(* Content-type: application/mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 7.0' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 145, 7] NotebookDataLength[ 582727, 11008] NotebookOptionsPosition[ 573081, 10692] NotebookOutlinePosition[ 573861, 10720] CellTagsIndexPosition[ 573729, 10714] WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[CellGroupData[{ Cell["Effect of killing cross-terms", "Title", CellChangeTimes->{{3.4692264520476522`*^9, 3.469226462093113*^9}}], Cell["\<\ Nicholas Wheeler 7 December 2009\ \>", "Text", CellChangeTimes->{{3.469226468184935*^9, 3.469226479849288*^9}}], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.4692265000834723`*^9}], Cell[CellGroupData[{ Cell["\<\ Technique for constructing random 3\[Cross]3 hermitian matrices:\ \>", "Subsection", CellChangeTimes->{{3.4692146952869*^9, 3.469214718801059*^9}}], Cell["\<\ In 2-dimensions I would find it most natural to construct hermitian matrices \ by exploiting properties of the Pauli matrices, which provide an \ algebraically convenient basis in the space of such matrices. It is to avoid \ being diverted from generalities by results that are special to the 2D case \ that I work in 3D. My objective, of course, is to be in position to construct \ matrices that represent arbitrary Hamiltonians and other observables.\ \>", "Text", CellChangeTimes->{{3.469226526309754*^9, 3.469226576959332*^9}, { 3.4692266148826313`*^9, 3.4692266635593557`*^9}, {3.469281513122273*^9, 3.469281611054699*^9}, {3.469281650026887*^9, 3.469281698239949*^9}}], Cell[BoxData[""], "Input", CellChangeTimes->{3.469217832417987*^9, 3.469281751510765*^9}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"?", "RandomComplex"}]], "Input", CellChangeTimes->{{3.469226860221754*^9, 3.4692268706968803`*^9}}], Cell[BoxData[ RowBox[{ StyleBox["\<\"\\!\\(\\*RowBox[{\\\"RandomComplex\\\", \\\"[\\\", \ \\\"]\\\"}]\\) gives a pseudorandom complex number with real and imaginary \ parts in the range 0 to 1.\\n\\!\\(\\*RowBox[{\\\"RandomComplex\\\", \ \\\"[\\\", RowBox[{\\\"{\\\", RowBox[{SubscriptBox[StyleBox[\\\"z\\\", \\\"TI\ \\\"], StyleBox[\\\"min\\\", \\\"TI\\\"]], \\\",\\\", \ SubscriptBox[StyleBox[\\\"z\\\", \\\"TI\\\"], StyleBox[\\\"max\\\", \ \\\"TI\\\"]]}], \\\"}\\\"}], \\\"]\\\"}]\\) gives a pseudorandom complex \ number in the rectangle with corners given by the complex numbers \ \\!\\(\\*SubscriptBox[StyleBox[\\\"z\\\", \\\"TI\\\"], StyleBox[\\\"min\\\", \ \\\"TI\\\"]]\\) and \\!\\(\\*SubscriptBox[StyleBox[\\\"z\\\", \\\"TI\\\"], \ StyleBox[\\\"max\\\", \\\"TI\\\"]]\\).\\n\\!\\(\\*RowBox[{\\\"RandomComplex\\\ \", \\\"[\\\", SubscriptBox[StyleBox[\\\"z\\\", \\\"TI\\\"], StyleBox[\\\"max\ \\\", \\\"TI\\\"]], \\\"]\\\"}]\\) gives a pseudorandom complex number in the \ rectangle whose corners are the origin and \ \\!\\(\\*SubscriptBox[StyleBox[\\\"z\\\", \\\"TI\\\"], StyleBox[\\\"max\\\", \ \\\"TI\\\"]]\\).\\n\\!\\(\\*RowBox[{\\\"RandomComplex\\\", \\\"[\\\", \ RowBox[{StyleBox[\\\"range\\\", \\\"TI\\\"], \\\",\\\", StyleBox[\\\"n\\\", \ \\\"TI\\\"]}], \\\"]\\\"}]\\) gives a list of \\!\\(\\*StyleBox[\\\"n\\\", \\\ \"TI\\\"]\\) pseudorandom complex \ numbers.\\n\\!\\(\\*RowBox[{\\\"RandomComplex\\\", \\\"[\\\", \ RowBox[{StyleBox[\\\"range\\\", \\\"TI\\\"], \\\",\\\", RowBox[{\\\"{\\\", \ RowBox[{SubscriptBox[StyleBox[\\\"n\\\", \\\"TI\\\"], StyleBox[\\\"1\\\", \ \\\"TR\\\"]], \\\",\\\", SubscriptBox[StyleBox[\\\"n\\\", \\\"TI\\\"], \ StyleBox[\\\"2\\\", \\\"TR\\\"]], \\\",\\\", StyleBox[\\\"\[Ellipsis]\\\", \\\ \"TR\\\"]}], \\\"}\\\"}]}], \\\"]\\\"}]\\) gives an \ \\!\\(\\*RowBox[{SubscriptBox[StyleBox[\\\"n\\\", \\\"TI\\\"], StyleBox[\\\"1\ \\\", \\\"TR\\\"]], \\\"\[Cross]\\\", SubscriptBox[StyleBox[\\\"n\\\", \\\"TI\ \\\"], StyleBox[\\\"2\\\", \\\"TR\\\"]], \\\"\[Cross]\\\", StyleBox[\\\"\ \[Ellipsis]\\\", \\\"TR\\\"]}]\\) array of pseudorandom complex numbers. \ \"\>", "MSG"], "\[NonBreakingSpace]", ButtonBox[ StyleBox["\[RightSkeleton]", "SR"], Active->True, BaseStyle->"Link", ButtonData->"paclet:ref/RandomComplex"]}]], "Print", "PrintUsage", CellChangeTimes->{3.469226872523498*^9}, CellTags->"Info3469198071-6311815"] }, Open ]], Cell[CellGroupData[{ Cell["\<\ 3D instance of a procedure that works in any dimension:\ \>", "Subsubsection", CellChangeTimes->{{3.469217840949679*^9, 3.469217876432064*^9}, { 3.469281778627599*^9, 3.469281793770417*^9}}], Cell[BoxData[{ RowBox[{ RowBox[{"M", "=", RowBox[{"RandomComplex", "[", RowBox[{ RowBox[{"1", "+", "\[ImaginaryI]"}], ",", RowBox[{"{", RowBox[{"3", ",", "3"}], "}"}]}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[DoubleStruckCapitalM]", "=", RowBox[{"M", "+", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", "M", "]"}], "]"}]}]}], ";"}]}], "Input", CellChangeTimes->{{3.46922668140446*^9, 3.4692266864062843`*^9}, 3.469226778219462*^9, {3.469226822827078*^9, 3.469226824995101*^9}, { 3.4692270284215603`*^9, 3.469227038481797*^9}, 3.46922709124992*^9, { 3.469227127088278*^9, 3.469227149389028*^9}, {3.4692818134862747`*^9, 3.469281815142478*^9}, {3.469281884901071*^9, 3.4692818976485786`*^9}, { 3.469281930705902*^9, 3.469281933741811*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"\[DoubleStruckCapitalM]", "//", "Chop"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.46921474066241*^9, 3.469214748566627*^9}, { 3.469216893800452*^9, 3.469216899077972*^9}, {3.4692819078151693`*^9, 3.4692819104859867`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"1.8253069485976003`", RowBox[{"1.2968987625532997`", "\[InvisibleSpace]", "-", RowBox[{"0.1600477885094611`", " ", "\[ImaginaryI]"}]}], RowBox[{"1.4644676967418526`", "\[InvisibleSpace]", "+", RowBox[{"0.3976268301909329`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"1.2968987625532997`", "\[InvisibleSpace]", "+", RowBox[{"0.1600477885094611`", " ", "\[ImaginaryI]"}]}], "0.5024366594197445`", RowBox[{"0.8336906335376579`", "\[InvisibleSpace]", "-", RowBox[{"0.025684399742365693`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"1.4644676967418526`", "\[InvisibleSpace]", "-", RowBox[{"0.3976268301909329`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.8336906335376579`", "\[InvisibleSpace]", "+", RowBox[{"0.025684399742365693`", " ", "\[ImaginaryI]"}]}], "1.9633806920459995`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{ 3.46921474935209*^9, 3.469216909854127*^9, 3.4692267041104*^9, 3.46922673672075*^9, 3.469226785987644*^9, 3.469226832097568*^9, 3.4692270454880543`*^9, 3.469227096782091*^9, {3.469227132289616*^9, 3.46922715679496*^9}, 3.469281829562409*^9, 3.469281911615641*^9, 3.4692819427430363`*^9}] }, Open ]], Cell["\<\ The hermiticity of the preceding matrix is manifest. We look to the spectral properties of \[DoubleStruckCapitalM]:\ \>", "Text", CellChangeTimes->{{3.4692820699395857`*^9, 3.46928211770991*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Chop", "[", RowBox[{"Eigenvalues", "[", "\[DoubleStruckCapitalM]", "]"}], "]"}]], "Input", CellChangeTimes->{{3.4692820545603943`*^9, 3.46928205633294*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{"4.039041884380291`", ",", "0.6287777454988355`", ",", RowBox[{"-", "0.3766953298157845`"}]}], "}"}]], "Output", CellChangeTimes->{3.469214936143673*^9, 3.469282132140272*^9}] }, Open ]], Cell[BoxData[ RowBox[{ RowBox[{"\[Lambda]", "[", "k_", "]"}], ":=", RowBox[{ RowBox[{"Chop", "[", RowBox[{"Eigenvalues", "[", "\[DoubleStruckCapitalM]", "]"}], "]"}], "\[LeftDoubleBracket]", "k", "\[RightDoubleBracket]"}]}]], "Input", CellChangeTimes->{{3.469214768915286*^9, 3.4692148615049343`*^9}, { 3.469214902100069*^9, 3.469214909575327*^9}, {3.469282145651359*^9, 3.4692821472917767`*^9}}], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{"\[Lambda]", "[", "1", "]"}], "\[IndentingNewLine]", RowBox[{"\[Lambda]", "[", "2", "]"}], "\[IndentingNewLine]", RowBox[{"\[Lambda]", "[", "3", "]"}]}], "Input", CellChangeTimes->{{3.469214869446455*^9, 3.4692148710880823`*^9}, { 3.469214917225119*^9, 3.4692149551635*^9}}], Cell[BoxData["4.039041884380291`"], "Output", CellChangeTimes->{3.469214873776822*^9, 3.469214921104436*^9, 3.469214956431567*^9, 3.469282153140931*^9}], Cell[BoxData["0.6287777454988355`"], "Output", CellChangeTimes->{3.469214873776822*^9, 3.469214921104436*^9, 3.469214956431567*^9, 3.469282153193878*^9}], Cell[BoxData[ RowBox[{"-", "0.3766953298157845`"}]], "Output", CellChangeTimes->{3.469214873776822*^9, 3.469214921104436*^9, 3.469214956431567*^9, 3.469282153280735*^9}] }, Open ]], Cell[BoxData[ RowBox[{ RowBox[{"u", "[", "k_", "]"}], ":=", RowBox[{"Transpose", "[", RowBox[{"{", RowBox[{ RowBox[{"Eigenvectors", "[", "\[DoubleStruckCapitalM]", "]"}], "\[LeftDoubleBracket]", "k", "\[RightDoubleBracket]"}], "}"}], "]"}]}]], "Input", CellChangeTimes->{{3.4692149905126057`*^9, 3.46921504474329*^9}, { 3.469215112662232*^9, 3.46921512567348*^9}, {3.469215283668429*^9, 3.469215316942308*^9}, {3.469282191289217*^9, 3.469282194293923*^9}}], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{"u", "[", "1", "]"}], "//", "MatrixForm"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"u", "[", "2", "]"}], "//", "MatrixForm"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"u", "[", "3", "]"}], "//", "MatrixForm"}]}], "Input", CellChangeTimes->{{3.4692151341841927`*^9, 3.46921514695152*^9}, { 3.4692824197298822`*^9, 3.4692824303908453`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ { RowBox[{"0.6642857866363209`", "\[InvisibleSpace]", "+", RowBox[{"0.`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.39013090913626614`", "\[InvisibleSpace]", "-", RowBox[{"0.0036894332186527185`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.6254239981004636`", "\[InvisibleSpace]", "-", RowBox[{"0.1239091525757127`", " ", "\[ImaginaryI]"}]}]} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469215147667408*^9, 3.469215331153808*^9, 3.469282362556719*^9, 3.4692824364348707`*^9}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ { RowBox[{ RowBox[{"-", "0.4999359503944`"}], "+", RowBox[{"0.046147951142502336`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{ RowBox[{"-", "0.331973903301307`"}], "-", RowBox[{"0.3092796898080558`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.7362566217751285`", "\[InvisibleSpace]", "+", RowBox[{"0.`", " ", "\[ImaginaryI]"}]}]} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469215147667408*^9, 3.469215331153808*^9, 3.469282362556719*^9, 3.469282436442443*^9}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ { RowBox[{ RowBox[{"-", "0.5127942728002159`"}], "+", RowBox[{"0.2090474576857117`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.8012013871793802`", "\[InvisibleSpace]", "+", RowBox[{"0.`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{ RowBox[{"-", "0.000045435463092141987`"}], "-", RowBox[{"0.22675433697262304`", " ", "\[ImaginaryI]"}]}]} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469215147667408*^9, 3.469215331153808*^9, 3.469282362556719*^9, 3.469282436450615*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "1", "]"}], "]"}], "]"}], ".", RowBox[{"u", "[", "1", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "2", "]"}], "]"}], "]"}], ".", RowBox[{"u", "[", "2", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "3", "]"}], "]"}], "]"}], ".", RowBox[{"u", "[", "3", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "1", "]"}], "]"}], "]"}], ".", RowBox[{"u", "[", "2", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "2", "]"}], "]"}], "]"}], ".", RowBox[{"u", "[", "3", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "3", "]"}], "]"}], "]"}], ".", RowBox[{"u", "[", "1", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}]}], "Input", CellChangeTimes->{{3.469215052048108*^9, 3.469215101783628*^9}, { 3.469215183402808*^9, 3.469215186221244*^9}, {3.469215368541212*^9, 3.4692154682597427`*^9}}], Cell[BoxData["0.9999999999999999`"], "Output", CellChangeTimes->{{3.469215389888215*^9, 3.469215409313437*^9}, 3.4692154753931704`*^9, 3.4692824690708723`*^9}], Cell[BoxData["1.0000000000000002`"], "Output", CellChangeTimes->{{3.469215389888215*^9, 3.469215409313437*^9}, 3.4692154753931704`*^9, 3.469282469120412*^9}], Cell[BoxData["0.9999999999999999`"], "Output", CellChangeTimes->{{3.469215389888215*^9, 3.469215409313437*^9}, 3.4692154753931704`*^9, 3.469282469189942*^9}], Cell[BoxData["0"], "Output", CellChangeTimes->{{3.469215389888215*^9, 3.469215409313437*^9}, 3.4692154753931704`*^9, 3.4692824692430973`*^9}], Cell[BoxData["0"], "Output", CellChangeTimes->{{3.469215389888215*^9, 3.469215409313437*^9}, 3.4692154753931704`*^9, 3.469282469294918*^9}], Cell[BoxData["0"], "Output", CellChangeTimes->{{3.469215389888215*^9, 3.469215409313437*^9}, 3.4692154753931704`*^9, 3.469282469342825*^9}] }, Open ]], Cell[TextData[{ "We see that ", StyleBox["Mathematica", FontSlant->"Italic"], " adjusts the overall phase so that the leading element of the leading \ eigenvector is real, and and automatically normalizes the vectors.\n\nI \ demonstrate that the eigenvalue/eigenvector indices are properly \ coordinated:" }], "Text", CellChangeTimes->{{3.469282478722128*^9, 3.469282563085622*^9}, { 3.4692827369432707`*^9, 3.4692827841524477`*^9}}], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalM]", ".", RowBox[{"u", "[", "1", "]"}]}], "-", RowBox[{ RowBox[{"\[Lambda]", "[", "1", "]"}], RowBox[{"u", "[", "1", "]"}]}]}], "//", "Chop"}], "//", "MatrixForm"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalM]", ".", RowBox[{"u", "[", "2", "]"}]}], "-", RowBox[{ RowBox[{"\[Lambda]", "[", "2", "]"}], RowBox[{"u", "[", "2", "]"}]}]}], "//", "Chop"}], "//", "MatrixForm"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalM]", ".", RowBox[{"u", "[", "3", "]"}]}], "-", RowBox[{ RowBox[{"\[Lambda]", "[", "3", "]"}], RowBox[{"u", "[", "3", "]"}]}]}], "//", "Chop"}], "//", "MatrixForm"}]}], "Input", CellChangeTimes->{{3.469215488928459*^9, 3.4692155778971357`*^9}, { 3.469215765940385*^9, 3.469215768670754*^9}, {3.469282590193666*^9, 3.469282722517325*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0"}, {"0"}, {"0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.469215521839983*^9, 3.46921554554714*^9}, 3.4692155786466093`*^9, 3.46921576982014*^9, {3.469282593401664*^9, 3.469282609440173*^9}, {3.4692826653795853`*^9, 3.469282690991503*^9}, 3.469282732060245*^9, 3.469282789601651*^9}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0"}, {"0"}, {"0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.469215521839983*^9, 3.46921554554714*^9}, 3.4692155786466093`*^9, 3.46921576982014*^9, {3.469282593401664*^9, 3.469282609440173*^9}, {3.4692826653795853`*^9, 3.469282690991503*^9}, 3.469282732060245*^9, 3.4692827896139708`*^9}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0"}, {"0"}, {"0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.469215521839983*^9, 3.46921554554714*^9}, 3.4692155786466093`*^9, 3.46921576982014*^9, {3.469282593401664*^9, 3.469282609440173*^9}, {3.4692826653795853`*^9, 3.469282690991503*^9}, 3.469282732060245*^9, 3.469282789623448*^9}] }, Open ]], Cell["I construct projectors onto the respective eigenspaces", "Text", CellChangeTimes->{{3.469282870301363*^9, 3.469282890201407*^9}, 3.469283182994102*^9}], Cell[BoxData[ RowBox[{ RowBox[{"\[DoubleStruckCapitalP]", "[", "k_", "]"}], ":=", RowBox[{ RowBox[{"u", "[", "k", "]"}], ".", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"u", "[", "k", "]"}], "]"}], "]"}]}]}]], "Input", CellChangeTimes->{{3.469215658563258*^9, 3.4692157046392527`*^9}}], Cell["\<\ and demonstrate that these matrices (1) are in fact projective\ \>", "Text", CellChangeTimes->{{3.4692831907037077`*^9, 3.469283222149145*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Table", "[", RowBox[{ RowBox[{ RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalP]", "[", "k", "]"}], ".", RowBox[{"\[DoubleStruckCapitalP]", "[", "k", "]"}]}], "-", RowBox[{"\[DoubleStruckCapitalP]", "[", "k", "]"}]}], "//", "Chop"}], "//", "MatrixForm"}], ",", RowBox[{"{", RowBox[{"k", ",", "1", ",", "3"}], "}"}]}], "]"}]], "Input", CellChangeTimes->{{3.4692157133143167`*^9, 3.4692157492356462`*^9}, { 3.469282925822097*^9, 3.46928296783354*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]], ",", TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]], ",", TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]}], "}"}]], "Output", CellChangeTimes->{{3.469215737679008*^9, 3.4692157499849663`*^9}, 3.46928291177169*^9, {3.469282955396996*^9, 3.4692829691792583`*^9}}] }, Open ]], Cell["(2) orthogonal", "Text", CellChangeTimes->{{3.469283242643386*^9, 3.4692832586584463`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"MatrixForm", "[", RowBox[{"Chop", "[", RowBox[{ RowBox[{"\[DoubleStruckCapitalP]", "[", "1", "]"}], ".", RowBox[{"\[DoubleStruckCapitalP]", "[", "2", "]"}]}], "]"}], "]"}], ",", RowBox[{"MatrixForm", "[", RowBox[{"Chop", "[", RowBox[{ RowBox[{"\[DoubleStruckCapitalP]", "[", "2", "]"}], ".", RowBox[{"\[DoubleStruckCapitalP]", "[", "3", "]"}]}], "]"}], "]"}], ",", RowBox[{"MatrixForm", "[", RowBox[{"Chop", "[", RowBox[{ RowBox[{"\[DoubleStruckCapitalP]", "[", "3", "]"}], ".", RowBox[{"\[DoubleStruckCapitalP]", "[", "1", "]"}]}], "]"}], "]"}]}], "}"}]], "Input", CellChangeTimes->{{3.4692157952352333`*^9, 3.469215801716996*^9}, { 3.469283045677854*^9, 3.4692831483267927`*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]], ",", TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]], ",", TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]}], "}"}]], "Output", CellChangeTimes->{3.469215803118528*^9, 3.4692830789548197`*^9, 3.469283151426627*^9}] }, Open ]], Cell["(3) and complete", "Text", CellChangeTimes->{{3.469283265426311*^9, 3.469283270609975*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalP]", "[", "1", "]"}], "+", RowBox[{"\[DoubleStruckCapitalP]", "[", "2", "]"}], "+", RowBox[{"\[DoubleStruckCapitalP]", "[", "3", "]"}]}], "//", "Chop"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469215813075145*^9, 3.469215833827187*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"1.`", "0", "0"}, {"0", "1.0000000000000002`", "0"}, {"0", "0", "0.9999999999999997`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.4692158352822533`*^9, 3.4692832805811377`*^9}] }, Open ]], Cell["\<\ Information now in hand permit us to present the spectral decomposition of \ \[DoubleStruckCapitalM]: \ \>", "Text", CellChangeTimes->{{3.469283363580503*^9, 3.469283400434251*^9}, { 3.469283622295648*^9, 3.469283634844095*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[Lambda]", "[", "1", "]"}], RowBox[{"\[DoubleStruckCapitalP]", "[", "1", "]"}]}], "+", RowBox[{ RowBox[{"\[Lambda]", "[", "2", "]"}], RowBox[{"\[DoubleStruckCapitalP]", "[", "2", "]"}]}], "+", RowBox[{ RowBox[{"\[Lambda]", "[", "3", "]"}], RowBox[{"\[DoubleStruckCapitalP]", "[", "3", "]"}]}], "-", "\[DoubleStruckCapitalM]"}], "//", "Chop"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469215850088599*^9, 3.4692158944966087`*^9}, { 3.469283292527835*^9, 3.4692832945493107`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.46921588223848*^9, 3.469215895662713*^9}}] }, Open ]], Cell["\<\ We have here displayed \[DoubleStruckCapitalM] as a spectrally weighted sum \ of orthogonal projection matrices.\ \>", "Text", CellChangeTimes->{{3.4692179252925653`*^9, 3.4692179603308353`*^9}, { 3.469283654395152*^9, 3.469283694728435*^9}}], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.46921796494283*^9}] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Technique for constructing random state vectors and associated density \ matrices:\ \>", "Subsection", CellChangeTimes->{{3.469283787394923*^9, 3.469283813392702*^9}}], Cell["\<\ One could construct a random hermitian matrix, then select one of its \ eigenvectors, which as we have seen is certain to be an already-normalized \ complex vector. The following nest of commands permits one to construct normalized complex \ column vectors with a single keystroke:\ \>", "Text", CellChangeTimes->{{3.4692849945523167`*^9, 3.469285054772773*^9}, { 3.469285414296677*^9, 3.46928545040493*^9}}], Cell[BoxData[{ RowBox[{ RowBox[{"RandomComplex", "[", RowBox[{ RowBox[{"1", "+", "\[ImaginaryI]"}], ",", RowBox[{"{", RowBox[{"3", ",", "1"}], "}"}]}], "]"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Transpose", "[", "%", "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"Normalize", "[", "%", "]"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Psi]", "=", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"{", "%", "}"}], "]"}], "]"}]}], ";"}]}], "Input", CellChangeTimes->{{3.469284065168026*^9, 3.469284067428851*^9}, { 3.4692851399503803`*^9, 3.4692851690495*^9}, {3.469285213527849*^9, 3.4692852645798264`*^9}, 3.469285409534153*^9}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"\[Psi]", "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469285279100391*^9, 3.469285283377507*^9}, { 3.46928536189432*^9, 3.4692853633255157`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ { RowBox[{"0.05864614031820224`", "\[InvisibleSpace]", "-", RowBox[{"0.6191609151072957`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.046319371118120116`", "\[InvisibleSpace]", "-", RowBox[{"0.6392331004396015`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.026832625385446677`", "\[InvisibleSpace]", "-", RowBox[{"0.44912800046957074`", " ", "\[ImaginaryI]"}]}]} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469285284831044*^9, 3.469285464589697*^9}] }, Open ]], Cell["To demonstrate normality, command", "Text", CellChangeTimes->{{3.469285478996846*^9, 3.469285486250483*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", "\[Psi]", "]"}], "]"}], ".", "\[Psi]"}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}]], "Input", CellChangeTimes->{{3.469285646122921*^9, 3.469285700920775*^9}}], Cell[BoxData["1.0000000000000002`"], "Output", CellChangeTimes->{{3.469285681994032*^9, 3.469285706976445*^9}}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.4692857209074697`*^9}], Cell["The associated density matrix is ", "Text", CellChangeTimes->{{3.4692857431395283`*^9, 3.469285757114037*^9}}], Cell[BoxData[ RowBox[{ RowBox[{"\[Rho]", "=", RowBox[{"Chop", "[", RowBox[{"\[Psi]", ".", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", "\[Psi]", "]"}], "]"}]}], "]"}]}], ";"}]], "Input", CellChangeTimes->{{3.469285762513204*^9, 3.4692858328133383`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"\[Rho]", "//", "MatrixForm"}], "\[IndentingNewLine]"}]], "Input", CellChangeTimes->{ 3.469217997005698*^9, {3.469285837608034*^9, 3.469285844855538*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0.38679960857072604`", RowBox[{"0.39850460377310165`", "\[InvisibleSpace]", "+", RowBox[{"0.008809409895730643`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.27965613368451`", "\[InvisibleSpace]", "+", RowBox[{"0.009725910847987634`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.39850460377310165`", "\[InvisibleSpace]", "-", RowBox[{"0.008809409895730643`", " ", "\[ImaginaryI]"}]}], "0.41076444083840374`", RowBox[{"0.2883403545677045`", "\[InvisibleSpace]", "+", RowBox[{"0.0036510242152158387`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.27965613368451`", "\[InvisibleSpace]", "-", RowBox[{"0.00972591084798763`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.2883403545677045`", "\[InvisibleSpace]", "-", RowBox[{"0.003651024215215836`", " ", "\[ImaginaryI]"}]}], "0.20243595059087047`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469285845566527*^9}] }, Open ]], Cell["which is manifestly hermitian, projective", "Text", CellChangeTimes->{{3.469285938922195*^9, 3.4692859532621727`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"\[Rho]", ".", "\[Rho]"}], "-", "\[Rho]"}], "]"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.4692858917466803`*^9, 3.469285919835451*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.469285898079356*^9, 3.469285922052423*^9}}] }, Open ]], Cell["and is seen from", "Text", CellChangeTimes->{{3.4692859764767036`*^9, 3.469285978724689*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Tr", "[", "\[Rho]", "]"}]], "Input", CellChangeTimes->{{3.469285863627309*^9, 3.469285866286008*^9}}], Cell[BoxData["1.0000000000000002`"], "Output", CellChangeTimes->{3.4692858682838984`*^9}] }, Open ]], Cell["\<\ to project onto a one-space; in fact it projects onto the \[Psi]-ray:\ \>", "Text", CellChangeTimes->{{3.469286004386826*^9, 3.4692860315855103`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Chop", "[", RowBox[{ RowBox[{"\[Rho]", ".", "\[Psi]"}], "-", "\[Psi]"}], "]"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469286035068903*^9, 3.4692860742418203`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0"}, {"0"}, {"0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.469286063885841*^9, 3.469286074979476*^9}}] }, Open ]], Cell[TextData[{ "Which is to say: \[Rho] refers to a ", StyleBox["pure", FontSlant->"Italic"], " state." }], "Text", CellChangeTimes->{{3.469286157268065*^9, 3.4692861727447233`*^9}}], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.469291828540143*^9}] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Construction of density matrices that refer to arbitrary mixtures:\ \>", "Subsection", CellChangeTimes->{{3.4692918376650457`*^9, 3.469291854528623*^9}}], Cell["\<\ Density matrices that refer to impure states share these general properties: \ they are hermitian, have non-negative eigenvalues that sum to unity. To \ construct such a matrix, we note that if \[DoubleStruckCapitalR] is hermitian \ then so is its square, and the eigenvalues of its square (squares of the real \ eigenvalues of \[DoubleStruckCapitalR]) are necessarily real and \ non-negative. To complete the construction we have only to normalize the \ square in a trace-wise sense: \ \>", "Text", CellChangeTimes->{{3.46929026560386*^9, 3.469290322293312*^9}, { 3.4692903645328703`*^9, 3.469290525857685*^9}, {3.4692906829465933`*^9, 3.469290683120606*^9}}], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{"R", "=", RowBox[{"RandomComplex", "[", RowBox[{ RowBox[{"1", "+", "\[ImaginaryI]"}], ",", RowBox[{"{", RowBox[{"3", ",", "3"}], "}"}]}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[DoubleStruckCapitalR]", "=", RowBox[{"R", "+", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", "R", "]"}], "]"}]}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Rho]", "=", RowBox[{"Chop", "[", FractionBox[ RowBox[{"\[DoubleStruckCapitalR]", ".", "\[DoubleStruckCapitalR]"}], RowBox[{"Tr", "[", RowBox[{"\[DoubleStruckCapitalR]", ".", "\[DoubleStruckCapitalR]"}], "]"}]], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{"\[Rho]", "//", "MatrixForm"}]}], "Input", CellChangeTimes->{{3.46922668140446*^9, 3.4692266864062843`*^9}, 3.469226778219462*^9, {3.469226822827078*^9, 3.469226824995101*^9}, { 3.4692270284215603`*^9, 3.469227038481797*^9}, 3.46922709124992*^9, { 3.469227127088278*^9, 3.469227149389028*^9}, {3.4692818134862747`*^9, 3.469281815142478*^9}, {3.469281884901071*^9, 3.4692818976485786`*^9}, { 3.469281930705902*^9, 3.469281933741811*^9}, {3.4692864919265003`*^9, 3.46928650412982*^9}, 3.4692865957917*^9, {3.469290547580694*^9, 3.4692906059959803`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0.21454419697780483`", RowBox[{"0.24729012555937335`", "\[InvisibleSpace]", "+", RowBox[{"0.012813691867376319`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.23465092737560506`", "\[InvisibleSpace]", "-", RowBox[{"0.08632630525273245`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.24729012555937335`", "\[InvisibleSpace]", "-", RowBox[{"0.012813691867376315`", " ", "\[ImaginaryI]"}]}], "0.4047239346901377`", RowBox[{"0.3219037435659417`", "\[InvisibleSpace]", "-", RowBox[{"0.10867391637885711`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.23465092737560506`", "\[InvisibleSpace]", "+", RowBox[{"0.08632630525273245`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.3219037435659417`", "\[InvisibleSpace]", "+", RowBox[{"0.1086739163788571`", " ", "\[ImaginaryI]"}]}], "0.3807318683320574`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.469290589247705*^9, 3.4692906070959883`*^9}}] }, Open ]], Cell["\<\ Hermiticity is manifest, and the spectrum has the desired structure. \ \>", "Text", CellChangeTimes->{{3.469290706478544*^9, 3.469290740323847*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Eigenvalues", "[", "\[Rho]", "]"}], "//", "Chop"}]], "Input", CellChangeTimes->{{3.46929061977216*^9, 3.4692906357570963`*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ "0.9103652289485474`", ",", "0.061084481126525945`", ",", "0.028550289924927422`"}], "}"}]], "Output", CellChangeTimes->{{3.469290626721427*^9, 3.469290636561084*^9}}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Tr", "[", "\[Rho]", "]"}]], "Input", CellChangeTimes->{{3.469290648626603*^9, 3.469290650972457*^9}}], Cell[BoxData["0.9999999999999999`"], "Output", CellChangeTimes->{3.469290652600896*^9}] }, Open ]], Cell["\<\ Such matrices \[Rho] are typically NOT projective, which is clear already \ from the spectrum (all the eigenvalues of projection matrices are either 0 or \ 1, with the number of 1s equal to the dimension of the space onto which the \ matrix projects). \[Rho] can be developed as a weighted sum of projectors in MANY ways (quantum \ mixtures cannot be resolved uniquely into their component parts), but we do, \ in particular, have the spectral decomposition\[LongDash]peoduced here by a \ single keystroke: \ \>", "Text", CellChangeTimes->{{3.4692908054500723`*^9, 3.4692908248948097`*^9}, { 3.469290855124675*^9, 3.469290873212194*^9}, {3.469290905873968*^9, 3.469290975502009*^9}, {3.469291022020553*^9, 3.469291123396865*^9}, { 3.469291159123447*^9, 3.4692911642517357`*^9}, {3.469291615552917*^9, 3.469291634606144*^9}}], Cell[CellGroupData[{ Cell[BoxData[{ RowBox[{ RowBox[{ RowBox[{"r", "[", "k_", "]"}], ":=", RowBox[{"Transpose", "[", RowBox[{"{", RowBox[{ RowBox[{"Eigenvectors", "[", "\[Rho]", "]"}], "\[LeftDoubleBracket]", "k", "\[RightDoubleBracket]"}], "}"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Mu]", "[", "k_", "]"}], ":=", RowBox[{ RowBox[{"Chop", "[", RowBox[{"Eigenvalues", "[", "\[Rho]", "]"}], "]"}], "\[LeftDoubleBracket]", "k", "\[RightDoubleBracket]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalQ]", "[", "k_", "]"}], ":=", RowBox[{ RowBox[{"r", "[", "k", "]"}], ".", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"r", "[", "k", "]"}], "]"}], "]"}]}]}], ";"}], "\[IndentingNewLine]", RowBox[{"Chop", "[", RowBox[{ UnderoverscriptBox["\[Sum]", RowBox[{"k", "=", "1"}], "3"], RowBox[{ RowBox[{"\[Mu]", "[", "k", "]"}], RowBox[{"\[DoubleStruckCapitalQ]", "[", "k", "]"}]}]}], "]"}]}], "Input", CellChangeTimes->{{3.469291228282639*^9, 3.4692912428497887`*^9}, { 3.469291285531163*^9, 3.4692914611598387`*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0.21454419697780508`", ",", RowBox[{"0.24729012555937355`", "\[InvisibleSpace]", "+", RowBox[{"0.012813691867376357`", " ", "\[ImaginaryI]"}]}], ",", RowBox[{"0.23465092737560514`", "\[InvisibleSpace]", "-", RowBox[{"0.0863263052527325`", " ", "\[ImaginaryI]"}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"0.24729012555937355`", "\[InvisibleSpace]", "-", RowBox[{"0.012813691867376357`", " ", "\[ImaginaryI]"}]}], ",", "0.40472393469013784`", ",", RowBox[{"0.32190374356594176`", "\[InvisibleSpace]", "-", RowBox[{"0.10867391637885712`", " ", "\[ImaginaryI]"}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"0.23465092737560514`", "\[InvisibleSpace]", "+", RowBox[{"0.0863263052527325`", " ", "\[ImaginaryI]"}]}], ",", RowBox[{"0.32190374356594176`", "\[InvisibleSpace]", "+", RowBox[{"0.10867391637885712`", " ", "\[ImaginaryI]"}]}], ",", "0.38073186833205747`"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{{3.4692914393702374`*^9, 3.469291488400276*^9}, 3.469291545741178*^9}] }, Open ]], Cell["\<\ I demonstrate that this weighted sum of projectors \[DoubleStruckCapitalQ][k] \ does indeed recreate \[Rho]:\ \>", "Text", CellChangeTimes->{{3.4692916475649023`*^9, 3.469291674395227*^9}, { 3.469291706794348*^9, 3.46929171752947*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", RowBox[{"Chop", "[", RowBox[{"%", "-", "\[Rho]"}], "]"}], "]"}]], "Input", CellChangeTimes->{{3.4692914936464777`*^9, 3.4692915401587753`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469291517371581*^9, 3.4692915501068707`*^9}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.469291877059348*^9}] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Construction of an arbitrary Hamiltonian and the associated unitary \ propagator:\ \>", "Subsection", CellChangeTimes->{{3.469291891390218*^9, 3.469291917452614*^9}}], Cell[BoxData[{ RowBox[{ RowBox[{"H", "=", RowBox[{"RandomComplex", "[", RowBox[{ RowBox[{"1", "+", "\[ImaginaryI]"}], ",", RowBox[{"{", RowBox[{"3", ",", "3"}], "}"}]}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[DoubleStruckCapitalH]", "=", RowBox[{"H", "+", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", "H", "]"}], "]"}]}]}], ";"}]}], "Input", CellChangeTimes->{{3.46922668140446*^9, 3.4692266864062843`*^9}, 3.469226778219462*^9, {3.469226822827078*^9, 3.469226824995101*^9}, { 3.4692270284215603`*^9, 3.469227038481797*^9}, 3.46922709124992*^9, { 3.469227127088278*^9, 3.469227149389028*^9}, {3.4692818134862747`*^9, 3.469281815142478*^9}, {3.469281884901071*^9, 3.4692818976485786`*^9}, { 3.469281930705902*^9, 3.469281933741811*^9}, {3.469291956554534*^9, 3.4692919722622757`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"\[DoubleStruckCapitalH]", "//", "Chop"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469291988886483*^9, 3.4692920105782833`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"1.9236778338410119`", RowBox[{"1.054667150155513`", "\[InvisibleSpace]", "+", RowBox[{"0.27442827975088613`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.8054833681388875`", "\[InvisibleSpace]", "+", RowBox[{"0.18347893227073486`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"1.054667150155513`", "\[InvisibleSpace]", "-", RowBox[{"0.27442827975088613`", " ", "\[ImaginaryI]"}]}], "0.9395288370709514`", RowBox[{"1.050237340891981`", "\[InvisibleSpace]", "-", RowBox[{"0.053527205183167226`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.8054833681388875`", "\[InvisibleSpace]", "-", RowBox[{"0.18347893227073486`", " ", "\[ImaginaryI]"}]}], RowBox[{"1.050237340891981`", "\[InvisibleSpace]", "+", RowBox[{"0.053527205183167226`", " ", "\[ImaginaryI]"}]}], "1.0502740033737927`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.4692919989826107`*^9, 3.469292011242577*^9}}] }, Open ]], Cell[BoxData[ RowBox[{ RowBox[{"\[DoubleStruckCapitalU]", "[", "t_", "]"}], ":=", RowBox[{"Simplify", "[", RowBox[{"ComplexExpand", "[", RowBox[{"Chop", "[", RowBox[{"MatrixExp", "[", RowBox[{ RowBox[{"-", "\[ImaginaryI]"}], " ", "\[DoubleStruckCapitalH]", " ", "t"}], "]"}], "]"}], "]"}], "]"}]}]], "Input", CellChangeTimes->{{3.4692912169462347`*^9, 3.469291217761121*^9}, { 3.4692920228277683`*^9, 3.469292095966671*^9}, {3.469292137990137*^9, 3.4692921771335487`*^9}, {3.469294444671832*^9, 3.469294492872142*^9}, { 3.4692946835956783`*^9, 3.469294692528886*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"\[DoubleStruckCapitalU]", "[", "t", "]"}]], "Input", CellChangeTimes->{ 3.4692921047779703`*^9, {3.469292208488894*^9, 3.469292257290618*^9}, 3.469294710426483*^9}], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"0.03405333767174848`", " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{"0.4872437566895003`", " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{"0.4787029056387511`", " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "+", RowBox[{"\[ImaginaryI]", " ", RowBox[{"(", RowBox[{ RowBox[{"0.03405333767174848`", " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{"0.4872437566895003`", " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "-", RowBox[{"0.4787029056387511`", " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ")"}]}]}], ",", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"-", "0.14219726281545925`"}], "-", RowBox[{"0.03012197655405354`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.21133897677815947`", "\[InvisibleSpace]", "+", RowBox[{"0.0638516190996317`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.3535362395936187`", "\[InvisibleSpace]", "+", RowBox[{"0.09397359565368524`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.03012197655405354`", "\[InvisibleSpace]", "-", RowBox[{"0.14219726281545925`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.0638516190996317`", "\[InvisibleSpace]", "-", RowBox[{"0.21133897677815947`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.09397359565368524`", "\[InvisibleSpace]", "-", RowBox[{"0.3535362395936187`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"0.1073666196727543`", "\[InvisibleSpace]", "+", RowBox[{"0.01545062527785537`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.43961053492102353`", "\[InvisibleSpace]", "+", RowBox[{"0.08853622691176194`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.33224391524826935`", "\[InvisibleSpace]", "+", RowBox[{"0.07308560163390658`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.01545062527785537`", "\[InvisibleSpace]", "-", RowBox[{"0.1073666196727543`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.08853622691176194`", "\[InvisibleSpace]", "-", RowBox[{"0.43961053492102353`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.07308560163390658`", "\[InvisibleSpace]", "-", RowBox[{"0.33224391524826935`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"-", "0.14219726281545944`"}], "+", RowBox[{"0.03012197655405357`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.21133897677815927`", "\[InvisibleSpace]", "-", RowBox[{"0.0638516190996316`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.3535362395936186`", "\[InvisibleSpace]", "-", RowBox[{"0.09397359565368517`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.03012197655405357`", "\[InvisibleSpace]", "+", RowBox[{"0.14219726281545944`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.0638516190996316`", "\[InvisibleSpace]", "+", RowBox[{"0.21133897677815927`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.09397359565368517`", "\[InvisibleSpace]", "+", RowBox[{"0.3535362395936186`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{"0.6204206832054407`", " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{"0.10003451393291946`", " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{"0.2795448028616398`", " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "+", RowBox[{"\[ImaginaryI]", " ", RowBox[{"(", RowBox[{ RowBox[{"0.6204206832054407`", " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{"0.10003451393291946`", " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "-", RowBox[{"0.2795448028616398`", " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ")"}]}]}], ",", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"-", "0.4620000235874484`"}], "+", RowBox[{"0.030453936326854766`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.20228072851676343`", "\[InvisibleSpace]", "-", RowBox[{"0.01920740634670809`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.2597192950706851`", "\[InvisibleSpace]", "-", RowBox[{"0.011246529980146691`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.030453936326854766`", "\[InvisibleSpace]", "+", RowBox[{"0.4620000235874484`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.01920740634670809`", "\[InvisibleSpace]", "+", RowBox[{"0.20228072851676343`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.011246529980146691`", "\[InvisibleSpace]", "+", RowBox[{"0.2597192950706851`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"0.10736661967275447`", "\[InvisibleSpace]", "-", RowBox[{"0.015450625277855363`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.4396105349210236`", "\[InvisibleSpace]", "-", RowBox[{"0.088536226911762`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.33224391524826913`", "\[InvisibleSpace]", "-", RowBox[{"0.07308560163390664`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.015450625277855363`", "\[InvisibleSpace]", "+", RowBox[{"0.10736661967275447`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.088536226911762`", "\[InvisibleSpace]", "+", RowBox[{"0.4396105349210236`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.07308560163390664`", "\[InvisibleSpace]", "+", RowBox[{"0.33224391524826913`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"-", "0.4620000235874486`"}], "-", RowBox[{"0.030453936326854815`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.20228072851676368`", "\[InvisibleSpace]", "+", RowBox[{"0.01920740634670811`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.259719295070685`", "\[InvisibleSpace]", "+", RowBox[{"0.011246529980146691`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.030453936326854815`", "\[InvisibleSpace]", "-", RowBox[{"0.4620000235874486`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.01920740634670811`", "\[InvisibleSpace]", "-", RowBox[{"0.20228072851676368`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.011246529980146691`", "\[InvisibleSpace]", "-", RowBox[{"0.259719295070685`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{"0.34552597912281074`", " ", RowBox[{"Cos", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "+", RowBox[{"0.4127217293775802`", " ", RowBox[{"Cos", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "+", RowBox[{"0.24175229149960908`", " ", RowBox[{"Cos", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}], "+", RowBox[{"\[ImaginaryI]", " ", RowBox[{"(", RowBox[{ RowBox[{"0.34552597912281074`", " ", RowBox[{"Sin", "[", RowBox[{"0.10021228047530581`", " ", "t"}], "]"}]}], "-", RowBox[{"0.4127217293775802`", " ", RowBox[{"Sin", "[", RowBox[{"0.6701810682539088`", " ", "t"}], "]"}]}], "-", RowBox[{"0.24175229149960908`", " ", RowBox[{"Sin", "[", RowBox[{"3.3435118865071525`", " ", "t"}], "]"}]}]}], ")"}]}]}]}], "}"}]}], "}"}]], "Output", CellChangeTimes->{ 3.469292112311696*^9, 3.469292150381111*^9, 3.469292184022196*^9, { 3.4692922141937323`*^9, 3.4692922589610653`*^9}, {3.469294454569338*^9, 3.469294481158*^9}, 3.469294521238549*^9, {3.469294702351466*^9, 3.4692947119522867`*^9}}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"\[DoubleStruckCapitalU]", "[", "0", "]"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469293365008029*^9, 3.469293384687332*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"1", "0", "0"}, {"0", "1", "0"}, {"0", "0", "1"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.46929337615837*^9, 3.4692933861181993`*^9}, 3.469294529356123*^9}] }, Open ]], Cell[BoxData[ RowBox[{ RowBox[{"\[DoubleStruckCapitalU]adjoint", "[", "t_", "]"}], ":=", RowBox[{"Simplify", "[", RowBox[{"ComplexExpand", "[", RowBox[{"Chop", "[", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"MatrixExp", "[", RowBox[{ RowBox[{"-", "\[ImaginaryI]"}], " ", "\[DoubleStruckCapitalH]", " ", "t"}], "]"}], "]"}], "]"}], "]"}], "]"}], "]"}]}]], "Input", CellChangeTimes->{{3.469294385073229*^9, 3.469294415584444*^9}, { 3.469294558517891*^9, 3.4692945968798647`*^9}, {3.4692947306424627`*^9, 3.469294738440052*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{"\[DoubleStruckCapitalU]adjoint", "[", "t", "]"}], "-", RowBox[{"\[DoubleStruckCapitalU]", "[", RowBox[{"-", "t"}], "]"}]}], ")"}], "//", "Simplify"}], "//", "Chop"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{ 3.4692946233915253`*^9, {3.469294661446395*^9, 3.469294665104959*^9}, 3.46929475554805*^9, {3.4692950308626423`*^9, 3.469295138972307*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{ 3.469294625941162*^9, 3.4692946671604357`*^9, 3.4692947583532553`*^9, { 3.469295052766923*^9, 3.469295140689179*^9}}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[DoubleStruckCapitalU]", "[", "t", "]"}], ".", RowBox[{"\[DoubleStruckCapitalU]adjoint", "[", "t", "]"}]}], "//", "FullSimplify"}], "//", "Chop"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.4692947709600067`*^9, 3.469294866974021*^9}, { 3.469294897983347*^9, 3.46929490590626*^9}, {3.4692951864849377`*^9, 3.4692952091606817`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"1.`", "0", "0"}, {"0", "0.9999999999999998`", "0"}, {"0", "0", "1.0000000000000004`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{{3.4692947939979*^9, 3.4692948377061777`*^9}, 3.469294869185815*^9, 3.469294907808749*^9, {3.4692951718796873`*^9, 3.469295211194586*^9}}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.469292366668371*^9}] }, Open ]], Cell[CellGroupData[{ Cell["Dynamical motion of the density matrix:", "Subsection", CellChangeTimes->{{3.469292377539757*^9, 3.469292385656146*^9}}], Cell[BoxData[ RowBox[{ RowBox[{ StyleBox["CompConj", FontColor->GrayLevel[0]], "[", "A_", "]"}], " ", ":=", " ", RowBox[{"A", " ", "/.", "\[InvisibleSpace]", RowBox[{ RowBox[{"Complex", "[", RowBox[{"0", ",", "n_"}], "]"}], "->", RowBox[{"-", RowBox[{"Complex", "[", RowBox[{"0", ",", "n"}], "]"}]}]}]}]}]], "Input"], Cell[BoxData[ RowBox[{ RowBox[{"\[Rho]\[Rho]", "[", "t_", "]"}], ":=", RowBox[{"Simplify", "[", RowBox[{ RowBox[{"\[DoubleStruckCapitalU]", "[", "t", "]"}], ".", "\[Rho]", ".", RowBox[{"\[DoubleStruckCapitalU]adjoint", "[", "t", "]"}]}], "]"}]}]], "Input", CellChangeTimes->{{3.469292402440531*^9, 3.469292469812379*^9}, { 3.469292672735067*^9, 3.4692926752190857`*^9}, {3.469292730697942*^9, 3.469292754221158*^9}, {3.46929280392817*^9, 3.46929281305052*^9}, { 3.469292902323305*^9, 3.469292949090929*^9}, {3.469292998580716*^9, 3.469293001239956*^9}, {3.469293038970461*^9, 3.469293045301762*^9}, { 3.469293103414632*^9, 3.4692931078972397`*^9}, {3.469295277663384*^9, 3.469295296931686*^9}, {3.469295371127367*^9, 3.469295380080386*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[Rho]\[Rho]", "[", "t", "]"}], "//", "ComplexExpand"}], "//", "FullSimplify"}], "//", "Chop"}]], "Input", CellChangeTimes->{ 3.469292491593742*^9, {3.4692931156635323`*^9, 3.469293118150292*^9}, { 3.4692931733903646`*^9, 3.469293175963242*^9}, {3.469295312418921*^9, 3.469295320329212*^9}, {3.469295354923224*^9, 3.469295359695004*^9}, { 3.469295420848489*^9, 3.469295455276648*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"0.45209405147206794`", "\[InvisibleSpace]", "+", RowBox[{"0.0005875986270142165`", " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{"0.22452360983082337`", " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{"0.013613843290453953`", " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "+", RowBox[{"0.00473900530512429`", " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{"0.14835281201993727`", " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{"0.01927354866464262`", " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{"(", RowBox[{"0.24381201912200212`", "\[InvisibleSpace]", "+", RowBox[{"0.06421783061145506`", " ", "\[ImaginaryI]"}]}], ")"}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.00043117729958144085`", "\[InvisibleSpace]", "-", RowBox[{"0.00916500899957405`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.00993374911723268`", "\[InvisibleSpace]", "+", RowBox[{"0.09428148372381867`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.012980678255022499`", "\[InvisibleSpace]", "+", RowBox[{"0.05204235397931399`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.01114352056628045`", "\[InvisibleSpace]", "+", RowBox[{"0.0013070749347672134`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.059357412872136084`", "\[InvisibleSpace]", "-", RowBox[{"0.12676060719007168`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.04048078614953941`", "\[InvisibleSpace]", "-", RowBox[{"0.026818471078752598`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{"(", RowBox[{"0.21060511879335742`", "\[InvisibleSpace]", "+", RowBox[{"0.04695575403926067`", " ", "\[ImaginaryI]"}]}], ")"}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.0008444039267763853`", "\[InvisibleSpace]", "-", RowBox[{"0.009688569698383005`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.048174977863947144`", "\[InvisibleSpace]", "-", RowBox[{"0.11514762473328208`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.023284765354923104`", "\[InvisibleSpace]", "+", RowBox[{"0.027823004257094035`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.005519623736702166`", "\[InvisibleSpace]", "-", RowBox[{"0.0005468672521031712`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.022095666325150844`", "\[InvisibleSpace]", "-", RowBox[{"0.18135601337539659`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.0391213341354547`", "\[InvisibleSpace]", "-", RowBox[{"0.010893498399361906`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"0.2438120191220021`", "\[InvisibleSpace]", "-", RowBox[{"0.06421783061145506`", " ", "\[ImaginaryI]"}]}], ")"}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.00043117729958144085`", "\[InvisibleSpace]", "+", RowBox[{"0.009165008999574049`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.009933749117232667`", "\[InvisibleSpace]", "-", RowBox[{"0.09428148372381867`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.012980678255022464`", "\[InvisibleSpace]", "-", RowBox[{"0.05204235397931398`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.011143520566280448`", "\[InvisibleSpace]", "-", RowBox[{"0.0013070749347672125`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.059357412872136084`", "\[InvisibleSpace]", "+", RowBox[{"0.1267606071900717`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.0404807861495394`", "\[InvisibleSpace]", "+", RowBox[{"0.026818471078752557`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}], ",", RowBox[{"0.27583638926452453`", "\[InvisibleSpace]", "+", RowBox[{"0.001907406781438161`", " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{"0.07942418382248992`", " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{"0.047555954821685065`", " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "+", RowBox[{"0.009036491524896488`", " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{"0.04872659916510897`", " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{"0.060518149207887936`", " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{"(", RowBox[{"0.20659209916537574`", "\[InvisibleSpace]", "-", RowBox[{"0.009749009935829068`", " ", "\[ImaginaryI]"}]}], ")"}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.00012899078496144377`", "\[InvisibleSpace]", "-", RowBox[{"0.012637231271530918`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.11350010739573532`", "\[InvisibleSpace]", "-", RowBox[{"0.035852530215920705`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.001682546219869241`", "\[InvisibleSpace]", "-", RowBox[{"0.05043514495557641`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.006001779932756909`", "\[InvisibleSpace]", "+", RowBox[{"0.001992917547618586`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.07792804512959696`", "\[InvisibleSpace]", "+", RowBox[{"0.03774839255250287`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.007704267247826108`", "\[InvisibleSpace]", "+", RowBox[{"0.040065966948047566`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"0.21060511879335742`", "\[InvisibleSpace]", "-", RowBox[{"0.046955754039260666`", " ", "\[ImaginaryI]"}]}], ")"}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.0008444039267763801`", "\[InvisibleSpace]", "+", RowBox[{"0.009688569698382997`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.048174977863947116`", "\[InvisibleSpace]", "+", RowBox[{"0.11514762473328206`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.023284765354923062`", "\[InvisibleSpace]", "-", RowBox[{"0.027823004257094032`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.0055196237367021745`", "\[InvisibleSpace]", "+", RowBox[{"0.0005468672521031642`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.02209566632515085`", "\[InvisibleSpace]", "+", RowBox[{"0.18135601337539656`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.03912133413545469`", "\[InvisibleSpace]", "+", RowBox[{"0.01089349839936189`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}], ",", RowBox[{ RowBox[{"(", RowBox[{"0.20659209916537571`", "\[InvisibleSpace]", "+", RowBox[{"0.009749009935829061`", " ", "\[ImaginaryI]"}]}], ")"}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.00012899078496141775`", "\[InvisibleSpace]", "+", RowBox[{"0.012637231271530915`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.11350010739573534`", "\[InvisibleSpace]", "+", RowBox[{"0.03585253021592068`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.0016825462198692825`", "\[InvisibleSpace]", "+", RowBox[{"0.05043514495557641`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.006001779932756912`", "\[InvisibleSpace]", "-", RowBox[{"0.0019929175476185515`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"0.07792804512959696`", "\[InvisibleSpace]", "-", RowBox[{"0.03774839255250288`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "+", RowBox[{ RowBox[{"(", RowBox[{"0.0077042672478261146`", "\[InvisibleSpace]", "-", RowBox[{"0.04006596694804755`", " ", "\[ImaginaryI]"}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}], ",", RowBox[{"0.2720695592634076`", "\[InvisibleSpace]", "-", RowBox[{"0.002495005408452357`", " ", RowBox[{"Cos", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "+", RowBox[{"0.1450994260083334`", " ", RowBox[{"Cos", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{"0.03394211153123111`", " ", RowBox[{"Cos", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}], "-", RowBox[{"0.013775496830020783`", " ", RowBox[{"Sin", "[", RowBox[{"0.7703933487292146`", " ", "t"}], "]"}]}], "-", RowBox[{"0.09962621285482826`", " ", RowBox[{"Sin", "[", RowBox[{"2.673330818253244`", " ", "t"}], "]"}]}], "-", RowBox[{"0.04124460054324533`", " ", RowBox[{"Sin", "[", RowBox[{"3.443724166982458`", " ", "t"}], "]"}]}]}]}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.469292494026765*^9, 3.4692929616437607`*^9, 3.4692930183413763`*^9, 3.4692930649585657`*^9, 3.4692931299070473`*^9, 3.469293177861431*^9, 3.469295326698163*^9, 3.469295394867865*^9, 3.4692954319589376`*^9, 3.4692954640160303`*^9}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.4693718598326387`*^9}] }, Open ]], Cell[CellGroupData[{ Cell["We construct a random observable:", "Subsection", CellChangeTimes->{{3.469371872425887*^9, 3.46937188612986*^9}}], Cell[BoxData[{ RowBox[{ RowBox[{"A", "=", RowBox[{"RandomComplex", "[", RowBox[{ RowBox[{"1", "+", "\[ImaginaryI]"}], ",", RowBox[{"{", RowBox[{"3", ",", "3"}], "}"}]}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[DoubleStruckCapitalA]", "=", RowBox[{"A", "+", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", "A", "]"}], "]"}]}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Alpha]", "[", "k_", "]"}], ":=", RowBox[{ RowBox[{"Chop", "[", RowBox[{"Eigenvalues", "[", "\[DoubleStruckCapitalA]", "]"}], "]"}], "\[LeftDoubleBracket]", "k", "\[RightDoubleBracket]"}]}], "\[IndentingNewLine]", RowBox[{ RowBox[{"a", "[", "k_", "]"}], ":=", RowBox[{"Transpose", "[", RowBox[{"{", RowBox[{ RowBox[{"Eigenvectors", "[", "\[DoubleStruckCapitalA]", "]"}], "\[LeftDoubleBracket]", "k", "\[RightDoubleBracket]"}], "}"}], "]"}]}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[DoubleStruckCapitalW]", "[", "k_", "]"}], ":=", RowBox[{ RowBox[{"a", "[", "k", "]"}], ".", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"a", "[", "k", "]"}], "]"}], "]"}]}]}]}], "Input", CellChangeTimes->{{3.46922668140446*^9, 3.4692266864062843`*^9}, 3.469226778219462*^9, {3.469226822827078*^9, 3.469226824995101*^9}, { 3.4692270284215603`*^9, 3.469227038481797*^9}, 3.46922709124992*^9, { 3.469227127088278*^9, 3.469227149389028*^9}, {3.4692818134862747`*^9, 3.469281815142478*^9}, {3.469281884901071*^9, 3.4692818976485786`*^9}, { 3.469281930705902*^9, 3.469281933741811*^9}, {3.469371339681447*^9, 3.4693713585981827`*^9}, {3.4693713896939793`*^9, 3.469371430004443*^9}, { 3.469371506123402*^9, 3.469371508167014*^9}, {3.469371555941104*^9, 3.469371576673642*^9}, {3.4693716987649603`*^9, 3.469371731915077*^9}}], Cell["\<\ We verify that these results do in fact yield the spectral decomposition of \ \[DoubleStruckCapitalA]:\ \>", "Text", CellChangeTimes->{{3.469371937321474*^9, 3.4693719576053677`*^9}, { 3.4693719883155746`*^9, 3.469372002570602*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Chop", "[", RowBox[{"\[DoubleStruckCapitalA]", "-", RowBox[{ UnderoverscriptBox["\[Sum]", RowBox[{"k", "=", "1"}], "3"], RowBox[{ RowBox[{"\[Alpha]", "[", "k", "]"}], RowBox[{"\[DoubleStruckCapitalW]", "[", "k", "]"}]}]}]}], "]"}], "//", "MatrixForm"}]], "Input"], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0", "0", "0"}, {"0", "0", "0"}, {"0", "0", "0"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.46937201087873*^9}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.4692180284160233`*^9}] }, Open ]], Cell[CellGroupData[{ Cell["\<\ The moving expectation value of \[DoubleStruckCapitalA]:\ \>", "Subsection", CellChangeTimes->{{3.469372126779868*^9, 3.46937214824971*^9}}], Cell[CellGroupData[{ Cell["The moving expectation value:", "Subsubsection", CellChangeTimes->{{3.4692182408089933`*^9, 3.4692182548323097`*^9}}], Cell[BoxData[ RowBox[{ RowBox[{"ExpectedA", "[", "t_", "]"}], ":=", RowBox[{"Chop", "[", RowBox[{ RowBox[{ RowBox[{"ComplexExpand", "[", RowBox[{ RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"State", "[", "t", "]"}], "]"}], "]"}], ".", "\[DoubleStruckCapitalA]", ".", RowBox[{"State", "[", "t", "]"}]}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], "]"}]}]], "Input", CellChangeTimes->{{3.469217193281069*^9, 3.469217195523158*^9}, { 3.469217558486568*^9, 3.4692176797901993`*^9}, {3.469217717911232*^9, 3.469217722187183*^9}, {3.469218279659792*^9, 3.469218296690194*^9}, { 3.469218331100212*^9, 3.469218341231523*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"ExpectedA", "[", "t", "]"}]], "Input", CellChangeTimes->{{3.4692183575066557`*^9, 3.469218370535376*^9}}], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"-", "0.001880048397779957`"}], " ", SuperscriptBox[ RowBox[{"Cos", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], "2"]}], "+", RowBox[{"0.040630951369277836`", " ", RowBox[{"Cos", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], " ", RowBox[{"Cos", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}]}], "+", RowBox[{"0.008471376132050738`", " ", SuperscriptBox[ RowBox[{"Cos", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}], "2"]}], "+", RowBox[{"0.7348182457074439`", " ", RowBox[{"Cos", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], " ", RowBox[{"Cos", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}]}], "+", RowBox[{"0.16031052068302096`", " ", RowBox[{"Cos", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}], " ", RowBox[{"Cos", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}]}], "+", RowBox[{"1.4140128618447463`", " ", SuperscriptBox[ RowBox[{"Cos", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}], "2"]}], "-", RowBox[{"0.008471464421149483`", " ", RowBox[{"Cos", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}]}], "-", RowBox[{"0.12682693134008766`", " ", RowBox[{"Cos", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}]}], "-", RowBox[{"0.00188004839777995`", " ", SuperscriptBox[ RowBox[{"Sin", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], "2"]}], "-", RowBox[{"0.008471464421149478`", " ", RowBox[{"Cos", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}]}], "-", RowBox[{"0.031346176434944036`", " ", RowBox[{"Cos", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}]}], "-", RowBox[{"0.04063095136927783`", " ", RowBox[{"Sin", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}]}], "+", RowBox[{"0.008471376132050736`", " ", SuperscriptBox[ RowBox[{"Sin", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}], "2"]}], "-", RowBox[{"0.12682693134008766`", " ", RowBox[{"Cos", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}]}], "+", RowBox[{"0.03134617643494402`", " ", RowBox[{"Cos", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}]}], "-", RowBox[{"0.7348182457074441`", " ", RowBox[{"Sin", "[", RowBox[{"0.2624757022287486`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}]}], "+", RowBox[{"0.16031052068302096`", " ", RowBox[{"Sin", "[", RowBox[{"0.3203083899642697`", " ", "t"}], "]"}], " ", RowBox[{"Sin", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}]}], "+", RowBox[{"1.414012861844747`", " ", SuperscriptBox[ RowBox[{"Sin", "[", RowBox[{"2.768773066039115`", " ", "t"}], "]"}], "2"]}]}]], "Output", CellChangeTimes->{{3.469218364235227*^9, 3.469218372403513*^9}}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Fig1", "=", RowBox[{"Plot", "[", RowBox[{ RowBox[{"ExpectedA", "[", "t", "]"}], ",", RowBox[{"{", RowBox[{"t", ",", "0", ",", "60"}], "}"}]}], "]"}]}]], "Input", CellChangeTimes->{{3.469217741490678*^9, 3.469217796996723*^9}, 3.469218393437517*^9, {3.469220149809889*^9, 3.46922015814336*^9}}], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUWXc81e8Xv/fKTEL23nte1yo6J6uikJWKhlAiCakQUkrSkmQXCRWyoqw0 bPFNQ0Zk773n/d3fX/d17vM855z3uZ/P+3m/X9fqgpO4C4FAIBEIzHf//7m7 8K7U17quRSS1/RKJP30Iov7sucqX/BNJDScrFzJuQOofPqdmbkRScRIpReYU ZBmxM3N08CApOY4q6+wN+X/oWl9b+iEpxL1p66l5KL5Hfd1QcApJjgreSWax UGa4dm2C5wCSKDO1td+vQmXgWuZtvUQkMfZ4jl82gs+tK1G8kxxI/C2U1RDC A1XkpYDMZ7eQ+Kz+cM5RAtTcW3DRtVxHogtXQU1yOTQazugcLRhGQp/O9HrA M2hKnhIfczmOhMTLBy2q7OG/wMWwiq3nkGAlSIoKlYaW1vkL5mXdQHWdtzTt 5YOf9D7OvNzxsPnxBE/0SxL8Is/ad3vawCZnQEL/xVFovTdt4CVaD+uvu3cl vy2BTsNx7kehRbBSEHXZcY4Cva0zf0elTGAuzeTztTuz0OeZZ29umQazo9eU pgzJ0E/v1ZJ/jQizlONq9tgCA+SpGv9fZTD9S9/Fr/UXDNS93dNN4odp5T37 9v8Sg8GTnmWGKn4wxT1fNneQG4buTeRtva0O4/nNhkmdnTAsla14oeA+jD1Z eWMiyQvDpR4vf3SPw2g4B3i4E2HESklMh3U/jNxpz7C5Iggjw2PxiTrpMJzI Jmxu/BRGg99wE1y2wNBHu/7n9FowxnPugfOjUzA4b1WXz90CY9kKzLXlFTC4 61FW16EnMG44clNpVAgGnjzh25W+F8bbMzcf8fjDAKn5X5EOH0xcPHtlwaAV +m+8bCk94QKTTLJzRy5QoJ/P/VxxlhtMJg+dL0+Igr5PLnEJVddhSjN9WLxm GvqC6p48vvwKphpdncLmD0KfZVkxe34rTKtHTV5it4U+bRsnA98WmA6cfZMh cxP6yE826P9SYbrW6mybXj70GfjN2LY5wMyOfJmtVj3Q59pZpjG5BDMnOPr0 3dih71kS0xpLAsy8vvj8QvBu6Bu/1kZnoA0zC98dU56ch/6DmsU+Gywwu0dd 4MebROj/5L7Cd6gbZiMf/qH/VA8D+z4wEZgvw2zr9BPt1hUY6E0vybk8A3Py GSeZyO9hMGlZOqdiCOYCjiv8uecHQ+dbA0/FNMDcN66FzGEKDB+6HAlPmmFe pKHyqsEsjOxdieMpPwnzXqF39yflwqilp3pdWhLMf9a141/2hDG331w8n1Rh wSV9rCR7FCYadC16rijCQrFj0V2mTJhin2o5nyUPi8xc14+ddqXN6Q7zq+hQ WMwK5Vnn64OZx3XfO6cPwtKco4H+jU6Yv7X/whnzXFg25tq2rSseFvZdd+LG HliOqW/t0rWHxa0Pc7qsXsPKTp3zQZO/YOmRy/eqvbmwen1HbOWRJlgN9zW9 o3oLVlvqTz8sjIQ1/aUXvmV6sCZ1XeXUdlNYm+zOTuPegLWaya/ErzWwgc53 nl3/D9b5X95vEb0FG90tx75xUWDd3eFIqr8RbF7ZO9PgXQwbbHVThqqVQI3S +PLzVBJsnAwp2RERhARm+zdkXnPYyNe+2TeghwRjScEMm1TYpJu0KIRVJAQa W7K1tcKm7UuBm/EfkJDzoPXdiWzYzDg2YLNwBQkdrX+/0RvD5gpnrrSlFhLp lsw99TuBalrnv/B6HokyjUuCVmSgJoYYV9PnI9FIL/XVvApQJ7XZY05eRKKD qpp/sQcSRBcnChxjkegZlT76mhcJ+1qsBSOWkBhwFKe+DCHhYvb7G+/tkBga 5PfLbTcS4u8Ijw28Q+KNDRJncwQSvjjfsNnBhcTgP5266feRMA7DZeiDRD8B HeqmJBK5Bc2lzrcg0bUpwGNWF4n6iwWRCepItGKSoaiG0b5v4ZuvfYhE7a4a y+4/SHyQHeSwMIVEPs+u+RVRJL4P76uSNEfCXNunwT208z3O+5Uts5FQb3zZ NeYLklgg50kQKxISOqPq1HqRpCG4Y+ONOxLONjG1h99C0rHFqy5/6pGgfpBo 5B+GpJvfu5oY5IE6Jr5Pu5GCpOxsIy2NcKC6Z9Q8ku9AEtV5O8N9E9i0y6b8 TupCOjnw9Sx9CRufdTNFHS2Q7pBA2++RLbAh231MKwyQLvV7WobhF1jr+3Y0 d8ddpKtdGnl7Xw3WTB53S0hfQropEZXiP8mwmr73M0PPC9yy69z72vNXYOV4 JHkryzBu+UnXOPJUBRY/nfBrlh/CLasKHDM9ibAoYHeN8CgI6cUs7VaVmGHB 94p640oi0nsk9rBU9sO8ilXBqRY5ZKDXXFAcjYeZ6ogW5vWbyKDosJOiyQQz 2luq485GIsOh0GC94EswnRXl/doakSGpmfkglyVMHVJRcfA9iQxfFsxtj5fD 5Ik8e97YCWQYFXzs+EoBJgLCRFVfJiCj1llhz930MPa3aVux6jtkdLjv5Bfu DWPSgSDWOIqMoYUZQS3/YDRwXOl93HdkzGwfvy18EEb64yJLj9AhYzNR/cGZ EhhxmIWSoX/IuCDr9zRfDoYHbTeM3rogk+DB0mfrT2D4Og/r6ehdyLTHl5C5 lwTDypeYKHsUkOlMvHHuIy8YGkvPfZ9xD5nuVUa87+iCodLm2m3uh5CpYLD5 k4wZDCXTr/MX0Nbbt3HVeb2HoSiLzprb75GZoHHke6kMDMWmddXeUUNmmSPJ 7fSPYSh34ty/a6zIfCC4r9eSAEMdpJW02lhk9k6XG433hGGe3F1PPnYic2zj +dn+Dhg+nXfQIyAVmStm89dU98Hwp4Ll870vkXmAb5nuahGMqB3evJMfgyxb QX/rV0kYeavqTXp5A1nUXUK52B7CqP5HPutjx5Hl8N0aIftNGO0Ial29uhNZ ruWzSr1wh7Hb7WrNN4eRpW4zRlPHBCaYf399Kr+CLNNSHfqhhTDRxf1V8pcf buUxEzP+Jg6TFcr8MY4auNXp6Wu7U+swbXGkOEr6KG5dU6u4fDcPZp1DdjT6 38FtSk6DH7qFYHHp/uSut3647VBd7MU5AizdNtD9VYG47bKaqQJDPyxzu03N B//AbZ82suNVsmFF0TBpJHgetw05n7AyiIKVfA45LS87ZGNt4GCx84NVzYiV RyQqsh2O9fUP2g1ran8i2GbNkC2QKkOOkoC1jG7HJ/PnkS3VpXU0nYHGkytG tZx8yFbTeCf1wyis36or32rYhmwTGruOfmuC9Ynl6sLKDNzOGTfB2ZMPGxYa Cutv8nC7DiG5fiEGNl4rVJAUCnG74xnLG8z+sLEZk/jmoxRuD20i7RI+AZsH NFw1OENxeyalYE6dxr9RXy6qDLTi9m8JLm+M5WCzhW1txlcDt8+ReE8fYQUq c78999EvyM5Kcb5XdAWoO/k4eJbrkF1kqNyt8hxQncK2dVLOIrt6Ao9xvQNQ b7Bms6eWI7uR+QXxn+ZATYjpRF0rZD9MrNnoQqBmccon97kju9s7sbZhGu8W +vx3IVkO2QPPXn03K0WLiytJzEvI/kCw5dE6D23/v6Q9xV+RPaVZ0ZOBicbT SxW//zuJ7IWhN023rwI1bJtkJ5sMstdo/pXhHweqi7YL2ZYX2dtGtEiSXUDV f5wSFJaM7OOJ97uUvwOVzfh079mryE61GCrR/gqbrX5hYXWXkYOTDmP2FMFm 3GEWplVADqmiOG+zTNi0EVrcZOxFDm23WXNbmh5k2iJzZ0gVOUyFzRRORMLG O3NuqyJf5HD4L43BLQg2HPa86Vt6ghyh2rYV15xgPfaYHN0pFuSIHs2Jv20D 6yqyLW8NHyNHRjKj3yMTWKsYkz7CtYgc37Z8UHmpQLtXW+6tZlQhJ2+LUHLj LCw3HzrX4/ICORXCLvn/pj1/e//NPnQeRk59nSa7f79hqSLPfaKkCzmdnoWw zZfAYmbfayn9a8iZ7dEXLHQD5gMP/3gLBOSsFNM7JuMHc3NZPftJNcjZ8uOJ ttpZmPMY6HzcE4icyztNpowOwKxr9WWp6ie4g2XiWYP5bpgZNrVKp/uKO4Sf L2fYq8HMebfu38V7cYch4+sTHlwwfZNHfueLStxx2LL6BaMCTG+zyy0woMMd 7nG9g6kIU7pHTBmOm+GO4F6qgr4dTLpsbz/vvw13RCsJnv/jARPJabuyPCVw R+Yl7TyfUBgf7JS+q0nCHWUV1gtssTC++4Iof20e7vjO6KXzKgfG0ukbazZ8 cMeAZWSg0VcYE9E9kD3pjDtW4jI/drfDaEZUyvNZAnJt662i85+GUYMI5ReC +5BLQrFnLw8DjEzd1lP60YFcWr4bEXlCMJLTnfhJhYJcphX8TQfIMBLSaq6i cQa5TjBqcQztgxGX5zvry+aQy8fSyjb0OIw4HqWfcxZArttxnnHCvjBylmN0 6YcEciX0RnS+j4CRsM/ZcdYxyJWrmCFm/RxGimwSFXoNkOur75fTkzT+W0l6 YeUTilx/yrsz7jTCqMUpn6Peacg1wbA2JtULo8WHimZm+ZGbaMmn8nEZxtTZ jsk/FEJu7jiK9zE2GCuTHj7X1Izc8r2W7xalYNyee5w32gq5dyt6rDzaCRNb zLwv1P1FbivfO/pKljBRKfnQrACQO4Dh01enQJg63VfEMOCF3A8suhg3omBa Wrjr1RkB5H4Ru2oWmwnTyXdHddO0kbtRkdzS9ANmUu3WWfjakPufrwWP2zDM KpZ4t5voIfd8ufvRLZsw++H8x+Mcg8gjbPGiZ5cCzHUnWbj/K0UeT1+uqQya Dt17eON7VifyhJaraxjEwmJfdIBQoRzyPGU46Pc3B5aC750/oSuCPB9jwzZ3 tMNyofVo1n9vkZe9bIk1hAxrhG/ne69PIq80ww5LwX2wFnH3QdudAuTVtVB9 XHQc1rd7/72gzI28p3rOCoxHwAZTHmuK9Sry+incPH77OWwEshUezedF3gif 56kSRbAxnMZw3Ogd8iaXlQ2UN8KmOd2u9FR65C2gb5O374XNN1WGL995I2+N +YLH3DJQiRHyT8quIm9HLEfuAzagmq/4qDWtIO9Uj/K8Ao2vHn++4tNMRr4t CqbaVTuB2pxYyMc5gXx8Pq4BJy2RQJRi2DkOyKdUFlqx5ooE7uoII1sJ5NtD /4wUE4gE6eRUd04/5LM1LzVRj0KCWtwT6kMd5Durv/dIhyoStNwUDcK5kC9A 8cf5m41I0B6WRsoU8t0XOHFdxQ0JmpyH6KR5kS+FaSz6DwMSlId4TD/LI1/B 0uXM6y+QIOF2tWz4HPJVD9KVKSISOJ8LiXqOIF/bzwfNP2n8uKFwnvy5CvnG vwj2BQUAtTFbdl01F/moeRlLcnw0fGNsB287Ij/ncwrL9yKgWm8r+9wXg/zS 9ytFAqyBymKcsVHNjPw61w6QpaZh80PdMvc0bb+Z+x/jpnuweerP8YBQNeQ/ ftT5yBUF2CQVDrqz0CH/Te3AkAZn2FBjZCj9XoP8T2WYoi8RYb1c4vor2Wbk f80VnSmSDOtGh09m3bmN/M0zWc0X/8CaweMT9L25KCD45q8I30FYYV7T2J7v ggIFYvqlLlowr9S5fUTsOQpUs9U1s/2AuYffJAW9BlDgz4Zt73svmF08olsR fBUFNts9Wba+hplak74/IbYoyFG7KvxuL8xopve8y+tHQaniW+rH+2E6PWnQ Oa0cBU2jk+3zRWFK4exLBNq64w0Fj2NlMCkbIBV+UgAFvS4WhdAfhQnVU/du z+mj4I2TBo9zlmDcYLaEEGWFgjHmTRn20TDmtJ5y9MQMCr7SP1pKUofRh4Zv FvddRMEyxcGmN00w0hC6bdbvNwo2C3j32rrDCPcZ+ZGXQSjYy7S5QGWCYc+Y ddVnh1Bwfukuc+ZLGGp99YAhKhuFGAd5ha0MYOiQwpU9H7JQSODnC7W1bhjs iD352WEMhZS/qBq9DITBy84xFGcXFMK8UnsLfhiUms0448+PQtbP97ovF8FA f+zWuEO2KOR6/0dwqjUMFCX8XO+lxVevnXhsNg0DCVqen7wvoFCk+1j6wj0Y iHrk+oOpDoWeHb1c8kwBBhJrDG/eXUGh/P10TftqYOBDn4uppjQKVWk/6Jl1 hoGRnvkUOzUU+iMjuJBIhEGlt5066WIoNMaVyWycDIOhCs1O54RQaJOOIjSl B4Oj4g7NE89QmH2mUi22DYacrbfd/XEAhaX9tr/5EQ9D68vD77/9h8K6qydk 2Y7C8Guyva+vHgqbB+Wk7heEEXcf7Yvzv1H4NGlTmOZ/R1GIdeXvGApfvn0w tiIJxmT9nx4xjEHhyK1JO1aOw7hY/dnD/j9Q+B2XHovnP5g0/uWu9B8Jheti 74a9SoGpCwXWjIF0KNwl1EHod4Jp6/5vV0tfogij9NWlI/0w80zcVfdwM4oc 0SruNxqG+YuJq4EBn1DkfCnjieBXsKDK6qc+vRNFQsGuveQcLEzmdPLEmKDI m70L31XHYeliU7i3VSiKbBzRqBCYhlVfwt+U6fcoyvH3hq5tHqypvvxgIvYJ RWVOtRQ+9Ia1oXkBKfpGFLU4d/EN/TxsmHXPBpGDUfT05CcZfAcbi6oXv/F2 oegVH/aUAD/YjL/3mZ+xA0VTAt4+nabxWzNb2vX9nij6jkrdoUjzz6r0Y+Fx Gyhaf9P8vqs/ErzP8E9vv4ai3UzJzCm7kJB3ju+4cgSKzkVO3OxYR8JYC51c myaKbWFKDtl6HInirpyzRg9RjM20Wj/zIxIPzfyot0tAMb7IiTUTMSReM7Y/ PvQCxSSaud/3hSDxhaYb8yUjFFPm0PcL+YfELwk5kR+dUUzb2oUisgeJXXZ/ 7D8qodieJ5EzpalInDOtUI20QTGz1sK3R0hIIp28xsJejmJ2/J0ei05I2nrr geh3FRQ76bBF4THN97Lldq3Ei6HYuWTFYXUp2vqfXwH+aSjm+8/6ZdNN2vkN p1YFBhQLlgg47d6PxHlJkkN+LordcX4hzmyMxG7rD1LMF1HscXp9V3o6Er8+ 2/6AwQ/FkoZnE40YkJgmQJ/J/ATFMhUFjvS40vB1k1xPBaFY/nkD3qAamn/f Vj5oa4ViZW/dfgnJIVGsQSbjvA6KVc88ivoQjoTRPXuGYQ3Fvmt8sLQbRkLO M8ZdL2VQrONSz7b5/Ujw5NO+nM6LYlOrandUWYBaEea2w+gniq3q2Zs00vS3 RN+Ng78fo/iWoJAtbvWwGewZLVviiuL8pObgtLuwIWnPfCOoD8UluTndKKaw fsGqUPcKK4ory9pafWWCtSKeE6a/klB8j1mHdN9NWNUKftJ6QAjF3aIHGyUC YYnbsefMqQoU98mQf5e/Exb3b4rMDZih+LUPHs/2LMNCUDUxOYmC4o/+znqf ugRzY7HXR/cOo3iJzCbfc0+YHnhG3f4uDcW/6u4hqSnBtErz1TBeWtxkdmPs 4yhMrokTle4EonivF3NF9xkYd7jipvNmFcXHQw9kXpCBMYmMj0+P7kbxxegH jzb7YWQ5i1X1HAdKsHzgdhE5CUN/jlobjnKiBFeDvXmOCAz2zZZz9exBCZG/ CTr6nTBIt0tf9/kASshNdYl/i4cBbRIGvl5DCQ2S+FZHe+gP2RIqcDEAJfS5 Ts9P8EBf1/iJV58+osRemfSuwJ/QZ3NIX/qCJEoc0hmpZY2C3n9FKy9f86PE MTOlvMRD0Hvz3qV9iQ9QwsXxQoISO/SimlxJVBNKXLiQf7O0CXp3DDRVmLuh xNXrC55mkdBD/SfGsO0FStyI1jncYQq9W3alJN0AlLiXHrDHnQl6xai3uJzZ UOLp+wqF1WrotRNOCb/0DSVSGohcETeh94W3V5KyGEq8+Wu4KWAAfUxVZMcN RpR4N3Vr6DUB+sJquZYeN6BEJbHuu24F9AvwXZJ0ouGt52ItqQuE/ipQY+ct QImfMhZpR3bBwK08xusCVJTo0om6N7wKg47NvE3d9CgxbPrr8pX3MLR/6MSp 8lSUWL9wzDSWQtO/vQlfzedRkuF6MkV2FkZvMqbz275ASfbHPcLFuTBWmvW4 0zkOJaXeu063KsPksf1dLh/LUPIg0fspjwzMCjf3B+x3QsnDO96FvOyH2SE6 Mvn5IZQ8Jb18TjMV5grlBJdyqCh5yTRI31YEFg7fLH96sA4lkx7f6YvmheWc Yif+E7R66cs33ynywcqla7EuO1VRMtcx+NZnAVjd2eC+hcUDJb/IXpKfFqH5 q7cVHpECKNkYeWHtthisB0pamD9ZQclfM+e+iUjAhmZbgHizO0oOl570OiAN mwl9+5bOt6LkrNixPX0yQN2rIPql9RRKroXZ7fCXA+p4fG2+wVaUYrMwK8pU QkIUQ6Gnww+U4i00CQcVJLRHfQmIu49S4vx7jvxWQ6Ko6a7Ry94opdGntbFF A4mJXjL/pKdRSn+felMCBYk/Q4qT1PhQyiRb6TlZC0lMRM2pnewoZckpe7FO B0laDdvMAnJQ6shlCcOTO5F0slRnyU0MpZw6hbmW9JAUln/869PzKOWxh3fw /m4kvYweXEvbiVKX0jnfSyOSKs04Tme8Q6ngrdvulO1B0q9PjLZLySgV7sV0 zNoASQODo//tP41Sj37RKY0aIWk6t/dq3DeUStDd2LxugqRFXomHScUolZa8 /B/fPlrMPVvWr4xSOXRzKW9Nafuzcyt2i6NU8dlJH5MDtHy9zcvnw1Gq8tuI 0V9zWr3yXx33ulCqntzP42vx/36slXguotSPp13DWy1p/Sa0q1YpolTnetuH VCsani8zNbKNKDV46uddXRsa3k76a3+rUWqqutnhPzskaROskhniUWpFsV7l jD1tXpYttqnDKE16+JW6eZQ2zxmxzRl2lOY+WvJCyRGJJz9J5apYo7TIx3e+ X2j3k9h+fQtKJ0rLSeWaHD2JhI7vGQPVuSi9c/LlSLgzEvYWaTnLxaD0ydBH qv3usDFrHpaqL4zSbkORxAAP2NAf4fkuswWlfQ7c+sHpCethFonyhEWUvsUT 6Ie0+5g5t/DyrwMo/eb1mbLEq7C0/CjQ8qc8Sr/b7nRfwx+WyHsspxwUUbrC 1/FEfSAsephe1hIIR+nvu61JyyEw3/ehOaZmE6UXW3bvt7kNM1Pr/O81C1GG SBJoKhSAGeVLXSmC7iizVX3JZkcOTHul1N9+I4Iyoo9yj7f8hklu3canf4dQ Rv5TZD/5HIxXmVy/9FUXZTRm3NwebcBYWNSW/LiLKLPvkITvISkY2SWtLXar D2WsQjZXct/DMNlZrY64gDIOuR3B7AdgSL/p+63mEpRx/fd+y4V/MHic4tDe qIQyF9mj7zT7wEC05aZjRxrKBODF7aoM0P/vm3S7Bx/KhHkdjL4fD/1Gegu+ l1pR5sFzBf5JFej7KPr5lAQtX9x/DM8Ofoa+Q92ZyYq2KPOC2ieVbQe969/J jOGaKJOj+vE16yj0Vmpw8Bmbo8z7E4mqHkHQm6g89WinI8p8fnClsJEDeh/+ PKQi8w9lGj/a7lRMg95kiYhgszso83tK/eNdHej9stkwJsOJMj2ibEajjdBH 0sW6a39QZsx8tN70JPTZRxr4hR9GmYWgGotX89BXnfhM4dVzlKHmvPjFfBv6 zVbmDxm8RVnmrpCjbgLQPywqxKgairJcbA7dtTkwEO+dbS8YgLIiu3Vd5Axg 0Gnv/cqSIZSV8+Qevf0bhnb/jXc/pIayet+aFkw2YIQ8k6UuFYiyJhtv/NMf wui+mPaWmp0oe0g5nMAgBWMXDu5uCbuPsi739rBUHYCJsUc5224Joez9AwVi hvEw83FHknL4BZSNDXzw8oUKzF4rSdyTTUbZ1CwPRdJnmINl16qWPJQtZpXW /DQC840X2tSiI1G2u+GpGerA0syVJz63v6DsyJrPf88aYfkDv5xE9S+UnVe0 tKWehJWgV2LuhodQjuku88mK2zT//ZMsxrgN5ThLBgZFBGCtjDshROw8ygmN fna/lgPr3tEG5G2+KKduGuCn9xs2mqpctcnfUG6X/+G1xHOw6WOYmZWliHLG rykhaxtAZX9abasti3IW7RwMxx4CNV37wzWtKZQ7yjxxt1QKCfz/nXn+mhXl nHXrOQTeI8FBadvjgeMo5+mWHuN/AAlxCxcL0u6i3JW4G4Jt/5DQshRarngT 5ULrTqTo+iCRkfjKqhRRLtJsnu25KRLJc43uplEo96QpPJBRAolHzXjYBFNQ 7tkhoZHzK0gMHNszc/UZymX+yLP7+R8S40s7cMYK5fLtjL/uykRiQaa51g0a ntI/7eqpwUisebb4cP4OylUd83zGbIfE3zEKna9UUK6pi47VSwWJ/0KNbour otyfk0+vttIjceBEWnFaO8r19CkO6XfSYuV3oy6uKDfmUmmTVkjTl1OjBweJ KDc/bPN5610k/souGhAvQ7nNcyOq3k5IrPa+6xMQgPKME9cS23YiMf9gzU6h ZJTn8OJkQQ4kxlm9fjg7hvICs+mX04eRGBBrfJbhNspLXdo5sI2mv4/oOlXa X0Z55aVmK98Y2jxsLipP2KK8tv/pjx2etHltPXuZwInyuL6sbGCMhNYbWa45 BihvGhwZ/0oICS8GxsdeMqG8DUmcafs8EjyO0yWFFqH8Gcb9vX9TgdqfyMVV +Rnlve50WRpdBepJ+/0fCD9R3p/Vu/yNJWz++qP8xDEE5e9xJMReocLG25lB 47+I8gWCE+ZcDrCmwnku6tFflC9Lvl4aoAGrYQeP+HdboXy1OI9cLwustBsZ cEiKoHybDNC9/QDLwQ84D/S2oTxV7eEHMx5YqG5djd1/BBWYCqRl8iZgQVCq yoN8FxU4NT9E832l+a/ru3g/tqKC9M5ez0EfmJO59t+ew+yoYGZMkbr+H0x/ vGRlGPMPFWxqah8NZ8A0eefa7g02VDhu6kC1CIIp2+pLtZYvUOFM44xHkR1M Wr8OaYvqQIWLFmHtwsowcXwgnXQtABX8WwT23twC41fUWHJi76DCTZucwtFO GHuu2akhuogK91oNJQ4VwGhrTP1zi9+o8PRI64P3ETAqMnBvo1wJFZ53um+I noIRvwy/Q9W6qPD6BOHcLR0Y7rZhaMB4VCjoiW6dYIfhIzaJx04gKpQ7yxtb D8HQwBaxLc70qFA9WJ5fUgFDoTlVfFslUeE/Nysx8RgYIj/6EdNjhQptY4P3 ws/D4HxHUGu+Mir0efqvThnBYN3r2mh9Y1QY/6QQve8qDOY5Xur5tYQKizva lVNyYPD1eJPWdgVUoLreqV7tg8EiMxsfU0dUZPqge9KGDwZ/OEZYX3FARc6t wyvZB2GIuMrGunwAFQWPxz5mCIWhPSOfW2uOoKJU3j6lE8UwFDW8RXVnBiqq 0C1VvR+HofnUvjQpd1TUtss4wSkOw66lF1nWplERX9ktu9vB8HA1a120Cyru X2d49PUujPibfiUwKKOitXmRonAljApUFWHtR1R0SHH56rcAo3UZN2/eWUdF l3lux/8UYOyWbS1V5iUqXonzfRj6GCZk/Olf3NZAxevjUvIdtTDJFF7qOC2J ind3//hCWYfJhZg7/51YQsWkfvLCkCtM+30SdTzwHRW/qMwcO7ATZpPPX4mf PotK2756ynD3wpKJWjmDPzcq8fIKf7zAC0vtZMZI2zxUEnP7Zl97AJY9zjpd uNiDShrble76F8HKneQlLi9DVNI71Sn1YwxWd5TIej24g0rGhXcrlMVgNX4P ix5jBirZHxmd7o6AtfhhgfIrUqh0Kis+QucjrHPpnY0p0kalc1RTyUfzsB7u sm+u9ioq+R5aKRuTh/UlRsrWcUCla2mv7IyOw8aJBot34raodGvJfirpMWx8 PpHfV9GGSg9NmcIXa2FT5PJLsUoTVIpLfC9hsUHj64a2qruvUCl16kzpKzJs fhZIbzY/gEpZBry2pDNAZZbTFo2TQ6V30dWTxxKBuq/iPd19GVSqGPK7Xfgd qCE517sKwlCpdqeMOBsDUHObdrU716PS98hfJWd2AfXP6mpQCy1fe3eYdaUX UFf4johEOqNSP5kywf8SCUzhewkduag0cbP/lnc7EtjP6aXwn0KlxdZo0cbt SOBSbpUVFEFlgoLhB2kjJOxo0aOPmkFl5sA5q6CrSNh+ZKzB+gEqczanjrXm IIGhvuKnSAEqC0lYhan1AXX+WEU8aT8qS18iiUTwAfWH6HVJcEJllZq84r4D QH1NkP0xPo/KOgKnDuldB+pVyUvO2r6obKA9m3x8H1B3PXr5S+MoKh+wvTEW wgabC4cf+HnxoLKdD7dOyi/YTC//4JxBQOWTD9PDviTCpiVXjPuP76h8Lke7 ZeA0bMwUasazElHZt6FOjFEBNu6ujRWXS6PyHYaxEtP3sJ4lybPLPx2VH0td Y/QIhnVyrzyOfUHlJAM2m/smsFbIWKzySRaV866pTX7/CasZ19e1nOxQuW3O T9J+GpadSClb22JRuZ+D0cu/GJbqEx+wZFFReVIltjwxCJbU54QFfAxQheRW evgfKyxQFfXhhiuqKPwjRJ6Rhbk03rNNVwZQhbLxqC18EuY4O5h+jjKhCghK yrwuhNmbc/T2hqKoYm1nXDlpADOXDK6VmISjiqNPKxs7M0zPX99a9PMCqpx5 dPaYOo1fr4qLEd7JoEpAY8TCpeMwJVfCIf2NBVXCRoUMnkrD5OE3DlXMLajy kDH7AY0PJqK/H/aQ1EGVeOndne0FMP5vKDvv53dUSTP4T37dH8b1EgLPzP9B lZyTpy6L7IGxzIisw4/jUOX9tdmvyARj0oYOgYF6qPI54SbHqSYYLTA8diCI FVUaP3Afv/EERq2W3oYX+qLK79/pb146wOiWMnUm39+o8m9ee7lGAkZqO9S7 riujyhhHnfHwCIw8u6cjX7mBKguqRx+z5MJI+HLtDylxVKEeGPundBlGwsiG 55xcUZX53DVl890wEr2r2dCwA1V3hLP5e9HDSBH9kf6Bs6gq/PJZzaNGGBn1 SG9UrEFV2S/qXAWPYVRd38v7WDGqqv/7fOrXURiNUI+/cl8EVXdtWucsicPo /Ixv7etEVDUR7F/nG4axi1zbPVL3oKqlrt/+nW9hbFM0uXG+H1WPHmaMcfCD 8YQ7U8tyXKjq7BvbF6QHE/vej4dVvUZVzygFted0MElPpxz7XyCqXnlbeu1T PUw25yZddjdA1dBvB+r7HsJUxpqsaPEcqj5luuAiK0K7Hxn+1PGzo2qKDCF/ 3wDM6NNrn6bSo+obw0fUc1kwU8lYcuztIqp+DCqMy6Hx4w+uZz2TB1C1dnKj qdUI5uzPfHzXPIuqLSf2biGYw9y/g99/ys6g6gC0eVqdhvlF4rd87zVUow8J Fg8wgSVFqlXZRQKqsei8KNveAEvvZ4sfOdahGtt0rV2aJSwblZm2Dc6iGt8p jrvfjsGK3Tw7wW0vqinuebEo6gVrR3BUvkgB1VRXah8WLMDaf0pU/ufbUI2S N6G4zx/WDdzC1p7bo5ruOY6qvwRYf/vYZCQ7EtV2S2idvBgGG7xmard8PFDN oP3oGgMLbFzVyqSk/kW1vVHBT+IfwsZP6u+3TWyodsA0TU2VGzYVbKdr3E1Q 7RCptv5LPGxemQigFhegmm3JhIu9KGxWhtzvDm5DtaM+HNTxNKASmlnfWcei 2glFrfjrCkDVTe5MdbiBaqf7jmlyvwXquTLBsl/XUe1sQnDzawpQH6+YisnS 6p23Tju3uwSo71S9v1Z2oZr31rotPwCoTbsPbjJRUc3vy8SzM1+B2s16/Drz LVQLCOTUXd8P1KHbTO1UYVQLoWj9fNhEi1NfGrpIoNrN8WMXpG1o+4/e/VA0 j2p30kKYP7TR8uUJnP4kj2r3HdLSzI/T6r0tkmEgodpjrrrdvf1AjXat8vX0 RbWnjRNtl91o/U68PE75jGqJYZy+WydpeJxiO9NtUS1FX5vtuQ9sUkfzL7LS 8r1cOPaKsgybFXnvJTJo+V5nhxjWBcHmpVHirOkFVHvrktZ1nA42pRut/qaq oVqhcN2V2XCan3ia9TC1E9Xe/5rccZsNNrweCQ+tj6PaZxPtfbl8sJ4CV8YH LqJa9eaxPqMkWFeL/+jnfAfVGopCrrVJwFqJl9XnkCeo9kumroCkDKsft/UJ 1+xBtWFGB2EbA1h++uRKLd0lVN9WGzKz7AFz4wSlscsHUJ1zkMf14k6YM+19 7dQ/gup8dG/aR5lg9s304Sydu6gusfv3l46XMHOZzurIqhOqyzp46Nj4wnSv vq9fnBGqK/kTs74ZwLTVVsOq1SBU13qn9KSiCyZv7A29nXEU1Xe1fGbRzoKJ MIbxpgQhVMfpw0G5NH6LhhyCcSKqm2wbn5PfB2N5o+YeCdtR3Uwx9EwqD4x2 zSr6yJWhuuV+3k6BfhgVotxne/EP1W1dsy0f58PIueAm1yhdVD9606CKNQSG 66PC9Mt5UP1Eyh/dMAsY1hdoZFSNQ3Xnj+ezN0Vg6PO7SMffFaju9pdOwo+m x45NdEdetUd1z9XYmKkSGGLWC/qaE4bqvnwqW8+Gw2ADo9HjbgNUv6r5JbjH DgZTnldyS9OjepC1/fxRKRi8t7fdXIcN1W94TZ5tmYXB+7L3uQLzUT38/o2/ ZpUwmGaXX9X6BtXvZfEf+nofBptb27fL0vBH1eVU6zvAEHscDpueRPWnQ0Y7 ixRgyNX3R0egI6onbWl7q7ICQ9/VTBKuH0L1VIkLkhm1MGx5bXfbmAuqZ+CW WLEYGO5bidK7JI3qWY7xrHHOMHJX9elumw5UzwtQvc5JhlHjmPD3nuaoXhT7 deEuAcY4o9Y+xLWgemnR0XN0TTA2c/mMSUkjqlfNhFktuMNEzxh3n4wzqjds F6jx3AmTM/VnzCzWUf0/pdxdQ0wwzcT/SmbgOqq3n+mQ+pMGMzvNLznJvEP1 7jCvuEPeMNOpzxjd9ArV+18wbKtHmL1x8A+HtCaqT3apLZb+hbku385zbx8g mc7mVu0zHlgsNtYscBxHMpO3kD5vPyx5ddKr2dcjeduDvLyH+bCscDkiZTgJ ybz1nfGhFrCSwBIoYx2BZKFhb7Y1EVi1fJWlJm2HZAkGxhs+47BGl3xc84YR kpX2kD1cwmH9+IgN28MPSFY/XvOvyw426JeMtTmckawV6Gh7WAo2MhpM86AD yVgcvntfJWy2nTrn9bwAyca/hPM/3Qfqmc1gdeGbSDadLZDd6QDUKb4iiWIK km2Vu9gVV5DgfaL2NUs+ko9qXBdIWkRCpg7ryPwrJJ/QlZL+v39su3v7+yVN JDvvrlG9PoNEBoXOrtC3SHYzOqc7N4lE5dHqiLb9SPY03WbkMoZEyzRIZWhE so9Frnkrzd96UjJ/J1Qi+Yqt9ZH9A0i8dW3UR5YbydeOLp4u7aX5YdcoRi5j JIeejPNU7kZietvYnGAPkm+76l15RvPfObV8ZZkVSI50777B0YbEPBmRHXbC SH50MfTejd9IzJ6Yuxx5Cckxl6WfLvxA4kuRiEsKT5GcEFibeuY/JMaUtriJ 1SH5eah7Vts3JIY2FTcOhyP5ZThbsVk9Et1cRBmqq5H8+l7ep/IaJJo+av/U QsP79rFNo+pXJMp4JP0sj0FyYezS75RPSNjgGP58tgrJH5Lje3ZUIKEpdbZP 4zOSK9L0x8JKkZBg/GD/ui2Sv7z6t7D0HgmnpbYF3FVA8rd3siwd+UCtz2Do HZRAcktJPfdB2v2BQuc1Q92R3Fp5XvRjFmxmv/2vzmQTyT0NBZQX6bDho3Dq b6gfkhd64bh7PKwWvW6pONWH5NWh3rN/n8LKOvdhzXeuSKZOhPlYRMPKbvmG b+oU1GBebgjXuA9LJWNFWtfuoYbI9sN5a6Ew/yqS4cfWGdSQ5FotOx8Mc4O9 LSYTSqghJ5BU3R1I89v4c43ZBzXI0n0dX/xgJvsjo/5iPWqY6HnR36W9X32/ zjxbaUENM4Md2zfOwvgLi1e+u5tQw3JvkcAFVxjz6nHm366MGrYHj0j1nIbR gyk9wlfcUeOo1bqq9UkY2ZnwPPHYAmqcsH+mW+UIwzoNZ+lsUlHD+biBkfZR GNov6lBQfBc13E4PmL86DIMefooHn+xDDU+38COCNjDwIlnPLeA7avhcUDx9 7xD0T9ksXZ2g4b3i23Seag79liZc14nsqHHN/+KVi2bQV8OyoftgHjVCQ7hC +/ZBn43CbuOUq6hxO6z4nq0x9C7zMg1kS6BG5N2jT2sMoLdArSr7LidqPHq4 kaoL0Bv2+X3ifBtqxMQ8z3qjB71edEUzCwGokWD87I1YB/ReJGvFGOujxrP5 5Fcx/tB7W9K7m5HWb9qLpExWPujN+7GtWO8OaryySky/Xgy9M0qdSkE3USOH mJC2ZAd9xqzTl9/3okZ+bvwLj3noy4Zc+V3VqFF8Ii6l9zH0y19fdKP5ZY0y ttjn9mTo/3DdQKe/FDUqy58mf/sOAw4d07dp751GlUdMkqEXDO4QD6Vb1UON esEnCR+2w2BHy7ir5VfUaKqPjlfJgaEip8X1OB3U+HH1cWzaQRhO1UrVMuND jT9yUU/5x2Ek+WVc11NH1Oi59TCaXh7Gqn3dhzhdUWNQ80GUfy2MTxvfcTpL Oz/af//htCtMyj5JXXz3HjXmDSLvdaTBtOP9xN5XAkhh2Ay/nS8Gc8pHLFjE XyBFyS8k4AQPrJjE8X+mOCFFXTr46s93sMqiYrV15QtSNH8GXTG1gdWGuX2K JluRAuTAS5pRsL63PpqtjwMphj0BPm/UYYNQeY/1uQdS9j309xb7Dzbe5Zx2 NEhCyqHJKxdYtwGVw44nM+gRUuySLp+/ngXUYpa/P1tUkXLsgJ/7kikSFA9v 1/nOgBTn175ne8ORUDmrs/5KECluR3zO2MvS+M2M88eNP0jxZPJ2+VaNxL0l YpEjjki57Orl9GELEiv4jt/0PYmUQO4Lp1ReIHE2RzDrFw1fyFfPE2kGSJLI cLfJ0kFKmM/54/z/kHSwe+6X/TekREh4ODwIRpKv1qjEPtr6/e/ux+hFkPTk gSK/WQFSHoecO+JfhqS8Zivm/gqkxKq6HZ4+hqTansZT60eRkth11s5lFUnt haNexaJISbl3xqYjDklDOyfIe0uRkq7nan1IB0mT5zj417yQ8nrM5VB1K5Km 9Z60kGKQ8jbe2VLPD0njuZ9Hmy8gpXD/afN8biT11Y74HbBByvtlp4OyhUj6 ddfp3jVZpJRnnDJLskLSZ1LEp7vhSPlsd9KUcwZJr/RKAmJvIqXm3H8SHn+Q FH67yi5YBSmNwbBaXYmkUx3uLrWTSPn++O0PsUwkaR65JNRggJTfmaJv/B8i iU7ul6lBEVI6yh7c+HkFiQ2Xbt14HY+Uf/9RHVROIvG+lzNLMBdSRle6WXvV kEhv5q5JOICUaTbzAT0+JHxI4zz41xMpCxIV5TEEJLglXJIboeGjmiV77m8G agCj8O1fjqi5I8mxOc8T1gNC90ouMKAmX963zK12sJYz3+UxzYeawtX6IS67 YbWr2Ks0lIKaslPC6vzbYEVra2DfuDVq6mFnVEgWLPzxscv/lIyae2wPnGuP hgVS03uT0hrUNHErM6QEwryqnVJ3fgBqWkYlzA2bweyjhh//vB6hpnP/MVuL UZgSfdLBHLYdNd2WG5RftcBExY4GVUsv1LywbRcDXQmMu7+oFiEHo6av+Osu x1QYk3fPkS6JQM2rWgJFxXdgZDm0difBBjWDTCMecHjBcNv01+cPn6HmjeOr Z9ztYagp69fHzIeoGe5zDqoQBlsTf/VspeG7d7udT1QOBhaiA7NEaPWiEvdP X2WHAXmbl6xP3FHzae6H2pZl6PeJSFMibqJmYpV8itI/6PvZnM/NSMOX0hZ3 9VYt9B3MdL71dz9qpk8yH/qXC71dN/UuSN1FzSzSVYWdsTS+Kze5oGOBmnk8 I6ToEOjd/7Sp8tkGahYp2rdPnoFeKQO5knod1CyF2oJ95tDL+zNCYU8Yalba 6NxN1aKtW64+jd6LmlVnM0+vi9DOv7C7s5SPmvXX+PTsGGj5U1w/RnGjZvOj cK63k7T6W7caKgNq/ny5NMH8G/oORPhrblFEzbaSM1WnK6CvpTCK5SZt/l1N rUnl6dDvxbx7SakANfv69vrx3ocBybk5UYso1BxeKja/6AcDE43qUtd7UXOS VVam4TgMNv6Wi2gZRM05sRiqtDEMVRqedvlGm+eyJkNrsBIM12jczIr5gpob +/3etnHByL93PGG3SlCL0dvuZGQ/jO+1u7yxEolarGoSSqOeMBFQJfNL4x1q sU9MrOxdgsmyNAutku+oJeAWFkXHAtNv26t35zailvLJwi9X1WDuVsXZsPY5 1CKLhDz4XQLzRkziRzteoZZW5wEHiiEs0Gl8iFYMRC083Dc/ZQeLNztcxe6I oZa1xQ4Zl2uwEsaw27vuM2rZs3bNfmGA1T1bLqnctEEth/rXH8UewuoqnH+3 dw9quZoYHKbx9boThUkyRQW13LewSemqwMb2NrtBUzXUuvCpbTqmGDbeP35a u6CNWlf1L945VA+bK/vE5HvTUOvamr7tW5offWgeMvAjBLVCPzBLsP5FAvue U2L/JFHrtt/PyXOuSLApY5osdUKtSMrzkpppJDw5XSZg9hq1Hs563Jb2R0IL 045kjReo9SRXx/oGHRJZAnq3ONL6jffcIvovEom7n9g+8+1BrWeKzeO7eWh6 UoWPfeU0ar0YSXif8JymHyXd6VJp+TMzztxckUfiRzuDLbw/UCvbReOQXT4S /7054mxqiFr5ElThQj0kbmw15ePLQK2if/WjHNVI4nKKDzy1C7VKk2OKLlgi SSalYZ2dDbUqHZxCG9uRpFHucsegGLWqBFQsFE4jaVf2ap+4EmrVta4K3h5H 0m4vtRcJ4ajV9KRqaIDGr3pbyjkbu1Hrh/WjQkMCkiheLpVHKaj1h8Mx5HkE kmRLVwYKs1Grs1n+wCYXkrjn98cU0Z6HnsgF/mNJSKQq8Nc+j0KtQdPKgQ8y SOzz5tZd4UGtMabIfJ5cJH7uL9lnP4taU9X2Qb47kZiUbPzl/hRqzd+UMv3+ BYk+FfHNC7KotbJnmlf1ABKN3WTHJZVRa4Na2hdJ08ucX+vU3LaiNqn89tvR //+fvpbxr3AatRkDrAP3jiAhZRev+7N41GZfHOMmrSNB7Olg8I3dqM1dWNRz 8hZQi/SqJicvoraAd2h2xXagSm1mcpJo5yUnBUyuSsDGTMA7ll4V1NYa2H95 aj+s2aiVaVzwQG09plsi6kKw+qSJzf6TPGrvUfz81XsSVn62XaHOcaK2mbce +2I0LJszFf0IfIHaJ6gqrza6YAEjZSsrllDbRcLdEnJh/rpZqvcXAmqfM85Y vH4D5qrYP7kz6qC2b6S4Ib0czNpbLVZz2KH21beOIyarMPOKXzjr9hfUDmqJ fxD+Daapl9otiI6ofYd/RyerN0wFPdFtFnqN2vf1LEPNjWDSuiwu4cUyaj8+ cU/uIS9M6FyrOdmgiNqxoXVN30dhXLm5iu07bR5J6QyXdpTBGCXOdE0vGrVT 6wwFbe/T9O7b5fpdwaidMR786ekpGLnaGtOdrIba2dvLzrRRYPjDJ7ct791R O5+8wibIAMNsW6O6J0tRu9hOs9DhDwxdxvt1efdRu+yq99HkNzC48Lv4Km8v an9KfEv4FwSDEar90xYHUbv641iGhCUMUuKENj8SUbuhT+6gswSN700PJ3Jp ofZ3Bue5lwsw0Oh6+EBUKmr/lk+JG6qFgQ+rT97M0ubdcaAL5eNhoJxqJLd5 CLX/eQkMunvAwJ/DJ4fXZlB74PHhyGyAQab/uosaL6H2aFE0eYoTBi2kBpvq dqH2VNt/bWr9MPh6WdT4Aa2f+Y1twd5FMCTYwffOcxW1V8VMpQvvwFCK6cdG ZiXUphrealh0gOGduUPQuoY6W1y/eOuowPCQc28aTwXqMEcQ+PwJMJKeRQ7J FkEdtmy9itIfMHq5dA9d9C7U4Zsr2gpXYNwsL/v8q3bUEeGZzbtuBhMHCsae ORBRR3Kn6uEvIjB57F0EpdcedZRDMtNMvsC0RfZgzq2jqEN+MWAaHgPTwx3p KkJPUUe7Rny6/izM3PrEdrXPEXUMtiXom7PBbJPunsqgaNSxi7/329YeFq5O 9FjErKDOsfL6wKcKsCjLdOaLrQPqnOxhlPizAYs/ct/0U2xQx1025IJDKizL b4twkYpHneBCH6bT47CaL1fWEVWIOjc/lhslT8LaXtlz+kpDqHOngTGkbQrW fi9PNJeEos7jnoQVi1lYH9iWzGnlhDqx44OaEXOw4eRAH37qDOokLatdrJqH jT/RT2yqR1Ank+3r6K4l2MyRkn33awN1sgW2y/gtA5W1vMp7jpY/X/qIU94q UJ1q3N44dqFOsdqL5PE1oL5d9PhrRo86ZbsmOmTXgTrPlBmVQkKdT3t1eJ02 kcDrZK/osBN1qq1CrZOoSFDdIO7qyUedBsfGB200vWVYGUup2o86/7nxNHKR kGCVRLW+1o06v3xPMVlsQYJDRPnaAm3+7cFvjCLokXDqOtMWNdo8uiMWQqoY aHFw5YuLJ1GnPwbKCUz/33+V7tFN1BlJubOyi4WW79W5y46/UWcy66em31Za vREPh4/BqDP3XuRiHisS1I4NvAv5jDrLX85mj7MhgY+nJMzrO+psNOWPyrID dfHsOQ7BFNQltW3IOHEAteDlr7eu/1CXcWCvUxInUM8q6ilxyKEu6/Sj5D9c QOUy0YwQFENdjrWOjh3csPn+gNin832oy8sow2vOA5s2cc3//YeoKy5c8uAr P2xcTm27RmFBXRn5LQ1UAVin6lZThNRRV5FiwbRTCNZDmqrP7vuLupqmfSG5 IrDmM8LwOrMfdfdfYb2YKAUrco+D7QcCUNf8hl12qwws309n6L9jhrrW95+P csrC0hxjky2LMuo6vtR0Cqc9bx/Yvz44noO6F1uOW/uqwbzpq8uZZTdR1+9v 5oO3ZJgriGdniHFG3YDh2YZRDZgTDSLseJKBumHU20YntWCWdLDjcGk66t5l +R6SoA0z/nvNdZl7UPchj2DZbx2YXjJX9CIPoW68Uq7mgV0wTXJXcHtdi7rP tFcv3taDKYlvh3MdTqNumqFR9ufdMGnuQ2J8loi6r8zvj2wATIRHflT1n0bd nCNtMjoI49+/dyR5CaBugYukk48BjCuMtq++60Ld917nk3MMYSzaq9Ms7ivq lgcUt48YwRjbBOsHVUbU/XybxCtlAqPxf+SDRXhQt2ZX5l6nERjd/bzGkJ72 ezRMm19OiYSRWa0Bm6NsqPtf2nzGPzUYefeTbVerLer+OhL/P4quPB7K743O jCiSokQphZJEZS3bPM/sM4gWJKGI+KJCSkWShKyprEmrbC1SUVlTiaSUlBTZ jX3ft9/7+/N+7r3ve59zzj3veT6f8VErWw2dVx4ccJXUQJ06Mdoim9PQeayU l7RZB3UaSjp2Jq2GTnvDHBvWCOq0eEc41xVAp5Nm8W99gl++ikbCqsPQ6Xu2 ujXLD3V6Gn+XWwpA5z3yU5NHBH+DMRcmYx9C56+Hfi4rN6DOmKHi5hoedK3V nMx3IfiZmvtstbwHujzdrr9UJfCZf+4ZujcKun7OZ3FdQlB3gbP0m2h16Da8 3mHQ1YS6wmsKu77+hO7PJbvnFsmi7pJvjjJiZ6HHRqo+KdAGdSWCFhvtWgM9 05Ivf4etQF0p3Wc+YcXQ+1AtpazxBequvz/7V1gI+uUc+g6DEOputHwgykmH /gEN09eaxH7lJYb6QcYw4PDxdMS4EepqnI69JXANBo193kXNJ6Luzi36lXQt GCx9U7kouBt19f81z/rXwhAze/vQsV2oy+Zts5lbB8OGK78qheii7gGZMtkJ oh86bryQ///321QdN91hCqOTU3xBeQ7q2l9eceHUEIxdLtfIO8dAXde+w/+G dWD8ZruQ8ZcvqOtXPHmntxQmX2pyT+iKoO6lU7e/qfwHU5qjAVsvRKBuiDKb 7CoKU892rJ0K80Td6OvX7Ph7YfrWDUa+4BzqxnJ3XlMchRnR1ifi3faomzjT UOIYDzPe61ovurug7oOjW+Sb/8Gsbs8X1gLi/Omrv+2TC4DZ6zVbTjWcQ93H X70vHVaE2XYLbdWYKtTNDlz7PLkc5tSVpO/X56Bu7s53LfVuMOe96KriUBHq 5vW6rFizFOZe5G3oXOaIusX3ljGtsmGuW9hmWdcC1P1gkeOVYA7zq0q6mmfu oO6nxdYPfhH+iwU19deSUPdLMaVm5U2YP/QzOeVQMepWe6ULmlNh/vSAzYM/ jahbu9lU63ozzF8e+ct8I4q6f+tHHb9fhvnwH2lpe01Qt+nazVhxJZgPC1ao 16hB3XYOrdT0M8wHiIR6JBijbtd0x1jkCZh3PzLuspiPuv1ZkYqVhL9Z3La7 LrQcdUc+pWzvn4N5tQTPr546qDvZmq8n0QXzAjC441Eq6s7NVbO0fsJcRYGI p6YW6i2Q7jK1LIG5MKaAoeth1BPWIB/weQJzdPFF9ul3UE9sl/SR5ESYHXAe Y+63Rb3lTtuOvQ2C2diEVZQNf1FP+iLbu/UkzGqOtaRdfIV6a2/aBCw8DDMV /24Xm0SjnvxLr3BlY5g5+Epy2+E1qKfSee+O+waYduq70OtwAvXUBV5nXF8G U2075q9E70C9HWurXrycgSkbjtv4D2fUo+2ZK5v+AZNQ6bfIjY96bDfJatli mMi0CtEbD0I9oyCVetojmFj+m96/WQT1zN9YDQZfhrEGUuLm8iTUc5HPXb1c G0ac3la5+uxEvRP6XzZoy8NwRbiowmQz6nlZtG09IAbDatK+H6SHUM8vbDk9 uR2GBBrei73XRL1LKcrGb7/DoCc7VmbJMtQLKaJZtBbCQGvFce1Fvah3bfiE q3Ic9GeVqfziL0e9+CVBp3YFQt9L8dcL/qtBvVubbl1wd4feD78krB6Po949 2osr162hp1UsOt5yGvVSD1Zcz+FCz7J7yqF1Haj36FTzLSIvdhsfD7y/9TTq PYuaTJ1ZD13x9HX8c1dRLydjWfY6Uegc+Rssu1IQ9fLeb8qnTRD+1hvVtuAy 6hU3UEsdWoHfsiL9hlA96n2YMK8KrgL+qUa9k4FOqFch4VaXkQ98mUqJGQ0q 6lWpXGqtTIeOGlZzmkUy6tWwE/sGYqHj3r1vvzocUa/u8LPJ5QHQEbhZdkfD TdT7d65cQPs4dJwT+SrqmY96rTcalxywIuaPnnusRtTX+WRcypcNHfc5su5v h1Gvr1xM7rY6dPx8IKz5YhT1hls2bimRBb7s4dok/RbUm5jV12oTAf65XVfE Bu6j3pzUPlw4BvwuxfgTpGeoL6DmYqjcTPh5jJ7bLT/UX2R00WzXF+hasC3F MJ6C+ksc423d86Dr8Vm7nz9FUV/iwlPn62nQ7dzyltNlgvpSCaWeOTegR+uL +Z3VQqgvVzkSNOMGfeRdksf921Bfkb84ep0l9M3tonjGHkF9FYr8TToTBhZU i1zwi0Z9be3dT0PWwKDCZ8Ggg86orzv57vGTDBh8VpXiY1mP+lCgnVmzk/DP g1obP6ujPo+5Jk3eDIZPbQlPNt2N+tZ7+Hfyw2F0Qq7Z7NYN1LeTPJjcshrG khr0iztfor5j7ZdbwmkwTt/8n2U+MT5x6EWCxXuYCDe9rXhkGvUDjvlHD0zD 9DJJSNV9gPrB24evSoXAdEbo12M2y1E/bORoJHUlzNCOdLsniqN+jI9xaJg6 kR+NuE75DNRPpBaFPCuG2e6jBQXqK1H/NkU9qNYE5k6QhleFKqF+2hXpSxtd YP7IuLcAdRfqPzYOu2hE+NmPgJOvi6tQP3vp3AXPICTJjsq2dxL45FR7nE9Y gSTTzjdxU4qonxfb6lN0D0m+I7t84q6jfrHV/rPt25GU0qtqW7cF9T+s/XRG tBBJn552hMZaon55k8FpDWMkdcscdjuzDfW/PMjyOlCH5IXiAzd0DFG/2lnB 098ZybJn/XogF/Vrt8S6PxxDsppevNR2F9T/2y98vDIQycAKaDAMQf2mbF+3 EQkkc0P2JL/7iPrtp/pdV99BsvHEwMOyRajfrWP/H20rMb6qdkqfwLd/psbJ KR/JHMPv37pPo/5IMdcx0hDJBpsv5xgR/ExeyjvyohbJW7f3k7MmUX+Os9Xu z1Ekr/4vZYynhAYCIncPk0eQTG7c5tf1Ew0WfllhqxSApNYHY2jxAQ1Eo4Ot TZchqeQz+KiEo4G42ZTVqWQkJR1fU/rfCTRYKX3MMkkFSR4PrR41PkeD1X8a LUpeI4l+w0337io0WHd7n1knG0lLD+678uEjGmywL9239AfMZzHE3hpcRgPV zkem1gMwl//oSoPbezRQf7xu1yU/mNvJqPfibkKDHe7XjDJEYfbxrje3gmPQ AMfPcMaVYOay21+V0r1owHrTzV6bA9OdziU1hxehgaGfLZPJhGnu/ZKXr/6h gZkgE6MPw+TUj+iKNwvQwGn5Mp0t8TCuX/Gu8y5Rv2vh1UojcxgL/37z7v5E NHB3EbdzlYDRv4eVlNRH0ODsW4krGWEw4qsmwDI6hwbhJ1bUbfaDwcpIL887 +WgQvTrmOE8PBpVSf//52o0GMaUrKc4TMBDs8+7O9lg0uL1WSjnNA/oeJPS5 aSWhwf2yuMKyrdDr5/dr7xYzNEjzkt7bQfTjRzKHRSoU0eBZxSqfTQ7Qte8Q xyO5Hw1yTicu5chB56GopT6iiAZ58qvvH20g/MWy1sF1HA2KKm/uCEqEjgfe X/ZrNKDB+7MyFQ/3Q3vDzU8VN7+jQfmGpEOlK6B9s/PHvzeG0KCyas1wWxW0 XT5tHRmShgbffW6FCEZA67Ap3eN+Lhr82iS7ZqMhtJ50byPvJer58z05i7UQ WoUMp2Vk7NGg0W8d0+E9tDwC5X90ZTRo23y7NvAitDjeecXKJfDorFnv9oAK LeovqOkXGtGgz/8u6d00tCx/eirQaSEaDKvI3Wh5BS2LXpPCtWrQYPzXvc0C XtAizjc68OU0Gsxcki9QUIOWbSz317keSCVvvb+H3gcth8tV8x9GIlWwTqHN PgNaHlo3xnhIIVXk8oOzAU7QMl93nUv4M3Wp2sYl9zZA67ElEpvfTiF1+d+U u2+boLU3S0wy/wxSpUMUtZqSoe2iu+H+wn6krtV4+Il8ENo3df9cIb4UqXL/ NtnKSUN7Y/YeiQhFpCqGpg5hDXRksvRe9jshdYu2UtDha8APlt7TdlMHqZoR m5/cWQxd7ouDVr2uQqrOzgx6URl0+3xfHOz5AqkGrco//wVCT6zilQ2fTyCV o7dlXnYO+oZZ2z6YvkOqUfuj69Q8GFit6vL9vThSd19T3WR7BgaKjQ6oiIwh 9UDnVtNbgzAkYzlZJ3gRqW5x2++saYWRuTjhc15cpEYPa9FWf4Jp0knWv9uI 1LgFik9lq2C6QsZ3NOoUUpMkpdfK/4SZaxurCgaJ8zzUnppUboI5iZT2+1NU pGZyepy2tsPcp4+Pc+xeIjXLsr5GvQfmfR9UupwoQuqbc8XPdCeQ5Bj1YZ/D PqQWhWWvoxL99XNepVEZsf990v0IBtEvz7w6F3SuGKnlj29Mc0SQTEvcZr/b BalfCoP+M1qK5IvPqCPXryG1+qv3L9MVSM5/v1H6tzFSaxv/Y+1bjeShW2++ y8kgtX7Q6vn+9UhRWHK0cj+BZwvFWO6gIlJ2dfBqa+4hlb/cIOqQClJOLnBS Eo5Gau+GrbNH1JFyjV5G3XccqUNa612ddiIl83LSocEIpI6zxX+7GiCl8OU+ 11ExpM7sF+CcoCOlouiFkJEQAtlp5OVJLlK+x/XMbslEEDrTruBtgpRq7bu/ g7YgLL7yK9rHDCmVCUORh58iLEssm79ghZS3H/wg3ARBMvPNsUuHkfK0xG7N mQCE1fmZf4KPIiXutupup2sI6ypv8cJckXL2xL8luRIIGxoic6PckWK+q6LQ ahvC5n7/jddPI2XL/nDnxbII20ge1+N8kTyd1bJfLAJBU/wI+WYAkksDbilm OiLoyJuduB2C5LCpY/SMfwhUDVb9/Ujie2BYSt7thsA1V3qdmYCkQvMQXUND hF1HV296Svi1e2i/5Kq9CHu9F8c8f0B8/yKPyb5pR7CO7/PIy4K5Bgzz7TqA YJf+719RDsxtiCyM1RpCOPrm2653+TDr8GdJc4UtgvvfF5srPsL0z6Wvh2eG ES6vP9v0p4Ho/6kT7zcIIoSquZr+a4Xx3PTkVY8mEaLo1gUtXTDW+PSFetsf hAQHSOgehVFNP/b9TQ4Ij9ME98yIwuD4xau7zhONd/arsWKSBAyqr6WezHuG kFvO37pAGgY8heUMyqkIb7srREQVoG9R7fLR4SKE0pn8M8uUoCeLJXf5+1WE z0set6/YCt1OJg6nXscj/Nx2tWSNLnQuui+av4Cov66zYYNCM/CFvjBfyUwj /HuwJWjLFehYKZMVqr4VodX2DF9jO7TvoAUvCyfw75QuNdSthTYXvkGG3jxC b/XyR3R/aH2WIXRtvyXCUISdmKEStIpKWFkxCH7GOU/c91RBy7lb4f+OEvXN kKe/H/CG5tlXx5feUkUk2W5I3B4FzTFFY6XeGxApeUZGmkegmUGmCBQdRhSU 9pjZuQOaBV9JZK/ah7jwVPxj/cXQ1CCuqHxnAFH4e5EtNEDT503X448FI4pu a1/KyIamKsEJsxwtRLEI0WJOEDR1PlQz3B6IuKxLw8PICpqlB+2O9DoiLudY yZtuhWabYkHyx1WIkg/8q/dRoDm3TsJeIxtRmpwaaPETWhTHFTtW2COutv2i ZZUOLRmFb8XF7RDX5I202/pCK70sWGBhGeK6VTJx9ruhtS8pwVzHD1HuNJ17 dAO0PX4+OGYqgKhQ7Tz53wS0Xzjz5csLA0TF7VEZxz5DxxHOpm9PLBCVInIO etwBvuWx7bIhyxFVuQIFZznQdTL3eg/zLuK2FOXj51dDd/zOxIMjwojqlD3r /Pug51NF35dHBD7a+ckXQ2Kh38K5tr0qB1Fn1Qf1cBcYsChYQPJwRtQ73d1y lQqDC/fLbfMERFTTYcW1wdCpmwtj3y9F5KX8EE7VhNHTbyz+JaxDNKZMvclc BGN6rDzuUy9Ek8Nyrk/+wjjpgG/anyrEfauPV+ZcgokrhQvFyMR5raOEoz98 h+krMt7B8g8RbXu208sfEvk1sstl4AeiHc9iuPIczIwOyr1bO4t4VOCBeY0c zO22G02xnEJ0PlwhVDsCc6Muxp6WixBdCgZz/5bB/A3zlMpoGcQTZ2BVqzsh m9mWS+5hiB41jp/4TCTd744t6t+J6KUe7tMjTeS17qJVS5URT0c9V+nvQbJc 5WjELYKvMz2/64l7QrZyFM93JuZ9DEmR49eQHBlsTJGdRDyfugmmiTxYKGbd eXMBov+CXQPzukjm/0r4eTwGMcDO656AGFLE3otVf72FGDi4r2hSCCnqtE28 vQTewRfV6/vmkGL66XvymwOIoeLi061jSHHirT5cbowYfmdwVV0/Us6kR4WY uCNGba/aUcVHyqV/Bxgmc4jRxU/NSxuREtJCjVHLQryxO/Jkfi1Sgp9nlFq/ RoxtPBadXYUUf7NHd6IfICa4Gz9NK0OK59eBc4qE/pJIWyqTi5FySPOllDCh 9+SrIt03XiGFFZfnyl6PeHddl3DoM6QoLlczDR1CvP+0fJN/BlJIpXH6Fa8Q H0Ia6/Q9JNf8vKtUQuCT9jX4iFsiklOOSdyNIs6feejoRXsCrxOvYo1WXUd8 3M+6bRmKZM2eq20SFxGzLmwoMCHy8igOLLgtj/gyuXlS9ySSnGFquek84qut JVLb3ZAkc2Kndwyx/03hXS1FB5iPiRKO83yOWNRwyEPCDOaobLkkiXLEkuPU qEXGMHslTD2kxgXx/dzaR3NMmPmavP91KOEXn9b+4XdpwbRJI/V5HsHHDxvL wyUrYUL1QJxc9xjiz94dfq/EYNz+pMieCCnE3+dXJj0RgrG4KXaXizpiQ9KP 2oRxGF2o0vrHdAaxSeX5WNQAjNBpep9LCH9pyb++4jIfhv2vT5copCHy/+7Z 7VELQ8Ip9MQ0Yr7bbftxpyoYNH+22zSS8I/emaXhNmUwkBrzoPg5gfewTGUZ 7zX0FYbHTVqfRhzNfNQO2dAbfuRm/8ESxAm98AVaGdDj4tjqo1GEOHvQEOUS ocvqnXep3QTifPdmW6lr0Hm8Uf9gvDXSKD6LfJeEAj82PVPr91KkCYp0JAoQ /fv373NTF9yQtjCxNHfSBzoUZlaoaasgTUQ5pabfC9pDs/41zNxDmuibwOE2 N2hf6DwxdPA+0pYaOoj/cYC2xGBN/mkJpInXMbZ9s4Y25gFLkhMbaStc5Hd9 NIM2QanfSNgXbeUU2bXAGFr/tr05Z1+GtFVXGq88Z0JrRZXXqeoFSFuzqig1 3QBav30d12tMRJpsevKH29qEH5YWhkEU0uR0zrfEbIU2+YtTu/1lkKZQbkMO U4Q2t9efxp/lIE3xgP66i7LQVikebXGdizSlevlFJ59AOxYVLBjoQNoWu4WD R6nQXvYzPHWhDdJUW3vrLL9Ah0PpcSO+JNK2O1e/M7IB/vI+6+3dc0hT73n9 yKAH+DVJZ5LejSNNy/12zHZf6EwXe5X2cBRpumdcnSSToPti15ThvCHS9Gf2 7F6kAj2Xg8od3xP1gP+OnVN50BtnLBJ8gNjPDBEQ+VcH/U1bIuCnLdJM4hKf ZKyCIW8fzca3fkjbI+Mfl5QGw/JnxZ9upCJt3+2j/lE7YfjroED7o59Is0xV 3+tlCaPbys281LyQZp9TPkaNhfHB3fvKushIc9R9+k9NESaSHQQEJcyR5lQY U6aQA5PcjuPx7e5Icyu1S1xUA1PXf/r+cQtA2glDTsC0A0xrSIX5KDkizeOL qmvvCExXeun+l2mMtNM1kwbVK2Bm0OZJBa8QaWcP/FP88ABmz7ZP5uIs0nzq Pyx9pQGz05d/mqyPQZqfXcZERgnMnSo7zk7PRpp/29WmW3thjs+k6L/NQNql /05/imqG+b0xnE0/Cbwu91o/D/CE+ReH878dI/AO8aAnnaIgSSjd78J7baSF jipddopG0tarF4MexyIt4qzYcav1SNrzXus3fxvSomZG9htnEXls009HWiTS rvnXIfFZIl25L+983xdpMQuKN6tVISlZ3kN0bB/S4kIeSmw4jKTHscWvKgh9 JoqGTa/sR1LuaIm0M1F/0lWPVmE/JBXoNFQVtiDt9or9n2fEiLG9Xu3nP0i7 G2/wso/If688c890v0DagzUKyU1bkfTkZPNwoAnSHt4RDq4uRNJtz/GG5P1I S1fody/dhaSwi+U6aY+Rlplac+DVXyR55mSZlGch7cmWPHom4WfmKqc8v5xA WtbTu1tuzSBJg6L5JlkIac81gldcDUeS6EFqcb4R0l7mus1eWgPzJQXd6gtu Iu2V3t72U5kw7+BhrMR5hLQChuwrq08wd/Wgd7r1O6QVfVxwd5cVzMkcCWIa HkFaiWFXKHTB7O3X7FSpNqR93JtjvVEYZmI1o1N4Y0grzzSEVS4wIzFd/zuf WP9ZoEF+CdEPhaXsfWpK6K3qpWDHWDhMeTqEp/4g7n+dlPnJMjGYkPffXhE8 gLS/Jzot8o/DeJiK5QucRlpD2XmdrK8wNuz9bptPNdJazj6YT7gKo+//mHur DCKt589ImKsEDJ/RSPPuI8b9miHHD3nCULeWZcCZV0gbjFizZ181DNndmA/4 pIq0MSpTSv8GDFopJKT9YiFtIvbX1PZRGKjfEMm4QfAx1e9av9ECBhwVf/y+ Rtz3+TvX74tJQf9iYxODIGukkyc3BVHOQJ9Byh1JyZ9IX7Anz3msFnp9C0eO pG1AulCGqVG3DvSUq/ZXRoYiXZjSsvVfIvQo+I4VEPeVvvigt3j1FHSHKxd3 +jUgfckLkZGyg9AtkD7mdl4a6ctEk3/l50NX6OGfX5YGIl3CUf3NszXQtT6t S63uPdJXFJTeSiH8o/RL/vC610iXWmnln1APnefFUxoaJpG+6njfkUgqdDLu x+lsk0G6zMcAdkAydK5Om3pmmYR02fUrN5+eg07KzvhDJ6ORvv5MhqjrIeBP HQypt7dAuvw3g/5DxdApsEQ9cHEO0jdu/vbdbD10rtHb87l4CdI3BTi+5PpD J6tUtPKTG9I3103G6zdB5wV/8WIZV6SraET4qNGhs3xr8cndw0jfGi5nu/Ee dMmH2oYfFkL69taXtNUC0BVGtbC/bYJ0DQPeBjEH6KZ0+rPN7ZCuFVO/kPIe uq/Inhq+34r0HX3uneMboWftkf4eZwOk63IWfO4Ogp4iLRXXL/lI178d//Rf O/QepxxKjn+BdNru4lNladA3sHbTN0cjpDPSzSwLFkF/ifQhLeW/SGeT+XrP /oMBU62x5AMEfobPl5ETlWFQJ+yh5EOinl2L77dGhsLgi17B8vKvSDd12PEx oBuGNNo3PXqghnQzyUMRro9gWJf/2kVuE9JtvJ+uMtgKo4dfzp8Q/Yb0w5Tp /xSew+gE7cdnLsGffSTntchOGIsq9B7xtUK604NGy190GM9XXZ4r+wnpLttV 0go+woTp8ILmXjmku+WdmXhgDBNNe3/lOsci3eO7eLyHBUxO3TYVeHcD6V42 Nvz9f2Dq4slkF6NVSD/Nz9hJPQzTAtmaz/5dQrrPPL12sQtMT7jZ+YiUId0v NGrTYD/M/DeiXcGWRfpFyT/etV4w82M+33duJdKDVLykUi7AbMzs0FEG8f6Q 3GKn8AUw271dbiTACelhDNFcz1CY2+n4Zn5IFOkRXw4sPLAU5s7vCaqJD0H6 1QMp++EGzL1+ci5wfArp11oHUxVXwVwvTLa89UB6jLvBuGgyzEt9l/BS7EF6 3HQoe1gB5nV0Ho/6ziE9Mehn7O80mN9jln/82z+k35KQby9WhfnDIuZhaplI v33rhPbDbJh3NPy26Rqhz3tKeUERO2DebunPqXwxpD94sfDnyXyY32ftyBlv RHoqmCla0WBeX/9LzBcG0tM/3TmNpTAvU/rEJXMG6Y/Me0o3GcHckHTNqg87 kf6kSWflkiqYKzqo/HzpYqQ/c7vsOGIOc5cKr6jFGiL9+fi3l3V1MAe+kYuJ /oSec0lW8O0hmB36dWTPofVIfy3mYp7aCrPJq/zDliUgPS8hJyXyP5ilxRyJ W12I9MKNlFGvPpipT6GV+hP1vtO7GUObINpQMeN/qY+Q/qG0o03pPNGvwC59 Z3ekl+3V1FpKgenFz9dnziggvdL584+/ojBFNrc4tmwW6b9i5lacloPxwu2i 2q25SK+TM3SwfgjjiuMHDi0OQvrfR7EvGCowFrnP7L2FOdKb3m0zW6YNowc3 Gdx5QODbPXD4eoYhDM12kYJrbyG9z+dRy9WvMOQ6FLtLvBrpg0KTGt5mMFjP Pvp18UGkj62NrmbawsBnEfIxEUKPE2kNCsotMGAY8dBilSTSpzWVT4o7Q/8t IZuNdkHIIBm+k2jwhN5vJ5Qu9RUjg5JRYF1aBr1LmI90ctKQISiS+/CpLPSY FxbxLX2QsdDl2UCcF3RnXkpsblyEDOFPj3QvfILupVsDT9/RRIao8sNA5/XQ ddF0V1CDEDLEQu982X0auij/VV/VYSBjWddN6Z2fofPaaMR5n1JkLDeMsZeT h04NG/mnvjRkSGZEPRI+A/yWOdFotyFkSItcGR38AvwUJYbIziRkrHYJhLoN wPdeOyJR5oKMNZ/8rpScA77VcvbTSm1krFM+U51RBXyTrZsDnVYgQy705Nrr isDfF6S62f00MhS6jjn5+ALfZYOLa7wVMhQNnZ4d+Qb8awJeIQ9SkaGUYTdt TKyvWLr/nBlxni0i1ixNwr8lN97bd/kRMlRdLKLWfIdOd8nzGboEfts+7f4t qASdf25Xzu8IQIa6spF8rx907fe2FxFfjAzNUJZbzQ/oaqGplOZkIkO7C3IK laHb70aYVPEfZOgY6pIe+kOP0vSNj2YUZOhlaBpG/oSepuEzD9sqkEEV2XrD WwV603paLq8oQgb9k4ISpxb6bV2VTxQReLOU13pu3woDGysCmopHkMEJlcqX DoSBe8IFLoHlyDA2FDXt3A6D6b1XZy98RYZJhlDCtyAY0iyS/VtjjIw9IqSW 139h6F10FEVcFRnm5aPeoSEw3H201LXgLTIO8Rpub2mEMQtPUSHZpciwS6/t XKENYyNCHludnyLDQbhafSYcxqNfBXPfE/udyz+WVu6EiQrXdS/LI5Dhycvq O34Vpjf/THgXE4YMr/TMnRbtMP1mi0h/XjQyvIVTAkAfZti3t0o89EeGT3ni yqV8mN27JPPo+xZk+G2+cWiCCrNVGzZN/PBEhv+VyPRGwr+4163qSxuQcZl3 yeAZwrxiU5WGZB4ygtPPByfEwnw4SUXD1wsZocLe3y72wHznmOTXnixkhP/n KeNCR9L6nfviH51CRmS5m+PeeCRxok9d+bQGGdGbjz7V7UOSS8vSzLxRZFy/ cnhSgYmkUNFJU6f3yIjpPMhYnIikB0O2tgMvkRHPMw8fJvJk3tnaTtY3ZCTe srhbS+S9SoPA4sF4ZCQN7s8p+ISkurQbip5myLjNsqy4l4uklsebmM8vIuNu woHG4AdI4mMlJc8GGfd7rUbdrhFj64jNgT3IeEizFtlzgVg/0/8jgNBTWozN Om0iP/6Rp2wcIPjJ6LTVlLEi3vfVUteS0Pdjg0M8EhdJ+aKilneJ+/A0+rBN mxaSUprzbJ0ckPGszc7zkzyRT+38XPYFIuOFjn3w02VIcktoKjtK4J0TcSTp +iySeHd+K1ouQMarJodnZ7qQJB/5TFprPTLytBxLbWphvm92up+biIyCK0f/ 0D/AfLTcMjMLPjKK6p0GNhH+v+WT6+tuLjJK1P4TFL1D+DWjNWL5D2SU/nbd +vMczH7rdl+/ayEyylXdGHnOMLun3N4yMQcZFRePWd4xh5mKn48/fWlCRtXm EwEu22E6S/J7bGA/Mr6fd48zlYVpmbUtmiaxyPjxzeOR5mKYCiCvOr9wHTJ+ nz1ZM9sOk6ytKZ+7WchoLj+zOfomjD1JCs+zrERG29qz1NMhMCZYe2n99t/I 6PA4t+/gaRi1/n00ypyEjJ5Vvuc37oYR0Z3jxtMrkTH6n3/VayEY9CvP+nif WD9ecLHt1ggMNPtPWFqEImNKPGAqoInw10W+7X0EH3OvL28wzoc+49o9KjsL kUleEqSjlg69Ox0aF5y8jEwBu2CTlbHQs23nxODwQmQuEr7i3egBXXRRyovj zsgUsQkN/2ALnYeSffuk8pEp+izsboYx8MOS98Zu6kXmUsHwnCgd6CjN6zux j9gvfiCiwksROsSTrm5/kI7M5Y8jGw+sgHa33x/X+WkjcyU5apRKhrbfpFHv pRbIlDaPFpHvgzaLa/Wkb5PIXJ1+bd3Cv9DaLqClG3wLmWtmr2v0fILW4FdG ts05yFy35wb3Wy606os8Sg6pQ6ZcSoxNzgNoFWDWfYVlyFSYjPX8//8Dqb+/ +Kv4YWQq7ooL9r8ALRV7h7+tDEOm0t34JEc3aPl8MrF4fjMylUcTnhlaQUuT pH6zcggyVXmJpds40Cqso11q+BKZ26rMuEKW0GpYHmhTJ4VM9f1LyutdoPX+ WsE3NAlkatZ/NHrhC23iU4KsMyrI3OHgXxkWCW1xEkc3DIQjU6db19T+DrSr C/x7fCgLmfoew990sqG92dJIracJmdSJx/uWvYeO1CCBvswYZNIuHP3R8RP4 /gOWBbV2yGQKrbco5EPnMf7SHWQCH3b479qYKeg6ljn+0VocmUaJxn+Za6Hn nkHJof/WIdNETshWZiv01mhdPzv6Fpm7U4v+DSH0S5mKzEo2ItP8pXrLHQdC L8aJQoGtyLTU63H0Pg2DsW99xI+nItOqJKXDJASGdl8EnvFeZB6qku6aIfLq N9uRMQ6Bx39dc0OWwzA2pjAbFF2GTDePV17bF8B4SVr7yW53ZB6f8BhbuBIm omQD7x6JR6aXYOvkS12Y2rh0/4bXBD9+6yvI4gEwq3Z/Ca3OCJn+qYEB/Bsw 22XaO3xtDpmXtlIXFD2EuVt+9TpKBP8hes8WHfsE8wOdV4sbbyAztMQllEn4 GWdqrjetAJkRvA2iMoQ/Xr9+YnXRGWRes4hb+kkcyWv7Uu/d34LMG/W7o+/K I9la86yy2EZkxjmILD+jieSY64v+WXojM6HrXYwpG8nlP4VLV7YjM8njvJSi JZInKm9WTZ9HZvKEdvysC1LkTc6smyH0dNdvYPUPX6Sw1Vepft6KzAeC6UmZ kUhxsGj4GOiPzIfhR2QD7iDF947qY/k2ZKYvX3PnQDZSIsdCCw4TeslM+Cm/ /T1SbnKeXbf/jcwn66MeLPyJlLtBD/0HEZlZqbyNDXxinPmuuqEcmc+3UlJf ThHrX5QcKMpG5ssX+ZsjRJES9aBglVINMl/pncp0kEXK+cDt+hJEfW9Ktqnq bUOK4+HhLXuikFnA5T+VQKRwTFT/ObUgs+jrve2de5Gi4MA7QXxXmCUWB7OL HZA8WfynM5mo9329pGbcaSR/CooK/SCHzI9HvuQcDyHwqhe0H/yOzPKukJ2s RCQfHHgeXEPw8WV8Wm+4EEm/pb+JGvgg89s++9zPd5EUczPbtVMImdVPPmmk BCLJhOsbl0TUX+uYsGU/D+b/GxDdkkHMN1XvWP3mB8zkW7VuoTxEZuvW5Njr uTCz4OvFw3GE3ttDhSTcEmGad8ZAy4CMzG6sEZE9BJPlt3+Eayojc+SJ5+TF Lhh7aZamM0RF5rhw3SmrShjtP0QyVyD2TzrSBjWyYFRZr3T9vpvInFuzjN92 CoYfvN/AdehAFsn7jEOhJQy1xccrqlYhi/L9X2OcLgxtvmpUtEkPWQuvPKnl kWCgYEPR79fiyBJuW2kmR+TXJb9+n7k7hCxRPP91qhT6Pr5YI268GFnLxo0/ PgqDHkbyhxjVN8havvcF4/Jx6Jb06BVKfIQsyScyRba7oXM076ZWfgmyVjt0 5S5bCR1t0rnaLC9krSneq86fhPbRt4FbqNnIWifz+snbv9AuLWKt66yBLDlv OeXEQmgzvhUs6XoOWQrfQx6evAutN6Rs2RZ/kKWoOiBvHAgt/YLVE/wNyFK6 sj9541FoseEvaLl6ClnKrUWr5njQ3KRa89B+EbJUUTHmlwo0n91xs1eBqHfb zUjxrKXQrGheHR/8EllqY6MRIUPQxC9r3KxhgyzNvTbCdj+gqbi47tatCGRp P35/WTcXmp7YuHZtMEOWjrAKeXkiND17/EKpMgpZeg7XfXt8oelTFvWG0Tdk GRRNTXw4BE2T+yj7P1ohC2XsvZLp0Gxw0l197iSy6KfLB7w3QnNsQzhfqxVZ zO9qbrsXQYvgHulD5/2QxVGN5yt1QUuI99vDdbuRxbtCciBXQuu6fseC5WeQ ZdTq9K8uC1orWLbPN8YhywS+Hnx+DdqujP+2+PoZWbtvav8KPwXtB0/rPjfL QNbeseR9jpbQAXJR1iauyDLfK/iVqgd8zV1CkjESyNr/+JiRlCx06lJPqYfN IcvaQZ9e1gLdp4wY6X1SyLItul94txR60l5UX1xkiSw7mcW659KgtzNgcchm gh/Hb3XqKsdhgJMbwTl0FFnOh6ZWj5+CwUWuRvKqBN4ufasFSnxh8Mt1DUfT u8hyFzlQYxkGw87e48scjJB1jv77TGAajBVaDlgXDyLLt2rSzvQpjMfGO5h1 H0HWhUOrDFfnwMRxLylWnSGyAn0OyGR9gKnVZO1DlYReI5/XFv5thlmjBnXK 0+fIiqZNpKZ2wpyo25vdtcT661XSVz0HYK707+LWzNPIiu+xtF80j6Tlzqb+ vI/IunnujOEPof//XqB+3TImspIXxWvcFkPSc0/NTXvKkXV/Q62g1hok06wU K9/lIisle7yPpIBk/5ZS7CDuUxpN6meFMpLztqeoflBAVsbXHUWx6kgeEGPv HxpG1mOb/Wl2OkhZZyojU1WNrKfd3tEqhF9xP2UGmTYgK/ts3NlxLlLcbF6/ NSLuw8uFufYlpki50nX4xbtdyMqN+WUUYUH4paXq22ITZL1RGNe0tEFK9g0t f5VMZOVnS61VcEBKQbTCscBiZBXhDsE+N6SUcJh+/cT73n6x6Ht9EinFL8Ij hQn+31uf/hV4Dimv/sYmHexFVmlXbLFpAFIyym73WS1BVvmZnPTVV5ASG+wn sJvA67PQz+i2q4Tfq0Z5JV5E1pcbY+ey4pBy8EdqhYA/sr4prDzik4wUrZtj Ni0WyKp+pm3MfogU4VS+2do6ZP0ECy3xx0iulRdsuZaDrNrK02v/vkDy3Z2f +keCkfXnYKxQaj6SHdf5bDXYh6xG75paAyK/N+Cx3qkkZLUIjr5d9I34/o1b vr8ai6y2G5Lp1bVIYnkv3ny8E1ldWeY+/3XAvA4/9cr3Tcga4dfIxi6AmV2y Yw5ggmzhAz8c+gxgtCmQz/0+juzF+fc4eSthVK75QymJGIutc1cO7oMRBxuv 0i1zyJZoWzwglwxDg4NX2SGhyJbk/P7edxqGqEp356/9RbZURurLPBMYjMzp 2/2mCtlr3Bk+ZnMwQO0LluV4IVu2WtxW7if0wzYfSo8usuW0/mHfE+hbsvVk U4A5shXiHyvkBUEPv7jP7exGZG+c9hEKsYXu7yXxje2FyFay5Xaaa0PXZ4Ft FeqnkK38duVneTHorGHVX6x4h2xVhdYnfe3A78f7Q05GyN4WlB2dVwT81Te/ 7birjGy1Tn+vkHjo2D94wUGAqE/T2GS/uTu0p1aua1dzR7b20zW68lxoF8mU aMxQRbaOeNea/vXQ5j9h+OHpBWTreeXO501A22LxmfPdzsg2+HW5OaQKWtNZ 1hbU/5CNuvs+mKdB68GvTu1PbZFNvyWXJu8PrfJ/C1YNhyGbOd8f2m8JLXNX /Dz3H0M2x77gWP52aOlrYiaLFyOb9yFs9xXC74b/rbVn/UO2sdIBDfNGaF1y LO63vDiyTcI2rZR/Ba16R+tHyhYie3fvyET/VWg9f+OPavdpZO/bXfIn3xla q5+563rWI9v8+dXCKzRoMzj6/Fa9A7ItV9retVgNbW9MqXV1kci2OqsSKD8E 7byxEIFdisi2/jN1tP8TtHf/5lgJpCH7ELWMl38POm478p++IPiyuxurcuUc 8I/MLpLxWI5shwUOSy32QufOwcmUuAFkO5fP/xigQLe4wLMp7Upku6pU5ubX QY9E6JIX9WuRfSzqZuKVbOiVo7/+pSmEbE/zHYcV7KHfPiNNW5KCbJ/G490W 72BIVSTUxH0a2X4M/S8KSTBUs9ZVRJCo1/+hSNaAFwxfKI1uGO9G9mXXh6dD N8BIXWqF/db9yI4cayAXBMJ4nOgD7WHivNEHMltDrWGCycnS1Sfwv55/9uN+ TZjoT/fINV6D7PgAyfCBVpiivrk9NU48/96SXdIbWDDz9PaloSWLkf3AQe5D 7wuY1a/cai9LPO/hm7GTuRtg9v2RX9uFifOni1fI+RP9P2tWe5pCPD/T+fZX wwUw93bHrM7HZGQ/LvI6v9wL5rVerXqJn5GdtZKn/LcF5u9vIpdcbkb2i3dD QSdKkKTxSeLzPWI+d/VHTR11JB0ih90r10T2a4+bzRQiTwXJmfV/70d2Xpl7 1GeiP05f8ljTwwrZhetYBjH+SPr4ROOcwy9kF59e1W07gKSm3okYqjeySz73 JSgdQtJ4XkSKTSOyPyi8Yw9+QbKw8NLv73yQ/fFc3EgeFcmSn9V2BBL4lX9z uxf4hMjPc9Rhvj6yPyvRdpusRbJc0gbnU5bI/nJBck4qHMnrH7reOEacv6qm K7NxBskycroJlzcgu1ql6ECGG5LFV5g4fQdk11y6sfDkHyRTQofiqFPI/lXn /NLAEEn918dO9RF6rVMzOCL0Bkm1tNvmTElk/w2RWFaljKT89Hg5e8KfGhra CxMSkJTU2rBnKeEvTVp5bkcWIens0kfUpQuQ3RJ+dbXKGSTt4R0eHzmH7LYW h4+jfCRtfOGYcpHwA76uzqmi/TDf0yO3ovs+sns6mr/t1YZ5GdqWiTrifP3U 3AsyRL+R9k7rjMF2ZA/GhKu0ScLc9r33TqRuRfYYQyvEewRmt6kmVCt1Ins+ OQRuZcPU+MudHM2fyCGPWvc6ycOUnV2s9Dcr5CwwVru5PRomy27WUT8sRM6i ybqxd+4wEZ0Wear9NHIkzFUfd26DMZlf4a5rOpCz4hHlYHYyjF5I+B7FG0CO FOWXsK8YjLR+Y8mct0aOTJa/o1gfDL8M6iqM2IIc2YXmErW2MCw3/ORP3DBy 1ttuLr5bCUPR2QsyThDzGxdXr9F8BIO+zR6GRfeRs8k+tXxuDQxMbvFw34TI 2fza1/tjGAycO6lq0F6KnK1OG6utXKB/9Xx8aWc9crYXTF1UqIM+GGrS32eP HI0VX7f28qDX42XY57/dyNFyvf835xX0ZDWf5/CDkbOj5EyovxJ0zwkfAREW cnSNBVP08qHb2L2147wHcvRrrhWN74auR+ffZPvcQw4cWleX3QpdsrlbAqWu IofGfzR6/Ax03r9W02zoixymp+4yZVHo1PPiZS/7iBz29EfltjvAb3/0XOlD CXJ4l81YdzWBn3IuTl/9IXKMxZoOW5cB/4zw3QozEnJM4o/7SFsD39bzcvrm NOTskZuOqR4AvuWbvnvL1yFnX2ZIVlQg8I/WXL50iHifhZZkhZE08EPuCcl8 vowcy8J77UKPgF/Q67S6vgA5B7nbSSUInUKeyx9szEeOzbcCmfM/oNOuVvvg ulvIOXzQUFvHGTq/5kje/GKBHPvWX7tHZqDL9Kp/IM8ZOY7HHVyzrkJX4x/j I4+IsdP4YJDbBugOmGxenpCHHJeLfnc3EXhqQV12FFHPscWL81qMoWdsMHPT SmnknLgR/zO5EXo/smw+DDghxyv1uejKRdB/1UlLg3UQOd5qqPgtCQYYWcZy kW3IOfumkhaxHQbeZ0hozP9Gjl9lh/cCSxisETz1xscbOSEjMk1D6TCyXqxz rvMHcsLOp808ocLIK5PfAdw45EQu1Jb67zuM7rlRZGFJ4HF99W7jxikYC/xu /aljDDm3MDD3qyFM1IdvI4eVI+f2J/HvoQ0wecx3odvbDOTc25fcy/aEycnK u3Xtn5GTevS1fAHRvwl6pVFMiXrTB9gGZ7fC9KX3Xn9jJpDz6Gy1pWYJTM+4 FtnBNuQ8C++NyOyCmbqBAhXnd8h5sfJcmpMfzMLY6tk4SeTk3Fn4TkECZm/d X7VwIx85rzffqG94CLPDtyjr/Yn5vOdyE4m6MEe7Whj4Vww5hfpPl5t/gblg DXnHY6uQU1yqv1XcHube0zQbJAn+3pmWcz+PwdyE/wmRbQTeH35bHAkJhXmF wqE56yDklNm3+DFlYZ5V/cZrHaHnTz3u8fPZMG+bnD/9uBg5ladmn+exYf64 8JKrtwg/+Dof+uV0Hcx7Leg/LXoTOd+vSHWqH4d594hmDjUUOTUSKQJ9FJg/ kpai1r4JOb+S1GXTY2F+1xG1/c+YyKnbWKTjqAzzqj8ybSTUkPP30pSJfBTM L1qTvolB8NvQrGX/bwTm6sKManUpyGlCd+9bB2AuhXtBabYBOS3JmeFWhTDn HJD3b90H5LTNtN+VVoC5DccbzQsJvvgH5V7WhMDsb67AczVF5PRKxf3bvRdm t1ZE/r3Qjpz+U99HlhD9d5XwUQ6DqHfwh9iiijUw42p1O4TorzljVwPVWe0w HR0Xzn8XjlySsPtlvbMwmSoVV9OYj1yKU2bCBKEHhaNvn8oBchd8aH+SQ4eJ pBH1BqKf4QoHWNeqicL4lZvnhvc3IVdimrdF8Q6MWqiF+rv8Q67kgUBoFYSR 8lMa3tZZyJXKLTK76wIjBq9ST2rHIHeNl5bfGk0Y3iIXp29zG7kbe+W+iZfB oOJ+gwzGFHKVjKzbvqrCwBPvTPjWjlzl9Lip8GswoLfZVvofCbmqC6uX8sah P+FeprplCHK3HRXbsNCa6N/vdL/euQO5au95O9+/hT6ykKvsyw/I1ZQP3BWg CL3sgORqXSZytf2L7CEUeuLf9K+PWITcnQ1Tp2f6oXvCUFnN5A5y9Qy0wt6Y Qbdj0fwP3VTkGtx0v+P9Crqa7gT9FxaBXJjMfKm1FrrcjpTbXd2LXPr+9vKh AOgSOmWXeekKcpk5cg1PO6AzS1Pa9f5y5HJWWA8fM4bO/wZvcDYT+3me8YuU n0GnxpB4uMQYco2qqtfwV0KnmNtGj0eDyDXZJqaW4gP8yZDwlEECv90RPLZ9 I/BH7QX3HuxG7t7uwIPrWdBJEdR87XoZuea8ohP1GdC5zj0+sJDYvz91KvDm UujcdePSt7+SyLUS0kqw9ILO8H1KFF1f5Fo7uD+R/A2df/zTeT4U5NqWZJZU U6FLt4XaTY5Drt369l9X70NXhsYhsdvE+iMX5HpMFkH3ZpkxEbtjyHWstyEv PgbdubppY5COXGe9eMmy79Bjtl7BXKAeuS4J1cpBO6BnfveJG29mkOs2IQaM JOh9xbBaN3cNuR4vAp0LHaF/j+TtcJP7yPVieXcd64MBOaebFXlJyD3909Vt 7RkYuFO5/sL1P8j1mdh7wjcUBp8xO2rFKpEbpL/+tG4WjIgouZ6Y7EBuSOXy 8S5dGEkrrEmPtEFumO3CswnvYJR3/pbGPQKfqxf6fCd+wljk3PaTdwOQm/gu /1LODExKX/lzTPU9cm+ZZQkeDYLJnHGjXawDyL3ddj945VKY2nuN+ii1FLkp C0NDT8nDtF9HUt0d4vmp8X6iGzJhRuxU4Io6gu+MzR4RPzRhJv730h2Bgsh9 amQZrcGB2ZvjqYy0GuQ++2ss0VwFc8szY6yOEPfpxTG4cc0K5gI39U1sJPDI mdNYSWuBuZ6JmaZPtch9HaUYN+hG+Em21nJ3Y+Tmr1+96s4YzKf0pTbDceQW Pltyc/cFmB/d4zih34Lct3Ty2v//vfvakn77H4XIffd9JPnpNSTpr5e5zCL4 LD3CX39oDZLMkyuigdBr2cife2IpSHKR32l/+CZyKy5/VSjcSuSxazaT2w8i 98vKkpRjr5B06a+ky/lPyK1Kzdm0loakkEFlFd8VyK3emZ72mehfQ8pcMgPG kVtTfkvZ14xYb/NiRTmhj1qrq4+21CPpzLNJQ3F95NZ1B6r+OYok5wrm6DtF 5Nb7ej8NJfLtvhc5eblrkNu4xFVN9yySdPx8gr3NkducbJvdRUHSaoPi2bB1 yG3btlczIZzIf6u4Gs0uyO0oZuXwJGE+cYW25EZC7117dHZMJMM89fmL7HUq yO1pVnmdpkT4K9dl3MEbuf0n1+vtz4a5Y1tGq99+Re7QguUFC/Vgdrw4Ln1t K3LHN04VHzWBmdGn7ImkHORO5vTRJWth5r/Riv3riefNcJrefbCD6V8ffKwe PEUe+b+yjxu8YOreRgUno2LkCUzlc6vnYHL+J4O5QAp5QmFPP10KhskDe77J 7ipC3uLHsZXNiTBBWdfzNzUeeZKDDj/uFMGogfSXEdYC5EmfPiRn3AgjkTsM aj8uQd7q6QPHJkkw3Mi5v1HnOvLWC5os3EOHoSsfngY4JiNPPpS7b/YIDDZ7 LauwcUHexqX0O+mBMEi98dIqeRHylFft0CWXwoDAje9a047IU0lWC3rcAX1/ 4IBU8hTytilsqbZaBL1vWvvrkhOQp5amuF5ICXpStz16/Oce8jRV17tl86D7 rvK6W6WVyNPOXv3a1gW60quV7/8dRJ7ODkkhkVDoLFkr/jj7MfL08pfuzckE fldPTFapL/KoNOHb9p+Bv1Fa5o7zV+RhqUCPWC90eBxtXHbNDnkMw7mdeWLQ XnV1J0uFhzzW14nLTtugnaHbZLdDCHlcs6Hvy3dDW/mMrXWRGfIMf/fKFnlA m6Ol48GNFcjbZdvh6noN2iSLrXsvr0CeaUvTK6lsaP1zrbPwXg/y9jr/XfCu GlpzZSKkVpogz6z3554TI9Cads0sYclz5O33rEpeswJas2RuXornIO/A+Keu Mi1orfxYX+D3D3nWvh92eFlAG/l60q9EMeQdIhcFrveGNiOn/YrQhTy7oNdV n+OhLVMqmizZijyHxS/WnnkN7esdBjaNEPUevfrEZUMdtGfM/KcnH4q8/yTT cqqmocPQ+8KGbKIe18T7AufXQMec9Q10v4G84+tu7d6sD/zSdcbzqQQe7g/i kmpsoPOunEDOwgbkndwc3XnRD7oin+1SNexE3qkn4dqqydAdKRYR8eM48s69 8v96mciXhb98d3oVIu+8gc8adTL09bmvXLKfirwLJaecG+RhYKV0SPxADvIC P7tQtB1g8OjYfr/lH5EX2bhPs70DCC1O+UjvQ160466L1xfBqODlCzt3E/q5 3sX5AkowWmaW0/uewC9+RN8pzgXG92oruS57i7z7izYmcvpgymGNWYXSQ+Sl RKxrH1kK0xsgaGeJMPLSJFZr3NkO002mJ4So75H3eI3Y5wkPmDWNK/lYWoe8 p3eFVz24BrPzV3SjjhF8ZSsKHN1N3Nf0ukxxnzzk5W4bn08bgfluatBSDzby Xl+5w7ZoR5LeY0OlbkHk5bVwIxbUIslvd87DeQK/Qv2B6mzCnwrMygrqdiKv ODZh9eE8JE0Euys+IfAvGaDZiT1C8rbXXpu+KiPvA68zNT8ZyfaFCT7/a7jM w6Huojg+MyUUkSiltJBEZAmtzpHC/BZaRbSILCVKSIkoVEKUIqGiFEVRb0WE RCWVEgotdmNfxs7M+/vzPve5957zOcv9nt/bkPs+Oar7cCSyo44W90/JRO7H yXV68ueQ/frukrT/GP/KLBt8SzyRXa+vV2UogdwvT0PfejoihzNTrM6mHrnf puuKL7VCjqKkQZXKF+RW2NdalHORo7dAckskw7My7/wN/3XIMVWJHuYz+z/n qv9euRI5OxekT3GKQ27NsR9KNYrIsemfXX7fFrl1pWcOX5RGzr7EE8v+jSP3 r7Jypj4HObYLnVXHjyG33q9suJHPnHe7uF36JXIbq70Mo1qY+6/bc1NKkNui vTAYqpn3bwY+NfBEblto8aeuD4x9IQe+6ocht73JTeZWDmP/kSP6i4yQ22U4 x5rL8Km36Tbzz0JuT8yb28MMn9fuEu92MOu+PseW+wyfyPwMIlCIXD4ppbGD 4WPnemTQJQa5Q/deerIZPhoxpyXEGL4jgv05Tw4ha9AxooHQQO64lRh7ryWy sjklo+6XkDuZ+dRshhnzX8SGeIX/Q4JziFXptBKEPxuvWvovQmLqm9QFcox+ 37TuEuepHBKi8tsOFkkzelnpe/A8cSRmfLrbqzgAk9YXHGveJCEhp2M8vSoH xkL9dizT/ojE3Msd24Iew+gXdek9H98jMa/5WqxOIoxKBauaNfCRUIxtWhYR CMPhgRfuucYisVwYjCZmwA9Jbzy/Jg4JNWuNEP46GHhf4DG6vBqJlVmVn5NW wsAM2Sz15VFIaDuq7BFIQV9coV1Y2jokdPO/3H3Mgt423ZFXpduR0J/n3Wbd D73rFJ79zS9CYl3Ze6//qqCr5nX/qRw7JDaquOfaf4DO28e3UXruSECA/JRZ 2dBxXJrF3XYCCaNfBdz8NGjfGhhX/5qxf7Ouc+TReOAZ3j+y2tAXCZNw6WqF CGjbYJGbPC6GhFlL9sKPAUy/4eqXIRsJEu0cTnpAi4u50Q65VCToOPFHy+yh OW7mcdmfX5DYqnJ4X/FpaPqiYOWzex4S27M+yRyKhCZ53wqrG6+Q2AXqJSIp 0Hjq59HqDd+Q2P0p7NT9XGjoL97cp/EAiT27uzS2fIeG84V+5hxXJGyb6H/N bdCgoV82bhmIxP5jGdHBAqjvOSV/TtQBCbtJKTMVWagvLVHKzC1EwuGS+3iJ GtTnb7E8ugqRcJIrf+KIUP9Z9PoT4gISLkna9tMsoZ4vqtVdPYmEq+bVOSlH oWH18saBvGQk3HL6S02CoCFcv0tx7Qokjpvu8GuJgwZBxxrJ1zwkTlQ81w55 Co0Xm+PPeXsg4X1AtlmlBJrUMrU3qaxEwqfTK7akDpoai7JeWG1GwteninLs h+ZnfhNtVoNI+IsYsETFoOXmytijRkFIBETFPHuwCFqjxayL6BtInF844mSq D23JBjKR6SFIBKdZz29l9H5x2U6lwjNIhBbNP7fcBzqNlp605m1AItzCV+99 BHTFvBgoZ/QscaW2lud0D7onbRanr3yHRDQ/YeuDb9A74vbhlC7D77bK4kWq asBfUb5MWTkFibtZAd8/IPAFwyPJ0m+QuGdYH+JsCYNVOmvpZnskUncndT08 D8MXmp2/vi9A4tmlZbmqdTDWaOu0qrYCiRdyIe4f+mH8Oewbb+lH4tXdViUX MZgI5H5YPMTkZ17Ow9BUPRBIirTJ8Rn+BabihlwKBKV7Oh+XWCLxtsKlj3cQ hIGLDKZtuY3E+0516xURyHLd4GSRoolEqU+YxMd7yHrdq/livQ0Sn6d2Frjk IFtU0y6hZwiJr1G0p3g5sun/ZrZMeiHxfWHG8tRWZF8xOYvpTD39SJtZyxUg +1Oq61oTHSSq9d0j2mWZ/pSqu7lxAolfb8s3haohZ/WcqeELGd51FlpDaoic /cWS+9WZ/T+1UamllsgJjkyUsSWRqHfq33vYFTn3rcjN6YlINPJ3zJp+Djn5 7OkW7zuRaAl49i4tFjnfvczqar4iwZOU9SEykPP3nvCc/VkkOm56qbe/Q05L cL7NUSbfupdV/g2tQU6rTE7Y8flI9GbpX1PrQ0499T7jtjcSA7O3YNlP5FSt tV3jzEVi0HNHt1sBcoqzVO6UM/k4XHkwXvoBcp4US2s3rkViTP84NysCOdFB 2kb73ZCYiDk7vNMLOZ4ib41X7UJCMBJxb8gWOeYO4vHWpkiyrRO2xxojRynP ZMuWdCSn5DxmrVNDNl83hsppQ3KaQk563Sxk53f+s3BIQVLszEcbvxFkX5BJ G9P5iqSkYcuLwhJki9nsd/NdjKTU7UEH+3RkvW2wk2D0PynDniojEs3MAwlF /NWXkZxTtOQol4l3tt5rntV/SM5T1prfwQWhzJPwXayDSCoEG34I1wKBo79S wbFBJBeb2iqVC2BCuGb6bZGFSK74dKPG6haMTc0KypNLQnLDDwlTpx8w0PDD xaqMsQ/05g+Kv4aBZX2ryMNTkDS6sSLpURL0uya4So4x/phYmUz2HIO+aVW/ df0bkDTL3vUoajf02nhuk61IRZKc72Ctawg9RwI/L5U1QNKiLvD5SUnoHMso SvjRieT2jZEH5fnQUS1/NruT4bUz8bZUTh20vw3lv9vXi6SlMD3Ptgh4eRWC rdx6JK0P5B4WpEHbx+ePvQtHkbQp/CR/JwpaW/49nWmWh+S+pb9KNvlAq9zk t/Q4KSQPnG870bQfWnYnn2ja8RbJg03DS0JMoPnxniUz2BeRPGQi8lVVA5pl by3jPN6DpNMD2TOljP6MLNfVsbNG8rCYktqRcWhasmOeqMMxJF1dtH9KNkBj yVE/nbA0JN1KMfjJR2gMsHpUocD4f1zdQncb088sNro/H7mK5Imwvf8GYqFR V3ckL1USSa8u14jrZ6FxpfHX2D+FSPqY+24wcITGdfZVaS/bkTz95BLvlzk0 7j1tZQxM/P2kY2N89aDx+t6lU6c/QvLs8QdbFi6Axvpv6ftoZSQDv//Xnz8F mjaF7xLRWY9kkO67OwfaGX2tF6PCykcyJLrCnPMNmuGAg4bZDSQvDtaP33sF zTXRp6oOMDwuE8OexvrQEpRVI7vFDsnwOxKd9f9BK0qcPfPPDckrg0sPBayG NglR0V6pl0heJQz+KGZBW+v4GxsNMySjb9OWeVrAq1izKGWlL5I3+Ae/2jyB 9jKBs//LMCRvJYYXxD6GzraJnpy3J5FMGEhea6AG3eIhlc8KEpG8Y/YqszIV evRg88vbAiTv9Tcmy6RAbyWOtrpYIZlhujY47A4M+By6+0+dqY+n8eYCdUXg L38rFfWnAsmsPgfvjwnA/5l1bHVCFZIvb11xnhYHQxvV66b6bUWyoKeZCIiG UfaFOQnJIkgWbR4rWiQDoxlRfPOZekgWx0mvz4uCMSv+DyC/I1m6ef3KsQgY Ty5ot+08j2RFbJSU5yUQTJU9sXCU8aeyK+XibDEQRJzQvRvF+PtzUy4rM4Sp t0Ce4XKGd03Mt1NbRUB45a+e9s5MJOs6W/q6g5A1ZW7EZ4ttSP41mjgczszP 68IfVoaoIlkfM6tRPRBZbiuep5kz5xs7l9uUspCVUBKp3HcNyRajDT+c/ZH1 cZOJdGkxkm03ttPTJpDV66+S05OAZHuHU/F9X2TP3ve7y+8mkl3oZ2g8hmzt shATz6VI9ly/+qLBB9lkVnYd5xCSfe0PVwUMMXqQdWfpTuY9PuQ9XOSF7OMP R8VlVJAciq5YnMdHtl/SMrP2ciRHeG03bT2QHdSiXlsyF8lxQ8GssV6mX3nk xu++hORk9OzQm+7IDuZqSSjKIynkrZhi0I1sf7cgoymAFMfQ0LfKFdkeHZYC 8Wakpl7bwfdsZ/T8p7qJ5xeRmtbmcnS2C7LNF1W59ZUhJb7RvzmzDdmrxwKK LvshNeNq9L6tjsie6xGcbTCMlGRralV3E6NX77Z+OfkcKekN+Rbh9sj6er9T 8fwZpGSifnxQb0DW/dvsvEBVpGRb2rH0ALJOPk15qPwKqbnrhdnOf5BlwiZs y9SRmhclpyO6F1nSOZp69aJIKa5Hpc17QLhGUzpwZxBSi4tmiv2YBMGTDTGW +/chpUTUdh+6A4JFd94UlScipbrnZHZIC0x0R53mxDP26px6YvHRA8Z2aRXM Hp6FlB7bT2+PHIym1EnNbhlFyuASOb/9FYwMXf+VeGY7UhtiW1pmCGD4auGn z5uESJm8UjxjHgr8r3VXKoq8keJCp91fDeArfW2UCGfsJd/nmLqXw8Dp30oF l5KQ2lptKRM1B/pXJ4ua9XUjtWO/0siSbOiLDdB/mDQXqV0tfb+zbKGPnX9Q Q5HZ3zMU/rAiCXrKeFuq898htdffJsJhC3QXfxCxiziI1IFpK07w26Drs7nK 3W9SSB0MH7YOvgydTdtfuTnbIXVIrhjmaELnjITMdxI7kXJKuKac8g06jFsx /vYGpA4vs5tu4Ant4dXLP2iaI+WartnzYS7w2qeutPY4hpT76olKqxzgWQ1c U5VZgtTx3NIc3l5oq5XPqH2lh5Tn5tg7p1nQduyHf2NeLVLeZY4h05Ohbb7X /ifGJkid2rn6yC0TaP2pX+biuQAp3zrONnUetKYZ6Rz1vIWUv325fm4YtEam VkVXT0UqoCNxAb0KWi97lHmMfUTq/AlX9u/v0BoXKvTnI1LB42tb3bygNe+L +YJxJn4Xz4uWCeWhdVDkhVNlBlKXp1dmXXkNbYyiDF1wAanwq8mxi/dBW+Lc fqdlBUhFzj/ul8kGnpRqm7GaKVJXk8De6B7wrt6aOf1WG1LX1STNvptC+/Lf Ex/N/iAVk1mrebAd2r9s/fu9fxlScWtTZw+EQ0ewsZz0+TCk4gu9R4O0oJPk 11arMnzufpN5d98bukW2jg+4M/7cs/qXpj8PuocMFJ/4M/mX8i/jSkku9AxY Kk5d/wKpR73EnjYO9Ikq5J0ptkQqw0cefe5DX3TP0nJ75r2nwhYVcTPoV3nn tOCODlIvpAL71CJgYOeqvX7XjiJVoJl94agCDBYnVvmeZvx9e1nPURgBQ1ZP XIutq5B615ppcpUDQx3qzmUD75H6eCdV5EU7jEic/LU4zQmpT5PKzdx9MBK3 M/vo3Q6kvljfeVf7HUaXvbj9TKEGqQqZ2CAWM//prlX1e9mKVKW7rP01TRh7 7pOtFjqGVPWnSONlSTCu7dukqaCAVF3QRQ4RChMLtd+5HPqC1J/6KfV1jH65 vGS//sFDSNVvPFvo7gETA+aChYeZ/GweOhkQvQcmM2V6JKyZfGvbPnBA5QsI pklOJrsNIdX+xB2yN4Fg56xpcXV8pLpmdCwiX4Agrq7h5Vqmv/Q4Owr+qIGg Zs11tZtMvfe9a/hzLBGEs/p8+k4x+c5fsu/NFBkQGtU680L9kRryq0m4zvR/ l/KGc+cCkRqpsfRbPgrCS8+WdoUy/Mb1v9vmuILw7mHD54fikJq8Zr6B+gfC p82697uSkRL2lCr83QnCV/MGp5u9QZpDbRk//gGEL0U85HKlkZ76sLB26gYQ pj+6cf23EGlRkY05N56CMH7ull9n9yAtbpcdt0IZhOf2WPx0k0N6xhu9U69j QXgg/MC9PyZIz5yfaW0uAUL9r+2re/uQlj6pseZfAAhFzMRiDj1AWqYiVd6D D4KypQceLd6NtJyW8oiIMwguR416XU9Cem7YneqYOhAYl0uviYtEeh5vwUu1 rTA5qNDqeiARacUkWW8LA5g0HsrylbREerEgcld9Gkw0LDZtO9uKtJKNhJ6n IkycDtvbKKeMtKrsFP7NaTB+K6aR/aMCae3gAY+GKhhNtRqPG3iCtG6D+3Yv AkaVLRROLjBGWt+wQ0f0DYzEF/YoHuUhvW64oW9lCjM/9bGCjrQgvdnlu7u3 NwzuDFbYbF6FtEmJuYVYO/A/7P8868oNpLlLSzVv7QP+Rk+RP01nkaZrC7sK TGBAQymhySYe6d10put0Oehbpeuyg9qCtHXvaT/je9D7WPK18RwW0rbXjCP8 dKBX83t9y3Ae0vv1JW6/KICeY7OGN9dlIG33s/Jprzl0R+77ZMsyQ9rBN/Ht ijroyj/jZlrP3Oek6FRhfxg6BeSFeSrbkHYp1GqKH4FOOp6oUt2LtKv96GBV CHRkaCSFpJYh7T6tSFRaFjoWP7afVR+N9PHUMHluErTfz4rJfXYMaU9q14rz WtC+seTfFGktpL17FNfl5gOPZ1N/4nM60qeiWskhc+A9/PplyWttpM+sfrp3 VR3wzriyVDkRSPtXn3JzOQw8B4e0NHWGR+DpTWeTRoB34OedpGMrkQ5aOCOy LgR4x79p57z+gHRI/o+kObLAi9m00FL7H9KXDiY8s0gC3jfxWzWPNyIdJuL4 7hJjj2Kvv062GtIRD1dVvn0D7QFp9oeVDiMdRYy0TFDQPlg1VesUc9+1rsIR vRro8Ju8NpTJRfpG5GVxdxfolI8xMBkTR/qm7s75Dxk+xSIR/1R9kL5VtVC9 IQS6zsXuFutbgHSiT8vGBbLQvTV8/umARqTvKjwx38X8Z5pSIxpDDJ/kNz77 r2hBr6TixqRTB5FOsTM69uEN9IZKbZp3egjpRykV19bXQN8NL0VlcVWkM7jx 97ycoV+5YrWI1Bmkn3Ye+i9jCPpf/n2gYkcg/UJ7+OdSGRjg3cwdcmD8K8hb sFCMhCGXyvma8V+RLtrfrGlUA8MSs/bMlDdHuoSTAb7OMJxefX289RDSn8zQ rjsIRrp2b7OPqUW6ssIh5UcujHPLfTy2VSP901vj1UwCxqskLl60H0S6Vn7o o+lPmLDNEVnhnIn079f5NYGOMPHbtFq5cDbS//Zd7Mjhw+RutYkkDWbdyN42 wT8Hkx//KlikbUe6+Z7CTE1pEKxundH9SwPpNpOmRU6JIIj5vC+z6B3S7bx0 rbsaIOhXrv6jzMSnK8zbqCYXhMYXm/Raw5HuXQU7ZAkQXk6c+pg9F+n+72IO 9E8QlspoDRK2SA96fvO64AhCQYJWhCXTf0bmxoUU8JElG+DZeeA40mM59jFj 55ClHFThb2CI9OTelamrpZG1ylR1SSFTb0LhYM7RRGStfkX/0tnwPyOjD4o= "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 1.}, PlotRange->{{0, 60}, {0.5525347812373504, 2.3701613140471767`}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{{3.469217781204506*^9, 3.469217798024837*^9}, 3.4692184025705833`*^9, 3.4692201622356567`*^9}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.4692194177727757`*^9}] }, Open ]], Cell[CellGroupData[{ Cell["Density matrix:", "Subsubsection", CellChangeTimes->{{3.469219422980452*^9, 3.4692194320005836`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ SubscriptBox["\[Rho]", "0"], "=", RowBox[{ RowBox[{"ComplexExpand", "[", RowBox[{ SubscriptBox["\[Psi]", "0"], ".", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", SubscriptBox["\[Psi]", "0"], "]"}], "]"}]}], "]"}], "//", "Chop"}]}]], "Input", CellChangeTimes->{{3.469219712342164*^9, 3.469219752397924*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0.40566609297943285`", ",", RowBox[{"0.4387092228890344`", "\[InvisibleSpace]", "+", RowBox[{"0.08260477477079191`", " ", "\[ImaginaryI]"}]}], ",", RowBox[{"0.1874654519574097`", "\[InvisibleSpace]", "-", RowBox[{"0.08166080605654981`", " ", "\[ImaginaryI]"}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"0.4387092228890344`", "\[InvisibleSpace]", "-", RowBox[{"0.0826047747707919`", " ", "\[ImaginaryI]"}]}], ",", "0.4912644524937844`", ",", RowBox[{"0.1861068784437498`", "\[InvisibleSpace]", "-", RowBox[{"0.1264855285906508`", " ", "\[ImaginaryI]"}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"0.1874654519574097`", "\[InvisibleSpace]", "+", RowBox[{"0.0816608060565498`", " ", "\[ImaginaryI]"}]}], ",", RowBox[{"0.1861068784437498`", "\[InvisibleSpace]", "+", RowBox[{"0.1264855285906508`", " ", "\[ImaginaryI]"}]}], ",", "0.10306945452678287`"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.46921976241181*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"MatrixForm", "[", SubscriptBox["\[Rho]", "0"], "]"}]], "Input", CellChangeTimes->{{3.4692198607083387`*^9, 3.469219869256524*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0.40566609297943285`", RowBox[{"0.4387092228890344`", "\[InvisibleSpace]", "+", RowBox[{"0.08260477477079191`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.1874654519574097`", "\[InvisibleSpace]", "-", RowBox[{"0.08166080605654981`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.4387092228890344`", "\[InvisibleSpace]", "-", RowBox[{"0.0826047747707919`", " ", "\[ImaginaryI]"}]}], "0.4912644524937844`", RowBox[{"0.1861068784437498`", "\[InvisibleSpace]", "-", RowBox[{"0.1264855285906508`", " ", "\[ImaginaryI]"}]}]}, { RowBox[{"0.1874654519574097`", "\[InvisibleSpace]", "+", RowBox[{"0.0816608060565498`", " ", "\[ImaginaryI]"}]}], RowBox[{"0.1861068784437498`", "\[InvisibleSpace]", "+", RowBox[{"0.1264855285906508`", " ", "\[ImaginaryI]"}]}], "0.10306945452678287`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469219871254052*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Tr", "[", SubscriptBox["\[Rho]", "0"], "]"}]], "Input", CellChangeTimes->{{3.469219937095704*^9, 3.4692199438209352`*^9}}], Cell[BoxData["1.0000000000000002`"], "Output", CellChangeTimes->{3.469219945281739*^9}] }, Open ]], Cell[BoxData[ RowBox[{ RowBox[{"\[Rho]", "[", "t_", "]"}], ":=", RowBox[{ RowBox[{"ComplexExpand", "[", RowBox[{ RowBox[{"State", "[", "t", "]"}], ".", RowBox[{"Conjugate", "[", RowBox[{"Transpose", "[", RowBox[{"State", "[", "t", "]"}], "]"}], "]"}]}], "]"}], "//", "Chop"}]}]], "Input", CellChangeTimes->{{3.469219441122024*^9, 3.469219490024575*^9}, { 3.4692195262821293`*^9, 3.469219541214224*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"\[Rho]", "[", "0", "]"}]], "Input", CellChangeTimes->{{3.46921950427905*^9, 3.469219507134242*^9}, 3.469219777657482*^9}], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0.40566609297943285`", ",", RowBox[{"0.4387092228890344`", "\[InvisibleSpace]", "+", RowBox[{"0.08260477477079191`", " ", "\[ImaginaryI]"}]}], ",", RowBox[{"0.1874654519574097`", "\[InvisibleSpace]", "-", RowBox[{"0.08166080605654981`", " ", "\[ImaginaryI]"}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"0.4387092228890344`", "\[InvisibleSpace]", "-", RowBox[{"0.0826047747707919`", " ", "\[ImaginaryI]"}]}], ",", "0.4912644524937844`", ",", RowBox[{"0.1861068784437498`", "\[InvisibleSpace]", "-", RowBox[{"0.1264855285906508`", " ", "\[ImaginaryI]"}]}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"0.1874654519574097`", "\[InvisibleSpace]", "+", RowBox[{"0.0816608060565498`", " ", "\[ImaginaryI]"}]}], ",", RowBox[{"0.1861068784437498`", "\[InvisibleSpace]", "+", RowBox[{"0.1264855285906508`", " ", "\[ImaginaryI]"}]}], ",", "0.10306945452678287`"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.469219508973494*^9, 3.469219549585433*^9, 3.469219780141765*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"Tr", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "//", "Simplify"}], "//", "Chop"}]], "Input", CellChangeTimes->{{3.469219634743085*^9, 3.4692196419438868`*^9}, { 3.46921996663728*^9, 3.469219982122014*^9}, {3.469220012915365*^9, 3.4692200377905397`*^9}}], Cell[BoxData["1.`"], "Output", CellChangeTimes->{ 3.469219645423074*^9, {3.469219968401895*^9, 3.4692199834835043`*^9}, { 3.4692200182537947`*^9, 3.469220039128932*^9}}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"Tr", "[", RowBox[{"\[DoubleStruckCapitalA]", ".", RowBox[{"\[Rho]", "[", "t", "]"}]}], "]"}], "//", "Simplify"}], "//", "Chop"}]], "Input", CellChangeTimes->{{3.469219565642324*^9, 3.469219599025258*^9}, { 3.4692200545650883`*^9, 3.469220069728835*^9}}], Cell[BoxData[ RowBox[{"1.4206041895790176`", "\[InvisibleSpace]", "+", RowBox[{"0.04063095136927784`", " ", RowBox[{"Cos", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "+", RowBox[{"0.1603105206830209`", " ", RowBox[{"Cos", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "+", RowBox[{"0.734818245707444`", " ", RowBox[{"Cos", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}], "-", RowBox[{"0.0084714644211495`", " ", RowBox[{"Sin", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "+", RowBox[{"0.03134617643494404`", " ", RowBox[{"Sin", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "-", RowBox[{"0.12682693134008766`", " ", RowBox[{"Sin", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}]}]], "Output", CellChangeTimes->{ 3.46921960608475*^9, {3.469220057953806*^9, 3.469220075906205*^9}}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Fig2", "=", RowBox[{"Plot", "[", RowBox[{ RowBox[{"1.4206041895790176`", "\[InvisibleSpace]", "+", RowBox[{"0.04063095136927784`", " ", RowBox[{"Cos", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "+", RowBox[{"0.1603105206830209`", " ", RowBox[{"Cos", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "+", RowBox[{"0.734818245707444`", " ", RowBox[{"Cos", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}], "-", RowBox[{"0.0084714644211495`", " ", RowBox[{"Sin", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "+", RowBox[{"0.03134617643494404`", " ", RowBox[{"Sin", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "-", RowBox[{"0.12682693134008766`", " ", RowBox[{"Sin", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}]}], ",", RowBox[{"{", RowBox[{"t", ",", "0", ",", "60"}], "}"}]}], "]"}]}]], "Input", CellChangeTimes->{{3.469220091460984*^9, 3.4692201383246393`*^9}}], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUWXc8le8bPufITLL33nscu+i+syoKIalEiQolIQohJZJKkiQUCQlFsknD zJfSkJG9996c3/n9dT7Pee7nXu/zXvd1fV6rS05iLgQCgUQgMN79/++egruS X+u7lpDU9ls44YwVxPzde403+ReSvp2qWsy4Dal/eZ2auRBJRUmkFOnTkG3E ysjWwY2k5KcUGWdvyP9L05pl6YukEPem7acXoOgeJevb+9NIOinvlWSWAOWG 69cnuQ8iSWO2ru5HAFQFrmeG6yUiib7XY8LPCD63rsbwTLEh8Y9g9rcQLqgm LwdkPr+NxOcNR3OPk6D23qKLruUGEl0439cmV0Cj4azO8fcjSOjXmdkISIGm 5GmxcRcHJCT6HbKoPgbfA5fCKre7IcFKgBQTKgMtrQuXzMu7gXJ2wdK0jx9+ 0Xo783AlwNZHR+7YV9vgN3nOrtvDBrbYA54NXJ6C1nszBp4iDbCR1b07+W0F dBpOcD0MLYTV9zF+J+d3QV/r7L8xSROYTzP5fP3OAvR75NmZW6bB3Nh1xWlD DRig9WzJv06EOQ0HVTv8CYPk6Vr/3+Uw81vfxbe1FQbr3+7tJvHBjNLe/Qd+ S8DQKY9yQ2VfmOZaKJ8/xAXD9ybztoerwUR+s2FS5wCMSOYoXHp/H8Yfr74x kRCCkbILr352T8BYBBtccGeEUStFUR3mAzB6pz3D5qoQjI6MJyTqpMNIIouQ ufEzGAt+w0Vw2QbDH20HXtDqwTi32wPnh6dhaMGqPp/rN4znyDPWVVTC0O6H 2V2H42HCcPSW4pggDD5+zLs7/SBMtGduPeT2h0FSc0+hDj9MXj5/ddGgFQZu vmopc3SDKQaZ+WOXqHXzursVZV+CqeThixXPYqD/k8vTZ9W3YVozfUSsdgb6 g+ofP/J7C9ONZ53CFg5Bv2V5EWt+G8yoxUxdYT0C/do2TgY+f2EmcO5NhvQt 6Cc/3qTt2gYzdVbn2/Tyod/Ad/ZI2xmY5ciX3m7VC/1nO8vVp9Zh1pGtX9+V FfqfJzGsM6XBbNblF5eC90D/xPU2GgM9mF38cTLl8UUYOKRZ5L3JBnN71fh/ vkmEgU/uq7yHB2AuKvov7acGGNxfwkBgDIS51pnH2q2rMNiXXprrtwLzchmn GMjFMJS0IpVbOQ7zAQ7yf+/5wvDF1sDTcb9g/j/OxcwRDRg57BcFj3/AgvC3 qmsGczC6b/Upd8VZWPAMvXsg6R2MWXqo1aelwMJnXVu+FQ8Yd/3Dyf1JBxZd 0sdLc8Zg8puuRe9VZVgsOll4lyETplmnWy5mK8ASI+eNE2fOUvt0h/F17C1Y yg7l3uDth9lH9T86Zyxhef6kgf7NTli4feDSOfM8WDHm3LGjKwEW999w4sJh WIlraO3StYOl7dG5XVa5sLpL52LQ1G9Yfujyo3rfB1i7wRFfdawJ1iJ8TO+o RMBaS8OZ6IIoWNdffulTbgDrkjeUT+80hfWp7pw0bhKs1059JX6thU10vvP8 xi/Y4Ht1v0XkNmx2t5z4j1MLNtztj6X6G8HW1X2z37zKYJOlftpQpQooMepf fp1OhM1TIaUckUFIYLR7Q+Y5CJv52rf6B/WQYCwhkGHzErZopiwKYA0JgcaW LG1tsHXkFf+thBIk5D5o/eCYC1sZJwZtFq8ioaP133+01Dir7O+kLLWQSLNs 7qHfBRTTev/FrAUkSjcuC1ipAyUxxLiGNh+JRnqprxdUgDKlzRp36jIS7VVU /Ys8kCCyNPn+ZDwSPWLSx7I4kbC/xVogchmJAcdx+ssoEi7nFN8stkViaJDv b9c9SEi4IzQ++AGJNzdJ7M1RSPjifNOGgxOJwX87ddOjkTABI+XojURffh3K ljgSuQTMJS+2IPFsU8CFuV1I1F96H/VMDYlWDNIaKuHU/1t4F+qikajdVWvZ 3YbEBzlB9ovTSOT16FpYFUFicUR/tYQ5EubbPg3t1UZir/MBJcscJDQY+52N q0ISE+Q+DmJGwrPOmHrVHiSpC3BsvnFHwvkmhvaI20g6sXTN5W8DEtQOEY38 w5F060dXE50cUMbF9ms3aiMpJ8dISz0CKO4ZtQ/l2pFEcd5Jd98EtmxzNP4k dSONLPh4lL2Czc+6mSInLZDmMH/bn9FtsCnTfUIrTBtpUn+kZRh+gfX+/46/ 44hCmrrl0bf3VWHd5FG3uJQP0kwLKxf9TYa19H2f6Xpf4bbdbsV1F6/CqkMU eTvTIG77RdM4+kQZlj45+jbL9eC2NXm22d5EWOK3vU54GIK0opa2a4qMsOhz Va1xNQ5pLyT2MlUNwIKy1fvTLTJIR6u5qDCWALM1kS2MG7eRTsF+l4YmA8xq b6t5ej4C6Q6HBusFX4GZ7BivLGtEuqRmxkOcljB9WFnZ3uci0n1ZND/iUAFT jnl2PPFLSDcm8Ojka3mYDAgTUXmVhPRa54U89tDC+L+mHUUqhUhvf9/JN8IL xqUCQbRxGulDCzKCWnpgLHBCsfhpJ9Jntk+ECx2C0YGnUWXHGJC+maj24Fwp jNrPQelwD9Ivyvg+yZeFkaEjm0ZvLyCDwKGy5xuPYeQGN/OZWD1k2OtDyNxH ghGlKwwae+WR4VyC8buHnjA8nv6uOOMhMtyriizu6ILhsua6He4nkeH9UPMn aTMYTqbd4HsfiwztOzjrPYthOMaisza8EhkJ6sd+lEnDcHxaV90dLWSUPpbc TvsIht9NuvVcZ0PGg8H9fZYEGO4grabVxSOjV7rsWIIHjHC/2/34Yz8yxjde nBvogJEzeYcuBLxGxsq5/HWV/TDy6f3Kxb50ZBzkXaG5Vgijqke37uTHIdN2 0N/+VQJG36p4kV5FI5OaSygnSzSM6X/ktT5xDpmO3q0VtNuCsY6g1rVrBsh0 PZ9Z8qU7jIe3qzbfmkOm+q04TR0TmGT88/WJPDMyzUh26IcWwGQX11eJ3yG4 ndtM1Pg/MZiqVOKLO7kLtzs9ybI9vQEzFseKYqRO4fZ11Uq/u3kw5xzC0egf hTsUnYZKugVhafn+1O63V3DH4fr4y/MEWA430P1dCbjDT9VUnm4AVrhcpxeC 23HHp82cBOUcWFUwTBoNXsMdw86OVgYxsJrPJqvlaY8szN/YmGx9YU0zcvUh DROyHI338Q/aA+uqfyNZ5g4iSyBFmhwjDusZ3ScfL/ggS6pL61g6HRUnV43q 2IWQpbbxTmrJGGzcrq/YbtiLLJPqu4//1wQbkys1BVXZuJP96SR7bz5sWqjL b7x5hzt1CMkNi3GwmSVfSZIvwZ0nz1neZPSHza24xDcfZXFnaBNpt5AjbB1U P6vOfht3Zmq8n1ej4mLMl8vKg39w53/PXN4Yy8JWC8v6rA8Zd86TeM4cYwYK 44Ad1/EaZGXWcL5XeBUou3jZuFeakVV4uMK1yg0oTmE7OjVOI6vaM27jBnug 3GTOYU39gqxG5pfEfpkD5VlcJ+raIOtRYu1mFwIlm10uud8DWV0/iLaNkIFS 4P39UrIisgaev/ZhTpK6LqoiMa4h6wOBlocb3FT7nqS9RVR/Kc0KHnQMVJxe rvzz/RyyFoTeMt25BpSwHRKdLLLIWqv5T5pvAigu2i7kI+zI2jaqRZKg4rz+ o5SgsDhknUi836X0Aygsxmf6zl9HVorFcKn2V9hq9Q0Lqw9FNnYajNtbCFtP jzIxrOkim2ThUy+zTNiyEVzaoh9FNm3XOfMjVD7IsE36zjAZ2UyFzOQdo2Dz gzmXVeEVZLP/nkbnGgSb9nvf9C/HIFuo9pHK606wEX9CluY0E7LFjuUmhNvA hrJMy1vDaGTLSKb3fWgC65XjUsc4F5Htv20lyq/kqXO15d5aRj2y87QIJjfO wUrzYbdelxRklw+74v+Hev/29cxFOw8iu75Ok23PH1iuzHOfLO1EdqfnISwL pbCU2Z8lqX8T2XMu9AcL3oSFwKM/3+7ZRPYqUb0T0r4wP5/de4BUh+wtPx9r q56H+QuDnY96/ZB9ZZfJtNFBmDtb4ydZcw85mCaffzPfA7MjplbpNNXIIfRi JcNOFWYvunb/KTJADkP6LMcLnDBzi1tu18tK5DhqWfOSXh5mdti+e29Ahxzu T/uGUhGmdY+Z0jlYIkdwH0Ve3xamXHa2X/RnRo5YRYGLfy/AZHLa7mwPYeTI vKKd5x0KE0OdUnc1NpCjvNJ6kYXKM/dcEuGrK0COH/SeOq9zYTydtrF20x85 Bi2jAo2+wriw7sGcKRfkWH2a+bG7HcYyYlJezDEg546+ahr/GRgziFR6KWCN nOIKvfu46WB0OlxP8ecAcmr5bEbmCcJobnfiJ2UZ5DSt5Gs6SIbRkFZzZfXL yOlIr8U2vB9GXV7saqig+vO2tDoS6gCjJ4/TzjtzIWf4U4+nQj4wep5tbPmn IHI+64vsLI6E0bDPOU+tXyDnO4UMUesXMFpokyjfh8j51efLmSkq/q0mvbTy voGcfyu6M+40wpjFae/jXoXIOUm3Pi7ZB2NFhwtn52SRi2jJq/xxBcbVWE7I RUshF9dTDa8TLDBeLjXi1jSKXHJ9lh+WJGHCjmuCJ9YBufYoXFh9uAsmt5l5 XaofRy4rnzv6ipYwWSURbfb+MHIF0H366hQI02f6C+kG/ZHrgUUX/WYMzEgJ db0+J4dcL+PXzOIzYSb57phuGiBXowK5peknzKbabjDxjiBXj48Ft+sIzCmU erWb7EGuhQr349u2YK7k4kcHtgXkFrJ42btbHua7kyzceyqQ28OHczojFJb2 Hd38kf0XuUMr1NQN4mGpPzZAsICM3E/oDvn+y4Xl4HsXHXUVkftjfNgWRzus FFiPZX+vRB7W8mXmEDKsE/672BdKQB4pOg5Lgf2wHnn3QdudT8ija6HyqNAB NnZ6/bukxI88p3vP809EwiZDHnOK9Sby+Mrfcgh/AZuBLAXH8wWQJ9L7Rap4 IWyOpNE5GJUiT3J5+WBFI2yZ0+xOT2VGnve0bXJ2fbD1ptrw1Qd/5Kk1X7ww vwIUYqTc4/Jg5OmIZ3v3gAUo5qveqk1LyDPdq7QgT8WrR5+vejdrIO82eVPt 6l1AaU4s4GVfRl5e77MBpyyRQJSk2zWxG3kVy0Mr188igasm0uiIBPLupX1O igtEglRyqju7H/IeMS8zUYtBgurTx5RoLeQ9r7/vWIcKErRcFQwi2JA3QOHn xVuNSNAekUKNOeS9z+94Q9kVCZrsh2mkeJE3hWE89i8dEpSGuU0/yyPv+2W/ zBsvkSDueq185Bzy1gzRlCsgEthfCIp4jCBv268Hzb+o+Lgpf5H8uRp5J74I 9AcFAKUxR2ZDJQ15KXkZy7K81PrGWQ6F2yAf+wsNph+FQLHeUf65Pxr5pO5X CQdYA4XJOGOzhgH5dK4fJEvOwFZJ/QrXzFHkM3P/a9x0D7ZO/3UICFVGPofj zseuysMWqWDInXEF+W5pB4Z8c4ZNVXq6sh+fke+JNEPsFSJsVIjfeC1Ti3xZ nLGZwsmwYXT0VPadCORrns1uvvwX1g0eOdL2vUB+gTf/hHkPwSrjuvrOfGfk fy+qX+aiBQuKnTtHRVORv4alvpnlJ8xH/ych4DmI/H83j/QVe8Lc0jHdyuCr yL/V7sG0PQtm60z6/4aYogBb3ZrQh30wq5ne+yHvHwpIFt1WcxiAmfSkIee0 UhQwjU22yxeBafnzrxA6UODkTfkLJ8phSiZAMuIUKwp4Xi4MoT0Okyqn74XP m6DAzVMGj3KXYcJgrpQQo40CceZNGXaxMO60kXLccRIFXusfLyOpwVi04Zul /R4oUK4w1PSmCUa/he6Y8/2OAs38Xn1H3GGU65zc6KvrKNDHsLVIYYARj7gN lef7UGBh+S5j5isYbn39gC4mBwXph3iErAxg+LD81b0lb1CQ/9dL1fVuGOqI P/XZfhkFlb6oGL0KhCE/5zgN55MoiHlldhZ8MCQ5l3HOXxYFrV/sc18phMGB +O1PDzuj4Nn7P4NTrWGw8NmvjT57FLx23fGR2QwMPtPy+OTli4JR7uPpi/dg MObh2Z8MPSj4/Lhf6XN5GEysNbwVxYiC+QdomvbXwmBJv4uppjQKVms/6J1z hsHR3oUU210o+FdaYDGRCEOKbzt10mVQcJwzk9E4GYZC5Zud3ERRcItGQ3Ba D4bGxOybJ3NQiHW2SjW+DYadrXfc/WmDQlK+O9/8TIDhjZWR4v/aUEh3zVGG 5TiMZJHtfHzMUcg8KDf1gACMuntrX14YQqEzpC0hqv4dQ0Hm1X9LKOQXfii+ MgnGZfyfHDPMQ6Go7Ukcqw4wIdpw/qj/DAp94NRj8uiBKePf7orfJVCoPv5u 2OsUmL703po+UBqFugQ7CANOMGM98N+1sgIUppe6tnxsAGafi53VPdqGwse0 igaMRmDhcuJaYEArCl8so3cMfg2LKsy+ajPGKBwKtu2lbrA4ldvJHXcChd/s W/yhMgHLl5sivKyeoPDmMfVK/hlY8yH8S5n5iiJs/27qHsmDdZVXJSaidSgi fbqlINoL1ocX+CVp21HEwu3yG9oF2DTrngsi30KRM1OfpPEDbC6pXP6PZwVF rnqzpgT4wlbCvc989BMokhLw9skMFd+aWdJuHAhFkQ8UCocCVT+r0I5HJNCi SMMt8/tn/ZHgdY5vZucNFOlmSGZM2Y2EPDdeB6VoFJmPmrzVsYGE8RYa2TZV FN3GkByy3QGJYmfZ54weoiiLaY1+5kckHp792WAbj6K8UZPrJqJIvG5s5zD8 AkXFm7mK+0OQ+FLTlfHKHhRVYtP3DelB4pdnuVEfz6CotrWLhvBeJHbZ/rX7 KIOiex9HzZalInHetFIl6iiKmrUWvD1GQhLp1HUm1ioUteXrvLDkhKTttx+I /FBH0VP22+QffUESy7uu1QQhFHVLVhhRk6Tu//0d4J+Coj491q+ablHPbzq1 yq2jaLB4wBn3ASQuSJDs89+g6B3nl2KMxkjsti6RZLyIoo/SG7rS05H49fnO B3TeKJo0MpdoRIfENH7aTMYnKJqpwH+s9yy1vm7S2dOBKJp/0YAnqJaq33dU DB2xQtHyt66/BWWRKPpNOuOiIorWzD6MKYlAwtjevSOwhKI/1EssbUeQkPuc fvcrYRTtuNK7Y+EAEjx4tf3SeVB0ek31jgoTUCrDXDmMalB0Tc/OpJHKv8X7 bx76E4hi24JCtrk2wFawR6xM6TkU4yM1B6fdhU0JO8abQY0oJsHF7qphChuX rAp0/TZRTEnmiNVXBlgv5HY0/R2OYnvNOqT6b8GaVvDj1oOsKOYaO9QoHgjL XCd7z51ORDHvDLkP+btg6cCW8PzgLhS7XnLh+d4VWAyqISYniaPYw39zXqev wPx4/I2xfS0oViq9xfvCA2YGn1N2fniCYl9195JUFWFGuflaGM8DFGsyuzn+ cQym1sWIindOoFifJ2Nl9zmYsL/qqvOmF8UmQg9mXpKGcfGMj0+OC6PYUuyD h1sDMLqSzaziSkFxphIuF+FTMPz3uLXhGBHFOb/ZmecKw1D/XAVnrw6KC/97 pqPfCUM0u/V1X/SguOx0l9h/CTCoTcLArBEUVyeJbT9pBwMh20L5L19EcX3O MwuT3NDfNeH4+lMZiu+TTu8K/AX9Nof1pS5xofhhndE65hjo6ylcfZUlg+In zBTzEg9D3617V/YnRqG4y8lLzxRZoQ9VZUtjfqL4pUv5t8qaoI9jsKnS3BXF r91Y9DCLgl5KjyjdjlwUvxmrc7TDFPq27U5JunkQxe+lB+x1Z4A+UcptTmdO FH9SXCm/VgN9tkIpEVcGUDzlG5Ez8hb0vfTyTFJSRPE3/wy3+A2gn6GafHJT AMU/TN8eziJAf1gd5/KjQRSvItb/0K2EAX7eKxJntqN4AydzaX0gDFSDKitP LYr/krZIO7YbBm/n0d8QYEbxLp2YeyNrMHSymaepWxnFR0x/+10thuEDw46n K76i+MalE6bxGlT+2/fsq4UIStDdSNaQmYOxW/TpfEc+ogTro16honcwXpb9 qNM5DyUki8/OtCrB1IkDXS4fx1HiENHrCbc0zAk1DwQc8EGJoxwfQl4NwNww DZn8wgslTkutuGmmwnyBrMDy2+0occU0SP+IMCwevVXx5NA0SiQ9utMfywMr uUVOfI7FKJG+cuuDAi+sXrke77JrD0q8Oxl8+zM/rO365r6N6RZKfJG5Ijcj TNVXbysvRImjRGPUpfVwUdgIlLAwj2NEid+zbv8Ji8OmZluAWHM0SoyUnfI8 KAVbz/r3L1/8gRJzoif29ksDZZ+8yJdWar7rYbYc/rJAmUioyzfgQUkWC7PC TEUkxNAVeNh3oCRPgUkEKCOhPeZLwNMnKCnGt/fYH1UkipjuHvMLQUn1fq3N bepITPSU7pFaQEn9/WpNzzSQ+CukKEmV6s8kR/EFWQtJDETN6V07UNKSXeZy vQ6StL7tMAt4g5LH/MQNT+1C0qkynWVXUZR06hTiXNZDUli+w9cnvih5YS/P 0P09SHoVO7SepoOSV9LZi6UQSVVmbGcyKlEyePuOO+V7kfT7E/2R5SyUjPBk OGFtgKTBobHvB06h5MPfNIpjRkiaedd37WktSj7T3dy6YYKkJR7x6KQilExL XvnOu5+65porHyCjZC7NfMpbU6p9zrvKPdR8is5PeZscpPrra165eAslq/4b NfpnTo1X8bvj3neUbCAPcPtY/D8fa0Xucyj580nXyHZLar7P2lWqpVGyc6Ot JNWKWs+X2VqZGpQcOv3rrq4Ntd5O2uv/ilFyuqbZ/rstkrQJVsl0KSi5qtCg fM6O2i/LliOpYyhFiv5K2TpO7ees6NYsF0pxHS99qXgSiac+Sb5TNkQp4Y8f fL5Q55PoAX0LjX6UkpV8Z3L8FBI6fmQM1rxGqV1Tr0YjnJGwr1DLWTYWpU6F PlQZcIfNOfOwVH06lHIdjiIGXIBN/VHuH9I0KOV98PZPdg/YCLNIlCNModRt 7kBfpM5jxncFfr91UOpN1rnyxGuwvPIw0PIXN0p92Ol0X90flsl7Laft+VCq 0uekY0MgLF0w9dPi90GpH3usSSshsNBf0hxXO4xSSy17DtiEw+z0Bl+x5guU JpL4mwr4YVbpSleKgBNKb1dbtuHIhRnPlIbwrE2UFnn4zqHlD0xx6TY++VeN 0nKfogbIbjBRbXLjyldalFafdXV9uAnjYTHb8p9ao/T+w+I+hyVhdLeUtujt IZS2CtlafVcMI2Rn1XpiH0rbv+sIZj0Iw/pNP243l6L02Z7ibZd6YMhBw769 kRWlL7PG3mn2hsFYy62THZEoHYCXd6rQwUDPf1Lt7ksoHeZ5KPZ+AgwY6S36 XPmM0g9eyPNNKUP/R5HPp8VzUfrpd7rnhz5D/+HuzGQFB5R+SemXzLGFvo0f ZPoIXZTOVfmYxTwGfVXqbLzG+1G62DFR5UIQ9CUqTT/c5YzSnx9cLWhkg77o X4eVpZdRuvHjkV0KadCXLB4ZbPYUpf9Mq328qwN9X7a+jUtLonSvCIvRWCP0 k3Sx/voESo+bjzWYnoJ+uygD34hTKL0YVGvxegH6axKfy79+i9KU3Je/GcNh wGx14bDBF5Rh7Ao57soPAyMigvQqqSjDyWLfXZcLgwleOXYCN1FGeI+ui6wB DDntu19V+gNlZD24xsL/wPCefwnuh/VQRu+/pkWTTRglz2arSaajjMnmG//0 aBjbH9feUuuIMoeVIgh0kjB+6dCelrCPKONyby9T9UGYHH+Yu+M2df/+wfei hgkw+5EjSSniLsrEBz549VIZ5q6XJu7N2YcyqdkXFEifYR5Wzla3NKFMEbOU 5qdRWGi81KYa+wplur89MUMdWJ69+tg7vBtlRte9vz9vhJUSPlnxWk6UWVCw PEI5BatBr0XdDT1QluEu46nKcKr+/kUWpZdBWfbSwSFhflgv53oWIhqOsoJj n92v58KGV6wBeUcQyqqZBvjq/YHNpuqz2uRulN3tf3Q90Q22vA0zs7P3oKxx lkbI+iZQWJ/UHNHWR1mLdja6E9FASdcuua61gbLHGSfvlkkige/7uRdZXCjr rNvAxl+MBHvFHY8G3VHWwzU9zv8gEp4uXn6fFouyV5/eFGjrQULLcmiFwj2U Da13TNH1RiI98bVVmTnKRpktsLwwRSJ5vtHdNBplHzdFBNKLI/G4GTeLQAbK Pj8sOHpxFYmB43tnr2WibObPPNtf35GYUNaBs2dQNt/W+OvuTCS+zzTXuvkH Zcv+tqulBiOx9vlS9MIDlK0+4fGc0RaJf+LkO1+romxTFw2zpzISe0KNwsV0 UPbvqSfXWmmROOiYVpTWg7K9/QrD+p3UtdKHMRcXlB13qbJJK6Dyy+mxQ0OM KLswYvN5+10k/s4pHBQrQdktt1EVLyck1njd9Q7wRTn6yeuJbbuQmH+odpdg HMqxebIzIRsSn1plRc8toxz/XLpf+ggSA+KNz9MFoZzklV2DO6j8+5iuU5Wd D8opLTdb+cRR+2FzWWnSDOW0/c987PCg9mv7eT8CO8rhxoqSgTESWm9mn80l o5xpcFTCa0EkvBycGH9Fi3I2JDGGnQtIuOBAkxRajHLn6A/0/UsFykAiJ2fV G5TzvNNlaXQNKKfsDpQQGlHOn9mr4o0lbP3+q/T4ZAjK3WN7Fn+VAptvZ4eM //Gh3HuBSXNOe1hXZneLefgX5cqTb5QFqMNa2KFj/t06KFcjxi3bxwSr7UYG bBKCKNcmDTRvS2Al+AH7wb7fKEdRjS4x44bFmta1+ANmKM/wXko6bxIWBSSr L5D9UJ5dsySW9ytVf93YzfPxG8pL7erzGPKGeenr3/ceZUJ5M2MNyRvfYebj FSvDuHKUt6mteziSATPkXet7NtZQ3sHUnmIRBNNHaq7UWZ5H+XONsxcKbWHK OiukLSYP5S9bhLULKcGkw2A66botyvu38O+7tQ0mrqoy5cafQ/lbNrkFY50w /kKzU12kHuXvtRqKH34PY61xDS8sPqH8k2OtD4ojYUx48N5mBS/Kv+h03xQ5 DaO+Gb6Ha0RQPsuR4HZbB0a6bei+oTvKv++NbZ1khZFjNoknHE1QvsJZzth6 GIYHt4luOzOF8jVDFfmllTAcmlvNu10B5b+7WomKxcEw+eHPuF4LlG8bH7oX cRGGFjqCWvMR5fs9/NemjWCoPqsuVt8K5Sc+ycfuvwZDeSev9P4eQvkljnal lFwYyppo0toph/KUs3dq1vphqNDMxtvUHRUYSnRP2fDC0M+TkdZXD6AC+/aR 1ZxDMExcY2FecUUFAYf4R3ShMLx39HNr7WVUkMzbr+hYBMMxI9tUdpWigjLN cnXxBAwvpPanSbqggrZthiO7GIycLbvMtCGECvjadsXdFkZGapjrY2+iwoEN uodf78Kov+lXAp0aKlibFyoIVcEYf3Uh1vWhgn2Ky1ffRRirz7h1K1IKFVwW uE5+l4fx20fqKNI/UOHqU5/o0EcwKe1P+zJ8PyrcmJCU66iDKYaIspMzyqhw d8/PLxobMLUYd+f7KUFUSBogLw6fhRnfTyInD/5DhS/KsycO7oK55ItXE2Yi UXHHVw9prj5YNlGtoPNHVOThEfp4iQeW28n0UUfyUVHU9T+7uoOwcuG806XL K6iovlPxrn8hrN5JXub0PIyKeqc7JX+OwxpHqYzngwRUNC64W6kkCmsJe5n0 6JtQ0e7Y2Ex3JKwnjPBXXFVFxdPZCZE6H2GDU+98XKEFKrpRTCUeLsBGhMv+ +br7qOhzeLV8XA42luk1tk/sQ8Xraa9tjRxg0/GbxQexIFS8vWw3nfQINj87 5vdXDqBitClDxFIdbAn7vRKtskTFp4nF4habVLz+1lZ9txYVU6fPlb0mw9Zn /vRm8xOomG3Ac4R0DiiMstoiT1VQ8UNszdSJRKDsryymua+GipXDvuEFP4AS knuj6/0DVKzbJS3GQgeUd027251/o+KPqN+l53YD5e/aWlCLPSq2d4dZV3kC ZZX3mHCUJyoOkDUm+V4hgSFiH6HjPSpO3hq47dWOBFY3vRQ+Z1Rcao0VadyJ BE6lVhkBDlQiyBuWSBkhgaNFjzZmAZUYA+etgq4hYeex8W/WMajE3pw63pqL BLqGyl/C5agkKG4VptoPlIUTlQkkY1SSukISjuQFyk+RGxLggkrKtXlF/QeB kkWQ+Tkxiko6/KcP690AyjWJK87ap1DJQHsu2WE/UHY/fPVb3QKVDh65OR7C AluLRx/4evKgkq03l07Kb9hKryhxTp9DpVPR6WFfEmHLkjPO/WcjKrnlarcM noHN2QLNhO2zqOTzrV6UXh42766PF1XsRKU7dOOlpsWwkS3Bvdv/GSo9krxO fyEYNsh9cjiei0pJBiw2901gvYC+SPkT1T7vuurUj1+wlnFjQ8vJCJXa5n0l 7GZgxYmUsr3tDioNsNF7+hfBckPiA6bsf6g0pRxfkRgEy2rzQvze2qhMci07 2sMMixQFfbjpisryPYSoczIwn8ZzvulqGyprbD5si5iCefYOhl+jFFQGAQnp rAKYuzVPa2fIgMrWtsZVUwYwe8XgeqmJByqf9G5lYWWEmYUb2wt/OaLyuYfn T6hR8fWamCjhAxcqBzRGLl5xgGnZUjapxmlUDhsTNHgiBVNH39hXM75G5Wj6 nAdUPJiM/XH0gsQeVE6Q2tPZ/h4meoZz8n6loXKawXe5DX+Y0HsWeG6hCZVz T532E94L45mR2UcfRaBy8fW5r8gA41KG9oGB5qj8+dktttNNMPbe8MTBIDlU bizhcrj5GMaslt9GFLih8p8/6W9e2cPYtnI1Bp9ZVO5Z0F6pFYfRug61rhta qDzOVm88Mgqjz+/pyH3iQeVFleOPmN7BaMRK3U9JVVSmHBzvUfSD0TCyoZvT JVRhdLuuZL4HRmN3Nxsa0aIKRwSLvyctjBbSHhsYdEIVoVfPax82wujYhfRG he+oIvNFjfP9IxhT0/f0OlGLKmo9n0//Pg5jkWoJV+9LoMruLevcZTEYW5j1 qctKQxUTgYEN3hEYv8y580KqCapY6voe2PUWxrdEkhsXxlDl+FH6OHtfmHh2 Z3pFVgNVnH3i+4P0YHJ/8URYdROqeMTIq76ggSlaGqX47/GocvVt2fVPDTDV /C7Jz/0IqoT+d7ChPxqmM9ZlRIp5UOUJwyUXGWHqfKT7W88niSop0oT8/YMw q0+rfYYihypvDB9S3LJhtoq+9MQ7OlT5GFTwNJeKjz85n/dOHUaVuqnNplYj mLc79/FD8xKqtDju20Ywh/meQz9+yXKjyiC0eVidgYUl4n/53iyoShsSLBZg AssKFKvyyxKoyqTzsnznN1gunit6eLIWVVlm6mzTLGHFqNy0bVgZVXlPs939 7wSs2i6wElz3oarC3pdLIp6wfgzH5AqVUFVltS76/SKsf1ek8L3gQlWNvEmF /f6wYeAatv7iLKrqurFV/yPAxttHJqM5Sai6R1zr1OUw2OQxU73tfQ9VDdqP r9MxweY1rUyNlwyoui8m+HFCNGz+ovx52ySHqgdN01RVuGBL/shMrbshqh4m 1TV8SYCtq5MBlKJGVD1SOuliJwJbVSH3u4Op+R/3ZqNMpAGF0Mz8wToZVR0V tBJuyANFN7kz1T4WVc/0n9DkegsUt3KB8t8hqHr+WXBzlgZQHq2aikpPoupF 6zS3PaVA+aDi9bVqEFW9ttdv+wlAadpzaIthFlV9v0w+P/cVKN3MDjcYb6Bq QCC77sYBoAyHM7RTqP0I0dD6Fd1EXae+MnQRRdVbEycuSdlQ7Y/fLSlcRNU7 aSGMJW1Uf3n8Zz6poep9+7Q0cwdqvLeF0rQbqPqIs35P3wBQYs9W+3hcRNUn jZNtfq7UfCdfOWiko2piGLvP9ilqPU7xnenmqJqir83ywhu2KGP5l5mp8V4t nnitsQJblXnF4unrqJqVE2JYHwRbV8aIc6bHUfWtS1qXAw1sSTVa/UvdhaoF QvVX5yKoeuJJdnRqAaoW/57iCGeBTc+HQsMbraj62UR7/zte2EiBqxODiKo1 Wyf6jZJgQzXho6/zVVT9VhhyvU0c1ks9rT6HxKHqb+n69yQlWPu4o1+o1hpV R+jthWwMYOXJ46t1NLaotqMuZHblAsxPEBTH/XajGvsQ99nLu2DetC/LaeAH qvHSvGkfY4C5NzNHs3V8UU18z58vHa9g1o/G6tjaCVSTsb+gY+MDM336Pr5P uVFN0Z+Y/Z8BzFhtN6xec0A1rQ+Kjyu7YOrmvtDwDFVU293ymUk7GybD6Caa nrGiGs4cDXpHxbdYyCUYh6KayY6Jebn9MJ43Zn4h4TeqmSmEnkvlhrGuOQVv 2ceoZnmAp5N/AMYENe6zvGxHtSNncywf5cOoW3DT2Rgyqh2/ZVDNHAIjDTFh +hVsqOaY8lc3zAJG9Pkb6VXcUM3548WcLWEY/vwh6uSfSlRz/Ucj7kvlYycm u6OuWaKax1p83HQpDDPqBX3NDUY1H17l7ecjYOgbvdGjbllUu6b5JbjXFoZS XlRxSTGjWpC13cJxSRi6t6/dXIcF1W56Tp1vmYOh+zL3OQMbUC3i/s1/ZlUw lGabX91aiGr3svkOf70PQ82t7TtldqJaTH1ujb49DLM+xRHTm6j2ZNhoV6E8 DJ/1+dkReA/Vkra1vVVeheEfqibPbsSgWqr4JYmMOhixvL6nbTwO1TJwW7xo HIz0r8boXXFEteyTCcxPnWH0rsqTPTZjqJYXoHKDnQxjxnERxR5vUK0w/uvi XQKMs8eslzxdRLWywuNuNE0wPut3zqSMAdWqZ8OsFt1hsnecq186C9W+7eSv 9dgFU7MN58wstVHtu+K73cMMMMPA91p68BGqtZ/rkPybBrO7zK84SX9Bte4w z6eHvWC2U58+tukPqg28pNvRgDB389BfNqm9qDbVpbpU9g/mu3w63d42IZnG 5nbdc25YKjLWfH9yE8kMXoL6PAOw7NlJq2q3geQdD/LyovNhRd4vMmWkGMk8 DZ0JoRaw+owpUNo6BcmCI14s68KwZvk6W1UqFMnidPQ3vSdgnSbZQfOmJ5IV 95IvuETAhsOoDUv0OySrOdT2dNnCJu2ysTabH5K1Ak8eOSoJmxnfTPOoapCM RRF79lfBVttpN88XNUg2/i2U/+k+UM5tBasJ3UOy6dx7mV32QJnmLRQvMkHy EaUuVoVVJHg51mUxUf0fV7/Bn7SEhEwd5tGFDCQ76kpK/V8/tt0N/3HFCMnO e2pVbswikU6+syuUuu9q5KY7P4VEpbGayDaqPw/THUYu40i0TINUui9I9rZ4 Z95K1bceGpl/nn1C8tUj1scODCLx9vUxbxlJJF8/vnSmrI+qh8/G0HNqITn0 1FMPpW4kpreNzwv0ITn8rN7V51T9nVvHW55ZgeQo9+6bbG1IzJMW5rDlQPLD y6H3qDqfmDM57xcVguQ4P6kniz+R+Eo48op8ApKfBdalnvuOxLiyFlfROiS/ CHXPbvsPiaFNRY0jN5H8KoKlyKwBia4uInQ1VUjOupf3qaIWiaYP2z+1yCP5 7SObRpWvSJS+kPSr4g6SC+KX/6R8QsIm28jn8yVILklO6OWoREJT6ly/ehGS K9P0x8PKkPDM+MGBDXMkf3nds7hcjIQzkjsC7ooj+b8PMkwd+UBpyKDrG+JE cktpA9ch6vxAwYuaoQZIbq26KPIxG7Zy3n6vN2lGcu+39xov02HTW/70v9BT SF7sAwf3BFgrzGqpPF2P5LXhvvP/nsDqBtdRzQ/CSKZMhnlbxMLqHrlv/6nx ozrjyrcI9fuwXDpeqHXdCtWFdx7NWw+FhddRdD+3T6K6BOda+cVgmB/qazGZ pEV1Wf6kmu5Aqt7GX+uMB1CdLNXf8cUXZnM+0usvxaO6iZ4n7V3q+9X/+9zz 1ZeobmbAsXPzPEy8tHjtsycH1S33FfJfOgvjnr3OfDt3o/qRQ8cke8/A2KGU XqGrp1D9uNWGivUpGN317EXiiTpUd7R7rlt9EkZ0vp2nsQlDdWcHAyPt4zB8 QMT+fVE4qrueGTR/fRSGLvgqHHosheoerhHHBGxg8GWynmuAH6p7X1I4c+8w DEzbLF+bWEP1qz5NFynmMGBpwnmDMIHq1/0vX71sBv21TJu6D9JQPTSEM7R/ P/TbyO8xTglB9fCwontHjKFvhYdhMIcO1aPuHn9SawB971Wrc+4SUP1h9Gaq LkBf2OfixIVxVI+Le5H9Rg/6PGkKZxep558ZP38j2gF9l8laccbGqP58Ifl1 nD/0hUt4ddNfRfW0l0mZzLzQl/dzR5HeF1R/bZWYfqMI+mYVOxWDAlA9l/gs bdkW+o2ZZ/yK51A9/13CywsL0J8D7+R2D6J6kePTlL5HMCB3Y8m1jrouZ4l/ YUeGgZIbBjoDVPuqiifJ//2AQfuOmfCz1OdXfSEuydAThjjEQmnWjqF6g8Dj ZyU7YaijZeLs4R2o3tQQm6CcC8OFTksbTx+i+s9rj+LTDsFIqlaqlpkcqv+V jXnCNwGjya+edj05g+q9t6NjaeVgvMbHfZg9D9WHNB/E+NfBxIzxHafz/qg+ NnA/euYsTMk8Tl0q3I7qCwZR9zrSYObk/cS+12aoQbcVEZ4vCvNKxyyYxJpR Q9E3JMCRG1ZNnvJ91niHGmpSwdd+fYA1JmWr7WsE1ND8FXTV1AbWvs3vVzBR Qw0gB17RjIGNfQ2xLP0yqGHYG+D9Rg02CVX3mF/4o8b+aH8v0e+w+SH3zEmD CtQ4PHX1EvMOoLDZcmcGzaGGbZLfxRvZQCli+verxRY1Thz0dV82RYLC0Z06 39dQwznL53xfBBKq5nQ2XnOhhusx73N2MlR8M2P/efM/1PBg8HL5rwaJ+0pF o0adUcPvrKdTyTYkVvI63PI5jxqBXJdOK79E4lyuQPbvRNQI+erhmGaAJPEM d5vsg6gR5n3Rga8HSYe653/b1aJGpPgF+wfBSPLRGhPfr40a93+4n6AVRtLj Bwp8ZtT+PApxO+ZfjqS8ZivGgReoEa/ienTmBJLqehtPb3ihRmLXeVuXNSS1 F4x5FsmiRsq9czYdT5E0vGuSvO8taqTrnbU+rIOkKTc2vvVo1Mgadzlc04qk Gb3HLaR7qPE2wdlSzxdJE+8+jzV7oEbBgTPm+VxI6q8b9T14FDWKV5wOyRQg 6fddp3vXlVGjIuO0WZIVkj6TIj/dfYAan21PmbLPIum1XmlA/DXUqHX7Ln7h L5Iiwqttg6n5NAbDGhVHSac73F2o91Xjx6O3P0UzkaR57IrgN2o//mSKvPGP RhKN7G9Tg5eo0VH+4Oavq0j8duX2zaxU1Oj5TrFXPoXE+57OTEGjqDG22s3c p4pEWjN3TYIWasywmA/q8SKhJI390D9L1FgUr6yIIyDB9dkV2dG7qEExS/Y4 0AyUAHqh8N/mqMmRdLI5zwM2AkL3SVDfX03evP8yt9vCeu5C14XpZtQUqtEP cdkDa11FnmWhtKgpMy2kxrcDVrW2B/ZPKKCmHnbGhGTD4l9v2/xPnqi598hB t/ZYWCQ1FZuUPUJNE9dyQ41AWFCxVezOp8azjHk2P2IGcw+//ezxfIaazgMn jliMwbTI4w7GWwmo6bryTel1C0xWcnxTsRhEzUs7dtPRlMKE+8saYbUN1PQR y+o6mQrjcu65UiW9qHlNi7+w6A6MroTW6VIGUDPINPIBmyeMtM18fREdj5o3 HdbOudvBcFP274+Zu1EzwtsNqhGGWhN/9zJNo+a98HZeEVkYXIwNzBbWRs2Y xAMz11hhUM7mFfNjR9R88q6krmUFBrwj0xSJDaiZWC2XotgD/b+a87no81Az pe3ptdt10H8o0/n2P0bUTJ9iPNzzDvq6buldkgxEzWzSNfld8VS8qzC5pLML NfO4R0mxIdB34ElT1fM51CxUsGufOgd9kgaypQ0MqFkGde/3m0Mfz69I+b3U /KtsdO6malH3LdeexJ5CzerzmWc2hKnnX9reWX6Bmg3XefVs6aj+U85+jBFD zeaHEZxvp6jxt283VDJDzV+vlicZ/0D/wUh/zW0qqNlWeq76TCX0txTEMN2K Rc2uptakinQY8GTcs6xYgZr9/ft8ee7DoMT8vIhFBmqOLBeZX/aFwclGNclQ OtScYpaR/uYAQ41/ZCN/Up//vGgcRcoYhqsMz7j8p4eaK5p0rcGKMFKrfiv7 CbWezQO+b9s4YbTnA3fY7S3UoveyPRU1ABP7bP02V7+iFrOquOKYB0wGVEv/ Vm9HLdbJydV9yzBVnmahVbqEWvyuYTE0TDDztr1mTx4RtZROFXy5pgrztyvP h3WYoBZZOOTBn1JYMGIQO95RiVpanQftNQxhkUa9JFYhH7XwaP/CtC0s3eo4 K3rHALWsLTikXa7DahjdHq/6MtSyY+6a+0IHa3u3XVG+5Y9a9g1ZH0WjYW0N Ln7Y54FaZ00MjlLxesNJg0Ei5RBquW9jkdRVhs2dbbZDpsGodelT20xcEWwW P3pSt2iHWtf0L9853ABbq/tF5frGUOv6uv6Rt1Q9Gm0eMvjzPWqFljCKM/9D Auve06I9+1Ar3PfXlNtZJNiUM0yVXUKtKI0XpbUzSHh8ppzfrAi1oucuhEv5 I6GFgSNZPQe1Hr/Tsb5Jg0SmgL5tJx1RK8Fjm0hPFBL3PD7y3GcZtZ4rNE/s 4abySWVe1lWqv5ejz4qfvaDyRwl3mlR31MrMOHdrVQ6JH20NtvH0o1aOi/ph 23wk9rw55mxqilr54hShAj0kbm435eUtQa3CnoYxthokcTolBJ62RK2y5LjC S5ZIkk75tsEqiFpV9k6hje1IUq9wuWNAta/mV7aQP4Ok3Tlr/WK7UKu+dU0g fAJJezxVXz6LQa2mx9XDg1R81dtWwd44iFo/rR8WGBKQpOHpUnVcH7X+sp0M eRGJJJmy1cGCAtTqbJY7uMWJJK6FA3GFX1CrN2qR70QSEinyfHXU+aY1ZFo1 WCKNxH4vLt1VDtQaZ4jK536HxM8DpfuP0aPWdI1dkM8uJCYlG3+534laC7ck TX98QaJ3ZULzIhtqre6d4VE5iERjV5kJCTHU2qSU9UdR+TL713pVV3bUJlWE vx37//f09YyeglbUpg+wDtw3ioSU3Tzuz6+gNuvSOBdpAwmiT4aCb0qjNldB Ye+p20Ap1KuempJHbX6v0JzKnUCR3MpkJ7WjtsQUv8k1cdicDfjA1DuN2lqD B/ymD8C6jWq5+qX9qK3HcFtYTRDWHjex2H3aidp7FT5/9ZqC1V9tVynzrKht 5qXHuhQLK+YMhT8Dz6O2I0X59WYXLGKUTFVlDWq7iLtbwjtYuGGW6vW5F7Xd jDOWbtyE+WrWT+70O1DbJ0rMkFYW5uyslmpYKah97e3JUZM1mH3NJ5QdXoza QS0JDyL+gxnKlXYLoj5q3+Hj6GT2gumgx7rNgsaofV/PMtTcCKasy58+e1mP 2o8c78lG88CkzvXaUw3rqB0fWt/0YwwmlJqrWX5IonZSOt0VjnIY13hquq6X hdqp9YYCR+5T+e7blYbdV1E7YyL405PTMHqtNa47mQ21c3aWn2vTgJGST67b ivegdj55lUWADkZYtsd0T11D7SJbzQL7vzDsh/fr8+xQu/ya1/HkNzC0+Kfo Gs8b1P6U+JbQEwRDkSoDMxaaqF3zcTxD3BKGNJ4KblXOova3ftlDzuJUvDc9 mshJre8HnfP8q0UYbDx79GCML2r/kUt5OlwHgyVrj9/M0aN2x8EulEuAwQqK keyWJWr3ePIPuV+Awb9HT42sL6L24KOjUTkAQwzfuwsbg1B7rDCWPM0OQxaS Q031h1F7uu17m+oADGWtiBg/8EHthc0dwV6FMCzQwfvhEjdqr4maShXcgeEU 04+NjGTUphje/rZkDyO73g3DXzbU2Xb2i5eOMowMO/elcc+jDmMkgdefAKPp 2eSQnL2ow5KjV1n2E8b8yvbSxJ5BHd75wu1wFSbM8nIuZpmjjjD3XN4NM5g8 +H78uf0u1JHYpXL0izBMnfgQqdGXizpKIZlpJl9gxiJnKPe2M+qQXw6aRsTB zEhHurJgC+po14rNNJyH2dufWK71X0Ydgx3P9M1ZYK5Jd29V0EfUsU249+eI HSxem+y1eCKJOicqGgKfyMOSDMO5L0eeo86pXnrxv5uw9PPdmwGNx6jjLhNy yT4VVuR2RLpIfkKd4AJvhjMTsJYvW94R8wd1bn2sMEqegvV9Mm76SjtR5843 +pC2aVj/szLZXEq1f9T7bNViDjYGdySzW91CnfiJIc3Iedh0sqeNOE31n7Si erl6ATb/xj62qRVHnUyWr2O7l2ErV1Lmwx8J1Mnh3yntuwIU5opqrwV61MmX OuaUtwYUp1rXNw7UfhepvkyeWAfK26UL/8yo9ZTvnuyQ2QDKAkNmTAoD6nza p8PjtIUEHic7BXtq/2usQq2TKEhQ2STu7qXm9+1k44M2Kt8yrIrXqLZAne+u 3I2cJCRYJVGsr3eizm+f0wwW25BgH1mxvuiGOu3Bb4wiaZFw+gbDNtUc1OmO XAyppqOug6teXvZBnYE4qCAw/N/+Gs3DR6gzmnJndTcT1d9rN7+TI6gzlf1L 03c7Nd7oBfuPwagzXyx8OY8ZCaonBj+EfEWdlS/ncyZYkMDLXRrm2YQ6m035 YzKsQFk678Ym8Ap1SW2b0k5sQHn/6vfbc8yoSz+4zymJHSjnFfQU2dhQl3nm YfJfTqBwmmhG8o+iLtt6RwcHF2wVHxT9dPEr6vLQS/OYc8OWzdPm799JqCsm VPrgKx9s+qW2XVcfR11puW3fKPywQdGt0RBUQl0FDQuGXYKwEdJUc35/Nupq mvaHvBOGde9RuqzMeNQ9cJX5cqIkrMo+CrYbPI+65jdtc1qlYeV+Ot3AHR3U tb7/YoxdBpbn6ZuOMK6i7slXmk4R1PtWwvr1gcN71L3c4mDtowoLpq/9Msvd UNf3X+aDt2SYf5/AShdngboBI3PfxtRhXiSIwPH4CeqGUcKNTmnBHOlQx9Gy cNS9y/Qj5Jk2zPrvM9dlrEPdaG6B8j86MLNsruBJfoe6CYrvNA/uhhmSu7xr 1lvUfa69djlcD6bF/zv6zp4PddMMjXI+74Epc28S/XN91H1tfn90E2AyIuqj in8V6uYea5PWQZj48aMj6dIK6r53kXDyNoAJ+bH2tQ8TqFvseTE51xDGYz07 zZ5S41UEFLWPGsE4yyRziXIn6n4OJ/FImsBYwl+5YKEp1K3dnbnPaRTG9ryo NaQtRN1vM+Z+KVEwOqc1aHOM2v/vaQsZPaow+uEXy+7We6j7+1jCX+Gf8D+K rjwc6u8Lz4xUJEUpRQolabOXbc6ZfQZFhSRRIkJCpL5UkuyJlK1SKtlapKKy ppIlRVJSZDf2fd9+n9+f95nrnnPe8973vufBM53Bjw45SexCrTpR2uIjZ6Hz VDHvzpadqNVQ1LH7zlrotNHPOsKaQ60Wr2sOdXnQaa9e+FvXALX429Ti1hyF Tp/z1a0ZnqjV0/i71FwAOh+Qn+99Moxag7cuTUY/hs5fjy86riLwHtNX2FLD g6516pO5jr9Ra2rui8WKHuhyd456vZ3gz/xL95D916Hr53wG1/EBai9wkHwX qQrd+lEdet2rUVtIOr/r20/o/lJkPLdYF7WXVtlJiZ6HniOr6+/4e6C2eMAS gz3S0DMt8fp3KBu1V2u/8A4thN7HKkklTZKoveHh7F+hhdAva9t3FFRQe5P5 IxFOKvQPqBm9VaehttJSfd0AQxiw/Xz22rgraqudjb4rcAMGDb0/XJ/PQu3d W3Ur6BowWPyuYnGQImrr/mue9a2FIWam8tApR9Rm83YemVsPw/qrvikGnUPt Q1IlMhPEPORiuIjf+Ba1j1S6GO0ygtHJKb6g3AnUtrm68pLnEIxdLVXL+c8U tZ36jv4b1oLx2+0LDb9JoPbFwsn7vcUw+Vqde1qbyPeK572qbSdhSn3Ub8el eNQOUmKTnURg6sWudVOhyagdGXXjGH8/TN+9ychdKIPa0dzdNxRGYUak9ZlY NxEvfqahyC4WZrzWt152DUPtRye2yjX/g1ntnq+sBbaonbq26oCsH8xG1Wz1 bLiC2k+/eV05qgCz7Waa2281oHam/7qXCaUwp6oo+bC+F7Wzd39oqXeGOa/F EQpD5aid0+u4UnoZzL3K2di5/AxqFz5YzrTIhLluoSPLu5ai9iezLI84U5hf U9TVPPMGtcuWWD76Regv5tXU3ziJ2l8LKTWrbsO89c+EJOt21K72SBU0pcL8 2YEjj/50oHbtFiONqGaYvzryl/l2DrX/1o/afb8K82E/UlL2u6B2043b0WKK MB8aKF+v9h612zm0YqMvMO8nHOIWp4DaXdMdY+GnYd71+Ljjkj7U7s8IV6gg 9M3s3rGohQQ/RsqSlPvnYF4lzv2buzlqT7bm6oh3wbwADO56koTac3PVLI2f MFeeJ+yubog6CyS7jMyLYC6UKaDvdAV1hNTIh7yfwRxdbLFNajLqiO6RPJ4Q D7MDDmPMg0zUWWG/89T7AJiNjltD2ViJOpKX2V6tZ2BWfawl5XIg6qy7fcRv 0VGYKf93r3CvDerIvfYIUzKEmcNvJHZaz6DOts4H9103wrR936Ve282ooyrw Ni1qOUy17ZoPjhRAnV3rKl+9noGpIxzn8R+HUYe2b65k+gdMQsXFxc5VqMN2 lqiWKYSJdIsgnfFzqGMQsK2e9gQmVvym9ys2oY7pO4vBwKsw1kCK31IagTqO ctlrV2jCiP37SidvCuqc1v26UVMOhsvDROQny1DHw6xtxyFRGFaR9PkkWYQ6 F0NX0BPaYUig4aPohx7UuZKkZPj+Owy6s6OlRAZRJ6iAZtaaDwOt5S6ai1NQ 58bwaSelGOjPKNn2q6McdWKXBnju8Ye+12JvF5wMRZ27m+9ecnWF3k+/xC2e /kWdB7RXwVGW0NMqGhlrTsRLPlwelcWFnuUPlELqClDniWfzXcIvdhu6+D/c sRN1XlyfTJ7ZAF2x9PX8/1iok5W2PHO9CHSO/A2UkfiCOjkfN+fSJgh9673e tuAk6hQ2UIttW4HfsjL15sIE1Pk0YVoZWAl8z0adM8Q8qFMu7lyXlgt8qQrx GbUNqFO57UprRSp01LCaU4i5SaeGHd83EA0dDx5U/eqgok7d0ReTK/ygw3+L zK4GN9T591+pgKYLdPwn/E3E/R/qtN5sXHrIgvj8xH9PVRpRp/PZ+GofNnQ8 5Mi4Fi1Dnb5SUdl7qtDx85GQ+qs/qDPcsmlrkQzwZY7W3tHbijoTs7oabcLA /29PsOhANerMrT6Ai8aA36UQe5rUj7oCKo76Ss2Ent/Scb77GXUXG1w22fMV uhbsTNKP3YW6S+1irVxzoOvp+WM/f9qjrvil5w5RKdDt0PKe0xWOuqvjit2z bkKPxlfT+2t1UFe2YiRgxhn6yHskXC5Loq4Cf0nkenPom9tDcY/uRN1tFLnb dCYMLKgWvnQxFHU1NY2fB0nDoPwXwYDDT1BXe/LD02dpMPiiMsn7kATqQp5m es1uQj8Pa2z6YoW6PKZ0ipwJDHtuDUswskNdy338+7lhMDoh22xyl6jnmMTh hJa1MHanQbewSxp17Wq/3hVKgXH6lpPmuc2oe9r6VZzZR5gIM7qnYKuKun6n fCMHpmF6uQQka39F3UDl4YjVQTCdFvLt1JHjqBs6ciKcugpmaMe7XePNUPeW t2FIqCrhHw249rn7UTeeWhD0ohBmu0/k5amuRN17FNWA2r0wd5o0vCZEF3VT giWvbHKE+ePjXgJUAq+nhqGXDQg9++F35m1hB+pmLpu75B6AJJlRmfZON9TN qna7ELcSSUad72KmtFA3J7rVu+ABknxG9njHPEPdQouD59uVkZTUu92qjsD7 07qycyL5SCp73hESfRR1S5v0zqoZIqlb6qjzOT3U/foow+NQHZIXiQ3c1DJG 3WoHeXdfByTLnL/YA2WoW7s12vXxGJJVdGJXK7uj7t9+IZcKfyQDy69Bn8i3 KdPHeUQcydygfQkfilG33bPfae19JBtODDwuEUHdbi2bk7QdxDpCxVP3Pur2 z9TY2+cimaP/varbD3VHCrl24fpI1ttyNcvgMepOXsk5/qoWyTuU+8kZDag7 x9lx7M8JJK89mTTGW4d6AsKJR8kjSCY37rzY9QP1Fn1daaXoh6TWR2NoVoJ6 IpGBlkbLkVT0Bby3nUQ9MZMpC88EJN1xkS4+aYJ6qyRPmd/ZhiS3xxZPGq+h 3to/jWZFb5FEv+msnSiGeuvvHTDpZCNp2eEDwZ/eod5Gm+IDy37AfAZD9L2e Kept73xiZDkAc7lPghuc01FP9en6PVcuwtxuRr0HZwz1drneMEgTgdmne97d DXRGPRw/xxlXhJmrzn+3FRPxWe+62euyYLrToajGugb19C9aMZlMmOY+LHr9 Jgr1TASZGHkUJqd+RJa//Yl69iuWa22NhXHd8g+diStQzyk/osLAFMbCvt9O PGiLeq6OYsecxGH071FFRdVPqHf+vXhwWiiM+KgIsAx2o17Y6ZV1Wy7CYEW4 h/v9YNSLXHvLhacDg4rJv/98y0C9W8WrKA4TMBDo/eG+sgPq3Vu3WinFDfoe xfU5a+ig3sOSmPySHdB78eKv/UpTqJfiIbm/g5jHj6cPC5cvQL0X5Wu8N9tC 1wFrjltCLeplnY1fxpGFTuvry7xFAPVy5NY+PNFA6It5ra3Tb9QrqLi9KyAe Oh55fT2oloR6H89LlT8+CO0Nt8vKb59CvdKNd6yLV0L7FofPf2+moF5FpfRw WyW0XT1rGR4UhHrfve8GCV6D1mEjutvDO6j3a7OM9CZ9aD3j2kben4t6f74n ZLAWQetC/WkpqfWo13hxPdP2I7Q8AaV/dBnUa9tyr9b/MrTY3X/Dyn6Dep01 G5wfUaFF9RU19VIm6vX5JpI+TEPLiuee/ie6UW94m+zNljfQsvgtKUxzKeqN /3qwRcADWsT4Boe+6qPezBW5PHkVaNnJcn2b7YdU8o6H++h90HK0dHvu4xSk CtbJt9mkQctjy8ZbbluQKnz10Xk/e2iZr4viFh1A6jKVTUsfbITWU0vFtxSJ IXXF36TE903Q2pshKpH7BqmSQQoaTQnQdtlV/2B+H1LXqT0uIx+G9s3dP1eK uSFV9t9mK1lJaG/M3Cd+7TZSFUKSh7AGOtJZOq/745G6VVMx4OgN4AdK7mu7 /QOp6te2PLu/BLpclwSseZuPVK3dafSCEuj2/r4kkHhPqHqtSj//+UNPtELw xi8NSOXobJ2XmYO+YdbOT8ZmSDVofxJFzYGBtdsdv3/kItX4xvbNVudgoNDg 0LYlHKQe6txhdHcQhqTMJ+sEu5HqHKN8X7oVRuZihP7ziEJq5LAGbW0ZTJPO sP7dy0VqzAKF5zKVMF0u5TN6ncDrjoTkOrmfMHNjU2Xe0EakPtacmlRqgjnx pPaHU9eQms7psd/RDnNln59mHRtFaoZ5fY1qD8z7PKpwPN2M1Hf/Fb7QnkCS 3fVPB2wvILUgNHM9lZivX/IqDEockfrxzsNrDGJennnzX8B/5UgtfXpzmiOM ZFr8ThvjS0j9mh9w0mAZki+/oI5E3UBq9TevX0YrkZz7cZPkb0uk1jaeZB1Y i+Shu+++y65Gav2gxcuDG5Aiv/RExcHfSG2hGMoeVkDKng5ebU0eUvkr9K5b b0PKmQX2ikLZSO3duGP2uCpSbtBLqAfOI3VIY4OT/W6kpF+9Yz2YiNRxtthv Jz2k5L8+4DS6EKkzBwU4p+lIKS94tVB/HoFsP/L6DBcp32N6Zrc+QVh4rl3e ay9SqjUTfwdIICwJ/hXpbYKUirih8KMPEJbHl8xfskDK+08XIWwPgkT6u1NX jiLledEx6XPnEdbmpv8JPIGUmHvbje2DEdZX3OWFOiHl/Ol/S7OXImxsCM++ 7ooU0z3l+RYbEbb0+26KOouUrQfDHJYsR9hJcouK8UHydEbLQdGbCOpix8m3 /ZBc7HdXIf0ggpacyel7QUgOnTpFTytFoKqx6h+GE++BfjHZ2AiBa6r4Nj0O SfmmQdr6mxH2nFi7+Tmh164h/RJrNBD2ey259fIR8f6Fn5J59xfBMrbPLScD 5how1KdLHeFY6r9/BVkwtzE8P1qjEeHEu6o9H3Jh1vbP0ubylQiuf19tKf8M 0z+XvR2eyUa4uuF8058GYv6nTnyUj0IIUXEy+tcK49mpCWueXEK4TrfMa+mC scbnr1TbbiHE2UJc9yiMql9kP9wsh/A0RXDfjAgMjl+O2OMzjJD5ZqyQJA6D quuoZ3JCELJL+TsWSMKAu5CsXukKhPfd5cIi8tC3uHbF6LAKQvFM7rnlitCT wZK9+n09wpelT9tX7oBu+722nm/XIfzcGVEkrQ2dix+K5C4gI9R1NmyUbwb+ wq/MN1L+CP8ebQ3YGgwdq6QyQlQpCK1W5/hqytC+ixa4PGw3Qqdksb52LbQ5 8vXSdL4g9FaveEL3hdYXaQtvHCT6PXTtmKi+IrSKiFtYMC4gjHOeue6rhJb/ 7ob9O2GMMEOe/n7IC5pn37gsu0tGJFltjFe+Ds23CsaKvcQRKTkGBurHoZlB pggUyCAKSrrN7N4FzYJvxDPXLENc5Bn7VHcJNDWIKSglCiIKfS+wggZo+rI5 KvaUC6LIzvZljExoqhScMMniIYpeEynkBEBT52MVfeV3iMu71NwMLKBZcvDY 8V5LxBUcCzmjHdB8pFCQ/BkRJR75Vh+gQHN2nbiNWi+iJDnZ3+wntCiMK3Ss LEFca/VVwyIVWtLy34uJdSNK54y0W/lAK70kUGCxAOL6NVIxNsbQ2ncnzlSr DFH2LJ17YiO0PX05OGa0H1G+2mHy5AS0Xzr39eura4gKytfTTn2BjuOczVXP jBAVr2UddrsPfPNTyjJBRD3buQJ55znQdSY7qofZirgzScnlwlrojt0df3hk A6IqZd963z7oKSvv+/okDlEzN+FyUDT0mznUtlfJIWqt+aQa5ggDZnkLSG6Z iDpnu1siqDC46KDsTveTRMEqWqyYNhjyvL0o+iOx5CX9EEpWh9Gz78z+xZ1C NKRMvUtfDGM6rBxuBtGvvUdlnZ79hXHSIZ+Uv6KIB9a6VGRdgYng/EWiZBVE y+tCkZ++w3SwlFegvBCiVY8yvfQx4V/DuxwHaYjHeGbDFf/BzOig7AeZxYgn BB6Z1sjCnPGx0aRDiogOR8sX1o7A3Kijobu5JqJj3mD23xKYv2maVBHpi3j6 HKxpdSVoM9tyxbUQ0a3GrozPRNLD7uiCfltED9Uw7x5Jwq91F6xZthrx7PWX 2/p7kCxbMXrtrifiuZ7f9cMFSLawE8t10Ef01ieFj99AcnigIUVmCvFC8maY JvxgvqhlZ/wsou+CPQPz2kjm/4r76RKN6HfM44GAKFJEP4pWfwtC9B88UDC5 ECmqtM28/US/Ay+r1vfNIcWo7HsCQTcMERObbh1Dij1v7dFSIr+w+4Nr6vqR ci71etDey4jXlSt3VfKRcuXfIYbRKsTIwuemxY1ICWqh3lL5jnjTOPxMbi1S Al+mFVsWI0Y3norMrESKr8mT+5FEvDhXw+cpJUhx/zbwn0IN4h3S1oqEQqRY q79eLUT0LyFCuPvmG6SwYnKc2NKIieu7hEJeIEVhhYpRqDDiw+elm33TkEIq jtEtf4D4GFJYZx8gueZnomLRSsSUb4HHneORnHRKPPH6csR06xOXbQi8Tr+J NlhzFvFpP+ueeQiS1Xsi2sS5iBmXNubtJfzyKA4sSBhEfJ3QPKl9BkkOMLXC 6B/imx1Fq5WdkSR1erfXrf8Q3+UnaijYwvyt60Ix7hWIBQ3WbuImMEdly94R j0QscqFeX2wIs8GhqkE/iH58nFv3ZI4JM98SDr4N/oVYtu4Pv0sDpvc2Ul/m 6CD+OGJ+tGgVTGw/FCPbTTTgZ++ui29EYdzmjPC+sEbE3xdW3Xm2EMZipthd jkQ9DXd+1MaNw+iiba1/jIj71bTt5dj1ARih03S+FEUhtuRGrbzKh2HfqOki eQJP/t99xm61MCSURI9PIerpdlZ2sa+EQdMXxkbXfiL2ziwLO1ICA8m3HhVm ziAOS1WU8N5CX35YzKTlMcTR9CftkAm9Ycdv9x/mIE7ohC3QSIMeR7tWbzUt xNnD+igbD10WH7yKbUiI891brFbfgE6XRt3DsZZIo3gv9lkaAvzo1HSN2jKk CQp3xAsQ8/v373NTl5YjbVF8cfakN3TIz6xU0dyKNGGlpJp+D2gPyfjXMMNE msg7/+E2Z2hf5DAxRMSnLdO3FftjC23xgep8z3qkidUxdlZZQhvzkDnJXhlp Kx3l9nw2gTbB1b+RkDfaqimyU54htP5te/efTQLS1gQ3Br9kQmt5pYfn906k Sa8pSE7Vg9aqb+M6jV5Ik0lN+HRPk9DD4vxQuIM0Wa0LLbd2QJvc5SljXymk yZceIYcqQJvz27LxFy1IUziku/6yDLRViEWaRXGRplgvt/jMM2jHgrwFAyNI 23ps0eAJKrSX/AxLXpSNtO2tvXXmX6HDttjFgK+JNGWH6g8GR4C/os9SuUcI aao9b5/o9QC/5s65O4S+0TRc791S9oHOVNE3KcnEfu1zTvYSd6D7cteU/rwL 0nRn9hkv3gY9VwNK7T4SeIHvrt1TOdAbYygceGgeacwgAeF/ddDftPUa/CxC 2t6Y+Gdpa2DIy1u98X0g0vZJ+cbcSYFhufNizzddQNqBeyd8r++G4W+DAu1P FZBmnqy638McRneWmniopCPNJqt0jBoN44PGB0q6iP7ZaT//p6IAEwm2AoLi L5Fmn3+rRD4LJrkdLrHtKUhzLj4Wv7gGpqJ++vw5JY600/ocv2lbmFZbHeqt SODt9nW7U+8ITFd4aJ9MJ/I7WzOpV70SZgaPPCvn9SDt/KF/Cp8ewez59sls mifSvOs/LXujBrPTV3/u3ZCMtIvH0ibSimDOs8SFndqINN+2iKa7+2GOz6To vs9B2pWTZ8uuN8P8/luczT/zkHa11/KlnzvMvzqaW3VqHGlBbvQ7nhQkLUy9 eOnjfqSFjCpetY9E0o6IywFP3yDt2nlRF4sNSNr3UeM3n8D7+szIQcMMwo9t /mlHS0TaDd86BMIWBD+Uc3gYgbRbCwq3qFQiKUHOTWTMHGkxQY/FNx5F0tPo wjflRH/iRUKnV/UjKXu0SNLBFWl3ItxahS4iKU+roTKf4NO9lQe/zIgSaxud 2i/VSEuM1XvdR/i/N+7Z57rvIe2RtHxC0w4kPTvTPOx/FmmP7wsFVucj6Z77 eEPCEaSlyve7Fu9BUujlUq2UVKSlJ9ccevMXSe5ZGXtLbyPt2dYcejqhZ6bb PN2/nkRaxvPErXdnkKRGUX93l4+0l2qBKyPCkCRymFqYa4q019nOs1ekYb4o r1t1QQbS3ujsb/dMh3lbN0NFzl+k5TFk3liUwVzEYa9UywCkFXxekLjHAuak jgcw9fWQVqTfFQJdMHvvLTt59Sukfd6fZblJCGai1SOTeINIK03XhzWOMCM+ Xf87dw/Svgg0yC0l5qHQpP3PjVYgrfK1YMdYGEy524Yl/ziGtLrVpmdKRGFC zle5PPA70v6e7jTLdYHx0G3mr5C4Xw0lF7QyvsHYsNeHnd5EvJbzj+bjImD0 4x9Tr20FSOv5MxLqJA7D59RSvPqIn+9XD3Kxdoehbg1zv3N2SBu8Jr3vQDUM Hbs571dagbQxKnO17k0YtJCPS/m1HWkT0b+mlEdhoH5jOOMmwYepfqf6TWYw YKfw4/eNL0ibvx/1UHQ19C8x3KsXsA3p5MnNAZRz0KeXdF9CwgTpC/blOIzV Qq9P/sjx5D9IX5hmZNCtBT2l2/srwmOQLkRp2fEvHnrkfcbyfiogfclhL7Hq KegOUyrsvOiP9KWvhEdKDkO3QOqY8wVZpC8XSfiVmwtdIUd/fl3mgHRxO9V3 L6Sha0NKl0rdWaSvzCu+m0ToR/HX3OH1l5G+epWFb1w9dF4QS2po+I70NS59 x8Op0Ml4GKO1UwzpUp/92H4J0Lk2ZeqFeTzSZTas2nJ2Djopu2Otz/xG+oZz aSJO1sCfOhxUb/MB6XJVev3WhdApsFTVf0kc0jdtqfpusgE6pXX2fSkMRvpm P7vXXF/oZBWLVJTRkb6lbjJWtwk6L/mKFUqFIH2b2jVvFTp0lu4oPLPPHOk7 wmStNj2ALrkQq7Cj9khXbn1NWysAXaFUM5t73khX0+NtFLWFbkqnL9u0FOka t+oXUT5Cd7CM5/AjFaTv6nPtHN8EPeuO9/c4PEC6NmfBl+4A6CnQ2Ob0lahX 917s83/t0OtCsU6IW450mnGhZ0kK9A2s21xl54N0RqqJed5i6C+StNbYaod0 Npmv8+IkDBhpjCUceo90/ZfLyfFKMKgV+ljicSjS9yx52BoeAoOvegVLS4eR bmS767NfNwyptW9+8igA6SYS1tecnsCwNv+to+xepB/xer5GbweMHn09f1rk OdKPUqZPyr+E0Qnajy/ct0i3Cee8Fd4NY9fzvUZ8MpBu/6jR/BcdxnO3r8he vwDpjsrbUvI+w4TR8ILmXgbSnXPOTTwyhImm/b+yHTqQ7vZdLNbNDCan7hkJ fLiGdI8jR/gH/8DU5TMJjgYbkX6Wn7abehSmBTLVX/zrQ7r3PL12iSNMTzgf 8xauQvrFkOubB/th5uSIZjmbhfTLEn+8aj1g5sd8rs/cUaQHbPNYnXQJZm/N Dp1gEPGDsgvtwxbAbLey7IhfFNJDGSLZ7iEwt9vu3fyQDNKvfT206NAymLuw L6AmNh3pEYeSDsJNmHv77D//CSGk32gdTFZYA3O9MNnynuDLLVe9cZEEmF/9 XdxDYQzpMdMh7GF5mNfSejp6YQnS4wN+Rv9Ogfl9JrkuVa1Ivysu1164HeaP CpuGqrxE+r27pzUfZ8K8nX7V5hsEfx4o5gRc2wXzx5b9nMpVR/qjV4t+nsmF +QOWdpzxJqQng4mCBQ3mdXW/3vq6G+mpZffPYjHMSxU/c0yfR/oT057izQYw NyRZs+aTHNKfNWmtWloJcwWHlV4uW4n0F85X7UZMYe5KfrBKNIHvy/Gq13V1 MAc+4Us+CiM964qM4HtrmB36dXyfNdG/t6KOpsmtMJuwxjd0OXFfcuKyksJP wizt1vGYtSlIz99EGfXog5n6JFqxbzHSP+jcvkWbIMZQUcN/yUlI/1Tc0aZ4 gZhXYI+uA4FHyX51jWUUmF7yckP6jDjSKxy+/PgrAlNkU7NTy/OQ/uvW3Mqz sjCeryyi2XoG6XWy+raWj2FcYfyQ9RJi/fdJ9CvGNhgLP2Dy0Wwn0ps+7DRZ rgmjhzfr3X9E5Nc9cDQqTR+GZrtIgbUGSO/zftIS8Q2GnIai94gR+QwunFTz MoHBevaJb0u2In1sXWQ10woGvgiTTwkTeE6kNMgrtcCA/rXHZpIfkT6trnRG zAH67y48sunYMmSQ9D+IN7hDb9VpxSt9b5FBScuzLC6B3qXMJ1pZWcgQFM5+ /FwGekzzC/jme5GxyPHFQIwHdKdfiW/+N44MobIn2pfKoHvZDv+z91qRIaL0 2N9hA3RdNtoT0CCCDNGQ+1+Nz0IX5WR1hJYdMpZ33Zbc/QU6b4xeu+BNnL9C /5aNrBx0qh2Re+5DnC+Rdv2J0Dngt8yJRDrfQIakcPDo4FfgJykyhHd7ImOt oz/UbQS+17oR8RJDZEiXXQwu+g/4FivYzyuckLFe6Vx1WiXw9+7Y4n9iBBmy IWfWRSkA/0DA9i2uesiQ7zpl7+0DfMeNjk6x1chQ0Ld/cbwK+DcEPIIe8ZGh mHZs2pDYX77s4H8mh5GxVdiSpU7ot8SmBweu1iJju6PZdenv0OkqcSFNuw0Z O8uMfwsqQuefexXzuxKRoapkINd7EboOetkIi/kiQz2E5VzzA7paaNuKs0qQ odkFWflK0H3xZujq96rI0NLXJj32hR7F6ZufTfYjQydNXT/8J/Q0DZ973C6B DKrwjpte26A3pafl6spmZNDL5BU5tdBv5aR0uuASMlhK69yVd8DApnK/psIh ZHBCVudK+sPAA6E8x6vLkWGoL2LUqQyDqb0Rs5eI/uxNWxhXFQBD6gUyf2si kbFPmNTy9i8MfYi8ThE7hwzT0lGvkCAY7j5R7JRH4GHNa7i3tRHGzNxFFsqY IeNYam3nSk0YG1notsPhIzJshapVZ8JgPPJNIPejCzIcSj8XV+yGiXKn9a9L O5Dhzsvoc4mA6S0/4z7cIuJ7pKbvNmuH6XdbhftziLWXUJIf6MIM+94O8ccv kOFdGr9qGR9m9y9NP/FJFxkXt9y0nqDCbOXGzRM/TiHDNzg8tZHQL26URf3n lci4yrui9wJhXqGpUk0iGBmBqRcC46JhPoy0Tc2H4EuIkFfV5R6Y7xyT+NZT ioywk+5SjnQkbdh9IPbJBWSElzrb7Y9FEifSM7hsGzIit5x4rt2HJMeWZek5 BJ5RwUcn5ZlIChGZNLL/goxbnYcZS+KR9GjIymqgDBmxPNOwYcJP5pyv7WQ9 QUb8XbPEWsLvVej5Fw7eR8adwYNZeWVIqku5qeBujYx7LPPyB9lIanm6mfmS 6F9i3KHGwEdI4mMFJccAGQ97LUYJ3pP4lte2+E8i4zHNUnjfJWL/TP8Pv6XI SLl1ZL0m4R//yFE2DXxFRlqnlbqUBRHvm7m2uRcynupZ80hcJOWKiJgn7kLG 88ijR9o0kJTUnGNl/x8yXrQdcy+TI/zpsYuOB3yQ8UrLJvD5ciQ5xzWVnPiB jKxrx+9EzSKJd/+3wsF5ZLxpsn1xrgtJcuEvJDWkkZGjYVd8pBbm+2an+7nG yMgLPvGH/gnmI2WXm5jVIaOg3n5gM6H/W8uc3nYrI6NI5aSgyH1Crxmt11YQ 5xf/dtrx8z+Yrep23WA4jYzS7c6MHAeY3VdqYx7viIzyy6fM75vCTPnPp2Vf CX5Wbjnt56gM0xkS36P9fyHj+wXXGCMZmJZa16K+l8DjR5XbE/UlMOVHXnNh EaE3v8+fqZlth0nWjqQvXd+Q0Vx6bkvkbRh7dicsx/w0MtrWnaeeDYIxwdor G5RzkNHh9t+Bw2dh1PL3iesmBB961vhc2GQMIyK7xw2niXpHT/pWvl0IgxdL Mz4/DEHGeN7ltrsjMNDsO2Fu5o+MKTG/Kb8mQl8X+7T3uSJj7u3VjYa50GdY u2/bbj9kkpcGaKmkQu9u28YF7ieRKXAscO+qaOjZuXticCgLmYuFgr0a3aCL LkJ5deozMoWPhIR9soJO6wSfvtXSyBR5EZqYZgj80IT90ZuvIHOZYFjWdS3o KM7pO73fC5lih66VeyhAh9idCOVH8chc8TS88dBKaHf+/Xn9hdfIXEW+Pkol Q9tv0qiXaCEyJU0jheX6oM3sRj2p6j0y16beWL/oL7S2C2hoB/ogU3o2Sq2n DFoD3xhYNUchc/2+m9yqbGjVFX6SEExBpmzSrSNZj6BVgFn3jVqHTPnJaPf/ fx9I/cMl38RMkKmwJybQ9xK0lO8frloVjkzFxNg7ds7Q8uVMfOH8PmQqjca9 0LeAliYJ3eatYsjczosv3smBViEtzWL9J8jcWWnCXWgOrfql/kfqIpCpenBp ab0jtD5cJ/iOtg6Z6vWfDV75QJvYlCDr3FVk7rL1rQgNh7YY8RMbB4j6tLq1 jWzuQ7uqwL+n1k3I1HUbrtLKhPZmcwOV3pXIpE48PbD8I3QkBwj0pY8hk3bp xI+On8D3HTDP+y2ETObCDWb5fOg8xV+2i+KCTHbY79pbU9B1Kn38s2UDMg3i Df8y10HPA70i65PNyNwru9BKagf01mhEnR/zR6ZxcsG/IYT+1UbCs6seINP0 tWrLfVuCL4bxC68eQaa5To+d11kYjH7vLeYyhUyLoqSOvUEwZHwZeIZVyLSu lOyaIfxqldXIGBeRebJrbsh8GMbG5GcDIiuR6ez2xkN5AYwXpbSf6Z5BpsuE 29iiVTBxXcY/8fhvZHoItk6+1oapTcsObnx7HJkXN5STxfxgVuXhUlrdOWT6 Jvv78W/CbJdR73CUPTKv7KAuKHgMc3cv1msptiIzSOfF4lNlMD/QGVHYJIrM kCLHECahZ5ypud6UbGRe420UkSL0MSrq9NqCAGTeMItZViaG5HV9yQ8eKiDz Zr1xZKIcki3VzyuJ7kBmjK3winPqSL4VtfifeSwy47o+3DJiI7n0p1Dxqg5k 3nG7sFrBHMkTFbcrpwm+JUxoxs46IkVu77n1MwQfEy8OrP3hgxS26prtX/SR +Ugw9U56OFJszRo++wci83HYcRm/+0jxub/9qdwoMlNXSN8/lImU8LGQPGui P+lxP+WUPyLlNudFlM0XZD7bcP3Rop9ISQx47DvIRGZGMm9TA59Yp3+obviJ zJc7KMmvp4j9r4oOFfxD5utXuVuuiSDl+qO8NYovkflGxzPdVgYpF/yVdcXl kPmuaOd2nZ1IsTs6vHVfHDLzuPzn//+1BGfv9n/2Ocgs+PZAuXM/UuRteae1 h5FZZHY4s9AWyZOFfzoTnJD5sV5CPeYskssCrod8nEPm5+Nfs1yCCLzqBW0G CT6XdgXtZsUj+fDAy8Cai8j8Oj6tM5yPpN+SVSJ6xH2rOmCT/SURSbduZzrx Y5BZ/axMLckfSXu5PjG3i5FZaxe39SAP5k8OiGxNbUFmU/Wute9+wEyuRetW ClF/646E6KhsmFnw7fLRmEfIbA9ZKO4cD9O8c3oaesT97MYaYRlrmCy99yNM jThv5Jn75OUuGHttkqI1OIHMcaE6T4sKGO23JpnKE/yZtKMNqmXAqJJO8YYD bGTOSS/nt3nC8KOPG7m2U8gieZ2zzTeHobbYWIXtR5FF+f6vMUYbhrZEGBQo DCFrUfCzWh4JBvI2Fvx+cwtZQm2rTGQJ/7r01+9zibHIEsEL36aKoe/zK2kx Aw9kLR83/PwkFHoYCZ9ubXdC1or9rxhXXaBbwq13YbwGsiSeSRVYGUPnaM5t jVwGstbadmUvXwUdbZLZmqwVyJIu3K/Kn4T20ff+W6nyyFov9fbZ+7/QLils qe2wDFmyXrJK8fnQZng3UMJpKbLkvwc9PpMIrTdXW7HNSpClsH1AztAfWvoF qyf4kshSDD6YsOkEtBzhL2iJMESWUmvBmjkeNDdtr3l8LBNZ21Hh1q9t0Hx+ 1+1euWFk7bwdLpaxDJoVTKtjA72QpTI2ei1oCJr4JY1b1M4iS33/EaFjP6Cp sLDuboIYsjSffryqnQ1Nz444dW0k8NAS2kZeEQ9NL56+Uvwqjiwd2yifHh9o Ksug3jSUQpZewdTEJ2tomjxAOfjZBVkoZeORQIdmvTOuqnOByKKfLR3w2gTN 0Q1hfM3FyGJ+V3E2XgwtgvskrS88RxZneyxfsQtagrzeH627iSxeMMmWXAGt 6/vt8lZ8QpZBq/2/ugxoLWdZvdxUhKy98O3wyxvQFjz+2+xbLrKMb2v+CvOE 9sNntV+a7kXW/rGEA3bm0AGy1y33PkGW6X7Bb1Qd4KvvWShxcx5ZB5+eMlgt A53aVE/VayuRZWmrSy9pgW5PA0ZqHxtZVgUP8xOLoSflVfXlxRPIOia1RPu/ FOjt9FsStGUWWXZVdarbXGCAk32NY/0eWQ7WU2vHPWFwsZOB3I4lyHLsWytQ 5AODX6PU7Iw6kOUqfKjGPBSGHbzGl9s2I+s/+u9z/ikwlm8+YPneFlk+lZPH jJ7DeHSsrUk30e9L1mv012bBhIvHalbdJLL8vQ9JZXyCqbVkTeuKIGSFv6zN /9sMswYNqpSMVciKpE0kJ3fCnIjzO+Pa88iKqpSMcB+AueK/S1rTCfxie8xt Fs8jaYWDka/+cmTd/u+c/o+F//97gfr1y92RlbA4Vu2eKJJeuqtv3vcbWQ83 1goSPoxMs1Co+PAMWUmZ430keST7thRjB1FPCm31z3IlJOcoJ23/tA1Zad92 FUSrInlAlH1waAZZT48cTDmmhZT1RlJSlR+R9bzbK3IboVfcsvQAYyJ+5vmY 8+NcpDgfefve4A2yXi/KtikyQkpw19FXHwj+ZN/6ZXDNjNBL8+3vC42R9U5+ XN38CFIyb2r4bstCVm7m6nXytkjJi5Q/5V+GrALcJdjnjJQiDvNifzuy3n81 63t7BimFr8LChZSQ9dHy7C///5Dy5m/0ncPTyCruii408kNKWsm9PgviPpWe y0pdG4yU6MCLAsbnkPVl4c/ItghC77df94i/gayvN8f+y4hByuEfyeUCu5FV Jb/quHcCUjRujx1p2Yms6heahuzHSBFK5pusI/D5CWYaYk+RXCsn2HLjDrJq K86u+/sKyYm7y/pHDiPrz+Hohcm5SLZb771Dj9CDRq+aWj3Cvzfgqd4pgq8t gqPvF1cR79+4+ceIy8hquymRWl2LJJbXki0u35DVlWHqfbID5rX4ycHfycga 4dfIRC+AmT0yY7bUDGQLHfph26cHo03+fO53b2QvyX3AyVkFo7LNn4pJFcgW Xe+qFNgHI7ZHPIq3tiNbvG3JgGwCDA0ORrCDdiFbgvP7e99ZGKIqJs7fiEX2 6rTk1zl7YTA8q8/4nT+ypV0Z3iZzMEDtC5ThSCJbplrMSvYn9MNOb0r3CLJl Nf5h3zPoW7rjTNPlo8iWj30qnxMAPfzCPudzacjeNO29MMgKur8XxTa2KyBb 0YrbaaoJXV8Edpar2iJb6f2qL3Ki0FnDqr9cfhvZ2+Vbn/W1A78fHw6deIzs nQGZkTkFwF97u2pXoiqyVTp9PYJioePg4CVbAR6y1Q33HjR1hfbkivXtKluQ rflcWluOC+3C6eKNaeuRrSXWJd2/Adp8J/Q/PTdBto5H9nzOBLQtEZu50K2P bL1fV5uDKqE1lWVpRj2MbNQ+8Mk0BVoPf7Nvf74f2fS7silyvtAq9zdvzXAU spnz/SH95tAyF3zR/SAim2OTdypXGVr6mpgJYpnI5n0KNQ4m9G743zobVhmy DRUPqZk2QuvSUzG/5YSQvTd08yq5N9Cqc6J+pGQrso17Ryb6I6D1ws0/27u/ IvuAcdGfXAdorX7hqu0+hWzTlxH5wTRo0zvx8m69HbLNV1klmq2FtndG1Lq6 p8i2OL/NX24I2nljQQJ7XiDb8s/Uif4yaO/+zbFYsADZ1tQSXu4D6Lhnx3/+ KhXZxxKjtwX/B/zjs4ul3IKRbbvAdpnZfujcPTiZFPsB2Q6l8z8GKNAtJvBi ahfRL6dtFdm5ddAjHrL0VT0d2aeu344PzoReWfrbX+pzyHY33XVU3gb6bdJS NCWI/LwbXbrNPsDQduGQvW6HkH2RoftV/g4M1axzEhaUQbbvY+GMAQ8YvlQc 2TAhgOyrTo/PhmyEkbrkcpudi5EdPtZAzvOH8RiRR5rD15EdeSi9NcQSJpic DG09TWRH5Z7/fFAdJvpT3bINLyI71k8ibKAVpqjv7k2N70P2g6V7JDeyYOb5 vStDS4l8HtnKfup9BbO6FTtsZEyR/fjd2JnsjTD78fgvZSFDZKeKlcv6EvM/ a1ZzmvIG2ekO977pL4C597tmtUoUkf20wOPCCg+Y13iz5jX2IjtjFU/pbwvM P9xMLgrYjuxXH4YCThchSa1M/MuDemRnr/2srqWKJGty6INSS2S/dbvdTCH8 VICsSX+1CLJzSlyvfyHm49SlT9XdfJGdv56ld8sXSZ+fqf1nO4jswrNruq0G kNTUO3GLSvCt6EtfnKI1ksZzriVZkZH9Sf4De/ArkoWEln3/cAXZn/+LGcmh Ilnii8oufzNkl1Y5P/B/RvjnOeown4PsL4o0473rkCx7Z6ODpz2yv16SmFsd huQNj51unqpGdmVNV3rjDJKlZLXjrq5CdvW2gkNpzkgWW7nX/vtuZNdcubno zB8kU0KGYoCC7F91Dq/19JHUHzXm2WeB7DoVveML3yGplnbPlCmI7L9B4ssr lZCUmxora0Pwu6GhPT8uDkl3Whv2LUtBdpNGjvPxxUg6v+wJVZTgT0tYxNpt 55C0j3d0fMQJ2W0ttp9H+Uja9Mou6fIzZPO1tTwLDsJ8T4/sym6CPz0dzVX7 NWFeirZ1oo7Qo35q9iUpYt5I+aBxjsiLPXgrbFubBMwp739w+nEOsscYGkFe IzC7c3tctWIdsucTguBuJkyNv97NUS9FDnnUstdeDqaOHYuWrFqBnAWGKreV I2Gy5HYd9WM2chZP1o19cIWJyJRwz3Z15Iibbn/auRPGpH6FOUnnIGflE8rh zAQYvRT3/TrvC3JWU34J+YjCSGsVS+qCEHKkMnztRPtg+HVAV37YL+TILDIV r7WCYdnhZ39iPJGzwWpLYWIFDEVmLkhzuYScTUuqpdWfwKBPs5t+wQHkbLZJ Lp2ThoHJrW6uCnzkbHnr4/U5FAb+O7Ndr90MOTvsN1VbOEL/2vnY4k5H5Cjn TV2Wr4M+GGrSPWCEHLWV33b08qDX7XXol79M5Gg4Pfyb9QZ6MpovcPjXkLOr 6FyIryJ0zwkdByHifG1DwSSdXOg2dG3t8KlGjm7NjYJxY+h6cuFdprc7csB6 fV1mK3TJZG/1X+2DHBr/yajLOeh8eKOmWZ+Ix3TXXq4kAp06HrzM5XnIYU9/ Vmq7D/z2Jy8VPxFr3lUTVqI68JP+i9FVPYwcQ9Gmo5YlwD8nlFh+oBg5e2Nd vCUtgW/lfjV1Swxy9slO36oeAL75u74HK8jIOZAelHHdH/gnaq5esVZGjpmG RLmBJPCDHiyU+vIWOeb5D9oXPgF+Xq/92vpx5BzmKpOKEDoXuq94tOk8co5U 5Uld+AGdx2o1D693Rs7Rw/qaWg7Q+S1L4vZXa+TYtP4yHpmBLqMIX3/eYuTY udg6ZURAV+Mfw+NPUpFjPz4Y4LwRuv0mm1fENSLH8fLFxM0EnhpQl3n9D3JO LVmS02IIPWOD6ZtXiSPn9M3YnwmN0PuZdeTToBhyPJJfiqxaDP0R9hpqLA5y vFRQoeoODDAyDGXDq5Bz/l0F7ZoyDHxME1cnnUTOxYoOrwXmMFgj6PnOOx05 QSNSTUOpMLJBtHOuSxM5oRdSZp5RYeTN3t9+3ErkhC/SXH3yO4zuu1lgZv4B OVFrjQ0bp2DM/7tlWcc8cu6if/Y3fZioD9tJDiXwvlcm9j2kASZP+SxyLlqJ nAcHEnrZ7jA5WZFY18FDTvKJt3J5xPwm6JFCMapATuoAW+/8Dpi+8tHjb/RR 5Dw5X22uXgTTM04Fx4Dg84uw3mvpXTBTN5C37aQ2cl6t+i/F/iLMwtja2Rhp 5GTdX/RBXhxm7z5cs2jTLHLebrlZ3/AYZofvUjb4Evcr56XsRLw2zNEi8v3/ WiEnX/f5CtOvMBeoJmd3ajlyCot1d4jZwNxHmnqDRBlyPhiVcr+MwdyE72nh nauR8+m32fGgEJiXzx+as0xDTolNy0WmDMyzqt95rPdHTlmPa+x8JsxbJeRO P5NHToXn7MscNsy7CC2NuBuMnG/zIV/P1sG8x4L+syJ+yPkevLpT1QXmXa81 c6iPkFMjniTQR4H54ylJKm3E/fh1R1UmNRrm9xxXOfiCiF+3qUDLTgnmt/9I PyJuiZy/V6b2yl2H+cXSqZsZo8hpaNaw+TcCc3WhBrXam5HThK5edw/BXBL3 kuLsd+S0JKSHWeTDnINfzr/1rchpm2lPlJSHuY0ujab5hH7wD8u+rgmC2d9c gZcqBJ96V8f8M94PszvKw//6rkVOv+f3kaXE/F0pdILD8EDO4A/RxeXSMONk cS9oaANyxiL8VVntMB0ZE8b/oI9ckpDrVZ3zMJm8Oqam0Qa5FPv0uAmCD/In 3j/f8Bu5Cz61P8uiw8SdEdUGWx5yhfwsa1VEYDz49n/DB32RKz7N26pwH0bN VEJ8HTORK3HIH1oFYaTUU83LMhe5q7MLTBIdYUTvTfIZzR3IlfbQuCitDsNb ZWN0j5xA7qZe2SqxEhhUOKiXxhhBrqKBZdu37TDwzCsdqmKRq5QaMxV2AwZ0 tlhJNtQhd/ui6mW8ceiPe5Cuas5B7s4TohsXWRLz+/3ut7vFkavykbf743vo Iy90knkdgVx1Of89fgrQy/ZLqNbehFxN3wIbCIGe2Hf9G8J8kLu7YersTD90 T+grqey9jlwdPY3QdybQbVcw/0ObgVy92673vd5AV9P9gJMhY8iFyfTXGuug y/l46bGIBcilH2wvHfKDroWex9Kv2CKXmSXb8LwDOjPUJZ0epCGXs9Jy+JQh dJ4cvMnZ8he5PPfYxUovoFNtSCxMfBC5BpXV0vxV0CnqvMntyRXk7t0pqpLk DfzJoLCkoTXINb7GY9s0An/URnD/4Q7k7u/2P7yBBZ0UQfW3TgHINeUVnK5P g871rrH+xHvBPZg85X97GXTuuXml6u9e5Fos1Igz94DOsAOKFO2vyLW0dX0m 8Rs6//im8rylkGtVlF5UTYUu7RZqN7kHucc2tP+KeAhdaWrWovfMkXv8kmzP 3sXQvUVqTPhYJXLt6o+Ql5yC7mztlDF4hlwHnViJku/QY7JB3nQBka9jXLVS wC7omTc+fTOXjFznCVFg3IHeNwyL9XOvkev2yt8h3w7690ncC9v7GbkeLK+u U30wIGt/uzynAblnfzo5rzsHA/crNlyKGkau98T+0z4hMPiC2VG7TBm5Abob zmpnwIiwotPpKSJeUMWK8S5tGEnJr0kND0ZuqNWi83EfYJR34a7ag3HkRlzq 85n4CWPhc8pnEm8gN/5D7pWsGZiUDP5zaockcu+aZAieCIDJrHGDPSyCz/fa HgauWgZT+29Qn6QIIzdpUUiIpxxMX+y4U3f/HXKTYy+KbEyHGVFP/5V1WchN 2+J27Yc6zMT+XrbLn7hPzw3MI9U4MHt7PJmR0oncF38NxZsrYW5F+i2L44bI fXUKbt6wgDn/zX0Tm1KQmzWntorWAnM9EzNN5UR9b68rxAw6E3qSqbHCtRC5 uRvWrrk/BvNJfcnNQPA9/8XS28aXYH50n92E3kLkvqeT1/3//93XFfXb1BB4 f/g+kvD8BpJ0N0hdZcUht/g4f4O1NJJME8ojgeBnycifB6JJSHKU221zNAm5 5Ve/yefvIPzYjSOTygT/v64qSjr1BklX/ko4XiD6U5mctXkdDUlBg0rbfFSR W707NeULMb8GlTim+00it6b0rpKPCbH/yKuVpYnIrbWIeLK1HknnXkzqi0kj t67bf/ufE0hyKGeOfiDwrvfxeh5C+NsDr7Jyson8G5c6qWifR5LWRe9Ar33I bU6wyuyiIGmtXuFsqApy23buV48LI/zfGq5a0yxyOwpZWTwJmI9fqSmx6RFy u/Zp7ZpIgHnqy1eZMveR29O87W2KIqGvXMdx2wvI7T+zQedgJsyd2jpa/T4Q uUMLVuQt0oHZ8cKY1HXEeeObpgpP7IWZ0efsiTuuyJ3M6qNL1MLMydHygxsE kTvDafrw6RhM//rkbfFoL/LIJ0s+b/SAqQeb5O0NApEnMJXLrZ6DyfmfDKZA HvIWhj4vuxIIk4f2VcnscUPekqfRFc3xMEFZ3/M3ORp5EoO2P+4XwKie5NcR 5hvkSZ61ljVshJHwXXq1xePIWzt96NQkCYYbOQ83aa1H3gbBvYv20WEo+NNz P7sjyJML4R6YPQ6DzR7Ly48g8jYto99P9YdB6s3XFncnkKe0Zpc2uRgGBG5+ 15jegrxtCSoBTzug7w8cWn23HHk75bdWWyyG3net/XUJq5CnkqKwYaEi9CTv fPL0z2nkqW/f4JzJg+5EpfV3i88jTzNz7VsrR+hKrVZ6+GcAeVq7JBYKh0Bn 0Tqxp5m2yNPJXbY/Kx34XT23Moo1kEelCd2z+QL8TZJS9x2CkYfFAj2ivdDh dqJx+Y0zyGPoz+3OEYX2yojdrK2NyGN9m7hqvxPaGdpNxzTTkMc1Gfq+whja SmesLAsUkKf/u1emwA3a7MztDiuIIW+PVYeT0w1okyi07PUvQ55RS9Ob1ZnQ +udGZ/6DGOTtd/i74EM1tGZLXVu96hDyTHp/7js9Aq0pN0zilmYg76B7ZYL0 SmjNkLp9JXYr8g6Nl3WVaEBrxef6vEsnkWfp82mXhxm0kaPu/IpXRJ41ucB/ gxe0GdgfVIA55B0LeFv5JRba0ldHklfpIc92yat1595C+wbbgc0j35B3IuKZ 48Y6aE+bOakjL4q8kxIpWZXT0KHvdWlj5h/kOcU/FLggDR1zljfR9QfyXNbf Nd6iC/zi9YbzyUT/XB/F3Kk5Ap2JsgJZi/ORd2ZLZOfli9AV/mLPdv0h5Hk+ C9PcngDd4aLXrv24i7z/3vh+u0r4y/xfPrs9eci7oOctrUqGvj7XVUvNjyHv UpGnQ4McDKySDIodqEOe/xdHiqYtDJ4YO3hxpTrywhsPqLd3wAhrwZS35DPk RdrtuRy1GEYFr17abfwJeVFdnK+gCKMlJlm9n1YiL3ZE1z7GEcb3ayo6iRH9 erh4UzynD6ZspU3Kt1ggL+na+vaRZTC9EQJ2FxH4poivVbuvDNNNRqcXUv8h 76m06JcJN5g1iin6/PkA8p4nCq15dANm54O1r5+KQF6mgsAJY+K+ptali/kQ /c7eOT6fMgLz3dSAZW6hyHsbfJ9t1o4knaf6it1LkJfTwr22oBZJF42zHs8z kZevO1CdSehTnklJXp31/yqu8rgY3y86M4qiSIqILC0opbThS/cm0rxLWVNJ i2ihRIpIKSpEKaJQSYiiUtY2RQslRFokS3vTvkx7zfze35/zufO8z7nn3Huf c5Gbf/PWArtsZI1cOKqSehG573sN7Wc+RfbqTM/lX+WQW8TlPcqJQ/b+t7d8 fhPI/XA/ovtQOLIj3Ir6pzD8l0yu15U7h+zse0uTXyYit8y8wafYE9n1erpV BurI/fIs5L2nI3I4M8Xq9rYh99t0bfFlFshRkNSvUqlGboXDL7NyLnJ0F0pu Cd+N3Mrc8zf91iNnq0rk8P/rpWae2u9Vq5Cza2HKFKcS5NYe/aFYq4Ccvf1z yh+eRm5d6ZlDF6WQYxN3XPnfH+T+VVJK1+Mgx3qR84rxPcit9y0bbuQz549c 3CHF4Gus9jKIaGG+f8OBm5iA3BatRUFQzdx/K+CZPtOfbSFFn7o+MviC7b7q 0chtbzoifSeLwX/4sN5iReR2Gcy15DL81O/tNvG7htyeqLd3hxl+st0lCncy 866vz7HlIcNPeF4qEfAOuXxylvpOhh9718ODLmeQO/TgtSeb4Uc96rSEGKPP iMA2K+0gsgYdwxoIEeSOW4ix95kjK5NTPOq+HbmT6c9MZpgw70V0sFfoCyQ4 B1mVTqtAWNN4zdy3CAmRt0kLZRn/vmn9JU7afiSmyW3fXyDF+GXF70FyV5GY 8eler8IATFpecKx9uxEJ2TVG06uyYCzEd6eyVjgS8y53bA98CqNf1KSsSs4i Mb/5evSaOBidFbTCpOE9EgrRTcphATAcGnDhgasqEsuFQWhsAvzglMbz+l1I qFqqB/PXw8CHfI/R5TlIrMqo/JywCgZmyGSoLaeQ0HJUsRLMgr7b7+yvJA0j oZ335d5TFvS2aY+8KZVHQm/+iTbLfuhdL//8b95dJNaXffB6WQVdtdn9pzJr kNio4p7j8BE67x7bTul6IQH+clNmZ0LHMSkWd7sKEoY/87l5ydC+LeB2ffZF JDZrO4e7xQDP4OFhnY3fkTAOlaqWD4O2DWY598cKkTBpyVxU4s/MG65eGVxD gkT7Ayc9oMXF1HCn7HQk6NviT5QdoPn2zGMyNTZIbFM5ZFN0Gpq+yFt4m0cj sSPjk/TBcGiS86mwuEkgsRvUikUTofFUjVv1BkMk9ny6cuphDjT0F23uU09A wmpPl/qW79Bw/p2vKUcMCesm+l9zGzSo65WNm19AwvZoamSQAOp7Tsmdm7Yc CfvJWSYqMlBfWqyYnnMUiQOX3MeLVaE+b4u522omXyfZ8jRHhPrP026kETwk XBK0HKaaQz1/mmZ3zQokXDWuzU10gwad5Y0DuXwkjmT1lxoHQkOoXpfCutdI HNu607flNjQIOtZKZk8icbzihVbwM2i82Bxz7kQSEifsZJpViqFJNV1rk8ox JLw7vaKL66CpsSDjlcUoEj7eVZRjPzQ/951os1yDhJ+oPmuaGLTcWhXtZtiC hH9E1PNHi6E1UsyygGb4P79oxGmrHrTd15cOT52BRFCy5YJWxu8Xle1SfM/o HVKw4Nxyb+g0XHbSsl0ZiVAzH90PYdAV9WqgfNYIEld//eI5PYDuyb1LUtQn kIjkx2579A16R458PKX9E4m7KksWr1AF/spyZSXlLUjcy/D//hGBLxgeuT9b HYkHBvXBzuYwWLVmHd3M1FPSnoSux+dh+EKz89cPtUg8v6Scs6IOxhqtnVbX LUTilWyw+8d+GH8BNuOtpki8udeq6CIGEwHcj0uGJZDIzXockqQLAknRNtlB Eon8reIGXAoEpVadT4u7kXhf4dLH2w/CgMX6U41XIfGhU81yZRiyXDc4mSVq IFHqfUWi5AGysns1Xv33EonPIp35LlnInqZhH9sjROJrBO0pXo5s+uXMlkkm /n1R6vKkVmRfNT6LKWlI/Eie+YsrQPanJNd1xmZIVOu5h7XLMPMpSXtzkz4S P9+XbwpRRY7OXJHQRQVI1JlpDqkicmyLJG1XzULiz6+IpFJz5ASFx0lb70Oi 3ql/3yFX5Dy0IDennEGikb9z9vRzyMljTzf7wPR3i//zwuRo5Hz3Mqmr/YcE T1LGm0hFzt8HwnMOb5HouOWl1l6InJagvL1ut5DoVq78G1KLnFbprCtHmX7v zdC7rtqHnHrqQ+rdvUgMzNmCZTXIqVpnvdZZAYlBz53dR/KRU5ShEl8+iMRw 5f4YqUfISSuS0mo0QGJM7xg3Iww5kYFahrbbkJiIOju8yws5nqLvjVYfREIw EvZgyBo5pgfEYyy5SLItY3dEGyFHMdd4y5Y3SE7Jespar4psvnYUlfUCyany WSl1s5Gd1/nP7MB1JMXOlOz1HUH2BenksTXVSEoatLx6V4xssb22R053ITnr 7uABhxRkvW+wl9AUR1KaLSItGsnsA7EFfJ0QJOcWLHXjMnpn6mbzLDhIzlfS XNDBBaF0Wuhulg2S8kEGH0M1QeDop5h/9DiSS7ZaK5YLYEK4dvpdkTgkV366 WWtxB8ZEMgJzZVWQ3PBDYqvTDxho+OFi8WkQSdBdMCieDQPKfatJlzwkDW+u THiSAP2usa6SYxVIGlsYT/Ychb6pVb+1/e4jaZK5+0nEHujd67ldpsIZSXLB AUttA+g5HPB52RxE0qwu4MVJSegcSy2I/eGB5I6N4fvl+NBRLXc2s+MVkrvi 7s7KqoP29yH8QpuLSJoLU3KtC4CXWyHYxt2DpKVdziFBMrSVvHh6Ir8Oyb3v PsnFR0Bry79nM01IJG2W/Sze5A2tspPfUm4FIGl3vu14ky207Ll/vGmHAMn9 TcNLg42h+anV0hnsY0geNBb9ukIdmmXuKHOe9CLp9EjmTCnjP8PLtdfYKyB5 SExR9fA4NC3dOX/agc1Iurpo1Ug2QGOxm++aKwyeI6UYlFYCjf4WTyrkbyJ5 TM1Mezszz8w2ur8Y2Yfk8Sv7/g1EQ6O29khukhiSXl2uYTfOQuMqo6/Rf8qR 9Db12aDvCI3rHaqS3zDx02mXeD9NoXHfaQsjYO7zlYqO8tGFxhv7lolMz0by 7LFHWxYthMb6byk2NBMP+P6yP28KNG0K3S26hsETqF0Yb9fO+GvdKBXWMJLB kRWmnG/QDHYH1E2Y8xcH68cfvIHm2shTVfYMvsvEsKeRHrQEZtTKbElHMjRe orP+JbSixNkz9UuQvDq47KC/DrRJTJvWK3UPyWuE/h+FDGhrHX+7V0MZyci7 tHmuJvAq1i5OVGf0vMnf/3VvGrSXCZz9Xv9G8k5caH70U+hsm+jJKtiGZOzA /XX6qtAtHlz5/F0hkvEmb9Irk6BHFza/jqeQfNDfeF86EXorcbTVpQfJ1K3r gq7Ew4D3wXv/Vl1C8lmMqUBNAfjL38+K+GuKZEbfgRMlscCvyTiqE7cUydd3 rjpPvQ1DG9XqRPymI5nf00z4R8Io+8Lc2PuMPgWbxwoWS8NoagTfdOY5JItu S/2XGwFjFvwfQM1FsnTzf6vGwmD8fn67decnJCuiI2Z5XgKBiMzxRaMNSFZ2 JV6cIwaCsOPa9yKY/Go25bDSg5l+C+AZrLBFsjbq26ltoiC8+ldXa1c9knWd LX3dgciaMi/ss1k0kn8NJw6FMvvz+tDHlcFMf9ZHzW5UC0DWkZUvkk3fItnY uXxvKQtZscXhSn1+SLYYbvjh7Ieskk3GUqWTSLbd3EFPnUBWr59KVk8Jku0d TkUPfZA9x+Z3l28Kkl3oa2A0hmytsmBjzy1I9ty49qrBG9lkRmYdJxzJvvbH q/2HGD/Iil+26yOSfMh9vNgL2ccej4pL6yA5FFmxhHm32b4JyiYdzDwZ4bXd svZAdmCL2q9iVSTHDQSzx3qZeeWRE7PnGpKTkXNCbrkjO4irKaGgi6SQt3KK fjey/Y4EGk5RQopjYOBT5Ypsjw5zgXgpUiLXd/I92xk//6lu4oUvUlPbXNzm uCDbdHHVkb4EpMQ3+jWntyFbZ8y/4DITn3Et0mabI7LneQRl6lciJdmaVNXd xPjVe61fTl5DSmpDnlmoA7K+PuxUOH8WKemIHx/VGpD18C4715+PlExLO5ba Ievks8THSklIzftPmOn8B1nGbMK6bD5S8yNk10zbhyypLA3dfxNIKfyHiput QLhWQypg1wyklhTMFPsxCYK0DVHmtheRUiR+dR+MB8Hi+LcF5c5IrbA6mRnc AhPdEac5MTuRWnMqzazEA8Z2a+bPGV6GlC7bV9dKFkYT62bNaYlCSv8SuaD9 DYwM3fgZd0YdqQ3RLS0zBDB87d2nz5seIGX8RuGMaQjwv9ZdrXjfghQXOu3/ qgNf8WujRKgWUuSHrK3u5TBw+rdi/iU7pLZVm0tHzIV+nfvTTPrOIbXTVnFk aSb0RfvrPU5gI7W7pe93hjX0sfP2qyt8RspqKPRxRQL0lPG2VOe9RGqf396w A1ugu+ijqH3YcqTspq48zm+Drs+mKvfKvyC1P3TYMugydDbteHPEaQipg7JF MFcDOmfEphdKzEPKKfa6UuI36DBqxZi7ikgdUrafru8J7aHVyz+qP0PKNUWj 5+M84LWLrLL08EPKXWei0iILeBYD11dIb0XqWE5pFm8ftP2SS/31WoCU5+bo +NMsaDv6w68xNxupE2WOwdPvQ9sCL9u0TYyep3bpHL5jDK01emUuniZI+dRx tqvxoDXZcI2b52Wk/BzK9XKuQGt4UlVkVT5S/h1xC+nV0HrZo8xjLBmp88dd 2b+/Q+vtEKEffypSQePrWo94QWvuF9OFE+JIXTw/rUwoB62Doq+cKhm+Lk+v zLiaDW2MowxZyNwXeu1+9BIbaIub1++kIoVU+IJjvuls4M1a0Wak+gOpawng YPgAeNfuzJwew+h/Q1XS5PtWaF/+e6LEpAKpqPRfGvvbof3Ltr/f+22Qur0u ac5AKHQEGclKnc9BKubdidFATegk+b+ql08ide+bdOHDE9Atum18wP0wUg8s /iXrzYfuIX2FtLNzkEr8l3q1OAd6BswVRDZ4IPWkl7Bq40DfNPncM0W2SKV6 y6H3Q+iL7FlW7uCD1DNhi4q4CfSrFDotjH+K1KtZAX2qYTCwa/U+3+tdSOVr ZF5wk4fBorgqHx+mP95f1nUUhsGQRZprkRWTb2FruvE1Dgx1qDmXDTD1UBKf JPqqHUYkTv5cksz096dJpWauDYzc3pXpluCK1BfL+MJf32FU+dXd5wutkKqQ jg5kMfuf9roVvm+kkap0l3G4rgFjL7wzVS+7IVX9KdxIOQHGtXyaNOS3IFUX eJFDhMDEIq1CF8ftSP2pn1Jfx/iXy0tt9fZnIVW/8ew7dw+YGDAVLDpkjVTz 0En/SCuYTJfukbB8hFTbjgE7lS8gmCo5ed+di1R7mjtkbgLBrtlTb/9m6q9r Rsdi8hUIbtc1vF73G6keZ0fBH1UQ1K69oXrrAlJ9hQ1/jsaBcHafd9+pNKT4 S23eTpEGoeEvZ17IY6SGfGtjbzDz36W84dy5/UiN1Jr7Lh8F4aXny7pC2pEa 1/tuneUKwnuHDF4cjENq8rrpBuofCJ81az/s6kNK2FMq/3cXCN/MH5xukok0 h9oyfuwjCF+LesjmANIij9/9EtkAwpQnN2/8oZGeJrox6+YzEMbM2/Lz7C6k xe0zb69UAuE5K7MatxSkZ7zVPZUdDUK7ULsHf+YgPXNBuqWpBAj1vrbr9D5E Wuqk+tp//iAUNRGLOsj8lq5IkvPgg6Bsmd2TJXeQltVUGhF1BsHliFGvG8x9 867EV0fVgcCoXGrt7e1Iz+ctfK26DSYH5Vtd7V4grZAgc8JMHyaNhjJ8JJcg vUQQvrs+GSYalmxtO3sTacW9ErqeCjBx+sq+Rtn5SK+QmcK/NRXG70Q1sn/Y I60VNODRUAWjSRbjtwe+IK3d4L7Di4BRJTP5kwt1kNYz6Fgz7S2MxLzrUXBL Rnr9cEPfqkRmf+pjBR4ORnqzy3f3EydgcFeQ/GbTMKSNi03NxNqB/9H28+ww IdLcZaUad2yAv9FT9E8Twwf9611XvjEMqCvGNlm1Ir2HTnedLgt9q7VddlKS SFv2nvY1egC9TyWzjWRzkba+bhTmuwZ6Nb7XtwxHI22rJ3H3VT70HJ09vLnO HWn7mspnvabQHW7zyZqlgfQBn7j3K+ugK+/Mka3/jJF2UnCqcDgEnQLywnzl cqRd3mk2xYxAJx1DVC1/j7Srw+hgVTB0pKonBCdFIu0+tWCalAx0LHnqMLuB g/SxpCty3ARof5gRlfNcCmlPavfK85rQvrH43xSpfUif6FFYn5MHPN7e+uOf 5yF9KqKVHDIF3uOvX5ZmKyN9RufZvtV1wDvjylrBsUTar/rUEZdDwDtwIDlZ TQ7pgNObziaMAM+uJj7h6COkAxfNCK8LBt6xb1pZ2Uy+wXk/EubKAC9q0yJz rb9IX9of+9wsAXjfxO/UPn2M9BVRx8JLDB6FXr81mUlIhz1eXfn+LbT7Jzsc UsxDOoIYaZmgoH2wSkTzVAXS17vejejWQofv5PWhdCbfm+GXxd1doFMuSt94 TBXpW9q7Fjxm+CkSDfu34iXSd6oWqTUEQ9e56D1iffeQjvNu2bhQBrq3hS44 HfAU6Xvyaaa7mfdMY9aI+pAD0vffette1YReSYWNCadCkU60Nzz68S30hsza NN9nPdJPEiuu/1cLfTe9FJTEmf+ncmMeeDlDv1KFjqgUG+lnnQdfpg5B/+u/ j1TsGfyvtIZrlknDAO9WztABLaTzcxcuEiNhyKVygUYsF+kC22YNw1oYlpht NVMuBuliTir4OMNwSvWN8dY2pD+ZoH13IIx07dnuEG2LdGXFgcQfOTDOLff2 2CGDdM0J9TczCRivkrh40aEe6V9yQyVba2DCOkt0pTNz/nd2Xm2AI0z83lqt 9G4H0v9sLnZk8WFyj+pEgvoCpBvZ2yf452Cy5K+8WfI5pJsfyM/UkAKBTuuM 7p8mSLcZNy12igNB1Geb9EJG/3ZeiuY9dRD0K1X/UTqDdNeVE4a1OSA0utik 23oC6d7VsFOGAOHlOJGn7KVI938XO0DXgLBUWnOQOIb0oOc3rwuOIBTEaoaZ b0V6ZN7t4Hw+smT8PTvtzJAey3KIGjuHLKXACj/9bUhP7luVpCOFrNVbVyx9 dxZpoXAwyy0OWTpv6J9r1P8HL7f+aA== "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 1.}, PlotRange->{{0, 60}, {0.552534781237358, 2.3701613140471767`}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.469220122967628*^9, 3.469220188229244*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Show", "[", RowBox[{"Fig1", ",", " ", "Fig2"}], "]"}]], "Input", CellChangeTimes->{{3.4692201707177057`*^9, 3.469220178981435*^9}}], Cell[BoxData[ GraphicsBox[{{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUWXc81e8Xv/fKTEL23nte1yo6J6uikJWKhlAiCakQUkrSkmQXCRWyoqw0 bPFNQ0Zk773n/d3fX/d17vM855z3uZ/P+3m/X9fqgpO4C4FAIBEIzHf//7m7 8K7U17quRSS1/RKJP30Iov7sucqX/BNJDScrFzJuQOofPqdmbkRScRIpReYU ZBmxM3N08CApOY4q6+wN+X/oWl9b+iEpxL1p66l5KL5Hfd1QcApJjgreSWax UGa4dm2C5wCSKDO1td+vQmXgWuZtvUQkMfZ4jl82gs+tK1G8kxxI/C2U1RDC A1XkpYDMZ7eQ+Kz+cM5RAtTcW3DRtVxHogtXQU1yOTQazugcLRhGQp/O9HrA M2hKnhIfczmOhMTLBy2q7OG/wMWwiq3nkGAlSIoKlYaW1vkL5mXdQHWdtzTt 5YOf9D7OvNzxsPnxBE/0SxL8Is/ad3vawCZnQEL/xVFovTdt4CVaD+uvu3cl vy2BTsNx7kehRbBSEHXZcY4Cva0zf0elTGAuzeTztTuz0OeZZ29umQazo9eU pgzJ0E/v1ZJ/jQizlONq9tgCA+SpGv9fZTD9S9/Fr/UXDNS93dNN4odp5T37 9v8Sg8GTnmWGKn4wxT1fNneQG4buTeRtva0O4/nNhkmdnTAsla14oeA+jD1Z eWMiyQvDpR4vf3SPw2g4B3i4E2HESklMh3U/jNxpz7C5Iggjw2PxiTrpMJzI Jmxu/BRGg99wE1y2wNBHu/7n9FowxnPugfOjUzA4b1WXz90CY9kKzLXlFTC4 61FW16EnMG44clNpVAgGnjzh25W+F8bbMzcf8fjDAKn5X5EOH0xcPHtlwaAV +m+8bCk94QKTTLJzRy5QoJ/P/VxxlhtMJg+dL0+Igr5PLnEJVddhSjN9WLxm GvqC6p48vvwKphpdncLmD0KfZVkxe34rTKtHTV5it4U+bRsnA98WmA6cfZMh cxP6yE826P9SYbrW6mybXj70GfjN2LY5wMyOfJmtVj3Q59pZpjG5BDMnOPr0 3dih71kS0xpLAsy8vvj8QvBu6Bu/1kZnoA0zC98dU56ch/6DmsU+Gywwu0dd 4MebROj/5L7Cd6gbZiMf/qH/VA8D+z4wEZgvw2zr9BPt1hUY6E0vybk8A3Py GSeZyO9hMGlZOqdiCOYCjiv8uecHQ+dbA0/FNMDcN66FzGEKDB+6HAlPmmFe pKHyqsEsjOxdieMpPwnzXqF39yflwqilp3pdWhLMf9a141/2hDG331w8n1Rh wSV9rCR7FCYadC16rijCQrFj0V2mTJhin2o5nyUPi8xc14+ddqXN6Q7zq+hQ WMwK5Vnn64OZx3XfO6cPwtKco4H+jU6Yv7X/whnzXFg25tq2rSseFvZdd+LG HliOqW/t0rWHxa0Pc7qsXsPKTp3zQZO/YOmRy/eqvbmwen1HbOWRJlgN9zW9 o3oLVlvqTz8sjIQ1/aUXvmV6sCZ1XeXUdlNYm+zOTuPegLWaya/ErzWwgc53 nl3/D9b5X95vEb0FG90tx75xUWDd3eFIqr8RbF7ZO9PgXQwbbHVThqqVQI3S +PLzVBJsnAwp2RERhARm+zdkXnPYyNe+2TeghwRjScEMm1TYpJu0KIRVJAQa W7K1tcKm7UuBm/EfkJDzoPXdiWzYzDg2YLNwBQkdrX+/0RvD5gpnrrSlFhLp lsw99TuBalrnv/B6HokyjUuCVmSgJoYYV9PnI9FIL/XVvApQJ7XZY05eRKKD qpp/sQcSRBcnChxjkegZlT76mhcJ+1qsBSOWkBhwFKe+DCHhYvb7G+/tkBga 5PfLbTcS4u8Ijw28Q+KNDRJncwQSvjjfsNnBhcTgP5266feRMA7DZeiDRD8B HeqmJBK5Bc2lzrcg0bUpwGNWF4n6iwWRCepItGKSoaiG0b5v4ZuvfYhE7a4a y+4/SHyQHeSwMIVEPs+u+RVRJL4P76uSNEfCXNunwT208z3O+5Uts5FQb3zZ NeYLklgg50kQKxISOqPq1HqRpCG4Y+ONOxLONjG1h99C0rHFqy5/6pGgfpBo 5B+GpJvfu5oY5IE6Jr5Pu5GCpOxsIy2NcKC6Z9Q8ku9AEtV5O8N9E9i0y6b8 TupCOjnw9Sx9CRufdTNFHS2Q7pBA2++RLbAh231MKwyQLvV7WobhF1jr+3Y0 d8ddpKtdGnl7Xw3WTB53S0hfQropEZXiP8mwmr73M0PPC9yy69z72vNXYOV4 JHkryzBu+UnXOPJUBRY/nfBrlh/CLasKHDM9ibAoYHeN8CgI6cUs7VaVmGHB 94p640oi0nsk9rBU9sO8ilXBqRY5ZKDXXFAcjYeZ6ogW5vWbyKDosJOiyQQz 2luq485GIsOh0GC94EswnRXl/doakSGpmfkglyVMHVJRcfA9iQxfFsxtj5fD 5Ik8e97YCWQYFXzs+EoBJgLCRFVfJiCj1llhz930MPa3aVux6jtkdLjv5Bfu DWPSgSDWOIqMoYUZQS3/YDRwXOl93HdkzGwfvy18EEb64yJLj9AhYzNR/cGZ EhhxmIWSoX/IuCDr9zRfDoYHbTeM3rogk+DB0mfrT2D4Og/r6ehdyLTHl5C5 lwTDypeYKHsUkOlMvHHuIy8YGkvPfZ9xD5nuVUa87+iCodLm2m3uh5CpYLD5 k4wZDCXTr/MX0Nbbt3HVeb2HoSiLzprb75GZoHHke6kMDMWmddXeUUNmmSPJ 7fSPYSh34ty/a6zIfCC4r9eSAEMdpJW02lhk9k6XG433hGGe3F1PPnYic2zj +dn+Dhg+nXfQIyAVmStm89dU98Hwp4Ll870vkXmAb5nuahGMqB3evJMfgyxb QX/rV0kYeavqTXp5A1nUXUK52B7CqP5HPutjx5Hl8N0aIftNGO0Ial29uhNZ ruWzSr1wh7Hb7WrNN4eRpW4zRlPHBCaYf399Kr+CLNNSHfqhhTDRxf1V8pcf buUxEzP+Jg6TFcr8MY4auNXp6Wu7U+swbXGkOEr6KG5dU6u4fDcPZp1DdjT6 38FtSk6DH7qFYHHp/uSut3647VBd7MU5AizdNtD9VYG47bKaqQJDPyxzu03N B//AbZ82suNVsmFF0TBpJHgetw05n7AyiIKVfA45LS87ZGNt4GCx84NVzYiV RyQqsh2O9fUP2g1ran8i2GbNkC2QKkOOkoC1jG7HJ/PnkS3VpXU0nYHGkytG tZx8yFbTeCf1wyis36or32rYhmwTGruOfmuC9Ynl6sLKDNzOGTfB2ZMPGxYa Cutv8nC7DiG5fiEGNl4rVJAUCnG74xnLG8z+sLEZk/jmoxRuD20i7RI+AZsH NFw1OENxeyalYE6dxr9RXy6qDLTi9m8JLm+M5WCzhW1txlcDt8+ReE8fYQUq c78999EvyM5Kcb5XdAWoO/k4eJbrkF1kqNyt8hxQncK2dVLOIrt6Ao9xvQNQ b7Bms6eWI7uR+QXxn+ZATYjpRF0rZD9MrNnoQqBmccon97kju9s7sbZhGu8W +vx3IVkO2QPPXn03K0WLiytJzEvI/kCw5dE6D23/v6Q9xV+RPaVZ0ZOBicbT SxW//zuJ7IWhN023rwI1bJtkJ5sMstdo/pXhHweqi7YL2ZYX2dtGtEiSXUDV f5wSFJaM7OOJ97uUvwOVzfh079mryE61GCrR/gqbrX5hYXWXkYOTDmP2FMFm 3GEWplVADqmiOG+zTNi0EVrcZOxFDm23WXNbmh5k2iJzZ0gVOUyFzRRORMLG O3NuqyJf5HD4L43BLQg2HPa86Vt6ghyh2rYV15xgPfaYHN0pFuSIHs2Jv20D 6yqyLW8NHyNHRjKj3yMTWKsYkz7CtYgc37Z8UHmpQLtXW+6tZlQhJ2+LUHLj LCw3HzrX4/ICORXCLvn/pj1/e//NPnQeRk59nSa7f79hqSLPfaKkCzmdnoWw zZfAYmbfayn9a8iZ7dEXLHQD5gMP/3gLBOSsFNM7JuMHc3NZPftJNcjZ8uOJ ttpZmPMY6HzcE4icyztNpowOwKxr9WWp6ie4g2XiWYP5bpgZNrVKp/uKO4Sf L2fYq8HMebfu38V7cYch4+sTHlwwfZNHfueLStxx2LL6BaMCTG+zyy0woMMd 7nG9g6kIU7pHTBmOm+GO4F6qgr4dTLpsbz/vvw13RCsJnv/jARPJabuyPCVw R+Yl7TyfUBgf7JS+q0nCHWUV1gtssTC++4Iof20e7vjO6KXzKgfG0ukbazZ8 cMeAZWSg0VcYE9E9kD3pjDtW4jI/drfDaEZUyvNZAnJt662i85+GUYMI5ReC +5BLQrFnLw8DjEzd1lP60YFcWr4bEXlCMJLTnfhJhYJcphX8TQfIMBLSaq6i cQa5TjBqcQztgxGX5zvry+aQy8fSyjb0OIw4HqWfcxZArttxnnHCvjBylmN0 6YcEciX0RnS+j4CRsM/ZcdYxyJWrmCFm/RxGimwSFXoNkOur75fTkzT+W0l6 YeUTilx/yrsz7jTCqMUpn6Peacg1wbA2JtULo8WHimZm+ZGbaMmn8nEZxtTZ jsk/FEJu7jiK9zE2GCuTHj7X1Izc8r2W7xalYNyee5w32gq5dyt6rDzaCRNb zLwv1P1FbivfO/pKljBRKfnQrACQO4Dh01enQJg63VfEMOCF3A8suhg3omBa Wrjr1RkB5H4Ru2oWmwnTyXdHddO0kbtRkdzS9ANmUu3WWfjakPufrwWP2zDM KpZ4t5voIfd8ufvRLZsw++H8x+Mcg8gjbPGiZ5cCzHUnWbj/K0UeT1+uqQya Dt17eON7VifyhJaraxjEwmJfdIBQoRzyPGU46Pc3B5aC750/oSuCPB9jwzZ3 tMNyofVo1n9vkZe9bIk1hAxrhG/ne69PIq80ww5LwX2wFnH3QdudAuTVtVB9 XHQc1rd7/72gzI28p3rOCoxHwAZTHmuK9Sry+incPH77OWwEshUezedF3gif 56kSRbAxnMZw3Ogd8iaXlQ2UN8KmOd2u9FR65C2gb5O374XNN1WGL995I2+N +YLH3DJQiRHyT8quIm9HLEfuAzagmq/4qDWtIO9Uj/K8Ao2vHn++4tNMRr4t CqbaVTuB2pxYyMc5gXx8Pq4BJy2RQJRi2DkOyKdUFlqx5ooE7uoII1sJ5NtD /4wUE4gE6eRUd04/5LM1LzVRj0KCWtwT6kMd5Durv/dIhyoStNwUDcK5kC9A 8cf5m41I0B6WRsoU8t0XOHFdxQ0JmpyH6KR5kS+FaSz6DwMSlId4TD/LI1/B 0uXM6y+QIOF2tWz4HPJVD9KVKSISOJ8LiXqOIF/bzwfNP2n8uKFwnvy5CvnG vwj2BQUAtTFbdl01F/moeRlLcnw0fGNsB287Ij/ncwrL9yKgWm8r+9wXg/zS 9ytFAqyBymKcsVHNjPw61w6QpaZh80PdMvc0bb+Z+x/jpnuweerP8YBQNeQ/ ftT5yBUF2CQVDrqz0CH/Te3AkAZn2FBjZCj9XoP8T2WYoi8RYb1c4vor2Wbk f80VnSmSDOtGh09m3bmN/M0zWc0X/8CaweMT9L25KCD45q8I30FYYV7T2J7v ggIFYvqlLlowr9S5fUTsOQpUs9U1s/2AuYffJAW9BlDgz4Zt73svmF08olsR fBUFNts9Wba+hplak74/IbYoyFG7KvxuL8xopve8y+tHQaniW+rH+2E6PWnQ Oa0cBU2jk+3zRWFK4exLBNq64w0Fj2NlMCkbIBV+UgAFvS4WhdAfhQnVU/du z+mj4I2TBo9zlmDcYLaEEGWFgjHmTRn20TDmtJ5y9MQMCr7SP1pKUofRh4Zv FvddRMEyxcGmN00w0hC6bdbvNwo2C3j32rrDCPcZ+ZGXQSjYy7S5QGWCYc+Y ddVnh1Bwfukuc+ZLGGp99YAhKhuFGAd5ha0MYOiQwpU9H7JQSODnC7W1bhjs iD352WEMhZS/qBq9DITBy84xFGcXFMK8UnsLfhiUms0448+PQtbP97ovF8FA f+zWuEO2KOR6/0dwqjUMFCX8XO+lxVevnXhsNg0DCVqen7wvoFCk+1j6wj0Y iHrk+oOpDoWeHb1c8kwBBhJrDG/eXUGh/P10TftqYOBDn4uppjQKVWk/6Jl1 hoGRnvkUOzUU+iMjuJBIhEGlt5066WIoNMaVyWycDIOhCs1O54RQaJOOIjSl B4Oj4g7NE89QmH2mUi22DYacrbfd/XEAhaX9tr/5EQ9D68vD77/9h8K6qydk 2Y7C8Guyva+vHgqbB+Wk7heEEXcf7Yvzv1H4NGlTmOZ/R1GIdeXvGApfvn0w tiIJxmT9nx4xjEHhyK1JO1aOw7hY/dnD/j9Q+B2XHovnP5g0/uWu9B8Jheti 74a9SoGpCwXWjIF0KNwl1EHod4Jp6/5vV0tfogij9NWlI/0w80zcVfdwM4oc 0SruNxqG+YuJq4EBn1DkfCnjieBXsKDK6qc+vRNFQsGuveQcLEzmdPLEmKDI m70L31XHYeliU7i3VSiKbBzRqBCYhlVfwt+U6fcoyvH3hq5tHqypvvxgIvYJ RWVOtRQ+9Ia1oXkBKfpGFLU4d/EN/TxsmHXPBpGDUfT05CcZfAcbi6oXv/F2 oegVH/aUAD/YjL/3mZ+xA0VTAt4+nabxWzNb2vX9nij6jkrdoUjzz6r0Y+Fx Gyhaf9P8vqs/ErzP8E9vv4ai3UzJzCm7kJB3ju+4cgSKzkVO3OxYR8JYC51c myaKbWFKDtl6HInirpyzRg9RjM20Wj/zIxIPzfyot0tAMb7IiTUTMSReM7Y/ PvQCxSSaud/3hSDxhaYb8yUjFFPm0PcL+YfELwk5kR+dUUzb2oUisgeJXXZ/ 7D8qodieJ5EzpalInDOtUI20QTGz1sK3R0hIIp28xsJejmJ2/J0ei05I2nrr geh3FRQ76bBF4THN97Lldq3Ei6HYuWTFYXUp2vqfXwH+aSjm+8/6ZdNN2vkN p1YFBhQLlgg47d6PxHlJkkN+LordcX4hzmyMxG7rD1LMF1HscXp9V3o6Er8+ 2/6AwQ/FkoZnE40YkJgmQJ/J/ATFMhUFjvS40vB1k1xPBaFY/nkD3qAamn/f Vj5oa4ViZW/dfgnJIVGsQSbjvA6KVc88ivoQjoTRPXuGYQ3Fvmt8sLQbRkLO M8ZdL2VQrONSz7b5/Ujw5NO+nM6LYlOrandUWYBaEea2w+gniq3q2Zs00vS3 RN+Ng78fo/iWoJAtbvWwGewZLVviiuL8pObgtLuwIWnPfCOoD8UluTndKKaw fsGqUPcKK4ory9pafWWCtSKeE6a/klB8j1mHdN9NWNUKftJ6QAjF3aIHGyUC YYnbsefMqQoU98mQf5e/Exb3b4rMDZih+LUPHs/2LMNCUDUxOYmC4o/+znqf ugRzY7HXR/cOo3iJzCbfc0+YHnhG3f4uDcW/6u4hqSnBtErz1TBeWtxkdmPs 4yhMrokTle4EonivF3NF9xkYd7jipvNmFcXHQw9kXpCBMYmMj0+P7kbxxegH jzb7YWQ5i1X1HAdKsHzgdhE5CUN/jlobjnKiBFeDvXmOCAz2zZZz9exBCZG/ CTr6nTBIt0tf9/kASshNdYl/i4cBbRIGvl5DCQ2S+FZHe+gP2RIqcDEAJfS5 Ts9P8EBf1/iJV58+osRemfSuwJ/QZ3NIX/qCJEoc0hmpZY2C3n9FKy9f86PE MTOlvMRD0Hvz3qV9iQ9QwsXxQoISO/SimlxJVBNKXLiQf7O0CXp3DDRVmLuh xNXrC55mkdBD/SfGsO0FStyI1jncYQq9W3alJN0AlLiXHrDHnQl6xai3uJzZ UOLp+wqF1WrotRNOCb/0DSVSGohcETeh94W3V5KyGEq8+Wu4KWAAfUxVZMcN RpR4N3Vr6DUB+sJquZYeN6BEJbHuu24F9AvwXZJ0ouGt52ItqQuE/ipQY+ct QImfMhZpR3bBwK08xusCVJTo0om6N7wKg47NvE3d9CgxbPrr8pX3MLR/6MSp 8lSUWL9wzDSWQtO/vQlfzedRkuF6MkV2FkZvMqbz275ASfbHPcLFuTBWmvW4 0zkOJaXeu063KsPksf1dLh/LUPIg0fspjwzMCjf3B+x3QsnDO96FvOyH2SE6 Mvn5IZQ8Jb18TjMV5grlBJdyqCh5yTRI31YEFg7fLH96sA4lkx7f6YvmheWc Yif+E7R66cs33ynywcqla7EuO1VRMtcx+NZnAVjd2eC+hcUDJb/IXpKfFqH5 q7cVHpECKNkYeWHtthisB0pamD9ZQclfM+e+iUjAhmZbgHizO0oOl570OiAN mwl9+5bOt6LkrNixPX0yQN2rIPql9RRKroXZ7fCXA+p4fG2+wVaUYrMwK8pU QkIUQ6Gnww+U4i00CQcVJLRHfQmIu49S4vx7jvxWQ6Ko6a7Ry94opdGntbFF A4mJXjL/pKdRSn+felMCBYk/Q4qT1PhQyiRb6TlZC0lMRM2pnewoZckpe7FO B0laDdvMAnJQ6shlCcOTO5F0slRnyU0MpZw6hbmW9JAUln/869PzKOWxh3fw /m4kvYweXEvbiVKX0jnfSyOSKs04Tme8Q6ngrdvulO1B0q9PjLZLySgV7sV0 zNoASQODo//tP41Sj37RKY0aIWk6t/dq3DeUStDd2LxugqRFXomHScUolZa8 /B/fPlrMPVvWr4xSOXRzKW9Nafuzcyt2i6NU8dlJH5MDtHy9zcvnw1Gq8tuI 0V9zWr3yXx33ulCqntzP42vx/36slXguotSPp13DWy1p/Sa0q1YpolTnetuH VCsani8zNbKNKDV46uddXRsa3k76a3+rUWqqutnhPzskaROskhniUWpFsV7l jD1tXpYttqnDKE16+JW6eZQ2zxmxzRl2lOY+WvJCyRGJJz9J5apYo7TIx3e+ X2j3k9h+fQtKJ0rLSeWaHD2JhI7vGQPVuSi9c/LlSLgzEvYWaTnLxaD0ydBH qv3usDFrHpaqL4zSbkORxAAP2NAf4fkuswWlfQ7c+sHpCethFonyhEWUvsUT 6Ie0+5g5t/DyrwMo/eb1mbLEq7C0/CjQ8qc8Sr/b7nRfwx+WyHsspxwUUbrC 1/FEfSAsephe1hIIR+nvu61JyyEw3/ehOaZmE6UXW3bvt7kNM1Pr/O81C1GG SBJoKhSAGeVLXSmC7iizVX3JZkcOTHul1N9+I4Iyoo9yj7f8hklu3canf4dQ Rv5TZD/5HIxXmVy/9FUXZTRm3NwebcBYWNSW/LiLKLPvkITvISkY2SWtLXar D2WsQjZXct/DMNlZrY64gDIOuR3B7AdgSL/p+63mEpRx/fd+y4V/MHic4tDe qIQyF9mj7zT7wEC05aZjRxrKBODF7aoM0P/vm3S7Bx/KhHkdjL4fD/1Gegu+ l1pR5sFzBf5JFej7KPr5lAQtX9x/DM8Ofoa+Q92ZyYq2KPOC2ieVbQe969/J jOGaKJOj+vE16yj0Vmpw8Bmbo8z7E4mqHkHQm6g89WinI8p8fnClsJEDeh/+ PKQi8w9lGj/a7lRMg95kiYhgszso83tK/eNdHej9stkwJsOJMj2ibEajjdBH 0sW6a39QZsx8tN70JPTZRxr4hR9GmYWgGotX89BXnfhM4dVzlKHmvPjFfBv6 zVbmDxm8RVnmrpCjbgLQPywqxKgairJcbA7dtTkwEO+dbS8YgLIiu3Vd5Axg 0Gnv/cqSIZSV8+Qevf0bhnb/jXc/pIayet+aFkw2YIQ8k6UuFYiyJhtv/NMf wui+mPaWmp0oe0g5nMAgBWMXDu5uCbuPsi739rBUHYCJsUc5224Joez9AwVi hvEw83FHknL4BZSNDXzw8oUKzF4rSdyTTUbZ1CwPRdJnmINl16qWPJQtZpXW /DQC840X2tSiI1G2u+GpGerA0syVJz63v6DsyJrPf88aYfkDv5xE9S+UnVe0 tKWehJWgV2LuhodQjuku88mK2zT//ZMsxrgN5ThLBgZFBGCtjDshROw8ygmN fna/lgPr3tEG5G2+KKduGuCn9xs2mqpctcnfUG6X/+G1xHOw6WOYmZWliHLG rykhaxtAZX9abasti3IW7RwMxx4CNV37wzWtKZQ7yjxxt1QKCfz/nXn+mhXl nHXrOQTeI8FBadvjgeMo5+mWHuN/AAlxCxcL0u6i3JW4G4Jt/5DQshRarngT 5ULrTqTo+iCRkfjKqhRRLtJsnu25KRLJc43uplEo96QpPJBRAolHzXjYBFNQ 7tkhoZHzK0gMHNszc/UZymX+yLP7+R8S40s7cMYK5fLtjL/uykRiQaa51g0a ntI/7eqpwUisebb4cP4OylUd83zGbIfE3zEKna9UUK6pi47VSwWJ/0KNbour otyfk0+vttIjceBEWnFaO8r19CkO6XfSYuV3oy6uKDfmUmmTVkjTl1OjBweJ KDc/bPN5610k/souGhAvQ7nNcyOq3k5IrPa+6xMQgPKME9cS23YiMf9gzU6h ZJTn8OJkQQ4kxlm9fjg7hvICs+mX04eRGBBrfJbhNspLXdo5sI2mv4/oOlXa X0Z55aVmK98Y2jxsLipP2KK8tv/pjx2etHltPXuZwInyuL6sbGCMhNYbWa45 BihvGhwZ/0oICS8GxsdeMqG8DUmcafs8EjyO0yWFFqH8Gcb9vX9TgdqfyMVV +Rnlve50WRpdBepJ+/0fCD9R3p/Vu/yNJWz++qP8xDEE5e9xJMReocLG25lB 47+I8gWCE+ZcDrCmwnku6tFflC9Lvl4aoAGrYQeP+HdboXy1OI9cLwustBsZ cEiKoHybDNC9/QDLwQ84D/S2oTxV7eEHMx5YqG5djd1/BBWYCqRl8iZgQVCq yoN8FxU4NT9E832l+a/ru3g/tqKC9M5ez0EfmJO59t+ew+yoYGZMkbr+H0x/ vGRlGPMPFWxqah8NZ8A0eefa7g02VDhu6kC1CIIp2+pLtZYvUOFM44xHkR1M Wr8OaYvqQIWLFmHtwsowcXwgnXQtABX8WwT23twC41fUWHJi76DCTZucwtFO GHuu2akhuogK91oNJQ4VwGhrTP1zi9+o8PRI64P3ETAqMnBvo1wJFZ53um+I noIRvwy/Q9W6qPD6BOHcLR0Y7rZhaMB4VCjoiW6dYIfhIzaJx04gKpQ7yxtb D8HQwBaxLc70qFA9WJ5fUgFDoTlVfFslUeE/Nysx8RgYIj/6EdNjhQptY4P3 ws/D4HxHUGu+Mir0efqvThnBYN3r2mh9Y1QY/6QQve8qDOY5Xur5tYQKizva lVNyYPD1eJPWdgVUoLreqV7tg8EiMxsfU0dUZPqge9KGDwZ/OEZYX3FARc6t wyvZB2GIuMrGunwAFQWPxz5mCIWhPSOfW2uOoKJU3j6lE8UwFDW8RXVnBiqq 0C1VvR+HofnUvjQpd1TUtss4wSkOw66lF1nWplERX9ktu9vB8HA1a120Cyru X2d49PUujPibfiUwKKOitXmRonAljApUFWHtR1R0SHH56rcAo3UZN2/eWUdF l3lux/8UYOyWbS1V5iUqXonzfRj6GCZk/Olf3NZAxevjUvIdtTDJFF7qOC2J ind3//hCWYfJhZg7/51YQsWkfvLCkCtM+30SdTzwHRW/qMwcO7ATZpPPX4mf PotK2756ynD3wpKJWjmDPzcq8fIKf7zAC0vtZMZI2zxUEnP7Zl97AJY9zjpd uNiDShrble76F8HKneQlLi9DVNI71Sn1YwxWd5TIej24g0rGhXcrlMVgNX4P ix5jBirZHxmd7o6AtfhhgfIrUqh0Kis+QucjrHPpnY0p0kalc1RTyUfzsB7u sm+u9ioq+R5aKRuTh/UlRsrWcUCla2mv7IyOw8aJBot34raodGvJfirpMWx8 PpHfV9GGSg9NmcIXa2FT5PJLsUoTVIpLfC9hsUHj64a2qruvUCl16kzpKzJs fhZIbzY/gEpZBry2pDNAZZbTFo2TQ6V30dWTxxKBuq/iPd19GVSqGPK7Xfgd qCE517sKwlCpdqeMOBsDUHObdrU716PS98hfJWd2AfXP6mpQCy1fe3eYdaUX UFf4johEOqNSP5kywf8SCUzhewkduag0cbP/lnc7EtjP6aXwn0KlxdZo0cbt SOBSbpUVFEFlgoLhB2kjJOxo0aOPmkFl5sA5q6CrSNh+ZKzB+gEqczanjrXm IIGhvuKnSAEqC0lYhan1AXX+WEU8aT8qS18iiUTwAfWH6HVJcEJllZq84r4D QH1NkP0xPo/KOgKnDuldB+pVyUvO2r6obKA9m3x8H1B3PXr5S+MoKh+wvTEW wgabC4cf+HnxoLKdD7dOyi/YTC//4JxBQOWTD9PDviTCpiVXjPuP76h8Lke7 ZeA0bMwUasazElHZt6FOjFEBNu6ujRWXS6PyHYaxEtP3sJ4lybPLPx2VH0td Y/QIhnVyrzyOfUHlJAM2m/smsFbIWKzySRaV866pTX7/CasZ19e1nOxQuW3O T9J+GpadSClb22JRuZ+D0cu/GJbqEx+wZFFReVIltjwxCJbU54QFfAxQheRW evgfKyxQFfXhhiuqKPwjRJ6Rhbk03rNNVwZQhbLxqC18EuY4O5h+jjKhCghK yrwuhNmbc/T2hqKoYm1nXDlpADOXDK6VmISjiqNPKxs7M0zPX99a9PMCqpx5 dPaYOo1fr4qLEd7JoEpAY8TCpeMwJVfCIf2NBVXCRoUMnkrD5OE3DlXMLajy kDH7AY0PJqK/H/aQ1EGVeOndne0FMP5vKDvv53dUSTP4T37dH8b1EgLPzP9B lZyTpy6L7IGxzIisw4/jUOX9tdmvyARj0oYOgYF6qPI54SbHqSYYLTA8diCI FVUaP3Afv/EERq2W3oYX+qLK79/pb146wOiWMnUm39+o8m9ee7lGAkZqO9S7 riujyhhHnfHwCIw8u6cjX7mBKguqRx+z5MJI+HLtDylxVKEeGPundBlGwsiG 55xcUZX53DVl890wEr2r2dCwA1V3hLP5e9HDSBH9kf6Bs6gq/PJZzaNGGBn1 SG9UrEFV2S/qXAWPYVRd38v7WDGqqv/7fOrXURiNUI+/cl8EVXdtWucsicPo /Ixv7etEVDUR7F/nG4axi1zbPVL3oKqlrt/+nW9hbFM0uXG+H1WPHmaMcfCD 8YQ7U8tyXKjq7BvbF6QHE/vej4dVvUZVzygFted0MElPpxz7XyCqXnlbeu1T PUw25yZddjdA1dBvB+r7HsJUxpqsaPEcqj5luuAiK0K7Hxn+1PGzo2qKDCF/ 3wDM6NNrn6bSo+obw0fUc1kwU8lYcuztIqp+DCqMy6Hx4w+uZz2TB1C1dnKj qdUI5uzPfHzXPIuqLSf2biGYw9y/g99/ys6g6gC0eVqdhvlF4rd87zVUow8J Fg8wgSVFqlXZRQKqsei8KNveAEvvZ4sfOdahGtt0rV2aJSwblZm2Dc6iGt8p jrvfjsGK3Tw7wW0vqinuebEo6gVrR3BUvkgB1VRXah8WLMDaf0pU/ufbUI2S N6G4zx/WDdzC1p7bo5ruOY6qvwRYf/vYZCQ7EtV2S2idvBgGG7xmard8PFDN oP3oGgMLbFzVyqSk/kW1vVHBT+IfwsZP6u+3TWyodsA0TU2VGzYVbKdr3E1Q 7RCptv5LPGxemQigFhegmm3JhIu9KGxWhtzvDm5DtaM+HNTxNKASmlnfWcei 2glFrfjrCkDVTe5MdbiBaqf7jmlyvwXquTLBsl/XUe1sQnDzawpQH6+YisnS 6p23Tju3uwSo71S9v1Z2oZr31rotPwCoTbsPbjJRUc3vy8SzM1+B2s16/Drz LVQLCOTUXd8P1KHbTO1UYVQLoWj9fNhEi1NfGrpIoNrN8WMXpG1o+4/e/VA0 j2p30kKYP7TR8uUJnP4kj2r3HdLSzI/T6r0tkmEgodpjrrrdvf1AjXat8vX0 RbWnjRNtl91o/U68PE75jGqJYZy+WydpeJxiO9NtUS1FX5vtuQ9sUkfzL7LS 8r1cOPaKsgybFXnvJTJo+V5nhxjWBcHmpVHirOkFVHvrktZ1nA42pRut/qaq oVqhcN2V2XCan3ia9TC1E9Xe/5rccZsNNrweCQ+tj6PaZxPtfbl8sJ4CV8YH LqJa9eaxPqMkWFeL/+jnfAfVGopCrrVJwFqJl9XnkCeo9kumroCkDKsft/UJ 1+xBtWFGB2EbA1h++uRKLd0lVN9WGzKz7AFz4wSlscsHUJ1zkMf14k6YM+19 7dQ/gup8dG/aR5lg9s304Sydu6gusfv3l46XMHOZzurIqhOqyzp46Nj4wnSv vq9fnBGqK/kTs74ZwLTVVsOq1SBU13qn9KSiCyZv7A29nXEU1Xe1fGbRzoKJ MIbxpgQhVMfpw0G5NH6LhhyCcSKqm2wbn5PfB2N5o+YeCdtR3Uwx9EwqD4x2 zSr6yJWhuuV+3k6BfhgVotxne/EP1W1dsy0f58PIueAm1yhdVD9606CKNQSG 66PC9Mt5UP1Eyh/dMAsY1hdoZFSNQ3Xnj+ezN0Vg6PO7SMffFaju9pdOwo+m x45NdEdetUd1z9XYmKkSGGLWC/qaE4bqvnwqW8+Gw2ADo9HjbgNUv6r5JbjH DgZTnldyS9OjepC1/fxRKRi8t7fdXIcN1W94TZ5tmYXB+7L3uQLzUT38/o2/ ZpUwmGaXX9X6BtXvZfEf+nofBptb27fL0vBH1eVU6zvAEHscDpueRPWnQ0Y7 ixRgyNX3R0egI6onbWl7q7ICQ9/VTBKuH0L1VIkLkhm1MGx5bXfbmAuqZ+CW WLEYGO5bidK7JI3qWY7xrHHOMHJX9elumw5UzwtQvc5JhlHjmPD3nuaoXhT7 deEuAcY4o9Y+xLWgemnR0XN0TTA2c/mMSUkjqlfNhFktuMNEzxh3n4wzqjds F6jx3AmTM/VnzCzWUf0/pdxdQ0wwzcT/SmbgOqq3n+mQ+pMGMzvNLznJvEP1 7jCvuEPeMNOpzxjd9ArV+18wbKtHmL1x8A+HtCaqT3apLZb+hbku385zbx8g mc7mVu0zHlgsNtYscBxHMpO3kD5vPyx5ddKr2dcjeduDvLyH+bCscDkiZTgJ ybz1nfGhFrCSwBIoYx2BZKFhb7Y1EVi1fJWlJm2HZAkGxhs+47BGl3xc84YR kpX2kD1cwmH9+IgN28MPSFY/XvOvyw426JeMtTmckawV6Gh7WAo2MhpM86AD yVgcvntfJWy2nTrn9bwAyca/hPM/3Qfqmc1gdeGbSDadLZDd6QDUKb4iiWIK km2Vu9gVV5DgfaL2NUs+ko9qXBdIWkRCpg7ryPwrJJ/QlZL+v39su3v7+yVN JDvvrlG9PoNEBoXOrtC3SHYzOqc7N4lE5dHqiLb9SPY03WbkMoZEyzRIZWhE so9Frnkrzd96UjJ/J1Qi+Yqt9ZH9A0i8dW3UR5YbydeOLp4u7aX5YdcoRi5j JIeejPNU7kZietvYnGAPkm+76l15RvPfObV8ZZkVSI50777B0YbEPBmRHXbC SH50MfTejd9IzJ6Yuxx5Cckxl6WfLvxA4kuRiEsKT5GcEFibeuY/JMaUtriJ 1SH5eah7Vts3JIY2FTcOhyP5ZThbsVk9Et1cRBmqq5H8+l7ep/IaJJo+av/U QsP79rFNo+pXJMp4JP0sj0FyYezS75RPSNjgGP58tgrJH5Lje3ZUIKEpdbZP 4zOSK9L0x8JKkZBg/GD/ui2Sv7z6t7D0HgmnpbYF3FVA8rd3siwd+UCtz2Do HZRAcktJPfdB2v2BQuc1Q92R3Fp5XvRjFmxmv/2vzmQTyT0NBZQX6bDho3Dq b6gfkhd64bh7PKwWvW6pONWH5NWh3rN/n8LKOvdhzXeuSKZOhPlYRMPKbvmG b+oU1GBebgjXuA9LJWNFWtfuoYbI9sN5a6Ew/yqS4cfWGdSQ5FotOx8Mc4O9 LSYTSqghJ5BU3R1I89v4c43ZBzXI0n0dX/xgJvsjo/5iPWqY6HnR36W9X32/ zjxbaUENM4Md2zfOwvgLi1e+u5tQw3JvkcAFVxjz6nHm366MGrYHj0j1nIbR gyk9wlfcUeOo1bqq9UkY2ZnwPPHYAmqcsH+mW+UIwzoNZ+lsUlHD+biBkfZR GNov6lBQfBc13E4PmL86DIMefooHn+xDDU+38COCNjDwIlnPLeA7avhcUDx9 7xD0T9ksXZ2g4b3i23Seag79liZc14nsqHHN/+KVi2bQV8OyoftgHjVCQ7hC +/ZBn43CbuOUq6hxO6z4nq0x9C7zMg1kS6BG5N2jT2sMoLdArSr7LidqPHq4 kaoL0Bv2+X3ifBtqxMQ8z3qjB71edEUzCwGokWD87I1YB/ReJGvFGOujxrP5 5Fcx/tB7W9K7m5HWb9qLpExWPujN+7GtWO8OaryySky/Xgy9M0qdSkE3USOH mJC2ZAd9xqzTl9/3okZ+bvwLj3noy4Zc+V3VqFF8Ii6l9zH0y19fdKP5ZY0y ttjn9mTo/3DdQKe/FDUqy58mf/sOAw4d07dp751GlUdMkqEXDO4QD6Vb1UON esEnCR+2w2BHy7ir5VfUaKqPjlfJgaEip8X1OB3U+HH1cWzaQRhO1UrVMuND jT9yUU/5x2Ek+WVc11NH1Oi59TCaXh7Gqn3dhzhdUWNQ80GUfy2MTxvfcTpL Oz/af//htCtMyj5JXXz3HjXmDSLvdaTBtOP9xN5XAkhh2Ay/nS8Gc8pHLFjE XyBFyS8k4AQPrJjE8X+mOCFFXTr46s93sMqiYrV15QtSNH8GXTG1gdWGuX2K JluRAuTAS5pRsL63PpqtjwMphj0BPm/UYYNQeY/1uQdS9j309xb7Dzbe5Zx2 NEhCyqHJKxdYtwGVw44nM+gRUuySLp+/ngXUYpa/P1tUkXLsgJ/7kikSFA9v 1/nOgBTn175ne8ORUDmrs/5KECluR3zO2MvS+M2M88eNP0jxZPJ2+VaNxL0l YpEjjki57Orl9GELEiv4jt/0PYmUQO4Lp1ReIHE2RzDrFw1fyFfPE2kGSJLI cLfJ0kFKmM/54/z/kHSwe+6X/TekREh4ODwIRpKv1qjEPtr6/e/ux+hFkPTk gSK/WQFSHoecO+JfhqS8Zivm/gqkxKq6HZ4+hqTansZT60eRkth11s5lFUnt haNexaJISbl3xqYjDklDOyfIe0uRkq7nan1IB0mT5zj417yQ8nrM5VB1K5Km 9Z60kGKQ8jbe2VLPD0njuZ9Hmy8gpXD/afN8biT11Y74HbBByvtlp4OyhUj6 ddfp3jVZpJRnnDJLskLSZ1LEp7vhSPlsd9KUcwZJr/RKAmJvIqXm3H8SHn+Q FH67yi5YBSmNwbBaXYmkUx3uLrWTSPn++O0PsUwkaR65JNRggJTfmaJv/B8i iU7ul6lBEVI6yh7c+HkFiQ2Xbt14HY+Uf/9RHVROIvG+lzNLMBdSRle6WXvV kEhv5q5JOICUaTbzAT0+JHxI4zz41xMpCxIV5TEEJLglXJIboeGjmiV77m8G agCj8O1fjqi5I8mxOc8T1gNC90ouMKAmX963zK12sJYz3+UxzYeawtX6IS67 YbWr2Ks0lIKaslPC6vzbYEVra2DfuDVq6mFnVEgWLPzxscv/lIyae2wPnGuP hgVS03uT0hrUNHErM6QEwryqnVJ3fgBqWkYlzA2bweyjhh//vB6hpnP/MVuL UZgSfdLBHLYdNd2WG5RftcBExY4GVUsv1LywbRcDXQmMu7+oFiEHo6av+Osu x1QYk3fPkS6JQM2rWgJFxXdgZDm0difBBjWDTCMecHjBcNv01+cPn6HmjeOr Z9ztYagp69fHzIeoGe5zDqoQBlsTf/VspeG7d7udT1QOBhaiA7NEaPWiEvdP X2WHAXmbl6xP3FHzae6H2pZl6PeJSFMibqJmYpV8itI/6PvZnM/NSMOX0hZ3 9VYt9B3MdL71dz9qpk8yH/qXC71dN/UuSN1FzSzSVYWdsTS+Kze5oGOBmnk8 I6ToEOjd/7Sp8tkGahYp2rdPnoFeKQO5knod1CyF2oJ95tDL+zNCYU8Yalba 6NxN1aKtW64+jd6LmlVnM0+vi9DOv7C7s5SPmvXX+PTsGGj5U1w/RnGjZvOj cK63k7T6W7caKgNq/ny5NMH8G/oORPhrblFEzbaSM1WnK6CvpTCK5SZt/l1N rUnl6dDvxbx7SakANfv69vrx3ocBybk5UYso1BxeKja/6AcDE43qUtd7UXOS VVam4TgMNv6Wi2gZRM05sRiqtDEMVRqedvlGm+eyJkNrsBIM12jczIr5gpob +/3etnHByL93PGG3SlCL0dvuZGQ/jO+1u7yxEolarGoSSqOeMBFQJfNL4x1q sU9MrOxdgsmyNAutku+oJeAWFkXHAtNv26t35zailvLJwi9X1WDuVsXZsPY5 1CKLhDz4XQLzRkziRzteoZZW5wEHiiEs0Gl8iFYMRC083Dc/ZQeLNztcxe6I oZa1xQ4Zl2uwEsaw27vuM2rZs3bNfmGA1T1bLqnctEEth/rXH8UewuoqnH+3 dw9quZoYHKbx9boThUkyRQW13LewSemqwMb2NrtBUzXUuvCpbTqmGDbeP35a u6CNWlf1L945VA+bK/vE5HvTUOvamr7tW5offWgeMvAjBLVCPzBLsP5FAvue U2L/JFHrtt/PyXOuSLApY5osdUKtSMrzkpppJDw5XSZg9hq1Hs563Jb2R0IL 045kjReo9SRXx/oGHRJZAnq3ONL6jffcIvovEom7n9g+8+1BrWeKzeO7eWh6 UoWPfeU0ar0YSXif8JymHyXd6VJp+TMzztxckUfiRzuDLbw/UCvbReOQXT4S /7054mxqiFr5ElThQj0kbmw15ePLQK2if/WjHNVI4nKKDzy1C7VKk2OKLlgi SSalYZ2dDbUqHZxCG9uRpFHucsegGLWqBFQsFE4jaVf2ap+4EmrVta4K3h5H 0m4vtRcJ4ajV9KRqaIDGr3pbyjkbu1Hrh/WjQkMCkiheLpVHKaj1h8Mx5HkE kmRLVwYKs1Grs1n+wCYXkrjn98cU0Z6HnsgF/mNJSKQq8Nc+j0KtQdPKgQ8y SOzz5tZd4UGtMabIfJ5cJH7uL9lnP4taU9X2Qb47kZiUbPzl/hRqzd+UMv3+ BYk+FfHNC7KotbJnmlf1ABKN3WTHJZVRa4Na2hdJ08ucX+vU3LaiNqn89tvR //+fvpbxr3AatRkDrAP3jiAhZRev+7N41GZfHOMmrSNB7Olg8I3dqM1dWNRz 8hZQi/SqJicvoraAd2h2xXagSm1mcpJo5yUnBUyuSsDGTMA7ll4V1NYa2H95 aj+s2aiVaVzwQG09plsi6kKw+qSJzf6TPGrvUfz81XsSVn62XaHOcaK2mbce +2I0LJszFf0IfIHaJ6gqrza6YAEjZSsrllDbRcLdEnJh/rpZqvcXAmqfM85Y vH4D5qrYP7kz6qC2b6S4Ib0czNpbLVZz2KH21beOIyarMPOKXzjr9hfUDmqJ fxD+Daapl9otiI6ofYd/RyerN0wFPdFtFnqN2vf1LEPNjWDSuiwu4cUyaj8+ cU/uIS9M6FyrOdmgiNqxoXVN30dhXLm5iu07bR5J6QyXdpTBGCXOdE0vGrVT 6wwFbe/T9O7b5fpdwaidMR786ekpGLnaGtOdrIba2dvLzrRRYPjDJ7ct791R O5+8wibIAMNsW6O6J0tRu9hOs9DhDwxdxvt1efdRu+yq99HkNzC48Lv4Km8v an9KfEv4FwSDEar90xYHUbv641iGhCUMUuKENj8SUbuhT+6gswSN700PJ3Jp ofZ3Bue5lwsw0Oh6+EBUKmr/lk+JG6qFgQ+rT97M0ubdcaAL5eNhoJxqJLd5 CLX/eQkMunvAwJ/DJ4fXZlB74PHhyGyAQab/uosaL6H2aFE0eYoTBi2kBpvq dqH2VNt/bWr9MPh6WdT4Aa2f+Y1twd5FMCTYwffOcxW1V8VMpQvvwFCK6cdG ZiXUphrealh0gOGduUPQuoY6W1y/eOuowPCQc28aTwXqMEcQ+PwJMJKeRQ7J FkEdtmy9itIfMHq5dA9d9C7U4Zsr2gpXYNwsL/v8q3bUEeGZzbtuBhMHCsae ORBRR3Kn6uEvIjB57F0EpdcedZRDMtNMvsC0RfZgzq2jqEN+MWAaHgPTwx3p KkJPUUe7Rny6/izM3PrEdrXPEXUMtiXom7PBbJPunsqgaNSxi7/329YeFq5O 9FjErKDOsfL6wKcKsCjLdOaLrQPqnOxhlPizAYs/ct/0U2xQx1025IJDKizL b4twkYpHneBCH6bT47CaL1fWEVWIOjc/lhslT8LaXtlz+kpDqHOngTGkbQrW fi9PNJeEos7jnoQVi1lYH9iWzGnlhDqx44OaEXOw4eRAH37qDOokLatdrJqH jT/RT2yqR1Ank+3r6K4l2MyRkn33awN1sgW2y/gtA5W1vMp7jpY/X/qIU94q UJ1q3N44dqFOsdqL5PE1oL5d9PhrRo86ZbsmOmTXgTrPlBmVQkKdT3t1eJ02 kcDrZK/osBN1qq1CrZOoSFDdIO7qyUedBsfGB200vWVYGUup2o86/7nxNHKR kGCVRLW+1o06v3xPMVlsQYJDRPnaAm3+7cFvjCLokXDqOtMWNdo8uiMWQqoY aHFw5YuLJ1GnPwbKCUz/33+V7tFN1BlJubOyi4WW79W5y46/UWcy66em31Za vREPh4/BqDP3XuRiHisS1I4NvAv5jDrLX85mj7MhgY+nJMzrO+psNOWPyrID dfHsOQ7BFNQltW3IOHEAteDlr7eu/1CXcWCvUxInUM8q6ilxyKEu6/Sj5D9c QOUy0YwQFENdjrWOjh3csPn+gNin832oy8sow2vOA5s2cc3//YeoKy5c8uAr P2xcTm27RmFBXRn5LQ1UAVin6lZThNRRV5FiwbRTCNZDmqrP7vuLupqmfSG5 IrDmM8LwOrMfdfdfYb2YKAUrco+D7QcCUNf8hl12qwws309n6L9jhrrW95+P csrC0hxjky2LMuo6vtR0Cqc9bx/Yvz44noO6F1uOW/uqwbzpq8uZZTdR1+9v 5oO3ZJgriGdniHFG3YDh2YZRDZgTDSLseJKBumHU20YntWCWdLDjcGk66t5l +R6SoA0z/nvNdZl7UPchj2DZbx2YXjJX9CIPoW68Uq7mgV0wTXJXcHtdi7rP tFcv3taDKYlvh3MdTqNumqFR9ufdMGnuQ2J8loi6r8zvj2wATIRHflT1n0bd nCNtMjoI49+/dyR5CaBugYukk48BjCuMtq++60Ld917nk3MMYSzaq9Ms7ivq lgcUt48YwRjbBOsHVUbU/XybxCtlAqPxf+SDRXhQt2ZX5l6nERjd/bzGkJ72 ezRMm19OiYSRWa0Bm6NsqPtf2nzGPzUYefeTbVerLer+OhL/P4quPB7K743O jCiSokQphZJEZS3bPM/sM4gWJKGI+KJCSkWShKyprEmrbC1SUVlTiaSUlBTZ jX3ft9/7+/N+7r3ve59zzj3veT6f8VErWw2dVx4ccJXUQJ06Mdoim9PQeayU l7RZB3UaSjp2Jq2GTnvDHBvWCOq0eEc41xVAp5Nm8W99gl++ikbCqsPQ6Xu2 ujXLD3V6Gn+XWwpA5z3yU5NHBH+DMRcmYx9C56+Hfi4rN6DOmKHi5hoedK3V nMx3IfiZmvtstbwHujzdrr9UJfCZf+4ZujcKun7OZ3FdQlB3gbP0m2h16Da8 3mHQ1YS6wmsKu77+hO7PJbvnFsmi7pJvjjJiZ6HHRqo+KdAGdSWCFhvtWgM9 05Ivf4etQF0p3Wc+YcXQ+1AtpazxBequvz/7V1gI+uUc+g6DEOputHwgykmH /gEN09eaxH7lJYb6QcYw4PDxdMS4EepqnI69JXANBo193kXNJ6Luzi36lXQt GCx9U7kouBt19f81z/rXwhAze/vQsV2oy+Zts5lbB8OGK78qheii7gGZMtkJ oh86bryQ///321QdN91hCqOTU3xBeQ7q2l9eceHUEIxdLtfIO8dAXde+w/+G dWD8ZruQ8ZcvqOtXPHmntxQmX2pyT+iKoO6lU7e/qfwHU5qjAVsvRKBuiDKb 7CoKU892rJ0K80Td6OvX7Ph7YfrWDUa+4BzqxnJ3XlMchRnR1ifi3faomzjT UOIYDzPe61ovurug7oOjW+Sb/8Gsbs8X1gLi/Omrv+2TC4DZ6zVbTjWcQ93H X70vHVaE2XYLbdWYKtTNDlz7PLkc5tSVpO/X56Bu7s53LfVuMOe96KriUBHq 5vW6rFizFOZe5G3oXOaIusX3ljGtsmGuW9hmWdcC1P1gkeOVYA7zq0q6mmfu oO6nxdYPfhH+iwU19deSUPdLMaVm5U2YP/QzOeVQMepWe6ULmlNh/vSAzYM/ jahbu9lU63ozzF8e+ct8I4q6f+tHHb9fhvnwH2lpe01Qt+nazVhxJZgPC1ao 16hB3XYOrdT0M8wHiIR6JBijbtd0x1jkCZh3PzLuspiPuv1ZkYqVhL9Z3La7 LrQcdUc+pWzvn4N5tQTPr546qDvZmq8n0QXzAjC441Eq6s7NVbO0fsJcRYGI p6YW6i2Q7jK1LIG5MKaAoeth1BPWIB/weQJzdPFF9ul3UE9sl/SR5ESYHXAe Y+63Rb3lTtuOvQ2C2diEVZQNf1FP+iLbu/UkzGqOtaRdfIV6a2/aBCw8DDMV /24Xm0SjnvxLr3BlY5g5+Epy2+E1qKfSee+O+waYduq70OtwAvXUBV5nXF8G U2075q9E70C9HWurXrycgSkbjtv4D2fUo+2ZK5v+AZNQ6bfIjY96bDfJatli mMi0CtEbD0I9oyCVetojmFj+m96/WQT1zN9YDQZfhrEGUuLm8iTUc5HPXb1c G0ac3la5+uxEvRP6XzZoy8NwRbiowmQz6nlZtG09IAbDatK+H6SHUM8vbDk9 uR2GBBrei73XRL1LKcrGb7/DoCc7VmbJMtQLKaJZtBbCQGvFce1Fvah3bfiE q3Ic9GeVqfziL0e9+CVBp3YFQt9L8dcL/qtBvVubbl1wd4feD78krB6Po949 2osr162hp1UsOt5yGvVSD1Zcz+FCz7J7yqF1Haj36FTzLSIvdhsfD7y/9TTq PYuaTJ1ZD13x9HX8c1dRLydjWfY6Uegc+Rssu1IQ9fLeb8qnTRD+1hvVtuAy 6hU3UEsdWoHfsiL9hlA96n2YMK8KrgL+qUa9k4FOqFch4VaXkQ98mUqJGQ0q 6lWpXGqtTIeOGlZzmkUy6tWwE/sGYqHj3r1vvzocUa/u8LPJ5QHQEbhZdkfD TdT7d65cQPs4dJwT+SrqmY96rTcalxywIuaPnnusRtTX+WRcypcNHfc5su5v h1Gvr1xM7rY6dPx8IKz5YhT1hls2bimRBb7s4dok/RbUm5jV12oTAf65XVfE Bu6j3pzUPlw4BvwuxfgTpGeoL6DmYqjcTPh5jJ7bLT/UX2R00WzXF+hasC3F MJ6C+ksc423d86Dr8Vm7nz9FUV/iwlPn62nQ7dzyltNlgvpSCaWeOTegR+uL +Z3VQqgvVzkSNOMGfeRdksf921Bfkb84ep0l9M3tonjGHkF9FYr8TToTBhZU i1zwi0Z9be3dT0PWwKDCZ8Ggg86orzv57vGTDBh8VpXiY1mP+lCgnVmzk/DP g1obP6ujPo+5Jk3eDIZPbQlPNt2N+tZ7+Hfyw2F0Qq7Z7NYN1LeTPJjcshrG khr0iztfor5j7ZdbwmkwTt/8n2U+MT5x6EWCxXuYCDe9rXhkGvUDjvlHD0zD 9DJJSNV9gPrB24evSoXAdEbo12M2y1E/bORoJHUlzNCOdLsniqN+jI9xaJg6 kR+NuE75DNRPpBaFPCuG2e6jBQXqK1H/NkU9qNYE5k6QhleFKqF+2hXpSxtd YP7IuLcAdRfqPzYOu2hE+NmPgJOvi6tQP3vp3AXPICTJjsq2dxL45FR7nE9Y gSTTzjdxU4qonxfb6lN0D0m+I7t84q6jfrHV/rPt25GU0qtqW7cF9T+s/XRG tBBJn552hMZaon55k8FpDWMkdcscdjuzDfW/PMjyOlCH5IXiAzd0DFG/2lnB 098ZybJn/XogF/Vrt8S6PxxDsppevNR2F9T/2y98vDIQycAKaDAMQf2mbF+3 EQkkc0P2JL/7iPrtp/pdV99BsvHEwMOyRajfrWP/H20rMb6qdkqfwLd/psbJ KR/JHMPv37pPo/5IMdcx0hDJBpsv5xgR/ExeyjvyohbJW7f3k7MmUX+Os9Xu z1Ekr/4vZYynhAYCIncPk0eQTG7c5tf1Ew0WfllhqxSApNYHY2jxAQ1Eo4Ot TZchqeQz+KiEo4G42ZTVqWQkJR1fU/rfCTRYKX3MMkkFSR4PrR41PkeD1X8a LUpeI4l+w0337io0WHd7n1knG0lLD+678uEjGmywL9239AfMZzHE3hpcRgPV zkem1gMwl//oSoPbezRQf7xu1yU/mNvJqPfibkKDHe7XjDJEYfbxrje3gmPQ AMfPcMaVYOay21+V0r1owHrTzV6bA9OdziU1hxehgaGfLZPJhGnu/ZKXr/6h gZkgE6MPw+TUj+iKNwvQwGn5Mp0t8TCuX/Gu8y5Rv2vh1UojcxgL/37z7v5E NHB3EbdzlYDRv4eVlNRH0ODsW4krGWEw4qsmwDI6hwbhJ1bUbfaDwcpIL887 +WgQvTrmOE8PBpVSf//52o0GMaUrKc4TMBDs8+7O9lg0uL1WSjnNA/oeJPS5 aSWhwf2yuMKyrdDr5/dr7xYzNEjzkt7bQfTjRzKHRSoU0eBZxSqfTQ7Qte8Q xyO5Hw1yTicu5chB56GopT6iiAZ58qvvH20g/MWy1sF1HA2KKm/uCEqEjgfe X/ZrNKDB+7MyFQ/3Q3vDzU8VN7+jQfmGpEOlK6B9s/PHvzeG0KCyas1wWxW0 XT5tHRmShgbffW6FCEZA67Ap3eN+Lhr82iS7ZqMhtJ50byPvJer58z05i7UQ WoUMp2Vk7NGg0W8d0+E9tDwC5X90ZTRo23y7NvAitDjeecXKJfDorFnv9oAK LeovqOkXGtGgz/8u6d00tCx/eirQaSEaDKvI3Wh5BS2LXpPCtWrQYPzXvc0C XtAizjc68OU0Gsxcki9QUIOWbSz317keSCVvvb+H3gcth8tV8x9GIlWwTqHN PgNaHlo3xnhIIVXk8oOzAU7QMl93nUv4M3Wp2sYl9zZA67ElEpvfTiF1+d+U u2+boLU3S0wy/wxSpUMUtZqSoe2iu+H+wn6krtV4+Il8ENo3df9cIb4UqXL/ NtnKSUN7Y/YeiQhFpCqGpg5hDXRksvRe9jshdYu2UtDha8APlt7TdlMHqZoR m5/cWQxd7ouDVr2uQqrOzgx6URl0+3xfHOz5AqkGrco//wVCT6zilQ2fTyCV o7dlXnYO+oZZ2z6YvkOqUfuj69Q8GFit6vL9vThSd19T3WR7BgaKjQ6oiIwh 9UDnVtNbgzAkYzlZJ3gRqW5x2++saYWRuTjhc15cpEYPa9FWf4Jp0knWv9uI 1LgFik9lq2C6QsZ3NOoUUpMkpdfK/4SZaxurCgaJ8zzUnppUboI5iZT2+1NU pGZyepy2tsPcp4+Pc+xeIjXLsr5GvQfmfR9UupwoQuqbc8XPdCeQ5Bj1YZ/D PqQWhWWvoxL99XNepVEZsf990v0IBtEvz7w6F3SuGKnlj29Mc0SQTEvcZr/b BalfCoP+M1qK5IvPqCPXryG1+qv3L9MVSM5/v1H6tzFSaxv/Y+1bjeShW2++ y8kgtX7Q6vn+9UhRWHK0cj+BZwvFWO6gIlJ2dfBqa+4hlb/cIOqQClJOLnBS Eo5Gau+GrbNH1JFyjV5G3XccqUNa612ddiIl83LSocEIpI6zxX+7GiCl8OU+ 11ExpM7sF+CcoCOlouiFkJEQAtlp5OVJLlK+x/XMbslEEDrTruBtgpRq7bu/ g7YgLL7yK9rHDCmVCUORh58iLEssm79ghZS3H/wg3ARBMvPNsUuHkfK0xG7N mQCE1fmZf4KPIiXutupup2sI6ypv8cJckXL2xL8luRIIGxoic6PckWK+q6LQ ahvC5n7/jddPI2XL/nDnxbII20ge1+N8kTyd1bJfLAJBU/wI+WYAkksDbilm OiLoyJuduB2C5LCpY/SMfwhUDVb9/Ujie2BYSt7thsA1V3qdmYCkQvMQXUND hF1HV296Svi1e2i/5Kq9CHu9F8c8f0B8/yKPyb5pR7CO7/PIy4K5Bgzz7TqA YJf+719RDsxtiCyM1RpCOPrm2653+TDr8GdJc4UtgvvfF5srPsL0z6Wvh2eG ES6vP9v0p4Ho/6kT7zcIIoSquZr+a4Xx3PTkVY8mEaLo1gUtXTDW+PSFetsf hAQHSOgehVFNP/b9TQ4Ij9ME98yIwuD4xau7zhONd/arsWKSBAyqr6WezHuG kFvO37pAGgY8heUMyqkIb7srREQVoG9R7fLR4SKE0pn8M8uUoCeLJXf5+1WE z0set6/YCt1OJg6nXscj/Nx2tWSNLnQuui+av4Cov66zYYNCM/CFvjBfyUwj /HuwJWjLFehYKZMVqr4VodX2DF9jO7TvoAUvCyfw75QuNdSthTYXvkGG3jxC b/XyR3R/aH2WIXRtvyXCUISdmKEStIpKWFkxCH7GOU/c91RBy7lb4f+OEvXN kKe/H/CG5tlXx5feUkUk2W5I3B4FzTFFY6XeGxApeUZGmkegmUGmCBQdRhSU 9pjZuQOaBV9JZK/ah7jwVPxj/cXQ1CCuqHxnAFH4e5EtNEDT503X448FI4pu a1/KyIamKsEJsxwtRLEI0WJOEDR1PlQz3B6IuKxLw8PICpqlB+2O9DoiLudY yZtuhWabYkHyx1WIkg/8q/dRoDm3TsJeIxtRmpwaaPETWhTHFTtW2COutv2i ZZUOLRmFb8XF7RDX5I202/pCK70sWGBhGeK6VTJx9ruhtS8pwVzHD1HuNJ17 dAO0PX4+OGYqgKhQ7Tz53wS0Xzjz5csLA0TF7VEZxz5DxxHOpm9PLBCVInIO etwBvuWx7bIhyxFVuQIFZznQdTL3eg/zLuK2FOXj51dDd/zOxIMjwojqlD3r /Pug51NF35dHBD7a+ckXQ2Kh38K5tr0qB1Fn1Qf1cBcYsChYQPJwRtQ73d1y lQqDC/fLbfMERFTTYcW1wdCpmwtj3y9F5KX8EE7VhNHTbyz+JaxDNKZMvclc BGN6rDzuUy9Ek8Nyrk/+wjjpgG/anyrEfauPV+ZcgokrhQvFyMR5raOEoz98 h+krMt7B8g8RbXu208sfEvk1sstl4AeiHc9iuPIczIwOyr1bO4t4VOCBeY0c zO22G02xnEJ0PlwhVDsCc6Muxp6WixBdCgZz/5bB/A3zlMpoGcQTZ2BVqzsh m9mWS+5hiB41jp/4TCTd744t6t+J6KUe7tMjTeS17qJVS5URT0c9V+nvQbJc 5WjELYKvMz2/64l7QrZyFM93JuZ9DEmR49eQHBlsTJGdRDyfugmmiTxYKGbd eXMBov+CXQPzukjm/0r4eTwGMcDO656AGFLE3otVf72FGDi4r2hSCCnqtE28 vQTewRfV6/vmkGL66XvymwOIoeLi061jSHHirT5cbowYfmdwVV0/Us6kR4WY uCNGba/aUcVHyqV/Bxgmc4jRxU/NSxuREtJCjVHLQryxO/Jkfi1Sgp9nlFq/ RoxtPBadXYUUf7NHd6IfICa4Gz9NK0OK59eBc4qE/pJIWyqTi5FySPOllDCh 9+SrIt03XiGFFZfnyl6PeHddl3DoM6QoLlczDR1CvP+0fJN/BlJIpXH6Fa8Q H0Ia6/Q9JNf8vKtUQuCT9jX4iFsiklOOSdyNIs6feejoRXsCrxOvYo1WXUd8 3M+6bRmKZM2eq20SFxGzLmwoMCHy8igOLLgtj/gyuXlS9ySSnGFquek84qut JVLb3ZAkc2Kndwyx/03hXS1FB5iPiRKO83yOWNRwyEPCDOaobLkkiXLEkuPU qEXGMHslTD2kxgXx/dzaR3NMmPmavP91KOEXn9b+4XdpwbRJI/V5HsHHDxvL wyUrYUL1QJxc9xjiz94dfq/EYNz+pMieCCnE3+dXJj0RgrG4KXaXizpiQ9KP 2oRxGF2o0vrHdAaxSeX5WNQAjNBpep9LCH9pyb++4jIfhv2vT5copCHy/+7Z 7VELQ8Ip9MQ0Yr7bbftxpyoYNH+22zSS8I/emaXhNmUwkBrzoPg5gfewTGUZ 7zX0FYbHTVqfRhzNfNQO2dAbfuRm/8ESxAm98AVaGdDj4tjqo1GEOHvQEOUS ocvqnXep3QTifPdmW6lr0Hm8Uf9gvDXSKD6LfJeEAj82PVPr91KkCYp0JAoQ /fv373NTF9yQtjCxNHfSBzoUZlaoaasgTUQ5pabfC9pDs/41zNxDmuibwOE2 N2hf6DwxdPA+0pYaOoj/cYC2xGBN/mkJpInXMbZ9s4Y25gFLkhMbaStc5Hd9 NIM2QanfSNgXbeUU2bXAGFr/tr05Z1+GtFVXGq88Z0JrRZXXqeoFSFuzqig1 3QBav30d12tMRJpsevKH29qEH5YWhkEU0uR0zrfEbIU2+YtTu/1lkKZQbkMO U4Q2t9efxp/lIE3xgP66i7LQVikebXGdizSlevlFJ59AOxYVLBjoQNoWu4WD R6nQXvYzPHWhDdJUW3vrLL9Ah0PpcSO+JNK2O1e/M7IB/vI+6+3dc0hT73n9 yKAH+DVJZ5LejSNNy/12zHZf6EwXe5X2cBRpumdcnSSToPti15ThvCHS9Gf2 7F6kAj2Xg8od3xP1gP+OnVN50BtnLBJ8gNjPDBEQ+VcH/U1bIuCnLdJM4hKf ZKyCIW8fzca3fkjbI+Mfl5QGw/JnxZ9upCJt3+2j/lE7YfjroED7o59Is0xV 3+tlCaPbys281LyQZp9TPkaNhfHB3fvKushIc9R9+k9NESaSHQQEJcyR5lQY U6aQA5PcjuPx7e5Icyu1S1xUA1PXf/r+cQtA2glDTsC0A0xrSIX5KDkizeOL qmvvCExXeun+l2mMtNM1kwbVK2Bm0OZJBa8QaWcP/FP88ABmz7ZP5uIs0nzq Pyx9pQGz05d/mqyPQZqfXcZERgnMnSo7zk7PRpp/29WmW3thjs+k6L/NQNql /05/imqG+b0xnE0/Cbwu91o/D/CE+ReH878dI/AO8aAnnaIgSSjd78J7baSF jipddopG0tarF4MexyIt4qzYcav1SNrzXus3fxvSomZG9htnEXls009HWiTS rvnXIfFZIl25L+983xdpMQuKN6tVISlZ3kN0bB/S4kIeSmw4jKTHscWvKgh9 JoqGTa/sR1LuaIm0M1F/0lWPVmE/JBXoNFQVtiDt9or9n2fEiLG9Xu3nP0i7 G2/wso/If688c890v0DagzUKyU1bkfTkZPNwoAnSHt4RDq4uRNJtz/GG5P1I S1fody/dhaSwi+U6aY+Rlplac+DVXyR55mSZlGch7cmWPHom4WfmKqc8v5xA WtbTu1tuzSBJg6L5JlkIac81gldcDUeS6EFqcb4R0l7mus1eWgPzJQXd6gtu Iu2V3t72U5kw7+BhrMR5hLQChuwrq08wd/Wgd7r1O6QVfVxwd5cVzMkcCWIa HkFaiWFXKHTB7O3X7FSpNqR93JtjvVEYZmI1o1N4Y0grzzSEVS4wIzFd/zuf WP9ZoEF+CdEPhaXsfWpK6K3qpWDHWDhMeTqEp/4g7n+dlPnJMjGYkPffXhE8 gLS/Jzot8o/DeJiK5QucRlpD2XmdrK8wNuz9bptPNdJazj6YT7gKo+//mHur DCKt589ImKsEDJ/RSPPuI8b9miHHD3nCULeWZcCZV0gbjFizZ181DNndmA/4 pIq0MSpTSv8GDFopJKT9YiFtIvbX1PZRGKjfEMm4QfAx1e9av9ECBhwVf/y+ Rtz3+TvX74tJQf9iYxODIGukkyc3BVHOQJ9Byh1JyZ9IX7Anz3msFnp9C0eO pG1AulCGqVG3DvSUq/ZXRoYiXZjSsvVfIvQo+I4VEPeVvvigt3j1FHSHKxd3 +jUgfckLkZGyg9AtkD7mdl4a6ctEk3/l50NX6OGfX5YGIl3CUf3NszXQtT6t S63uPdJXFJTeSiH8o/RL/vC610iXWmnln1APnefFUxoaJpG+6njfkUgqdDLu x+lsk0G6zMcAdkAydK5Om3pmmYR02fUrN5+eg07KzvhDJ6ORvv5MhqjrIeBP HQypt7dAuvw3g/5DxdApsEQ9cHEO0jdu/vbdbD10rtHb87l4CdI3BTi+5PpD J6tUtPKTG9I3103G6zdB5wV/8WIZV6SraET4qNGhs3xr8cndw0jfGi5nu/Ee dMmH2oYfFkL69taXtNUC0BVGtbC/bYJ0DQPeBjEH6KZ0+rPN7ZCuFVO/kPIe uq/Inhq+34r0HX3uneMboWftkf4eZwOk63IWfO4Ogp4iLRXXL/lI178d//Rf O/QepxxKjn+BdNru4lNladA3sHbTN0cjpDPSzSwLFkF/ifQhLeW/SGeT+XrP /oMBU62x5AMEfobPl5ETlWFQJ+yh5EOinl2L77dGhsLgi17B8vKvSDd12PEx oBuGNNo3PXqghnQzyUMRro9gWJf/2kVuE9JtvJ+uMtgKo4dfzp8Q/Yb0w5Tp /xSew+gE7cdnLsGffSTntchOGIsq9B7xtUK604NGy190GM9XXZ4r+wnpLttV 0go+woTp8ILmXjmku+WdmXhgDBNNe3/lOsci3eO7eLyHBUxO3TYVeHcD6V42 Nvz9f2Dq4slkF6NVSD/Nz9hJPQzTAtmaz/5dQrrPPL12sQtMT7jZ+YiUId0v NGrTYD/M/DeiXcGWRfpFyT/etV4w82M+33duJdKDVLykUi7AbMzs0FEG8f6Q 3GKn8AUw271dbiTACelhDNFcz1CY2+n4Zn5IFOkRXw4sPLAU5s7vCaqJD0H6 1QMp++EGzL1+ci5wfArp11oHUxVXwVwvTLa89UB6jLvBuGgyzEt9l/BS7EF6 3HQoe1gB5nV0Ho/6ziE9Mehn7O80mN9jln/82z+k35KQby9WhfnDIuZhaplI v33rhPbDbJh3NPy26Rqhz3tKeUERO2DebunPqXwxpD94sfDnyXyY32ftyBlv RHoqmCla0WBeX/9LzBcG0tM/3TmNpTAvU/rEJXMG6Y/Me0o3GcHckHTNqg87 kf6kSWflkiqYKzqo/HzpYqQ/c7vsOGIOc5cKr6jFGiL9+fi3l3V1MAe+kYuJ /oSec0lW8O0hmB36dWTPofVIfy3mYp7aCrPJq/zDliUgPS8hJyXyP5ilxRyJ W12I9MKNlFGvPpipT6GV+hP1vtO7GUObINpQMeN/qY+Q/qG0o03pPNGvwC59 Z3ekl+3V1FpKgenFz9dnziggvdL584+/ojBFNrc4tmwW6b9i5lacloPxwu2i 2q25SK+TM3SwfgjjiuMHDi0OQvrfR7EvGCowFrnP7L2FOdKb3m0zW6YNowc3 Gdx5QODbPXD4eoYhDM12kYJrbyG9z+dRy9WvMOQ6FLtLvBrpg0KTGt5mMFjP Pvp18UGkj62NrmbawsBnEfIxEUKPE2kNCsotMGAY8dBilSTSpzWVT4o7Q/8t IZuNdkHIIBm+k2jwhN5vJ5Qu9RUjg5JRYF1aBr1LmI90ctKQISiS+/CpLPSY FxbxLX2QsdDl2UCcF3RnXkpsblyEDOFPj3QvfILupVsDT9/RRIao8sNA5/XQ ddF0V1CDEDLEQu982X0auij/VV/VYSBjWddN6Z2fofPaaMR5n1JkLDeMsZeT h04NG/mnvjRkSGZEPRI+A/yWOdFotyFkSItcGR38AvwUJYbIziRkrHYJhLoN wPdeOyJR5oKMNZ/8rpScA77VcvbTSm1krFM+U51RBXyTrZsDnVYgQy705Nrr isDfF6S62f00MhS6jjn5+ALfZYOLa7wVMhQNnZ4d+Qb8awJeIQ9SkaGUYTdt TKyvWLr/nBlxni0i1ixNwr8lN97bd/kRMlRdLKLWfIdOd8nzGboEfts+7f4t qASdf25Xzu8IQIa6spF8rx907fe2FxFfjAzNUJZbzQ/oaqGplOZkIkO7C3IK laHb70aYVPEfZOgY6pIe+kOP0vSNj2YUZOhlaBpG/oSepuEzD9sqkEEV2XrD WwV603paLq8oQgb9k4ISpxb6bV2VTxQReLOU13pu3woDGysCmopHkMEJlcqX DoSBe8IFLoHlyDA2FDXt3A6D6b1XZy98RYZJhlDCtyAY0iyS/VtjjIw9IqSW 139h6F10FEVcFRnm5aPeoSEw3H201LXgLTIO8Rpub2mEMQtPUSHZpciwS6/t XKENYyNCHludnyLDQbhafSYcxqNfBXPfE/udyz+WVu6EiQrXdS/LI5Dhycvq O34Vpjf/THgXE4YMr/TMnRbtMP1mi0h/XjQyvIVTAkAfZti3t0o89EeGT3ni yqV8mN27JPPo+xZk+G2+cWiCCrNVGzZN/PBEhv+VyPRGwr+4163qSxuQcZl3 yeAZwrxiU5WGZB4ygtPPByfEwnw4SUXD1wsZocLe3y72wHznmOTXnixkhP/n KeNCR9L6nfviH51CRmS5m+PeeCRxok9d+bQGGdGbjz7V7UOSS8vSzLxRZFy/ cnhSgYmkUNFJU6f3yIjpPMhYnIikB0O2tgMvkRHPMw8fJvJk3tnaTtY3ZCTe srhbS+S9SoPA4sF4ZCQN7s8p+ISkurQbip5myLjNsqy4l4uklsebmM8vIuNu woHG4AdI4mMlJc8GGfd7rUbdrhFj64jNgT3IeEizFtlzgVg/0/8jgNBTWozN Om0iP/6Rp2wcIPjJ6LTVlLEi3vfVUteS0Pdjg0M8EhdJ+aKilneJ+/A0+rBN mxaSUprzbJ0ckPGszc7zkzyRT+38XPYFIuOFjn3w02VIcktoKjtK4J0TcSTp +iySeHd+K1ouQMarJodnZ7qQJB/5TFprPTLytBxLbWphvm92up+biIyCK0f/ 0D/AfLTcMjMLPjKK6p0GNhH+v+WT6+tuLjJK1P4TFL1D+DWjNWL5D2SU/nbd +vMczH7rdl+/ayEyylXdGHnOMLun3N4yMQcZFRePWd4xh5mKn48/fWlCRtXm EwEu22E6S/J7bGA/Mr6fd48zlYVpmbUtmiaxyPjxzeOR5mKYCiCvOr9wHTJ+ nz1ZM9sOk6ytKZ+7WchoLj+zOfomjD1JCs+zrERG29qz1NMhMCZYe2n99t/I 6PA4t+/gaRi1/n00ypyEjJ5Vvuc37oYR0Z3jxtMrkTH6n3/VayEY9CvP+nif WD9ecLHt1ggMNPtPWFqEImNKPGAqoInw10W+7X0EH3OvL28wzoc+49o9KjsL kUleEqSjlg69Ox0aF5y8jEwBu2CTlbHQs23nxODwQmQuEr7i3egBXXRRyovj zsgUsQkN/2ALnYeSffuk8pEp+izsboYx8MOS98Zu6kXmUsHwnCgd6CjN6zux j9gvfiCiwksROsSTrm5/kI7M5Y8jGw+sgHa33x/X+WkjcyU5apRKhrbfpFHv pRbIlDaPFpHvgzaLa/Wkb5PIXJ1+bd3Cv9DaLqClG3wLmWtmr2v0fILW4FdG ts05yFy35wb3Wy606os8Sg6pQ6ZcSoxNzgNoFWDWfYVlyFSYjPX8//8Dqb+/ +Kv4YWQq7ooL9r8ALRV7h7+tDEOm0t34JEc3aPl8MrF4fjMylUcTnhlaQUuT pH6zcggyVXmJpds40Cqso11q+BKZ26rMuEKW0GpYHmhTJ4VM9f1LyutdoPX+ WsE3NAlkatZ/NHrhC23iU4KsMyrI3OHgXxkWCW1xEkc3DIQjU6db19T+DrSr C/x7fCgLmfoew990sqG92dJIracJmdSJx/uWvYeO1CCBvswYZNIuHP3R8RP4 /gOWBbV2yGQKrbco5EPnMf7SHWQCH3b479qYKeg6ljn+0VocmUaJxn+Za6Hn nkHJof/WIdNETshWZiv01mhdPzv6Fpm7U4v+DSH0S5mKzEo2ItP8pXrLHQdC L8aJQoGtyLTU63H0Pg2DsW99xI+nItOqJKXDJASGdl8EnvFeZB6qku6aIfLq N9uRMQ6Bx39dc0OWwzA2pjAbFF2GTDePV17bF8B4SVr7yW53ZB6f8BhbuBIm omQD7x6JR6aXYOvkS12Y2rh0/4bXBD9+6yvI4gEwq3Z/Ca3OCJn+qYEB/Bsw 22XaO3xtDpmXtlIXFD2EuVt+9TpKBP8hes8WHfsE8wOdV4sbbyAztMQllEn4 GWdqrjetAJkRvA2iMoQ/Xr9+YnXRGWRes4hb+kkcyWv7Uu/d34LMG/W7o+/K I9la86yy2EZkxjmILD+jieSY64v+WXojM6HrXYwpG8nlP4VLV7YjM8njvJSi JZInKm9WTZ9HZvKEdvysC1LkTc6smyH0dNdvYPUPX6Sw1Vepft6KzAeC6UmZ kUhxsGj4GOiPzIfhR2QD7iDF947qY/k2ZKYvX3PnQDZSIsdCCw4TeslM+Cm/ /T1SbnKeXbf/jcwn66MeLPyJlLtBD/0HEZlZqbyNDXxinPmuuqEcmc+3UlJf ThHrX5QcKMpG5ssX+ZsjRJES9aBglVINMl/pncp0kEXK+cDt+hJEfW9Ktqnq bUOK4+HhLXuikFnA5T+VQKRwTFT/ObUgs+jrve2de5Gi4MA7QXxXmCUWB7OL HZA8WfynM5mo9329pGbcaSR/CooK/SCHzI9HvuQcDyHwqhe0H/yOzPKukJ2s RCQfHHgeXEPw8WV8Wm+4EEm/pb+JGvgg89s++9zPd5EUczPbtVMImdVPPmmk BCLJhOsbl0TUX+uYsGU/D+b/GxDdkkHMN1XvWP3mB8zkW7VuoTxEZuvW5Njr uTCz4OvFw3GE3ttDhSTcEmGad8ZAy4CMzG6sEZE9BJPlt3+Eayojc+SJ5+TF Lhh7aZamM0RF5rhw3SmrShjtP0QyVyD2TzrSBjWyYFRZr3T9vpvInFuzjN92 CoYfvN/AdehAFsn7jEOhJQy1xccrqlYhi/L9X2OcLgxtvmpUtEkPWQuvPKnl kWCgYEPR79fiyBJuW2kmR+TXJb9+n7k7hCxRPP91qhT6Pr5YI268GFnLxo0/ PgqDHkbyhxjVN8havvcF4/Jx6Jb06BVKfIQsyScyRba7oXM076ZWfgmyVjt0 5S5bCR1t0rnaLC9krSneq86fhPbRt4FbqNnIWifz+snbv9AuLWKt66yBLDlv OeXEQmgzvhUs6XoOWQrfQx6evAutN6Rs2RZ/kKWoOiBvHAgt/YLVE/wNyFK6 sj9541FoseEvaLl6ClnKrUWr5njQ3KRa89B+EbJUUTHmlwo0n91xs1eBqHfb zUjxrKXQrGheHR/8EllqY6MRIUPQxC9r3KxhgyzNvTbCdj+gqbi47tatCGRp P35/WTcXmp7YuHZtMEOWjrAKeXkiND17/EKpMgpZeg7XfXt8oelTFvWG0Tdk GRRNTXw4BE2T+yj7P1ohC2XsvZLp0Gxw0l197iSy6KfLB7w3QnNsQzhfqxVZ zO9qbrsXQYvgHulD5/2QxVGN5yt1QUuI99vDdbuRxbtCciBXQuu6fseC5WeQ ZdTq9K8uC1orWLbPN8YhywS+Hnx+DdqujP+2+PoZWbtvav8KPwXtB0/rPjfL QNbeseR9jpbQAXJR1iauyDLfK/iVqgd8zV1CkjESyNr/+JiRlCx06lJPqYfN IcvaQZ9e1gLdp4wY6X1SyLItul94txR60l5UX1xkiSw7mcW659KgtzNgcchm gh/Hb3XqKsdhgJMbwTl0FFnOh6ZWj5+CwUWuRvKqBN4ufasFSnxh8Mt1DUfT u8hyFzlQYxkGw87e48scjJB1jv77TGAajBVaDlgXDyLLt2rSzvQpjMfGO5h1 H0HWhUOrDFfnwMRxLylWnSGyAn0OyGR9gKnVZO1DlYReI5/XFv5thlmjBnXK 0+fIiqZNpKZ2wpyo25vdtcT661XSVz0HYK707+LWzNPIiu+xtF80j6Tlzqb+ vI/IunnujOEPof//XqB+3TImspIXxWvcFkPSc0/NTXvKkXV/Q62g1hok06wU K9/lIisle7yPpIBk/5ZS7CDuUxpN6meFMpLztqeoflBAVsbXHUWx6kgeEGPv HxpG1mOb/Wl2OkhZZyojU1WNrKfd3tEqhF9xP2UGmTYgK/ts3NlxLlLcbF6/ NSLuw8uFufYlpki50nX4xbtdyMqN+WUUYUH4paXq22ITZL1RGNe0tEFK9g0t f5VMZOVnS61VcEBKQbTCscBiZBXhDsE+N6SUcJh+/cT73n6x6Ht9EinFL8Ij hQn+31uf/hV4Dimv/sYmHexFVmlXbLFpAFIyym73WS1BVvmZnPTVV5ASG+wn sJvA67PQz+i2q4Tfq0Z5JV5E1pcbY+ey4pBy8EdqhYA/sr4prDzik4wUrZtj Ni0WyKp+pm3MfogU4VS+2do6ZP0ECy3xx0iulRdsuZaDrNrK02v/vkDy3Z2f +keCkfXnYKxQaj6SHdf5bDXYh6xG75paAyK/N+Cx3qkkZLUIjr5d9I34/o1b vr8ai6y2G5Lp1bVIYnkv3ny8E1ldWeY+/3XAvA4/9cr3Tcga4dfIxi6AmV2y Yw5ggmzhAz8c+gxgtCmQz/0+juzF+fc4eSthVK75QymJGIutc1cO7oMRBxuv 0i1zyJZoWzwglwxDg4NX2SGhyJbk/P7edxqGqEp356/9RbZURurLPBMYjMzp 2/2mCtlr3Bk+ZnMwQO0LluV4IVu2WtxW7if0wzYfSo8usuW0/mHfE+hbsvVk U4A5shXiHyvkBUEPv7jP7exGZG+c9hEKsYXu7yXxje2FyFay5Xaaa0PXZ4Ft FeqnkK38duVneTHorGHVX6x4h2xVhdYnfe3A78f7Q05GyN4WlB2dVwT81Te/ 7birjGy1Tn+vkHjo2D94wUGAqE/T2GS/uTu0p1aua1dzR7b20zW68lxoF8mU aMxQRbaOeNea/vXQ5j9h+OHpBWTreeXO501A22LxmfPdzsg2+HW5OaQKWtNZ 1hbU/5CNuvs+mKdB68GvTu1PbZFNvyWXJu8PrfJ/C1YNhyGbOd8f2m8JLXNX /Dz3H0M2x77gWP52aOlrYiaLFyOb9yFs9xXC74b/rbVn/UO2sdIBDfNGaF1y LO63vDiyTcI2rZR/Ba16R+tHyhYie3fvyET/VWg9f+OPavdpZO/bXfIn3xla q5+563rWI9v8+dXCKzRoMzj6/Fa9A7ItV9retVgNbW9MqXV1kci2OqsSKD8E 7byxEIFdisi2/jN1tP8TtHf/5lgJpCH7ELWMl38POm478p++IPiyuxurcuUc 8I/MLpLxWI5shwUOSy32QufOwcmUuAFkO5fP/xigQLe4wLMp7Upku6pU5ubX QY9E6JIX9WuRfSzqZuKVbOiVo7/+pSmEbE/zHYcV7KHfPiNNW5KCbJ/G490W 72BIVSTUxH0a2X4M/S8KSTBUs9ZVRJCo1/+hSNaAFwxfKI1uGO9G9mXXh6dD N8BIXWqF/db9yI4cayAXBMJ4nOgD7WHivNEHMltDrWGCycnS1Sfwv55/9uN+ TZjoT/fINV6D7PgAyfCBVpiivrk9NU48/96SXdIbWDDz9PaloSWLkf3AQe5D 7wuY1a/cai9LPO/hm7GTuRtg9v2RX9uFifOni1fI+RP9P2tWe5pCPD/T+fZX wwUw93bHrM7HZGQ/LvI6v9wL5rVerXqJn5GdtZKn/LcF5u9vIpdcbkb2i3dD QSdKkKTxSeLzPWI+d/VHTR11JB0ih90r10T2a4+bzRQiTwXJmfV/70d2Xpl7 1GeiP05f8ljTwwrZhetYBjH+SPr4ROOcwy9kF59e1W07gKSm3okYqjeySz73 JSgdQtJ4XkSKTSOyPyi8Yw9+QbKw8NLv73yQ/fFc3EgeFcmSn9V2BBL4lX9z uxf4hMjPc9Rhvj6yPyvRdpusRbJc0gbnU5bI/nJBck4qHMnrH7reOEacv6qm K7NxBskycroJlzcgu1ql6ECGG5LFV5g4fQdk11y6sfDkHyRTQofiqFPI/lXn /NLAEEn918dO9RF6rVMzOCL0Bkm1tNvmTElk/w2RWFaljKT89Hg5e8KfGhra CxMSkJTU2rBnKeEvTVp5bkcWIens0kfUpQuQ3RJ+dbXKGSTt4R0eHzmH7LYW h4+jfCRtfOGYcpHwA76uzqmi/TDf0yO3ovs+sns6mr/t1YZ5GdqWiTrifP3U 3AsyRL+R9k7rjMF2ZA/GhKu0ScLc9r33TqRuRfYYQyvEewRmt6kmVCt1Ins+ OQRuZcPU+MudHM2fyCGPWvc6ycOUnV2s9Dcr5CwwVru5PRomy27WUT8sRM6i ybqxd+4wEZ0Wear9NHIkzFUfd26DMZlf4a5rOpCz4hHlYHYyjF5I+B7FG0CO FOWXsK8YjLR+Y8mct0aOTJa/o1gfDL8M6iqM2IIc2YXmErW2MCw3/ORP3DBy 1ttuLr5bCUPR2QsyThDzGxdXr9F8BIO+zR6GRfeRs8k+tXxuDQxMbvFw34TI 2fza1/tjGAycO6lq0F6KnK1OG6utXKB/9Xx8aWc9crYXTF1UqIM+GGrS32eP HI0VX7f28qDX42XY57/dyNFyvf835xX0ZDWf5/CDkbOj5EyovxJ0zwkfAREW cnSNBVP08qHb2L2147wHcvRrrhWN74auR+ffZPvcQw4cWleX3QpdsrlbAqWu IofGfzR6/Ax03r9W02zoixymp+4yZVHo1PPiZS/7iBz29EfltjvAb3/0XOlD CXJ4l81YdzWBn3IuTl/9IXKMxZoOW5cB/4zw3QozEnJM4o/7SFsD39bzcvrm NOTskZuOqR4AvuWbvnvL1yFnX2ZIVlQg8I/WXL50iHifhZZkhZE08EPuCcl8 vowcy8J77UKPgF/Q67S6vgA5B7nbSSUInUKeyx9szEeOzbcCmfM/oNOuVvvg ulvIOXzQUFvHGTq/5kje/GKBHPvWX7tHZqDL9Kp/IM8ZOY7HHVyzrkJX4x/j I4+IsdP4YJDbBugOmGxenpCHHJeLfnc3EXhqQV12FFHPscWL81qMoWdsMHPT SmnknLgR/zO5EXo/smw+DDghxyv1uejKRdB/1UlLg3UQOd5qqPgtCQYYWcZy kW3IOfumkhaxHQbeZ0hozP9Gjl9lh/cCSxisETz1xscbOSEjMk1D6TCyXqxz rvMHcsLOp808ocLIK5PfAdw45EQu1Jb67zuM7rlRZGFJ4HF99W7jxikYC/xu /aljDDm3MDD3qyFM1IdvI4eVI+f2J/HvoQ0wecx3odvbDOTc25fcy/aEycnK u3Xtn5GTevS1fAHRvwl6pVFMiXrTB9gGZ7fC9KX3Xn9jJpDz6Gy1pWYJTM+4 FtnBNuQ8C++NyOyCmbqBAhXnd8h5sfJcmpMfzMLY6tk4SeTk3Fn4TkECZm/d X7VwIx85rzffqG94CLPDtyjr/Yn5vOdyE4m6MEe7Whj4Vww5hfpPl5t/gblg DXnHY6uQU1yqv1XcHube0zQbJAn+3pmWcz+PwdyE/wmRbQTeH35bHAkJhXmF wqE56yDklNm3+DFlYZ5V/cZrHaHnTz3u8fPZMG+bnD/9uBg5ladmn+exYf64 8JKrtwg/+Dof+uV0Hcx7Leg/LXoTOd+vSHWqH4d594hmDjUUOTUSKQJ9FJg/ kpai1r4JOb+S1GXTY2F+1xG1/c+YyKnbWKTjqAzzqj8ybSTUkPP30pSJfBTM L1qTvolB8NvQrGX/bwTm6sKManUpyGlCd+9bB2AuhXtBabYBOS3JmeFWhTDn HJD3b90H5LTNtN+VVoC5DccbzQsJvvgH5V7WhMDsb67AczVF5PRKxf3bvRdm t1ZE/r3Qjpz+U99HlhD9d5XwUQ6DqHfwh9iiijUw42p1O4TorzljVwPVWe0w HR0Xzn8XjlySsPtlvbMwmSoVV9OYj1yKU2bCBKEHhaNvn8oBchd8aH+SQ4eJ pBH1BqKf4QoHWNeqicL4lZvnhvc3IVdimrdF8Q6MWqiF+rv8Q67kgUBoFYSR 8lMa3tZZyJXKLTK76wIjBq9ST2rHIHeNl5bfGk0Y3iIXp29zG7kbe+W+iZfB oOJ+gwzGFHKVjKzbvqrCwBPvTPjWjlzl9Lip8GswoLfZVvofCbmqC6uX8sah P+FeprplCHK3HRXbsNCa6N/vdL/euQO5au95O9+/hT6ykKvsyw/I1ZQP3BWg CL3sgORqXSZytf2L7CEUeuLf9K+PWITcnQ1Tp2f6oXvCUFnN5A5y9Qy0wt6Y Qbdj0fwP3VTkGtx0v+P9Crqa7gT9FxaBXJjMfKm1FrrcjpTbXd2LXPr+9vKh AOgSOmWXeekKcpk5cg1PO6AzS1Pa9f5y5HJWWA8fM4bO/wZvcDYT+3me8YuU n0GnxpB4uMQYco2qqtfwV0KnmNtGj0eDyDXZJqaW4gP8yZDwlEECv90RPLZ9 I/BH7QX3HuxG7t7uwIPrWdBJEdR87XoZuea8ohP1GdC5zj0+sJDYvz91KvDm UujcdePSt7+SyLUS0kqw9ILO8H1KFF1f5Fo7uD+R/A2df/zTeT4U5NqWZJZU U6FLt4XaTY5Drt369l9X70NXhsYhsdvE+iMX5HpMFkH3ZpkxEbtjyHWstyEv PgbdubppY5COXGe9eMmy79Bjtl7BXKAeuS4J1cpBO6BnfveJG29mkOs2IQaM JOh9xbBaN3cNuR4vAp0LHaF/j+TtcJP7yPVieXcd64MBOaebFXlJyD3909Vt 7RkYuFO5/sL1P8j1mdh7wjcUBp8xO2rFKpEbpL/+tG4WjIgouZ6Y7EBuSOXy 8S5dGEkrrEmPtEFumO3CswnvYJR3/pbGPQKfqxf6fCd+wljk3PaTdwOQm/gu /1LODExKX/lzTPU9cm+ZZQkeDYLJnHGjXawDyL3ddj945VKY2nuN+ii1FLkp C0NDT8nDtF9HUt0d4vmp8X6iGzJhRuxU4Io6gu+MzR4RPzRhJv730h2Bgsh9 amQZrcGB2ZvjqYy0GuQ++2ss0VwFc8szY6yOEPfpxTG4cc0K5gI39U1sJPDI mdNYSWuBuZ6JmaZPtch9HaUYN+hG+Em21nJ3Y+Tmr1+96s4YzKf0pTbDceQW Pltyc/cFmB/d4zih34Lct3Ty2v//vfvakn77H4XIffd9JPnpNSTpr5e5zCL4 LD3CX39oDZLMkyuigdBr2cife2IpSHKR32l/+CZyKy5/VSjcSuSxazaT2w8i 98vKkpRjr5B06a+ky/lPyK1Kzdm0loakkEFlFd8VyK3emZ72mehfQ8pcMgPG kVtTfkvZ14xYb/NiRTmhj1qrq4+21CPpzLNJQ3F95NZ1B6r+OYok5wrm6DtF 5Nb7ej8NJfLtvhc5eblrkNu4xFVN9yySdPx8gr3NkducbJvdRUHSaoPi2bB1 yG3btlczIZzIf6u4Gs0uyO0oZuXwJGE+cYW25EZC7117dHZMJMM89fmL7HUq yO1pVnmdpkT4K9dl3MEbuf0n1+vtz4a5Y1tGq99+Re7QguUFC/Vgdrw4Ln1t K3LHN04VHzWBmdGn7ImkHORO5vTRJWth5r/Riv3riefNcJrefbCD6V8ffKwe PEUe+b+yjxu8YOreRgUno2LkCUzlc6vnYHL+J4O5QAp5QmFPP10KhskDe77J 7ipC3uLHsZXNiTBBWdfzNzUeeZKDDj/uFMGogfSXEdYC5EmfPiRn3AgjkTsM aj8uQd7q6QPHJkkw3Mi5v1HnOvLWC5os3EOHoSsfngY4JiNPPpS7b/YIDDZ7 LauwcUHexqX0O+mBMEi98dIqeRHylFft0CWXwoDAje9a047IU0lWC3rcAX1/ 4IBU8hTytilsqbZaBL1vWvvrkhOQp5amuF5ICXpStz16/Oce8jRV17tl86D7 rvK6W6WVyNPOXv3a1gW60quV7/8dRJ7ODkkhkVDoLFkr/jj7MfL08pfuzckE fldPTFapL/KoNOHb9p+Bv1Fa5o7zV+RhqUCPWC90eBxtXHbNDnkMw7mdeWLQ XnV1J0uFhzzW14nLTtugnaHbZLdDCHlcs6Hvy3dDW/mMrXWRGfIMf/fKFnlA m6Ol48GNFcjbZdvh6noN2iSLrXsvr0CeaUvTK6lsaP1zrbPwXg/y9jr/XfCu GlpzZSKkVpogz6z3554TI9Cads0sYclz5O33rEpeswJas2RuXornIO/A+Keu Mi1orfxYX+D3D3nWvh92eFlAG/l60q9EMeQdIhcFrveGNiOn/YrQhTy7oNdV n+OhLVMqmizZijyHxS/WnnkN7esdBjaNEPUevfrEZUMdtGfM/KcnH4q8/yTT cqqmocPQ+8KGbKIe18T7AufXQMec9Q10v4G84+tu7d6sD/zSdcbzqQQe7g/i kmpsoPOunEDOwgbkndwc3XnRD7oin+1SNexE3qkn4dqqydAdKRYR8eM48s69 8v96mciXhb98d3oVIu+8gc8adTL09bmvXLKfirwLJaecG+RhYKV0SPxADvIC P7tQtB1g8OjYfr/lH5EX2bhPs70DCC1O+UjvQ160466L1xfBqODlCzt3E/q5 3sX5AkowWmaW0/uewC9+RN8pzgXG92oruS57i7z7izYmcvpgymGNWYXSQ+Sl RKxrH1kK0xsgaGeJMPLSJFZr3NkO002mJ4So75H3eI3Y5wkPmDWNK/lYWoe8 p3eFVz24BrPzV3SjjhF8ZSsKHN1N3Nf0ukxxnzzk5W4bn08bgfluatBSDzby Xl+5w7ZoR5LeY0OlbkHk5bVwIxbUIslvd87DeQK/Qv2B6mzCnwrMygrqdiKv ODZh9eE8JE0Euys+IfAvGaDZiT1C8rbXXpu+KiPvA68zNT8ZyfaFCT7/a7jM w6Huojg+MyUUkSiltJBEZAmtzpHC/BZaRbSILCVKSIkoVEKUIqGiFEVRb0WE RCWVEgotdmNfxs7M+/vzPve5957zOcv9nt/bkPs+Oar7cCSyo44W90/JRO7H yXV68ueQ/frukrT/GP/KLBt8SzyRXa+vV2UogdwvT0PfejoihzNTrM6mHrnf puuKL7VCjqKkQZXKF+RW2NdalHORo7dAckskw7My7/wN/3XIMVWJHuYz+z/n qv9euRI5OxekT3GKQ27NsR9KNYrIsemfXX7fFrl1pWcOX5RGzr7EE8v+jSP3 r7Jypj4HObYLnVXHjyG33q9suJHPnHe7uF36JXIbq70Mo1qY+6/bc1NKkNui vTAYqpn3bwY+NfBEblto8aeuD4x9IQe+6ocht73JTeZWDmP/kSP6i4yQ22U4 x5rL8Km36Tbzz0JuT8yb28MMn9fuEu92MOu+PseW+wyfyPwMIlCIXD4ppbGD 4WPnemTQJQa5Q/deerIZPhoxpyXEGL4jgv05Tw4ha9AxooHQQO64lRh7ryWy sjklo+6XkDuZ+dRshhnzX8SGeIX/Q4JziFXptBKEPxuvWvovQmLqm9QFcox+ 37TuEuepHBKi8tsOFkkzelnpe/A8cSRmfLrbqzgAk9YXHGveJCEhp2M8vSoH xkL9dizT/ojE3Msd24Iew+gXdek9H98jMa/5WqxOIoxKBauaNfCRUIxtWhYR CMPhgRfuucYisVwYjCZmwA9Jbzy/Jg4JNWuNEP46GHhf4DG6vBqJlVmVn5NW wsAM2Sz15VFIaDuq7BFIQV9coV1Y2jokdPO/3H3Mgt423ZFXpduR0J/n3Wbd D73rFJ79zS9CYl3Ze6//qqCr5nX/qRw7JDaquOfaf4DO28e3UXruSECA/JRZ 2dBxXJrF3XYCCaNfBdz8NGjfGhhX/5qxf7Ouc+TReOAZ3j+y2tAXCZNw6WqF CGjbYJGbPC6GhFlL9sKPAUy/4eqXIRsJEu0cTnpAi4u50Q65VCToOPFHy+yh OW7mcdmfX5DYqnJ4X/FpaPqiYOWzex4S27M+yRyKhCZ53wqrG6+Q2AXqJSIp 0Hjq59HqDd+Q2P0p7NT9XGjoL97cp/EAiT27uzS2fIeG84V+5hxXJGyb6H/N bdCgoV82bhmIxP5jGdHBAqjvOSV/TtQBCbtJKTMVWagvLVHKzC1EwuGS+3iJ GtTnb7E8ugqRcJIrf+KIUP9Z9PoT4gISLkna9tMsoZ4vqtVdPYmEq+bVOSlH oWH18saBvGQk3HL6S02CoCFcv0tx7Qokjpvu8GuJgwZBxxrJ1zwkTlQ81w55 Co0Xm+PPeXsg4X1AtlmlBJrUMrU3qaxEwqfTK7akDpoai7JeWG1GwteninLs h+ZnfhNtVoNI+IsYsETFoOXmytijRkFIBETFPHuwCFqjxayL6BtInF844mSq D23JBjKR6SFIBKdZz29l9H5x2U6lwjNIhBbNP7fcBzqNlp605m1AItzCV+99 BHTFvBgoZ/QscaW2lud0D7onbRanr3yHRDQ/YeuDb9A74vbhlC7D77bK4kWq asBfUb5MWTkFibtZAd8/IPAFwyPJ0m+QuGdYH+JsCYNVOmvpZnskUncndT08 D8MXmp2/vi9A4tmlZbmqdTDWaOu0qrYCiRdyIe4f+mH8Oewbb+lH4tXdViUX MZgI5H5YPMTkZ17Ow9BUPRBIirTJ8Rn+BabihlwKBKV7Oh+XWCLxtsKlj3cQ hIGLDKZtuY3E+0516xURyHLd4GSRoolEqU+YxMd7yHrdq/livQ0Sn6d2Frjk IFtU0y6hZwiJr1G0p3g5sun/ZrZMeiHxfWHG8tRWZF8xOYvpTD39SJtZyxUg +1Oq61oTHSSq9d0j2mWZ/pSqu7lxAolfb8s3haohZ/WcqeELGd51FlpDaoic /cWS+9WZ/T+1UamllsgJjkyUsSWRqHfq33vYFTn3rcjN6YlINPJ3zJp+Djn5 7OkW7zuRaAl49i4tFjnfvczqar4iwZOU9SEykPP3nvCc/VkkOm56qbe/Q05L cL7NUSbfupdV/g2tQU6rTE7Y8flI9GbpX1PrQ0499T7jtjcSA7O3YNlP5FSt tV3jzEVi0HNHt1sBcoqzVO6UM/k4XHkwXvoBcp4US2s3rkViTP84NysCOdFB 2kb73ZCYiDk7vNMLOZ4ib41X7UJCMBJxb8gWOeYO4vHWpkiyrRO2xxojRynP ZMuWdCSn5DxmrVNDNl83hsppQ3KaQk563Sxk53f+s3BIQVLszEcbvxFkX5BJ G9P5iqSkYcuLwhJki9nsd/NdjKTU7UEH+3RkvW2wk2D0PynDniojEs3MAwlF /NWXkZxTtOQol4l3tt5rntV/SM5T1prfwQWhzJPwXayDSCoEG34I1wKBo79S wbFBJBeb2iqVC2BCuGb6bZGFSK74dKPG6haMTc0KypNLQnLDDwlTpx8w0PDD xaqMsQ/05g+Kv4aBZX2ryMNTkDS6sSLpURL0uya4So4x/phYmUz2HIO+aVW/ df0bkDTL3vUoajf02nhuk61IRZKc72Ctawg9RwI/L5U1QNKiLvD5SUnoHMso SvjRieT2jZEH5fnQUS1/NruT4bUz8bZUTh20vw3lv9vXi6SlMD3Ptgh4eRWC rdx6JK0P5B4WpEHbx+ePvQtHkbQp/CR/JwpaW/49nWmWh+S+pb9KNvlAq9zk t/Q4KSQPnG870bQfWnYnn2ja8RbJg03DS0JMoPnxniUz2BeRPGQi8lVVA5pl by3jPN6DpNMD2TOljP6MLNfVsbNG8rCYktqRcWhasmOeqMMxJF1dtH9KNkBj yVE/nbA0JN1KMfjJR2gMsHpUocD4f1zdQncb088sNro/H7mK5Imwvf8GYqFR V3ckL1USSa8u14jrZ6FxpfHX2D+FSPqY+24wcITGdfZVaS/bkTz95BLvlzk0 7j1tZQxM/P2kY2N89aDx+t6lU6c/QvLs8QdbFi6Axvpv6ftoZSQDv//Xnz8F mjaF7xLRWY9kkO67OwfaGX2tF6PCykcyJLrCnPMNmuGAg4bZDSQvDtaP33sF zTXRp6oOMDwuE8OexvrQEpRVI7vFDsnwOxKd9f9BK0qcPfPPDckrg0sPBayG NglR0V6pl0heJQz+KGZBW+v4GxsNMySjb9OWeVrAq1izKGWlL5I3+Ae/2jyB 9jKBs//LMCRvJYYXxD6GzraJnpy3J5FMGEhea6AG3eIhlc8KEpG8Y/YqszIV evRg88vbAiTv9Tcmy6RAbyWOtrpYIZlhujY47A4M+By6+0+dqY+n8eYCdUXg L38rFfWnAsmsPgfvjwnA/5l1bHVCFZIvb11xnhYHQxvV66b6bUWyoKeZCIiG UfaFOQnJIkgWbR4rWiQDoxlRfPOZekgWx0mvz4uCMSv+DyC/I1m6ef3KsQgY Ty5ot+08j2RFbJSU5yUQTJU9sXCU8aeyK+XibDEQRJzQvRvF+PtzUy4rM4Sp t0Ce4XKGd03Mt1NbRUB45a+e9s5MJOs6W/q6g5A1ZW7EZ4ttSP41mjgczszP 68IfVoaoIlkfM6tRPRBZbiuep5kz5xs7l9uUspCVUBKp3HcNyRajDT+c/ZH1 cZOJdGkxkm03ttPTJpDV66+S05OAZHuHU/F9X2TP3ve7y+8mkl3oZ2g8hmzt shATz6VI9ly/+qLBB9lkVnYd5xCSfe0PVwUMMXqQdWfpTuY9PuQ9XOSF7OMP R8VlVJAciq5YnMdHtl/SMrP2ciRHeG03bT2QHdSiXlsyF8lxQ8GssV6mX3nk xu++hORk9OzQm+7IDuZqSSjKIynkrZhi0I1sf7cgoymAFMfQ0LfKFdkeHZYC 8Wakpl7bwfdsZ/T8p7qJ5xeRmtbmcnS2C7LNF1W59ZUhJb7RvzmzDdmrxwKK LvshNeNq9L6tjsie6xGcbTCMlGRralV3E6NX77Z+OfkcKekN+Rbh9sj6er9T 8fwZpGSifnxQb0DW/dvsvEBVpGRb2rH0ALJOPk15qPwKqbnrhdnOf5BlwiZs y9SRmhclpyO6F1nSOZp69aJIKa5Hpc17QLhGUzpwZxBSi4tmiv2YBMGTDTGW +/chpUTUdh+6A4JFd94UlScipbrnZHZIC0x0R53mxDP26px6YvHRA8Z2aRXM Hp6FlB7bT2+PHIym1EnNbhlFyuASOb/9FYwMXf+VeGY7UhtiW1pmCGD4auGn z5uESJm8UjxjHgr8r3VXKoq8keJCp91fDeArfW2UCGfsJd/nmLqXw8Dp30oF l5KQ2lptKRM1B/pXJ4ua9XUjtWO/0siSbOiLDdB/mDQXqV0tfb+zbKGPnX9Q Q5HZ3zMU/rAiCXrKeFuq898htdffJsJhC3QXfxCxiziI1IFpK07w26Drs7nK 3W9SSB0MH7YOvgydTdtfuTnbIXVIrhjmaELnjITMdxI7kXJKuKac8g06jFsx /vYGpA4vs5tu4Ant4dXLP2iaI+WartnzYS7w2qeutPY4hpT76olKqxzgWQ1c U5VZgtTx3NIc3l5oq5XPqH2lh5Tn5tg7p1nQduyHf2NeLVLeZY4h05Ohbb7X /ifGJkid2rn6yC0TaP2pX+biuQAp3zrONnUetKYZ6Rz1vIWUv325fm4YtEam VkVXT0UqoCNxAb0KWi97lHmMfUTq/AlX9u/v0BoXKvTnI1LB42tb3bygNe+L +YJxJn4Xz4uWCeWhdVDkhVNlBlKXp1dmXXkNbYyiDF1wAanwq8mxi/dBW+Lc fqdlBUhFzj/ul8kGnpRqm7GaKVJXk8De6B7wrt6aOf1WG1LX1STNvptC+/Lf Ex/N/iAVk1mrebAd2r9s/fu9fxlScWtTZw+EQ0ewsZz0+TCk4gu9R4O0oJPk 11arMnzufpN5d98bukW2jg+4M/7cs/qXpj8PuocMFJ/4M/mX8i/jSkku9AxY Kk5d/wKpR73EnjYO9Ikq5J0ptkQqw0cefe5DX3TP0nJ75r2nwhYVcTPoV3nn tOCODlIvpAL71CJgYOeqvX7XjiJVoJl94agCDBYnVvmeZvx9e1nPURgBQ1ZP XIutq5B615ppcpUDQx3qzmUD75H6eCdV5EU7jEic/LU4zQmpT5PKzdx9MBK3 M/vo3Q6kvljfeVf7HUaXvbj9TKEGqQqZ2CAWM//prlX1e9mKVKW7rP01TRh7 7pOtFjqGVPWnSONlSTCu7dukqaCAVF3QRQ4RChMLtd+5HPqC1J/6KfV1jH65 vGS//sFDSNVvPFvo7gETA+aChYeZ/GweOhkQvQcmM2V6JKyZfGvbPnBA5QsI pklOJrsNIdX+xB2yN4Fg56xpcXV8pLpmdCwiX4Agrq7h5Vqmv/Q4Owr+qIGg Zs11tZtMvfe9a/hzLBGEs/p8+k4x+c5fsu/NFBkQGtU680L9kRryq0m4zvR/ l/KGc+cCkRqpsfRbPgrCS8+WdoUy/Mb1v9vmuILw7mHD54fikJq8Zr6B+gfC p82697uSkRL2lCr83QnCV/MGp5u9QZpDbRk//gGEL0U85HKlkZ76sLB26gYQ pj+6cf23EGlRkY05N56CMH7ull9n9yAtbpcdt0IZhOf2WPx0k0N6xhu9U69j QXgg/MC9PyZIz5yfaW0uAUL9r+2re/uQlj6pseZfAAhFzMRiDj1AWqYiVd6D D4KypQceLd6NtJyW8oiIMwguR416XU9Cem7YneqYOhAYl0uviYtEeh5vwUu1 rTA5qNDqeiARacUkWW8LA5g0HsrylbREerEgcld9Gkw0LDZtO9uKtJKNhJ6n IkycDtvbKKeMtKrsFP7NaTB+K6aR/aMCae3gAY+GKhhNtRqPG3iCtG6D+3Yv AkaVLRROLjBGWt+wQ0f0DYzEF/YoHuUhvW64oW9lCjM/9bGCjrQgvdnlu7u3 NwzuDFbYbF6FtEmJuYVYO/A/7P8868oNpLlLSzVv7QP+Rk+RP01nkaZrC7sK TGBAQymhySYe6d10put0Oehbpeuyg9qCtHXvaT/je9D7WPK18RwW0rbXjCP8 dKBX83t9y3Ae0vv1JW6/KICeY7OGN9dlIG33s/Jprzl0R+77ZMsyQ9rBN/Ht ijroyj/jZlrP3Oek6FRhfxg6BeSFeSrbkHYp1GqKH4FOOp6oUt2LtKv96GBV CHRkaCSFpJYh7T6tSFRaFjoWP7afVR+N9PHUMHluErTfz4rJfXYMaU9q14rz WtC+seTfFGktpL17FNfl5gOPZ1N/4nM60qeiWskhc+A9/PplyWttpM+sfrp3 VR3wzriyVDkRSPtXn3JzOQw8B4e0NHWGR+DpTWeTRoB34OedpGMrkQ5aOCOy LgR4x79p57z+gHRI/o+kObLAi9m00FL7H9KXDiY8s0gC3jfxWzWPNyIdJuL4 7hJjj2Kvv062GtIRD1dVvn0D7QFp9oeVDiMdRYy0TFDQPlg1VesUc9+1rsIR vRro8Ju8NpTJRfpG5GVxdxfolI8xMBkTR/qm7s75Dxk+xSIR/1R9kL5VtVC9 IQS6zsXuFutbgHSiT8vGBbLQvTV8/umARqTvKjwx38X8Z5pSIxpDDJ/kNz77 r2hBr6TixqRTB5FOsTM69uEN9IZKbZp3egjpRykV19bXQN8NL0VlcVWkM7jx 97ycoV+5YrWI1Bmkn3Ye+i9jCPpf/n2gYkcg/UJ7+OdSGRjg3cwdcmD8K8hb sFCMhCGXyvma8V+RLtrfrGlUA8MSs/bMlDdHuoSTAb7OMJxefX289RDSn8zQ rjsIRrp2b7OPqUW6ssIh5UcujHPLfTy2VSP901vj1UwCxqskLl60H0S6Vn7o o+lPmLDNEVnhnIn079f5NYGOMPHbtFq5cDbS//Zd7Mjhw+RutYkkDWbdyN42 wT8Hkx//KlikbUe6+Z7CTE1pEKxundH9SwPpNpOmRU6JIIj5vC+z6B3S7bx0 rbsaIOhXrv6jzMSnK8zbqCYXhMYXm/Raw5HuXQU7ZAkQXk6c+pg9F+n+72IO 9E8QlspoDRK2SA96fvO64AhCQYJWhCXTf0bmxoUU8JElG+DZeeA40mM59jFj 55ClHFThb2CI9OTelamrpZG1ylR1SSFTb0LhYM7RRGStfkX/0tnwPyOjD4o= "]]}}, {{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUWXc8le8bPufITLL33nscu+i+syoKIalEiQolIQohJZJKkiQUCQlFsknD zJfSkJG9996c3/n9dT7Pee7nXu/zXvd1fV6rS05iLgQCgUQgMN79/++egruS X+u7lpDU9ls44YwVxPzde403+ReSvp2qWsy4Dal/eZ2auRBJRUmkFOnTkG3E ysjWwY2k5KcUGWdvyP9L05pl6YukEPem7acXoOgeJevb+9NIOinvlWSWAOWG 69cnuQ8iSWO2ru5HAFQFrmeG6yUiib7XY8LPCD63rsbwTLEh8Y9g9rcQLqgm LwdkPr+NxOcNR3OPk6D23qKLruUGEl0439cmV0Cj4azO8fcjSOjXmdkISIGm 5GmxcRcHJCT6HbKoPgbfA5fCKre7IcFKgBQTKgMtrQuXzMu7gXJ2wdK0jx9+ 0Xo783AlwNZHR+7YV9vgN3nOrtvDBrbYA54NXJ6C1nszBp4iDbCR1b07+W0F dBpOcD0MLYTV9zF+J+d3QV/r7L8xSROYTzP5fP3OAvR75NmZW6bB3Nh1xWlD DRig9WzJv06EOQ0HVTv8CYPk6Vr/3+Uw81vfxbe1FQbr3+7tJvHBjNLe/Qd+ S8DQKY9yQ2VfmOZaKJ8/xAXD9ybztoerwUR+s2FS5wCMSOYoXHp/H8Yfr74x kRCCkbILr352T8BYBBtccGeEUStFUR3mAzB6pz3D5qoQjI6MJyTqpMNIIouQ ufEzGAt+w0Vw2QbDH20HXtDqwTi32wPnh6dhaMGqPp/rN4znyDPWVVTC0O6H 2V2H42HCcPSW4pggDD5+zLs7/SBMtGduPeT2h0FSc0+hDj9MXj5/ddGgFQZu vmopc3SDKQaZ+WOXqHXzursVZV+CqeThixXPYqD/k8vTZ9W3YVozfUSsdgb6 g+ofP/J7C9ONZ53CFg5Bv2V5EWt+G8yoxUxdYT0C/do2TgY+f2EmcO5NhvQt 6Cc/3qTt2gYzdVbn2/Tyod/Ad/ZI2xmY5ciX3m7VC/1nO8vVp9Zh1pGtX9+V FfqfJzGsM6XBbNblF5eC90D/xPU2GgM9mF38cTLl8UUYOKRZ5L3JBnN71fh/ vkmEgU/uq7yHB2AuKvov7acGGNxfwkBgDIS51pnH2q2rMNiXXprrtwLzchmn GMjFMJS0IpVbOQ7zAQ7yf+/5wvDF1sDTcb9g/j/OxcwRDRg57BcFj3/AgvC3 qmsGczC6b/Upd8VZWPAMvXsg6R2MWXqo1aelwMJnXVu+FQ8Yd/3Dyf1JBxZd 0sdLc8Zg8puuRe9VZVgsOll4lyETplmnWy5mK8ASI+eNE2fOUvt0h/F17C1Y yg7l3uDth9lH9T86Zyxhef6kgf7NTli4feDSOfM8WDHm3LGjKwEW999w4sJh WIlraO3StYOl7dG5XVa5sLpL52LQ1G9Yfujyo3rfB1i7wRFfdawJ1iJ8TO+o RMBaS8OZ6IIoWNdffulTbgDrkjeUT+80hfWp7pw0bhKs1059JX6thU10vvP8 xi/Y4Ht1v0XkNmx2t5z4j1MLNtztj6X6G8HW1X2z37zKYJOlftpQpQooMepf fp1OhM1TIaUckUFIYLR7Q+Y5CJv52rf6B/WQYCwhkGHzErZopiwKYA0JgcaW LG1tsHXkFf+thBIk5D5o/eCYC1sZJwZtFq8ioaP133+01Dir7O+kLLWQSLNs 7qHfBRTTev/FrAUkSjcuC1ipAyUxxLiGNh+JRnqprxdUgDKlzRp36jIS7VVU /Ys8kCCyNPn+ZDwSPWLSx7I4kbC/xVogchmJAcdx+ssoEi7nFN8stkViaJDv b9c9SEi4IzQ++AGJNzdJ7M1RSPjifNOGgxOJwX87ddOjkTABI+XojURffh3K ljgSuQTMJS+2IPFsU8CFuV1I1F96H/VMDYlWDNIaKuHU/1t4F+qikajdVWvZ 3YbEBzlB9ovTSOT16FpYFUFicUR/tYQ5EubbPg3t1UZir/MBJcscJDQY+52N q0ISE+Q+DmJGwrPOmHrVHiSpC3BsvnFHwvkmhvaI20g6sXTN5W8DEtQOEY38 w5F060dXE50cUMbF9ms3aiMpJ8dISz0CKO4ZtQ/l2pFEcd5Jd98EtmxzNP4k dSONLPh4lL2Czc+6mSInLZDmMH/bn9FtsCnTfUIrTBtpUn+kZRh+gfX+/46/ 44hCmrrl0bf3VWHd5FG3uJQP0kwLKxf9TYa19H2f6Xpf4bbdbsV1F6/CqkMU eTvTIG77RdM4+kQZlj45+jbL9eC2NXm22d5EWOK3vU54GIK0opa2a4qMsOhz Va1xNQ5pLyT2MlUNwIKy1fvTLTJIR6u5qDCWALM1kS2MG7eRTsF+l4YmA8xq b6t5ej4C6Q6HBusFX4GZ7BivLGtEuqRmxkOcljB9WFnZ3uci0n1ZND/iUAFT jnl2PPFLSDcm8Ojka3mYDAgTUXmVhPRa54U89tDC+L+mHUUqhUhvf9/JN8IL xqUCQbRxGulDCzKCWnpgLHBCsfhpJ9Jntk+ECx2C0YGnUWXHGJC+maj24Fwp jNrPQelwD9Ivyvg+yZeFkaEjm0ZvLyCDwKGy5xuPYeQGN/OZWD1k2OtDyNxH ghGlKwwae+WR4VyC8buHnjA8nv6uOOMhMtyriizu6ILhsua6He4nkeH9UPMn aTMYTqbd4HsfiwztOzjrPYthOMaisza8EhkJ6sd+lEnDcHxaV90dLWSUPpbc TvsIht9NuvVcZ0PGg8H9fZYEGO4grabVxSOjV7rsWIIHjHC/2/34Yz8yxjde nBvogJEzeYcuBLxGxsq5/HWV/TDy6f3Kxb50ZBzkXaG5Vgijqke37uTHIdN2 0N/+VQJG36p4kV5FI5OaSygnSzSM6X/ktT5xDpmO3q0VtNuCsY6g1rVrBsh0 PZ9Z8qU7jIe3qzbfmkOm+q04TR0TmGT88/WJPDMyzUh26IcWwGQX11eJ3yG4 ndtM1Pg/MZiqVOKLO7kLtzs9ybI9vQEzFseKYqRO4fZ11Uq/u3kw5xzC0egf hTsUnYZKugVhafn+1O63V3DH4fr4y/MEWA430P1dCbjDT9VUnm4AVrhcpxeC 23HHp82cBOUcWFUwTBoNXsMdw86OVgYxsJrPJqvlaY8szN/YmGx9YU0zcvUh DROyHI338Q/aA+uqfyNZ5g4iSyBFmhwjDusZ3ScfL/ggS6pL61g6HRUnV43q 2IWQpbbxTmrJGGzcrq/YbtiLLJPqu4//1wQbkys1BVXZuJP96SR7bz5sWqjL b7x5hzt1CMkNi3GwmSVfSZIvwZ0nz1neZPSHza24xDcfZXFnaBNpt5AjbB1U P6vOfht3Zmq8n1ej4mLMl8vKg39w53/PXN4Yy8JWC8v6rA8Zd86TeM4cYwYK 44Ad1/EaZGXWcL5XeBUou3jZuFeakVV4uMK1yg0oTmE7OjVOI6vaM27jBnug 3GTOYU39gqxG5pfEfpkD5VlcJ+raIOtRYu1mFwIlm10uud8DWV0/iLaNkIFS 4P39UrIisgaev/ZhTpK6LqoiMa4h6wOBlocb3FT7nqS9RVR/Kc0KHnQMVJxe rvzz/RyyFoTeMt25BpSwHRKdLLLIWqv5T5pvAigu2i7kI+zI2jaqRZKg4rz+ o5SgsDhknUi836X0Aygsxmf6zl9HVorFcKn2V9hq9Q0Lqw9FNnYajNtbCFtP jzIxrOkim2ThUy+zTNiyEVzaoh9FNm3XOfMjVD7IsE36zjAZ2UyFzOQdo2Dz gzmXVeEVZLP/nkbnGgSb9nvf9C/HIFuo9pHK606wEX9CluY0E7LFjuUmhNvA hrJMy1vDaGTLSKb3fWgC65XjUsc4F5Htv20lyq/kqXO15d5aRj2y87QIJjfO wUrzYbdelxRklw+74v+Hev/29cxFOw8iu75Ok23PH1iuzHOfLO1EdqfnISwL pbCU2Z8lqX8T2XMu9AcL3oSFwKM/3+7ZRPYqUb0T0r4wP5/de4BUh+wtPx9r q56H+QuDnY96/ZB9ZZfJtNFBmDtb4ydZcw85mCaffzPfA7MjplbpNNXIIfRi JcNOFWYvunb/KTJADkP6LMcLnDBzi1tu18tK5DhqWfOSXh5mdti+e29Ahxzu T/uGUhGmdY+Z0jlYIkdwH0Ve3xamXHa2X/RnRo5YRYGLfy/AZHLa7mwPYeTI vKKd5x0KE0OdUnc1NpCjvNJ6kYXKM/dcEuGrK0COH/SeOq9zYTydtrF20x85 Bi2jAo2+wriw7sGcKRfkWH2a+bG7HcYyYlJezDEg546+ahr/GRgziFR6KWCN nOIKvfu46WB0OlxP8ecAcmr5bEbmCcJobnfiJ2UZ5DSt5Gs6SIbRkFZzZfXL yOlIr8U2vB9GXV7saqig+vO2tDoS6gCjJ4/TzjtzIWf4U4+nQj4wep5tbPmn IHI+64vsLI6E0bDPOU+tXyDnO4UMUesXMFpokyjfh8j51efLmSkq/q0mvbTy voGcfyu6M+40wpjFae/jXoXIOUm3Pi7ZB2NFhwtn52SRi2jJq/xxBcbVWE7I RUshF9dTDa8TLDBeLjXi1jSKXHJ9lh+WJGHCjmuCJ9YBufYoXFh9uAsmt5l5 XaofRy4rnzv6ipYwWSURbfb+MHIF0H366hQI02f6C+kG/ZHrgUUX/WYMzEgJ db0+J4dcL+PXzOIzYSb57phuGiBXowK5peknzKbabjDxjiBXj48Ft+sIzCmU erWb7EGuhQr349u2YK7k4kcHtgXkFrJ42btbHua7kyzceyqQ28OHczojFJb2 Hd38kf0XuUMr1NQN4mGpPzZAsICM3E/oDvn+y4Xl4HsXHXUVkftjfNgWRzus FFiPZX+vRB7W8mXmEDKsE/672BdKQB4pOg5Lgf2wHnn3QdudT8ija6HyqNAB NnZ6/bukxI88p3vP809EwiZDHnOK9Sby+Mrfcgh/AZuBLAXH8wWQJ9L7Rap4 IWyOpNE5GJUiT3J5+WBFI2yZ0+xOT2VGnve0bXJ2fbD1ptrw1Qd/5Kk1X7ww vwIUYqTc4/Jg5OmIZ3v3gAUo5qveqk1LyDPdq7QgT8WrR5+vejdrIO82eVPt 6l1AaU4s4GVfRl5e77MBpyyRQJSk2zWxG3kVy0Mr188igasm0uiIBPLupX1O igtEglRyqju7H/IeMS8zUYtBgurTx5RoLeQ9r7/vWIcKErRcFQwi2JA3QOHn xVuNSNAekUKNOeS9z+94Q9kVCZrsh2mkeJE3hWE89i8dEpSGuU0/yyPv+2W/ zBsvkSDueq185Bzy1gzRlCsgEthfCIp4jCBv268Hzb+o+Lgpf5H8uRp5J74I 9AcFAKUxR2ZDJQ15KXkZy7K81PrGWQ6F2yAf+wsNph+FQLHeUf65Pxr5pO5X CQdYA4XJOGOzhgH5dK4fJEvOwFZJ/QrXzFHkM3P/a9x0D7ZO/3UICFVGPofj zseuysMWqWDInXEF+W5pB4Z8c4ZNVXq6sh+fke+JNEPsFSJsVIjfeC1Ti3xZ nLGZwsmwYXT0VPadCORrns1uvvwX1g0eOdL2vUB+gTf/hHkPwSrjuvrOfGfk fy+qX+aiBQuKnTtHRVORv4alvpnlJ8xH/ych4DmI/H83j/QVe8Lc0jHdyuCr yL/V7sG0PQtm60z6/4aYogBb3ZrQh30wq5ne+yHvHwpIFt1WcxiAmfSkIee0 UhQwjU22yxeBafnzrxA6UODkTfkLJ8phSiZAMuIUKwp4Xi4MoT0Okyqn74XP m6DAzVMGj3KXYcJgrpQQo40CceZNGXaxMO60kXLccRIFXusfLyOpwVi04Zul /R4oUK4w1PSmCUa/he6Y8/2OAs38Xn1H3GGU65zc6KvrKNDHsLVIYYARj7gN lef7UGBh+S5j5isYbn39gC4mBwXph3iErAxg+LD81b0lb1CQ/9dL1fVuGOqI P/XZfhkFlb6oGL0KhCE/5zgN55MoiHlldhZ8MCQ5l3HOXxYFrV/sc18phMGB +O1PDzuj4Nn7P4NTrWGw8NmvjT57FLx23fGR2QwMPtPy+OTli4JR7uPpi/dg MObh2Z8MPSj4/Lhf6XN5GEysNbwVxYiC+QdomvbXwmBJv4uppjQKVms/6J1z hsHR3oUU210o+FdaYDGRCEOKbzt10mVQcJwzk9E4GYZC5Zud3ERRcItGQ3Ba D4bGxOybJ3NQiHW2SjW+DYadrXfc/WmDQlK+O9/8TIDhjZWR4v/aUEh3zVGG 5TiMZJHtfHzMUcg8KDf1gACMuntrX14YQqEzpC0hqv4dQ0Hm1X9LKOQXfii+ MgnGZfyfHDPMQ6Go7Ukcqw4wIdpw/qj/DAp94NRj8uiBKePf7orfJVCoPv5u 2OsUmL703po+UBqFugQ7CANOMGM98N+1sgIUppe6tnxsAGafi53VPdqGwse0 igaMRmDhcuJaYEArCl8so3cMfg2LKsy+ajPGKBwKtu2lbrA4ldvJHXcChd/s W/yhMgHLl5sivKyeoPDmMfVK/hlY8yH8S5n5iiJs/27qHsmDdZVXJSaidSgi fbqlINoL1ocX+CVp21HEwu3yG9oF2DTrngsi30KRM1OfpPEDbC6pXP6PZwVF rnqzpgT4wlbCvc989BMokhLw9skMFd+aWdJuHAhFkQ8UCocCVT+r0I5HJNCi SMMt8/tn/ZHgdY5vZucNFOlmSGZM2Y2EPDdeB6VoFJmPmrzVsYGE8RYa2TZV FN3GkByy3QGJYmfZ54weoiiLaY1+5kckHp792WAbj6K8UZPrJqJIvG5s5zD8 AkXFm7mK+0OQ+FLTlfHKHhRVYtP3DelB4pdnuVEfz6CotrWLhvBeJHbZ/rX7 KIOiex9HzZalInHetFIl6iiKmrUWvD1GQhLp1HUm1ioUteXrvLDkhKTttx+I /FBH0VP22+QffUESy7uu1QQhFHVLVhhRk6Tu//0d4J+Coj491q+ablHPbzq1 yq2jaLB4wBn3ASQuSJDs89+g6B3nl2KMxkjsti6RZLyIoo/SG7rS05H49fnO B3TeKJo0MpdoRIfENH7aTMYnKJqpwH+s9yy1vm7S2dOBKJp/0YAnqJaq33dU DB2xQtHyt66/BWWRKPpNOuOiIorWzD6MKYlAwtjevSOwhKI/1EssbUeQkPuc fvcrYRTtuNK7Y+EAEjx4tf3SeVB0ek31jgoTUCrDXDmMalB0Tc/OpJHKv8X7 bx76E4hi24JCtrk2wFawR6xM6TkU4yM1B6fdhU0JO8abQY0oJsHF7qphChuX rAp0/TZRTEnmiNVXBlgv5HY0/R2OYnvNOqT6b8GaVvDj1oOsKOYaO9QoHgjL XCd7z51ORDHvDLkP+btg6cCW8PzgLhS7XnLh+d4VWAyqISYniaPYw39zXqev wPx4/I2xfS0oViq9xfvCA2YGn1N2fniCYl9195JUFWFGuflaGM8DFGsyuzn+ cQym1sWIindOoFifJ2Nl9zmYsL/qqvOmF8UmQg9mXpKGcfGMj0+OC6PYUuyD h1sDMLqSzaziSkFxphIuF+FTMPz3uLXhGBHFOb/ZmecKw1D/XAVnrw6KC/97 pqPfCUM0u/V1X/SguOx0l9h/CTCoTcLArBEUVyeJbT9pBwMh20L5L19EcX3O MwuT3NDfNeH4+lMZiu+TTu8K/AX9Nof1pS5xofhhndE65hjo6ylcfZUlg+In zBTzEg9D3617V/YnRqG4y8lLzxRZoQ9VZUtjfqL4pUv5t8qaoI9jsKnS3BXF r91Y9DCLgl5KjyjdjlwUvxmrc7TDFPq27U5JunkQxe+lB+x1Z4A+UcptTmdO FH9SXCm/VgN9tkIpEVcGUDzlG5Ez8hb0vfTyTFJSRPE3/wy3+A2gn6GafHJT AMU/TN8eziJAf1gd5/KjQRSvItb/0K2EAX7eKxJntqN4AydzaX0gDFSDKitP LYr/krZIO7YbBm/n0d8QYEbxLp2YeyNrMHSymaepWxnFR0x/+10thuEDw46n K76i+MalE6bxGlT+2/fsq4UIStDdSNaQmYOxW/TpfEc+ogTro16honcwXpb9 qNM5DyUki8/OtCrB1IkDXS4fx1HiENHrCbc0zAk1DwQc8EGJoxwfQl4NwNww DZn8wgslTkutuGmmwnyBrMDy2+0occU0SP+IMCwevVXx5NA0SiQ9utMfywMr uUVOfI7FKJG+cuuDAi+sXrke77JrD0q8Oxl8+zM/rO365r6N6RZKfJG5Ijcj TNVXbysvRImjRGPUpfVwUdgIlLAwj2NEid+zbv8Ji8OmZluAWHM0SoyUnfI8 KAVbz/r3L1/8gRJzoif29ksDZZ+8yJdWar7rYbYc/rJAmUioyzfgQUkWC7PC TEUkxNAVeNh3oCRPgUkEKCOhPeZLwNMnKCnGt/fYH1UkipjuHvMLQUn1fq3N bepITPSU7pFaQEn9/WpNzzSQ+CukKEmV6s8kR/EFWQtJDETN6V07UNKSXeZy vQ6StL7tMAt4g5LH/MQNT+1C0qkynWVXUZR06hTiXNZDUli+w9cnvih5YS/P 0P09SHoVO7SepoOSV9LZi6UQSVVmbGcyKlEyePuOO+V7kfT7E/2R5SyUjPBk OGFtgKTBobHvB06h5MPfNIpjRkiaedd37WktSj7T3dy6YYKkJR7x6KQilExL XvnOu5+65porHyCjZC7NfMpbU6p9zrvKPdR8is5PeZscpPrra165eAslq/4b NfpnTo1X8bvj3neUbCAPcPtY/D8fa0Xucyj580nXyHZLar7P2lWqpVGyc6Ot JNWKWs+X2VqZGpQcOv3rrq4Ntd5O2uv/ilFyuqbZ/rstkrQJVsl0KSi5qtCg fM6O2i/LliOpYyhFiv5K2TpO7ees6NYsF0pxHS99qXgSiac+Sb5TNkQp4Y8f fL5Q55PoAX0LjX6UkpV8Z3L8FBI6fmQM1rxGqV1Tr0YjnJGwr1DLWTYWpU6F PlQZcIfNOfOwVH06lHIdjiIGXIBN/VHuH9I0KOV98PZPdg/YCLNIlCNModRt 7kBfpM5jxncFfr91UOpN1rnyxGuwvPIw0PIXN0p92Ol0X90flsl7Laft+VCq 0uekY0MgLF0w9dPi90GpH3usSSshsNBf0hxXO4xSSy17DtiEw+z0Bl+x5guU JpL4mwr4YVbpSleKgBNKb1dbtuHIhRnPlIbwrE2UFnn4zqHlD0xx6TY++VeN 0nKfogbIbjBRbXLjyldalFafdXV9uAnjYTHb8p9ao/T+w+I+hyVhdLeUtujt IZS2CtlafVcMI2Rn1XpiH0rbv+sIZj0Iw/pNP243l6L02Z7ibZd6YMhBw769 kRWlL7PG3mn2hsFYy62THZEoHYCXd6rQwUDPf1Lt7ksoHeZ5KPZ+AgwY6S36 XPmM0g9eyPNNKUP/R5HPp8VzUfrpd7rnhz5D/+HuzGQFB5R+SemXzLGFvo0f ZPoIXZTOVfmYxTwGfVXqbLzG+1G62DFR5UIQ9CUqTT/c5YzSnx9cLWhkg77o X4eVpZdRuvHjkV0KadCXLB4ZbPYUpf9Mq328qwN9X7a+jUtLonSvCIvRWCP0 k3Sx/voESo+bjzWYnoJ+uygD34hTKL0YVGvxegH6axKfy79+i9KU3Je/GcNh wGx14bDBF5Rh7Ao57soPAyMigvQqqSjDyWLfXZcLgwleOXYCN1FGeI+ui6wB DDntu19V+gNlZD24xsL/wPCefwnuh/VQRu+/pkWTTRglz2arSaajjMnmG//0 aBjbH9feUuuIMoeVIgh0kjB+6dCelrCPKONyby9T9UGYHH+Yu+M2df/+wfei hgkw+5EjSSniLsrEBz549VIZ5q6XJu7N2YcyqdkXFEifYR5Wzla3NKFMEbOU 5qdRWGi81KYa+wplur89MUMdWJ69+tg7vBtlRte9vz9vhJUSPlnxWk6UWVCw PEI5BatBr0XdDT1QluEu46nKcKr+/kUWpZdBWfbSwSFhflgv53oWIhqOsoJj n92v58KGV6wBeUcQyqqZBvjq/YHNpuqz2uRulN3tf3Q90Q22vA0zs7P3oKxx lkbI+iZQWJ/UHNHWR1mLdja6E9FASdcuua61gbLHGSfvlkkige/7uRdZXCjr rNvAxl+MBHvFHY8G3VHWwzU9zv8gEp4uXn6fFouyV5/eFGjrQULLcmiFwj2U Da13TNH1RiI98bVVmTnKRpktsLwwRSJ5vtHdNBplHzdFBNKLI/G4GTeLQAbK Pj8sOHpxFYmB43tnr2WibObPPNtf35GYUNaBs2dQNt/W+OvuTCS+zzTXuvkH Zcv+tqulBiOx9vlS9MIDlK0+4fGc0RaJf+LkO1+romxTFw2zpzISe0KNwsV0 UPbvqSfXWmmROOiYVpTWg7K9/QrD+p3UtdKHMRcXlB13qbJJK6Dyy+mxQ0OM KLswYvN5+10k/s4pHBQrQdktt1EVLyck1njd9Q7wRTn6yeuJbbuQmH+odpdg HMqxebIzIRsSn1plRc8toxz/XLpf+ggSA+KNz9MFoZzklV2DO6j8+5iuU5Wd D8opLTdb+cRR+2FzWWnSDOW0/c987PCg9mv7eT8CO8rhxoqSgTESWm9mn80l o5xpcFTCa0EkvBycGH9Fi3I2JDGGnQtIuOBAkxRajHLn6A/0/UsFykAiJ2fV G5TzvNNlaXQNKKfsDpQQGlHOn9mr4o0lbP3+q/T4ZAjK3WN7Fn+VAptvZ4eM //Gh3HuBSXNOe1hXZneLefgX5cqTb5QFqMNa2KFj/t06KFcjxi3bxwSr7UYG bBKCKNcmDTRvS2Al+AH7wb7fKEdRjS4x44bFmta1+ANmKM/wXko6bxIWBSSr L5D9UJ5dsySW9ytVf93YzfPxG8pL7erzGPKGeenr3/ceZUJ5M2MNyRvfYebj FSvDuHKUt6mteziSATPkXet7NtZQ3sHUnmIRBNNHaq7UWZ5H+XONsxcKbWHK OiukLSYP5S9bhLULKcGkw2A66botyvu38O+7tQ0mrqoy5cafQ/lbNrkFY50w /kKzU12kHuXvtRqKH34PY61xDS8sPqH8k2OtD4ojYUx48N5mBS/Kv+h03xQ5 DaO+Gb6Ha0RQPsuR4HZbB0a6bei+oTvKv++NbZ1khZFjNoknHE1QvsJZzth6 GIYHt4luOzOF8jVDFfmllTAcmlvNu10B5b+7WomKxcEw+eHPuF4LlG8bH7oX cRGGFjqCWvMR5fs9/NemjWCoPqsuVt8K5Sc+ycfuvwZDeSev9P4eQvkljnal lFwYyppo0toph/KUs3dq1vphqNDMxtvUHRUYSnRP2fDC0M+TkdZXD6AC+/aR 1ZxDMExcY2FecUUFAYf4R3ShMLx39HNr7WVUkMzbr+hYBMMxI9tUdpWigjLN cnXxBAwvpPanSbqggrZthiO7GIycLbvMtCGECvjadsXdFkZGapjrY2+iwoEN uodf78Kov+lXAp0aKlibFyoIVcEYf3Uh1vWhgn2Ky1ffRRirz7h1K1IKFVwW uE5+l4fx20fqKNI/UOHqU5/o0EcwKe1P+zJ8PyrcmJCU66iDKYaIspMzyqhw d8/PLxobMLUYd+f7KUFUSBogLw6fhRnfTyInD/5DhS/KsycO7oK55ItXE2Yi UXHHVw9prj5YNlGtoPNHVOThEfp4iQeW28n0UUfyUVHU9T+7uoOwcuG806XL K6iovlPxrn8hrN5JXub0PIyKeqc7JX+OwxpHqYzngwRUNC64W6kkCmsJe5n0 6JtQ0e7Y2Ex3JKwnjPBXXFVFxdPZCZE6H2GDU+98XKEFKrpRTCUeLsBGhMv+ +br7qOhzeLV8XA42luk1tk/sQ8Xraa9tjRxg0/GbxQexIFS8vWw3nfQINj87 5vdXDqBitClDxFIdbAn7vRKtskTFp4nF4habVLz+1lZ9txYVU6fPlb0mw9Zn /vRm8xOomG3Ac4R0DiiMstoiT1VQ8UNszdSJRKDsryymua+GipXDvuEFP4AS knuj6/0DVKzbJS3GQgeUd027251/o+KPqN+l53YD5e/aWlCLPSq2d4dZV3kC ZZX3mHCUJyoOkDUm+V4hgSFiH6HjPSpO3hq47dWOBFY3vRQ+Z1Rcao0VadyJ BE6lVhkBDlQiyBuWSBkhgaNFjzZmAZUYA+etgq4hYeex8W/WMajE3pw63pqL BLqGyl/C5agkKG4VptoPlIUTlQkkY1SSukISjuQFyk+RGxLggkrKtXlF/QeB kkWQ+Tkxiko6/KcP690AyjWJK87ap1DJQHsu2WE/UHY/fPVb3QKVDh65OR7C AluLRx/4evKgkq03l07Kb9hKryhxTp9DpVPR6WFfEmHLkjPO/WcjKrnlarcM noHN2QLNhO2zqOTzrV6UXh42766PF1XsRKU7dOOlpsWwkS3Bvdv/GSo9krxO fyEYNsh9cjiei0pJBiw2901gvYC+SPkT1T7vuurUj1+wlnFjQ8vJCJXa5n0l 7GZgxYmUsr3tDioNsNF7+hfBckPiA6bsf6g0pRxfkRgEy2rzQvze2qhMci07 2sMMixQFfbjpisryPYSoczIwn8ZzvulqGyprbD5si5iCefYOhl+jFFQGAQnp rAKYuzVPa2fIgMrWtsZVUwYwe8XgeqmJByqf9G5lYWWEmYUb2wt/OaLyuYfn T6hR8fWamCjhAxcqBzRGLl5xgGnZUjapxmlUDhsTNHgiBVNH39hXM75G5Wj6 nAdUPJiM/XH0gsQeVE6Q2tPZ/h4meoZz8n6loXKawXe5DX+Y0HsWeG6hCZVz T532E94L45mR2UcfRaBy8fW5r8gA41KG9oGB5qj8+dktttNNMPbe8MTBIDlU bizhcrj5GMaslt9GFLih8p8/6W9e2cPYtnI1Bp9ZVO5Z0F6pFYfRug61rhta qDzOVm88Mgqjz+/pyH3iQeVFleOPmN7BaMRK3U9JVVSmHBzvUfSD0TCyoZvT JVRhdLuuZL4HRmN3Nxsa0aIKRwSLvyctjBbSHhsYdEIVoVfPax82wujYhfRG he+oIvNFjfP9IxhT0/f0OlGLKmo9n0//Pg5jkWoJV+9LoMruLevcZTEYW5j1 qctKQxUTgYEN3hEYv8y580KqCapY6voe2PUWxrdEkhsXxlDl+FH6OHtfmHh2 Z3pFVgNVnH3i+4P0YHJ/8URYdROqeMTIq76ggSlaGqX47/GocvVt2fVPDTDV /C7Jz/0IqoT+d7ChPxqmM9ZlRIp5UOUJwyUXGWHqfKT7W88niSop0oT8/YMw q0+rfYYihypvDB9S3LJhtoq+9MQ7OlT5GFTwNJeKjz85n/dOHUaVuqnNplYj mLc79/FD8xKqtDju20Ywh/meQz9+yXKjyiC0eVidgYUl4n/53iyoShsSLBZg AssKFKvyyxKoyqTzsnznN1gunit6eLIWVVlm6mzTLGHFqNy0bVgZVXlPs939 7wSs2i6wElz3oarC3pdLIp6wfgzH5AqVUFVltS76/SKsf1ek8L3gQlWNvEmF /f6wYeAatv7iLKrqurFV/yPAxttHJqM5Sai6R1zr1OUw2OQxU73tfQ9VDdqP r9MxweY1rUyNlwyoui8m+HFCNGz+ovx52ySHqgdN01RVuGBL/shMrbshqh4m 1TV8SYCtq5MBlKJGVD1SOuliJwJbVSH3u4Op+R/3ZqNMpAGF0Mz8wToZVR0V tBJuyANFN7kz1T4WVc/0n9DkegsUt3KB8t8hqHr+WXBzlgZQHq2aikpPoupF 6zS3PaVA+aDi9bVqEFW9ttdv+wlAadpzaIthFlV9v0w+P/cVKN3MDjcYb6Bq QCC77sYBoAyHM7RTqP0I0dD6Fd1EXae+MnQRRdVbEycuSdlQ7Y/fLSlcRNU7 aSGMJW1Uf3n8Zz6poep9+7Q0cwdqvLeF0rQbqPqIs35P3wBQYs9W+3hcRNUn jZNtfq7UfCdfOWiko2piGLvP9ilqPU7xnenmqJqir83ywhu2KGP5l5mp8V4t nnitsQJblXnF4unrqJqVE2JYHwRbV8aIc6bHUfWtS1qXAw1sSTVa/UvdhaoF QvVX5yKoeuJJdnRqAaoW/57iCGeBTc+HQsMbraj62UR7/zte2EiBqxODiKo1 Wyf6jZJgQzXho6/zVVT9VhhyvU0c1ks9rT6HxKHqb+n69yQlWPu4o1+o1hpV R+jthWwMYOXJ46t1NLaotqMuZHblAsxPEBTH/XajGvsQ99nLu2DetC/LaeAH qvHSvGkfY4C5NzNHs3V8UU18z58vHa9g1o/G6tjaCVSTsb+gY+MDM336Pr5P uVFN0Z+Y/Z8BzFhtN6xec0A1rQ+Kjyu7YOrmvtDwDFVU293ymUk7GybD6Caa nrGiGs4cDXpHxbdYyCUYh6KayY6Jebn9MJ43Zn4h4TeqmSmEnkvlhrGuOQVv 2ceoZnmAp5N/AMYENe6zvGxHtSNncywf5cOoW3DT2Rgyqh2/ZVDNHAIjDTFh +hVsqOaY8lc3zAJG9Pkb6VXcUM3548WcLWEY/vwh6uSfSlRz/Ucj7kvlYycm u6OuWaKax1p83HQpDDPqBX3NDUY1H17l7ecjYOgbvdGjbllUu6b5JbjXFoZS XlRxSTGjWpC13cJxSRi6t6/dXIcF1W56Tp1vmYOh+zL3OQMbUC3i/s1/ZlUw lGabX91aiGr3svkOf70PQ82t7TtldqJaTH1ujb49DLM+xRHTm6j2ZNhoV6E8 DJ/1+dkReA/Vkra1vVVeheEfqibPbsSgWqr4JYmMOhixvL6nbTwO1TJwW7xo HIz0r8boXXFEteyTCcxPnWH0rsqTPTZjqJYXoHKDnQxjxnERxR5vUK0w/uvi XQKMs8eslzxdRLWywuNuNE0wPut3zqSMAdWqZ8OsFt1hsnecq186C9W+7eSv 9dgFU7MN58wstVHtu+K73cMMMMPA91p68BGqtZ/rkPybBrO7zK84SX9Bte4w z6eHvWC2U58+tukPqg28pNvRgDB389BfNqm9qDbVpbpU9g/mu3w63d42IZnG 5nbdc25YKjLWfH9yE8kMXoL6PAOw7NlJq2q3geQdD/LyovNhRd4vMmWkGMk8 DZ0JoRaw+owpUNo6BcmCI14s68KwZvk6W1UqFMnidPQ3vSdgnSbZQfOmJ5IV 95IvuETAhsOoDUv0OySrOdT2dNnCJu2ysTabH5K1Ak8eOSoJmxnfTPOoapCM RRF79lfBVttpN88XNUg2/i2U/+k+UM5tBasJ3UOy6dx7mV32QJnmLRQvMkHy EaUuVoVVJHg51mUxUf0fV7/Bn7SEhEwd5tGFDCQ76kpK/V8/tt0N/3HFCMnO e2pVbswikU6+syuUuu9q5KY7P4VEpbGayDaqPw/THUYu40i0TINUui9I9rZ4 Z95K1bceGpl/nn1C8tUj1scODCLx9vUxbxlJJF8/vnSmrI+qh8/G0HNqITn0 1FMPpW4kpreNzwv0ITn8rN7V51T9nVvHW55ZgeQo9+6bbG1IzJMW5rDlQPLD y6H3qDqfmDM57xcVguQ4P6kniz+R+Eo48op8ApKfBdalnvuOxLiyFlfROiS/ CHXPbvsPiaFNRY0jN5H8KoKlyKwBia4uInQ1VUjOupf3qaIWiaYP2z+1yCP5 7SObRpWvSJS+kPSr4g6SC+KX/6R8QsIm28jn8yVILklO6OWoREJT6ly/ehGS K9P0x8PKkPDM+MGBDXMkf3nds7hcjIQzkjsC7ooj+b8PMkwd+UBpyKDrG+JE cktpA9ch6vxAwYuaoQZIbq26KPIxG7Zy3n6vN2lGcu+39xov02HTW/70v9BT SF7sAwf3BFgrzGqpPF2P5LXhvvP/nsDqBtdRzQ/CSKZMhnlbxMLqHrlv/6nx ozrjyrcI9fuwXDpeqHXdCtWFdx7NWw+FhddRdD+3T6K6BOda+cVgmB/qazGZ pEV1Wf6kmu5Aqt7GX+uMB1CdLNXf8cUXZnM+0usvxaO6iZ4n7V3q+9X/+9zz 1ZeobmbAsXPzPEy8tHjtsycH1S33FfJfOgvjnr3OfDt3o/qRQ8cke8/A2KGU XqGrp1D9uNWGivUpGN317EXiiTpUd7R7rlt9EkZ0vp2nsQlDdWcHAyPt4zB8 QMT+fVE4qrueGTR/fRSGLvgqHHosheoerhHHBGxg8GWynmuAH6p7X1I4c+8w DEzbLF+bWEP1qz5NFynmMGBpwnmDMIHq1/0vX71sBv21TJu6D9JQPTSEM7R/ P/TbyO8xTglB9fCwontHjKFvhYdhMIcO1aPuHn9SawB971Wrc+4SUP1h9Gaq LkBf2OfixIVxVI+Le5H9Rg/6PGkKZxep558ZP38j2gF9l8laccbGqP58Ifl1 nD/0hUt4ddNfRfW0l0mZzLzQl/dzR5HeF1R/bZWYfqMI+mYVOxWDAlA9l/gs bdkW+o2ZZ/yK51A9/13CywsL0J8D7+R2D6J6kePTlL5HMCB3Y8m1jrouZ4l/ YUeGgZIbBjoDVPuqiifJ//2AQfuOmfCz1OdXfSEuydAThjjEQmnWjqF6g8Dj ZyU7YaijZeLs4R2o3tQQm6CcC8OFTksbTx+i+s9rj+LTDsFIqlaqlpkcqv+V jXnCNwGjya+edj05g+q9t6NjaeVgvMbHfZg9D9WHNB/E+NfBxIzxHafz/qg+ NnA/euYsTMk8Tl0q3I7qCwZR9zrSYObk/cS+12aoQbcVEZ4vCvNKxyyYxJpR Q9E3JMCRG1ZNnvJ91niHGmpSwdd+fYA1JmWr7WsE1ND8FXTV1AbWvs3vVzBR Qw0gB17RjIGNfQ2xLP0yqGHYG+D9Rg02CVX3mF/4o8b+aH8v0e+w+SH3zEmD CtQ4PHX1EvMOoLDZcmcGzaGGbZLfxRvZQCli+verxRY1Thz0dV82RYLC0Z06 39dQwznL53xfBBKq5nQ2XnOhhusx73N2MlR8M2P/efM/1PBg8HL5rwaJ+0pF o0adUcPvrKdTyTYkVvI63PI5jxqBXJdOK79E4lyuQPbvRNQI+erhmGaAJPEM d5vsg6gR5n3Rga8HSYe653/b1aJGpPgF+wfBSPLRGhPfr40a93+4n6AVRtLj Bwp8ZtT+PApxO+ZfjqS8ZivGgReoEa/ienTmBJLqehtPb3ihRmLXeVuXNSS1 F4x5FsmiRsq9czYdT5E0vGuSvO8taqTrnbU+rIOkKTc2vvVo1Mgadzlc04qk Gb3HLaR7qPE2wdlSzxdJE+8+jzV7oEbBgTPm+VxI6q8b9T14FDWKV5wOyRQg 6fddp3vXlVGjIuO0WZIVkj6TIj/dfYAan21PmbLPIum1XmlA/DXUqHX7Ln7h L5Iiwqttg6n5NAbDGhVHSac73F2o91Xjx6O3P0UzkaR57IrgN2o//mSKvPGP RhKN7G9Tg5eo0VH+4Oavq0j8duX2zaxU1Oj5TrFXPoXE+57OTEGjqDG22s3c p4pEWjN3TYIWasywmA/q8SKhJI390D9L1FgUr6yIIyDB9dkV2dG7qEExS/Y4 0AyUAHqh8N/mqMmRdLI5zwM2AkL3SVDfX03evP8yt9vCeu5C14XpZtQUqtEP cdkDa11FnmWhtKgpMy2kxrcDVrW2B/ZPKKCmHnbGhGTD4l9v2/xPnqi598hB t/ZYWCQ1FZuUPUJNE9dyQ41AWFCxVezOp8azjHk2P2IGcw+//ezxfIaazgMn jliMwbTI4w7GWwmo6bryTel1C0xWcnxTsRhEzUs7dtPRlMKE+8saYbUN1PQR y+o6mQrjcu65UiW9qHlNi7+w6A6MroTW6VIGUDPINPIBmyeMtM18fREdj5o3 HdbOudvBcFP274+Zu1EzwtsNqhGGWhN/9zJNo+a98HZeEVkYXIwNzBbWRs2Y xAMz11hhUM7mFfNjR9R88q6krmUFBrwj0xSJDaiZWC2XotgD/b+a87no81Az pe3ptdt10H8o0/n2P0bUTJ9iPNzzDvq6buldkgxEzWzSNfld8VS8qzC5pLML NfO4R0mxIdB34ElT1fM51CxUsGufOgd9kgaypQ0MqFkGde/3m0Mfz69I+b3U /KtsdO6malH3LdeexJ5CzerzmWc2hKnnX9reWX6Bmg3XefVs6aj+U85+jBFD zeaHEZxvp6jxt283VDJDzV+vlicZ/0D/wUh/zW0qqNlWeq76TCX0txTEMN2K Rc2uptakinQY8GTcs6xYgZr9/ft8ee7DoMT8vIhFBmqOLBeZX/aFwclGNclQ OtScYpaR/uYAQ41/ZCN/Up//vGgcRcoYhqsMz7j8p4eaK5p0rcGKMFKrfiv7 CbWezQO+b9s4YbTnA3fY7S3UoveyPRU1ABP7bP02V7+iFrOquOKYB0wGVEv/ Vm9HLdbJydV9yzBVnmahVbqEWvyuYTE0TDDztr1mTx4RtZROFXy5pgrztyvP h3WYoBZZOOTBn1JYMGIQO95RiVpanQftNQxhkUa9JFYhH7XwaP/CtC0s3eo4 K3rHALWsLTikXa7DahjdHq/6MtSyY+6a+0IHa3u3XVG+5Y9a9g1ZH0WjYW0N Ln7Y54FaZ00MjlLxesNJg0Ei5RBquW9jkdRVhs2dbbZDpsGodelT20xcEWwW P3pSt2iHWtf0L9853ABbq/tF5frGUOv6uv6Rt1Q9Gm0eMvjzPWqFljCKM/9D Auve06I9+1Ar3PfXlNtZJNiUM0yVXUKtKI0XpbUzSHh8ppzfrAi1oucuhEv5 I6GFgSNZPQe1Hr/Tsb5Jg0SmgL5tJx1RK8Fjm0hPFBL3PD7y3GcZtZ4rNE/s 4abySWVe1lWqv5ejz4qfvaDyRwl3mlR31MrMOHdrVQ6JH20NtvH0o1aOi/ph 23wk9rw55mxqilr54hShAj0kbm435eUtQa3CnoYxthokcTolBJ62RK2y5LjC S5ZIkk75tsEqiFpV9k6hje1IUq9wuWNAta/mV7aQP4Ok3Tlr/WK7UKu+dU0g fAJJezxVXz6LQa2mx9XDg1R81dtWwd44iFo/rR8WGBKQpOHpUnVcH7X+sp0M eRGJJJmy1cGCAtTqbJY7uMWJJK6FA3GFX1CrN2qR70QSEinyfHXU+aY1ZFo1 WCKNxH4vLt1VDtQaZ4jK536HxM8DpfuP0aPWdI1dkM8uJCYlG3+534laC7ck TX98QaJ3ZULzIhtqre6d4VE5iERjV5kJCTHU2qSU9UdR+TL713pVV3bUJlWE vx37//f09YyeglbUpg+wDtw3ioSU3Tzuz6+gNuvSOBdpAwmiT4aCb0qjNldB Ye+p20Ap1KuempJHbX6v0JzKnUCR3MpkJ7WjtsQUv8k1cdicDfjA1DuN2lqD B/ymD8C6jWq5+qX9qK3HcFtYTRDWHjex2H3aidp7FT5/9ZqC1V9tVynzrKht 5qXHuhQLK+YMhT8Dz6O2I0X59WYXLGKUTFVlDWq7iLtbwjtYuGGW6vW5F7Xd jDOWbtyE+WrWT+70O1DbJ0rMkFYW5uyslmpYKah97e3JUZM1mH3NJ5QdXoza QS0JDyL+gxnKlXYLoj5q3+Hj6GT2gumgx7rNgsaofV/PMtTcCKasy58+e1mP 2o8c78lG88CkzvXaUw3rqB0fWt/0YwwmlJqrWX5IonZSOt0VjnIY13hquq6X hdqp9YYCR+5T+e7blYbdV1E7YyL405PTMHqtNa47mQ21c3aWn2vTgJGST67b ivegdj55lUWADkZYtsd0T11D7SJbzQL7vzDsh/fr8+xQu/ya1/HkNzC0+Kfo Gs8b1P6U+JbQEwRDkSoDMxaaqF3zcTxD3BKGNJ4KblXOova3ftlDzuJUvDc9 mshJre8HnfP8q0UYbDx79GCML2r/kUt5OlwHgyVrj9/M0aN2x8EulEuAwQqK keyWJWr3ePIPuV+Awb9HT42sL6L24KOjUTkAQwzfuwsbg1B7rDCWPM0OQxaS Q031h1F7uu17m+oADGWtiBg/8EHthc0dwV6FMCzQwfvhEjdqr4maShXcgeEU 04+NjGTUphje/rZkDyO73g3DXzbU2Xb2i5eOMowMO/elcc+jDmMkgdefAKPp 2eSQnL2ow5KjV1n2E8b8yvbSxJ5BHd75wu1wFSbM8nIuZpmjjjD3XN4NM5g8 +H78uf0u1JHYpXL0izBMnfgQqdGXizpKIZlpJl9gxiJnKPe2M+qQXw6aRsTB zEhHurJgC+po14rNNJyH2dufWK71X0Ydgx3P9M1ZYK5Jd29V0EfUsU249+eI HSxem+y1eCKJOicqGgKfyMOSDMO5L0eeo86pXnrxv5uw9PPdmwGNx6jjLhNy yT4VVuR2RLpIfkKd4AJvhjMTsJYvW94R8wd1bn2sMEqegvV9Mm76SjtR5843 +pC2aVj/szLZXEq1f9T7bNViDjYGdySzW91CnfiJIc3Iedh0sqeNOE31n7Si erl6ATb/xj62qRVHnUyWr2O7l2ErV1Lmwx8J1Mnh3yntuwIU5opqrwV61MmX OuaUtwYUp1rXNw7UfhepvkyeWAfK26UL/8yo9ZTvnuyQ2QDKAkNmTAoD6nza p8PjtIUEHic7BXtq/2usQq2TKEhQ2STu7qXm9+1k44M2Kt8yrIrXqLZAne+u 3I2cJCRYJVGsr3eizm+f0wwW25BgH1mxvuiGOu3Bb4wiaZFw+gbDNtUc1OmO XAyppqOug6teXvZBnYE4qCAw/N/+Gs3DR6gzmnJndTcT1d9rN7+TI6gzlf1L 03c7Nd7oBfuPwagzXyx8OY8ZCaonBj+EfEWdlS/ncyZYkMDLXRrm2YQ6m035 YzKsQFk678Ym8Ap1SW2b0k5sQHn/6vfbc8yoSz+4zymJHSjnFfQU2dhQl3nm YfJfTqBwmmhG8o+iLtt6RwcHF2wVHxT9dPEr6vLQS/OYc8OWzdPm799JqCsm VPrgKx9s+qW2XVcfR11puW3fKPywQdGt0RBUQl0FDQuGXYKwEdJUc35/Nupq mvaHvBOGde9RuqzMeNQ9cJX5cqIkrMo+CrYbPI+65jdtc1qlYeV+Ot3AHR3U tb7/YoxdBpbn6ZuOMK6i7slXmk4R1PtWwvr1gcN71L3c4mDtowoLpq/9Msvd UNf3X+aDt2SYf5/AShdngboBI3PfxtRhXiSIwPH4CeqGUcKNTmnBHOlQx9Gy cNS9y/Qj5Jk2zPrvM9dlrEPdaG6B8j86MLNsruBJfoe6CYrvNA/uhhmSu7xr 1lvUfa69djlcD6bF/zv6zp4PddMMjXI+74Epc28S/XN91H1tfn90E2AyIuqj in8V6uYea5PWQZj48aMj6dIK6r53kXDyNoAJ+bH2tQ8TqFvseTE51xDGYz07 zZ5S41UEFLWPGsE4yyRziXIn6n4OJ/FImsBYwl+5YKEp1K3dnbnPaRTG9ryo NaQtRN1vM+Z+KVEwOqc1aHOM2v/vaQsZPaow+uEXy+7We6j7+1jCX+Gf8D+K rjwc6u8Lz4xUJEUpRQolabOXbc6ZfQZFhSRRIkJCpL5UkuyJlK1SKtlapKKy ppIlRVJSZDf2fd9+n9+f95nrnnPe8973vufBM53Bjw45SexCrTpR2uIjZ6Hz VDHvzpadqNVQ1LH7zlrotNHPOsKaQ60Wr2sOdXnQaa9e+FvXALX429Ti1hyF Tp/z1a0ZnqjV0/i71FwAOh+Qn+99Moxag7cuTUY/hs5fjy86riLwHtNX2FLD g6516pO5jr9Ra2rui8WKHuhyd456vZ3gz/xL95D916Hr53wG1/EBai9wkHwX qQrd+lEdet2rUVtIOr/r20/o/lJkPLdYF7WXVtlJiZ6HniOr6+/4e6C2eMAS gz3S0DMt8fp3KBu1V2u/8A4thN7HKkklTZKoveHh7F+hhdAva9t3FFRQe5P5 IxFOKvQPqBm9VaehttJSfd0AQxiw/Xz22rgraqudjb4rcAMGDb0/XJ/PQu3d W3Ur6BowWPyuYnGQImrr/mue9a2FIWam8tApR9Rm83YemVsPw/qrvikGnUPt Q1IlMhPEPORiuIjf+Ba1j1S6GO0ygtHJKb6g3AnUtrm68pLnEIxdLVXL+c8U tZ36jv4b1oLx2+0LDb9JoPbFwsn7vcUw+Vqde1qbyPeK572qbSdhSn3Ub8el eNQOUmKTnURg6sWudVOhyagdGXXjGH8/TN+9ychdKIPa0dzdNxRGYUak9ZlY NxEvfqahyC4WZrzWt152DUPtRye2yjX/g1ntnq+sBbaonbq26oCsH8xG1Wz1 bLiC2k+/eV05qgCz7Waa2281oHam/7qXCaUwp6oo+bC+F7Wzd39oqXeGOa/F EQpD5aid0+u4UnoZzL3K2di5/AxqFz5YzrTIhLluoSPLu5ai9iezLI84U5hf U9TVPPMGtcuWWD76Regv5tXU3ziJ2l8LKTWrbsO89c+EJOt21K72SBU0pcL8 2YEjj/50oHbtFiONqGaYvzryl/l2DrX/1o/afb8K82E/UlL2u6B2043b0WKK MB8aKF+v9h612zm0YqMvMO8nHOIWp4DaXdMdY+GnYd71+Ljjkj7U7s8IV6gg 9M3s3rGohQQ/RsqSlPvnYF4lzv2buzlqT7bm6oh3wbwADO56koTac3PVLI2f MFeeJ+yubog6CyS7jMyLYC6UKaDvdAV1hNTIh7yfwRxdbLFNajLqiO6RPJ4Q D7MDDmPMg0zUWWG/89T7AJiNjltD2ViJOpKX2V6tZ2BWfawl5XIg6qy7fcRv 0VGYKf93r3CvDerIvfYIUzKEmcNvJHZaz6DOts4H9103wrR936Ve282ooyrw Ni1qOUy17ZoPjhRAnV3rKl+9noGpIxzn8R+HUYe2b65k+gdMQsXFxc5VqMN2 lqiWKYSJdIsgnfFzqGMQsK2e9gQmVvym9ys2oY7pO4vBwKsw1kCK31IagTqO ctlrV2jCiP37SidvCuqc1v26UVMOhsvDROQny1DHw6xtxyFRGFaR9PkkWYQ6 F0NX0BPaYUig4aPohx7UuZKkZPj+Owy6s6OlRAZRJ6iAZtaaDwOt5S6ai1NQ 58bwaSelGOjPKNn2q6McdWKXBnju8Ye+12JvF5wMRZ27m+9ecnWF3k+/xC2e /kWdB7RXwVGW0NMqGhlrTsRLPlwelcWFnuUPlELqClDniWfzXcIvdhu6+D/c sRN1XlyfTJ7ZAF2x9PX8/1iok5W2PHO9CHSO/A2UkfiCOjkfN+fSJgh9673e tuAk6hQ2UIttW4HfsjL15sIE1Pk0YVoZWAl8z0adM8Q8qFMu7lyXlgt8qQrx GbUNqFO57UprRSp01LCaU4i5SaeGHd83EA0dDx5U/eqgok7d0ReTK/ygw3+L zK4GN9T591+pgKYLdPwn/E3E/R/qtN5sXHrIgvj8xH9PVRpRp/PZ+GofNnQ8 5Mi4Fi1Dnb5SUdl7qtDx85GQ+qs/qDPcsmlrkQzwZY7W3tHbijoTs7oabcLA /29PsOhANerMrT6Ai8aA36UQe5rUj7oCKo76Ss2Ent/Scb77GXUXG1w22fMV uhbsTNKP3YW6S+1irVxzoOvp+WM/f9qjrvil5w5RKdDt0PKe0xWOuqvjit2z bkKPxlfT+2t1UFe2YiRgxhn6yHskXC5Loq4Cf0nkenPom9tDcY/uRN1tFLnb dCYMLKgWvnQxFHU1NY2fB0nDoPwXwYDDT1BXe/LD02dpMPiiMsn7kATqQp5m es1uQj8Pa2z6YoW6PKZ0ipwJDHtuDUswskNdy338+7lhMDoh22xyl6jnmMTh hJa1MHanQbewSxp17Wq/3hVKgXH6lpPmuc2oe9r6VZzZR5gIM7qnYKuKun6n fCMHpmF6uQQka39F3UDl4YjVQTCdFvLt1JHjqBs6ciKcugpmaMe7XePNUPeW t2FIqCrhHw249rn7UTeeWhD0ohBmu0/k5amuRN17FNWA2r0wd5o0vCZEF3VT giWvbHKE+ePjXgJUAq+nhqGXDQg9++F35m1hB+pmLpu75B6AJJlRmfZON9TN qna7ELcSSUad72KmtFA3J7rVu+ABknxG9njHPEPdQouD59uVkZTUu92qjsD7 07qycyL5SCp73hESfRR1S5v0zqoZIqlb6qjzOT3U/foow+NQHZIXiQ3c1DJG 3WoHeXdfByTLnL/YA2WoW7s12vXxGJJVdGJXK7uj7t9+IZcKfyQDy69Bn8i3 KdPHeUQcydygfQkfilG33bPfae19JBtODDwuEUHdbi2bk7QdxDpCxVP3Pur2 z9TY2+cimaP/varbD3VHCrl24fpI1ttyNcvgMepOXsk5/qoWyTuU+8kZDag7 x9lx7M8JJK89mTTGW4d6AsKJR8kjSCY37rzY9QP1Fn1daaXoh6TWR2NoVoJ6 IpGBlkbLkVT0Bby3nUQ9MZMpC88EJN1xkS4+aYJ6qyRPmd/ZhiS3xxZPGq+h 3to/jWZFb5FEv+msnSiGeuvvHTDpZCNp2eEDwZ/eod5Gm+IDy37AfAZD9L2e Kept73xiZDkAc7lPghuc01FP9en6PVcuwtxuRr0HZwz1drneMEgTgdmne97d DXRGPRw/xxlXhJmrzn+3FRPxWe+62euyYLrToajGugb19C9aMZlMmOY+LHr9 Jgr1TASZGHkUJqd+RJa//Yl69iuWa22NhXHd8g+diStQzyk/osLAFMbCvt9O PGiLeq6OYsecxGH071FFRdVPqHf+vXhwWiiM+KgIsAx2o17Y6ZV1Wy7CYEW4 h/v9YNSLXHvLhacDg4rJv/98y0C9W8WrKA4TMBDo/eG+sgPq3Vu3WinFDfoe xfU5a+ig3sOSmPySHdB78eKv/UpTqJfiIbm/g5jHj6cPC5cvQL0X5Wu8N9tC 1wFrjltCLeplnY1fxpGFTuvry7xFAPVy5NY+PNFA6It5ra3Tb9QrqLi9KyAe Oh55fT2oloR6H89LlT8+CO0Nt8vKb59CvdKNd6yLV0L7FofPf2+moF5FpfRw WyW0XT1rGR4UhHrfve8GCV6D1mEjutvDO6j3a7OM9CZ9aD3j2kben4t6f74n ZLAWQetC/WkpqfWo13hxPdP2I7Q8AaV/dBnUa9tyr9b/MrTY3X/Dyn6Dep01 G5wfUaFF9RU19VIm6vX5JpI+TEPLiuee/ie6UW94m+zNljfQsvgtKUxzKeqN /3qwRcADWsT4Boe+6qPezBW5PHkVaNnJcn2b7YdU8o6H++h90HK0dHvu4xSk CtbJt9mkQctjy8ZbbluQKnz10Xk/e2iZr4viFh1A6jKVTUsfbITWU0vFtxSJ IXXF36TE903Q2pshKpH7BqmSQQoaTQnQdtlV/2B+H1LXqT0uIx+G9s3dP1eK uSFV9t9mK1lJaG/M3Cd+7TZSFUKSh7AGOtJZOq/745G6VVMx4OgN4AdK7mu7 /QOp6te2PLu/BLpclwSseZuPVK3dafSCEuj2/r4kkHhPqHqtSj//+UNPtELw xi8NSOXobJ2XmYO+YdbOT8ZmSDVofxJFzYGBtdsdv3/kItX4xvbNVudgoNDg 0LYlHKQe6txhdHcQhqTMJ+sEu5HqHKN8X7oVRuZihP7ziEJq5LAGbW0ZTJPO sP7dy0VqzAKF5zKVMF0u5TN6ncDrjoTkOrmfMHNjU2Xe0EakPtacmlRqgjnx pPaHU9eQms7psd/RDnNln59mHRtFaoZ5fY1qD8z7PKpwPN2M1Hf/Fb7QnkCS 3fVPB2wvILUgNHM9lZivX/IqDEockfrxzsNrDGJennnzX8B/5UgtfXpzmiOM ZFr8ThvjS0j9mh9w0mAZki+/oI5E3UBq9TevX0YrkZz7cZPkb0uk1jaeZB1Y i+Shu+++y65Gav2gxcuDG5Aiv/RExcHfSG2hGMoeVkDKng5ebU0eUvkr9K5b b0PKmQX2ikLZSO3duGP2uCpSbtBLqAfOI3VIY4OT/W6kpF+9Yz2YiNRxtthv Jz2k5L8+4DS6EKkzBwU4p+lIKS94tVB/HoFsP/L6DBcp32N6Zrc+QVh4rl3e ay9SqjUTfwdIICwJ/hXpbYKUirih8KMPEJbHl8xfskDK+08XIWwPgkT6u1NX jiLledEx6XPnEdbmpv8JPIGUmHvbje2DEdZX3OWFOiHl/Ol/S7OXImxsCM++ 7ooU0z3l+RYbEbb0+26KOouUrQfDHJYsR9hJcouK8UHydEbLQdGbCOpix8m3 /ZBc7HdXIf0ggpacyel7QUgOnTpFTytFoKqx6h+GE++BfjHZ2AiBa6r4Nj0O SfmmQdr6mxH2nFi7+Tmh164h/RJrNBD2ey259fIR8f6Fn5J59xfBMrbPLScD 5how1KdLHeFY6r9/BVkwtzE8P1qjEeHEu6o9H3Jh1vbP0ubylQiuf19tKf8M 0z+XvR2eyUa4uuF8058GYv6nTnyUj0IIUXEy+tcK49mpCWueXEK4TrfMa+mC scbnr1TbbiHE2UJc9yiMql9kP9wsh/A0RXDfjAgMjl+O2OMzjJD5ZqyQJA6D quuoZ3JCELJL+TsWSMKAu5CsXukKhPfd5cIi8tC3uHbF6LAKQvFM7rnlitCT wZK9+n09wpelT9tX7oBu+722nm/XIfzcGVEkrQ2dix+K5C4gI9R1NmyUbwb+ wq/MN1L+CP8ebQ3YGgwdq6QyQlQpCK1W5/hqytC+ixa4PGw3Qqdksb52LbQ5 8vXSdL4g9FaveEL3hdYXaQtvHCT6PXTtmKi+IrSKiFtYMC4gjHOeue6rhJb/ 7ob9O2GMMEOe/n7IC5pn37gsu0tGJFltjFe+Ds23CsaKvcQRKTkGBurHoZlB pggUyCAKSrrN7N4FzYJvxDPXLENc5Bn7VHcJNDWIKSglCiIKfS+wggZo+rI5 KvaUC6LIzvZljExoqhScMMniIYpeEynkBEBT52MVfeV3iMu71NwMLKBZcvDY 8V5LxBUcCzmjHdB8pFCQ/BkRJR75Vh+gQHN2nbiNWi+iJDnZ3+wntCiMK3Ss LEFca/VVwyIVWtLy34uJdSNK54y0W/lAK70kUGCxAOL6NVIxNsbQ2ncnzlSr DFH2LJ17YiO0PX05OGa0H1G+2mHy5AS0Xzr39eura4gKytfTTn2BjuOczVXP jBAVr2UddrsPfPNTyjJBRD3buQJ55znQdSY7qofZirgzScnlwlrojt0df3hk A6IqZd963z7oKSvv+/okDlEzN+FyUDT0mznUtlfJIWqt+aQa5ggDZnkLSG6Z iDpnu1siqDC46KDsTveTRMEqWqyYNhjyvL0o+iOx5CX9EEpWh9Gz78z+xZ1C NKRMvUtfDGM6rBxuBtGvvUdlnZ79hXHSIZ+Uv6KIB9a6VGRdgYng/EWiZBVE y+tCkZ++w3SwlFegvBCiVY8yvfQx4V/DuxwHaYjHeGbDFf/BzOig7AeZxYgn BB6Z1sjCnPGx0aRDiogOR8sX1o7A3Kijobu5JqJj3mD23xKYv2maVBHpi3j6 HKxpdSVoM9tyxbUQ0a3GrozPRNLD7uiCfltED9Uw7x5Jwq91F6xZthrx7PWX 2/p7kCxbMXrtrifiuZ7f9cMFSLawE8t10Ef01ieFj99AcnigIUVmCvFC8maY JvxgvqhlZ/wsou+CPQPz2kjm/4r76RKN6HfM44GAKFJEP4pWfwtC9B88UDC5 ECmqtM28/US/Ay+r1vfNIcWo7HsCQTcMERObbh1Dij1v7dFSIr+w+4Nr6vqR ci71etDey4jXlSt3VfKRcuXfIYbRKsTIwuemxY1ICWqh3lL5jnjTOPxMbi1S Al+mFVsWI0Y3norMrESKr8mT+5FEvDhXw+cpJUhx/zbwn0IN4h3S1oqEQqRY q79eLUT0LyFCuPvmG6SwYnKc2NKIieu7hEJeIEVhhYpRqDDiw+elm33TkEIq jtEtf4D4GFJYZx8gueZnomLRSsSUb4HHneORnHRKPPH6csR06xOXbQi8Tr+J NlhzFvFpP+ueeQiS1Xsi2sS5iBmXNubtJfzyKA4sSBhEfJ3QPKl9BkkOMLXC 6B/imx1Fq5WdkSR1erfXrf8Q3+UnaijYwvyt60Ix7hWIBQ3WbuImMEdly94R j0QscqFeX2wIs8GhqkE/iH58nFv3ZI4JM98SDr4N/oVYtu4Pv0sDpvc2Ul/m 6CD+OGJ+tGgVTGw/FCPbTTTgZ++ui29EYdzmjPC+sEbE3xdW3Xm2EMZipthd jkQ9DXd+1MaNw+iiba1/jIj71bTt5dj1ARih03S+FEUhtuRGrbzKh2HfqOki eQJP/t99xm61MCSURI9PIerpdlZ2sa+EQdMXxkbXfiL2ziwLO1ICA8m3HhVm ziAOS1WU8N5CX35YzKTlMcTR9CftkAm9Ycdv9x/mIE7ohC3QSIMeR7tWbzUt xNnD+igbD10WH7yKbUiI891brFbfgE6XRt3DsZZIo3gv9lkaAvzo1HSN2jKk CQp3xAsQ8/v373NTl5YjbVF8cfakN3TIz6xU0dyKNGGlpJp+D2gPyfjXMMNE msg7/+E2Z2hf5DAxRMSnLdO3FftjC23xgep8z3qkidUxdlZZQhvzkDnJXhlp Kx3l9nw2gTbB1b+RkDfaqimyU54htP5te/efTQLS1gQ3Br9kQmt5pYfn906k Sa8pSE7Vg9aqb+M6jV5Ik0lN+HRPk9DD4vxQuIM0Wa0LLbd2QJvc5SljXymk yZceIYcqQJvz27LxFy1IUziku/6yDLRViEWaRXGRplgvt/jMM2jHgrwFAyNI 23ps0eAJKrSX/AxLXpSNtO2tvXXmX6HDttjFgK+JNGWH6g8GR4C/os9SuUcI aao9b5/o9QC/5s65O4S+0TRc791S9oHOVNE3KcnEfu1zTvYSd6D7cteU/rwL 0nRn9hkv3gY9VwNK7T4SeIHvrt1TOdAbYygceGgeacwgAeF/ddDftPUa/CxC 2t6Y+Gdpa2DIy1u98X0g0vZJ+cbcSYFhufNizzddQNqBeyd8r++G4W+DAu1P FZBmnqy638McRneWmniopCPNJqt0jBoN44PGB0q6iP7ZaT//p6IAEwm2AoLi L5Fmn3+rRD4LJrkdLrHtKUhzLj4Wv7gGpqJ++vw5JY600/ocv2lbmFZbHeqt SODt9nW7U+8ITFd4aJ9MJ/I7WzOpV70SZgaPPCvn9SDt/KF/Cp8ewez59sls mifSvOs/LXujBrPTV3/u3ZCMtIvH0ibSimDOs8SFndqINN+2iKa7+2GOz6To vs9B2pWTZ8uuN8P8/luczT/zkHa11/KlnzvMvzqaW3VqHGlBbvQ7nhQkLUy9 eOnjfqSFjCpetY9E0o6IywFP3yDt2nlRF4sNSNr3UeM3n8D7+szIQcMMwo9t /mlHS0TaDd86BMIWBD+Uc3gYgbRbCwq3qFQiKUHOTWTMHGkxQY/FNx5F0tPo wjflRH/iRUKnV/UjKXu0SNLBFWl3ItxahS4iKU+roTKf4NO9lQe/zIgSaxud 2i/VSEuM1XvdR/i/N+7Z57rvIe2RtHxC0w4kPTvTPOx/FmmP7wsFVucj6Z77 eEPCEaSlyve7Fu9BUujlUq2UVKSlJ9ccevMXSe5ZGXtLbyPt2dYcejqhZ6bb PN2/nkRaxvPErXdnkKRGUX93l4+0l2qBKyPCkCRymFqYa4q019nOs1ekYb4o r1t1QQbS3ujsb/dMh3lbN0NFzl+k5TFk3liUwVzEYa9UywCkFXxekLjHAuak jgcw9fWQVqTfFQJdMHvvLTt59Sukfd6fZblJCGai1SOTeINIK03XhzWOMCM+ Xf87dw/Svgg0yC0l5qHQpP3PjVYgrfK1YMdYGEy524Yl/ziGtLrVpmdKRGFC zle5PPA70v6e7jTLdYHx0G3mr5C4Xw0lF7QyvsHYsNeHnd5EvJbzj+bjImD0 4x9Tr20FSOv5MxLqJA7D59RSvPqIn+9XD3Kxdoehbg1zv3N2SBu8Jr3vQDUM Hbs571dagbQxKnO17k0YtJCPS/m1HWkT0b+mlEdhoH5jOOMmwYepfqf6TWYw YKfw4/eNL0ibvx/1UHQ19C8x3KsXsA3p5MnNAZRz0KeXdF9CwgTpC/blOIzV Qq9P/sjx5D9IX5hmZNCtBT2l2/srwmOQLkRp2fEvHnrkfcbyfiogfclhL7Hq KegOUyrsvOiP9KWvhEdKDkO3QOqY8wVZpC8XSfiVmwtdIUd/fl3mgHRxO9V3 L6Sha0NKl0rdWaSvzCu+m0ToR/HX3OH1l5G+epWFb1w9dF4QS2po+I70NS59 x8Op0Ml4GKO1UwzpUp/92H4J0Lk2ZeqFeTzSZTas2nJ2Djopu2Otz/xG+oZz aSJO1sCfOhxUb/MB6XJVev3WhdApsFTVf0kc0jdtqfpusgE6pXX2fSkMRvpm P7vXXF/oZBWLVJTRkb6lbjJWtwk6L/mKFUqFIH2b2jVvFTp0lu4oPLPPHOk7 wmStNj2ALrkQq7Cj9khXbn1NWysAXaFUM5t73khX0+NtFLWFbkqnL9u0FOka t+oXUT5Cd7CM5/AjFaTv6nPtHN8EPeuO9/c4PEC6NmfBl+4A6CnQ2Ob0lahX 917s83/t0OtCsU6IW450mnGhZ0kK9A2s21xl54N0RqqJed5i6C+StNbYaod0 Npmv8+IkDBhpjCUceo90/ZfLyfFKMKgV+ljicSjS9yx52BoeAoOvegVLS4eR bmS767NfNwyptW9+8igA6SYS1tecnsCwNv+to+xepB/xer5GbweMHn09f1rk OdKPUqZPyr+E0Qnajy/ct0i3Cee8Fd4NY9fzvUZ8MpBu/6jR/BcdxnO3r8he vwDpjsrbUvI+w4TR8ILmXgbSnXPOTTwyhImm/b+yHTqQ7vZdLNbNDCan7hkJ fLiGdI8jR/gH/8DU5TMJjgYbkX6Wn7abehSmBTLVX/zrQ7r3PL12iSNMTzgf 8xauQvrFkOubB/th5uSIZjmbhfTLEn+8aj1g5sd8rs/cUaQHbPNYnXQJZm/N Dp1gEPGDsgvtwxbAbLey7IhfFNJDGSLZ7iEwt9vu3fyQDNKvfT206NAymLuw L6AmNh3pEYeSDsJNmHv77D//CSGk32gdTFZYA3O9MNnynuDLLVe9cZEEmF/9 XdxDYQzpMdMh7GF5mNfSejp6YQnS4wN+Rv9Ogfl9JrkuVa1Ivysu1164HeaP CpuGqrxE+r27pzUfZ8K8nX7V5hsEfx4o5gRc2wXzx5b9nMpVR/qjV4t+nsmF +QOWdpzxJqQng4mCBQ3mdXW/3vq6G+mpZffPYjHMSxU/c0yfR/oT057izQYw NyRZs+aTHNKfNWmtWloJcwWHlV4uW4n0F85X7UZMYe5KfrBKNIHvy/Gq13V1 MAc+4Us+CiM964qM4HtrmB36dXyfNdG/t6KOpsmtMJuwxjd0OXFfcuKyksJP wizt1vGYtSlIz99EGfXog5n6JFqxbzHSP+jcvkWbIMZQUcN/yUlI/1Tc0aZ4 gZhXYI+uA4FHyX51jWUUmF7yckP6jDjSKxy+/PgrAlNkU7NTy/OQ/uvW3Mqz sjCeryyi2XoG6XWy+raWj2FcYfyQ9RJi/fdJ9CvGNhgLP2Dy0Wwn0ps+7DRZ rgmjhzfr3X9E5Nc9cDQqTR+GZrtIgbUGSO/zftIS8Q2GnIai94gR+QwunFTz MoHBevaJb0u2In1sXWQ10woGvgiTTwkTeE6kNMgrtcCA/rXHZpIfkT6trnRG zAH67y48sunYMmSQ9D+IN7hDb9VpxSt9b5FBScuzLC6B3qXMJ1pZWcgQFM5+ /FwGekzzC/jme5GxyPHFQIwHdKdfiW/+N44MobIn2pfKoHvZDv+z91qRIaL0 2N9hA3RdNtoT0CCCDNGQ+1+Nz0IX5WR1hJYdMpZ33Zbc/QU6b4xeu+BNnL9C /5aNrBx0qh2Re+5DnC+Rdv2J0Dngt8yJRDrfQIakcPDo4FfgJykyhHd7ImOt oz/UbQS+17oR8RJDZEiXXQwu+g/4FivYzyuckLFe6Vx1WiXw9+7Y4n9iBBmy IWfWRSkA/0DA9i2uesiQ7zpl7+0DfMeNjk6x1chQ0Ld/cbwK+DcEPIIe8ZGh mHZs2pDYX77s4H8mh5GxVdiSpU7ot8SmBweu1iJju6PZdenv0OkqcSFNuw0Z O8uMfwsqQuefexXzuxKRoapkINd7EboOetkIi/kiQz2E5VzzA7paaNuKs0qQ odkFWflK0H3xZujq96rI0NLXJj32hR7F6ZufTfYjQydNXT/8J/Q0DZ973C6B DKrwjpte26A3pafl6spmZNDL5BU5tdBv5aR0uuASMlhK69yVd8DApnK/psIh ZHBCVudK+sPAA6E8x6vLkWGoL2LUqQyDqb0Rs5eI/uxNWxhXFQBD6gUyf2si kbFPmNTy9i8MfYi8ThE7hwzT0lGvkCAY7j5R7JRH4GHNa7i3tRHGzNxFFsqY IeNYam3nSk0YG1notsPhIzJshapVZ8JgPPJNIPejCzIcSj8XV+yGiXKn9a9L O5Dhzsvoc4mA6S0/4z7cIuJ7pKbvNmuH6XdbhftziLWXUJIf6MIM+94O8ccv kOFdGr9qGR9m9y9NP/FJFxkXt9y0nqDCbOXGzRM/TiHDNzg8tZHQL26URf3n lci4yrui9wJhXqGpUk0iGBmBqRcC46JhPoy0Tc2H4EuIkFfV5R6Y7xyT+NZT ioywk+5SjnQkbdh9IPbJBWSElzrb7Y9FEifSM7hsGzIit5x4rt2HJMeWZek5 BJ5RwUcn5ZlIChGZNLL/goxbnYcZS+KR9GjIymqgDBmxPNOwYcJP5pyv7WQ9 QUb8XbPEWsLvVej5Fw7eR8adwYNZeWVIqku5qeBujYx7LPPyB9lIanm6mfmS 6F9i3KHGwEdI4mMFJccAGQ97LUYJ3pP4lte2+E8i4zHNUnjfJWL/TP8Pv6XI SLl1ZL0m4R//yFE2DXxFRlqnlbqUBRHvm7m2uRcynupZ80hcJOWKiJgn7kLG 88ijR9o0kJTUnGNl/x8yXrQdcy+TI/zpsYuOB3yQ8UrLJvD5ciQ5xzWVnPiB jKxrx+9EzSKJd/+3wsF5ZLxpsn1xrgtJcuEvJDWkkZGjYVd8pBbm+2an+7nG yMgLPvGH/gnmI2WXm5jVIaOg3n5gM6H/W8uc3nYrI6NI5aSgyH1Crxmt11YQ 5xf/dtrx8z+Yrep23WA4jYzS7c6MHAeY3VdqYx7viIzyy6fM75vCTPnPp2Vf CX5Wbjnt56gM0xkS36P9fyHj+wXXGCMZmJZa16K+l8DjR5XbE/UlMOVHXnNh EaE3v8+fqZlth0nWjqQvXd+Q0Vx6bkvkbRh7dicsx/w0MtrWnaeeDYIxwdor G5RzkNHh9t+Bw2dh1PL3iesmBB961vhc2GQMIyK7xw2niXpHT/pWvl0IgxdL Mz4/DEHGeN7ltrsjMNDsO2Fu5o+MKTG/Kb8mQl8X+7T3uSJj7u3VjYa50GdY u2/bbj9kkpcGaKmkQu9u28YF7ieRKXAscO+qaOjZuXticCgLmYuFgr0a3aCL LkJ5deozMoWPhIR9soJO6wSfvtXSyBR5EZqYZgj80IT90ZuvIHOZYFjWdS3o KM7pO73fC5lih66VeyhAh9idCOVH8chc8TS88dBKaHf+/Xn9hdfIXEW+Pkol Q9tv0qiXaCEyJU0jheX6oM3sRj2p6j0y16beWL/oL7S2C2hoB/ogU3o2Sq2n DFoD3xhYNUchc/2+m9yqbGjVFX6SEExBpmzSrSNZj6BVgFn3jVqHTPnJaPf/ fx9I/cMl38RMkKmwJybQ9xK0lO8frloVjkzFxNg7ds7Q8uVMfOH8PmQqjca9 0LeAliYJ3eatYsjczosv3smBViEtzWL9J8jcWWnCXWgOrfql/kfqIpCpenBp ab0jtD5cJ/iOtg6Z6vWfDV75QJvYlCDr3FVk7rL1rQgNh7YY8RMbB4j6tLq1 jWzuQ7uqwL+n1k3I1HUbrtLKhPZmcwOV3pXIpE48PbD8I3QkBwj0pY8hk3bp xI+On8D3HTDP+y2ETObCDWb5fOg8xV+2i+KCTHbY79pbU9B1Kn38s2UDMg3i Df8y10HPA70i65PNyNwru9BKagf01mhEnR/zR6ZxcsG/IYT+1UbCs6seINP0 tWrLfVuCL4bxC68eQaa5To+d11kYjH7vLeYyhUyLoqSOvUEwZHwZeIZVyLSu lOyaIfxqldXIGBeRebJrbsh8GMbG5GcDIiuR6ez2xkN5AYwXpbSf6Z5BpsuE 29iiVTBxXcY/8fhvZHoItk6+1oapTcsObnx7HJkXN5STxfxgVuXhUlrdOWT6 Jvv78W/CbJdR73CUPTKv7KAuKHgMc3cv1msptiIzSOfF4lNlMD/QGVHYJIrM kCLHECahZ5ypud6UbGRe420UkSL0MSrq9NqCAGTeMItZViaG5HV9yQ8eKiDz Zr1xZKIcki3VzyuJ7kBmjK3winPqSL4VtfifeSwy47o+3DJiI7n0p1Dxqg5k 3nG7sFrBHMkTFbcrpwm+JUxoxs46IkVu77n1MwQfEy8OrP3hgxS26prtX/SR +Ugw9U56OFJszRo++wci83HYcRm/+0jxub/9qdwoMlNXSN8/lImU8LGQPGui P+lxP+WUPyLlNudFlM0XZD7bcP3Rop9ISQx47DvIRGZGMm9TA59Yp3+obviJ zJc7KMmvp4j9r4oOFfxD5utXuVuuiSDl+qO8NYovkflGxzPdVgYpF/yVdcXl kPmuaOd2nZ1IsTs6vHVfHDLzuPzn//+1BGfv9n/2Ocgs+PZAuXM/UuRteae1 h5FZZHY4s9AWyZOFfzoTnJD5sV5CPeYskssCrod8nEPm5+Nfs1yCCLzqBW0G CT6XdgXtZsUj+fDAy8Cai8j8Oj6tM5yPpN+SVSJ6xH2rOmCT/SURSbduZzrx Y5BZ/axMLckfSXu5PjG3i5FZaxe39SAP5k8OiGxNbUFmU/Wute9+wEyuRetW ClF/646E6KhsmFnw7fLRmEfIbA9ZKO4cD9O8c3oaesT97MYaYRlrmCy99yNM jThv5Jn75OUuGHttkqI1OIHMcaE6T4sKGO23JpnKE/yZtKMNqmXAqJJO8YYD bGTOSS/nt3nC8KOPG7m2U8gieZ2zzTeHobbYWIXtR5FF+f6vMUYbhrZEGBQo DCFrUfCzWh4JBvI2Fvx+cwtZQm2rTGQJ/7r01+9zibHIEsEL36aKoe/zK2kx Aw9kLR83/PwkFHoYCZ9ubXdC1or9rxhXXaBbwq13YbwGsiSeSRVYGUPnaM5t jVwGstbadmUvXwUdbZLZmqwVyJIu3K/Kn4T20ff+W6nyyFov9fbZ+7/QLils qe2wDFmyXrJK8fnQZng3UMJpKbLkvwc9PpMIrTdXW7HNSpClsH1AztAfWvoF qyf4kshSDD6YsOkEtBzhL2iJMESWUmvBmjkeNDdtr3l8LBNZ21Hh1q9t0Hx+ 1+1euWFk7bwdLpaxDJoVTKtjA72QpTI2ei1oCJr4JY1b1M4iS33/EaFjP6Cp sLDuboIYsjSffryqnQ1Nz444dW0k8NAS2kZeEQ9NL56+Uvwqjiwd2yifHh9o Ksug3jSUQpZewdTEJ2tomjxAOfjZBVkoZeORQIdmvTOuqnOByKKfLR3w2gTN 0Q1hfM3FyGJ+V3E2XgwtgvskrS88RxZneyxfsQtagrzeH627iSxeMMmWXAGt 6/vt8lZ8QpZBq/2/ugxoLWdZvdxUhKy98O3wyxvQFjz+2+xbLrKMb2v+CvOE 9sNntV+a7kXW/rGEA3bm0AGy1y33PkGW6X7Bb1Qd4KvvWShxcx5ZB5+eMlgt A53aVE/VayuRZWmrSy9pgW5PA0ZqHxtZVgUP8xOLoSflVfXlxRPIOia1RPu/ FOjt9FsStGUWWXZVdarbXGCAk32NY/0eWQ7WU2vHPWFwsZOB3I4lyHLsWytQ 5AODX6PU7Iw6kOUqfKjGPBSGHbzGl9s2I+s/+u9z/ikwlm8+YPneFlk+lZPH jJ7DeHSsrUk30e9L1mv012bBhIvHalbdJLL8vQ9JZXyCqbVkTeuKIGSFv6zN /9sMswYNqpSMVciKpE0kJ3fCnIjzO+Pa88iKqpSMcB+AueK/S1rTCfxie8xt Fs8jaYWDka/+cmTd/u+c/o+F//97gfr1y92RlbA4Vu2eKJJeuqtv3vcbWQ83 1goSPoxMs1Co+PAMWUmZ430keST7thRjB1FPCm31z3IlJOcoJ23/tA1Zad92 FUSrInlAlH1waAZZT48cTDmmhZT1RlJSlR+R9bzbK3IboVfcsvQAYyJ+5vmY 8+NcpDgfefve4A2yXi/KtikyQkpw19FXHwj+ZN/6ZXDNjNBL8+3vC42R9U5+ XN38CFIyb2r4bstCVm7m6nXytkjJi5Q/5V+GrALcJdjnjJQiDvNifzuy3n81 63t7BimFr8LChZSQ9dHy7C///5Dy5m/0ncPTyCruii408kNKWsm9PgviPpWe y0pdG4yU6MCLAsbnkPVl4c/ItghC77df94i/gayvN8f+y4hByuEfyeUCu5FV Jb/quHcCUjRujx1p2Yms6heahuzHSBFK5pusI/D5CWYaYk+RXCsn2HLjDrJq K86u+/sKyYm7y/pHDiPrz+Hohcm5SLZb771Dj9CDRq+aWj3Cvzfgqd4pgq8t gqPvF1cR79+4+ceIy8hquymRWl2LJJbXki0u35DVlWHqfbID5rX4ycHfycga 4dfIRC+AmT0yY7bUDGQLHfph26cHo03+fO53b2QvyX3AyVkFo7LNn4pJFcgW Xe+qFNgHI7ZHPIq3tiNbvG3JgGwCDA0ORrCDdiFbgvP7e99ZGKIqJs7fiEX2 6rTk1zl7YTA8q8/4nT+ypV0Z3iZzMEDtC5ThSCJbplrMSvYn9MNOb0r3CLJl Nf5h3zPoW7rjTNPlo8iWj30qnxMAPfzCPudzacjeNO29MMgKur8XxTa2KyBb 0YrbaaoJXV8Edpar2iJb6f2qL3Ki0FnDqr9cfhvZ2+Vbn/W1A78fHw6deIzs nQGZkTkFwF97u2pXoiqyVTp9PYJioePg4CVbAR6y1Q33HjR1hfbkivXtKluQ rflcWluOC+3C6eKNaeuRrSXWJd2/Adp8J/Q/PTdBto5H9nzOBLQtEZu50K2P bL1fV5uDKqE1lWVpRj2MbNQ+8Mk0BVoPf7Nvf74f2fS7silyvtAq9zdvzXAU spnz/SH95tAyF3zR/SAim2OTdypXGVr6mpgJYpnI5n0KNQ4m9G743zobVhmy DRUPqZk2QuvSUzG/5YSQvTd08yq5N9Cqc6J+pGQrso17Ryb6I6D1ws0/27u/ IvuAcdGfXAdorX7hqu0+hWzTlxH5wTRo0zvx8m69HbLNV1klmq2FtndG1Lq6 p8i2OL/NX24I2nljQQJ7XiDb8s/Uif4yaO/+zbFYsADZ1tQSXu4D6Lhnx3/+ KhXZxxKjtwX/B/zjs4ul3IKRbbvAdpnZfujcPTiZFPsB2Q6l8z8GKNAtJvBi ahfRL6dtFdm5ddAjHrL0VT0d2aeu344PzoReWfrbX+pzyHY33XVU3gb6bdJS NCWI/LwbXbrNPsDQduGQvW6HkH2RoftV/g4M1axzEhaUQbbvY+GMAQ8YvlQc 2TAhgOyrTo/PhmyEkbrkcpudi5EdPtZAzvOH8RiRR5rD15EdeSi9NcQSJpic DG09TWRH5Z7/fFAdJvpT3bINLyI71k8ibKAVpqjv7k2N70P2g6V7JDeyYOb5 vStDS4l8HtnKfup9BbO6FTtsZEyR/fjd2JnsjTD78fgvZSFDZKeKlcv6EvM/ a1ZzmvIG2ekO977pL4C597tmtUoUkf20wOPCCg+Y13iz5jX2IjtjFU/pbwvM P9xMLgrYjuxXH4YCThchSa1M/MuDemRnr/2srqWKJGty6INSS2S/dbvdTCH8 VICsSX+1CLJzSlyvfyHm49SlT9XdfJGdv56ld8sXSZ+fqf1nO4jswrNruq0G kNTUO3GLSvCt6EtfnKI1ksZzriVZkZH9Sf4De/ArkoWEln3/cAXZn/+LGcmh Ilnii8oufzNkl1Y5P/B/RvjnOeown4PsL4o0473rkCx7Z6ODpz2yv16SmFsd huQNj51unqpGdmVNV3rjDJKlZLXjrq5CdvW2gkNpzkgWW7nX/vtuZNdcubno zB8kU0KGYoCC7F91Dq/19JHUHzXm2WeB7DoVveML3yGplnbPlCmI7L9B4ssr lZCUmxora0Pwu6GhPT8uDkl3Whv2LUtBdpNGjvPxxUg6v+wJVZTgT0tYxNpt 55C0j3d0fMQJ2W0ttp9H+Uja9Mou6fIzZPO1tTwLDsJ8T4/sym6CPz0dzVX7 NWFeirZ1oo7Qo35q9iUpYt5I+aBxjsiLPXgrbFubBMwp739w+nEOsscYGkFe IzC7c3tctWIdsucTguBuJkyNv97NUS9FDnnUstdeDqaOHYuWrFqBnAWGKreV I2Gy5HYd9WM2chZP1o19cIWJyJRwz3Z15Iibbn/auRPGpH6FOUnnIGflE8rh zAQYvRT3/TrvC3JWU34J+YjCSGsVS+qCEHKkMnztRPtg+HVAV37YL+TILDIV r7WCYdnhZ39iPJGzwWpLYWIFDEVmLkhzuYScTUuqpdWfwKBPs5t+wQHkbLZJ Lp2ThoHJrW6uCnzkbHnr4/U5FAb+O7Ndr90MOTvsN1VbOEL/2vnY4k5H5Cjn TV2Wr4M+GGrSPWCEHLWV33b08qDX7XXol79M5Gg4Pfyb9QZ6MpovcPjXkLOr 6FyIryJ0zwkdByHifG1DwSSdXOg2dG3t8KlGjm7NjYJxY+h6cuFdprc7csB6 fV1mK3TJZG/1X+2DHBr/yajLOeh8eKOmWZ+Ix3TXXq4kAp06HrzM5XnIYU9/ Vmq7D/z2Jy8VPxFr3lUTVqI68JP+i9FVPYwcQ9Gmo5YlwD8nlFh+oBg5e2Nd vCUtgW/lfjV1Swxy9slO36oeAL75u74HK8jIOZAelHHdH/gnaq5esVZGjpmG RLmBJPCDHiyU+vIWOeb5D9oXPgF+Xq/92vpx5BzmKpOKEDoXuq94tOk8co5U 5Uld+AGdx2o1D693Rs7Rw/qaWg7Q+S1L4vZXa+TYtP4yHpmBLqMIX3/eYuTY udg6ZURAV+Mfw+NPUpFjPz4Y4LwRuv0mm1fENSLH8fLFxM0EnhpQl3n9D3JO LVmS02IIPWOD6ZtXiSPn9M3YnwmN0PuZdeTToBhyPJJfiqxaDP0R9hpqLA5y vFRQoeoODDAyDGXDq5Bz/l0F7ZoyDHxME1cnnUTOxYoOrwXmMFgj6PnOOx05 QSNSTUOpMLJBtHOuSxM5oRdSZp5RYeTN3t9+3ErkhC/SXH3yO4zuu1lgZv4B OVFrjQ0bp2DM/7tlWcc8cu6if/Y3fZioD9tJDiXwvlcm9j2kASZP+SxyLlqJ nAcHEnrZ7jA5WZFY18FDTvKJt3J5xPwm6JFCMapATuoAW+/8Dpi+8tHjb/RR 5Dw5X22uXgTTM04Fx4Dg84uw3mvpXTBTN5C37aQ2cl6t+i/F/iLMwtja2Rhp 5GTdX/RBXhxm7z5cs2jTLHLebrlZ3/AYZofvUjb4Evcr56XsRLw2zNEi8v3/ WiEnX/f5CtOvMBeoJmd3ajlyCot1d4jZwNxHmnqDRBlyPhiVcr+MwdyE72nh nauR8+m32fGgEJiXzx+as0xDTolNy0WmDMyzqt95rPdHTlmPa+x8JsxbJeRO P5NHToXn7MscNsy7CC2NuBuMnG/zIV/P1sG8x4L+syJ+yPkevLpT1QXmXa81 c6iPkFMjniTQR4H54ylJKm3E/fh1R1UmNRrm9xxXOfiCiF+3qUDLTgnmt/9I PyJuiZy/V6b2yl2H+cXSqZsZo8hpaNaw+TcCc3WhBrXam5HThK5edw/BXBL3 kuLsd+S0JKSHWeTDnINfzr/1rchpm2lPlJSHuY0ujab5hH7wD8u+rgmC2d9c gZcqBJ96V8f8M94PszvKw//6rkVOv+f3kaXE/F0pdILD8EDO4A/RxeXSMONk cS9oaANyxiL8VVntMB0ZE8b/oI9ckpDrVZ3zMJm8Oqam0Qa5FPv0uAmCD/In 3j/f8Bu5Cz61P8uiw8SdEdUGWx5yhfwsa1VEYDz49n/DB32RKz7N26pwH0bN VEJ8HTORK3HIH1oFYaTUU83LMhe5q7MLTBIdYUTvTfIZzR3IlfbQuCitDsNb ZWN0j5xA7qZe2SqxEhhUOKiXxhhBrqKBZdu37TDwzCsdqmKRq5QaMxV2AwZ0 tlhJNtQhd/ui6mW8ceiPe5Cuas5B7s4TohsXWRLz+/3ut7vFkavykbf743vo Iy90knkdgVx1Of89fgrQy/ZLqNbehFxN3wIbCIGe2Hf9G8J8kLu7YersTD90 T+grqey9jlwdPY3QdybQbVcw/0ObgVy92673vd5AV9P9gJMhY8iFyfTXGuug y/l46bGIBcilH2wvHfKDroWex9Kv2CKXmSXb8LwDOjPUJZ0epCGXs9Jy+JQh dJ4cvMnZ8he5PPfYxUovoFNtSCxMfBC5BpXV0vxV0CnqvMntyRXk7t0pqpLk DfzJoLCkoTXINb7GY9s0An/URnD/4Q7k7u/2P7yBBZ0UQfW3TgHINeUVnK5P g871rrH+xHvBPZg85X97GXTuuXml6u9e5Fos1Igz94DOsAOKFO2vyLW0dX0m 8Rs6//im8rylkGtVlF5UTYUu7RZqN7kHucc2tP+KeAhdaWrWovfMkXv8kmzP 3sXQvUVqTPhYJXLt6o+Ql5yC7mztlDF4hlwHnViJku/QY7JB3nQBka9jXLVS wC7omTc+fTOXjFznCVFg3IHeNwyL9XOvkev2yt8h3w7690ncC9v7GbkeLK+u U30wIGt/uzynAblnfzo5rzsHA/crNlyKGkau98T+0z4hMPiC2VG7TBm5Abob zmpnwIiwotPpKSJeUMWK8S5tGEnJr0kND0ZuqNWi83EfYJR34a7ag3HkRlzq 85n4CWPhc8pnEm8gN/5D7pWsGZiUDP5zaockcu+aZAieCIDJrHGDPSyCz/fa HgauWgZT+29Qn6QIIzdpUUiIpxxMX+y4U3f/HXKTYy+KbEyHGVFP/5V1WchN 2+J27Yc6zMT+XrbLn7hPzw3MI9U4MHt7PJmR0oncF38NxZsrYW5F+i2L44bI fXUKbt6wgDn/zX0Tm1KQmzWntorWAnM9EzNN5UR9b68rxAw6E3qSqbHCtRC5 uRvWrrk/BvNJfcnNQPA9/8XS28aXYH50n92E3kLkvqeT1/3//93XFfXb1BB4 f/g+kvD8BpJ0N0hdZcUht/g4f4O1NJJME8ojgeBnycifB6JJSHKU221zNAm5 5Ve/yefvIPzYjSOTygT/v64qSjr1BklX/ko4XiD6U5mctXkdDUlBg0rbfFSR W707NeULMb8GlTim+00it6b0rpKPCbH/yKuVpYnIrbWIeLK1HknnXkzqi0kj t67bf/ufE0hyKGeOfiDwrvfxeh5C+NsDr7Jyson8G5c6qWifR5LWRe9Ar33I bU6wyuyiIGmtXuFsqApy23buV48LI/zfGq5a0yxyOwpZWTwJmI9fqSmx6RFy u/Zp7ZpIgHnqy1eZMveR29O87W2KIqGvXMdx2wvI7T+zQedgJsyd2jpa/T4Q uUMLVuQt0oHZ8cKY1HXEeeObpgpP7IWZ0efsiTuuyJ3M6qNL1MLMydHygxsE kTvDafrw6RhM//rkbfFoL/LIJ0s+b/SAqQeb5O0NApEnMJXLrZ6DyfmfDKZA HvIWhj4vuxIIk4f2VcnscUPekqfRFc3xMEFZ3/M3ORp5EoO2P+4XwKie5NcR 5hvkSZ61ljVshJHwXXq1xePIWzt96NQkCYYbOQ83aa1H3gbBvYv20WEo+NNz P7sjyJML4R6YPQ6DzR7Ly48g8jYto99P9YdB6s3XFncnkKe0Zpc2uRgGBG5+ 15jegrxtCSoBTzug7w8cWn23HHk75bdWWyyG3net/XUJq5CnkqKwYaEi9CTv fPL0z2nkqW/f4JzJg+5EpfV3i88jTzNz7VsrR+hKrVZ6+GcAeVq7JBYKh0Bn 0Tqxp5m2yNPJXbY/Kx34XT23Moo1kEelCd2z+QL8TZJS9x2CkYfFAj2ivdDh dqJx+Y0zyGPoz+3OEYX2yojdrK2NyGN9m7hqvxPaGdpNxzTTkMc1Gfq+whja SmesLAsUkKf/u1emwA3a7MztDiuIIW+PVYeT0w1okyi07PUvQ55RS9Ob1ZnQ +udGZ/6DGOTtd/i74EM1tGZLXVu96hDyTHp/7js9Aq0pN0zilmYg76B7ZYL0 SmjNkLp9JXYr8g6Nl3WVaEBrxef6vEsnkWfp82mXhxm0kaPu/IpXRJ41ucB/ gxe0GdgfVIA55B0LeFv5JRba0ldHklfpIc92yat1595C+wbbgc0j35B3IuKZ 48Y6aE+bOakjL4q8kxIpWZXT0KHvdWlj5h/kOcU/FLggDR1zljfR9QfyXNbf Nd6iC/zi9YbzyUT/XB/F3Kk5Ap2JsgJZi/ORd2ZLZOfli9AV/mLPdv0h5Hk+ C9PcngDd4aLXrv24i7z/3vh+u0r4y/xfPrs9eci7oOctrUqGvj7XVUvNjyHv UpGnQ4McDKySDIodqEOe/xdHiqYtDJ4YO3hxpTrywhsPqLd3wAhrwZS35DPk RdrtuRy1GEYFr17abfwJeVFdnK+gCKMlJlm9n1YiL3ZE1z7GEcb3ayo6iRH9 erh4UzynD6ZspU3Kt1ggL+na+vaRZTC9EQJ2FxH4poivVbuvDNNNRqcXUv8h 76m06JcJN5g1iin6/PkA8p4nCq15dANm54O1r5+KQF6mgsAJY+K+ptali/kQ /c7eOT6fMgLz3dSAZW6hyHsbfJ9t1o4knaf6it1LkJfTwr22oBZJF42zHs8z kZevO1CdSehTnklJXp31/yqu8rgY3y86M4qiSIqILC0opbThS/cm0rxLWVNJ i2ihRIpIKSpEKaJQSYiiUtY2RQslRFokS3vTvkx7zfze35/zufO8z7nn3Huf c5Gbf/PWArtsZI1cOKqSehG573sN7Wc+RfbqTM/lX+WQW8TlPcqJQ/b+t7d8 fhPI/XA/ovtQOLIj3Ir6pzD8l0yu15U7h+zse0uTXyYit8y8wafYE9n1erpV BurI/fIs5L2nI3I4M8Xq9rYh99t0bfFlFshRkNSvUqlGboXDL7NyLnJ0F0pu Cd+N3Mrc8zf91iNnq0rk8P/rpWae2u9Vq5Cza2HKFKcS5NYe/aFYq4Ccvf1z yh+eRm5d6ZlDF6WQYxN3XPnfH+T+VVJK1+Mgx3qR84rxPcit9y0bbuQz549c 3CHF4Gus9jKIaGG+f8OBm5iA3BatRUFQzdx/K+CZPtOfbSFFn7o+MviC7b7q 0chtbzoifSeLwX/4sN5iReR2Gcy15DL81O/tNvG7htyeqLd3hxl+st0lCncy 866vz7HlIcNPeF4qEfAOuXxylvpOhh9718ODLmeQO/TgtSeb4Uc96rSEGKPP iMA2K+0gsgYdwxoIEeSOW4ix95kjK5NTPOq+HbmT6c9MZpgw70V0sFfoCyQ4 B1mVTqtAWNN4zdy3CAmRt0kLZRn/vmn9JU7afiSmyW3fXyDF+GXF70FyV5GY 8eler8IATFpecKx9uxEJ2TVG06uyYCzEd6eyVjgS8y53bA98CqNf1KSsSs4i Mb/5evSaOBidFbTCpOE9EgrRTcphATAcGnDhgasqEsuFQWhsAvzglMbz+l1I qFqqB/PXw8CHfI/R5TlIrMqo/JywCgZmyGSoLaeQ0HJUsRLMgr7b7+yvJA0j oZ335d5TFvS2aY+8KZVHQm/+iTbLfuhdL//8b95dJNaXffB6WQVdtdn9pzJr kNio4p7j8BE67x7bTul6IQH+clNmZ0LHMSkWd7sKEoY/87l5ydC+LeB2ffZF JDZrO4e7xQDP4OFhnY3fkTAOlaqWD4O2DWY598cKkTBpyVxU4s/MG65eGVxD gkT7Ayc9oMXF1HCn7HQk6NviT5QdoPn2zGMyNTZIbFM5ZFN0Gpq+yFt4m0cj sSPjk/TBcGiS86mwuEkgsRvUikUTofFUjVv1BkMk9ny6cuphDjT0F23uU09A wmpPl/qW79Bw/p2vKUcMCesm+l9zGzSo65WNm19AwvZoamSQAOp7Tsmdm7Yc CfvJWSYqMlBfWqyYnnMUiQOX3MeLVaE+b4u522omXyfZ8jRHhPrP026kETwk XBK0HKaaQz1/mmZ3zQokXDWuzU10gwad5Y0DuXwkjmT1lxoHQkOoXpfCutdI HNu607flNjQIOtZKZk8icbzihVbwM2i82Bxz7kQSEifsZJpViqFJNV1rk8ox JLw7vaKL66CpsSDjlcUoEj7eVZRjPzQ/951os1yDhJ+oPmuaGLTcWhXtZtiC hH9E1PNHi6E1UsyygGb4P79oxGmrHrTd15cOT52BRFCy5YJWxu8Xle1SfM/o HVKw4Nxyb+g0XHbSsl0ZiVAzH90PYdAV9WqgfNYIEld//eI5PYDuyb1LUtQn kIjkx2579A16R458PKX9E4m7KksWr1AF/spyZSXlLUjcy/D//hGBLxgeuT9b HYkHBvXBzuYwWLVmHd3M1FPSnoSux+dh+EKz89cPtUg8v6Scs6IOxhqtnVbX LUTilWyw+8d+GH8BNuOtpki8udeq6CIGEwHcj0uGJZDIzXockqQLAknRNtlB Eon8reIGXAoEpVadT4u7kXhf4dLH2w/CgMX6U41XIfGhU81yZRiyXDc4mSVq IFHqfUWi5AGysns1Xv33EonPIp35LlnInqZhH9sjROJrBO0pXo5s+uXMlkkm /n1R6vKkVmRfNT6LKWlI/Eie+YsrQPanJNd1xmZIVOu5h7XLMPMpSXtzkz4S P9+XbwpRRY7OXJHQRQVI1JlpDqkicmyLJG1XzULiz6+IpFJz5ASFx0lb70Oi 3ql/3yFX5Dy0IDennEGikb9z9vRzyMljTzf7wPR3i//zwuRo5Hz3Mqmr/YcE T1LGm0hFzt8HwnMOb5HouOWl1l6InJagvL1ut5DoVq78G1KLnFbprCtHmX7v zdC7rtqHnHrqQ+rdvUgMzNmCZTXIqVpnvdZZAYlBz53dR/KRU5ShEl8+iMRw 5f4YqUfISSuS0mo0QGJM7xg3Iww5kYFahrbbkJiIOju8yws5nqLvjVYfREIw EvZgyBo5pgfEYyy5SLItY3dEGyFHMdd4y5Y3SE7Jespar4psvnYUlfUCyany WSl1s5Gd1/nP7MB1JMXOlOz1HUH2BenksTXVSEoatLx6V4xssb22R053ITnr 7uABhxRkvW+wl9AUR1KaLSItGsnsA7EFfJ0QJOcWLHXjMnpn6mbzLDhIzlfS XNDBBaF0Wuhulg2S8kEGH0M1QeDop5h/9DiSS7ZaK5YLYEK4dvpdkTgkV366 WWtxB8ZEMgJzZVWQ3PBDYqvTDxho+OFi8WkQSdBdMCieDQPKfatJlzwkDW+u THiSAP2usa6SYxVIGlsYT/Ychb6pVb+1/e4jaZK5+0nEHujd67ldpsIZSXLB AUttA+g5HPB52RxE0qwu4MVJSegcSy2I/eGB5I6N4fvl+NBRLXc2s+MVkrvi 7s7KqoP29yH8QpuLSJoLU3KtC4CXWyHYxt2DpKVdziFBMrSVvHh6Ir8Oyb3v PsnFR0Bry79nM01IJG2W/Sze5A2tspPfUm4FIGl3vu14ky207Ll/vGmHAMn9 TcNLg42h+anV0hnsY0geNBb9ukIdmmXuKHOe9CLp9EjmTCnjP8PLtdfYKyB5 SExR9fA4NC3dOX/agc1Iurpo1Ug2QGOxm++aKwyeI6UYlFYCjf4WTyrkbyJ5 TM1Mezszz8w2ur8Y2Yfk8Sv7/g1EQ6O29khukhiSXl2uYTfOQuMqo6/Rf8qR 9Db12aDvCI3rHaqS3zDx02mXeD9NoXHfaQsjYO7zlYqO8tGFxhv7lolMz0by 7LFHWxYthMb6byk2NBMP+P6yP28KNG0K3S26hsETqF0Yb9fO+GvdKBXWMJLB kRWmnG/QDHYH1E2Y8xcH68cfvIHm2shTVfYMvsvEsKeRHrQEZtTKbElHMjRe orP+JbSixNkz9UuQvDq47KC/DrRJTJvWK3UPyWuE/h+FDGhrHX+7V0MZyci7 tHmuJvAq1i5OVGf0vMnf/3VvGrSXCZz9Xv9G8k5caH70U+hsm+jJKtiGZOzA /XX6qtAtHlz5/F0hkvEmb9Irk6BHFza/jqeQfNDfeF86EXorcbTVpQfJ1K3r gq7Ew4D3wXv/Vl1C8lmMqUBNAfjL38+K+GuKZEbfgRMlscCvyTiqE7cUydd3 rjpPvQ1DG9XqRPymI5nf00z4R8Io+8Lc2PuMPgWbxwoWS8NoagTfdOY5JItu S/2XGwFjFvwfQM1FsnTzf6vGwmD8fn67decnJCuiI2Z5XgKBiMzxRaMNSFZ2 JV6cIwaCsOPa9yKY/Go25bDSg5l+C+AZrLBFsjbq26ltoiC8+ldXa1c9knWd LX3dgciaMi/ss1k0kn8NJw6FMvvz+tDHlcFMf9ZHzW5UC0DWkZUvkk3fItnY uXxvKQtZscXhSn1+SLYYbvjh7Ieskk3GUqWTSLbd3EFPnUBWr59KVk8Jku0d TkUPfZA9x+Z3l28Kkl3oa2A0hmytsmBjzy1I9ty49qrBG9lkRmYdJxzJvvbH q/2HGD/Iil+26yOSfMh9vNgL2ccej4pL6yA5FFmxhHm32b4JyiYdzDwZ4bXd svZAdmCL2q9iVSTHDQSzx3qZeeWRE7PnGpKTkXNCbrkjO4irKaGgi6SQt3KK fjey/Y4EGk5RQopjYOBT5Ypsjw5zgXgpUiLXd/I92xk//6lu4oUvUlPbXNzm uCDbdHHVkb4EpMQ3+jWntyFbZ8y/4DITn3Et0mabI7LneQRl6lciJdmaVNXd xPjVe61fTl5DSmpDnlmoA7K+PuxUOH8WKemIHx/VGpD18C4715+PlExLO5ba Ievks8THSklIzftPmOn8B1nGbMK6bD5S8yNk10zbhyypLA3dfxNIKfyHiput QLhWQypg1wyklhTMFPsxCYK0DVHmtheRUiR+dR+MB8Hi+LcF5c5IrbA6mRnc AhPdEac5MTuRWnMqzazEA8Z2a+bPGV6GlC7bV9dKFkYT62bNaYlCSv8SuaD9 DYwM3fgZd0YdqQ3RLS0zBDB87d2nz5seIGX8RuGMaQjwv9ZdrXjfghQXOu3/ qgNf8WujRKgWUuSHrK3u5TBw+rdi/iU7pLZVm0tHzIV+nfvTTPrOIbXTVnFk aSb0RfvrPU5gI7W7pe93hjX0sfP2qyt8RspqKPRxRQL0lPG2VOe9RGqf396w A1ugu+ijqH3YcqTspq48zm+Drs+mKvfKvyC1P3TYMugydDbteHPEaQipg7JF MFcDOmfEphdKzEPKKfa6UuI36DBqxZi7ikgdUrafru8J7aHVyz+qP0PKNUWj 5+M84LWLrLL08EPKXWei0iILeBYD11dIb0XqWE5pFm8ftP2SS/31WoCU5+bo +NMsaDv6w68xNxupE2WOwdPvQ9sCL9u0TYyep3bpHL5jDK01emUuniZI+dRx tqvxoDXZcI2b52Wk/BzK9XKuQGt4UlVkVT5S/h1xC+nV0HrZo8xjLBmp88dd 2b+/Q+vtEKEffypSQePrWo94QWvuF9OFE+JIXTw/rUwoB62Doq+cKhm+Lk+v zLiaDW2MowxZyNwXeu1+9BIbaIub1++kIoVU+IJjvuls4M1a0Wak+gOpawng YPgAeNfuzJwew+h/Q1XS5PtWaF/+e6LEpAKpqPRfGvvbof3Ltr/f+22Qur0u ac5AKHQEGclKnc9BKubdidFATegk+b+ql08ide+bdOHDE9Atum18wP0wUg8s /iXrzYfuIX2FtLNzkEr8l3q1OAd6BswVRDZ4IPWkl7Bq40DfNPncM0W2SKV6 y6H3Q+iL7FlW7uCD1DNhi4q4CfSrFDotjH+K1KtZAX2qYTCwa/U+3+tdSOVr ZF5wk4fBorgqHx+mP95f1nUUhsGQRZprkRWTb2FruvE1Dgx1qDmXDTD1UBKf JPqqHUYkTv5cksz096dJpWauDYzc3pXpluCK1BfL+MJf32FU+dXd5wutkKqQ jg5kMfuf9roVvm+kkap0l3G4rgFjL7wzVS+7IVX9KdxIOQHGtXyaNOS3IFUX eJFDhMDEIq1CF8ftSP2pn1Jfx/iXy0tt9fZnIVW/8ew7dw+YGDAVLDpkjVTz 0En/SCuYTJfukbB8hFTbjgE7lS8gmCo5ed+di1R7mjtkbgLBrtlTb/9m6q9r Rsdi8hUIbtc1vF73G6keZ0fBH1UQ1K69oXrrAlJ9hQ1/jsaBcHafd9+pNKT4 S23eTpEGoeEvZ17IY6SGfGtjbzDz36W84dy5/UiN1Jr7Lh8F4aXny7pC2pEa 1/tuneUKwnuHDF4cjENq8rrpBuofCJ81az/s6kNK2FMq/3cXCN/MH5xukok0 h9oyfuwjCF+LesjmANIij9/9EtkAwpQnN2/8oZGeJrox6+YzEMbM2/Lz7C6k xe0zb69UAuE5K7MatxSkZ7zVPZUdDUK7ULsHf+YgPXNBuqWpBAj1vrbr9D5E Wuqk+tp//iAUNRGLOsj8lq5IkvPgg6Bsmd2TJXeQltVUGhF1BsHliFGvG8x9 867EV0fVgcCoXGrt7e1Iz+ctfK26DSYH5Vtd7V4grZAgc8JMHyaNhjJ8JJcg vUQQvrs+GSYalmxtO3sTacW9ErqeCjBx+sq+Rtn5SK+QmcK/NRXG70Q1sn/Y I60VNODRUAWjSRbjtwe+IK3d4L7Di4BRJTP5kwt1kNYz6Fgz7S2MxLzrUXBL Rnr9cEPfqkRmf+pjBR4ORnqzy3f3EydgcFeQ/GbTMKSNi03NxNqB/9H28+ww IdLcZaUad2yAv9FT9E8Twwf9611XvjEMqCvGNlm1Ir2HTnedLgt9q7VddlKS SFv2nvY1egC9TyWzjWRzkba+bhTmuwZ6Nb7XtwxHI22rJ3H3VT70HJ09vLnO HWn7mspnvabQHW7zyZqlgfQBn7j3K+ugK+/Mka3/jJF2UnCqcDgEnQLywnzl cqRd3mk2xYxAJx1DVC1/j7Srw+hgVTB0pKonBCdFIu0+tWCalAx0LHnqMLuB g/SxpCty3ARof5gRlfNcCmlPavfK85rQvrH43xSpfUif6FFYn5MHPN7e+uOf 5yF9KqKVHDIF3uOvX5ZmKyN9RufZvtV1wDvjylrBsUTar/rUEZdDwDtwIDlZ TQ7pgNObziaMAM+uJj7h6COkAxfNCK8LBt6xb1pZ2Uy+wXk/EubKAC9q0yJz rb9IX9of+9wsAXjfxO/UPn2M9BVRx8JLDB6FXr81mUlIhz1eXfn+LbT7Jzsc UsxDOoIYaZmgoH2wSkTzVAXS17vejejWQofv5PWhdCbfm+GXxd1doFMuSt94 TBXpW9q7Fjxm+CkSDfu34iXSd6oWqTUEQ9e56D1iffeQjvNu2bhQBrq3hS44 HfAU6Xvyaaa7mfdMY9aI+pAD0vffette1YReSYWNCadCkU60Nzz68S30hsza NN9nPdJPEiuu/1cLfTe9FJTEmf+ncmMeeDlDv1KFjqgUG+lnnQdfpg5B/+u/ j1TsGfyvtIZrlknDAO9WztABLaTzcxcuEiNhyKVygUYsF+kC22YNw1oYlpht NVMuBuliTir4OMNwSvWN8dY2pD+ZoH13IIx07dnuEG2LdGXFgcQfOTDOLff2 2CGDdM0J9TczCRivkrh40aEe6V9yQyVba2DCOkt0pTNz/nd2Xm2AI0z83lqt 9G4H0v9sLnZk8WFyj+pEgvoCpBvZ2yf452Cy5K+8WfI5pJsfyM/UkAKBTuuM 7p8mSLcZNy12igNB1Geb9EJG/3ZeiuY9dRD0K1X/UTqDdNeVE4a1OSA0utik 23oC6d7VsFOGAOHlOJGn7KVI938XO0DXgLBUWnOQOIb0oOc3rwuOIBTEaoaZ b0V6ZN7t4Hw+smT8PTvtzJAey3KIGjuHLKXACj/9bUhP7luVpCOFrNVbVyx9 dxZpoXAwyy0OWTpv6J9r1P8HL7f+aA== "]]}}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 1.}, PlotRange->{{0, 60}, {0.5525347812373504, 2.3701613140471767`}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.46922019352507*^9}] }, Open ]], Cell[BoxData["\[IndentingNewLine]"], "Input", CellChangeTimes->{3.469220688367992*^9}] }, Open ]], Cell[CellGroupData[{ Cell["\<\ We kill the off-diagonal elements (which is to say: all the interference \ terms)\ \>", "Subsubsection", CellChangeTimes->{{3.4692206938845167`*^9, 3.4692207155950737`*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"?", "Diagonal"}]], "Input", CellChangeTimes->{{3.4692202497446127`*^9, 3.469220259663927*^9}}], Cell[BoxData[ RowBox[{ StyleBox["\<\"\\!\\(\\*RowBox[{\\\"Diagonal\\\", \\\"[\\\", \ StyleBox[\\\"m\\\", \\\"TI\\\"], \\\"]\\\"}]\\) gives the list of elements on \ the leading diagonal of the matrix \\!\\(\\*StyleBox[\\\"m\\\", \ \\\"TI\\\"]\\).\\n\\!\\(\\*RowBox[{\\\"Diagonal\\\", \\\"[\\\", \ RowBox[{StyleBox[\\\"m\\\", \\\"TI\\\"], \\\",\\\", StyleBox[\\\"k\\\", \ \\\"TI\\\"]}], \\\"]\\\"}]\\) gives the elements on the \ \\!\\(\\*StyleBox[\\\"k\\\", \ \\\"TI\\\"]\\)\\!\\(\\*SuperscriptBox[\\\"\[Null]\\\", \\\"th\\\"]\\) \ diagonal of \\!\\(\\*StyleBox[\\\"m\\\", \\\"TI\\\"]\\).\"\>", "MSG"], "\[NonBreakingSpace]", ButtonBox[ StyleBox["\[RightSkeleton]", "SR"], Active->True, BaseStyle->"Link", ButtonData->"paclet:ref/Diagonal"]}]], "Print", "PrintUsage", CellChangeTimes->{3.4692202613773823`*^9}, CellTags->"Info3469191461-5372129"] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Diagonal", "[", SubscriptBox["\[Rho]", "0"], "]"}]], "Input", CellChangeTimes->{{3.4692202780205193`*^9, 3.469220288279228*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ "0.40566609297943285`", ",", "0.4912644524937844`", ",", "0.10306945452678287`"}], "}"}]], "Output", CellChangeTimes->{3.469220289954276*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"a1", "a2", "a3"}, {"b1", "b2", "b3"}, {"c1", "c2", "c3"} }], "\[NoBreak]", ")"}]], "Input", CellChangeTimes->{{3.469220329181509*^9, 3.4692203452593307`*^9}}], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"a1", ",", "a2", ",", "a3"}], "}"}], ",", RowBox[{"{", RowBox[{"b1", ",", "b2", ",", "b3"}], "}"}], ",", RowBox[{"{", RowBox[{"c1", ",", "c2", ",", "c3"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.469220347121428*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"Diagonal", "[", SubscriptBox["\[Rho]", "0"], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", RowBox[{ RowBox[{"Diagonal", "[", SubscriptBox["\[Rho]", "0"], "]"}], "\[LeftDoubleBracket]", "2", "\[RightDoubleBracket]"}], ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{ RowBox[{"Diagonal", "[", SubscriptBox["\[Rho]", "0"], "]"}], "\[LeftDoubleBracket]", "3", "\[RightDoubleBracket]"}]}], "}"}]}], "}"}], "//", "MatrixForm"}]], "Input", CellChangeTimes->{{3.469220360875667*^9, 3.4692204435825577`*^9}}], Cell[BoxData[ TagBox[ RowBox[{"(", "\[NoBreak]", GridBox[{ {"0.40566609297943285`", "0", "0"}, {"0", "0.4912644524937844`", "0"}, {"0", "0", "0.10306945452678287`"} }, GridBoxAlignment->{ "Columns" -> {{Center}}, "ColumnsIndexed" -> {}, "Rows" -> {{Baseline}}, "RowsIndexed" -> {}}, GridBoxSpacings->{"Columns" -> { Offset[0.27999999999999997`], { Offset[0.7]}, Offset[0.27999999999999997`]}, "ColumnsIndexed" -> {}, "Rows" -> { Offset[0.2], { Offset[0.4]}, Offset[0.2]}, "RowsIndexed" -> {}}], "\[NoBreak]", ")"}], Function[BoxForm`e$, MatrixForm[BoxForm`e$]]]], "Output", CellChangeTimes->{3.469220445068574*^9}] }, Open ]], Cell[BoxData[ RowBox[{ RowBox[{"R", "[", "t_", "]"}], ":=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"Diagonal", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", RowBox[{ RowBox[{"Diagonal", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "\[LeftDoubleBracket]", "2", "\[RightDoubleBracket]"}], ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{ RowBox[{"Diagonal", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "\[LeftDoubleBracket]", "3", "\[RightDoubleBracket]"}]}], "}"}]}], "}"}]}]], "Input", CellChangeTimes->{{3.4692204827443523`*^9, 3.469220523920662*^9}, { 3.46922055738896*^9, 3.469220563808167*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"Tr", "[", RowBox[{"\[DoubleStruckCapitalA]", ".", RowBox[{"R", "[", "t", "]"}]}], "]"}], "//", "Simplify"}], "//", "Chop"}]], "Input", CellChangeTimes->{3.469220597495656*^9}], Cell[BoxData[ RowBox[{"0.6308099912082596`", "\[InvisibleSpace]", "+", RowBox[{"0.03986687415676307`", " ", RowBox[{"Cos", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "+", RowBox[{"0.14182107817986891`", " ", RowBox[{"Cos", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "+", RowBox[{"0.09627668554402627`", " ", RowBox[{"Cos", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}], "-", RowBox[{"0.04704522650676235`", " ", RowBox[{"Sin", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "-", RowBox[{"0.03859053009749735`", " ", RowBox[{"Sin", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "-", RowBox[{"0.1175399614770487`", " ", RowBox[{"Sin", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}]}]], "Output", CellChangeTimes->{3.4692206029182053`*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"0.6308099912082596`", "\[InvisibleSpace]", "+", RowBox[{"0.03986687415676307`", " ", RowBox[{"Cos", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "+", RowBox[{"0.14182107817986891`", " ", RowBox[{"Cos", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "+", RowBox[{"0.09627668554402627`", " ", RowBox[{"Cos", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}], "-", RowBox[{"0.04704522650676235`", " ", RowBox[{"Sin", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "-", RowBox[{"0.03859053009749735`", " ", RowBox[{"Sin", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "-", RowBox[{"0.1175399614770487`", " ", RowBox[{"Sin", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}]}], ",", RowBox[{"{", RowBox[{"t", ",", "0", ",", "60"}], "}"}]}], "]"}]], "Input", CellChangeTimes->{{3.4692206125305347`*^9, 3.469220650392456*^9}}], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUV3c4lu8Xl03I3nvvvSvnJBoSEjISyQrZqTRIkrIS35KkYaUiSWRGi1CK ynq9vMOeyU783t9fz3Wu59xn3ec+5/OxD/GS8aGjo6Ono2PL/P/XrCJJ/sNn 4hJM85ccTL0tDLd6dp0Tzv0BU626t3TEkuBxj7BXhwDC1HneI9wVt6G8h6H7 qV0UTHa8bY94chSqUjaftr06DpORDOWpdE1Qt/vvxWlBa5jkT1U35n0O77pX bwnN8MDEgUtWSbMv4KPu8vknDxJgnBzU8+2BATSnLPqY2K3DeNSrk/8tCEH7 7t/Grq/GYCwraDnuSSV0di+E2NQNwkjLt8TcUh74wRThLSSQDSP2L6Mka0Xh p+6882CwAwwTrGJ9DNihO2XOPFSqFaiz/eyxhzWBsHtKID2uEijS31qtLmfC QFggu0t/KJDLvvAmix8AYu74pqy+GpDNex5+YK2EoQt/FlzCvwEpWCvgQQM/ kLt/D0zI74HB7+9YGp3MgBL80tnGLh8Gpb/FZN3/BFSm0M7yi1uAeKZt6fjV YKDmaB8ULD4GA90CbVWn3sGw7mxz9M86GAA2Vn3HHBj+/GLXIL0IEF6syVv1 McOIZ3Ddbs0oIKga/lDj04eRZU3DItcf0F/K1NPF9hVGU6Zfbr2mA/3bC3/5 X9KEMfkStZBXqdDXZa+1WMQNY7VBBV2DU9B32ngs158Fxu3VpY059kOfTPwN j4NWMD42mZ1jXAi9PUd4TxxLhYmYZwJ0PozQe7eVvHp7DSYFA9K8049Dr898 reb1cpgsUWVrqW+A3h29FeyR3DC1ezxefUIceiWv7A6pcYWpvicb6YLR0Ms+ aZGeOg7TYf5nF827oZeemWnJzgJmWJX+uIToQy9D49FFhjSYyR09VX/vFvRu 22y4a+oHswaFYzLNc9Cr+Nj9x4tMmG339bq6cBB698bYkFYmYE7n1sxpbkfo Dfd9UdXjBXMX5p8VKcZDb5HMT/8Nf5hrsffv3VEOvcMRl/ZFJcJvvnLFrfYk 6NMQZi2KeQ2/PXgoO09yQ9/FjyUbZnTw+2nYw5AYM+jrVn938NpW+L343f3R f6eg33h5+SqzM8zv0hHtepYD/Y/b8yslLWC+e+4/o+5VIKRwNY7NFsMflSJP Vt03MCBU9fjOiUr4c/6Yak9KFAwUD6em1xNgQbKt8Zz5PBDJN54KdFfBok/h ZE3JBAy1Chy2+5QJi1XulUmsT4CkoBrCo9MDS2z8l91O+ALpchr9+9R5WHoe J7guTAHyjrW95971wfIfd/OdVwhAeX3wvkSUJqxY8nNyErOBytPtqD8qDCu3 W7uJJs5APaXA+GJ+BFZNjU9dmvkJw5ID32W6PsBq0qyx7f5MGI6sYuxib4XV gUIGqXx7GG7R7oq+0Alrl/myGl2+wojvRq1u2CKsdbaeuFmRDCMvf6WYHpKF v/KXNY9vs4KRVae2F4sP4G+U0apOACuM7tz/MCWBDv42z3zY8qEZRi9e6Azd IwrrIgWpnVIJMFrdNBbPoQLrgUddHkdbwOjvGasW82lYr+eTD/9JD2MK/cfu mPyAf1yfZ3drNcKYo++DowKZ8M8ztobvxiUYiw175Xf4A/wrN4qnDO+AsULy hZ8XF2GDYca2AtZgrCVPMcb1F2w4FojGZ1fD2PBj9ZlH7rBR5DbssHgWxv51 nnAcTYGNVd4yBTtDGOdRXUg1MYVNq8/Ri08XYFyqyFM6ZDds5sRafmIqh3GV vRHeW/6DzRkj7tueYTCuuc2sMY8H6aSWpl+5Z9FkFha56GdIt6/zsNiNZZr+ dr26HctIF1by5sobJ5q9OmORd5eQLvu6xOTwa5q/3OjGpSKke+99xYGPH8Y2 1oyPilsj3RSM1WEEjI1OnsjzZ8MtAmI28qc6Yaw9PU62QwO37Fx6lXxPB8ZK tiUwFGjhFt9O4YWWmzB2PamAQV8Ft6SVXDq6OAtjXlrqTcOfccubRMpHORsY M1J0yOrIwi0k7/0adiUwxvqQUyHeGOnZofS/Sxww+uvLl/pJTqR3Wzrn09MK owGCxS/2WiJ9/HfiV2YVGNV6cFrq6k+kLymxMNRLhJH5KmMHv1ak3/Texpy6 B0bCYy94Oucgw+Pv+UW738OwhyppedAAGVqWx1+kasOweFpnKYEDGWYlNat6 coHae/FED0EKGbcHvGk5dRao9nX+QltHkPEHQ/v4HU2g7NP49DpLDhnXVHl+ k3KAwrKjSKeuHZmk7ZzW1NmA3Hz4WzXfNWQKyiGxN1KBfMDi+aNoQ2RmMlhU m8gGku9pEN56ApnVjprqG7ACSZf/k/f2dGQ+FBezI+Y0DG0O1RALTiLz/Q62 g/x2MPTYv7ZAmoDM7xdtHI/Vw9AZB9dPeWRknhDLcC9WhSGbtvC8h7+RxdBf ItiMCYaYHiYYTKYgy9FUr6jEcBhsNdh6TCEdWeIqii51DsFg6os1ptuxyPKk b+qaxEEYdG468O8cAVk6tuik+dXAoHJEcE/0L2RZVIq6U64MxH9t3w27hJBV 7GDtg/X/gNjTOqPjuAdZd0XSPdlLD8TqIHeW3OPI6pdtWZYeCsQHhdR1+Uxk TWm88aafCMSkoP8sj3gj66uRjiZF2p668HRoORuQtY+T/3PoGyBGWDW0ZG5H Njo9l++1ikAM0W6/LlWHbIouuX1MGUAM3Rmlzf8H2axjKGQ7OiBGHZQ+aEZA tvBC5YnsYCBeNnasZryIbFntp+ap/UC82bViX34A2Rrmy/9q7QNi/u/IHMIe ZBsWXmE4VwnE+mMrTOwlyL4Vdm79IAfE3tHiR5+3I7uOTxw/100grh0e1t4X g+xHkprFnTdgUPLkWRHW48h+sZxDPi8QBi0nh3ZdDEH2zxu3DYxp+zR3uyRd Iweyz8n374yrgMGvV5/VlTfjVsED0pZfZGCILieuYacNbvW689Tp+DoMGXrv JzHX4ta/2g1nkl7C0NPokbCfKsghc4Qh5pckDLVcVNdcskGOvRf3JUonw9DI Q5n3cVuRI+NzZ9Zr2ryVfe/G/zAfOdW9RqoHxYF0h+2r2TYl5Dz0OSvsDx2Q yrnfnknrRM4z2laqzFQgtdvubjKmIGfTv5JszRIgbQgeZ3nKjpyj3h725reA LBCpbsiwH7k42njYnaKArFbBQ+bzR64jWZHRl8yA7PBfZPKqFHJd2FTUvSUL ZD/hVy//a0auxz7dE4XMQD6764nkrVnkam6//rh6AsiJTLGdPX7INa233fXL VyDfdtObOz+E23jvTvOSyoGctz2e03YQtxnT5bYu3gZy6SOGT6uFuM3dz+4K WzSQ3yQKZXVO4ba4r/TbJTyA3Diz+Mc7ALc90X/1R8eC9r5+Ftz+Eo7bvtzz eWapDOQvO9d059Nx2x96oRMuHED+rpZ2qOMPcnPoe6dUngVyV15TeqsLckuO 1p9sDADyj5JCI/uDyK1zT9Cy9ShNtt8i+SAXuS1sQmR+2AC5856JUMUF5D6y pfkfEYHc8Z/VGxMx5D75Wrp3TBfIbQerywRJyH3B/9zreXkgf+yKfcFEs58m 1pm+Lgjkt4abE+f6kPtRh1owMyuQq65PONx7gdwVcfFW29Zo+Q7q3N4ih9zN BgOKIlNALrCaG+CYR+6pnFSixncgp9WmKCwUI/em7WiN0QcgX5l+XKrzGHl4 GfD2rkogn7kZyuZ2C3mMTs7bOGYD2f3GbJdYAPLEGTk2XPQCspLM0xlzLeTJ nCjNvuYAZJHHjFI5rshTlMsSlb4HyBzHu2VOryDPF8ZqzQJVIP1hjeEca0Ce wTe87C9o/TQq2/5ymQV55gMDRqq5gNRPsdmVuYK8TJLv372n9dd3r/BPpSeR V6hTPLd9HkjNVX9quOeQV/Xq6ehftH5rWKtVCMlB3p3GX52GfgGpClsvm6gi r+2Uku5EC5DKHsj65Hghr9eDWK6FGiA9U3mmcDwSeU/b90xsPAdS4R9J/zPG yJvIrNPMmgukfI7f/cb+yJtdfSOP9yaQHl/bzXyJFk9JECVG/ApNDv1w4n49 8jZK73BTjAJS3q9nDfuckLez6z8jbX+avQ+PFu1LkHc4YYbP1JXmz6JClYEN eVdM98xaWAPppceeoNME5GOfftBmYwakajGW+f03kE/i4UqRszaQ3l0Yo0RK IJ+2w6ErXrJA+pLI0/zxEvLtZnnqEcQPpL6dhFytFeQ7Yvcpj4VWz/Fxs1vK TMgXeJc88hiB9DdhU800GfliyJuqO52AvI0ndlw9Cvky1cVO9QQBWT72Tv6B bOR7ctroZUQckLd/Tqp2m0O+uobDi1xZtPc3KM0jZI58w3bJFyxo/ZCs+X15 eRn5Vu8+eTvYB+Sn5sYMobbIz0n+yBA9B+TPP2bW46jIbxj578ZLcaBw8tSq V/Ai/7W7wXclIoHysGgjYCcb8t8j3yC8uQGU5qtfOT9eR/4ytSLpww+BMkNZ 0JySR/6e+sGi6+1ANbvy03zUHAVUyHavl+SBSviyrFThiAJmakGr6aYwzKDf If7uBwrYR17fqW4Hw8pfdE97WaDAeeamD14XYDh0+K58kQIKpNkSWf7dguGM bX9XWPNRIC9r7UDWExh+1cTKN6+MAu1qup1fu2B4jq/SyfcZCgxF2gqeHIOR rb7u2zi+o8BCfaAr4waMyD/meX1dAwXZmK/df8gPI9sf9znFRKKghG0eabsq jNjtifjwoAIFdbIa5X8hjHgFnQ9a/ICCliSCf5gTjIQt3clrKkBBV9WV5xxB MHKpJdfMxhUFgyP5Z4viYCSxpdr2pQMKxtXr6JlnwUja4PXBDWcUvMN8MGqg FEYyVvadsTiEgs9sT9ac/QAj/9FRjtxdQcG3WVc3+Ppg5NaE6O47jSjYRXq8 q3QORlKK7pT1+KLgmGrDVSsaT7qqzbIv6j0Krkf0tQyLw8i5iyp312NQiLtu mSNWF0YCkqij30RQSIGZz05sH4w4H7VfGrmOQia2WhmVx2DE/HfbV6tZFDqY daD7UCSMqNpPO/4JQaHjJH/RqRswwnX9QfMhZRSKUo0/du0hDM/mCQ7zuqDQ jYiHj2UrYfjLi475xyYo9IqpV8WZDMOxi/td9yWjULPNYtCfFRh28tArdR9G of4snrI0LhhWkcxau3wchRlVrYw+mgK1WXZnuEEACgtH+J73tAPqrTLmsSQy CqvXxTX89QXqUUW1Bv4qFHa0qd2jcwso4xdSEtSforD/zr0u/VpAefm+ZFBY H4XPq3Wdim8HytleamfpXhR+xDqZ2cMMFEZpiY9C+Sg89V6Mcuk8kPkFHtV5 KaLw5suiZWVhIHW37WGMF0QR3of67N8rgXSvOXiDqI0ixhetdeXnaPzEffX3 bD6KxBtdiG3zhiGLO4cb4iJR5I4ia+bpLTDEzc1a5e+OIk/5M59I5sJgTfMD /qooFOn4/bwjrAeI63Vb6wW2owh5yJgidpqGHz6q3+RWQJHFrx+WPvIC8dD6 YX6vZyjKWn+IPbgMiIzVTGF9+1BU7NmApPBBGKi3zfpTGYmimtkndZomYOAC 98jbmUsouitx0TIwEQZ2WdG/yT6Bog5nLrsIKMAA5+6FTyefo6ifL2dQwzsg DO2O/Hy6HEWjHe7G+nsAoSaxOHCqGEVTditk8qwDIcdmgpv7L4o+1HlZVHsX CFfb47m7alH0lfTOWh9DIJzRvXJ9JhtFP3F97uDqAkLo85jezgwU7fnnSH4T CoRwzyBO+jUUnZwkLXlxAuFCcKDHTS8U3egLZt/6FAipc8UbrowoxtOyJvF6 LxCK/5pq3viGYvJVCTrHqED4cm9xI5wDxYwKeS1Z44Cw2ufzwEcQxawyc53L pWBAs+pp/EdjFHO/ohrkVgcDQZohnlk3UCw0rDKWyRUGynUHeL24UOyKp3lG 6TIQ6coz724dRLHbNl+LnDOBeCSzlP/LOIoV73StpdcBYmV1HZkuF8Xq1Ea+ PvsKgxJ/e763CqAYmXVjcZMVhui3ffDtaUCxheUkticFMLTLquH5f8EozjIi JGFvDkPxyrVcRb4orvFey6LgApD4MY9+LRLFz130yDhAm5cuPr/ujPGieHLg ZOFiCpBfOpz9OWqP4g9cz9Q8UAXK1qvO9UXdKP7RKI007w2Ujx1VPyc3UYL7 d6N2Vi8MH0hxPza4ihIKUduedWXDcFWswuUrWihhsuahxOUKI3LdMSaSHChx gn5DgsaHR9Y9y36K+KPEa/4d7MFDMJZ3jjFccStKfM5Kulr8CMbZDEWiPQxR gijeT0f1gvGg54479dZQkkXh3LILFSbUzv24FVWNkmLFn8MzC2AiQWspdaMZ JbU1hac7fGFiwD1lOjACJV0Mq6gWYzAZszBC5ymKkqdqWTxiimGy9Zv3v+Y0 lIwDp76aAJjiYXFN9yeg5O33hY6LajBl71rwL6UNJZ/tXfyuNQVTqaktTRCL km/bLa0DSmDqY5DU2msSSv6wy2wuCIappbeiNep+KDn2g2o+pAXTss6nuB+F oeQ/F70G0TmY3scJT97aoRTPwBUTx5cw7VflViuSglKKxzsrbobD9GWDwzqb KyhlOiKr1aYH05kB+T+CnFHKNiDsGdMCTD8Cgv1qDUqdmGlSxNcwXfS4VOCE MUqdjeB+dD6KJidwPa2+ilLJy57ilUYw/ZBs+e6gE0o9Ov/iztwKTGdUrN8x 7EGp15ubfGrVMB27ZW+dNz9KtcbbpPpGw7RPx/PtGjkoNciay/ZoO0xbakqt 3VZCqT/J0/H96zAtqc10MomI0oysubFbj8HU3IxsQN0DlOay+rTzyVuYqk+4 Ff/lG0oLJ0//3SMNU/HiFcdYL6C0bIfAG0osTFn08qY256G0Bs/OqNghmNwc 39unvoDSRod99CV3weTrGOJQ+16UPtBd8cKFHiZ5ZNV2RjGitJMIIWjJCyaq nlryZZ9Bac+jjKoZ72HC5aWagMQ8SkcOHS74Gg/j6V6qA5xUlI6RPX8ikArj SsVMsrtWUfq6d54MmyWM1RSVaXdoofT9sfkcC2YY7VGp4gqbRelPv9NvVSfC 8F/XzOOvzFBGhL4jJj8JyMaHXbkXG1BGToD3pL4VkFrqhRd2NqOMhpKj/Qca X3XrvIzWbCiz60C/AiUehpK8nBMnF1HmwDHJbRG096acaHBfYBRlHEM9Vxno YDDDk7d4jBtlTmaOtMteAKLu+TZfXwrKRBSpvC43hYGbIXlMMWooc7E66MGu FSCsvF7zSxRCmWttLxJp850Q+I3u99FalEkfmA8/fhr6Jwzvb6zGosy9OQO3 3/rQH+nKumTzAWUK6c9axs5DP/sjf7hCs1fGX6vFXQZ9JQFnzhAtUKZGcUP4 YTD0ubMZaGZLo8wHk1302urQJ9KcFZlzDWW+Hrgy+XYCeinDNpeztFGmx/3T T9sn0Ft7J/apSyrKkEPZGgb9oPcR6+MBnnmUmYqzfhKiCL2Zzlbf7Z6jzFJm WvoGFXr/u8fNyyWKsnSFnedTH0Nvft9purpZlGWvFvCR9ITed/w9Yy0klOVv c7YplYTe6Z0RenvMUFZy4J7xTgL0Ke448o5UjLLKs0SZL9nQFzQTf9YLUFaP XmaruzP0NSks99gWoexO/hML04LQL1v87V36DpTdq1hIvPAD+tMNzyioUFH2 kPF4C8ctIHBc/JKUwYeybgfUX+YcAkKmUqD3SxGU9XEPuafODQNK3wa/zw6j bEhIeXztVxho5lxv7eJE2SuZxkf6rWBQ8UTTM9kXKJtSeH5XICsMDkeFvFxI Qtk7bxpU1z7B0BHWjktXFlH22cDuDVFzIKk3WHovHEHZ17MJo09pfOH+lT+d oYwo27jl83eTBiDztDKk0/i/7A9F23yX7UBhCONTX09F2fUQN6ssfRimI1hf TV1BOebLufpK8zB8Jejv1B0zlOPOIElUlcEI88UikvkhlJN/4zvXrQGjHImu QZaJKHdwS/gdQUUY51TXOEyJRbkjfK9jC2jvIfHZDY1jrCh3XGElwOAxTGy5 kWYnGYpyp60u7XSUhInxue/fXXVRLuZokyKVAJPO76ovNGWh3PUQRu7IbJh8 f0Prs9Y0yt3PuE7JFIKppBcN4eEElCtciX+tJgxTZA9eT+FwlCtzj0l4JwrT +pcmGug7UK76XbSzizhtvnXH9nFkotx7pdMqc5Iw3apvJ9fIjHLtySF/r0nD DKe3aXbCF5T7+Tvgi6QszBwwefol+iXKDTr55L6Wh5krN145nAlCubFaz1Br BZip1FF7NpGPcvPSbrsoijBDYW891haAcn+vOvFFK8Ms2+qOICkxlGecsBvm UYVZVbJJdzQJ5blsD1Q+UYdZyxd3nFi2o7xQxZ5E0IRZV1vHrU/rUF5GZJfL L22Y9X/11EZ1DeVVL+1QO6ULs6FdXL7mrSivRzH8x6gHs+FPzPZR/FF+5z6d r/f0YTZYFyy5NVF+T4n6Q11DmD0ReaxgiwTK2/EqhX02hln7KM3dp/NR3uWM 7G5PU5jdvvuSsLsWynsRJPiXd8CsxJT9O/0glA/aJTSSagYza/G9fM90UP50 Ie8bBYSZ7wpOL5ykUD5mK+f1ul0w82hCqN5xHuUTQ1ndDpvDTNAfl5Z//Cif /pNBfcICZnR99H6l+qD8PZN/G5f3wPS83/n3Rh9RPj935ZvwPpguUShm8MpE +VKGP49eWMG019eKn8dKUb7KfyZijzVM82Wu0FnQzrfqUgUjbWHKz/7antIz KN91hzi21Q6m2Bu42D7zoDxhvbf6sT1MPtWcPvdaAeVnP3Uc/eYEE0TFV/lf w1BBwLUmT90dxmX5bulNmqKCZ1y6FjUQho8qXSfkEFHh5GjylvNBQCXLPxyW H0WFCOuELt5goPqT/7XO7EKFBMELURgOlHME7iK7u6iQdv7M3m4af2MsO5p5 oxwVsobCRYKjgJx+tkxikA0Vnj31q8s5B6RyVi/hPn9UeL3NK1UvGkh7jt5Q 0jyKCg2R7h6tF2BooH5aMsodFb6bHaZfiYUhwWK3J2+UUaEv7+CPtDgYDHP9 xc5ehQpU1n2FivFArPf+NaAagwpLnWb7Ha7BQIjiXKKuDypuoRf9WiEKhM+S uaeVXqHiVp1lB75SIGg6MW3dZ46KAse7emn7oP+B3nXWp/yoKJVedqzzF/SL 890/tP4JFVWakqm6AdBXsC82jbkPFfV+nzyZ/g/6tosyFNQeR0UzGcuZ3zeh d+Cji9k2eVTcd0g28pA89KZcmAl93YKK9rEbq2VvoNfK37rm5y5UPFrWH8Nt Db38+Uz7Mr6hou/QG8aQIeiZ2lXwUIsWbxh35vWOCOj5vvf1C34PVDyPYdu0 mKHn/cu3ugZnUfFq6MHM1GyaHHosoJ52Pu2hqsiMJvR8O3mUVaoYFe9+Y35w 8B30TJz5ppwgjIp5mxT5Eifo5Qmy2fdTEhVLtd4+5aDtD0tOkcUXJ1HxjUeO VtAl6L0q6M1y2g0V36WdrWjngd7vpjfVhkpQsf2to6laPvSpyMad/fIcFX/N 6rxNMoa+5FMLdXOCqEiS4rKYaIe+tVddO4q5UXHSZqLVyhP6I+Iexi2fQMXF S822xQvQv+Q5OJaegYqbpXk/2a4BIe5LwqknWajEz3V0sKUUBipVKVqKCagk aWbio2wORLcVhlRCGSopBwtMXPsFg2ycvpccTqPSji9fF/f8g6Hte21Ff0Sg 0p5/z6ILb8JQdrddgZ0cKh3SSKRjloeh9Ua9VvkiVPJJ2cX+0RpInyBj6GQh KqVav5LenQ0U353mnE6DqJR1Ia0gTxMoX8dy3P4+RKXHz4PU6N8B1UhJNddO BZWqOBQMmsZhmGlLQf8+ZlQabLtzAI1hJHk9uHq/OSqN/4349qAdRsY5bkuN uqLSgpqd46YnjJor7WPe2IPKrElsng3XYHTC79XNS6mozFszPCIpCmMmW4Nz OB+gsvjEu8CLpTB2pVKkrckBlXWszkft+AXjHBYdeX3rqLw9+sjfnAAY3/9R PVZNH5Utn+rH/v0H43FXvZk0ylDZto+H2e0mjFcew98uWajsyjadVCsP48Ma dFyEHajsbdLKI/oGJri6fNwPqaJy8MnC29HWMKFn9EXyvigqn717Rax3CCbs LY3rKdOoHPfZ45FJBEwETh5Jcq1E5eQDC1wPrWDi0rx728hOVP7va+IFFlmY SNGj0E+pofKDQ+Ljp1Zh4nZJRfGoAio/6Xrp9OMbTNw7riogRjtf7mT5YfsT mnxY/IKlISrX9vTpPI6h6ScR3NPjUfmjW/ADNto8ShGS3yvjicpfiQwcoZow EcPIJmAejco9nnfOdTPBRJD36Ovr5qhMoqiN0vDHhKOtXewPmv9Jn0aH/AqY MOmfQ/4zqLww5vBuaxJMiCpG/T4bjsobAeNa4TR+snTwgo+5IKqwTF/M6TWF 8a/hXm3qc6jCE8rLjjww/rjayKr1A6qIzheeKRyD8Yh9N989zUMV+dOmw5xv YRz3B7VFmKCKxnKHfeRtGv8Z3SrhpI8quL6iYU7Drxkv5lUOhqGKVUxydrE4 jDlsLzou1IwqDvQyrNsWYIxXvsBjSz2q+LHsJw88htGEofsFjLmoEnqdaGdx DkbNpKr/mMSgSjRHeP0zOxj5k8m20TmEKik897LObsKIywYm7EJUeSU2bcN/ FIbFJDV+dmxBlbrcy7Xn9YDaahkUXSeOKp9kBJXJ7EA9F+gUuH4PVXoVgeFF NVB6MuiGn3mgyqb2zeoDgkB+aLpSsc6CqqyvFBRfTgPZ0cLq/Tl3VOU1qM4U /gDkrTOXT3iroqqCKTl4JAJIl3pbhp9boapmfRThIA1vbw9Zkj6ugKrGuHV/ hSwMrR1ieZQqjqoHLPXlL3+DoZirvVk5h1HVobklfawIhvaIP/Qqv4eqx6yO btpegqFt8VqPNHNR1a/9d1ClEwxWvQgTereEqmG2V/skNGDQySNdMOADqkZ3 iu6NZwTiX4tzdcfuoGq8Q2nFBAGIhfFhikYcqJrSvVv20CsgOqNJ6RYZVL3j 0p325gYQuTNYti+eRNWHhMB/UsdhoOPWuf1ufqj61IMuIMEYBv6zuXuLLQxV X5Eyu6dp+PL498FTXBmoWu+tYnl4FAYMZIyaEFH100h9eU0DDGzbwZNw5xKq fjtpLy1zGwhz4sFNmbGo2js5kpJ4Cgjdlatk4xFUpQRHr81aAOHjX6mgbH1U nWpSzdx3Dgg1f/cGpkyi6hJfn8Yj2v6p1C9Im/2Kqpu+1z+tUYBQdT0/M5UN 1VirTTwdhIHQ0ONqzcqAarxbx1ZLDgKhba3Mvusuqokdy8pgjgMC8VNa67A0 qsm/3KfuUQWEZa5ty98DUE2TYfnjmykY4H9QdFOiFtWMnIo8eGVgwGiP5+3z cqiGxU4rgU4wcKyVK+lHIartX2dO/5AEAzdmEz6HB6PaYZtKNYlGGKg92/dt chTVjj7y+RC1CANzCpnHDrmims+CgPs3VSAql/9haVlEtbN3I2/GZQAx37XQ 8FMzql2eklfpbwHiyMhuq152VEsy63qvvw6D6gZ3bSrPodp9qu7iqC8M1i/M CrV4odp7zd9u1qYwFN1jqBzaiOqcH4IVBchAqjmTWSGPqC4kJPE2RAhIQ9Hh f1d5UF365BfnFmsgM+WLft1Ziup629SToiuBbL12evpbAKrvOE6Q75oEcvC8 cuTfT6huWZHUoCEN5LTCzwU2gOrOLhNzgzeA3D5zwSE9FNWPP8++YfwWyOMu ryxXPqB6wKaVXPoCDb/o7sxXdUT1yEOrdZMqQJHwf/Nc3BLVL+YXO1kcA4r+ bB6VOIrqCcvOs/czgGJV60/nF4vqN61YE5dagHLsnduSgweq3815I2v7Dyih 6y1O5Ruo/njWr7ZYFyiXvcd+uLCg+nNzIUd6P6CkLU2xjFxB9deZn2bccoCS U/Y7mDMK1RtGo65VfAdKUQZTgSktvhZTRRkuZqCUPRCfM11F9e/JP2v8aPyk qpeDccIa1fsGrx5uDAVK/Z4YrbAXqE7V1Z8WKQBK48xs16odqk/HUxPC+4Dy rj/LVcgV1Ze6M6XatwHlvZBvapYZatCp7q5WsABKU+nU9XhR1GC78Mf+0jmg NJTZKV05hhq8HY8nu0uBUqNmojgfjBrisvZXtSlAqdCuXxO4jhoKp+klbwgD 5XmXreeMMmpoNr+solgDJV/xMdGAGzWMRY8f2nEZKNmQIzZ5ADXMjeZzj+0D SqqROO8RCdSwdrwyGcsFlNg/32dE61HD82bh1fe0epww9nscvgs1AkqNOodP AOUw3a0I9q+oEdn2WZpFFSjmXEaDS1qocZ15ssbqDVAk43RSA2j+MuQvsgTF AIVDI8UnTAU17ptzOaTuAfLqE0lecz7UeHlRe+b7DyD/+M+RfdMaNXr/RMk5 zwE5lUFXYmQINag8LKHRVUC+2FfzZdMCNWY0s+pzLtH669pxyd8nUWPFWpW9 wRLIngopxcvJqEl/svbIEAeQ7b8ZP8/6gpoc16wLtnQBec/Dzwn7OVBTMH9g Xi4byNtLV9hHyKgp/S4ELD2BrMv9ojWPhJqqQ3TJfkpAVuldPNXLh5r6/9J7 E2eALCf+dZeEOmqCmJzi0wogS4yrbi3VRM39xhUR7eeBLGrdsXegEjUPO1k2 zpgDWfjw/s6q86jpHtHNxc1Gk5kICsmjqOmX7u+m8w3IIoERKsFXUTOsdPXJ 4TtAFk/MeTntg5rn228snj4GZBmP2OKGCdS8OiFufkcByEqzhFUBP9S8yVKS RpsvZG0jRtXSJ6iZrWBG6HsFZFMt4vd3P1Ez3/ybyno0kC07Z8sUYlGz1PP4 GcldQD4s6HqXdytqvrk4/wFZgXx8eoZ9TyhqvrsXz3P8K5DDD2iFozdqtlcL HLvyH5DjJQKEio+g5q9fhc8KjgL5zqHGjF1tqDm0YLTSLAvkZ13YOjuEmota rhnsZUDu8RSy7axGLYmCB83p7UDZURJ1REEftZTe6/C/or3PI77BnrcPopbO 0LvjP12BEp7gV0T/D7X2iFHXhceAUhyS4yivhVp2JlH7TV8A5cPvLSzSn1HL 9QjL7aNRQBmy2urgxYVawbdUtR8yAFVgBsV1c1Dr7Ivai02tQNWaKSsQp/mL +2LdSrkJ1P06xf+CGlHrDmuIj5IkUM9OsTeGWKHWI0W68n3DQE0TA+d7dKj1 bHf6ZsBzoOatT/80ZkCt18flrJMjgFoVIusXcgq13l6quFtqCtTPh3gEvCJQ q2Xm39duC6D2nKdmCH1FrU6PvYx0NkAdEfRY+0zz199x00TFGajzbLbWisdR axh6g+1PAHXd7HuMdhvSwpTNOx8Mw4yPwz5XXUWtFenAnvyzMLxVBppy2lGb rsU1YaQJhrcVj1ikPEFtptgYmfN7YJhXvYmrcRO12Y3z6ra10eR7c5OBJqjN NdfilG8Hw9yTwQGVYajN92Tqt/EvGObkEWPfI4vawsd5kr64wTAL47eG2/So LSFiqHh8CKgbjSHt/fmoLfPdtXHJF6gLMDESrYzaitdj3G5MAnX08qGzyd9Q W21X3pJUKC3f+B651AXU1lptuflqEajN1oN2lR9QW//ltNq+aKC+GlgmaGaj tkkAz8cBOqDmmg23/2VEbTNZQ8+wq0BNOPtIgIEZtc37XP8y0/BR0G0HR48z qL33Vsx/2bT7sitwyf+siNqH6Fta32cDlbc7tc27G7Uda6Z9nKWA8ltayXAm FLVdI3g2p/KB8iX/a8JcI2qfoLgZCND65fL7uDdvtFA76v30Az8afur1N1/O otk7f4HXZH0/kJ9nHh6e60LtWH3DHzdp/X5p2sOZ+B9qX8+PZavupb23uiNV VQOonXOVN3LrDJC8FTYnuEZQ+9FOI66HNPxlwFL2eKYetQsW3Yr1V4DE7Har ++Awar/wySceY4ChZ6yBsm+lUfvdHqN9ZcIwtMXsHfuBOdT+tOFGsbgPgxXT vT3TnqjdVhl7sVcWBgOWzssQVlH7W3CB8KknMCg/fsNz4Bpq/1T8/IpeA4gU IVMBZQHU7iXOHLxdDsSCL1cWw4JQm3iHd0zNGIinjpD2W9Dipdgaxb2tB6LJ JmMZQxtqj7EclXCg8TcO1v9uvX6F2lNvY6vGmmGAUjl9oeU+av8+U2B/0RoG mvZ1387XRe0lrc/TPJ0wkL9qfz7mKGqvjc5cKzwCA6mbDo/3k1F78yGfrCkB BmJSMtKKrqAOo7NRXYcXDER12jD+/YY6bNxHj5yg4bXIZZFjLjaow9kS+3sl CAaipcttvkyhDu+IoG+YKQxca/Pjtx5DHWGGZ30TrDBwf53psjXtvITMLluv XzBQ116juNqCOrJmv973F8AA1U5ZZV0ZdZSOBhk7RAKRPx1Lf+5GHfXoLc+/ 0PI7mCFj3P4f6ujcuSOzhxuIaXarKiL+qGP4Wv2/BiIQe+uXrvrT7G3vfMdu 9JyGfz6aKsq6ow7OHblUFg2Difu7fKZHUGcP59QflX0wOKX43ntpC+ocUIvz eywIQ+pCVxbzd6GO3X4hgigVhnwFfy3vSkYdR98Su4xyGMqbX+wn8qOOx6Me k6u2QFKt+KGYUY063m9PlWxIAinCqr+ch5bfyQEG2agpIL2VjKhrpulHCmtu 9U8EsscbjUJLXdRJTL0ycKCRtt+ztXmuDaDOy/Nal3l1YaS63d+nvg91KrM+ LCbRwajIqcyR6ZeoU1vpGsDwFUbPit9WD6f5+/j7qv1iIIzpnKJOlQuiTts2 0eZgUxhLimLqfPIHdb6pl20fZYUxirP6b6Mi1Onz65fvyYfxpFhxImsF6gxe Db17KBzG+/UP3HlIRB1qHjNnK8KE0oOuGEk61BlvvBe3mwsmQi9VCD6jxTtD 1F6qHYCJ12kH30lRUOfP30+B+s9hYunWw5xv3qizKuI2WBINk3rW1wNarFBn w/C3g+I+mAy8sh7I8AR1GRwSWh4IwuQDhp3fB5NQlzVcfKcQFSa/ZAlV9Oii Lmfay5c3y2FymZ9TtzoedXlL9iqyxcKUmPWNgqfyqCvUSsiOs4UpUzHe0hlt 1BUfC+f6KwlTh939ZoYGUVeWmeVKxBRM+S6+MjutirpKcjnLUzUwFdlaaH5N CXXVd+kG+STC1IXmbP2TJ1FX51jzENEJpi4RnjYcZ0VdwwvujkfkYSp6o/2r 6k3U3X53/nPHPEyFKZF2q35DXaxKNNvXCFPHnd4fv3kVdS1/SpQ3pcLUgTT7 Zf9m1LWaf6VkehSmtPqi5c9Go64d9757r1RhimuPZvVNV9R11CByq63C5Mhw iPIWLdR11bssen8JJmvKjI4ttaOuh4m8Ao2vTl4X/Lo9uRd1vc2atS7/hkl7 E9EDs59Q96RFgMmfGZgU3H7euOYR6gZbcVr4TMLEzz2vWObqUDfCtsymewwm 0q613aAbQt2Lrksnask0/h0ZIbfnO+rGed4N1hiE8bxUl2DpYdS95rvj7AMC jFvXxEptlKJuelhcypVfMJZpE+bzZBfqFiRyVR1ohVE6o2m1ZEDdpykvm+qb YeS/HPNFXxvUfZHh0K71AUZU/JUTZbhRtzo3m8TXAMMHk4vYviag7pfXSuz9 5UBJL1zfe6gSdTtrWgUO0ua1XPe//LdPUbe78ZTU2+dArtJnlKU3QF1S2yv9 vEIgkb/fdOWl9csiGY4FZsPQESVpq8Zi1F0bJfsP3IHBlSAHfYkHqLs5fTXC NhMGrVT/EIUoqMf4R/liUzqND0kPUZ3Poh7bSluiXioQmYl/FbreoR7XRvCt AhrfCp/94H1vEvX4GXnuCyUCYezX2PHpb6gnwlZRdP0qEE5yt6r68KKe5LYj L//GQf8Cr+Cybz3qyfGv1Z2Kgf4bez2YPcJQT1n0/qfBC9Cvzl/RVHQG9TSk 8fuhc9DX+9M+vVEa9XQVKP3vo6Avo3/vE0NW1DNSSxgxiIA+1xD1bfULqLdD R2WuKBT6NDoIe1VqUW+XYftfkWDo49LdUZg9g3p7doQyJQVC798mxuYRQ9Q7 YM637Z8/9C7lZMhHcKCe3d5K0RBf6N3o+8zPOYR6jgdd5EknoI8/YfaQrCLq udqvax32hD6jR3lSLx6gnofzA5OP7tDnL92uwXUB9byPmVsYuUJfwew9o50t qHfyxLBN8RHom5tJv8ITgHrBJxNdxBygf9+fdBlZK9SLCFE7kXII+ks+L65O 96He2civpzZtgCClW3VLKgH1LkaHnQ07AIQcakZoRivqxcXyx1H2wYBiMrIe SEG9a1erUhwtafy2/ckW/Il6yUmud5pp8/woaLwaHkO927cfPn+2Awbr5s2a LJJQ757lg2fS/TBkFB8Q+uc26j1YyC2+HQ1DmXdFWZxp8RXb5xRergKSy+9T ezzbUK+OK+uhM43fsdvQl/2k5UdKuJnJpAIj7876Bh66g3ojBmm3oltgdPun 3a9NuFFvgpp6c84XRituWH1RS0O9BfPklP58GMvTfXrZagn1mTcSr5VLw0SQ c04bDyfqby25lqD0FiZ6nvVX2OxHfe6jCfH3j8GkOUuZniw76ovUxF9OvAdT 7PW1Hr+9UV/y5JWYDVOYOpn2TLdGEfXlhOMuRfTA1AdX+n9NW1BfPSr2vIcg TAdR5JI26FBfRyHm3I/XMF2jWf1BeR71DX5cOmvlADP0yfknlBH1Ta9cjHr7 B2Ys2zQbZjdQH3QvnDa4BTNX6rjkJSJQfzfpfMQzHZip048rN5hD/X03o8Ol v8HMLOfp2r4A1D8I50JvB8OsuNx/mZWaqH9o5mwIByfM7j4S/ObLPdR3un/m 1OXnMOudEmdaT5PdrKMCl61gNqbUxf8sLX+Pv6cDgsZhNrPwdta2PNT3fhrp T06E2Tw/Bm4jDdQ/6RLh56wEs8+o2fT6u1A/mDXc58snmC0Vvpmi+Av1w6vC vHf7wOxTOtcPZX2of8Y31KuaEWYf3C+pMhhF/QsCIcc182A2delSuNgt1I/9 EOyRbw6zZ/k3GPPFUf9qxKljIkMw68ZyK14zF/VvyAYdTYuBWaOhL4K/zqN+ 6vdANyZJmOWqct+axYj6GbEBLtF1MDNYSMdmeQj1s7ROHplzg5mnbSEelbR6 5RD9nXzWYCbUyiqxipbfoxQ/h/67MKO9b2PMjFbfwh2+hw8Zw/TE7+cDCbGo /yLb225HFEzb/TVXXuBB/Yr9J2zKBWBqvanzLfNO1H+z4nVQqQKm8v75C1hm o/47J08rXtr8Hm46IP7RHPWbA77JBvXA5FmeB7YKF1G/PQbWPjXCJLOx7QcO Wj/9eiL1LPomTAidGnWJovXfxOogB1kbxoS+a3bF0fpjjstmeIcwjGbwtour ZaL+omxD/W0aPuDU1nhp/wT1Nw/kBu/vgOHNHCbWVm004Lvv3vEyGChjyaYv cv+ggfDLL0+2OgHFZ6WlLT8VDSQ+7Yz1MQPycOS1W/dY0UBpVkJHhBNIU+IN vqN5aKDBkMIWvgCkSCYmuttENNAVWie1E2BoMyJuayYvGuxAwq3Y5zAku52n QeUoGuxytA7oo83jyBBOmXFmNNhzsm63/gUgltKNJZq4o8GBS+piqd4w8Lv2 +YspOjSwu3Xvz9gBGEDF6kd9N9DAsWhru7kuEO42Bn/YvYYGrrXn83NEoX/j X8vMng008OiYvLBED/2hVqyJo9xo4E11c7SdgL4ZZk+mjgo0OLnSplHcCX1n rz+dqrNBgxDO7cwMNdDHLa3xZrgGDSJlnhLdH0Pv663H3shPosE5Q9HKquvQ 63f5jUDuGTS4ZHUjjScUepVKM3Q6TNHgyrE1v0Bn6Fks5zxe240GiREB8BGh 5/vDJUVCMBqkXOsTllKGntr4T+NKsWhwK2f/3Dlu6Hnllq/vR4v/Tll1S+cK 9FQL+4d1hqFBzkeVR+pD0NP++A3XI1o+j3rvnktogZ7pbsmVABY0KJxhOzRU Br3i14xsmwvR4Dn9OVXTLOh1PterapqIBi8Fx+kzY6H3UeBhfS3afVSqOffN +EHvMuez9RrafdVCy6t9NtDnwjpZIryCBo0OxkmPDaHv099TowxH0OCj/5MT 65LQj9HbL5Il0KD1ovAOJ2bo/8gT+CZgEA060hP5X8wAwcEiuenXMBr8KFie ZvsFhOk03snRPWhA/Np9v74QiDt0fd+dzUEDCmVvlBBtv865E26RaPmOLVfZ hEXB4PO926I8/dDgj/TtTQVLGMpx82357YmGLOFOnslUIO/z63j4dQINObRl 1SeCgVwWmU8NzkBD7unp1b3LQBE5/XHg2zk0FD159RYDO1CmZdjPerWjoYZn xftz2jB8q7JUwawQDXUlY9N+1cDwGuOr4nZmNDQkWB/V3w0jHmNZTlLn0RCP UBZmnWBU0cE7bdAJDQ/b8in6XISx27Y+PQ3eaOjMQZx/zwxjE93mFT/d0PBo 69O30jdhfPvUvNVjezQ8nhiVdEkUxq+77rB+5Y+GvnvMj9D2w3jn5PdShgA0 DGTkkjfRhAnhsGCxHS1oGNLUO3e7CiZc6pdZNk+i4bmdYdcPtcJEB8tAy0ky Gl78u9PxhQNM0te2ec3XoWFcNZssxwBM6uT9ptMNQcNrUT9mAnxh0i3/wpvj fGiYrP+wpnkOJmML9g90EdHw5nzQNQUavn6YPr2rWhsN/yszPnyFASZrXRoe uv1Gw+xgRqmhZJj8NjuoI/IEDR+odUyZ0fA26cBO52ASGuaN33tz7yFMTvn+ d+FVMBo+KfKLX1WByXkMpXPIQsMSH71DTjT8Pd8v7ptUjoblspsSFTtgcmZH cXFNOBpWDrVO8HyCSWpwf3vVFzSszb1dGWIHkz9jJMsjVtGw8ahXXHsfTDZd itrvdxUNP4pq2qqegMknl4NELRvQ8HP3mti1KZi88WDtgHsmGn797+PocBRM +i1Gqv2l9UfX4fSK3XQwiQ+Djv52QMMeHvfYhzdgkv9dVolWAhoSOlSsN/hh ghr76v2WXjQkJS+KuN2HiTL6o88Vs9FwkjW5XLAMJkzXz30QLUHD2U/OlyJN YXztu9eKaQcaLsTLW31/D+NVslH7edfR8N9mLSX5F4wrsrcZc0egEffSpAD9 OoxSBCIOJ7GjkUBFJckzAUaTCck6SxfRSDQ8rqRhG4zqT81V3HmFRnIzonvO ycJI7PCJrBuDaGQ4vP/M7H4Y3qZ6cMHaH412sCZI6ogD9amf66GCLjTapfbu Q/gMUC3X7G3Wd6DRgfAd3EuZQIlxvSCq/xiN7P47W2XsCxSJxn2Fd+zRyPHN a/doEyDXP39dJWqIRh6bmsX/iEBmMHDzc+hBIx/ZQDsoA1KxS7xFkxwaBVgW LV2+AiR7vsYsmTk0ikyW2c2kDEPPt7uquJih0bkX7uN71mDomIha9aAQGl3q zE5L/AJDfKfyC0P90Oi6CB+BIxwGeU9WdFGG0Ch1h12cjQUQM5bSB+Jo9jI8 UpRvCgFR4i3/hzYiGmXFff76fQIGyhiENI6UotH9QubTfHUwYLVmEqzEj0aP P+8Wc0wFwkzXJ0Ui7X/RVEzTneNAuFd5//uTbjQq2Vbn16sPhENNqk6mNH/l uqtcYsxA4GFzzR4UQKMqJ4OKoz3Q35tTyNpD0687F+6a+wz6n8UK/V6n5dOU 84Ju6BL0J5R51c/1o9Gnt5NFsnbQHyiz1uB8F43aKMoHvWWh37nJ+PLfF2j0 ndn7T8Ei9NuGx53Vt0CjXyqP7o620GTeiy/EPNGo35qIKtnQfyTKMjShHo2G QkVHAoOg3z+w+PzTUTQazjiSXALQf7k6PGM/GY0mKjN1Z3mhP0+qhPd5LhrN 9n7r1aZCf/uRVUPXq2i08I8zJrwS+v8JDDXGfkOjNWkrhYrrQDBg0cnxuIlG m7sT2paOAiHih7vU5w00ZvR9H26sCYRq6f6rs+ZozHaDTjiaDgYYkzK2y15G Y66SHQ21XTDg+GJ36oFANBb+U7kVzgKRJfqogagxGksKzr+8fACIfnt83zRx oLGcqdaR95JAbBPI9C9QRmON2Cf5e97DYM6MnuanFDTWzRu2SrwNQ0zMp04K BKCxUbPMXKs/DO14vum6OwyNzTnv7bThgqFS+zXrG05o7JSd8svRGUiZuRVX xh3R2K2+9cIdVSB9SZA08/mGxp4kFtmef0Bmql18JFGFxoFKsSFHHwM5wjKO NV4DjWMqIlhPTAFl56OUlTAtNI5/W2+ROwOUgDmS2NZwNL7exhLbOwuU2zD6 YmMBjVN/HarnnwPK2xCn+Bmafgbp3qrtPFBGvDI+/T2NxllTIwY3/gCVfbP1 YLQ+Gt9f0Q77uABUDa3VxWXa/zyG6FK6JaBaDzKx0O9E4ydcHya2LwP15N+a WzxtaFwiuk0xagWo8WcGOvqJaFyu4OL1cg2oOftUT/sooHGVdl7u1F+gvvR2 YuCg5V+3fbpfaR2o75u5eN7R5Ka9xkJeG0DtOvWnIGkTjT/Zxx2+vwnUIZeM NGsPNG5zb0/rpQPqRIrbRPFxNP52UrCdnx6o89tSm/M00fhn5HFWW0agLo3r 8qo0oHFfzDOLG0xAXVVYb8+m1X/wxmLsR2agrvzYVXE3EY2pt6GejpWmz9gd pPwejccfXV/dzg7UudaN3Tq2aDzz/IdB1Fagju/Y/XbqDhr/eSMZ9pIDqIOh 3sylNPsr7/1Lprho8V5W2X09GY3/fS2fUOIG6scb+015pNGEvvefohcPUF8X 75IxGkcTluG9Xvd5gZr3T2u+LhtNOObSc3v4gXqz8MURxgU04fnb388nANTz zc+OhBuiiRCLopCNIFB9rpjQW9GhiYxETdoHEaAauKmfb5xEE0UVxrZNUaCK vWOb6mFHEzV9W1ZT2nzc4sy9EjOCJgZWlNgySaC0PtMXOvYaTfaf5QjLkQeK q5RPrvQONLG54lTSrQgUM42OnGsiaHI49eEErxJQZLO62PSPool7gYFXoiqQ p7m6as5IoIlXWUzue3Ugd7mRCxnD0cSv9nPfhgaQa677uXICmoR1HjscqQ3k pNXGnpcWaBI18CTthS6Qo8LNjFMQTc6PzbdN6AHZy9Cq5aYQmsQu7GRV0Aey rdt8oEENmlzdvGbhaQjkncvqo5aLaJLE/j32nhGQNUVaBQIa0eSmoFjdL2Mg S3V5JpV3ocl/Mj6rPKZA5tMx6z7NgCbZ6mUG1tuBzGrmcfhEAJo8MFoLu7YD SJsMx/LHM9Ekf7dFyTszIC0nRI4buKNJsU3q+D8A0u+2sDvx/WhS6tKraIxA mu6qEUui1eOVj5xXhDmQJnMPKM3T7uNN6Knc0t00WamuwUgJTerPV/WN/4+C 6w6n8n/DJbOEhCTJKEkqK9nPc/Y50pKMQirKqiizKYRsJSQ0hIykIRVRpKKU kuJYZ9jj0DchSb/39+d7fd73GfczPvd9Lhcqwe+9mH22x9CkJkpo2Wo6cMec WxaPh6DJW7O7jIODwP15/RfZ+hKavB/fHnQrDri/4Vl1uxWaNN2ZyOcQeM2z 2NGZTcTb4pjRqtJMxB+1vZC3F03YUiRx50DgyUq9kVmujSZdNf3GBH/nKdfu OPpxC5rwg+I92C+Ap3VlVeECfTQZ0DG4ttwVeJu9la7Pv4gmI5y2eocFwKMY 9b/w+44mP66e/52aB7xdXHH1Q5loMjP3Ye/SEeD5BcjusX+Ppos/u6+QCgHe k9EQzlgOmspGLtq6TRl49SIM5YO/0XSZ6YPTsS+B1653U9xDBk1Vc/52SIgC 799epzIJDzRd43BHklEA/CWrGjeK+qCp9mIr80hrop/iVkbai6CpQWBq1oLL wKeM0sjT1WhqvN68kbwZ+LvvSanyZNHUvJv3N7QV+AcLml2Hv6ApKeXShuoz wPdtiGPldaIpnbXJeW4V8M9O/tllnYWmVn9bEsxrgB+zrF6YtgFNdzw8XXWa uO+vKmSv1XFDU9sjaoLnEsDP7jL/k9WNpo4r3qlMFwM/f++1xNff0dS56diO LTuAXxL8Y/75K2h68KLc+QBiHz5e2539+gCaHjF5fv9xKvCf2gc0CqTR1Fvg 2v3TBPjPf28tuKGPpr45YjJ6HcCvnHfFQvg6mgbY30PfUOBXHFy3/egnND0l udv3vgbwn8ndyT9qjqbnXv6+OfoG+GUyhxrmpNA0PODGZx1P4N/fcW1R5BM0 jdamz/eWJOL7aPtzch2axnWN6BXcJ+KPSZguYqNp8pXLBwZsgH8lQuew/TE0 TWUaX9b8BfzIFyKR4dpomjHbVeOeDvxg44Gfi0TR9MaDiP/umAP/yN+spXYL 0fTO4fXqvG7g28l2V9fuQdMCpc+71cKATw5zk3AMRNN7n4LCXYl9sMH+4xqP j2hablzL7/QB3pyP/6jNajStGPWSU5YGXs8Hv1UaP9D05W0Z6t6HRH80v9ho rYimDYuc7nyfBl7SBo+iU0No+vGlUIvCdaK/SqLI78TRtNm/QGQPoY9tDHcZ DBD2Ojp/uX+5CDyZSnO5m5vQdKw0QbNRFrhRC+e+5YSj6URDru7YHHBd54a+ TuSi6e+eSjPZIeCaTtYXc56hmbDi0A6HGuCMFVgKTyqimYTBfMfTJcBpeDnI 5JegmdQ2xUPZGcDJS7VpM2lCM8UL9KCek8A5sOXppbtcNFt53TlMzBU4eHDF 9mJAM/Uy/zhta+Coid5xyGSj2dpPsanbjIGzYLdz0gfCns7g7Zu+q6H749Bf 2UNBaKa/4FnhFRnovq7vbxnghWZbVjY9LpuFbu/m5XtuvkYz8y39Va0D0E1S lWdvd0Uz0q65d3++QvcKz0O9K5+jGd1HvlnlJXTN/PE1uLAXzbZG6nSSiqGL o1LVk1+OZjtvUvrd0gm+sewKrS0FzfY83/sj6iJ0Va65Lvn7Mprt/er3p/AE dD0Kz3Ka1EGz/YJLIo0u0PXgyIaRpx1o5i5+U3rcCrqezHPLjQ1EMy/1cqWl RtD1KiRDYvYlmh03/7jaSB26mqe0Hoo7o5m/Xe9GRynoGk0zXP14KZqF+M4a n56Bbim/AJsAdTQ7F7uUnN0H3UZpkoJYKTQLz9W2fvUFug/L7uPIDqJZdDXJ rqeK4EN93L+CCDSLb3M4IFYI3e1LNMg1RHyXfx731k4DjgyF1bTMCM3SF0cG bIsAjlHG63rlXDTLWpt13tcXOK5h19+UyaHZbdLjS1ecgBM/9G3D+zQ0y9/3 /soTJnCqqkdNDxSgWXEAL4vgu5z/hpo32Iuh2YPE3/mzqsDV3vn3z2k7NKt4 vbaSNA3c27t7hUMPo9nLLss3bj3A5RWWXlK+jWZ103uaopqAt+aB5KzPLjRr 0gnvaSwA3iOVReQHpmjWk8JZ7LiX4EcKIac2VKHZYMnUsjN06DHKFj3kX49m gnoptRv60BNt8ELtbyaaTf8139y7EHo3JJ5XTSlE88Xu6S6+FdAX0nMxyWEB msuev+9x5S70vX/7VX3qK5ovu/bmxJMU6Fe+u5IdUoDmao0TkbM+0P9821tD oedobmS08360Mgx0Nu4aiPqL5qa/a++VFMLgisGkffX6aA4vjIpajGHQ3kmT /j4SzamhBQV/3sBg4kTAtwwWmrOoynfVbWGwLjzKousFmm8XS8xj8mBwqkMv +YEwmtu8F7pz3BeGNJvJUX6L0Nw+IeD21TkYsoE7P/IV0dxp18DNyjgYCpm5 yK7xQvMD8vuy+UowlNUdvPKsD5q7t37MkrgLQy8+pO6r3oDmXpmk67pGMNR2 O2fz5DiaH9//+Jrdaxj6QVubZxCF5v4aa9PO2sCwcEbioaQSNA/uy7iaw4Hh pXEUQ/EqND9buPhKwzEYVhF9JYf+aB52NDR5/A8Ma/x+qBLmi+ZRuj+TlkXD 8Br7uRVflNA8duJwwv/1uPqyjqJaOTRPKm+Lc7sDw0r6/B8ZpWh+9bR1TKw+ DC/OkyvXtEbzDMvq6AcvYWjWjz9VQMR7Q0g/snU7DPVlkN7S96F5Tl1uxFwH DH3QzIp3DEPzu5cUw9d4wdA9tf6YrYfR/J517IWt0zB0KSMxJ+4Wmj+Unjt/ IhKGDty+cCJGgOZPmv3OXiP09OYdK6/eSETzitSe09W3YUj4mS51dQuav9xr H9KnC4OfZnvXyQShed3KhmDJKhhMM3l66RbRLx/vlPo7smFQSadZ9vk0mjd7 aJwI9YCBb3oz0/JEfq3rU33zJmEgofHLR18PNOc+POMzIQv9k1G5IfvK0Hzi JdM9wQr6cutBSWUVmv8Orzj0uBX6rA2FH+UR+c8xNh5oPwy94z9iepIm0ULs o5yLVhj0GnnWHVjaghZK7Ry7mmfEfVch+HPcEi1W3dhtO0gHvqOy89bAULRY ffDNbumvwPs9qXsmMRctNgwW73AaB56lIUnEaz9a6N9btS38HDFfa00MCiTR Yovv5a2FksQ+L71Zpku8j1PBjClCP7dmzXh+HkYL2vNh+sonwImIXWKbtxkt rM65UKlU4BioBj16L4wWtiJUTHaFbv0EZyHjJLRweFduWS6ArtuFc8sVnNHC OU7bvOsMdCktOiWWUI0WR5bKmKxPh05Vpx28Rz/QwrsqqXHrHuh4iDleaw6j ha/XkgPestBh/dTT9PgoWgTIJ/+K+Qjt4yvENjsT/kJeyV4qjIX2zOea3d0u aHHO58rKBia023BP5lgsR4twxaUPhoShXfabs/xsJFpE1abQJV4Bu+N75zkZ H7SIOy7HXncO2A9UqM/r49AiWenqMZYZsBPbxV5NLEGLq28UhDymgR1inNLK bECLDL/U1OjHwD66V6dgahAtbqxcpn3XD9g++166pZxCi5x3aVXvNgI7cNfX 8cXqaHHXX9GmfwjYsSxv1yIltLi3Kr1fLB/YRQzBU4YWWjx4v/z0Wjdgf9u6 VkJcBS2eBGZIM9SgXZKlLy59Di0q1JVyDndB+zbdC0oXrNCiuvH6lkhCn6f3 d/Ct6tDidciK93n20D62/ZitsD5a1K/O3P9GDjp2mjWCIaBFY5Pyz94m6KgI lTu8PhstvpzOihaJh05d/sx09ghafF+rorzGCjpLtX4GyxegRfuX7FKaGHSZ SOwYpRPxcs6torq9Ju4zY2/HIFu0GGxR9bljSejlZVbGQYZoIQi9Na/2D3D2 rbyZeFMaLX7qqKXwnwKnbIkuJHqjxWy4+gsNPeCe7AmpKNiFlvM35uwiC4Db uiOjO28cLUXYGr0HC4EH1TKhBzzRUlpvzeLbhN5RyPvT2diAlmrda13UFKGn U9pTg5mAlpox+f9hC/TaaMSSjmWg5XojrUjXy9D7TqTp5yoftDSMX1dycxH0 lc0v1a+9jZYMs/X/VOZgINbRV96XON/aV3zFsgIG5jxeBaych5Y7L29Y6xIM g8fb932haKGl4+DGHVk/YMi60jHsxWq0dEkp4b8ogaHy1OhP2rVoeQg3BXV6 wfCq4vkZqUQ+Pmm6N5V7YLjPz0KUTeTjR35gaH4LRqgZEpLr09AyQKD3zskF RrLHkwOPB6DlqWsPnc4owcivLQsC4/3Q8jxNf/z6dxhl6KtOhYajZfj4o4iK FBhNKfDWteGhZXSmoWL7ThjtPLJ1b3kkWsYzHhf/WQwCNRMTX21/tEz+uZmk 1ACC/WOcpr7DaJkmrHlfpQkEWf/27Q/ZjJaZ8oor1b+BoCVXhEoKQstbmhKx mh0wJvqQAVei0DLPaOa3NhfGdP8W2quIomURY+TIxj4Ysz04q3djL1qWOnS2 6I/AmG/tcfVxFbQs8/xEMfoPxiJFyt7fVkPL56dePjCdhrEUBSUDfQKf6tiH qyznYCyjL4mRdw4tX2fmxFOEiedjtQ+sndCy/l7KH8ZCGLtcrDp6sBstP1ZF em6VhrHwW215zsFo2fwp6PsOORg76tBDveaBlq0cT9puJRjb0T35xP0dWnb+ 2PvIXhXG1m89c8LRBS35QtZq+zRB8K8sqkKkHC0Hllok7tcBQaNF1aa1T9By dPXGv4f0QZAigWWnr6Hlf5tVvY8Yg8CWnPDdvhQtp+hL2rwtQCC13P2WYjta ztovYBwnw+irmgwNxj6E+Ucmyk4yYfR40NgjxS8Iiy59Tz5tCyPPRa6fOR6H IJPx7t/5vTBi73HcVKsOQb7o+dFwVxgeGx5X1QCEVY1ZrFhv4r5M3W3/oxjB cMmh+dfDYHDj3Kt/VTkIJuq2x29Ew0DJp6eM9DsIlga0zpwEGNC5YhJofASB uUfrWdE16Ne4WHowrRTBKV3gV1EKvQsdF6xc6oVwoKC7u/oJ9ET8sN4jeItw +PnnbbWV0DPv4ejZLy4Ivh2P171/C7y/jHHVrVUIF1VDuO1dwJX3HyUtkUCI 0fPe0d0DnDuKP2a4pgiJZKcX/CGC33U2+bB5CNfc4NrwL+j6qpbbLV2NkB2g JzY2A10Kes0SDYT9nEj1gIl50HlwKEok5CDCvbsiu2YloUPF1Sk2PRbh4dPJ l/NkoT1hUf7v87YI5fUDG4UVoX2h2ZIO5xmESnZbprgKsK9YN/vXEf5eDb9f KKkB7HUpdzef1Ed4M1sZLKMFbY374q19vyN8WHyvT24jtJ3rjlkrOo3wWeWG raIhtFnuDlNOR4Rvm5JqlE2hbVG3tFt5PwJ7sGu1Bg9aB9MLRI2OI3TfWR+5 /hK0Nldd0jQOQOhxCR4w0IXWhj38BfvSEAYV31iZtkJr4z69BIuPCKPNS4vJ odDaUWNRamyC8F/8ASkrLWidDtT+7ncSYYpR4rurCdpUHS9VthH1mp3/54tj ELTZ0v1s5+1GnOeyOkM3EdpSpLKa1k0iClVs3Wp4CNq6T1/NPX0CUUTRb9Z4 C7CNtn/1KOciigWk3zNfBOx00h/vu42IEl+qCXUA7SJiYuMKmoiSm/qkKQ+h /Qwz4a3CGUSZIQO/rXuhI/pt1/OLpxGXMvaq79gInSuNteTj7iHK3wlt3i0E nc9jIp2dzRGVXD5u3lsA3YsnG/7mf0LUaPb47UnwbeHWw/SDixE1dRMLj34A rndS18qIFESt+Cf7/G4C92uW+YSCKuIG5oIXIQzgFZ8vGKl1RTSqzL4QnQo9 Xi0dx1RUEE2W1+nHeUFPe075mdd/Ec0Ch/lJltBrvf3kpnhlRNQzoaX1Qt/6 2fjQHeKIrNyvEvmG0N+xOW4tvRjRWmjmeZE4DFAO3XK/QuCx3VXNu6QDBu4u Ppe3yhBxt9KxxifhMOhRs3nxOw/EPUFXzz23g8HXrCsyPCIf+6+Vm6q0YUiZ rpz19SGiU6JEct0XGHr5OHxJ51JElxFdcn0eDEs+vdH9SgrxAMvuZ+MpGLa1 K/20tRPxUN7ZO5+3w3Da/g8/3OYQDy+4s6dFDYZb7p6c3H8I0cP1vWjrBIxI ixe0jDYjer34Ud7xDkYoTtL1f68gHl2h6MHJhJETF2bi9BchHg+G5T2+MJLp fPFt+ypEvxb3hgEqjLxsufP4kBKiv37c6RFFGOlu2fTxEBF/YOIjnbERGPlt NZZfexExeKSt82c1jC5ed6c+YD3iaat5CVOXYXSFh2TOQyL+s/lr4c9hGF09 jx9bHYsYKrxt/J8pjGr+x3+XQPRP2AH/2wukiHN89aBKAjHix+7q36IwqvS3 5aX2NGLUBf1OwRyMLtJdflgyEjFmyZI/PZMwMjEUHfO5CjHu5o/l7DEYaaXt bZDKREzUbdrSNAAjZc79RabHEJNf3t/zhgMjcXaR8Z4HEVN2JpysbIURZzsv D6kexFTO0eSHTTCidSnk4lHC3zVf6/t338GwQFXNpakFMXPe+sbslzBcskOz TrAZ8daqIYmYB8T96sxu0LFAzLlfvza0EIaang+uUyMj5sFdWiDBz88WZi8/ QkUs2n/4wsHLMPj+QFPdvzjEe2O0Gw4xMOjzcu+/Z9mIpedXv9hO7NuF3UpP JwWIZdm836YnYcDyzsceHmG/umu/n6wt9MVnJdGfMxG/Oju41igAX/7eZfc/ NxC/jW4591QKeEV6u7/76CG2nVXILBEFHrluYiMrFbEr82vrtSnghuyIxgEv RK7Oo8nEceAqfj11vKAJkV95Re7iAHAqLn2rOpKEONCxa6dfK3AWhY2pZRB4 DPvoHjvSBN0ngnNTRboQR2el45zfQdc3dd3X/ncQx2MFhbtfQhfZrbXdQBjx 54rGd6xn0Fme8fyuJ2H/V1FxHzyETiP5kEESkf+0WZzw5kLoqFKRPNOhgDjz 3lt9/W3o2DERX80j6vd3nxWqEfxxuElJZ2UN4r/hdS7LLkN7ctejHZ+KkCR0 WvzM4hhoJ9N0tF6UI0lkYX/GgjBg/121sl/2HpLEMt6U/z4N7JpzvXLBZ5G0 UDu3Zcwf2JeP5svJ1SBJ8nnEz14fgg8P7x844ookaSu3Je0Ev90lmF4dGYqk JWzKps9OwCYdPtj3+jSS5LzUt721BbaZUe2jOAaSFGbme7+wBjaYZ87bpICk 5Zc4lx5Rgb3Nep7RGS6SlJdX5xdYANtdt/mnOyJJpSC77oYRsCPvLzYPqEaS mslZ/lWCfz8Imzsw/R5JGvXO82M1gd17IM8iVxdJmo7mqy6oQLvab9Fge+J9 rU518ZMl0O5+/WKKrQeS1h8Q+3HYEtpLJ7/nZAYiaUPPKNvhI3QsKPryddlS JOl6NNdudYYOZzsLergmkvRHnhVbjEDHi3i93qubkbTZ98ZV3TPQqcGPjsoh 8DMN9j4inwld8zXHz7Q8R5L57K6d4jrQFaSlFxZmgyQI3WI8UwFdP07IDXhy kESNXrCwmw3d4wKxdW/UkLQ9LaOkcDlwRb+KffrUhaRdK0LTMu8C11qm6f1W Ao/dNw6HJhoDNznoIr+oBEkO+fo2/g7AW9EQQ118HUkHn9RPWqYCf+3c9iUn vZHkbnq/W0/z/78vtggtlELSkaqr7zSeAD9v790C6hMk+bw5kCHeAj0rPbdu fCqJpONWjLA/btCzp7pqevdGJPl93OA9OgE9sS+kNu2LQlJgy2+LZjnoGV+q s9lwHpJCHLs16+5Ar4rmi+1JrUg63Vkn/dQAell2HU7zw5B07kDhdGEN9PpF p2+4eAxJob1J3Cwb6E29o3u0ogJJ4Z6BDYk86H16Va1Kl7B3cdTpUdgJ6P1u tTF/oQmSov3ImQFC0Ptfmbt9ExVJMb+0Lh5Jhr6FH9RNxq4iKT5E6theVehb FSXt8CocSYmzE/bWpdCny1PNLb+CpMuhbCRoWZ9lR7w6wUdJV4VfrtNrgj5W 8OtZIxEkpUXnya52hb6dlW5FG4j3MyRj/yiMQZ9t6Rp153EkZSb59Uicg749 ++u1zX8h6Yac/YdZKeizYYvNaK5H0q10izJBNvRt0w1Yn7AdSXeUNbK5G6GP GnRYQ3wQSXk3JaKaq6DPuK5lcfZWJBVojPm+2QZ92ptJVxSKkVSU3+L4tAP6 FLmJ+/kEviXrK8hFPtAnPECsoMtIKr1/a33WLPQKvE91exL4PjKIkkuKg96W 2Nj4eTeQVFbu8zdcGXqfH13axC5F0lMzm76AIujN2vzYofMvkl5QVJ7ubYBe 5+U9CY27kFT9VvjWtr3Qaxq1u/hpKpJqrIZiYAh6Fc7Dkp8/kfTW5onTGgno aaj4bvGRmNf6IitYTtzveSIXpaarkPRhQZf64gboCb1Ji/5FxN9UJtI/GQc9 hg4TU161SGIv23PynRTwrz23NqM4Iqnj+KBd5THgnzz2fs8kDUld786alH4C /raYVkfnH0jih9z5dy0J+AuC/tOe00fSSPtELKH/eSH73DZ9JuZhzDD62P4T wHOMinCdIeb5R7zyrt3NwDOd6rijcRhJk5bUZeYpwJs/NRy4gJi/6dTvM7q/ gNtfXMl2oCBpZsy7c40dcD+OcacPzyLp380rOVLLgHvbPme3rROS5/9eGykU DNyEy83Xpo4jWXhXhcdkK3DPXDpZ1ZyKZNHCHVuHTYDrQ5Zce9UZyRJC/I3d GcB1fmVSFyaL5EX7gpY0zwB3l0SJ/2UFJC9+vHDi3T7g0pUN1o7dQ7KMZPb3 ykrgmk8VdY88QbKsu/7zB8rA3Xxt5aH/ziFZ7sWbrP///6VNfx4/8xBB8jKF vaHXOoGro9i0Q0waycuPCQ4lWAJ33cDCLzcBySvehtHDsonn/Yzcjikkq6gq rAucA672iZe9Wl5IVg0ulPTeD9wN8gr/JZgjWf2zxdj+l8DV23IgYMkOJK9Z 9/mLrSpwjepJFRULkbw2zL2MGQpci6eTJwISkbyO/TvdnAtc2k/fq7c8kKxj EH9ajwzcHW6eSfQtSN4Yp+ay5jZwHSdPHa/VRrJuTxlJaQFw3a8YKkyUI9nA grVayg24JzSMHUd/I3mLwHdwag1w4yvS5zZaIdmUIfxhOBK4mcmnkisNkGx+ I/1+dx9wi34NbC7LRjJp58uAd8S++zDP8vijGiRbPZKZn6ENPKl1xX8k2Uje tiinJyEGeCqRh54285G8w23L27Bh4G3MXDjup4FkW/n98d7FwNsWp7pBhotk u6M/fV0lgee0We3YWaKeDm+idtseBZ6X4bHAFjckOwfdX26xEXjhHaw+/gCS XYX+eGo8Al4So2LRRCeSDyYwni00Bt71yFvPdj5DsvvyFPHxF8DLDZP1fkPE f+QOx+E7GXglq/8xPrYj2UtX5+4LQn+VOe0uz9+GZJ+K4Ok71sCrWLb4g3YM ko/T65gxn4FXTd9D1buBZL8vS9L97ID3qjNJNZ8493d2HrBvB17NF/+Sxa+R HDhQaGzpSjyvVBR8GkdyyMnJ6NW9xPfl/2zPySH59D9y6yIvwv5N1aef5pB8 LiZx7Y8xwv/XnzG6BN4X5NuDWv2J+Bx81QOYSA6/tfZt1QwR/yZZ0oFuJEfq +C/LPQ+8jP3f88NskRxd/vJInDDw4ifMmqaikRxLkSw/QeB9flr3S+0HJMd/ dBRzlAae7yl5V+1fSE5yzLUHYv72x9ydV0rU63LPj3zN5QT+5lNhhu+QfNXX YkoyG3gmeTvLkiORnBH5LbXtLlHPOYnIbTeRnCWr3vdyA3Cn1Y7/uq2M5BtZ x43yHgKX414eTN2N5DuPxb6dJOan5CM/4shlJJdwTRQWNxHz1c8+qHIfyQ98 LrpP7CHmpX6ZTTIPyY+mPpex2cCVHRiVQmJ+nkl57ckn9C4nfORBLB3JFdee 5CZ4AufdjoQW8gYkV60R+uUvAE7pjAs34yKSa82uXyVNA+difIngRS2S6970 92qdBY5f3drOtEVIfmdjuFlaCDj7Qw69/2SK5IbO0IhfUcDZ/mnis9R7JDd6 fPjaIQkcGLV7Y2eE5E8Ty1fXJANHf6LmRXMRkr+cd/cvUACOlvgCX59lSG5Z +OB1YiZwVjGsPv2kIfn71Tm5QDXgKH4WH1lB4M1Ws3JzygOO3KvX9guIee8o Tn1M0QHOUoMJw6lGJHdv4Quve0CcQ4daKAfJ3NpNtjJGwFn2x0t2y0sk92w/ nTNZARyVUwrOk/uR3Nf29mcnAmfthzeL5Yn5H3SXo9TWAcdw/v2rxuuRPDzu eqXQCjiU1WTVn/uQLDhdzE/6BJw9FtIXVhHv/xD9bRBkCxyvXU/5xkT8Py/T wp3bgBPm1uV+1hfJkyuTm6kuwMkKXJJdTey36btdGtp8gg9feL9hHtFPfwy1 Ty7xAE7HhUP75B8jZZ5VrWzXCeCu1Wtb5JeIFKHCF05v3gF32/BXkt9/SBFZ WJ53XwW4QWLqUX/HkSLRUGx6vgG4Tct6j1b/Roqkdl6EB7HP/vEe+Z+qR4pU zM2POwOBp/csW3hsK1KWWl09qKZOzK8Kw2JtMVLkCxOLJYKB98VXYbXEWqQo Lrz068dH4C+yY049mkKKcsO5SzWngH/Bpje0YSlSNK2OPDj0GXpCWmTey9cg RavwwB9rTeh5dlO61lEUKesXOtEMz0DP7xd5kr92I2VTw842ES3oPWVcflaH ODexMp2XFwp9QULDHj03kWJWaGiV8A36Hlt+SH5I5GO5cGNKkA70je9SGd5O nJMbNLQYrdDvZlKzt14TKTTtlSd0N0L/9Qe3H6cEIIURs6xSMQL6P1MO3b1F +LO2ktwxqAsDRvP8tnxajJTthaLXPkfCgNvXs6LFAqTsWjiP/4zQ08lym3Ok tyBlt+efDbf1YeB52onHN22Rsqf+V1BMNAxwtzw8mFaNFId1Y69OdMGgcJPF hj4bpOy9NLRonyEMrrFk5my+ghSnwZ49FEJvkQ8o1k3MIGU/q+vGeg4MOimp KiRGIeVAQeugnBEMntimosBTQoqbRLP+bBwMRnDM9VRTkHLYs/FMDw8Gr1Rq 9YX9QopH/ds3jcYwmP1BT2fvJaR4r6uRKUuAwdw5++5LqUg5eqlyb1YPDBZQ vLe81UPK8cEndyJNYfBuouKa7M9IOcEqFRxLgsGc73+iHpQixb+gyNiuDwav L3uwxus2UoIkcsPAHAYTrDj7F91BSojnjQ9rCf141o8eUf8HKafrMxSkB2DQ I77q3fkhpJxbl7J/2hIGd+Qxql13ISX0UkIBJwUG9V+Legu3ISVsMPrnuyEY XPKTXrt2P1IussItHiAMjFJCKmybkRJVcDbqWioMvH6r+2o4HSkxEkGfL4zA QHqKQnmNB1LiPE+s8CLDgEdN34GuLqQk1Pu426TDwObjLg73VyMled3h+6YC 6J97qn+K2YGUK5dcf2tQof911Uy/EQ8p6aw9cT/HoJ+R8Taw0QspGVl2t1o7 oF9U0X1PG/F+5g/7Jy8aoK9G6ldwoSNSbl1z5ETdgT4DxZ4FCzcgpXDQxXDF XuiZE+wP8jyMlHsW+1nzmNBT4gciFXNIuZ/s6ty7GXqc5wfs03FFymOTg1H3 ZYD/VDfF5PI/pLy4dLidXAe8oyzHe/mrkFLdeWR87UNCf9xTWrkyDSk1ep4i kjeB2/B3d0/EM6S8afPe+O0UwUfs1dINDZFSv8GHUkHMP6etLIlPzNf7C0cd bu4BTqqpp9W3FUhpWnc8zEsXOGKh2pH5mUj5ctY3bYcKdEfdnz9+UxUpXz/7 FRsugm4RntvS0kmkfF9z4pXiNHTFiHxLM9FHSlvIyZa/fdAld8E+LcYdKe2N /kO8ZujMfWbUqyaDlC61gH9vX0Kn5eI3//GJeeMEBMkVl0BHZ7uM8bxepPDq g9clX4eOi8FRpPoKpPSuDLEMjIaOzcZ1pSr5SOn3O7V7XyC0j3rS96yQQ8pg 3WkPPATtJTarS2ItkTKy/MzZNTuhPXhz96jnNqQIjp69vJDQl1t3qb0wfoKU 8Vfn8sbWQ/tadvTMMcL/T/nzFV8VoV1KKDjCZQwpvzxDm56JEvr7vyFbs0ik TL240Js1AeypT/R/94n4ZpaEzYRxgf3nMakhhMB31j1C+shHaJcof3Zk5Xqk zD27uNq6ktC7A1nhJ+chdf7iSBO9Aminbm8ztQ9B6oIDUdsVUqHdd5gcarAT qSJl0Qf/REB7fnlCZuBCpIpLXAri+EH7QN7WqsNfkLrQOSauzgU6DG53l/+c QKrkg9hbhdbQEZ0c22ufhlRpkbgniSbQ0b8jkJp5A6lLHOPf+2tC546H5ZW6 c0hdei+B4ygHna9ijsd+T0CqwvzEX5bzocs8u73bLwepSgWXV4l1QPe2s8p7 zigiVfnvFYORBujmPVnvLEP4X7Urhfm5HDgo4uQuN4RUjd+pJ65fBs5Ak+bm XWVI3cDKeLOJAbyDFRrrO3YjdVOTLVOU0MOfV7hpDW5Cqr794vpOL+CTKckn ttQjdYtbaGNsAvSsv7Xy9iMKUknnD3/tJ/bxkoUvGy9WI5UqqmpXNQB9cSaH HgVoI5Ue19Z6dQb6xTydNxRLInVrhnUHdSX0z9o6zsZcQeqeMn3+TTcY/CYZ ukJmGKkOZiPuQYEwxJifubgnEal7a3L7t0fDUNkSn7MkVaTub1Icmi2G4ag1 frmRAqQetPviQ+jD4aEL56umCftunbGCwiYYYd0NDx83Rqrn0Nx/Dj9hhBCb deCKVB+/p/66wjBqNZLs5dqI1GPTfpNiCjCa6jx8UuItUv3Orw/q0oLRDrOE p0pEfv4iPb/LTEGgbPV3zL8UqYFx2afjrEFgZ7dSL1MLqSFL7f8ecgFBzPqD C9qnkXo6Y8l5U18QlGf9q/gmhNRzqu/nLwkDQVekjJzTIaSG5keEDaSAYO5l +cfcZqSGb7QUrs6DMUVdhqWJMlIvPp6KTH0KY9qVdrfss5EabfZA/GgDjBnt 6lpv5IvUmBqvGGoHjJnyWXcnbZAaz1otuUIAY8YeUSXzipGa+Kkz/uc8GNvY LWds/Bmpl+3SpBuWwJgKa+4lMxKpKZ07k2+pw5jog+ESA6Jf0twWLg02BMHA miUb6YZIvTZUe3UHHQQ15ZsOnwhDaqbf2WWaDiBIvRAhI3YMqdnTRul/vUBw 6Pazsy8/IfXWuXGlr2dAoG35WWleOlLviBRkFiXA6NB5CZ//iO/z4g6phN2E 0TvJ1VWbViK1YKnyTceHMOpQuM3TfBtSi679/5c7GBWbx/k3Z4LU0nzWmq4B GNm9f/qBEwupjzYK5ZfNwPCPsaNTZjpILXtcuS5eEoZjGw5eyCbyfV6zaYPZ Jhh6mElcRwQ+L5gD92URhtBkj5AkUd/qT7d1B21gsMHn8NZVRD+97pQ3TAuE gW/zz8sYSyD149Qfs59V0Pd1b33Tbi5SP+8+WP7hFvQ5fvujsYSod3NJg0Fu BPR2/c7ZHknsg1b3a+vtWdAzeO6KrdFSpHKbtyg9/wp88RQ1npIXUns2Zqde KQdeyrZzVYeIevbFiMr6ZABP3WyxncEJpA5jy0IVQl/S+qymJ1cgdTTT/OIU meBzJpRLBN+hjk3dmd+0GjgBQ2nqZGLeJkpO/L4wBN0ev8J0lgkjdUqCHbC3 EboePX2wK4+P1N/upB8GpdAlRuYpL1NB6p+XBUclL0Onm7j1VRdinueUZQZ6 A6CjIbC6k7kBafOCgt2qHKDD9Jft0d3xSBP60s1JM4X2sh4NrQ06SBPZyHDy XQntZpnzvVYXIU3sUkkrax6wPxwjW1tTkSbRq2Crxge2x52zTHkK0iTx7KeZ N8CWCd3rbhWDNKnrvVub70LbazM3RT020mSmrN8Wx0JbhGjXD/depC21eUy5 eAzadi7KN3/4H9LkS1ZUu+yENi0vufzW20hTlAg322IAbYtNekRufUWakttQ uYwCtM7FrLWw+IU05Zc2+gO/oXX2gJmfUTDSVq14VvKqA9pEK+vcXi9GmlqQ mnZGFbStTH6sdncYaRpfovNO3oI2/MDqWEbY09wwrm4dAW2+O9cvvuyNNK1L 9tlrDkNb0fTJCtk2pGn3VC+fY0Hbz7Qv31jzkbYBNa9+1wE2fbood0MZ0jZd T1hSKg3s3E4LhZo6pOlN/oqP/g/apf997A/SRZqhjbPEga/QflG25+S+RKQZ 3Xt90bQcOoRfBcjoVCLNREJn/tIM6Ei4v65Owx9pZm5XzoycgU714Jv6MVNI wxUH/bPJ0OU+Vtu9JRNp5MD68aA10C33RFZ1N4Ef9Yuez05x6H4fs3AEzyCN dWme2/xG4OTncUgBc0izmcze7U7s7x1jHRd2+iHN/TNbX+cY9I4ds5e7fQ5p HvtnlKYCoO/YycumN4SR5iVQWlBzBvpGLt5iRZ5Amu9CxxaHWOjv2XJx2TY+ 0k6kh1RpXIYBZ6m0GMcHSAvQvJYnyICB5gPZdIP3SDtFbguOuAuD5fOemb+N RtqZpt8HdtyHIY3UleQowv/5/cutlJ7AUJxpwdE9tkiLOO24orQOhnceudRs ReAVJREifPoDDBfZnkoj9jPtUlr6KP0rjMxXYh68ZY20hEetVR08GMnU2t89 44u0ZNJ0fv4gjHCmGT2n3yLtSpNi0olxGF1Vmpcwpo20VBfjEItpYr+ovbyn 64m09BGHg+L/YDRGbcWJyjSkXT8VbPVVFEbLLl2Pvkfkmy2ebnBDCkbZZjeC HH8j7WbqU2UveRidXilvt3g70nJWt4psVgaB9PLYBwEEXrkPpwTzNECwSt0i 6ybR/3dJy7691waB1sblI0XhSCv8tKU6VZ/YhyZ1USFEv9xztr97wAQEaxjp Q6rpSLs/HJSsgyBYvj+9flwPaQ9D0kKmmCAQTfQc26yFtDKx8oM1O2B0ZAh3 iRDxll/9vjXeDkY/RNdfjCQj7bnGlKGDM4zmXpLNUnNDWuXDZSs13GA0WMoz XCUKadW4RUTgA6NUkun8/qtIe/XRTvDsJIwu3N4XL3QMaa+dAr9HnIKRBs/M ySPE92+GUl/uCIORiHdrjW3PIu2D6Lfk3iQYHiQ9KbBsQdrHlMlTpWkwfKU7 1GkHgcdnDYVDp7Nh2OSNrkgUA2nfwG7zknswFHy9eYswkR8nqKXVogEG3lsz f+qLIY0v8uuV+GcYcJj5LK14AGm9KfIFzYQ+60kOYyZeQdpQ6Z7Tnv3QNzn4 Wfcckd/EQItKqjD0ykZ+vKi3BWlTgb/EDiyCnmvFX08KPJA2IyI3riMLPWq5 5J+RRL//U7N9VaMK/M2BT50+vUO6hONXN4EFcAMVdOOsbJG+qPI2o0IBuHKa n5mrNyNdapWvdpQAOGW64jGunUiX7V00rpYNnPkyfxUUfyNdntH2RRAI3Q53 fazXziF9WWF+WcV26HrS6HOjgI10ZV/Kads56Ix1SVralIt0leYlLmrfoFPI V2PPIzOkq23uRgHBh8O8qk82uiFdI/2eRkUkdCxK/OUeGYP0NX9Oi0a7QHu2 kt5+tSGka7kwB/cYQbup+fX9wtZI136l8EFdCtgc9b8nbpCQvkGjp0TQB+zE H/err00ifVPkw+SKamCz6mIczZSQrjcY6h+dDuzFjzp6zyci3dB6u/0eX2hr b/CRKSf8G91XNlVnQttjaf/6n4h0kyVDymOq0JZ6ocxSeADpZv7l/yqmoS1c 8XSY8yzSLb5f5EU3QVtI7bOOwttIR9PddXuIfX7KRzXx4XWkk7PU7qqHEvv8 p8WShp9Ip/4bixlzgLZrBtlxM8+Rzjj44milLrSVz/sXMR6BdFZd7M5L4tDW peV/6s8PpFtrORrs4QBbyveG21nC3vbYtQrqT4HNuNb/+oYE0neOTkyPJQH7 kku50UNfpO/eWdNe6QHs5pPPDWxkkb7nUVLVJRK0r4le/d1MC+kOCi637JSg /Tztz/FpQPreEJ0IdWIfc2xcEp93IX2/5TtW5W3oePL1mfrOJKQfuJWqc+kU dK4/7zD/YCXS3YTdpO1sCH3DOzcwR+DrUf/v67gQdBXKl/VsOoF0b53G8ko2 dBvMu/7YYiHSjyZez7j0ELqr2b4V/wUj/cSeLa4aB4Hj94JcrJSH9NOcY8N2 tcC9He0qlJSB9HMU848amcAdX0p7KP0K6aF5C0vH/YFnca9ET0wO6Re98wJj VgOvVU3Uzmwl0hMmu+a/iICeBfm7rLOJ82THop4YJ+jZuvP6fi8fpF+pDHlr bwg9yQzdGtM2pKeHyceN90DvMgdBh68z0jN6+MdfVEKv/bJbV1PSkJ7FeGAT kwK9KV7/Wbwl3r+9eJviahr0iW3qj1Eg8rnjplY3+hj6zGxTH9gXIz3v+eTJ 8tXQ53MthFr8COkFS96rhaZAXwZv/fW/65Be5HHjk5Uw9L2R4xjcrUP6vWr/ s0v9oU+g5MOZIPq1VIGl3cGHftmuPey2eqQ/PLqyNc8G+g23f7vMeIH0x7X/ RR6vgX4bpwG7rL9IL1d6a2iiD/0+s/TXcy+R/szvOk/oFvRHaJwQensf6RXv fBM/yEB/2vuSOutPSK9aRbO4Ggr9+T+EtfMJPF8GLh92GYf+x5e3eK6LRXrN B8E1rf3Q/6K0zqlYHul1GrX0Hx+hv3Zn28w9I6S/PZU2UWEJ/XUhtPKKeKTX f/a5HVEC/a8NfIfsApH+QYu0czuhT6pjtxmPXkH6x/Pyc8vioL88kaa+jpjP ppahIs4s9BfvXFeq+AfpzTrVjoU+0J/FVfGgLUB6S3iK2Ml26I+zD4/97yrS v7M9yiysoD+ooevFeuJ9tp7FIdHn0O9qW6NirYD0jmhZmSZt6KfLPBQaHEF6 V1df1bVr0L9OWcJKfDHSuZsrfA6JQ7/E1Y5TB74gnR+XpKQTDH392e3P4/uQ 3st3e/uL0Fu1jgbVQc+QPmBqElBtD32ZA0c/HCLmc6Sf99nGCPqslj/ZIKyK 9DHL8vMr8qBPRfzURFoV0n9cjdPplYfecXfWUDjRr5OUzdFBE9CbvEhWl0X0 27/saMh6CD1v4g0D+WPImP/LafSIOvRc9tn7JOI0MoSt9a7rJkOPi7D/a1Nj ZIj/Zk/W+gL/l9C/M9Y7kSG7Z8O9wU3A14z3vhVMfC9XLLTvYTbwJmLWd11w QsYyoe8SZ6SAV3vC0ZH5DhkrSkPdpQTAc7vkHXxcHBkqYntkW12AZzTZPWdW iQxVl3UvbzUCb+GHxw/KCf9rFjUrGxYDt1z5yaL/xpGx9mB+/ZwycBPfSaZx hZCx7tmZoLexwPWymNp+uw4ZOjK71iTNAJd+2bbFKA8ZG4+sad7rBdzV3xey H65Chu6LmQsabOCKrJg9WWCCDAO5TxtHWcDpPy60PecIMjZ753Q8eQqc9+09 dU7zkLGlJjgmVAs4D33sP0xYIsPUWiTXrBI411janx+lIMO85XL11E7ghD1X sncnnmH/KvbDHuAcqzWsNgJkkAaKfx0LBo6ze1GX1nZkUE+YymhLAmf7jU6X MgIf+p+32r03gUM+6iLxpQwZrIu2tFuGwDF+K9YvewMZ1lJcV6d3wNHLWaT6 /jcytqcfO63oBBydX/NTQ8uRsUvtz9XmceBol4iIrJJGxu6i6NLECOK58m2h 8XJk2G2Wf79VETgbFt+nduYgw6Hqdp9oMWEv5j/b9EBk7GPqzqtBwp9yuX9V CDKcP79YcfYrcLDAqNQPkeG6z8rIxAM4W+U6CkqKkXGw5/vOiVng2JOP+6yz Q4b7MTfv0iTguC81rJXiIePI1I9IH0Lf+B9Z3h+6CBleF87dWkvgeVG5w4kh jIyjixZV8K2BkyZr0MAg6nE8Jf1bNgc4hRqzpcxRZPjnP5JUEAdO8+IlYasW IiNIDzU/ZwJn4Lrds32Ev5DnjaR4XeDMPXYAhxFknGvsDxJ2AO76n7ukfq1A RvTECu5/BcCNfHA866sHMmLP3p0tsQRuZo6Q0y5DZCSIGS3z/ALch6Ipn6jr kXFFaac1h+gXdkFwXSYfGVdzOo9kJgB39L7jVe3VyEjf4BXmoA7cf48zVvqT kZGFEeWfrICn6mzGZm1Fxo2GJV9iuoC3sSWo1EodGbd3Z4/STwDPbHL689NY ZNzp1BEXEgUe48GxITaRf/7hZ+ovCL24a/Lt8kk3ZBSM0y1CNgJvb4WZjU08 MopDmh0Ma4B34Nd+uQTC/v0FrifG7YB3JHvd8YgSZDyIG40vGgKed+ks5LQg 47HCqbtH/v//L9ffzb0shYwnN8VqNWSBd1zK937nRmQ8W5fS2ZVHnDuJGjnR kVHxSG06wxR4PssjB77/QEaV+f2lez4Cz2N7wNXgp8h4+cZ845KDwDskXFri SuRTu6Oe+WESeE7Mw4qlWsioa7M7FB0DPFstYZtlfch4d5B/jqoCPKvHWrOv 9yGjYcQ3/d9D4MG/uB4WFRmNAX8fVdCBZ6gymlRJ9OunfzEfA9nAW6s1cf49 kd+XS8sG9Y8Bb7mRsoqoMjJaZHMXCISI+Xe9nCVyExnsNdUm7trAHTo71BO6 FBkd4TPb1ROJen1y/WuyBBldvM0HuyeA2/Bq979pYl/ws4vi9lYBt+DH6XxX GWT0zvbdUtQA7rXDhY3rXyJjYJ9aWUs0cKOHZB31if0xuiyte6cNcD0ShGTu aCNjLODLxOJy4DoMGDTMBiHjx1cp8ffEvmHVPhrSeI2MCX3WyugLwDWzMDO/ uwMZk0kR+rQ+4G7wg19NA8iYFlQzFmwFrurpbzPJGsj4Yz2z72UpwU+Drx5W Ivz/Ldrse04OuBJhJw4+NkLmPAnfi2YhwPn3qOCk2UFkCh0pujbdBZypNVks nxJkCtf1lTwhA2dsvIJ63QaZYqvVak/mA2dIZ/+jv4eRKRHm1KpH7JNewfOp 5a+QuYiTNiLwAw7fujxV2wyZUpbN84u+AYfnyPU+24NMmSwpBU8z4lxr26RI KjJl/7DWaxL7p/fdxRFTYWTKOxLkRISwz/JdE96CzGXl1ba3vIAz/tRibJ07 MpXkZzxdPgHnt6oDJy0Zmcr+m88pGwJ3QawNKakUmSpffC+3XQOuzLyvS09X IVNNtyg/dQ64KpFXGnNqkKmR0Fdpewi4m1Tp7DeByFwzqvZ5yTvgkj6l97zX RqbWVqfeTxuAa5ei8XtsGpnaBWkzcZeBe9Tb6+qqcWRuEGuWZk0R872zOTks C5mbDkutFnMC7i2Lu9KFScjUe80yfv0KuFXrlu6Sf41MQ/WIbWGawO0UXx03 64pM466ZwNkxYn69hfxFgpFpZrE59rkt8Kgf4yMKryDT4rrvzaCnwPPqtf5s uQSZZPu++v/CgFfxTcpXgcBja1Oz8oAC8I/NF7X/OYTM7Zuk9HJPAz+LOaur IYPMnfEs+kEO8Bs9a49ud0DmHlb18c5C6NlU5JYpIOJxqSmqabaEnum1e/Y7 f0TmAdW+70k50KttnETzJep36LzayHZx6N03Ybj89V9kepily7/7Ar0v3qq1 /VyKTL/HER5V7tAX6KzJF9dBpj8taOioAPruvO9JnEhHZuA3b5+VBH/4bLum 5+gPZIYccRltnA99f16XP528gczT0zbHz8RAv8ZoUWCKPzLPXaL90JGDftbN OBFXMjIvKJmcaM8i+Fppct3LZcgML9KZiNUk+M5/LS7LCXuR5qqBpqXQX2h5 YW0u0T/RjUunhkwJfnV0vqhsOzJjXcRCrtVCf/te3aD5q5AZL5j5w7KGfkFP ZP5Tot5J5wVnpr9B/9woHoB3yLwiw5276woDixyiMuvzkXn1Vkuo/SAMyC1O Twk2Rma6fr2Q2AkYUJzs0SxTRWZGbWX4k1kYUBLa5FZxFplZtqUihyNhYJlq 6drQbci80ZsTpSANAzIMzegOK2TeDkwTr0uHAZGjP9Xu5yIzVywmJkAd+ieT 140/JeqRn35OcnUR9PNLbzWNiCGzcJ1f/FdD6P/QNM/TThmZxc/dpcOroP/B ZE14N4HX/a0OyQYM6L+iixWln5H5oMNaltcE/b6xpUHHTJH5+CikXN4L/VbK amsXfkfmkzkDBRLBl1dNB7yMfYTMZ4maaT98oO8/s72nijWQWamqtPzmJPS9 WmD09u9FZFY9WHx953noizuxIr2a6KfaLxPZ9y9D3/LuxW+e5SDzzaEB1f3K 0Nuh/yzSbxaZ7ybab0vlQu/11qOfBxYi86NCTe7Rp9C7dIBUqBaJzNa9ScXr O4E/6nj/cesXZLKHIza0Hwb+zcO+G5YT89J5Juh+zDjwbe4UKcvaIZOX7fJw iNi/Ze/HJTuI/hrh6Ty7qwXcczel9Sx3IHPspKqZ/UPgGohKMnuJev4nvPSF GLFPBro9RfeuRObUmpmXh7cDZ6/0NXb5dWT+fiIgy7cCR35ymfaOCGTOMri1 dQeg+27neWZ3KLLme757u9ofuh5vr5J/YI2sBTOVzOY56KJ8s8x3bkWWaOz9 hvAo6GQnmZoOnkKWhHKOtYEMdAb/WkBts0LWonupjbwM6FwVdErc0QdZUpYx Oy5rQMfHiHRddiGyZD6d/Uwqho7I8Hk9G2eRtdTVz+aHEXQwvlytBENkyf9w +3qzGjpkPyxZGkjYUwzcr2bNgfaBExu3261CltIfx6O/50H729mrl3SPImtl 6O7nuWrQ/qBgpdiPdGSpimwX20WG9tx7T7573kOWegxz999D0H7HWE/jrzCy 1kiTbxZEQPu9o3/1U7WRtTbFfHRPLrS/8v5rXL4DWdrLt5jOf0PoWbqj1dOT yNLJ1ou81w8dC8X3JRu8QNYmjfXNe8Whw/Lx7vGjBB56dzVVRbWg4xwlnK6Q hCzDDao+D1nQ8S5Pap/JDLKMHio9c/GCzpU107fOrkCWyRZ50YUx0Hk2WPP9 nDGyzCqlbZ4UQWd/hue2rZrIsiRJ3Dj4Abr2/UqoHLJBFr5ZMCI1Cl1s63Or /tohi2I1Z1whBd0HHR0lwj2QxbT978tSgu8y1sGbZ6rIsmobVakm7pv0156b dJqQtc2l39v7MnAEER9cNZchy8ajQ7i2Gbi5KVv8TbSQZTv6bdfxCeCJepY5 nTmMLPsTTdnKcsS+9XvQofIDWU5n6rb42wHfTIi8o4E4P5xU4rWaDT2/RmMC L+cgy1P+7pOmP9DrYZR66Fo5srwzchacJeahnfrGhLkeWb530jJbnKGvxn6g KjwNWaeehn66yIGBmLrPwWVEf5y1OK2sPx8Gft/fOagajKzzNQEeXeoweDhH 2uLVR2RFfPASMnKDIZPdVhzqPmRF7XLbzrsIQ9keLO/YVGRd+uZyPSEPhueZ LIuzFkdWAme3YV8/DD/9el7ySAmykt23Xbgi/v+/hz8T24vIujLE+AhaMOL6 PiUzIhBZqcfJSsMsGLnX9rle/xiy0ifMj6R5wciv/7JEzrkg63qI0WNKLIwa t04phOxGVtac3ryxYhgN3H3fXOwMsm6Gr992vRFGSzakDu4g4s0RX5PBEMAo Z/NDZ/sWZOXGr+qbkAaBJLiPuUgg666sksFNXRDoq9/oNbFEVmGaXKj1ThDs qpgrbjVD1j1lqQ/TfiDwGvlpO7sFWfdvSSy/cxkEZ69zVrvuRNb/KrjycKq6 L1xEpEFFg0KlwZwSUrKWiO4ZRKRIJCIiKlNJUaYyRCmlkkxRkfTVF0lIvgz5 yXzN93Kva7qlpL4Uv/P9uZ+9z97vWut9117rOc85BetFXS0KQHj5L+1NpfrI +uvRn4LfjSC8uv4cN6wCWX9v/DGdPQ7CWMFFfXOGz4WXU01t+CCMeF5X4p6A rNd9u2NntYHwTHbPJdtyZJUYfGksqAaha017Wv4iZJXevC13+DUIzdcK9Wwj kVX+xchp/hMQbiw1P2Emh6z3rMGHxSkglMrTyk3RQNY/6QlCj3gY5Yq8ayjz QFbVn206yy7CaEHLuit5BciqteEGVfrC6HkT62pbY2TV5V8p93WFUROv1Sfq GL5/mqMtueYAjIr5Ll8g04OsRueOPfVMPMpCTkqxmf2a31y6eX4bjAQWjw6L LEZW21K1LnV1GFHb/2GBA2NvZ/U5jyhpGL50StT9+Rlk9axd+0xXBIZVDk3n xl9DFie49kffOAzVhJqFkbuRxd8kHw6tMCTmGjZ87CuyPieV3P+RAoL9M38u 9qpCQuTojGY3degv2E7/EZuHxKySnJWyCtBv0KUaqXsTidnLLI+8k4a+Dw4t F8pZSEjVPPii8A24fZiUNO8eEvPXEbq1/cA9bbhLc1k2EtLnvwafbQGumM0l n8lwJGQ3G89pKQLOplld43lvkVgaPWwZxvR/NZriBakRSCznXb+1OQV6jyX5 rMscRULhVv+6uFDosfeb+Jldj8SqrzGeBqegO1eokvvmIhJK1JaCIRfoni2c wU9qQWLDdDia7obO1j3NKMqMVW01Isa3QScd+X1QwgUJ9YLmj2nq0PHxm5v8 RRUkNs49L2OhAB0HRryUxfWQ2OS63m5qAbQLK5dFl19DQvtt3YMnM6D9aq2k 9Sx7JHSX+wtsv0K7wU69B69PI7H1tOLG2f3AHncpS71jhMS22n/8XrQA+5Xb W8lIeSR2rPcudv4A7KgAOasRZj2ELBNdWAhsl5fez8MpJIzYpay3j4BN49jk 2/NImGgfi/e6C+ydKvuCn/shYRor3boiDtjGZ0rHtpQhsZtfKF8VAmxL7Qo/ 2QYkSHRyCTgF7OMWNnk71yBBJ0s+XucM7IS3bRKf3iBhsd7D4f1ZYFfteHCs IgWJvQU1i47GQ/v8r1IzJPqR2AdqlWJZ0H7k4RGFO8VI7K+JOZNZDO0VKy9t LW5Fwm7/qMauBujQFps3VqaKhH0/3csTQMdTybRbgzQSjj55ieFT0Kn3Qbm/ 6wYSTn8W7F4vA521TYoF7l1IuFz2nqxUhS6vl98v7TFAwk22/qkrQvfyL+6n nJh4empeW5LlBT3Xtup7zHNH4kTR12rTMOjVjHkbbnUdiZNmVsH8ZCafn1tS 7eONhP9hGd76SuAEXrj35o0tEoEjfrcqO5n+uGf8X3GGP0GBLZTrV+C67u9f mrUWiZCEpOcPFaHPUVI2wG4XElfeyV3cEAg8FycT5TNtSMTuCdL5Jw54Axf7 xG+OI3G1o2PQLQP4HleUnV6eQSJx/J7Fw08wcCJTLfRsPBL3169SVFaFQeci lXPjMkg8KAhp+IAw2P53YFjgTCQyDDkRx2xgyNx7UvDPYiRy9qeNZl+CYdWM VXfjmOcf94s+2J0Mw9d8LIsvJiKR5+NiLciH4e+3j5osMUXi+eV1xcqdMPKk Jr4jeBCJl7IR3h++wugM43dXO0KQePVgQMldAkbpHxelqzqReK1p1iqpAKM3 ciqMjwch8aYo+0qODoy2rvH6Q3GRKDWTNGTqXeEizaAlS+2QKG90Hxs8AkLT 1A2r3lsg8d6xOvNyIAh990g/UQpD4p8RNVuVOBDeWbZfaHUFierAmLlVGSAs 6n71TrwSiY+zRkrdi0DYeOW+wHAGEv9LoH0l60HIm7Pd/bwvEg3yeRtyBkD4 2TJCI9UGiaZH8ztYUyD8amd+/+ZCJFp1veOGZEAoXLv1RM0JJNjl9TuvqIKQ +1eh21QTEp17tCZUEYT1Sxcef6GFRHdHQk61DQhf2u+ITp+LBMft6yEPTxDe SObb38hEom/cauGciyD0+syKi2TiwQ95XvHoFgghSKH3rjYSg/NkAok8EM45 GnjlHYHE8G0/taEKGK1rv7/tWjMSwnXNPVfaYTRWarCi+m8kvhToXlcdg9Fd G+Pzo5h89W3xLqxtg5HfLRK8D6lIfPe1Ep4ohZGnd1OOdh5F4pfuSVZBHIzM PrbOmL0Aid9JF35Y+8Hwk76HTocDkZj6GZcxYQ/D5p7zDaQskBQtejJjmyoM ReTNCYzKRFJ8RVFu50IYkv96zSvdCEmJc1UHg3/CYIHtOestDkjOM+S/LKsE QeOrXy/URZBc8m61F+sIDMzc5Llb/H9ILl+r9d/9zb88+uv3m2kkV4QbfojV Av6iHXpX2zqRXGVmr1Q/Bbw1TbtVk2uRVKm52X7gDvRRMfWZToFIqqtnRv5i +sn28KfzXrYjqRn7l85dD6b+Od/4yn0Zktp7PsX3/vf9ytVIOasFSBo0zTVz a4Je0ZC/VaIykAQdue+Sr6HHsighwdUdSaObKmmP06A7w7voiesvJI1/bLWg L0PXdOS+eM/LSJoeMP3z2Qe6nJ1MXll4I7m7cN/jhP3QWW+T7vO6BUlSzsVW 2xA6TQtjjx94iyQddFq8eT10VDYN/+OsiOSeztC/AuZBh8WIWX71ViT37og/ smwc2nnb0woeaiBpnXJ/QVEntF/6Yd18MBVJm+ncN/bvoF2D/HJ3xWokbQ8X e0wx+ZbjoD4mVYjkwbKaZakJwE4lmuckqiHpsIZduTMQ2B7rS/a8M0Py8CXB 6X5HYBtOVexYwUHySP+P1RGmwFZorTb1KULyqKnY/5Q1gC2Z9y63xxFJt4cy 56ploG06sMzYXQ9JDwkl1eOTwJ6hNOv32i9IerpvapvHBbbU9RVkArPfiWoM f1oF7NVZv9ZtTUHypNoebct8Jv/rrIsKopE8HXOo99stYHvNZftayiDpN+oZ d+MCsB9M6K9MlEUy0DzIQM8V2L0Fl3SzTZA8+/TyINsc2pV/kxtPJCIZLH0r KUgH2s9G/r2sl/HHhZMPd8mvhPZmkXI5nWwkQxtefH0rCh1bZS+pqjF4wrQr Ug8PQUf6kYDW79+QjPrOmcx4BZ0JcnUe83SRjCZ++BrrQpe0rH7oBQrJ2NS5 I5wX0HXjSLOF0wiS1wi9boUC6H62Y93v9XVI3kmJLb3F3O/xSVTs5p1I5pnp h8ekQt+2P89ivgYhmX/XfEpNAfrifyqf+bYLyYIxF/+qe9DHZxW4CRl8f9+5 ekw8GfrjRA5pHDRFsvBLRm/mMujvGX+p8sMfyeJdRbbGScDTsubevC6JZOln HhGSCLya8KbQXCkk35n8eqe4CPiywvwdcwaRfJ8svf1NAvAPfa/3ae9Cstpk u/qvOODz11X+yd+GZO1ti8zb82Bg3Y+W4X0Mnjqhq4JeDAwcKWoK/7gSycZb CQt8L8NAQ2hqxVZmvnk0K2qxBNPPG6uy6BlItu0snvGM6f+3nDS4oVeFZHvS pzMWYiBwHCtWUOpAsnOEPyYMA0Fkrn/Fi9lI9hj99ogVAcHjeM3U5YweOUkL +9RCQVATlRY0zkayb2TDweoZIBiIShRjFSDJNzJoOnYeBNNhNZk2W5AU3NxL i/+GwcV+clKrGH8ODbu9zwyCQSXr/a4Gp5EcxWBD418wuHHV4f2yb5D8fOPa S24gDOq2SyfXWSM5NpS9MWQCBreFbz/rb4/kOLzJVvRjxspps5cw/phIbFz1 ZhwGdT4OaOQy+evnoOC2/SkY1AgNfdqnj+Sk4dTCX19gcPXeJwuKa5D8k7j4 ym1vGFxotfe8uhaS04MqonpCEPxJmuqqIpESMTQMavEEAV+vfMTDBKlZ163G fYf+e3+/6uFDP6TEBe5ei91B8OTL2Mx0V6Qkd5znPROAINpk9++7yUhJXUt0 sHAFgavrIZOfZ5GaN5DTIuwHgeEZlyPn8pCSNni7J9YZBIsfBCQft0JKhj+E 1Ydh4EVVg5xDIVJLt08XHuuGgUtyCbaii5FaniC7efYhGDBfLT7aTiClsB2V TOyA3zXlKD6lh9Sqd/Mlmv4AP0Vea0THGiklokN4NBX49kciancuR0rZLqAw gg+8Jsv0DO9EpDafebqn6hT0vzohqOk/jJTOzGAdO1no93G7eVd8BCm9y6Tc 0CvoV/a89+QAs97gFp8vNQV9tyMCZB2dkTJ9pXDO/ApwI1Ye/Xs8BimmFXDq 0QCuSepsXtZvpMh/isy864E7q/tCx8oipCxabRYlLAFO9A5uz2o2UlaOSj9X FwLH8ln48BRz/j7+WFeBPXDkwrXc1B8iZTcRm92YBr3Pm8Svr6hF6tD5g3Eu u/77X0zSiwfhSB0WVzk9LoDeAyuaQsZbkDoS+8M2PJqpx+6YrznM+Puo7HtY ogm9EvI+UfsVkXK7d31t1ifoqXIZ17RHpDzWOc3R84WeqLpRxdM3kfLM1fz8 YSn0mMecpXdrIuW95XfzgSLokXvUFx/qidTJ4uqiwUPQPbppsajaBFK+JrdS z86A7krFDInrR5Hyr3WNmJMO3Zk+P30c7ZA6Y73l+B1T6I5ebpV4twCpoE4R S7VB6A4Qn6Re9CF13rletzgGut3XOFzmRCAVMpyykt4I3U7mRp4YhNSl054z uxqg29FNmPxxHKnwSf2BE37Q7WzmlX5UF6moS7Nrp5dBt+fbEqXzXkhFz2ku uPoauoPuL/UbMUUq9lr6rVUO0H31qfq9snSk4uVOBj+bCd3ZL3zXvGPsu5YG zkYZDP7Tp+N3SiN1Q3Xe7gYz6BY8nG+yYAtSSc86NI8MQc+COa1bbX2QStbP WfwtFnr0bY76lI8idbfM/98wLehxM3yUX8PY++DToopMf+ipzf0dnMPwP+NA 7yPd5dA7s/p4xdl9SGX15l2tLIZeBU/HlVFRSD3+QtgJRKDX7sbdvfvmIpUX uAwDM6H3TNU+E9XNSOVP89dL7obe22F1e0tCkHq5IHRMNQ562aOlLYf8kSrV LIz0WgEcu/VLLixk5sujdVyn44ATsP7+AglmvmLgmek1EeBcvzZr6QJGb1Wp OWIvh4BTbSjvnDeAVM2ftTyWA3D6Kc+5cqVI1dmmVnQ0AOfP4R8bVr5CqnHR rbAZRcBVN5vcMLUXqWZvGefrmsA1GpfOUWTwt9bEG69LA66NbVH8N0Yf7cpz 17xaAlx3D8lyiYNIdYZFiRCMPoKWssXOZCPVzRHldE4BN3p/i9Vyxr+cHRfK vE8BN3nVFVZ8P1J9tycfzOQD96HHoU+2TLx5EwEhiXbAfb7x3sCcu0gJ9n47 vL4OuG88g6OVtiM19NQbCncCt1JRtlUjF6lRqWFF8iVwP5q56OdQSH0+5jrV rQrchgHTDh6Dd6yC2+2TAtxWkQDdOEZP46sdSkQXAZedcrHDcgdSE8Ht925E MPVVeXlQ9CykfrbbBG/4lxkH7tFJrkJqUrfBvsgTuG1VE9qdzH5/rpsbUL3A ba5QzeFlIjX9uXpFjzVwP/lOGIruRFqE2jV58gNwa4YHhE7NSM/KLuuYZQDc 99tmXky5jfRssR1FN/MZe06m36mxQFrSqTBZZS1w/8oK9FeqQ1qqROfM61vA fTR6yKdLDen5cs9szecCN9WePBkrh7R0gMbW3hDg3phjqJ+9BmlZrbU/xY4B 99ylxI8K00grpMn479EDrukLgaCtCOlVU/H7OI+Au7Xn0YWARqSVDs7V8VUA rtoyWaO8R0gry4iO3xYHrvTqhlx3b6RVfS40qZ0FrqiKkXrSQqTVayefvxEC Z+LxPkf2aqQ3hX87xW0BTk/kPSNZHaS1ud57/QjgNMt4HP/fS6R1DYc3zy4B Tk1I2/58RHrrHbeFyZuBU96v765ih/S2H9wx9SzgvLbmPSx3RXqHtUP9Wzng /CVYHdFajzTkt+dbMnx+WiD2NE4X6Z3zbK72M3x+/N6tT/YE0ibuDd7+/sDJ 0X4vdegu0qaV5nskGH5nz1O/6sicz1pTrXmH4Xf2YfWpnzZIkxdM52sw/H6k X66y4SHSdEfZaKkpcHIzTfKoYqQttu74uLcIOAVPDio8YPyzN7HwCU8TOIU2 I885qUhbj+nEBKQBp+yx7PMkZr/99DPPObKMfbnyEQoOSNt+ORtsnAGcttbK cF9FpO2vG8cFM/YK5LM0guYg7ag79/7LUuD8e87Or0AJaae25vwv5sCdK4zo va2KtEtQSrlKJ3DXHG/k8B2RdlNwa3Rm6nt9wVh3wVykPZ3//d7C8PNE1gvy hxjS3uLvZkvLMHoq17rp5Ib0yZyYZSxGj4+umyUGM/Hy/6ywrfgtcEdGX7bp 3EE69OzOC2k/oS90xbeliS5Ih8lLxXdGQF92fNEVXzbSEW+b0pbIQF99rljz xDmkY8RcKy5rQf+ajd+KXHOQvhkfLentDv3vXQ5EqJ9E+ra2tVz2T+gf3UB3 JHYjfadFXo0bATwZRQPNtYuQfrDiqfm+NOA5jNibTFoinV4S6HhVC3gXnRIm sxj/ZTkZ+XwoAV4GtT1EcBrpx1mN17e3A49bfZ4waUA6j3U3w+8Y8KbFfPUS mXjljxx9kTcB/OVi4rQFg+/5Vc1KQRjwtVJH9aNXIv1y04+2NUz9uKugZ9bK xUi/aiodPMjc/weWlOX9qUH6dcDlXzc0ge/2qEOgw/CpRM5Kqq4Y+L4WoomW jB5L36yUlyCBHzxY9eveRqTfOfI0jdqBH+aQ6bCnAulKkTwIOgb8yFtLG2LS kP6QGWDxF4Mn8mLj8mwPpGt2oxNTL/LD5rOdF39C+uOw5GllBk/wxuSSI51I 18c2hDkxeE51LpBX/YZ0o9adG3cYPEfn15YOtCHd3OiS1cTgsaqU+57vjnSb v8ar+QTwYdaMKeMhpDuWTVSZtQF/fZ3l8jaGD12v37aHugJfSvNp/ELm/F6H qOGiceCNbL7xPH0z0n0zLX+PXwRe1bBFvRGjN17Givma0oy/vX7/riGRHhrM 1XqgATyLra5jrkw+GY3xN2ovBt7q4qn0kkikv2wEKxkC+r+Ey6jPZPB/9/3k F+kK/REhOmVZIUj/XJocUToO/eZSpRIuTL75VeScxPSn/YujzxuHdiD955B6 zhZp6GvVJtIfKSM9Pf29yCuFqZ/0SyhFxf8Dh/N3Sw== "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0.4}, PlotRange->{{0, 60}, {0.385947141987003, 0.9900019477147122}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.469220654816236*^9}] }, Open ]], Cell[BoxData[{ RowBox[{ RowBox[{"R1", "[", "t_", "]"}], ":=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"Diagonal", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "\[LeftDoubleBracket]", "1", "\[RightDoubleBracket]"}], ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}]}], "}"}]}], "\[IndentingNewLine]", RowBox[{ RowBox[{"R2", "[", "t_", "]"}], ":=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", RowBox[{ RowBox[{"Diagonal", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "\[LeftDoubleBracket]", "2", "\[RightDoubleBracket]"}], ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}]}], "}"}]}], "\[IndentingNewLine]", RowBox[{ RowBox[{"R3", "[", "t_", "]"}], ":=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", "0"}], "}"}], ",", RowBox[{"{", RowBox[{"0", ",", "0", ",", RowBox[{ RowBox[{"Diagonal", "[", RowBox[{"\[Rho]", "[", "t", "]"}], "]"}], "\[LeftDoubleBracket]", "3", "\[RightDoubleBracket]"}]}], "}"}]}], "}"}]}]}], "Input", CellChangeTimes->{{3.469220792542262*^9, 3.469220872975093*^9}, { 3.469220913378285*^9, 3.469220920823278*^9}}], Cell[BoxData[{ RowBox[{ RowBox[{"f1", "[", "t_", "]"}], ":=", RowBox[{ RowBox[{ RowBox[{"Tr", "[", RowBox[{"\[DoubleStruckCapitalA]", ".", RowBox[{"R1", "[", "t", "]"}]}], "]"}], "//", "Simplify"}], "//", "Chop"}]}], "\[IndentingNewLine]", RowBox[{ RowBox[{"f2", "[", "t_", "]"}], ":=", RowBox[{ RowBox[{ RowBox[{"Tr", "[", RowBox[{"\[DoubleStruckCapitalA]", ".", RowBox[{"R2", "[", "t", "]"}]}], "]"}], "//", "Simplify"}], "//", "Chop"}]}], "\[IndentingNewLine]", RowBox[{ RowBox[{"f3", "[", "t_", "]"}], ":=", RowBox[{ RowBox[{ RowBox[{"Tr", "[", RowBox[{"\[DoubleStruckCapitalA]", ".", RowBox[{"R3", "[", "t", "]"}]}], "]"}], "//", "Simplify"}], "//", "Chop"}]}]}], "Input", CellChangeTimes->{ 3.469220928372014*^9, {3.469220965857654*^9, 3.46922102511075*^9}}], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"f1", "[", "t", "]"}]], "Input", CellChangeTimes->{{3.469221125974845*^9, 3.469221130396557*^9}}], Cell[BoxData[ RowBox[{"0.2048107795491107`", "\[InvisibleSpace]", "-", RowBox[{"0.0007933278861625571`", " ", RowBox[{"Cos", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "-", RowBox[{"0.01386330428847683`", " ", RowBox[{"Cos", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "-", RowBox[{"0.03464070420688588`", " ", RowBox[{"Cos", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}], "-", RowBox[{"0.0051189007517392845`", " ", RowBox[{"Sin", "[", RowBox[{"0.5827840921930183`", " ", "t"}], "]"}]}], "-", RowBox[{"0.0064639452017809605`", " ", RowBox[{"Sin", "[", RowBox[{"2.4484646760748454`", " ", "t"}], "]"}]}], "+", RowBox[{"0.11932522436138936`", " ", RowBox[{"Sin", "[", RowBox[{"3.0312487682678637`", " ", "t"}], "]"}]}]}]], "Output", CellChangeTimes->{3.46922113223833*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"Evaluate", "[", RowBox[{"{", RowBox[{ RowBox[{"f1", "[", "t", "]"}], ",", RowBox[{"f2", "[", "t", "]"}], ",", RowBox[{"f3", "[", "t", "]"}]}], "}"}], "]"}], ",", RowBox[{"{", RowBox[{"t", ",", "0", ",", "60"}], "}"}]}], "]"}]], "Input", CellChangeTimes->{{3.469220948320191*^9, 3.4692209497099657`*^9}, { 3.4692210350363607`*^9, 3.469221069937456*^9}, {3.469221105666695*^9, 3.469221108206068*^9}, {3.4692211677100897`*^9, 3.469221216176262*^9}}], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUl3c81f8Xx2WTvbOy93ZxSZ2TRCGrSCohDSNZSRpGkpCQSlaLKJKMa++9 ufcS5UtKkiRZKdHv/v76PM7jPT6vz3mcz/O8jv15d+lTdHR09HR0bCn/f+4q jZNr6RxfheYvH4a+FjdB8sjuSyJZVGipaHLdeXYZno6IuPcLIrTe9j2zrnwW ikcY3r60DYZ2ON+hPGoOTW9/JwvP80JXcf3T8t6P0Krz63Leo2joTijVfyx/ Gdpvr5wytP0LPWfnzmw2r0LPnp9E55IZ6BM8J6dygg/Ib5fPW9dMwECWiFwL fQNQmQI9hAXTYGB9bMavUA2GdBadJnwPwaAjwc/v2iy8vb1g4re9Cwb/hfdk NgfCSPV5A2LIDSAfWOBIyNkPozPzanSDCOQHX+z8jDhgbM+cYFIkCSgSt74z 9RnAf/7e7Efe+wHFqVeo6HkOjGd9/SdDUAVKQofxyZoDMNFzdmk2fhoo9Xad ow5a8OHK0vKRgAGgzJpmCFFCYZK/xLf2kQZQucMlpuN3weTLgK/S3XFA1Zhx fbaYBB9365y88WsGqPtOa5strsHHtz//m5UzA+rRL8pZLu/gk+8bJ2vbbKB6 Hq8tOFwHU0x+5OKrW4Dq17hXtKUJpjK0Dgi9cAFqABddw6YifNb50R46VAPU c3vFDejl4HPn690T9NuA6u6Tf+F1EEy7+tbs0QgGqt2d7Oj8Ipj+paGf60wF qlHt0P6mOPhy+/ubrTe1gSq5dcjpFR/MyL1SPV+SAJT15GVBi6MwU+2TQ5mY AwrFX+bnyT74aq8mReTYD5ScjjF6MQb4OvMtLYP4HCgBjYzLAl4wG5YvSHeK EShGUX0hDqvwTcjrjkeSG5A39iSaPvGAuT1fo9RmxYEcdF0zc0cOzL3L20wS CgWycjCXtMYL+O5/NmTF5C0MvjOPmVy9CvNZX87VpifDoNadR2O987BwZTE/ VyEK+s8rsXmORMLi24V7Bm9/Q8fsyfsxljtgSTnXlVWnAjqYvgoo7vWDpcsu KiO3afUoG8fQKeMCy5LdDZdMFqH17LWA8CFnWPaLjNufWQQtSUGpq3rqsNxk 6LhtzRea6/t+Rvofg5VTz79VvZqFJs3mwn98C7BSfpwUx5oHjQFvBzULBWGV TSDi6MnT0FBnskNFzwJWnbutVOvkoIGvoFyBgwdWCyKF/op8grpJv7hW5g+w ukmc7Al8CrVvfnOZCKTBL5sfBRl9rlCTyuVo+vQG/Hry/OI5ZUmoTiG9d7Js h19Lx012Xh+DqrxP+sr+NrC2V4CTczwNKofOQfzSIqzd73o7bugElWLCey3v NsPaTMTTwhQhqAiNc/Bt9ILfRsRz1+aHoHzZ+XG5gzf8jvtBtNmfAuUxf5cH XrjC7/+eM2zPtody4oB1DXci/NE81vvjHw+UM4rcGC3eC38i+FMbjvQBafYf 99jdEPhD7jqZWBoPpO/J/0302sK6XISGG7cFlG/tsK/2fATrwQa/tb1YoXzv JftHJnmw3j7fsqWlHcrTTnl/vPAC/m7LSSBvj4YKdh2RcZEh+Ot97MjTUFOo SPEWTTaYg7+1/HIBQ/RQSYz8VpNvABtcnT/2aDZA5QrFcnG3NWy4hlfxx16D qu7MjwQmEdgoNoj69NkYqis5Vj2IF2GTYd6mFP5ATS0Bv9K7wKZDjmhUWiXU vuXtLxvjg83co58PrYRAPVPCvw/3KmDzN1+RvK0+NMg2tHoWT8O/jPC9bUzF 0BjuJuryoRf+zRvw3Hf1hyajYsYrUy+Qbvvq95LjqdD0ly8t7V0c0vm/qrhe 4UirH1ZdlvADuEVQzFruHBnav7emaaxW4ZadqyXx6drQ0dVJV1/EgVtOk0WW OxKhM+9C1qFFLdxSEfOpVdYaut19Dp05uhW3THrsV7d9BT07WbufxikgPTsU 3rvGAb0Cy+SfHRlIf3T10qmRLuirvLjp/3cV6aMGx/uYlaE/Krh6YDctfvXK VF83BgbkuiaPHr2A9P88uJkTzGBgIqvGd2URGZQgyLc6BwYN1PPUJtKRwU50 dPgrIwzGuifkf9FGhqeD2bl7moEsE3Zt9j8pZOj49fV1ghaQ3S96XcQxZPgh qVE+kgXkzOfnpPvLkVHQNLBBdiuQKe/s6N7yIeMOr4qOcyFAYfzMezXnMTK6 JW4MlNN4qZW5ZZQpFRlvlpuM0h8EiuPYAn/IEjK++u/mpFUDUC5GmYojFRmp DD1fH2gAJfmimLqVFTL+UeH9OZkBlNysEzsPaiOTlK3jHzU2oJC+vAvKWEUm s+AM+uBgoDSYmaXsKkMmn4xJ9oYpoDS/cTJZtkGm5GYF/q32QGmUb9MXp0Om iq/eYg71QKnIsdJqoyLTBHeR7GM1oLzUfzGMH5CZSW9FdTYNKPc+Z9pOByGz 6jEjgh4rUC43jQ7eDkNmu8gw47ALQDn66fcPYh8yX8xrMe38CBT9k+cWdWhx Zj/bAQFboGx1Pb2NYxmZm1esHVxqgfxus+LrQRIyz4rdPf5CBcg5p1ufcGxD Fp7dI6eWUoHs1Wo/uYcXWfTPSvjuYgKyqqXnK6HzyHIswT04JgAGv2iovc8z QJbI0txr5A8wmPX8leRxJ2Tp36J950wVDG6xKcTNfGRZUQx+UKwEA68Yd9Kf lkRWsQPVj/7egwEHRwNJTkZkPZO2tyjJD/oNfw38OxKKrLcbYivej0PfpaGr b7ktkLVkur9RwRJ6y45cOZiDyEane2SwWgF6pOfZpt3dkC2159zi1HvomOLr /sC/hGx1i8Xrmvugg7/3E/e+OGT7LLLGcIkE7aYPjkQdmkJ27VORAlyJ0FrA z1ixJInsnZv39Yhm0JQTJGbg5oPsC3Lvd0aWQuOC14GnjYO4VchSam+vNDTu zpvt3GaNW90fvHR0+wv1i08PKJZfwa0xtT9cCjyhrvGlxxfjetxaOEU4vToM tU/y1Xb5v8Wt61p1F+PeQHXquYaVA1uRQ/owQ9iwJFQVrQQ2mLAih/nVfTFS 8VA5TifNZR2LHOee3U70+g2VUuZj3sJbkONuJzm17DRUhFzQ25nDgByVC8KP /1Gh/Ivk8+7PIsjxQejYC4vdUO7TISyZ8Rk5mY2fvEkphHK2j6d0YgE51dyn KyfEgVTXzTzc8wQ57TpT/ZfogBSfuP2mawByXtSyUGGeApJ/UXzf5GXkzLi/ /lG0A0i+VlfL359DzsaNV2kar4AU6e+y8bETOb94nLA3SQZS4bbuz9NiyMXR zcvuGAykn+oJ7FmeyKWj3dTk5QzllhEF22/Q1g+nBoVe2wXlNSM3H0+IIteV fwo6yTJQYfqR7Wj6ReR6eurt7HNmqPi0Y6yAIQG52ntuPa2chcoH/pVmyRrI 9V13h3NvH1S5blTbN5ojN9/D73yTxVAN+zpTTN4hN5Euq2vlPtRo57MdXELk juyj3yFxAupsWv59OdWM3HmEkiVtU6i/NM68n/Ulcvemn8rfqwQNzp7cdyfY kYeD4HGbFAKNBZUyBtduIM/hLe0b4wgtj9Z15uE68niWSY3O6ECr52jYoE4I 8lw5e6lsUQ7aDKrAw/ka8jzpV/VlZoX2sd9d4laRyDOXkTCuPgjdO5Rr7XQk keefzZcqgxbo4fuxGJqRiLx8DHh/Nwl6vl40qThchLwGnovWDmnQlygtL7CP jLwWEpYqJ+Kh32XprBJ5CnmPDWQze16DAYGlh4O+m8gbaeBQd9UdBkbequfK KSJvymxh2k2a39TPZt76owZ5c7NYgpPMYDBpX1DILhXk7WWs1Mih/d87fgke DptB3okKPvbXNL8Td/HO/JoF8i56e01XcgF5uObROfIX5BMmi2f1LALlxDO/ XBsN5FO5cSF0mMavjKHQuaVm5NtJ7HP8MAwU6nemnHNvkM9mTlFntgOoLNVd B/0Ekc/9UTjXchVQ9YSzyHW9yHfBfmR2swCox949+HpsHPlimLXbWbOAeu2T UgwtP3xplbHP+BKBmi6w88kYbf8rn09h4teB+sbBMYL7OPI1SBkfVaD5xcbk h7GpachHptwz0DoL1O6az870Jsj3OXqe38gZqP2d4yq3Sci3ZmT2w9QKqL0l vyJ2DiI/+/dH3da7gNoaGsp92wL5JR6v5TppAbVc+kbn2RfIr3XI7rq7DFCf FvOobk4j/x6Wlyd8BIAaYzhe9ucw8h+2bXvGogJUj7Yj/3x1kd/74cfpp0jz q+foF1P3IH/Yx38qOx2BymYPPxJvIH+Kmti5ER+gDD7uZGpeRf68CwZvAiOB cvdW5D3FfOSvqTu4wpUKFJs99hpLXMj/2Tb+imkLkMtkcn51fkT+3w/z6ife AfkEWbndShwFOD+2MoQuAJnptDmvdw0K6AdtxL4Rh0GTm5c63ftQ4OZD34cS QdAvcfD5fYZqFEj/GDtWEQt9hHzCx5unUaBINVfq4GPoNW9MIp/MQYGR2onc Wz3Q7ZHy5OTPNRRU/mhbtioH7Um2LbpiYyi4S9Xnd5IRtGX9yqE6ElDQPujW TjVbaH19QtcoagIFLzM3trhfgeb3Xy/NabahYI+qDrmPAg2Xz/r8CX2Igh+C bIQ8Z6D+HPFLTKY0Ci7XejszbkLtqmCG8Hc3FGJjvpn5WABqkrvIvzvVUEjC 5tnkDhWoNn3ZYnz4LApppzbIDSNUcSx3GGlNoNDeybGz/o5QMUdV0/UvQCFn lbUCDh8o/3xi25bqKRTyDRL4kRsJpF/34r+3klEoslZb1yQVSHKununpUij0 gPlA8H+FUOaTeH/BJxaF8m08q0JaoHRgfEfjYxIK1afe2OR/B6V2M5ysy+9R iDL5dHfhApQs6JSafrZEoRmVuhsWzFBSoHTggVswCv0NfNfxWRxKooU3twsa ozBPzS+OcB0ouab39OdxfxSWZ+a3FdsHJSkPHzL6PEFhQxvNuyQXKOnELJO9 gih8INXyrV0QlIpv7Z4WnkNht8mzonOxUBrf8FLDtQCFg1WiXG4+hjIxtuOO A/woHBv4+KkMCcragr6Vjc2jcFZNzefaHiAlXMxoKqXdX8I0quz0EcoDvP3e HLmFwu3WKz5La1Dhn1hkLL6Gwu9TeYvucEFlHFeK2PEbKPxjUn1ZRQ6qapcZ 7j8SRRFGFQuDViOoYSN6RnXsRBGRwNOXXW2h1rsm6+/DPhRRq4msWz8NdVOW sx+DzqCIg3W1mXYyNLIptx+qlkGRszvNj7zXhEZKdWgPQy+KXFalnIvqofXT qN2pD16iyBPWbykjzNByTNNelt8MReaaxT5duwwdDCxOMX7ZKPLvTe4vJRHo WE77+5tog9v4HhPYB0nQ+WVCTSf6Gm4jXrXSkVuA7oEI1l3jPrgtyuBKeLcH 9N+zcme26sRtDxRYUy5sgQHous3y1Ri3vRRIyZPMgoFi8vcTr2ZwW//Pgn7/ ERoPFT0OLljgto8fiJ/ELsDgeotOkQozblvpa1ltpc317l/oQv7So6hY/n+S IgeAIn/Z2cGzB0U10jy1G2eBEm7faTwbh6K7Y1b2escAZfhKiPvBZyh66GLE EUF5oCqWzD3WvIGiZ05z+tQ1ATWw4lrCNzoUDT30MPzsCaBWHUjw2LWMorf3 yKfw/gXqulFpXv5tFH2s/Sa3+iEM6Vv+SJ8gomiJ1M7qU/ow5G3XK3v9A4q2 cXX2c1FgKE06SealKYqObDh8rPCDoaZ7TqJT4yj67dvkqjsnDE3F660LuqPo 5jtf9q0vYejfrB39mh6K8Xb8kSgzh2GB1LWc+1IoJlcere0yBcMy0RWHF5tQ zOA5317WSBhWzj1tHHcTxSxSspyKt9PiZfVHN2RR7Ph1FZ+jNTAs633PqPIy ivn5k8KZnGFYiDfCWGcaxa67mtwt/AXDW35oc24ho9h9675cpxQY+sJPHK3e hWIvdjpX02vDUGtS8KRNNorVqE735ffBUOZ1xmRzXRTrFw34SJsDh84zy0a0 R6PYR9bNlX+sMGS0S8FS0ALFln/FseXlAPXfQQfPm7YozjItLGFvAtS6gAEZ wd0oLkp9prU+AdSLfZ4pA0wort6saZpzBaiqGZG8z5xQHN9UO9lsA8o74bG+ qq8ofvCxufcaCSjXwwKkO5pQ/NLVE3ctaXztkOWMn7yN4vHe356v3AbyyYU9 f8dYUfyR88WqRyq0eunOlowwQfFWgzuTix4wKLVkV1uMKMHzs0ErdRT67nGE hdQ5ooR8MHc+JQ164+r5pc6TUcLwzwlFLmfoCesWmYnIR4mT9JsStPm562S7 VrNGJEqUCRiz+36ANv0TLwT++KJEZ2rcjRdPoFVn6D+G9gMoMS7+nm7KHVoI B0jt37NQkkX+0q8jU9C0/9NvxhB1lBR70RmQkgONJ1omtH+TUFJLQ+R7P23+ v6bS6nfKHCWP6JdPmc5A7eugS8bXqCh5rprlRNgLqFEa4NFAAkpGguO7Ki+o qtzTxOmthJL3m587rKhCpbu0a5UYA0rmm68Mas5BhVz2AdfhWZSs79lr5fUK yrcsHNq1uoSSVNuU9hxfKFvnycKCGJScoU6ZfNCEMp7VyqBOmr6NI7p1ogtQ anqpcqdgI27n/e+6ocMbKLnvcH7rVD1uV3AjlyYGQAmLcs3ZXMTtRtMymt26 UPzg0uZ/pbW43cbLP59pGYrNC+4f2f8Zt5+cb1TAMigWYY8OFKTtDwnkeXI5 GIpZ63XmE+lwe/wvV3GSARRvGzGoWL2C259cfv1gYQ2KLfcei+HWx+1l//7x q1ZCccZItrwCCbd3RVknnA6FEi6ziw5Ua9w+wZrF9mQHlDyS/2bk0ILbl+K/ R73/C6XWP2UmiktQipE1K3yrC5RJMHW/DJxCKS6Ltp159UBizSmVL/ZCKZH4 7+tmUlDO8+vKi0eyKCXTL1jxKRwq9FjcCPFUlFLn3Rkc/gEqAzvEqhgEUMrg 4CmC5G6o6uEfF0iioJTl29LXR+ihtmU33/LkdpRy3Dbms+oO9W6nTayUB1DK 9Rijyt1maLhlu+v4bguUCvpwMKcvCpocro/+G6xCqTCZyye9p6DZPKD/vkwK St3yeCbNthdaULLxDdM5lMqcWcwwZYY2E8GAKCMZlGr7mZRcGQNdvkePadrE o9SgbqWt4wx0X8GVs/k0ve8vTHIu74eeW0eu2wyYoNSPP1q3NNmhL3P/r97D 71B6G31/WHYcDO779PN3QAhKywryeRIsYLDWjlr6nzNKqys62LewAll9mKWq KQGld1u+l/8UBZQtD10Nr6SitKWLJHegCVA8JGN6TNpQ2sHP9TcDHW0+Niso vi6O0p4p0z0ytP/Za1ed6cUNlA7MVS4rNgJq2WWRKrUolL5a6fNo9xpQ/+h2 7WoWRemb3a9jaP1jiKiuYJ74DaWT/lsMcLtA44t2zFprBkqnL+gd/UmAoUe8 0Xz1Dij9nD5kb/giDLVn85nLMKF0kUC1Jk8RDM20PR9UO4jSVQqbIo99YZj+ +AntJdr5FsPd9FpqNJ4e5FkNFUHpPsvr3+pnYVjy4fGApQmUHjneNmSTB8PS SrUe9H9Q+qMfW93EGRgWp4v20b2C0nORVnnnFWCYW2HW89ZflF5NuZO0OQVD f/IWsq8rogzdc/LlhKcwNJ52duLcZ5RhrxQ8JekKQ9WCI/a351BGoNvJulAS hhKNXZ/PxaKM5H/pxJ1jMOSi94RFvgJllH6MS/emwZC8ZpL74BmU0aWX3nrc CaifXZyYXcdRZqfAyeXvQkDN+rut0E0LZcwVno9foQLVfl+LCWsHyhy1VHuT YQeUXHYhMU4JlDl1/Hy6Gg9Q9m9zzfXmQ5nz54ujqvuAPM1hTed7FWWupxAP v7cAMh/ThawkNZTJ/2/PpqgJDBivMPHYJKNM2Y/oLy/poD/C5WddyxuUadjS OWhYB31R39Ps2/JRhqpgk31kB/Rc/++QYVQuyvw9f9QilQAdoUwqAncqUJY5 IouguAjtl1Vy08SyUJbn7qREeRG0hUXvlNQZQ1m5itMLb9Wh5Z69QdKNKZTV 6Hrx7vQcNOf54yqrAsoSx+aaV19CU5OMymLZX5Q9sCXggZACNHI4qc2FyqLs Yf6y8JwpaNjlXHwm/Q3Kusmveek9hbqlwd1375FR1pu442CrK9SGHR58b6KM shcsru10kIQaqVg1/Q9sKBt2rFFhagyqRu6F9x78jLK3zjPyBKVBZV4yjyV9 McrejTD7w+gEFUmRej+9rFA28+6tTynCUJ5id+NJ9FOUfb4WVaYqAqQGnUKz e64oW3Q8LLpJFEgMM8f07U+ibGVTqNMRcSg7sVbjVb4dZZsVLygvSEIpdbie 7HcRZXviz6/flIJS98m2mrJelB366dUrKQOl7KeOlme9Q9kJx1NZZXJQ0p26 KRDAiLIz1a5+VvJQ8vzRX0NWO5RdlDq6+5MClKTfOj4ya4qy6zcc+UOVoOSl zbicsQTKMc7afuZVgRLy6Mr3P/wox2VjScpTg1KB3yt151xQTrjULAY0oNTP V7PK8BTKSW/bfWRYC0qnJrUNp9dQTuWaseo5HSjzn906edkI5XQ/6W8w6gJJ mOPV+oVulNu5T7svnQAkStX+vmQ2lDN7pfZYRx/Kc+26H06eRzlbPkX/TiIt f1LH3x4ClDtyUWaPqxFUJrqtH0mmnXcfkxD4ZQxV2SZc/SpElPPZLTydsAuq u8e2qyS9RLmwrZy3anZDnb0bMtK9QbkYP9ajB02g/rVRdj+Vtj9piEFt1hQa bg+rheftQrnsrLUBkX3QxO14IaKcpr9LZ0ooyAZaHaQLvXYEoBzlwfjMVlto 03q1x/CFCsqN/R2tfGoP7VxfhAyIN1DuR1v/sQFH6BjIfBoiuxflBZ2rnqkd hx6HYPqHfcooL1lfFtTsAr06UxxvouJRXkmuyMzZFfo4fRnF1GxR3mg+52sM zd+WK9Sc8XiH8q6RSZpT3jB46t7vVxPaKO/5JX7LZR8YrC8r+NFJWw+0iqbw +QJZcDZxNYOK8lfeRGS/8APymZ+PfzQwoHy00JVgDKDNl3Em5ZLPUP7O5Yvm b4OAvHk5RHW7OMqnfgjY5hsMFJNYzgenr6D8070+35hCgBIR2cpnSI/y+S/P 1GRcAkq13rWXVa9QvozbPUE3FCg/go/vG29C+bqg4ye6aHyWlJiRCalE+fZR J223a0A1Z1PKzziB8oO7DtKvhdP4va3l2745lH/37AD1TiRQb+oLSe04iPJT rPueK0TReLR/R3apJMp/P7fnYm00UF/vUaH/44Hyq+Rd+w/dpPlnEZLmuShU 2EIv2lcqSvN/H6TTxApRYav2r0P8hUCtObWSp/cHFQTdKKO0fkItfh2nqmeK CtuTilzIw0B90vEvZD4WFZQb46d0vIB6q1Whm+U3Kuj+9PRM2qDpqw05qv4U FXZJ753/mQhU08Flm7+LqLDPTibITg6oIsoMKW4PUcE+fPN3UQVQpn7YVXSo ocKxovdhPFZAyT/8hEHoJSqc/lDBeP4DULzTUt+2SKKCP0/Krf5A2jyxmpCf V4MKl9GfW5MZyO9e7y/R6USFG34HUhLSgBzL2RiT8AgVHg4wPzrQBIOja1yZ 36VQ4dm/T3KvHGHwsrqB+zxNX6Fm/UuOWRgUcZZR+3QQFZruhJT28MLAPpEz 1hseqPDNerbLwhV6eXxN68/GoKJxb9+K2Qa0kjXG69WsUNFsIz/0eSK0rCgY zkQ1oKKdegwdsxy0SJg+KDSOQ8VTt3ezt1pBU7hAYdMdUVQ8XyuRKPcBGmvM tu/ryUbFS3N/BKMCoZEuI+3pTCwqJliVSO1Jg/p9Xa9dnzKiYuqVOznPNKCO XbOGnectKj4t8FGlb4KaD/9lWbJfRMWCsX1F7o5Q3fvJYzs7ERXLOeT1Gr9C FdnoPl8BOyo2Gm+plr4ClfNtduecZlCx23scI3igUs7+YsEdI1QcSq9q/ZAN FYHJy7yqt1FxovuBJRKh/D9Z4VkzOVT8uh448KgHyt1ucOn23ELFZVVbh3+u NL8qfYaNnqZn86jae5dlINWYVR96aYtKrHFsrnU3gXRX2ZO3yhuV+Ko+T0vS +Hvj01mna1dQSXy2yftqIZBSggyPpU6jkqJo1sJ/JkCqa/h2dr0JlbQtLgcb D0M5Y4pxv48wKu0IPbye4QXlp7IoL0jaqLT3JSF8fQPKJy/e0fWJQiWbd7zM RxOh4mL6T+9VCVRyZvseVy0HlYp+jx9+vYRKHoZdvKIVULlgQqdjuYBKvp7P 74daQdWg6YdEtyVUCnl4XWz0A1R3PjAdOuKBSpGdJ54YBkLNyO5dOk5KqBRv ucz12AJqZ9T2vuIMRKV7fTFXWGSgnq3avAyfoFIe5Y0jdQAa6r4OL0wvo1Kx 496WHXnQeEY6OV52FpWqR95pPw2DJiE+CCvUQqW+cQYOPw1ovtG6T2izCpVG XB9cessELXtt5B+MrqDS5CfVLzT/0com/ffWwGtUWp451LQ1DtrSWffL6Amg suji84vPZ6BzSPxuz4UtqCx3wegzZz10Zdtc3nrdHpXVf/XbB92H7gCvtx0l PqiMf9fUTfZCL2uudQnhMSpbhMWnvRCH3v5ABT1OU1Q+RC/Nyr0MfcnXWH2a vFD5DMv+j/89hQGmk3KPGfVR2e/WuK3pJRg44SHnpqKCyqEcAbX5tjBA2t/T 1vAElW/zpqeG/IPBI8+YKuf2oPL9FC2miWEYzLm96vBUDZUfC7UEmhXC4Her hhvj51C5ROy7tcAxIJ8/8iGeYo3KNVkR1Zd1gfxiRwHP6QlUbpMWUvrIDuRx kfsMBRqo3P/s5f19H4HC2XKXizSDyqMKwPC6EihE4aW6cE9U/phH8RdKBMqx Hzc0rcdQeU71zPjVs0C5olZIx8yKyiuv/lpOAVDuld0Mft2Hyv+0EisthYDy ImixscALVVhL5BXefAdKufNLU5VvqMKnV5ki0gKU+qPWE91zqCJWYb0lLIPm n/38tdJosbzRR99pGn9qU++XTEygikZt8NgBC6CUvDUayo5EWsvcur9UBijZ hO4phXVU2d30qFz0N1AS6p59G0xHFcu9BLmIAaAERm9E5I+hyqH2jqSZXKDY Z0/dSBRGFReLY/9srgFFjSAz03AbVc70/PQhOQKFzkE5pqYGVfxtbryTUAdy v9qKtu8sqoSSRc2jGIGcOvVjbMkJVaIOFZbOjgH5+Ms+C9vPqPLgyNs7FbE0 /qmmcn7+iCqPx7w3trvBYKLDX/4371Hl5Qk6r2giDO7xGSHZGqJKrYfy3oNf YCDr1Ny62z9U+eQb+ueHKfS5FFBnXsSgylyjSsq+S9D7SGSyte83qqzyv1N/ Ugg9YzKPjNbyUJW10tD1kAh0W8fs4XnRhKoaDL9aK+agQ8EmummlG1UNHHNP 8ElD+/G9x4MuzqAqvnBc83aEtnuz6tFP76HqQWuSqkQDtHK1SbG8FULVkIdB iZF3oUnz+9ecz3WoGjEnp/y+Axpv3Oy/6jGJqnG7KM2Ev9AwmfTNOJsLVTOn dFa+nIZ6F/5D9Qu0+3L1P97BdKgz+WxU7OCFqkW3kpUeDkAt8bqBQWsBqjZr /DxqZQTVR64XSm3Q9PZEPF7OOQ9V0QF5mf25qDpEtb29mQ2V7XsvrbbTvm9C kU7x8ChUimewy2h5ourMpdcNb7ig4ibT8jCDHqr+7DnhzL4HKljoBcoXTqHq +naupZMhUJ6xKdZVkY5qjP518TW0eX+/o6H36hiqcbb4Kgh+hHKOQ30q3VGo JiwsUX9eGEjTOuqly3tQTcqz16nDCkhvBc5ztBeimnLNlUXpCCBNcFtqWjKj mi63WlwoCUibcuxmhq2oZuw2Jkf5BuWE3WHBJxtRbW9pXJ06bX6OMOW32a2D ajYsOw5HO0D51MrYkFAyqjkdmV2YiIWKE1yd/NdOoppbQVossR4qfhAGRSub Uc3rn4Vs0jJUpvzdtavTDtWC7H7XfFOGKpsvfz14I1DtavYLR1MXqN7u+UeU 4IFq0b+cfmTehRr6T/eNPhNQLdGCNWa1A2p+Pxct72dFtac/zlS/0IF6iY1h 8RU9VCswEXagPwMNcisSO/IzUa0spW3+aAY0NH25X73TFNU6jBSkuZihSaCv NzvJGdW+R01FB7yD1i0ty06cjKhOFHWzM46A7stK5j7h+1DdxGAxy2Uf9Ljk WYm/I6C6lcP1b+Fc0IuEobBnG6jumvj8RnMG9P1RjCM97kV1r0ID8ueT0D8w 7+BulYrqQd2dUiwqMGBnok2XVYDqt5i/VVlUwOC26EPMijWoflfuKotPGAyG mDC1ZbujeqYJ16EEMxgkf9PjLv6A6rknHj0p4gSysmrJW/ksVH9zVWt+kArk 0DcHtzxpRvXqtKYdS+lAbtfQYnxL099acfCWoDtQuJw2sz7S9PcPTQ0bKAPF 5rv18ZEXqD66FCzrtACUuPxuktU3VJ/iZfELLafxyPmKd9Eoqs9rpNZm0Hix 0PKLt94J1desVNjr9gJ1W829gLRtqEHvWX34AwdQjUWevFjeRA2Om1Y5WyhA PUy62hVtjxpC2f8tyqYB1edS65fVS6gh1XQe9roCNdTKx6PYBDVUPtDFn1EE aoTkwNl9GahB2EgajZkHaviXvrPHv6AGiMkqvCwF6qXsrpKSYtTYTywN7LkM VO8jZcv7g1DjoOPehnmaf3Tk3fpMlQs1jge+5eJhA6rh+y5f4aeocSbp7FHt AaAKDZ5OafyIGv6Fv/MOPgDKd27Svg011LjcE7tywQUoNVUjMsXzqHFjVtzk gTxQbmzw1654o0Yiy6s7NL5Q9jOXZs36oEaa/K6xdyVAYeacuHJvBTWyTQaU /4YCudbaXaT6CWoUurpdlNxN6z9sVwdExVCj4upiC7ICWezGK36VHajRUyno cv0ebT5Y6jFOz0SN4eHn+TnHYJAx9kpmx1nU+LBssNYuAwOPVNWOkM+hxoqm 8132IuhPoTM1CA9DTYmcR+1JPdCzpe4Ea6wzaio2awuU3IVuQcnDp48JoKb2 hya3IWfoUljlPVAxi5pmYlN/RWagY3/Yl/lr+qjpm6yi9ZgBWp6qL/165Yia Ia+rrzZ2QXOjV/lqNjtqRvZadX1KhKbPiy0sfoOo+YD1/ClFSWg0EcmOIV9A zScKdMX7PkPDZZPEe2qRqJm/J+mfVwHUX8pkaWwyRs36a6UPC42gpryoNGVl GjU75jf63ppCdSzhtSLvYdQknzBnpLOGqvPUQ50OND3v+xMNlZ2g0vOq9O2m SdT8DKO+9iehIoTjJzEzEDXni2SeXfal8c1u3+qqDWquSXmPZIfQeMX8avHT VdSi63COnm4EktIIm43fYdRiCg+TvmwGZckuolsre1CLnfishrsbygQkV3Zn vEQtroUOx2xbKC28fU9pUwu1+PPmfhKHafM420XjP1aoJeLGG9d7FEo1xvdT d26ilsQ2fQW3D1AquOeFf1wgakkPOjesnqbFx+YZP9uglsKtsKOx36BU05B1 v3opaqnufra63Y92H/n8cW0f1NL83ZFYsgKlRdySL5VOoxbhzXfVfaFQJtSQ LV4WjVqGXryt/9FB2f3sXXnesqi1S0bf1f8GkDRPctK9DUEtk3fO68zsQPqQ bHVa4wJqmSeH3UtLhPIXJbmOXXtQy8oiW0tTECpi9e7OslBRy46+o6s5DSoj AhvjowpRy6Hq+ymn7VCVUH6v88xT1HIO5P03lw3Vb4jlDcdHUevkp6N6gq+h TueKvz14o9bZ9LD+lwSov3PUYqYuFLXOHcz22lUFDddGSEYUWj6Cm78/OtMC TUzqHK/LNVHr8hU+w7/7oYmiWpRuq4Ba4QR9amIfNOcmZTF+PYtat7LD2SpH odWNLvzL1VbUyrjBF7R1HjoZLrqLsNij1pOdBlyPA6Hz5/S54eFl1MpZOfqC sAZdk0RC250TqPX6VPa4CwP01KeG9axGolaTmcG+IhEY0A2MZhOj5a9t8+gn 00wYyGYl/+3fglrdpPCrozIwyKe5m91lCrWGFDpL6NVh8POwSOcFV9QaHZ8/ cL8YyBavUdn+CWqNP+CbUSUCOb/7lvl7Wr3MsByTOGQCFLeMS1b6Fqg1Vx9e PtMOlLKGeA4ZZ9T6eTHH/qoVUJlYtm6a0M6vanZ+5yUD1UYry4NA0//ny/zN 54eBmrJl9JdNAWr9e8wvYzQGVMqe4HrBBNRmdDKo6XeHIY6m0FdsaqjNxnPs 8MkvMIS27qObTqjN2RH+c80Hhs4179/zOg21+aaFTvsbwdCDVZIyfTlqizDk v5tlhaGqF/rCY6WoLSG928Z9GIaGKnoeXNyG2jK7hpvf58DQV/ar4h63UVvx mA/xUBAMLceeqjnggtpqoVsKek1gaE2VYr0ijtraDx5Im/HA0NJX65mqfait X6Z2r24chr60ZD9eqETtHeQmdoMCGKK0fcooM0dtXDh8rSgUhiroDh+XKEJt M865JeV9MJQSE+ow74falqqRZ54KwZDn1ckRkwrUtt0vPCY6BUMGzLLu3SKo 7XD6le3dYqBuHCg5f34KtZ2jTFo5woFac7N2zKAPtU88GTG8YQPUoO9r/BY7 UNuj/tyrTUmgKjScE6tpRm3P/xhkgmk8Ju/d0xR7F7WDRDS2no0BinB65T6u 16h9Sa85bNIRyMWCo3nqJ1H72kGnZWc5IJsntxqr+KN2TML1/ywbaPwVi0sV v4/atwu22bUkwMD3FQnJVmvUTu4sbNt5DAYCfVTZOulRO5Nx9LXGb+j79mVd fjAEtd9c1ozg04GuHn99P34Z1CaltqzE0UFn4ykr9Tu0/dUkZy+GPuggrZdV EtNRu/XnDfsVb2jLnWDaGkuLu7lF232NoDWHO77MrRu1B9SKdnxhhZbnud/r maZR+92Z93Ij2dBUVZx36x6tHiZu+D20C4DGvq+xoSu9qD31jJmzC6HhG3fJ 7NQL1J4f11qt/g/qrlBCnN9GofbSeps3oQBq5u1ZnJqiUfv3tqMTr0Kh+sLo t8Y7e1CH4VB0xyMhqOhxja9/Oow6rAHiO4WnoDxz2CphzB51OO+8eZNYDKTY 3ZLpL2JRh++VuQJbOI2nT8T2e4mgjnDXWFqkDZSWj1HlWXJQR3wmgGtdEkr+ NFw1sqLFMsws1wPnoOTohkaxWhrqKMpm/JqrguIxJfD+8hB11Hbr+JyKgeLL VZWqHUKoo+3S/mHcEYp38Ex/vMaPOvpXjjscloNiUc+Ni9OIOjseLnb2L0Lx ti8T3v6yqIPlMbv2NUCxYcL69eAB1Nk7JFHcmADFl8zM8p9roY7FYomi0TEo HnlrxxbpjDq2PPvSS1Sg5PB8MCGTGXUc1Md5VH9DyaqY0hm2PNRx1o0QzVyF 0tfCzdznOVHnhKGcPG1eLYuaXJTaVEUdj13tmhE/gRTcEO53yhh1PE29DJfm ofz65EmO2TOo42vBaXrqG1TkOwc4r9PeH2hTZP12BirnVIi5an9Q56rz6snq j1BT5rQ8qdWLOpGuD33VJ6Bux75Ravs66tw8bRzyaAzqh0pC20JdUSfJP/L2 9WFofCJfrl8zjTo5MVzlll3QeqbokFmyH+q8vP2msbYd2s7sPbHq8gF1Xt89 1KPZAu1eZay85xZRpzIrbZK/Djov3T19zb0LdXrLFNnfF0Nv5k8pRb8G1CFX dQkeeA19zyS8jmV9RZ23Dee21xdAf/a39A9Dq6gz2V1CePYcBnqDSss93FFn 5SO4eKcBOeOUmWL0Gur8+fLx7H80/8T08hOrCRV1/n2/EWiTAhQvBnGHituo y7ikdLUxCSidQrt/SZWgLttad4xuAlBlkqyrOsxQl2vTNzknDqjBCtrJfcuo K8DImykcA9TmeL5T7pdQdxtbae6tGzDEHpasa74TdSW5D79Zj4Qhi2bmo68y UVdW4E/NuTAYipIs7CjVQV0l0cy2iSswRHKdpsYooK66FA7aXYKhSRfb6kHa uo78p/fNwTDM+HM9eE0GdQ1Uo6f1AmFYaiNy5H0+6hprKy/k+sGwnqfy8EsV 1N2t37O+zReGTWTnJwfvo66ZsR9TnDcMm8ummZifRV1LE37ujbMwbOrR5tGZ gLq25iTR86dh2HD2o5DsN9R1OHBEbvIkDCs2qL1YdUZdZ/u/mgddYZhzpYBn 5gTqnnB6ZNh6HIbmnol/yXmDuh4uJqYGzjDUPD12+6Q06nqe/Gz94jAM3f3c 68gQi7q+njFHxA7B0LFuwk/iU9QNPK968rYdDEl23JNXsULdkKC+c/+sgfpO MIwOafuvhvqH+FsCNeGH68tH+qgbGS4Q+WkfUHc+tGYspH3PzRvltx32AmX6 aIvpG1p+4uOcH7TT+t2tqLOd4x6oe//+44J8YyA3OC5r3X2Muul7H+VLvQfy MWGnJ/tsUffRctaL+6EwuKz0S/OtHuq+sM94HlEOg6KRyaXEWtSt4Up97KQD fclNMScuSqBuQ+2DrN5B6H0ysCNzJ+37W33uZ+7xg57CnxHk7A7U7etKSdMo hK42R1JSylvUnYxOTGFShrZNztLdrnWoO613Jzm0A9r4OKjWqTS9s1MJiQun oVX5k7txSyLqLpvE336fDc1nKLcMPtqh7trPuHi7PdB0Z3e/itEh1N14HBvb 9hEaazWH84uHkcC8GXOzWAoaNDa/06nWIWHrq5vRivVQNyMUFuFsiwSeY9FR mS5Qe330u32yLBIEt964zvcXanTs9WOXFpCwrSoqIiYdqpZDX29fy0aCpOf1 sE0jqOwGtfOPk5AgKxJ5LXAEKsov2/1eZUOCYnvEla8Xobzy9yxlaREJasHh l08IAYkSZHvwDRMStOXDLlHLgMSYNO69TxcJetRrIRaHoOzAl2CJXYFIMLp+ Nbh+icanX1xevNuRADpXLuglQ6mq5qGtuzSRsGfycmC+NpTUf3MyvaqPhH2J oQFSA1DiG2DDyh6AhANwye++L5To/zqWNW2OBLv5kPMcnFCy7WHA72ba+x0z L56LKIASoZO7mkVtkHDUKtj7lwWUqFvK7UrYgoQT6xe8fL5CiYuu5YrLAyR4 vAw6+zEGSvK+1st6zCHB80jgGSdFKOVUutv9WhoJvqwBp3rboDT2vpJNTjoS Asr9PfacgjKpVt/DEWlIuHjaz72SEcr69U37ZrOQcEXwvJvGMyDdc/hQ0OuH hPAW3xPZJlAeEGaq/es4Em4EnnPZ9gEqTq237KpGJMTK+By7EwaV5xnSD/0o RELCoPdRJkmoiq+7NfDtDxLuhnsdCa2B6jrTyNHvl5CQMX7W8dQfqHNqa7yo l4GEJ7fPHHr/EOrrXz7vYWJHwnPj0wftiNCQLggbzLlIeJ3mYWscDE0KT79X akcjocnR1YLvJ7SGH+tNneFEQrvXgIzPCLQF/hy5L2SIhJ4w+NPWQONvkspe BxYkDOdtzw9NhM7jR8VuWbYgYfb3BMdHLeiFL2wqDDFIWOCy/mwsAn06RTzB l9uQsCJTV3ufDvqlXYNMDQSQ8M8yy3d/PwxcvOWcGCaLeoyuXGbPSDAwbfDY 7icJ9diCrkluZMGgnR79Brs/6vFnHu9/4wtk0Qxnb24J1BN505u3leZ/Qmzp fuAz1JNo2xl+aheQB6Z7lX16UU/m3SunegWgyPGKFKpFoJ7iDwntbZxA8Y86 VpGqgXrqDLfZApaBUrF+Qd2lHPV0hP9O9owB5Y/+eHJhMuoZqPlUKbQAlfB3 voUchnrGOJYcXgBUT1FWgsUd1NvtYOX1LgWo913bw+Ac6pl51uwhXKH5u5eq O88rop7lNTWxBA+gvh8qey+8D/Vsk9OXZmi8Wuq85fE5AfUccrf2mOjAEGPA VbXUWNRzrr6cnSEKQ5wt6rqvpVDvRP+3K6v0MMRdyWBjFYx6HlNHHWxmaf3C 3nDTbBD1PNe61V/Q/PjGg5EFjTHUO8+5g5mhCqhfU/N3L62hXpD0y/HjT4Ha 56UZmmGAepf0RUnlt4BaoBThZE1AvWsWsXd4/YAaubrz+lMW1Lvu8ueMtxNQ 7f+eMB1sQ72YQC9oRaCKe4kc/WqNerdvvhPZrgSUiaBnMldpepIz9i9c4gFK xmFumXJafh4UVXaQ14By0J71UHoo6j0ZfXgpugPIRYUzW+xqUe/5PJvdhyIg H87/IR8vhHoF9JdUjFJh8Pcv7YDwl6hHUnV6N38GBrUniK+51VGvGjpK9lnD QAexS07NC/UaDhHjnurDwNEjVz++TkG9rqsixo7M0LckrDh66QPqjfe9zax9 Dt0mXW/7pipR79Mn82DhBOg6qBTt5k1bn/lVbu0fDJ0nh54EEZxQb0nq/j/5 vdB+6+4RUb19qM8S4OgaPwXNmweDO/8cQn0OLRm1WV9o1p/m5c1KRH2e799/ m/+Cpgv+3xh2rKK+qOeNZAZ2aGQertfTDED97Yp2Lq4p0OA8+K9p4hLqy36W UKmTgHpfgX3cXsKor+5a2nxJC2oFjpjeZRxGfR3J8DvDVVDDyKhVx38W9fXH rI4R9kA1C2V8jmsG9XekiSgl9UCVVOx0zB9W1MfDn5Z/OEKl9bubnz/No/5e wdeNVjRe3HWXmrd+hPr7KZdvv/SC8vnCojjbcdS3TjJ3ZlmGcjcjT6GlONQ/ aMOvcOoqkL752Ov9lkV9J47xxWZmIN1+8MetWh/1j3W9rJdKBJL5BtHl7zPU d4sJjrsmCiTR9r+DmadR/7SZyWFa/yEx8xC4N6tQ35uRS85QA0hsoxWWxXao f75xdOF+OZAUBA4Jt9PyExSWU7OMQHJOHzJvJKH+pZ3+t+y6gJRL1BJYDEf9 q+s7HV4fgvKtmcVeg8GoH1nJJsPxH5TfPGeuXiGE+jeDqfNep6FClNBpclMX 9eMJj6vaF6Ci+fxpnrlTqJ+46HNTPhQqI6O51YO2ov69IuLB6wxQ5dj/p8fB F/XTfBm3f4iHarirk/RVC/UfqfbP7RKCGmMmLQvhb6j/7Gt6RfpjqLW0jGEc o70vL/dM1G9lqPO58mirUCnqF8v8kyg1hgY/p5d39F1Rn/Sha5a3DRpF+i6k n65A/eqs+6TzttDYIRO8d42Wv1ZRDRuVk9BsyFnY+oeC+p1v/4jdnIPm3+cM 8Kk56vfda/3yORha6oxT472oqD/Cezz8cSy02SeNuP3XhPrfWOOLhYqg865b gWt5Ner/aHO6FmQEXR65D4d4rFF/OUrOYrAZugmbdJJitHrZ+Ff9KX4YegYU px7XjKMBfe3N17Ou0PswJ/nvXQc0YLl88Ir5V+g7cUungq8CDXhWvwnS/4X+ /1ZusDNYoIFgKWnSNRoGLB63fCynnRcNiHxVxw0Dz3beTX2Zggay86Jml2Rg 0MLwUVdDMBoo5U/zDRfAYCq3opPCVTRQ93wzQdCHwcmOsFJZOzTQ/7z/4o/9 QD7zi0I6SbvPmDVaUlscyHndkpnhCmiwW7WpJWAeyJ9aTihqtaCBmfU/r9JG oIjkc118MocGlgHGPKs0f21ule6Z1YkGtvdCyomngXL+SlrUi/1o4FBRdjzU EChJAgtbOAho4Px+kaGGAyj5K0vFioNocOKfxouNcaA0MCeU8VuiwSkZb1so Akovwb79+QYaeO3NXY24DhRKYJzONl80OH92KqPZkTZf15vrRiygQVC89B4m Gt+6tx3/0mOABpdeH/9q9gcoNbHez2YK0eAaOe1OTC9QcqU7uvcLoMH1lbd6 XY+BErsSqvjbDw1ubeMf4wgAylkZUer7PjRIMLaNtDYFyq6u+n6SKBrcPXFb KVEYKNwiQ9aUfWiQGtnZNzgL5NEdK+uLCWiQ+Zz5An8NkDM9a4wFD6HB0849 Yg4JQD763+0359+gQe5cWOMDNyALjLip7FlFg2Kd31xizDB4MWLnvcP1aFDu qFd6bAQGpbvKJhlN0aDmUoBzVj4MtA9evH62HQ3a6r/lytjCAOtVv23L6Wjw 3mocldOgp7cm/d1eRTT44Cc67e0D3Uv0905SnqPB57uH418BdAv+AVAPQYMf owOjWlPQ6XTKhlsuBImMp5sDiBrQ+kWU/+zReCSyxdKJhNJBK297VUV4MBK5 XhnXVVOgBWdnW1RikSiyRNoKIdD0upmRAnZIlBRafBNhCY0/6TdsiFxIlDXS PNwsCY2GveK83FQkqofnZZs1Q33nPeUCpYNI1Hn22SLmPtQlZh5/fbIfiQbt 0gtdZ6HW+49NedxPJBrPujzg2AE1x3qHWJe7kWjCmb7TmguqPeQWJONykWiu 9fbTnQ9QFbnalZ08hUSrQwK3Bkugskq25WoITY/dRTst/mioZA9KrdzViUTH tNvDDk5QEfTwof1yNBKP1nZdeaAC5b9V6P3HfyDRdZJFZmQDylOfnRqKSkXi acY9naIDUH7QU+Pv3QIkeiuGnz/2FMqVW/DaHwkk+lnUCmYFQbnYx5frx9eR eOHc7+oP5rT1t1cncr2QGJqk7y4jSjufVrwjmh6JYaWBrCfnoPwhm2bxNyIS o+prTbNo8/ivT5ohvRQk3upmCR/9ARV+o24HWn2QmDBsVyuwAJUMYUFLUXxI vDuZ/ttmESpf2y5JJgciMXVuWi92CaoC35CUKQ+RmLmm5d+6DNU28yGjUilI fMYQWki3CjV7dobmXRVAYh5Xy+yOX1BrVW/IG66ExFei3ArBa1DnHbZTQooT icXyR9zf/IH6rKC/N8z0kViz4/t7xb/QKInBVwjbkNhoThR234RG8j0LNQZz JLbZRx7M/AdNCQ8TY7No9THgKdQjQA8toj+0ys9rIHEoyI3VhhFavvZTmq8r I/FdWL5pLBO01oYNJtTEIHHqPtTSsUK7H+HgwVPfkbjWfPbVHBd0zcW8c8yc ROJGX/GsIg90dz+b+NEug4b0oxsK7rzQ80JSTli0Cg05FpKyRgSg7+ii/fI1 UzSUlqi607INBlojr5jtF0VDBWXG7n+iMCgb/oj+RyIaqhJsWI3EYfDqo5/r AZxoqAUPTS9IwCAldnq9ZgQN9Sw+hRdJAlnuR8uGeD0aGjmq13yTArJ/+AqT PQENwe3ibwVpIFesWOKheTQ09WnSc5MB8jqrFU+hPBruD+Hwz5ADikGozzCd NRpaX3d89ZbmV315bdXBGQ0PJjye5VMEStaDjQAoQUOnh98UDtD41DHBb/2Q BQ2P5+i5x6gAZa5mReHkZTR0LwrLalYDKjvnEpFVGQ3PVHe+21QHqlTpuf07 FtHQp11AmNavqVqpPxoMafr8yS4Hg7SAavjSktP7BRoG/5d357UOUHf8l2P9 h4yGl2cWu2d1gWqgES3+7Sgahi/vZJUnAFU1a8z/hyca3vh3838UXHc81e8X l1BoaZGojFRCSMo85+57JakoJKlQ0aCUCiVkJ2RvJSSUvffe3CEqUigaqHwT qX6f35/P6/OM87zPOe/n/f7jXqq1JnA3aP7NtLNGrUCRXo+4vcDlE0yNdk9G rZD1Gyv69gFnePPrXC5x/wgZ2zkxbeCUJv9n7fIetWKVXuwx1AFOUOZlH5t9 qJW0d97JVxc45mdeMpWIcSqFml2nD5zN82n7gkZQ66lR8MQfAPbwI8nFC3Wo lWM+oLAPgR33UKjmgChq5dvKnb5KBvYh0VTh8yWoVeJ4MTGHAuxFB003rE5F rTpffnF5OvQeOTEeWEbMb9bJYJyegJ6FXb4P9d1Qq33ayCUlCHpSfFUTxQk8 eOax/Zs40N3Y7+l2uwa1xpV2x2ywho6wzZbf/o2i1pfhgVazxdBe8fvC953O qPUt4s5cZBq0jb2/9SG3AbXm/3ZYrPkCrRr+SHFYitrLe203rrgJjf3nhf6b f4Laq31E9x+Qgkah8jfaD0RRW1w71zWwBhr2Bf7pkbmB2lse/3kjLAR1Tyne 33sWUHurWeoyxlOonbhy4bd8CWorLjfQ9TGE2l1ZiUu1PVF79/XIhMVhUJ1C a7Qu1kTtfTt1O8l7oMp8cfLn2m7U1n37/o9HP1TKnEpS35SH2qRwf+VqNyj/ baO27nEPatNZu0783Qxl4/zrzA++Q22DP7xg3TooHZd7sPpmF2ofzHOtciX0 2EJKuvOcLmqbnJWZLBOGEgWaazhzArXNN7Zs+pUFxbbNkZn6y1D7RM+lg3sP QlF5t7m9sDJqn7639s6171C0/ful1xOA2me1yp4XREJhdsHB3bR61HaYtH77 QwsKDe71CvLcUdvx8ZJVam+g4F9DK6yzQ+1rx7LR0QMKOsvLNmcQ328tO+L4 XA4KClJCg1jmqH27Zi75K+HHC2Kuj3AaUdvrWlKv0nlifl6Gb/RP1PZTpC9y WEbsN35eo4w4L2joi9rT51C4X+YPll5G7dCHYafGD0Phc/XiqqojqB3J3Bem 8B8U7Xwl7uPLQu3YhaE622goqqmfEVYn7p+U6/09VReKL/qVciKkUTvVbqfs +7dQopZdFGrUj9pPJXuPyHhC6bInzsdsV6N2dreLl7UClP7pkfsmJo7aed7S +YmtUC7gtm7wjAJqF++rHxm8ABWbP5CSn1NRu/yr/VqplVB5UPv1on1HUbvm 0SqqRR5UhUbMlLuMo3abqGXqy19Q86S2ojpOH7W7avh56+Og1vrux4YbBL4c 56eCpvpQJ1sgGuaSi9pvBv+zZd+D+vJ1o8/EF6H21Itghc7V0KxI4f9xsBK1 Z9qeqE79hRbZGDsBeQPUnhut0Fn9CVqlg1Xd//mhjoDEp4NmddAuXmaxeO4S 6kjcpbuMXoXuxbmb8g4R36XjTngusYbu2Yw7EcZ3UEe20DlI0RB6rno7xJtZ oo7SxKNkR3notWBWyxVdRR31xaWZD1dBb4vPldgvBaizV7qnoHAB2Oodd8Nt ZFGHdOhvy28usOfGa3YbVqIO/cI6zqYa4BwV8BgeTUOd/T5Kg6Qs4GQVXPIU akQd42TKR5to4PxJeyv/UhF1TMssvvneAy7zwTFFMyI+C67T78wrwL2v0Fvy ZhZ1Tk76C3ZaAbddSWAX/0HUsV2avHLaAHj8Z+KP+dmhjr1sseQaTeCpR9+U k5tEncu6XfKassA7HjeY5yWJOs5Hx1TMVwDPnZZ45z9v1LnpuLDPdR54Uc5M 95gPqHM7cA058QPwMpeT+QPVUcfriaJhLRt4BQtjZ5HAw6+adHS0CnglmhF7 letQ5/6A2aklmcDLL2gdSfuGOmE/LjsoRgEv/abEpyUE3tHLfa4d8Abew6A9 ttpvUSdhW8IdR0fgufz5tHTAC3UekQr8H1oCz2RsWcDOx6iTfrz9YRETeIon 7e/e5UOdrGvvEwYI/v4V+pxrqY86uQ/m0he2ALc6nV71Ixl1ijJX5W1eBtw7 bLWYW8tRp7xhW8X/f7+gZTDkeu0L6tQM6TfZjBLvzcGZMblfqNP4y7THtwc4 MQrS8c6fUKdHyWu08ymw3zm/jLfORh0ePXZyOhLY7mG3vM8S+L6yzp1b40no 17bKhy5rUWc0fHi5uQX0aoosk/9vJepM5MyKu9Ghp+bVQJLlGdSZbF0hk6QO PUzzNSfVdVDn1x/dPWMi0JWo7N7bU426y22jrRzLoS3E5N7WX/dQd/Wd5+ce ZkCrf838+sBE1BWPabpSFA4tXl2HlzCJ+TKdMz4LF6DJPdAsL8cTdTU1jZ/7 SUFdirkts60fdbXn6rNzMgk/V3JzpZgK6kKl5jPePqj5+UY3RCALdVlUqQxZ E6jiyuQmPFFGXaMlD9KY76HSusFxs/k+1D3czp962RHK/00/enLpDupaHhpP rgiC0ltGek9tr6LuqXXHE0ckoeRYUORrORbq2vZ3JQhnQPH+C/Eb3aioax9P ilPVhKKjrY/bnN6j7uWTBTFHG6Dw1gVnK+0p1HWW2xblfhgKikWSB0plUPfG h9iIx8NQsJw2E2H0AHXdM5c/bLsE+e7PJtmTxHmeFz1Cp39D/tJCI/8wTdT1 Vf0RIu4HeTmNOc1qLagbOGMXTPjhPMedu38vHUDdkOKBIJtUyDuwyeDCEXvU jXA1DAhUhzxa7fU1b3ejbqx+tV9uDeRZyGjwdSxF3SR+dZ9+I8gL2P31dNF1 1H3c+MT77xvI4w0s0Iw5qJvhL+G11R7ydQdrfyuvQ91sw8C7+39BftWP7m3a m1A3b+XfO1d8oOB4uTSdsgJ1izhO7jFroXB1pXngo1LULY8cda1+BIUjl7HP NhR1ayyO3fygCkWdasuuG/ShbqN0241lVVDcbTRl77UIdVvf6V3fbQglE4se FzWMoW5X6gtn81dQtuHyEisjfdTlnJO74nEOyk9mtvi2/UHd/p2RjmmE3i0r 3oKDJqj7Ls/twsxqqM6cnTYvlUXdD9emHCSToSZx2un2mwXU/ax1+jxJBWod PWPMkutRd6aGaRtsAPU7zp9weuaEunNe5WcK+qFhpc2uVal8qPuXoXLqtR00 zLcpvY2RQb0lXWuttntC09D0+oOa7agn+Xr4aF0ptDXHDvz0/IN6m5OOmEzQ ob1B+Gi39HnUkz/ddGQlFzqq2R5bq46jnvJE1kHLaejKsxQ8sncn6qlnbz7g dRu60x9Sb8uLo95ex7D9mcugxzBAQpbLQj2cvcGY3U70118UcTNEPVrZZ7p0 EfSm/5q9K2iEega3rahUKrDXpcvl75JAPRNBKoZaA3usKXDxwzHUM2sp1i+e JPz4w+Ak9QLUOxGkqDvkBpzUDsmxrmOod3bNKq2d0cA1PnGX0RaFeg5VIZ37 TYEbX+j/NjEI9RztxU45rAbuSKrMdoUM1Lu2LvS/gC7gyf8IvtL3BPVu1q72 zwwEnvUV658yG1Hv9oWH0m0EX0X8iEieSEE9L4k1uZ8EgNdoVHqvaz3q+daH 04VrgTdpvbS+loJ6QZfXvtpxG/rElvpztxHxhUpGXGLpQJ+yytYdVwg8I5rW 85/7BX3k8tJlq7ajXqxTZKRfAfQdStmRdpbAL0laXDHDCfrMORd3x39Bvcct UVUtKtB33DCc0ZSNehnOEoc/foK+o6JdO8k81MveHP1xSTr0Gaw7T/sxhHq5 7Rtct9lA394r3bEKDahXdD12JUMG+jap3zk09x31ymUlH9sNAe/v2X+2cnGo V90Zt9cnFnj9O3tO/u5BvYabG9vTjgHvWZz5sSxH1GuVjz/ZtBZ4N/p7Ja28 Ua+zR+rHWA/wUFQueyuBJ9s1wU/wPvAWneAqUQtR7+W2TVJbDYBbyfd6q+Z1 1HvNTnxBWwLcq9vvekcS+R++vZlq0wBc+eX7NPK3ot4Eb8uFVEJPuyw2z1ms h3qTHil89b+BsyH6Jd/ZUdT7oSQTPlIC7OIfckNNBJ4LXrKVcmrQOyE7Fp5Z h/qLVB4fIk9Cr1vEh7KW3agv+Epu7HQm9C5ferTU/Dzqr1TbuvyRPPQokIQ0 NQtQX+btNisZCejwdeJuErFFfYWA9O/Ig/bQmLFtAs2ov1Nzu491GLTFWd89 OBGM+hr3d+Qki0LLi/y1d5peoT5DZ+e/TX+h4Wd8ZUbjI9Tf/yHroX45NIiN GFzxKUN94zDlbVY3oF79SsNH+bWobz6hcjDhG9T6LFUsPReN+lbhOSOVOVBT drBq+EMG6p/BXS6D9lD9pVHr0cAD1L8QpZosNQqVq46nSsY4or4TOVdDNwXK KydvoxoZ9a9NqrVYWkHZHYvHC6flUP9WTJ6lmySUmgZtVxtMRP07NPXpuJdQ QjrJr3ovDPW9pvO9y8OhmJJ49tZwIOr7xWtIvDaGohMC6g/c1qP+fUZB1u/l UBi879jOPZ9QP/THHpJkGxS87LG59P456kcJKDzf1AMFKpL9Z56ooH78Oglp 2T7If4QpTQ+I+FMUhAMV3kC+RgGfsmEN6qdpzs8pvoO8sfaKpoqXqP+M8eWs ygfIK6x7Fxf7DvVfmA3y1L9AXmpJwMpHH1G/8Hw3RfM75OU972gbDkH9sls1 udq/IO/tg+qNUf+hfnVg3mb9v5C/Uy60dd4L9RviH9+nCEB+hGQzxN9B/dbs 8N8MESiQFvbs9I9A/a4qn/P7V0JB3bnpho/pqM/pdnl5kOBv76imf7ctUL9/ +DztiCQUnX41eHKnHeoPfrPIP7YFii1PDBZ6Lkf9EX5DmeMKUOKoIXLZk4T6 42v0HpxUgtIE842GUVWo/1Ve5c8ZdSh7n7fLZC0xf5YuNuCgB5W5ipKtLkR+ F44tZlwmQ/XeZ52Fn6cRFp2dKbzKhJpz4ew9iv8QRP1fhrqaQN3GB5LXjVwQ VsW2/LtjAfV8WsvC1GgI656VXfSyhvrP8CzY+QXC5s4EVqADNPb8tfCMF0TQ EDuzKM4TWutS/2SapyJoyZpcTvKDttqGQ5e3EmP93bTBx8HQXrND/A/xvgLT dHvpsxjoLN6+0pZ1F+GAneS254nQlT2/hh58BeGwi2hEfip0J3t2yaxKRLCM nnQqfwE9ZRmmB02J8089ffu2muBnxdkHMhK3EOzKeg/UV0BvlNueq4VKCI5v Cna0NwP7TOryzZ+/IFz7+iSquwPYdSH+TxIEEG79jRLksIEjRctWlm1FuLfl 5rvXQ8Cp6p56qB2IEKDmcPDtKHCXrFOvXFON8IBsWTnyCbisxWNBheoI4UeM dn6cBq736cMXNngixNhAzOf/gFuywOU83I+QeE1tydQ8cD+kCQWrn0J47CN7 bYYPeMs1P+o0JSBkRK0Z+SUEPKXgP1+95RCyMwQPLSwDHjXcubo8BCGv5GcN 32rgmWqomKU8RihuHVcRkACe1SmeVg0doeLVQPzSTcR7ID760lwZofZzu8gy OeBZHMu6VEHko2mh4saq7cA7sGN/rLE0Qsfy7A9rVYC3Ly4jnc2H0LspyURC A3hSDdZlfwsQ+naF1ElpA3euOrcj8jLCq4khebn3wGUXr0p/1ofwNnWnz05/ 4D7J7iJv3oowanVjfLcqcK+0fr40E4EwIdFkoN0PXG1NQcHV1ghfOWuyyB7A WZBRPZdchvD9/qkVBtuBU1LFDXBHhIVFv9nmLsCR/VHSGPUUkc9KPlb1AbB7 BXvuvk9E5C/fv1/jDLBvVT9LthhFXHItOltXFHrr6IN2cAhx1afdTvstoEdR 1Oi8SwHiGoaF7EEV6HY10W078wpxXaoH5wg/oX9fndr90QxR0qprj8VT6Bi8 9/EL2w5RjnNu7vwvaD0gaX/cbghRQfVB5sUOaLGnZeTVNiBuv1903CkZmv0t J86/34qozFxceZNB1Pu69pz2v4iaFYl3/SKhLuwm58cCEZ/Whkb1IHuo7X25 y/3hNKLO9c8jIfpQK3HI8YaJPSKqadGixqD65osVEzH1iOTgkz/jSqBq7wjp 3Qw/IvWzT3pSEFQu9Up9f0gYkfWEK5yuAWWjSYvvixLnGfLPlz1bCqXfyvk4 s9cQjaxlHHLeQOla78//tfQiGlcypfJeQMmBlH8bVlchHpG81FnkBcUJT9S2 eHIRTV0ibpcdhWLBFY0iSqsRj3ErdlUpQpH3/nF38g5EC7X3w7V/oWhj0ane uOeIlg+EQxvZUNgefqC7XxLR6osquTUNCsPFv1qarEc8xTr6o/MWFF476aWu 04Z4Js09tdcICi9fIzVLdiPaLU415clAoedB1mkg5p+zbhfqn4HC7O4iOy06 on3lt+I3LVD4raG6siwF8eJGiXPD8VB0YHzMeOV3xMs3YMOoIxTVjqiYxHxE dOLZto1TofgAw+Xgq1JEZ/Ug1y8SUDwV2X1jSyDi9Qf5SlNfoCQDxnZf0ka8 8WVg8Ec1lF5Pumuy8x+iqwFf8GwYlFksWpVzqAfRPX0b/LaD8kNJOg91ZxE9 Tzk/WrwCKm8pScypEvO9vx2pnhOCqvqLieWx0oi+d9UHJ/9CzarFIp8eGyEG JX/b8GqKeD+H65s5yxAjhy+G5vVAQ8urP8yzM4gxjobPM1qgMY1Wd/IIgUc8 387OxBpo8lV7yfodj5iy+ZNwQC60GG/WE2waR3x20u7u6TBoH/u9heHniJg9 RUsyC4CO5r1J8RZEvb64I19p5AmdT5br3gmSRSxMfD+nfRW6j6jzuzM5iCUq deKqF6Bn1beFw8niiGVVKXsUbKDH+bd4SLY3YvXQSafVJtCr9uj0kUgi/rpL +g+WGkKvf5BT/xEVxIa/0ll/qdD7plp0Ps0Asfn+QtuMLrAVP2/eJ0LE1yb9 evzTHmBfbQrw2qeF2JFdLjSsQuifZWdWjBP11q0XK9+nAOwZ9z2rUqiIvZ03 yR2bgLPzLeiH+iJyT5hZ160HjuXfjJ48VcS+r3tvl6wAjm92LtuLhjjgvj4+ Rwg4zxoGo7XrEN8s+6/s8V/gtMqJ72qvQByK5/bHzALnbXb3s88uiO+U8n8+ mAbO1KH1yuvTEEcqHq69Nw6c2X9Pw0L4ED8YXlF3HQbOXP7iessjiONvDhk7 9QNnxsHqnvlPxM8XVC+dJfz4+K7coYIixK8LK4NOtACHtzxLQIqJOB04mXmk BjjlGx69oX9B/LGxs4VVCpzYS3e2nSH46b9nWYR6AM5V5ZSTO8MRf+kECezJ BA7tdo29NoHvfLuD7M5HwBF7UEdR7UD8c9wAZWKB/TI8uHSC4JN/n3dYiYcB O6rpza3I+0jid13qtjwA2IdtfwVtWUDSktim4jlX6C3hZ1xTDkKSiOIT3pQz 9J6JqnJ70oykZWXeP8YuQK9I6y9Z+wgkib2i7Oq1hJ4Dtu6ptRJIWmsve6DZ BLo5q+ZfOF5E0vr5RQ6VhtAteO3jqvrDSJLaUJ3+VA86jzd2eK56hiQFc93N dzdB65hHqsjVVUjaPii79GoOtAo/PT557gOSdp5a8s1OH1pU/CP3er9Ckuo5 Tv3+E9Dk/iRBJPkrktS/lGbpfYHGpy5ON+0eIGmPY1KEqhs0DOw8SRvgIkn7 hsPZdfFQzyCLfmz0RJLuwiHjpUpQ5yt9fIbQ0yTw2LtvvhxqO9sC4n/VIInq t1jk7SuoucLKa0ndhySG6PgPNqFHH9Vt6BiqQBLrQdebhnmoupqeFyh0DElG UbE5mRug4tAM3xbFOCQd2ugRFZ8B5afMg/WWxyDpSJKdx4N9UObr9GolczmS jsoZnr/bAqWNSycymZZIMktXP+xsBqWS3PYP648j6fhOCR27cSjxd556utQe SSdy/soT71WJmFOIvAARj7X66HJDISjO19H0J91G0umi1p/6kVB8WfFQboMc kmy1n79VU4Bi2u1RsUgyks5WRbTIFUGxpjWTvn8ISfZk19z1dCgmDS67HpaG pAtNp2KX8qD47KfR1CgiP5cNGJ6/baA4wy7/3zcfJDl1KTt8nYGSxWuzVVI3 IMn58BqTYW8ocUn2v/xID0nXeXN6nLVQKpjQZ2P4HUk3zd8qNKZCaZafhZZn IJJcBxtXluyGMoeG+1zjBCTdPpX5K7MOysnJSmFrLyHJYyzkXcJhqFA9PCDB fwFJXuevtz14D5W7//bFZhP43ftqme95BaoMc3k/zNYhKeC/7ffOhkKN5V0R F+UxJN2/ueKSxRaoFVQRld/tjKQHCzPHDF9AbZGUhbtpIpIiBGp2qPVAvcoW +8QdxH2S1h7rWFgBTWfEYvXTOpCUEq1XOJkIzdoHOcLfPJCUKiWX+E4FWtby FQsWEOc9lZtybDoAre1+czaXVZGUv9t3bUgQdGqk9QY9sUZSYfGFP15S0CW8 uMqP4EtSic7hD9eeQdfAZX+tUmJ9JWVTiUUb9OzJjzmapYSk6maBlAOEfoio EO9/P46kOoNPAfAJer4f4szeO4+k5sNFlluFoTep7e7L7DIktT4zgA320Pup haagUIWkjsVDssvbgK2WvUaum8h3l+UVQf6dwHbWZwTEzyKpp1Dw488gYOdR C0eoG5HEWR7T9ukLsD8H9UVb9yCJZ6ec/fYAcDZ/+CYrfQRJL6tqQzg5wDm4 jbJd9xeSXombXm0h+PGmgvytj41IenN54mjFJeAk1GxrXLUCSUMt7lovuoFT 0bd38EMAkt7JiEk9UQVO34HcFG9i/5Gbqf9iQoDzadUtnSKi/8fY+97fJ/jy 13IxdqIZksYVOxo9DwGXT/PR3QFi/MnLOuN6HnD574nG5Igi6cvrmUCH1cD5 OzMhwTVC0pSG36WTV4Dzn2+UxclMJH27L3XoCAc4HyjqMycJvvkx9kKDqQGc XvXwpq5IJP3Up4rrhgOnyNZAz2MeSb8iX86r/geciCn/tlyif+anHAa3HgXO 5U9yj6Y2IekP41/NhiLgUK9capIQQ9K/5IePV4gDZ02mus2iSCQvmtvmw38D 2IPFgTm2RkgWOFR+7mc/sB819Ci5VCJZmH9E5S3Br5via1o9OpAsetxFjDMP vbzu2ftyPCQvLxCZaTkOvX6cNzHZekhebatelisFPSO0KWOvUSSvrWxKeOIG PYH7dmX7OyBZfL2FR8wg9Kjyv1bvVkXyxmZPuifhT96HKIQfr0Py1h29bJMt 0F60e6DAPwTJ2zxtC5ke0Dbqo5kTvhTJO17NReu+gzaxe3+st0UjWSVIxmrr I0Jfak28Vp1C8t5Jx4nZrdC4Q2Pk3u0xJGszBDo++0DDyfRjnwtHkKybFP38 7Qeoj113kPHNBskk45prLRlQJ32OJH0vHMmUpyZmlUuh9qyPkRNHCcn0ReM6 ueehpkRsyUh2H5IN8lctilWEqsFjCWObkpF8QPTxaHAAVD6L2xf5rQXJB232 Nnt+hoqAP5eMCL9FPlTRnuliCOVujseeHz+EZJN1J+87ZEGZx8ufSefuIvno xR+O1sugNEbY9XdwBpLNmnyPmFyEksa26C8tQ0g+vnmjJrMLSpb0vHGYU0Dy CZfnG/RUoPhkrbRL+jkkW/P/Pi+XD0V1U4+8om8j+XQwo1RkHxSR9sQN1Lgj 2XZD+NLpSih8vckzavMzJJ9NHTZ7SYbCUCkpQaYHku1VlTIqm6HwjI8Q63Yc ki+U3/iVagiFh2NOXJAm7nOZ3sgM6IVCqxs3V/vlItmJLRbtdBQKfRXrVUYI PJxPnBg/9hoKO+LTe1PUkHx9PHOfvjUUqSQlql0n6uvm1Z9+8mNQlPXJ4MNL HyS7/iP3i9pDMUOiHP0KkHw74MG2b1NQvJBVeuf6eSTfXffapd8ZSjpXaL0o IvDyStnWXDUPpcWBQ42x35Dso+Qs/uQOlJUfj7q6phjJfsU1Z4MEoPxl3OH9 FiuRHEhZVnyFyIfooZbL8kwkh5g/OQbhUF3QLWF57yaSw0a/pStsgJrwjZwz 598gOcJRb3ZZItQeU0HTvhkkx/r0RQ4Q9fAzY3FXbyaSE1bLfqhRhvreaKrY yzIkJyVc1kzLg4a8Od4p/1gkpxYs6btaAU33+uUWiRkjOeed1vrlPdBmYosj Z7yRnHvhnu2MKbTvvy539z/ivPzZ3sJXr6CDvKU08AWRj9IV9qbpo9ClWBj9 4tlpJJfHFD0JPg/dkm9ESouI/Fdt5f/PeRJ6FgeuYczKI7leJy6C9At6xpV/ vi7xQnJj08ex7e7Qazr0+U6lNZJbDmvsWckPvdXTZSsnq5Hcea6D+2YZsH2z /Yr81yO5e2aDfF0osEcT1lYMSiGZfcfW+SmhH3VdyK/3E/l+GfF37XUZ4LxO dbP8mYfkVzIGNpZpwJW1vuEdsxXJb7IiCyhKwD094BsSS/Tv270jAjtygRvf 2ytEIuJ/V7/LZJUmcHvEbMmH25E8auT6+Gc5cBfsrqobEP31YaD5xyACT+bJ DadzRH4nbNdS6huBB08d4ooI/vg8bf0w0wB4R48M5wR/RfKka9ZISDfwbO9v O5K8AcnfhOZ2u5gA7yL19JxQEpJ/hNG8TgwQ48vxiQNkJP+UDuVQrYBnJ9rF ZO5H8q+MITnFEeAdkxoXMCfs4W8Nxati54CH2V1+GopI/lN9vW72K/Bke45Y fDZECp9B/eqhK8D9k6AQHVePFP7MSsumFuByrUPZvopIERQpTnu+Cbip83yf 7e4hZYl97nSUM3Av3z+wcUkeUoTbsrTvtAFXg6JqPDWMlGWKad7ntgDnB2vr R6etSFkRkNxlfB04Wb0MV44CUtYYRJyWkQXOKnrSUSoVKesyH2QJE3xclv2C nyOBFAkR//++dQHbOoAspnUSKVJtt/3rbhHvZ+3PqYJSpCgYnM090wvdE/am R8z1kLI989RvQwXoFnKIuP5KCCk7RSxpGm7QtdnBuGtsCCm72owHBLdDB5Mh mE55hRQtA22+NA9oCVi7VfjrKFJ0MjUMgvugOS60sLfzIFL0RVTCXZSgKedX mnuDFFLIbXLbGf3QMOQyIjP2HCmGBssOTqhCrbNt4LYjhUgxyhSK6fWBmqcn tmxfxkbKIRG+kdI3UN2e3VfydBAppq3/uQT4QcVU4IrpMm+kmO2Yqr0yBOXx LQkrhTKQYuH/SfQ44c9PvAimasQi5SRrKGnnMJRs8q4vzXFEyqmn/RNrNaF4 i8GJgEg3pNgIc9QXgqBIy8HbiS8FKXbnO91G30OhfYb5Bc4DpJxrbW7q3AcF BTmL9Xv2I8VhR92qwmAokFi/RROPIuWif4VFwijkR9gxvBWI/F6eKEr10YZ8 5ZWxrhmNSLnCejF5KQTy3l6yk6x+ixTnp8/2HSUcSlYrb8caOlJchJ94gi7k RRzJV64NQMrN80kd28IgL2Y7XtaNRopra+z6leOQV2IkLtsShpTbO8JP/tKH vO9FMiMng5Hi4R/8dDgc8pk6j2++B6R4Tvj9aPkE+SWpB79HiyHlHstLLxeh gJJ8fXKzLlJ8n7r7xkRCwXg7rNiwGikBwi69d79AYXrmd2NpU6QEnb+y0Z4M RXdyj2/84oWU4NYLtoejodhRV63CtgIpoTvsnmtPQsmNiJivMb5IeehvPSdH hdKomRfSp4SREs0yDfoxBRUS1K9+gr1IiU04mtL/BiodVYdvNHxBSvy3Y0WV bVD16uHVR20XkJISYz7smwo13/R+5ogS+cicsNLYaAENBxWVQ7xUkZKtd5LF x4RGja/qnvFEvT0PtT4xtgeaNq9NCrnWh5QCrdO+z1dB85/um6ceUJBS6W/3 mtwI7b0KO7LNSpBSPXh2elsedDSZPywg9CClTu284LJk6CzDlngxov+aBhxU +m5Bd5zhBeMtS5DSqnyBUn4Oeiie316u80BK+92LZsmm0FN8UGF9bgRSenZc 9rRXhd4o84HCB2VIYbs7Rh3cBGz+yX8iHueQwu11ytIQBbaDjP2BTgLfgZtX eX/+/3/4W28Me21EyutO50/vCX0WdOqHRvF6pAzJXPvXTPjZUTHOY79EpLxv vbEjNA643o0RF7sI/Mekb+pf9wNu58FYZdtdSPnodOvI8evAW2X+VLHNGCkT ja7n8AzwDrwYXNzJRcqXDW7uW42Bd08hwUDfCCmTF93DRPSBVxRSRz68AinT tbfTpnYC711roZ7dGaT8WHennCsBfUIv7t+QkUbKf+c9ekqFoG/rjh22OUVI ma28O5YwA316UlSTKKIf58U85z3fQZ+Ri5/l9RtIWbD1Xnm2C/rMleN9coj6 +1t6T96wAvpOaO7apvoZqYuW+2ipPYU+i/sLJxgXkbr4lK/R+kjoO6TZU7yf i1TBQr/Tv72hj7TXd1ZLHKlLhf1dhp2gTzF+ya9pN6SKnAgIarSCvuXX9lYt V0XqstzAlExD4E30f09bb4zUlYJBRQ+0gFf9bvZNTwhSxczvtzsrAO/B8z5R i0NIXZMdPGy+FnjmdpzjiYJIXb/owX/6i4Anvat32DUfqRKmoSKyk8B9vU/V pSMMqZJPwzYveQPch7kZ+luuI1Xqz8PdXwg+prMVcnIvIHXzoXBmbzFwZjip QQkGSJWbi7wSFwYcsnP2b9IXpCociPL1uAPs97HNewKI/banRMfbXgD27Zzw +PJapCqzYpt2MaD3WXfm48dHkbqrx4QpZAa9u//rPX95HKnqx5a3DtpDT0k3 Kjy+itS9Nh6dgcHQ7ZzecuWGClJJd+y4H/ug/TD9Z9Vh4r5UoS1Hq8ahjR5l dXZZFVLpQQP9EfPQquM9E5xVjtT9sYZvqNLQrDj+okWQhlTTQvWRZBuo19px sfmdHlLNdL7YulyHuv2lZi9TnJFqUffko5Ef1NrOfEE1Yr+TPRKfFrKg+rxs +MQOZaSePsq+wKmCSu5QUS/h96k2g4GTmT1QYToaI17RiNTzn/5+N/sBpfG/ lgio/EDqBacSZ1UBKLG1y7HvR6Re+uX0c8l6Qr+93dVbdAmpTnd2ugxthyKa ufWd2VykOguOzhVqE3qxoFHN7CZSrwclugYZQkF4ccddHQuk3lxz7M8ZK8gf X7lFaPNipLrGit3RdoR88wPLSkO1kHp7S/siMU/IG59YvDtiGqke6d6e4+GQ FwWqii9vI9VLRV+gOg3ybEKUw7+eQeq9glmfyBLIO8TX8SzHH6l+OrlLL7ZB nmXMnODXLKQG1NkHUN9Ani+z4DX8Rep9lvyyjZOQ1zOkcjXOBKkPugfv/+CD fK2Vk1nN5kgNOxq1sk0M8qtvbDk3TMwPHzQOTZGFgjMR048StyA1ykZkzQ0N KJTvEup8RkJqzKf6iIN0KOJ3WLfG1Q6p8U7u4gpmUPQ78c7Hg2ykJv7SjP5j DyUr70odW6+N1JTb05JcNyjVWytgr07kLy3ozCbPZCgfHhL6SK5D6tM1Usnm eVBp8tQgWsgUqc9i+mRVGwi/cfD0aZ29SH2Rzto6NA61a2q4Zj9eILWsbpey zi5oKFM7erLhN1K7Zn/r/KiCjlKb2fm3BD69R04Xd6RAZ3oAZXutIlI5OW27 n3hDV9icVO8/on/7bWN2HmNBz64LG7qTK5H6qpYvXVUJehK/UQ86EvUxKHVO TngF9IoWDrRdKUDqO85eyTIu9L7O0QzaZIjUUZXEyIfFwNbHXzEP/ZD6IUBo 9YX//361N9ovSQep42MXg2luwJ4VVxKd6kDqZ+SJbDoJHMMO7akH55D6NV73 3iyZ8N85Mx+vb0Tq1Gzqoh554EzctXGSIfL//Yio+9MlwFUVTky+No/UmZwr c3c/AddJbCovnsB/VvjVNYtO4GadF6DuzUHqnC3p2+4XwH33fqXIu1Gk/q55 enFZGPDETD7NlPgg9a/UqvGxa8DTTtR6GHcbaXwuN2yqzIB3Ism94kU10vjZ b4ejtIF3C9QZLbeQJqjCsHSUBl7IuTeDxkJIW+Kf08/iA17ycqmG1AykCY+t N5Eh9OVTmcL0xgKkLUP37vkm4D1LiaTGyiJtRdzYfk4G8NKDXi/RkkfaqlnD 5qxA4MWPdwSOfkHamsMFlHuXgBdY/Uok9QPS1uVsrLYi+N9Z5kHus0ikSQh7 6ezdTehhWb+fhD6lSdp8Kl61Hnhqb6y+OBHxS9UcVh+fA96Si2FpNAekbd5Y mlNL8F/f3NX6tKtIk3GRUYytAm5yxmLV0PVIk2P7pV1NAa7NE/ZexyykKShP yxp6A1dObvBPuxrStvsfS9xqB5w3x876B8QiTRkVIl4qAQdaqIamc0jbFRcs 9mIlsD95vVQfLkea2s//7vt9B3bIt0Uqz94hTTO74Z52MfSy+a1jfeyRhhtP OyeSoUf5wfxrSV2kka+3TrtshW63uL63vF9Io7LVLhgvha5YPweqbjbSWP58 Nos6oaPLoznJaBZph38mHrE1g1bFV9QhcwbSTA8LduvrQAslt00pUBNpx7Iv 7hffBM1WGa4ib8aRZmmjS24ZgcaECj+n+Uyk2fa+Ule6BHVHTJ5fG+Mi7dzJ ecnZa1B752t1Vt8OpNlPSi6uc4OaQtWdZinBSLvopvXlvjdUf3xyhWf+HWmO IuY8s0CouimEh3A50q5E36ySC4PKzW3t73eEIu2aQkzaZCyUDypEujoh0lwK Sh+UpkBZ3l63VyeuIO0WeeCGdwaUJgvkP7v6EWluPXOnDj6HknT3AVW5JqTd ObnBQLIIiltvSYMIkY+7X7XUx6qgWKjYw0ZmG9K8Xc03vmiEopOvFHv0ibGv 8E0B1w4o5Dm8fvGNqC//qOivdC4Unn3tdKWEyEfQ1pI+McKPi7sFmu24h7Tg /P6qN++hYNSXWlIhirRQ0q/09Akg7LCIzMQC0h72SIRcmYaCl7v83EYbkRZp te+m3i8oWMgrIrcQ+0d/MTu99B8Ugv3PFyUbkBZ364YBVwgKk4SOcv87hbTE pdG7k1ZA0Ubx0T3l5khLjiyRsl8HRfkSUmXVRD08lu8X3CMFxXa2Lvb3JJH2 JG92kk8OSjRogtduvERaBkm8r10RSjeOKH7fEI20zO691ZHqUCZlcimK1oq0 7BPHMk5pQblG5Mksk2SkPf/sEqqEUHEmJPTQBzek5d2MujnLhMqMdZnq1/yQ Vhzxcv/9o1CjaVJ6pobAt0xuVsPsBNTM/Mtv912MtIo8cWk5G6gtLym8kJSI tNquo5OlV6HeiiEf+6MTaR1CfaFjIdD0Tm3RtN59pHWF/7z1IgqaW4cvnk7X R1qv3PozronQUrBYTOQ0UR99cHSPWDa0+W/RJW83QNqwC69frw26Nh121Rkn +GFE8L/apb3QNY+ZUTpeSBsLX/eU0w/dPS1bFlvrIe3TC1PX8x+hp1A/bSqg B2lf9a/ZaExC7/q1eqIL6kib6ogw/PcTeq8UJAloEfwwM87bFCkAbOm/DtLS d5A2e/2/JacIvXphSexedeK+84Jrp5VWA7sgroC0hsB/4aFG/+wGgn9dTJ/Z pCHtn4xJbR3hRzUs0y32yyJ90XPnzPvbgOOwJuqFuAfSBfTCH5qpACfOfsWa q5ZIF2ovcJPTBE6jhkTtlWtIFzbn2kzqAWfccG0t6zHSRSseMcrXA5d/4uPE kyCkr9jsqOhL6DHxu9kTLzSQvspTf7lJE3AVBNTe7FuG9NVjotMyicBVOWti kkrMX8cYYE9eJ/g9bfUVpyNIF89MLyw3Aq5SjV+pgQrSJZdfi/ZTAK5s+bcs 2XVIl3KkuJr8Be7qR8nKUt5I38QRs5LpA86C13/p8RFIl9nzFidzgDPsbLKc 6oZ0uehsuXIf4FTdF83bbIP0rb9dhfysgBP5hSYe3of07VbMCVPifucLPpRK VSBdsXZ9h+wK4OxdcX36B3G+stxozuQHYP+Tn1+BIUjf5ZMXWl4N7LrtHaUG CkhXm/Bw9osGtofRrUYWMV/D0OiYqSOwtbrCwpyakK4l9klqagvh1zly0kvj kK7jXPyv/Bf0Gr4WyklZjnS9l/fe+/VAz09K2QCnAOnkBJkMWQ/oAevgFYcF kE79NxUwZQbd1SfPzW5/jnTG6cqLFarQ9VvR8eIHEtINt5vvNh2GTrt3uqfe CiPdND+kyp8EbTLpbDXSdqSbrbdKOSoJrUank5Xiq5BucVPJW/Y7tLgWtZ1+ 9QfpJ/VbWBWPoOnVl/GH3wl8TqVEKvnfgqYVSpKLFGqQbiNgs/LoYWikVv57 92M10s+1/uNO80N91Y2Dl4ZXId1BqbO44hXU83+oVpAsQfrFB3Gx/nlQd0B9 OMJ/I9KvmO61ljsNNf9VJHckbEC6c4kgeVobakwl1MM2fkG6iyRna+VqqL4p MbRInljvOnzp89F6qNxv84jPqRvptym6XXLxUGEcqbg9l4jXI03kxbQzlJ8P X+x+eQbpXkv7H1YaQlmU1OtbT62Qfs8h7XqAPJQODAmf63qAdN8uZ/OjC1Cq fuVfQPkdpAeokXXluFCSYtOu7RuK9KDwVZums6Bk245UK+UTSA/+ObSo0huK G+SDQgz4kB5q/mw0wBKKb6T+NdQJRPrDipvNxzSgmPoh6+TjrUiP3MzIlF8G xTvEDvH6a5Ee7bkuaHoUirfuC/PWW4/02NGRy5UVUKx1/ItxBFGfCYzcwwHh UGxrV6rbQuCdlHlnz7ELUPxUlbzHkRg/Wn5AQp4GJQKu78PG9JGeaiPT+LUA Spw9dXfp5iI9rezn1WJ5KJm/NpsoFI30p2LtMh7hUBrx0Us/MQ/pz84ldRsI QBnddL7LgOjX7Gpn9zUEXqK7HkzzpSL9xXqW4psRKB9h82mtWov0gvrvPpfr oLIzhkrK70F6sWSzhpY6oTeVFL9nJSO91CnuPX8KVP+xPTr94jbSqzbT9CI8 oFZziZKg9hukN9+KminXh/rvVSkPT8UivbX3wiPvHGjImYlz2rgP6R3bScZG 0tB4effCCaM1SO/hfXo2vABNc+YHf28eQforNb0zQmXQJutnzG0m+vmN3+pV PYrQ9vWSpCqF6K+hoQ9VMTHQXkxPyz/viPSRoBBJpRvQSYMTchb5SP/y8X3v YU3okTp1jWxC9MOUfvGdjWnQ4/xyodvyFtK/RQQpja2Dnraj019+ZiD9J2WP n8sM9DqYa0uY2iL9V6yIJskGeovMeA9XJiJ9fnpoVIQDvX/MuXUHiPj+JfpB Qh7h/255bL/7BBmL/rP8elYW2CWq4mnudGQIGKrFqYYC++saoHb/RYbQYyHm PB9wpH4dWXWEjYylc69+1jsCh1Fc4PVbHRmixs9Tg4aBc1H5U6PoADKWp3sf PmoMnPs61SHZu5Cx8q853+Ya4KQ3mdX79iJjtaly9sQu4FQUG9m9e4yMtVn8 x/MSgdO+UKlmdwcZ4vwvhd0IvuL5m15IOI6MDebPimm3gTNgYqf+cSsyNr7w sF0xCZyX5qMLbd+RsWmJ6ep+gv+6Y51cMBEZW6x21KR0Aqduo2lSIhcZsgV/ LznoAifng6d8UwMytopypDSyCL4U8V21qRMZ206nt/6VAo5LrF/5u3xk7Ch1 c2kOBM6RHLXNe4n1SqsObQ2ZB47iqQTXW7uRoXJ2K8fCHti/ecwbwweQoVo5 f1fuFbBblKfrn8shY/fabpWvLGA/CHfTaxZHxt66GwEe24G9iswlV3ggQ9tQ 8IlOBfTWt3rw3dZChi4vrHrWmHgPr9wNmiLuRxrP+u/SDeipOzXn68FBhuGK d9aWLdB1QWP1InofMoyiL7lKWEJn1IeAs3LOyDgk8zuCMw0d1TffPWHvQ8bR Peva90tAu6BbS6j0SmRYHzfQ1DoHzd4bFP4Jf0TG6dGXxjNEvT7hJduFiiLD 9pKNw4sQaGxb3ZDZ+BQZ9ndvp2wrgQbpY3ek95Yg46KoaPmIIdQf+G3NlzKO jMvh0X2Jw1DnffR3x4w8MpzT85etXwq1gtrsSMe1yHBRQ4XeeKg5pOlcqMxC xs2yTtJ9Vajen3jnsvssMtyoFpZMwv8tMV8j+aQJGbc7P7oImEFF36sj9Kg6 ZNw95hxa/QXKyw/UdX8m8PN6tyjL1QPKCttjzfWJ+vKxD27euxZKm4+Vv7lE 1JffzMZ3359CyfRwqdBlfWQEumcs5OhDyW79rwb0EGQEL9EUP8+G4ge7s+5t eoSMkJB6dcIPFAue85lPXYyMh5LGhsPzUBR56bHl3B5kRDwePBsfDEXkql13 tokhI1rZ3tNMFopECsd1fhxCRmzRbMKaIiicjC0VdJlHRgJ6F3cbEOOoq65f G5GR1CbGDhgi5lckHan/hoxHRxK/0q9AEYXvdMUDBjJSB5WW8gtBUZS+15mI k8hItyuVrYyF4iX7PNb1+yDj6TRd76YKFIdF1E0mpSAj6ybHTKMOSvaNfB5P uI+M54utr0wfhZK5qN2hig7IyA36ev/ZJyjlioz53I5HRsH6Wxlnb0NZ893v R0qI/ilKXlIvtxrKOduit6UbIKN0R/jgUBpU/Fwl07WIhozyfJlfsdpQtUur XG9sARlVus/XmHZBtft9iwXzZcioadJVESPeu8tTb6t+FSKjceDoGb8AqP1a FpmcFoSMltMjt6mboK50MfVacxcy2r44Rv/Lg/rA2PeVhl+R0f0voOv6K2ik 5F055LYfGa+2VmvZKkLLhNzwSibRX2+85o1kH0BrS6nqiM5OZAy933P67Qy0 PRVOUeqsQMZI4rMgC8L/nxce9P5D8MNX8ai3xoehmz3AL+5D9MfUNfbM8mLo OXzIYEW9CjK+cVcsbZeCnpLOXGkVX2T8DPFWp32AXneSd4BULDJ+TVYzFu+H 3lfR7qzjRHy/DeeP17wAtgb9+WjwIDL5hB3v6dwE9ut7x9yodcjkP/ss5tcQ cLYH8e1KJyFToPFDThHh7x37yp2NNyJzibxM/dV04BTs8In99xiZwp6W/WrL gPPdkGW+VQuZosNRXyadgLtTVIAlE4nMFfqcRc/6gGu1Z2H7+BwyVyWsWH9e B7iBKe/yb5xE5urfrJ0KycDN3/bfjEEbMteZe8OoIHC5aYo/G0eQKV5cbZJi D9ypf1jko4lMyXXz5626gScoObTvwgQypZz33JbSAN6asbRi+2vI3MR2DBuI AZ7UIQOOrBAyZVSfpUf+Bd4mk4jcUh9kygV/qDA5A7wNn68Ln5VC5tavMr1i LcBbKb5YOKYFmdv3W451KwP3z6D7polgZCo+jZoPCgPuBwODkMP3kKm8hLOS NQvclotTsw95yNxlt0J+iSVwU60EYkYEkKnWwNrXUAvcW3tknzobIlND1vuA J6F/DVZzXNOPIlPTo/o0BAB37UaXug8E3vuG5q8vTAGn/8bOIS8iHzp6ewLL TIATdTTD9G8qMvXiHJNdSoBjPLEpmnQMmeRjH1q/ewK7cKnyjFEUMqlFMkPP PwLbeoW41HwVMhlrLX9cNAS2UJnO+RxrZO7v4UiNr4de5paEjBviyDTatULt iSv0jDjWWJnFIdP4Pot+ehh6XMOcHscR+Juyqi8PZkI3Q9Hl2tlHyLSqe1bH 0Yf2Es3fTMs7yHQq8D5XZQsNt5uk49r/ItOZ5vLp4iTUhy7OfeQ7gMzrfQ4X pG9A3dPNkTL3/JDp+uvwZbcAqJm0suG/sB+Zt/1p35TWQo30toTR/svIvCup deV1AlRlalw96L8FmT66W65rv4DyptGpqM67yPTrXDP7SRvKrt9oW6ofgsxA qyU3Y+qhVO/rV5O1Jsi8Pzn/m2UIJZucvYxemyEz5M6k268+KN6wtfzvC09k Plz17m+GNRTt4q/uZ31EZkQKz+PYBBRa/yiBi8T3aPVW/iVXoOB509S+s5nI jK2v8CpagAIp1SUyt4l6SjB5IWjnA/lpzfs13iQjM2nsse/6lZBvLLzBjUbU /6PrUUsboyFfwtSuZcsSZD5ZEhBwTRby/k2dkP39D5np0beXyT+DfKE/62Rm ifWZO5zuczUgX9nX3lyVqJesMtuVXlWQf/XmZ1k+GjKf7zcL3c2A/P5HI82K b5CZ+8Zw9fseKLDMvyd2LAmZBRchPMwCCv6hwtoYB2QW/d29njQChdU1KUJr 9yCz9IFC1LcLUJQQ0tbsykFmxRbJDck/oTjqm0DF7wBkVuUujzO+AyU5QuPn 7ZSRWUteJM23FEqHhpZpMP4gs549k/g8DMp3nBt+Uk/E33RmfMtJKagIuvvk gMQXZLbMvH604glUCX12djyzE5ld6+ueXCyBmsTtb03siPh70ou2SZOg9tae VInJTcjk7Hua0dEGdSeFD5YNM5HZbxGStXMQGqDWd+luD2S++uyt/NoOGrW+ jRgikY9BN5fnAdPQpOVpIfn+IDLfJ1rlfeKHFprUmk1nSpD55b1SacZ26Lg6 IGQpMIPMqatbdI7lQeft1/6ZLKKfvwusqVyiA12+2TMm1gRfzG6dr7Ezgh6G 69yuzW7InCuaJK/rh57Glo98UubIXGC8q288Bb2kLVNr6CRkLTrf0izvDGxl FS+1qlvIWjxfweT8BXaCiBLDSBtZQoHP27x8gbNUb2ZuMBxZwlKPDXevAs4l u9zy34eRJZod2fk+ltBnW9usew2QtUI/4GCYHHAVZcJCl/9D1qpu915SFnDv CCYUnZ9C1hprp8PfNIHb6bVixX/bkLXumw03uRp464yHEoZ5yJK4flLGcBh4 pss58Z4/kCX52/ziHB/w7r/d3L6B2E/a40jZExngVYfe+6sngqwtgkZLDpGB N/FrrWV6J7JkA5hH/pyBvmVfDhyKYCJr60py8lNv6NtuZFD6wQlZ28J1v5o+ gT5dgdc7Z68hS3HDXu1FTdDH+BMDhxORpZSo5pP9EfoMlRaq+r8ga5fcTo7F UuhjBtsWOk4iSy1DYYvQdujTk5cxeLkDWRrKWy7ksaBvx+w5h0pLZGnmSZZa 2UPfSgkzvb/nkKW1d52QSADwviSMniXnI0unYuXhomfAq3tSr87/HVn6JOGk 0x3ACzU58uikO7KwafGXFV+BZ8Z1Dy2aQBbF4O++8hUEfx8L/HrgF7Jo3b/u nd0FXM66RVrJxshimnxnrzEGro+B3d0tJ5BlMPB1UzXxHu3WWftq9ydkHbD6 6OAQBpxXu+YKN/5E1uFzbwTqOcARt7l58YAEsky+9h26PAPsnIPXRzqWIevY lZ5EqbXAxvCXRarjyLJ0a9zrfBR6zQopnm3ZyLILybGXfwVdjdcTo30CkXV+ XUZRz2/orA7B+xN5yHKIfbzYXQo6itflpy5djyzH1Kh43gloeyRqFFWyGFm3 Sjy67w1D011z7YEkCrLc9Vyl1BdBo8fVq+L688i6U3ft3JAsNHjjC73nH5Dl 3WHPr2kDdQnnV4nbfkOW7yEbo/f3oLbA6TIpMRlZ/n1WccFpUMO515kcaoes 4OEjGh8+QpV5027RvjRkhdoeuPtwKVT0wozCxgPIeviJ0QXbodwqcYI2Loas yMtkyc8sKFu8/TH1QAWyomd0z0bZQ0nNCpGMC/eRFXdTs4ASCMWR9sZBh4h6 Svir9r+KrDyeqq4LVxIKqZQmQ4UkU/WipNZSpnsGpHmgkigpDZJmKoo3oiRD A0JRr2S85mueue5AoVJmmTM10He+P89v7732Oms96znP8zvTBt5C2l2j4T+t 65AVcXstHV4NqY88hWk2+ch6Ka4SZtYPKVlTZyXtvyArxk+xY2QupEy/IJPB uYSs1/OXbojQgWR7z43wpwFZ8U9kPSgrSGpPnO30NQpZ/y2Xrpo4B0lepxrc Smch612kxJLoh5BkLJqz/WErspJURRyskiBpRfKRLicLZKXETyb94UOSgj+n Q38+stK1x/++HoEkg2AlpZ42ZGX4RJj+////2Wyx2DA7ZGW1mvvN/ABJ+Vmi V50Z/OUaDvKTKhh+tiq6w9ZDFic4dOmRLEhOVrWzqhBDVsGg0VHpt5BizU2x I6KRVczqfpX9HFLncJ+Zmqojq/RlYL9TAKR+enRunW0dssonDXQX34K00gsn BTcUkVW159vVEldIL2OvkXLegqyaRN8CVwdgtzwhJGWZ/vGPNVlyWZBlFV2s up2ZP2HO7eAbBpAd2zM34R6Dzw9yaz9paEDu3J/LA3nuyGquuOZ0TwY4vn8V FYe2IuuLsvJ7vRmQv49SgGfayPp6vWq8dQQKNKIETnfuI6tjnbwXNEBhJ28s ZJB534EnuS/Gn0Pp4/leI2rxSMw4Pk3oqAG1awwVhmueIzEzN275QgXgSpw+ QsdHISG2eIddoQxwr0nGOSmrIjGnMnJQ4QfU7T+0KFnaGQlpFUKvqo3xe3az J9m/kJC5MXz9Sj3w1rQtC766AomF67fPrs8E3ncV9emW7UjI/ft9xx3Gn5pY GWl1jSGxpP1RyHrGH4dxl7heOInEctj8uSUA+N+P3UuQY+IphLSp+HuCQK/a 5FSsCBJKw/edDc+D4Jpg4eg9YyRWUf8k9diDIGfbn+U/xJFQiWn+GbIHBOPd Nqk2bCRW//VCU3MQaoSvCZk+Hwn1/ZreIwYgPKjzU7biPhIaScLqKA0Q3r6n mT47EAltyRuyVgogjLk9x0ysDYl1DqoHpuaCMF+8eiQsDIkNeTWRbxk+rhfN Dp1QQ0JviVvX/mEQtp3SF+/LRGLjBUVtsTaG77Q5e9vlkTCoKr2YWg/CPkvR 1XocJLaoumQfY/RnZ/mnxktMfPBYLDIvA4RNsVRR1BQSRh85rLx4EJYNuLPU YpEw3nAi4PRTEL5LjRhX5iFh6ifTsMwfhAHSC14pBCNh3pEhX+4BQqcFcrEf c5Eg8aj9pfMg3NL6cXqzIhJ0mMQbFUb/zom2mNg1jISVqpNt8RUQ1FwPblqh hYR1UuX84wEg8H3h++n1ZiR2w9oS0VgQILG4u08Kib2V9y/HZAN/oFC0xaYH iQN7+zRNeMAP3f2mocYCiUNtdEt7F/C3WCebrpyNxNHJueaqDH9e7DSJqv2E hL2Py+8SdeDN0e+uEUgg4biQ+84Boe6pvesU+RgJZ62Hi2JPAze5/58ftuuQ cDsi265aAtV37Ybi+vOQcO+9GFLSDFV3jwW1x/kicdW9nnIYhkpv6Qis/oKE R+CT5FeKUH5bZn7AKQMkfAuX3lrtDsWPzapUep2Q8LO8qlvqD0XRcpq7bUqQ eNDU1O0YDYXsrDlyOtlIBI08s3pVB/lj5f8VXS1G4onH1EyzLshfUWZ06t00 JMKkbNM7p4CzO//Gtr9Mvi9UlRTV1CEnrbV2RlUvEpFJHrwyhOxDgykzA6cj Eb31q/eJPZC1VPPlaAJTj9jKbQbizpAxkGom8w9T1bi9UX2vbwP7878HqZEA JN60iUSaM/64M7zS+weFRMJZ+11diZA+J6N53M0PicQ/xeL3SiCNfHGhXJtZ T/ZRyVZrhtRXvS6ngmYikbbQ26VsGFJXqCpuWlmOBDuyc9VJcUhJT6vtfaON RJaWWYOEAqSc6jtP3F2KRE7ma984XUjZMnmQ1PRAgmMmsZXR0ymaU/J/ihyR KOCfHOq2g5TN329uYvQUUXy4IsbHHVIcY27rPzyMRGnv2v1r/CElcTDo28cH SFS435csj4ZUuYsldQFMv6pn9nJOZkJqWKFmsKI1ErWBtKsEF9IMqJ3zEs2R 4MknrI7rhLRRp4K9p2yQEMRLN7GmIL3S9k2I6xIkGvRc/HtkgZ1l+eJKODO/ Hwu423zVIaOIPqo6W4hEs6XOmDpCZrtVjRHDn8TnpsC4ij2QLW/YNLmuComv jsM2Ts6Q4/TltbEP058Oj+Si+BDgrJDZnT/1AoluKVl3IgE4bTeKKkrvIfE9 9OLaHkadJor7GMkw9RpM0nukPgSFh/btWNPFrP9YYIJVH6BI416jHCcfiVHX nf1nOFA8XTLiV/YCJH7pnWMl+UNJCkupjLyNpEjm22kG6lChnZuv3eiP5Kxl mf81z4NK6apf1DotJMWvlR+8PgGVvTff7+tyQlJqa0daPoP3iMmPOWxEclHh itMsO+Ce9pNzLfZCcomyzv+/z9ya6a/5c4uRXOa1tcxPB+rWyovtElYgqWR2 aBV3ivH7CluSxP4iufK1U935duCtvSRXVlmIpIrE5ZuyVcBzt/knjTsDyTWV wY37woEv8nk8zmwdkhoaMXd/3QL+tjm3WxzDkdTyS9F96gT8K52XsotLkNTp L2jdugP4CaZSsotMkdxgWRfQshH4TX9ljse/Q1I3sWXrLUUQiAxMOxw3iOTG ef29yrNAoDK+xosSR9Lg/GRocR8IjCZzWsyuIWkokDRzFIBgb19XfPN1JEF3 6ahEFggc0z+16rsiaRS8JupNFAjOWbVnG9ojuX18oxXtAwLX5GOt2sx5032m kwNnmfXPjY/lApE0z9j9JnAvCE40dN9alYMkudR+/4atINj/2tyjgo8kffXC LCHjx41tcxeWdCJp2eyZckkKBGvmzvvoJouk9ZYAu8UjIBCr+1Xiw9Rr1/MX czObgd9SLOG5zQjJPX//yzlUCPyU2f5xOSNI7j+S7TQVD3zP8uuWy5l8DuZX Lo4IBD6hyXp66BaStis/lmxzB77U8cLGPhkk7drGV3ibAs9L7SNZU4PkcVPR WjVN4BmsPjawi9nv+Er2WoUs1PV8W9L4OA5J55PrPkh9gzoMXxsmehjJi33O /o9vQq3vgVM1Tz8g6W5x1VDfAWo44tyZbOb5yjuf7o8WUD00kZ37cADJm+de mcgz+pO2OJ/aXInkvdGvv6PZUDYq5fn2O1PPf4lx1+16UKa8uMmwmem3X4Rk 79dUKN3F9Vyy4RuSDwn9zwpJUJwjCDGOYM4HvaD35OhA0fgxl3kVz5AMHrGr PfgOinRX2m66PAvJ8Od+nJC3UJD3cNPYBib+sx8vN+mrQ4HUyfC3PzSQjDBn vxfGQf7RHLkvT+WRjB5ufTmf0auKfX07DzLxXpn9XPZeGfIW+if03PqMZNwz 6SDLl5DzR80lZXo5kglmm7zuR0DWXxfj+PTZSCY+tZhaqwBZ8q9m/wrfg2TS kL1b+TPItPzYV2DOxE81vTxwYjlkBMfOm87RRzI9/MGJWWHAHg75O56wCsmM weiWmMXAttcGj5lsJLNNMvdvfwLp3/n9IuvrkcwNq637tgjSfVrO4cEWJDkD 7YRHEKRvfbp+/ew8JAuNfxUqzof02YaORhkGSBaHyWzOCYS03tbeHxuY+Sgd UEk5JANpHS81VU6pIllhvFnjlz+kjbvL/hI0I1kVahUTKgXpihZLXKolkazp d1DQvw/pNlNE6PAQknXbrwbXz4b0ZMuLN4XLkOSHBM519QG2wtgu56JYJIV9 sfcWiAM74nF16KZpSH7Ylj3tvTdk6OfBSAuJZOOTustWopDROvxJsI+5r7m3 Y6j/DmRGRyzcFMTg7YvRHye/GZB1WVX02PokJL8+mde61hOyjwimGyekIdlh ZCg4cQNyHUK5jlFZSHYFW9Oz/kDe7Vn+kVMOSPZ8dyyOuQqcvS+3H7vJ8NHA 44dp39whP/S/070PmHmf6O4KPXQeihwW7rXuv4Dk761T834NQrHycIFhqhDJ yaAFvqEuUNy5U+Xb2+lIzdi69Wq9M5S6rlQYzZBHas7DIFsrB6h4ulmY8qYa KanOuPr+Nqg87turyO5HSsYwz9LvGFRpsqxCs0yQku3owYojUJ3qr+akTiGl sBlXGR8ALiW+I/KxDFJKhdLigkngvv4jUrvhJlKriKb+4xFQN0OnakKuDCkV Xhx/bDvU7d3x7IPXN6TUDlzK8O6AutdSZl+ca5FS/2byQs4H6kYWlft2xSCl 6bTgzmsN4G1WCW5MDUdKe+ir08Za4F0b/RQ0WYLU+svvLMvPAy/DvFi+5j5S utOv6x5YCLyBtriYSG+k9H3IpT1s4Cs9tr6UqYSUwbwl064eBD6psUo3Sg8p w5COjjlTwHfxMKsrY/aDUkrl00jg+51pUlLhImX0+tZ7TWPgRzeJfWiwRcpY xyo4pxP4qQmr+kpfI2XKVrhm4Qv8vIaSJcXmSLGg9+gXTeAX0sKg0utIkaWZ Zi5c4HMkH30OjEfKwvKe5rQLwE9bFBHsUIOUVcOe+YGLgB/jOn5oFvO88/Cq iRUZwPfX68zKckVqd8fQp6RDwD97XkyjOBupfWfyCrf9BT61TV57miRSB8b8 XvOjgL+iXCJshgFSNjcO+tubAG9IK0b3ajRSR2atuTDSBbzMiKfp5ZuRsvMb 3+/1L/BuWJvSMxWROr6wGBZpAc/w2rnq5iykHJ89Uo6tg7pRmw08n7lIOakc na3vCnVx+m949YNIufzzR7gvk+nn5GhdUSJS57IrMrttgBvn/cNUyODJ1Tgk 4so0Bg/bnRqGcpC6vOufU+GmUDvXrfHLQqbfty84T//Eg8oCxbMS1T1Ief3e 1HnmIlR0TjuR53IIqXu3xar+LoaK2Ue11FoSkPJ7+DJEyRbKLL9ffKfwCKkn 75u07HqgqG/1zp2nupAK2xS34IcfFC3/dfm3pBdST/Pdft7RgcIdFQ4fSyqQ iqybXxTjBvnVJ/ZTq34iFb2vJV5vCeQvfOtps/QCUrEtCQ9KsoFz/MPOC59m IvVmkDjQNQNyjWRWp0h0IpXgvhjdYyBncYBnsJcyUol/O1QlzCF7Zg1PU2EA qeS7KXNCv0PWrI9+Y9JPkEqb6zmk7g+ZivHj238z+bCfWNZnrYMMKzXtbWEt SGUpymeTAmCHKh3w8V+MVE7s98hmd0j/TTZKnj2MFEcr4+7pZZB+iQxVXheF VMG/ug5//SF9QWuf8vcMpIo635s+nMH43aopywMNSJUaa6oqu0HaI8ssX3Om XuURcaJpPZDmFvlll9U+pConldtZtpB2rj3v3836SNXsjyhq4kGal7pG14NS pLhpy6PPmEJaopOd5RCDL/78kDvTMiHth7/IX51kpIQussceaUG6xUmtl9I+ SDVUBmxXiYJ0Tnaw6TDDJ41qkivZi4BN6KaK9/1AqvnOvRmEL7B7XCTOFDH8 8PmryNfmKciIGLRf26OB1NctN/NdzkPmaW2T5csIpFpDf0dO74Asiwdma5Lq kGofu+QRdACyje3CKn6oI9Vl/eOIag3k0D3+qSEHkOp55wIZ2yDXaZ/qoZsM vwyccJj6rA4cZ00bMd9upIaKvn0++xzyZUVWmfxkzo+ssM0VmQ/5hcekHxwx Q2qicc/11T+hUO/B20Lfl0j91uMdynSGwp+6Vqpmp5GafGRhSLVAUb5ce/pb J6RnUCa/z5VBie08uuv9ZqTn5OpezgqB8kzPrnDeI6Sll77fbyEJFUFftPxV epGWuaS5scUDKl3Ug/zZCUgv1FGeED0B1UoVetXnZiKtECXrZqkP3PW+bQfZ vkgrTQXs/hoP3MfdMlNu7UivOiip66oA3LFAkbPKzHk1WZGR0FlQl3BHQ9x3 DGn1szcFa68AT3TjaaP8V0hrVP1OzukH3j4JUf4ZJt46rx/nv9UDb3BIeeIB D+kN31ysLxLA1/P5rd5fi7Te1u/rxXKBf2mbjqF1CtIbwx3nha0HflLrf5Tr cqQNxr8NacQCv9vQrjVRDOktu2y5eUtBsFThvP+oAGlIbEzc4Q8CkyO7Va0m kd4mtedB2wwQODVPPg/bhbTxSZ6Lmxvjby9MsJ7rIG1aYmEp3gOCaAlvp0Y3 pFkrK7TCbUGQ7p0m0R2ANHnTVFqTB4Kijxf7311Bmm7K7+OYgqByJCbtNHOf 1cYt1daZIKjirhgRtUfaOijjbbsWCEpPVFkJe5DeNaR7/xKjX7M4D2K/GCO9 l37vPHshCOI+H5ER8Ud6/+CV69ujQeAfzstYuxTpQ4+2+19fD4JTudLKe7WR Pqwn+SKNw+hl+6ythipIH/0gTBy0AMHc9Lk1D5n77K8+L1jD6NP61rje741I Oyo48o8xev2Jkv2M8zVIn8zXaXs6AXyr+BW3Ku8i7Xzs52i9N6P3i4L8InOR dplVKCbD+PPEjD/vfZj8zsXdX8yKAt7exi9zOYeQdhtQMMjOg7rHU68e7lBD +nJgJzlmAXWaeYukbjN4u/ZPoo12M3DzVfYseXgeac8r225GTUBthopfuQeT 3x35OQHN3lBTMa5QnzOBtHeeIGqRLFQ3hIzGH2Twc1/UochHByrb5S5Ob7VE OjjgXwmXk1Dau2tN4PAU0qEbdi19PQElAzVqtduYfMPr5dd+84bi0QGpn3Z2 SEcue2exOwqKpOeekH0nj/SbWP6jzY3AuWdmXaguhXQC62n0xROQ5zZ5Mp13 GenE3uOpCWOQM7RjQFfUEOnkB1olXXcg23NZkZXcH6TT1o1/WDkfslYLm+aY jCPNFnC6D0ZARke7RFczU9+sSz6/HmsBOy84ysSL6Ufu0p1zarIZfaYoH6f9 AWlOznJ5cRLSCkJa7e08kC483K5l1AipAyKr01PbkC6ZkQBXT0DqJmyPX2+C dFnMJauUMUiJkhsJaZBFutIcjzL6LEVdx+CMhRzS1d8lLqjNh2Te/N+7Twwi zfXj3TkaAclh6mbhPWyk+Trhj8O1IPkW554NNCMt5NvHCrIh+f60tWfMFiP9 wU2TLU1AcuqPM202T5BuWjxWbvYBUqbdoc773Ef6U1Zeo6cDpJy6doctxuC9 xfbe98wRSBnzqrhap4B06/Qdf0ZuQWqkulTiz1VIt0cvk9Zi9KwTv2Oa1imk u0zbFB2fQ7p17fILe+8g3dP9n06kJrD3sXbmE7eR7rvvZtSYDRlXrGtCVfYh PagNO2UJyEz/slOrl4v0qGvdxbsOkONRBksOMPdNyIV5c0YgT+IuJSbGzP+v zGNPGD/K2WUeaqx/BulJG424f2QgX0tLK5vL1PPv39HM08+hYF6M4tsDl/4H 2Ke9fQ== "]]}, {Hue[0.9060679774997897, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUWnc4V/8XF2Une++9947OSVFJRqFQQkYkWUlGpIGSIpUk34aMUkbI3nvv vfcmW4Xf5/fXfc7zvO/rvF7n3rPuc8/fsuGzIyIiIiYionj5/+uxzKeCFbVD mzBdc+dlwlw3RPYcv8sa1wHTBuojDMgDn3pYbZqZEKZatG3N7b0ho4ek+6uh F0yWJYmS1QlAWfdOJMsSHYx7fDycQ5UBbd3rt/QLhmGgXkh75lg0dBzysGVh ioH+B+MZHsYB0Cm/emnYxRj6jpW3f3hQCt3PVrRceeqgu8i6xKDlOvTk31JR 9X4EXQ/befLaa6B3ZkmSqBWh07CBF+8TwcCJBaaIoGxo+xdSKXwtBgbdblCa 9btC62B3guzbSRiKm93nV5SAlurykM2XwjDit7Zu5t4Cja53I42eusAow0+X wv+koT5Qha8+SwFGv7rP8tU/hdoE96cROi9hrPv34JygDlSpTws7DlLCuEv6 JX3DeKj4Ju/zZlsDJg65tmX4H4ByZe4X32Qfw0Ss7DnmZEsoHbgfPNofAJPy y9U+nQVQ8h8nB4mEG0zWph4fJmaDol154wMrMTBl5VJwQtoLCrbYk89xV8HU lrRyonkH5EsunhOuXoDpZ4vpVMFykPufd7Jd7zrMCH6XuPUzHHIMrrB+/egC M/nOX9qHF+CXJnXibqAnzJ6X5FWlPgPZ1+v+zqvywuzMfEysagJktX03Y3D5 AXMB35iI7A5Clk9MC3HyQ5hndnpuG2ENWVZ6YUwZ6zD/XZyiprAIsh7oa1PY XYOFE7MPJec4IWtsr61YoRIW+pL2Iph9IPvei7H4SHJYdLvuvaHVDb9MqJro Uk/DErnImtktRcix03vR13MeluKmbxa+i4TcH6TDKtcFYVkpYYavegXyparM Kbw5YbnB3ubR+jkoGFse8Xl/ElbkIpdu05pAUX3Zbv1fO1jxW/2WKPwQSjyk cunc6WCl5vz1Xo0MKI02c3oQZQ6/GTKEqc6PQlmHkSj7Rzr4/dXtw62AY1AZ RhUSkhEFvzdar3x8dROqD1kmHsKvsHpcjr39WyzUvPjQ8OdcGqx2r7xS6d6B +u7nZaM5/LAmlmhFLp8DjdncFzi2d2DN11K855kXNH+mI6tXEoR17vqSu1qr 0Npy7ubsqCVs2CXM532fg84/Ag3zVPWw8etK9lPyJOgmF6bjljoCmxSM9y2u 2UMP06fkE6WSsJkSxPyPdRz6JNR0V7f0YWvtipbmgwEYEiFPe9lzD7a1GQ8f HoqBYebdf+JVQ7D9uq57SO0SDO+fK7Km1YIdddWb95Y6YaRvJ29RWgB2ni6r GpyJgtHjLmYOZv2wM5hAwhN/HkY/xdPZQCP8uc8QXWLWBGPGvPlci8Pwp63u 2ovMMBj7PKiWVHUa/grel7Y+ogtjizomnWmy8NdLZUfOiRzGZYWNfO5Mwd/q pYoDFdUw7nxuLtxKGv6xfQlv43kM4x+jfPZ0KOHfjctmn3xOwnhzDz1fJMC/ QgZB905iGN/cDS5eToNdmtrlEzIlMMG8EvK+fxN2rQLzGJ7cgwmZz7FhZ0pg N0Pl4fgkIY+O0wrtfk+EPZIlg0z4AxNnlS1EZyhhz+QL+8OYXJjQ51KtedoG e4kWk8Yb3jBxps3lyb8rsLdDnyZkqAwTx5y0zxW/gH3dWp+Nr+swIbEfcFJo EvZjA7WrDmXABP1PeRlqWdhfUqF9beUG46sp71rWi5CIZ3Px55VoGG9gXWk+ 0IhEp9sucDzZgvH/JIQVb/+HRG7fcx7kmBL0c2S1wzQSxYRyzU9mwbiC6JWS GmMkKrd9YMzACGPrL/vlwwXwABOHvuDNNhhzSH4i9DYJD2hu/gx7JwdjbMsh Dmz0eMC+jXW95gWMVtcbZPlb4IGckPFKAX0YZflc5rffg8QWm3fteupgWJCl wfJvFZJ8ao1PPFEOPS+cO2xl45GkZms2NVwWupupr+SxxiDJMrf0r5446Kb1 P3P7ZB4ePOqUU3PTGzrjeAYjw+7hQesXuy2/pqBj3vnFt+uNeDD4l1Yv8QXo 0CzuYW9bwIMdJA2zb6Shbe32JTcxczz4R5zu92gstF2sLKNb8sVDvIamfyQp oLVs29FYJwoP6XjFEnt5QauSh+ML1U085Bw7SlkyAS2pr4L6o1LwUGS5MAPV eWiRy+r43EiEh3Jmb3CYFEOz96ljz2g58dDwkTSBD5LQFKfH9uPxMJIeUtqQ mIuBxs5tYk+rESSVuKyuqEQOjTy/xp3pdZDUKChAI+A2NPhOh8jqn0bSO0kV J2vHoH6u1WlfdgJJ3zdTnGM0hHrnHxF5X3eRtHxD38SyEOoPJPjYyMci6RzH yyvJ4lCX3MxC5jqGZLTHe+zWoqHOTrJX3tYEyZSvc7kcOwR1ChkNsZqWSHY5 3MYrxB3qmLRKOYTckSwoM/Fe2wjUUXx/EKZQh2RJfQvBXOegjvankB9rFJI1 H5B77pAHdeIkF7QejyPZhojXmwxRqDPRzFGVVUdyjnP5//17BXURq66fjz1H 8uOeREmniKFu+FPI3I8gJHeI0U6LcIV6zZLQV9XeSP6s5ElO/xDUf0s/Eqli geQ/p5pLhc9Cg2i9qxyrO5L3HWasdc2Bhkzz68cdmJCCSMGsNV8YGs/Fdn5R ikEKYbO4vkMvoXGtyif7eAhS6AWMjxkSQVMCUV2VP+F8dMPN1Yl+aGF6bnaH jxIpilYz/sqchpZ7jJ/HdqqQYpJ1m+RuNrTM1QkvUFYipZxdECPNC2htqj0q QbOHlLV7r5VUdaA9o0R7Xc4PKVcE+zWDMqFDOoS7/3QyUjGf5dVu5IOOrzaH b1VKI5XNm6+m1v+g88NRXo2cTKQKKVy2THGELjalZuEeUaT6MaFov9kFXS/U YyJ/myHVX9miO0/TodvrduRXGV2k5rtIEtDFDd0ThTOpFntIfcr/dAhvGPQY KIUQO7gj9cvatugse+hlObS3cEkcqXNXWD7sd0Cvxys5/jeeSD3CfDlZ9zj0 1pWyVuZ442FJm6ncYU7oc4jZJTNSxMNGtdFua0TQ982WjENdHA/fkdUVJ52A vpmekWDKSTwc+/rvGHsN9POUjJhkBePh0t3vMdLfod9g7jCloxIenra9el4r EvrviumkOC4hDXU9HaWpF/S/M3fKECXY8nJlZU7m0J9t5fbDlgppLkZ7+tw7 Bv11Qo8TLUSQxm9fWD6SH/q73rEeemyDNJ/suucSSKG/P3eM+pMs0lQ3hH7K nYP+3vur3eZdSLOocNS8sQn6m/8ktltE4xH6t4v0oxnQXywfX3rgGB5RJYqr 23gN/YlQAdcd8cgVB8MHFD7QHwIH1oh+4JGgJuKjXFeh38bY0lz+Lh5JUvy5 JncS+pW+FlzfCccjje/svmmLQj+xRy7FKyY8skbMcs2MGvpqpv2+RAUhLbWi 7bNsb+gLPfl4+MUDpOWeLnQscYK+E82eX6sYkFbuHbN23WXo3W4rWX1aibQX D1TvDiH0XghhplfTRFrHLN7eGXno2ZmdKTgmirR+1+9mrQpCT6wAtVPoeaT9 2CzhQkoO3Z0p69QTVki7EBs+JNUKXaKT2h384Ui7bzCdp1IBnbkJKhY5p5CO ngRfH8+GzjM/9/6KUyOdiuOqvkkMdDhGq+kLvUU6Xa6z4lfDoP2PtYx8hjHS XW6JJ3W8B+1hFvwxW/ZIF6RiUuRvA21Z5QssURpIFzX3IybYGNr0eeuq06mQ LjGOzCtCB1pnQ/vD/40hXePBXOkv4tAqoRNMb8eBdMM59JSpnNDSdnvd5aI5 0q3ecJrKpSHk03WnpBo+pGdp44xrWIXmBj3qKkoSpBd/dNunawKatS0G//VE I72mapPpSBc01Y1fY9WaQHqDBRH5uRpouvJYJiamHult/gukWc+Dxt2BmPYo OaS/fb5nbi8FGlM8xIiNV5E+hFSumjwOGh1r964+LUb6mNwnn+lfQKOy8eVn 5dtI/915PIDzATTSe4tPfhVD+hJeDQthL2jYe/aEijoS6dvaX6nIXoeGP0Me 0jwE/MnHSwzq5tBIlnDt/HI70m+r6yyf1INGIXr6ZVpiZKBc/K9e/xg0XgB1 Ab0LyMD1YTvxkiw0RmpYNwwxIYOssdEDG35oHCMvjZvIQYYTZF+vOjNCk1aU hJMIGTJcNKz6TCYOTUWsRW80TJDhxtuxqU8IzXrLL7OUk5EhYGxfXNMUmpeq 4lbu6CFDlCTHzR5naDF658h7UAcZkm6rpHsEQUvmmIJ2KDcyFBRd2KCJhlbe 5fesMwbIMGkY5neyAtroqZ97lVUiw87bpOLhPmh7bWGwfkEYGQ+PVZL4rEA7 33KVv1MKMip77j5J54SOc6TBE5zjyBj81uUtlyd0TRw49+iXDzK+G3sykPME ul92hB3lPIWMaRKJvBc+QI+Os4H3LRVk7CkcTgxtgN50jXsBhR+QSWzMMGtT EAZCNxLvsR5HpmMSzjsR6jCokehhPOOHTOc9QzUlDWFwIfjQ0OU/yORLWlph 4wfD+FfdsZgfmZ4bDJHtRsLwhPhIXifh/OfoP2ejk2AEh0fZvr9GpgYJ+bam dhjpyHv9CxuQacTTgNlxBkbZROamZ4mQab3whvnBPRg1C0rg9mtAZgrS4Pcf GGH0VeAQmVA1MnMZfB49Kg6jDQMAHkLILBddItiFMLp/3uzlJjcya48OXHcz hTHJ+JnZq03IbC6+nULtTJgfY9gfVw8gs4sn43JiEIx5/Wu6u8GMzEGFcgpa 0TD2MrajdfcgMr8hPec1+APGvhoIu55ZQuZvBo553hUwlrfEpsj4HzIXRz/a Y+iDsUqz56df/Ubm9tFPx3+swFjtHT6beD9knhEveqRLCmPV8ieKmbeR+Z9H X80kJ4wV+dnlMIcjC23BFnWgPIylnqNqCGNDFiFSBkOO0zD29uP4zlYXsqgZ yLzMtoQx/3sd+rr/kOVc9NluI08YM18iiS0KRBbr0evsC09gTG5/QIf7OrJ4 iT+0DP4AY8T1FJQvviHLE48Pn/izYbTR+caIz3NkiSsomCxsgNFIMprWRllk +XmoV+zSGIwaVtBMJWciS7X+hvPaNoxStN0XW5dElv5ourTnNDBS6GDUR3kK WQ+K66pUqsMIEyNptEsPsrJ62PtaGcKwxzWi8BtLyCpZEFT01x6GfpYok5KH IquJfr6OXCQMctoLqDzyQNbrmqfM+mVgwDjUqV6fHFl9JdpvPmyA/mCMe3Ve EFk/ks9H9ZBC79Cw9U7xU2RdKOcYv+cLXY7slhszSci6n564JcoKncGLTJWe C8hG/0GRsjUbOhK+ctvGOiKbqr+evOAKtE2VW78ke41sD1X8AuttoZnKcvG7 8idkeyNMHnX7AGFfJtl42MmNbF8Zo5K446B+eIM8L3gd2Zp/pzS79UDNtOLC eQ45ZBsbUR3nuA3V96W+1PGJINtGU8VmJT1USVQc/P7JGdk5vg1ys56D8l8Z KfbCnMguHeMoVzoHZe+sa/tUu5D9eMiG9o0QKI0yVzSfLkN24zv3zZiEoOTz F3UR/zPI7mB/2LmoDIqj7v6IMtVHdh/jt4HXr0IRPVMqN1MGsj87IRRF9w8K SlvITUxtkf2DXHpi/lvI/6KcHxqQhOw/eTXz7ZQhrzCjWtA/E9mraGqbadoh j/pxC5btIXvPrslYjivkRjbONEsRI/v8/OimzWHINfLxsBM9j+x7fS6UVF8h 94yJUGY+P3LQ1fzhyjoFuT7/xKNDWZFD8NdjOcsJyJ0as310Txw5VBLotcmD IO+Zfit7YTZy6EbFXcrggXyXyzQuqbTIceWBuLNFARSEbhO/z7BCDle37MBD 5lA4PJ5wULMGOR5Yab38sQXFtzJW3FcYkeO1flPipSgoibX3rG59hRzJmub5 xHJQ+l5pSdNaHzkKJKaavjVBWW5Tt7nwOnI0s7uPmdyA8t+fpI5snUaOMfK9 jX1yqDxJdXKjpQ451reeUiR9gao0Qykiw5vISTbFwnVeC2qUPnFqnz2FnFLl Mie/+EF9cEMHE/875MT0/EsGbNB4Pv7XloY8cl74cOrGdjY0y4dKWc+LI+dd /6svz65A6+uL8n9nE5Az7MZ8wsYzaPskG7GSK4ic/5nfyftPHNqzSVhvG/1C zkqV56OrttC5mvl61SsZuWh/l8hG90Lf12EMklVALiGvI9/aY6D/r3VcLpUb cqn9uSpCYw6D52o9Xe6HItc14j0uwv48vLZw2mrcFrmyGDUoXUYI9chw+6tF IXLVRj99lPyRsK/VrBvdmkGuIc5+ogkbGG9pC/FN1EBuMqG7W2YTMLEXSP6R wQK5OZJr3aO+wGQ412r6Q1HklpVmXWy2hylWLSu32lXkNlP+NXFyBqbZNe6q 0JUg9818sqsByTAdIfkrXOsocgeBaV+eE8wQ9d5O6YpA7tflCSYbEjBzffCF 4bYqcn87tdEqswAzNX3EW9eCkLu4QVvP6TvM8nppGYpbI3eHYVT1FxeYvWUV gQFlyD3TMaE1IgOzWfqGvMY3kHvXTKGIfQVmf//Tnn5jjjx0gw/UTNJhTkh6 /CCUII+wdVvmC3eY08/wf9M/gTzqU/wy9Qow52LxeFS5CXkMnNy+HVqHuYek ubFZK8hzbalUGLNg7vnb45LnPyKPtwftR18vmIsg6rV8fB55wrasOLNVYC5E YSqD/TPyfPRNfbOyDXO35R7/fZqIPFn7+wwSuTB36UDMxYc9yFP3UD/c3gfm ZDMrLv+2R55h8jiKj0dhdt/WLoRVHHnWwhYf9v+D2Qr5sGFtW+Q9SB4XSGUJ swFnRnte/EFeGt0qzaRimJUdfPz8UiXysoYt/tXhhZle0dWrpMzIy9/MlDMe CDPetyr2P+8hrxSdplfgCMzQ7EaeildFXpULdorcx2H6vVBCwQIn8p7tzkw1 I4apj9mDRla/kNeUbcB504bwPOcKLrzLQl6rywfFX5bDZOi5bB8NF+T1HLnw pekhTFz0K481J0LeAH7fazcmYDz7ZF0P3yLyhtp+5qPQhnFaBeKQm77I+35m NfYkKYz++pEmpvsJeat+R0TmhsBg3Rl2h6kd5G1VyDU0nYEBs213gUOHkLf/ 9ujh9TPQN3si/uGFKuRd/iMbKkMJPVRRnAe6J5CPjbg5IP4ptN2X5ftTJIl8 Akz0joq6hH27804+BzvySYmYnK8gh+ap93EXmraQ7/jZfqHxh1DP1vmt6tMy 8p215D7iQcjfLt77LJKlyGfiarVDQgRVKeyDmtXDyOcYNdXA7wdlKSY+rXkf kc8jUSwrQx1Kenf0gitTkM8/1/m/49tQZMhXlC9jjnzB9akhhH6RL8u9/8dS EPkiBlfdrW9DjmmM4e3HD5Hv3YqSxW9FyKqiTFE45Y18CcTe2oGr8DPw2/rG yVXkS2PMl6FNg/QQf6KaUXXkyxPeY/3gAqlFvr8Oia8jX4XacWJZSfhOPXDX Uo4a+ZrOPpgvnoOve9H26cwEvJ4rVZ0GSZD8KoHuvxthyDfmSlE07ABJ7Z7Z 62eSkG8hSC/pljAk5cxNzL9B5NuMeh6xNwHJOnavrg0IIj9RQptv+Cf46vsr v/uoLfJT5jLZcVtByiXxU1GOfsjPWH9J/wc3/Ojzi9FDUeTnHnynqjkA6UTu uec5DZFfdHmIrzEGMiaeCFH/jUZ+BWI+qiuXIIuVLfKFlg3yazJeW19khl9v PR33XlYg/ynhhCG/Dsiz/956Uj4G+Y1UZ2uoI6HQP0b97Ioe8luclUyPNYKS mzMvudYbkN/uyq13krRQBhfDeerpkf/WrYyH+U1QIfdtlvPIDPI/iFK92K8L Ne6VOFlojvzPEnyP3yCHuiLx2R3KFuR/k1Mk/qcKGgXyGNSk0pH/2+CJPXYt aGVvSr9RRIH8WcuPp78SQZvLapSn+HvkLzlQ26pWBO0NvA9PXqhB/g5hg3iz o9D1XimO1kwZ+f/dstCNVoQBH+1XE1ORKEB6P05RZBUGNzfP/TSwRAHal6Nc v9II84vrVIVPFwoI5tivdEvBqEKAzQ0iIhQ4d8D9DbMwTFDVXrl/+QEKXGTI CvxCqK85XemH54pQwFpo20npE0xaS1+3ZxtFgdu69zRNuAn5mdDv6bGGAgGX S4UnBmBatbr8ZQrBX+itg7SeMTBdq6ieT0OKAu9fho5HscBMh7hlqZA6CiRs P8ySYIVZ7XUz3aRcFEi7EvC4jB1m0+498z/ViQK5ZT6XzDhhjiFgnyRQDwXK RW6LrXAT6mEWy/+/Bwg0hN36G8wLc6XD3ac2j6FA52+nRm5+mKeu3p3QU0aB YVO7uCxBmDc4qlDWlIkCM/lWrnpCMB96WMKDfRkFVnktjo8Lw3weSwlNNUHP 30emDD6iMD92lKb5jhIKHpwznKQTh4UDV8JIv8WiII3B2ewkSVhgcWZwPJSO giyZOiEgDQuCZmufDo+gIB/bcbMuWVgQ5d0Qgx8oKH5PQ+KmPCzwl142jn2E ggrjyrsHFWCB8fhU0ocCFNQ8Ldf0ThHm/6WWO5LvoKDOd8kP8sowP8Ad7GZ/ HgUN6UXcalVhPiOJ04oiDwXN7vCfsFKH+UDnb3yGvihoM8DFuKUB89qR9xda T6Cg83GWqfBjME+svVFrpIOCtxPoc4QQ5rJzIbU4AAUDqA6HFhyHuWuCJtdz COdDXMktLmjBHFlF0QncR8GIThLJuZMwGz94rSqIBgXj47ZbWE/DTG1ssWRm Mgr+IFn7mKoLM0ZjxgKCcyj46/qSh44eTLcP3nhP2P8E6+QnmD0NYKrKxnD9 0CwKtr8ZmqEyhCmVlDHTQwYoOPCvN/fTeZiMl1ok895FweWq5sstpjDheveC pFMMCjGZ532WvAKjY7o/rP0yUYi7OMuz3BJG1XRYP4MVCokKpumYW8FI+F/O 1DgbFFJf+jIbYgtDrl3WN999QyGroAiZiRvQyxutkngqA4Ucp8MO+DpDd/ti Q/NWEwp56D1up3eBrjCig0GGb1HoMbOfF7pDx+Ham0pEEyj03PfOqW5PaOuV 8PLIdEKh6BF3NhcvaE2J++zYao9C3746FMTehabBl2Yye/EolHXEJlzBBxoK l784XSLcX+R55WqdH9Rlaps69rCiUHXvJTnre1BTOvrw8800FGo9doF4OxCq pveFDFs/o1Df53Mdz4OgUqRKYeziHApNkJ9OEH4I5UG6c7K8Yyi02XbsjHEw lDy9Iy5w4CAKHyBmb8pkh6LjA4Ym22EoTCW3ZczwA/IzD/3I1olDYSbr9l5C /8i9dURQLZ8ShXki0izbuuCX5xW9F+cHUFisNGxC3gmyKhlyZ8SkUVjht6Nj xC5k2jAec3+8isLH+LSXfr+An2f3XI9/cELh00b8nkaCkPFAxqFo5zEKnw/c 20nLgQyy0pzKgjEUvpzWH0CrB+m9zxJUsvVR2H4k5+CtEUjfFnueeiYBhd1o o0KbPSDDZi66oO8zCvui2xEZUvjJOhky1+2Jwo9cz0WFx0Am6/J4hCAvCj// IM62JA1Z1h+VDeQKUfhtC+l/58og+7dxbHsJAf/z/rjgd1PIqRL3djJeR+Ef MsVfqecgb4jSLHqHC4VzrsbKON+DQuUvbIsU31C47Ll3ZgMdFDfeEZgfJkXh hmITdYl4KDWY9PAh+4vCXctyxU9VoazzSp7F4Q4Untefq9O1giqZAxFmWgT8 jXvVBsnrUL0/9MZ6UweF93987qQIhtopV/LhnpMowkhzebjmBzQumiZcuJuG IhqNTRs6u9BusssxaPwQRXR2v/kkvIAOH6tDJOQJKGIkFUJEKgidH3PfcjCY o4jds+OUlXrQvSHqQMKtiiLhej95T8TAQCTNsHcAOYpE+z3/8lkaBtPP1YaK /EORTynOEsRlMFRvz7nP9ABFflELKZXOwoiXgm1XEAmKlGocyOfzg5Ht0k/f XyejSP2NIbxPC6Nev+/KKBajyHD9m7OoCmO2MdLPt4lRZPavR8t/DTDWbtTF EhuAIusShib7VjCuIXZS795FFNmzkOy3XIfxuMYhLa2zKEr+lMKqKBjG/6xx HfyTi6L0eZNT3OwwYXi+5naNDIpyzpXd8P8BE3HfJXn8RlBUhD1uZVALJiZr tXsbHVBUTtfXS6MLJoU8l1jcOlD0qM/Fv7FOMHnl4U7pMysU1f6qGPh3Fyaf dZqEFB9CUYM+OlKLFzCZfYY9xuwxippTLD7NF4TJrrrYH45UKGqrVkfHngOT y2fdHM1tUNTFMeG1jx5M7le40WvfQlHvtw84ekdgilwp7ctBSRQNqr36Uc2D YCfcPndZFEXDzq7TfNCFqQPj94jbuFH0VVOIHxk/TK6fXOCiu4ei/xlxzt7c gcnhUeWnX96haFJ7umlHC0xWtG95r6+gaIapdsXRJJj8rNk4NMaFovk9fXKf AmDS9+TRY8eEULTSwuU/ClOYPEfn/5Y2GEWbhkioXaVhkrXDri0+FkV7rN7c 7T4EE0Ml4u+YaVB0dFximjC/TPxHrUpTOoei83YlxvGZMGG29k/xSQOKrs8Y l1E9hYkj2Wkh8BBF95xmZdwJ+1DJG96Sn/YoRrboH9urDuPO/bdIRLdQjH01 4U7CDIzl/HJQUEMUE7ytPnm4GMYuzdbf6mJGMamt5vOer2F0I/MfeWEKiuG/ bSktbRgVvhYXdKYMxRzIzowNfoKhfslomXv0KPaTY1Gf8TJ0l6bdn5iuR7GC uPv5vgrQNZXLNzhrjGJVfMyiY5TQdaT4dGZHBYr1CgNJai50uEq8ryTUS7Gx pHY35hfQnpTDp/Q3EsUWJByG/K9D2/SzTdq1JhTbl32Re5YZWn24eeiSklCc /KeQcPoitDTVa7N90UVxeqXcKNYKaJFISBPT0kNxjhz9AwGx0MwvldxAZovi QupjLlMe0Li3znr/JzGKSxd6DZzThYYlvUSjEkkUV0WqM5n8UL/SJqZ1VhbF j5f994t9B+rJDk+XBHWi+FltRcH7LVAnd8dQ8VQDihtX10TMJEKtq0c6x58a FLfUvbxvQKjnFaak3cxaKO7Q8Ns52xRqpCx7VJcsUNzN4FEflxRUfyvgX04g 8PFpYz/18CBUQ+jFIsF1FH9o/CNzbgCqZprtfR4Joviz7hP8Rj+hKvFhb5a1 A4q/Met+nvMEqnzDD4weI/D/MHBjl8caqmwLZSr861D861Uip8eqUGXTtrsW l4HiP0ejuhdpoer2veRDZGkoXmgrpn1hGqpiLb0E3oWjeNVUYUZeEVR1E4m8 dbBG8RbH87x8r6FacPTeiPkQivfOTz0LuQnVj1zr8xf+Q/FxF58/yyehevtw cPviQxRfKBWPOn0Xau7qvD/0ag3FNxn6pD7+gFrKQOqfxBdRfN8+tOrPONR+ /Tec9DgSJchz1ayMWaHObIPy3ftslKCnmtn5fg7qmV+Lj521RwkOy+iXpEFQ P3JQt6l5ESWkSbYqcxag8VVKnMDAE5RQMU28Ss8HTYGkZ9fD3VACk023b5hC s8fNj6+enESJC/rZElwl0JKT7ikerooS3m89XwS9hLauYM7yb04ocX9BUKy/ BtqtX/YMdsSjxNNj7eWK/6B9keSfq6gISryfkN+YtodO4r+N1x01USJReew5 voPOsHskur57KJEWGin6tgW6GM2eb4EpSpRL/7bQU4duLsnD9VfjUKLh/of1 L7eg+718ucUvwvnODsNne/HQw/aTj+z8MZSYuZtakk4DvaTOKoyqkijxu+Gq OeUJ6L3d5bQXR48Sf3lo1q55Q+8I11TUGB9KHq5wEWYag77PYx6MQvwoycLC VXyLBfr+FSoSlyuiJK9j46UaPeg3kOMsnLdBSbECv1W++9Af82/hCXcFSioc kXzqkw39w1PcEX8I92tYDwi2z8MAVy7TtytPUFI782mRFC8MGJ85/o+VCCUN yI5efGwCAw/c0IaDGyUvmc2tDD+BgW/0ayF37qCkdUrME9ViGKhnef0m9SVK Ou3rCkSsw8D4rdrbuvoo6Wm0UzAvBgPrDK+fLraipH98sulJSxjYJXZnz3RB ycdbl5bfv4SBvRMKR5r7UfKFLnnIZg0MbE0cuvP7Bkq+jc3hN9iFgdk/phEn DqLkp2WH/GR5GOj4lPea/CFKpmixmBA7wEAOUcMlxecomRVVtWQRCwNRQNpg 3YeSRdNewZmtMOAYHp34OR4la9SF+WhIYUBVRF71mRtKtoZ15jkchQEincak yKMo2Tf86EKJK/SXybl135dFyQl5xUW2L9B/T3aF7KItSi4+nHjs3gf9ilGx kZMRKLnZHcXTcAT6JtOJFycKUYpI/ESu0Enoixh+JfubE6Xomz/Nd/+A3oHl YLPxIyilym5tpHEfes6w5tNlsaGUlspqnOVp6O5I7YnaKEEpPZMH84E00G15 U9zhahRKWb1IeFQeC10uGeOD5e9QKpR0Pk83Bzoi9/IoTL6j1EtBfzLnAOjg Pb0eeHIbpd5r0RiH60B7mtOhLwvuKJXuL7vU2gFt/df1lj4eQ6n8mLKja++g 7c7FR8Unz6NUZc6FUCYbaGNFV387RKneNS+BSyvQ6jQ/osAOKDVBR+bq8wta uV6d+/SnAKWWpKMLY+9BC6EH/e4nRaltPXHKIm1oefP+E/8YL0oTO+ZfHKGG lqs5zwsG/VGaOljvy4F2aJFJXE4L5EZp5vjBVYEYaCHLYvlo9gClectugbYV NLfg8pjULkqLjxCFOYhA8yetjzVCzSituBvRG7IEzY/qxsxDFVAaOASEv2ZC sy/fP71pHpQ+o5rp0eALzUF+T0d4L6D0BVPtkiUtaH73V8W4kB+lr3h009BS QHP1V99h97Mo7RBx3UKuBVqIS099j0lHabcfO0kX3kCL+M9vt9aUUNq34cnG bUtosYhSEz1A4P9ojlPrjRC0vMIfN2OJUPoF2ffnhPrT0h3WKhPQgNIxQscG +n5CK79WVMHveJSO12oR++cDrbf5z5BX2qD0DyvrO9zHobV5rP1veRlK5/iv ViA5tMnLrX+6JYLSDblMlg9eQTs1GRnFSiZKd3UlfPtyGdqDCl9zeEah9Mi6 ynY1P7Tv2xb4RX5B6Q0Z85eUadBJHlkhl/oIZbi+/Fcd0QDdZ28G1FVQoIxI uRzjz5fQPTZClc1OhzJyI2XWnebQ41vPP/I9DmV0OCb+sc5Ab2qghPfIT5Rx iRSX/UACA9QnfNeKVVDGOzXfv7QOBt67kb8JzUCZoEa9uvEXMCi+eKGLkhdl 3pDfshPhhiFVw5+SYkso81GYKOP0JAxl7z+7P34aZb6diNh3SoFhmWf7ZPPk KFN8L/PtD3UYISVVLxiIR5mapd2m7pMwongqe1ziA8q0XT11kEgfRiw32rRC GlCmv/mFmtglGHk0wmTvNosyk9Drcv4ajCT1Hmgse4MyS2n8n31dYKQ6G1h6 fVBmm/dGT7w3jIwZVmacaUJZohrzx1OlhHn2EenIJXGUPRQYwOerA6OUCnRb 73+iLKXq54Ij9TDKciw71XkEZWlWakzjDWGU53m8zcIflGVIWvit2gWjAnSP RS9dR1lWa7qnjRYwypfJ8enyGMpysSkLW4/AKMcNfY+TSSjL12pesmkPo7Qy JzUMGVFWODTA4sk8jOz/u8l8lx9lJY5/3uRxhZG59nDPotcoK7NT8+LnBoy0 5NZdI7dHWcX0RYnTPjCSnvOZJUsVZdWc6CoHiWDk2eCflvlClD3Gr2zl9ghG 7DSO7PSHoqxWn/lfUkoYUZm9/UZGE2VPRQa8inlBiO8Bsl0qR5Q1Iq6pK4+B YbEhp/k8Aj+TvEW7SzwwlMJbaJYcibLmHnT7C/EwJGx9vJ5LA2WvjVsoMaXC 4MErNjw3BlD2+ruA5q+KMHB9/vX4VhrK3rwQ73QsD/qrOaM3XI+grFf54n8O FdDnbqqflXMYZX396NX+nYHeEruciclrKBuoqNzxogl6KQXfuBG5omxofCBF bi90R271RHQoomzsI3pPqiXoeFL/7gVjBMp+1FSh+eAB7dVk6nSSfij7ZcMi WXEb2kkGIvSuyKBsql38kCUJtN5fs9r+9x1ly3RUTqexQuPJix2rrgS8qj2L 8ZPvoX6eXN83UxBl67MD/Xv5oe79Gbm3owS+ncK1P4mloEbsCnvoUQJ+79DS udcZUH0obNlAoQNlh97Qz0ioQuXqwTL5NFuUnSG7zGWsBRVEbL2Kiz9QdqE4 8NdMNZQLlFhWrQ2h7O87X87760GZRXGUw0FvlN2UqV2ka4PSpD8xkZGmKPtn eik44SKUUmsn9750R9n9Dwz86gNQ8tjunLioKcodvKRS0GwDJayHE78UuaEc Be3li9emofhB+TOdakGUO1wT+HvbGYrJp4jWHn5COfopZns3dSgqKM5KuEmw WUm+9c2RQ1HK1p0cQj+S4+I7bmDTBUV9js+fp6ehHP+xrvL+L1CsnZ9nlh2G ciKXnVWNPaF4+bKI44MplJP0OZDSqAUlN0qMeaPZUE7uzRs+HVoo2WegFIg/ gXLKWZKvioagND14xT/7LsodbSujVEmBsqDxrYSDT1EOVy7eS/OBcvfmU8dG eVBO5/DCmthpqAjsNHd7PYhyZyWCHD4xQ2WKnYrq/S2UMzzDMsA+AVXrrSUP VRpQzsT+u+HLDKgxOy9wRCMe5a5+7FF7ZAD17uODit6ZKGdbfPP7Hjc0Cg1p 0UWcRznHQRJ+rwVoWikRu757H+U8WaWprodAq+29ZrHZSJQLCX8weLYEOlPW Vw5rfka5ZylsRhXh0NWayrfKvIZykbU/qjQJ+8c/ZrXQCW+Ue3+wN1V6B/os RYkV93RRLt1X5j69PAwfHdUspGVFuezoio2nhPxTs5J+8MEE5fKzzZ1ImmCk PSXcwEoP5Sp/Pzq/cQPGiBsrGwqNUa7+CHu1izqMvX7vytccinItkmlHp8lh XAT9/hoeR7k+h37BnniY0CQ32ppQRrnhR65vjdxholTlDLfDKMpNfCY9XIcw ifoT0abZKDdb8i7oBA1M5g68/xzoinJLQ7Kb+YMwJeVOrafwBuXW/lbdUEyB qZgvBgOFNCi3w2Yx/N0Hpg/QeWQsL6PcnvJvY+HTMG1l2sJJU4ryJMaPa/5j hukcDvu+siCUJ3fn1GSZgBlyKvU9/zWUP/w8Pf1FBswY9Hz+9ZUa5em/nxKm CISZ5xpve/S6UJ6lbiAmyABmKjf3s3wLUZ5zxp3mLzfMrDWN87cpoTw/KdkD jwWYZY3M1bnQjfIiArFbC3kwq8gaRR42ivKSx+Wd7UJgVueY4ZxzCMrLWVaP DJnCrP5sKa28Gcor+10xuShIsMnrTDs/ovzRt6u1zasweyLivkEIA8rjr5Bj p0tgViZIg+T8K5TX7uTKKA2HWbrR9w66Z1Bed/WniPplmJkreuvq8QblDWlP v/spDjN5sm6FTPIobyI1RCuxAzOBpl+bN46ivLnCffb3mzBzLFGfMes9yl9V ExQ6sg7T6yZrvWTPUd72WLXM/d8w/fGN2JerKyjveNJJbW0JpnUSigJFtFDe RffwSbt5mBqvjpaquofyHgZp+t0zMHX3RJCCEyPK+5tvXssfg8nwqNGf6ddQ PsjqrYvUMEwe4b7S2CSM8sH2Gt7/Efb1UCfnuy6E+EW4BT170EXYv3//7rUm 6P0SQvPrbB2M/Lt+1NWCoO/rs/TSwmoYufio8OXzCpRPfWncIFMBw1bGPDqO h1A+Ny5mlKGIMP+aaFBmEPQ3ZolQ9mdA9+Uy07T0ZpRvy6tjOpcKXRTz14k7 llG+u+QmT3EKdOTNG3VeSUD50fqfip8ToE2yxX92VxnlN8bA8kYM1PVPDXKT EPz9mR67PvgGalKhkTqKEI/9xUceBlFQFVPa2VbfjwoH10T9SyOg4sNRylWh QFSg2K4PUQiHslI7v8g+HlSg2XOJ/PKUUF8mc471u6MC40G69ywhUJQXmVVa 5YUKbBSZiaGPID+byunltC4qcB+5mP43CHLWF8lHrk+hggDjn4KbAZB9N9ef QcUJFUTZ31cN+0GmTt6CcFQCKkjxYqvRXciwPJAYQv0EFeSFxvvLvSCt1MYw uWYFFVQkHk8pecCPiRmLAZKHqKAhJ7aS6Aopkzy2I9kvUOG4csNfNhf49ijG k+opIyroaLgeenoDvtaQbWh2SqLCWS2GI7vX4ev3yEssZ2ZQwfBUNvste/im PNQ4tH0QFUzOmQmOXoMUW+o7RzII+s3P/5O5YAU/FDMPOMa3oMLVS/+pVV6B tAtr/Y/oL6OCraXWSRVzSB9i2zfuEkYFx2uT+skX4eevFuGrJ9VRwcUxxIzD GLKGe7RcS/VRweOWxLVnRpBj/NNO1W8PFbw9m27u60M+46fh4JtzqODv4+bt dhaKuJJfv/P2Q4WgQMag8dNQkl1VrWBtgwrBj349M9GGssCF9rrkAFQIe2r+ pprQ324sV6/xRKPC69cfUr5pQE1coubUMCH+77T/+8bbD3X5Mk5WFGqo8N96 XPJrH2hY6undNXiHCsnnYxPu/4KWuno7mWPPUaGAJvrDJXno7O1Juvl6DRVG H7+IOiQGI45O0ZVCDKgwpfQ80qcGRtk5ZMRf1aHC3ET4ixXCvFTr6VrU74oK 61phz/rjYZxfY7X7mQYqbP9+GmZ0AsbrCj17zxD07n548qRqDCZcKVoyao+j IuleSHAGL0ymHuO/qB6PilTfgx+LFMPU6dNJLY+9UJH28uOH7y1hasC2tn37 FCqy5T28H/KOkN/qtotLXajI7fggYE8dZu7IfaOpVEdFAdagex49hHqWner1 yAkVJb0Cfa8yw2wPk3pUxSQqygkF3O3Igjkw9BUhDUZFpY573rrGMBc3eFV7 5Skqqj/w9ypeg7nNu+0HjsijIsj73VaKhHntWePP7zpQ8cSor8c3OZgP2+CZ y7+Kiqdf+LjztsB8vbMJ9VYYKp6Du66vXWDhgCCH1/lxVDRa8r5FfRgWpLZK eMLuo6Lp+zs376fAglFNs9Mtgj4LPa8bW7qw4Bwg7mpjh4pX/952cp6FhXsU LD9biVDR9qvn9bEQWHhsYSDq44yKjmYeDpdEYOGR24En4lGo6ELubtdYBQu+ Z/v+jRP8u/9ysz1hBwsO60QbRKmoeMfe1Sb3ICyc9aZytPZDRT+mW9bSn2FB aGHHhJBrioEVLlfjtWB+y4U3UrMRFR953LRkG4H5Ei421jxDVHzC73z5eQDM B/J9/tl6DhXDW29YHOKGeZXksGPzFaj4MtDJzKcA5qZmgu//a0HFaBnHiysW MBfOSl3V9QkVY4eum9r9gTmpOxIn31ai4sdnDsb9b2G2Utfi/nUC3wQN+wtG qjBr0q/XGLyKiqkxtoYaXjBjc3v7uS0fKmaeuaafwQTTwxJil7P+Q8WcbZtz IpkwbbL+hCXKExXLTK106X/DlMLp1lXxdVSsdmrhd+6ByWeBNhsFhPg1BMCf qhKYGEsePMFM4N+VxPPN5wWMB5IWhFHfQcW5nWHqMVkYef23uOXgPiqu0OhP arDCcLqLRF9HDSpu8BcVviaCIUVmydM9Vai4fzbO5Uwz9B+tmbu1ZoVKDO+v NKe7QOftuTLiPWJUYk1vTKIyhQ5GltPhLOSoxFWlGWh3DNryitTWpSRRSWSZ S47tMLQIpQ2ftwhBJSmSZxTu69DYc0AY1XRQSZ7l3///7av3/0Rj8DQblTRw IDIwBao5qLevcD1ApeMmek59UVDJLClS6lSISjqOBScU/aBcdv3eQAcDKp29 J8kRbgulzo1PlFY5Uckw8t3azFkofk3SIeaph0omiVQNWvJQ8NYvFijTUMk8 3zc+lh1yp/iYJ3elUelq87zfJjH88m98N9l4GZVsJyxMDOYgy4arbtU3E5Uc t+ulktvgZ9QNy5Gle6h06/BRUpI8yGBqVH+zSIpKnnxfh658grRp5jTXfHVU uqvMnv0rFNLIuw8+vryESvd0nzync4UfmxnGfsYE/AeWfxxuXIIf/qd8IDka lUI8nKAS4Uf8qmPBCi8qPQvuY+URhVTntKSAOVFUiow9s3KXFtIc/doslm+i 0pu03Jq2bUh3GtH0vqSGSrGVYh8lRyAj9yjz61ZCvD/2vr37uAYyL5AKRnm7 oFLCEoXRSBpkK/vuPqMKQ6UU4rvi6tGQYx1TSbzyG5XSmWeJowIhr02Yi6ey DpWyJS71LTlA4aPkM4pphOeRDzU/T+tDiUZBcf7zNVQqMVZ9+kkZSjsvK0zN BqFSnT+rhikpVF5+Gi9y7zgqNUeEMKYuQfWpmrK0sieo1PFla5GiC2rPsofw r3eh0lBT9/vCBGiMDJxrf1eGSuPjp7xYwqG5LXWG4StB78zWL303L2ilezXR 4H4RldZ4X+8LaUO7l/n1k3+foTKZu6lV2AT0eJBIkLlaojK1LL/knAv0flgc vtVAg8q0i4s7p7agr+m+3K0jm6jM7vgokoQSBoU440UE+VBZyiqz/C7h/e+g yjO/SYTK8tyBz7vyYPTKW712YYKtPKB3WfEEjI5/nN831URlvDi+vmwKY+OJ KnZ1hqiszZRaqjcC45eXuJMkCHhn2n2ffXWC8VZtSs3hDFS+YMAgbOcPE8nj l+acBlD5EvXQajkpTB5+TR/GdRyVL9d9LeZ9AZM3ZMZTLZhQ2TrE6+k9dpgs c78zaaSAyvY6WhcJ/WSKQfDVGrcXKt84SCOoJg1TV+hP7gwT9N0q7V15/Qum PlAv2j+9iMqeAV8K1hGm+udvvxzXQOW7mm6hRnUwTfvEnJo+DpX9/2qapBrD tGZ9qYVmDCoH5VLwUw/C9LU3Tx1SVFE52Ktjyckepu/3Xz41yIbKYYof8qpX YDr6yafIyydR+cWqc7AQYT5PjHsr3DyKyq/SVC88IIHpVOaTHpCOyjEuB3lG wmD6x+YNDXFrVP5PonnhGGFeTzzz5PdDAp/Ps+9y3n0g4PFUxylJoXJSosPD HTGCv/DuB5zmqPzdTsHINIPAJ33ObW8alTP497kyNWD6WPIzIqYIVM4eqZuj q4Jp+nfB3/ieoHJ+3OvsW4YwNZyRd4TRHpVLLtsENfTBVAL36jnzaFSuZJc2 EL8GU/aMJbUV7Khc2/2HI3gBpngaT19rbELlpleV05NeMNn+mMqkwAGVe+iu BH54ApPixCNMSQuoPNAsprfHCBONJv3BsnSoPBq2wWbxHiZuVDkVOfWi8jx5 WAZzGoy/O0H3T4EQn+WqS/c81WFccis+oNkEldcfCuq2lsNYPruCHZEQKu/u 54+HdcFoE4O1vKg7qtBuzjMR/4PhDEG526fTUUV58syd5TPQ/YPU/9DvUVTR IH/MLccJXZ/WdiLDlVHluERZhfsSdL4/4Zog5IoqZ901aDejoP3LhRJR83Oo YvjK+5eqPbRlJoSSD5miiklO1hUfNWht8G/7mfcSVa7uSyfvDkELq3cwVfI9 VLHjv2EIadD0rszjeyLBn5N24ub9B9DImTrVGfYEVW5dn4gtN4X6zCr2jA0z VPEM4ztxSBTqbHcivFl+oMrd1CuzOn+gVlpoQvZqMarca4t5HtIINXSHBe4M 3kCVBxvdSnUfoJrmolG0mRqqhLIxDFC7Q5VwxKazBzmqhGsYBumfhEpzmUZD hteo8vLqM9EXLFCRHPJMT7kZVaKDapta56CCsac+MoOg930C6W2GAiiPPb6t rZGPKp9qT3CYhEM51N12zGRHlcSFgNI31lC2f6W6I2ITVb4fKXDoVYSyvobR 3C+PUSVDfoeGgxTKWpse+1QtoMovU6XMyz1QNr5frQjlqFJw19087huU0068 uXWcDFVKY1OJRu5B+cWZu2e3ulGlqng+kd8QynOuomVzPKrUj4ues+WHCrlH cw7yfajSSmq79mUDKsoehP1u8EOVLrGPb6droNLJyXRioBZV+vWGUCwGqkRV 48cWxVBlxJV96oYzVP3pE7EcLEWVyZcXw74DVI+KsUnCH1RZ7m3plZ2A2vGZ mOBSHVRZ3z0c4J4NdbtlVG1Geajyh1dXKDMUGoS5fL2JeVH1oH25u6o0NMV+ Px/AYomqFE+IWH2IoHl6Y+XMYByq0nzXKMpvh5b3myQCb56gKutaNhV4Q+vf Ka0cTiZUlQpMitcph466453ev46gqvznSd2Q19DpfyL2+1IoqqpU863UXYcu 6bDlR8QEfK3D7zT1aaA7lHVBYe8Hqp6S7R5/PgI9stvbYlVTqKpnzBja+hN6 Oi5/09yXRlXTmGddJpegj0pynv7MV1S1KKzzeyMOfe+dKh6sn0RVq1Ey/p5d 6Bdt3mmXPYCq9gdP1LK3QH9K707/SDeq3hAJvHX5EwyIHbGp/H0BVV11C5ni PGHgPZHLdyMC/9s3d/JHTsEg+SmTCb13qOoToWzDzw6DTiEJ3qS+qBqQ6UF+ bQEGyz1eJDdmoOrD4sKTcUswRGfft+mkjaqh9WSBvcswZDLU4KWhi6rhXUaF jCsw9OIHc+scIR4vR9/tGKzCUGn67neqU6gavTCl9GQNhmbaHDoiHqPq+21Z t8p1GCY9MFlO6oSqn0l8fhBtwjCb5nf/PYK/JJqKuaNbMMz/7Gmfkyeqfmc/ Iuy1TbBJ6iFmH1UzhMxs0v/AMEshLeViDKr+kv0ct/AXhklGxIv/xqNqwdHF fpF/MDT1fkR9NRVVS0+pstjswVCJbJIgbKJq1fmgC+/3CXwHrJfIrqBq/ZWG 572E+e3SElFKjw6qtjgyNzASwxBrvrRQxTSqdnpakxschMGWj5Oe5xVRtS/g 28knh2DwPvmhjHhmVJ14DYVE5DDQSJOy9VoDVWc/hu4cpYQBxzgRPY9wVF1K 6VDyooIBotCpw8QSqLpdfv37Ag3080RW431C/HabMuZEaKEvqTY8W/oJqhH3 7grb0EGfdHgGtWIlqlGvRMT1MEKvPKdCpkgQqvFx5T2vYINucWruhi8xqCYs drB+nx26kmxd2fbvoJqEogG5OqEeijKe+M9VEdWUdMcD07ihU6rlGX3OZVRT N5UqmOeFjiyiV7QD/qgG1nd2hPmgA5ropGX4Ue2MN7VbrCC025gPbxcboZr+ A9Pv3cLQ9pctu8VAENUuhH+YoxeBtrce+wLUD1Dt0tt54XOi0KbZu+5gPIRq V74o2YSIQ+tMZlk+41NUs0kLiCuXhNYYezaD/seo5pBf27cnBa0XTlWkSx1D NedqRhZCv25l+GZ0xukWqrm1WV7wlIWW/t7s+2HiqOY1mPQ8VR5aUhgom23H UM13ZvV/FFh5XE3vEyZrSZR2ifZFUiStd+but4SkTd8kEUkoIkSlQipJkiSp pD2t2rXRvt32vW77KiGpkN/9/Xk+531n5pnnmTkzp3ZqPzC9A/Cv0RHU9JjX 3SijBkzbm7NflOdQ8/6/hxRrdWAe+SXO888KNf24mjxeHQQmUUomX+8TagYK bi9s1wCmzuYnTw5LoOZzCdslXi1gwsd1v51CUDNMKe2AgTYwDZQNvh9vRs03 B5edHuoA87QxZah8FDVjyJSUMgIw3YV+jW5dQM2EIwGTfwGY78zTBXIsUPP9 iS5ZDQRmy4z4up+zqJlpK2VzjQRNXBG3JcwbUTPX8VLEezI06e371DdzGjU/ uuZ0T1Kg6YnNb8+5o6hZ9pBDSJoGTb2si4x7PqhZqR1Pt5mE5t3XbjfdFkHN 2rkjLlH+0OxeFVjdeRU1206EdYq3QIvG+db9Qd9Rs5uHuPHkDWh59WeVri83 avaXjWuw5/fWNWOtOySLUXNCaf9LEWtoHdi1Pf8KD2rOsLqqzddA27Hg4+Vt bDzfnrsvhcRCW0WSp4RtHWour9RZbJuB9uwpz5m5AdTa3GS7necWdEbrbZRb Y4pafA82HTosBl1ifrL7rO6glpBWuqtfCXQ9fy6sdGQetXa9/dvLuR66PbmC dm1YRi0Z8xhuegJ0z7sJtFh3oZbiZn2dBwbQc6bmUBPDDbX23wh5vSYIeg9u s5zdbYVaGrt16kkHoDdMsbe5cgy1dAaG/np0Qu/iMdXE3RqoRQx+tKf4DvQd E/tKedWMWjS9vSdXdkJfjIJgaAz7vf7ftgCdMuj7JuDiqOiAWkczXItcz0H/ wYc8yvy+qGV8XmI2nxP6XeRPmn+XRq0T26vEF5OhPzWhLMzrDGqdZF4+evAo 9LNKK8YzL6OWzX1+9+vfYYBrzwUfrx+odV4zPzUrBAZ2p9kWvNJDrYuz1gM/ NGGAKnD1wzMe1HJ8u2Grai8MmCrfLSllx3/dLAUdPWDAqrWfLhCGWre5jzum SrGfJ72Cg8+hllvJUuSXCvZ5EzHZw0zU8rr+pknpAtveRnWrbg/U8lGkrb7I DQOKvy3c1w6jln//jGpCKgxs3Lujov4Yaj19FnR6wgj6e5OV4983oFYIQyNI 9if0x3udWGE+Q62wP/1ltqHQf7H+0cHEPtR6k+79PUYH+mWT8q7f34daMed2 Sw4NQF8XbTpa6iRqJYg2HZfwhD4v5ua7/dmoldLo4mUtC32y7j05JoWoleG9 IzOiGnpLQz6Kf5RErRyNT8N9DtBrYvli8/tvqFXwxZ5fbAv0DO16JSnOgVol 0VspFhnQY6fJ41jAtlezyTKmYxG6Tw8dlOc+gFoNJRxtgq+gqx09yk9HolaL c8I6EwJ0UR+Xh8YyUKu376dt833oFGTaq+STUOtrWoBsPR+0Ba0NCZvPR635 mncqX1egtS/99d0nJ1BraaRQm28KWuX/66g1p6D2WuGpo+Zl0PzxdQ2emEVt zv2rT7i+h2bOD69rD4qhNs9h4TMRYdBkVm1zM84atYXv0VxGrkHjUofZ5+kF 1N7x6qTnBmtoSFxI+2v9A7UlPzj7KxpA/bkt9/UrFlFbaTI60lEaav79PT6z 7Rtq71uTl/hsK1QPxj5c3ZKN2gd3MLM+/IGq5tl8D7+rqE08tlL1uxUqJgPm 3VPY52kOAi3iJVDBa/UsrsYetQ89UOojJkP54bySv3MBqG0YSR4/Gwqf32RF JLu7oLZJvsW3h/fh88ZjYO4UidoWrU6/E6/CJx+3+x5V+1H71OyjdfVW8ElC aFty1iBq226M3DKnD2XNrCNeWfmobS+ZI7pNHcoiHjl1pxej9hWdBml1SSjz TsxL49yL2s6mo8oneKDs/uc3K+oWqH3L8Y+G6zKURYOKZm0Jarv5bSNFjLHn xwtO2i6pqO31TtGgtBk+Kd8cI+2sRW2fYqLpSBF8ingk9za1C7Ufd5mf3pAI nxVfPUshbkPtoB9XLiq+gM/NL4TFnnKgdujmB9cPe0N5CIP/flMmar+We+3u 6AgV161NBi2FUDuamPXomSVUXnHZbSvOvh/3X+2zbAZUeXMGf8hj20++PvSa Pc9WZ9x+dGYfm4/0J0txf3ZBzfK2E4/XO6F2wWe5QuIi1DedvRUseAi1S/oJ FWdHoPHU/tEt2SaoXb5ownzIBOa9PeerfEVRm6nkNVKfAM3Eud/DPbtReySY tfmEBbTLDouPibD1M/n+l9AdGnSoOOsd+cTGP1vNI/FmH3QSvgjIcN1D7cW/ OgdGuaD7/E6qhbwl6my2DbVyLID+Wh1q4KIL6vC5p9o9i4eBoR9mxiIlqCP0 suJqdjCw7uw6VGItiToS9fMP/jjAoN9Ux47a46ijrm6Y6iMGwz5XtuZk9aKO 1tKnlPeJMPxzJWPZ/hzqwEf1pDYNGDll6cBrXIU6FI+EhN8VMPJ5n7+R9BfU 0aOIxUsaw6i08pE7LFnUObLhSSxjCEbdV+1Iq1FAHaNajpgrjjDa+l+XigYD dcwCrkc/X4ExqSWB/Ds8qGN5bCKy0B/GLj2zpLQdQ53TAv9FDIvCWNqUbR/D B3VsOxtec8bD2JcyY2/jVNSxDye+UlGHcanhibryd6hz5VTWS9PPMH5cLGub kS/qOEvJvbhrBOOuQJc8xM7PzbGw529ZMB4udVLYJA917iZuflZzGcY/xHI4 CPChjuclj6dzv2G8MiFn8pU66jxU+REo5APjzYJPH4Wx/fvNnwv4/z7d2n1s nd8i6gTmdPmfjYHxxok7W47aoM5zVwNfv30w/knrsuD+KNQJIxT7pJew9/FG 2wctbH9vOPY96DwC40HJ/jc+jaDO2/J33iu9MH65909aLDv++EfCXjL2ME52 KhaeuYY6KQZ+9w4twvjWexXn95qjTsaWFferD2CsXWF0f/tW1Mlucbr7kh/G gv2LT9rYo05ByIhrcTSMHWrOrntERJ0SC7NbYyow+kehJWqCC3XKd9Tc5C6C 0XflcT39/6FOQ0ya84luGBnNivXvZeNrsZO66mEHI3ceXcDAGdTp3B3iGLsA I5vfFRZ8OIg6gxl3HOb5YHjnl8jrd4pQZ76EYRugD4M86pfzjumhzpJXwZms TmB5REadf0pHnRW68umeczDAMtv5s28v6m5o4LeSZ/f7wNcOPW/lUFe0h2Va lgedTe32dAlZ1N355rjxJFvvkQ2yjsqjqCttU3F8Syu0O21ieGzVRN09k8lH LeegVfjn2+Wdyqi7L2XnYS83aP7umPSvdhJ1DzoGHUrkhiZmrfGvd5moi79u 0n/JQyNQY4SGDqMuNX+atiMb6qXSnsbTtFBX382KQqFArXB+hHlvIuoar6Pg U2uoZDde3qkg1DWvyiHkzEL5xfB/Abcfo+5Jf0Wd/jvwKWPmutKNEdQ9v22r 5u5QKPGLfG0iroa6F4sC6w+ZQNHhzb8LBaxR19Ge9/RFPijIrVi59nceda8L PP3p2wB5lo+1OC/NoO6tUr5HiX6Qo/XfLfolX9R1c3i2o4YBH4zXVnXy+qOu l/C29Km1kJm0f3X80SrUffgpmMZZChlEW0f/4xWo63+Fv1vBDdKFvoYflMtA 3aeizy/raUPa3kVFT7RC3ecVghx2i5DK/7W2TSgUdcOcQkJ8suC9oVOVRKk0 6r7ZIaQY7wTvtyW9GosRRt23VS+KqpTh/amrwb9+U1A33lnYaHwKUtW0hZgD N1E3ZWfo+IY4SNPjXnV1kIS66bUirnJnIV3npKy/aTPqZt8I20KXgIxrud69 EnOoWyAp+vZcP2TOBleIh7PzW1z/6uCDMPiQfHBxSPgc6n6+tb021gxyknJ+ rFfuR91q6fBTFfyQN73cWVHmhrr1TLEfo0wovJhTudpYB3WbXV/7rHsMxQou kYyOLNTtkBMXk9GHUt6alV0/dqBuT3NEGnUDlCVLiB3P50NdlttOytnP8Nmm xOSN4VXUnWzb5RBDgKpdrupfC8NRd9YjatWn31Ajc2AgLIGN74eSRPBwLtQR b5HiRztR94+X5EcpVWCe9JEYnq1Awmrlt8dIs9DkWyBcHkBDwrpuqVGbRGgu KS9JD9qChC2qMpujpaGNIrt9+EIEEiQG5KwkhKE7cP9pE7IiEmR9475jG/TM 3Dx9obgfCbvV5R9YB0GffjjLl3gLCWqPFd5HbgLWWtvjzx3lkUDX3v1PfAWG 8sjfXQgfkHBoLPkZoQCGT7c+z/LLQoJh0B45q5swsiHiWmmIKRJOTCofff0N RvUEvj/xYsdjFfx++ON7GB0e3ap2RAcJZ3CvS589jN1WXSO+TxAJDi9UIsVG YPxF1LY9Z24jwYmUrqYTBRM7VjyPfRhFwvVZ1SpLK5iIOLTcZCiFhNsvMyzv iMKk8N4hPudUJLhT98296oBJP+Pl13ZqSPCay/QuCIbJX/bRexSpSPAJVxPu MYQpSxnum1wcSHhMz0r+vRmm8nRve1MSkfD0xwGiaA1M81y9fOq8BhJerJVN FWfC9Om0KvWGeiSECwjvkGyH6STR6MDFXiREyXL6yfbC9NTVhsLrJCTEqi8v KQ7CzE5vE834ASQk0WfOK4/BjL7k2/YvrUhIM+9r2zcDM/YKWcm9Skj4cKGR rP4dZtzd9nRyX0BC/u2SdK1FmPHdfKtl4R0Siv0ydhJWYOZRfaf139VI+Bz+ 9jF5Lcy4FZ0fY+/PhOqU4N90LpixG5ej9rUjoaHowYVDW2CGYT4nWfADCS2N Lh1H+WFmx54LL0uUkdDJukA9LgrT4w9+uBZwI6Hvm0Wm2S6Yjgv0FfrriYRh DgOJ/2Rh+uTd6NRltj4mtuk+OaUE05y+qzL2svn+Iq3898w+mEpa8mItyCDh +4FdF89rwBRV6OuTZQoSftF4uy7qwmSbRpLCARMk/DFbQ79CgknL7DsqvI4I q8/Pf7jGgImeH5r/thUgbHrU8dTVmP19MoJ9b98jbA2r+uduAeNK5o9j/3ki CCTlX/KyhrGAUcG/ezcj7Kx/red3EUZJz680i7LtqfGeWf3KE4au2Y2L00kI mpLGV974wGBOwKWNLYBA2E/texsArKU47y3KNggME/m8pJfQP3/wi7EDE8Ey dNapIA06l1ZKddYVIZxOGBgozoYOL73DFUkRCOfymw5/KoR2PgEc2e2M4Nib pVBbCS2UKrnGut0I93fdGuzphzqtS1XQ2IDgq3rx6MAIVA89Hqw1GER4QrL8 ODwFlbFXrjsPpyIEHz+ye3wOyh82xjas10d4eRZeTv9kz69G6KZwCyHiuuqG r8tQmuBx0lrlIMLbB5LX51dBcVfAsdK+CYT4F9uGF9dDYeyXK2ZBrQgp8euO /eGGvI/3fmiyFhEychdKVvFBzk4nv6wmaYSc6gnltcKQ1RC9pqCUHV9hd1f4 RnHIqMupIusSEEqna7m4pSB9O38sf2YJQsWfwptb5SH1eoqnzgFEqNucMsav DCknUv1FYtj2m8TfGAurQdKwecqbwa0I7XsDy8S0IGkb/WxrlApC92S/tNQQ JI4rzmygRCIMxOx+sPsRJB0KXKPcwEAYsbo5sV8FkiGt6G37H2QXboW+Viek lJ3SPcSXhvClZVsyyQNSm9eXXJGiIHx/fJpHXx7S1U0fjl75gvCL/t7xGBMy Kpf5DxlWIvxZ/bv5hAtkhQdsF5FGxFVW0mEqTyA7w3Py/Pp8RI6CQ4fUzkDe RlG6+KA44jphpz8aB6HglV3vjSw+xA3XQ1N0NkGRvVnuyWOXETmbi62gH0ry jH4fO9SOyL13bAs5A8rMf5JopG7ErVP7nQ5ZQPnobesbUpOI2+gWkkeVobIx 9OLPmTZEgRiPluMcUN2YuFt6OAhR1KrhgEUCNGy6Fm+qGo8o1WK3dGERWqJy 1U8mRyOqF0bc8wmBvuAdCnUORoiaIuX7/O2hv5lTw7eCB1H7xvRwIAFY6+2V JFtU2ABVNakvRmFQq0ii3CsFkRRwauFVLgwyG1JDJNjxUaYfxL3xh6Ezz3JI 3x8g6r1r5YxTg+G74ef4XWiIBhzL+UkbYWStcM685lbEI9YSF9/3wsj9gvPe RYmIx0Uv12d7wegtnheGYgqIJi7P3fJNYXRKSDJFtBzRrLVwb5EijBkvhMwc YZ+3fML5tLwZxoXERXI3iyFazaiQqmNh/NJIL1cqA/G0numP+tsw/tGDuv7h KsQzsXdjmo7AxLqPe7SonIjn1sSYtEnABO0y7xcLAUQ769r1nfMw4X5RlRTO RLT/+C2ntwom0oKihpYmEC9tF7ZjhcNEV9U5iYBAxCs3QWTEESaWF24HiKwg OrXZ1kxQYJKfe2CWtA7ReZ+/64wwTMqsjnxZws7fjSeZSl9nYHIPs0lTwhzx 5kxX349imFS+4z9lvh7RVX9VwK8gmJTjnmn69g3xbpwc/D4Hk0JPfkvp30X0 WHt47p8WTK4SiUoJykX0PO0cvYYHJlifnq0ssfnw/na8eGk9TGRvfnhkmG3/ 4b19fbMrMOHpsP5UayqiLy/v75EFNl6BQTe+EkT/yG8i3V9hYrWefOPAFOIT FeZB5gSMZ+lPv3N8gvi0JNWkgr0fnDr3rWrLOGII69LTDCaMvdFJ94ysQXzp aJAaXwVjask1tvns++GrdtdHlMDop7f7HITMEKN2TnH6psNIY3XQw8pSxKRT 5+7ZBMFQisvjKbUFxJSv1DfmvjC0/XLkdw9ZxDR36Y9HPGHwQRiWnmfz+yFi aEnrGrBMQ94larLjLe4/5cRnDD3tS0fv7NBBLLtMeLLRALqjy9dlyzxD/Lyy I3mFAl1OOxJWTsci1uzomZg6AB0iJw9ztC4jtp40ty4ThOYMyaYe5h7E9i8H 3XJ5oCnZlT8iPhSx665g+Pv1wExz944LZNdDf3hr58tfUJ82Y9YZ5YQ4qJS5 8GQOan97PuSRyEMcLnzGf38CaqyaL9Q+Ydf7RO8xQ6dOqLy2aFy4oxBx2kHl 8nkmVMjljQxtYNf7lz9b/E9WwedFrmYHmzjEOb/ZxOMl8Gk0fPr1/duIP7bX V+nlQdm3dROVLwmIP5OSx4Bd/6LFAj5jVxAXtf3XHkiE0jMyAuu3nURcrr0o uTsaSmpcSjXD0hH//qePEmFQYtgtYyrzCPHftIKVUBAUz7yTxdUrSORw3Xhn sy8UX38eyysehcR1XONhazyhWGdXn6v2MSRuCKvIWXKFYkjc7Rd7Colciu/a vjpDsbvVQdNH40jkzvf+MeoAxctq4tpchUjcon+Wt+cslJi5XDgZKIRE3m7y 3iZLKGnYYrL32QMk8ttLHq40hlJrXv9bOwlIFFxeffGjAZRxhxx6HWqHRJFH rEeZFChrfnPihchVJIqJFMcl6MKndBGpHPo8EsUTIsrfqMPn2J7pycVsJEpo 3h1+rgzlGSkbyVJKSJQ9obPznjhUbdAYi0vNQ6J8n+TGa++hms7r3PjXB4m7 T2/4do4ANSFd9ucU55CoYtfy6dBJqLd2dOa8fRiJWjcvnhcIh6bcYu+t1oZI 1PlzzHCjEjR7uVjHBDcgETwOaiwXQIvhpjNEua1IpPis4RrohtbpqG9yWwaR eORF2PtEEegUHNbsu8eBxGPbPV6Ex0Nnd2dm6CF1JB5/c87jiQZ0hUW91byi gUTzuH1GzubQw/V31+wEGYn/7RbWPjcBPfn0kc0m+5F48v2KNPt70mvT8VZv 9XEk2mRXLxDY/fm1n2Tp7m4k2mqlDqjKQr9ynFiB2hEkni96XiWVDf3ZS6cC atj5tSe5pgvSYEBd4Nl3v41IdKg4HbaxDQaSXl75S/yHxCv6dM/fZ4G1KcRj eBc7H04Ney5+mQeWDtGl+/xpJDobbTNmeQPrfLKZbPV2JN5oW9Jt4QfW4wKT pKeOSLx1YkC2PAZY780Pitm5I9G1r3xL7n5gVV81LQlj43U7nbiYWAas/pXk CvFIJHqMBg6+NgLW7K8GmctSSPS6cKPmyRB7Hjo5LnaIHf/9L5aZnleBtaKe pl+ViEQfJ1L4dQ5g/X1s5dnBtu/7U/7++afA+nX18anEXCQ+vsVz2WIXsKb/ SBfzb0bikz/zZgZpwOqhamrXmSMxyKMb2WMXq/zSeJ22MRKfry1RUGUCK/GV Ukoz+/4Ln1g+aWtg+S58qL74EIlh3H6/Bb8C69z7tFJCPBLDA51GON2Apfsz uG+zPRLf8JvV/eEBFu/qSe72KiRGhep+mI2AgRIx7jub5JEYIyYVMagMA6ap yd5pqUhMkPrqWHEY+q+Y9yxapSAxKa7tRG4v9H33bqa7TiPx/e4CUpID9Dma MVWODSExc/9D/kB/6LUyqvb+IonEDzkOf73EoKcxc88uDQUk5mobjV1Pgh6C ogTRi63Pj2TxXIsa6OYf7g0K50VipVG2pQwndBSYzHFsdUFidZI+iNhDhyqf o6GzDBLr1vRLbq6B9vjXh5TvrUEi88O68QV/aHvDxb22KwaJLZtf1kzNQJt4 t+CnSDa+tnN7UgYOQ2v08V+PK9nvu4VMrlXxQMuHhWvfT4QjsffKpGnhZWih zaxyw2gk9lfd1UxrhOY+6ZDk0SIkDkrwir1TgeabS16pwhlIHL4V8+9lIDSL FlEMD9GQONqsMfR4DprKSuB8yHkkTijWlXsegyYn2v1ucikSp7ys429kQJPs 09Azk+JInOmZ97vIB8yhnv3HlYeR+FXN5/Kpq8CMNd1li8tI/PZY7NjxFmA6 KfOMpJgi8cdomhpDDZjkh1d3iwgjcYFAEdIJBuYO93NvPnMjcTGkY1nlJzSu mGkFvMhC4vLXi30yptDYpPZqXHAbEv/S/5WIZENjZoy15ssWJP6LfPaWRwga 38XMN7ZuQ9LqJbkHHDehMU6m6f1lOSStPVZgt9AJjbkbnunyXkDS+sSjh6Y1 obFTTEJkuAxJnBzDygNhwFzzzHHpCHtc2vSfC2/LMjAlL7uJKJCQtDmLa77q P2AyrjbFyAohaSt3REdhITCvX9KIzBtDEp/tvvx0MWAmSI2ptvEjif9jxet3 d4A5bJqssdEGSUKCFh4v+6BJqljz2acOJIlcnj0TQIAme96TTYEOSNpe6Unz jICmD7+N1j5tQZL4LkGFGyvQvEF2/RPCGyTtupnIffEUNJ88cD2ETxJJMgpN zca7oEXQNeG+MtufnKftB4YHtNwc+SDkWIokhe6lUJ1BaOl/UW249gmSlP0l rGSioTVje0D3jqNIOjjrOPlLBtod2ko11oshSYu+tm76AbT3jtDkvNSRpPMm NHVgDDoMHotS7hYiiWhYcr2K3S/lq7ui1RaRRE4wNv+4ETqDL+890/wNSbTV E9rpF6Dzb8Mj5RvWSNLP3Lo6TBG6Kn9V2p9SQdLhTW9HAnyhW27Q3+Ea+/7R swcrPaeh26t6IklsD5KOFdYmurDngx690nxjApKMBU49vpgMPXtW+Nx2VCDJ 9NIPR2tu6HH10fPMeokk84qHx40vQc+ngLdeY6uQ9N/O7eqMBuhdV3ivpUMZ SSddUkV0laGXWFZo/IudX2uO3xekMqHXNTD1qf0ZJNkE0PO4NKA35Xr/U5sk JNmKBG+c+wi9HTJux4y7kHQ+hmXeQYLexf9Y+g1KSLJXUYr/WAl9W/s/3/Lt RpJDwc3FGAPo23U36iOHEZKu0MoZvk3QJ8c7wuDmQZJTM2+okyn0SbtLdK9n Icn55MkJsx7oE84kP+Rg83djIlGDYA19a2Jj+jMzkHTr2oKP9Cj0jlrKGzxI RJLrP1LnJnvo/TiIz3PY+nPzfSL37Sv0Pj781VZDFEn3BHpcOp2h9/jn3IGf NCR5RclVFi1DL+9Vf9pXdv4fKDkLvXOHnoqAqBzftUjyySk5778Weq5ZHJYh CCLJj8ydc9UXeoR3VWREn0NS4Il3ZhAM3frLQ0OzbL6CRr7FyYpAV1fLqGQt m5/njrq/uCOgy0a16O2HeSSFPWgP6WLr4fSZFiPBUSS95pMcK9kDHV2EL7A7 EElvXl9Rj81g6ycpgrVqEkkxWRvar7H3Y/kn3f7h7PjeD2oKbmZCS/ky/xKd fT7d4b7tvAm07I7Lss7sRVLmr6YP3d3QHLRmSnV4Dkl5PPYmcSPQdOZhDNFB D0kFL7PfBVwAJnOL3rWYm0gqkuH46TwLTFxrufnSYSR90n71nLgIDSeaQzkJ bH2UV4yPyt+FelMzm/cSbP1XGakd2MIBdVYrJ7/NcCKpps/D++dDqL2+/rTH 7C8k1dvVtfZyQ83Ltwt6Zmz+G+dFpMueQnXDqo2FZq+Q1Oxu65wgCNXb1GMq 1TWQ1MaV/vlJOFTZmxQ2qGgiqeP5Cv8NCahs/e/PpDe7n3RL6J+1jIVKo4NJ YRzjSOpNDskiK0HFSPlLcZ+7SBo4OLxWIR0q/JniD5wGkTT4aa/xVnWoOMSz J6D7PJJGjri+XSiAil2SV/VWtyJprKvyRx97DecOe+gyehJJk7b85E/lULF1 lnAvVxdJ03PWzxL1oWKP2+/iosdImnVNHg5shAqbF3IjnM+Q9G390n4XY6hI u5Aj3OaFpB9BVK+TXVApxFthNxSBpIUdT1soVlAZEnR0WnQjkhbj+6UUh6FK uW1B0HgWSb/VFK/x2kHVwPsx/+0hSPpbfKPs1xeojm9vDRoTQPIq/U98/Veh 5tHXTaebrJDMkfjRsqIKatmj+docaSSv48qJTRWHOoegp7yPCEjmrEnWcq+B htd87iUi95DMrRjrbbeL3e9XGe5VJyOZxzeywfAGMP001Itz0pC8Tf+5jYQk NE0aEv32+iBZIPFJMudNaH7bsFjWVYZkYa5HP781QIutwXSVZxuSxWrcHpXd htbfB+QqRl8iWVb/fPqZJui0GREjneFDsnzi6d8GstBFLdr0iWceybu5LKlq d6BbcfD4SFkNkvfWGHatk4eeJRP558xeJGvqa62K9YCBZ4nC4ptSkKydqKYf 0A4srSZj1JBDMoFLOdhFiT3PKHNt+BSNZFKNlDy9EwY93x1bDtRHsoE+99FJ FRjm5ePy6CtC8pHE9S+bHsDwxZAn0xVs/8e4Vg3n9cJwafe+X52HkXz8wu89 0ftgZFvQ+caxzUg2qf7p4usDI9YucQme8Ug2V/haerUfRuKpqdHWRki2eDS1 6T81GJkq+GjYsRHJlpMjJmRfGJULr2pJtkfyKb3+N7tZMGqVWrh3Kzve0wmd k/zqMPqkOK6i/D2Sz3K27PvjD6P5r35URj1H8rkL9XdGhmB0QDjAmPkVyXbV lRX1GjD6l0fwaC8TyRcVyrZ+CIAxfguO4AU2P5ceFVq8HoEx6f7yh21dSL4y mR3zQAvGlL1mmAZEJF/VS5u9HAhjquq68qnvkOyckKRhOgZjSuM1UnoHkezC +c4TdGBMIpCzUXocybcuvKmTC4KxLbtH5AZckOxaHSa4ZQJGf34sAusOJLsp BJ9aJMBo2+F729rvI9njUUACKxhGUybCN6Q/RrLnpM+PqikYdY/mmJ9IQPJ9 PS/ddIRR/SC5TpIukh8m3H34MgRGedrn+L+x+fLldGm6NwMjdc/eGZ8+i2T/ C1e325NgxGtuiNOffT6g2sHWKBRGDojdim7Yh+SnCudStWZhmKVneTOM/f7Z I+slKQoM30918V2QR3Konon/j68wVLJzu9O9J0gOe20a1dkLQ2atIQr/sfUY /s0s+2MNDE79u7Zq+QiSo16eYD2MgcG1rLW5Vmz7iZNWatstoH+nA2EkOBjJ Kbqn9FYxoE+o8u6LymtITn1qfXL0APRy/1f3e+Q4krM0bR6mboWuLwc6CAaI 5I+PzvWQyqHt7S23dxksJBf3nZ+Ty4DWZ/TrB3aEIblM9cI67khouf/MdHkh HMkVXReV229Dk3PzN6XJUSRX73EgF9gB85rFqs+0ViTX3rtkHmkCDV83/nK+ O4JkpsIVT3sVqJULq5Pp9kVy813HF0fFodqi4VCkNNt/a5NTstomqHyzP3I4 7QeSu25da/s7Bp9P8//Li/JHck+989RQC5QN7n+5S6kTyf0S1/9VlkCpS+X2 gycpSB6qvqnw9BV87JoTJxxZheTRHbcIN3yg4NWDuMOOdUged7p9/L8bkBeU 3yTysRvJk+WudngGckrPqJJtI5E8I3LnrowhZCuoJHzN+4zk2Ut3g7gIkFWb skZhRhnJc6VusV93Q2ai5puRPeznHwLuBa3CkFHBczS2wxLJPy94MPPWQ8Yu Dd0x0wUk//p4b/T1PKTnhf4baqxA8jKv57LnIKQHjZb287H5+2PrveV8A6Qn xwaftGXjW8m7L21QCBkbDnTz/7uBlNWbH2iqJkBGdHEHRwcXUtacfnhEMAQy 3UmnZYbakbLug4/Nb2/ICvfPlGFKImUj5yMXlhN8WIbTMmx+KFwnff3LrSDn Rd2VkeZVSOFO94tKNIA8Z5eAvfcDkLJlnX/2E00oeGLq+OJwGVJ4TzyudZaF j+PHjuosyiJlW0oA6wQ/lOg+Lcu21UaK4OonPwmroTTmvYLJsAxSRBOCdm7o hc+V+6u5rLKRIvb32f6ZGqjwKKP+FTBGys5jwYymHKg63ljZG1aNFKmlkKuv gqCO2A1XVtj29+iFVeylQ7P/4mnvp2x7e5nGjPXm0NJ5o+gAnzBS9pltru6z hzYln9Ov3oUi5eBZj3q/AOiYooiW9Xkgheh+rnW8nb1P1xQFarYhhbJ+l2nR BAxsYX5eZ78TKTT/rs7ny8DCerfuST6kHAoz6KXsgMHU2dL4ztVIMfmwbzjy LIxcuwcy/eNIMdeesXW5AaPbn2hnz99BikXZu/EjPjBa9NfHQ/gBUk4xhaf+ JMPYz/EHF4pdkGJj2uzQUgTjPmc84lbY+Tnb5zebyIQJfqKmkzP7/oWple/m P2BS0KyH+c8WKQ5Ouc4qa2HSL2908cV9pFxedFrYIAiTi8zEzwYjSHFy3+3S Lw9TJ/GOlRibf+d1I0sftGAqNyuwEHqRcsM/wtXfAKa5BgzOiK4g5dY2s79n rGD6+O2Hu72+I8U1jNddyxGmg+g1jQW1SHHbVbua1xOmK7f6ra9URopHnLfn RDBMf4s1/X2qHyleyoS1xbEwwzu85psTO3/3s349CMmFGbl45vfFQ0jx0U7f eKkGZvYNeZ0REEGKb5m9L6UXZvZ73bE8HYWUx3rS3NtnYUbhYUV5tg5SnjT2 Pf6xCmb458V5FuqQEmT6YksNL0z/bPvyuJn9HNxn+DRKEqbryIybjf5IeXGW a9tNNZgOtei+z+5flJdTn54fpcG0BblJTT4JKeFOd4VkzWGab8/HPvFNSIlY VA/9aw9TJYaOz7bbICXKbU609Q5MnZ2u/at/FSkx6xLCkwJgahU9SN95Himx /mfEPSNhMihzt91Tth4StolFnsiASbH7Z9Pes/lIetkuqfIZJiLEFHUlhpGS Fqcn0z8B474qTSzldUjJVOaIYxfT2E8/U76jH5DyIatQ4TE3jFnY/ORfewIp +WV792jvhVE+j+5NN5WQ8pExkcqHMHKugaU1IYGU4sZolUkjGM6Oj52O8EHK 5z4BtRc3YOiQVsH9MXb9Nfz6rf2jCPrTpFs3jR9GStNxm5y6KOg7ZOT+NDoR KS3va/a/84aesbbQXg52/XfavtxtpgddkuXl0y3rkTLYclA0vxVaqo/7RrPK kTKiHBHyLAeaPffqVm9hImXMdz2fQxg0kX5syV91DinT2MYlfgrqi409WyrY 8X0J17n/iwS1t58egHs5SPn6K2Y1UxqqDeiRu4R+I2X+/dWle1NQrinx+tmr PKT84uy+blEPn8yEbyVUsvvFki3x2/40KH265wVZyQEpv0sSLnEHQXHTvsa0 e2w+VsS2Toxeh8LCFSGn5hWkrnK5ebbIHPLGOzh6SYhUjuYB1gstyDmeULvN xR+p65Tplo474APH/gfDPzWQuuHR+069VZDxc9XX457JSOUcFTSWGIb0PZcY vJ1se9x4t3G5AlJPkLoUf4QjlefV6KGWeEgel12n8FgJqVt/GVQm+0HS+g0x /dJpSN1mlEW+fxkScjj8lm45IFXg/fZiK0OIX64Sw7sqSBXm9NI+uB/im970 Jzz7jFTRs1M5WwUhQfu8fabSWqSKlRjtm1iCxINBrxbKq5C6c3ve+9JeSCpX EigxYscr4SKhGFYEKUPt/Oq/2P6lmn1ir0VBGt22XK1VDKmye+YkDbwhfdix QolLHqnyj8wiZM5B5mfKvemHDUhVHCkWWdGDDxN2Zq6F5kjdg7LPO5Qg98j+ zL3O+5C691UAb9oWyP/RaDw6E4xU1YWfj32+w8cO9Vxb7ptIVU/5fF8rB8rk CD3vhNj+NTmVVm8Lg0+zJfsH2rYhVfvsszszd6CcaWB+ImULUnG7jXMECap7 ukTSWzOQSrpRPeciA3UcXSubuCOQSmlWdTDcCA26E3fO/HcAqXqPVp1dXc/+ 3ldOZJ56iVSjhYjjtubQfm3noQ0C8Ui1berep3QZWNeXHdcdZufH7tSy6K/r MCg30Jpr3YZU+1nRNWV3YLBjwynFv25IdeQ60WbuB8OKnTLjO7mQejX0VpFU EAw3nHeL045C6nXZl7GzYTByab30hpuzSL1N6rrpHQ+jLx3KAoSDkHqHuXT6 aCqMycJJ7eO8SHU/JaIvmg1jyQePmy3nIPXeF819o+z+rDgRFar9Eanerie2 p5XDeNSXylL5GqQ+5Ly11rUOJrZObN8eYYfURy9Cv9BaYcLFgy8tuASp/jK5 7bw9MNFm+3iLpBdSAzI7i3qHYFLRcIuNrTBSnxIX4+ImYfL6yt+3zzmQ+owp HHh1DiZzNWvzB6qRGmKlcUt3ESbnmHJ2m+8hNXTG3GbjP5jaGSba7N2L1Fe3 b+q3rocpyk252NlspEZsDN3/hgemTh23SL3O5j8yJFfMXgCmHHeYdZ9/h9S3 0p3rDojB1A2mVUKfBVLfZfyaXSUFU9eu0DfX5SI1nijUXqsIU+fW3UhrYceb 2HiwOGQfTB2JbajIY/OdctIs/rQmTO25EnT1rDJSU6ddniohTHF4UwfC2fWU cevFrV8MmGzgfbDfcRNSP2zIsSk7CpOBh7rvnN+D1JznHYcem8Kk3pW7Tf/2 IzVf6pea+UmYWCqR7Cz/jdTCDKEdUmdhIsonbcyKXS/FeHDdrANMEEV/r4cF pJY2mM7mXYPxzkYnmXOySP1seaPD+zaMX1hbm6b0H1IrpkJKjnrC2Ped6d9v seu9bn3709FAGJ3L2mPLO4DUhuCF22kvYPQ8rxE1mW2vSUrwjGsEjHSKHm2y u4LUdjA9wJsCw8kyQvzPDZDKcmnr1GXPy4quzY5ndyF1eN3P0o1NwPK+UR8l 8gSpo8ECCS2dMJD/1KRCZQ1Sp9JMXC+MQ992bT6PzepInZ9oEw9ZCx1ze8vv TrP5+3Xj54bTm6A99a2K69oQpC6v459T4oO26/vUYibZ+fsnYVxa9v//bbf+ XPIWQ9rqVOfEx3LQNM+1xqLRG2lrdYOfmSsDs8fmTX0CIo3zROvZWV2of247 dveyE9I2FUbTCwShtoo0Mc9ZiTSenY6KD2ehhveyw+0XDkjb6knYbFwBVU6L 22JFniONb3TTnEQEVEz1dF3bWoI0AXpX8+wNKL+7+gIh6ALShBLjPhQcgc8K 2g/9NRORJrr5eqiPLJTN0f1XOcsiTcyR7Gq8AqXNdTMTL2yQJt7CayXRDiVN lr2v6mKQJnFgAGffQ3Hn8Lehx9lIkwpNkSp4AEUMlsgPwWWkyfx2Xe9jBYU/ 6DX7nf2RJm/FmDRRh4LOBxdVm9h4FUsF6yR5IH+xS47oeBlpe6RG3s+OQb7x 0dW0nyFI2/sg42lBMeRNVW8VnJtAmuqkh7NPKOQVLPdvdfqHNDWDI2YmjpBX aT/jEXofaeqpYlqSDMjnvmYBTwOQpsk7JfZ1F+T7kpJ7bjkjTds551/BIhRA b4GvxVek6XbcH/JhQqHS9oLVjez3qHW83CQePh5Ov6DJQUUa6bVEvKQHFL2V N1/FmkUa5d9X36/mUCJwNfGtaCvS6DYfLxWqQMm3YJc+XTY/euV+ho82Quno Rj+qRC7SDORP7DdhQdls/YUTGb5IO+InJyiZC583N3PrFLHPG36ZX/waCOW4 0JHx6iTSTDIDix4RobJ/4GG/nzTSzAWtokxFoZoubXvFmZ1fi1tK3pLfoabo 8YOoh91IO0Wo0iuMhvo261QXO3Ok2VX/a53jgGbdqo7YGQrSLirV5xR2Q4vo SPLW9l6kXXryKuxRBrQs9Ys1h9ch7arJQWspG2jLv7XJJUgNaa6sy9Omn6Br H4eOZvYNpLmRdRqkwqGbs1R2Hd5GmkcsV9qcM3T3pXJxPRNA2v2LsTd8paHX xVP2ktR6pD1scD5h+gf6dBQG35Y1Ic1XlaQj1Qp9yw8CnqZUIy1goX/1R28Y sJE/pH5AGWlPTySN+FoCa53qF/k5BaQ9K7xVaaYGLBOJzZ+/H0dayE56ojQ3 sCLjOy5Gv0ZaqKeA/9wIsMaDy+XJKkgLGxm+8rEQBhWyovh21yDtNT3dyDcY Bs/1xty1KkLam0T3A2YOMBgxGkxfYNdT9ObDwtJUGGxK7lI1OYe0mLMS5V+y YHCFsF3x3SukxeYvXMuRhiGZxw/kJwaQlsBbK+ERDEP0h6wymWikJdm9adRf C0Nnxa+o3mLrN6XY+e42Zxhypf3itDRAWpqgnmLvMAz5/y7WlvZBWsalHZ2x RjD0UtvfZSYZaVmfvj+4UgZDkZwvv42rIi1HtFJNcx8MRV0K8BF4grQ8p1dD HFEw9Pra5Qt7LJBWUOX4pG4rDAXvfqSrH4S0op1U3eceMHQ/8exEjSjSSm6I TFvNwdDVjTF/T0wiraxu9qX8KRg6YTM7NzaHtHKpT7RvDTCk3VWr9uoY0ipv v5gvIMCQyFMvRoAG0qqbHKK938Pg9/Q/07vGkFYnTzQ8wt5/Ks8F3Sey+0WD u8CKkD8MvuibWJhtRhqzbSqJ9QcGz+qVTZqx77coFZ9IZOdXqb+pvp/dL9q8 gjdc6wHWt1bvTA4C0rpVdc+szweWUx3vIl840np9+LYyFYG1h5HA/5hd7/39 Y0UvX8JA3YG+TvdOpA37B4oq3YT+qODVnTyeSJsZH2oyUodeqXSDMzXs+v9K yHHfHgs9br8Mky6w8X577q80KgDdXZCwFMXW9wL5gI/LPHQ95rln5lmItH8R PvA6AzpW9aQW2B9E+uqfll/OS0L7mYsKlzcUIn2tgeorlafQVq01fSyVgvSN S90LnxyhNdIj66rHBNI3GabG+LOgla+f6Oa4gPTNcd5GpobQ4qtvF6HYj3Q+ kz0pk3uh+dFIRi7RE+n8yRz/ZURAM1/jydm0NKQLcXRw3uGBpmiiRCPkIl3k RFIO1Q2aNHyuX/3civTtaR62PLPAbPvC4zd6EeniG0z4Oq2AeTs1uQm1kL7L SqEkqh6YcpzT4bXnkC6ZtXL5og40ZlvuL1eoRrrMphYxtWRoVA5QPXhbDOly NnHVK2LQkD3nfntHENIV8u64VPpBw9H8ZyHKN5GutPWYTOAy1C/x+6vIf0G6 8nmZFgt7qM8Ukjo7z8ar8nH5nlQ31N+tfc91/h7S9/M3Kn/Rg/oTSvL6p9j5 OnDxbW92LtRTpY56X9+F9INlN3095KGe6F9ttlsF6VoG695pF0K94dr+ydWh SNdpCyr+ZQj1TtQjXUnSSIdTO7szRqA+ZtSpbr8t0okTyT8v34T6qQv/pWVy Ip1yVWurIjc0EEkuY5bzSKf9rlQcjYSGeKbGOiUjpOvdN6ZGqUHjLuvWZ99H kG7AM2htWQWNcfvE+zd1I/1I6GVXYUtgimuq3i8BpB+T+P28ZQ6YVyKTK83e It30gEDtIWFokh7OsvtvEOnmRdFj65Oh6WGxXKZdCNL/Y6isKkNo+mrD6r9/ CenW/+mra9pBc01drb/+ItJtRjoM5/9Ai+7lZ2smWUi3vXz2YlogtGQ83Tx4 +CfS7e+5RcnlQmvUvGGHSxXSneMyuQU3Qru3oegLDbZ+XFRRtikc2pdlb2u4 KyP9Vn498bEKdFzKtlGKFkW6W/24y1pz6DxUEpZSbYH0e2bOT4tnoPODVMNM FVtfXoOrk109oEt0m4e141qk+8xvH/yeAF2t0U/kWvOQ7nc3/s97AnQrmL6S 9uxEesAGdaELzdDtUuoRFhiP9MDAT/vY+0R3sbvlrT9sfp+JGhqwlqFnldr+ nU/ZeJ6/7TsfHgA9mv7GnfIOSA/dY+9pLgk9F3XysjddQHpY9q/X27Kh5/ka Y4t5eaS/Ru+cRn3oyY777/T3MaS/qeFt9u2HnsalRzuWe5EefTziC+0q9LBa j138jx/pMX1KGznWQ8+k2GZLfba/uHN5kh/DoGeiWCzo4yzSE+ZoureUoWfg wx+ppTNIT77VYq5WBj0Na4xmytj3U9dYX50zhZ7MzB/02+z8pPt/eZw0BT2B /c27eNn+swRvx593gx7bmB8O2g1Iz47c8EmKD3r2qRoITysiPU8huK8/FroX cl/6PPdCekGmxGKYFnRn3lKu2p6C9CKd1G0mDdBtl5+T6M7WQ0mFjjKvDXQL 1n9fF26O9PIu0zM+vtBl1THIaS6I9CqbYTeKOHQuhV34pnwX6TUzjqH/MqAz oNxtoYLNR+M/34Yb3dCRSEj1k+JDerdMsaatIrRluUjzfXyD9F6v5SOST6BN s8mE2cl+7h86YDMwD61F/J/r+4hIH45I8rcogpby/67eMwtD+hehFwOGRtDU R9L9lb4d6V+vN89vzoEmO+XvehS2/2+tPBtrxYC5sP2mucUDpC8Eeu+jjgFT 7FvA9P/1vDhbTF9zCBovxTW/qMtH+m+D5f9K0qAh6bBr/itDZKzidLyvfQvq 6e2m6YFByOA4n/RysR/q4p1mn6edQcba8rH32SSoEzz5innzEjI2SEt8uhYH tUGWX9xN/0MGp6dlpyo31O6Q3ydhJY6MTawXM7NOUJNzsShD8i0yeAgtq5Pa oeZ0JXhPVSNj62sewQvaUCNWv82D6IwMvt96u2UjoXpydcYxkzpkCJzwhpF1 UF3RIy5TM4MMoZxi4yh7qM4uOe206hsyRAWWL1g1QnUep4iG0G1kiDkfcBNT g+r6v9wm16qQId7sGNT1Eqp/tjBFfrUiQ0IlKS5kBWpUsm7HTkYiQypgrND4 DNS4fThjqN6MDJkvEk28VVAz0PNNf/kJMuQPWY427oFaY7GbrEPFyFBMeLHs HwS1PRZyhgrDyNizoWWL3i+ou37urzfPYWTsPccjvcES6iXXdMWoLyND9bOe xudSqGexnoSo7keGmqT3YU9ZaEgP32oVLIgMjf7lG3++ApPssPnffVVkaOse 8Ms3Bma5pEy5mSwydF85RrrkQtORzGjNxjhkkMzGqr97QrPLv02hTVPIOMRs EZsQhDah8gKbfbuQcWQvj+o7V2ir30G+afQCGYaP9Wg2LGh/UP+8R2MtMkz0 iq/0JULnOs4dmQfmkWFVllTWQoAesTylf7PbkXF611hH4FvoGandWhAkhowz 7hIzRzZCb/JtEQ3heGTYaYcKVDVDv4bcxkkuNj6nLG+7IltgvbhLF8+qQYYz 1WXq0iywFu9duEvlQMaN9osOO27CoKmR3APyLWS4LhpdueMLQ+u7+uSX7ZDh 9oj6TYkfhsw7KmQU2PbviWpe7XkNQ7EqX0wepiHjgc6uG1ppMKwa78ZKYPPr U7/t15QWDF/ZvY6P9AkZflYbbr38BMOxTQmiaV+R8Xh2+beeAQx3WpBD7dj6 DXSfvbPYDiNrHn4pe16EjGdbB1firWFEXor2cv0gMp5HtXmYTcIIdTlTiScP GaH7qjk2XIURy6GXuXIKyAj7VOiV/QdGHD68MGHeR8Zr47R15x7AyHXjtDWS 5sh4M/r2oeAWGLkRd7MpVxMZ0TdebCwPhRHHcB1OLh9kvNvg63tdEkbOyAvx jUcjIy7UjVs6CUaO0Pk2e2cgI1HB6XGrGoyo/lEZCS5ERnK+7RavIhjhOXa3 c4Sdn9RD5k/302F42HzzWSU2vvReA74hJgyny/2+xPsPGVmXIDjIAoZdWKfL wz4gI3tlvyBxGIYPxtHn27iQkfdE9sU3BxiaS/A3ytmDjMJdoiKRCzD0VrTC q4Ctt6L0za8M3WHo6AEpOy4bZHxqno9IDYLB55f6ApduIqPizMSuU2IwqLKT la7Ojq9qviea5x2wyqP6dTSVkdEgWPbuUi4MfF74dmnHKWR0WgQm7+6Dnt6S wFbJVcjonvbe08P+3rQ+TJcLZveDvjsuqb5z0FXlWB+xgV1vQxFWGVMc0BHv +PPRPTY/M0NKefHy0HL2mpWQTDsyvl7bpW2WAc3m5+deF+Qi4/vabR83aEOT oclbQ4dGZPySWS45dwQaIsV2q5pLI2Mpe5Yk0Al1mcHZDgcIyPhDH/xUfhpq 2ksXam+bot7qC1WV0s5QefwlmTYqhnprlgsZLStQntYs+6SrBfXW+6XWeD2E z9Lda03Pr0U9TrG3Bvu3Qlm6oapUWyXqbUoJqR8Kg1KzUl7XthjU4yH4Hg2S ghKRmGf/7GdRb2vj3SZiMnycy7uZZaeIetusnYy+sffdKTuPmou9qCfw7Wxr ZDHkS+Y7fdDwRj3hG6ckDFiQG31+7Mb5edQT/X3i0tIqyLFxMt9dKol6OzyO 57+TgOyznn5MS0vU27XuyIZjJPiQyHvju/l61JP0ZRz/ewY+qFkm/3jyHfVk tpAiE7zhw9oLvzeHSqOeXLDOF5N38GH7OjWviE7UUxQ5qLW6Aj7cLFYhCwmj nlKE6oOUccjeMbO08Zso6u2V2t1isRFyuOJUfu4TRD3VeNld6+Uhl1CdoOfJ xqe2Z5dDhh7k5f1QHky7gXrqGaJ5VvZQ4Hrfr8LhKuppHhRYz+ULHz3fFW2a MkI97cItRtlJUNygc2Yl9TLqEYicb2zqoFTZVTn3szzqYcWaGZ4vUBYSzZw4 uIh6ZP0VjQIe+CyeLqPu1YZ6DOPvzdsModLXloDODNTT7/oiXuwE1Rdz72/9 /Av1DluNX7zI7sd2R3X2XFNDPSO73rWfWqAx5dFHh70jqGf8pf3YlXlgLvka FZfeRz2zq8wIMX5olu1qFbQTQT3LO+UHnU2h9bWfR2ioAeqdC3xvL90NXSxf A+p3P9S7IBCfzfwNPbq62h132P4uhr1dc1cMesPDiw9ydKOeY8yL8LaTMHBq 09dkjES927kejfdZMHRhSerLNNvfXV1XsX2rYXhb7rkTz5tRz73sul2/JAzn X3n57bQC6nnX2XOon4WRP3Hf/1dxlYdT1XfR0kSjelVSUSmVKSWZyt5Scs+A VCqEJFMkkTGljJWpqBCSKBpRhIwRmechU1zCvRepJCl85/vz9zzn2dNaa5+1 bdfMIMv/sLk22xe+3t/gE6tCIOtGs/GD4CfQL+1V0sXnjazg7iO7+wdgQHNx 0TbrU8i6fZa+FsYPA2X06c5pRWSFcQ9VwzYYPBh36J0w0989+/0iPBYMZkeb ojeDb8TYXsv7NsDZ2gUV9hxkPXDb81bjFnCC+D2q155FVsz0zlnfXgCHUzR9 xksIWXHeUvSDKuDuKzga2+2KrMf8W6IOjQA34M1vW0NGT4lBYv1jy4BbbhSc o6OMrKQVIvJxcsDjuyqbs/Mysp7dF/KidIEnNxjc51+IrJfrllZOOADvmJ1K 0lQJsl4/EliTcAd49pz30f8YfaRJzLHQTQOe1/6cvupjyHr7bCrtXwPw/M5u rRP8hKx3O37PJI0B77qmcoC0G7KybsRp6vcD75LMtumVBsh636sVNLcVeCaS wrs2Mu+8vaMNaeXAY5xFVCODZ8G9SBHT98ATjrg6fwEg68Oo+umlL4A7KL8q Li0YWR9ZnKc5scB9rdCivYDJX/r49ohNKHDtir8fG2Xyl02pKAhfB+7mhSnO Lxchq1Kf7VHiBJx6RVOrpzuRVZ1y84OTBXBcr0kPyE8hq26hvMCmE8BZLT51 VBWR1XCmXaeWwePVaUpw11JkNeV637uiAoNqV/88XqaKrNbVUp3S0jBQWrm2 efU4sjrKL9sECEJ/qUJXQyaj/y+bN6fu4YN+tTmlhjFLkNXjWfm7dwy+vjZR 2hAjjaz+net9oQX6vOkLBwMZvX67n/fwdyz0/E3psjz1Bgm+s7OaLKXh87Zl WjI1l5GYm5e8bqUotNQuura94yASC4QPmxUJQrNHQrxclBkSiyoejYr+hIYe +5Nzn3QgsXQLsaeyD+pj3K48NmAjIXjlh6d7M9SZ1h4VrgxAYuUujYXN2VA9 u/j69pElSKy+xTvs8wIq8h7IFTovRmLN17CIXbFQFlpWGrpWAQnRiL4twdfg o4de/WlVCSQ2/Ai03XsRiu7d7flVzHwvTu1O45pDYeXMaGvZPSS2zviiphbk bohi55w2RULypIzfmApkq804yj4fQ0I6rakqXhreRXP+7fZ1R2LH4itCuqKQ vldVpf3jWyR2WkgYTC+DN6KJh9hmzFs+v/rRi1mQevCZwVxFFST2rHEePPkD Xp9V1E+osEVCyVFsx4I+eFG1c26t8ywkVCpLL6U3w7OPIgV2K4eQ2Cdhn3Pm EyRrWzyZLzMXCfASnrM8C5JsRLtUVf8hof65gJX/DJKE+9pNhlyROCBvFWoX DUkGo729WUz9mkGCLWuDIVluOad1JAUJrf6s9WVe8Oz+y9eZ35h5k3ja3OUi vAgtVGHlFiBBRwk833IGXq/pCdEuT0BCV8LG+KM7pO6T+2XcOoqEXlrFirOh 8AbiRPyrzJE4BlIl855A+p3Aj+/cfyJxvCLQLTEHMnfP/uduy+BlcHxY5mA9 vN/4ieeleAwJoz66++sg5J20qTm7lIXE6allWhJC8KEqoinGDZEwv2H/t0QS imtiPxl6JiJhubL2tQVCyahH0T59LhK2sndWPbGDCn/PW9vSxpFwNhX6KlEC 9RyfC0VTTL+uQ5ciSjqgkXC9eY/xx4SHazNl8QOa0kJHDSOfIeF1+/6bp2LQ GuZiQPbkIHGzSOT6VlfoctYqH5o4hUSQjodCaTB8Gcn5S00fQiKkvZ1jmQDd aXo/rzp1IhE+FqP7tA7Ym2a3Su3fhcRDiQ1i2yShr3X1eZYvw/dHaV71nxC+ uuW922IRikSCWo+flT70r4xYJpLli0Ty8fjhJG8YUObW/sutR+J535xHWlEw UOiiN9bSi8SrC+ZHB1NgUN2o1Rv5kXhzY0vOtg7gbHto7c9vj0TGSj/7Tz+A E3LAxK2bwT/z0YC4NT9whuqLzL0ikHgve6hFQBS46rMvr99Vg0RudtLNZAXg 3gpxdKHzkCg4JKDG+FdupUbZ9jfLkfjQYP2dY8bs0+FpT81wJD6alCfecAXe DguROGMGj9IhqZPbg4F3OLD0ehEzz3LXwMVlCcCzJi03BzF4VM0dKrDOBp5z +EatGkCi5jbtJFALPLfTHO9BBp/69a+2Jg8AzyHTM0HQB4nGZ0vbWdPM/kww crONQqJlj30wVwh4GirpAkOaSHz+ULv/piTw1kVqa7yxQqJDR25cEoHL67c8 FHUNia7228nl+sBN1Q09V+qBRI/lj1M2tsz+XOq5rv4FEr1jR5YvZPanmHF1 uZQuEv1eb4qfRQDnk/9nwZB9SHCWCLkSr4Bj1WJiSDL48CIvSXGLgTMrnlLJ VkRiNG1PmOR3GBQW49QfLkLi538HsbIVBh7RCiGOMkj8cjoycr4ABjZbCfL3 eyIxuceBlRYM/Wt6Zs+VmkJyTvaLWSqS0Fs5k39bUQjJVUUb7Vhm0Glj1Lzq xTMk12yW+///skPUd0R/83wk1/qqfQqSY/zrhdyYP5lIbjhkJF47Da0sCZwR dkNyU5JN3cWv0CKQwX9i9W4ktwi4XRWqhKaaUGO+UB8kt1fcazvxABrOXzaK 4tdDUlo60X+Sud9YUkrd+pJIyga9VYi2gTrpU13r3jYiKa9TF9qtBNWirpqd vyORVEjpVrsuBpUbwmt0pnWQVFo+MrR5PpQrq/u4BMQiubdx8SHLRih5s+zP 3sOTSIKCyC+B9/BxldljzXdrkVS/tz3+eTwUhSfWn9P6gaTGbyVd+gZ8kM21 VvZbgaTmCc2pbxeg4KvLVuO4MSS1so49v30c8h2FtL/G1yJJipiflFeD3OPS nbGRi5GkPRznN0nAe1eWmHWGKJI6HdfeuiyBrBbi/UTpJyT19oWaCY9BplOv 7e/mLiSPxj5clt0B7w6Hp7k4/kNSf+ZlrlERZNi9fimYroTkSdMcm+lnkF52 bH2eZRuShoUVwnG3Id380i2WrhqSxps+l+x3hXS1uqAFd8yRNPUedOwzgfTj kyp3lvGQNOv7vdFPE9JfH87/FWeG5FnNeTXbZCDjwOKVZeeY/i2fCl0uF4J3 K3d0uOTfQ9KGX1zy3F/I3Hj/JM98DpK21jtbl7Ahy/J7RObLAiTPl6Pv6zLI 7i96mv9NBUkHKR35wymQE239R+ZlD5KOgae6f0ZAnv+aL4k7jiF5adg2+O5V KNBzCN9iOI2kq7bHXkULKHRrHZSIeYWk++sbnM/a8CHzKXefYDSSVx2eHly/ Dj66FKbPMRtG8lp9+o/8OVDy66amvrM6kj7yxXGmXPjkm7l7kmuDZMCvnr8J mVDRkjGflt+M5C3it5PGHqgKeh+cEDaBZFDc4qGedKjRGf3sHMxB8g6h2CWa BnVu9ybVJUqQfBAbVBDxApoOcqfc5MaRjPn5WFlREpr3KWcbJwQgGaeVmdqU DC27cySLLJYjmfCj9/GKJ/B5o836+70MPq8OKfsGxkFHJ+3wkPH3ZEq09rSU KHR+WN0uVrUUybTv5s5lMdD1yDM5LMoayXcPQqzmR0G3RK1SSvQ6JLNGE7oT haE78L6pN8cfyZyD2Sc17kP3iOffgxym/4JvXwmvcOhJfNjb9eM6kkUHJovE VkDPpEnGEosqJD9GCarm3gY2oaVv+e4MkqXftrw1EgT2XYkVx40NkCw/oCo9 GQzstjJ3z18MfpWRuomRS6B37QIv2RltJKtHLEQVA6H3WFbISbkLSNZpeNxr Xgi9N3MTaviY/hoibi9zugG9WeOmPTKIZNPwk4D/+Jl7m1yFiZuQbN2fMyuV uffnP23aEPMVybb7dW6686BPfOpi+PorSHYM9X8f8YE+VfXALWFHkfyi/s8m iA/6qPP+yUN1SPbcX94rdQ36jnuHHAm2R7J3aKth+SzoM/K5ufimLpL96nsb ra5An6G74KYTO5AcvKdHz/8HfUcdjHS3RyHJ5Vl+TPSAvkOXlRWrGf4Mo6ea xiT07U7hGbYyevt2904G2xX61kmuTrFg+Pydm7TDaxx6p+c/abszD8kxyE0S uwS9bdaJyqL7kRwPb9iQOwa9qe5iDYd3IjnBGYw0ugi93mbTtGUvkn/VppdP jkKvrl7b3N1iSE6F/3cz0h56hT3qHZPckOJTU/NotgX2Az3R56VeSM0NOzLm xAW2vmNCjr0VUvMHre3+swb2kowDZy/VI7XoTrixrgX0OCTcVppSRWrJQHLz SB/0iLa+/HiQDynBvfk6QWegu0yNhqY9SAn1c7HcFLpXfbA8MKyBlKgqih8w gE7llpgOkXlIbShayt84BR2WO25udVRCSpxoHzkbB+1hIfWezsuQ2mbgkuXX D5+HXO+8EAhFapfba52yi9D8piL55MpspBRmeyoYrISmEdbn527FSCneIEW4 mdAkE+X08hogtTeiv3/RNDTkKhWE3tVBCja8rYh+BA1L311oKx9HSj3peqrM Aai3tPYM4/uLlGam6GXtm1AnZ9+8W9EDKRYMnf4iA7WPUq8bhgQjRZZmH7Kv hdp1s3zLfhxHSlsnQGaWI9SAs+n0x39I6bbor7i9CqoNzi0Qv56I1BET8YmN WVB1w6F3RrMFqWP93zvTjKCyiv/Ehf44pE6czy/aPwOVW+4O87EWImUwHpTU EA8VYbZCCXePInXqimGw+UGoWFUyz9tEDinT+dsdxwah/PWnp1NpfkiZBf0+ 6XsLyo2vP8hzX4zU2ZUfYZUslG8a0ms9vxUpy5iwzU/qoOzf4P63t+8hZbPl 9EJFJygb0pAMOsbks30p++3TaigbfWu/tvMPUva7/zWdyIZyga5b/K9qkXLI Kc/mnIJyRZ2wmD32SDkdiIhznwXlrtfqNriTSDlXWvgtfAzllR02e66/Qcrt 6O5zDzShQv7txgBpZl4eHXyHpThQ8YLaPFD1A6krZ2r35ARCpULz8KpXJ5Dy 4sWuo3dAZY2LDORnIeXtaDu7sx6qXLFOxnQMKd+/ygPnL0H1DhWBXfuXIBXg vaByRhiqfxqZpTRGIhV053HEBmOo1Tm7M13pAlKhIg6eqbOhtpC755WMO1J3 4uGMegLUKRXZ+LJnIXU/tV3WjAv1O/m3k1YNSEUpJ//3MwjqU5WexAtfQyq6 0PmPjxw0yGsFmN8eQepR3YriRGdoVPG5oHiEmc/zUcJgkA+aD8yxKyvlIvXK VRhdE6G5sDpbVcsWqZSZfgkBLWjZG9k5qZqPVMaya98lg6FVYrJpowaDf+Z9 neb3O6H1rrfOuq3M/N+Lrc8hG6F1+gefoBrTf4Fslr/dWvhc8GL/AD+jjw+3 FCxmgqFthYfkHF8mfvFAquYdPmgzPhsbt10EqdIDMhKbnaEtQXzVwKIapMri kudlcKGt1yFkxZ1XSFVMbf7KMob2dSs/5OpTSFWfjCtur4d2ut109okFSNVm rEs4rwntrnd3q2bYIdWwIsJnVja0R6/X0wnSRKrJXuhMmCy0Zx7bpXVEFqmW ilCNLfHQXrWlXD0HkWrbtnhT5ipob7sa5/XHFakOnwA+4ia0dxs0pwpMItXV M6enYxrau97JKudFINWz72qh/UVob4qe92pmC1K9kX8fze6H9o/COoaNTPyv 4y5e4QbQ/nK/149EX6QG9X6aSlRDe7DS5Yt8c5HivraHrP3QbiV28ciGUaSG F/HEyAxoVxXPXb+sEalvVhbTXZLQvsA1w3MHs0++F7O7LsRCW9UJv0W7C5Ea 22icN2cFtAXN4fQZMnoa92yLuesHbZqFf7RTmfwTbfqeW//A58kvPVEJjF7+ 7qk3yraFz8kpMZ4NzUhNhWnvpbrh85HApxK/9yLNRx386/AJWiPZ/93+rYD0 ojwFt/cR0PzPyPAqey7SS0VST2ovhubgbC13y3CkBV1klLq9oFnseu288FKk V8ptnphnBU1qKuMNJXeRFo0XctZRhAbPz4W8A+eR3jAdeqznGTQsXyIbmjuG tLjhYgUnUah/cuPcxncFSG8TmjMWOR/q2uCW29YrSEteuNoo5c74hbMXv69+ g7R05d83uSNQt+657oixCdI7fX9eZDdDrQM1UU4dQFqeba93iYBacbfh5ZFH kN6jxtu1IA9qMmqLZ9Uy9Ss9sFwetQtqdnHjv/2pQlrlN/u79BOofj9aUyg5 jPS+o8a1+SJQredvIH29BWlIaUs5HAxVvzXND1UMIL1/iX5IHx9UPcsKkKlY i/QB63p7Z2eosjPROrPvKNKaJdo6/FyogqRbnkJpSLM2lcs+MIaqjRPi16aq kSavai6VYfaBUPxpF6vdSNPthcMFmlC1eijdSzwGaV2lfVV62VAlvcRwcE4P 0nrhWS++ykLVYanVdusDkD76XSHQJR6q/I3+LF5ViPRxOtV24Uqoqn6mcMP7 MtInR909NRKgete5IXnrLqSNwjSCPXdB9XOdPL9MB6RN9ix+mFEANcprZkv+ Z4v06damlFFtqGm/qLFJJxppc4/YD9s7oFZr/MMHNpPfUtSy4YwN1L7iO6S6 dCnS1oVyfdETzPwDn4dcFEHa9syfX81+UBeuSsjoyCFtP79ogaAQ1AslSRfG Mng6JAcKs+KhPsbM5tyDD0g7fxNVycmHho+lmyI68pC+5r7/avwENJs5Pb68 LBBpn/WLQjv8oGVlHlw1UUPaL78xfpUQtFReVZgXpId04DyL4hty8FndyeL4 b0D6XugtAXtr6JB/VN837wvSkfJHRZImoGOY2HiFEkf6QfN6KbYfdCbsrG0u X4H0o7WvtY/Fw5f5t6cMQhYj/TjP1SREDr6kuEmnuG9C+slp9Quf8qBbEuys XB4j/fxJQ5hqG3QXjUer+aQg/YoVnXDJCnoW/ixkZ89HOmXobPqrceih79xR fu2K9JsQ2ZJBH+i55SU7LDAL6Yydv1s3MX6x2G6gS5Lhc2ZjAccwDnomlt+/ SDJ8eu9yY/KuLLAltkwmBTB8zhM5sqg6B9ja5+ZGvJ9EuiB33Xp+EtgXnkhP O7ohXWTyVVa9DdiB8VkuPxg+lfC9Ag8rYMfvmi1xmYn/KdFF9+04sFOlnpn5 JyFdoYWnGf/HzrJj7z3Vh3QVT8Bx2wpgZ3cG+ec/Q7o2qN7ndByw0y0cFUMz kG6Qe3D3AVNP0l+TJpUypJsazJ80MvXcvafef9wC6VZnmcylBLA9lEBESx/p duHxskOtwD7J+bAhmam3831+2zULYO/MviY4weDZbRzAyx4DNl/h5SMHpJHu nX3439h16KlaNz7bIRHprwlrl8oKQk/oj9MFO4yRHtTsE7OMhR7t8/SqK5lI czkv5R7JQM+8YtQzXYT0cKCzelsOdL+TjRDsnEB6dAccESKg22zkuD9lhPQv p7pL/hbw5djmHcrnipCeWB3lVzAGXcGyRcWHpZCezD5zn7lvO/OsFP08nJCe OiWdvFsQOvqDGvoctiI9M/Mr2y4WOhZYjPTE2v0P8P1Iww== "]]}, {Hue[0.1421359549995791, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUV3c4128XVglZ2WRl771JnWMmhOyEELJ39soWsvfmazVEQ1ZSKiWhfiWV 7CQRCaHk/b5/fa77Oufczznn81znObeprxOvCwkJyX4SkkOl//+euJsm8OTF xCYkZDpyGBPIIWdMI4yt4i3Eyz6LrzJAqBljcxpmRrg8bik5tXoDbo8deH/N JBiiyz/I3FnrhvsZe9de3nGESLlHzTdmdKBb60/UMoshhE23pAaeb4PH77dz WH/Qw6WCawxbGtnwVO53RGNlEgRWk9+pkTkD/RkbLqomf8F/iGbkjoszvFj4 ZTSwPwD8jt5gy6jthUGtnyo2dxbAW9p3jvzOWxiqWOH97mIPHr7vbr0NkYCR yM3EHioPcDvwKFeC/Dm8vnYpPMlZAFx+SNbq3hGBN+/XfY26J8FZ9j+9jAtb 8PZgoDMrcwk4vRa50dEXBO/k1qwnfczBccje9s69dBh18D/d0H8YHMXteLSW SOB9xqqm39EBcPj25FAqOsNYl6+ySmgiOFLOP1h7nwIfFn5IkLxGcMx8uWM2 rg6fWLx5Xoj+AacwCbMQej0Y11pizo5rgwv9/gpu10ngs78n5dlPfuASfpcj 8mAWTFR82+NTEIeLOT0sU39WYXLQ7ddi+jx4HCa/7MGwClORv9bPBoyA14/7 ez/D/GCa8Y7Pg0op8GMzO/+y7DlMXwv4xvsyDfy/3Qz3jUOY0ZC7kPh7AQIX k04//L0KM+9/fl4U0IXgI2+6NJWdYdan1drIhAChwY4iDOHpMHfQ783tqH0Q /u+juj57D3yRW+kPf9cNsbpdwyH32+DLi1sak/uPwOXnnOH5rxRg3sGnW0sq GOJt6a7x75yFrxnLrVTJspB0v2T0pdkjWBC4Ke575yqkRP5ra0s7BQtdXnX/ TS7BFZPcS3fjM+DbwveSMpV6uMpl+eX0sDAsaX1LkFjkhFymg2vinGyw9LHx XzZLOORxHR4P7OqEZX+30A3N95Av3F70b1INflR89X5QmgOFJ+Z9vgltwmrk 2vUGoQQoPe+reS/7Dqw+N3X7oH4byhzwpMp/bvCT8bYQlek0lNvWoJxEDfy8 5l/lG3MCKnWEXPhoB+Dnxmu76nxvqGL9ntOYEwBrGrLs/10vg6rU0rxLYp2w 9n41X/n9NlQ7NFZX+nbBL9EGBwq5dqh+VRnSzL8DvyLsxcYygqFGoVtXPesJ /HrFtNG4oAA1+ZXjvTf8YZ37ZW+Y5hrUrKyy2toZwbpfXNqp8hao1bJWGmhy gvXHqpZHtnygNjNV/t79bNhgXOVZNJOA2rdqNILUvrDhUv+98+YiEOhF6O8/ 5IGN+3ZtaRSNQNBl5Xf6exM2DzFdPnfBFQgBrzcC6IJg0+aloXiPABAKxbSv 6FHC5o04lr9ss0C4vU0R7uIFm/9UpgcDa4DwjLuy5osz/DZeuVE25ACEkbhf A4J68Lu6PsRblBsIb5iLFF0Y4fcvO83j8eNAGHg19aVbC7Z0mGhoJkqA0Hlt bcI0HbYKBt5PqFoDofrOq/MJVbC1cLmmOY8FCDG/DQ9RssC2mop39I93QLBI 5/10iw+201ZUjE/lAYEv69WRbnHY/lx/4CjBFGq/iidQtfLDjrTtq5U9Oqit vcJ+L+Ez7FxmLOo9OwS1VtPTmewOsPNm4ELW3XSoJfWNOsvwE/4IXJZyPKwP NU3B7ZLxVfCn/8eTfU/6oXqCv/5soDX8PVJ39c3RJKj2CaivkuGBv562Z2vC taFqK8NCt2QVdmlfrGhJ90Ll70sN/6TEYdchtpPxSjRUfDcwzRafh93bygmz X9ShfJR1n7twM/yzqGNPKOmA0jwrne8a+2CvLFbn2cHbUDAcY9ZQfwj2fijT FTj4Q37BEzVaQjeSHN1cvmNXBHl2khvtJ42RxP9me3y7JWR/H2rJM3HEfcwc RgLeb+CKHcHWbsMD9x3fvJNeKgspHL7nRMiqcJ/rG7b151mQuNDlw535F/e1 p8w+5TeCyx2TGA2KuG/a+ZSkyU2IiZdKjXj0GfdTQnN+NDVEPGLvd5Cdw/3n NsNcxgYgaNTxZx2DD+5PeD0xRCYK/ndNAplHlXD/zZvaSvIp4F3anqPt4of7 R1OaKhy+gptBy0pQgiHu33M+THZVFy5E9atc8UA8IAJBPl11YO+dvxgV2o4H zrB/GP1GCpabcl+mJWPxQNgmAIszmJTL4+Mxon/Na0KDVh/ovpnWcDQg4ue/ v926KgMnfINaw5OJ/ivcUvfHKkDeVtsjU9wBSZm1A3v5qUBkjd39wl0SJD3m 0f7cOxT4KylWJL2J2DFrd+T+PAgNOkTka8QiafJ9zQ/7zUAWHdx+xhHtNz8n Txv2wjF7MfvOj4ikbw8MfiuUAu0J+eJLQMQ7YvQ/p8vASMxGxPdNFR7kMbHc kTgE5v12TVoxDnhQN7hsf3AwnIsLpVe1kMGDXmXTlL1z4FgNDIr3R/BgTp8Q I5UpuO4z/8h1TAQPtn/z5LB4CB4T9+dtyRvx4OThFv4qCfDj4OWfOhqLZAcV N8QXSyBwv/I32OpBMnFbNQVFCgjmazFRObqFZGfiYtRjLkFYuPdy+H1dJCsf PnSayQRiL6hSRiwQ/fs2jCzsH0Cczfsf56zuI9kiR65dkxgkuDMkGF8bQXIl Ny6fEwchpS/5KhMYIrntVafglABIo77VU27OhORxdxui30xBhusZZ/6FU0g+ vE8282InZPPx+l5Iu4wUF0t0WrL9oED9esQRYj8pMnqvtH+agMIred+ocBMp 7swPPxIygKIPSdfN2izwEIn82dddQlAafG9Q8dgCHioa9F6b+wRVpz7026qJ 4qGetdt/pPWg6mvzdvbPZ3joC9vWgbA2qI47xdd2vRopZV3imGizoObGoeM6 e0FIaZXWz2n9D2rVzP29JauQMuo2tUCtJ9T23Xr+woQaKV/8K1BU0SXOKzKz pB/PkHJV4NPxuLtQd0JLJvZUGlKxGPDovOKFuhYmZg1dJqRS93cxYr0K9RyC FI5slEjlVHjN0vEv1McI7/ukfBCpUh6s2N9wh/pPI8JR5bpI1Tyn4Lo5Cg0y 4wed84eQ6h1lmK+GNjREUz22PiCBVH9kekLSWqGhT8h4+gQbUvNaHYgZ5YaG va38dZL/kPpklF4KTzo0KliUUJz8gdTetRlZHtvQ6MBSnVRFjdS5L94U3XOF xngOY89/b5C6Y5W1au8tNJafekC7jxg/xWLbpK8Bjc1XM5bULZGGTL26Na8Z GtvG/8VwiCKNhNN8xyQnEUstbw0HIM2ZF0X+v0ig8cbLmCyPUaQJkdEXI5uD xpJB7uOBu0hTVvBnhv05NMYer+ARakGaR7s3S6RuQqOd3KJ4NiLNV+fzppo5 0Cj7ZFOW7RnSUr+kp7QMhoZ/DDfCb0shrZzs48ceNtDwGN/vq0OktSoKCo8+ QexHOKnpjiPSRu4JyeXwQYPcDzfFNWOkrXF5v1hPBvWT/7VLsEgjbf9gak3H ItQnuhz4kka0L8sfs3k1BPX8M6n99uV4mKF4mWH6NtR1ZScTfBTwsApJxcBG AdQZdpMP+gXg4bih/ce4zgPB9rFX/ld5PNyocOeXrDbUjuu552wDHn5V6nJd RwRqLcvLcklFkY5awTmjLRRqjsuf1RTWQzqrff27EwiVnUb2/rlnkW6p7OqE 5GvIzxy2Xfuki3R7xl87lZ9AnsFrQ8WXukjPcAALNNogl4rxTxvjC6RXdl8z siiBrILX6hODi0ivz2Ugdj4drr74I8bylRvpbUcIZO7RkB5Yss8kwhfp45Qt eqKcIHmDPL4jRg7p8xabS5LNIXF4fjlgtxfpGyrIg7N1Ib5HvD+YswHpX5F2 SNWJQczdMa8IrqtIP9nOQHmLEyJZDmo0lMgg/Zqnx3wHLYQ2Opfyd4ggA+sb zorBNQhwL2MP9ExHBrHES+Gjc+CrSib9MI8TGY6rDFlOjYIHvc+CfyEFMhgv CcstPgeX860Wx2ufIINTZSzteic4Ct0j59wj8l0yHVv8dwNs3tC5XmTzQ4YU Mtl+igowP9P1L3SIDhlKOq7UMmTB6SoFvWj9KWS46TUbwxkP2lWq9c1MscjQ y6N+TigYjhnPtvZzkiDDm//ylWXcQDYod/70CtH+JekHo5oNCDs9ITmQQcRb aror2oYgQH1+n7Q4CTJSLle+NDoBIpPP2Hf3xyIjV9VWg7UMyC142WoS+Rhl zM/EO/HBcdcBE9fJKWTUIr923osJdFZSawgCJshoZfKsllwMjPeSbyebryKj Z/HMfA2CpahsqkbUFjLGzOyJHbcEO+8Aj/SHRL48CQ7vMS9wmonOvhj+BBkb Lym3BsbBxdyWwQNpRcjY3WO2QVsEXk8bvUVziPm8JvdTaWoGf9WmQNqDecj4 xSQ9UvsJBBmUVlm/HkfG7eLGh5MfIST/Qc6IVToy0cw8PRC+ChFM6+xp7xWQ SSlo90orJ1w2bc6PoiVDpuRin2KuIEh9WxobyhqNTKUzV8bbr0A6p9TNLxcM kalFvIHHrAqu+p7dS/pqjkxjDyYbUgchu4jmnka0DTKLzpjc2xSAAt8Jg0jh QGQ+Ie61na0GhQSXmAaedWQ2DUo9LmECRaPqlmtnypA5guzRE6dIKJW801wY pIDMg+Jyb4b+g8rMqXS6+efIPBVkzOK+QJzXl8wc9xP91x942pD+g6qnLy/j dylkOUSWXF7FBNXqV47up/BFFi7j2uljYlB98/TDj+x0yCJb1CswStSFrO0Z HEVxyKIzPe7mbwk1EQG5oLqNLDZiWzeovYi6UexQ0CobsvgEMa00xEGtZG5u rAqRP+6BrLxmEdRGeEQ1TowhSyHZ6eDPzVD7yG/t6isSZLlu7N4Z+gRq9/zE 7jFRIsvDosR/jB+BoKiSXyEviiz/TddoNK8Cwan679nAcWRZEOtJ1CcDQnJa +UXVDmT5G/jx+RdOINSOa0wpRCArXfdv6lg5INyPM3ideBhZBckYTTj0gPA4 8MEz+ofIqmosndtmD4QnzccigpaR9XSRwfszQUDoUT7Qz/0HWR2n3diXrgDh Fvtsm0c6sgaLJdgnVxH3ax++nfAryHolsKqGrw0IIYbubb29yFrR3f3lwSAQ jMYqh7b9kPXOwQ+i1jNA4FZ/+l7NA1n7jTa8fm1B7Zem/iHHXGT9VETfkkkL tXVWp0bagpB1ZVpyXUwAau3ifTaWmJCNVExf+aka1NIGlOSM5SAbW6BrhIMJ 1LT7SnCKLSObRHdczx9XqDn3QnGJWx/ZLIy6dGVzoDpztdrs9gVkczt+8uwn aagW3Vd28UAtskWI/+edMAhVD1JMudbzkK2a4nveGBlU3s0l/+GYjGxLfRyz 0RFQOvZf/KIiGbLttTb8FmGDkncPI339zfAIQ5UC5es2KB42Ldx9qY1HVKIM 5QRWobArdpYluQiPJChHxr50htyMzB0FuSd4pFCIIu/SPshJVCk5OTSOR64x 5TVyV0B2TM27yLOUeGT4541h/zG4uqcrsv87CR6ZmVKZ5bgEGTTakzatMnhk Y+jJ5lMGSBPyoMi5uY3sHNc/c7OdhuQYO5qL8jzILlXiLvtoERIfNTB0r24g u0bKho5nCiQw0R5kP9eH7Bddabx6HkPscrZpc20ssoebF8e6nYfoZc8RSnol ZM/QEsyj/wuRItRv49fbkP0Oz/EuFyUI4donbPpmHdmf0b4Ypv0Pgj4aNT2Y NUH2sV2LmXY/CHg675vXRoLs379PbzrRgN/UPN+gmh+y//voQ0l1DXysk/uP xIciB/3zHa57J8HzTKm/u8IWcgjcT5K1nwO3ZlPNzEkH5FCuZ9ChiANX37qr zPREf/28CuvbR8G5QJ0wINSLHHbxYl7nuuECn9dD1WN6yOHn3xZ70AacmFTF Mj+kI0e8g2Zu829w9DS733BAHTkKjIYarPPAUZjLM2+FDjmajtt07ZcFR90G fxLhVeToFp8fuj4Ejs/vnlK+m4Icw+wBMxae4HS949jUBpFvhuLfxh4FXFie OvBhvgg51n+nHWqsA5cMivsPmsaQk3yelctUEy4mvVw69WsJOdnf1sr8mQT3 sSjaNqY85JTsk9auiwSvGIn1Kd5I5MTWLmvjI+AbcqOaMjILOc2qTnputYH/ eT0Ou54t5AyLOp9rsAqXmM6JPjqWhpzpnt/rNzIghI9s5ardFHJW2oR0VopB mNGu6qQkI3I+Vc6cXnOGqN3pnu9PjZCL7mevTNEHSFBqMJ6n30QuweDD1/8r gcT7c1YqDleQS3XnvDCtDSTDXuuK0jxyXdj/j4uoZ6/Yy3D+PkiPXPeY1Cl9 piDLlFTmTLAocr0oSktsqoZslo3uvVsnkGuC8xPJnBNkj72cXhNzQ25ywbDf Z+cg1+RIG8U1MuTmaHoRkFcHeaRH4o4mcSK3jBTb8rAr5N0+HP+TjAS5zyrd n9NegPztY3eVh1eR27uL/HxMExRkK3orN7kidxxYfuz0gEKem3m2ZQnIXdBX b7EhDoUNXHLXdE8g9/WTG6+ll6BIgDy+XeAwcj8c1DH0uAlFBTvOFnnsyP3W JK+/zgeK9q62yc3IIPfC2znNKWkotnNrV4oYRO7ds/I97KtQ3KJiJtNQgEfp P8erWrRC8e8Om+v1hnhUyPHN3awAKJG/86j6SwAeVZvnk34pDyUX/jxemTiN R409/K8fXIeSFP/dC2eK8eiFH4+E8B6UVB+It3hwF4+GBtJVRwRDyc2mz4OC e3g0/bcDZ5sylDR7HuCLZMej1RG3Cle3oKT2rIaFqDoevbe3xyjeASXpuZzk hm54dCDB6KprOJR4aHH7Karg0UmKikPVx6BEvbLa1e04Hv2Vvpzw6S+UHFhM HCClRx5SiopYKnso7vWbLSW0IQ+t/rPjjQ+hOOBKM+v7LORhS1/+o8sDxewl p1VHSJGHb5i5fTYWijqoJRk0iFiS/nhw7BQUGVlquVMxIo+ymYsCtwYUfvyd LvqgG3kM3t+9dXY/FIy7+N8SICCP5ZFxr00nKDC1ph2WeYM8DrakYrl9kP9o qSpAiBgfNGVWN5QAeVlX2Vh/uCFPDF/EBU/i/VhdHcxZCkOeVOda3kM6kHu6 siGBshV5yhfWyrTJIPvfPb14Nh/kefYzO6cjBa42DFKE/05FntfyHSaWC5Bh 4XZp3/x35Pl0aZpm/RSkkzM9TvsqgzwrOzKp0pSQGin2n9eGJ/LsqFvrDnpA iqaeV4quN/KSRseSug9AMv3Hga+mJMh7ZP9wDCENEt6YH7xvcw95+ZkZ3BX0 IX7A9had0x7ySgpbmD6hgLiRg4FRvfeRV1m1SN3sGVye09PLm6dEXg2DT4Kz CXCZ4rB99TF65DWw5z4cqAmx6grq+uaKyGvh57B9gARi9u+O8qAb8p6Pq53J 6YGoVn2Plw7dyOueNz/IFwmRCbk/tJ0XkDewQfTebTWIiNre92ndCHmjOrwq NbYgvMKPO/rUE+RNfnkrhfi+hM0/u/aKngd5sz+vBTgS9bFVlZpCVh7ylq4q nvupAKE/L23curCEvPX7Q3Vi1yD0boFU33YW8rYwdUnTtUBotQAXtxsxn06h f2xVPhB6n7WfRC8deZ+oauyXkYDQzRNMhYycyDtkEP/94SKE2avS8jh4Ie+Y 3bN3xo0Q9iNWu+QkkX/G71DP5EUIr3d0zjceRd6lOMNGXyGISCRZy1sjQ97N vMzsf3MQmXpORuvTGvKR1L+JuFoDUbd1RIu/siMfZQezC7cDRP/zXvjub4R8 TC+tjZq5IVZJesFGsRD5uD+Xqhwfh9hdDer5Ukvkk9/PS2VnDXHtwi5dz+iQ 7zjThfVlFoi/xpiTfeEo8p0Uqp+IfAsJjSffaF49j3znDCRay85A0lPeEAu5 euRzsfMtlaCD5OmQA2YLz5HP1/d2QtcQpB4MOZmWm4Z88XkqVp/0Ie38pUAl G2L89c9a/9g1IYsjQ/tzy2/ku7eS9PUaCWS12dUxy3xFvt59L16r9kC28ctz Vu/eIt9bIWPC2WOQE/F55tUHUuSbUMnJWNiBXBrlouyWHeRb0H8XEtoOuSXF vcE895Dvr+85/SIFyGsgm+75vY78ZJcrFITXIF/gi0sG01fkp8ud5rrfAvll QfUrB5uRn71egPykDxTQSHxbHHqI/ALtrqvvJaEgOEOr7HIb8ksNNH10XYKC MU7e802ByK8yvtS3eQ0KZS8+377jhPyaK9I3k9ygMI7sta4KBfKf3hdQyCIE hS+KcpZpjZDfivFebN0cFJE/zWpNC0V+R8EtD8UaKFKH0TW1TOT3VDlm9tQB ilz7zmxVkSL/Jf3o4xbcUJTMwv7o5CXkj7F9JDQ3DkXlFB3LVrbIn+pLShdU AkWNwQ5FcizIn3tZd4fUGoqadFjeqe4if3lu6mweKxRVukzWFl9D/vqthHvi bFCUnu6eWx2F/C12MUmP2aHIz0P5BHMW8nc8Drc+ywlFBjSnhG6cQP4+4Uui q8TzOdOpQ6STkH8w3fdPMg8UfjlofF4tFfnf/fR4xc1H1ANVUifNYpF/0tKl 4p4AFJ5L2541eoL8C10OfoaCUEhJwqL4Tgj513jOacwKQUHLsTdl/UPI/yfR kjFcBAqMko6RUm2iAK2xQVujBOQHpdTXR7SgAOtd3RSQgry/v3z0791FAd4j GmdHZSAv+vXkvVenUUB+VmmXVB5y/YrU2aN5UeC4nuxQqQLkTLua61boo4Du TYkqOSXIOR3YTvVfAAqcDeHTclCD7COm/hEGSigQQ0WT2q0BGdyu+eQu9SiQ 4kdxzkwT0l6/7lx1GkKB7HcHJBa14UpC9rGtbAcUIFRsjbDpQfLSf1yZO7dR YEBujiXIGOK+rTWTeYmgwH+FEwtUJnC5I7jD0q4JBcb/fuioMYXY7KEOjthl FFh5Nmw7YgmRj/8sS8ZFo8C2+IDURWsIb6OWbC+4hIL7s57s/bOB0L4E2nWb aRRktumslbCDS4qt+btRIyjI/fBeUJ89BFYolUtXFqCgiECLro0DBIj0VI5v ErFs6nXWn07gN7pxe3y8BQXVftR9S3EGX/OPsX/T/VBQ26yq66greJ9SFN9p eI6Cp9tL09vcwDPjOo0RWxYKWnEV2J/2AA+BZoO7/5xR0CEuW3rOE9zp/OUO eQmgoPvX9H0RXuBmOndQm1EGBQMNk/5j8IGLy3doQ9JEUDCy9TKhyQ8uzmh/ TD1kjoJJLJHBGABusu2tOEqHgpkRISffB4Hb+BcdnuUxFCyaCjjiEwzuc1bl WiwSKFij4/X9YCh46pmqnfF3QMHr1y52l4WBN8VgSJRbEQreO+x0VT4cfAUD RER6ifE9QXbnByLBb+h1h+NuLwr2f7CWdYyGABW1oOJdaxR8fcJs/1YsBA5Z rfjWhaLgx9rTbzPj4FKWe7TIJ2I/5ij06oUSICQqNeVIJLHezTcnTpknQ8Tb pjOvWT+i0L797EN32SHa/6PshZAhFKKS/W3O2Ayx5W+DW5gEUehodov9m1GI r3e3rJTRQiHRR+lzch6QePKV+4mcVBSS/+nunr0LSTsrlx7EfEAhvTN8QWcE 4Er2fq6cjn4UMo39t93SDun+9CWMXcEoZNvyKYbOEK7aHbX8uXYOhfzp8lKH AyFb1I1GyzgUhYpHyCpPP4Z8zaY6m6sGKFS7Nytw0xIKDE6m32P8gULN0g+v US9CoRkZi8j4Ngo9zgy9O0gPxbY6/7jbT6HQ4EMLNXEClJzbmhjnWEOh0RXZ h2kqUGolyyL43R2FvhstDug7QLn6dSsPdn0U2ojuN25ahwpB+fYWdxYU2muu fXcoGSoPDvdd9I5GYSZa28nnzVBledCIf4eAwtwnVF1ENKGq/bC9fnsjCov4 MC8mj0I1U7EFdD9GYfVXQxu6u1DdNVL49OcrFNbdvR5enwU1Bz/tlDTNo/AZ yRQSMgGoOSXATPfAB4XP2TknurRDTaJLpQyNJQq7ZGhQPjWEms4L+rNe4ijs +4ArS2AKar7Ok7LXL6Bw2NIOc0Ig1NI8EA+yCkXhBM7RkjkyqBXveamgbYvC Vw3v8GiVQK1G/zMT2TwULorMrKuVglrj7n1O1oYoXHPDS3z/Y6i1SEq01LBG 4Rvjei1OllBrdqT0mtcYCt+nFlR89A1q9XzmhCkoUfiR+r4u3kioVUy+sEJ8 74Rfek7gZTqoZffMmGB7gsLvSjufThGg5re4NqMTHwpPviw0QBWoefn5TW9y Ngp/+xM4UjkINUXVc1xMcSi8Lm5isecANXbl3vnb7Cj875zEJ/t1qGHfLTjh 2YIiFGmHHHqSoXpksyDvuR+KMHR+medmh+qYvuw7ZzdQhHPxsWdUM1QL17tR MWujiKx+RLD6KFRdeFcp3HkURY6FW/0p84DKn2mHJKbVUETnmkLsn12opOPP 6eVnRxGbQ8tpXQJQLpX6rb6XAkXiXpyvVg2EYvJCo02/vyiSbrBOW6UPRfvE ZnxOf0SR/KGUSHI+KNj8+WDejBpFGv9rtXw7AnkfrUp3q8ZQZGjiALWfFGQV DgiXPs1GkTGHwrD3ByGTSX35qCQlikzPin8l7h/p/+rNvibUosj6gvljqjRI +e5NIfbtCYqyr9WH1C/AZdPLwswUuigqcEntC81DiBGcNrQXykdRyd/DpkEF EPHyRrbiu08oqhx+4eEn4r5VrEpuqBCAovh3S1JTBy5lwy6HFh2K6seklzRx QkCb7ernJnUUNd/PS3F4HfyoqAKovhKxXcK9S8EvwZNfX8FGch1FL5Kfmvlc A65FD49rhVujqF/qhIl2GDiF529vVS2gaDh1wIPrJmBXDDSl7VMoGp9JLs4g AlYC9z7u29eOohn0pUWhe3CmfM6a2vM5ihbkyRycHAV9pgkqwqIDilaxPAnU bQbNibSVKb5YFG0qtp66mQjHNC/Pf7pXhaJ3OJaNmGxBoX/ua1o5kb+74nJX hDzIWUtwVh3gQdFnvCwiM5Qg/0clapq8F0WHa68V6M2A6r2xM52hiKIfhODA rQ7QeDKVccuZyDfT+J8/SxbodT7duLdC9F8SvzgR5QYmnyUPDpeuoujGzb8G cwCW5+dZjAktKLonk9VhwAK2M5MFNX/8UIzijqBQ6zI4yZoxZaMJijEoduSx PQFX78sTn2l6UYyj3WhfTBmxf5ePlv69i2KCajM+84HgJ3LsXsMrFhSTehA8 flofArYK/TK9uFFMBalO3eWD4H1UtwK/BaGYgY6CwOURiLyt5WKjQopi5v3P sxcaIFbcJFjt9zEUs9e33TOOhjhmYa7Hst9QzN848SOXJCSJZZ+uY1pCscKz 7zPbr0CmAk1/Mz0jilWNe+4edYSsVxMWStesUezaeRKPJBXIIU/g5i4fR7EH zqI6Zl8hL6X3jwZ5FYrN+oTvrGhDcdMk3dxiM4otPRLL0wuDkqX2WLv7xii2 yfhRsroZyqQUtjMFXqA4RYeqgzkbVDSVKttr66E4A9XC9s3TUDk94EhtS4vi HPZFuWRxUHUlz2SVKwjFpQ78ftq+BNX3qBu/ENJRXNmy4TwDL9TokJ4//DAD xbHJcsvTEmpGfiov/+xHcTOjNnGuXqj9xHePMZsVxW2rXZ4EbwDB+rqqeaQH irusM9uNiAFhpFVcNksOxUOLg7LicqGuea5un9YUil9eEhD99BzqmYKYAzIr UDztxH99Cn+hPlBHbd+MKornZcfbXpWF+pdMBvdCLFG8fE5u46srNHDUXPjD uojiDUozmVgKDc73rj/hEkPxltQckeIRaKg/krQZeQrFO8Y1Hv06CA2TFd30 r0ZRvE/q5zlDNWik5yhcZAxF8cHLVet1vtCo5kVju0C0v3trkvGPAI3n/EcW XnSi+KQwibDVB2gMYvKjF45F8YWwW72ttNCYoOEcJdqN4j8Hz9tQakFj2hLf YwFXFP9zlPbXhVAiphUYOsSGEqT+PendN6Hxcv3KN1sPlKB54iPEPAONvt0s BvEDKMHKyvXQlxUaLc4qwNW3KMHj/sr6uSE0ymUufFw1RQnR7sg13svQSB4y oL/4BiXkD0ukhbdBw1spi95Hmyih7jgu8N93aCga/URBcREldO6m9UjyQINF yYV31WEoYUx+zCrJAhooGwqmNYjnW59dXJ28AvXtcgUT9k4o4Xij5IrKQ6i3 D/rsVUrk89jT589eh7rdmwzsvQIoEUVostS2hzoRhg6tGXmUSPptvVKeC4Q7 77oYpIpQIkufImXzORBUO+yVOH1RomblYleTHPG9OU59zrETJW5oslrsvwg1 13MXhphFUOJe3rMf58qghis0Mes/d5R4ribES0sGVRtRZDf+xaPEcsJcUsBH KCfdV2kuOo6SKuyOZ9QvQ85nwYJ/zCYoqam8VmGvB9lJtUVM94ZQ0tAi/nss LWQpkXw3OSGFkg5Z9Yl9ZZBmod0afrgGJT2ald98uQApsxvak7+JfEEvX/CQ i0FSUuudp4XsKJlK9r1Tvx3iDi9WXxu9hpK5AlHkXjEQs07B8+94D0qWa9Ka X9WFiH+95zoUxlCy4XxldQsNhGqJDfkNsaBka5TMj9dvIeiJZqrPFwmU7Cp5 fOxXKfgnDJlJHq5HyaftZqnMTuBN230zcfMuSg6/mxtVFoWLZXkHprsXUPLD r2B+61Vwum8rKq8/hZJz9OR+4ffB9un1DNOfWyj5Q6roQVk0WGinqen5E+1b hmKUPTpwero/8ksnD0rtd++ymqIGrYqYuR5NEpSiTjas2/cfKKcX6Erlx6IU C+HzGn8JiIbmXfghQ8Q8j31BxwE4Tt75vcBL9BebIkm/KAys6kUbHvRErLCb /SHlB/A7fXXuuU7EwMEvdO0uyAVIrL91Jp53SuVu4GAE4AEdy74iRCkzS53e H5pwSnrs1sBNIrYLfE9LdwhMbW4tnyJdRamL2W7nZEfgrLYKXeyl5yjl37zd aFYIDvXf311SVECpiMErG5fswUV5i9T59iBKJS5yahYKgofJn1nDFjeUyiK/ mUmcR77LGlphaUwoVSJ4YvzjHQg88MYmJt4UpQiaI6J/wyH49OnDt2OkUKrZ wTGEWwPCRtg+ZGeso1R71NoTpICoVNrC7slglBrsYLaPz4e45yRcbC/rUWp0 tP56nS0kfPd+1fgfBUpNrStv9fNBMt9/rCsWTSi1IW2TS9kCaU+bKHXvOaI0 V11lf/Yg5IzMvltn2EVp4T5Zpju5kMf80SL65C2Ulp167PjOBvJtPdQv9oSi tC7H3F+2BSj89jO3JmUApX1yxGSqDkDZAg8hfcMGpUNvdUU9GoAKDt+XlwZW UTruleHAbBZUnnwxQ+9ri9KFFL4uwtxQ1TcWG3fOFKWrhUhu632BasObJwVn W1D6ulb2nscN4r4l8C6r9jRKP4y+W9ysBjVPDf7a+3Oi9PMfu0PvtaFW6kNk S5UWSr85f5KUxAhqcxxkfzp3oPSn4SxVUWuoXSk+B49qUPoLfPAxvQAEXS22 usbjKP2jha82wgcIBTzF0uukKL3F4zlGCAXCxI7OYbcJlCF5bpM0/wjqeMpn CSGHUOZgbAxvhC7U2Uw8sHkkjTKUKrXdh19CXUZ+h/oNXpShXX1uSTCBuvbq YLGSPZRhbFz6qTIKdZ/mzxPmh1GGzZE+7dU5qPtt1MPBvIgyXEeUhBynoJ5y 4HGb9WOU4X1t07vpCvUsJleLRnNRRig15tyV71B/ZFzx6qQtyohr1G4e9YN6 Zt9uuZp5lJHefp51ZwPqDzHSJ+UR81VoXRbXC4e69bektOJkKKPqQf/0MwnU jT2T8+P6izIn+JQc/BOh7t7+3y3PO1BG86PNHzJKqEttMmOpT0WZkzkx+SVZ UGf5+eXQSW2UMdQnyEgzQx3ngImruSHKnNn/fKCvBAifKhjA5w3KWHQuu1gf BUJuhh3tSxKUsQmk31siAEH7tUe+qyPKXJg9p8h8C2rzzJW/17GgjFtpzPA1 BaiV5zTpPlKIMt5mBI8TnVAzaOBdoDOIMsF9y5UXn0D1Clcy9RdRlImIZFD9 ewqqw9zuDlSfQ5lYBaW3WUNQtefxaA0PokwqIfZQxweoIiG9YzS3izJliQxB VD+gtNtAPta6G2WqjyvTVgVCSbUxaZ61AMrUbZxrUtiC4vivlSs+IShzy4Uw YX8ACnUYLR2cv6PMY11lvRY2yHk1rSqgRfxfz/6dm9Uuh+w2mu057RGUedkW G/WBD7JqxJgax26hzDuhF3f2S0IGg2nsTEUNynyY+HG64DakUW4nZJXJosxE IcOCuAqk0p/4vJ/jPsoskNtymWtCosGXjcv0d1Fm6WHs/YV+iI97plF3JRxl fobUmUYZwuVBlChd9EaZTekXy/RvIFbU60OvsCrK7Hz9kVxvBVFKTb7p3O0o s1fFyKc2DuE6n69O+o+hLKm1cvewE4TEwbT2hyGUPURna3XhKwQtBw1e03+O sjTPY39ueUFAyo58pn4CyjLMs7j6q4GfqMyU520/lGU7cP3jIgV4UYY+uMlL gbJcvBrGTqPgpv7k3UseNpTlOzHa96kOnL/bH8vbUUFZYVsvFfMgcGJdSGe8 5IayEuH7brzShPPJ/B/SvHhQVrawkFeXDs5NCEql/y1CWaV7Evk9E3BW8Wxd 2QjR/9ibx5TKN8Aq3abNY3gBZXHVKrolHCynx9zP1/airC7N0i9RPbCSLx1V P9qIsgbicRdrWMA6+krwkwGi3eQU6zj7HNj0XDtVxk7Mx8L1pknubbBbEeq9 +ZNYj02C5lPqWHA8KK727UELyp6vHlNNNIYLCVPDI95ZKOv80PvmP25wDWSq HD6yhLLunw/wBS+B+8v+y5oX36Ksz05RwUoneF+p+HKszhxlg9ikqNxSwO9d UBshJRllwxT7YqYtIZDx9Xyj8gGUjTazXrcRgEtRPTtGM50om3I1/rNBL4Q/ 3z7qff4OymbcOHLmyVWIIhiL/zyfj7I5L5qfHbeFWOP6xH0UNChbTvrhltQ2 xAeWVlx050bZ1gjpywxycEV6YmdQQw1l24qebKSRQNoy6bsd198o29Vm43Fg CDJu6Ja20q2j7NOfiaYbnpCVT9VVo2WLsh8vfhIYI0DutjNbyNw8yk4m+hWf CYC852Z/jP2J/3OuloxmACE/u+r45T7i/fgxIbPZ9RkK6WxOm4qfQNlff555 KtyAwieOO5UO1ii7feTc5M1wKPL9Q74+xIRyB8yTnleyQPH10OtfmNNQjiKA 8zjrHJSoaTpnjrqgHE1ma2vWbSjpcbv7Sy8U5RhunhQ6FAulymE3kvaxoRzr wHhJnDGU1h2mLZG+i3KcCwG0f7ihjGyIUFSjhnJ8ZOTxgUtQZuuRcCzkHsoJ 85f9XuqEsrrqU5nX6FBOQkPOyyUFymZOaGdFXEU5Wfv+qQlLKGc4UnLssznK KUXaWVgR9bQi21kq0psod6x47cXwGpQbSMQ+yyGgHN5POaHXC+Xm5rsdM/oo p/OO6/ajq1BuUl5oofQM5fTX7gir2UI5cr+xyWlHORM6vdI7YlDOt/usezUF 5SwkJ+jEt6Fs2/Obi9VtlLORv8xevgllT3q4KN2J/TivKiBI1LdlCbnxgy0e KOd8ol/68k8oU1VnpHNJQDl3bQ/VX8R5NctAOfFoFeV89Gm0Xb5DaVxY9geb JpQLNG4xer8ApaxTekqC0SgXZbN5oWsGSoSu2ngv6qJcnEOxj+QkFFeNU99X JvYv2VU9tHIcihkVUtsFxVAu2z8uI34UCmeclN/8I55fl0J732AA8tUD82Wv LKHctYzWRw/6ifqr7kbyRwqUu5VrPij9BHLfsI8z8JGgXEdFyTRjD+Scr1sq zbqFcq/uCVN+ug1X15xUTwoboNybzgHm07cgI6mrj8ruE8q97/U++vAGpPN8 Ptwf2oJy0y/vKNTWQ6rng8fWwekoN//aEphrIeXooSOXuJ1Q7vvo9qnkKkj6 LHvNZ+Alym3MgL1nCSREt0ZM0hLr3/k64/a5EOKdZc1vU1Kj3N5yYqBxHsRZ f86dlHiK8qS/RKIeZcNlu1qphCpplD+09TJF/irEBto9kX+1H+Vp//nk1KVB jOLNG4VDoyjPREpfzpoCUU1X+Mm8u1H+yKG7DamJEKlVaJ5KYo7y3IetWv/E QQTpr6Kv7eMoz8+00+0dA2E/8k4oW0+hvAh7+bPJSAjbp90+kSqB8pI8+PpM GIQer/u784sN5eUEZz/1BUMIQWrf7cUElFcWT5pXDIQQRWGfsUt3UV5dVnS1 wQ+CV1svsUvrobyG0uCfIz4QPPpw5B1xPsrrqvsdTPOE4K/sv8MOEfM10GQ8 vOsGIbwOvjKHe1He5GQbu68rhCQeMIl7wIDyFqfPCkxfgFB6w5+n7ZVQ3sb0 r7SZA4Q+tWt3biLWf966UvWpHYRVSR05RKOJ8s72mtrKNhBelXlP9CQxP/cL X4yarCDiqVSdsfwmyvu4p5zlMIco6py5H+aUKB/oK34h4wxEX+KdbmJ0QvnQ oCHvPSOI2eWRIJkSQvm4WKa4WT243Prz1fEUeZRPTryfYaEDcZXGal/PTaN8 eppNYb8mxBfZctNumqJ8QUHVjevqkNi4Edgoko/ypTqV13k+QdJ1lRnGk/dR vnK9oqkgHJLvFP8lU3NH+SbTsvrL9yH1TZmFqB4xv27aoiprObgqocWWRjuA 8r0PCitevYZMzRNb4sxXUf6pV0G5lh9kiW25LVe7ovzQQF6JVDNkqwoGmzeS oPx0UlbeQVHIlTVk6LzCjfLzipk54c8hl/Dnx2QDKcovzl3NWnWFPMYq6dlJ Yn0ruRmZrgchL3LwybAEH8qva6ZnfCJA3hT79VBCHspv/UxLP6MF+cdFDM8r Z6H8btWVK89mID+3+PWcJKLCfuPUVKK+y589El7uRoMKZP9Skm/zQIG41XyY zwIqUN1MThJ+CAVe1PaBWe9Qgc42KaHcHgoIGywlK7dRgZkqMZ7hLxS8e+8h vxqJCkc6Ey6nlELBv1j6Xp91VOB2j4/5pwaF3H2mvRycqMDPFhcdOAaFSlEi eyUZqCDcfznyWwhxv8l2N7XaRAWJ4NiI8yxQaPD9c5H9DCrICsaEvb0Hhadi z/6WkEAFxbfRofrmUHhC/6Xs33pUUIuPCn74CwrFjE8wvv2KCiAXeUkxBwpp iidn1W6ggtZ0ROB1WShYOPH7s50IKuhlhQfwjEBBp82Hbtl2VDgNYX4FPlCQ QP/aZPgHKpz5EepLTQMFOlnkGiy/UMGyPMT78g3I3ztkv3uOAhXOGQZ7/taH /JYOI4rIRFQ4/+eSh9c3yLeZuXH7bCsqOF8LcptJgbzd4VgPUjlUcD8beNFa GPKKRx6+CV1CBR+KAJdXzyBP+gTdizcVqBDi6ufUQQq5Jz8pRc88QIVIZl9H qVrIefmBZ7HIEBVin/icJ2hCjv5EecKSJCpc4fOyzYyB7GOjD6nnB1GhbMLN 0mUHMn5kPz1NUECF6oyL5p+KIf2xWa1FYBUq1Ku7mp1RgbQ8bVJplS5UuFXi bKIeDKnK/FdGZon9eGzpoM/wExKKzvizMUSgQr/HCJ/XGMS7yKZv/UlBhcEY 2HnWC3FqqX8WpA6hwmjj0evhWRBLyrbzdJYMFT51Z8a/DYWoje/6j3SI92Nq ZM9WygEi9kRk33MR61vcnqSekYFQ71HbaDpi/1Zpjb6os0Hw+w8U2dzUqLDB 1/OggAQuuUYdXwJbVNhRlspfWYAgjgdpnYf1UGHPoMLn1DAEbPtr6wXPoyKp A61ubRsE7NMP1eIwQsVDQdHcuxXgr/CWKY+onxRpU5Z/WyWBX76fwOd3g6jI WG433OoDfgKW6oayZKjI1vqqkcoSfJ8KUG/dT0BFrmfHY11OgG8N6YEhKm1U 5Pt40/qhEPhO3RjwGs5CReEVLtkjNOCnQSfTNjuDipIHMg4FrINfX6zXUuFd VJRj/Ts9OA7+If2FEssSqKgs4dUp9AQC7N6pr1Q5o6I6jufE3oDASxef2Z9l Q0UNC0OPj3kQ1EPtF0VThoq67t1aCpEQrHw0hI3KCxUNoiU4rjpDyOSgT/C3 LVQ0ySn9tWAAYfdpB+t2bFHRooFqUFMOIrpuV+/IEvlsuiIIZewQtWQRqytg iorOc+csjBfhskuBu92Jx6jovvVSsukNxHVO6l1Rk0JFX5pjZAc6IYEvKZx7 hJhvmBJ72/1USBbwuRPCy4eKGckf2Y6KQMawxui7otOomFN2ajWMDjLzbhy2 zG1GxcKWjudvtiCr5QvVGm0sKlZ/KA5Leg45SR6fj9aMoGKbuPXHHxehQF2T jnw9AhW74PkdPSMoFOZkv31KFBV7zVXSapSg6PBxR5pVFlQciGJTtySD4rck 56KXbqDicHYK060fUNL896vZbVZUfFv3e/nQKJTG//4Vf+o8Kk4MvS9/UA/l bM+tGBPbUXF29mQwK3Efes9ukehLrGfh930j/2CoSHep2D3BjYo/qIWFXtpD pfI2W2ZFByr+4inYE9SByg/iaX+fL6DiliLZ+xgJqFJuFvlJZYOKu6eCb31g gqrI7R3z7B5U2m83nyL3F6q65VwGHHJQiTzA0iF9Dqo2mJ81xdmjErUMn8Si D1QLaNHG0bqjEt3y8vbJ31B9Ok3IqnAOlZivdzyti4Vqv2fMY0f2oRK7e2LO AUqozhhJDT09gEpHhc/YO+RBNSFtQ616B5X4v3CJ9XBB9d2JOx0EbVQSqVn4 zVEP1T13ec/GErGkw92+MBmofvT3TjJdPirJccdmjnYS7c1UP1RjUUlp3NBW QYsY30eed+kHKh0rYRPJHiTyH3vR/0wPldBqdn3Fkng+1+N3KX6opMN865Hh FFT7Bt6O5W1EpVP/RWRc84Bqg9P1ksN/Ucko+6QN+TpU8/SuDbnboJKZMaOQ SxRUrSyIhelKoJI19cRaHxlUtc/SPKeiRCXbgWsPebKgKmI0dDPcH5UcU4LT otmhSmXJ8M1CJCq56mpaEd+nyqn6Bs5aRCVPUloBVSmo1I7W/Lvchkq+jz6s FtyHivLh2WEnoj3suH/qmQEolxu+r8xthkpRf45b3DKHMt8rIQb251EpruMQ H/VnKK19X3B23gSV0hWqOvtXoXij8Iy4hhcqVYoPL51ggQIT3rS7652oVPut tL20CvLPXS/ttkpApcaGiwnbopDnjLnhN4j13+bb47qrDjm+Fdi58ByVnrJL GYtdgAxK46+9XQKo9OL9DkfyEqRxfTj44RUNKg3lP/36hThPVehuvKCbQaUx ervYqiuQmDRS2ffiMSp9p0i/zdICUX7RVuNdRPvKM+voIDUIt1ral3ubHJXW EwT0X/dBiLOjj85uDCpta6yyShtCUFH4dbNeNlTa3euaTR8F/812n/ELvai8 /0HyrUUH8N3/l+Z4ChMqk0eYRZ78Bh7B3O3SbDyoTK16VK8uAFwNuLl3+RdQ mW7zO/P+v+CUuG3pMpGHysx326YdksDe/OUTQ552VGYPiLvZcxisQ4qpGzOz UPmojFE4RxGYhacTeHVFUJn/B7tuGB8YPbtYxHmkBZVFrs8zjN4APa/ZudNf SVBZ0r11UkEJtFa3gmrpVlFZTjjqenYvaJy9fqTCipiv0pdTISunAG8UWCcd jUVldYokbllO0DyqMh+eS8xXQ/zxk4AfoPOhqVo7juiva7TncfcRGHxWP/v9 MdHfIECdbjMPTJnKavykTFDZJD/0voorWNl5P2wgJcZbtN+zC1cFu2eNHd7V RH+bT2sHuqnBqd73mXzDGCqf35Nq2p0A142RpM2gEVR24fM0gRbwHHqwEX+D yOeh07B5OR78CkkIH6hoUdnXba6sj6iXk9cPmKW4oXJQOq/WQREIbuf3bCt9 g8pht+y+6e5AuOAJ729uxPqj35RkpryCqMGuVg/0QOXUI4zj1AEQN/nfhyOv M1D5qrpJnJE2JO6P8mp3ZEDl3PMZIlmskKwOR/7UlaFyeT3ZJcZuol5e5f3h pY7Kt+W2aTnIIJeSpizu+EtU/mQ4gaIlUN7feYb7gyUqT/mxz3t6QSXr29Y6 fmNU/pJrlX4ToEr2hNxSsT4qr3wY+SAzB9X+KfxWx7lReX2XJiagDWqoPAcP XZtB5R0efcG7qVBTFaA2/dELVUhd+wJUpKC245t2drMlqhy6QsIWTgKE4y7L w6U3UIX2pnpP139A6OS21l4fQRXGkTDn3Xqok64rjlyyQBW2X21UEAp15Xnt Jb9eoQo3y1rrZQOoP5BGo2DCiyr8atJWfdxQ7yhVdOtPCKqI2Hn+I12F+jaJ sx/8OlFFMraRoNsHDftVSqzFRVBFrvaLfkoBNOgcVuxjv4Yqyv28qwNu0BAd +JGGmsinvmhfSH0MGprlNKj3wlFFk6b0uBEtNLzldvpeVIQqJ2Xez2ZOQcMq U7WOThKqGJozpb6+A437thff63WhypmQMzKMSdB4qOt9XmEOqliWZIxaWEMj uckDMvs5VDn3YCCyUAwadu53zvydQhWHaXK+sV1omN16+MpUGlVcSbVesI9A Q59AkqKPJqp4Csf62tZAQ5F5qk3fD1Tx03/AXBEEDReurYWciEaVS97bXVMn oUHQNJq3jXheeLaSEx871H+OPrt7nphfzN1AigtLUJ/uvvNmaBpVEh4+0K74 AfUKdJ+oXhxHldSX5LEfVqDuPwvz25cXUeXq6JkHTKtQ55HYP3ZFBVVyp0u3 jdeAsP3bbUB5D1WKluYVr/wCQtzB7Unf56hSviXj/3QdCAcYY/L3P0aVRton i8d+Q83PkTGx58WocpP9sFDwFtQ4OFI8q6tHlduCZ51ad6D6RYvk+lkSVOk+ tvxJmPj+XY09aPbQBlVG3FkGmfZD6cRTv/HyHlR5F+RIYUwKJQmphrM7v1Dl Y8x17SsHoVgEW3+JIKrMFcADEgoo8KGJ90vYRpWtPrebS7SQzdA6fVeyGVV2 h24vChP3i0y+0MfEfVl1/4ddISd6yCArfGpxbwJVqVezK8aYIEWYRHrdTR1V 6f98+sTIDIn/7XqtJ4ajKiu5EKsRC8QXPKZTu5uKqrxcnZlPjkD0i5Uwgf5Q VBUSJX25xw7hXx9Jy2iqoaq4gjGFGieEnPjDnOz4F1VloFj7EhcEvqn2S6yN Q1VF/dnYFm7wq0tM7mR4jKpqlpLd33nAMygp8UhDI6qCY8i2EC+4CkV9sdsw QVVtr8eKjnzg6KJbFhWUh6qnQqn9ywTAxj3aO/YdG6oaxVvefC8EZjJFx2Ja UlDV7GrVIoMw6BuY7qiSOaCqdfF3odMi8D8Krjuey++LI6kkIVLSMFJJshrm OZ/9IZUyQhIlW/Ye2Zll773JTlJmKkmDQklIRSGkhZDv7/n9eV733DPe73Pu ufeP58F0dttxqT2oeKHwyKUwSZCrajk58LANFS9VX8t6KAWiOztEDtSwoKJF 49PB1UPAa/Usnf8eIds+4Rck5iefLpdl8RdCdnxtrO0iA2I+rFt0C/xR0W24 5GaVHMh3y2p8O0TY8574+WxKHkgXM2Yer3dARf/fquv3KsAJlJtNO86DiiH/ XaeaHAXtNlUBCakYVIzkfOWffgzOcz4d4N65HxVjtu5oenMcTJvKvol0Ef4S Ra785VUC80DSAX8GomKaVPURTWWwec0quO6KPipmH1tyJA47h94SRgOZ8FdA oVa0q4HzatyM/DEinkqDdxLHEbw5twwpvMxCxdtXxC45k8FvLGsD04PAo8HB LquSAgGe5TwVk4dQsf06m6A4HUK6yzp/3ypAxSfKJYxLk3D9Z+aalc3VqPhs 7pR7bhRE7J0nL77sR8V+g7SBXb1w467Wit8Jgs8JKfnU7SaQIGlefQCJepoe ffdUfw0kXmnSbr32BBV/JF77m1QESXm6G1nCSlFxafW54ZZpSN1Rvb0tXgiV Nr26soPbEzL/vAn4N1mKSnyhG0+cFIZsUcGs2NVRVBJUqvGObIMccRLjc5AF Ku3J/ze0gQNykSIRwleJSnv1C7gYpZDbSXbQf8GKSpKbNFRCNSHvRDPrfTIT leTdkjLXxEE+peLjSnswKh0/qPKCfATy7zrdyzFWRiWVD5/++Q9AwV4ta4O9 06hESgg/1OoDBVGLExqMWFSiqx++sLobCmZF+zPXK6GSxr/+GyrtUMgMr8/a +BKVTtd6t3ibQ2H6rOi2w8aopGMhMnt/AxROCMw8vU+sG+zo3LVYDkVSz/V6 6w+g0oWeq6ePnYYiq08SHCeI+C6F8F9z/QlF2Ye657S/oJKF4v2quiQoeh4Z k+69EZVsZk0+/FKEoh/DgR+rSlDJIX8dj+wQFG/eeFtzcxwquZ6rQAd/KBZn l7468guVvLi0HarEoFimkfNeZg8q+bX9zZnpgOIjknZ3utpQKcg1+5WUFRTL no2qvXIelcIk6aw2XMR5qlgy/HgIlaJGpmVLq6CYZ5rj9eguVIqNjzOdOAtF v70/5gW1o1IS83icxB8o6mE1MMvWRKW0lZH2KylQlJ92weQ3EX92TfDPAhUo sreO/cRF+CswPyj66QMUyafLdvrkoVKp0CttkUAonDP8tvDWE5Uqut2DTCSg sHC46V5TKyrVBu+8nfUUCnWMHh69sYRKd48//DxsCwUrW7dIaO5ApcYZa37h zVCQqRk4MiKPSm15PFTDWig4znD9+msWlbo2GhW8XYR8o+YRjw8zqPSyja1/ azrkfRl6+T3ODJV6XUrX6qpBni1Hw2FHXlQaGv5z5XUI5FpODAv23UGl79U3 JF7wQdbVVYOIIqIef3cVynxfhczDi2cLfNJR6e9YkzLfFKRPlynMJ9miMvu2 qdP67ZB6wUPTUz8BlbcF0N3HnCFB6fYFb5l1qLwz/ULgOhOIW1AfhxUOVBa9 4xIlqQmxDbxFC8PnUVlqMi/HQRxuJDhd4zf5h8qkM6udy30QkrxXGjAHlem2 Ar272iAo7HfVm++RqHwiVGqYVA4BsRUrrgKqqKyVQ/lqlgLXrqTKVZf9RGXd +4Y/roeA93urTWncOqhs2Oe4XOYEHtcvHZ+h+6HyxdnwtS+MwdXm69+3HCuo fGV9zuY5DXCK3E/hlEJUtha9K7TlKNi/lF9pnY9BZXuVl+JHRcGGbWFLm14Y KrvojUsbcIP53bzXr6oHUNnTYeW49xJcmu06v1RxHJX9IreQs77ARV2+U9Q1 c6gcVCip+eA1GLxbc669gJDDWkl6Yy2ga9Pwe6Kf8Bf9Tt90XRmc5b4Z5lVj gspxv+xtJJPhdNDw6aa//qicsinU9WQwnOSM7zsoIoPKmfsyrzk4gGYlbd9j WS1UziPVhccbwSlrflXi+Ebl4vPP4uuZcGZHYcqVyxOoXO76KfOdAuhw1PQ1 aLOgcs3Nv8Ure+Dcj/WO13r3oHJ9GU/tbi4w6ivMK/tM2G98tK+JtAimuudM W4cWUbltRK3DbAzM7jS9zuTtQ+XHi7o913vA8rqG+vSLHlR+xmc7WNYEto8t gmPWq6Byj1TQ2ItScLgT80C9KA+V++lps3NJ4KyY3Pbm+SNUHjSp+bslEFy/ reyJnKhB5bGE0U0GhuDd/WNJe/duVJ6sXBD0oYPfspKbnDGhP/uUWyRbDgLE JY8tdNug8uI/lSPjnBCcuRsebilElU1XUowdGiGKS+tispUPqhw9qlUVJgyJ OSvLfZuYqKL092FFZRkkuSd/vn36PqpA89Fb/cchWV3gknTtPlRRpwqXiOpA yuCeM6VqEqhyat3NIuYnSE1xaMx2O4YqZ5+xFdg7QNppyZwzxhtRxejMRE5T FPE+LFfZtokbVUwFzmd9FoIMtJ9O22yFKlcGXmZuKIGM3sSua8anUMU6g5Qu cxQyjaqbV0xTUMX+Yl2q3iPIfG+U81m+G1VcxPYl+56FrNNqwQ8e+6KKx5e0 xPxRyGpg/yEisRtVfMs2xXddhWwBy+33ao1RJdDOP3ZuGbItJLncsoj8rsv8 ihEMg+yKrW8lD7KiSuRv8xvE+zT7K3vBYOsYqsTcfRdlVgA5nFaTmptWUCXR WzMiUg5ydhzz/WpL7E9Taw2rIebL3vFrG1v3oEo2m1zowCnIkXT2It+/iir5 jwuDV4cgZ/8XAYziQpWS8G1Be60hR4SxZnfCX1Sp0IwMOLEIOVsK/ribAKrU bl695hQKOSxbwo2fVKNKfa+jbyo/ZL/QM3m3neCnMWnMuzUPsm/yHUx9v4oq bYbnPL/IQDYt87+Nn1hQ5fHOLg+uFsj6QdfnvhCDKk8/qrrJa0JWnIm++mgw qrwsqHYxGISsA+rO5IR7qNJrKebkbwmZ9Rb8r/R7UGXgYJJD0TxkKkmQtOfC UOVjrY/tbz7I2GfygX9pLap8cf1uI5QD6bEDBeIZ+qjyTfGSFUka0n5P3mnN EEaV323MKzc0ILV4854oNSK/v0GNl+sGIGVBY+6fvxaqrDKkTd+bQwrZbv5Y YCCqrnvJb7w/EJKefTw4G1uPqkLvR/Xa70F8M9owmi+j6u5sbZ1JOsTNG0f+ nFdHVfFLHdqb+yDusG/opX/9qHposvy00RzEFJysd8x2QVVc8GAs7IdIbaNz Rc+3oirt/jf6znoIn8jQCL5mhqoafsZUKhXCrt+M+W/LGVTVWUvFWBMI+fJy 9s40B6rqd95VuzsLwaU753WeTaHqhShJlREfCPIUj3XIIuKx2MKjeDAFAmjd qxsZ8qhq0xLz4oQu+DN/aTvISKKqgzWvqQ0f+DXk7TzrqIeqrgKxfyJegi8r ZwznPR5U9XzAF14WCd62iuOLDdtQ1c82fmcXEzyXJX9teeSPqkHbttRMsYNH ZcPT0OwBVL3+MIG+4QG4X8/a+zJ6FFWj7PkHD/iBW6jL/JElYj1WKPGqujK4 3rrzbwUQVRM7trJZLoLLQkPCFRsVVE1zTEoKqwMX69prbyQIf9k7BSVLHMGF /e7M+vYRVM3vTG7plAbnx42BZ9pNULXEZdvZr1PgXHHjRq8AGVUrdqd8XVcM zo0fdc1bulC15tl2731m4DxHcfo5kYGq9W5pmxki4KIhfvEhZRZVG0WF8s1H wKWzb6NdzgSqtr5IPxaaRsyLoLjao6dQ9ZHnjmdF58BNls59m43A56l4xsUO fnDfzuvzlN6Aqi96hH+N94CHSGuLFF5C1dfemWFro8FTY3NlOF8Oqr7dt0t4 rwZ4xRRppVpOo+r711nVtHXg/Ztnc10NYX/UbzfV7BH4OrUL0P9Zo+pk/x7b AjXwv3B70jT9BarO+ueyPFyGAPyuIWNfhqq/pEQSPjdAoLS2lU9RHaquBIk2 i8lCsFjztbdibqjGKp1/hjwLIQc6noxM30W1tYNi45fKIPQoq9z9b+qotll2 76Y8cQgzGqwDi3+oJvJhn7HINojq0BpSF+xCNYmI4p/YD9Gjx7VuG3Oh2sGj +0NN4uDGQs/WAL4hVFOIPlCZsxFion9+Lz+ugWoM5YP/7VqFuOinHf4bglHt xJfyeLVGiF9jI+77qwrVtOIO7TP2gHgXjsEhzwhU01WtaPRTgPgP8hLxUutR zWBS+nTmD0igRoal/0tDNeOEys/NlZCQXyOsc/4qql3Gw+7D1pCwojHJumcA 1Sy+VW/8tw8ST3FIy07yoJptskyO8BgkpiSVPQqrRTVHco2CSi4kvq8fOTk3 j2qus7KdRsaQJLifWmtK4OOVWmvkIwRJGrcjG3Nvoto1mtxc+ltIctvf7v8r AdWC5m4HNyZAUurFQM9YKqqFZShse68FSXXH+ARbB1EtmlFXvrwJkjqS/Ls/ OqJa7K8jJKEuSOqxbXIxJPBOZpeo2tUDSa8s1oQ2rEW1DIFtO0XfQFKnevep vgeoliuxIVJiCJLu7N5cmUhGtaKjS38lP0JS8nJ1fM02VLvFmLaQ/gJJDv8F f320gGrV+sP9ctOQhLoSQT8tUe2OVTfl6E9I4pB9xlAoQrX7Xm01SouQ2N6/ 2VOsDdVaI2t3q61ColvYnQraKVR7lJEfTWGHRJEcoyuM96j2tCJhmcEJCY88 D9z3ckG1ly2hVic2Q8JFTzuT2ElU6+12f3uaH+J/8SheOFSGagOjVjRtIYi/ VvdjoJTQ/8ymKXJeAuJCpdzzLbVQbWKL6s2LUhDHuoVsm0PsnxGX/ndZDmLd pYfReA+qLdB539moQsy5q5/fxHEhbAx/G+utA5G/xm24Nzch8KR1/nfNECLa LNKLVRBB4NZ9uyATCI9uvNn/QBlh94tM9UgbuC6jrajJ7YWgwHuZNT0QAhsl hhtUPRAURXXss8MgoG5TQ8tqOYKaPG04/wb4N5x/N3khGoFCPXaiOAGuxYWx dRrPIzB199+7lQq+3YPfrzvNIZw0F9pXlQU+kuMVPld/I5x135h4uwC8Ch80 +oduQzh3/d+au2XgSbYu5rIi/BulzDo2VoMHW+O32ne7EExLP3xorQe3ias/ OC6YIZjff3XyYRO4/tm+/wdHDILNs/bGJ+3gKimpZW7FjeAwVHfg2RNwCZvq OzbwEsF1pjC5+zm48Hmdrw4NRPBaTV7b+xqcn7z08s7LQfDfHOb8dgCcS2si ZY8S+Ybs8fz4fgScGx7cbv2qhBAha3P6wxg4/3Hgb+e1RbhJNmr+PAUuJizi +RUqCAnapw5+nQOXxevHdHoIfFPNIPXbH3BtmZbfPkPEm+Uqu+77ErjVfnX6 sFCCkB8q6vqbBdz72N6cekXwU5K85fMiB3iKpP5XckcLoaJk7ZkVLvBKG2w1 kCbwq22Yb2PhAx+1x+VTBrMId59OSLNvAz8u6elvp1cQmgbfZazfBf4sH2Lj yyMRHnx7xsklBgHcS0xZRjxCx0qTB89+CDwcd0ibzI7wfFPFF35pCDJt/s+f 8y3Cm8Mx7cJKEDL/cndNGcHH4OSIuNgnuA7Xz9dhLcKHgoOhB8MhLP7t6Sdz RPyT2zo0lAYg8vz+9L9iaxBWWJdfG7hDTN4v4YC/CYgsxuJpMjch1jzgMNMw HpGt8cQJhcsQd1A36E3wMcR1rikVKhshvv7s6sez3Ig8U/KOJwwhqXWe9WLz NsQtDEPR09KQ7LNTWY+miyhQ4N+rzQYpCsFrNvscRxQyfnnEsBRSk3MOtRuZ IAo3/v5i7ANpJPHOF2xLiLu370i+pAVpY2FW6mFjiGK9ln+tFiFjaz3PVnRE lJC5WWb3HDIKHFmRNIW4P7r+vGMOZB6YaSYddUeUnBrmcnWBzKLbmz42tyAe Yq5p9mRAltApEmkfIR8ulLzqKwRZwdpxXOvkEeXYzuz2n4WsMeOIKzNsiAoX 3XuC2yFbic9Uhngf4NGmrICwJMgOETpzQdAPUXH7Y7koa8h+fGDqv2wO4oHh 9u1zjBpk//13oS+NkFX7+BIS+CCHt3zE7wUdEWUVacnjkLMnNPNd4B5E8o2L 8+kNkHPAvcYQNBGp30KLs6MgR8ohW/QbER+DWaGfb0Ksm7fTdxJ4qxf2bShW IPbrGPKNUBE12Zbu31pP2D+eaNeyEfGUiYhN5RBkLx5W0JmZQdRqZgrXVkP2 gz889ywGELWFrr6oD4LsayO/5H1OIOq6J/rd14NsOb5FLa03iOf6mg63SELW wAN+5QZ2REPZT6MPViHLRXRx1ImIx+jmhtjHryGL3fvXUhYRv/G0DPlpEWRG bHssMyGCaKqu9+uFF2SuPW17ulwV0XxNgW6/CKSPbN6RXHEY0dLkGcfAb0hX cTj+SpkL0br5x92hTkiLE2qejAhDtPeA7WMOkLpXp2K8aReiY/+VrgkqpFxe agy+ewrRRS7Ke3obJGdsLbn9IwLRY/rd8K9WSFwWn4/77YAYaOqSt4abOG// nHV+QvgL/qHd+pcDYmO0tZUiLyJeD5Abnl2FmBTrDY4fVhGjcn5sH/wO0WdY yNzeRL0njdrF1vZAyONAKalnVYipDppVJZ0QtOiZoqUajJjBcvBFVhsEHvuQ 2//PDDF399SGiBrw7VDx9zQJQcyverrPvwy8CkaQv2QHYhGU0NzywL3sWJ3W 0i3EWxfNAy7FgdORZrPfKnKIFd9p2foRYB/0WkJ0yAex+pp486lAsGGNeFuu SPi7vXnNe6oXmE8Onyw/Q/B9J+vTXyVnuEwqSddjpiA2SLcLytiCySa7AJ2U 9Yj3W3KPSJiBYV583OgcwXfzKX9tYSPQE/n6U1SgGrF15KIjnw6czb7a/IDD ErH9qtrN9ZqgtWf0v33HiX59tLqzfJUKJ/X2JX7h40F8Er3S9VsFNIPG9YMC tRC7dr6fmDoCJ3v+POI4LYP4vKKRY1QatHLuOrsd8EfsVk0TfyMB2jYtew44 jCK+euFJfr4LzqlXHOZlJez1XdA3ad8KRoplu11GCf03M8f8GrjBVHgu6w0S +u98t2ZUcoBZ/kNuM06iXYa4/tzPXwXLKmuv+7Y5iCMZfQOpC2CnGuHSdYaJ +FHq9vzNOXDcW9T1jJgf+Lkpnj/k//87vf1gZ48x4sTQGS3HAfDal3juF20e 8ZutzFWLHvBNZzkTb0nEP7OyOepCJ/jnfnakfiTq+deOF53q9yA4cf/nYjmi fv+d10CRNIj27N42piyL+N+3A8aCcXBz+FVJ8RZjJLF5r/fZFAGx+zJyQ85s R9K6tI67f70hvlTkYIfADSTxDlIOvzKClO74svzUL0jitxY9+UQH0oT0Wafp /5C0dYnVplkT0i9bSsZsiUWS8PbW4lJVyJwWaLa5QehLGKjsDtgFuUaf1i/J vULS/mHR9c6VkDs5N1m6wQ1JB03X/TBXgzwnyftF4q5IkrHsfXjiAuQ7GRR+ LicjSW76XrnqNORPupm8mZ1G0hGH7EQZHygw5OYmbWlHkpKHjYVABhRKyMs/ SjyGJJWVM1rrpaAwgFdw5/MoJIH/seNLjVDYt/FSh7w+ksjsO0VmNKBo17v0 QcExJFHD1nB+GIQiU8WxXyJcSGJsnPj12hqK0n4oBxD3QZL6zZdDj5ag6Nmz KmM1QtbcUvf4bgQU/cpIMA3KQNKp5LTKsu1QzKdW0ZPjjKQzO/yTM0qgeF+C tof6NiRpZ5v73zwOxfLXY7Sqh5CkJ6ZpFdAJxUc5laLmWpCkXyx31kUfig9v Zf9n3YSk8we3KZtPQPGu8vDa9BgkXahcFSfmWTH725O9ddZIMpEb26TJAUUf i1vyGNVIulT/dF4tCYruHNtXdjoISVeUqj7ISkDRtaI8sVcEPhYtiZ1i9VBE 2vUv1jQeSdZk75qtdCj825NbXmWDJNsO07T1/VBY+suqgTSPJHsNRuCyGRSe 6fDUxjwkOb48ZDPzGwp+xHD+WNqFJLf+v6q9/FAgRGp8deMvkjwNPkg8LoD8 /LeVVf72SPIefry5QR7yJVrVNvV6I8l/POZj5lnIE9rjpNMXgaSIP/tDLGIh +7ok46aUEZKiPbmvGu6BLMb0Ej/fCJJurvw+p1kNmWz3t5+9z4ukRPa2A7I9 kGZdVfd+5x4kZfOfe77CDYmi7hvr7pQiKTdF9c5sFsS/6qaZRq5HUoGwWNZH aYgLmqZMOPoiqVTsu0PHSbg5WXja6RMPkm4V9xs0DEG0TciHl5ODSKo82Ei+ ZQsRy9eHcMMKkm7LX+ePiYLQU8KpA4fMkXTnru2/IGEI5s+3KCPGJ6lB+ewX 11sQ8EPG0CaoCkn3W493WyrBtWf7gmdfdyCpmbKrwbALvL/yv+BPXkVS6xP2 3JOG4KHowTUrSeDbrjEVAcR97bHzyNJ2PyQ9OVtvtHcD2B3M0vpumoKkp7c0 YLs1WG6T1L6lQfh7vmZEdFMXXPbeevFFsgOSXho5rWU7CMZVtx8ZmpQgqefO 2q/zUXDu8n6Ou49ykNS7KbVrahrObOVL3faY2N9vfqjiw0lgpIWq9oz0IOlt y4OY3kri2iyTJLG/DUmDgrrOndwga8KyV0CNkIfsJ/WaroJIpDN1pJQFSSOd vorV3cBbKer3N4qQP4rwChfKAK8ei2OwOSF/9iz4LzUGRDZIn+539kfS+Ovj n6LnQGa+uPesLyFPSD5/HHgGVIscHGfFCftTQSYlbrXAoD1LuFdKxDv9/nek DR9ofbn1K5uf0P+uEHb1ohOckz3Dpt0wgKQf0cJntHvB2M/dXsnQA0m/xqsV mApw2SzQlT/VB0nzalRBlQSwXBu/zlSewG8x6e2SzB+w233gjEEjgc/Sd5vh vXrgqNeXlPBbFEn/5cTncwuCe1SRWxXNA8msf/eFsnmA1/zzWwnZ25DMfqbR cn4A/OLibUsrjJC8ge2z9Ic0CJJkKnLkPUIy3xW5+zXCEPmOq6TGvwTJ/M0d mYU+cOMQb03xE38kC2419E8dhhj6YrN7DQ3JO54E0gOzIO5ubXZ3fjOS9x54 9VpnDyS3vnzlLx+C5H2BV+4w/SGVPffJ5bWeSD4w+DdF5SOkMR/E1RZzIVk6 SsR4bx5kPGPw+q6/i+Rjsw6TC3shp+fYIleDLJKVGOzPv4VC7mX9bTKvJpCs kp1S9eEL5P7McREsMkQySavNtbME8tn5BZz9NiOZUqqj37we8kN84hoPLSGZ zjqhXGMFBaxhzOfWr5GscZuHNU0SCiYO6ccnvUfyyY35YzcioPCsmPny/Q4k nzY79iTwGxTecYvm3TKA5DNNz8rcNaGId/RD7JtOJOsIXIy2KYciC5GA3FNE vHp2vxxMuKCobuX1e0EqkvU7rmvr2EHRskqqS4Ilks/v3nGU+RKKFav3n5wn 8r3gXrVdVRqKHaT83yqoIdmEbdlK7DYU5239LvU0DsmXbjDucRLnadfp73qu wki+sj1h/VwzFE80HF7juwHJFgWj+m/JUPzv+P0d3wDJ1jJSJc1PoGTdw407 9tQj2bbRY7FAE0rWn+FnM9RHsj39MTPiFRSvjsNw8SKSHV/zpjjqQfG3CNsh z1Eku1y4MHHuPRS/1P6wNqwFyW4TZcfVTKC4+MLBBJf9SPZ0ng8TH4dit66d AiuvkOz9H3lgozUUq3TEGCucRrJfxM19P75D0d9QDS+9HiQHCLx3H3CBogrq kw5Vgu+g3H1PWoj5ok9j17Yl+AuVchEsvAaFS69U+ytykRx2t80iih0KE5Wq TvXmITmSwnXXieBj3zt6w+gckmMMCs9BAhQcO3Jc6WYokuPGfhRLbIf8uges 105EIDnRQXWBKwvypXbxj3E1Ijkt9E3SuxLI4xSZTRkSRHImn+iXtkOQ69gS st1cGcnZmfZHi2ohp19QclWQieSCunVvnJsgW3Rn1f3cGCRXflTcuqkHUgcP /0lAwl+NbciV37qQEjgfduz0BSTfXnh1Z3AQkg++k9NUJfi6x22tWzwGCYF6 UYnM30h+qJyeSFqEG21BFwuu/UXy446v4/t9Ier0vFEUqy6SO88qHNnMBuFz 51+9PWKK5BeWz/uGuCDE5spR6cVNSO7+vV28PRaC1KOjtrw4juTX1664lG6F AKhUUDtA8PE2cZXfTQS8fo0/yK7+guRBEQ0zoyJwD3iZvENHE8lD5Ul1FClw 0RjnC9Yg8Plw7DP7gRpwPOO6avqeeM58fHhYh+co2B2W2nSulZDHTnnnzzeC pRrKxWTzIPnLuye/hhEu91rMW4S1IXnyCj/l4WO4+Knv5PDfaiR/mzOJL9MA gyN7b3v45yB51rv8c0w3aOdebkqVYUHyD46/8u46cErHPOBfCrH+K44WdOEd MPI8v5skEPU2vzO2l2oMFGmD/7S1CLwXS0bEJD8DLpgnLJVoIXlZQdKZ1xJw eUP3648mSP7X6ta+MAMUhSbTA49NkMKi8ZBvxAkYWRvTmvV4kMJW1mzU0Qkn K2aM2NSrkbKW825R1S4427Wc3tPMgpR11jVzyS5wrojWWVDbiZQNXeVK17rg wmXDsiCt9UjhkiwKttwDpqtcLr8LqUjhjsh5qeUGV+pGvh74QNjnmUrfdvw5 WL12jTtK1kLKFo3ESyKicPXsm+d2jxApAmU3yzd4gGPK6GiqUTtStnGG//nx ElyOGKy1nlVEinCXX3i7F3h+u8XX4roWKbslPXrLiPsti5Vc0s1MpIhEOO+M lwD/HctnS8ijSJHQsKi5/AqCAnfoUrwOIWV/memypgSEqIeLcLTvQMpBTiOa gg9cFw7/W59pjpTDXVrv1u6HiA/bnIRLmEhR1FBiKfKHWF2h9mmrx0hRLlPQ uPEG4s6InM+ZTUOKGqd0grsUxGtmWgapVSKF3CW2nzEAiWovfy7MEP41NbhO T8pAylKOb36UJFJOlXGkvgqF1KG+PGF9XaSc4WT5fG8I0hrCloKPJCJF9+kf 94gwyDDo33GxoQ0p+ge+P3Aagcxdh1UVCvmQYhg+tfG8AmS+uymrtFqClIvq I9kHRyFbXvHqBrldSDEtHZjkPwrZPSHa7+RdkWK2oVduhXhfy9r53pn1Q4q5 1QufsU+Q4zIxT/LkR4rl0ycdL45DTm1IsYbSGFJsDrTz3LkBOVPb+VUqipFi F95kmDkGuTt8n80/3YYU+8n6glAlyKXH+194a4QUJ/Xq2asxkGsD2+/njCDF pfTWcT1inoRbP+6eY0OK+4bCQFCB3FwOtryJOKR4WmU/3xcHubVrC+akvyDF +2na1s0TkNt0eVDz4hJS/A4kXFxUg9y2PRI/hr2Q4h9+o3Q0AXKb4SLJgcgv cDLsV+cU5N5+lrHauAcpIepBqjUIufl9MjpTH5ByvdT3emoS5EbbRd22O4iU iA3urwKmIdehRjax3xgpUVZOO6zJkHuy+eZJLYKfG09tr5xNgVzxO1/HKdFI iT1gXqU0Czl/2qWgvhEp8eEmf8WokPOAuz9c3QkpiZPnKRvTICf0eeM++WCk pKjrRv36DjkMmXFqzRukpGXq5Q4MQc6a97JvDI8gJePHufrmLsh2z/O3/j9e uakGo9cLIEtVSn5cTAEp+TOGf2zjIDP+pfnx+iCkFJGMOM9cg4zPPO4CjulI KZs0VthhCOl29Qm7nxL1VaF6UZ2FCWlldGcJEQ2kVMWaXBg/Aqkfn38QSzmM lDrFS9ereCCFqsgkiRDxNYebvyc/hoQJ03+zcxuQ0jpsMbevFhK4L569dZHI v13Wai1XDsQrXO1kyeVASsc7G+k3XhAbdKa5/msTUnoO2Aday0CUt2hJGv0V Ul77OiSf3gUR33bkPajtQkrfK8dyhY0QbjYuGWBL8PnO07n/3xcI9Tr4nt6z GynvX7hMfeqFEMHKMU7HfqSMiLj+96QNgh5y1btGTiHl01OPA7HpEEAfZkbN LyNlfKenmlsY+O+ZO/KtJBYpXx29tM+7gd82peOLZ+uQMvnY2xIvgw/frjee LPuRMr3dx3evFnjJdGzoFhNHyqydbxynGni4MsqFvhDny9wDv6LvB8FtNG3u PxWiXn4JXGvs2wauzomduasySPlj5d9zjwNcpKcvqD5KQMpCc8B45m9w3iJT nrrOHylLvIFLgR/Bac9r1exbRP2vXAnebPESHPV3/qSZaiJl9V6IuGYTONxT /dMWXYlU1k2hirKl4EBVfLI1lpDXmF4/tTUJ7H9Mt969NITUtXfCLi0Hg32l 7cyYixFS128Idx91BPvmI80r5+qQynkhIuqxMThw57NIxRgjlasmMrdMExzM K2zfLqYhdfPaqPqbiuAwvhjjt08HqbwG0c9cJMAxMt/DfP8SUrdU3Bg14Acn vco7pPsxSN3KevOPGis40/7psgoqIHWbbiyn6Cy46Ov++neeB6lCpXG71w2B a9TlsUheM6QK/4uXn+4i8Gqw+SM9gtTdZxKYr+6Ch/bzLd2dhCz2N8kpPQ68 b2ZXDKR5IVXiZPJ1/2vgq/NxyuSnJVL356ZkXLGFa7JTS/QVGaQeUk/rOMyA gMQntLkQeaQe7tFhcuhD4D3GVuuNt5Eqd27T02FrCBqfE6f2PEDqMTP/F5E3 IFSXEX4gMwCppGvmfV/fQKTZxB/XjYQ/KscevZYJiCrrCmlVMkUqPerdQOL/ v08pOfH4ZyBST6RpDlF3QswRu+Cj+4n8dO/Ifc4xg7gw4WqLo3ZI1VeevuLu BvE8AdQ98w1INWwv/HoqDOLj+nfmh1CRekH9otXeNKI/YuyDv91H6sWebVMr 5ZAQJL+lT+MwUi/pvbbtbYGEOdq65JYfSDUbjpwl5k+irt7wXLAmUi3MaA4B nyCxlk3S4zGBl9XU6k/9X5C0dvKTO0UDqbaODS4y7JCkdb8u+gnB19VFx/l1 WyEp7qBeTLs1Uh2vHXQf2Q9JT1eewmAWUl3Wjv29owRJC0uxuccJfN2isryj NCF5x9LXZ8fskeq55dy/y8aQfGRk8GfwNaR6p/FeU3KAZFqcdSZrF1L99jxj 5Q2E5BNcCWOniHz8i4MDJxIgmX7aoNkkGqlB0mrsrUWQfMziNk1eEqkhdQuh SQ2QvMsuaOfhQaSGKdest+uCpOUgl2nnl0iNaLeOoA5B0ss3b/YmvkZqtLo4 145ZSErJvqw0egOpN7uHo3+xQJLBbk5XMX6kxuklb+7ihSSe3IJb8qJITRjW is0VhcRWnwKRRWJ/shnnFg8FSDTnvvf//zdRU6ceJp6mQyJ7q+PySWmkZjj6 CkroQ0L6akfr9CxSsxaPpvyzhgQp6q1FHaI/cv3mhPp8IL6eJ3ZmuRCpRVGX dwXmQFyDof3WOqJeSrcI5xjUQpzswnRNfgFSb6W+EZV5BLHFBqdeZ6YjtbpY fe/IBMRcHzlmLNmD1Pvthw8pH4aoy/3qBf5/kNrMnKjiQ4g8upA0tkBGamt3 nszkWYjgYhsMUyb69dGwgEKyG1x/IPrAfUgfqS8XlpV/tUCQd6fT1DYOpL7S vnT3eS4ExnUVsK8h7PVWdskXBkNAndg3VpIBUt9wylb5mYP/6L1lrkfE/oEr qQfPqYP/dsvhbC5FpA4+YCmWkQK/FnadoztXkTosbCm2gRt8zVKiosU8kPrB vTvr4w/w2b/GJPQuE6kfe48J3e8D7w05hSftfyN1TDorKf4ueHHWdITdJfrj SwQHn20aeEoZpTn2FCF1YtzuBs0HPOzLTtr9vx6/YT/nrovg3h9qvsueDakz GSohC2RwN27+1ElNQur3hQLWHnFw37jq0D+RgNSf2ht9S9eB23B/q9QBop9+ Vzr9DZgCt77qb98UtZC6sGHQ1fAFuM0NcYv2Efj/vUL6IV8N7nIB77+2uSB1 ua3UjisO3FNv2RE3KqSuCvNMjLuChwTuOLlMRRqLu4dZiz54DKz1M7tZjjS2 1x9Gk5XAszI79ONtKaStlWYYOewEr9KYuj/9/EhbF145oM4C3p1KtjVNSkjb ML5VR+Qz+G4YNp/X1kQaF/p2L3WAn12aWp8vF9K408dP9JbAtZ9OQj8e8iCN Z0HzSXkk+D/Lv3csygBpW87WUUKuQkCFvdv5965IE6jc0WqsBYGZc4celn9B mpDZ1F2erRBcsuOi5og70oTbzspN/IWQNsnSIE5FpO3eca/ywRCEfr43LGrN izSx12FFzrkQrp7TNUtpQNohlEh8KwU3XOzPZOt6IO1w+g3e6s1w83mtgOZQ ANJk5/9Eh/2EmATev4uBU0g7WvEoROkuxP5Yspa9VYk03HHJJYsMCcXy7bdl GEgjuz2dc98LiadrvSoPZCCN+lrWVms9JP7Z7nidGYw09XAWM9YXkKxwDlZP mSHtxJjFh8FqSH7BIyiwtBNpp6D7/O04SDFx26dw6Q7Szs5naV/Rh1RXhYbH k7JI0z27tltNGVL/RPcEjB1H2rkKuxOCuyDtapJc/Y42pBmu7+uYY4G0D+8N WS/0Ic3ITIXc+RnSmXNPTkovI824Nb8ltwPSSx2u+GTLIc10x0YlrxLIYFma EDx9H2mX3ZzqtSMh4/Rx8az9BJ9XXg3KSV2FjPi35soWI0izvLgktOAKGb0b aj/y6yPNelZoTbsPZHLQJ7n4uJFm56M4HR0MmYeYzJ5CL6Q5cBr060dCJuP1 WNsgUT9OKZ4tYsR9S/fu9PsdfkhzlUgtmk2DTL2+mckkY6S51927eS8XMk/w r+X8GYo0L/I7j+ASyJSzXbefewZpPj1/TU9XQSbXaMbelVtIu3Zxu4ZQPWQM Bp6s2t+JtIAZRbnxFshIvxru5S2PtGBvgx3Vj4l8upPXKLAg7foGT3bv55C+ MORfbWiBtPDklBl6H6THD8y5VbohLWpvwxve95AuLsDCtdMIaTduD7QMfYK0 kumV7vsJSIslLRYXT0Ka6NPdiTI3kBbfsy3GaQ5S43Yev6H/EGkp0/qX1v8H KdoKitStBUhL9/LQ6OOA5CKBPWPEPKBlrU+Rz+aGpLnS9XuLNJCWLz6w9ogw JNrffMgcbUJaYe3CLIsYJBRqNIlHE/olJME3zyQhvv96Y22qA9IqLpwrMVWE uP3FWnQlb6TdTXx7IloPok3X6RXe3Yy0+2ILCvoXIDIP64ytypDWVCu4U8wM wsc/9ZhLhCDtwUu92XvOEOptw3Os7ynSHhm5vQ32guCe1//2JhH5dUwltZ0O hKDD+R98eceQ9pzjTex4DPjznJVbZ0bw8TJh3qs6GXwPbjBeof5A2iuxrZe9 s8BLN3rx4abdSOutOapJJ97v+WfEuFceIO0N6B3hrQBXQb2wn5kE/gMv3HYO 1YFTXdAN1bdkpL0/n8RRTNzffDMsPi7NIm148s53p4dgOxWY4zApjLRR9/4B 1S6wUn57u7c5Bmmf1/55sP4VXPk10Hl4lsBrPEGgtHcALm/tNssO9EHahOiR uKxRMCkp1T37SgdpU9W63lZf4YLBxA8zH3+kzai5minMguHJOw/hrRbSvj9P 1PxvHvTl1AoT84j4fhreOdL1D85xKRwyNyLOo98T/buS2EHvg629gS2xvuD2 Z53pRjhXOXKk0J7gY2kt/5wUHxj4ZLLyCBLnx0q8wsDCdjBS73/nF2mCtP9E dB607wGTjeYeE/sakM5a5VIWvQ8uOV7l+RfCRDq7akK8vjRc0VMUfjaQgnSO Z3U+YkfBsiE8SVxuAukbDPrMZlXBNqk8Sv0Lob+xKY/RuBUctGIj9/muIJ17 t4Pk9VlwCn+m7OhshHSeQLVNOh3g8qdrpXKQWOcb3zgnkgXu8dKMAFIC0gUY 717PuoGXidWp9AuSSBcsK77TeAp8z3eu56nfinRhB4q3zioETFVJn9x0F+m7 enmNRd5AUPHdaQ7OCqSLHPmAs5UQ4l4x3FS1Eel7l705wowh/MS1Y7qrR5F+ OLQ2trEVYl4Wp+afCES67KS/S1gKxH6xLn2QxYd0Bc1T53QdIJ6Nd/zjQivS FXmnhL/vgUTSyNXcD+xIJ2eKlIj6Q+rz8BiFjXuRTv3ve8R3fUibLj3x76YB 0hmXmu2aZCCDc9udP1PlSNfcbyCvOwpZigmdNpoLSNe9HdMSToJcSbEWr5IN SNffapyrJwS5BVseb/Y5gHRDT6lg0Z+Qtz2g2vaEK9IvqnWqN+VB3h97y/Ht vkg3zU2SCveC/POUbyqL55Buxm62We8s5DeievCebUi3fPpf3xwbFFiG1Emn SiPdRurF3aZBKLhD9Q/s8EK63c30tPBaKFgWFP4dNop0+59WvnoRUKj4KP1u Zw/SnXSPmYhdgkLH7SdeF9CR7tKwljynBIU5X77stHiIdHeh3r3NfFD4lL1e THA/0j19c9eHT0Hh5OmWVqtPSPcevfpN7yEUsZYNGjoR+/0oKi/FMqCId/VN YQLBp38RZ/WcCxQJKUW2LQYjPWj9QHyzJiGfna0jqyE9xKbILUIcivgou/ev +CD9+ksXA70VKGLjctD/7Y/0CFmyilgf4a/eTsKXwDMqgWfXXDkRz6m3+s7z SL8xP8LaHEzEO5m7kN+B9FiDW2MRRlDokPOl+xAH0uObPJ+cU4DC4wlvBUiL SE/azSgT54KCxW/FtGgCv5RAgai5MSioeeWmpJ+E9LSxz/bNTVBw2eNwoilR /5mMmrMRCVDALWgVzULgkbfp5DZxGuSfVtxjDmVILzATeTxTB3kTl5GrOAvp Rffnne+KQ553L9n/8BjSb1lmd2uwQ25co1DoxbVIr3v4M9S+HbL52RtjNnxF +l2hJwqKcpD5T+L86//3yz3H9E9suZAxaiPoEFaC9JbdNNVEf0hLSQvjHR5G +hOv5N+NapD4dQ1JI9MN6U9f2eYFV0JCQ3jBhQE2pD/fT9I6tRPiIxacIzcQ +fT0T90aXYHYo2z6/ynyI71XqtWgzBZieAzU3dMnkd4flLDO+T1Ej/Dv22DJ ifRBWdXLHPch3LNohTQ+jfShMD6eHkm4fn5PStzMFqSPjHxpSU2FkBNDUkSD I/1zVIyQlAcEmOJHm3+zSB//bPbkzwRc423bxMpN1OOEkqJr6znwjtT9OKis g/Tpr59enT0Krusly5eIyxf9u9rdazuKwInHekcabRXpPxKjpMYF4Grqivsx 3Qyk/5o2GawMBit/4ROD+4jzYp5yJMz9N5i9VfC4YMCC9MU0zqMkMzDJ+4Mh v6qRvjQ3MsbZCwYv3MWbrxL1+o9xO7aPDNonhN8lWhN8/pcVBpm1cCLMaUvD GkQG6x+jGQtRIHtN1Kz7UI0Mdk3ZdJlYODq1/KTvdg4yOPI5mEsscGDJRozN 0x8Z6/8Ozj90AOF3JiKdoizI2KhVVRA1CltdZthSuwh5U3HwWT0t2GNjwq+4 idDfvGrAsrsNpFkWVQ9rE+t8uocqJg+D4rL+TaXRNmTwl7Odr80Caoyn4ttN e5AhyPZ2gw83nFJwm7FcJvS3G9y6S/MDnR9LLX05McjYUe1/hXsWzj/7bHmo mtDftU6Xb8AYTF/1vFs+PYGMPcYH2nJfgLkS/5ZA4/XIEK1bvWqjAjY73gXH rWghY+/GXmGFcnBQ/LWDLYDIf9+l4qerwuC8W+dQnelxZBy45+P+JBLcVISn m7O8kCFtsbfX0Bp8t5z6FHhRHxkyzUsBYoPgbxYq9uJHBzLk+bulZ9QhUO/q dfWlGWQca/eI8N8PoZ6z/U7PryNDSXNtoXIThA08f/yC3R4ZKv1xrQtaEHni o1vOi0RkkCbK/1z1gBj5E2ufmNkgQ5P7o4lRJySmHh7eNUn4P5Vy1XubEfHe HHiitKCNjDMiy4m9c5By8UvHf/1jyNA7IvDsxDZI35dwIXqTHzJMzmscVbSE HI3A1yo2RLyXxt5q/V6BnB/nhzewSSPjylUzm+oYyE242OVt0YIM6wC/3H0N kPf8BTm+dzMy7DZubPysCfmmcs/XvghFhn1Cyhtifud/b+I3P9qNDJfi21xb 10PBX7O/3JYRyHCXRYlXGVDoXNzxj6MJGZ73X5CiZaDwi65zTcAnZPhQDY2Y j6DoDPew1mQxMvxefHVn14ei294Jv94CMgLOucS2TkPxJo0DDyXDkBH0kbXc 2x+KjWXslmdKkBFqfePJMX4oLvx2J8s4CRlhv3d8/FkKxWMk7th2cWRE+pas VKpBybZ/Tlz2dGTcWHdU0Oo1lFD+fBfw34mMmJiHcnvNoeTKuhfLt8eRES+k pTm6BCU+28rzNR8iIzF/2CLjBpSEb42P5ylARsoh60B9USiJXFxTxcaNjLT6 hcwt9VAS3LiS+HkAGZkYfLdbA0qcrZYzRT4iI7uL93XECJTo8/6yo6ggI087 a4buBCVyz1UYsebIKBiWWs/GASVr7vmy7zJDRrH5PdHmNCh+xhY1/Pg2Mkrn 6Kqe0lAcNnBq/y4DZJR79uortEOxqnMgb0onMqrWmDjN6UHRJH9impgYMmqi ZqJvTUFR1BqL6eouZNRt9Sqx8IMiiZucp18R/VGfs+6hGDGv7m3y/Co5gozG 2yKLaUpQ8OR8QE9/GjJaVKq26L6EAsr8F86hW8ho61CR5r0E+fd3LDerEHw/ fqd3OSwC8hLefNIRs0VG56XPftRdkLt0Sdqm1hgZXdMOKf/VQu550xOpkuXI 6P4v4qXbIOTw9rBdKHuDjMG9rYpXJCGtVUpo8iPR/0NBS6dEb0Kq+yblay1P kDHy6cilD78hRTr2wP0Xasj4nHUryrAFEnMOmFRmXEXGjGDyB62zEFPUd2ZY ZRAZ311f/950F25MrbVo2W+NjB993OufCUNU/JNnb18uI2M+JliO9gXCBGZu tC5WIWNxtpWx5gSE/LD8tvwfUV/Lmkvn26ohaNQ998RuAWSybHAIUfaEa//q 2A3tpJDJZnErdXEEfExjOrZ6BiOT/fGXynoyeMwvn5BMIOR14iIPnYvBtU0m SPvfG2RuCDQakOUCp1bqR8EXc8jcOJo8PesI9tO0xzwddcjkVutlvfUGrF9s cX15xwiZPJncW62UwVx8m4oi5x5k8i2rH5TIAdPfuy60HjRBpoBBMIytBaNr EiSjU6PIFLzbqpNrDXq8nPaa23uQKSSwZGXcDVq59Ol6rxxkCrsc8RNWAKbZ d9ISJyJz12uHuHepQIqair7NS9gTkblVnLQKip5n9c3qWZApduNLk85lkJ9M 4TmQRNjfOyPyircT5CwHrP4JtSFz/wmj8e5DoPBTMZ60StiXLE1eiooDZd/3 T0xvEfEeWte7WX0BKCwOr/t8CfuHzbnF1xmBxtFm84pjhL7sI/Xjjx7AmfKm Q1l5BB4KosEnAyXg3GE7+3XGi8g86t96CSLgQuZwXO8gkc/xkSW3le9wyWM8 hGWOiE9Z9UjkfR0wj5Nw+6/cFpmq6Q457g1gs3Xa8kjMI2TC31t3juwEB9Y4 fLQqhEzyuS9PfwaCU+nxvocaR5FJrRcZqfoKrv7buPRO8CCTwW/0y04TPIKa YvpLryLzRE+v8MRWuMYxPXnavAWZpw5zyxZ6QwD6xcpfIPjUilanXxqFIG77 b57CIsjUVW+1Hy6D0Leb/vp5EPuN22+196rBje+4RvvrPDId64ItW65A8viz jeVcO5DpQnOfspuFVFbZftLtQWS6vbGx3ekBaYIUoXuifsj0Xjxr7xMBGYdD S7M/HEemXzjthxQ/ZB5+/3x0cwkyA4QUnd5nQtY+0bbOiQvIDFXZ46ZUDdkL UhH+J28iM+zFloUpJcixkD4oFayJzEjjdZ6pDyHneXm68KAaMqNnl5bVNSH3 YKPkOYYzMmOuzfosvoHc4A01MxCOzHiej6slJpDbfyomMsUDmYm5/f7nJiFv N/3LuiYi3hS5p2zrnCDPtL7Tg4cLmWkPm4LqVyAvw8rpVm0CMjN1qteah0Je t6zN0N4JZGaP51/fuhny/k6UeN6pRmaeW/L6xymQL+yQv8Rbj8zCdRERrqKQ f7TWOVmI4KM4xY9L/BbkM7Ne/Qz5gcyyA47RfQqQf1ZaLPUOIZffv7I5qAXy tU1cbKqFkVl1Qj9WngH5mqp2kxMnkVkzpMn3qQfyVfroQfxkZNbZQUKcIeTv VS6d6Sb061flt5I+Q/7a2LQqMjsy792USP5hC3nDy5pz55KQ2bRHaHvOPORV Fix1SRP101KzKV3rGuS5P6g97gfIfEBm3cmyHvIUI4W/+N9A5sPXv7Oq4iD3 N4nM45WGzI7LE3suCkNuiST5XiAnMjt/v8/jLoRc3Tger32Xkflya3uhXQPk ZB3g/ZpAQmZPcf2+nSTIUeK8LHuQ8Nd7vLTkeRdk52bwfZAn6nvAMKb84DBk ftxTI2jMgczBb8GH3ptDxu+HA0++jSNz2Me9KmIOMlhdG7rujyDzU5Zx7RQb pG24wcNzgujX6U9S90r2Q+Lsmlf91CZkfnfeo3yuFhI+m8vOlhH18JN9S/M6 ZYh/J6Yu4kLYW9i71GZ+CmKfnj01qHQKmX/rZ8kCAxDz+MaDL/5PkbnC+Pjw sSncjHRKXQ6YQnVWq84n4i4QWea8n2e9AaqvWWpi9q5C+POLn9n1LFCdI7Kq K+g6XF8pudS4cRHVN1YkvfiUBsE3xIflS71QnVst4nScGAT+FHRh+xKG6jzd vq/+/z2q1WiOgAkPqgv8MOvLaQVf+b2BvH2VqL7N7aKI5ih41abQuE/4oLrQ soHdXxbwMGFMPw37jeo7/bXvF4qAm2rzWosLiOp71p5ad4YMLppnDkcssKO6 aART+99lcIqW3msyO4DqezeTc0qDwZFVc+asDeF/X4LKjG4h2J+dcp3mIeKV 3H5MibUD7Lg8/2lx6KO6VJZsaMVXsJHhYW6mEvJhsYO9huvBqoVndUCeH9Vl SyT2cOwHy3rvPnsdB1RXOLTHtlYdLLfN7AhTe47qR2uF7hlbg8VYVsOPZ0Oo rnhMgIMzgli3chj1IPJVbtp8tv4WWNZ2RL+U0kJ1NdKG7EvPwarm1Qf+i0S+ 2LFmmnsGbLZobhI46o/qFI3V443cYNuXiZ4PhFGd1r0YYnEYrv4c2N4GRDxM nZ+vt2iBwx1j77XfQ1Fd493MrlZHcBI2bpl7SeR30virjU0cOJf7q+4s6kT1 058/NgjWguvl2vlNHIOoftZyiP1hL7jD4xqKljOq68y8OWP/GzzVLGmGLzeg +jmnnixhfvC+cGhp8hiRr5HP42MuenBtbk6Ey8Uc1c1jKq3FByGEr+rghdk0 VLcSKKnvWYZQ2yWBbfIEHjZp+Wt8heH6czU7WekgVHcoSM7ovwAR8U+0Ms+o o7pXg393yCjEcD8bjCInobqvqrewHCvENJLuHTO6jerX2l0tR0Qh1sxqX1bi VuKR/9ya7agZxJWJRnftIPi9fsbs1KcQiKfZ7c5UIPAMf2OcfqMI4gfflzwq dEH1G6PaCl++wv8qru5wLP8uLlmlMlJGkQpJVhmV5BwVeu4RKkpaJKOfEiEr ZIUQMh5kPo8dSRKiRKRoIamkNCSVUZJkvPf757muc5/vOZ9x3eckjPaWmbkw 78cdp89fFoJEt2OhCmeeIevyoMkTYPwz+ONP4jdlZCW5bJP5xoIka2Eu3w2m P/aYvkPyCUhq2OOXtfwostK8dSu2X4RkeXul116bkZU+s55n+Cokuw+Zyh6l kJUVvI5OewzJd7N+up/RQhZHSDHVZAjYPFvTf0xpICs3ekX/mAiwdWM4Pi+/ IqtAXEYrSxPYtk47VvJVIqsoWSKQMgN2cMnTw+E5yCpZvqhtwhXYqSa3uNff I+ta9jxpbjyw89TSeleVI6tcaa69WTmw822MpuIZPiuKpsunOoCdPpyRyFFC 1i2NP7MFzH4VMZKYFdOJrOqILGPLfmCf/JRyWpaZ9/bHndF83cDe+b1zsxWD 7x39kY7yR8BepjM0cKkGWfVJKTJHb0Pyp7GDX+RDkNUwYmiziJk3z055rX0F sppYX/NrMyD5SEvlq4Y1yHrAiRs6EQvJYs6RnWw3ZD2c1tORCoKk21n/1PUZ PNssP/g2u0PS4boBnriNyHpSFtngbg+J/1Y6Nvxk9Ntx7I3pMxYkrjSPfNPD zPeiLjjJXw8SiseJHXr7kNUtue6tqiokrI+T6LvE+Lnnkd+JcFG4rHEx5e4r Y2S9U1C4rssL8QX9UoZhH5HVd67tz8cxiF+xjrUyg5m/f71sKLyEOL7Id78m 7iBrOPlO5p8MiA6WE77nw/hvdNS+PzcWomy1HBbprEfWGCmiticILm4vvrfP PQZZEzNHaq4dhwhxfq34FXxI8B7neeGgCiF/lSMPOEUhwXencPkSOQgRUnfr aclEQlDK3LZRFIJXRqmy69uQmOf6t9CVF4KM5c4JxqgiIdyaPSL3C8573hpq qxtAYpEiodv2CQIr5CWPlXoiIer/85xPFwTMKLYVvzyIhHh36n3lFvAvC5iW 181BYsmG7fO7auBcYIJ2E3cbEpIXv5mHXAU/l6jfDtt+IiH9+TJ7Qwb4+ns1 lle8R2I5bOl9Hws+pSydtd55SMixPynGnAfv2XZJ6V3ySMj/jHLWdwPvMy3X 9j7ejcRqSrt80A685zdKms7aIaGY2/OXbQleTRvL1SN7kFgzG4rGO8GL43dH LzUBCRUrtbAxPfDKbeFVqGRi1fIXj3NUwatNPckixgsJjQX+EmZy4L2U+yh4 82kk1tsrHZgRAe+QmTWc591IaN19kn2VB3yW/ogB9gQSutKeA1Y/waeN33Wh eAMSm86s0BD8BL6cqnWFr7SR0Gt74HGzC/zS93usc9iAxFYll9pjLXCuSnpo 3W0zJCBQaq5YNfj/EnvYe6MeCcNX9ay7RRColNbSofwViR1ajrEnr0Dghxtz tg3dQMI4WvTlshg4X6I+5918JyR29lfLPgyEoNDDrz82SiBBp84rVjwGIdaD nzpPBSNhpnTicJMPhJ7yzqvLY/jfXd4qfjwWwqJNNRcW3kNiX2uUdy5z739b 8WCTNy8SNtMiO5UkIIZv4cCy4udI2EW4/GtWgUsWYyMvozSQcFjy7Jo9Quze PjXLq4CEs3r80ryTEFdwY0jR+g4SnkclPis1Q4J+opzyESUkvL57sJt7IOGV 1a5YCQckfL26KPufkOjq5atdU4NEYFzyjfwVkBSn6lHYPoJEsOyEg4kuJEsG Gh3wb0EitMhK5gsFyUmlDreaBJCIbJQJWuMF7HOHPETNGf6iTX11HjBUfOww 3C/cj8SlN2++OnAhxXDR9pgSZyTiHfXTBWsgJSHDRrcyEomEsXSz/OeQ0rt8 iqO2HYnkwBk+kwFIlVVz+FbWjETqwsO3vsxAqvm5Xda8/EhcSbn7X7gEpPo0 LR6cK4REppL8CmXmXmS3qNhm7kUiuzywvQUhtdjC+unXWiS4Bn1hjpaQWm4y b/DvUSTyWrfpCTlDakmwrUEk44fCfTk/CoIh9cpUWLPRKiSKP83N3pkKqYFl Ra/Cs5AoPW23d6AMUvcXKej/UEeibKpJKLwZUhVmTxvOUUbiRoRirXIPpHzu UH1/hPFD5ZIwl5afkJK2l//1/RkkqrK/rHYSghSj2sQuX6af2+omL+fJAfvz ni1XJhh/19UURBbqANvH6eNldUZ/9SbzDJh9nM1/qCnl7CkkGjqcRr/aQnJY smSkpQcSD76vs1obA0knr1X3X7RE4pFX1IKHXEjsvCwV3u2GxGO+7/VONZCo 1SibeCAAiXbZ0jWFX+DyW0/BTqoaiR5TzXEVRi8j7cIBvIy/R8p1L6uMQqi/ HXd4zXUkfi02wrZuCJHO15Y2ZfT9233P0Kl6CKq1uhBXJY7EpK4rqzwGAmU3 zrWPYvqdSg74s9cDzt3u4OuJKEViZiKGO34QfC01Q8LyXZCcW3OVR08FPN/R dInHBSQFltWU9IiBezs7IF/qDZJCfg+tz02A25fhacW6VUjOf9s9b8U7cF15 NEp/XArJhQb9lfeawWVOtfUr2dNIimT+tjtWAs6iHtctyx2RFJ/DJ86fAE5u DrEZalVIStiK3833BQeVew8TQiWQXNq48iTLFo7rewTRlVNISito/v9/faxI w1FBiqm3LNSgJVoTbD0tlrpKX0FStp/21JAEGw7vTf0AbSTlTQ6ufjYDNhuE 2udfUkByVcGJ526fwUZh0L3R5TuSivO8AyTawMbvjdu5EE0k15wIV62sAFu9 O7ONhD6Sa1uTXu9Pg2NHlIKrK52RVFXNvTAZBHaDZiX7fpshqR5doXPlBNj3 KLcWtzLzaA41fDQwBydtY6fhzFgktUyfx77fBP+N/tCYffoMSZ2y9wZBK8BF YqFgqLcQkpvEhr4rCIArlb1tklRFUs9tOqXpB5yBb4+zQ5l59DsXmDh0goft h21BP8eQBB2Z3/Nuw9mK/UEVr5KQNExam1OcAz4bJoeyw04habzfeHr4NARc v3llUqoCyZ3VFsVx++D8Pu3m3O8sJEkZOystAwgauFd28DcfkqY95yvOLoQw Q7M3CsabkNy9NdZWagzCBaQT7//uR3JvRqZITQ9EvOJ0Pbg4gKTV0doTM0UQ nf9DgCiIRNL205+VYcYQ/7ZApCN9CZLHjfmfKqvB5QEjbuMcCyQd8iX8HklA wlgxX6meH5LOTuu7F36AZJEqtq7oHyQ9fjjHJAZA2ua58Wt9ApH02uWrv5G5 h/Rl3ESediDpcy3i66tdkK5311e67RGSAa75RrLLIXNN0cdXBwuRPN9+8+fd uZA1X+/4ihI2kiFa97OODkKW32DM4YM+SIb/7vvHrWLutZY9DsMMPheJP+7b dSG7er9C2MZ/SEZnLfjedxNypMRwB580kpd+rzoeqA05rkXldq/1kIwnNvbK lUNOU+9Uk98QkgmZtGWdJnAknE17/Bh+k8Zsn1pfA84hifoMZh8hU1heJpPq wMnM8E2nxJBMy4iuZ18FzpuO35pPXiKZ/ouzeaMKcMVCll5S24pk1s6q6y8K gYtBDjnFSkjmpD9RcVcGrmO+TpCeN5Lcnx854nnAjXh688QWxl/5Jn+XXVcA LqdXQerIfiQL0xclmHKAW1ndd5oXkSz+qbBgaBVwG0zDTeenI1lqsjk0Kgu4 zXG8/hYMn2VXds2skwNuowe7OHEZkuWjdp4P04Fb9XfmUBgz301j72HH5cDN l7fqiriJ5K20S44CqcCNHjHpSWLwqB7hvs+VAu5JzzdHX7gjWWtUY7U9GbhG zSl865jv76Q+ff5hKXCX/jnlrc/4oX74MxGYAJw+pdwH8YNINu6YbFwhDhyu Z6fn021INqWKbqmLA46NSHPxs3EkHwwrVhwUBY6knL0RL9Pvox1bVCdjIKe5 M4N/ug7JthSz3JSFkHPaadPnpp1IPhmyl9sYBTkSknWJn+4i2cGOE3GPgGzi gcU+pU9IvviRF75YCLJ6LXL3C9ci2b2tlud6GGSdlPxz5K0Hkj3f+0eHQiBj cmpm+iPjr35D/U5Hf0jtH1G/p8PgN5C0mxaYgpTOQ35dHxj8B785NOX6Arvu a9H1WkZfw4nxlR+8ICn8cslA3FUkJ74OpBx0g/jFitdvjTPv/zOYEZscgdgJ O42L9m1ITicsjkxxgUv1JVxPtRSkeA0MfLuc4eJZE/nHcmeR4ru8Z8x9ECJs tsSnRGQjJTDgdHKxE1zYt9Ry/bsupITjEw6b2UOw8838CvGTSC38Utg19AnO X/L/vNuayRfVv2safQwCwv+TWcx1QkqifxAfHQWv5yOHzpw2Rkpyy2y1Yy94 3DKf8lUYR0o6bskGwUPg1ip7e/p+EFLL+lWKcnvgtMjVpZF/M5CS24KrdxyA /xY57FQqCkdKvnGRUOc02As6jfRNaCK1mngzdDwLbII2ivmuy0JKsb2wY3w7 WAuwFw6eOoqU8oGz1WH9sFfWuc7xAROrfDDKlIwAKgtVGjRFkVI7sTikQBW2 s4b73tYyscZo34lNT2FTqJSr7wiTv8H7mulDN1AnpQVX1yNSOnPO6RxYAor8 phsTPAOR2hhBygxWgSKZnVa6jwcpPTFpHl9r0FCuX7Z2mon12f39wjOw2TR5 ZfBNJh/kK1qvZMP2QVNRtghT37Ag6LraDqBjDBvE5d4jtUPTLKnuC+x9IDJy Y8oMKeMqOb9dkWA9qrHyuyXzPQu+27xTA1t56x++nvuRIh/UmLg8A/uXvwRe V29CapdpuBrPGXBeNrP7BcHkm720FI9bCqfHdlfIfUpFas+R1RMrq8F9eduF NWcJpCz6R9+WH4SzocIHdoqeRurAeHRBRw4EzDfY5VOij9Qhf+sYOyM4H1s2 FPT3PlJHBdaeGRuA4Mp98xw/P0Pq+JImWKoO4Syhmg66GykX7akX+2sgbt3r 4DyLHUi51j6q+XoI4gMVD7+p2YCU+w52lg8PXH51c77AzuVIee/V/i/NGJLY Wy1FbsUgFXzGec7bdriy2u/dL0dLpEL/bf5yygPSgzhEQYswUuHBgm2zUpDR K2J9Qp/Rb3Q8hy1/GLIacnguMH6mkq+/UbcdBM4Wr1XSLxg8UjcXLv4VDZzH fIsiVqogdeWe598QTeDuv+A5WbYEqUzWjncSHcB9G8NWqLiDVPZz8fu5npBr XTTNK7sQKe7+90W60pDbfubtYOsepPLel15qroU8wzCf5z5RSBU6+nnsOwJ5 BWGn9/1j+CseIQ4M8EK+oJzgqWkuUqVeUuiVC/kHZwK2WNgjVTbbrzRvJ+QX vjebbRNH6saFCuGUb5D/I/jk02JXpCpFzo+qxEDBmmseHfcY/qqSTbtur4eC /VoDo9piSN1eIVtLdkKB/4BZvtl/SNXlfcvu8YKCtKu2sz7aSNWrV184uQwK Sh3lTruHINVwUcd+lqlX2dIcGXIKqftfrhvH8zKxlemzKROkHuxQU1LwhIIS GUHeDZVIPcwq5K8cZOqJZUxXrUKqdVrhM+swFATS6+qDFZF6YpV1/007FBx8 L5X4rRypZ5XLuaeMoUDzk3TIuA1SHeLsEJ4ayJ/yluKXPoPUCxeJY5fVIf9u 07J5hgNIvWyN3a6YA/k+Yypnzigg9Vp5waqqpZCvvkldoJcXqZ6QcF4iEvJe tTwYvTeJVG/f3L6eGcg793Gd8CN5pPq2BtxzcYM8mYffB3p9kfo8fjYw4QDk ony2UbIsUgO7fx1VegLch+ovK38+QmrwmgtUbwMuZeQv36mL1LCj/UyvCnAM /xpVlv9FauK15bk1fyFr3ETL89B2pIXv6HjfZkNyY9d1gxFNpBfJXLfatQCS vL65SU8mIi16Vm3T+0BI1GhedfynMtJLNBUm+B0hPs/V49+QEtKSUVkvk3sg znFhrBL5BGnpr8tvqZhBrAa/VsBvLtJyORKephvhYtZnV8tLFkjLz8Ra9BVB hNWqkj9W25Bebb1Ax10OLqy65a+5fBfSyhJzx1IEIGh4+rbCgkakVU4HdK7z gcDxhB2P2N1Iq7b9u1E3BP7KCbPeFkz99aG/3D50wVltE42OhKNIa31w2e1B gPu8LUpNzx8hrWvwbYPgHXBd8uBKodsBpDelOYilbgDnWzUCIy8XIK3358Oo ah441AQaZeYvR3rr3sPP7srAsY1iFfpqXkhD2esy8xg4fDRE4rOXPNLbFlpe +sQL+8wXiRZNiCK9w6ndxdMTzLf/SPO3RqSNm3eZCg3CTm/pwZtaTD5r1SP1 tMOAvgr2ov8x/ZEBxovU2kFntWdm6bt6pOk3937UG4PKlInmkywepM02bX28 uwYURDYUfghi4t0J1Vc/q4OinzDvmBcT7x3ViTqbAxqb/rbLb2Xe20dfd56/ BDal8bx9W8vUsxrxObedC9tvh58WdYxF+uDl7THnNgBdVNcfamWG9BHdBZmV 9WAh6C0l998mpG26X5SN7IKDzj02XquEkLbzzWhY2wO2/ukRs3NCkHaQc+g4 dgIc9l2B0XQppJ3uaX66MgHOtZsNer0ZPTgf+/u7Kwxc99j+AZO9SLsINAqK SoB7ePSQfXk80q6FUVKsHDg7af6fw3lxpD2H5fRq70JAqGOf6Z4hpL3jvpDj u+B862V94XyGfz/tskMaPRD8bV1apCyD73mfbQE5ExBuL+LdutII6RBZ4die MIisHWsyOnUN6bC7nTlLJSB6ZauPkp4k0lH89vcjNCGOf8rpjnEA0kmxF+e5 OEGSSFvib7Yi0ilae2UKJiDZvOvQ4kYfpNO6ZNd9CAN23G6hqMczSGcvu7bL IgfS5o64mfPQSBfndVze8hoy2YbFh29cQbqUdYXr4QhZe+TNe/kVkC77fvxm 6ThkdQbbPuPPR/rGJfXmgRDINu08uGa0COnK9X+6V4lD9n1O7c5oE6SrOuu/ WmdBzobwS3/tgpC+fTZiMlEdclLkn7o0Mnq8I7NH+Ekt5EyIVdhPMn6sr1su K0QCx1RI6G8XiXTjkc/qhq+Bk151avxYONLNvKXg6wicj+8yndPzkG7JPWtW MQ7cldty8NA3pFt3og2zb3EtSqdjGhg9P/4274yyOHADJ3vu2jB6ehbdHmLD 7Kscfo2VTzYg3aGZlpimDtzbN+/wmK9C+kWHXV5nLXAfTetJz/mJdLenWtUi ArjPXqDnjADSb6TGH5p0A/cxWpY8/oj029t3X5+3B249q/e8fyHS7w+Hf6sZ A26RwJxFuwSR/jjHfGosCLiRcXEXhX4h/Zm7bJG6KHBtxb48tPmN9IDxpxUO GcDVrCjP9A9DevBriWa2GnDGrzWcbC1F+keUp+HrWuBUYOu/O4xfRzRgjwQB HKeS1T/8GT/8bBeyo7uZfdbwzLLYrUj/dn/uccEecurM7ZZbMHqckEwNqx+D nIMbCYtbS5GerDmWzNyX2WN7Gn7ZGSA9fUi1UFsUssMWmq1+kYb07OzvmpMZ kC32dHhuQvL/ABmobJA= "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0}, PlotRange->{{0, 60}, {0., 0.8918521289303568}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{{3.4692211004434357`*^9, 3.469221120615958*^9}, { 3.469221162180147*^9, 3.46922117723672*^9}, 3.469221220917165*^9}] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"Evaluate", "[", RowBox[{"{", RowBox[{ RowBox[{"f1", "[", "t", "]"}], "+", RowBox[{"f2", "[", "t", "]"}], "+", RowBox[{"f3", "[", "t", "]"}]}], "}"}], "]"}], ",", RowBox[{"{", RowBox[{"t", ",", "0", ",", "60"}], "}"}]}], "]"}]], "Input", CellChangeTimes->{{3.4692212429694443`*^9, 3.469221246758875*^9}}], Cell[BoxData[ GraphicsBox[{{}, {}, {Hue[0.67, 0.6, 0.6], LineBox[CompressedData[" 1:eJwUV3c81e8Xlz0ie++99y7OSVFJSCUrpMwkK5UUSVJCxrckqcxKJMmmtAil qKzr4t5rb9nE7/7++rzO63Oe85xznjPeb7tzHlKeNDQ0tDQ0LGn//5qWxct+ +kpcgineokOJ9/ghpWv3JcGsXzDZrJ2iJXIbsrsEPdr4ECYvcx/nLLsHpV10 nS9sw2Ci7V1ryDMXqEjYetHy5iRMhNKVJtK8h9o961em+K1ggjdR1ZD7BXzo XE0RmOaC8YNXLeNnXsFn7eXLzx7HwhjJv+vHYx1oTFj0NLLdgLGwN77/LQhA 6545Q6c3ozCa7r8c/awc2jsXzlnX9sNw04+4rGIu+MUQclqALwOG7V6HidcI wW/teYf+gKMwRLCM8tRjhc6EWbNAiWagzPSyRh1RA8KeSb7k6HIgS/5otryW Bn1BZ1gdewOBVPKN+46oJRCzxrakdVWAZNb15BNzOQxE/F1wDP4BgwEafo/r eYHUOdc3LmsB/T8/ML23NwVywGsHa9tc6Jf8EZn+qBEoDIHtpVe2AfFCy9LJ G4FAydQ8xP/cFfo6+VoqzjbAkPZMY/jvWugDFmbdY5kw9PXV7n5aISC8WpO1 7GGAYfeA2j3qYUBQ1v+lwqMDw8vq+gVOv6C3mKGrg6UNRhKmXrPd1ILenfl/ fK6qwahskcq5N4nQ02GnsVjACaM1/nkd/ZPQc95wNMuHEcbsVCUNtx+AHqmY 226HDsDY6ERGpmE+dHcd5z7lmgTjkYV8NJ700P2gmbR6bw0m+P2STiefhG7P +Rr1W69hokiZpamuHrp3dZexhnLB5J6xGNVxUegWv77nXLUzTPY820zmD4du 1om9yYmjMBXkc3HRrBO6aRkZlmzNYZpZ4a/jOV3opnvvskiXCNNZI2frHqZA 946t+gfGXjCjlz8q1TgL3fLZJ369SoaZVi+PGwuHoHtfpPXgyiTMaqVMn+c8 Bt3BXq8qujxgNmK+sEA+BroLpH77bHrDbJOdT/euUugeCrm6PywW5nhK5dns BqFHTZC5ILIM5ty4yCa+nNBz5XPRpuk2mHsR9ORcpCn0dKp+OHSTBeYWf554 +t9Z6DVcXr7B6ADzu7WEOwozoTe7NbdcfC/Md87+Z9C5CoQEjvejMwXwV6nA nVm7EvoEKrLvnyqDv5ddlbsSwqDv+VBicl0PLIi3vL9kNg9E0u0XfJ1vYdEz f6K6aBwGmvmO2H75DxYrTpTHMz+DQTnlc1xaXbDEwnvN+ZQXDF5Lov2YOAtL L6P5NwTJQNq1tu/Sh25Y/nvCzOQ6AchvDz0SC1ODFXNednZiBlC4Oo/pjvDD yr3mTqKRA1DOytG/mqfAqrHh2avTv2FIvO+nVMcHWI2fMbQ5kAZDoRX0HaxN sNqXTyeRawdDTZod4RE/Ye0aT/p7x+8w7LVZox20AGvtzafult2B4dd/EowP S8K67DX1kzssYXjVvuXVYhashxmsavkxw4jJgScJNzZhvXH607ZPjTByJaI9 0EIQNoTyEtslYmGkqmE0ZrsibJxxccwO3wsjc9OWTWZTsFHHIxv8mxZG5Xpd 7xu1wz+OrzN7NN7D6DGvxy58yfDPPaqa5/ZVGI0KeuN95CP8KzWIIQ/tgtF8 UsTvK/OwSTdtUwZrMNqUIx/p9Bs2j+UJx2RUwehQtur0UxfYLHAeOrp4EUb/ tZ86NhIPm6vcJXK2+jDGpbyQaGQAW5ZfwxdfLMCYRIG75Dkz2MqMMv/CUApj SvtCTm9Lg61pA8577kEwpr7D9H0OJ9JILE29OZFOlZmYZMILkWZ/+xGR28tU /Z06tbsWkSaoqPJ6pT3VXq2h0IcrSJNxS2xi6C31vqzw90t5SPPx9PWjPLww urlm6CJqiTSTMFqLITA6MnEqx4cJt/GJWMuebYfR1uRo6TYV3Gay9ObOQy0Y LdoRS5engdu82gUXmu7C6K34PDpdRdyWVHTVZXEGRj00VBuGmnBbZRz5s4w1 jBrIH01vu4fbBk8fULMtglHmJ+xyMQZIywrF/13dDiN/vn2rm2BDWuelS55d zTDix//81T5zpI35SfzOqAQjGo/PS9z4hbRFRXv1deJgeL7C8Kh3M9Jund7B mGgBw8FREe4OmUiX/TO3YM9HGHJTHlzu10W6puWxV4maMCSa1F5M2I50M+Lq FV1ZQOm+cqqLIIH0O/0qm85eBIpdrY8A2wjS/6JrHbuvDuT9al/epssg/Zoy 19xgJpCZdhVo1bYgg6St/ZoqC5Aaj/yo4olFBv/MQdb3FCAd3PvyabgeMjLo LaqMZ8Cg13kQZDuFjCouxrp6zDCozfvl9M4UZDwcHbkr8jwMbA1UE/N8kPFR G8shXlsYyPapyZPsRcaPi9bHXOtg4MJRpy85ZGQcF0k98VwZBqxbgnOezCKT vo9YgCkDDDA8idWbSEAml0SPsLhg6G/WY3OVS0Gm6LKCq+0D0J/4ao3h3lVk etYzeVPsEPQ7NBz8d4mATG3btJK8q6FfMSSgK7wTmRYVwu6XKgLxX8tP/Q5B ZBY5VPN44z8gdjVPax3bj8y7Q2me7aMFYpX/CaYsd2T2zjAvSQ4E4uN8yoZs CjInvL9d2UsEYrz/f+bHPZD5zXBbg/xBIEa8GFjOMEHmHnber4GVQAyxrG9K M0IWGh3HnzXyQDyn2XpLohpZ5B2zehhSgRhoEqbJu4AsVpFkki0NEMMOSR4y 7UWW4HzF8YwAIF4zPFZFfxlZ0lvPzlN6gXi3Y8WudD+y1M+XrmvsB2LuXGgm wQJZhgRX6C6VA7HOdYWBtQhZ2cCE7ZMMELtHnj/9aoysWp7RvBx3gbh2ZEhz fwSyHo9vFHXYhH5x34tCzG7IeqV0u2zOGeg3nxjYfcUfWb9u3tMzpO7TrJ3i NO9ZkXVWttckugz6v98orC39jGz8ByXNv0nBAE1mdL2JFbJ53H9hf3IDBvRP HxhkrEa2dc36C/GvYeBF+HDQb0XcLnWcLvKPOAw0XVFVX7LC7fuu7I+TvAMD w0+kPkaz4vbUr+3pb6nzVvqjM++TbGRX9Riu6heFwfss3013yCP74a/pQX9p YLCU892FpJ/IfkHTUpmRAoOtNnsaDEnI3vCvKEO9CAY3+U8yvWBC9pHTbnZm KUDiC1XVp9uHHNtbuFjtw4CkUsZF4vFCjuPpoeFXTYF09L/QO6tiyBGxJa+d Ig0kb8E3r//7ghzZnp3j+YxAurj7mXjKFHI0tt7KrhoHUhxDVHuXJ3JM6ex0 +vYdSPecdWYvE3EH94Mp7sFSIOXsjGG3ocqGNFnNi/eAVPyU7stqLu444W17 nSUcSJVxAuntY7gj+jvtTjE3IL2fXvx72gd3PNN981drL7W/fufd+xaIO749 9Cw0VwTSN5M17fkk3PGXVuCU43Yg/VRJOtw2h5zbdU8nlF8EUkdOQ3LzceQU H6nzfe8HpF9F+QZ2B5FT6yG/ebMLVbbbJv74IXLutT4n9csaSO0PjQTKwpHz +LbGf0QEUtt/lpVGQsjp+1aye1QbSC2Hqkr4+5EzwufS23lZIH3uiHrFQLWf JNKevMEPpHf6W+OXupHzaZtKACMzkCpujR99WIScZdExljvWqPH2a93bJoOc jXp98kKTQMqznO3bPouck5mJRLWfQEqqSZBbeI6cWzYj1QafgHR9KrtY6yly cdPhvd3lQLpwN5DFORm5DHznrY9lAOnE7ZkOEV/kijY4Vn/FA0gKUi+mzdSQ K228OOPmUSAJZdNLZDojV0EWU1iyBZC2n+yUOr+CXN/oq9TzlGHwL3Mk+2g9 cvVXcrO+otbTiHTr62Um5Jo/4zdcxQGDvWTr3WlLyM0g/vHDR2p9/fQI/lLs g9wC7aJZrfMw2Fjxt5pzBrmVb5wP/0Ott/q1GrlzGchtYvjdfuAPDFZg8zUj ZeS2mVTQHm+CwZLH0p6ZHsjt8TiKY6EaBguVCuVOhiL3ebuu8c2XMJj/V9zn giFyxzFqNTJnwWDu9rleQ2/kzqi6ncN9Fwazb+5hvLqM3EX+5EjR61Q58NOp R3XI/V5yl7N8GAzm/Cms32+P3O0d/xlo+lDtfXq6aPcSuYdip3mMnaj37S1T pmNB7hVji5m9VjD42s3C/zwBeVinHrdYm8JglQjT/IF45BF7slLgoAmDHyJG yaHiyKN59PB1D2kY/BbH1fj5CvLsYXrh5s8Lgz0mhCyNZeQ5bvslh4maz7Ex 0xRFBuQ584A0nI0wuB67pWJ8G3kiSVvKJvZA2sEVNaYahjxpqiJnu/yBJBt1 P/fgA+R5dt7gdUg0kHZ+ja9ynkWe2vojixzp1P7rl+QSMEOeIds7EXup9XBH /efy8hLyrD549q6/B0gvzAzpAm2Ql530mS58Fkhff01vRFOQVz/03+3XokBm 56pRLeNG3psPAh6IhQL5ScGmnwkL8j4k3SZU3gZy443v7J/jkLdEpUDyyBMg T5MX1CdlkLerrr/gVitQTK//NhvZjXxKJNu3S7JAIXxbVig7gnymKv6rycYw RKfbJvqhHfnsQm+ZqNrCkOI37fMeVP3LjA2fPCJgKHDogWyBHPIl2RCZ/qXA UOqO9RXmHOTLSV87mP4Mht40MPPMKyBfq4p2+/cOGJrlKbf3eoF8A6E2/L6j MMzmdWLH9jbkW6g740S/CcOy2Vxvb6kgPwvjzUdPeGF4Z3aPfWQI8ovZ5Azu VIZhW4uQT4/fIL9W+nvZPwjDHv6X/Rc/IL/5IMEnyB6Gg5bu5zTkIr+T8srL 7f4wfLUpy9TaCfkDQnlnCqJhOK6pyua1HfJH12npmKXDcFL/rf5Ne+S/z3go rK8YhlNX9l/Ya4P8hTa+1Rc/wfB/NOTjD5aR/136jU2eHhhOGRfec/8d8ncM Zu8unoXhhIL7JdR5yD+qXH/DkhGGb2gy7Q+j+rMR0tM0JArDl64oPdiIRAHO 2uXtUdow7BdPGfkhgAJyjDy2Ivth2MHFbmk4DgWMbDRSy11h2Gyu5bvlDAoc Sj/YeTgUhpXtpo79PYsCJwd9hCdvwzDHrceNh+VRIEw5xvXmExiayeEf4j6O ArdDnmRLl8PQt1dt89lGKPCGoVvJgQRDUYsHnPbHo0Cj9aL/3xUYsnfTKT5B QYHedK6SJA4YUhJPX7vmioL0ypYGn42B0ihtEqzni4KCIV6X3W2BklLCOBrf j4KqtdH1615AcZFXqectR8Fj1jUWWilAHotIiFV9hoI+JvscezWA/PpjUb+g FgpeVuk4G9MK5IvdlPZiCxR8yjyR1sUIZHpJsc8C2Sg4+VGEfPUykHj5ntZ6 yKHg1uuCZUVBGOxssaCP4UUh7ie6rD/LYfBhY8AmUQOFDK9YacvOUvnJidW5 mRwUijGIiGo5DQN77x+pjw5GofvyzGnnt8EAJydzhY8LCr3gTXsmngX91Y2P eStCUKht7mVbUBcQN2rZ6viMUYg0YEgWOU/FD59V73LKotDi909Ln7mBeHjj CK/HCxRmrjvMGlACRPoqhqCevSgsUtgnLngI+ups0v+Wh6CweoavVsM49EVw Dr+bjkDh3XGL5mfioG+3JW1lhgcKH71wzZFPDvrY9yx88S1EYW8vdv/6D0AY 2BP69XwpCocffRDl4waE6rjnZyafoXDCHrk0rg0gZFqPc3KuofATrdcFNQ+A cKM1hrOjGoXfSJrUeOoD4YL29VvTGSj8heNrG0cHEAJfRna3p6Bw179jpMpA IAS7+7PTrqDwxMTgkgc7ECICzrjdPYXCmz0BrGwvgJA4+3zTiR5FuJrWxN7u A8LzdWP1220oIlsRq+VKAcK3h4ubwWwoYpDPbc4cDYTVHs/HnnwoYpmW5VAq AX3qFS9iPhugyInryv7OtdDnr37OPf0WigQGlUcxOEFfqXYftwf1/HV3s9Ti ZSDSlKY9YCOiyD3r7wUOaUA8nlbM+20URZ6bONXQagGxvKqWRJOJIrUqw98L v0O/2HrXz2Z+FCExby5uMcMA7Y5PXl31KLKwHM/yLA8GdlvWv/zvLIoyDQuI 2ZnBQIxiDUeBF4qqfdTYmxcBg7yYQ7sWgqKXrrilHqTOS0fPP/dHuVH0zpmJ /MUEIL0+evH3yGEUfex0ofqxMpDZbjjUFXSi6GeDpMH500D+3Fbxe2ILxTjn 3mumd8PQwYQTrv2rKCYXtqOwIwOGKqLkrl1XRzGjNTcFDicYlumMNBJnQ7FT tJtiVD48vOFe8lvIB8Xe8u5iDRiA0ZxL9MHyrCj2NT3+xvOnMMaiLxTupoti RNFeGooHjPm/PGais4LiTHKXlh0pMK5y6VdKWCWKizz/GpyWB+OxGkuJm19Q XFNdcKrNC8b7TiRMnQlBcUf9CsreUZiIXBimcRdG8bM1TG6Rz2Gi+cfpf42J KB4N9j3VfjDJxeSU7NOL4vc+5h9bVIFJO6e8fwnNKF64b/GnxiRMJiY2NUAk ir9rNbfyK4LJz/4Sa2/7UfyXbVpjXgBMLr0Trlb1RPHRXxSzAQ2YknY4y/n0 HIr/c9SpF56Fqf3s8OydDUpw9V03OvYaprwrnGuE4lFC/mR72d1gmLqmd0Rr awkljIelNVp0YCrNL/eX/3GUsPELKmRYgKmnQLBbrUSJU9MN8vgWpgqyi/lO GaDExRDOp5fDqHIsx4uqGJS4s+wuWm4AU09I5h8OHUGJp5df3Z9dganUso37 +n9Q4u3WFo9KFUxFbdtXe5oHJZpjrBO9wmHKs+3lTrUMlOhnzmJ5uhOmzNUl 1u7JocTfO1MxvRswJa7J4BtPQEl65qwoNleYnJ2W9qt9hJIcll9Mnr2DybrY lJhvbSgpeGdq3UISJmNEy1yZw1FSuo2vkhwFk3u7uRMbn6KkGpdJWNQATGyN 7etRXUBJgyOeuuK7YeJtJHGg1RwlD3aWvXKkhQkuaRWTMFqUtBci+C95wHjF C3OejPMo6e5Cr5z6EcYdX6vwic2hZOjAkbzvMTCW7KHcx05CyUjpy6fOUGBM 4TmD9O5llLx1OkeKxRxGqwtKNNvUUfLR6HzmXkYY6VKq4AiaRskvc8kpVXEw tO6UdvKNCUoJ0bZF5sYDyfCIE+diHUrJ8HH76lrCYFOd4IJJI0qpKRyz+0Tl q87t19CKGaV2H+yVI8fAQLyHQ9zEAkoddBXfEULtN8U4vUd8Qyh1LNB9lY4G +lPduZ+P7kAp37ThVukIIGpfbvHyIqFUSIHS21Jj6Lt7LochUhmlrlT5P969 AoSVt2vecfwodbPlVRx1vhPO/KCZc6lGqeS++eCT56F3XP/R5moUSj2c1XOe 04XeUCfmJeuPKJVPe9E8ah56WZ/6wHVFlCrhrdHgLIGeIr8LF4h7UKpaflPw SQD0nGDRU8+QQKlPRrtpNVWhR6gxPTQzFqW+H7w+8W4cuslD1tfSNVCq68SX 3zbPoLvmftQLxySUIgWy1Pd7Q/dT5uw+rjmUmoy2enZOHrrTHCx/2hai1FJa UvImBbr/e8jJzSGI0jT57ZcTs6E7t+c8Te0MSrNW8XmKu0P3B96u0SYSSvO2 OFgXi0P3lEmIjsUulBbve2hoQoAe+V3HPwwWoLTiDFHqWwb0+E/HXPQwRWkd Wim2Ew7Q0yC33GWTj9ImvKcWpvihV/r5jw/JO1F6n3w+MeIX9CbrX5BTIqP0 YcOxpu0pQNh+5Vt8KhdKOx9UfZ15GAhpCmdOvxZCac8T5x6qckKfwo/+nzPD KH3uXGlMzXfoa2TfaO5gR+nraYbHey2hX/5UQ6F0EUon5F/efYYZ+ofCzr1e iEfp+5X1ymtfYOA4c9vV6wsoXdi3Z1PYDAZV681PL9ij9NuZ2JEXVL7w6Prf 9kBalH6/7etPo3ogcTXTJWdQ4/klb5PruBPIdEE8qhuJKL1xztkyXReGaAhW NxKXUIbxWpauwjwMXfdfn7xvijKcqYNiFSUwzHilYNDMFmVkK71mO9VgZHuc k795HMoc2hZ8n18exthV1Y6QI1HmOM/bqDxqP8QV3lZzZUKZk3IrfnrZML7t dpKteCDKnLe8anJMHMbHZn/+dNJEmUiXBnkKASYcPlRFNNxHmVvn6DlDM2Di 422NrxqTKPMo9RY5TQAm41/VBwf3okz+SsxbFUGYJLlxuwtS7ZWciIz9IAxT ulfH62m/oUzVh3AHR1HqfOuM6tmegjIfFc4rzYrDVLOurcx7OpRpvXNu/aYk TLOfNs6IbUWZ33N+38SlYfqg0Ytv4cUo02/vmfVWFqav335z9IIfyozWuAda ycF0uZZK4Xg2ysxLOu8my8M0mbXZtcUHZdZv2POEK8IMy+oufwkhlKUftx3i UoYZZZJRZ/gAynLYHCx/pgoz5q/u2zMZoqxAmUUcqMOMk80xthc1KCsltNvx jybM+Lx5Ya28jLLKV3epnNWGmcAODi+zJpTVIev/o9eBmeBnpvvJ3ihrsl/r +0NdmAnQBnNOVZS1KFJ9oq0PM6dCXfO2iaCsLbdC0FdDmLELU99z/inKOl6Q 3uNuDDM791wVPKGOsh4EMd7lXTAjNmn3QdcXZf13CwwnmsL0Wkw3T6Emyp7P 566UQ5j+KWf/yl4MZSPZ2G/V7obpp+MCdcdmUTYukNn5iBlM+/91bPrHg7LJ v+lUx/fCtLanzp/EUyj70Ojf5jULmJr3vvzR4BPK5mat/BDcD1NFcs/pPFJQ tpju79NXljDl8b3st2shylb4TIdYWMEUT9oKzV7q+WZtCn+oDUx62920KA5D 2Y77xFE2W5hkredg+boDZQkb3VXZdjDxQn3q0lsZlJ350ubywx7GifJvcr8H ohyfU3WO6gkYk+ZJ0ZkwQjn36GQNyhkYclG4RcgkopzvyJ1tl/2BQpJ9MiQ7 inIhVrEd3AFA8SH9a57ejXKx/BFhGAzkSwTOAtsHKJd0+cK+Tip/oy9xSbtd inLpA8FCAWFASr5YItbPjHKFL7xrMy/BYCmzh2CPD8q93eGRqBMOgxYutxXU nVCuPvSEW3MEDPTVTYmHuaLcT9MjtCtRMMD/3PlZpSLK9eQc+pUUDf1BTn9Y WctQjsK8P18+Boh1p//0KV9FuaV20wNHb0LfOfnZOG0vlN9GK/y9TBgIX8Wz ziuUoDyb1vJRnmIgqNszsO03Q3m+kx3d1H3Q+1jnFvMLfpSXSC5xbf8DvaI8 jw5vfER5pYY7FG0/6MnbH5XE2IPyOnO+vsn/oGenMF1ejTvKm0qZT8/dhe6+ z46mO2RQfv9h6dDDstCdEDEd+PYryttFba6WVEK3pY9V9W/qfS4lvZGcVtDN m8uwP7UN5b0GKunPDUDX5O68JxqeKB/EmXarLQS6fu57+4rXFeUvY9AODUbo +vj6nbbeRZS/EXgoLTGDKge6+tX9QPmkJ8pC0+rQ9cPXhVkiH+Uf/GB8fOgD dI1f+KEYK4TyOVtk2SJ76Obyt97/WwzlizXevdhO3R/m7EKLr3xRvtItU8P/ KnTf4D/NdN4B5T8kXSxr5YLun8Z3VQYKUb713TFjlVzoUZKOvvjtOcr/mdF6 F28IPXfOLtTO8qH8oATH3vFW6Fl707HrOSfKT1iPN1u6Q29I9JPoZQ+UX7za aPN8AXqX3PtHk1NQfqs45zfLTSBEf4s9++w/VODlcOlvKoa+cmWyhnwsKoib GnkqmgHReYUukVCECooBfOM3/0A/C7vX1aMhqLDr2/dFi38wsHOfjfCvYFSw +FcYnn8XBjI6bfNspVHhsFocDaMsDGy812mWLUAFz4TdrJ+tYPALpA745qFC otUbyT0ZQPYyMWO370OF9IikvBx1IH8fzXRef4IK2S/9VWg/AMVAQTnLVgEV KrbL6TWMwRDDtrze/fSo0N9y/yAawvCdjYCqA4AKY+shPx63wvDY9nsSI06o sKBie2zLHUbMFPYzbu5FReZ4Fvf6mzAy7v3m7tU7qMhdPTQsLgyjRmwBmexZ qCg6/uHMlWIYvV4u1NJgh4palpfDdv2Bse1723J61lFxZ/jx9Uw/GDvwWTVK RRsVzV/oRq3/g7HoG6cZ1IpQ0aaHi9H5LoyVu+Kc4z1UdGKZiq+RhbEhNRoO ghEqnjZq5hKuhHGODs8Th5VQMcA3/164FYzrGHwTfySIihcfXBfpHoBxO3PD OvIEKkZ/dXtqFALjZyaOxzu9RcU7Bxc4nljC+NX5Ey3DO1Hxv+9xEUzSMJ6g Q6adVEbFx4dFx86uwvi9orLnI1Ko+Kzjtf2vHzD+8KQyn0gZKpbam3/a+Ywq HxGNMNdFxZquHq3sSKp+POFEcjQqfnYOeMxCnUcJArL7pNxQ8TuRbnugOoxH 0rPwmV1ExS73+5c6GWDc//TI21uIioNklREq/hg/ZmMb9Yt6/4Tn+6O5ZTBu 1DuLvGGouDB69ANbPIwLy4fNXQxExU2/MY1gKj9ZOhThacaHSkxTVzK7jWHs e7BHi+o0KnEFcrMiF4xlVxlYNn9EJeH5/Av5ozAWsv/uhxfZqCR73niI/R2M 4QH/lhADVFJbbrMLvUflPyNsYvY6qIQbK2pmVPya+mpe6VAgKllG3sl4Lgqj R3cWnBT4gkpHaaWYdyzAKLdsntu2OlTyZjpA6suGkdiBR3n0WagUeItou/cS jJhKVP01ikSl8O3BdYW2MPw3jWWzfQCVErgepl/cgmHHTYzdjaj0RmTKmtcF hkTE1X63bUOl2qxrNZd1gNJs7h9eK4pKX6T4FUmsQLl0xv7MxkNU6pYHuldV QO5KpRkqdEelLc27VQf5gfTEeKVsgwmVmd/Iyb+eAtKxvZYfL7mgMrdeVZrg JyCxTV87dVoZleWMSQHDITB4tbtp6OVBVFavCyMcouLtneeWJE/KorIhsh0o k4aBtcNMTxPFUPmgua7stR8wEHmjOz3zCCofbWxKHi2AAQvRJx6lD1HZ1dJl y+YqDOyI0XiqnoXK3q1z/uX20F/xKkjgwxIqB9nc6BFTg357t2R+v4+oHN4u vC+GHojrey/VuqajcszR4rJxAhDzY4LkDVhROaFzj/ThN0B0QKPibZKofN+x M6nyNhA5U5l2Lvqg8hPCmX8SJ6GvLeXSAWdPVH7hRuMXawh9/1k/SGEJRuU3 g2mdU1R8efJn/1mOVFSuO61kfmQE+vSkDBoQUfnLcF1pdT307djFFXv/Kir/ 8LWTlLoHhFnRgIY0qtw9MZwQdxYIneWrJEMKKpMDwtdm9gLh87qEf4Y2Kk82 KKftvwSE6vV9ZxImUXmJp0ftKXX/lOvmJc18Q+Utr1tf1shAqLiVm5bIhCrM VUbuRwWBUN/lZMW8DVW42UZXiw4BoWWtxK7jHqqIuKanMkYDgfglqXlIElVk X+9XdasAwjLHjuWfPqiiTrf8uXIS+ngfF9wVq0UVA/sCN24p6DOwcL93WQpV 8Ln9yhl76HNt5oj/lYcqBzYYkz/FQ9/tmdivwQGocsS6XEXsPfTVXOz5MTGE Ki5PPT+FLULfrFya62EHVPFc4DvxQxmIiqV/mZrmUeXig9C70alAzHXK1//y CVWuTcoq9TYBcXh4j2U3NZ54046PuhvQr6r3wLr8Aqo8omgvjnhBf93CjECT G6p8VJ9ztjKGgfAufcXAd6jK/ilAno8Eg9UX0spkTVFVQEDs3TkBGBwID15f 5URVSd9vDk1WQGLIFf5uUoSqOjtU48PLgWS1dn7qhy+q7jpJkO2YAFLAvGLo +idUNS+Lr1eTBFJS/tc8a6o9B8fx2f7bQGqdjjiafBZVT77MuG34Dkhjjm/M Vz6gqt+WpUzyAhW/aJvkKh9B1dDDq7UTSkAW86l8KboHVa/kPrff6wpk3Zkc CnEYVWOXHWYepQLZssaHxjsSVe9aMsctNQHZ9YPz0lEXVH2QWSlt8w/IgRtN 9qUbqJo9413zXBvI106P/nJkQNWXZgLHaL2BnLQ0yTQcjapv075MO2cCObNk LoA9FFXrR8Julv0EckEqQ54x1b8mY3kpDkYglzwWnTVeRdWfd35Xe1P5SUX3 dvrxg6ja03/jyPtAINdZRGoEFaMqRVt3SigPyO+nZzpWbVB1KoYSG9wD5A+9 6U4Cjqi61Jkm0boDyB8FvBLTTVCNRnlPldxeIDcUT96KEUA1loi/dlcvAbm+ xFbh+glU427LnugsBnK1ipH8vD+qiUrb3dAkA7lMs26N7yaqyZ2nFb8tCOSX HTbu04qopt74uoJsBeRc+WyiHgeqGQqfPLzrGpAzIFNk4gCqmRnMZ7nuB3Ki gSj3cRFUszp2fSKKA8hRf39OC9egmvvd/Bsfqfk4ZeidHYyo5lds0D50CshH aFJCWL+jWmjLV0kmZSCbcRj0L6mj2i3GiWrLSiCLR2sl+nGiWqrsFSb/SCBv V0vwDKL688iM42iiBZBWn4lzm3Gj2usrmtM/fwHp13/HWLcOolr33zAZh1kg JdJpiw33oxqFiykwvAJIV3qqv23tQbVp9fS6zKvU+rp5UnzOF9VWrJRZ682B 5C6X8Hw5HtVpfWuOD2wHkt0Pw5fp31B9+02rvG0dQLJ48jX2wHZU58/tm5fJ ANLO4hXWYRKqS344B+buQNLmfNWcQ5WVB2jueCsASal78Ww3D6rr/kvujpsG kozo991iKqgOIjLyL8qAJDamzFasgeoHDMtCWi8DSdiqbV/fW1Q/Ym/+ftoM SIJHDrRXXEb1EyGdHJwsVJmBIHdnBNW9k32ctX4ASehMiFJADKoHFa8+O3If SKJxma+nPFH9cuvtxfOuQJJyi3peP4bqN8ZFze7LAUlhhrDK54Xqd5mKkqjz haRpQK9c/AzVM+RMCT1vgGSsQfz54Req55r9UNoIB5J5+0yJXBSqF7ufvCC+ G0hH+J0ecLOheuWV+U/IDKSTU9OsFoGo/uFhDNfJ70AKPqgRjKdRvbWKz/X6 f0CKEfMTeH4c1f/8yS/McwHS/cPvU3c3o/rAgsFKozSQCjuweaYf1Rc1nFJZ S4DU5S5g016JGmJ5jxuTW4G8qyjsuJwOaih81OJ9Q+3P414B7vesUENr4MPJ 305ADo71LqDdRA0LEcqG4CiQn5/LPCarjhq2RmEHjF8B+dPcNibJJtRwOs50 zyUMyAOWbEc9tqNGQIqy5hM6oPBNo6j2Q9S4+KrmSkMzUDSmS/JEdVEj+ptV M/kuUA5oPf/n/x417jOf81QQB8rFSdb35w6gxlN5mtL9Q0BJEgGHhzSoUbgn ecvvJVByNqZ+G9KixtuTMlZ3QoBScU7a+9xZ1Hh3texBsTFQvh7m4vMIRo2m 6X/fO/cCpesyJVXgO2q0u+2jp7EGyjC/29pX6n29bXeNlByAMs9iYyXvjhpD 0B1gdwooG6Y/IzW/ItVN6ZzLATBEnx30tSIGNVYkz3TlXoQhNiloyGxBTZom p9jhBhja8Xx4b0IBajJERUpdtoAhbtUGjvebqMlqmFO7o4UqP5ydOGOAmhyz Tfa5tjDEORHgV34ONXmeTc4Z/oEhdi4RVgtJ1BQ8yRX/zRmGmOh/1N+jQU0x IX35kwNA2Xx/rrU3GzWlfjq9X/ICygKMD4croqb8rUjn2xNAGbl2+OKd76ip sjtnSSKQGm9Ml0ziX9TUWG26+2YRKI1W/bblH1BT9/WUyv5woLzpWyaop6Om kR/X5z4aoGSZDrWub0NNU2l996AbQIm9+JSPjg41zXqc1hmp+Mj/3tFjbudR c19K5H8Z1PeyzXPM/SqHmodpm5o/ZgCFuzOx5fRv1DxWPeXpIAHkOUkF/emz qOkUwrU1mQvkb7nfY2frUfMU2VmPj1ov1z5GV1aqombYx6nH3lT81O1jtpwu i5qXI7iNNg4A6WXakaHZH6gZpav/6y613q9OuTkQU1DzVm4US1U3td9qj1dU EFAz8wZ3KNs0DJ6W2xrnGELNpyYGHE+o+EuPqSR7uhY18xadn+uuwCCjc0rn IQpqvvLMJbrSwUAh8xnpd+Ko+cHCYH+JIAxsM/3AenAGNb9sOpP3PoL+sqnu rilX1Gwpj7rSLQ39fkuXpQgrqPkjIE/w7DPolx277d4Xg5q/5b++oVUDIlnA mE+RFzW7idOH7pUCMe/b9cUgavzE+9yjKoZAPHt88MBeqr9kG4Pod3VANNqi L6FrRs1RJhexo1T+tp35v5S3pag5+S6qYrQR+sjlUxFNmag5dyHP7ooV9DXs 77yXq4maSxpfp7jaoS931e5ypDNqro1M38w/Dn2JW0ezDwyi5tYTHmljAvRF JqQmFUShFr2DQW2bB/SFtVvTr7ehFguny/FTVLwWuizk6miNWuxNUXMr/tAX Lllq/W0StbiH+b2CjKHvZos3r9UIagnSFfaMM0Pfow2Ga1Y/UEtMareNxx/o q22tll/9jFrSpn8+9uZBH8VWUWlDAbUUXPwNj4YCkTcZi6n8XUs1fNvLb9T4 DqVKGbamoZbW/ftSFpxATLJdVRLyRi39t6r/1ROB2F23dMOnEbV2tn9gNXhJ xT+fjeWlXVELZ49fLQmH/rgDHZ5TVH8s2Cf/Ku2H/kn5j6cXN1HroEq0dzY/ DKgKXF/MRdSyPSBAEKbAgBf/n+Xdd1DrmFeRbWopDOTML/YSeVHL7WmX0Q0b GFQu+yWfWolap9+dLdoUh8EQy95SLmp+fPvopMMmYfCdeEhtIw9qhQqqs/nE AcmtUi3fXBu14hKv9x18T93vGZpcNwmo9fqyxjVubRiuavXxrOtGrfL0T4vx NDAidDZteOo1atWUO/nRfYeRi6L3VIOrUOvz3A27xTMwqnWWMlnKh1otO4Qb A4xhND6Mof3ZHGr9UC3ZOcIMo2QH1TmDAtTq8e6V7cqFsfgoUSLzG9TqvxH4 4HAwjPXqHrz/pBe1KDmM7M0I4wqPOyLFqPkYe/8weg8HjAdeLeMv1EKtaaLm Uk0fjL9NOvRBgoRaf9e/nNF9CeNLKU8yf5xCrVUh5/6icJjQsbrl17QftTb1 547K74eJM9c3ztAVoDbd0dimx/ww8ZjO5Gf/LdRmDhY1EaDAxLd0gbIuLdRm T3r9+m4pTCzzsmtXXUNt7qJ98ixRMClidTvvhTRqCzQTMqJtYNJYhLt4Wh21 RUeDOdbFYfLICe/pgT7UlmZkuh4yCZNei29MzyuitoJM5vJkNUyGNueb3ZRF bdXd2v6ecTAZ0Zih6+uD2lqujQNEe5i8SnhRf5IBtfUjThw7LguT4Zut35WT UHvng/mvbfMwGaQwuEf5G2pjRZzp/vcwedL+48m711Hb/LdYaUMiTB5Mslv2 +YLalvNvFIxdYFKjJ1z24iXUtuXc//CNMkxyWKhX3XVA7WNqRE6VVZgYHjqn uE0NtZ10rgk/WoKJ6hID16Vm1HYzkpWj8tWJW/zfd97pRO3Tpo0a1+Zgws5I +ODMJ9T23etn9HcaJvh3XjasfozaAZbsez0nYPy3xRum2WrUDrEpse4chfGk my23afpR+4rT0qkaEpV/h4bIWLShdrT7gwC1fhjLSXQMkKSg9k2vXRcfE2DM qjpKYvMlaicHRSdc/wOjadZBns9MUTsvjqPiYDOM0BhMqdwxQe0XCa8b6hph +L9Ms0Wvg6j9KvVoq8YnGFbyUYyT4kDtqqyMQZ56GDp0p4Dlewxqf3urwNpb CuTk/I19h8tRu726me8QdV7LdP7LffcCtTvfn5V49xJIFbr00rQ6qD3Y8kY3 Jx8GST/vOnHnofYiCVzPZMDAcQVJy/fPUHtthOTTdx/6V/yP6oplofbW1I0Q mzTot1T+SxQgoQ79X8UrDclUPiQ5QHG4gDosKy1xOolAZCSuy3W8Qx2OzYCU PCrfCp75dPrhKOrw0nM9EogDwuif0ZNT31BHiKWs4NYNIPhyNit7cqGO+I7j r9ejoXeBm3/Zqxp1ZHjXas9GQu/tfW6MboGooyj86Et/BPSq8pY1FIShjpok /jx8CXq6f9slv5dAHW05cu/HMOhJ7d33TJ8JdQxUYof1QqDH6ZzqjroF1Nml pTRbEAg9am2EfUpU+7v1W9eFAqCHQ3tXfsYU6ljsCmSIPwPd6w30jcN6qHPQ jGfHPx/oXspMlQ1hQx3bfeXC57yge7PnKy87Nf5jhxxlB09BD2/szGFpadRx stvQOOIOPQZPcyReZaGOm8Njo88noMdHslWN4zLqnHY122vgBD15Mw8NTL6i ju+pIevnx6Fndjr5Opcv6gT4xjmKHIXe/X+TpaQtUCfknMqphMPQW/R1cXWq B3Uuhn4/u2UNBAntihSJWNS5Eh50MeggEDIpqYGpVHvRUbzR5P3QJ38HmQ/e QZ2bNyoSjplT+W3rs23Yjjp34p3uN1LnuQuovRkaQp179568LNwF/bXzpg17 b6POQ/PHhZK9MGAQ4xf49z7qPF7Ien4vHAbSHggzOZxBned2mfnXKmDQce6s hXsz6tRypD9xoPI7Vmvakt8RqDMYezeNQQmGP1z0OnM4DXWG9ZJSwptgZOeX PW+NOFFnnJJ4d9YLRspuW35TSUSdBbM7Cb25MJqj/eKa5SLqMm7G3SyVhHF/ h8wWLjbUZSu6GavwDsa7CnvLrPejLqdLbMwjV5gwYyrRkWZBXaHqmGtxD2GS ta7Gbc4DdcV9r0duGsOkb1KhdrUs6soIRl8N6YLJT060/xpoUFc1LOqyGz9M +ZNl4v/9Q10tuchLv97CVLV61SfFWdTV+3X1ouVRmKa9k3tK0QR1ja9fCXv3 F6bNW9TrZ6j6oB1xXi8Fpq/XcsiKBaHunsHLIYVaMF2rG12qN426+++GB0v+ gOkZ9vM1PT6oewguBd4LgBlRmf/SylVR9/D0xXPb2WFmz/GAym/pqGv/6MLZ ay9h5nRCtHFdBuo6W4WdWbaEmchiR5+L1Pjc1s/7+Y/BTFr+vfQdT1H39ItQ H1IczOR403EaqKCur2OIt4MCzBRSMmh1TVE3gDnY89sXmCkWvJsg/wt1gyuC Tu/xhJkXNE6fSrpQ94JXoEcVPcw8flRUoTeEuhF8506q58BM4tLVYJG7qBv1 KcAt1wxmLvJu0ueKoO6NkLOuQgMw48yUEqOeibq3pf1dkiJhxmDgG/+fS6ib +POMM4M4zHBUnGBLp0Xd1Cg/x/BamO7Pp2Ext0bddA3f47POMP2i5ZxbeQjq ZhJ97D3XYDrQ0jKugvq+TxO8j/Y+gGnN/Zujphuom7/L68hhQ5gan3vZFxuJ uq8yTtvuCoMp23UzxQVO1C07cMq6lA8mNxra3zHuQt3KFY9DCmUwmfPPh8+c ms8P9u6W3NT5PdRwUPTzbtRt9Psh7d8FExe5HtvIRaBuaySsfXkPE4yGNp+2 W6Dun2cSheF3YVzg7Ihj2ALqjq/2bydpwqjAT/WOa1uoO8thPbRLEEZSuVtF VVJQd1G6vu4eFR+wa6q9tnuGulsHswIOtMHQViYDc7M66vE8OtH2OgDIo3eM X2XNo57g62/P2OyB7LnS1JKbiHpiX0yiPE2BNBR6M+UhE+opzIhpCbHD4KRo vddIDuqp0SWwBC/AYCgDA809IuppC2wMthJgYCskmi2NG/V2ISEl6iUMSO/k qldyRr3dx6z8eqjzOPQcu9QYA+pZ+Nbu0Y0AYjHNaJyRC+odvKoqknga+uZq Xr6a2EI925SHf0cPQh/KVz3tuYV6xwrYWs20gfDgfcCnPeuo51RzOTdTGHo3 /zVNW/xDPbe2iYglWugNtGSOG9mBeqcpzsdsxqFnmtGdoa0c9XxXWtSet0PP xVsvJmsPod459p2MdNXQwympVjlUg3qhUi+IJ7Kh+y2ba6XsJOpd0hcur7gF 3d7XKvmyLqDeVcvbSVyB0K1QnKrVZoR6113XvM84QNdiKfvJmk7Uiwvxg88I XT+fLMkTAlAv4WaPoIQidNXEfBlTuIp6KZkHZi9xQtcb51xd7zXUu19S1dS+ Al1Vgj5B7YGol/lZ6anqAHS1ZldyZNOg3tPuB5dim6BrqlN8xY+a//xplsMD JdAtetPApjEP9V7SXlI2Toduh0vdysZxqPeaf4w2LQq6n545oqsxgHrlKg49 097QvcxeuFGdi3o10PRmvzX0ODJPFAkuot77o4bx2frQ82X97AidPep99nl2 akMcejF85xWSGOo1XxHcZc8IvZ+5zlT6Ue21JcfxvpoGwtG9dxr+kFDvV97y FMsfIEwlcU+M7EU94vfOR3X5QNyl7fXhYgbqkcn7wgSo+3X2BCFlcAX1Rpcr rIPCoP/lvh1h7l6o91fy3pacOQxkOns1zbmjPlOwvfsdCpD2e7c9+T6G+ts1 pVXHA4BUEppLCUhBfc6pqdV9y0AWOv+578cF1Bf2vZFCxwrkKSnWix7NqK/m XvbxkiYMpZQXy5nmob62eFTSn2oYWqN/87yVAfX1CVYuuntg2G003V7iEurj cfLCjD2MyB89ndR/DPWP2PDIe16B0Xs2nl31HqjvsJ04/5ERRsc7zcp+O6K+ S/OLd5J3YWzn5Lxlti3qn4wLi78qDGO3nHZZvfFCfS8Ls+PU/TDWPvGzmM4b 9c/Qc8gaqcO4YFCAyK5G1D/X0D17rwLGHeuWmbao+pdMgm4dbobxNqa+Jt9+ 1L+ybnLs1VGYoK1p8ZivQf3oKhbp7X0woZUzR6N9FvVvhv2a9vOCCefciMqT 3Kh/R/dJdeMsTETlHejr6EP9u/P+N+Wo+PpJ8tTuKg3U/6/E8Mh1Opiocax/ 4jyL+hkB9BIDd2Dix0y/llA+6j9WaZs0peLtwYMmDgEDqJ8z9rDy4ROYmPT6 L+IN9b5nBd4xq0owMY+BNEfvoX6Rp85heyr+nu8V9YovQf1S6S2xsl0wMb3r +fPqQNQvH2ge5/oCE5SA3tYK6nvUZN0rP2cLE78jxUtDllH/vYtHdGsPTDRc DTvgHY36n4XVbZRPwcSza/7C5nWo/7VzTeTmJEzcfrx28EQa6n//7/PIUBhM eC+Gqqy7oX7HkeSyPTQwgU/8XebsUL+L60TUk9swwfshvUjjBuoT2pSsNnlh nBL15uO2TtQfvLMo5PwIxktoXV7KP0D9CeY7pfwlMG68cemTcCHqz3xxuBpq DGNrPz1WjL+j/kKMrOXPjzBWIR12gHsd9f9t1ZDv/IExedYWQ84gNOBcmuCj 3YARMl/IkXgWNOArKx90j4WRO4Q7WksRaCAcHF1UvwNGdCdny+6XooHMtLDF JWkYjho6lX6biAb6QwcuzByAoR3KhxasvNFgF3OsuJYoUF54Ox3O60CD3Sof PgVPA8V8zc56YxcaHAzexbmUBuRIpwhh3Ww0sP3vYoWhF5DF3u/Pv2+HBscq 354INwJS3cu3FcJ6aOC2pf78HxFIdHrO3ke70cBT+owtlMDgc8eYvQ0yaOBn XrB07ToM2vG8T5eaRYPQO1J7GBRh4OVOJyVHUzS49OrEmMUaDLgKqVT1C6DB 1faMpLhvMMBzNjc/0AsNbgnxELYHQz+3b1kHmRpP4i7baOu9QExdSu6LnkOD VLcExbsCQBR7x/uphYAG6dFfv/8ch74SOgG146/Q4FE+43meWuizXDMKUOBF g+yve0SOJQJhuuOLPJH6v2AysuH+SSA8LH/081kXGhTtqPXu1gXC4QZle+MB NCjVXuUQYQQCF4tTRj/1fIW9XplLF/R2Z+Yzd3WiQe2lYKesQugtjBKY2zBB g4bMVzQDV6E3tsSjbrYXDb68myiQtoXeM1Jr9Q730aCFrHjotDT0OjQYXlsv QoOfjKf/5i1Cr01w9EXdvWjwR+npg5Emqsx95ZWIKxr0WhFRKQN6j4eZB8bW o8FAoPDwGX/o9Tnz/PKLMTQYSj1+pwig91pVcOqBQTQYL0/TnuGG3hyJIu6X mWgw0/2jW5MCva3HV/WdotFg4R97ZHA59P7jG3gf1YYGa5KWcmW3gKDHpJXp loQGW3tiW5ZcgBDy64TE1w00pPf6GGyoDoQqyd4bM4iGLLdpBMNpoI8+PnWn dBQachTtqq/pgL5jr/YkHvRFQ8G/5WxwEYhM4S56wvpoKM4///raQSB6W3hV NrCioYyxxvGP4kBs4UvzyZNHQ7WoZ7kWH6E/c1pH/Us8GmrnDFnG3YMBBsaz vnxUewaNUrPNPjCw6+WW055ANDRjf2hizQEDxXZrVrePoaF9RsKfYw4wmJZV dn3sKBo61zVH3FeGwW+x4qae39HQfZBJuusfkBhqFp+KlaPhGYWocy7ZQAox j2aOUUXDyLIQ5lOTQDZ5mrASpI6GMe/q9mZNA9lvdlCELQgNb7UwRXXPAPke jLzanEfDxD+H63hngfzunH3MNPV86uDDVZt5IA97pH5ZD0bD9Mlhvdt/gcK6 1XwoXBsNH61oBn1eAIqaxuriciga5tCFF9MsAcWqn4GJ1gQNn3F8Gt+5DBTf 9eoUrmY0LBLeIR+2ApSYC31tvX1oWCrn6PF6DSiZ+5XPe8qiYYVmTtbkOlBe n7an206Nv3bnVK/CBlA+NnJwfaDG37DPUMBjEygdZ//mxW+i4Re76COPtoAy 4JiaZOWKhi0nWpO6aYAynuA8/twdDX/48rfy0gJlfkdiYw41/t+hJ5lt6IGy NKbNrVSDhj2RhXtvMwBlVW6jNYOa//7bi1GfGYGy8mt32YObaEi5B3U0zFR9 +k5/xQ9oOPb01upOVqDMNm/u0TqEhtMvf+mFsQFlbNeed5P/oeHfSvGg19uB 0h94mrG4Fg1XPvoUTXJQ/b2mtOfWbTT89710XIETKJ9vHzDmkkAj2u5/8h5c QHn7fLeUwSgaMQ3t83jEDZScfxrztffRaPtsclYXL1Du5r86Tv8XjbjWe3t5 +IByubHweLAeGgkwyQtY8wPF87oR7YEtNJISq076JAQUPWfVy+/H0Uheib5l SxgoIh9YJruY0UhF14bZmDoftzlwrkQOo5GeJTmqRBzIzYW6Aq5v0OjAxe1B mbJAdpLwzJLciUbW1+2LOuWBbKrWlnlTCI2OJD4Z51YAsnR6B4uuCxqdyNPz iFMG0hRHR/UFETTyKInM+qgKpA5nUj59CBp513zt2VQDUvUtbyd2UzQKanc9 EqoJpPjV912v96JRWN+zpFfaQAoLNjVMADS6PDrfMq4DJA99y6a7gmgUtWDC LKcLJBvn+TN61Wh0Y+vmXnd9IJksq46YL6JRPOvPqIcGQFIXaubze49Gd/lF av8YAkmiwz2+tB2N/pPyXOUyBhKPlmnneXo0ylAt0bPaCSRmU7cjp/zQ6LHB WtDNXTC4ReeaO5aKRrl79hZ9MIXB5djQMb0TaPTcOnHsH8DgXEvQ/RgCGhU7 dssbIgxOdVSLxAuj0RtPGY8QMxicyDqoME99j8rAs1nF/6PguuOxfL9wySxJ spJkFCUVkexz3uEdkgYZZZQoq0JmU0i2lJDQtpOKVFaRilIkZXuHPV76JoT0 e35/Pp/7ee5z7uucc93X9X58UIhntfLKnaqoX3G2tH2ISuh7D0a/lTfqV1/h k1lPA/a4Q8vyiSDUf2+YQ3ceAvavW7/J5lGo/3HCIvBuLLD/wMuqDibqNz6Y zGYReC0y3tOV6YX6LXZprQrNRP5XLPI4B1G/XYwk7BAAHAmxd+Kr1VG/u3pA j9DvHPmaPSc+70R9bmCcW3sFcDZeX5e3RAv1BzW0b64+DJwdnnK3Foej/iir rc52CXAougMVPi2o//PGxT/JWcDZxxZWPpqO+rMLnw6uGgWOj7/EAZuPaLC8 yXWNWDBwno8Fs8bvoYFExLJdu+WBUydAl3f+gwYyBk/OxrwGTofWHWE3MTRQ vP+3U0QQOP8O2peIuKHBBtsHovRc4K5c17BV0BMN1JebGUWYE/0UuzbCRgAN tAOSM5ZcAy5lzJQ8U4kGepuNGsg7gGv5SEyRI44GRj2cvyGtwHXObT488hUN SElRW6rOAde7PpaZ1YEGNOY2h4V1wD0/NbfP/BYamP1tiTeqBm60TB2/qQYa 7Hl6tvIscd/fkM5U03BGA6vjSrxXIsDN7Daay+hGA7s1HxRmCoCbffBmwtsW NHBoPLln5x7gFgb9XHzxGho4X5a86E/wYbFaT+ZbJzQ4rv/qcXEycF/Y+Dfw lqOBJ+9wzy994L76syv3tiYaeN8XEtfqBG75ouvG/DfRwN/mEXqHALfMeZPF iU9ocEbU0vuxCnBfSj7IPmGIBhde/7kz9g64JeJH6xdE0SDM/3aThjtwH++5 uSyiGA0i1WmLPUWJ/D5b/ZpSRYPY7lGt3MdE/tHxM/ltaJB4/dqRwf3AvR6u cczmJBokM/Suqf4GbkSFQESYGhqkzXdXu6YCN0hv8NcyAu/bT8L/e2AE3ON/ M1ZZC6PBg2OblTk9wLWW6KmqsUSDXLkmS6VQ4JJDXUTs/NHg0ZfAsMMEH2yx +bzBjeiHUr0abpcXcBa8/Mb2q6BB2ZiHpPwK4PR+8lmnwkOD1/fEqQefEv3R XLHVXAYN6pfZP/gxA5yrW9zyzwyhwefXfC3St4j+KrxC/iCIBs1+uQIHCH+8 X2ef9qAyGnR2/Xb9ehk44uVGkne2oMF4UbxqgwSwryxd+H4/FA0m6x9qji8A +/DC8LfJB2jwp7fcUGIY2AZTdQWsUjTklx3eY1sNrPFcE/4pGTQU0V5sd7YQ WPWvhxjcR2gotlv2aGYasLKS97fpf0FD2Uu0wN7TwDqy80VUDgsN195yCBU6 DCx0XmNRYIKGyiV+sermwFISfGCb3oqGal9iknfrAWuJpcPVTwVoqDF07473 euj5PPxX4mgAGm5f8jLvujj03NruZ+LvhoY71zYWl8xDj2fz6gN3XqOh0c6B ytZB6CEpSrVbOKIhad/Ch7lv0LPG/Wjf2hdoSPOSalZ4Dd2zc97al+zQcFeE RhepALpZCpW92c/RcO8dyoBLKqE3ZK6btl1HwwOvDv68chm6yzfcEv2TiIYH v/nM5flC97OwDPupzWjoxIsSaHCE7ifHt4y+6EBDV+E7KybMoPv5IpeHMf5o 6KFcKrdKF7rfBKeJzFeh4Smjz+t1laG7eXrjU2EiPz/rvq12YtA9lqKzvlgC DYO95/XOzkKPmI//fn9lNLwQs4qc2Q89uimivJjlaBj2UN38zVfoOSZxiCUx iIaRVSTr3kpCD/Wz//LC0TCuzfaIUB70dKxUIVcT+V37dcpTPQVY4hRmo8wO NExdHuG/OxxYumlv6+Sz0DBDLeOitzewDofeelciiYb3SMVR1+2BFTf8fcvH VDTMPvTx+nMGsCqrxgyO5KJhgT8ng9C7rP+Gm7fYCKHhk4Q/2fOKwFbf+3fu rA0alr1VKyfNAPueZR9/yDE0fN1t8s6lF9icvKIo+XtoWDtzoPFKI3A2PBGd 99qLho0aYb0NucB5prCM/EQPDXuTWMvtDhL6SDr4zJZKNBwqnJY5R4Ne3UzB o34f0JBXJ6Z0ezv0RmpXKP1NR8OZv0Y7+pZC35aEi4pJeWi03DXV0bsM+oN7 L1+15UMjiYuP3a7nQP/H99+Up1vQSObmO9/nSTAgn7O2PTgbjZQaJiPmvWDg 1e73OnxlaKSru/dxpDwMdjXsG7zyF40M/tQ8KsyDoTVDVw/VbUMjqNDNb9GD IRt7VdrHCDSihuTmzr2DoYRJ/+9pDDRiUuVzlK1gqDbsinF3BRpZCCVkMTgw NN2plfhkCRrt/8j34JQ3DKs2k6/4LEUjm3j/ezcWYHg/PPiZLY1G9vsG75TH wnDw7OX2ajc0OiJ1KJMrB8MZPUFrz3uikWvr5wyRHBiu+JR8qGozGnmkk25p 6sJw2737O6Z4aHTKqfim9VsY/mmqlqUdiUZ+Kmop5/fDCH9awtGrBWgU1J92 4z4LRlbFUnSEy9HofN7y6/UnYURB8I0k+qJR6ImQxIk5GFH581Qh9BQaXdH8 dVUmEkY22Cys+SqLRjGTx+L/78eVZTrza1ah0dXStliXBzAit537M60QjW6c NY+O2Q4jy7MkS1V3oVGaSVXkk9cwPO/Dnc7VQKPbfNsjWi1guD+N9J5mi0b3 ax+GL3TC8CfVjDi7EDTKiZIN2+ABw4+UBqJ3HUOjR+Yxl3bNwHBUWsL92Dto 9HTFwkXfCBg+cu+Sb/QYGj1v9jl/k/DTO/asvXE7Ho3KknvPVt2DYf6XmtT1 zWj0+qBNcL8mDH2Z79skHoBGtWvrg0QrYShF/0XU3Rw0+vygyM+uHYbkNJol Xk2hUbObim+IGwx+15qdkVqJRq2bk72zpmAwvuHrZ8JfGrGfnvOalICBqSsP gw8Vo9Hka4ZrvBn0P6wDOYW1aPQnrOxocSv0m+vwP8tiotECfeuRjmPQN/Ez uvfqJBoLfZZ03BgKfbrutUdWfUNjuQ6WdfVL4r4r482dMkbjdbctrYZowLWT d9gVcBGN1zu/s1zxDTh/pjTPJdxD4y1DBXvsJ4BjokMS8HBE4+2P1u0Ou0DM l5q+du5SNN7pfW1XnijB50V3SjQfojFOB9GnCf/cmjHr3jSKxqavRmhrnwMr PGalVZY2GptdcKRSqcDSVgx89pEfja0EqJh4GHq2xzvw6cWhse2HUpNSHnTf y1tYLX0QjR1i1Y26z0G33LIzQvGVaHx8lbj+5lToUrTfw3lG7O9ZebVh1wHo fIr3PTa4oLG3x8ojnhLQaf7C3eDUMBr7SyX+jv4MHRNrhHY4LEHj4DcSUXkx 0JH+SrWnxwGNL3hdX1vPgI797NP3jWXROEx21ZNhfuiQ+O4gNR+Bxldqkmgi b6C980fXBXEvNI49Jdm+6QK0P1GgvqqLQeNEuRsnmYbQntAh9GZSHI1vvJPm c5uB9mC9pFbGRzRO80lOjiyG9hMHNXKnB9H49loZ9RwfaPc69Nol6Qwa3/+Q UvlhK7QH7Ps2sVwZjXP8ZPcPDEN7DNPzcL4cGj9alzoglA3t+XTeC7o6Gj/5 uPqsmgu0f9+lJiK8Do2fB6StoCtBhyhzu/AKon5lynL3j3VDx27NS3KXzNG4 quHWzgjCn6cOdHLN3qLx2+A1H7NsoGPc4qQVP1GPuvXpTu8koXOvYQPoIBo3 NMr/6muEzrIQyWObb6Px17MZkQJx0KXJnZ3JHEPjH2oK8hvMoKto468gqRw0 7viaWWQqBN36InvGaAR+rAvrqC5viftMz9Mu8AAaD7Uoej0wIfyyjJle4A40 5oXcXVQzB6xDa+8k3FmBxr80lJK4L4BVslITEgh858OUK1S0gH26N7gsdx+a LN56fx+ZB+zWPWk9WeNoItCu0uecBxyoEg854oYmK7Q2LL9H+B3prLmuhjo0 UepRc1SShd6uFe4qjHg0UY3O/g9boG+/Sgzp5E002ay7MeLwNej7IND4a50n mujEbSq8swz6SxYXba+5gyZ0w83/FBZgMMbOW8rbHU129RdcNymDwQW3N/5r F6HJ3mtb1ByDYOhUx6GvFDU0sRvauifjJwybl9uFViijiWNSIbeiEIZLkyO/ qFejyVHcFtjlASPrChanJe9FE68UzTvyvTDS72Ms2M5DEx/yEx2juzBKTRMR 3XwDTfx5Wh/sHWE0cyIx4NRpNDlz86n9OTkY/b1zSUCcN5pcNN0+cesHjNG3 K06HhKJJ2MSz8LIkGEvK9dTcz0aTyHQd2Y69MNZ1fNfB0nA0iaMXF8wtB56S vr63ui+aJP7aQZKrB57TOKux/yiapPCrPlZoBF7Gv0NOwTpoki4lu1b5O/Ba HgpQSQFocldVJEa1E8YFn9Lh+mU0ydKd/aPOhnHNv3k2CkvQJJ8+enxrP4xb Oc9r3bZBkyLbrpbtozDuXXNKeWINmpS4f6Ho/gfjEQIlH+8poMmrM6+fGMzA eJK0nPb212hSFfN0nckCjKf1X6VnnUOTt+n34yj8xPPJmifmB9Gk7lHSHH0p jF8rUBxz7kaTz5UR7rtWwHjY3bYsh0A0af4S+GOPJIyfsO2l3jyGJq0sd1NL ORjf0zP13PUdmnT9PPjMRhHGN+8652tnjyZcPnOlQ6rA+1dypUygBE0GVxkn OGkAr8G4cptaMZqMrd/69+h24CWJYMlZol/+26HoeVwPeFbk+B+E9DeZpq1s 8zQGnthq17uyP9Bk3mYJ/RQZxt5Up6nQ7RAWH58sOc2AsVOB489kmxCWRf1I PGsFo68Ebp07FY0gnvbh38WDMGrjdspg41sEqfxXJ8IOw8j4yISiijHCuoYM ZowncV8mW9r8LEDQWXl08a1QGNq68OZf5V0EfWWrU7cjYbDwywt66gMEE23T rvvxMKhxXT9AzxWBcWDjy/ybMKByucg5pRDBPpXnU1YEfUvtlqxd5YZwJLen p+o59Ib/ND/Aq0U49qppd0059C56Onb+qwOCd2fxpo/vgfOXPqG4qwLhsmIw u6Mb2FJ+Y6SVwgjRWp57enqB9UD25yzbECGBbF/BHSb0XVejVzsL4aYL3Bz5 Dd3flB72rKhEyPTXEhqfhW5prWaRemL/+xHK/pOLoMt5+IpA8BGERzkC++ZF oVPhsH1MKoHP0xdTrxdJQEf8suw/Fy0RSusGt/LLQsdSw5WdDnMI5e1t6cIK 0H7dvNmvloPwZuTjUlEVaN+UlLPjtBbCu/nyIPGN0NZwKM7c+zvCp+WP+iW3 QtuFnmg1wd8ITQq3rWR1oM3EMlQ+FRC+b7taLW8Abct6VriU9iO0D3WvV+FA 61BqrqCuN0LPg80Rm6OgtbkySlXPH6HXMWhQWxNa6w9wlxxKQRiSfWdm0Aqt DYe04o0/I4w1ryogh0BrZ7VxkZ4+wn9xR8TMNkLrTID6D5/TCNP0Qu99jdCm aBdV3nYcYX7x3Fe7QGizovlYLbJEXOS4Pk0zAdqSxDIaN00h8pXt2qVzFNp6 zt54eNYXUUDWZ15vJ7TrWnxzK2UhCvmnPjJaBu2ppDnPnC+IIl+rCHcAHQJC QhPSGxBFt/WvoDyFjnOM+PfSQYjiw9o+uw5CZ+T77leXzyGuoh9U3rMVutbq bZSKfYQo9SCk2ZIPul5FRzg46CPKOX7ecTAXepZP1f/NbkBUaXb7407obf7W YzTnZYiqmgl5Jz4B2/Nq99rwJMSNcc8P+dwB9rcMo0npdYhbGEsqgunAKbiY O1rjiKhbnnkpMhl6PVo6TxL6BfVX126P9YDejvul597OIRoGjHCvmkCfucXp bXFrEFFL3zSlD/o3z8eF7BFCZD78JpKtAwOdO2LVaPmI5nyzr/KFYZBy9K7r 9R5Ei8NKnoWdMJiz/ELWOm1ES7mTDc/DYMitesfyD8cQDwTeuPDKGobeMq+L cz4j2nwr31apDsPyNPmMb48R7RNEEmu/wvDr4rCVXSsRHUc1yXVZMCL64nbP G1HEI0zrXw1nYMTKuujLrg7Eo1nnHzRZwEiK06efLn8Rjy15cKBFCUZack5P OR1GdDv8UbB1EkZXCOe2jDUhelT8LO38AKMU+xV1f68inlgj68ZKh1HfS7Ox 25cingqC1b3eMJrucPl9B4GPT4tr/SAVRl+3PCg+uhrRb3vs2VFZGO1p2fb5 6CfEgIRnGuOjMPrHbDy7JhwxaLSt61cVjC3f9KDOfxPiWbNF8dPXYGyNm+j9 p0T+57PVYO4YjK1fxI2pikIM4d898c8AxlT/436I/4gYesTv3hIxYh3fPKkU RAz/aVn1RxDG5P62vFYn+vHKpe1dvAUYW6a5+phoKGL0ypVzvVMwOjkcGd1U jhh75+fq9nEYbTU9WC92CzFBs3Fn4yCMljgM5Bt4Iia+fnzgHQtGY60j4twJ fJL2xp8ub4VRB2sPNzEuYjLrROLTRhjdGBV8+cQ04k1v88c5H2CEp6jk2PgN MX3R5obM1zBSuEe1lrcD8e66YZHoJ8T96tBer2GMeP9xnVpIHgw3vhrapERG zIIc0wBCn5/Py1x9nIqY73TskvM1GPp4pLH2Xwzio3HT27bRMOT1+uC/l+mI RRfXV1gQfLu0R+7F1ChiSSbnj8FpGDR58LmXY4RY1e3kI2EF/XEZV2mvmIjf HGwPV0sDV+rRNde5TMTvYzsvvBADTr6W5Q8vTcS289LphYLAIddObmXeQOxO /9Z6cxrYwXsicZDAg63xbCphAtiy386cym1E5JZfl7w8CKyyqO+Vx4n+GOzc t9enFVjLQseV0mYQR7w0Tx5vhB7foIfJAt2IY/MrYh0+QPd3Zc23fvcRJ2J4 eZavoZvs0tqhTdTv15qGD8yX0FWa9irHnej33/kF/fAUunSlgodIsYgzhrH8 O/Kgs1JB9FynFOLsR0/lzfegc89kXBWHqN/fQ2aoROjHkUY5jbW1iP9GNjnK XIOOxO5ne77kIYnvrPC55dHQQTbV2FjxHEkCSwfSloRC+991awckCpEklPau 9M9ZaK++0CcZdAFJS9Uftoz7Qfu1E9mSkm+QJPoq/FefF6GHR5wGjzshaYWZ y8oOQt/u482sj7iEpJXtlG1N9tBOOubc//YMkiQ9lHe/t4J2Q92aZ7GmSJKe XexZYQ7tYJS+aJs0klZHsaKeUaF9t/ki3XMsJMmvrsrONYZ2V83mX67GSFLI zay9rQvtEY+XG/lXIUlJ/zz3BqG/n4QuHJn5iCSVOofFMarQ3ncky/jhViSp 2hmtu6QAHUp/BINsKpG0sUtZ+HQhdLjeupxkdQxJm48I/TxmAh1FUz/up/sh aUvvWLvtZ+hckv/1m8xKJGm6NdfscoBOB2tjWth6JG0ffVlgPAqdFXFafTd0 kLTD+/YNzXPQpcKNvHK/BEkGQZ7HpdKhe7HqxLmWF0gymt+3V1gDugM3aoWG 7kcShOzUmy2D7p++koPu3UiiRi5Z2tMOPRM8oU3v1iHJIiWtMG81sAW/CX35 0omkfWtCUtJzgG0u3vhxF4GH5e1jIQl6wE4MvMzNf4Qk2+zt+/1sgbOmPpq6 /CaSnJ/XTZkkA1dtwWLlaXckuRo87tFS/f/viy18S5ch6XjljQ8qz4GbdTAn l1qMJK93R9KEW6B3rfuurS+WIumUGT10zgV6D1RVzlhqIMnn8xbPsUnojakQ 23YoAkkBLX+MmyWhd2KVxg7tBSQF2/Wo1j6APgXVCourbUg621W74oU29DGt O+0XhyDpwpG8mbxq6POJTN1y2QtJIX1X2Rn7oS/5geaJMgKfMPeA+gQO9L24 oVS57S+SLo/ZPwv1hb4fZluzl+ohKdKHnO7PB33/lbjaNJKRFP174+XjidC/ 9JOy/ngSkuKCxU4eVIT+dVdW2L4h+i1hftLGvAj6NTmKD0sTkXQtpB0JmdBv 0hmnbH4ISTf4X2/SaoR+ZtDbeV0+JKVEZkmsPwz9e8td8rcQ76eJxsxJj0O/ VdEGZYdxJKVf9ekVuQD9B5zq1I0mkXRb0ubTvBj0728XmlXdhKS7qcYlvEzo 363pvzneHEkP5FUy2Vuhnxp4TEV4EElZd0SuNFdCv15ty/JMMyTlqox7v9sN /eo7SNelifnLz26xe9EJ/bLsBCcugW/h5jJyvhf08w8SFHQVSUWP727OmIc+ nueZHvfvSHqmfUXyaiz0tcTExC3KRFJJqdffMHnoe3ViVWM7Ma8vDPf3++dD X8aOYtsuAs8KisKLg/XQ57C6N75hD5Kq3vPf3X0Q+gyuWBa8uIGkarPhaBiG PumLsPLXf0h6v/+5/QYR6K0v+2H8+SyS6vLNYDVxv2cJXBabqUDSpyXdysvr oTfkjmnk73wkNZYIDEzFQq+O7eS0Rw2S2mUOnP4gBtybr8wNKXZI6jw1ZF1+ ErinT348MEVFUveH8/pFX4C7O7rVzmECSdzgB/9uXgXuksD/1Be0kDTaMRlD +H9O8CGXbU1KSBrXiTzp5Ascuyvhh2e3IelnnPw+y2bgGEx3PlBxQdKUCVXG KAk4i6dHApYQ/TST/GNW8zewBwrK222JeLPjnl0brIH9eZw9c2weSf/uXL8v JgPsezb3La0OIXnxH7UIviBgx19rvjntjWT+fWVuU63APhd1urI5CcmCeXt2 jegD24ssqnbDEckifNytPWnAdnijXxsqgeRlhwJXNs8Ce59Iod81KSQvL146 +eEQsGny2mrjBUgWF838UV4ObKPp/J7RUiRLuG5/9UQe2Dturj363wUkS1a8 y/j//1/aNlf80k0AyTLSB0NudgFbQ7Zxj9AKJK8+yTsabwLsTYNLv94xQfKa 96G00Ezi2Yn+sHMayQqK0psCFoCt7vu6b6MHkhWD8kQ9nYC9RUr6v3gjJCs3 GY87vQa21s4j/iv3IHnDpqavVorA1q0jlZUtRbJaqGsJIwTYxi+mfP3jkbyp /U+qERvYpr+8b9x1Q7KGdtxZLTKw97i4X6XpInlrrJLjhnvAtps6c6pmI5I1 e0tIckuA7XpdR3ryOZK1jZnrxVyA7auiZzc2g+SdPO+h6Q3AjitLXdjKRLIB nf/TSASw0xPPJJZrI9nodurjnn5g5/8e3FGSgWTS3tf+Hwi++7TI5NSzN0g2 eya+OE0dOGKbCuZE25G8e9n93vho4ChEHH3RzEHyHped70NHgLM1femEjzKS raSc4jwLgLM7VnGLOAvJ1id+eR8WBY79DqWT55ORbPvuiqXVCeB46JwMaDmK ZIfAx6uNtwInrJPZz+1H8mG+OXeVZ8C5Si9bNtmBZOd4+sulesC5FXH35V6i fq6rk4QnKoDzMFTC850Wko8/YNn+IAOncP0/+mciPw9NjZwKwn+V2FuWZpsh 2assaOaBOXDKZJZ/Uo9E8ilaLSO6CThVtANUrUwk+3xdmepjDZw3XVcVs6OR 7OfgMGjTAZzqr36Fy2uQHDCYp2dymHheK8v7Mo7k4NNTkev7iO9L/1ldWIXk s//Ircs8iP3vKL748hfJF6IT1H6OE/G//YrWZCD5klRHYKsfkZ+tt7K/KZLD 7qq9r5wl8t8mQTrSieQIDT+ZhxeBk+b0I5u4n8iRpa+Px/IDJ27SsHE6Askx FNFSXwLvizOaX2s+Ijnus52Q3QrgeJ+ROqw+ieSrdg9tgJg/p+icRUVEva71 /sxWXU3gbzQdqvMOyTe8jadFM4Gjn7W3JDEMyWkR35Pbcoh6LohE7CbqnSGh 3P96C7BnlE79vrcGybczTulmPQU2y7U0iLoXyQ+Khb6fJuan8DM3/HgikgvZ +tLLG4n5Gmh3VniM5Cdel10nDxDzUiezP5GN5GfTTSXt7cCWGBwTQzEkvxTz OJBN+F1W2OiTGOL8ZTefP4x3B9aHPfEtZA0kV27g++3HA1bRrCM7jcivxvDW DdIMsC7HFfIqqpFc+26gb+N5YPnUqnWlEPPyYb/OjhV8wHIKPvrxix6S67tC wn9fAZbFl8kmsU9IbnD79K1TFFgwZv3OmpiXL5Or11cnAmv7ZHVFcz6Sv150 9cuVBtZG4SXeXtJIbln65G1COrDW0c2+/KIi+ceNBckAJWDJNgmPriHwblcy c7HPApbkm7c2S34jubMguZiiAaxV2pM600S8np1c/k1PiHXoVAoh+p1ds81K XBdYMnMeEjsrkdxrcfb+VBmwFM5IO0w5Ibm/7f2vLgSW2qd3y6WEkTzkKkmp qQWWzuLHN/TUkTwycfh6nhmwKOvJir8IfuSdLeBe/QKsA8YrLq0jzv9T8I92 oBWwPPa94OoR+f+6Zhrm0AasUJdu1/MEf06tTWymOgIrI2BlZtV5JM/kdKuo cwk9fOnjlkVXkDyno356pRuwOi8dPST1DCmLzGokun2BrabVtswnASl8eRX2 7z4Ae/fIN5LPL6QILC3NeqwA7EAh5St/fyJFpL7A4GI9sBtl+k5U/UGKqHpW uBvBZ/84z/zOfECKWPSdz3sDgKP1MpN/fBdSVpndcFZSJuZXgW6sVoAUqbyE ApEg4Hz1ll4voooU2aVRv39+Bu4ya8b0s2mkyNdfiKo+A9xL+/tC6iWQomp2 /MnRJugNbhH/KPUGKRvzjsyZq0LvyzsrauwEkLJ5qb2pzjno/VORJfrbEinb 6ve2CWyEvjN6pec1iHV9M4NFWSHQH8g34tabiRTDPB2z+O/QX2zyKfHpBFJM lm5NCtSA/ol9CiMWt5FCrlfZSG+FARf96oN1G5Biqr7WV3MrDNx6cq84yQ8p 9GiZctlwGGiiHM25S8QzNxPdM6QJg7qLfHZ+WY4UizzBm00RMOjy7bxgwShS 9i1dxH1J+OlEyR33V+gixdJ9bsu97TD4KsW3+A7x/YG634HRkTDI3vnUOaUS Kbabxt/4dsMQf6Pxlv69SDkYNbzskA4MbTBh3N+RiBT7od4DFMJvkY/I1k7O IsWJ2X17MwuG7OUUpRMikHIkt3VIUheGfHcrSHNWI8VFpHn7fCwMhbOMtBSJ 74+5N5zr5cDQ9fKN/aGTSHGre/+uQQ+GMj9paRyMRIrnpmrxkngYerhg0xOV hJQTUeUHM3phKJfiufO9JlJODT1/EGEAQzkJshsyG5HiyyzinbwKQ/d/zF15 UoQUv9x8Pet+GLol82SDxx2kBIo8DAUjGIo3Yzktu4uUYPfbn9QI/3jehxZe R/TP2bo06RWDMOQWV/nh4hBSLmxKcpoxgaE9WfSqw3uQEhIVn8tKgqHtbwU9 +X8gJXQo8teHYRha+YtWo2aPlMvMMOMnCINjlOAyqyakXMk9f+VmMgy+fa/5 ZiQFKdEigU2XRmEwNUm6tNoVKbHuvms8yDDoVt1/pLsDKfF1Xq77U2FwxylH 28dKSEncdOyxAQ8GFl5sP8NoR8r1qMN/VKgw8LZydkCXhZRU5oHYX+MwQE97 H9BwHClpGdZ3WzthQFDW9UAb8X76T5vnFfXQXy32OyjPBil3b9qxrjyAfm3Z 3iVL1ZGSN+Sos+Yg9C7wnALdjyLlkbETcxEDegt9QKBsHimPEw879O2AXofF /oc0nJBSrO985bE4cF9oJulf+4uUiqhjHeRa4Jxg2j3KXoeUqq7jE2pPCf/x SG7t2mSkVGu5C4jeAXb9X8ve8BdIedfmufX7GUKP2Cil6mgjpW6LF6WMmH9W W8lVLjFfHy+dsL1zAFjJBu5m34l+adx0KtRDE1hCIeoR2WlI+XreO2WPAvRc ebx44g6Bz7cmnwKdZdAjwHFZVfQbKT82+L6RnYHuaIHvKfpaSGkLPt3ytx+6 JS/ZpES7IKWjwW+Y0wxdD1/q9imJI6Vbyf/f+9fQZbL83X9coj9Y/oGSBYXQ 2dUhrreIgxROXdCmxFvQeTnoCqnuFVL61gabBERC5w692iKFB0gZ8DljeSgA OsbcaQfWSCJlqPasGx6FjsL96wtjjJEyuvrc+Q17oSNoR8+YuzlSeCfOX1tK +Mtd+5Qq9EqQMvHmQtb4ZuhQa4+cPUnM+y+pi2XfZKFDjC8o3JGHlN/uIY0v BQn//d+wleFlpExXXOrLmIT26S+0f497kTK7MnQ2lA3tc8Wk+mAC33nX8BXH P0OHSOnL42uJ+i68vLzevJzwu4MZYb7/kLp4eYS+Vi50UC3aDGyCkLrkyBUL 6WTo8B4hh2jvRapASaTzXDh0ZJfGpweIIFVYJCqQ5QMdg1m7Ko81I3WpQ3Rs rSN0at/rKf31C6miT2Lu5plDZ2RiTJ9NMlJXCMQ+T9CHzoE9AdT0TKSutIv7 6KcKXXuelpZrEvFXPYpn2UlC15voUzE/4pAqvTjht8li6DbK7OjxuYdUudxr 64Q6oWf3efkD52SQKv/3uvZoPfRwnm92EG9E6rp9SYymUmChgL2r5BBSVf4k +966BqzBRtUd+4qRuoWZ9m4bHTjOZSqbOy2Ruq3RiiFI+OGmNS4bh7YhdbvN 8rouD+CSKYm+O+uQutMlpCEmHno331177xkFqaSLx74NEHy8cunrhsuVSKUK KlpXDkJ/rP7RZ/6bkEqLbWu9MQsDQu4OWwqWIXVXmnkndS0MzFvZzUdfR+qB ku3cOy4w9F00ZI04kZ+t4ahrYAAM0xenL+9NQOrB6ocDFpEwXLLS6zxpHVKd GmWH5wtg5MoGn4cRo0h1tv7qRfjDkeFLFytniP1dumJ4eY0wyswJC5vQRar7 8MJ/tr9gdFZ1cy04ItXL54WfJj+MmY0mehz+iNSTMz5TQtIwluwwclrkHVJ9 Lm4O7N4IY52G8S/kyEj1E+j9U2IAPHmzv+N+hUgNiM08G2sOPGvrtVrpakgN XmXz96gj8KI3Oy/pmEHq2bSVFw28gVea8a/s+yKkXlD8uHhlKPC6I8Ql7Y8i NSQ7PHQwCXgLr0s/P2xCathWE/6qLBiX1aSb6Msh9XLxdETyCxhXL7e+a5OO 1EjDJ8In6mFcd1/3Zt2TSI2u9oimdsK4AZeZM7UHqXHM9aJreDCu53alcFEe UhO+dMX9WgTjW3sk9fQ+I/WadcqK+pUwrsBceM24jNSkrr2Jd5VhXPDJSKG2 NFJTXJauCtIB3uCGlVtp25F6c7jmxh4a8KpLtx3zvYTUdJ/zMqq2wEu+FC4u dAKpmTO6qX89gHf03svzrz8h9e6FCblv54CnbtIktygFqQ8EctPz42Fs+KKI 13/E91mxRxVC78DYg8Sqym1rkJq7Sv6O3VMYs83b7W60G6n5N///yx2MCS1i /VvQQ2pRNnND9yCMWjrNPLGnI/XZVr7sklkY+Tl+YtpQHaklxeWb4kRhJKbe +VImcd5X1du2GG6D4afp4XVzp5BawRh8LIEwjPoH+ESJ/Kq+3NMc2g9D9V7H dq2LR+rbLimdlAAY/L74orieIFI/T88Z/qqE/m8H6xotWUhtsnQu/XQX+u2+ z6msLEJqc2G99sNw6Ov+c98igqhnq+vNzTZM6B26cN1KVwKp7Oadcq++AVc4 SYkj547U3q2ZyddLgZO0+0LlUeK8/dGCEl5pwFE2XG6t7YPUEWxZqkD4S9N+ s5kpYn0s3ejyNJnQc/qUKA0in/HpB4sb1wPLfzhFmfwBqZOFvn8uDUOP2+9Q DZklSJ0Wafc/2ADdz1482ZfFQeofV9JP7SLoFiJz5GXkkTr3OveE6DXochE2 v+G4H6kL8uKDff7QWR9Q1cXQQNNFgUEulbbQafDb6oRlPJryfe1hpRhAR0mv ysYtm9BUYCvd3nstdBimL/ZYn4emQlGFrcxF0P7pJNnc3BRNRfqkrZS40O72 4DxDioymonj+y+w7aBcPOehqFoWmYrf6djXnQNtbQxdZrTY0FZ82f18QA23h gt0/XXvRdNX+Ysrlk9C2d1m2EaHfTKUK11Q57oW2jR6S2a330FRWJMxwpza0 LdfvFbjbjKZyLsOl4tLQuhCtZmz8C03lX+/fPvgHWuePGProBqHpujUvC990 Qptgea3LW1E0VQpUUk+rhLa1icVKOcNoqvI1Muv0XWjDT8xOma9oqrplQtk8 HNq8925efs0dTTdG2WRuOAZt+TOnyySIfNV7q1YvMKHtV8rX74wFNN2Cqjd+ aEA7bSb/4ZZiNN12K35l0Qpof9hlLF1dg6ZaU7/jIv+DjhX/Pg8EbkNTnf0O Ike+Qcdlid7Thwh8dR+9vWxQCp38b/zFNcrQVF9EY/GqNOiMf7ypVsUXTQ1d rp8bPQddykF3tkdPoSmucfbLJEO363hNz85baEoOqJsI3AA9ks8lFC25aEr9 quW1Vxh6PkYvHcUzaMqMWuSyuAFY2Vkskj+R7/6pTEtXgr/3jHde2uuNpq5N 7ds1TkLf+EkbyXvn0NTNaVZu2h/6T56+ZnCbD009eHJLqs9B/+jlu8wI4n3v pXYttjEw0LvzssxuDpr6pgZXqlyDQQexlGi7IjT1V72ZxUuDweYjmTTtD2h6 htwWFJ4DQ6WLXhq9j0DTc41/jux5DMMqyWvJV4j9LjqtNpN7DsOxBrknDlih afhZuzVFtTCy93hUsxmB1xWRYP6zn2Ak3+pMCjihaVRK6hjtG4wulmM43zVD 0/hnrZWdHBhN3+jUM3sSTRNJM9nZQzDKmqH3nn2HptcbZa/6TsDYuqKs+HGi f5Md9YKNZwh+UXr9SPM4mqaO2joL/4OxaKU1vuU30PTWmSCzb4IwVhJ1K/IR kV+mcKr2bTEYaze8HWg3g6Z3kl/Ie0jB2MxaKevl5mh6f32rwA554K1YHfPE n8Dr4dNp3iIV4K1TNs64Q0LTHJLM94/qwNu4dfVofgia5n3ZWZW8neBD/dor wc/Q9JGDTc4RfeBtoKcOKyaj6eORwEQNBN5qp9S6CeL8T4NTgqcZwBNMcB/f oYqmJUKlztV7YGx0GPcJEPmW3vixK84axj5F1l2OIOK9UpnWsXWAsYdREhlK zmha/lRmrYoLjAWJuYcpXEHTKtwpwPOCMSrJYPHAdTR989ma9/I0jC216I/j 80LTt/YBP8LPwGi9e/rU8aNo+m44+fWeUBgN/6CmZ3UWTT8Jfk/suwojQ6Tn uSbE/H1OmjpTlAIj13tC7PdYoGmTivTRs5kwov9OU+AKwQffwXrHykcwHHSr eSd/KpqyAltajeth8KM549d2ITTlCvx+I9wEg7azTStkifr2JUnlNhP+rDcx lJFA5DdcdOCs+wD0Tw01aV4g+GRysEUhmR/6JCI+X9bSRdPpgN9CR5ZB782C b6d5RD1nBSQnNCSgV+kh+VfEPJr+U7J6U60I3B0BL+y/fECaiN03F54xsAOk NWPNrJC2rPwevUwa2JKqTYz1OkgTW+etfoUHrBJN4ejDXUiT6Fs2oZQJrMXi f6VlZ5AmRW/7yguAHtscL3O1v0iTycsuKbOA7ucNXrdz25Em7005a7UAXTGO V1c1PkCaQvNKR6Xv0MXnrXLgmRHSlHb0II/Qw6EeVacbnJGmkvpIpSwCOpcl /HaNiETahrmzgpGO0JEpp+WkNIS0jY6MoQO60GFgdMuJ3wxp6m+kPymLQTtL +a/vbTLStqj0FvL6oT3h5+Oqm9NI2xbxNLGsCtqZtdF2hrJI0xoK8YtMhfbl zzr7LiYgTcfcwuaAN7R11HuJl7ogTfexvIEyA9qKV/jV/TJBmv7KYflxRWhL vlRiwj+INEO/0n9lM9AWJns21GEeacY/LnMiG6EtuOZlZ95dpKGBZe0Bgs/P eCkmPE1DGjlDKUc5hODzX8Yr6/9DGvXfePS4LbTd1M6MnS1DGt254kS5JrSV LvoXPhGGNGZtzN4oYWjr3uh3Zm4CaeYb7bQPsKBdzPu2y3liP4sYNWnlF9BO vznw9rYQ0vaOTc6MX4X2KMdS3aenkGa5t7qj3A3am0+/0t4vgbQDz65WRpGg Y0Pk+h+GakizlXa8ay0HHRdN507NINIOBmuEKxN8zNrvmPCqA2lOJh+Y5feg 8/m3l8p745B25G6yRtQZ6Np80Xax80ukufC7rLDeT/gbzoXBBeI8bnX/vk3w QXeeVEnvNm+keWo0lJa3Q4/2olvFxkuRdiLhVlrUU+ipavcu+y8Aab4Hdh5W cQaWTwW5QO4h0s6yTo5Y1wD7XuRhvqs3kXaBYvRZJR3YE6tMn66oQlpI1tKi CT/gGD8q1BIiznPZMysgej1wWpUErQ3XIC1+qntxRTj0LsneZ565CmmJdvm9 0fbQu2vvLScPT6RdLw9+b6MDvYl0zWqDH0hLDZWKneiFPhlbXqe3PdLSermn Ksqhz0bm7o2kG0jLoD/ZH50EfUke/xm/J96/t3y37HpT6BfaNhAtLYy0By5K tWPF0G9olfzEJh9pWa+mTpeuh36vm8HUgiKk5a78qBSSBP1pnM23/hJ457vd /mLGD/3vJFnaOW+R9qjK7/wqP+jnyXmxJgn8i6SZ6p1cGJDoPtDeRszj0xNr W7P2w4COxfdr9HKkFdf8F3GqGgb22w9aZ8wirVTuvY7+dhjwmqe9XahE2kuf Wxy+uzAQruLL974QaWUfvBM+icNAysfCWvMGpFWuMzW+EQID2T/51bNfI+11 wOoRxwkYKL62031TFNKqP/FubnSCgYqiWvsCAr9alRraz88wULO3bfbRDqS9 P5MyWWYCA7XBpqVl0Uira/K6F14IA2+1vYetfZH2aSNprwXhT6piduuNXUPa 54tSCzKxMFCaYKq8yRxpjS3D+ax5GCjYu6lIlsi/WaPKLs8LBjLYCm6mfEhr CUsSOt0BA7E2YTH/XUfaj3a3EmMzGAis767YTLzfrmV8VPAVDBy2qlYwl0Ra Z6SEeKM6DNDEn/INjSCtu7u/8uZNGNgkL2ImLIo09o4yr6PCMCByo/PMkSak cWOvymkEQf9AZseruD6k9XFd3v8m/FaNnXZV4AukDRro+1fZQH/64IlPR38h bXSA07RfF/rNVj/fwr8OaeMmpRfXZEG/gvCZyRSiHj9vxGr0SUHfhCtzOCwV aVOUHZGBk9CXuExCk+mFtH+ZkZDxFHrfxekEcMeRvvi3/dhxZei95nXwefhZ pPOba93STIReR36/twY7kS78p32qxhu4v/n+nTPfi3SJA1seDW0Drmqc592g MaRLFvAdepoJnMnozd2XDiFdhu+HyDkx4NT42tkxPiB9TVGIqxgPOC5RnkGn hJCuIHRAotUROLpTPQuG5UhXdNz0+m4DcJZ+Kn5SSsTfsKxZXqcA2KXyz5f9 N4F0NefsugV5YCd8EE1h8yF908tzge9jgO1hPG1xrxbpGuL7NlydBTbtmlWL 7kOkbz2+ofmgB7DX/1ja/lQR6ZoVs5dU2oEtsGb+dK4+0rUlv2wdYwJr4BSf xf1jSN/heb/z+QtgfezorT30D+k7q4OiQzYC66mXzadJE6QbmAs8NCwH1k2m etOzJKQbtVyrmt4LrNBXcjau15EOTuvan/YC62SNTpUu8T5psOD3ySBgObjm d28k8KL6GoiriwLL4naXY4k90mlz79X77gCLfMJR5GsJ0pmXrUzv6gBL773Q gMRtpJuLsQ/bfwCW1v1lih9nkG6RevKsrD2wNH4vTg55jvR9SnM3mieApV4o ILBODOmW+ZFFCeHEc/n7PL3VSLfeIfVxlyywtix/TO26h3Tbynv9ggXEftH/ WaX6I/0QQ3NRNRLx5Ev9KoOR7tBUseb8N2Bhrm6RDwnphw+Z6eq7AWuXZGdu YT7SnXt/7J2cB5YN+ZTXJmuku5508Sy6CizXVTo1YmykH5/+GeFF+Bu/46sH QkSQ7nHpwl01As/L8p329CVIP7FsWRnXHFgpEtr19HVIP5WU+j2TBaw8lfki xijS/bKfiUoLA6t5+crQdcT3gVqo2pQOrMFb1i8PEfGCXzWQ4jSBtVBsC7bD SL/QMBDIbwvszb/2if2WRXrk5Br2f7nAjnhyKuPbcaTHnM+ZLzQBdvp9Pvt9 2kiPF9KVcf8K7KeCSV+om5B+XW6vOYvol/bcoNp0DtJv3O86nh4P7LHHdjfU VZCeusUj1FYZ2P+K09b6AdIzMLz0ixlwFB0M25lmSL9dv/JrdDdwtrYEFpkR /XXPMnOM5gscw6mZphdRSH/QpSHMJwgc+pOTw+3E+bOPvVSuIPzivqn3q6ec kZ47QTMO3gqcg2WG+/fHIL0guNlWpxo4R347ScYT+z9ecth3who4xzM3nQp/ hPQnsWNx+cPA8Syah/vNSC+WPpNz/P///3JzzsNry5H+/I5QjYoEcE6JeT/u 0kD6y01JXd1ZxLq9oK49Fellz5Rm0gyA47U6YvAHMe+VRo9XHfgMHDcL/xtB RD+9fme0daUzcI7yFxUeJs5Ts6eO8WkKOPaMY7JFakivbbM+GhkNHKuN/Ptl epH+wZl7gaoAHLPijfNv7ZBeP+qd+u8pcOBfbC+TgvQG/7/PymjA0VEYu1pO 7P/lX/TngHbgqG2cvPiRifSvUTJD208CZ7WuvILgGqS3SDxcwuMj5v/wtQyB TKS3b6jSd1UH9vD54d4QCaR3hs1aKCcQ9fpy+K++ONK7OTuceyaBXf/G8t/M GaRzM/NjD1YCO/fn2ezDK5DeN99/V1YF2DeP5TVsrkT64CGlkpZIYEcOS9ht X4T0MZmUnr37ge0Wzyf+gDjfuP/XyeWlwLYd1K6fD0D6z29iwh8JvmHWPBtW eYv0ye3MtZGXgG1obGiUY4H0qavh2037gb3FB343DiJ9hldFX7IL2Ipnv88m Ev0zZz576HURoU+DbhyTe430v/k7vC9IAlsk1Ne5WBcZi0S8LxsGA+vfs9zT hs7I4Duef3OmG1jTGzKYXoXI4K/tL3xOBtb4RBn11j5kCK1XqjmdDaxhDadn f48hQyTUvlWL4JM+3qvp1a+RsYyVMsrzARbXvDRZ3QAZYibNi/O/A4tjx/Y8 z0WGeIaYtLshsb5x95RAMjIk5pibVQn+6ftwedSAHxlSdoQ4ESD2Z3pvCPuG DJnSKqu7HsCaeGE8vskVGXJSs+6OX4D1R9GWlXINGfJ+Oy7I6wB7Scx+0tUi ZCh89b7WdhPY4ou+rTpbgQwlzfzs5AVgK0Rcb7j/Bhkq8f3lVkeBvU2R1v4u EBkbxpSaVn4ANulLau9HDWRs3GXf92ULsK2TVP6MTyNDPTdlNvYasE94etxY 9xMZW4SaVzCnifne25wYmomMbcfE1gvZA/uucc6KvKvI0HrL1Hv7BtiVm1bt k3qLDB3l8N2hqsDuEl4fO38EGXrdswHz48T8evL5CQQhw9B4R8wrK+BQP8eF 511HhvEt7zuBL4Dj0WfeZCKODLJNf91/ocAp+y7mLf0dGbsam+UHpYF7crGg za8RZFhsE9N6eBa4GYx5TRUxZOyNY9KcWcBtcK85YWGLjAPMqlNdedC7Ld8l nUfk41idX91sAr0zagecHD4j44hi/4+r96FPXe+qqTdRv6MXlUYthKHv0KTO 6rd/keFmmCr14Sv0VbxXavu1Ehk+xeFula7QH+CgyhUm8PIzDRw+wYP+Bx97 EyaJegZ89/RaS+iHJqsNvSd4yAg+7jjWsBj6596WvpjKQMbZmf2nzkXDgMpY fkCSHzIuRJn+1JCEAeadWIHDiIxLcvq+HRmEXitKrH0tg4ywfI3JGFVC7/zX 4rh6AhkRRooBBkUwkGdySe0hES+yYdX0sAGhr04sFpRoQ0aMo1DwzRoY6Dio GbhYARlxvNk5pjkM8Hojsl8Q8a5e5J2b+Q4DC2N4BN4h47o4eyHnMAwus72S XpeFjBt3W0JshmBQcnlqUtBOZKRur+MT8oVB2ale1ZJ1yEirKQ97Pg+Dcnzb XMrOISPDqkjgWAQMyigWqYXsQsbtvvtXpFfAoDhdNbKTgYx7ASnCtakwKHDi l9LjB8h4KBQd7a8MA1OJmyZeVCMjO/WC6Pp8GOAW3W0cFUBG3iafuG86MPCp cZG7tRwyCl65rgirhIEnU9VhPaeR8XiXbaI2HQaua2JZUSMynnSaS3AaYcA7 pijwpB4yik9A0rWDMGAmr6S2tAUZzxe0pUmEXl434/865gkyXiaopvz0gv7/ DA+eKVBCRrmi3Oo7U9D/Zonu+79hyKh8svzW3ovQH+u7JrVqOTJqvk5mPr4G /at7lr97eQcZ744OKjrJQ1/n9pcRPrPI+DDZcU/sIfTdaj3RNCiCjM/S1Q9P vIC+VYOkPKVwZLQevFqwuQu4Y3aPi1ubkNE+Er6l4xhw7xzz3rI6Hhld5wIf R08Ad/+DfHkJK2RwMh2fDhP8W/JxQrST6K9RjsbLnI3AvnBnhZaJBTLGTysa 2jwFtragKKPPHxn/8a+qECL4ZLDHXfCgPDKmN8y+PmYBrIMrbraX3kTGn+c8 slQrsKSmZNT3hCJjns6uqT0CPTldFxk955G52P3D+/V+0F1sUSn1xAyZS2bL Gc0L0E35bpLt8AOZgjGP68OuQFf7VQODoSBkisjfN9cWh66g30uobUxkLnuU 3MBJg651gWeE7TyRKWYSveeaCnR+Dk/VbM9FpviX802kAuiMCFvUu3UOmasO ++z/qQud9K83ykEbmVI/Xb7dqYJOiU8rVwUQ+8kGOCmZs6Bj0HerhbUiMuXm 7E78WQQd7+dvRGl6IXNtiOWrh0rQ8SR3rdDPFGQqClgI7SNDx8NHz3+4FyBT OZph+fcodDzQ01L5y4fMDSvId3LDoePRib/bkzchUy3JaOzAQ+h44/lXr9QC meqrdxosfkf4WZqd2QtfZGpkakU8GoDOpcKHErXLkblNZXPzQWHoNCm2nDjR hkytHFVFwY3QeYESRpNOQKbOFkWvp0zo/JAldkj/DzJ1n8q9dPSArrXVM3fP yyNTf6eU4NJo6DofpPpxQQ+ZhuUr9j/Ph66BNPfduzYi04Qkctv5E3Qf+h1f PmyJTHy3ZFRsDLrbzS+s+2uFTIrZgl6ZGPQ429mJhB1DJsPqv6+rCL1L3wTv XhL4mLWNKVQR903qW/dtGo3I3O044Ol5DVi88E+HVWWQud+tk7+mGdgPk3b6 6ash02rs+75Tk8ARdC+xP+eKTBvfxkx5SYJvfZ50Kkwg0/5c7U4/a+Aa8pH3 1BPxjl0t9FjfDr2/x6IDrt1DprtUzvPGOehz000+erMUmZ5p95ecJ+ahg/pO n7EZmd4PUtJbHKC/2mawMoyoz5kXIV8us2AwurYpqMQDmeeNz8pvXwyDfx7v HVIMRObFan+3bmUYOnZ/hfGbBmSGf/Lg03WBYX1LMxbVDplX9rlYcC7DcKYb 0zMmGZlR3x1vxWfByCJ9mVhzIWTGsyx1+gdg5MW3i6LHifonuu6+dF34/38P fy6mD5F5fZj+GTbC6OGPSenhAchMPkWWG2HC6KO2prrtJ5CZOml0PMUDRn// lyFwwQGZt4J1iykxMKbXOi0dvA+ZGQtai8YLYCzA8rGR0Blk3gnbvPtWA4wV bkke2vMJmfeFN6TReTDG2vHUwaYZmQ/j1vVPrgCeKLiOOxL55UjIad/RBN52 5dt9+kbIzEuRDDHfC7x9ZQsF/6vgyuOh6r9wJQotKkqKFqUsSXlpwzkimrtY IqWFRLaISlFSyZKyRBRpsy8VSeUtSba8WeqVfYYww4yxTSnFL+F33z/v5957 Ps95zvOc7zmf+5lp3YasnOXzasdOgsh98If1H11kPU2WXJp2E0QBd7vWHDFH Vr6KmLNFPoiuvdDeVLIFWS8eTeT/aQDRDZULvOByZP29cXQqawREkcIr28wo ZL2+lmRiIwBR6PNPxW4xyHrTvTtyZiuIzmV1BtmWIqtY71tDfjWInGs4KXky yCq5fUfhyBsQma0RbbENRVbZN0OHeU9AtLHE7ITpUmS9Z/VlFj0AkXSuVs4D pr7/pMaI3KNhiDejvL7UBVlVE9t15K/AUH7z2uu5eciqteH5V/rA0EVj62pb Q2R9yrte5uMMQ8aeq058WoOsz1Lakqv3w5C4z9L5su3IanBsM69j6lF6+aQ0 m4nX9Dbo9sXtMOhXNDQwYwGyWpeof9HQgEH1fR/m2zH5tldfcA+TgYGgU2Ju zxn9dK5Z80x3BgyoHp7KiY5GFjegdrR7BPprAk2DSVNkCTYphkAL9Is7Bw+4 Mvr+Gl/8cPQBCPdNH1vk+QGJGcemNbloQE/+DnpCfA4SM4uzl8spQY/eF7Wr ureRmCVvebRcBro/2DVfKjNFQrom+ZvSD+B1Y3z83HtIzFtL6Nb2AO+0wS5N +SwkZC5+DzjfDDxxmyDv8SAk5DYbSTUXAnfTzC8jucVILAkfsAxm9r8aTYn8 pGAklvJjEzY/gC7XeO+16YNIKCX0rI0KhM5DZ36NZdUhsfJ7hIfeKejIEanm vA1EQpn6K7/fCTpmiaYJ4huRWDcVgia7ob3FvAnFmpBQs90QOrId2umrP/tm OyOhkd/0MUUD2j7+cFG8ooHExjkXZS2UoG3/oOd6CV0kNjmrHJicDxxRpXx4 2U0ktN99Sn4yDTg3aiWtZx5CQnfpWaHtd+Do7dyS/OYUEltPr9g4qwfYI06l SXcRie21/5x52QzsVy7vJK8uR0JfxavI8QOww3wVrAZ9kIDL8mILXgPbqcDr eQiJhCG7hPXuEbBpHB5/dwEJY23XaM97wN6pujfgOfO8SaRMy7IoYBudKxn+ qxSJ3YLXilWXgW2pXXFGjsmXRAcn31PAPm5hk7tTGQk6UfLxWkdgx7xrnf35 LRIWKu52788Du0o/2bXiARJ78msWHosGzrzv0tNmdyOxF9QrxTOAczTzqNLd QiT21UScSy8CTsXyoK1FLUgc2De0YVc9tGmLzx0uVUXiUA/dxRdC21PJlIQ+ Cgl779y4kElo3/Jhfc+XOCQcJubvVpGF9trGFflu7Ug4XfMar1SDL54FP4PM 9ZFwkat76ozQsfSb2ykHpp4emjcXZ3hC582t29znuiJxovB7tUkwdGlGvAux Yvg/aWoVIEhk+vmFxdXeJ5A4e0SWr1IJXL9L99++tUXCb/BMQmU7sx93jvxP gtGPv18z5fwdeM77epZkMHxcjol/nrkCuu0l5XwP7ELiernClXV+wHdyMF5/ rhmJSHN/nX+igN97pVvi9ggSN9ra+lzSQOB+fb1DgR8ScSP3LTI/Q++JdPXA 81FIPFRZuWK9GvQ5FqpeGJFFIjn/cv0HhD7O337BvlNIpBlwQ11toN/Ma1z4 z0IksvelDGUFwYBa2sp7Ucz7j3vEkncnwsBNb8uiK0x+ud5O1sI8GPh555jx YmMknl9bW7S+HQaf1ES3BfQiUSAX6vXhOwxNMyq/0XYJiVfJvcpus2GIHr0i U8VB4o2maYukEgzdyq4wOn4OibeFWdezdWCoZbXnBMVFosRU0oCZd0ULNf0X L2H4KmtwG+47CiKTpHUr35sj8d6+Ov2aH4h8zGWeKDP+/GdQ3VY1CkR35feJ rMKQqPaLmFOVBqLCjlflEuVIfJw5WOJWCKKG6w+F+ky+/8bQPpJ1IOJL7XC7 yPihXjF3XXYviL5ahm5Iskai8dG8NtYkiL4fMHt4WwaJFl2vqH5ZEInWbD1R 44EEu6xu53U1EPFevHaZbECi3VzrlxqCqG7JguMvNyDR0RaTXW0DooJD+uGp kkhwXb4fdvcA0a1EwaFbqUh0j1gtkLoCIs+vrKiri5AQXH5e8SgBROCv1HVv MxJ9c2X9iFwQSR3zu17O9KuBO2fU+ytg6BPn4fabjH9Ea5s6r3NgKFK6r6K6 AIlv+bqxasMwtGtjdF4Yo6cfi3ZhbSsM/mmezf9wH4mfPlaiEyUw+PTeg2Pt Tkj81j3Jyo+CwVmua43Y85H4E39p1PoMDDzpznQ4wuhnciwq7dchGDDzmKcn bY6kWOGTadvVoD80V8ovLA1JiWWFOe0LoF/x+03PVERy9oWqgwFj0Jdve8H6 r8NIzjUQFJRWgrDh1e+XGjOQXFy+ypN1FHqnb/LYLfEvkkvXaP13fguuDf3+ 83YSyWUhBh8itUCwUH/LjVY2kitNDynXTQJ/deNutcQaJFVrbnP234VuKqIu 3cEPSQ2N9Ku/mX2SE/J0bgEHSc3IFzr33Jn552LDKzd5JLXNP0d3/ff7lRtX FazmIanXOMfUpRG6xC7/rfofftBR+Cn5BjotC2NinF2RNLytmvI4BTrSvAqf OI8haTS61YK+Bl+mru6N9ghF0mS/ycRXb/ji6GD8yuIEkrtf730csw/a62xS vd80IkkqONlqG0C7yevI4/tLkKT9T0s0qUBbZePAP45KSJq3B77wnQttFoOm edXbkdyjH31UfgQ4/B0p+ZkaSFo/eDi/sB04QaPWTQcfImkzlfP2UDlwNpDf 7i1bjaTtkSL3Sabfcu00hqVfIXmwtEY+KQbYSUSTVJwaknar2ZU7/YDtrlJs Xk4geSRIeLrHHtgGkxX6yzqRPNozuirUBNhKLdUm3m+QPGYi/u/6DcCWzC3P 6WTq5ZIpe6FaFlqn/EqN3HSRdJ+trHZ8HNjTlGf+WfMVSQ+3Ta1zecCWjl1G xjDxTlRjyNMqYK/K+L126wMkT6qba1vmMf1fZ22YvxmSpyMOd/1IALbnHLaP 5SIkzwx5RN26BOzkX9uWxy1E0s/MX2+LM7C78oN0swyRPP/0Wh/bDDjr/5Ab T8QiGSCTEO+vA5zzV/+W77qP5KWTmbsUlwOnaUaZgk4mkoH1L7+/E4O2rXJB auodSAZrVyQd6Ye21KO+LT+/IRn2kzue9graYxQ+uc/VRjKcGPUx0oUvMnLb Ai+RSEYmzRnkvoQvt442WTj0IXmT2NKhlA8dz/TX/lFh9Hb3QWRJAnO+R8dT kZsZfeeabguJSILu7RPPIr6fQzLvntmkuhJ0R4+tP/fDCMn8YaezVfehW8DK dxFlIPn33RuuEonQEzXj8IaDxki+/pbWlS4PPZ0jBaqjZ5As2lVoaxQPfC1r 3u3YWUiWfOUTl+OAXxPSGJgjhWS58e/yFQtBICfK05fqRfJ9osyOtzEgOPyz zpvTjmS18Q6N31EgEKytnMjbimTtHYv0O3Ohd+1o88BeBs8nkbPSlgjoPVrY GPJRAcmGhJj5Ptegtz4wqWIrg6dpKCNs0WxmnzdSY1FTSLbuLJr2jNn//zqp d2vLP0hy4j+fsxAHof1wkZJyK5Ltg4JhUTAIr+acrXgpjmSn4R/3yBkgfByt mbSUyYcbv6BbPRCENWEp/iMtSHYPrjtYPQ2EvWFx4qw8JAWGeo2uF0E4FVyT bsPUQ3h7Dy3xB/oWnVGQXsn4uX/A5X26P/QpW+9z1juJ5BAGGBj9hr6NK4/s kytC8uutmwU8P+jT5cgkftqD5HB/1sbLv6Bve8iO82cPIDkCb7NWnGGu16fM Wszw8SuuYeXbEejT+di7IYfR41if8M6hU9C3ITDwaTdzf9xgcsHvb9C3as+T +UVVSE7ELbp+xwv6FljtuaixEcmpPlWxLSIQTsRPfqliITXDwMC/2QOEgi1l g+47kZoZazXi0//f9/uVmZk+SEkI3TwXuYHwybfh6alOSEnqX+Q/E4Iw3Hj3 n3t3kJK+GWdn4QxCZ+fDxmPnkJrbm90s6gGhwTmnoxdykJLRe2ce6QjCRcm+ icctkZIV9GP1Eeh9WVWvYPc3Ukt2TL127YDeIIUYW7FFSC2Nkds86zD0mq2S GOIQSCntQGXjAyD4MmkvMamL1MryebMbJ0DwQFFrUMcKKWWiTXQsCQSHjobW 7pRHav0B39ehAuA3WqamecUitfncU/OqU9Dz6oSwpucIUjrTA3QOyEGPt8vt exKDSG25Rir0v4Ke9R73n+yPQ0ovQSCQnoTuO6G+cvaOSJm8Urpgdh14ocuP /T0SgRSzCjh0bgCecdIsfsYEUuQ/haZedcCb2XGpbfkrpCxabBbGLAZuuD6v cxUHKSt75bFVr4Fr+SxkYNIeqb2C4S/5h4CrEKLlopGJ1IFfkVkNKdD1vFEi dlktUocvHoxy2vXf/8XEv0y+itQRCdXTI0Lo2r+s8fJIE1JHI0dtQ8KZeeyu 2eoj55E6JvceFmtC12xF77B9K5ByuR+7JuMzdFY5jWgeAqTc1zpIbfGBzrBP QytO30LKI0fz64cl0GkWcZ7erYmU119/mvYXQqfCo+7oQHekThZVF/Ydho6h TYvE1H8i5WOckHR+GnRUrkibHXsMqbO1zqFSqdCR7j3mbX8AqXPWfx2/awId 4Uut4u7lI+XfPsNSvQ86fCXGqZfdSF10rNMtioAOt9V217hhSF0eeLCc3ggd DmaGHsjoJei0x/Qv9dBh7yJK/DiCVMj4tt4TZ6DD0dQz9dhWpMKCZtVOyUOH x7ti5YueSIVLNeXfeAMd/g+XnBk0QSryZmrCSjvouPFU435pMlLRCicDnk2H jqyXPqvLNZC6mQKOhmkM/tOno3fOR+qW2tzd9abQIcycZzxfB6n4Z22aR/uh c75Uy1ZbJn7ituxFPyKhc5vNMe8yIVL3Ss/+L1gLOl0MHuXV2CKV/HlhRfpZ 6KzN+ROQnYBU2v6uR7pLoWt69fGK84weM7pyb1QWQZeSh/3ysFCkHn8jDghn QNeBW/f27JVGKtdPHv3Soetc1V5jNS2k8qYEKpK7oetO8Kc9xReRKpgfOKwW BV3soZLmw4z/SjRfX/VcBtwDKosvLbiEVFm4jvNUFHB9VR7On83cr+h9ZnJz BnBjb85cMp/xW1VStnhBP3CrDRQdc/lI1Uys4bPsgNtDecxReIfUJ9ukirZ6 4E4cGV23vACphoUJwdMKgadhOr5ukvFnk5esY6wm8AxHZLJXMPhbaqKN1qYA z8a2MPrHTaQ46+esfrUYeG7ukmWzDyLVHhw2g2D84b+ELX4uA6kOrhi3fRJ4 4fuarZZqI8XVv1TqdQp4iSuvs6IZPXTfGU+eLgBepvvhz7bfkeL/8r0cdwB4 zzfe75VKREq458cRlU/Ae+sREK68Han+p17weifwKlfItWx4jNSQ9MAKsgB4 H02dtmUz/eGrq/Nkhxrw6ntN2vgM3uEKXof3A+C1zPDVjapBamSVXbHYQuCx H1xps9RD6lcA5/6tUGa+KivzDxdDaoxjE7Duf8y1n7lO4gekxnXrDxV6AK+1 6pd2O+O/iVgzPaoLeE0Vatn8NKSmvlYv67QG3mefXwZiiPQMatf4yQ/Aqxno FTk0ID0zq7Rtph7w3m+ffuVBAtKzxPULb+cx+ZxMvVtjjrSkw+tE1TXAe5Hh d1b5I9LSxTrn3iQA79HQYe8vqkjPU3hmazYHeEmHyJORCkjL+G7Y2nUZeLek DLZlrUJaTmvNmLgr8C4ExX1UmkBaKUX2rPkW4Jm8FApbXyO9cjJ6L/cR8LZ2 PrrkW4+08sE5Oj5KwFOXlzPMzUJ6vazYyB0J4Mmsqs9xO4G0mvelRvXzwBNT NdSIl0Fao3b8+VsRcH893mvPXoH0ppAfp3jNwO28et9QTgdpbZ7XnjMEcJtk 3Y//W4C0rsHA5lnFwK253Lovj+Fj612XBYmbgVvWs81N9QDS20d5wxoZwH1j zc8sc0Za39qu7p0CcF8IV4W2/Is05HHyLBk9P80XfxrFxN851+ZGD6Pnx+9d uuU8kTZ2q/c6exa42drvpQ/fRdqk0sx8NqPvrLkaN+xfIs1aXa15l9F31hGN yTEbpMlLJvM2MPp+tK1MdV0m0nRb6VCJCXBz0o1zqSKkLbbqf9xTCNz8JweV khuR3hP3+glfE7ivbQafc5OQth7WifBNAW7pY7nn8XuR3kc/85CSY/LLUQxV skPa9tv5AKM04La2VIb4MPwcijWKCmDyFSpmbPCXQtped87DghLg/u/CgTP5 q5F2aG3K+2YGvDmi0K476kg7+T8oU20H3urjDVwBE89FyaXBkZnvtwmHO/Kl kfZw/N/PZkafJzJekqMzkfaSKJ8lI8v4qUzrtoML0iezI+RZjB8fxZrGBSxE +uxXpe1F74A3OFTQqpOIdOD5nZdSxqA7cNmPJXEM38GK0tHtodCdFV143YeD dOi7xpTFstBdlyPe9OsC0hHizhXXtKBn9cYfhc4MX7ejwyW93KDnvdP+UI2T SN/RtlbIGoOeoXV0W1w70nebFdV5ocCXXaGnuWYB0snLnprtTQG+3eAh43EL pFOL/exvaAH/ikPMeAbDX4aDofeHYuCnUTsuC08h/TijIXYHB/i86ouE8Wek c1n30s64An9K3GdLXAbSeYPHXub+AsFScQna4jzSz29oVgqDQaCVNLQtnPFD wabR1tXM/Lgrv3Pmcib/V40lfQeZ83//4tLciRqk3/he+31LEwQuj9qEOoye ihWspD8VgcDHQizOkvFjydvlirNJEAT0Vf2+r4l0uT1f05ADgmC7dDvzMqQr Z+SCvysIriYsqY9IRvpDuq/FCwbP1SsNS7PckK7ZjQ7MvCgInsd2XMTo9+OA 5On1DJ6AjYnFRxl+6yLrgx0YPKfa5yuqDSPdoHX31l0Gz7F5tSW9zUg3NThl NDJ4rCoVfuYx8VrPbng1jwABzJw2adSHdJv8ryrTVhCofLJc2sro6cubd5xA ZxBIaz6NXuCKdJdd2EDhCPAHN996nroJ6e7pln9GrgC/asCiztAWaX7asnma Mgzfnn/+1LCQ7u/L0UreAHyLrc7DzouRHoo4a8gpAv6qosnU4hCkv20EK1kC er6FyGpMZ/D/9Pl85qoz9IRe1inNuIj02JLE0JIR6DGTLpntNIn070LHeGY/ 7VkUftEokI30xGGN7L9koLtFm0h9pIL01NTPQs8HzPy0rZhaofh/1x56lg== "]]}}, AspectRatio->NCache[GoldenRatio^(-1), 0.6180339887498948], Axes->True, AxesOrigin->{0, 0.4}, PlotRange->{{0, 60}, {0.385947141987003, 0.990001947714712}}, PlotRangeClipping->True, PlotRangePadding->{ Scaled[0.02], Scaled[0.02]}]], "Output", CellChangeTimes->{3.4692212534804688`*^9}] }, Open ]] }, Open ]] }, Open ]] }, Open ]] }, WindowSize->{1006, 984}, WindowMargins->{{15, Automatic}, {Automatic, 15}}, ShowSelection->True, Magnification:>FEPrivate`If[ FEPrivate`Equal[FEPrivate`$VersionNumber, 6.], 1.5, 1.5 Inherited], FrontEndVersion->"7.0 for Mac OS X PowerPC (32-bit) (November 11, 2008)", StyleDefinitions->"Default.nb" ] (* End of Notebook Content *) (* Internal cache information *) (*CellTagsOutline CellTagsIndex->{ "Info3469198071-6311815"->{ Cell[2019, 62, 2380, 38, 259, "Print", CellTags->"Info3469198071-6311815"]}, "Info3469191461-5372129"->{ Cell[306859, 6146, 869, 18, 104, "Print", CellTags->"Info3469191461-5372129"]} } *) (*CellTagsIndex CellTagsIndex->{ {"Info3469198071-6311815", 573510, 10705}, {"Info3469191461-5372129", 573621, 10708} } *) (*NotebookFileOutline Notebook[{ Cell[CellGroupData[{ Cell[567, 22, 114, 1, 114, "Title"], Cell[684, 25, 122, 4, 62, "Text"], Cell[809, 31, 89, 1, 64, "Input"], Cell[CellGroupData[{ Cell[923, 36, 158, 3, 51, "Subsection"], Cell[1084, 41, 690, 10, 107, "Text"], Cell[1777, 53, 90, 1, 40, "Input"], Cell[CellGroupData[{ Cell[1892, 58, 124, 2, 40, "Input"], Cell[2019, 62, 2380, 38, 259, "Print", CellTags->"Info3469198071-6311815"] }, Open ]], Cell[CellGroupData[{ Cell[4436, 105, 203, 4, 36, "Subsubsection"], Cell[4642, 111, 829, 18, 64, "Input"], Cell[CellGroupData[{ Cell[5496, 133, 278, 6, 40, "Input"], Cell[5777, 141, 1759, 38, 86, "Output"] }, Open ]], Cell[7551, 182, 207, 5, 84, "Text"], Cell[CellGroupData[{ Cell[7783, 191, 187, 4, 40, "Input"], Cell[7973, 197, 219, 4, 40, "Output"] }, Open ]], Cell[8207, 204, 420, 9, 42, "Input"], Cell[CellGroupData[{ Cell[8652, 217, 305, 5, 87, "Input"], Cell[8960, 224, 156, 2, 40, "Output"], Cell[9119, 228, 157, 2, 40, "Output"], Cell[9279, 232, 174, 3, 40, "Output"] }, Open ]], Cell[9468, 238, 492, 11, 42, "Input"], Cell[CellGroupData[{ Cell[9985, 253, 385, 8, 87, "Input"], Cell[10373, 263, 1060, 26, 86, "Output"], Cell[11436, 291, 1056, 28, 86, "Output"], Cell[12495, 321, 1064, 28, 86, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[13596, 354, 2375, 63, 156, "Input"], Cell[15974, 419, 164, 2, 40, "Output"], Cell[16141, 423, 162, 2, 40, "Output"], Cell[16306, 427, 162, 2, 40, "Output"], Cell[16471, 431, 146, 2, 40, "Output"], Cell[16620, 435, 144, 2, 40, "Output"], Cell[16767, 439, 144, 2, 40, "Output"] }, Open ]], Cell[16926, 444, 441, 10, 107, "Text"], Cell[CellGroupData[{ Cell[17392, 458, 1013, 30, 87, "Input"], Cell[18408, 490, 847, 22, 86, "Output"], Cell[19258, 514, 849, 22, 86, "Output"], Cell[20110, 538, 847, 22, 86, "Output"] }, Open ]], Cell[20972, 563, 162, 2, 39, "Text"], Cell[21137, 567, 327, 8, 42, "Input"], Cell[21467, 577, 154, 3, 39, "Text"], Cell[CellGroupData[{ Cell[21646, 584, 543, 14, 40, "Input"], Cell[22192, 600, 2099, 58, 86, "Output"] }, Open ]], Cell[24306, 661, 98, 1, 39, "Text"], Cell[CellGroupData[{ Cell[24429, 666, 819, 20, 64, "Input"], Cell[25251, 688, 2049, 58, 86, "Output"] }, Open ]], Cell[27315, 749, 98, 1, 39, "Text"], Cell[CellGroupData[{ Cell[27438, 754, 345, 8, 40, "Input"], Cell[27786, 764, 723, 19, 86, "Output"] }, Open ]], Cell[28524, 786, 241, 5, 39, "Text"], Cell[CellGroupData[{ Cell[28790, 795, 615, 16, 40, "Input"], Cell[29408, 813, 682, 19, 86, "Output"] }, Open ]], Cell[30105, 835, 255, 5, 39, "Text"], Cell[30363, 842, 86, 1, 64, "Input"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[30498, 849, 178, 4, 51, "Subsection"], Cell[30679, 855, 423, 9, 107, "Text"], Cell[31105, 866, 796, 20, 110, "Input"], Cell[CellGroupData[{ Cell[31926, 890, 180, 3, 40, "Input"], Cell[32109, 895, 1029, 25, 86, "Output"] }, Open ]], Cell[33153, 923, 115, 1, 39, "Text"], Cell[CellGroupData[{ Cell[33293, 928, 380, 9, 40, "Input"], Cell[33676, 939, 112, 1, 40, "Output"] }, Open ]], Cell[33803, 943, 89, 1, 64, "Input"], Cell[33895, 946, 117, 1, 39, "Text"], Cell[34015, 949, 289, 8, 40, "Input"], Cell[CellGroupData[{ Cell[34329, 961, 191, 4, 64, "Input"], Cell[34523, 967, 1484, 33, 86, "Output"] }, Open ]], Cell[36022, 1003, 125, 1, 39, "Text"], Cell[CellGroupData[{ Cell[36172, 1008, 225, 6, 40, "Input"], Cell[36400, 1016, 683, 19, 86, "Output"] }, Open ]], Cell[37098, 1038, 100, 1, 39, "Text"], Cell[CellGroupData[{ Cell[37223, 1043, 126, 2, 40, "Input"], Cell[37352, 1047, 90, 1, 40, "Output"] }, Open ]], Cell[37457, 1051, 161, 3, 39, "Text"], Cell[CellGroupData[{ Cell[37643, 1058, 225, 6, 40, "Input"], Cell[37871, 1066, 653, 19, 86, "Output"] }, Open ]], Cell[38539, 1088, 189, 6, 39, "Text"], Cell[38731, 1096, 87, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[38855, 1102, 164, 3, 51, "Subsection"], Cell[39022, 1107, 676, 11, 107, "Text"], Cell[CellGroupData[{ Cell[39723, 1122, 1328, 30, 142, "Input"], Cell[41054, 1154, 1509, 33, 86, "Output"] }, Open ]], Cell[42578, 1190, 159, 3, 39, "Text"], Cell[CellGroupData[{ Cell[42762, 1197, 163, 3, 40, "Input"], Cell[42928, 1202, 214, 5, 40, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[43179, 1212, 126, 2, 40, "Input"], Cell[43308, 1216, 88, 1, 40, "Output"] }, Open ]], Cell[43411, 1220, 846, 15, 174, "Text"], Cell[CellGroupData[{ Cell[44282, 1239, 1199, 35, 157, "Input"], Cell[45484, 1276, 1171, 25, 87, "Output"] }, Open ]], Cell[46670, 1304, 248, 5, 39, "Text"], Cell[CellGroupData[{ Cell[46943, 1313, 193, 4, 40, "Input"], Cell[47139, 1319, 683, 19, 86, "Output"] }, Open ]], Cell[47837, 1341, 87, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[47961, 1347, 177, 4, 51, "Subsection"], Cell[48141, 1353, 881, 19, 64, "Input"], Cell[CellGroupData[{ Cell[49047, 1376, 179, 4, 40, "Input"], Cell[49229, 1382, 1501, 33, 86, "Output"] }, Open ]], Cell[50745, 1418, 618, 13, 42, "Input"], Cell[CellGroupData[{ Cell[51388, 1435, 195, 4, 40, "Input"], Cell[51586, 1441, 13510, 322, 777, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[65133, 1768, 178, 4, 40, "Input"], Cell[65314, 1774, 710, 20, 86, "Output"] }, Open ]], Cell[66039, 1797, 613, 14, 65, "Input"], Cell[CellGroupData[{ Cell[66677, 1815, 476, 12, 40, "Input"], Cell[67156, 1829, 760, 21, 86, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[67953, 1855, 434, 10, 40, "Input"], Cell[68390, 1867, 821, 21, 86, "Output"] }, Open ]], Cell[69226, 1891, 87, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[69350, 1897, 127, 1, 51, "Subsection"], Cell[69480, 1900, 361, 11, 42, "Input"], Cell[69844, 1913, 779, 14, 42, "Input"], Cell[CellGroupData[{ Cell[70648, 1931, 461, 10, 40, "Input"], Cell[71112, 1943, 14378, 332, 869, "Output"] }, Open ]], Cell[85505, 2278, 89, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[85631, 2284, 120, 1, 51, "Subsection"], Cell[85754, 2287, 1879, 44, 136, "Input"], Cell[87636, 2333, 245, 5, 39, "Text"], Cell[CellGroupData[{ Cell[87906, 2342, 341, 10, 81, "Input"], Cell[88250, 2354, 658, 19, 86, "Output"] }, Open ]], Cell[88923, 2376, 89, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[89049, 2382, 151, 3, 51, "Subsection"], Cell[CellGroupData[{ Cell[89225, 2389, 124, 1, 36, "Subsubsection"], Cell[89352, 2392, 796, 18, 65, "Input"], Cell[CellGroupData[{ Cell[90173, 2414, 130, 2, 40, "Input"], Cell[90306, 2418, 3545, 87, 315, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[93888, 2510, 344, 8, 40, "Input"], Cell[94235, 2520, 50991, 842, 348, "Output"] }, Open ]], Cell[145241, 3365, 89, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[145367, 3371, 108, 1, 36, "Subsubsection"], Cell[CellGroupData[{ Cell[145500, 3376, 380, 11, 42, "Input"], Cell[145883, 3389, 1107, 24, 87, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[147027, 3418, 158, 3, 42, "Input"], Cell[147188, 3423, 1475, 33, 86, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[148700, 3461, 150, 3, 42, "Input"], Cell[148853, 3466, 88, 1, 40, "Output"] }, Open ]], Cell[148956, 3470, 449, 12, 42, "Input"], Cell[CellGroupData[{ Cell[149430, 3486, 150, 3, 40, "Input"], Cell[149583, 3491, 1155, 25, 87, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[150775, 3521, 319, 8, 40, "Input"], Cell[151097, 3531, 175, 3, 40, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[151309, 3539, 317, 8, 40, "Input"], Cell[151629, 3549, 921, 21, 87, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[152587, 3575, 1097, 25, 179, "Input"], Cell[153687, 3602, 50968, 841, 348, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[204692, 4448, 159, 3, 40, "Input"], Cell[204854, 4453, 101535, 1672, 348, "Output"] }, Open ]], Cell[306404, 6128, 87, 1, 64, "Input"] }, Open ]], Cell[CellGroupData[{ Cell[306528, 6134, 184, 4, 36, "Subsubsection"], Cell[CellGroupData[{ Cell[306737, 6142, 119, 2, 40, "Input"], Cell[306859, 6146, 869, 18, 104, "Print", CellTags->"Info3469191461-5372129"] }, Open ]], Cell[CellGroupData[{ Cell[307765, 6169, 156, 3, 42, "Input"], Cell[307924, 6174, 188, 5, 40, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[308149, 6184, 228, 6, 86, "Input"], Cell[308380, 6192, 311, 9, 40, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[308728, 6206, 826, 23, 65, "Input"], Cell[309557, 6231, 715, 19, 86, "Output"] }, Open ]], Cell[310287, 6253, 909, 24, 65, "Input"], Cell[CellGroupData[{ Cell[311221, 6281, 237, 7, 40, "Input"], Cell[311461, 6290, 878, 20, 87, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[312376, 6315, 1052, 24, 156, "Input"], Cell[313431, 6341, 51080, 843, 360, "Output"] }, Open ]], Cell[364526, 7187, 1561, 46, 90, "Input"], Cell[366090, 7235, 859, 26, 90, "Input"], Cell[CellGroupData[{ Cell[366974, 7265, 121, 2, 40, "Input"], Cell[367098, 7269, 882, 20, 87, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[368017, 7294, 552, 13, 40, "Input"], Cell[368572, 7309, 152915, 2515, 361, "Output"] }, Open ]], Cell[CellGroupData[{ Cell[521524, 9829, 404, 11, 40, "Input"], Cell[521931, 9842, 51098, 844, 360, "Output"] }, Open ]] }, Open ]] }, Open ]] }, Open ]] } ] *) (* End of internal cache information *)