeZcAAAgAElEQVSt3L1Lnj9v8bHabFUZI9XBYcPCwrRZxwB18Oi4uDirNoS3bt1KvV5PR0dHu3S1vHv3LkeOHKlldHp5eXHJkiVW297evXvp5+ennSfR0dFlYhKIQ4cOsVatWlqGojWOubS0NPbt21fLynB1dWVERIRdMuFINZDs5uZGvV5v1bFcTp8+zcjIyFyzsg0cOJDHjh2z2nbzYotrSmpqKkNDQ83G6urQoYPVM3AWLFhAnU5HBwcHqwfkZ8yYodUztmzZYrXtWGrigJKw1LGSlpbGDh06aMd+9+7dC599vIyy1D65ffs2Bw0aVKRZMS0hr1lFLdVF2VL7xGg0cvLkydoD8GrVqnHz5s0WKaOt2bLuZmI0Grly5Uq2bdtWu8fqdDrWr1+fM2fOtOoEQ2lpaXR0dGS5cuVs+hC2tNLT0zlv3jx27NjRbIIuBwcH1q9fn6NHj7b6PVoCaeJpJ4G0Qlg6kJaRkZHrwucOsJFez2f+uPDlqpCcPk3+3/+R339f8LhcNmK1m+ydO+oA/d26kQMGUNm0iSuXLjUbILpq1aqcOXOmRYMjGRkZbNCggdZlwaKV5N9/VycUyMP58+fp7e3NDwDur1mTigUz7y5cuMCosDB2KleODf6okDRr1oxr167N/bRq50416JZfVtvmzWrX4GKyR2WMVIM9PXr00Cq0er2eLVu2zPu7l8LSpUup0+no6upq97F0srOzOX78eK3xX758ec6ZM8di3zcpKYlNmjTR9mdERESZnAUwPj5eC8A3atSo1N19FUXhqlWrzGad9PPz44oVK8rEU+uTJ0/SwcGBrq6uFs0uSk5OZnh4uNYVEFAHPx4yZIhdZ4+15TVFURQmJCQwICBACwJ5enpyxIgRFg2eKorCXr16aYFwW3VR37t3r3a9GD16tEXXbemJA0rC0sfKqVOn2KpVK62B37t3b6sHAy3NGmNf9e3bVzs/mjdvbvF74dKlS7XrkLu7O+Pi4ix67bX0PsnOzmZ0dLT2cKtWrVrcvn27RdZtK/aqu5koisKNGzeyS5cuZj1zateuzUmTJvHatWsW21Z2dja9vLyo0+lsklVtTRcvXsw3MaNRo0YcP368xYfNkUCaeNpJIK0QpQ2kPZyKW6FCBZYH2AXgWID/8PDgd7Vq8eLgwcx5+23yxg0Ll946rH6TTUkhx49XAzrh4eSuXbz4v/8xIiJCS+nX6/Xs0qVLqW98W7du1Rrew4cPL1kF7ddfSzz+nMFg4KsNG3IpwK516pTqqVt2djbj4uLo6+ur3UArV67MSZMmFbzeq1fJZcvyz2pr2FDtnlqQs2dzZbWVhcrYunXr2KpVK+3JuZOTE7t37869e/eWat0zZ87Usits2T2qMKZuJqYgvZubG6dMmVLi7tFpaWls166d1njs27dvmZ7lkFTPqYEDB2plDgsLK3bgPT09nUOHDtWuN46Ojuzdu3eZHCtp+/btWhCmNMHNo0ePMjQ01KzLiLe3N4cNG1Zmvre9rim3b99mTEwMvb29tX3zzDPP8IMPPihVo/7y5cusXr261nXQ1tmdGRkZrF+/PgGwSZMmperib82JA0rCWsfKsWPH2LRpU+36MmDAAKtmy1iStfbJ1atX+fzzz2sBtXbt2pU6ILxmzRrtfHN1dbXaLM3W7C4+YsQIrRts3bp1rTZ2pKXZu+72qMTERD7//PPa/diU8TdmzJhSd7c2ZZsuWLDAQqUtO1JTUzl58uRcQwW5uLiwRYsWnDZtWqn3nwTSxNNOAmmFKG4g7dHBIXUA2wEcAvALV1ee8vFhRtOmVJ5/nhw5Up2d8f/+z6KTAVib6Sa7eeVK65b52DFy1Cg1oDZvHnnhgrZ9U3aMqbE3efLkYg3wrSgKIyMjtZtKsZ8YKgr5zTfkjBnk7NlkaYIpOTn8pEsXvvTHE9finoy7d+9mp06dtAqbs7Mzg4ODLXNSZ2WR//2v2v0zPzk5avDtkay2C+3a8QOAJ8LC1Jld7chgMHDevHlmmUUeHh4cPHhwsYMEI0aM0BqJZbUBpSgK58+frz3Jd3FxYXR0dJEbtFevXtXGHgPAwMDAMhUwLIrTp09rg7a7uroWaRauxMRENm/eXPveVapUYWxsbJnovlqQhQsXElBncy1OY3P//v3s27evWZfoKlWqcOTIkTx37pwVS1wyZaGBd/jwYfbo0UPrfuTk5MSePXsWe+iBxMRErXEzffp0K5W2cIqicOjQoVrgvbgzzeY3cYC9g6/WPlYOHjyoZcvr9XoOGTLEqpOMWIK198mFCxf43HPPadeSzp07F7uhvmnTJlatWlWry0yePNmq4+Rae59kZmYyPDxce5jXoEEDq8zmbEll4Tqbn3379jEkJMTsnuXt7c2hQ4cWO9tq0qRJBMC+fftaqbRlS1JSEqOjo+nv759r8rp27dpx9uzZxc72k0CaeNpJIK0QhQXSCp2uuG9fJr3yCo1Ll6oBl02b1IyrMjR9cXElJCRQD/DEkCHk/v3W3ZiikAcOkMuXk7NmqcG1P6SnpzMqKkq7oep0OrZr1467d+8ucJUXL17UxlNq3Lhx8TJs0tPV7K3Q0D8z5g4eLP04dcePM3nAAHrpdNTr9Vy1alWh32Ho0KH09PTUjrcmTZpYbeDmAuXkqH+XFSvUwOIfWW036tfnaYBZ3t7kiy8WvI5ffyX//W+rjNX2qIyMDI4fP16rrJvO1bFjxxbYVUdRFL7wwgvacVPWgysmK1as0AaHd3R05NChQ/PNPLl9+zYHDBigZRa0bt3a7g3i0lq7dq2WoVe7du1cXeezsrI4ceJELeio0+n47LPPPnbdPF577TUCYI8ePQpcbs+ePezVq5c2qD2gDjwfHR1d5oOlZamBZzQauXDhQrPgfOXKlYs0eYupAefi4lLo/cpW1q1bRwcHB+p0ukJng7X1xAElYatjZc+ePWbjRkZFRdklA68obLVPUlNT2b59e+3Y6NGjR6FDZmzfvl2rlzk5Odls/E1b7ZNbt26Z3VubNWtm9XF/S6osXWcLcvz4cQ4ePNhsCIIKFSpw0KBBhY4NtnXrVq1OUFauWba2f/9+jhgxgn5+flqgF1CHMOjSpQvj4+MLbR9JIE087SSQVohHA2mKojBx+3aO69uXr3p5cQLAEQAjoY4h07NnT65atcq8i80TdpFOSEhgfYAnQ0NJC45VUKCcHPLUKTVDKg9bt25ly5YtzQY0jo6OzpUxtGbNGu1JzKRJk4q+7SNHyDfeIHv0IIOCyKlTyaNHLfe3vX+fPHOGJ5OTteDYyJEjzRbJzs7m/PnztUo7APr4+HD8+PFlMjPKVBn7ZNUq8sqVghc+coSsXz/vsdqio9VupwkJahDagoG21NRUDhkyxCwg6efnx9mzZ5s1hoxGI1u2bEkA7Nq162NZ8UpISNBmJdTr9QwNDdUG1jUYDBw2bJiWVdKoUSOrDh5ta0ajkaNGjdIqi926deP69evZpUsX7b3y5ctz3LhxVpnB1laCgoIIgGPGjDF7PzExkT169DAbN6VWrVocP3683SZLKImy2sC7ePEiIyMjtYCtTqdjixYtcg02bjAY2LZtW60BZ8mxfizh3LlzWtC9W7duZplA9pw4oCRsfaxs27ZNu746OTlx7NixZe5hi633ycmTJ7X7pk6nY3BwcK5j5ZtvvmHdunUJqIOkjxgxwqaBSFvvk/T0dPbu3dvsYZWlx60qrbJ6nS1ISkoKhw0bZjajtLu7O/v06ZNrZtnz58/T0dGRrq6uZfLaZQ+KonDXrl0cMmQIa9asqR2fAFixYkUGBQVx2bJluYaPkECaeNrZ4hzQkSQeUydOnECbNm3wSvPmaHD5Mirfvo3aANwBOOj1yKlUCb+FhCBw6FB4BgXZu7g2sWHDBmx55RW8OWwY2q1dC+h09i6S5s6dO3jzzTexceNG3LlzBzqdDs2bN8fEiRPxySefYM+ePXB3d8euXbsQGBhY+ApJYPVqYP9+4PffgRdeAAYNAnx8rPYdfv31V7Rv3x4//PADmjVrhmnTpmH58uU4evQoFEWBs7Mzunfvjrlz56Jt27ZWK0dpbdiwAa+88goSEhIQGRlZ8MIGA3DwIHDmDJCWBly6BFy/Dty+DWRlAZmZgIMD4OoKeHurr+rVAV9fwN8faN4caN0a8PAocXkPHDiAOXPm4MCBA7h//z50Oh0CAgIwZMgQLFmyBNevX8eQIUOwYcMGPHjwADk5OWY/H31PURSz35HM83em9/N67+HfPfyeadmHf5qWefi9vJb94YcfsHv3bty5c0f77jqdDiTh4eGBwMBAVK9eXVvnoy9TGfMqw6M/H95+QS+Sef7/4Z+P/ruwF4Bc/87Jycnzb6/X66HX66HT6aDT6bR/P/zz0ZeDg4P2M6+Xo6Oj2b8ffTk5OWk/83o5Oztrv3d2djZ7ubi4mP3b9HJyckJERAQuXryIdu3awdXVFceOHUN2djYAwNfXFy+99BKmTp0KHytew6ylWNcUO/niiy8wZ84cJCUlgSTKlSuHkJAQhISEYMKECbhz5w769euHzz//HHq93t7FzeXBgwfo1asX/v3vf8PLywvvv/8+Pv/8c+zatQv37t2DXq9Hx44dERsbi549e9q7uPmy17HyySefYOLEibhx4wZcXFwQExODefPmwdHR0WZlyI+99snx48cxYsQIpKSkQK/X48UXX8SAAQMwb948pKWlQa/XIywsDB999BHKly9vs3IB9tsnly5dwvDhw/Htt98CADp27IiEhATUr1/fZmXIz+NwnS3IxYsX8e6772L79u24du0aAMDV1RWdOnXC66+/jlGjRuHOnTv45ptv0KNHDzuXtmx68OABtm3bhg0bNuDIkSNIT0/Xfufj44P27dsjIiICderUQdeuXZGUlITWrVvbscRC2IcpTmTNc+CJCKQtBNBMp0Omjw8qtG2LdlFRqNS1K1Clir2LaHOf/POfuPDqq2g7fTp6xsXZuzjAf/8L3LwJdOqkBlr+8PXXX+Ovf/0rkpOTtfdq1aqF2NhYuD60XGG8/vc/GF1dkVW1KmCjhg9JvPfee2Zlf+aZZ/DXv/4Vr776aplsgD0q38oYqQbHbtwAMjLUn7Vrq4G027fVZVxcgMqVgYoVgb17gRMngFq1gNOnATc3oE4d9W9+8ybwR5AAAwcC771X6nLn5ORg8+bNiI2NRVpaWqnX96TSPRRAN/3bFIh6+N8PvwBox+7Dgav8glcPB7EeDWjpdDotmPVogOvh3z3889KlS/jpp59yfZfy5cujfv36cHJygqIoWqDQ9PPhIGJeQcP8goAP/xvIHdizBU9PTwwdOhR/+9vfULFiRZts01oepwbe77//jrlz52LFihW4bbquAYiMjERwcLAdS1Y0W7duxdatW7X/e3t7IyYmBn/961+Ldf+0F3sfK6tXr8bUqVPxyy+/oFy5cnjjjTcQGxtr13u3vffJgQMH8Morr+Dy5cvae3369MH69evh5eVl8/IA9t8nP/30E4YOHYojR44AALp27YqPP/4YderUsXlZTOy9Tyzpxo0bmD9/PjZu3Ijr169r77/44osIDw+3Y8keL/fv38fRo0dx+PBh/Pjjj9rDOZMdO3agb9++diqdEPZji0Ca/R/DWcDPAH4lcSUjA1cPHYLHvXvo89tviIiIeCwqlZZULSUF1QH8WqmSvYuiunsXOHJEzRqrUQMYOhQbPv8ccXFxSElJMVv0ypUrGDVqlJ0KWjrnzp1DbGwsrly5gmnTppX9446EOwD3GzeAzz8HHB3/DJ7du6cu4+SkBsyqVwd69FADoVWqAOXLqxmACQmAnx8waRLQoAHw9ddAfLwaTNu4Uc1Iu3QJSEoC6tYtuDy7dwP/+Ie6voYNgRYtgJYtAWdnbZGffvoJb7/9Nnbs2IFff/0VgBrwycnJgV6vR3BwMHx8fPIM4pjeM/18+N8FvfdwVtOj75n+7+jomOfyjwaK9Hq9lgn16HumQNaHH36ITz75BPfv30eFChXg7++PK1eu4Pr163B0dMTEiRMxa9YsuLq6at/tcXfp0iW88MIL+Omnn1CpUiVs27YNgYGBOHDgAGJiYvDf//4XycnJCAgIwIcffli0bFULMmX53bt3T3tlZ2cjOztb+7fRaNTeu3//vvbz0denn36K77//Hnq9Hu7u7sjKysKyZcuwZcsW9OvXD2+++SbqFnauiFK5ceMG3nzzTWzZsgWZmZlmv9uwYQM2bNhgp5KV3K1bt/D+++8jPT0dc+fOtVvg43ExcuRIjBw5Eh988AHeeustzJkzB++99x5mzpyJqVOnPhHX1eI4c+YMJk2aZBZEA4A9e/Zg6tSp+PDDD8t+ncYK6tevj8OHD+PMmTMYPnw49u/fDz8/P/Ts2RPr1q1DtWrV7F3Ex9aBAwewYMEC7N+/H7/99huAP+tziYmJSExMtHMJnxw7d+6UQJoQVvJEBNIC5s6FNwDXzz5DvbQ03N+3D9/t24evX30VKT4+8OvUCS9HRmLgwIFlIoXfmqqeOYPvAbBGDXsXRfXss0CzZri9aBGuvv02Lowdi505OUgD0KVLF8ybNw+NGjXCypUrMW/ePNy9exf+/v6YPn06vG/cQKXLl5Hj5IQrZaSb5K4dO7Bt0ybc0+sxYcIEtGnTBr6+voiPj8dXX32F2bNnY86cOWjdujVmzpyJ/v3727vIalbY9etmWWZNvvoKbwCod/CgmjXYvr0aJGvQQP1ZpYqacZZX1+DMTODjj9X1jhgBVK2qvt+zp5qZ9uabQHQ08NZb6t+/KE9vb98GLl8GTp4EjEb1Pb0eDzw98eOvv+JwejpeNxiQDTV1PTo6Gm+//TaqVauGL774AgMHDsRXX32FXbt2lekuTXnJycnB9OnTsWTJEty7dw9eXl6YO3cuoqOjtWU2bNiAmJgYxMfHY9WqVXj//fcxfPhwO5baMmbPno133nkHOTk5iIyMxMcff6w1YgMDA3Hy5En88MMPeO2117B//3507doVderUwbvvvouIiAiblFGv12tdNUvTtWnhwoX4/vvv0ahRI5w5cwZ6vR4nTpzAvHnzsGfPHqxevRqrV69G5cqVJahmYTk5OVi1ahXee+89LZO1UqVKGDt2LObMmYOcnByMGDECO3fu1ALWLVu2tHOpc/u///s/rFy5Ejk5ORg0aBBCQ0PRtm1bLFq0CJs2bcLy5cuxfPlyNG3aFNOmTcPLL7/81AWFiiMmJgavv/464uPj8c4772D69Ol49913MWfOHMTExNi7eFb3aMZVt27dsG7dOtSpUweff/45YmJisHr1anz88ccYM2YMFi1a9MTXofPStGlTfPfddzhx4gRGjBiBPXv2oEaNGnjxxRfxz3/+UwLXRbRjxw4sXrwYhw8fhsFgAABUq1YNERERmD59Ojw9PfHSSy9h37596NChw1NxDhbXuXPn8O233yIlJQU3b97UhsRwcXFB7dq10bZtWwQGBqJ8+fJIS0vDO++8I0E0IazJaqOv2YBpELlNH3zw55v37/P3Eye4+7XXuK1mTc5zceEkgMEA6wGsXaMGhwwZwp07dz6Wg5IXKCuLPzdrxugyMhCpoihcuXKlNg19fYDL3Nx4vk4dPggNJbdvN5sQIDs7m8HduzMU4DpHR2a2akVGRakzRubk2PGbqN9l0KBBDAf4F3d3njt3Ls9lNm7cyBYtWmiDgbq7uzM8PDzP5S3OYCAvXSK/+4788kty3Try739XZ+2cNUudmXbpUvLTT7nnzTfZCOCny5cXb1KGmzfJ994jFy0i85sxKCODHDlSnTX1xInifYcHD2g8fZo7hg3jEi8vrgP4DcBDOh0H9e/PkydP5vmxw4cPs76DA3Vl5NgvCkVROH36dJYrV474Y0KUpUuXFrj87Nmz6eLiQgCsU6dOroF6Hxfnzp3TZlT09vbm4cOHC/1MRkYGw8LCtAlJvLy8OH/+/MfiOr5z507qdDp6eXnlGhDYJCkpiYMHD2b58uXNZpocOXIkz58/b+MSF19ZHAQ7OTmZwcHBdHZ21gZM79atW76zvu7atUs7v9544w0bl7Zgw4cPJwC6ubnlW/5vvvmGnTp10ibpcHV1ZWhoKNPS0mxc2oKVxWNFURTOmjVLux57eXkVOkO3Jdlyn1y8eJFdu3bVrjPPPvtsvnWUhIQEbdZ7FxcXTpkyxWbX3LJ4nJDkoUOH2LBhQ21yoLCwMJtNhFNW90leTHXizp07a9dg/DGZy8SJE/Od0MU0CUZB9aGnxalTpxgTE8NGjRrRyclJ24flypVjmzZtOGvWrHxn3ZXJBsTTTmbtLESegbSHKQr5v/8xa8sW7uvXj6tr1+bbzs4MBdgYoLNORz8/P44YMYL79++3beGt4d//5tVmzehr55vsuXPnGBoaqjVIHBwcGBQU9Od04snJ5GuvkT17ksuXk+fPq6/588k+fXjlmWf4D4ADAL45ZYrdvodJZmamFgyMaNyYxjffVINFhXxmypQprFq1qlnl4dEZJ0skO1udaTM5mdy2TQ2WvfeeGiybNYuMjSWXLCE3bSL37lXLmp5uNqNmiSpj2dlkfDz54YdkYZXG7Gxy61a1XEWcHW3btm3s2LGjNjulo6Mju3Tpwp07dxb+YUWhoUEDntPp+B+A55o0IV9/XQ347d1L3rlTpDLYgqIonDlzptZgq1ixIpcsWVLkz2dnZ3P48OFaY7l169a2CdRayPTp07WyR0VFFbtRZjAYOH78eG3/lStXjjExMTadTa44UlJS6OjoSBcXF168eLFInzEF1SpUqGAWVIuKiiqzQbWy0sDLysri1KlTtcY/APr6+nLBggVmM13m59q1a6xVqxYBsH379nY/rm7duqXdfxo1alSkWaANBgNjY2O1mSrxx0ywc+fOLROzVZaVYyUvRqORU6ZM0eovVapU4caNG62+XVvsk2vXrrFXr17ag742bdoUeVbKVatW0cvLS7vmzpo1y+oBtbJ8nJBq4Nr0QMjBwYHDhg3L90GJpZT1fWI0Grly5Uq2adNGe+il0+lYv359zpgxo0jXr7t377JChQrU6/V/thueEufOnePUqVPZvHlz7RoEgM7OzgwICOCUKVOK/GBEAmniaSeBtEIUGkh7WE4O+fPP5L//zTvvvsv93btzeOPGHO7szJYA3f5ouPv7+zM6OvrxvPB88gmPvPqqXW6yiqJw0aJF9PX11S78NWvWZFxcXN6NF0VRAy0REWRAAFm1qvozIoJcs4ZX1q9nZKVKbAmwR6NGzMwv+8nKkpOT6eHhQQAcM2YMaTSS775Lrl9f5HWcOnWKISEh2k1Rr9ezffv2TExMLPiDRiOZlUWmppJff01u2EAuXvxnwGzWLHLhQnLuXPX3J0+S166R9+8XWqZiV8ZMGYFnz5JFrSjm5BS6bFJSEkNCQrSgiE6nY5MmTbhs2bLiVdIVhdywgVkxMfzM2ZnfArzu5UX6+v75mju36OuzAlPGg5ubGwGwQoUKXLRoUYnXl56ezqCgIG2/9enTp0iVVHtJTU1lnTp1tMZpaSvIiqJw/vz5WuPO0dGRYWFhzCgkyG1Lt2/fpoeHB/V6PQ8dOlSidSQnJz8WQTV7N/C2bt3KNm3amGUDR0RE8MKFC8Vel6IoDAkJ0TJFU1JSrFDiwu3bt0+7No4cObJE60hJSWH//v3NHmw999xzds1mtfexUhTZ2dkcO3aslgVSs2ZNbtu2zWrbs+Y+uXXrFvv166edGwEBASWu4y5evFjLmvXw8LBqVvDjcJyQZGJiImvXrq3dh8aMGWO1gHVZ3CcGg4ELFy5kQECA9pBMp9OxcePGjIuLK1Fw8fTp03RwcKC7u7vNsv3s4erVq4yNjWXbtm21a73pOGrYsCHHjRvH5OTkEq1bAmniaSeBtEIUK5D2qF9+IX/6iVy9mr9MmMCvu3Th27Vr8zknJ1Z86AlA06ZNOXnyZKamplr+C1iSwUC+8w53z5pl05vs6dOnGRwcrFU2nZyc2KdPn7yfcmZnq4GYL75Quxl27UoGBZGBgWSdOuQ775CrVqlZTH/5C3MCA3nax4f7AK5wcGDqX/6i/n7TJvKrr8zXff9+kTOfimrNmjXU6/XU6/Vcu3btn794/301m66YQQtFUbh+/XoGBASYNfZeDg/nhWPHyFOn1G6smzapGWWxsWRCgvp67z01ePfVV2om2pUrpfq+xaqM/fgj+c9/kvfulXh7D7t8+TJHjhzJSpUqmQVdp0+fzqysrFKvPysrSwvYvDZwIJmYqAY///Ofgj/41VfkhAnksmXkgQNqENMCTF0y3d3dCYDly5dnfHy8xRofp06dYrNmzbRGcnR0dJEyb2xp0qRJ1Ol01Ol0jI6OtnjDa+PGjVoQX6fTsVu3bna/ZhuNRq1xtWbNGoussywH1ezRwLtw4QIjIiK0c0un07F169b87LPPLLL+RYsWUafT0cHBwWJ/w6KaOXMmdTodHR0duXnz5lKvT1EUrlmzho0aNdKOnUqVKjE6Opq3bPygqiwGA/JjMBg4cuRILbvG19eXu3fvtvh2rLFPMjMzGRYWpgU3GjZsWOKA/sMURWFcXJx23lWoUIEflKQeXojH6Tghyc2bN7NatWpa+2HixIkWvxeXlX2SlZXF2bNns2HDhlp91sHBgS1btuSSJUssEkhcu3atlon7pLh16xbj4+PZuXNn7SG96QF7vXr1GBUVlW/X/eKSQJp42kkgrRClCqQ9LCuLTEpSM37eeYfp0dHc3L07J9eowVp/VJ5M4420atWKM2fOLHIXHZtJTiZjY7npo4+sfpM1Go2cO3euWbcRPz8/Ll68OO8G8r176nhd77yjZlEtWULu2kXu26cGAI1GMiyMHDPmz88oippddeIE/z11Krs7OLA7wGW9epEff6y+HpaUpK47Lk7terhuHfm3v5ELFpBr16rbu3qV/JiQGlMAACAASURBVPXXIo0JNmrUKO2Ja66nQVevkj16kCtXFm/HKYrazTIlhb/t3Mm1ISFcVq4cZwGcBfC98uX5da9eNK5erY5xlpSkZlE+1CXTUopcGTtzRv27bdhQpEy3/Ny9e5dvvfWWFlgwdWkcPnx4iTJGCmM0GhkQEEAAfP7554sWuFmwgPT3/zODzc+PbN6cfP55day+v/+9WH8LRVE4d+5crbHh6elp1af3u3btYvXq1bWuN/Hx8VbZTnGcOnVKu07UqFEj3zHuLGX//v3a392UeWGpSmlxde7cmYD1xtpKTk5mWFiYWVDNx8eHUVFRdunqa6sGntFo5IIFC+jn56d97ypVqnDq1KkWCcQ/6tixY1qDZ+jQoRZf/6Oys7PZqVMnAmC1atWscn3MyMjgmDFjzI6dpk2bct26dTYZ/6qsBAOKIysrixEREVpQyt/f36JDglhyn9y9e5fDhw/XhkmoV68ev/nmGwuU0pxpmAJXV1cC6niXlgw4P47HCakGgHx8fLR2w4wZMyx2Xtlzn2RkZHDq1KmsW7eudt1wcnJihw4duGbNGqtcO6KiogiAkZGRFl+3LWRlZXHZsmXs0aMHK1asqO03nU7H2rVrMyIigrt377bKvpNAmnjaSSCtEBYLpD3s3j3y9Gny00/J6Ghy1iz+b9IkLurdm4G+vnT8oxJlyibq0KED4+LimJ6ebrkylMTx4+TmzVa9yR4/fpw9evTQKmcuLi4cOHCgeaPt/n0yLY08cuTP93JyyM8/J48ezX+A+p071Qy1fLqbXL16VRuLon79+rkHKb19W+3aeOiQGjTbvFkNzPXvT3bvrmaQmbpDzp6tdol8NEPpp5947+xZdgsIoBPABg0a5J9SPmUKOWCAGgR8VE6O+j1TU8nLl8nPPlPHgpsz588yzJ+vZnmtWcMfVq/miO7dWeGPwVj1ej07duxYtLHBSqhIx8mJE2pW3GeflSiYpygKV61aZZaB5+LiwuDevZlsoYyRwrZv6vrYvHnzoj0ZfvBAHa/vs8/UAOLw4WrWZEAA2bZt4UHYBw+0p/WmxreHhwfj4uJsNkDz0qVLtW1XrlyZW7dutcl2H6YoCqOjo7UstIkTJ9p0UoDU1FR269ZNO+58fX1tMs6RyciRIwmAwcHBNtlefkG1ESNG2CyoZu0G3qFDh9itWzft/uPs7Mzg4OASd3spjszMTDZt2pT4475grS7Up0+f1jJ1e/fubZPMUntMUPC4BkhItbt2aGiotr+aNGnCY8eOlXq9ltgn2dnZHDNmjJY9V7t27cKHkLAAo9HIyZMnawPKV61alZs2bSr1eh/n44Qkly1bpgVP3N3dOWfOnFLfB229Ty5fvsyYmBizB+cuLi4MDAzkli1bbHJfN2Xcryzuw2s7yM7O5rp16xgcHExvb29tn5kejISGhvKzzz6zybVdAmniaSeBtEJogbTly62zgQcPyHPnyB071EHWZ82i8u67PPnOO5zSrx/r1q6tVaZMXbYCAwO5ePFiu/Xpt/RN1mAwcMaMGWYDN/v7+3PVqlV/3kAvXlSDUgkJfwaLliwp3myQiqIGLSIjC/zc6NGjtUZUkbu7KAp544aaSZaaqs5iuXev2rX3Ielvv813XV05C+Dmxo2pTJ/+Z6bctGlqFtyxY+o6du0iO3VSM7XS0tQA3uefq1lqc+f+GTD77jty9Wq1O+vRo2qQJisrz1lIFUXh2rVr2axZMy0A4OHhwcjISItnJRR6nBw+rJZ/x45iz5i6Z88edu3aVavM6/V6tmvXjlu2bFEX+PFHdd0ff5x3INLCXn75ZS2YUqqMlUIy8pTsbKZXrcoTej13AFzl4MA9L7xAZccO9fizIUVROHnyZO1v0KBBA5sN2puUlKRNslG7dm27jS9Fqk/QBw8ebNOZPhctWqR1R7HHjKL2CqpZo4F369Ytjh07VhsHz3Qsr1ixwi771pSp7ObmZpEucg9buXIl9Xo9dTodFy5caNF1F4UtJyh43AMkpDpGZXBwsHavbtmyZakybkuzT4xGIydOnKgFsqpVq2aR7sDFlZ2dzejoaLNx5bZv317i9T0JxwlJxsfHaw+3PD09SzU2qi32SVpaGkeOHGlW73dzc+MLL7xglW7NhcnKyqKnpyf1er1NHpwUh9Fo5GeffcbQ0FCtW6/p5e3tzeDgYK5bt84uk7xIIE087SSQVgjTDrLJTTYnR80u+vprNUg0axb5n/9Q+eQT7nv/fb4cGsrq1atrlSpTo61nz55ctWqVzWb+stRNdv/+/ezcubMWKCxXrhxffvlldZplo1HN/po7lxw8mOzWjXz7bbU75eHDavfFYgZfSKrBqK5d1UkICrBr1y6tK0F4eLhFGlRbt25lOQcHegNc9eab6vfbuVPNXpswgRw2jJw8Wf3/9OlqppuXF1mjBjlxotqN9KOP1Fk0Dx9Wg0WZmSXbD1Sfek+aNImVK1fWjidfX1/GxcVZ5Iac73GiKOTSpeSMGeQ33xS5/CkpKRw8eLDWjdEUcI2Pj8+7vAcOqJleY8bkn6VoQZMnT9YqNvlNuV4aCxcupI+HB98CuFGv53lfXypNmqhdROvUIWvWJGvXVscDHDFCDczv3692bbairKwsDhw4ULsudenShVevXrXKthRF4ciRI6nT6ajX6zl16lSrbKckDAYDY2JizGb6HD9+vMWvy7t27aJOp6OXl5fVZ28rClsG1Sx171EUhevWrdMywAB1DKZRo0ZZ5dwtro0bN9LR0ZE6nY7z5s0r9foUReGgQYO0RnZZmKXO2hMUPCkBElLN2OnRo4d2rLZv375E2Xwl2SeKonDGjBlafcjHx8d8PFc7MRgMHDFihJY96ufnxz179hR7PU/ScWIaK9U02VClSpW4YsWKYq/HWvvk5MmTjIiIMHtoUb58eQ4YMMDiDw1KWj69Xk9PT0+rdOEvKkVRuHv3bkZERLBWrVpmbb6KFSuyR48eXLZsmV3LaCKBNPG0eyICab/99hvffvtt/n/2zjy8iXJ9//ck6b7vdGFvS4FSlrKUTUChBQqCgAIiKCoqHkTxcDiIoKIe+al4OIorRQURQUHEr6DsyCpS9qVAKRQo3em+pmny/v54OkmapmnaJk1S3s91zZV0Mss7M+9M+t65n+eJi4tjXl5eTBCEer/ok5KSWFxcHHN1dWXe3t5s5syZBiuwtaiQpkt+PmNJSSSeiOGC69ezqmPH2E9r17Jx48bVEkEAyuMybtw49sMPP5jN1tucL9mSkhK2YMGCWl+k3bp1o20VFlL46KZNJBotWcLYww8z9vrr5FoqLTXNASxeTOGEDYQSFhQUqHMhBQUFNSvR9qJFi9QhLQbzKaWlUdL6kSMZGziQsUceYax/f8ZiY8l5ZiZOnz7NxowZo/61WSqVskGDBjXrl0G9/USlojxgw4ZR2GkD5Obmsnnz5tX61TIgIIC98sorxiWvPnSIsdGjGZs8mVx9ZmblypXqEAtTJaJftWqVWqRwdnZmy5YtI2G3qorClFesYOyZZyi0ODqahDXtPGydOlEfmjKFsaVLKSeeGbh9+zaLiYlhAOXmmDp1qkmFnhMnTqhzwnTq1MkiObqMQalUshUrVpil0mdSUhKTyWTMwcHB+nJoMhqITJ06tZao5uPjYzJRrbkDPF3xRiKRsEGDBjVpAG5ukpOT1WE7sbGxTf4xJzMzU10ko1evXlYhvmpjrgIFrUkgEbl586Y6LyIANnTo0EY5yRtzTpRKJXvnnXfUP1x5enqyzz//vDnNNwtFRUVs6tSp6h9kw8PDGyXKtMZ+olQq2eLFi9Xip5+fH9vQiCrwpjwnR48eZRMnTlRXYRUNANOmTbM65xdj5NoFwCIjI1t0v0ePHmWzZ89mHTt2rBWF5OrqygYPHsxWrlzZ4gVbjIELaZz7nVYhpKWmpjJBEFiHDh3YiBEjmCAIbP369XWWS0tLY76+viwsLIytXr2avffee8zb25v16tWLVdUTUmVRIU2boiIK+fvuOxLU3nyTBLbNm1nZrVssISGBjRo1qlaVQkEQWFBQEJsyZQr79ddfm+6qKikhQa9GeGrKl+yuXbtY3759a4UTzn7qKU3et4oKEreWL2fsm28YO3KEsaysxoVuGktWFu3rzBmjFl+0aJG6qlpjf91TKBRs+PDh6hCEevPcpacz9vPPjE2bRqLZu+9SiCZjVBBh1iwS18yQzFcbpVLJ1q5dy7p166buR25ubmzmzJmNHrjX6SdKJTkBX3uNMQO5pORyOXv//ffV+erE/jJ16tSmiVMpKSQixcXVmx/PlGzYsIEJgsDs7e3Z8ePHm7ydTz75RJ37xMnJiZIJV1VRH1iyhLExY0iQnDqVqoGmpZFQuXEj5ee7fZvysC1ZQkJiv34ksGnnFjQDJ06cYJ07d2YAJQlevHhxsxydSqWSzZgxQy18vPnmm6ZrrJn5/vvv1dVdBUFgI0aMaHJeqIKCAubm5sYEQbBYcYPGYA5RrSnfPRUVFezNN99kQUFBtcIJly9fbpFQmMYgl8vZ0KFDGQAWGBjY6Gfwzp071aLh/PnzzdRK05Gbm8vmzJljkgIFrVEgEUlKSmJ9+/ZVP1dGjhxplAvY2HOyatUq5ubmpv7utYaiMg2Rl5fHJkyYoP4fMzIy0ijnZWvuJwqFgs2fP1/9A2lgYKBR1Yabe0727NnD4uLi1M448Uf+2bNnmzUvoqkQ/9+YPXu22fZx9uxZ9uKLL7Lw8HB1Wgjxf72+ffuy5cuXm83Zb0q4kMa532kVQppcLlcLFKdOnapXSJs7dy5zcXGh0MEa9u3bxwRBYGvWrNG7basR0rSpqKCwwA0bGHvxRRLVVq+mkNC0NFaQn8/+97//sQceeKDWr0ASiYS1b9+ezZo1i+3fv9/4f0z/+ouSoteEKBn7JVtQUMDmzp2r/qdYEAQ2qEcPdvC//6VE/QkJtVdISWGsvLwJJ6QJpKQ0Krn90aNH1f9YxsXFGeX2S09PV1c4NFjVcccOuoarVpFIoi/3XVkZuYmWLqWwziaGczaGgoICNn/+fLULSAz9fP/9940agNbqJwoFOQ2XL2fs4sU6yyqVSvbDDz+w6Oho9a9xdnZ2bMSIEWz//v2mOBjGXniBikLoVmM1A3v27GEymYxJpdJGJ2L+7LPP1IK4k5MTe/3f/2bKpCQSIVesYGzSJBLG3n+fxGDdfnX7NrnT9Dn+Kisbroy6cCFVEBVDQ5soNmzevFntynJ3d2cJuve7ERw5ckR9LsLDw81SXbAl0K30GRUV1SgxTKlUql1Fa9euNWNLzYMoqmlXFPPx8WFPPvlkowZWjRng/f7772zAgAG1EtxPmjTJZE7RlmTp0qXqZ+L27duNWmfBggUMoFyfLZEM3tQ0t0BBaxZIRM6ePcuioqLU/1/Fx8cbdL42dE6+/PJL9fPW2dmZLV++3CJ5AptDZmYmi4uLUwtqffr0YZcuXap3+fuhn1RUVLA5c+bUKhBhqNBUY8+JUqlkW7duZcOGDVML9+KPx/Pmzas15rIVRIesqcKYk5OT2cKFC1lkZKRa2ASoqEJUVBRbtGiR1brsDcGFNM79TqsQ0rRJTEysV0jz9/dnU6dOrTO/S5cubOTIkXq3Z5VCmjYKBWPXrjH266+MffABCTIrVzL222+UQ0upZNnZ2ezdd99lAwYMqJVfSiqVstDQUPb8888brgiVkFDLQdTQl+y2bdtYz549KY8RwB5ydWWbhwxhZf/9L7Xvrbdom3/+aR7HmZkoKytjgwYNUlvTL+oRhET279+v/odi6dKlhjecksLY5csNnwuVisTSN9+k69uC5y4xMZHFxcXVCv0cPHiwwZAosZ/8sG4dY+vWUZGIa9dqLXP06FEWGxurPleCILCoqCjzlDlXKMjp98orTSpw0FhOnz7NHBwcmCAIRokfX375pVp4cnR0ZIsWLSLB9u5duuaffsrYgQMklDV0bhYvZmzsWP2ibEPMns1YZKT+0NA33mDs2LFGbe69995Th5gEBwezfUa4KhUKBXv00UfVfe29995r/HFYIUlJSWzYsGG1Kn0aU3lOdCW9+uqrLdBK89IcUa2h75709HT21FNPqX/0EASB9ejRo0WrqZqLPXv2qJ+Tr7zySr3LlZWVsT59+qgHzLbgajBEUwsU3A8Cicjx48fVA3+JRMImT56stxhVfedkw4YN6jQhjo6OzXYRWwO3b99WRwMAYDExMXqfL/dTPykrK2MzZ85U55Xr1KmT3pyExpwTsXDVgAED1IUfALCOHTuyRYsWmSSVgSUpKipiLi4uTCqVGhRi6yMtLY0tW7aMRUdHq/Omij+GREREsJdeesngGMJW4EIa537nvhHS7t69ywRBYB9++GGddZ544gnm4+Ojd3tWL6Rpo1QydusWY7t2Mfa//5FocPw4hXddukRuFMbYrVu32NKlS1nv3r3VA1zxAd+1a1f2yiuvaL448vNpEH/hgno3+r5ks7Oz2VNPPcVcXV2ZE8CiAPZ2x44st29fCj8bPZoqbp4/T+4qG2bFihVMIpEwiUSiNxH0+++/zwRBYHZ2drWdAAUFJJg1l9Onydll4BdFc6FUKllCQgLr2rWrut+4u7uzJ598ss6vjt9//z1zBNhfzzzD2HvvUd9klOdl5syZtcJ32rdvz956662Wyd9z+jQ5rVqAmzdvqgf17777rt5lEhISmI+PD7MHWB87O/bGP/5R2/GoUlFxjcZw+zYVWmhG5S529y5jP/1EotykSYz17ctYhw7k7GskcrmcPfvss+p/4Hv27FmvM2jfvn3qvtGtWzeb/DW7IbKzs2tV+vTx8WEffPCB3sGrWEV49OjRFmipeWmsqKbvu0epVLLVq1ez0NDQWtuYP38+KygoaMnDMTvZ2dlqZ2J0dHSdQhanT59WP28eeeQRmxdDdNFXoGDIkCFNFgNaGwcPHlSH1UulUjZjxoxa36m652Tr1q1q17y9vT2bP3++2XLrWoqUlBQ2cOBA9bNh2LBhtUKk78d+UlBQwCZPnqx2e0ZERLATJ06oP6/vnMjlcrZ69WrWq1cv9Xe5IAisS5cubPny5VaR/N6UJCYmMolEwjw8PBr83zQ3N5etWLGCDRw4sI5hoXPnzuzZZ59tVqoPa4ULaZz7nftGSBPn6/uy/Ne//sUEQdCbJ82mhDRtVCoSbs6eZeyLL0gMe/ttErNOn6a8ZzUkJSWxBQsWsO7du9exHC9q25Yd7teP3dIa1Gh/yW7YsIF17dqV2QPsAYC97OLCdg8axOSvvUb7XbmSsRMnyA3Uirh48aI6EXRMTAwrKytjSqWSTZw4UT2QUxcnuHaNBIm33qLcVY0IKa2XGzfo+lqQvLw8Nm/ePPV5EH+NXLlyJVMoFOz7779nTgA7/txzrPjqVbZo0aJaeYp8fHzY888/b/OOiYbIzc1VF0uYN2+eev4333zDOnt7szEAmymTsd8HDmTKZcvofjEF771HIZ6mrEJYUkI5Bg2xfz9jc+ZQXz96tFZoaF5eHouNjVX3gbi4OHUCXblcziZMmMAAStBvC3l5mktFRQWbN29evZU+//e//6nDWlubKKLLxYsX2bRp0wyKatrfPYmJiWzUqFFqN4RMJmMPPfRQqxysaKNUKtkjjzzCAKo0KroaVq1apa5ma41J4U2JMQUK7keBRGTnzp3q3IwymYw9++yzrKKiQn1OFi5cyNq2bav+fM6cOS1W9d1SXLp0Se3UFASBxcbGsszMzPu6n+Tm5rJx48apHdI9evRgZ8+erXVOysrK2IoVK1jXrl3VwptEImE9evRgH330UavvN5999hkDqFCLNkVFReyTTz5hw4cPr/WjsCAIrF27dmzGjBls3759rf57mwtpnPud+0ZIO3z4MBMEgW3ZsqXOOsuWLWOCIOi1wosnaNOXX1LVSIWiRfJTmZyCAsp19u23JOi89RZja9eSwKNTCSYxMZG98MILLCwsjG0E2MqaLwhnZ2fWr18/NmbMGOYIsI41AxiJRMKGDRnCbsybR9s7e7aWUNdaUSgUbPTo0QygKo2iSNSvXz8mLyujnFazZpEb7+OPqViElSe3bionT55ksbGx6kGtj0TCRtU4J4ICA9X/qDk5ObEJEyZYZbUmc1JWVqZ2Cozo2ZM97+rKPgfYAUFg10JCWPWXX5J71JTiaF4eFVhYvtx02zSGb7+tHRrauTNjMTGMPfYYhYbu3MmSLlxQ5/aRSqUsNjaWubq6qt1q9RblaKXoq/Q5dOhQJggC8/LysrpKi+amPlFNdJaIrisxPGnVqlWtzknTEKtXr1Y7oyMjIxlAlRVbQ7hQY6ivQMHzzz9/3wokIrqOs+jo6FpOmZkzZ953z5bExET1/SIIAuvdu/d930/S09PZyJEja6UcAKhAgDhPJpOx6Oho9tVXX913z1oxzcSwYcNYXFyc+nta7EOBgYFs8uTJbPv27a1eONOFC2mc+52WuAcExhhDC3Hq1Cn0798f69atw6xZs+rM37BhA2bMmFFrnUWLFmHlypWQy+Wws7Or9dmZM2cQHR2NHX5+6Ovjg/z27aFwcYFKKgWTSKCSSnH9wQehdHQEAHimpcEvORkqiQRMIgETBDgVFsK+vBwqiQRKBwfkdOkCJpXSNqRSKJycUOrvr96nR1oaZHI5mCDQNiQSCIxBVbNOlasr5O7u9Z8ElQpShQJMEHCvoABHjh/HkAcegI+PDwBAKpfDPTMT3jdvwvPuXVS5uKDS3R3FwcEoCgxEpacnIAhwy8zEgK+/xu6oKKzNyMC9pCT45OcjFEBbACqZDEnx8Rg/YQLs7e0BxgBBaN4F1MO9e/dw9OhRDBkyBL6+vibfvqBUwiU3F6Vt2jRp/QMHDuCbb74BADw0aBD+06ULgs6fh315OYrbtMGlLl3wQ24uhg4bpr4Gpsbnxg0onJxQHBRklu0bew0YY0j8/Xd479iBnJISfAH6byM0NBQPP/ww+vTpY5b2NYTY/qFDh5rtGhhCVlkJ9/R07Fy5EjElJQgGUN6mDTzi45Hdsycqvb0Nrt/Ue6D9X3/B484dXI2PR5WrazOPonE45ufD7/p1eN26BfeMDDhmZwP5+YC3N44tWoQKb29cvHgRX3zxBYqLiwEAjz/+OMaOHdui7TQWcz+HRI4ePYoff/wRBQUFAID3338fwcHBJtm2pe+DppCWloZff/0VFy5cQHl5OQBAIpFg4MCBmDp1KrwbuHesDVNeg9TUVLz55ptQqVQIDg7GO++8Q9/FZqSl7oOmcPnyZWzduhUpKSkQ/+2cM2cOhg0bpl7GFu8BXRp7DY4cOYLvv/8eZWVlAIAOHTrg9ddfh5OTk7mbqhdr6EPJyclYs2YNsrKyAADPPvsshg8fbvT6tt6P9F2D3NxcfPnll7h27RoAQBAEhIaGYtSoURg4cCAEM/x/3xxa6howxvDMM8+gqqoKAODq6orQ0FAMHDgQAwYMgEwma9J2reE+aC7Jycl4++23sWPHDsTHx1u6ORxOiyPqRKdPnzbbGNcqhLT09HS0bdsWH3zwARYuXFhrnZkzZ2LXrl3Izc2tsz3xBPkDkACoBAkDAgB/AIEAjgGorlm+PYDuAKQ1y0tr/u4AQFaz7uWa97KaZVIBfKe1zycAtNPaBgAMBmBXs/4tAFdq9ilOhwAk1SzrBuBVneNwBOBXs6wbgEIARQAUADJrlvEE4FAz/yqAIQDGANgHoEfNPssB3Kh5fwVAcZ0zZnv0BDABwFcAspu4DS8AAwD0BhANIBnADwAumaKBRjAFQDcAewCcaKF96iMQ1H9LAGwAUGbBtjQWf9C12w1AZcLtOgNYCLp3UwHcAd07dZ82pkcG4KWaff7cAvtrCCcAYaBnoLKeZSQA/gMgA8DfAE4bWJbD4XA4HA6HYznmzp2Lzz//3NLN4HDMyqZNm7Bp06Za84qKinD48GGzCmlNk+pNTHBwMPz8/JCYmFjns5MnT6JXr14G15/2+OPoX49TYk4j29JT+w+VCuGMIVYq1b8wYxAYgzwtDUqFAhKFAsEAgiUSCEolJCoVBKUSo4KDUV7zi4ZQXQ23nBwIKhVO/f03fv3lFzwdG4tBACQqFfyvXIGkuhp5nTtDolKh0s0NVa6uSIqPh1NxMdwzMvBERgb8L1yA/82b6OzmBgFAb29vVDk7o0KlQmZeHnyDgqAKCoLC0bGWK08fzvfuwaGsDEqZDCo7O3qVydR/q2Syet1shw4dQkJCQqN/MTQWQaVC9+3b8ZhcjnPTpzd+AyoVwnbsQElhIeQ9e6IkPBztnJywuObjP//8E2vXrq3zy7hJYQxtLl/G5GvXkNepEzKiooD6+lQTMOYauOTmosNff6HSzQ23Bg9GRHGx1fzaZsw18Lh7F+3+/hsflZfj0sMPo8rDo9H7sS8rAxir4/5yz8hAmY8Pujg4QC6XIyMjA0FBQXBwcDBqu825B9zT0yFRqfBI27aNWs/UiNdgwJw5WKRzDbTPiVdJCYZ88gmciooAAEwqRYW7O8p9fVEUFIT8Dh2QHREBpbNzi7bf3M8hXZrSTxqiRZ5FZmTHjh3YvHkzpk+fbrO/fpv6Gpijnxiipe+DplKfW8XW7wGg6dfAWlxUYvut4Ro01RVk6/3IUB+yln7SEC15DczxnLWVZ6khrl27hnfeecdmv485nMYwffp0TNfRCUTDlTmxCiENACZPnoz169fj7t27CAkJAQDs378f169fxz//+U+D6w4aOBBTdUJCbYGijRtx7pdf4D1rFrrOmAFUVwPLlgH9+8NjwgRAoaB51dWIcnfXiFmMATk5wJkzcHBzA3Jz4V5UBBQVAcXF6FJcDJSWAg8/DDg5IXrwYEAnLBYAkJwM3LkDZGYCaWmAvb1m0hbPOnYEnnyy3uNISEjAY23aYFS7doCDg2ayt6/93sWlaQKSuzuwahW6t28PDBnS97XDnwAAIABJREFU+PVHjgR8ffWfAwBr167FsGHD6oQVm5wzZ4B164ATJ4D/9//ouExEQkIChg8frv8YzpwBUlOB+Hhg2jTE1IQXvfzyyybbf3Mx6hqcPQssXYque/cC774LdO/e8IZTU4G7d4GkJOrnvXsDEyaYruE1GDz/NoLR98HLL9Nz46+/gDNn4JacTM+PM2eA06eB118HLHAe7qtrYKVs3rwZ8fHxNtt+wPavga3cB/V9/9j6+Qeafg2s5Ts5ISHBaq5BU8+JrfcjQ33IWvpJQ7Tma2ALnDlzBu+88w4CAwMt3RQOp9XSIkLap59+isLCQmRkZAAA/u///g937twBAMyfPx/u7u5YsmQJtmzZghEjRuDll19GSUkJPvzwQ0RFRWH27Nkt0UzLc+sWiU6DBwMSiUaE0kUQgIAAYMyY+rclRuwayptw7x5w6RJQWQn4+QFKJQl3cjlQUQEEBQGjRwMGcnVIq6ogAPAUB9ZyOW1Hm+RkOo727QFHRxIyunatv11VVbQdZ2cS3saPB7ZuBdasAQYNonOjS3k5DeJdXABd+6a1fIn06QMUF5OI9vzzwPvvA+3amXefP/8MfP45MGkS8PjjJJDaKr1707G89hqwYAHwz38CcXG1l2EMuHIF2LuXBMvMTGDoUCAqikTYsDDLtL210a4dTVOnauaVlgLHjgENOIjxyy/A7dtATAzdE2bOG8XhcDgcDofD4XA4pqRFRtUfffQRbt++DYASZP7yyy/Ytm0bBEHArFmz4O7ujpCQEBw6dAivvvoqFi9eDAcHB4wbNw4fffRRnSIDrZaUFMDDg0St5mJM4tFBg2hijMSr0lKgrIym0lISvyIjDW6iy+7dWAZAqlAAbm4kWjk60rp2duQEO3KEBt0TJ9K2Gzq+q1eBbdvovZMTiWNBQcBvvwFvvQWMGAEMH07HeO0acPIkOWQYIxHSmhk+nETQJUuAefOA1atJYDQHp04BO3YA/fuTcGfLIppI+/YkqL7+OgmSN28Czz0H5OaS6+z33+m4HRxIPJsxA3joIYNiMMdEuLrWFTb1sX07cOgQvZfJAH9/oEMHEtejo+kebkLoLofD4XA4HA6Hw+G0BC0ysk5NTTVquW7dumHXrl1mbo0Vc/06EBpqluqaBhEEjfutkTkX0qKjsePHHzGkUycSOUpLacrKoteaXEp44w0KEdUlKws4d44G4S4u9OroSGGpKhW55crKgOBgIDER2LmTxDnGyKWWlAS0bQvMmUODcBcX2u7evUBBAbnaXFxo0n7v7k77sQRduwJffAEkJADff09uMUMOvaZw9Ciwbx/w2GMkbuhz8dkiouA7ejSJp+++S+exTRuaevYk4WzECMtdX45h1q+nkNsjR0hQu3QJOH6c+qtCQdetTRsgPJzcbUOGkOBvwryCHA6Hw+FwOBwOh9NUWoFFpZWQnw/k5QGjRlm6JY2iJCgIZwDkdO0K6BZ8KC0FFi0i0UOfiCYuk5JCYllFRd3PXVyAhQtJ7OvYkZxVa9aQwygkhNxIkybVFU1kMgoPLSjQuOy0Q05jYqhd9aFQUEiqtgjn5GQ6QapNG2DxYgpz++knuu4DBzZfRGUM2L+fhLThw4Fhw1pemG0uCoUmR19ZGR1PXh7dIyUltMzVqySyymRAdjYJsDdukEPN0RHYsoWE3ZEjKZeap6dlj8kYSkspPHrEiNbhHtSmtBTIyKDcjnv30r2Vk0P3pJMTMGAA9deUFGDzZnK3pqSQ2/Srr8jdGhwM9OhBIb0dOlj6iDgcDofD4XA4HM59SisbrdkwKSnkuKhPcLJFDh2ikM558+pfJjRU87lSqQkrFV/lco0Q1KsXsHQp8MEHlOsqJIQG5J9+Cnh5Ad7eNPXsSWKENozRtsrLadsNVRUsLCQxRhtBoPWcnUmg69qVnG0eHhQ+2Vjs7IBHHyWhaM8eCl9tjkCgUpFj79QpEgljYpq+LXNTWUmupIIC+CYnYxyAjocPkyhWXAy8+iqdW5mMXIve3nRuvL3JNenhQWLL5MmUO23JEioqcOUKOTtTU4HLl6lPeXjYhpBWUUHOLDc36752higv139vnTtHjjMHB1omLAyIjaXXLl004d5VVZTr8PnngenTKWT3wAESGK9cAS5coG1xIY3D4XA4HA6Hw+FYiNYhpLUG94ZcToNKE5Vutjj37tGAeORI4/NTSaUknhiqZjl+PDBuHA3G8/NJ0MrP10wpKXQedYUTQSCnkqMjJTdXqTQFGfTh60uOMdHNJgpwZWU0oN+yhQSc3r1pOw0JaTduUBs8POj4xLx/gkDnKCKChMFm4JaTQ46siRMbTvjeUhQVkTgmOi7z8ykkc98+Om++vgiQSBAMQGlvTyKot7cmAb2DA7kO9VFYSNsMDyenoJgAXztPl0Jh2JG3bRu52yoqgE6dSNTp1s0ywpufH123LVuoP1iz+FdcrBEtb96kfl1VRffH4sV1HaK9e5ObTLv6sD7E6r45OfS3nx8VNBCLGjBGBVHqo7oaePNN2sbw4RTuzUNCORwOh8PhcDgcjglpBQoUUO3mZukmNJ+hQy3dAtOydy8Nmpvi1GoIQdCEXLZt2/j1T5yg0EeZDOEpKZgGIPDCBRJkvLxIRPP01Ahv2nnj8vOBjz8mseXTTymk0JAgJ7J7t0YcAMi14+GhmSIiai/PmNEhmUJ1NaQAStq0IZePKYpVGItKRYKWtlDm708CBgAcPkyCqkymcZNFR5OjqH17oHNnXN6/H19t2YKhMTGU38xYLlyg1x496l+moUIlHTpQBcmLF8kNJob/enmRsBkdTSG3XboY367m0LcvFaD4+msKYbQWLl4kQfTGDWDXLhKwAeqjvr6Ux6xfP7r2+oQrMXehMXh6Ul/ShyAYvKaOJSWUSzEpiSq8OjjQdezalc7tgw+av1Iuh8PhcDgcDofDadW0CiHN1nHNyQG++44Sw7eGBOllZRRmN2aMdboF+/WjwXR+PkqLiiAF4JaVBfzxBwlDHToATz1Vd73qagotLS+n8FJXV5pvjOD13HPk4ikuJpeW9pSaSmKEGNZbUQH8+COJYkFBNK+0lBxAovAmFkuoqkLHY8cwQdyPOUQ0xqidolj299/UlpwcEh9FYUMqJbFMWzR54AGaDDmRmprDLSmJHExhYU1bHwD69KEJIEdVcjI51FJSNAKbINQvpBkjojaG4GC67rt2UWijeP3NjUoFbwAOYg46XQ4eJJHRy4vCTjt3pmsfHm76Z5aXV/1CWgNUenlR5db0dAoJPXmS+uq+fVTB9q23gKefpvu4tRTg4HA4HA6Hw+FwOC2KFaoc9x/umZn0prWEdbq4APPna8LzrA1RjAKQceMGNgIYExuLftOnk2BUX+jYxx/ToHzJEuC332gb3t6187N5eekPZRUdWd7eDbdPpaI2fPst5QCLiKBcUaLQJyIIwNWrCExNhTsAiaGQt4ZgjBL55+eTy08qpb83bKB54rYlEhI5GKMwzMGDSZT09qbzoStO1Jxns+DtTaG+phJr7e2ByEiatDF0XlNSELFzJz4F0O3//o/u4YgIEpia2v+fe46Eqy+/BN5+u2nbqA+VikQmUSy8dQvIy0NkURFeAhCQlKR/vRdfbDlR3NubwkWbQ3AwMHMmTQCJgOfOUd7Gvn0Ni2iFhST8cqGNw+FwOBwOh8Ph6IELaVaAW1YWuXZsrbqiIWxRFJRISAjTR1IShYw9+ijlXpLJSGDKydHk2BJxciI3UVNDyFxcgCefBLZvJ2faiBEUOrd0KTnTiotJDPnpJ8DZGdldu0L2999QNTTwv3CBEvfn5lJonkRCoZZifjmFgpabN48ccs7O5M6Ljq6d5N9ack5lZ1OxCnNjSEDy9ERRSAhUAHxu3KBwX4DOkb8/ha+OG0e5z0QHY0N4egKPPELVK69da35YaXY29d2cHBKSbt+m+fb2QGAg0LYtMkNC8N3WrYjo2VP/NlrSWTpiRONCQY1BKqV+LIYcG2LBAirY0aYNhXD360d5DNu0MW2bOBwOh8PhcDgcjk3ChTQL4w3AvrS0eeFp1kJ1NYV0traKejk5JGpNmgRMmUIClG5V0IqK2oUPGkoUn5xMbiBtJ5u2o8vOjva1fz/w3/9SaNobb5BTprqaqhiGhABLliBl1y6sXbcOw3WFtNRUmsTcZUePkohy5w597ulJIXqiQBYQQP0wPZ3+trMDxo41ySk0OZWVdFwPPGDZdvj5IbNnT8wH4L1gAWbEx5NrMTmZ8onl5VGeuG7d6t+GSlXX/TRrFoUobtpE4YiGkMupuEdODrnhdB2RlZVAWhoJexMm0OfdupHzsGa/eRs3IhWA0hpCyz09yUGmVFpGtJ0xg87R1avkAv31V2DZMrpHO3YkF+SUKcYLoxwOh8PhcDgcDqdVwYU0CxMKgEkkmvxYtszJkyT4vPKK4cqbtoaYN23ixPrDvZycaDI2p1VhIQkuRUWacE2plEQEb29ysw0dSk6Y3FwgIQF46SUSWL74ggSTCROA9HT4JyVhEoDOBw9SjrTYWNpecjJw6RIJZUFBwAsv0PYrK2meXE77T0+nsFWZjNpy9iy5oAwl6reUyCHi4EDOOVM7l5qLpycVJxg4UDOvoTxqH39M4qb4HAgLo+T4ixYBe/aQOB0SQgLqtWt0XW/eJCE2PZ322a4dOVq9vckFp0379sDcuSY/VLPRsyc5+Czl0B05kiaA+nliIjn5zp+ncNi9e4FRo7iQxuFwOBwOh8Ph3KdwIc3ChAEo8/W13nxiRiKVy8l5Ex3dukQ0gEK6Hn/ctNvs358mpVKTyF/b0aad9H36dArBW7ECiI8ngeH8eRJf/Pzgk5oKDwByd3daTiQ2FoiLM9yOe/dIyHFyAv7zH0q8L5c3HJr7888aR52Pj8ZZJ743t7NJrBZpCxgShBgDhg0joebiReDyZRI6q6tpvYICEqh79qTQ28REmu/uTnnA+vXTCE+t4DkCwLpyk0mlVFwhJkYzT6EwLCIfPUrXMzycqtAakxeRw+FwOBwOh8Ph2AytQki7sG0b3NPTwQQBkEjAJBJI5XLI5HJUeHlBJZWCSSRggqB5lUqhdHCAUOMGYoLQ4g6Ivb/+ip4A9ty4gdSNG1t036Zix44dAIBb332HcwEBuOrnB6UNHYvYfvHVaigoAHTOo9tDDyFcqYRLXh6qHRwgT02FIisLF9LSUArg0PnzKDhwAGUXLhi1C/uiIvTbsAF2FRU4O3Uqiq5cIZecEbhmZ8O5pAT2GRlwKCuDfWkpZHK5+vO80FBk1JdvS4edO3cCsMJrYCRiu8XjaBRKJdxycuBx9y7cKirgKpfDNTcXVU5OkCkUcC4qguT0aRTeuweFiwuYVIqi4GBUeHtDUl2NQgDFublgBw826xhs/RqI7W/SNTAxnQ4eRJc9e2BfXg4mCKhydUWJvz8K2rVDTkQE7nXqpFfwtNpnkZFY0zVoKvw+sCy2fg8AvA9ZA7Z+DLbefqD13Ae22n4ASE5OBgBkigXtOByOyREYayjuyHo5c+YMoqOjMR+Abor4tgA6AzgOoErPusUAVgEYA6B/zTxVzaSseY0CUA3gjNY87ddLABIBPALAXmu+uIwUQCSAmwCy9WyjD4AnAbwF4BqASgByrVdFE86JJfAD8DyAPwEctWxT7htkoPMeoDNdAvCHEeu3AfA/AM4A/g3AOPnMMA6gnH/eAIoA3DWwrCuAkQDyAOTXTHnQf6+2NqQARoOuQQCAEaDrWQTgDuh5cQzAdQD+AL4C8DaAGwCCAAQC6AVgKAChZv5fADIBZNS85oKeMRzLIAUwEHSNegLoBMAXdL0yASwEfTdxOBwOh8PhmIu5c+fi888/t3QzOJwWR9SJTp8+jT59+phlH63CkSadNg0RsbHkLlOpIGEMkspKlFRUoIuzMwQAAmMQVCqaGAMTBEQHBMApPx8OJSXq+dqv7unpgCAgxM8PYAySmu2L2ylt0waFbduibWIipFVV6vVQ8yqVy+F3/TpK/f1R6eFRZx+SP/9EVU4OZnfrhnA9lfl8U1JQ5uNDVQHt7KAUJ5kMKnt7FIaEoNLdHZ5379b5TCmTQSmTgUmlZg2V2rFjB/pv3ozeHTui65tv4oWWrO5nAnbu3IlNmzZh+vTpiI+PBwDIKitR7eBgM1VUd+7cie82bcL0adPwxJgxmGEg7MyurAwdjx5Fu7//BpNIcPaxx7A0NJTy9LUgDsXFCDl9Gg5lZbhx8SIuXb6MyO7d0alHD1S5ukLu6orMqCgobSBUcceOHdi8eTOmTZuGcePGAQCklZVwKiqCrLIShbo5yxhD50OHUOXiggoPD1QLAgoCAlDl7q4WIvuKy1ZXo8vKlVjRvz9SHnyw1mZyysvhefcuOlRWIkwuh1NBARxKSwHGoHRwQFJNGLC0shJ2lZWQu7vXe531HYMtYc3trwaQDOBWcTECrlyBQ0kJFvXogdKAgFrLqZ9FU6di/KhRUFlD4YdGYM3XwFjEa2Crx2Dr10Df97GtwfuQ5bH1Y7D19gO2fx/YevsBcqS9/fbbNvss5XBsAdtSPephwODBmDp7tuUaMGNGk1a73b8/1uXkIHzhQkRNmkS5keRyzevZs5RM3cNDM197meHDKbn8Z59RTqWKito7yMujnEtDhlD+JAcHyl2l/TpqFJCdTZPuZ46OlHDezq5eMc4nJQWOAKRjx+LxJ59s0nmwNJs2bUJ8fDxmzJhB5zUhgfJPjR5t6aYZzaZNmxA/bhweb6gv5uQAW7ZQaNmIERgjCJTrzNeXqnYGBFDuNkOFBkzMtW+/RcLTT2PNE0+gz8CBmiqjM2dSAQRd7t4FDh4EBg0CnJ0pB1VDOd3MhUoFl6wslG3ejIUAoo8dA4qLgdBQ+tzVlfLr6YqyTzxh/D4OHECgjw8GGPOckcvpXi4uRu/ISJp36hSwYweFCwcEUB69oCC6v0ND1WGGmzdvxrhx4+g+sEFsvf0A3ceP9+qFcQkJVPgjNJRy4A0bRvnwLFngwwhayzWw5WOw9WtQ6/vYRuF9yPLY+jHYevsB278PbL39Z86cwdtvv41A7dzJHA7HpLQKIc0myc9HuY8PdgMItbcnsczDo/YyPXoYt6033gCqquoKcTk5VGUuJISSmmt/lpdH7yUSqgJ46JD+bScl0XJDhtQW2GpEtsitW3EFQLm/P3D9Ogl/rq70auWDvjqoVMC6dUBZGYlJrRF/f+Drr6k/+PtTHxGF1Oxs4MaN2hUnWwClvT0yABS2bUuCQUOkpVGhBcaoeiVA/U236IGnJx2jqUXBjAzg3DkgKwu4cAEDd++GJwD/a9coKX2fPlTkoU0baktznY3t29M9agwODlTBU5uoKDoPmZnU9jt3SFw7epTOYceOCPb0RB8AjgUFlq/IClBRhZwcKq5xn1Hh7g5MmwYcOQJcvQocP04/lkilgJcXXcupU4ExY6iPcTgcDofD4XA4nBaFC2mW4sYNVLm44IwptiWRaMQtbcLDSQBriBEjSMCQy2uLbZWVJKyUlJCDRXt+ZSVw7Bhc8vNxHcCQc+dIiNKmpARIT6cBn5ubRmQTJzc3GhQyZh1hlJ98Avz2G7ByZeuutKcttLRvT5OIMddi+3bqB6KDLSCABvgtdQ0zMmifjz5KzjXRwZafT1VIr16loglOTuS+6tABaKxbsrychEUXF+qj2hQXA6mpJGKMH48Lzs6YsWIFVv3zn+b55TI8nESv8nJy4DUWe3u65trXXS4nMfDSJSA3F85Xr2IcgLADBzTOtbFjSYQH6N5uyfDf4mISD1u7kKZSkZPYxUU9S6pSUZ/18aFn6N27wB9/kLB44wZds/PngbffJidpRAQJ/2PHAp07W+5YOBwOh8PhcDic+wQupFmKGzdQ7u0NecNLtgwSCQkPTk615+vJ3QaAHHDbt+NeRAT+kZGBjY88gqgJE4DSUnJ0lZbSoO/6dRr8FxWRqFZWRiIcQGLawoXADz+QW0ZbZBOnwkIaLLZvT387OppFsAk6d45cMJMnA717m3z7NoMx59bTk1xNJ0+SuAOQWOPvT1OvXnVdUaZk1y4gOpr6anAwTSIlJSSGlpbSdezUicTB+lCpgNu3aZvZ2SRWuLlp7oOYmLohvhERNNWQk5WFXBMeXh0iIqidSUlA374NL28MDg7AgAE0Abi+cSPeW78evYYPR1RMDImVomhXXQ188AEJO0FBmtBQf3/9obemoKiIXLIvv9yyAp6pYIyecyUldC6vXaO+lZdH4fh2dvRZaSm9f+019aoShYIEYXd3evZFRWkcwZ9/TvNnz6bzc/YsiWpHjgAffUTP6xdfBEaOrCXOcTgcDofD4XA4HNPBhTRLoFQCqako0Uk2bVNs3gzk5eFabCxw4AAlMXd3p0mkPkFKoSBBTS7XLBccrBHg8vJI3CgtpbAmLy9NzimpVCOyubhQ/qDQUBpM6opwRiaqDwPQZfducub94x9NPyf3C8OH0ytjdM20Q0MzMoCOHc237+JiClfWSbwPgOYvWUJuwldfJSeXPi5fJiEwKwvYvZtE3Nu3qb94eABhYSQGdu5MrrPMTBI/LFVIo1s3EpOuXjWdkKaHagDlPj51w5pVKhJmMjLIHXX2rEaArKoi99SoUXTeAgJMc54cHUlEz8mxvvDF6moSwcQpMLC2g/Wrr0gIU9TUXc7NpT7n7Ez9y8WFBK+wMHpeurnV2nyZv7/+vJuffELPxgULgMGDgaFDNZ8lJwO//07utuxso599HA6Hw+FwOBwOp/FwIc0SpKUBcnmdqm02Q1UVDepHjEBRU5xHdnbkahLp1o0mXRjTDFarqzVCm/akVNLnu3fTe939AOSKGzqUBuTaIpyrK5xyc/EfAApnZ+Cdd2zT/WIpBEFzPhsTUnb5MnD4sDos1C0rC26AYeeYyMWLtJxu/sADB8g15epKQkPbthR+2a5d3Xxf585R+GKbNsCkSeS0Cg2l5XJySPjIzSWh6OJFWmfWLHK3WQJnZxKyLCUo2dvXFtcUChJr/vqLRLWbN4G9e4E//6T7x9+fHGtdutTvaG0I8VgzMlr2uCsrqW+UlJBoW1JChS3u3aP5QN1jGj++tpDWvTuJiW5uNDk7a9y0TeXECQqpHj+eRDRdwsPrF461OXcOWLOGnG7R0SSANiVcmMPhcDgcDofDuY9pHUKapZwiTeXGDcDFBRXaYpIt8ddfFPr20kvAzp3m248g1HW51cfSpTQI1hXaMjPplTESMMXQU8YAlQqDf/gBeQBK/PyomqWnZ+1JKiXHiaWqQjaVU6dIFBo71tItqYuHBwld2dnA1avocOoUXgXQbccOEkzbtgUeekj/uleu0P0uhlZWVwMffghs2kSiRfv21CeLi+nzuXNJsNNm+vT6BVMxJ5hIZSWJKH5+Bg+pC4CAS5eACxdoWV9f0xY5WLLEdNtqLnZ2dJ4efZQmgK6D6EgUixo4OWlEp8OHSYgSQ0PbtDH83A4KotfsbNO0ubiY2iUI5NoSBXpHR3Kiipw4QYIgoBGKMzM1xxIURKKiKJK5u9d9NhiTl7IxFBYC/+//Ud+eP79521IoyE3400/Ad9/R8y04mH7IGDKEimQ00Nc5HA6Hw+FwOJz7HRtToPRTrRMaY/X07EliQWKipVvSeMrKgGPHaDBpTUKgIGhyvOkOBCdNqv23SkW5vT75BFLGsBrA5KgoRHp706D1zh0aeDNGecDatSO3h4dHXaEtIIAG29bE339TJdf27WlgbOkKjLqEhGgEK8ZwLSEBm3/5BYPDwsgpWFRU/7qXL5MAs3cvOc6+/JIEUk9PEjkEgdxlHTuSWOPjU3cbjXEdOjrWFdf04A3A684dYNs2miEIFJLs50cOrXbtKJSvtSKT1c1Xp0tWFoVgi4ULXFwoVDE8nCqddu2qER/9/WmZnByjm2AHwCclBdi3T+MmO3+ekvNXVdFCvXvTfezoSCKYbnt79dKEXLq4WN6hqlJRUYHKSuCtt5r/o1G/fjRVVZHT7sABEn///JPyBP7nP+TojIurXYSEw+FwOBwOh8PhqGkVQprN4etLky0KaYcO0eBSOz+PrSGRkEC2dy9ux8Tgy5s3MaRbN+CRRzTLKJU0GL98mf5WKEhk0xXaRo8mIW3PHv1Cm+haaU5YV2M4epQG3iEhVH3U2kQ0XQQBVS4uuAYgJyJC43Cqj549SUT79FNyFrm6UnL1Z56hXF0WEj7+AnB17FhET56sCQ3NzSUh6Px5cmO1ZiGtIR54gKbqajonGRkkcJ05Q8Lvn39S6Kyfn6aYgSCQszIoSCOoubvXzk/2+OMa9xqAwEuXNPedmxsJZ1260PO2TRvahyG3oHjfWgu//07naMEC0wpb9vYklsXF0d9KJX0fXbxI54eHe3I4HA6Hw+FwOPViVUKaXC7HG2+8gQ0bNqCwsBBRUVF49913MXLkSEs3zXQkJZED4LHHLN2SphESQgNSWx5o3b5NIlNEBK7GxFDVUF2kUnIU1RemJQptDg702ru3fqEtL4/yyY0ZU1dkEycvL5NUIvW/cgVYt47cWP/9r/U55UzB9OmUcP/yZWDHDnJ22tlRmJq3NzmMxo6tW322pXB0pDa1bVt7vkpleL3KSspd5e9PYpLoZPPxMW2IqKWRyUj4Cgqi6/jUU3Ts6enA6dMkcP/2GxU1KCykYiNbt1JfDgsD4uM119nNrVZlSgWASxMmoOcTT1js8EyKQkGhzDNnAhMmmHdfUilVqI2JMbxcaSm1x8ODhL0HH6RnpLUL9hwOh8PhcDgcjgmxKiHtqaeews8//4wFCxYgLCwM3377LcaOHYuDBw9isL4Ey7bI1asUumbpkKGmEhVl6RY0j8pK4PXXSQD7z3+AP/5o2nZEoQ0gUVE3IbootN25QznxvLw0Qtvt2xqhTSql/G4bN1LbRGFN19XWgJjyEIDI7dvJKfjRR7YtdBqdGNCyAAAgAElEQVTCwQGIjKRpyhS6jwoKSIhJT6fwQWvMZ9fQ/a5UknNKdLCJOd7EEFGZjPJY+fuTq6qlHI7mQqHQhBQmJwO3blEVX0Gge6l3b3p/7BiJkllZFI69fTsVthg/nkJ4dcP6TSBIWw12dsDs2cbliGwpKisprH//fnInr19P91uHDugtlWIsAEEMo+VwOBwOh8PhcFopViOknTx5Ej/++CNWrlyJV199FQAwc+ZMREZGYtGiRTh27JiFW2gCGANSUqhaGscy7NxJ7qA336xdac/UiEKblxeFI+oiCm2lpTT479CBRBRdRxtAwkrbtrQdbRdbzfvAs2fxGoCioCAErlpl+yKLsYguGG9vmnQredbH3r30GhKicTZZWoBxcdGE2QEkWOTmUqL7XbuoOmZaGokWL75o+9e4vJwKRFy5QsLg+PEk0vfqpRGO7twhAfHZZ0lcu3CBwkAvXqSQz1OngH//2/bPhSF8fS3dgtr4+gKvvUZTfj7dS0eOAJcuoe2VK/gMQMn27SQKDxpk6dZyOBwOh8PhcDhmwWqEtK1bt0Imk+G5555Tz3NwcMAzzzyDJUuWID09HcGGElnbApmZNIDs3NnSLWkcjJGTLjzctkN4Ll+mQfiiRTRgtyTaQhsA6DouRaGtsJByJAHkatIV2lJT0TUxEVcB5LVvTw4fX18KCfT1pZA4S4tEzaWigo5Xt/pmUykvJ5egKM67uWkS5YuTpV1tjo6Uxyoxka77a69Rkviysobdhpcvk0Dbpg1NLX0sd+/S+Q0OJteYPtzdyRnq4WG4gqqjI3D9Or3v04cmgO6P3Nz6RbTUVBIn/fxsv/9bK97ewNSpNAHY+e23SHj6aSzq1Qs9AgPrX6+ysnWLnxwOh8PhcDicVo/VCGlnz55FeHg4XHXyOvXr1w8AcO7cOdsX0lJSaFCrmz/J2klJAX78EXjiifoHxrZAeDgVFNDnELM2tIW2jh3rfq5U0jXZvRuFwcFYceUK3rC3J9Hh5ElNTi6FgvK0xcZqxDVfXxoE20Lurdxcci7JZMDcuaYRRcR8UyUlmpDQ9HQq1CCXAxMnWlZoVamoouLx43StnnuOQjqLikj8GzLEcLjfrVskviqV9Le3t0ZUa9OG8pOZKn9eeTk5Ji9cILH95k1qp5sb8OST9T8vxJBVQ0gk5NT8+29gxIjan0mldcOptdm1C8jOpoIG7drR9RwwgPKs2WpYvZWjtLfHAQBPh4frf2aJPPEEieNubiQOx8VRqDaHw+FwOBwOh2MjWI2QlpmZiUA9v2KL8zIyMlq6Sabnxg0aYNiSq0ulAvbto8TStuak08XOzjZENGPYuRNYuxYYNAhnoqKwd98+PDloEIbMmEECSkEBcO8e9bmrV+nv69dpAAuQkOHhQULNY4+R4ODlRWKbNYQ6AsDu3cDhw+RsmjbN9G1ycwMiImgCNMUhtBLY66WwkKpP+viYvk2ZmVR1NTUVeOklEhlkNY/pkhIqkiGTkTBaH/HxVE323j3KLZaVRds9fpzcQDEx9HlzuHYN+PprCq9UqejeateOwvkiIynHmVY1zSYjkZAo9tRThoUzXZ59lnIR/voruVDXrQO++UZzzUeOpFxfnp7W0dfvF1Qqupf37SPB/8QJYPVqui6dO1O/mTwZ6N6dXxcOh8PhcDgcjtViNUJaRUUFHPSEIDnWhIBUiAKAvnV37EByaSmYTAZIJGBSKZhUCkilqOjY0aDzRqishKBS0ToSCQ1SzfAPvKS4GO03bMC9SZNQUhOql5ycrH49I4bvWRlO167B89w53Js4EYqzZ+t8bgvHYAhbbL/Hn3/Cb9MmVHTtivSpU5G8cyeAeo4hMJCmGoSKCsgKCyErKoKssBDS9HQUnT6NgPXrIdTkZFPZ2aHa0xNKDw9Ue3oCKhXkgYFQBASYxcVW5xqoVPDdsgWe+/ejPDISmePHg926RU4rK8DtxAm4nj8Plb09FH5+UFy/ji4Abl282Kw+5HDrFoJWr4ZQXY3cGTNQEhhITi8RlQqdKytRvG8fchuTO0usAtqjB6SlpWAAVDrt1L4G544cgcOdO6j29YVCLHSgg31GBtzs7cGGD0d5WBgqO3euvZwo4DUTmbc3OpaUIHfdOhQaEA/rvY9HjABGjICkvBwuly7B+fJlOF6+jOqMDKi2baPnmqlChptJ/vHjcIJtPYu0MfpZ2qkTuSyfew52GRlw++svuF64AMcbNyBLTETpmTMo7dsX+fHxLdRyDbb4faANb7/lsfVjsPX2A7Z/DLbefsD2j8HW2w8AV65cAWB4/MzhcJqHwJiY0dyyREZGIjAwEHvFROA1JCUlITIyEl999RXmzJlT67MzZ84gOjoaIQDsAQhaU4+a6UMA5Qb2OxZAP515KgDVAJQAbgH4qYG2x4MUSXEd3dcbAKIBvAFgcc3flTVTRc1y1ogDgDcBnAWwxcJt4RDjAbwC4DSoL6lMtF0JAC8APgB8aybx/RAA1wEUAygCkAfgXs2UByAbhu+xxuAI4B0AfQFsB/CxibZrShwABAEIARBcM4mBkkUAzgM42IjtyQCMAtAfdE5/rHnVxxoApQBebXSrDSMBEAGgJ4DBALoDuAQgp6YtWQAya16zQM+ulmJ9TTv+ZcJtOgBoDyAF+u8hLwBVAMpMuE9D9AQwE8BvAA610D6tEQ8A/qBrcqOeZYIA5KNl+yCHw+FwOLbI999/jxkzZli6GRyOWdm0aRM2bdpUa15RUREOHz6M06dPo4+YY9nEWI0jLTAwUG/4ZmZmJgAgyECY0PKvv0Yv3ZxGjAFKJR6WSg06zGT5+ZAWF0NQKiGoVEB1NQSVCoJSCSiVULm44N/h4Qbb7rVrFySVlep11NuqeV8cEwOFry+CvvgCP2i5H0pKSnD9+nWEdukCF19flPXqhbKoqHr3IygUsMvKAnNwgMrBASp7ezAHB7Pl/PHdsgWe+/bhztKlWFxPXrfMzEzs3LkT8fHxekNzLYm0sBBKDw+D19+a26+Lx4ED8PvxR5R36wbPf/wDiTUOIHMfg6SgAB3LyyErKantZisqgsAYSqKjwaRSON28iWpPT5pq3GzVHh4NutjE9j8yaBCiN26EtKAAuVOnYtawYZhl8qMxPZkZGdj/yy+Y0LcvghiD0s0N5d27G7WuLC8Pnvv3Q1ZcjOKYGJR3746nDfTXgK+/hvOVKxi2cmWz2iypqIDL+fNwSk6Gw+3bYLdvozArC96+vpAEBaG0WzcE9OkDCALscnNhl5cHu/x8CNXVUNnZIXv27BYLvfPZtg3OV6/iwcWL633Wmfoe8Ny7F043b0KWnw+FlxfKu3ZFaVQUVA3ldWsCktJStF++HGWM4WZYGP718MNW/yzSR0s9S4NXroTTlSuQKJWo7NgRpT16oGjIECj9/Zu9bVv6PtAHb7/lsfVjsPX2A7Z/DLbefsD2j8HW2w+QE+3WrVuI064Iz+G0UqZPn47p06fXmicarsyJ1TjSFi1ahFWrViE/Px9ubm7q+e+99x6WLl2KtLS0OsUGxBNkTqXRpCgUlKNId6qooNd27Si5dn1kZgJffVV3vr094OREldAef5xyX9VHYSElVHd01FQG1Dcgzs+nbcXEAG+91dgjtTz37gEJCRTaFRNj6dY0n6oqYMECyt/13nt6w+1aHKWS+pO9PZCRQXmz7t2jPGNlOl6esjLgwQdrFzzQzsV2+zYVT7hxg3KDtebk4wUFwLZtdP/99Rfd8zNnGleV9KefgM8/B7Zvp/xeTWXjRtqWUkmhdhERQFQUJeWvrxCBSkXPhaKihvMlpqRQQQRf3+YL/enpdC8//TQ9I1uC0lIKJd62jYop5OXR/MBAyt81dCgVLzBF9cnXXgNOnwY++4yKIXAMc+UKsHcv5VC8eZNyByqV1N/atKE8fc8+a/s5PTkcDofD4XA4TaIldCIrGI0TU6ZMwcqVK7FmzRr885//BADI5XJ8++23iImJsf2KnQA5c+zsSEBoCv7+wCuv1BXgtCd7e8PbOHGCJhGJRCOqOToCISHA2LHA+vUkigwYQMnq3dxocnGx/mIJpaXA++/ToLt3b0u3xjTY2wPvvkuDRWs5/1IpCWMA0KULTSIVFRpR7epVElZu3qTk9GI1SXt7Wr+oiES40FDgjTdMk6TemlEqSQS7e5dE7eJiSoYfHKyZQkP1C1DdutHr5cvA4MF1P1epaLvFxYYTtsfGAg89RM8UY4UuiUQjghqCMWDrVnoeyWS0j8BATdXQgICGn1PaBAXRc+f69ZYT0lxdScwVBd20NBI9z54l0evWLeDPP4EpU+g8N5Wff6btvvji/SGilZVR36yq0kxXr5IgX15Ofaxjx9qfd+9e+zm+bx8Juv36AX36kAh/5AiJ+SkpdH2++476XdeuwMCBwLhxhiuJcjgcDofD4XA4jcBqhLT+/fvj0UcfxWuvvYacnBx07twZ69evx507d/Dtt99aunnWgVTaPBcKQIPvyMj6nXGiG+WJJ4A7d0j4+PtvzfqCQINaNzdg4kTjXDQtiUpFYsylS8DzzwN6CljYLGYIKzMbTk5A27Y0aYddq1TkyMrLA3JyyFly7hwJahUV5DxycKBBcEAATS4utJ2mCtDWRHIynYPJk+nv8nISANLTSQBLTCRhZfFi/euHh5M4dfUq3cvl5STuXLhA80SHTnQ0sHRp/X3Gz888xwfQM+KVVzQFB7Ky6PjOnqVqp2lpJICEhJBL0ZCDVtxeaCgJaQ89ZL52G0Lsy489Rn/n5dHzMSRE//KVlfS8NhTWnJoKrFlDgtCUKaZvc1NgjJzToojl5qY5hooKEryrqkgArqoiQfHGDc13SGQkiWHi+q6uwAsvaLZ/4gSJXtqcOkXbs7cHvL1pfXt7mhwc6p7DBx4gMdrenj47fJjuIT8/etbY22sq6yYlUbXajz6iz5cto8rN7dub9TRyOBwOh8PhcFo3ViOkAcB3332HZcuWYcOGDSgoKEDPnj2xY8cODBkyxNJNaz2IzrKG8PMDVq2igVV5OQ3OdScnJ8PbOHmSwqLEfepO7u4Nb6OxrF0LnDkDvPoqDb451oVEQqKZIAD795PYsHQp0Lcv9amcHCA7m6a0NBJfLl0iESM4WCOuiZOPj9lyBJoUhQLYs4eEsqgoCqUEAGdn6qdiX2WMXDv1HZO9PYlpO3cCf/xBgg5jNL9dOwo5jIwkJ6klhVdHRwpZ1Q5Vv3UL2LSJPpNIyLForDMtLIz6hkJhlsqxjcbHR+PG1Mfff5PAo1LRc65fP+rjYihoVRWFzLu4kLjTlD6sUpF4VVpKIpejY22Ry9OztrvzyBESscVliouBo0fpvVxOywYFUX8SefZZjVhYVkYhxTKZRujKzSWR1MmJRC93d+p34ue6YcJ9+5JLTPxcFMMa47Lt2VNzPAkJJGhGRFAl0D59gIsXyek3dy49I65cAX7/nUS1mzfpu4cLaRwOh8PhcDicZmA1OdKags3lSLvfSE4ml4y2+KadOyswkFxjhigoILGhvlxu2hw9Sm602Nj6HT0cy3P9OuWFYgz48EPK0VUfSiX1o8pKEl5Eoa24mD6XSikHXlAQhYhqu9haKBl+g2Rl0cC+oACIiyMxoTltu3OHQgGLiui4o6MpL1SnThQ6aW3CYlUVcOAAiUuBgcDDD1M7jeHoURKJgoJI0HF3t57raoj8fOrnO3eSeFpRQQJUp04kdBYXAwcPkjOxXTv64UJ0csnldIwjRmi2d/48nT/R6SWX09+FhfS5o2PdXJD9+1OYvsiWLdRnRAFLEChE2MmJnrFt25LApC1yBQZqxD/GSLyzdGh5Whrlibxyhc7dM88Aw4ZpPlcqgY8/phxpEybUXV+prP8YPv+c1uvXj9xxHA6Hw+FwOByb477KkcZphYSH06SNUqkR1YzRcNesoUGovb1+V1tYGOVsunuXBledOwMLF5rneFoKxmxDLGgKBw9S/jo3N3o1JKIBNODt2rXu/IoKjXPNy4ucJqdPk2MJICEtIIBCRL29SSjo0qVl3UwqFeVqunWLhKPnnzdNSGW7dpSD7O5dyg915w7l69q3j+6Ttm2BMWMazmXWEqSkADt2kIAeG0tOucYIfaWl5CQ6doz+dnXV5JELCqJXU7taTYG3Nx3rgAHUD86dA3btojDDw4dJSLOzo4IPAOUA8/IiV5fo5NIW0lxc6Hi1Ra4uXWjbYqh9x461P9d1+z36aPOOSRAsL6IBdMwKBbmOx42r25+kUjrvBw5QKLCuK66+YygsJOdaZiaJ3kFB5B4dNgyIj6+/CAeHw+FwOBwO576DO9I41s3t2/rDSktKaDA6YQI5PE6dojCkSZNISPD2poGpl5fmvZOT9QtUW7eSOPT889ZRmdNUMEYFLL77jgSAFSuan+9P3z4KCjQCmzjdukWv4eEUjqedfy0ggNph6n6RkQG8/TY5Mh9/nCpOmvN6VleTACAKaxMmkMBiKUpLKcy6sJDE0vHjmxdqWlKiySUnTpWVwMiRgLWG/ldUkMB77ZrGVenuTmG8W7ZQbrEPP6T7wcPDOkJWbQWVyrAgW1kJ/Pe/5NJ78MHGbTcxkZyEp09r8r9JpeSInDePihfUlxuPw+FwOBwOh2NxuCONwzGUy0ZbA+7dG/j6awpdys+nEMBr1yi/m8iMGdZdGe/HH4EvvwSGD7cO54epUCopn9exY+QQ+fe/zSMqCQKJpt7etV1sYkL/4mKNuHbiBAkdALmAnnmGcvqJLraAAE1IW2NJTga++oocY8uWtUyCfJlMkxDfGC5coPunXTvTComMUU67Xbvo/pszh0IMm7t9NzcSoEpLqXJpQADd5w0VEyktJTecGDppboqL6bj37KH+3q8fuREHDCDBLDCQzsXw4cDMmcD33wOffWZ94bjWTkPny9GR8qWdOkV5A40VKSUSjZMQoGfXkSPA7t3kKkxOJtF6+vTmtZ/D4XA4HA6HY9NwIY1ju2gPzqVSyhWlS2UluZTEUB1DnD9PAoM+N5uxSdGbyubNJL4MGULii7U754ylspIEwjt3KPm3JZyjYkJ/bRgjkSU7mwQZhUJT3ECppGU8PDTCmkoF9OhB7+sTObULCvTtC7z+uvXmWUpKIrccoEm+3q4dTf7+TRN2iorIyZOcDHTvTtfblOFwEgmFsPboQWGihpL9i4iinpjvSwwLDQ6m69vc+4wxEu2vXiUBLSOD2unlRfnPJk7U3053d8pz9/77wKFDtcM473cqK+mebOh53RD9+wPr1lGfnDixaduQSkn0HD6c/hYLPNTH77/TM6NHD/N/Z3A4HA6Hw+FwLAYX0jitG0dHGkAHBja8rIMDORfu3CFRrapK85mrK+UgmjzZ9G3ctIlywQ0dSpX8WpM7xd6eQgxnzbKuSnmCULeC7dy5JKJpFzXIziZx9eBBEgFlMnI36VYPLSkBtm0jwTY+vvkFBczNtGnkyEtL04SD7t5Nx+/oSEnqo6KM2xZjJB7u20f30LRpmqqkpkQQSBC9fp2ENGPo04ccYenpJHIlJVGeMoD6ZUQEhZ02BqWSzp1KBXzzDYWvOjhQ2wYOpFdj8rbFxVHVy5Mnybl2v+fgqq6mohybNtGzYtWq5j0Lvb2pH6elmS7vpERCwrw+VCr6MUQU5zt2pP4XF0fP9tbkMuZwOBwOh8O5z+FCGocjEhGhEQAYo5Aw0c1mTBgZQI4ZZ2eNk83T0/AAqjWLaAAdz5Qplm6F8UilGnGsRw/N/KefJsFEW2BLSiKxNT2dPnNzo+Tnrq4U4mftFSadnGoXBFEo6Fju3DG+KEJeHrB9O4kVfftSzrKmhsQaQ1gYuQYLC43LsWdvD3ToQJNIaakm35qxIX9yORVOuHaNhLy2bSm8r1s3ygHXoUPTwpUfeYRCO//4o/nFAGwVlYpE3HXr6L7q3Rt44QXTPAtHjKCQ/+vX6xa+MTUSCblv//yTCmxcuEB/b9pE90TnzhTO+9BDVAjEmp8NHA6Hw+FwOByDcCGNw9GHIJAg4upqfN4plQq4eJEG+WJ4oCBQCJkYKtq/PzlkAE045/DhwBtvtD4RrTXh6UmTriCzcSM52AYOpJDI1FTqAwAJqkFBNEmlQK9e1i2u2dnVFZ3q48IFEn+6d6f+Pnt2yzgOO3Uiwe/kSeNdabq4uuqvKKxLTg4JMGvXkmDq4kL779uXjlsQmt4GERcXqrD6888k3JrDyWfNHDsGJCRQQZCwMOBf/6LzayrEvIHHj5tfSANIMBs9miaAwp137SJH68WL1I7sbODll83fFg6Hw+FwOByO2eBCGodjKiQS4KWXSFArLtY42URXW2amJlyUMaoK5+REwsXu3eQCEqf6woesmf/P3nmHRXmn6/+eoQgoCApSBKkWxEJssfeSGBNTNaYYU0w2ySYbs1nz25LsydnNZrMnZ5Ozm805abvGFBNTjMZoTOzBLogtauhNUEQEqcPMvL8/7n3zDjDAANOA53NdcyEw5TtvQd4P9/M8RiOwdy+lki2lbV2ZrCxgwwbux6eeajzEQp0wef489/mhQ8Dp0yz5692bYi08XJNs/v7uK9esUVgIfPQRxUBuLgXiF1807rMWEuKY9+TjQxGxcWPnJVZTLPud7dzJJvPXXksR3qsXz++6Ou7PvDz2WQsN5fsOCur4+x0xgs/Xk3pq1dSwF2RqKrfj888zPeaIY2bSJGD9ep6LtpT425O+fYGlS3kD+PP/ypWW73/iBB/jTmXwgiAIgiAIQjNEpAmCvdHrtQRTbKz1++h0bDaenc1EU1YW+0yZzfx+nz5MvVx/vfPW3Rmqq1nGVFTEi8D4eFevyDEYDJRIubl8j7fc0ry3lb8/JzQOHcrPzWYmUy5e1ARbaiqlI0C5dsstLI9UJZs7yjWDgYLp0CEms556iuvMz9dup07x/Y4aBdx6q2PWMXYsxV1dXefLSE0mrvvcOQo0y35no0ZR1ql99BoaKGOKirivNm5k0uiGG7imu+7q2Bp0OpYE9yR8fCghf/ELYPFix6Zxhw3jz+IDBxx3TNqKtzfFc0v87W8c1lFRQTk/aRL7vFlOIRYEQRAEQRBcjog0QXAVsbGNRZvRyARbaSlvbTUfVxQKnZAQyhhXiZdTp4D/+z8KtBUrbC+F7WpkZrKPXXEx8OyzwLx5tm1zdYpjUJAm1xSlcXLNZAKOHqWQBLjvLZNrAwbw8a4iM5N9n6qr+b4nTtTkR2KidqFvMDCx5sh01fTplLb79rHfVHupr2di1N8f+J//4eCAgADum2HDeBxb63fm5cW0nU7H5FBICPv/LVrEnleC7ej1wOrVznutiRM5UXfuXO5rd+Uvf6Gc/fJL9mA8ehT4+98pAocNowhcuFAGUwiCIAiCILgYEWmC4C54elKYtJZYsKSyEnjvPf7b15ePsywPDQnhBZcjBdvWrZyuFxwM/P73tjep70ooCsXJG29QuvzXf3EaX2fQ6XhBHxCg9cV65hnu0+JiTbCpcu3cOb7mwIGNBZvl1FFHUFXFyZTqFMLlyzlIoyW8vZlWa4v8fGDLlsbloLa+l8REvkZhoW33B7hdz53jLSeH58ojj7CcMDKS27Ot86SqipNJ09O57R9+mI+1hbIy4P33tZ5dUVFMZFlLYp0/z/OpJ5V6OpprrgH27OG+Hz3a1atpmX79OBlanQ6dk0OBvW8fj93PP2dy8tlnbRt+IwiCIAiCIDgEEWmC0FXx9wd+/nMtwVZaSrmQns50GwA8/rhj5JbZzBTap5+yBO7FF7tnSqK+nheyJ0+ygfjcuY6TV+pgir59G0+PrawEjh/XSgsPH2aPKYBrCQ+nZOvdm6k1e+wHRWFft61bgTNnKJ0mTLCflPX25tCNjAyWigJcu6VYaynlpdczmZOd3fLzm80UDxcv8uP583xcdDTLNdVk4IQJba/VZOI2372bQyNuvJFipj3liB4eFIAFBUwamUxMuA0cqIm1hAQm+tasYanoggW2P7+7UVfH8uXJk92jRLlXL+Chh/iz0WDoOpIyNpZ9N594gp9XVLBHYEsSraSEZbO2TLQVBEEQBEEQOoyINEHoquj1lA3BwY176JjNHG5QWtp6eghgWWZlpZZms2WqpNHIEqRvv6VUWLWqe04cLSxkAqSmhgmRkSOdvwZVrk2frn1NUXhBbZlcq6lh3zCDgfvQcphBeHj75FplJeXhjz8Cw4cDP/uZ/eVhWBhw883899WrjfusHT/O41aVB9YYPJhpvbIyoH9/fk3td3b2LJCSwhTPzJnsNThxIh/T3iEY9fWUcd9+y2mSs2d3bJBGYKAmxoxG7rPCQoq1Y8co6p59lhJk5kzgu++4blsTb+6C0cjjcN06it933tGmFLsaT08eE336cIhEV0QV7S3x5psUviYTj/cZM1h63Nb/A4IgCIIgCEK7EJEmCN0NvZ5yQRUMrZGfzwv5hgZ+7u3duDQ0OrrxxXxVFfDxx0ycrFrFRuHdkbo6luIFB7Oc0ZX9yZqi02nDLFSBqigsd1QnhZ4/Dxw8yFJUgBfWISEsa2tJrikK5dT27TwOli51TpNzf39Ko6Qkfl5fT1HYGmpvwdOneZyfO0fxV1dHkThhAqXX1KkdK4GrqKA8Ky1lGu8Xv7BfysfTU0vdAdzu1dWajJ44kYJ740a+ttqvrbiYQsQdS/rMZsq/NWu4ztGjKWDdRaIBFFBJSTwvxo/vnvJ/xQqeG59/zuEKO3aw5D4igufylCnAHXd0z/SwIAiCIAiCE9EpiqK4ehEdJS0tDWPHjkVqairGdLZnkSD0VNSE08WLjctES0tZMnjddbyf2Qw8/zwTS3fdBSQnd35qoruhKFoir7CQwsnDw7Vr6ijqfj1/HkhLowStqNDkWkCAllrz9qZQvXCBZYXz5rn3vq2tBe69l8dodDTLIidOBEaMsK3fWUsYjRQQe/dSWM2fzySis8sTL1zgYIvhw98DhdoAACAASURBVJnMM5uBP/+Zwjs0tHGvtcBA15ZPHjrEJFR2NvfDI49QVLkj588Db73lPEnsKqqqWMa6eTOP5ZwcTqQNCGAJ+KBBTFguWEDh3FV/xgmCIAiCIFjBGZ5IEmmC0NOxTDgNGaJ9XVG0XmsAExzx8bxg/uYb3oKCmDoJC9PKCbtq2uHcOWD/fuCee9i/qquV1TXFcr8OH86vKQovqNXkWmEh8OGHLDFUFCa5GhrYEy4ykiW/7niRrdezFHL/fopdk4lJo8JCICaGqZzISO5HW/nxRx7TV65Qys2Y4br0V2goS/M2bOBAhKQkYOVK7qeCAoqRI0d43z59KNRmz3b+sI9XXqGsiYgAfvc7rsGdk14RERSv+/d3P5FWWgq8/TYHo1y4wPPZ05Nyc+5cTnj282MyLzWVk0E/+4zCfPRonk/x8a2XjgqCIAiCIAgARKQJgtASOl1zEXH//UzHXLrExtYlJRQyahnhxIlagq2rYDSyjO/wYTahNxrbJ2C6Ejod5WdQEMsEs7IoQOfPp3y6cIEy6tQp7mcvL2066NChlFSOnhRqC716MfU0fjyFQVkZ5VJODstT9+6lRHjgAa6/NbKyKKyqqlgeu2yZe0yffewxnlcvvwz8619aubX6V7WaGq3PWkGBVgLqTCZN4nFzyy3uLdAsmTSJ5emFhV1flluiJlCHDGHvypEjObTE25tlt/v2NZ44XFMDfP01yz8rKoCvvuIQjZtucu37EARBEARB6AJIaacgCJ1HvYhTm+O3xNWrnNQYFsa0kysu/i0pLWUqo6yMMmn8ePeYMuhIjEZgzx5eWIeEsM9dU9mkTggtLOTtyBGmWdSUW2SkdgsLc/1+tERRWKack8My1ZakaEMD8P33wEcfMYX2wgtM7rnT/j9yhEmhJ56grOoMJ06wh1xUFBNvXUV82RuzGXj9dQrkO+5w9WpaxmCg0D51imlJkwn41a/aPzjAbGafRy8v4N13W97vdXXasJKmqKWi48a5d8m3IAiCIAgCpLRTEISugipY2qK4mMkHReEFXUhI49LQsDDnXKiZzSxpPHmSpV4rV1IudHcKCtjEvrycpYtTp1ov3fTyatwQf8kSllCqCajCQk7HNBop0ZKStAEGUVG2TX91FDod92VL+1NRgDNngG3bgB9+YF+1G29kssqdJBpAsfvEE1znjBmdm76Ym8uJqCaTVrqs9lmLjOzYNNKuiF7PVNZbb7GUOTra1SsiJSVMh507x/L54mLuK72ex/I113Tseb/9lufrCy+0Lk99fFr+2XvgAPDii5TPPj4UatdfD0yb5p6l34IgCIIgCA5GEmmCIDiXhgaWEKploSUl/NxoZMne//t/jhUaV65wkt3x42y2/cwz3beUU8Vg4BTSM2dYorl4MROBncFk4r4rLKRMO3iQCT+A5Z9qYi0qipLNHbZxaSmwdStFxZAhPBYLCzVBoPZXi4nh9nEHsWYwAG+8QVF9332dW5PRyP54ajloQQFToidPsmfYpEnADTdQuBgMwKZN7NFmywTgrkRVFfCHP/C9uUsp+ubNwGuvcVJwbCyPzxEjKKn9/Dr2nGYzh3L06gW8807HU4hmMxONX30F7NoF5OXx/Pf15TonT+aQCXtNthUEQRAEQegEkkgTBKH7oaZhLPsTqX3XKivbFgXZ2Uw89e/ffqmQlwd8+inF3TPPAIsWtX/9XY3sbF4AV1Qw4XTHHfYp6/PwAAYO5A1gGWVVlVYOWlgI7N5NSdXQQBlwzTXavg8Kcq6oOnCAvaICAzl1Vh2sYTQCRUUsBc3NZYLHZOJ0w5tuonh0Jd7eXMfatZy+OnZsx5/L01NLGioKy3v/+lcKtdBQSjUA+PxzJjbLy7mf5syxz3txF/r0YSn34cPAzJmOS8EajUyYnTpFMTtrVsv3nT2bt45KM2t88w2P7T/+sXPnvF7PKc3JycBzz/E837SJUu3ECR4rtbWUfu5cLisIgiAIgmAnHCrSSkpK8Nprr+HQoUM4evQoqqursWvXLsyYMcPq/ffv34/Vq1fj2LFjCAgIwJIlS/CnP/0JvXv3duQyBUFwNXo9LzTbSkkpCrB+Pfv5eHvz4t+yLLSlvmtmM/uC7d1LifDmm91/Ol1dHaVQWhoTVvfe27nSQFvo04cNzocN4+dmM/uVnTlDeZmVRXkBUFRZ9lqLirJ/rzVF4YV+cDDLiGfNYurK8nU8PVnep5b4NTRQLOXmuk8SKy6OZXTW+ld1hJISHhs//MBt87vfUZLs2sWptefPs1n9H/5AOWLJ8eM8z7p6n7UJEzi9My2NiarOYjSy/+PJk1p5ppp2BDg5szWRZk+BBvDY/+orTn+dOtW+z92nD2X0XXfxc3UScGtiXC0DFwRBEARB6AY49Leas2fP4i9/+QuGDBmCUaNG4cCBA9C18ItWeno65syZg6SkJLz66qsoKCjAK6+8goyMDGzZssWRyxQEoaug0wFPPtl4YmhuLic1qn3X7rxTSxsBvMj74gte1M6cSSHRlQWALZw7x1Ixg4Gpu7FjXVOmqNdrPfBUamqYklFTaykpvMhOTtYmKapirSOpQ5XiYpZx5uezv9isWUBCQtuP8/KiuIqLa/u+GRlAdTVL8RwtZu2RCrt6Fdi5k43j+/UDVqxg4u7oUeDhh4HMTL6Xl16icGyKwUA5o5Zhq+m26GgOrOhKosTfn6WThw5x2nBnfiYoCrB6NaUcQDkZHc3nTUqilHS2uD9zhsJTlV2OJDCw9bLOwkLgwQd5rAwYACxcyG0j/dUEQRAEQeiiOPS33nHjxuHy5csIDAzEZ599hgMHDrR439/85jfo378/du/ejT59+gAAYmJisHLlSnz33XeYN2+eI5cqCEJXwc+vueiw7LtmKW0Aljdt3sx+aD4+TE6426RJe6EowJdfMjU0eDAlmrsl7/z8uLbBg/m5olB2XrzIfxcUUEgoCveXKtYCAtjHq62m+LW1lEVHj1Jo3Hcf5ZAj+PFHTtYEKKYse6z5+zvmNTuK0Qj83/9xu15/PeWqhwfLXV98kefEr38NzJvXslTy9mYPw6IiCsq8PE4+3bGD51NkJPvvBQU59711lEmTeK6oQyesYTbzmAwMbPlc0uk4kOPmm4FRo9yjV5ifH2WVq0uTAR43N97IRPCOHSwF9fPj2qZO5fcs//ghCIIgCILg5jht2MBnn32GJUuWYPfu3Zg+fXqj71VWViI4OBhPP/00/vznP//09YaGBvTv3x9Lly7F22+/3ew5ZdiAIAhtUlzMcsLSUm0SnocHS9PU5NPIka5eZedQFPZhOnGCqY++ffme3KFZfkeor2+cWisoYA+zsDDKMXXSZGQkSzb1eoqi9HRKNJOJCbTx4x2feqmpYSoyN5drVAcuBAcz/Th6tP1fs6SEYrGtckCzmZK5Vy9+npXFnnaWPcGuXGH/uHnzOiaXzWauJy+Pcu2WWyhOugpr17IMeuVKnkd5eTyPzpxheWZ+Po+tX/2KMl7oHBUVnBy8ezfLYEtLef4+9RRl5uzZrl6hIAiCIAhdnB4zbODkyZMwGo0YN25co697eXkhOTkZx44dc9HKBEHo8oSHMyUD8IL4wgVKmqIirY9RVxZplZXA11+znDMpib2fVHHSVenVq3Hq0GzmBbc6JbSwkEkis5nSpr6e/a769QNuu42lY/9ONjscPz9g+HDeADZiV6WaI/aD0Qh88AG3za23tny/7Gz2QQsLY1IKAOLjm98vMJAJtY6i17OsMyLCejloU9LTtaEH9ur51hZmM2VZWRnFp7c3k4s1NVzL9u3cVleucPsC3C6DBnGK6fDhwLXXOmet3Z2+fYHly3kDeJ7s3s1zt6rKpUsTBEEQBEGwFbcQacXFxQCA8PDwZt8LCwtDSkqKs5ckCEJ3xNOz8aRJgOmltjh8mAm28HD3SdsoCnDsGLBtG/t63Xmn1uS/u6HXc/uHhmoJL4OBCatPPwU++4wX6DExTBJdvqz173KmsAEo8EaMaLlU0JLSUsqbQYNsl26enkyPbdhAAayWyKpcvMhyzYwMJvea/IHK5Rw/TnkCsARUHfQQHW3bJFejkQKstla7JSTwHFCf/+xZTZTV1jJhlpfH7/fty+mxAF+rvp6CVlE4AGPoUJZEjh/P/WLvIQBCY2Jj2y69NptZUpyYyD8WqGXJgiAIgiAILsJmkaYoCurr6226r087R8nX1tYCAHpZuZDw8fH56fuCIAh2p60LspoaiomGBl54DxigybiBA/m5s4cXlJez6Xt2NqXA/Plt9w7rTpjNFCY7d1Jsvvwyy+4qK1mKl5/feEJoUBAHFwwbRmETHOweZa+nTzONo6a6VKkQFaWJIWuMGsWyuM2bgcceo4QrKeFE2/Jyvt8lS5hA++QTHqf2GFZgD+67j8mj7GzKvvx87ktF0QY9hIRokuzCBaYNa2p4Gzu2cWkqADzxhDZhtaGBsi0ggOLV15f9twwGpsz696cg8/Xl8/zXfwFLlwL330/ZlpXFtX38MZCayqTUNdewZDUyUgSOK7h0ibd332Ua1d9f66+2eLHjeiAKgiAIgiC0gM0ibc+ePZhtY++Ks2fPYkg7Gsf6/vsC0Jqoq6urg5/8RVgQBFfh58cm7KWlWkloYSHTYOrF/yOPUM44GrMZ2LSJkwZDQ4F777VertedKSxkKWtxMQXH3LlMEgGUJP37a4mjqipNrP3wAwWJ2cx9GhWlTZwMD3eNIJkxg6mynByWg6alsXm/hwelzahRFEdN0ek4SOL3v6dEDAqiWPX05PTIKVO4jZ5/nuWMN9/sHJF2/jwng1qmwdT+hFVVPEdUSWYwaNtg0iT2wTt+nKWfISEUXb6+lF/DhlGeBARwe/Xrp31fvamMG2d7Cm/3bmDLFuA3v+ExkZjIG0AhuWULZdqRI0wOensz9aiWHYeEuIeQ7e4MGAD885/cJxs3Art2sSfk0aPAa69xP6xeDYwZw/0i+0QQBEEQBAdjs0hLTEzEmjVrbLpvWNOpeW2glnSqJZ6WFBcXIyIiotXHr1q1Cn2bTNNatmwZli1b1q51CIIgWMWytFBtWGkwUOYUFbU9pdBs7nxqrbSUEi03lxLooYfcp8zUGRgMnMCalkbx9eCD3A6t0aeP1r/suuv4HIWFmlzbvZsJJk9P9pZTe3dFRTmnz5xOp8m/ceMoZktLtR5rrfWM6tuXQum99yh3Zs8GHn2UIur++/k+hw+nTBs1qu21mM2NyyVra/kcmZmUYwYDy0gtJVmvXnxNla++4jkB8Hu+vtzOJhNFWFQUk0Sq/PLzo1zz8dEmud5+e+vrPH2a51x0NM/H1pJ7bb3ff/6T+3vu3ObfDwoC7r6bN0Xh+8rO5m37dqbeli+nuFEUoLraeX35rGE2c3DC1Kksde2OBAUBK1bwBnBq7pdfUnbm5VHGrlrlyhUKgiAIguBk1q1bh3Xr1jX6WkVFhcNf12aRFhoaiuVqc1g7M2LECHh6euLIkSO43eKXaIPBgPT0dNx5552tPv7VV1+VqZ2CIDgXb2+tt1NbfPwxk0GRkVpJaGiobVMSTSZg3z5gzx6mc1au5MV/T8FsZtIpIoK9z264gSmtjohJb+/GQwxMJpZE5udTDqWmAnv3UnCFhnLfqn3W/P3t+76soZYODxhAsdcSublsjl9fr5UvzpjBPlLnzlFYPfMMv3fhAvvI9e/P51clWU0Ne62pf/g6fJii0pKiIr5W794UdxERfK2gIH5suk3uvJNpOl9fxyX8Ll0CDh6kBPXw4JrU8zAqqnnZZ0ts28b9/h//0faxpNNpAxWmTqV8zc/XRG5ZGfD669xvcXFMiUZHO1d079lDyXfddc57TVczZAiTaABlZlVVy2m0rVuZbJQyUEEQBEHoVlgLUKlTOx2JWwwb6Nu3L+bOnYsPPvgAzz33HPr8+6+677//Pqqrq3HHHXe4eIWCIAid4JprmDAqKmJJkslECRAWRqk2ahQlW1OKi1nKdPEiMHkyZUlHEzhdkcJCltdduMCkyX332bdsy8Oj8fCJ2bMp6/LyKEoyMlhGC2i9ttRyUFVMOZt9+9izLzKSx0NlJZNgt9xC2ZWcTFlw6BDLRFXGjOFjrJVDAkyD9e3LlJj6fR+f9h1vTZLhDmHGDGD6dJ4T6n46fpzvNSNDS5hZS5mpmM1M8g0eDMyc2f41eHk1Lqn29+e02OxslhAfPMhjq7aW6cmJE7n9HSUXDQbgb39j2q+dFQHdBp2uZdltMACvvsrJrRUVFGpqf7We9EcJQRAEQRDshsNF2h//+EcAwOnTpwEAa9euxd69ewEAv/vd736634svvojJkydjxowZWLlyJQoLC/HXv/4VCxYswPz58x29TEEQBMdh2XvJaKQYKiykWMvK4sVvU5H21VeUIQMHsoyzjRL3bkVNDcvn1DLOFSucUzZnWWqpppzVPmvnzlHenDjB9IufX+PJoI7ss2ZZGjxsGIVVUhK/npjIYROffkrB4+lJcRAfDzz5JKeHRka2PX1Sfd9dATUxGBrK5N6uXcAbb1Bk6XSUJa2xYQOTiL/8pX3W06sXe7eNHMljo6yMa1m/ngMhNm5kjzo1DRkfb9uEUltZv5493O691z7P193w9uY22rkT+Pxz/jHjwAEOmujfn6XRd9wB3HSTa8tzBUEQBEHoMugURVEc+QJ6vR46nQ6Kovz0EQB0Oh1MJlOj++7btw/PPvss0tLSEBAQgCVLluCll15Cb7WRdBPUyF5qaqqUdgqC0HVRlOYX1b//PRM3Y8bwQk9tjt+vX/dtpm02U57t2MFtMmdOx8s4HUV9feM+a4WFLPUbOpQCSxVrkZGd77NmNDLddPQoB1q0NZnVYGg8efLiRX49LIxpvu422bWoCPjHPzjVMyaGwnnq1NYfYzIBL7xAmf33vzu+/LKhgSm18nLuk8JCHueBgRwY0dl+ZnV1LKmNjwf++7/ts2Z3xmTie1Zv9fWUX4WF2ueW36+rY99BtadffT1/tly8yH57mZn8d+/eHFoQE8ME4c03Wx/0IQiCIAiC2+MMT+RwkeZIRKQJgtBtqajQGmjn5/NiT1F4wRcVxRK+YcNcvUr7kZYGvPUW+0yNHdt4Gqc7o/ZZKy9n0kXtt6bTUWCppaBRUbb3WVMUPteOHSzdHDcOmDWr/SLs6lXKm/Pn2TuruwjY0lKmi86cYdJu5EhgwQLbhOuxY8C6dZRZAQFMfMbE8DZokONLp+vr2XcuO5v7NSSEx9B77/H14+L40Zb+iQAft2YNE3lq6tVdURTKXkvZBVAYX7rUXIKpn2dn8w8IdXW8b1OmTGHJc69eLEdWP6o3NdEZGsrPq6uZLM3OptBMTeVrDR9OaV1QwLUOGQL84heUnXFxPausXhAEQRC6MCLS2kBEmiAIPYa6OqYuVLE2fDgwfryrV9V51DLOPXuYEvr1r1m22FVRS/vy87UeXuXlfJ+RkVpiLTraerrwwAFe2F+6RFE6dy4nWzqac+coGgIDHf9ancFoBP76VybJZs2iRGtPYtFgYAppwAD2LczN5a26WuuZFxMDTJvmPHFSXc1BD9nZ/LenJ48Pf3/2u2vpPdbVAUuXMg35l784fp0mU3PZ1bs3Ra01AVZXx20dEqJ93vRXzn79uC8yMqxLsF69WLYaE9Py9/38uK/aOg7S01kuX1zM8uipU9m/8tFHKVXVRF9pKaeBlpfz+cvLeZ/QUIdsVkEQBEEQ7IszPJFbDBsQBEEQ2sDHh8kIW0vBLl2idIuKcl1j/NZoWsZ5552ObcjuLHQ6iq/gYK3P2tWrFFWlpVpzfDVdqIo1o5F9zk6cYN+vxx6jPHAGDQ18baORx4rayys21vYpmM7C0xO45x7KF1tTW5Z4e1NCA9xH48dzX5SWalLt9GlKOmfRuzeHFaglh2pp7kcfaWWHd9/NxFlcnDbUYfduisGVK9t+DUXhfrYUXr6+TFNaE2B1dRRYly5R6NXV8fFNmT6dk269vRvLLR8fTQQOGqQNr7B28/WlBHPUz6jCQuDdd9lLLz4eePxxYPRo7WdNVVXjHpUhIdo2VRQOIOnXr/nzZmTwfJ0xwzmyWxAEQRAEt0FEmiAIQnckP58DC9TG+GqPtagoDi7oiISwF4WFwNdfMxlyzTVdp4yzo/j7s4xPRU0X5uezf9bbb3NbKAq3xaJF3D/qdFdH4+XFxvu5uZQ4WVnAkSMUGwMHUj5MmOD8fVRfzwTR2LGNX9vegzd0Ooq5AQP4Pq31LGzKxYuUjvbcP5ZDFCZPBm6/HTh8mOWoVVXApk1cW79+TA4ePMjj5dIl9otrmgqzLImsr6e8tmT8eKa0TKbmEszHh6XJgYGUTE1TYOq/+/Th5FN36mOokpdHyZeVRVn32GPArbc2/9lXVdXyxFl1AIk19u8H/vUv4PXX+f6HDgXmzQNuuMHxvfcEQRAEQXApItIEQRC6I2PGsERSFTYFBSyfNBh48T98OFMwziYjA/jwQ065fPBBir2eho8Pkzq5uRSKOh1L9CZPZkIoJYU9wLy9KT/V/l0DBzpOrPn6Np4uqzbHV6Xatdc65nWtYTSyvHXvXkqgAQOc2w+wLYlmMlF+Atr+iY21/+RWHx+WH4aHM/lUUqL19tLruc88PCjJrAkutYTYmgDz8aGcnD+fYsndEqsdRVFYsrtnD0VaaCgnciYmWpd9isLzqiNDH+67jwNR9u4FPv6Y6dotW4BnnwUGD+b3HnywZUknCIIgCEKXRUSaIAhCd6VXL6aJ4uP5udnMi/GCAucmSNQyzoYGpmBuvhkYNco9UyyORlGAH38Evv2WvdRmzQKWL2f6R8VkYkJNLTXcu1dLp0VFac3xIyIcJ9aCgpgEGzvWtoRWQ0Pne4oZjcAXX7Bn3sCBHKgxc6b7iQi9Hrj/fq3H2vffU6Ko4jM2lmvvTILv4EHggw8onuvr+bWgIIqxWbNYmvjdd/y3M8tQ3Z3qaor60FCWiw8d2vqxazDw3OtoGXVkJHDXXbwZDDyvv/mGP+82buSwkJAQ4JZb7J+kFARBEATBZYhIEwRB6Cno9byYs+WCrq4O2LCB4iYqimKjI+WglmWc48fzOZKT2/883YHiYl5o5+Sw19WSJdYbmHt48AI9MpKJJLOZ8rOwsLG48fLSElGOFGu2pJXefJOvHR/P9xYdbbtYM5spH9aupehNTgYeeaSxXOwM58/zuewlbnU67TyaMoXrP3+e+yYnh73L2hqY0ZacrK7msTFsGNOjo0Y178NVUkIp25NFmqKw/+D+/Uye+fvz2AkJse24ranhRz+/zq/F25tl2YsW8fPqah4TmZkcZtAUtdS2J/5BQRAEQRC6OCLSBEEQhObU1DBltHevVg4aHq71WRs0qPXEjTqNMy2tZ5dxWnLoEAcP3H03S8lsLafT6ymmoqM1cWOZWEtJYfllbW3jxJojS0EtURQ2nc/OBk6epNTw8OAxEhdHudaSyMrNBT75BNi6leVwzzzTuJ9cZ7l6lX2sZs1i6awj0Osbi8+mve3MZibLTp0Czp6lbAsNBZ5/nqlRa8yZw1trJCYCn33GoQDuPm3V3pjN7C+4dy/71cXEMLnn789SYFvR6SgqHZF67N2bQrUlqbptGwX0yJFMzs2daz95LAiCIAiCQ9EpStNZ5F0HZ4w1FQRB6NGYzcCFC1qftfx8livpdMCvf928qbbRCOzbx9I0RaEMGDu2Z6YuDAaWb4aH8/O6Oqa07C231H2klhrm5VEqeHlpYi06mgmqzpZftoWisPm92l8tN5fbYcWKxuVzxcVM1WVmcvuMGEFJaG9ee42i5fnnnTeBtKGBAnn7dm6HwsLG5ZkxMZRtAwZQHqo91mxNUanU1wN/+Qsb3E+c6Ih34n4Yjdy2hw/zOEtIoMQdNMjVK2s/p06xlPnkSQ59MBopv8eOBRYupFjr6lOMBUEQBMEFOMMTSSJNEARBaBm9nqIjPFxrOF9RQXFjbTLd44/zAvGBB1i66O/v3PW6A4rCpvA7dnD7PfkkPzpK5Fjuo8mTtV54amJt/35KHU9PCjXLxJq9p7fqdBRCISE8XkwmiqTIyMb3O3SISaolS5isckSz+9xcTq697TbnSTSVzZt5HsTEAIsXs0TTsjwzL4+SLTeX5b4mExNMMTHcVvHxbYu1Xr2Y+DtzpvuLNJOJ+3LtWr7vm25i37GBA129so4zYgRvAH9ebNgAHDjAgQUbNzKdtno1j4UhQzhcQhAEQRAEt0BEmiAIgtA++vZtuRTq2mt58V9QAPz9740TUc4qNXQlubks2SouZknX3LnOT+NZ9sJTxVpRkdZj7cABYNcuSjTLUtDISPvvHw8P7vumXHcdRay6bXbtooCMi+Oa7LGON9+kfFi+vPPPpZZnnjyp9XGbOtX6fb28gN/9ruWyTUAr1QWYYCsoYDpvwwZOnJw6lVMh20rpJSYCmzaxhLU7Sms1gbZvH+VjdDQHPYwa5eqV2ZdRo7T3VFtLmZaTA5SWUrLde682NEYQBEEQBJcjIk0QBEGwHw88QAFw/jwvfPPy2MNLbY4/cWLbvZ+6IhkZwBtvMPkUF8ft4C7lZnq9NjRi0iStFFRNrB04QHnTuzfQv39jsdbZxFptLSVIcHDjIRNNE2LV1VrPK29vChN1cEF7Sx4BprQOHqRE69OnfY9VFEqMtDQ2ss/KoohUyzPDw5kQao3WJFpTPDwo6D78kGWoM2eyYX1iYtuPjY9num/TJvbe6y4YDEBqKo+d6mr2EbvnHh4L3R1fX6YoVaqqrKfRzp9n0nTaNJYGC4IgCILgNESkCYIgCPbFw0MTN+rUyZISSrXu1hS9tpYC7euvOZnv8ceZQnNEqaK9sCwFVcWa2tcsN5cCavduSrTIyMZTQa2V81rDYGAfq5QUluXNnt36/RctAm64gcdJVhbXsn07E0n+/sDNN7cvkfPWWzzWli2zGmcQIwAAIABJREFU/TEqqancp9nZQL9+fO/jxmnTM/v1a/9zWkNRKPxeeomJtOHDgWefZY8sW/jhB8qUoUNZbt1dqK7m9q+tBUaP5s+Q/v0d81oXLvCjtem57kJLIjg9HVizBvjnP3kOR0fzjxQ33WS/Y1QQBEEQBKuISBMEQRAci2WpYVtcvMhSpuhoJrrak+xxJiYTJ2Xu2UOZceutTKH5+bl6Ze1Hr2fj+wEDmBg0m7kf1MTaoUMsvVQncap91iIjmw8vMBiAdevYRH3kSJaWTp9uWypMp9ME39SpLHnMz6fQCgqy/f0cO8bbz37WOPlmNDI5WF7OnmUtyYYhQzg9NCzMcULi4kWWcRYXcy0//7nWg9BWKir4PquqgM8/p+QcOpQpvvDwrjXgo7aWAvPaa5mMnDaN28XR4n33bh6z997r2NdxBAsX8jxJSeE5l54OfP898J//yfN06lT2Z+xIolMQBEEQhFYRkSYIgiC4D+XlvCBMSdHEiipuBg1yfcNtRWG533ffAZcvA2PGMIXW3vJBd0avp0QKC6NYUxTKwvx83g4fpkD08NASa4MGAUePAuvXc7tMmAA8/HDnylu9vJhCsyWJlp/P9YSHA8ePM9WVlAR89hlw9qw2PdNg4HqffLJlSRYQwHSYI+nTh7em00zbw6RJ3D8ZGZzeWVZGkbJjBwXitGmOmYRqT2pqWFp8+DDldFQUz3dnDU+ore3aveUCAijUFi7U5P6mTdyeah/EgADgxhs5IVYQBEEQBLsgIk0QBEFwH4YOZSLo8mWmofLyWMJ24ADFWkKCa3tBFRcDH39MubNkiXuXhNkLnY6DIgYOpLxRFC2xlp0NfPQRy0Hr6ihB7ruPJWa2JBDtxd69bNavKEwZVVczSefpybLA6Ghg/HitPLM9CTdH4Odnn+NYp+P5Mn48xeCtt1J6ZmdrE0LdkaoqTpM9coTvYfx4HlvOFtJXrzKJ2R3w8KCAVCWkwcCS4aws68k+s5k3e0/uFQRBEIQegPzvKQiCILgXOh3lR//+TBYpCnDlCqWayeT89VRVsdxMp6McWrmSH3tquZROR4GYn8/0U0YGBdXs2Uy/5OYC777LUsOYGDZCj43lYxy1zZYt40CA7GwOBcjIYNIoKYniLD6eMq1pKaqjyc/n4IIZMxz7OomJTHEqitafsC3KyihEY2Kcl/QsKAD+9S+mA2NjtVSdq0qit20D5s93zWs7Gm/v1hOdaWnAc8/x2BkyhOdvW0M0BEEQBEEAICJNEARBcHd0OiaIbEkRGQy8OI6O5q1v346/rsHA1My+fUz6qFMUBw7s+HN2By5dAr79lkKkf3/gN78B5s3TenKZzUzuZWdTIu3Ywf5kERFMxsTFUaL062c/sab2bxs0iFMva2v52pmZwOnTTDR6elIaTZ/u+ImqR46w1PXCBZbITp7sWImXmAjs3MltbqsM+fFHnitqCXVcHG9RUfZfa3k5Rd+WLRStN90E3H9/8+mtzsRsZmlpZ35GdGVCQnjepqcDW7cCf/4zU4yjRnFgyk03da+SdUEQBEGwIyLSBEEQhO5DZSVTL6mp/DwwkPJE7bMWGNi2vFEUDjzYsYMlghMnUvz0dCorWTZ57Bjlw29/y4bmTUvD9HqtFHTaNEq0wkKmszIyKFPMZj6HmlaLjWWazV74+jIlN3w492dpKUvcMjPt9xrWSE8H3n6b8i4pCXjsMa7B0enF4GBKzaws20XapEkUcDk5FHBqb0JPT8q0ESNsnyDaEmrfthMnuE+WLQOSk+27rztKZSWPw+42SdhWoqOBp5/mv/PzOSDk++8pnb/5BvjVrzjwYfJkTs3t7LEgCIIgCN0IEWmCIAhC9yE4mPKipoaloHl5TMAcP06h0rcv8MQTLfcFSklh2kqvpwiZO9f1/bTchYoKDlpYsAAYN8723kpqEkxNg9XXc7/k5PCWns77hYdzeIEq1uxVbqjTaVNJJ01q+/61tUxKtUd+nTpFgXb8OI/BJ54AFi92Xv8pnQ5Yvrz9jfMDA4FrruFNFY5qkvDy5Y6vp64O+Pprbpc+fVg+OXas80trW6OsjB/l/GZC84knmObcs4dlnwcO8Bj48EPg/ff5s3PECOCWW5hW8/Z29aoFQRAEwWWISBMEQRC6H35+TNuo5Zi1tUxdXLpkXW6cPw+88AInPCYkAM8/z8SGoBEVBTz1VOdlSK9eTE2pyamaGoqbrCze1Ab0YWEUanFxvNB31oX75s1cT3w8j4X4+JZL3EpLmdL77DOgoQF49FHgtttc08DdywtYuxaYNatjx66lcLRlaqbBwPPKsjTy4kWWDHp7c78uXEhJ544N7VVR2NL01p6C0ciU6b597EU5dCjw+9/zWLrtNpZkf/EFp4CePMkBDWfO8Ny4886e2ytSEARB6NG44W82giAIgmBnfH15gTh0qPXvKwoTGMnJTFxdusR0T1BQz7tQNJl4YV1QwPSJJY5IFPn5Mf2XlMTPKyq0tNrJk+xTp9dTioaEUK5FRrIvmiOYNIlyJSuLrw8wLaeKtagovvbVq8D//i+Pkyef5ORJV6Z0fH0ph374wTkSODMTWL+eJaV9+7If3JUr7H2WkADce6/j19AZAgJYmmzLYIbuSH098N57PM98fHj+LVvGoSAFBeyh19DA/btyJW8mE3825uZSolr72Wg2a/0SBUEQBKGbIiJNEARBEMLCgM8/10oON2/WSkHVUsPExO5dzmQ2sxRvzx4KmZEjmVZxdpqob18KzeRk7oOyMu6ToiLg0CEmwLy8KIvUfRMWZr+L98hI3ubMYY88tbfasWMs/b3+euDaaynQ7rqLJavukLjS6djT6uxZ4LrrHC+AY2MporZsYUJJp+M+2b6doiUqimlCdyrntKS2lvutpzXUr60FDh8GDh5kCWdyMvDwwxRmKiUlTOk23XceHhRtoaEtP/+bb/I8TU5m6nTu3O79c1MQBEHokTj0N78dO3bgww8/REpKCoqKihAWFobZs2fjD3/4A8LCwprdf//+/Vi9ejWOHTuGgIAALFmyBH/605/Qu3dvRy5TEARB6Ol4eACDB/MGsMeTZR+vEycoKboru3cD77zDC945c4A77qCccjU6HXuOBQcz8WU28yJf3S+7dwPffcc0Vnw8S0BjY3l/e4ik3r05xXDYMEqFkpLGfcgSEjr/GvZk2DBKkuJiluQ5ArMZ2LuXr1NTw3PmwQeZ2svLY4ngiy9SQv7sZ8ADDzhmHZ2lpoZlxo5KNrobVVWUZ0eOMFk2dizwyCPWhy2oZa+Wcs1W4uKYZtuwgceDjw+TwFOmsFQ0Pr5z70MQBEEQ3ACHirRnn30WV65cwR133IHBgwcjKysLr7/+OjZv3oz09HSEWvxFKz09HXPmzEFSUhJeffVVFBQU4JVXXkFGRga2bNniyGUKgiAIQmPUiz+1FLSujhfdrWEydb2L8mPHmCA5e5ZpogceYMrIXdHrKYgiInhhbjJxImhODnvgbdvGr/n7a2m1uLjGfbzaw9WrnGR48iTw+OOURW1x6hQnQsbHs9+YM0uDo6MpFc+etb9IUxQm89asoSwbNow984YO5Xs8dw5Yt46ipndvNqSfM8e+a7AniYmtJ6u6CzU1FM5paTx/JkxgD7zWkniqSAsObv/rLVjAW20tp39u28afM2+8wdu4cewlmJBA8d3TSucFQRCEboFDRdprr72GqU1+Ib/uuuswY8YMvP766/jDH/7w09d/85vfoH///ti9ezf6/Ps/95iYGKxcuRLfffcd5s2b58ilCoIgCELL+Pi0/n1FAV5/nf2+VIETFeW+JU1lZcDOnUyNNDQAzzzDxvBdrbeRhwflkdoTzGCgUMvJ4fTJkye5b/r1o/iJiOC+aSvpfvEip3BmZ7M8bfJk2/dlSQlL2779lkIvIYG3uDj7TSJtCQ8Piq0zZ4DZs+3znIpCMbd3L5NukZHAb39LSabXcxv97/8CR4+y79jy5ey11dY5A7Af3oEDlI7R0c49X4KCuvfEzrIyDsOIjmZCbNo0SjRbjsHyct6vM/vD15c9FtU+izk5HFrQ0ECpd/w4sGpVx59fEARBEFyIQ0VaU4kGANOmTUO/fv1w9uzZn75WWVmJ7du34+mnn/5JogHA8uXLsWrVKqxfv15EmiAIguC+KAovVHNygPR09tLy8KB0iInRGuS7upeWwUDBk5bGRMpTTwFjxrh+XfbC21sTVwBTMbm53C8lJRxcADCJpKbVoqO1tOGlS8A//8lyUb0eWLSITdZtkUIqc+cCM2eyrM2yv5pOBwwcyMSfI8uEhw3j/i0q4ut1FLOZgwv27qVYjIkB7ruPH3U6HvMbN7JcMDMTWLECWLq0fdvqyhVKv4MHeb4MGsR9Eh/P9J+kldrPhQtMUZ4+zdLMxx/nMIz2SPLycvv3jouNBX75S/5bUZjatLZ/T5/muTppEv8wIQiCIAhuiNN/c66qqsLVq1cRbBEXP3nyJIxGI8aNG9fovl5eXkhOTsaxY8ecvUxBEARBsB29nkJqzBheJF66pPXxOnyYDfwfeogyzZV4elKKzJ3LnmPu2gjeXvj6soQvMZGfX72qpdVUgaPXs0/UqVNMXnl7szTtwQeZZOsInp6UQfHxwPz5lAaZmRRrzhgCcOQIe9w99lj7H68oXGNDA/D115RxixZRclmi03H73HYbMHp0x46l6GjK3LIybpvsbEqgHTsoUYYNY4mo0DaFhdx2587xeF64ELjmGu6n9h5zCQnWe6fZC52u5XLrLVt43On1vI0fDyxezI9drXReEARB6LY4XaS99tpraGhowNKlS3/6WnFxMQAg3ErvkbCwMKSkpDhtfYIgCILQKXQ6ICSEtwkTmOy5cME1/ZhUKaKi1wP3399zkz7+/hweMGoUt01eHvCPf7DkzGBgCmr8eH48d47pKHv0OQsI0ERrW9TWUkp1NCXYqxcliqK073EmE8vtUlKAO+/k+3788daTSdOmdWyNllgOlLj2Wq3vXXY2p8YKLWM2c3+dPMkyzuBgllKOGNE56dTQwJ9fruCXv6S4/eYb4KuvgE8+Ye89f38gKQm48Ubg9tvb7lkpCIIgCA7E5t/SFEVBfX29Tff1aSHWv3fvXrzwwgtYunQpZs6c+dPXa2trAQC9rPyn6OPj89P3BUEQBKHLodfb1qT+yy8pc9Qea/37d1zgmM2UInv3smeVZS+onirRLKmpAdauBTZtAurrgVtvZQknQIGTlcVU1LZtFElxcUxHRUU1ntrpCNTG8LGxTLQlJDD51Z79NmMG5WBlJSVeaxiNfL19+3j/xERNwvTpwzI7X9+OD2xoL0373rWG2QycP8/ed+3t77dpEzB8uPtNXrUFRQEyMph03biRx8kjj/AYtUefw5AQxx/nLaHXaynSVas4+GDTJp6PJ09q6cVBg4B775WUmiAIguASbBZpe/bswWwbG9eePXsWQ4YMafa1W265BaNGjcI777zT6Hu+/258ak3U1dXVwa+NHgmrVq1C3ya/4C1btgzLli2zab2CIAiC4HJCQpiC2rqVgsBy8mRsrG2lVmYzpcihQ0yoJCWJOLPEaARSU4G33mKJ59SpLLm1LFsMD+dE0IYGoKBAKzksLwfWr+d+io+nXIuJsX+D/HHjKL8yM9nPbutWitCEBL5ubGzbaZwhQygkzp5lKtIaVVUsbU1PB6qrmWKaPl1LIqWnA5s3c5tNmQK4Y6/a4mLgnXfYl00Vj/HxbQ8RMJvZt64zPeRcgdnMkuTvv6fgjIoCXnwRGDnSvoNCFiyw33N1ln792H9vxQp+fuECB4qUlYlEEwRBELBu3TqsW7eu0dcqKioc/ro2i7TExESsWbPGpvuGhYU1+rygoADz589HUFAQtmzZgt5NpmWpJZ1qiaclxcXFiGhjhPurr76KMbaUSwiCIAiCuzJlCm+WkydzcrTJk7fcwl5ULZGSwkmTpaXAkiW8fxv/f/YoTCbgjTcoxObMYTPz2NiW7+/lRVkWF8fPa2s1qWbZID8ykvImJoZiprMX92pZsHos5ORoQwuOHKH8mzu39edQxdKZM81FWn098PnnwIcfsg/ZPffwOfv35/fT04F33+VxFxwMPPusbSWpriA8nL3s1P2yZQtlU79+2tCCYcOay+S6Op5TXaWZvcnE/ZGSwv6L8fEUS9HRPU+Uh4a2Xia/fj17rF1zDSfYzpnjvtOTBUEQhE5jLUCVlpaGsWPHOvR1bRZpoaGhWL58ebtfoKysDPPnz0dDQwN27dqFUCv/+Y0YMQKenp44cuQIbr/99p++bjAYkJ6ejjvvvLPdrysIgiAIXRJrkyfz8lpOz5w5A7z6KvDjj5Q6q1dzaqTQGA8PJq4iIymI2ouvL1NbI0ZQwly+rAmcfftYBqqmCNXEWlBQ50SHtzdlwNCh/PzyZdtFXWIixVJNDYVRbS0HXxw8SJk4diwllFpCmZbGiaWnTnH7PPooBwm480RXvZ6prKgoHvN1dZzSqu6XH3+0PiG1tJRTTd2dhgZONtXpWMI5dCgFeVdL0jmTiAhK4c2bgddeY3pz8GCK6dtuY1pTEARBEDqJQ387qq6uxsKFC1FcXIxdu3YhPj7e6v369u2LuXPn4oMPPsBzzz2HPv9ubPv++++juroad9xxhyOXKQiCIAjui6+vdRmgkpnJpMrMmcDNN1OMGI3uLUBcRXKyfZ5Hp+PFev/+2kCJ3FytFFRNRgUGalItNrbzCShbpoj++COb9YeHawMEamoo0UwmCrQpU7TeaUVFbOr+8ccUaI8/TlnTFY8fHx+eK+r5UltrXWSeP88eY+0dyOAs6uuBo0eBAwf4nh5/nNNNndWnriszdSpvBgPl9tatTFm+9Rbw5pvsi7dqFf9QER1t35JYQRAEocfg0N+S7r77bhw5cgQPPPAATp8+jdOnT//0PX9/fyxevPinz1988UVMnjwZM2bMwMqVK1FYWIi//vWvWLBgAebPn+/IZQqCIAhC12XOHOC991gOevw4sH8/yxIHDdLK20JDe0YJmKKwz9z333PypLMapuv1WhnojBkUIbm52uCC1FROwfTy0hJrUVGOkVXl5SwBra3VBk4MH84eZ3PnNp/CuWcPxevq1cD8+V1ToLXEv3vwNqO8nB979wb+9jeW5arCs6XHOIPaWvY3PHSIImj0aEohnU4kWnvx9uaEzxtv5Of5+doAjuPHKZaffVZEmiAIgtAhdIriuD/HxcbGIj8/H9ZeIiYmBtnZ2Y2+tm/fPjz77LNIS0tDQEAAlixZgpdeeqlZTzUVtfY1NTVVeqQJgiAIgqIAFy9qpW15eZRJTz7p6pU5FrOZU/22b6cIiY0FFi3S+n65mspKNsY/dYr7pbqaUi06WpOdAwbYT3aazXy9s2cpVuvq+HoDBmhlw4MGUZrV1DD11JOEwrp1TCdt2EBplZXFck+djqWB6j6JinJOQ/uSEibQTpzgOTxmDDB5smvk2fnzXMfcud1LqlqiKCyZtTaUIi+Pqc4pU7pODz1BEAShEc7wRA79HzInJ6dd958yZQpSUlIctBpBEARB6ObodFoz7smTmTRywuQil7J3L3t75eZSfvz2t9qAAHchIIC3oUN5EX/hgpZW27mT0zk9Pdm/acgQrl8tvewIej37aA0cyMSiOighK4tN69XUYmwscP31PU8YXLlCeRgUBFx3Hb9WWakJ6NRUNq3PywMeeYSlro5aR0oKh2CEhwN33QVMnMiknKsoLqZcdKfJnfZGp2t5suv27cD771Og6nQshb7hBoo1mRIqCIIg/Jtu+qcmQRAEQRDg6dl2Kqu2lhMc1dLEyMiukUQ5coSposxMpqueew6YNcv9k1U6HRAWxpsqO/PzOTSiqIhN5RWFkzvVZFR0NJumd4TTpymG4uKAxYu11GJmJsWRK0sZXYWHB9NmlgQEcNKj0Ui5eeIEt7kjSqIvXaJAO3GCQu+BB5gACwy0/2u1l6oqitWeUApujfvvZznt9u0sBf3sM+CTT7hNoqMp1n7+c56/giAIQo+lC/ymLAiCIAiCw6iv5wX80aNMdzmy5NAeFBUBu3ZRpNXUsM/RggXuL9BawtNTk5gA31NODiXXuXNMB6k92OLjKToHDmz7/aaksHdeTg4F2oAB/LplanHKlLbXV1XFhJQ7HQOdJTQUuPbaxl9LSQHWrmVZ34ABwC9/yeEdbUllo5GlmRERbe+TkhL27/vhB/aqmzePYsbbu3Pvx57s2cPjrqeSk8PEqL8/h1YEBfGYKCriObl9O7BmDdOc48ezB9v06ZJWEwRB6GGISBMEQRCEnkxgIHD77UwqlZSwtC07Wys59PcHfvEL90mp7dzJMryHHwYSE7uuQGsJPz8gKYk3RWFj/Kws4PJlSo66OqaYLBvk9+tH0WU2a0IoM5NyZ9Uqlm92ZDspCvDOO3xetbdaXBxfvytTU6OVs+bkAP/zP5zsGBEBPP00++vZur0KCigsfXy4bRISuF8s+5sZDEyhvfUWxcyiRRwk4C7nlCUVFd2n1Nds5s+KoiL+bCspYQltVRV/dgwe3PwxJ09SnAE8/vv1Y0n2kCFMLX7/PQeKFBQAmzYxtebvD/zHf3CoR1xcx9OjgiAIQpfBDf8HFwRBEATB6eh0vMgMD2dSyWjkxWJpqXtd8N96K8sRu5tAs4ZOxwv5fv34+bx5bAav9vLaupWyICCAcmj/fuDqVZa6rl7N/l+d3U433kgpl5kJpKXx+SIjNbEWHt710mp6vTa9NDOTCbWOJhujo4EHH9RKZb/6igImOJhJvosX+fz33QcsW0Z5487HbmVl5/rzuZKyMoquykrgu++YLDMYtO/rdDyHQkKYKLPG4sX82ZeWxnRuRQVLxqdNY/l1djbw/PM8x6qqKNOOHGFvuZMneU7cc49z3q8gCILgMtzoN2NBEARBENwGT09ebLZ0wWnJvn2UK5GR9itxunyZF/RNJZ4rG7G7GlViRUYyFVNbyx5Oa9dS4vj4cOLjtGn8d2Ehy0A7uk90Oqar4uMpma5c4etkZnKf79xJidS035i7s3Sp9u9Zs9ifrKMyUK/n+4+K4nPV1HC7bN3K7dSvH8ULwGSTu3P1quv3p9lMAXn+PFNkFy5Q6Pv5UYB6eAD33tv8cYrC5F9AAEtm/fxYphsWpv2RoLUy2tpayuiDBwGTCRgxgv3S1LLo3bu1ya4A13LXXbwBTI9aijvL9/Pdd8CkSV1XUgqCIAiNEJEmCIIgCELHqa5mOWFtLS9SLfurhYS0X1CUlVHSpKezJHH8eMesuytjNrOfndoDLTISeOYZ9v1S+6sdOcL79OpFGRofz3I2NYnVEQIDKSjGjqVoKCigqOvK2CttaTRysMP+/RQ/48YBTz1F6dKV5O/Vq44demA2U3hZk7sbNnBS6pkzjfu0eXhQQI0YQRnW0sTN4GDgoYfav6arVynPjh7lcZ2YCMye3fx1Ll60LvdVWlpXXh7w0kuUrhERFHuLFlF4S281QRCELomINEEQBEEQOk7v3sCvftW4v9qOHcC2bZQ2cXEsMWyr79L588DbbwMHDjBtNW8ekJzsnPfQVVAUCoaXX6Ywi4oCfv1rbiu1XPCaa3gzm1lulpXF27ZtFBRXrmgps9jYjvdz8vBgn7a22L+f6biEBOencQwG4JtvWJ53992OKak0mYBjx4C//IUCavp0YOFCCmVbqK7m1Fx1n0RFuU6umM3cV+HhHXu8ycSyyrNn+fPg4kUmycrKtL5kV68Cd95pPZ03YACPkZEjue/CwymegoMds+/KyzVp7+FBaT9xYsuyubS0Y5IxNhb4178otr/5BvjySyZJ+/RhL8SZM1kO6u/fqbcjCIIgOA8RaYIgCIIgdA41aRERwVKohgamlbKz+bG15vQXL1Kg7drFnkQ33gisWNG1UjzOYtcuXoz37Qv89rfAnDktCwa9nmmxgQMpdxoamIw5d05LrKlliarECQ+3v7BQ+7kpCnuFqb3VoqIc13uvrg5Ytw7YuJESbcUKfs2eTfQbGthHa/9+vsaYMRTG7ZW/BgPQvz+Qmsr+Xt7eWoIwIUHrj+cM6uoosUaMsL7OqirKspCQ5t8/eZKN981mvg+zme8lMJBJrZAQDljo25fHgTVsmSJrD0pLeR6dOsVjYsYMSrS2hmgkJHS8PFctk7/vPm7HL79kueeJE9z3Fy9STI8YwcSnIAiC4NboFEVRXL2IjpKWloaxY8ciNTUVY8aMcfVyBEEQBEFoD0eOAI89xovZxYuBBx5wbFlZV6e0lImyhITON/i/fFlLq+XkAPX1lCSjR2tizV4JspoayjR1aEFVlSaM5szRelB1lqoq4KOP2AC+uprllStWMPWj0lppoS3U1/O4PXCA5cwjRrBEz5pcag+K0jhBmJ/PtQYH8xxx5ICCqipOtjxzhum4ESN4LJSVMbV15QoTdqGh/Hj//c2f4/JlitqAAJa5hoU1nlzqDuTnswzdYOD7mjKF6U0vL9se//rrPPeuu86+68rM5PbLzOT2W7TIvs8vCILQw3CGJ5JEmiAIgiAIrmHgQKaghg3jxe22bVoSR4Rac0JCOi9sVNRpoOPHa/3OTp9mie2mTRQ7AwZoUi062nbh0BQ/P8qZESP4vBcuaFLNHqm0+nr2i/vyS6aqJk6k7Bk8uPl9CwpYZvfEE0yCtYfz5znYoaGBybOpU1vui9Ve1Cb2EREUc/X1QG4uhY89JdqhQxSyFRUsM7x0iUIQ4MfqaoqzAQN4fAwZQpmXlMTEVEvnpeV0WXdCUXicpaRQ9IWEMKE5fHj7ZWpYmDZowJ4kJPDjhAnWv28wAKtWAaNG8dgYOlR6qwmCILgYEWmCIAiCILiGiAjKj/PntSTOli1M4vTvT4Ezf77jSgDdDUXhNqitZYmds1D7nak9z2pqmFLLzKRcO3CA+2DKFPZUi4+naOlIKk6no5Cr60WTAAAgAElEQVQIC6OIaovaWpbctfZaW7awPG7CBAq01vqT1dTwo6+vbeu9epXv32RiL7oJE5h0c3S/t1692i4jrK6mGNy3j7KtoYFiurKS2+y225o/JiODj1Ob9/v7a5MtKyr4XC+80HFp6i6YzcAPP1CglZRQ2qu92Tqa5rz9dvuu0VYuX+bPhk8/Bf75Tx7DSUkciHD77e4pMAVBELo5PeQ3U0EQBEEQ3BK9nlMnIyPZq6iuTps8WVraMySa2cz+Zx9+SBGSlORckdYUPz+uISmJF/CXLnF/lJezJ9i331LAqGm1uDjH9bT76COKIbW3Wlxc8wEJs2axRNQWuVVTQ5HSVj+sK1colY4d4zE4cSI/zp7d8ffSHhSlufDJyuJ0ycpK9tY6c4byrKiI3w8IYOP6uDgmrqxxzz0tv+b337McsytLNKORpbeHDnEfxsWxL1lMTOfLoV1FWBjwxhs8drdsYe+/Eyd4LPz5z8CgQcDvfsey7ODgrvs+BUEQuhA94LdTQRAEQRC6DD4+nO6XmGjb/Wtq7NtE3pmoAm3tWpadRUcDN9/MxJO7oNM1LimdN4+9pjIzKXbS0/n18HBe8I8ebd/JkzNnAj/+yNdLTdUGJKhiLSysfWXAx45xqmRL5ZKXLlEonTzJY9HWRvTtob6e+/v8eZa5qtMtBwzgdqusBJYutT4VVa/ncTJwIOWh2ousvp5CLTOTAunwYT73gAHsA2ZLH7o9eyixuyIGA4+P/fuZIoyLA+64g9upu+DnxwSamow7coSJ3pMnKdVSUljOfPfdrl2nIAhCD0BEmiAIgiAIXZO6OuCVV7Qy0ISEzvXychZmM7BzJwVafj6FyfPPUxo5sqm8PfD0pKSIi+PnV69ykEBGBkXGsWMcJBATw/0RH8/Ss46mZOLjmX7r25dyVe2t9v33wI4dLNcbNsz257twgamlphQWAv/939w30dEsKR47tv3HUkMDpU7ThJ6iAG+9RclVVcX1q/TuTRnYrx8HMAQEWG/UryYAW2LcOL5OaSmwdSsTjtnZwOrVwEMPtb32Cxeap/3cnZoaSsNDhygTR49mCXJwsKtX5njGj+cN4HGXm8vj1xp5eRTQ7v7zRRAEoYsgIk0QBEEQhK6Jhwf7QGVlsR/SwYP8WnS0Jh1CQ92r1Ck/n5Jj40amhLqKQGsJf3/Ki9GjuS9KSrg/MjM5PMJkoiRKSGC5amio7emujAwOBjhyhNto5EhNHphM3Jbtbf5eWQn06aN9np9PqaX2DrvuOuCmm6yXFDc08PHqpFM1SVZWRoEVHc2ebkOHAsuWNX6sTsfjsVcvSrIFC5jiGzjQvmm3gwcpaM+coZj79a+BG2+07bGXL2uC1N2prGTvutRUbvsxY4DJk91vUqiz8PKyPlwD4H5dsYLn4fDh3Me33CK91QRBEDqBiDRBEARBELomXl7We3llZQG7dwPbtzONY2tjeWdQW0tJ8/LLXHdXFWjW0Okoh8LDOUjAYGBKRt0nlZUUbAMHamm1iIjm2+D0aWDNGuDoUUqvO+5guZplysvDg+mttjh2TJNY/v5M0PXpw/Xs3cukzoABlGdRUfy+NXbu5P0BSreiIpbaBQVRSAwcqImcltJQc+e2vd6OYDbzeP/gAybQwsI4lXTxYtt6DBYVMdFVUEAh6s6UlbF3XUUF1z1xInDttY7r0ady5Ajl7cSJjn0dR+DnB/zmNyx7/f57JhVffJGp0QkTWE4+frxMAhUEQWgHItIEQRAEQej6WPbymjiR5XsXLriXRAOAIUN4c6eUnKPw9tbeL0D5ofZWO3CA/eF8fJiQSUhgcubrrym/AgOZolm6tHOJrYwMprMUhYLp2DEK1+PHKfB696Ygyc7Wkmo//3lzGTZ0KL8WEMD31a+ffZNknWHdOuDtt9l0fvVqpuraI2jr6iil8vMp1N56S+tBFxnpHrK3uJg9wH74gftsxgxgyRLnlaKePcvX6ooizceHvQ3nzeOxnpoKbNjAff3pp8AnnwCPPAKMGsXjvLXyYUEQBAGAiDRBEARBELojnp62NRr/8kuWGyYk2G/inaKwTNCyhFClJwi0lujbl33Hxo5liqqoiFJt+3b2urtwgQmvBQs4aXHw4M73u1uyhPsiO5sSb+hQDi/w9KR4iozk60yaxOOlpf5kAwe6b+P6KVN4/I4f3zHpFR/P7bRnD8tA+/RhAmvvXkqY5GTKOWejKEwMpqRw3wUFATfcwPU4e5pvebkmhLsyHh5MoU2YwM/Ly7UpvBkZlNki0gRBENpERJogCIIgCD0Tg4GlfKdOsZ9XQIDWWy0urv3TQM1mNsDPymLy47HHerY4aw29ngmojz9m2icyEli0CBgxguWg69dTllgOLeio6Ozdm/3VRo5kGVtxMaVBZiaHDFRVcfJhXR1fKyjI3u/WscTEWJ/w2R7q67ntFy5kqtNs5nbKzGQCz5koCnDiBGVeYSFF9223ua4U2mwGNm+mbOxuBAUx9QlwuxsMze9jNgOvvsrE2pQpXXdKsiAIgh0RkSYIgiAIQs/E2xu49142kc/L03p5qX21wsN5kdlWA3OzmSLuww954T9/fvNm8wIxm5l0+uADSprwcGDVKiaN1JSROnlSHVqwfTvwzTdsKK/TUXbFxXWsrE+nY1+2iAiWB9bUaK+TmsqSz1Wr7PueO0t9PbeJI0tJKyv5MSCAH/V621N4qpDuzHRWgMfGqVNMoJWUsBT3rruYGHSlkK6s5HsMCXHdGpyBTmf9nLp8mfvkq6/4BwJvb/alu/lmLdkmCILQwxCRJgiCIAhCz8bLS+sJBfDCOTubkxmtlWeqqALtgw9YphgXB/znf7LRvjv0lXI3KiuBd9/lxNLIyJb7eel0HAAwYABLLhsamFKrqmKj+dRUPiYqSttvYWEdky1+flpaTVG4RndJEV65Arz/PksuV65kyaujCAvjvuiInMzMZIIwKEjbH7GxtifZjEbK6/37WWo4eDDFanR0+9fiCIqK+DEszLXrcBXBwcDnnzPF+eWXnDr88cfARx/xjwwJCcD113MoSE+dmioIQo9DpyiK4qgn37t3L1555RWkp6ejtLQUgYGBSE5OxnPPPYfJkyc3u//+/fuxevVqHDt2DAEBAViyZAn+9Kc/oXcLk3jS0tIwduxYpKamYsyYMY56G4IgCIIgCI0xm4E776RsS0oCHnqIAk1oGZOJF+Th4SwR66hsLC/XUmTZ2UwL9e7NC/4xY3hh76gpjgYDkzmqMHLE61y8CLz3HpN4JhOPq0cfZYmjO2Iw8DzIzOStvJy9uAYN0raTtbXX17N88+BB9rFLSuJ7dTdhtX078Mc/AmvX8j0JnJ76v//Lc+HHH1kWHR5OgTpxInDrreyFKAiC4AKc4YkcmkjLyMiAp6cnHn30UYSFheHy5cv44IMPMH36dHz99ddYYPGXtfT0dMyZMwdJSUl49dVXUVBQgFdeeQUZGRnYsmWLI5cpCIIgCILQPtTStz592J8qI4OJpvh4XlC6S6rJnfDwsE+fqaAgYNw43kwmoKCAYu3oUU4jBLgPLCdPenh0/nUBljCWlbGnGsAS0cGDeYuI6FwSsbAQWLOGCTSdDpg1i0MXIiLssnSH4e3NIQ5Dh/IcuHxZk2q7d7MH3oMPavevrqY8O3+eScPkZGDyZKB/f1e9g9YpKeH+cDfB5wrU8tt9+5gkfPhhCuzf/54/D6urOUX2ww+ZcFy8mOdGbKz9zkFBEAQ3wKGJNGvU1tYiLi4OycnJ2Lp1609fX7hwIU6cOIGzZ8+iz7/LKN59912sXLkS27Ztw7x585o9lyTSBEEQBEFwKS2lo+Lj2UfIXSc9OgqzmTdnT1VUqarS9kdWFnug9erFsttRoyil7FF+pr5ORgY/1tayTDQhgb2j2ivUvvgC+Mc/KKXmz2fvvuDgzq/T1RiNlI9BQSxV3b8fSEvj9pk+nftE7cvmrrz3HrBzJz/2VBoagPR0CrQrV3icT5vGhJ5OxzLtM2eATz6hTPviC55nxcXc/6tXO7bHnyAIggVdPpFmDV9fXwQHB6OiouKnr1VWVmL79u14+umnf5JoALB8+XKsWrUK69evtyrSBEEQBEEQXEpL6ajMTMqVnoLBAHz2GXDuHDBnDiWJK+jTBxg9mjd18qS6P86e5YV+cLCWVouOZo+8zr5OYSFfo6KiY6m06Gjgnns4ndLdxVJ78PSkTNuwgSm+Xr1Yvjlhgjb9sbycokqdztpaX0JXEBjYc8u26+pYfvvjjzzGk5JY0t40nffww7x99BHL3B95hF9XFIo3axJt82YeA5Mni2QTBKHL4RSRVllZCYPBgEuXLmHt2rU4ffo0fvvb3/70/ZMnT8JoNGLcuHGNHufl5YXk5GQcO3bMGcsUBEEQBEHoOB4eLPOMiaFMaguTqeuXO9XVUU5t2MAL5mnTWMrlDlhOnpw+nT25hg6l8DpzhuWFnp7cX2qD/JCQ9oswvZ7JHFv7Z9XVNRcHY8e6rqeUolAA+vvb93jMyQEOHaLADAgA5s3je2w6hKC6unG5rFqWGx/PgRKuPkcqK3teE/2qKp4fR45QhE6eDNxyCyezWiMhAbj9dvb3Mxi0fazT8Y8N1vjyS56Lnp68//jxfI3kZMe8J0EQBDviFJG2ZMkSfPvttwAAb29v/OxnP8Nzzz330/eLi4sBAOHh4c0eGxYWhpSUFGcsUxAEQRAEwXls2sSJgPHxvBCNielYOsoV1NQwfbJxIy+6x4wBHniAiRV3pVcvYPhw3hQFuHRJ6+W1fTslQEwMRaAq1uyVlFE7qVRWAq+9xr5tam+1jk4ctRdXr3JNd9/deQmqKBRoKSlMMQUGAjfdxBLOlsp9IyOZZqqu1tKDqanA99+zpNDbG1ixwnVTPG2R4t2Fy5dZfpueToE5bhyHB/j7t/3Yu+4C/v534PBh2xJ8b71Fob1jB4eQrF3L8tmgIB4v99zDPoGuKhMXBEFoBZt/MimKgvr6epvu69Pkl46XX34Zv/rVr5Cfn4/33nsP9fX1aGhogPe//1pR++/Sh15WRm77+Pj89H1BEARBEIRuw8iRvEg8e5bJHU9PygJVrIWEuN/QAoOB0ukf/2Dp6vjxFGhDh7p6Ze1Dp+P2DQkBJk2isDl9mo3lVZGj1zMRpaajOjJEwmymKNiyhaJh5EjgxhvZW23fPpY09unD1xg8mL3cfH0d855borKSHztTUqooPI5TUiiHw8P5focNsz3h17s3BcqoUUBeHo+xnTv53Lff3vG1dRZ3H/ZgDwoL+TPo1CmWW86YwXO7PSI5MJCJw337KODaeqxeT/GelAQ8+SRQWsry8J07ef55eFDKxcZSxrpbya8gCD0am0Xanj17MHv2bJvue/bsWQwZMuSnz0ePHv3Tv++55x6MGTMGK1aswKeffgqAfdMAWBV1dXV18FN7KAiCIAiCIHQX1D5disLSNjUdtXMn8O23nHo3caKrV9kYoxE4cYLy6e67eZHbHfDyalxSduWKtj9SUijDhg9nOioujmKtd++Wn89opDz75BOKpehopnp8fJjeGzNG66mXkcFbejpTc6tXO7ecsaCAx58tqaOmmEwsydy3jyIkJoaDEuLiOiaBz51jKunQIW7re+7hra3BC92hTNrZKAqQn8/j+9w5lq9efz1wzTUdT8ZOm0YJeuVK+6echoQAjz7Km8nEhGhWFp9PrgUFQXAzbBZpiYmJWLNmjU33DWvlB6eXlxduvPFGvPzyy6ivr0evXr1+KulUSzwtKS4uRkQbfwlatWoV+jbpXbBs2TIsW7bMpvUKgiAIgiC4DJ2OoiA4mOKsoYEXj+44tdHPD3j66eZ9rrobgYGNh0gU/n/27jwu6gL/H/hruBEEBRVBRE4vvPAOzdtM08ry3My20tpqs7Xfbu633W2/W2u13+3c7bGdVmblUa1WZqZYmvcRXpnm4I23IoIccszn98erYUAQBphhGHg9H495mMMw85njQ/HqfaSzcsva9gbYZnnFx7M90dOTFXtLl7Ky5vx5oH174Omn2ep2bWVW6Zl6I0dyTtm5c3UfCO3bx2qy6lQfFRZy+2ZaGkPADh2A227j61ATu3YxQNu1i5VHkyYBU6faXyX39dfA0aO2yr527dQSeD2Gwfds/XqGqK1aseIvMbH2n72mTYHf/Kb2lbSenjy/wsOv3yb65JNsS+7Thz83SxVxiEjjsXDhQixcuLDMdaUXWzqL3f+GCQsLw/Tp0x3yoHl5eTAMA9nZ2fD19UWXLl3g5eWF7du3Y0Kp0u2CggLs2rULU6ZMqfT+Xn75ZaetNRURERGpU97eDASqYjZzrlRcXM2qiWqqoYdo1/L0tM3n6tqVM+EOHeLFOsvLw4OzwQ4c4O2TklhZ1qeP/Y8THFz1UHvD4BD42FgGII5o/T1/no9rTwtmfj7b7bZs4T8nJzMEbNWq5o9vGJyXdeYMcP/9DHWqO5uuUyfez08/8di8vcvOu7vekPzGxGJh6+aGDQxs27Zl+21CgmNbyOuqHT0hgefee+8B//u/nK3WvTswahRbQeu6RVpEXKKiAqrU1FT0cvICH5NhWKefOt65c+fQ6pp/sWZmZqJbt27w8PDA0aNHS64fM2YMdu/ejZ9//hmBv/TAz5s3DzNnzsTKlStx0003lbt/6wv0ww8/KEgTERGRxuXrrxlqGAbbqKyzvKKial5ZcuoUsHkzg4nOnR17vA1Rdjbwn/9w6cLp06wi7N0b6NLFVh0VFeW46qiLF4E33mBFWFCQLSiKjWVbaE38v//Haru3377+ba5c4edixw5W6CUlMUS73kbG6jp7FggNrf3rZBgMBs1mVssdP87jvekmHm91FRTwXHLnttHCQlb67d3L1yMhgVVeUVH1bwZjTVy8yMUn69axujIvj0HqnXcCEyfy+dZm/p+IuJ26yImcWvM8evRotG3bFn379kWrVq1w/PhxvPfeezhz5gwWL15c5rZz585FcnIyBg8ejJkzZyI9PR0vvfQSRo0aVWGIJiIiItKojR4NDBoEHD7M0GDnTlab+PhwdlnfvgzW7HH8OCs71q9nO5WrNiS6i4wMYMECYOVK4OpVYMgQ4N57WeVz+DCDnB9/ZPjk7c33Iz6eAVtt5j2FhgJz5rD119pWaV2M0K4dH6N//+oFPxkZ168oO3+e88qsWxytbXSOHvweFuaY+zGZ+FxatQIGDOB7c+QI52/VxMaNbGH9f//PMcdXl/Lzge3bWaGXm8u5hmPGVH92WU2dOcNg3tnFDqGhwKOP8lJczBmTX33FkHf5coarv/sd27VFRBzEqUHa/fffj0WLFuGVV15BZmYmQkJC0L9/f/zhD3/AgAEDytw2KSkJKSkpmDNnDh5//HEEBQVhxowZeO6555x5iCIiIiLuKyCA7YZdu/IXRuvWyUOH2PZZlSNHgHnzGPj4+HDO1d1365fO6zl/noHjmjX8pX3QIODXv2Z1j1XnzrwYBlvorNVRGzYA+/ez+ss6Wy06uvqD3b28GJDGxbGN7dIl22Okpla/8iozkxWIpZ09y+PduZOfsZpscbTKz+fzTkqq/vfWlq8vN4dW5fhxHmd0dNnW5aws96tmys5meGatHuzRg5+Jum5vPXiQVWJxcVW3LDuKpydbjUeO5N/z8hg6V/TzbM8eviY1nesnIo2aU4O0hx9+GA8//LDdtx8wYAA2bNjgxCMSERERaaBMJtuA7htvrPy2584Bc+fyl8kmTTiXatq0up215o6++IIVL8OGAffcU3l1j8nESquwMLbSFRcz4Dx4kLPUtm5lKBYdbWsDDQmpfrtd8+asPuzbl+Fddb7fMBg0WWe5Wbc4HjzI8OPWW4Fu3WoWoF25Anz0EauCAGDRoso3nbrS7t2s7LPOw7O2zO7dW3chUG1duMAAzdnVg/bq14/H8/33wLhxrjkGf//rB6mvvMLq0fBwtmQPGgSMHavZaiJiF62zEREREWlsDIOBUKdODNG6dKl+ZVRjdMcd3ChZk8DR05ObBdu35+t/4QKryNLSgNWrgRUrOBD+hhsY4sTEVH+xQ1UhWkEBB/vHxDAsatGCj1FYyEq7Y8fYBjl+PD8TNZkNduEC8OGHwDffsLWyf3+GjvU1RAMYoCQn2yr71qzh8S9bxtdh/Pj6W7l05gzDzx9/ZCg+ZAjn9NUk/HQkX18GyKtX83jCw117PNd64QVWzG3axLD3yy+5CbR9e56Dv/oVZw+KiFTAqcsGnE3LBkRERERqoLiY1RjW+Wrnz3POVtu2trbD1q0bxjByd1BQwPbHQ4eAEyfYrmmtjrK+Hy1b1v79yMlheGA28zHy8zkXr107VjDdfDMreGryOOnpwPz5vH/DYFXktW2v7qKwkG3PU6YwcPz976u3gdXZDIPVg+vX8/xt3pzhT/fuNV864QyFhfwMREYC//iHq4/m+oqLgZQUBmo//MDFIf36MZSMj+drW5+DYBEpw+2XDYiIiIhIPeTpyYAgIYFztjIzbdVR69ezWu2JJxpfm1NaGgfV1/VcLB8fhiDdu/PvFy/a3o9vvwVWrWKL4dChDEpquqUzIIAD5w3D9hht2jBQzcxk9dCRI/xcVGd+m8UC/PGPrEYbNYpz9q63wMAdeHvbPgdTprCiqj4wDLbdbtjAwDUsjNspExMZhNc33t7AiBHABx8wKL52Fl994enJz+2oUfz78eOs9EtPZ9vvwIGuPT4RqXcUpImIiIg0ds2aMSzo3ZvVGWfPNp4QzTC4nfHDD1kR9vvf236hdpXQUF769WNVz7FjrJI5cIAXDw9Welmr1cLCqldFZjKxrbNFC7ZeFhQwQEtLY1CzbRvnt1m3vyYkVH5/Hh7A//wPKxrdbTj/9Zw8yT/Dw6t+ba2VYdZZd9V9P6pSXAz89BO3cB4/zvf+rrv4ePW9anTqVODzz4G33wZeesnVR2OfqCheKps7+NZbwOXLDNl691ZrvEgjoyBNRERERGw8PYGIiKpv9/XXvF1cnOsGmteGxcJZWB9/zBApPBx4+GFg+HBXH1lZ3t62wAxgS6a1Wu3779mS1rSprf0zKan6IaiPD9ChAy+l57eZzfZtfwVYFdWQnDnDP+05F1q2ZPXS+vX8TAUG2kK12Niah9KFhdycumkTKwb79mWFlzu1y/r5ARMnAu+8w89TVaFsfXO9oDIzE1i7FvjqK7bGx8SwnXnCBP5MFJEGTTPSRERERKR68vPZrnXqFP8eEWELeyIj62ebmZXFwllIixez6qhdO7bvjRpVv4+7IkVFrFBKS2PF0uHDrGSLjLRtnrSnoqoqFgurFCsaGG+xuN/rZo9Nm4AlS4B//tP+aqPiYr4f1qUFp04BP//MhQVz5ti/ACAvj9VnW7bwXOvSBRgwgJVu7qigAHjxRbYLT53q6qNxnKIiYMcOVrP+8APD16Iivs/9+zNUGz26+ktDRKRWNCNNREREROofPz/ggQeAK1fYDpmWxl8ov/+eX4uL4yyu+jig+803GaIlJAD/+7/AoEHuGwR5ebHiKTYWuOkmVslY34+NGzlfLSCA70dCAivOqvNLfVERt4kuXsyQ6IMPyn//2rUM8azBXbt2PC53V1DAVtXqtOx5erIyKSyMIe2uXWzJbd7cvpl2WVkMz3bsYECZlMRtos2b1/x51Ac+PsAttwD//S9flzZtXH1EjuHlBfTsyfB640Y+vx9/BDIyGNavWsW2+fbtWa02cSI/HyLi9hrAv+VERERExCUCA21D8i0WVuCkpXGmV32dsTZqFGePNcRuhmbNgF69eCkuZruhtTrqp58YpGVm2kKviIiKQ8SCAuCzz3i5cIFBwK9+VXGoFBvLQHXfPmDzZoYmMTG2x2jWzPnP2xlMJm6urY7MTFYnff01kJvL2VnPP8+KsspkZPB7jhxhGNevHy/u2DJ9PV262BaZ3H23q4+m9rZs4QKF9HSea4bBZQrTpjH8NJs5F66oiD8P//MfBmuTJ/O86NaNAZyIuCUFaSIiIiJSex4ebCmMjLTv9nl5rgnbYmPr/jFdwdOT1WHt2nGuVl4eg5p9+xgCrF3L199arWad67RoEfDllxyk3q0bt3FWtrUyOpoXwwDOnbMFdytWMFwdMoQXdzN4sP23vXiRocl33zFUSU4G7r236uqj06e5gXP3brZyxsbyccPC+P41JB4e/ByePu1e7cDWzzVQtrX23DmgSRMG87GxDMVKt1C3acNz6McfuWTh6lVWi2ZlAVu3ciusgjQRt6UgTURERETqlsUCvPqqbUi+I1sC8/IYQmguUVn+/kDnzrxYLGyxs4ZeH38M7NnDfw4M5HynuXOrrqQqzWRi0BAWxk2G+fmc2ebubYn2KChgIDZ8ODB9euWVbIbBCqUNG/h6N28O3Hor8Otf2zanfvopX09Hz7pzNetCi/ru2DEGZceO8T3JyeEyjYkTbbe59dbK78NkAsaOZbXhokXAzJm2sNpi4efgWmvWMHi78UagRw/3CRtFGiEtGxARERGRulVczCHs1u2TWVlsG4yJsQVrISHVu89z54D581ltNXs2wxyp3JkzwPvvA6tXA9nZ3D7ZtSvn3Fln3Vmr1Zo2dc4xnD3L8LNtW/euwiooqDy8NQxWJK1fz4CmdWt+Rjt3Lh+YZGXZQs7Dh3nfTzxRf9ul3V1uLpdLrFvHis2MDM6n69zZNoOwbduaBf05OaxW9PMD7ruv8s/IO+8An3zC6rX8fIangwczwGsoc+VE6kBd5EQK0kRERETEdaytU9ZQ7fhxBm2PPWZfNVN6OsOgdetYBTJ0KPDgg9UP4hqTY8eA995jVZSnZ9lKqtKz7sxm/rNh2IKfoCDHbmZduZLhp68vAwtrBVZQkGPu39WKi4G9ezmM/vx5VholJvI52lNhVlzMsDEiwvnH2lgYBj/fR44wqDxzhhWamZmswuzTh1tSg4Md83hnzzJMM2x3LmsAACAASURBVJmA//mfys+dggK2+X78MVtAL1zg90VEADffDMyYwXPR3asTRZxIQVoVFKSJiIiINDBXrzJMqypoOHIEePddVpJ4e3Nr5fTpQIsWdXes7ubUKbbUbt/OCpnRozn4vbKFADk5tk2gTZsyELJWq1mrB2tTrWYYDDLMZl7S03ldWBhDtcREVua4isUCpKQw5IuPt//7CguB1FR+Pi9f5sKGgQOBqCjnHOeqVQxb4uLqf+VacTGrIGNj+brUxeMVFZXdnPrvf/M9ionhcURHOy44q8iaNcDrr3Pb8U032f99+/dz6cfGjWy7Tkzkn2PGsGJORMqpi5xIM9JEREREpP7w9WWAUpkNG4BHHmEAdOednC/VUCqYnMnDg9Vod93F7YH2bIUMCODSgW7dGCp16mSrVvviC1u1WkKCbaZXdarVTCYGZeHhwKBBbLOzBnc7dzIkresg7fx5tvidPQssW8YB+XfcAcyaVfX35uUB27axmig/nxVOAwaUHVTvaPn5fM02bbLNVouP53tRH2ereXgwPD16lMfo6OOzWPgeHj7My7FjXABReunFffdxWUBdvTbDh/NnW0oK35OuXe37vk6dgD//mf9cXMz/yZCWVnH4nZ/P1lHNVhNxOlWkiYiIiIh7OX0amDOHgYG/PxcVWFsCW7Sof8FBfeLIjYmlq9WsbbmRkY6tVisqYphWV/Lzgf/7P2DhQm5W7NyZwWNycuWvW1YWW1R37mSlU8+ewA031O2yhawsW8h5+DCrOwMC+D6MGsXgqL44fpwVpRMnssqqtqwLHFJTOX+xe3f+bIiKYsVZhw6cAehKhsFgdt8+hv/2bji21+uvs1W6SxcG30OH8jMs0sioIk1ERERE5Frh4cAHHwAXL9qCgzVrgG++YaVGQgLbFlWZUZ4jX5Nrq9UOH2ZrZkXVavHxHNhe3Wq1qkK0Xbv4eNbHsKfKriJZWcCHHwIrVvA5tGrFNtgePSr/vosX2Xa3ezePNSmJFWg1PY7aCApigNezJ6uXTpzg+XHsWNm2xvogKorv1+efM+Sq7iD/3FzbjLPDhxlinjnDz9jgwTz/O3Z0zCZgRzGZgHHjym7ydGQ7ab9+nPO2cycDuz/+ka9H377cMjpggHsv9BCpR+rRTxYRERERkWoIDeWlXz9WAR09yuDg8uXGF6IVFTGU6NjRMRU+1eXhYatCGzKkbLXaDz9wW2W/ftwOag29HLEJ1NOT4cGyZfx7RIStrbFNG/s/B0ePchZVcjIrHIuLKw/RTp1iBdT+/QwUhw0DeveuP4GVpyfnfkVH23f7wsK6rfwDGDq+8w7fr0mT7P++wkLgxRf5HrVowfd6wADeT2Xz/uoDLy9gyhTgrbdY9VjVJs/qsIaoALBnDz/Pmzbx3Pj0UwaWv/0tX6+YmPrzWRVxQ2rtFBEREZHGxxXBgTPk5wNLlvCX5YwMzj576CFXH1VZhsHgKTub4dPJk7ZqNWvoFRlZu2qZnBxbdeKhQ5xV5u/P6qT+/e27j4sXGcz+5jcMGV59tfzzOHkS+O47PkZICAOc7t3rV+VTdVkswAsv8LnX9Wy1WbNYAbhoUdlAqbiYr7W16mzAgLKLCcxmzp1z19mIZ88C8+Yx9L7tNuc+1pUr/PmQmcnP8MWL3ABq73kh4mbU2ikiIiIi4mjZ2cArr7C9zFod1bKle81Wu3IF+OgjYPly/nNSEvDUU1W3IrqCycTqMIAVc9aFAmYz29A2bOAm0OBg/nJfk2q1gAAGWt27Mxg6eZL3X50KpdBQ/pmRwTlTVobBuVvr1/N+O3UCJkzg/LSGUPlosQAjRjCI3LSJQaF1tlp8PGfeOWu+2gMPAI8+yiCtZ08+/v79rDQrLOTnIjq6fPVUVQtJ6ruwMIbewcFswW3b1nmPFRgITJtm+3tGBl/Xa+3fz8rMG290TWuyiBtRkCYiIiIijYuXF3DTTQwOvv0WWLWKv9Baq3Hqc9tTRgbnw33zDVBQwODp1792r2ChSRNuLeza1VatZg1xSs9Wq2m1mocHg4nS4cT27QyEQkLK3jYnh0Ff6aDo8mUGHcXFwN69DPouXGDL57RpvB93Cl0BhmWFhbwUFPBPi4VVe82aVTxbzWwGFi9miHj77QwQi4tt92NtrezcmQFpRTIz+VndtIntxxYL/ywq4vcXFzPY/sMfGN6FhHDm3tixvM/w8IYRVlYkLo6v8UcfcfuwvZs8a+vac8Bq1Spg6VJbdWJMDDB+PNCnj2ariVxDQZqIiIiINC7+/pzXZZ2tduwYf6G1zvPy9QWeeKJ+/vJ48iQ38w0cCEyfzqo6d2atVmvThm2Y1mq1tDRbtZqvL0OHXr24BMDeajWLhdVVCxfy/u6+G7j//rK32bqVlWZt2jC0a9WKbaGXLgH/+hdDtQ4dOCS+TRuGUNnZDGMrq9I6eZKBVHa2LXiyBkiFhdzm2b799QPQrCyGvFu2MGwqHT5Z/+zRg4FTTEzF97FuHedk/fgjX4trBQRwEP1f/2q7ztOTn6lDh3gubNtmaw387jvexmTi7Tw9+b74+Fw/SLNY2H5cUMDXzMeHwZiXFy9nzjCgDA5mqOntzdtevAicO8fWTUfM0quv4uNZRfn55wy4rJWbrvDYY8CYMfzcWOeqLV7M96BrV37WJkxoGC3xIrWkGWkiIiIiIlYZGfwF/nrBgKsZBsMZd50NVR2GAZw+bQs527RhsBQWVnYT6LWBZ1ER8NVXnB138iSDoSlTOBfq2uqmK1d4/9bZaufO8fu8vRm4tmjB8Ke42PY9AQGs0vnrX69fmfbaawwkzp+v+OtxcQwt7ryz4q9nZDDI2LyZz8/Lq/yfSUkMHyMiKr6PEyfYrnfmDL/H25sXa4gVEMBqv5gYPo9rX7fISFYkDR7M18DHh99fUcCckQFcvcpKQnuq9QwDWLuWr9ENN7BC1DCA1FTg+HHen3WWXng4MHIkEBtb9f26o6IiYP58hrcPPFB/zu3MTAaxa9awMrNVK2DoULba3nADP8Mi9VBd5ER1GqTNnDkT8+bNwy233IIvv/yy3Nc3bdqEJ554Ajt37kRQUBAmTZqEZ599FgEBARXen4I0EREREalzhsFKnZgY95ut5s7y822hWloaK5is1Wrx8QzaUlLYnnbhAiu+pk1j9Z497YHFxRx8/9VXwI4dHAjv6ckwrU0bBkuhoaxEi41lpdr13vvMTB4fwPDJGkRZQ6z61q742WfAxx+zEqx9e+BXvwIGDbL/OFetYvtmYKCtJTcuruJZXIYBrFjBdtsRI7hIoKLXMTfX9l7362er1jp/nu3AcXENZ5bXlSvA22/zs3XvvY7b5OkoxcUMtU+c4DnYpw9bfUXqoQa1bGDHjh2YP38+/Pz8YKrgB+WuXbswfPhwJCYm4uWXX8aJEyfwwgsvwGw2Y8WKFXV1mCIiIiIilcvMZGBTWGibrRYfz3CltrPVtm1jq1tYmGOOtSHx8ys7W81arfbjj8CCBcC+fbxdjx6cuXXzzdVrz/X05Gv/8MP8e1aWrVrt8GE+3pUrHMZeVcVis2bVW3TgaqdOsXLvT3/irLTqGj6c4Zl1ttquXbZZdfHxfL1atmQgs3Qp36tx49iuez1NmnBeWrduZa8/coRBHMBqPOscvZgY992eGhgITJ0KvPsu2yonTqxfAb2nJ1/jyEhWo1Vk6VJg9WqGbDfeyPddpIGqk4o0wzAwYMAAJCYmIiUlBV27dsUXX3xR5jZjxozBnj17cODAAQT+8n8W5s2bh5kzZ+Kbb77ByJEjy92vKtJERERExCWKirjhzhocXLzI4MC6CbRfP/t/qbdY2Ob28ce8v8mTgYcecubRNwwXLnDxwurVrF5q1YrVTYWFZavVrG2gtZm1VVzMlkOzmeFQQ6vGMQzHBjeXL9vOjcOHgd69geRk4Nln+b5MnMglBTV15Ypt8+uhQ6xuy80FEhMZ4owY4bxNo8504ADwn//wM/voo64+mur55hu2Ix86xKpBLy+GoCNHcllFQ6kelHqvwbR2fvDBB5g1axZ+/vln9OvXD926dSsTpGVlZaFFixZ4/PHH8fzzz5dcX1hYiNDQUEyePBlvv/12uftVkCYiIiIi9cKlS7a2w/PngVmzqg4mLBa2ES5ezJbCyEjO8hozpv61/tUnx48zQFu3jn8fPLjs4oXS1Wpms23WVnQ0q2WaNuVr7azXODeXFTz1dfNrXSsqYnXfkiWct/X73zNodhSLhUHahg2csQawPdW6QCI+npVr9anCqzJvvgn89BMwcybQpYurj6b6rO/16tWsPLxyhdcPHw7Mm+faY5NGoUG0dmZnZ2POnDl48sknEXadEvW9e/eiqKgIvXv3LnO9t7c3evTogZ07dzr7MEVEREREaq55c25A7Nu36uqeggK2QX36KUO3hATgqaeAIUMUoFXGYuGA/02bOENq9GgGaC1alL2dycTgJCKi7CbQnBxW/p0/zzbR0tVqjqyW2bIF2LjRVp2YkMBjdFWQs28fN5M++KBrjiE3F1i0iIHKK69UvJnywAHg4EG+VtVtkfbwsG3hBRjkHD7MEHXzZm4bHTuWVXHu4IEH+PNh2TL+XHHlJs+aCAoCZszghtyDB4H33mPo7Y4VgiLX4fQg7emnn0ZAQABmz5593ducPn0aABAeHl7ua61bt8aGDRucdnwiIiIiIg5VVVhx8CDw5z+zaubhh4HbblP1kj08PLhpctIkzpOyd7thkyacqwYw6Cxdrfb557bNkHFx3DrZuXPtAs2ePRnMmc3At99yEH+zZrZQLTq6bobJb93K2XE//shAZvJk/lmXLl7kMRgGcN995UNPq4ICVhqmpvK1b9fOtrSgugs9goI4J69HD4avJ05wSYTV/v0MY63vh72bRuuKyQTceis3ly5axMq0+rLJ0x4WCyvqNmzgxti4OOCee7icQ6SBsDtIMwwDV69eteu2fr9sZzl48CD+9a9/YdGiRfD29r7u7fPy8gAAvhX8B4Sfn1/J10VERERE3F5MDCvQLl/mL5wHDtQuOGhM/vjH2n2/hwcrfNq0YQVgTo5t1lZKCmehtWhh2wRak2q1Zs1s1YmFhZylZw3utm9ne+ktt9TueVyPxcL2yYULWZXVqhXn7Y0fX/ebIE+fBj78EPD3B+6+m4s5rse6VMDaIm02s3pw9Wp+X3JyzdpBraFcaU2a8D3duJFBZ2AgjzEykhWM9WFJhJcX27zffpth2r33cvtrfVZUxCUTmzYxBLQGaNHR+nkmDY7dQdq6deswbNgwu2574MABtG/fHo899hgGDBiA8ePHV3p7f39/AKgwqMvPz0eTKspAZ8+ejeBrfjBPnToVU6dOtet4RURERETqTEAAgwWg7Gw1a3DQrBnwyCP1/xfnhiAgwBbi3HYbwx/rkPxly3ibiAhbyBkZWb1QwNvbVvlkGLalFM7wxRcMXU6dYnj0xBPcXOqKduFjx7g8o0UL4K677G/rK90iXVjI+zGb2YrrKO3a8VJczGo1sxn45BNeXn0V6N4duOMOvmdhYa4LgaybPOfN42dxwoT6HUidOcOZj50781gjIlx9RNIILFy4EAsXLixz3eXLl53+uHYvGzh79iy++eYbu+709ttvx44dOzBixAj897//RY8ePUq+NnDgQHTs2BHz5s1DSEgImjZtio0bN+LGG2/EkiVLMGHChDL3deONNyI/Px/bt28v9zhaNiAiIiIiDYY1ODh3jhU4jYl1blxGRv3ZWHrtZkh/f7aAenjYqtUCAhz/uCdOMNBLSKheK+aTTzKYvesufn5cNW8vI4Oh4XffAePG1U3b8tWrDJlqU3V3/DhneZ09y89jQQEXUyQkcL6aq4Kh/fu5kGTq1PrfHnn5cuWVhyJ1oF4tGwgLC8P06dPtvuPjx48DAO64445yXzt16hRiYmLwyiuvYNasWejSpQu8vLywffv2MkFaQUEBdu3ahSlTptj9uCIiIiIibsnb2xbQVOXwYVZH1XW7nqPl5rIN8csvgcxMoFcvtifWh6ULgYGsTurenceUlQXs2cNW3L17bUsNrNVqERGOOe4TJ9hmumIFK7qsCxHatWPL3/X87W+ur2LcuZPv5bRprEqqKzt2sE2zXTtbBWBoaPUquKKibJWiRUUM1qzVotbNnwDbgXNy6q4Fu1MnLiDw8eF8xfbtnf+YNaUQTRoJpy0bGD58OJZZy6F/YRgGHnjgAURHR+NPf/oTuvyyzjc4OBgjRozAhx9+iL/85S8I/GUOwYIFC5CTk4OJEyc66zBFRERERNxLVhbwwQeAp2ftggNXysgAPvoI+PprID+fFT/TpwO//H5Q73h4sOV20CBerlxhwJKWBmzbxkomb28GXzfcwOClplsKk5MZKFo3T+7bx+2TPj6cr5eUBHTsWP77XB2ibdzI1uTevTkXqy4lJvJ8SEvjjLhvvmE1nzXkrO6CBy8vbg+NjQVGjWKFndWPP/JzGxxsu/+YGOdW3kVEAMuXcwbZffe5pjruzBm+plqMImJ/a6ejREdHo1u3bvjiiy/KXL9z504kJyejc+fOmDlzJtLT0/HSSy9h8ODB+Prrryu8L7V2ioiIiEijdPGibZbX0aOsoCm9GTIujsFCfVNUBLzwAtv+iouBgQM5kDwmxtVHVnMWC5CezkUCJ08yJDSZuNCgdLVaTUNOw+B9r1plu88bb3Tsc6gNw2AF3caNDBmHDnVtoFtYCBw5wvPjhx+A779n4Pzaa46ZtVZUxHPOev5dvMjQ08+PAejgwWzBdPRrUFQEvP8+2yfrapOnYbDdfMMGPt8xYzi/TqQeq1etnY5ius4PlKSkJKSkpGDOnDl4/PHHERQUhBkzZuC5556r4yMUEREREannQkN56devbHBgNrNqZc4cVx9hxby8GDYNH84KtNatXX1EtefhwbbAqCj+PTvbVq22ZQuXSAQEMFDr14+VTPZWq+3ZAyxYwNbF4GDg97+vOgwyjLoLsiwWVkqlpnKxQf/+dfO4lfH2Zoi8ZQvDPYuF742jFhZ4edlasG++mcHpihXcVvnJJwwVBwywhdrVrYar7HGnTAHeesv5mzwNA/j5ZwZo6elcunDnnaz8E5G6r0hzJFWkiYiIiIiUYhhsO2za1NVHcn31ZQZaXbBYOPMsLY1ztdLTgfPnWVlmDVrCw8sGXxYLWzk/+gj46Se20916KzB5sn0B3PbtrMSyVsO1beuc1zs/H5g/n8P5b7uNs+Rcbc8eHlNqKl+r0aM5r61Zs6q/d/duvlYhITV//NxcBlCnTzPUvnSJAdjYsUCpBXy1cvo08O67nJXm6E2excWc/7dxIz+n7dqxajQ+3n3axqXRa5AVaSIiIiIi4iQmU9UhmsUCfPYZq3QSEmoXHNREYwnRAD7Xdu14AWzVamYzK5i++47VavHxbMc9cIDVTUePslrvt79liFadiqaWLVlBlJrKiiI/P963dWnBL/Ooa+XqVeDf/+Y8shdfdH2l0vHjwHPPccNl8+bAr38NTJxof+Vfbi7wxRcMkkJDbSFnVQsertWkCWfYJSUx1M7I4HtdeqbZqVOsGrVWq1W3qiw8HLjjDm7ybNGCrbSO8vPPwLJlbE299VYGiyJSjoI0EREREZHGJC+Pl1WrODQ9JKRscFDTdjGLhcFKbi4rlKS8pk1tQUtxMSvUfvoJ+O9/gfXrWeXVpg0wfjwwdWrNZqtFR/NiGAxtzGZePv+c191wAwfo11RODvDhh5zZ9dJLQOfONb8vRwkK4vE8+ig/e9UJvwAGYE88wRZps5nvyZYtPBdiYnhudO9evUDTZLK1YJeWnc3tm9u28Tijo3mb9u0ZeNqjUyfOo/v3v/mZGT3a/uOqTMeOwCOPMIwVketSkCYiIiIi0pgEBHA+WUGBLTg4cADYupXBQXQ05yHZO1OqqIjVPJ98wraz7t0VpNnj6lXg22/52l26xPliffqwyujwYeDtt1k9Zm3RjIur3pwv68KDNm2AIUMYgB06VLu238xMzmy7epUzuurLjLtmzfh61YavL4Okjh0ZOJ47Z6seXLOG4acjdOjA0OzCBdv9v/8+W2RbtODn4PbbeR5WFggOGcIW3u3bec45YpOnh4dCNBE7aEaaiIiIiEhjZxiciZSWxmUA9sxeys8HlixhK1hGBgOIadOA5OTG1b5ZXZcusaJr5UpW7/XuzWCza1fbbYqL2a5oDVrOneNrOnIkg8uEBLZvOmNuVWYmQ7drq+HOn2eI5ukJ3H133bcEu1JRUfWr3KojO5sViVu2cAtoy5Zlq+E6dKh4S2dhIUO4rCz7NnleuMD3TeenNGCakSYiIiIiIs5nMgGtWvFSlfx84I03uJ0wJ4dD1P/8Z0D/Y7tqn37KyqniYg5xv+cehiXX8vTk9TExDM8uX2agVlzMwGXNGlaWWeeexcY6bivlrl22TaPWajhfX7afBgUxLK3LZRYXLnDxQv/+3HrqClWFaIbBJQdt2vA1i4rie2ivpk2BMWN4sYba1pbcr7/mdX378rbFxfy7lxfDtilT+JmqbJNnejrn5R04wKUVnTrZf2wiUo6CNBERERERsV9xMfDOOwwN7r6bLWZRUa4+KvcQGwsMH84KtOq0RQYHs3INAHr1YrWa2cyKtdRUVhhFRbE1sG9fVjTVtFpt0CAGeNYgZ/duYN06hjCtW3PIvb8/gyJPT4avffuyIup6PvkE2LePlVdeXrx4etr+jItjINuxo+17TpwAPviAj11QwG20Z84wKPL05J/WMMnLi5/H0FCGfhUpLuafJpPjK7KuXmV76e7d3Hjp68v32hp0VlUpVlrpUHvAAN53adaFALGxtqBz6lRu8vz8c7Zlm0wM2w4dYoB29Cg/G7fdxrZSEakVBWkiIiIiImI/Hx+2JlrDnA8+4HWlf7EPDnb1UdZPPXvWvnLPy4uvdWwslwZkZtpCr5QUYMcOBjfWBRIxMdcPlypSetPoiBFsG0xMZBXc+fMMdnx8uB0zNNS2/bUyRUVsY718mYHWtZecHAZHHTsysJs/nzP7fHz4HPv3B776Cnj55es/xqBBrMiqqMIPYPi0ciVfJ09PPs/Sf7ZqxcepLBBctIiBYE5O2SCwdCAYGcnbms3Al18y0AoLY3haVMTw89ixsiFg6T/btGEQan3Prn3vWrbkcR45wg2vFgurRAsKgOXLWd3Wpg0DtDNn+M+TJ/O1dUYrsEgjpCBNRERERETs5+3N6qEePRgSnD1rq46y/mL/4INAeLirj7RxaNaMSwr69AEmTWJIYw3WfviBAY817EpMZMhWnUAlKAiYOJGXima3ZWQwyKnM1Km8VObQIeCxx4A9e7hkYcoUXoKC+Jnq04dhXFERQ6OiIs4Is/4ZElJ+Q2Zpffow/Dt1it9z7SUw0L5qLWt1nDUELCri8VmPKyKCVZqDBnE77qFDfM2aNGFI+OmnwObN17//wYOB++5jkFmRdesYCKal8XELCjhXLTOTr88XX3DzZvv2bB2OjlaAJuJgWjYgIiIiIiKOkZ/PjZMdOzaugeYWC/DNN6x6ev55Bjb1QUaGLfQ6coRh0v79tsrBmBhWfdWUdXZbYGDZtsyaOHwY+NOfgLFjq7c11p0YBgO3q1dtYWBBAQO506dZTWYNBK/3vhw/znDuzBkGiNawzmxmxWBcHD+DCrKlkdKyARERERERcR9+fkDnzlXfbvduVlK1bevegVtBAcOzTz9ldVZsLIfj15cgLSSE88v69mXocv48Qz+zmS2gnp6sWLLO8goNrV71UunZbZW5coXVUpXNbouNBT7+uGFXT5lMrOi8diFAQYGtRTouju9HXBwDymtFRfFy5Qq3fG7fzgDuvvu4MbeyqjwRcQgFaSIiIiIiUncMg1shL11i8GYNDuLjKw4O6qPcXGDhQs7Aysxky+Ts2cANN7j6yK7P25tthxERwOjRbAe0tuSmpLBdsLiYzyEhgQFbRRsga2LPHmDVKgZvpTeNXlt11ZBDtMp4e3O+m7Uld+lSXh8RYZt1Fx7O4DMnB/juO25X9fRkkNm/f91uUhVp5BSkiYiIiIhI3TGZgFmzOKvKGuR8/jkDtvBwhgZ9+tTfYGDxYg7Dz89niDF9OtCli6uPqvpCQ3np358VUUePssLp4EFg2zYOv7dWqyUksLqtpjp2ZFjXrBlbTK3VcO3a2e6/RQtHPTP3YzJxSUFkJDB0KKvNDh3iYoM332Tb6/PPA8OGsYLz8GHOUuvTp2G2wIrUcwrSRERERESkbplM3CbYpg0Hs+fk2OY8bd9e+82WztS0KVsl77nn+hsi3Y2PD4fTt2/PQPPCBdtstVWrgK+/ZpA2dCjg788AzJ5qtTNngAULuPGzsBB4+GFWXmVk2Kqv1qxhqHrnnc5/nu7CYmGouXw5kJ3NoDY6ml/z9wcefbTxVu+J1AMK0kRERERExLUCAoBu3XixWOr33LQxY3hpqEwmzjJr2ZJtnlevsoosLY1z4NavZ4hWulrt2plwR46wam/DBr6XQ4cCd9/NiiuAoVy/frwUFla99bOxOHeOwePq1awS7N+fFY/XLnKoKEQ7coSVbHFx3BAqIk6jIE1EREREROoPe0K0jz5iZY51KLuCA+fx9WWQ07Ejq9W6drW15K5cCaxYwbbM+HgGORs3cn5XkybAbbcBd91VeVtoRcP3r5WeDuzdy/fb3mo4d/Pyy8BXX/E1HDoUeOghLhWw188/s4rN2iZqDTlbt1b1moiDKUgTERERERH3YZ2ldvAgh9hb20RLD2WvSXBgsbCC6uuvgb/9rfwgfOHr2qoVLwMGsFotLY0tiE8/za2gnp6c33X33WxJbNas9o+bnc2gaOvWqqvh3FFBAV+7wEC2NY8dW70QDQBuvplbO60tuRs3At9+y/uMjweSkhhCikitKUgTERERERH3YTJx6PqwYQxYrMHBpk3cZhgQAEybxkDNVw6/1AAAIABJREFUHhYLw7PFi4Hjx7kp8cyZ6gcZjY3FwqBm8WIuKmjbFpg8GejenW2G337LBQOtWjHISUjga+rpWf3H6tSJFXEXLthmq33zja0armdPhkjuJjeXix22buXSgHvuYUDZqlXN7i8oiK9Fz57cwHrihO31atNGQZqIgyhIExERERER99S0KSttkpLKBgf2bJgsKACWLgU+/ZTVQPHxwF/+wra6+jyjzdUKCoBly4D//peBY0wM8D//A4wcaXvdhgzh3LPDh/l+7NnDoDMmhq2GLVowWAsKsv9xS89uS062zW4zm/neu5MrV1j9+MMP/HvPnpxH54jqPStPT1buRUfzvTEMx923SCOnIE1ERERERNxf6eCgKm++aduI2LUr8Pvfc/C9VO7sWeA3vwEuXQISE4HHHmMAVBE/P6BzZ14Mg6FbXh6wdi1neRkGEBZma9GMjKxetVrp2W1VKSpiEFeTarjaKipi8LhvH/DUUzyO/HyGi8nJ3AAbEOD846iq3Xn9em7Otb4fLVtqtprIdShIExERERGRxsMwgO+/BzIzOVdq8GCGb4ah4KAqLVuy2mz4cM4/s5fJZGu1jY1loHboEKvJdu5kdZafH6vV4uKAHj1Ybego+/cDX37Jx7YGRdWphquJ3Fy2vX7xBYPHLl14XUAAK/Iefxzwqke/jrdowYrOtWu5NTQ42NaSGxurmYEipZgMw31rPFNTU9GrVy/88MMP6Nmzp6sPR0RERERE3EFGhq3t8PBhoLCQwYo1ZImJYcWTOJ9hAKdP871Yv56tox4eDN6sQU5kZO3abTMyWBFmNjMsKl0NFx/P+W6OqlbLyOBW2ZUrGZz17s3FC926Oeb+na2oCDh2zDZb7eJFvjbDh7vnHDppdOoiJ3JqkPb+++/jvvvuq/BrZ86cQatrhihu2rQJTzzxBHbu3ImgoCBMmjQJzz77LAKuU+qqIE1ERERERGqldHCQlsaB9sOGAYMGufrIXMPVlXm5uXwfrJfcXMDfn5VqnToB7dtzc2dN5eXZQtS0NM4rS0gA7rqrdsdtsQAvv8wALTubs/YeeIChrDvLyOBrFRHBwFGknquLnKhOakmfeeYZxFzzAyQ4OLjM33ft2oXhw4cjMTERL7/8Mk6cOIEXXngBZrMZK1asqIvDFBERERGRxsbLiyFNXBz/fulS7YIad5WaCixYwJlmM2e67jiaNGH1VrduDKdOnbKFXnv3cltnYKCtejAionrVav7+nO+WmGirhnPEsgKLhcfYpAk3l06Y4P4hGsDFHfbMD8zI4OsZEqIWaWnw6iRIGz16dJVJ4JNPPonQ0FCsXbsWgYGBAIDo6GjMnDkTq1evxsiRI+viUEVEREREpDFr3rzq2/z8MwO3+HggNNR9gwOLhfPiPv4YOHiQz2XwYFcflY2HB9s6IyNZ4VVUZGvR3LYNWLeOwVVcHEO1uLjqDe43mRjEVSUzEzh6lO/3L7+rlsjPB3bs4AKF1q1ZzThwoG0mXGOxeTOwfTvPH2vIGR3dOENpafDqJEgzDAPZ2dlo0qQJPCvoPc/KykJKSgoef/zxkhANAKZPn47Zs2djyZIlCtJERERERKR+OHkS2LSJbXzuGBwUFQErVgBLlgDp6UCbNsDs2cAtt9SvAfjX8vJitVf37gwB09NZBWY2s1otK4vth0lJtmo1R4Scx44Bn3/OiquICAZqbdpw3tr27Xw9e/TgDLHQ0No/njsaOZKvudnMUHbbNr5f0dG8vkMHoFkzVx+liEPUyU/JoUOH4sqVK/Dx8cGoUaPw4osvIj4+vuTre/fuRVFREXr37l3m+7y9vdGjRw/s3LmzLg5TRERERESkataqo6NHywcHMTFshSv1+069cvgw8MQTnAWXkAA89RQ3cdZmmL8reHgAUVG8DBvGuWRbtrDFcMsWbp8MCLAtLIiLY1tnTSQmsgpx9262Lm7fzgoskwno0wfo39+xW0bdkY8P59e1b8/A8cIFnhubNwNLlwKTJwOTJrn6KEUcwqlBWkBAAO69914MHToUQUFB2LFjB1566SUkJycjNTUVkZGRAIDTp08DAMIrKH9t3bo1NmzY4MzDFBERERERqZ6KggNrdVROjquP7voiIhgMjR3LEKihaNqUVVEAZ56lp9s2T+7ezdArMZGVUS1asA2zqmq1/Hzgs88YBF24AHTpAtx+O0O87GxudvXzc/5zc0cXLrANNzubQW2PHq4+IhGHsTtIMwwDV69eteu2fr/8MJk4cSImTpxYcv2tt96KUaNGYdCgQZg7dy5ef/11AEBeXh4AwLeCFdN+fn4lXxcREREREal3TCagZUtebrih6ttbLK6rAPPzA/72N9c8dl3x9ATateNlxAi2fKalMWBbuZJBp3VhQXw8q9VKB2JZWcDChcDy5dzq2a0b8Mc/AqU7qK5Znifg67tnD7BxI4O06Ghg2jS+vu46R1CkAnYHaevWrcOwYcPsuu2BAwfQvn37Cr82YMAA9OvXDykpKSXX+f9SYltRUJefn48mTZrYe5giIiIiIiL123ffAQcO2GarRUUx/BHnCAoCrMvvevbkbDNrtdrOnQw127Zlpdr69QyDCgvZonvPPaxik8odPgwsW8YQsmNHVu790oEm0tDYHaR16tQJ77//vl23bd26daVfj4yMxMGDB0v+bm3ptLZ4lnb69GlEVLFJZfbs2Qi+5v8ITJ06FVOnTrXreEVEREREROpMTAwrnfbu5dICHx8gNtYWrAUF1ex+9+0DPvgAGDSIiwOkPE9PVkpFR7MV9PJlbv/84APOPvP1ZQXVXXexujA62sUH7CaaNeNneMAAVmaK1IGFCxdi4cKFZa67fPmy0x/XZBiG4fRHuUbv3r2Rk5OD/fv3A+ATbdmyJWbPno1//OMfJbcrKChAaGgopkyZgrfffrvc/aSmpqJXr1744Ycf0NP6fxhERERERETcgWEAZ8/aqqNOnOB1w4YxDLPX1q3Ahx8ymAsOBu6/H7j1Vucdd0Px448M0HbsYGvn0KF83TMz+X5cuGBbamANOVu2VJuiSD1WFzmRU5cNnD9/Hi2vSaNXrFiB1NRUPPbYYyXXBQcHY8SIEfjwww/xl7/8BYGBgQCABQsWICcnp8ycNRERERERkQbBZOLQ+9atgRtvBPLy2CIXGlr191oswJo1nOV1+DDQqhXw0EPA+PGscJPr27yZweO+faykuusubpX85fdQAMCoUcClS7YFEmvXAqtXM2zLzuZstdhYvdYijZBTg7Tk5GT07NkTvXr1QnBwMFJTU/Huu+8iKioKTz75ZJnbzp07F8nJyRg8eDBmzpyJ9PR0vPTSSxg1ahRuuukmZx6miIiIiIiI6/n7c7NkVb7/HnjhBbYlRkcDTzwB3Hyz6xYYuJO//Y0z6sLCgIcf5iyv64VhzZtzs2mfPkBREXDsGF/z3btZxWZdamCtVgsNbZjVaidOcIHAmDE1bzsWaUCcGqRNmTIFX331FVatWoXc3FxERETgwQcfxF//+tdylWpJSUlISUnBnDlz8PjjjyMoKAgzZszAc88958xDFBERERERcS9nzrAltGdPVkhFRAC5uWUrqqRiI0YAffuy4qw6waOXF2enAXzdL160VautWQN88w2r25o352y1mBjA29s5z6EuGAaf34YNDBBbtmQlnoI0EdfMSHMUzUgTEREREZFGx2IBTp2yBTknT/L6iAhbdZQ2JtadwkLg6FHg4EEuj/D2ZvAWHc0WUGu1mjuwWNjyumEDw9rISLYdt2/fMKvtpMFx+xlpIiIiIiIi4mAeHgw4IiOBIUOAnBxbqLZtG3DgAOelNUZFRQyx6pK3ty3AHDMGyMiwLZBYvRpYuRIICeHXe/bkP9fHarWDB4EVK7hswfpcoqIUoIlcQ0GaiIiIiIiIOwsIALp358ViYQteY5OeDixYwPll8+bxNXEFk4nVZ6GhQP/+QEEBcOQIQ7Wff+ZtfviBrZ/W8K15c9cc67V8fBjOTpnCBRgiUiEFaSIiIiIiIg2FhwcQHFz5ba5cYaVUQgLnfvn7182xOcPBg8D8+cCWLazyGj6c873qCx8foEMHXgyDm0CDgxmsffMNK8BCQ22hWlSU66rVoqN5EZFKKUgTERERERFpTLKzubBg925WULVta5vl1bq1e7TypaayAm3XLlafTZgA3HVX/R6GbzKxrTM5mZerV23Vaj/9xDAwI4OVbNZgrVkzVx+1iFxDQZqIiIiIiEhjEh7OGWpZWbbZahs3At9+CzRtCnTsyPlY9TFQMwzgT3/iUP/QUOD++xmi+fm5+siqz9eXr3XHjnxeZ8+y7fP8eeDrr4GvvuK2zNLVap6eNXusU6c4P++WW+rnfDYRN6IgTUREREREpDEKCuLw+549geJi4PhxBmtXr9bPEA3gcfXtC/Trx1CorhcLOIvJxGrAW27h3/PzgcOH+X7s3cvg0McHuPlmhm7x8VW38BoGK942bOB9hYSwtbRVK+c/H5EGrIH81BEREREREZEa8/TkAPyYmKpvaxgcou/r6/zjqsjtt7vmceuSnx/QuTMv1mo1s5nz7b77jte1amWrVmvb1latZrFwc+uGDaxECw8HJk4EOnXiDD0RqRUFaSIiIiIiImK/s2eBt94C2rVjiBMfzxZER1WxGUb9rYhzBWu1mnWTZp8+rDAzmznnbuNGhprt2vHrFy/yEhMD3H03EBur11PEgRSkiYiIiIiIiP2CgthiaDazOmrVKrYZWqujYmLYhlhd585xgcChQ8C//13zeWANnb8/kJjIi2EAR49yptqyZXxvRo4Exo8HIiNdfaQiDZKCNBEREREREbFfkyacU9a3L1BYCBw7xlDNbAZ27GCo9rvf2V8FdewY8P77wPr1bD0cMoT3qyCtcjk5XCCwbRvn2t11F3DDDZqBJuJkCtJERERERESkZry92doZHw+MHs2WwkuX7AvRfvwR+OADhm9+fsC4cWxFDAlx/nG7s8xMYPNmIDWVr3OvXkD//lUvHxARh1CQJiIiIiIiIo4RGspLZQoKgPvv55yvli1ZSTV5MhAYWDfH6M4OHQI++ojB48CBnJfWpImrj0qkUVGQJiIiIiIiInXn/HkgNxeIiGArYkQEcOYMEB0NeOlX1EpFRXE+XY8eNZtDJyK1pp9SIiIiIiIiUnfatAE+/hg4cgRISwMOHAC2bmWbaEwM20QTE4GAAFcfaf3j7c3ZdCLiMgrSREREREREpG75+gIdO/JiGKxSsy4sWLmSYVtjDNIsFs49s3dRg4jUOQVpIiIiIiIi4jomEzdNtmoFDBjADZSNrW2xsBDYuRPYtAm47TZW5olIvaQgTUREREREROoPX9+qb7NyJeDpyTbQqCj+szvKywO2b2dra24u0KUL0LSpq49KRCqhIE1ERERERETci8UC/PgjsHEjg7fYWCAhgcFaUJCrj65q2dnA5s3Ajh18LklJQHIy0Ly5q49MRKqgIE1ERERERETcy5gxwOjR3PZpNnNpwZdfct5aWBgwbhwQGenqo6zYTz8Bn33GxQH9+vESGOjqoxIROylIExEREREREfdjMgHh4bwMGsQ2yUOHGKzV5/bItm2BYcOA3r3ta2MVkXpFQZqIiIiIiIi4P39/zhjr0qXq22ZlsQrMw8P5x3Wtpk25VEFE3JKCNBEREREREWlcFi0CLl0C4uJss9UCAhxz34bBajkRaZDqJH5PSUnBsGHD0KxZMwQFBaF3795YsmRJudtt2rQJAwcOREBAAMLDw/HYY48hJyenLg5RREREREREGosxY4C+fYGMDGDpUuCf/wTeegv47jsgPZ0LAKqrqAhITQX+8x/g4kXHH7OI1AtOD9Lee+89jBo1Cr6+vnjuuefwwgsvYNCgQUhPTy9zu127dmH48OHIz8/Hyy+/jBkzZuCtt97CxIkTnX2IIm5v4cKFrj4EEZfSOSCi80AE0Hkg1RAZCQwdCjzwAPD73wPjxwMhIcDWrcA773DOmr2uXgU2bQJefRX44gugRQvnHbcddB6IOJdTWzuPHj2KRx55BLNmzcLLL79c6W2ffPJJhIaGYu3atQj8ZWNJdHQ0Zs6cidWrV2PkyJHOPFQRt7Zw4UJMnTrV1Ych4jI6B0R0HogAOg+khgIDge7debFYWJHWunXV35eTw+Bt2zagsBDo1g1ITgZatnT+MVdC54GIczm1Iu2NN96AYRh4+umnAQBXrlyBYRjlbpeVlYWUlBRMmzatJEQDgOnTpyMwMLDCNlARERERERERh/LwAKKiAB+fym/31VfAK68AW7YASUnArFnAbbe5PEQTEedzapCWkpKCjh07Yvny5YiMjERQUBBatGiBp556qkygtnfvXhQVFaF3795lvt/b2xs9evTAzp07nXmYIiIiIiIiIvZr2hQYOBD43e+AUaOA4GBXH5GI1BGntnaazWZ4eXnhvvvuw5w5c9C9e3d89tln+Pvf/46ioiI8++yzAIDTp08DAMLDw8vdR+vWrbFhwwZnHqaIiIiIiIiI/QYNcvURiIiL2B2kGYaBq1ev2nVbPz8/ALZWzn/84x/4wx/+AAAYP348MjIy8Oqrr+JPf/oTAgICkJeXBwDw9fWt8L6sX7+W9fr9+/fb+zREGqTLly8jNTXV1Ych4jI6B0R0HogAOg9EAJ0H0rhZ86Hr5UiOYHeQtm7dOgwbNsyu2x44cADt27eHv78/8vLyyg06nDJlClauXImdO3di4MCB8Pf3B4AKg7r8/Hw0adKkwsc5evQoAGDatGn2Pg2RBqtXr16uPgQRl9I5IKLzQATQeSAC6DwQOXr0KAYMGOCU+7Y7SOvUqRPef/99u27b+pcNJxERETh06BDCwsLKfL1Vq1YAgEuXLgGwtXRaWzxLO336NCIiIip8nFGjRuHDDz9EdHR0SRgnIiIiIiIiIiKNT15eHo4ePYpRo0Y57THsDtLCwsIwffr0at157969kZaWhvT0dMTExJRcf+rUKQBAy182mnTp0gVeXl7Yvn07JkyYUHK7goIC7Nq1C1OmTKnw/lu0aIG77rqrWsckIiIiIiIiIiINk7Mq0aycurVz8uTJAIB58+aVXGexWPDee+8hNDS0pNw0ODgYI0aMwIcffogrV66U3HbBggXIycnBxIkTnXmYIiIiIiIiIiIiVTIZhmE48wFGjhyJb7/9FjNnzkS3bt2wbNkypKSk4K233sKMGTNKbrdz504kJyejc+fOmDlzJtLT0/HSSy9h8ODB+Prrr515iCIiIiIiIiIiIlVyepCWk5ODP//5z1i8eDEyMjLQsWNHzJkzp9wCAgDYuHEj5syZg9TUVAQFBWHSpEl47rnnEBAQ4MxDFBERERERERERqZLTgzQREREREREREZGGwKkz0kRERERERERERBoKtwzSrl69ijlz5iAiIgJNmjRB//79kZKS4urDEnG47du347e//S0SExMRGBiIdu3aYfLkyTCbzeVuu3//ftx8881o2rQpQkNDMX36dFy4cMEFRy3ifHPnzoWHhwe6du1a7ms6F6QhS01Nxa233orQ0FAEBASga9eu+Pe//13mNjoHpKEym82YMmUK2rZti4CAAHTq1AnPPPMM8vLyytxO54A0BDk5OfjrX/+Km2++GSEhIfDw8MD8+fMrvG11PvPz5s1Dp06d4O/vj/bt2+O1115z5tMQqRV7zgPDMPD+++/j1ltvRVRUFAIDA9G1a1fMnTsXV69erfB+a3seuGVr59SpU/HZZ59h9uzZSEhIwHvvvYft27fju+++c/qaU5G6NGHCBGzevBkTJ05Et27dcPr0abz22mu4cuUKtmzZgsTERABAeno6kpKS0Lx5c8yaNQvZ2dl44YUXEBUVhW3btsHb29vFz0TEcdLT09GhQwd4eHggJiYGe/bsKfM1nQvSUK1atQrjxo1Dr169MHnyZAQGBiItLQ2GYeD5558HoHNAGq4TJ06gW7duaN68OX7zm98gJCQEmzZtKvnladmyZQB0DkjDcfToUcTGxqJdu3aIiYnB2rVr8f7772P69Ollbledz/ybb76Jhx56CBMmTMCoUaPw/fffY8GCBXj++efxxBNP1PVTFKmSPefBlStXEBQUhBtuuAFjx45Fq1atsGnTJsyfPx+DBg3Ct99+W+Y+HXIeGG5m69athslkMl588cWS6/Lz8434+HgjOTnZhUcm4nibNm0yCgsLy1xnNpsNPz8/Y9q0aSXXPfTQQ0ZAQIBx4sSJkutSUlIMk8lkvPXWW3V2vCJ1YfLkycaIESOMIUOGGF26dCnzNZ0L0lBdvnzZCAsLM+68885Kb6dzQBqquXPnGiaTyfjpp5/KXH/PPfcYJpPJyMzMNAxD54A0HFevXjXOnj1rGIZh7NixwzCZTMb8+fPL3c7ez3xubq4RGhpqjBs3rsz3T5s2zQgMDDQuXbrkpGciUnP2nAcFBQXG5s2by33v008/bZhMJiMlJaXkOkedB27X2vnpp5/Cy8sLDzzwQMl1vr6+uP/++7F582acPHnShUcn4lg33HADvLy8ylwXHx+Pzp0748CBAyXXffbZZxg7diwiIyNLrhs+fDjat2+PJUuW1Nnxijjb999/j88++wyvvPIKDMOAyWQq83WdC9JQffzxxzh37hzmzp0LgK0OFoul3O10DkhDlZWVBQBo1apVmetbt24NT09P+Pj4ANA5IA2Hj49PyefdqKSJzN7P/HfffYeMjAw8/PDDZb7/kUceQU5ODr766isHPwOR2rPnPPD29kb//v3LXX/77bcDQJnfmx11HrhdkLZz5060b98egYGBZa7v06cPAGDXrl2uOCyROmMYBs6ePYsWLVoAAE6ePInz58+jd+/e5W7bp08f7Ny5s64PUcQpiouL8eijj2LmzJklbc2l6VyQhiwlJQVBQUE4ceIEOnTogKZNmyI4OBgPP/xwyfwPnQPSkA0dOhQAcP/992P37t04ceIEFi9ejDfeeAOzZs2Cv7+/zgFpdKrzmbf+87W37dmzJzw8PPR7tDQ4Z86cAYCS35sBx50HbheknT59GuHh4eWut1536tSpuj4kkTr10Ucf4dSpU5g8eTIAnhMArnteZGRkoLCwsE6PUcQZ3njjDRw/fhzPPPNMhV/XuSANmdlsRlFREW6//XaMHj0a//3vf3HffffhjTfewL333gtA54A0bKNGjcIzzzyD1atXIykpCe3atcPUqVMxa9YsvPjiiwB0DkjjU53P/OnTp+Hp6VkmVABY8RMaGqrfo6XB+b//+z8EBwdj9OjRJdc56jzwqvom9UteXh58fX3LXe/n51fydZGG6sCBA3jkkUeQnJyMe+65B4DtM1/VeaHhuuLOLl68iKeeegpPPfUUQkNDK7yNzgVpyK5cuYLc3Fw89NBDeOWVVwCwZaGgoABvvvkmnn76aZ0D0uC1a9cOgwcPxp133onQ0FAsX74cc+fORVhYGB555BGdA9LoVOczn5eXV9ICfS1fX1/9Hi0NyrPPPos1a9bg9ddfR1BQUMn1jjoP3C5I8/f3r3CFaX5+fsnXRRqiM2fO4JZbbkHz5s3x6aeflsyGsn7mdV5IQ/bnP/8ZLVq0wKOPPnrd2+hckIbM+tmdOnVqmeunTp2KN998E1u2bEHHjh0B6ByQhmnRokV48MEHYTabERERAYBhssViwZw5czB16lT9e0Aanep85v39/VFQUFDh/eTn5+vckAZj8eLF+Mtf/oIZM2bgwQcfLPM1R50HbtfaGR4eXmG5nbWs1fovVpGG5PLlyxg9ejSysrKwcuVKtG7duuRr1lJu6zlQ2unTpxEaGqr/8ypuzWw24+2338ajjz6K9PR0HD16FEePHkV+fj4KCgpw7NgxXLp0SeeCNGjW/74JCwsrc711AO+lS5dKbqNzQBqi//znP+jZs2e5/9YfN24ccnNzsWvXLv17QBqd6nzmw8PDUVxcjAsXLpS5XUFBATIyMvR7tDQIq1evxvTp0zF27Fi88cYb5b7uqPPA7YK0pKQkHDx4ENnZ2WWu37p1KwCgR48erjgsEafJz8/HuHHjkJaWhuXLl5dUHFi1adMGLVu2xPbt28t977Zt23ROiNs7efIkLBYLZs2ahdjY2JLLtm3bcPDgQcTExOCZZ57RuSANmnUobnp6epnrrf9zsWXLloiIiNA5IA3W2bNnUVxcXO566/ynoqIi/XtAGp3qfOaTkpIAoNxtd+zYAYvFovND3N7WrVsxfvx49O3bF0uWLIGHR/m4y1HngdsFaRMmTEBxcTHeeuutkuuuXr2K9957D/3790ebNm1ceHQijlVcXIzJkydj69at+OSTT9CvX78Kb3fnnXdi+fLlZX7BWrNmDcxmMyZOnFhXhyviFF27dsXSpUuxbNmyksvSpUuRmJiIdu3aYdmyZbj//vsB6FyQhmvSpEkAgHnz5pW5/p133oG3tzeGDBkCQOeANFwdOnRAamoqzGZzmesXLlwIT09PdOvWDYDOAWl87P3MDxs2DCEhIXj99dfLfP/rr7+OgIAA3HLLLXV2zCKOtn//ftxyyy2IjY3F8uXLK5wbCDjuPDAZhmHU+qjr2OTJk7F06VLMnj0bcXFxmD9/Pnbs2IE1a9Zg4MCBrj48EYf53e9+h3/9618YN25chf/xN23aNACsUEhKSkKzZs3w2GOPITs7G//85z8RFRWF7du3q41BGqQhQ4bg4sWL2Lt3b8l1OhekIZsxYwbeffddTJo0CYMGDcLatWvx6aef4sknn8Tf//53ADoHpOFav349hg0bhtDQUPz2t79FSEgIli9fjpUrV2LmzJl48803AegckIbltddeQ2ZmJk6dOoU33ngDd9xxR0nFzKxZsxAUFFStz/zrr7+ORx55BBMmTMBNN92E9evXY8GCBXj22Wfxxz/+0VVPU6RSVZ0HJpMJiYmJOHXqFJ599tly7ZnInG29AAABbElEQVTx8fHo379/yd8dch4Ybig/P9/4wx/+YISHhxt+fn5Gv379jFWrVrn6sEQcbsiQIYaHh4dhMpnKXTw8PMrcdt++fcaoUaOMgIAAIyQkxLj77ruNc+fOuejIRZxvyJAhRteuXctdr3NBGqrCwkLjb3/7mxEdHW34+PgY7du3N1599dVyt9M5IA3Vtm3bjDFjxhjh4eGGj4+P0bFjR+O5554ziouLy9xO54A0FNHR0WX+29/6e4GHh4dx7NixkttV5zP/9ttvGx07djR8fX2NhISECv89IlKfVHUeHDlypOTvFf3efO+995a7z9qeB25ZkSYiIiIiIiIiIlLX3G5GmoiIiIiIiIiIiCsoSBMRERH5/+3YgQAAAACAIH/rFQYojAAAYBBpAAAAADCINAAAAAAYRBoAAAAADCINAAAAAAaRBgAAAACDSAMAAACAQaQBAAAAwCDSAAAAAGAQaQAAAAAwiDQAAAAAGALolmEojl6PcQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002BFB8A90>)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "WARNING: using PyPlot.mesh in module Main conflicts with an existing identifier.\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7ff23ca63250>)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1896,9 +1259,16 @@
     "full(D[nz1,nz2])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple test case, sliding contact"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -1906,17 +1276,18 @@
     {
      "data": {
       "text/plain": [
-       "Dict{Int64,Array{Float64,1}} with 7 entries:\n",
-       "  7 => [4.0,0.0]\n",
-       "  4 => [2.0,6.0]\n",
-       "  2 => [2.0,2.0]\n",
-       "  3 => [2.0,4.0]\n",
+       "Dict{Int64,Array{Float64,1}} with 8 entries:\n",
+       "  7 => [2.0,0.0]\n",
+       "  4 => [2.0,4.0]\n",
+       "  2 => [2.0,6.0]\n",
+       "  3 => [2.0,2.0]\n",
        "  5 => [0.0,0.0]\n",
+       "  8 => [4.0,0.0]\n",
        "  6 => [2.0,0.0]\n",
        "  1 => [0.0,2.0]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1927,33 +1298,36 @@
     "    ContactProblem, SmallSlidingContact, Element\n",
     "using JuliaFEM.Core: PlaneStressLinearElasticityProblem, DirichletProblem,\n",
     "                     get_connectivity, Quad4, Tri3, Seg2, LinearSolver,\n",
-    "                     update!, get_elements, BiorthogonalBasis\n",
+    "                     update!, get_elements, BiorthogonalBasis, StandardBasis\n",
     "nodes = Dict{Int64, Node}(\n",
     "    1 => [0.0, 2.0],\n",
-    "    2 => [2.0, 2.0],\n",
-    "    3 => [2.0, 4.0],\n",
-    "    4 => [2.0, 6.0],\n",
+    "    2 => [2.0, 6.0],\n",
+    "    3 => [2.0, 2.0],\n",
+    "    4 => [2.0, 4.0],\n",
     "    5 => [0.0, 0.0],\n",
     "    6 => [2.0, 0.0],\n",
-    "    7 => [4.0, 0.0])"
+    "    7 => [2.0, 0.0],\n",
+    "    8 => [4.0, 0.0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "el1 = Quad4([5, 6, 2, 1])\n",
-    "el2 = Tri3([1, 2, 3])\n",
-    "el3 = Tri3([6, 7, 4])\n",
+    "basis = StandardBasis\n",
+    "\n",
+    "el1 = Quad4([5, 6, 3, 1])\n",
+    "el2 = Tri3([1, 3, 4])\n",
+    "el3 = Tri3([7, 8, 2])\n",
     "del1 = Seg2([5, 6])\n",
-    "del2 = Seg2([6, 7])\n",
-    "sel1 = Seg2([3, 2])\n",
-    "sel2 = Seg2([2, 6])\n",
-    "mel1 = Seg2([6, 4])\n",
+    "del2 = Seg2([7, 8])\n",
+    "sel1 = Seg2([4, 3])\n",
+    "sel2 = Seg2([3, 6])\n",
+    "mel1 = Seg2([2, 7])\n",
     "update!([el1, el2, el3, sel1, sel2, mel1, del1, del2], \"geometry\", nodes)\n",
     "for el in [el1, el2, el3]\n",
     "    el[\"youngs modulus\"] = 90.0\n",
@@ -1968,7 +1342,7 @@
     "end\n",
     "prob = PlaneStressLinearElasticityProblem()\n",
     "push!(prob, el1, el2, el3)\n",
-    "bc = DirichletProblem(\"displacement\", 2; basis=BiorthogonalBasis)\n",
+    "bc = DirichletProblem(\"displacement\", 2; basis=basis)\n",
     "push!(bc, del1, del2)\n",
     "con = ContactProblem(\"cont\", \"displacement\", 2;\n",
     "                     contact_type=SmallSlidingContact)\n",
@@ -1977,7 +1351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -1986,64 +1360,67 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: slave dofs of element: [5,6,3,4]\n",
-      "INFO: normal dofs: [5]\n"
+      "INFO: slave dofs of element: [7,8,5,6]\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "JuliaFEM.Core.BoundaryAssembly(JuliaFEM.Core.SparseMatrixCOO([5,3,5,3,5,3,5,3,6,4  …  3,11,4,12,4,12,4,12,4,12],[5,5,3,3,11,11,7,7,6,6  …  7,7,4,4,12,12,12,12,8,8],[0.00955015,-0.0206644,0.194034,-0.419847,-0.132539,0.286786,-0.0710448,0.153725,0.00955015,-0.0206644  …  0.139949,-0.064678,-0.419847,0.194034,-0.0206644,0.00955015,0.300562,-0.138906,0.139949,-0.064678]),JuliaFEM.Core.SparseMatrixCOO([5,5,5,5,3,3,3,3,6,6  …  11,11,4,4,4,4,12,12,12,12],[5,3,11,7,5,3,11,7,6,4  …  11,7,4,12,12,8,4,12,12,8],[0.00955015,0.194034,-0.132539,-0.0710448,0.0206644,0.419847,-0.286786,-0.153725,0.00955015,0.194034  …  0.138906,0.064678,-0.419847,-0.0206644,0.300562,0.139949,-0.194034,-0.00955015,0.138906,0.064678]),JuliaFEM.Core.SparseMatrixCOO(Int64[],Int64[],Float64[]),JuliaFEM.Core.SparseMatrixCOO(Int64[],Int64[],Float64[]))"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = "
+     ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO: tangent dofs: [3]\n",
-      "INFO: normal dofs: [6]\n",
-      "INFO: tangent dofs: [4]\n",
-      "INFO: normal dofs: [5]\n",
-      "INFO: tangent dofs: [3]\n",
-      "INFO: normal dofs: [6]\n",
-      "INFO: tangent dofs: [4]\n",
-      "INFO: normal dofs: [5]\n",
-      "INFO: tangent dofs: [3]\n",
-      "INFO: normal dofs: [6]\n",
-      "INFO: tangent dofs: [4]\n",
-      "INFO: normal dofs: [5]\n",
-      "INFO: tangent dofs: [3]\n",
-      "INFO: normal dofs: [6]\n",
-      "INFO: tangent dofs: [4]\n",
-      "INFO: normal dofs: [5]\n",
-      "INFO: tangent dofs: [3]\n",
-      "INFO: normal dofs: [6]\n",
-      "INFO: tangent dofs: [4]\n",
-      "INFO: slave dofs of element: [3,4,11,12]\n",
-      "INFO: normal dofs: [3]\n",
-      "INFO: tangent dofs: [11]\n",
-      "INFO: normal dofs: [4]\n",
-      "INFO: tangent dofs: [12]\n",
-      "INFO: normal dofs: [3]\n",
-      "INFO: tangent dofs: [11]\n",
-      "INFO: normal dofs: [4]\n",
-      "INFO: tangent dofs: [12]\n",
-      "INFO: normal dofs: [3]\n",
-      "INFO: tangent dofs: [11]\n",
-      "INFO: normal dofs: [4]\n",
-      "INFO: tangent dofs: [12]\n",
-      "INFO: normal dofs: [3]\n",
-      "INFO: tangent dofs: [11]\n",
-      "INFO: normal dofs: [4]\n",
-      "INFO: tangent dofs: [12]\n",
-      "INFO: normal dofs: [3]\n",
-      "INFO: tangent dofs: [11]\n",
-      "INFO: normal dofs: [4]\n",
-      "INFO: tangent dofs: [12]\n"
+      "INFO: slave dofs of element: [5,6,11,12]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "JuliaFEM.Core.BoundaryAssembly(JuliaFEM.Core.SparseMatrixCOO([7,5,7,5,7,5,7,5,8,6  …  5,11,6,12,6,12,6,12,6,12],[7,7,5,5,3,3,13,13,8,8  …  13,13,6,6,12,12,4,4,14,14],[0.21522,0.0105929,0.0105929,0.000521371,-0.147011,-0.00723572,-0.0788018,-0.00387854,0.21522,0.0105929  …  -0.0109405,-0.222282,0.000521371,0.0105929,0.0105929,0.21522,-0.00017379,-0.00353096,-0.0109405,-0.222282]),JuliaFEM.Core.SparseMatrixCOO([7,5,7,5,7,5,7,5,8,6  …  5,11,6,12,6,12,6,12,6,12],[7,7,5,5,3,3,13,13,8,8  …  13,13,6,6,12,12,4,4,14,14],[0.21522,0.0105929,0.0105929,0.000521371,-0.147011,-0.00723572,-0.0788018,-0.00387854,0.21522,0.0105929  …  -0.0109405,-0.222282,0.000521371,0.0105929,0.0105929,0.21522,-0.00017379,-0.00353096,-0.0109405,-0.222282]),JuliaFEM.Core.SparseMatrixCOO(Int64[],Int64[],Float64[]),JuliaFEM.Core.SparseMatrixCOO(Int64[],Int64[],Float64[]))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n",
+      "normal tangential = \n",
+      "[1.0 0.0\n",
+      " 0.0 -1.0]\n"
      ]
     }
    ],
@@ -2055,7 +1432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -2063,31 +1440,342 @@
     {
      "data": {
       "text/plain": [
-       "12x12 Array{Float64,2}:\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  -6.0   0.0   0.0   0.0  2.0  0.0  0.0  0.0  4.0  0.0\n",
-       " 0.0  0.0   0.0  -6.0   0.0   0.0  0.0  2.0  0.0  0.0  0.0  4.0\n",
-       " 0.0  0.0   0.0   0.0  -3.0   0.0  2.0  0.0  0.0  0.0  1.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0  -3.0  0.0  2.0  0.0  0.0  0.0  1.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0   0.0   0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0"
+       "8x8 Array{Float64,2}:\n",
+       "  92.0  -15.0  0.0   0.0  -74.0   15.0    0.0  -12.0\n",
+       " -15.0   62.0  0.0   0.0   15.0  -14.0  -18.0    0.0\n",
+       "   0.0    0.0  6.0   0.0    0.0    0.0    0.0    0.0\n",
+       "   0.0    0.0  0.0  16.0    0.0    0.0    0.0    0.0\n",
+       " -74.0   15.0  0.0   0.0  110.0  -15.0  -18.0   12.0\n",
+       "  15.0  -14.0  0.0   0.0  -15.0  110.0   18.0  -48.0\n",
+       "   0.0  -18.0  0.0   0.0  -18.0   18.0   18.0    0.0\n",
+       " -12.0    0.0  0.0   0.0   12.0  -48.0    0.0   48.0"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "K = full(ass1.stiffness_matrix)\n",
+    "dims = size(K)\n",
+    "K[1:8,1:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16x16 Array{Float64,2}:\n",
+       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
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+       " 0.0  0.0   -2.0    0.0   6.0   0.0   0.0   0.0  0.0  0.0  12.0   0.0  -16.0    0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0   -2.0   0.0   6.0   0.0   0.0  0.0  0.0   0.0  12.0    0.0  -16.0  0.0  0.0\n",
+       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0    0.0   0.0   0.0   0.0   0.0  0.0  0.0   0.0   0.0    0.0    0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fixed = [9, 10, 11, 12, 13, 14, 15, 16]\n",
     "ENV[\"COLUMNS\"] = 300\n",
-    "C1 = full(cbc.C1)*3\n",
+    "C1 = full(cbc.C1, dims...)\n",
     "C1[abs(C1) .< 1.0e-9] = 0\n",
-    "C1"
+    "#C1[fixed, fixed] = 0\n",
+    "C1*18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16x16 Array{Float64,2}:\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
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+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
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+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
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+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D = full(cbc.D, dims...)\n",
+    "D[abs(D) .< 1.0e-9] = 0\n",
+    "D[fixed, fixed] = 0\n",
+    "D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16x16 Array{Float64,2}:\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  -12.0  0.0  24.0  0.0   6.0  0.0  0.0  0.0   6.0  0.0  -24.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  -10.0  0.0   6.0  0.0  12.0  0.0  0.0  0.0   0.0  0.0   -8.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0   -2.0  0.0   6.0  0.0   0.0  0.0  0.0  0.0  12.0  0.0  -16.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0    0.0  0.0   0.0  0.0   0.0  0.0  0.0  0.0   0.0  0.0    0.0  0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C2 = full(cbc.C2, dims...)\n",
+    "C2[abs(C2) .< 1.0e-9] = 0\n",
+    "#C2[fixed, :] = 0\n",
+    "for i=2:2:16\n",
+    "    C1[i,:] = 0\n",
+    "    C2[i,:] = 0\n",
+    "end\n",
+    "C2*18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16x16 Array{Float64,2}:\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
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+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16x16 Array{Float64,2}:\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  12.0   0.0   6.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  12.0   0.0   6.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   6.0   0.0  12.0   0.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   6.0   0.0  12.0   0.0   0.0   0.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0  12.0   0.0   6.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0  12.0   0.0   6.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   6.0   0.0  12.0   0.0\n",
+       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   6.0   0.0  12.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DC1 = full(dbc.C1, dims...)\n",
+    "DC2 = full(dbc.C2, dims...)\n",
+    "DD = full(dbc.D, dims...)\n",
+    "DC1*18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32-element Array{Float64,1}:\n",
+       " 18.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  ⋮  \n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0\n",
+       "  0.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = zeros(16)\n",
+    "f[1] = 18.0\n",
+    "g = zeros(16)\n",
+    "A = [K (C1+DC1)'; (C2+DC2) (D+DD)]\n",
+    "b = [f; g]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: zero line 17\n",
+      "INFO: zero line 18\n",
+      "INFO: zero line 19\n",
+      "INFO: zero line 20\n",
+      "INFO: zero line 22\n",
+      "INFO: zero line 24\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i=1:size(A,1)\n",
+    "    if sum(abs(A[i,:])) == 0\n",
+    "        info(\"zero line $i\")\n",
+    "        A[i,i] = 1.0\n",
+    "    end\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2x8 Array{Float64,2}:\n",
+       " 0.406155  0.659049   0.219683   0.439366   0.0  0.0  0.0  0.0\n",
+       " 0.149452  0.0       -0.102834  -0.0562157  0.0  0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "u = A \\ b\n",
+    "u = reshape(u, 2, 16)\n",
+    "u[abs(u) .< 1.0e-9] = 0\n",
+    "disp = u[:,1:8]"
    ]
   },
   {
@@ -2096,39 +1784,27 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12x12 Array{Float64,2}:\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  -3.0   0.0  2.0  0.0  0.0  0.0  1.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0  -3.0  0.0  2.0  0.0  0.0  0.0  1.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0   0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "C2 = full(cbc.C2)*3\n",
-    "C2[abs(C2) .< 1.0e-9] = 0\n",
-    "C2"
+    "# tie contact\n",
+    "@assert isapprox(norm(vec(disp)), 0.8223229928825583)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# frictionless sliding\n",
+    "@assert isapprox(norm(vec(disp)), 0.9363129098080032)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -2136,36 +1812,136 @@
     {
      "data": {
       "text/plain": [
-       "12x12 Array{Float64,2}:\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
-       " 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
+       "2x8 Array{Float64,2}:\n",
+       " 0.0  2.0  2.0  2.0  0.0  2.0  2.0  4.0\n",
+       " 2.0  6.0  2.0  4.0  0.0  0.0  0.0  0.0"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "D = full(cbc.D, 12, 12)*3\n",
-    "D[abs(D) .< 1.0e-9] = 0\n",
-    "D"
+    "X = [0.0 2; 2 6; 2 2; 2 4; 0 0; 2 0; 2 0; 4 0]'"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2x8 Array{Float64,2}:\n",
+       " 0.406155  2.65905  2.21968  2.43937  0.0  2.0  2.0  4.0\n",
+       " 2.14945   6.0      1.89717  3.94378  0.0  0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = X + disp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3-element Array{Int64,1}:\n",
+       " 7\n",
+       " 8\n",
+       " 2"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eldofs = Dict()\n",
+    "eldofs[1] = [5, 6, 3, 1]\n",
+    "eldofs[2] = [1, 3, 4]\n",
+    "eldofs[3] = [7, 8, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002615EAC8>)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.1,6.1)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "using PyPlot\n",
+    "\n",
+    "function get_coords(X)\n",
+    "    return [X X[:, 1]]\n",
+    "end\n",
+    "\n",
+    "function plot(X, args...; kwargs...)\n",
+    "    X1 = X[:, eldofs[1]]\n",
+    "    X2 = X[:, eldofs[2]]\n",
+    "    X3 = X[:, eldofs[3]]\n",
+    "    PyPlot.plot(get_coords(X1)[1,:]', get_coords(X1)[2,:]', args...; kwargs...)\n",
+    "    PyPlot.plot(get_coords(X2)[1,:]', get_coords(X2)[2,:]', args...; kwargs...)\n",
+    "    PyPlot.plot(get_coords(X3)[1,:]', get_coords(X3)[2,:]', args...; kwargs...)\n",
+    "    axis(\"off\")\n",
+    "end\n",
+    "figure(figsize=(4, 5))\n",
+    "plot(X, \"-k\")\n",
+    "plot(x, \"--r\")\n",
+    "xlim(-0.1, 4.1)\n",
+    "ylim(-0.1, 6.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 0.4.2",
+   "display_name": "Julia 0.4.1",
    "language": "julia",
    "name": "julia-0.4"
   },
@@ -2173,7 +1949,7 @@
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "0.4.2"
+   "version": "0.4.1"
   }
  },
  "nbformat": 4,
diff --git a/src/assembly.jl b/src/assembly.jl
index 970fffd..cd8ee71 100644
--- a/src/assembly.jl
+++ b/src/assembly.jl
@@ -63,6 +63,10 @@ function assemble(problem::AllProblems, elrange::UnitRange{Int64}, time::Real, o
     return assembly
 end
 
+""" Run postprocess for assembly. """
+function postprocess_assembly!
+end
+
 function assemble(problem::AllProblems, time::Real, nchunks=10)
     ne = length(get_elements(problem))
     kk = round(Int, collect(linspace(0, ne, nchunks+1)))
@@ -75,6 +79,11 @@ function assemble(problem::AllProblems, time::Real, nchunks=10)
             info("Assembly: ", round(j/nchunks*100,1), " % done. ")
         end
     end
+    args = Tuple{typeof(assembly), typeof(problem), Real}
+    if method_exists(postprocess_assembly!, args)
+        info("assemble: doing postprocess for problem ", typeof(problem), " assembly")
+        postprocess_assembly!(assembly, problem, time)
+    end
     return assembly
 end
 
@@ -180,4 +189,3 @@ function Base.(:+)(ass1::Assembly, ass2::Assembly)
     force_vector = ass1.force_vector + ass2.force_vector
     return Assembly(mass_matrix, stiffness_matrix, force_vector)
 end
-
diff --git a/src/mortar.jl b/src/mortar.jl
index 83859c2..44becd5 100644
--- a/src/mortar.jl
+++ b/src/mortar.jl
@@ -752,7 +752,7 @@ function assemble!{E<:MortarElements2D}(assembly::BoundaryAssembly, problem::Bou
     field_dim = problem.parent_field_dim
     field_name = problem.parent_field_name
     slave_dofs = get_gdofs(slave_element, field_dim)
-    info("slave dofs of element: $slave_dofs")
+    #info("slave dofs of element: $slave_dofs")
 
     for master_element in slave_element["master elements"]
         xi1a = project_from_master_to_slave(slave_element, master_element, [-1.0])
@@ -762,21 +762,15 @@ function assemble!{E<:MortarElements2D}(assembly::BoundaryAssembly, problem::Bou
         abs(l) > 1.0e-9 || continue
 
 #       Ae = get_biorthogonal_transformation_matrix(slave_element, time)
-        nnodes = size(element, 2)
+        nnodes = size(slave_element, 2)
         De = zeros(nnodes, nnodes)
         Me = zeros(nnodes, nnodes)
         for ip_ in get_integration_points(slave_element, Val{5})
             xi_gauss = 1/2*(1-ip_.xi)*xi1[1] + 1/2*(1+ip_.xi)*xi1[2]
             ip = IntegrationPoint(xi_gauss, ip_.weight)
-            w = ip.weight
             J = get_jacobian(slave_element, ip, time)
-            JT = transpose(J)
-            if size(JT, 2) == 1  # plane problem
-                w *= norm(JT)
-            else
-                w *= norm(cross(JT[:,1], JT[:,2]))
-            end
-            N = element(ip, time)
+            w = ip.weight*norm(J)
+            N = slave_element(ip, time)
             De += w*diagm(vec(N))
             Me += w*N'*N
         end
@@ -796,44 +790,53 @@ function assemble!{E<:MortarElements2D}(assembly::BoundaryAssembly, problem::Bou
             N1 = slave_element(xi_gauss, time)
             Phi = (Ae*N1')'
             N2 = master_element(xi_projected, time)
-            S = w*Phi'*N1
-            M = w*Phi'*N2
+            #S = w*Phi'*N1
+            #M = w*Phi'*N2
+            S = w*N1'*N1
+            M = w*N1'*N2
+
+            nt = slave_element("normal-tangential coordinates", ip, time)
+            #println("normal tangential = ")
+            #println(round(nt, 3))
+            nt = [1 0; 0 1]
+            ntS = nt'*S
+            ntM = nt'*M
+
             for i=1:field_dim
                 sd = slave_dofs[i:field_dim:end]
                 md = master_dofs[i:field_dim:end]
                 add!(assembly.C1, sd, sd, S)
                 add!(assembly.C1, sd, md, -M)
+                add!(assembly.C2, sd, sd, ntS)
+                add!(assembly.C2, sd, md, -ntM)
             end
 
             # construct C2 & D
-            nt = slave_element("normal-tangential coordinates", ip, time)
-            nt = transpose(nt)
-            ntS = nt*S
-            ntM = nt*M
-            info("normal dofs: $(slave_dofs[1:2:end])")
-            info("tangent dofs: $(slave_dofs[2:2:end])")
+#            info("normal dofs: $(slave_dofs[1:2:end])")
+#            info("tangent dofs: $(slave_dofs[2:2:end])")
+#=
             # contribution in normal direction
             for dof in slave_dofs[1:2:end]
-                add!(assembly.C2, [dofs], sd, ntS[1,:])
-                add!(assembly.C2, sd[1:2:end], md, -ntM[1,:])
+                add!(assembly.C2, [dof], sd, ntS[1,:])
+                add!(assembly.C2, [dof], md, -ntM[1,:])
             end
-                # contribution in tangent direction
+            # contribution in tangent direction
+            for dof in slave_dofs[1:2:end]
                 add!(assembly.C2, sd[2:2:end], sd, ntS[2,:])
                 add!(assembly.C2, sd[2:2:end], md, -ntM[2,:])
                 # set lagrange multipliers to zero in tangent direction
-                #tangent = nt[2, :]
-                #add!(assembly.D, sd[2:2:end], sd, tangent)
+                tangent = nt[2, :]
+                add!(assembly.D, sd[2:2:end], sd, tangent)
             end
-#=
-            nt = transpose(nt)
-            normal = nt[1,:]
-            tangent = nt[2,:]
+
             for nid in get_connectivity(slave_element)
                 ndofs = [2*(nid-1)+1, 2*(nid-1)+2]
-                add!(assembly.C2, [2*(nid-1)+1], ndofs, normal)
-                add!(assembly.D, [2*(nid-1)+2], ndofs, tangent)
+                add!(assembly.C2, [2*(nid-1)+1], ndofs,  ntS[1,:])
+                add!(assembly.C2, [2*(nid-1)+1], ndofs, -ntM[1,:])
             end
-=#
+
+            =#
+
         end
     end
 end
@@ -1038,4 +1041,3 @@ function assemble!{E<:MortarElements3D}(assembly::BoundaryAssembly, problem::Bou
         end
     end
 end
-
diff --git a/src/sparse.jl b/src/sparse.jl
index 9ce0e46..0f32163 100644
--- a/src/sparse.jl
+++ b/src/sparse.jl
@@ -23,6 +23,10 @@ function SparseMatrixCOO()
     SparseMatrixCOO([], [], [])
 end
 
+function Base.convert(::Type{SparseMatrixCOO}, A::SparseMatrixCSC)
+    return SparseMatrixCOO(findnz(A)...)
+end
+
 function Base.sparse(A::SparseMatrixIJV, args...)
     return sparse(A.I, A.J, A.V, args...)
 end