{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":44611,"title":"¡Busca el extremo!","description":"Crea una función que calcule el extremo de una parábola (máximo o mínimo absoluto) cuyos datos son proporcionados por el usuario en base a la siguiente ecuación. \r\n\r\ny = a*x^2+b\r\n\r\nEl usuario indicará los valores de *a* y *b* mientras que los valores de *x* se encontrarán en todos los casos entre -10 y +10. Se debe redondear la respuesta al número entero más cercano.\r\n\r\n* *Ejemplo*\r\n\r\nEl mínimo absoluto de la parábola y = x^2-5 es P(0,-5).","description_html":"\u003cp\u003eCrea una función que calcule el extremo de una parábola (máximo o mínimo absoluto) cuyos datos son proporcionados por el usuario en base a la siguiente ecuación.\u003c/p\u003e\u003cp\u003ey = a*x^2+b\u003c/p\u003e\u003cp\u003eEl usuario indicará los valores de \u003cb\u003ea\u003c/b\u003e y \u003cb\u003eb\u003c/b\u003e mientras que los valores de \u003cb\u003ex\u003c/b\u003e se encontrarán en todos los casos entre -10 y +10. Se debe redondear la respuesta al número entero más cercano.\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cb\u003eEjemplo\u003c/b\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eEl mínimo absoluto de la parábola y = x^2-5 es P(0,-5).\u003c/p\u003e","function_template":"function y = calculo_maxmin(a,b)\r\n  \r\nend","test_suite":"%% TEST 1\r\na = 2;\r\nb = -8;\r\nmin_correct = -8;\r\nassert(isequal(calculo_maxmin(a,b),min_correct))\r\n\r\n%% TEST 2 \r\na = -2;\r\nb = -8;\r\nmin_correct = -8;\r\nassert(isequal(calculo_maxmin(a,b),min_correct))\r\n\r\n%% TEST 3\r\na = -5;\r\nb = 0;\r\nmin_correct = 0;\r\nassert(isequal(calculo_maxmin(a,b),min_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":132597,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":37,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-16T07:17:45.000Z","updated_at":"2026-02-05T15:59:02.000Z","published_at":"2018-04-17T18:00:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCrea una función que calcule el extremo de una parábola (máximo o mínimo absoluto) cuyos datos son proporcionados por el usuario en base a la siguiente ecuación.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = a*x^2+b\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEl usuario indicará los valores de\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e y\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e mientras que los valores de\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e se encontrarán en todos los casos entre -10 y +10. Se debe redondear la respuesta al número entero más cercano.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEjemplo\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEl mínimo absoluto de la parábola y = x^2-5 es P(0,-5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61157,"title":"Scaling vertically parabola by evaluating its area over an interval","description":"Let p be a quadratic polynomial, with its axis of symmetry being the y-axis. Considering the vertical shift of its vertex to the origin, find the positive constant, k, that scales the shifted parabola such that its area over the interval [-a; a] (a\u003e0) will be equal to the area under the original parabola (see figure below).\r\ninput: (p, a)\r\noutput: k\r\n\r\n\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 447.8px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 223.9px; transform-origin: 408px 223.9px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eLet \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ep\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e be a quadratic polynomial, with its axis of symmetry being the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ey\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-axis. Considering the vertical shift of its vertex to the origin, find the positive constant, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, that scales the shifted parabola such that its area over the interval \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[-a; a] (a\u0026gt;0)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e will be equal to the area under the original parabola (see figure below).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003einput: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; 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\" alt=\"Scale parabola\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function k = scale_parabola(p, a)\r\n  k = x;\r\nend","test_suite":"%%\r\np = [1 0 1];\r\na = 2;\r\nk_correct = 7/4;\r\nassert(isapprox(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [1 0 -1];\r\na = 2;\r\nk_correct = 0.25;\r\nassert(isapprox(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [5 0 0];\r\na = 0.5;\r\nk_correct = 1;\r\nassert(isequal(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\nfiletext = fileread('scale_parabola.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp') || ...\r\n          contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n\r\n%%\r\np = [-3 0 1];\r\na = 1;\r\nk_correct = 0;\r\nassert(isequal(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [-3 0 2];\r\na = 1;\r\nk_correct = -1;\r\nassert(isequal(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [-3 0 2];\r\na = 3;\r\nk_correct = 7/9;\r\nassert(isapprox(scale_parabola(p,a),k_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":4993982,"edited_by":4993982,"edited_at":"2026-01-16T14:57:57.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2026-01-16T14:57:57.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2026-01-10T14:36:03.000Z","updated_at":"2026-03-29T13:27:42.000Z","published_at":"2026-01-15T14:52:59.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLet \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e be a quadratic polynomial, with its axis of symmetry being the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-axis. Considering the vertical shift of its vertex to the origin, find the positive constant, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, that scales the shifted parabola such that its area over the interval \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[-a; a] (a\u0026gt;0)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e will be equal to the area under the original parabola (see figure below).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003einput: 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i0Q1AiK+v76JFiyoqKt58882dO3eOGTMmODj4zJkze/fuLSwsnDRpkq3muW4BAQEvvvjiyy+/PHny5Oeff54Qkpqa6lrH0AceeCA4OHjz5s0VFRURERF5eXm7du0qLS2tqqpil2Q7xFxnrFixYurUqZMnTy4pKUlPT69j/Eqlsn///jk5OXPnzh07diwhZP/+/UePHrVarX/++SezJZoQUlVVNX/+/F9++eXhhx/eu3fv999///TTTz/yyCMufHcATqGaazl3mBXFrSI0IUStrknrI9ENQAghJCAgYO3atePGjfvoo49Wr159586d5s2bR0VFrVmzJjIy0vml1sGDB6empr711lvLli279957J06c+I9//GPevHkNHU+nTp02bNig1WrT0tL++uuvzp07z5kzJywsbPr06Xl5eewsukPPP/98u3bt3n333UWLFt13330zZsy4detWWu05sxdeeIEQ8sknnyxatKh58+YxMTFff/31xo0bc3JySktLmc1dnTp1SkxM/PDDDz/++OOOHTsuXbp04sSJIizJg/diH2alVrsWrRSy6puzdu1aQsjs2bNrfYROZzeNltPg62WxWK5evdqpUyepByKUq1evBgYGsjtLyEH9v1RQu40bN27YsGHbtm3sVWHxzZs379ixYzt37mzbtm1Dn2symWbMmFFUVOTa0yny/HUym82lpaUdOnSQeiBCuXz5cocOHdh7B+TOYCB/L/EQQkhaWh1Z7jp+qdxtRwGOugIQxvXr14cPH56SkmK7ajcajXq9PigoqHXr1qIN49SpUzExMTt27LDdU1hYePz48eDgYGqLM4B8sVPcDTnMiuI+lyQ27EQ3jroC4ElQUNBDDz300UcfXb16Va1W//nnn59++mleXt7ChQvFnJyFhoaqVKq33nrr7NmzDz/88I0bN1JTU4uLixctWmTr0Akgd+wUdyM6YLphhJ42rSZC63QkKQkdQAEaz8fHZ+HChQEBAV999dWuXbuaNGny4IMPfvzxxwMHDhRzGEqlMiUlZdWqVbt3705PT2/atGmfPn02b958//33izkMANc14jArihtGaG5FN6bRAHxQKpULFixYsGCBtMNo06bNihUrVqxYwcurNegMbAAeNOIwK4q7rUMz2CGZ3fUUAABAWo04zIrinhGavbGMOeoKAABAco07zIrinhFao7Fbe2avyQMAAEilcYdZUdwzQhP71AH2XIF3q6ys3Lp164gRI7p37x4WFtanT59XXnnl/PnztgdkZmaGhYVt3LixtleYN29eTEzM9evXCSFWq3Xbtm29e/cOCwt77rnnbt261cjhbdy4kcdjMxrDZDJNnjzZ9p0C8K9xh1lR3LBSjIGjrgAIIYQYjcZXXnklOzu7Y8eOcXFxTZs2PXPmzIEDBw4ePPj+++8PHz68oS+Yl5f33nvvhYaGxsfHd+rU6Z577tmxY0d1dfXkyZOFGD+A52j0YVYUt43QOOoKgBBCiF6v//HHH+fOnfviiy/ajs04c+ZMfHz82rVrH374Ye5RylwpKSm2/y4uLjYajTNnzmSi+6lTp955550XX3xRoPEDeA5eU9zEjbPcxH6TGRLd4K2OHDmiVCpjYmJs4ZkQ0rNnzwkTJly5cqWoqMi1l0XPaoCGMRjsIhEf24DdOUKjAygAIV26dDGbzQbOjoZ58+adPHkyMjLSdo/ZbN68eXO/fv3CwsKio6P37NlTXV1te3BMTIzBYJg8efKMGTMIITNmzIiIiEhNTY2LizMaje+++65tLbmqqmrr1q3R0dFhYWE9evSYNWsW+zrAarUeOXJkxIgRXbt2DQ8Pf/PNN+teyTaZTCtXrmSWvYcNG3bw4MFJkyZNnjzZZDIxy8aJiYmpqak9evQIDw/fs2cPIeT8+fMJCQl9+vQJCwvr3r37iBEjDhw4wDQrvX79ekxMTGJi4t69e6OiosLCwqKiorZu3Wo75Ipx7ty5SZMmde/enTt+ABdRnT5dOsyK4rZZbgY6gIJLXNug19DmdeK8y+OPP56WljZ37lzm6MlHHnmkXbt2Ds+z2rBhQ/v27V9++WVfX9///ve/c+bMUSgUzJHJDF9f31deeSUiIuKjjz564YUX+vTp06VLl9dee+2DDz4YNmzYsGHDQkJCqqqqkpKStm/fHh4ePmvWrJKSEp1Op9FodDodc6r0N998M2fOnHbt2i1dupQQkpqaeu3atdrOUzGZTC+99NKRI0dGjx49aNCgn376adasWVar9eGHH7Y9JjMz8+TJk0uXLi0uLg4PD8/Ly4uPj7/33nsTEhK6dOmSl5e3c+fOOXPmBAYGRkVF2Z6SnZ09atSoiIiIjIyM5OTk8+fPJ/99WG9hYWFCQsLIkSOnTJly+PDhjIyMK1eu6HQ65jhqABdRnT75WHh18wiNDqDgEvapM85r6FFqLryLRkNqP2XRsfvvv3/79u2vvfba4cOHDx06RAhp3rx5bGxsfHw8dfTkgw8+mJqaypzG+NBDD02dOpUJY7YH+Pr6RkVFmUwm5gHMMZF37tzZsGHD/fffP3ToUEJIdnZ2RkbG+PHj33nnHSYTHh0dnZCQ8O67765ataqysnLLli2dOnWyBewhQ4ZoNJraaqezsrJ+/vnn+fPnJyQkKBSKuLi4iIgIrf3+SbPZ/MYbb8T8PSNZs2ZN06ZNN23a9MADDxBCRo4cGRUVNWPGjNzcXFuEvn379ooVK5h19JEjRy5evHjPnj1jx45lnkIIeeedd8aNG0cI+ec//+nn57dr1y6DwSDt+V3g3vjr9Mnmzlluwkl0s/+BACTl2gS6oeGZERoaunPnzpycnJSUlBEjRhBC9u3bN2HChCVLlrCzu4MHD2bCMyEkODi4a9euV65cYeKx87755psmTZqwD1eOiIhQq9W//PLLlStX8vPzL1y4EBcXx4Rn5o3i4uIcvpTFYvn222+Dg4NHjRrFXEkoFIrhw4d3796d/bD27dv37NnTdvPVV189fPiwLdYSQoKCgqg5elRUlPrvGYyvr+/EiRMtFktubi5zT8eOHW2xXKFQDBgwwGg0FhcXN+jfAcAOf50+2dx8Dk0I0WhqVqDT05HoBoE0NDvj2jS9Mdq0aTNu3Lhx48ZZrdazZ88uXLjws88+GzBggG2WTB2ve889Db5Av3XrVlFRUfPmzS9dusSeFldXVxuNxtLS0pKSEpPJ1KNHD/azqJs2ZrP5jz/+6NChAzu97O/vHxQUxH5Yhw4duOdOVlRUnD59+rfffjt69GhOTo7RaGR/tXXr1v7+/rabrVq1UiqVZ86cYW7ec8897H8K1MQBD6gqbp64f4SOiamJ0EwHUCS6QQAFBVKPwBGmZuqRRx5Zvny57U6FQtGzZ8+33nqLm8duJKvVarFYbt68uXjxYu5XS0pK+HojNupKorCwMDEx8dixY4SQpk2bdu3atW/fvj/88EO9r4NIDEIRJsVNhI7QFRUVGzZs+OKLL27evNm8efORI0fOmjXLlv7ih0ZDtNqalKJW62KiELyJCOFWnIjeunXr5s2bHz9+vKSkhNr37Ofn16xZM37DklKp7NKly+XLl3fs2NGxY0fuA06dOhUQEJCbm8usYTMuXrzo8NX8/PyCgoJ+/fVXo9FoO/v5zp07ZWVlrVq1cviUysrKZcuW5efnb9q0adCgQc2aNWPe9KeffmI/rKys7Pbt28xXCSHFxcXl5eXdunVr+HcM4AS+t0HbCLgOfeXKlWnTpn300UctWrSYOHFiWFjYzp07NRoN/xsb0AEUGkilavD/RHgLF94lKCho9OjR58+fX758eUVFhe3+ysrK1NTU8vLyxx57rMEvaq9JkyY+Pj4Wi4W5+cgjjxQXF+/bt8/6d+Gc0Wh87rnnBg8eXFBQoFKpunXrduDAgcLCQuarpaWl3377rcNX9vHxefLJJ4uKivbu3Wt7tcOHD1+6dKm2wRiNxnPnznXt2jUqKooJwNXV1dnZ2dRC8rFjx06fPs38d1VV1VdffdWiRYvo6OhG/UMAOERNoHntnSXUHNpqtW7ZsuXkyZPz58+fMWNGkyZNrFbr5s2bV65cuWXLliT+kgCEoAMoeLUpU6bk5eXt3r3766+/7tevX6dOnW7evHnkyJE///zzmWeeYc9lXdO6dWulUvn111+HhIQMGDBg2LBhP//888qVKw8fPjx27NiKiort27efO3duwYIFKpVKoVAsWbIkISHh6aefZvZV173bKjY2duDAge+99965c+eY3VaZmZm1PZgQEhQUFBkZuW/fvtdee23o0KEVFRUZGRlnz55VKBTspWij0ajRaJ5//vkuXbqkp6efPHlywYIF999/f+N7jAPQeD3MiiLUHPrixYv79u0bNGiQRqNhWh0xOy87d+584cIF9sU+D9Rqu5CME6PBmwQEBKxdu3b9+vUPPvjgsWPHtm/fnp2d/eCDD27evFmr1TY+y92mTZuEhITCwsK5c+cePXrU19d3yZIlS5cu/f333xMTE998801CyAcffPCvf/2LqceOjIz85JNPOnXq9NZbby1fvrx3797MxmiHlErlunXrnn322YMHDyYmJp45c2bVqlXsym2Kj49PcnLypEmTfvrpp7lz577//vt9+vT55ptvBgwYUFBQYAvS/fv3X7Ro0WeffZaYmHjr1i328AB4JsA2aBuFtaF7PJ1z4MCBl156afXq1WPGjHH+WWvXriWEzJ49u8Hvp9OR+Piam8J8U41ksViuXr3aqVMnqQcilKtXrwYGBtYxAZKE679UIIXr168/9dRT/fr1Y7cKb+jTg4ODN23aZFvb5pE8f53MZnNpaWmHDh2kHohQLl++3KFDB2ozgiwYDHbbNtLSXNhPVMcvlVBz6EuXLjFFJQcOHBg6dCjT/2/hwoVCdddDB1AAN7R58+Zhw4ZduHDBds/Ro0evXbuGqi5wD9Q2aL63+woVoZkuwRs3bnz11VebN2/+1FNPtW3b9vPPPxekUozB/qfJzhbkLQCAVwMHDrx58+b06dPT09P379+v1WoXL14cGho6evRoqYcG4ARhtkHbCJg0MJlM2dnZa9asYXrvVVdXb9q0KSUlhekOWFu+Iicnh7onIiKC3f2/Nj4PPaS0TZ11OlNioiUkpDHj553FYikvL/fg3r8VFRUKhUJuWW6z2Sy3IYFNr169Pvzww5SUlOXLl9+5c6dly5ZPPfXUq6++2qJFC6mHViuz2VxWVib1KOyYzeaKigp2kxYPU15e7u/vL7cst09hoZI1hzYlJlqc+MXIzc09ceIE+56cnBxbkzv6LRo3wnpMmjRp2LBhzH83adJk8uTJBw8eZLoDdu7cmd/3skydSlatqtkYrdeTqVP5fYtGslqtqFUBoPTr12/Hjh18vVrbtm2zkULzOAqFUCVTjeGzbVvNDZXKIsB2PqEiNFNB2r17d3ZMatmyZdeuXfPz88vLy2t7YlRUlOtVGGq1bQVaOWsWefllF19HGBaLxWw219aKwQNUVla2atVKbhNWuY0H3J2fn5/c/orNZrPVapXbqHhkNBoDAwPlNocmR4/W/Lda7eS/v1qtVjtd7y3UOjRT6GE2m9l3Wq3Wv/76S6B3JISzEQ0HaQAAgBD0euG2QdsIFaF79+7t5+f3008/VVZW2u68cePG6dOng4KCWrduLci7qtV2a/XYGA0AAEIQrNMnm1ARumfPnpGRkTk5Ofv372fWD6qrq7/44osLFy7Exsa2b99eoPe1a1mOPVfgHSorK7du3TpixIju3buHhYX16dPnlVdeOX/+PC8vvnHjxoiIiFOnTknydB6ZTKbJkyfHxMTUdlg1gLMMBrv4ItiZikJFaKVSuXjx4rZt2y5YsGDMmDELFy4cOnToe++917Nnz+nTpwtYMIWN0eBljEbjv//97+Tk5IqKiri4uEmTJqlUqgMHDowePfrAgQNSjw7AE1HHv8fECPQ+Ai689+rV67PPPlu3bt2333576tSp++6774UXXnjxxRcF30ehVtcsD2Rn48Ro8Gx6vf7HH3+cO3fuiy++yHTYJYScOXMmPj5+7dq1Dz/8MHXmFQA0FrvTJ9V2mlcCnm1FCAkODl6xYsXx48fz8/OPHTu2YMECMbY5slfsdTr6YgfAsxw5ckSpVMbExNjCMyGkZ8+eEyZMuHLlilANggC8lmCnQXMJG6GlQU2aUdENHq1Lly5ms9nAuRKdN2/eyZMnbd1+Kisr09PTo6Ojw8LCevToMX36dNtCdXV19f79+0eOHNmjR4+wsLDw8PCEhISCWg64rqqq2rp1q+11Zs2axb4IsFqtR44cGTFiBNPo980336z7OCmTybRy5crevXuHhYUNGzbs4MGDkyZNmjx5sslkYpaNExMTU1NTe/ToER4evmfPHkLI+fPnExIS+vTpExYW1r179xEjRhw4cIApdrl+/XpMTExiYuLevXujoqLCwsKioqK2bt1aVVXFftNz585NmjSpe/fu3PED1I/q9CnkUYoy217GF42mZgVaq0WiG2jsJJV7iYmhPhEef/zxtLS0uXPn7ty5c8yYMY888ki7du2oUo+qqqo333xzx44d0dHR8+bNKyws1Ol0U6ZMSUtL5mdwtwAAIABJREFU69WrV2pq6nvvvde/f3+tVtusWbPMzMxvv/326tWrH3/8cWBgIPU6SUlJ27dvDw8PnzVrVklJiU6n02g0Op0uODiYEPLNN9/MmTOnXbt2zHlWdR89aTKZXnrppSNHjowePZo5enLWrFlWq/Xhhx+2PSYzM/PkyZNLly4tLi4ODw/Py8uLj4+/9957ExISunTpkpeXt3Pnzjlz5gQGBtq6MmVmZmZnZ48aNSoiIiIjIyM5Ofn8+fPJycnMVwsLCxMSEkaOHDllypTDhw9nZGRcuXJFp9N5cLM/4Bm7ilvgk449NEKzT4xmMhI4MRrY/v68dj/JydQv8/333799+/bXXnvt8OHDhw4dIoQ0b948NjY2Pj4+MjKSCdVHjhzJyMj497//PXfuXOaeRx999OWXX87KygoODs7Ozh40aNB//vMfpm1kXFycVqvdtWtXYWEhFaGZ1xk/fvw777zDdCWKjo5OSEhgWvlWVlZu2bKlU6dOtoA9ZMgQjUZTW+10VlbWzz//PH/+/ISEBIVCERcXFxERobW/eDKbzW+88UbM35U4a9asadq06aZNmx544AFCyMiRI6OiombMmJGbm2uL0Ldv316xYgXTbHjkyJGLFy/es2fP2LFjmacQQt55551x48YRQv75z3/6+fnt2rXLYDCEh4c34qcCXkOUbdA2npjlJtgYDd4lNDR0586dOTk5KSkpI0aMIITs27dvwoQJS5YsYRK8er2+ZcuWo0ePts2t+/Tpc/jw4VmzZgUGBm7btu2///2vrauzQqGobT/kN99806RJk4kTJ9qOnY6IiFCr1Uwr3/z8/AsXLsTFxTHhmRASHBwcFxfn8KUsFsu3334bHBw8atQoZlQKhWL48OHdu3dnP6x9+/bs46JfffXVw4cP22ItISQoKIiao0dFRdl6Nvn6+k6cONFiseTm5jL3dOzY0RbLFQrFgAEDjEZjcXFx7f+6ACyibIO28dA5NCEkKanmxGidjqSlSToaAMG1adNm3Lhx48aNs1qtZ8+eXbhw4WeffTZgwIBRo0YZjUZ/f/+WLVvW9tzq6upr166dOXMmPz//0KFDx48fv+ce+vL91q1bRUVFzZs3v3TpEntaXF1dbTQaS0tLS0pKTCZTjx492M+ibtqYzeY//vijQ4cO7PSyv79/UFAQ+2EdOnS49957qedWVFScPn36t99+O3r0aE5OjtFoZH+1devW7DMkWrVqpVQqz5w5w9y855572M0jbZcaAPUTaxu0jedGaI2mJkITQnQ6rEZDDff9ZejShX2LKZt65JFHli9fbrtToVD07Nnzrbfemjp1KrMiW8frWa3W77//fuHChX/88QchpGXLluHh4Q57jFitVovFcvPmzcWLF3Nfp6SkxPVvqnbUhUJhYWFiYuKxY8cIIU2bNu3atWvfvn1/+OGHel8HkRh4wK7HVKmE2wZt47kRmmBjNNTOU3IqrVu3bt68+fHjx0tKSqh9z35+fs2aNWMiU0BAQGVlZXl5edu2bZmvlpeXP//886GhofHx8a+//nq3bt2WL1/euXNnZsvWxo0buRFaqVR26dLl8uXLO3bs6NixI3cwp06dCggIyM3NHTJkiO3OixcvOhy5n59fUFDQr7/+ajQalUolc+edO3fKyspqO4GgsrJy2bJl+fn5mzZtGjRoULNmzZg3/emnn9gPKysru337NvNVQkhxcXF5eTlzUgBAo7CLJIRPcROPXYdmYGM0eLqgoKDRo0efP39++fLlFRUVtvsrKytTU1PLy8sfe+wxQoharS4vL8/MzLQd4ffjjz/m5eX17t376tWrJSUl0dHRoaGhTHguLS3V6/Vms5l7Bt0jjzxSXFy8b98+2+sYjcbnnntu8ODBBQUFKpWqW7duBw4cKCwsZL5aWlr67bffOhy5j4/Pk08+WVRUtHfvXturHT58+NKlS7V9s0aj8dy5c127do2KimICcHV1dXZ2NrWQfOzYsdOnTzP/XVVV9dVXX7Vo0SJagJMBwbuIuA3axqPn0FSiW6/HNBo8z5QpU/Ly8nbv3v3111/369evU6dON2/ePHLkyJ9//vnMM88w09lHHnlk/PjxKSkpOTk5Y8eOPXHixBdffNGzZ8/hw4ebzebg4ODNmzdXVFRERETk5eXt2rWrtLS0qqqKOpuOEDJs2LCff/555cqVhw8fHjt2bEVFxfbt28+dO7dgwQKVSqVQKJYsWZKQkPD000/PmDGD1LfbKjY2duDAge+99965c+eY3VaZmZl1nBYaFBQUGRm5b9++1157bejQoRUVFRkZGWfPnlUoFOylaKPRqNFonn/++S5duqSnp588eXLBggX3339/3TuzAeohbo0Yw6MjNLHfGB0fjwgNnicgIGDt2rUjR47cvHnzsWPHDh8+3LRp04iIiBdeeMHWaMzX13fJkiUPPPDA5s2b586d27x589GjR8+bN4/ZTLVhwwatVpuWlvbXX3917tx5zpw5YWFh06dPz8vLY+er2a+j0+kSExPvueee7t27f/DBB8OGDWPqsSMjIz/55JOlS5e+9dZbTZo0eeKJJ2bMmPHuu+86HLlSqVy3bt2aNWt27ty5e/fuBx54YNWqVVu2bKntO/Xx8UlOTlYqlXv27Pnmm29atmwZFxe3Zs2a119/vaCgwBak+/fvP2bMmDVr1pSUlHTt2pU9PADXsSfQYm3fVdjyS3Kwdu1aQsjs2bN5e0W9nsTG1tzMypJwY7TFYrl69WqnTp2kGoDQrl69GhgYWMccSBL8/1KBYK5fv/7UU0/169cvJSXF5acHBwdv2rTJtrbNL3n+OpnN5tLS0g4dOkg9EKFcvny5Q4cO7CJ8sQkZSur4pfLodWiCjdEA8rV58+Zhw4ZduHDBds/Ro0evXbuGqi6QHSlS3MTzIzTBidEAMjVw4MCbN29Onz49PT19//79Wq128eLFoaGho0ePlnpoACyib4O28YIIjROjAWSpV69eH374Yfv27ZcvXz5r1qzdu3c/9dRTn3/+ucOtXACSoY7KELjTJ5unV4oxsDEaQJb69eu3Y8cOvl6tbdu22dnZfL0awF1Uipu9ciowL5hDE2yMBgAAl0ixDdrGO+bQGg3RamsCs1brMS2lwBk5OTlSDwE8RE5Oju3gDfAKEtWIMbxjDk3st69hKdqbDBgwgJePVIvFYjKZGv86slVWVib1EARkNpu5DVhcEBUVNWDAgMa/DrgHqkZM9M263jGHJoQkJdn9Q+PEaK8RFRXFS4Q2mUyVlZVU72tPUlBQEBoaKvUohFJSUuLv7y/QJmnwWAaD3aqoiDViDK+ZQ1PZCWyMBgCAurGPylCrxZ/XeU2EJqgXAwAAp1E1YqJPoIl3RWhqkxX7nx4AAICN2gYtxTZdb4rQxD5Is9MXAAAAbOzFUInqlrwsQrPTFFQGAwAAgKHXS57iJl4XoXGQBgAA1EvSbdA2XhahCQ7SAACAOkl3VAbF+yI0DtIAAIA6SHdUBsX7IjSxD9JIdAMAAJt0R2VQvDJCx8TU/Ldej43RAABwl6RHZVC8MkJrNHbXRNh2BQAADHnUiDG8MkITHKQBAAAcBgNJTq65KfXxDd4aoanTJxGkAQCAWvSUrkaM4a0RmthfHGVnSzYMAACQCamPyqB4cYTGQRoAAGAjpxoxhhdHaNSLAQCAjZxqxBheHKEJ6sUAAIAQwukjJoPwTLw9QlNJDARpAADvZDDYrXVKXSPG8O4ITeUxUC8GAOCdZFYjxvDuCE1QLwYA4PWoGjF5TKAJIjR9kAbadAMAeBuqRky6w6woXh+hiX2QZneTAQAAj0dNoOWR32YgQnMSGuwfFQAAeDa5prgJIjQhnKIAJLoBALyHLGvEGIjQhBDUiwEAeCUZT6AJIvRdqBcDAPBCcq0RYyBC/w31YgAAXkXGNWIMASP0H3/8MWLEiDCOefPmCfemrkO9GACAV5F3ipsQ4iPcS1+7dq24uLh58+aBgYHs+6mbcsEUCNh+WunpMryeAgAA3rBrxORxVAZFwAhdXFxcVla2ePHif/3rX8K9C5+mTauJ0DodSUqyO/wKAAA8hvzOmuQSMMt97tw5QkhYWJhwb8Ez1IsBAHgJedeIMYSK0Far9fz58/fdd19ISIhAbyEI1IsBAHg8WZ41ySVUhL5169aVK1cCAgK+/PLL6OjosLCw8PDwhQsXFhUVCfSO/KAqBXAeJQCA55HlWZNcQkXoP/744/LlywUFBdu2bfvHP/4xceLEtm3bfv755+PGjTtx4oRAb8oDqqEMzqMEAPA8Mu4jxiZUpVh5eXmTJk0effTRDz74gCnerq6u3rRpU0pKysqVKzdt2qRUKh0+MTMzZM0aMnNmjp/fUeaeiIiIyMhIgcbJ5TN+vJJVL2ZKTLTwlKi3WCzl5eUBAQG8vJoMVVRUKBQKPz8/qQciCJPJdPv2bV9fX6kHIpSKioqysjKpRyGU8vLy27dvWywWqQciCLPZXFFR4e/vL/VAhFJeXu7v7+/jw0/A8iksVLJqxEzjx1tE/M3Pzc21TVMtlpD//Gd8ZGStVwhCzaHDw8N//PHHjz/+2La3qkmTJlOnTo2MjMzLy7t06VJtT9y3b7zBIOUSsGXqVHYJt89bb/H1ylarVaFQ8PVqID6r1Sr1EMBF+NNzawqFgse/Pp9t22puqFSWqVP5euWG2rhxgMFAdu+eXdsDBNxtxRUQEBAaGnr8+PGSkpK6H2k2R0VEREmWeFCrbSvQfjt2+G3fzsurWiwWs9ncqlUrXl5NhiorK1u1auWpc2gfHx/mG5R6IEIpLS314O+uqqrK39+/ttSduzObzVar1YN/fEajMTAwkJ85tMFAduyoualWi/zvplar1Wo1IUSnIxcv1vNgAXdbMVlB7v0KhaJJkyb1Pj0+XoAxOYnaGId6MQAAzyCPGjGDwW4pvDZCReiUlJTevXunpaWx7ywpKcnLy3NyC5bBIF2QpprLoF4MAMAzyKNGLD3dqTMUhYrQsbGxSqUyIyOjoKCAuaeysnL16tXnz58fOnRoaGhobU/088ux/bdOJ117bJxHCQDgYeTRR4yqtWJHPYpQEbpv374vv/xyfn7+8OHDZ8yYMW/evNjY2B07dvTv33/27Nl1LCfMnHmUfdOZPIAgNBq7lp+SjQMAAHgij0bc7PSwSkWGDTta2yOFitAKhSIhISE1NTU8PDwrK+vLL7/08fFZtGjRli1b2rRpU8cTVSq7iwu9XrppNPuHh6VoAAC3Jo8+YlRuWKOpayACVoopFIqYmJidO3deuHAhPz//0KFDCQkJztRSUidWxMYKN8Y62S+iI0gDALgxdmBUqSSpEaMKxFSqehLtAkboxqCCo2QlY+xrGxykAQDgvqijMqSYQ2u1dkVNVKTjkmmEpirsJCsZY19kSZlwBwCARqA+wKWoEeNm2eu9SJBphCaciwtpSrWoejFMowEA3JHUE2hq/7BKVf8Emsg5QnNLxqRZCGZfamHbFQCA26Fmr1IcBU3N4anZX23kG6EJIUlJdhc68fFSxEfqZ4lENwCAe6FqxERPcXMn0E4OQdYRmnAWC6QpGWMHaSmbkQIAQMOxU9wSFYixOZPfZsg9QqvVdvFRmmotqigf02gAAHdBhQ3RN1lRS7R1b4CmyD1CEznsvKJK7lAvBgDgLiTtI+ZagZiNG0RoYh+knTwShGdo0w0A4HakbsRNnZDR0Pd3jwhNpQWSk0XPNFP1YmjTDQAgf9QmK3GruKkTMqhFW2e4R4Qmctgezf6nRQdQAACZ40ZIcblWv83mNhFa+u3R1L8ugjQAgJxRuVZxa8SoVpiuHUXtNhGacE7UEHt7NFVigHoxAAA5ozZZiTiHbmSBmI07RWgiea4bbboBANyCpDViLm+AprhZhJb4RA206QYAcAvSbbJqzAZoiptFaEJIVpbdTbG3R6NNNwCAzEnXiNvlBp8OuV+EJpzt0aIGaeonjWk0AIDcSNeIm7sB2pkTMmrjlhGaShpIkOu2YdeXAwCA5AwGqRpxN34DNMUtIzSRtmSMKtnHtisAAPmgasTE2mTFb36b4a4Rmrs9WrwgjTbdAACyxQ4GIm6y4p4A3fh3dtcITTinRycni1i2hW1XAAAyJNEmKyEm0MStIzSR8PRobLsCAJAhiTZZ8dKfhMu9I7SUp0ejTTcAgKxItMmKqlaeOpW3CwP3jtCEkLQ0u9lsbKxYuW606QYAkBVqk5UoNWLc/Pabb/L24m4foQmnrlvUXLcE7woAAI5QZ002Ziey0/hq8OmQJ0RoqlhPvFw3dYGGejEAAKlQH/2i1IjpdLw1+HTIEyI0kaoVKLZdAQDIhOg1YgYD/Z68XxV4SIQmUrUCZU+jxe5tBgAAhBBpNllptXw2+HTIcyK0NK1Ase0KAEBy1GRW+Cpu6gCrxjf4dMhzIjSRqmSM2naF064AAMQk+iYrbv02vwViNh4Voal/JmqRQChUOgXTaAAAMYm+yYo6wIrKpfLIoyI04eS6k5PFynWz3xIAAMRBTcXUaqE3WXEPsBJu1dvTIjSRJNeN064AACRhMNjNZwWeQAvUf7s2HhihqWOvxMh1Y9sVAIAkxD3JKj2d/wOs6uCBEZo4OvZK8Fw3TrsCABCZuF1KqPy20BNo4qkRmoif68a2KwAAkYnYpUS0+m02j43QEuS62VdT2HYFACAocbuUiJzfZnhshCbi57qpTXhi7PQCAPBW1DkZQm6DFj+/zfDkCE04F1WCB00cGg0AIALunich30r8/DbDwyO0Wm33Q9TrBQ7SODQaAEAEVEZUyE1WkuS3GR4eoYnIuW6Vijz5ZM1NJLoBAITATnEPHixczJQqv83w/AhNODNbYeu6J02q+W+qkAEAABqP2mT1xhsCvY+E+W2GV0RoKtctbF03lQHBNBoAgF9ibbKSML/N8IoITUTOdaN7CQCAQMTaZCVtfpvhLRGaiNnDBN1LAAAEIspR0JLntxleFKG5PUwEDNLoXgIAwDuxjoKWPL/NEClCV1VVzZ8/PyIi4tSpU+K8o0NUrlunEywDje4lAAC8o7qUCJN31uulz28zRIrQe/fu/fLLL8V5r7qJmuu2wcZoAIBGEqVLiUzy2wwxIvT58+dXrlxptVpFeK96iZfrRvcSAAAeidKlJD3dbllSqvw2Q/AIXVlZmZKSEhgY2L9/f6Hfy0ki5bqpPQBIdAMAuMxgsEtxC3MUNJXfVqsly28zBI/Q27ZtO3To0Lx580JCQoR+L+eJlOtm/2zRvQQAwGXCb7KSVX6bIWyEzs3N3bRp07hx49QSpgkcof7pDQYSGyvA29hf5fm8/bYA7wEA4AWE71Ki1drlt5OS7LbNSkLACG00GlevXt2mTZvZs2f7+PgI90auoVYXhOosYt+9xO/oUQHeAwDAk/kdPepz6FDNbQHmtjqdXbGQWi3oaZbOEipwWq3Wzz777Jdffvnoo4/atGnj/BMzM0PWrCEzZ+b4+d0NZhEREZGRkUIMctcuEhjYynYzNpacOmUKCbHw+R5jxiiXLPEpLGRu+W/fXjZkCJ+vLycVFRUKhcLPz0/qgQjCZDLdvn3b19dX6oEIpaKioqysTOpRCKW8vPz27dsWC69/3bJhNpsrKir8/f2lHohQAlatsv23JSTEFBlJeP1dLSz0iY9X2m6GhFjef98k0F9Dbm7uiRMn2Pfk5ORERUU5fLBQc+jc3Nz169dPmjQpOjq6QU/ct288VVEvqAMHzOybCQn8X7JYXn/d9t/KjAx0L3FfMtmPAC5QKBRSDwFcZTD45eTYbrE/Ufny4ot284rXX7dInt9mCDKHLi0tXbZsWVhY2KxZs1z7wzCboyIiokRYvB42jKjVNfntnBy/tWv9eC5BmDmTvPii7VartWulLz8QRmVlZatWrTx1Du3j48N8g1IPRCilpaUe/N1VVVX5+/srlcr6H+qGzGaz1Wr12B/f2rU1/61S+c2cye9HjE5H2Bl0jYbMnOlHiFCfY2q12vnCLEHm0IWFhfn5+SdOnHjooYfC/vbll18ajca4uDgnO4sJe0YkS1aWXTmAIIdqUN1LMI0GAHAG1eaT73mbDOu32QSZQwcGBo4fP95stksg5+TkFBUVxcbGduzYMTAwsN4XYf7hxPnHSkuzq+XWavn+NUhLs/slS0+XeJMdAIBbELLNJ7djlazCMxEoQoeEhCxdupS6c968eTdv3nzppZfCw8PreK6fX47ZfHfNXKcj06aJ0c9FrSbffUeeeOLuTb2eaLV8x1CNpiZIJycjQgMA1IPb5pPX9WHqeIwlS6RsH+aQ7M62mjnTbj+SaJ24hgwR+ABpNAEFAGgQIdt8co/HWLaMx5fnh+wiNNU3W6htyo4I22iM2mIv2jI7AIA7ErLNp4TLzzodcb5+WrwInZKScuLEibpT3Ayqk4sg3b4cEbrRGL1JANNoAIDaCNnmk2ofJtrxGAbD3cSwQuFUnJbdHJoh3hmR9oRtNKZWm9nb0tmXhwAAwMZa4zRHRVka2FqjDtz2YaLVBVFXBvXGF5lGaCqfIdTxU45QFwexsXzujTKOH19zQ8wMPgCAG7H/eCydPZuvF5Ywv83dOFbvxF2mEZpwIqVoJWMqFcnKsruHxxm8+emn7TL4mEYDAHDZn5NhefRRXl6Vu71KtOMxXLsykG+E5paMibZuS13aMJuveMPOp4iZHAAAcAuCrUBT26s0GvGOx6ByphqNU1cG8o3QhJCkJLr8WbRmXFlZgm2+on4ymEYDALBRk02eoih3e5WY+W3qe3LyqkPWEZpwLp7E3KMk4FujCSgAgEPUBJqn8Cxtd08qC+v8W8s9QlOHdFIrxEK/NfuCi8/NV1TwF22NHQBA5uxXoPlKcUu1vYpwlmgb9NZyj9Dk78sNq5WIf/QflWbncy0c02gAAIow52RIuL2qkXN3N4jQhEgQm224O7P5WZCWqlQdAEC2BJhAS5vfTk+3m3819BtyjwgtIQE3X1HTaAAAb8adQDd6I5TD06vE2V5FHB380dBVdUTo+nEXpPkJ0liNBgCwYecnVSpezsn44Qe67EzM06tcq99mQ4R2CrUgzc82ZmoXAfsqAADAq9g6VjP4OCdDpyP/+lfNTZHz21SYcO0bQoR2liDdQKmLROS6AcA76fV2H6mNnkBzl5/F3ArE1+I3IrSzBFmQpi6rcCQlAHghvg+alLC7J4N6L5fn7ojQDSBIN1BqaQLTaADwNlRLzEaXcEvY3dPGtkO4MYvfiNANw383UCrsowkoAHgbXifQEnb35LJaG/XuiNANRl3e8bAgzV5xwZGUAOBVeJ1AS7v7mXd3I3RlZeX58+erqqqkHY1bUKsdtDFpFCoJgm1XAOA9qC4ljZhAM+FZqu6eQrgboY1GY0JCQt++fefPn3/8+PHq6mpphyVz1E+dhwVpTKMBwAtRH3eNm/Byl59F6+4pkLsR2s/Pr3fv3lVVVRkZGePHj+/Tp8+yZcsKCgqsEvbbbCCRZ55ZWXbVeo1dkKaOpMQ0GgC8AX8TaFktP/PlboRu0aLFunXrTp06lZ6e/vjjj9+5c0en0w0ePHjgwIGrVq0qLCyUeahWKEhystjHT/C8Q5p9sYdpNAB4PP5WoD1s+dnGrlLM19d30KBBW7ZsYUL1yJEjy8rK1q9f/9hjjw0ZMmTz5s0lJSVSDbQ2CgVRKO7+t8jbidVqXndIYxoNAF6FmkC7uiOKu/xMdYF0X45ruZlQzcyqMzIynnnmmcrKyuXLl0dFRQ0dOvSLL74wmUwiD7Q27J+p+DNPnndIYxoNAF6Cvwm0HHY/C6Se3VbXr18/duzY8ePHmdmz1Wq9ePHia6+9NnDgwM2bN8uh9ps6qISfZpwNwecOaWoajb3RAOCpeJpA63QeuPxs4yBCW63WwsLCVatWRUdHDxo0aMWKFefOnXvwwQdTUlJOnDhx4sSJd999t0WLFitWrFi/fr34I+biee9Tw3F3SLsepNmvxc8BHQAAMmMw8DKB9tTlZ5uaCG21WgsKClauXDlw4MDHHnts/fr1V65cCQ0NXbRo0S+//PLVV1+NGzcuICAgICBgwoQJq1evbt68+cGDByUcug031Sx+rptakHY9141pNAB4PCquujSB5jbfdvfdz1w+zP+VlJRMmzbt7NmzzM2OHTtOnjw5Li4uODjY4dNCQ0MDAwNv374t0jDrk5VVUy9GCImPJwUFog6AuUqwXRno9WTKFPLJJy69VlJSze+dTkemTfO0XzoA8GY8TaAPHpR497PBIPhpHHfn0Far9datW/fdd9/06dN//PHHn3766d///ndt4ZkQUlVV9cwzz7z99tvCjq4h2MkN7rWVCKgF6U8/dXUqT11OYhoNAJ6EjxVonY688ILdy4if39Zq7TYTCeFuhA4ICPjkk09ycnIWL14cEhKiqO89g4ODZ86cGRUVJeDQGojKb0iyhsvdIe3iGNiVDzqd2MVvAAACMRjsTvBzKUEo7dnPDL2+5vsQLkjfjdD+/v4hISFNmjQR6n1EIXnJmEpFZ9ddHAOVrMHeaADwDNQEuuEzX4OBxMba3SPy2c/E0SWCQNMojzrbivpxGwwShDaVym4CzP1lcham0QDgYagJdMPz2w7PxhB/93N6Oj0GgS4RPCpCE06um4fzmxuOamfjYhsTTKMBwMNQE+iGV3ZRzUkkWX42GOwmUGq1gBVqnhahiQxy3dwxuHihgGk0AHiMRq9Ac8/GEH/5mZvfFrSA3AMjNDfPLEmum/rVcaVqDNNoAPAYjVuBplYMmRcQefmZOOowKuhmWA+M0ISTZ2YHbNGo1fT7vv22T4NfBdNoAPAAjZtAFxb6yKE5CZXfFnoCTTw1QhP7PLNUJ2dyF6QnTmzT4JdgwzQaANxR4ybQGRkBhw7VzHDEb05CJOow6rERmsl1W62ShWcG1cYkJ8evwUEW02gAcGtf6u5MAAAgAElEQVSNm0BrtWTVKqXtplTNt0XObzM8NkKTRp1mxqfGVo1hGg0Abq0RE2g5VIcRKfLbDE+O0DLBQ9UYptEA4KYaMYGWSXWYhCdoIUKLgVs11rA9YJhGA4CbcnUCLZ+jqyTJbzMQoUVCVY01uNcYptEA4HYaMYHWaiU+uoohVX6bgQgtnu++s0RFmW03G9ZrDNNoAHA7rk6gtVq7yB4SYtm82cLnwJzGTqqLX6SGCC2q1atL2TcbVjVGTaPFb2cKAOA8V7tw63T0tPXzz0v4G1aD2fYEiZ9mR4QWVUiIxfWqMWoajXOjAUDOXGqP6bAsKyREmgk0m9UqQZodEVpsajW9RbsBVWOYRgOAWzAY7D6gnJt7cgt0CgqkqQ6TCURoabhYNYZpNAC4hYbvT3J4sqSXEzBCW63W//3vf0899VT37t27du06dOjQ/fv3V1dXC/eOboTqNabXOz2TxjQaAGSOmkA7F2nlcLKk3AgYodPS0qZMmXLq1KnY2NixY8dWVFTMmjVryZIlVVVVwr2pG6G23ut0dkUVtUpKsnsairoBQG4avgLNrQ6TpHeY3AgVoS9fvpyWlta5c+cDBw5s2rQpJSXlu+++i46O3rdv38mTJwV6U9dIcoA0cXSFGB/v3JSY/euu12MaDQAyQn0oORGeHVaHid87TIaEitC//fZbVVXViBEjQkNDmXsCAgLGjRtnMpmOHTsm0Js2lEJBFAopU8VqtUsNQTUaTKMBQKaoPdD1pbi5hThS9Q6TIaEidHR0dE5OTmJiIvvOixcvEkLat28v0Js2iEJR899STaOJyw1BMY0GABlq4ASaCc/s6rDkZLkceiQHItVyG43GDRs2pKen9+7d+9FHHxXnTevGzjA3uAcnr5KSyIgRDRwMptEAIEMNmUBzi7cTExGe7QgeoU0m0+TJkyMiIt57771//OMfqampbdq0EfpNnUElUqSdiO7fTw+m/iCNaTQAyEoDJ9Dc4u2UFCGG5cZ8hH6DO3fudO7cuXPnzseOHWM2X61du7ZXr161PT4nJ4e6JyIiIjIyUoix7dpFAgNb2W7GxpJTp0zCNa+xWCzl5eUBAQEOv/r++6RPn5rB6PXkjTcs8+aZan25MWOUS5b4FBbevanVlgnzr+S8iooKhULh5+cn7TAEYjKZbt++7evrK/VAhFJRUVFWVib1KIRSXl5++/Zti0X61lRCMJvNFRUV/v7+Eo7Bp7DQb8kSW0SxhISYxowhtf9GpaQo3367JgCFhFh27TLV9vDy8nJ/f38fH8EDlghyc3NPnDjBvicnJycqKsrhgwWfQwcFBa1YsWLFihWZmZkLFy40GAzvvPOOyVR74BHXgQNm9s2EBAF/A6xWq4K9+m1PpaIH8/bbPnVPjC2vv15zQ6/32batkSOEulmpbnDgPur40wN+6PU+hw7Zbtl9OnFs2+bDDs8qFdmwwVxH8bZCofDSvz6riMrKysaMGdO3b99ff/3V4QPWrFmzZs0aMYdktVrVaishNf9LThbqjaqqqn7//fe6H5OVZTcYQqxZWXU+gT16lYq/wbriypUrlZWV0o5BOEaj8fr161KPQkD5+flSD0FA169fNxqNUo9CKJWVlVeuXJFyBAUFzn8WFRTYfcSpVNa0tHpe/vfff6+qquJxvGxpaVZxIyGtjsAnatfPFi1ahIaGWiwWWXUWy8qyK7pq2HlTfHNY2l3XYdDslR7qJBkAAHFQK9C1NwNzuLdKwu6etq3YzOZbuREqQq9bt65Pnz67d+9m33njxo3Tp08rlcqWLVsK9L6uoX6dpK2MTkpqSNdutdru0RLuGwMA72Qw2H1oUh9K9g+k9lZpNFIWb1OdUgiRXcWtUBE6MjKysrJy/fr1RUVFzD1VVVUbN248f/78Y4891rFjR4He1zVqNfnuu5qber3EQToriwweXHOzniBN/YJj5xUAiEmvt4u6tYRc7t6qwYMl7rxNFZMvWSK7TilCReioqKjnnnsuPz9/2LBhL7zwwrx58wYNGpSWlhYRETFr1iwZluQNGWL3s5E2100Iycx0ev8VdcVKZckBAIRDzUNrn0Bz91ZlZgo6snro9XQn8GXLJBtMbYSK0L6+vgsWLNiwYUNYWNgPP/zw5Zdf+vj4LFq06OOPP5bbBNqG2yVbWtR46prZUxetkg8dALwE1aKklgm0ViuvgzEcdgKXIQErxZo0aTJ06NCvvvrqwoUL+fn5hw4dSkhIUCqVwr1jI1E/JGkbjRFHv8S1zuzVartaC52uzuoyAAA+UNWptUyguedWSX4whlZLL4fLLb/NELWWW/64jcak1YCjNbAaDQAioybQ06ZxH6LXOziLUtpwSB31q1bLt9UoIjSNPY2WwxZ5tZpOvzgO0lQLXAlP7AIAb+DEBFpue6uI++S3GYjQNCa3bLXKIjwzNBoHm6QdxF/qFy09XcAxAYCX406N7XHDs+TnVnG3VyUlyfogakRoB2S4IJGURMaOrbnJ/T27ix3JMY0GAIFQLUqo0/YcbX1+803pk8lUPbnkE/p6IUK7jS+/dKKTCXVBiNVoABBCnSXc3K3PKhV54w2RhlYb7vYqOee3GYjQ7iQry4lN0jiVEgAEReXnHIVnautzQYE4I6uVey0/2yBCu5m0tPqCNJVuwt5oAOCRwWBX40LVqDrqTCLt1meGu2yvoiBCuxnupR+VuiEEx2kAgGDqPCSDu/WZOppIKu6yvYqCCO1+uNekWq19FKauDzGNBgBe1HlIhlbrIJMsh/BMWFtn3SW/zUCEdkvcTibx8fZBGg1MAIB36em1HZLhsHGYrDLJzB5a+Vw0OAMR2l2p1XTxhV11Bvc4DfQBBYDGMBjsgjDrQ0anc7DPWFbh2Uaeo6oNIrQb46Zr7NqNyerUawBwd7XssKL6ehJCkpPlvs/YXSBCuzduu7GaII0+oADAl1p6fMqwcZgnQYR2nbQnX9kkJdGXqzVBGtNoAOCFox6fBgMJDbV7FMIzvxChXaRQ1Hlgs7i4FRk1a9LsII0GJgDgAuqjIymJqFQOT8VAeOYXIrQrFIq7/1Hrgc2io9qN1TTQQQMTAGgMaoeVSkU0Gm7bbY3GnXYxuQtE6AajQjL1ayqhrCwyZEjNzZoEFBqYAIDLOBNobnh2r03GbgQRusG4BzbLZ1763Xf0TDo0FA1MAMBVnH7WBrWGG54lb7vtqRChXUGFPPksSBPOmvTdIE2tDiFIA4Az7D/aDElp3EOr5NB221MhQruIajYrnwVpbisfg4HEau0bmOh0cknNA4Bs2S+KGdSa+HQ191QMN2rR5XYQoV3H7RYik6jHDdJ6PYk1yDU1DwDyxPqUMBBVvCFJtuGZu+/LMyBCu85hc2yZcHAElkGlJcms29h5BQC1s/+I0BO13qCy3ZRbeGZy77ZdNh4DEbpRqO7XslqQ5pZvJJMkA1HV3JbPWAFAblifDwaiiic1l/yyCs/E/kRqDwvSiNCNRW1Els+CNCFEpao5c42hJaySMb0eO68AwAH7JsHszw3m0l8+4Zl7ppYnQYTmAVUobXd8hQywZ9I6otETdc1t+eTlAUAm7FuUGIhKRzTMf8utcpuzF8zTtmUjQvOAuyAtq/wxle62m0YTBGkAsKfVsqtebfltuSW3qfBMOPtgPQAiND+4C9JTpkg2GC72la+eqO2m0TjzCgBs7HdY2bJuzPRUPuGZEHLwoN1Hl0d2BUeE5g21IP3pp/IKfOyJPrvogxCZTfkBQEL2O6yYlBt3A6fkdDrywgs1Nz0vv81AhOYT+1fEapXXLzRhBWkDwc4rAOCwz6jpidpAVDIMz9zlZ1mtjvMIEZpPthVfqoJaPtTquyOkd15hNRrAy3EKxOJJGhP85BaeqVMvk5LklX7nESI0z7gbnOTGdr1pVzJGHTAHAN5Gr6cKxGS49mxrTmKj0RCNRqrhCA4R2hsxM2l651VyslzalgKAyOwTx3qiNqjUcps9E/vmJMRzl59tEKG9FJOQp3Ze6WMxjQbwOgYDMcTaFYilq5JktbGKodfTzUk8dfnZBhHae6lUJK1AbetFQAhRG3TxoXrJBgQAojMYiDZWrzLoa+5RqZOy1HILz9Tyswwz8EJAhPZqKhVRF9gliZIM8aGhqOwG8AoGA4mPNUwzsJJnKpUqK02GkY8akuc1J3EIEdrbqVR2u8RUxKAxaOPjEaQBPBwzK1UZ9Gqir7k3KUmG4Zlhtd6tw/XI5iQOIUIDfTmaRJKJwYAgDeDB7iaNDYY0Yr+zWPaF0Varh1eHsSFCAyGEPv0jjcQzf8AI0gCex2AgoaHEYCB0ePae0OcmEKGBEEKIWs2+dlaTu4kvBGkAD6PTkdBQQlh/5ndRpwuADCBCw9/sKyNtF9exsThFGsBDaLV3tz2rCCe/jQm0/CBCAwsr160ihmRyt8IzPh4NxwDcnlZbs59YQ9JVxFDzNS+pvHI3iNCSUSjkF/Y4JWO2v+HkZLTuBnBj7PCsIoYk9tk59otcIB+I0BJQKIhCQQghycnyW+XllIzZ/lunoxvWA4D8MU332d246Pw2JtByhQgtNioky64US61m/ylTtSR6PYI0gDsxGEh6ul141hAdCsTcBSK02NRq+vAr2WWP7c9yyyJ2MVmvv1sICgAyx+yZtOtlTQxJhLW6hgIxeUOElgb7mpV73Kn07P9os1R2FxHMZkp5Tf0BwB7zwcI+r06lImkqLQrE3IiwEfr8+fPTp0/v0aNHWFhYnz59Fi5cWFRUJOg7ugvqWDe9XmYzafvEl9qgy0rWs7+OfiYAcmbrSWKjUpGsNIPaoKu5CwVisidghD5w4MDo0aOzs7P79u07ceLEoKCgzz//XKPRIEgzqINZdDqZbTu2n0ar9dqCAnq5CkEaQIa0WnopignPKq18N0DrdHfrZ4FNqAhdUlKybt26gICA7du3f/rppytWrDh48OD8+fPz8/Pfffddi8Ui0Pu6Ee4fiLxaYVPj0+tV6dq0NARpAFnT6ewWnsnf5yirDHq7v1WNRj5nNxoMd5OICNIUoSJ0Xl7euXPnRo4c2bdvX+aeJk2aPP300w888EBubu4ff/wh0Pu6F7WaPoFcXgGPOuAtOVlFDFR+nhASGyu/dXQA78PsqqLWyzQaUlBAVMRg9wU57bCiCnHk2ChCOkJF6CtXrrRo0SIyMlLBuihq2rRpixYtBHpHN2W/uYkQuZV2U3/G8fGEkKwseszYhQUgLWYaSv1hJif/nQiLl2l+m1vOlpwsn4sH6QkVoadMmXL8+PExY8aw7zx37tzp06eDg4Pvvfdegd7XHSUlkREjam7Kq7SbqiXR302UJSXRf+N6Penfv82hQz5ijg4ACCGFhT4TJ7Zhp99UKlao0+nsUnOy2QDNXFWww3NiIsKzHfF2WxmNxrVr1966dWvChAlKpVK093UL+/fTpd0yCtJUSdvfI9No6BR9YaHPE0/4yChLD+AF9HrSvbtPYWHNxTFzyvPdUMfkvtlfk80EOj2dUFcVKSmSDUaeRJrxmEympKSkQ4cOTZo0adSoUXU8Micnh7onIiIiMjJSyNGJxGKxlJeXBwQEOPzq+++TPn1a2W7q9eSNNyzz5pnEGl2d3n+/1dixtluWZ581rVtHCImMJMePk1GjlOxPh9hYcuCAOSrKLME4hWQymW7fvu3r6yv1QIRSUVFRVlYm9SiEUl5efvv2bc+rUd2+3e/FF/3Y94SEWHbtMqlUhPlhKpcs8WHNUk3r1lnk8VNOSVG+/XbN5wYz7NqGVl5e7u/v7+PjCSm63NzcEydOsO/JycmJiopy+GAx5tClpaUvvfTS7t27x4wZs3jxYg/+jKub1WpV1F6qqFKRAwfsoppcwjMhRK0mrAjts22b7dKXGTaVMxs+3G/6dE/4W6JYqW5w4D7q+NNzUwYDeestHyo8M3+PtpyXz6FDPtu22b5qmTXLEh0t4hhrtW2bDzs8q1RkwwYzO1VHUSgUXvrXZxXY+fPnn3zyybCwsLfffvvOnTt1P3jNmjVr1qwRekhSqaqq+v333+t+TFaWlRCr8D8WlzAjY/6nUlFfVKvtvk6IVa2WZJRCMRqN169fl3oUAsrPz5d6CAK6fv260WiUehS8KSiwqlT0X1xycp0PUqmsBQWSjJZSUEB/lqSl1fOU33//vaqqSozBSaGOwCfsHPrQoUOTJ082GAxvvPHGggULvHb27DymtFumF4vs5SuDgSo6z8oiiYl2k36mgzeWpQH4xa1/JoQsWGCma6y0WrsH2ffblwq3ElajQWezWgkYoXNzc+fMmXPnzp0PP/wwPj6+SZMmwr2XJ5FvKSO1PZoqECUkMdFIJeqZv0Z59UoDcGdMvzCqneeCBebZs0vtHqfX2/3hyaPBJ/faoqaiDRwRKkIXFRXNnz+fEJKamvr4448L9C4gNm4XNHtqNSkooK/U4+PJrl3CjgvA4xkMZN8+x/3CqDvpFJc86re5e6sGD5bDuGRNqHKenTt3Xrp0qWnTpq+++ipVo9GxY8d169a1adNGoLcGATF/6rY/fmYjh/01MPORQXUwHTfOQQM1AHCSw8y2SkUKCgghxEztnEhPpyeqMshvc/dWZWZKNhh3Icgc2mQy/e9//yOE3Llzp6ioqNDe1atXrTJdaAUncFqBcpeaHV7XY1kawDUOw3Ny8t3w7ODR7L89tVomeWS7Y6pVuF53iiBzaKVS+emnnwrxyiALaWl2R+fExzv8qEhKIjExdlUhtvPk5fGJAeAGtFoHme1al2+5+W3Z/LFZrXcPxmAycTKY1bsB8XqKgedgOgraUE2LWBwuSycny6ljGoBcMX9YDheeaw27VCqZynhJzWq9e80gp0HJGiI0uIT6I3OU62YwHyjUHyQy3gB1syWc2Bxe8to9h0oly2YCbVNQIIeicreBCA2uqq+u28bhsjTzAYRj5gC4HG6pSk6uc+2W2mgsj/ptaCREaHAVJ9cdMGtWHQ9PSnLw+YKMNwBbbZntpKR65sP+O3bQ9dtIJbs/RGg3Jn1ss891KzMyfA4dquPhTI6OGjYy3gAMh5ltJgVVd2bYLyen1dq1ds+RX34bXIAI7a4UCnkcUmmfSfNJSKj74SoV+eEHZLwB7DBTZyqzTf7eUlVP2TO3fhs7mTwFIrRbsvWAkT5IU8tdnH7dDtWR8aY+oQA8HvNHw506p6U5NxOm+m/Loz8J8AIR2v1QMUyv5/T8E1l9/bodYjLe3Brv2FhkvMGLMFNn6nfemcz2Xdz+28hvexBEaPfDTWJptVKfTuF0XTdbHTXe0mfvAQRWW1GYU5lt20vIr/828AgR2i1xe1zHx0sapLm5bqdjbFKSg88jlI+BZ6utKMzZzDZDludLAo8Qod0VkyVmow6rEJtGU8WOynq986NhDgBA+Rh4g7qLwhqwQ0qnY1+VW594QsJWIDhhViCI0G6Mm9OSdhG37N137W43sO6rtvIxTKbBY9Q2dW5ws3r7/LYlJOTGO+/wMkIX6PUkPr6mfBV4hAjt3jQa+q9dwiBtCQ42Hzhgd5dzC9I2DsvHMJkGD1Db1LmePtu1vZb9X5bl9dctwcGNH6QL2CtaCNK8Q4R2e0lJdHJLypm0Wm0XYPX6hoZWxyfSYzIN7qyOqbOzRWFs3OMxJMpvGwx2p9wRgitpniFCe4K0NHreKeWaNHVQRu2HatSBKR/DZBrcHZ9TZ9srUsdjSFS/za0HrfVMTHAVIrSHoMKic41DBEP9mbo0FEymwd0x+/t5mzoTGR2PwQyEapSCrV68Q4T2HFlZZMiQmpvcBJR41Gr6AGlXrxfqmExLeQkCUCdm6sytlXR96szgtg+T4ngMbnjGTmyBIEJ7lO++o2fSkqEOkHau0ZhDzIca9+9fpyOhoUh6g+zodHxPnW2vy97VJNHxGA7DM7XzE/iCCO1p2GvSVquUI+F3K5hG43gyzSS90c0b5MCW3eF56kzkcjwGMwoqPOOcDuEgQnsaJt2kVksdnomjS+vGTXhrW5lm8vmYTIOEbBVh3A7bjZ06E0frRFlZ4rcPY0bB/gaZP0n0MRMOIrQHktFVrUrVyM1XXA5XpsnfFWTobQTic5jWJoRoNI2eOjO426sQnr0DIjQIjI/NVxTmo4H76cB8iDzxBJLeIBKDgQwe7DitnZxM0tL4CGA6neTbqxCepYIIDcLjLkjzEUKZBmTciUtmJpLeIDhbWvuHH+zut6W1+aniYt6G/epS1EwjPEsFERqEx/1Y4W+nVN1Jb8Rp4J0tNnOvDnmoCKPeiZqeS7S9ir1qxpSXIDyLAxEaREF9svCxIG1jS3pTUOkNvHPYhITwVRFG4S4/S9eyi6k8lVGNi3dAhAaxUHkxPhak2Zjy9doqvdGGDBqJ2UnlsAkJn2ltG71e8uVnitWK5LbYEKFBRMIsSLMxSW+HcdrhxytAvZhks8OLPN6qtR2+pY0MwjMD4VlkiNAgIrWazpEJ0LqTabXkcHFar8fiNDSAbcmZu4vP1uqO/6DFXX6mOvSB10CEBnE1+nhKJ9XxAZqcTBQKxGmoS93lYGlpji8B+SGbwyVBcojQ3k6hoI+QE5wAO6Rrw/QK5X7IEsRpqEXdsZlZchYwYspg9zPIByK0V1MoCPm7PFVU1MJd41p2O/NuiNPgjHpjs7DF1HJdfgapIEIDIeIHaVEWpNlsi9Off+7gq7Y4jToy78TMmxUKx7F5/fr/3979h0VRJ34A/6wsCrmrmGAl68Mu6IlmxXWiPMV9WQpLL+uQ7PFHKdBJj3WRmV56We3S6Rld5VOkZfqIZKdmovd4GfnABRRdJJVAglkJq8cqiYCwoCAL+/3jg+M6M7ssy87OzO779fT0wDjLfGBg3/P5LcxwMG4hWH+EIs1+BulAQvsvVhrRUVTew+qQNpnIo48KfU2tljzyiLP6NB1Hhpz2H87btOlQ7aee8soYZu7ez+LNfgaJQEL7L+7WU3TqsPewOqR37/ZOczNTn3aS05iX5Q/oHCon2SzIUG1eWVnsvZ/Rvg1IaD/nKKS9t7gH621IyFFjLExOFxby/CttUcDULN9jMvVvRaVQ8M+h2rfPu9lM+BYn8dbCXXgMlTgktL/jrrLL3cdG2MuzNrIWeNQY9/pJSQ7r08y6ochpH0AbtBMT+X+96Viw4mLyyCPeXZeD1f3sxWWv6ZXpcFGQJiQ09D+yszqFvRfShLDjUeBRY1zO271pTqvVqjfeUKPOIUcmE3njDfVtt6mMRp5ao/04bW+vmcUavE2I16Y+03imPw2EtGQhoYGQq91erJD2Xm2WtWQSd1CrVzA5bbPxv1NnZweh6VtGmAZtnY5kZwdxT6AtOILPoXIiK0uUvTFoZ5b9w4pXB6CAy5DQ0I9bkybebHJmXVuwtcYGRLO5vt5hT6R90zeq1NLEjNB21BTErAsmJpFGh+3cyc5j7ngUkAgkNFyHN6S5A2oEId6oMV50PbIBu6hRpZYOpqeZd4Q2sRukLey6YK7grh3mldFhWVnsZnXEs5QhoYGtuJgsWHDdkfR0r4QQ903Ku6PGeDFN3+vWWTUaK/cEGtUKBaJaTExrtqPnOo3Gum6dlQ7SFn8VEN61w4TvA8/KYj+40MdQkCwkNPDYu5ddw/BSR51eL/qoMV5aLVm7tuv771ud1L3sW7+91Org32g3c3o6USictWanpZHcXPL9961r13ZJYvNEkdYO48az0YhJ11KnFLsAIFH0mZ7+SbPmQwnLYCAnT5I9e/o/pW9n3poeOiBa2zEY+jvKuf3QNKcJIVlZJC2NRESI3Zrqc0wmUlJCSksHeAxi6qU0lZuavFG2gXF3lhR+dJjJRPLyeOIZS5ZJH+rQ4JDBQHJzvRvP1O7d7FFjYgztdoLWzJz0UpOrUU1XrUpMRAP4UDE1Zjr+y1E82/c06/Venz01INbOksKPDkM8yxoSGpwRrf7H6i0Ub2i3c7SX2mYjRqPDdkpa52P6qnfuxAjwQbCfMeU8mPX6/uHZkuhp5sVqaBZ+dBhtgWJdE/EsI2jlBkmidQv7SSFGI0lIkOpbLzEYiMHQX18pKXE4vo0ZIUSresRrHfyy4mI7NqXXk9RUOXQl8A7eFrKOb78mCXNNg0EOPyu4yksJffz48fT09I0bNyYlJXnniiB79C3Mvn2bdkhLNaTJ1XdAF6Oavl0bjUhrQuxSmX4wIPpDS02VXjs2LzEGb3MnPQv8SACe542Ebm1tzc7Obm5u9sK1wKfQbaRlFdIUK6ppU60jrLTW60lCQv8HPozW7QabyvSHI7NaoEiDt222a8t5Ip5lSvCEbmhoyMzMrKqqEvpC4Jv0epKWdl2+pafLaAonjWpCSG5ufxo5GllG0Sxnvl2mei2byqJjbkQypdWSxYvJrFnyfGThtjV7sR+YhjTiWb4ETOje3t7Dhw+/8sor3d3dU6dOra2tFe5a4Mtyc4nFQvLz+z+V2Pwr1+n1RK93tWJN2VevydVKpFYr5R75fkweEzLoSCbEV9oSxJhbxSLCXAzwHAETura29qWXXgoKCtq6dWt1dTUSGty3f/9164vR+VcyDGnKvmI9YI+1PW6o0/m+9L+EhGtHvIamj8nU/wENY+bTQZHR84dLuDvEeWvlbfAZAiZ0QEDA0qVLMzIyRo0aVV1dLdyFQDrKypRCjQXMzb3u/U7mIc1geqzJ1amrhLMbpxOOspCGNBPeVEQE0euvnc8K8oYGJWsXQvuvTD8+ffpaJBPigSVZfaSuzIs79Vn+v67gZQIm9NSpU6dOnSrc1wepGT/+FsK394ZncOdf0UnSPjQAmqlb05Zwk6m/SurGGqL2OeqyCYO+zOD5ciTb4536jK5gGCTJzYcuLy9nHbnjjjtiYmJEKYxnWa3WtrY2tVotdkEEMWZMCP0gMZEUFHTFxXV5/hohIeTgwZB5864dMRqt4eEd8+d7/locHR0d3d3dgYGBXrgWISQkhPmGc3sAABypSURBVMTEEPqLv2lTf9bSqP7mm6Dycp7djiVIo7FqNNbwcGt8vJUbyRcveq8kbW1t3d3dVivP3icep3r9deWGDcynVo2mKyfHGhIi3Dfc1dXV3t4eHBws0NcXXVtbW3BwsFIpucByQ2VlJWvodHl5eVxcHO/JvvANy4XNZlOwmhF9xbx5IfafzpkT9PDD1u3bBXg31Ou7CgqC5sxhDigzMkhoqHeqYzbxRt3Q2tfVWlkXIV0NDUpCSHl5kNmsJISUlLjZ++vB4tEW9bvvthJCNBqrIE9p7vLan57yww/t45lotV05Odb4eO9c3VcpFAoR//pEJLmEjouLW7FihdilEITVau3q6goJCRn4VLmhk5btO93y81XNzcL0u82ezZokHTJvnhcmSSuVysuXL0vn9tGCTJvW/ynT2M8ausXqOR5s6zfTLstkMP1/QgIxmfo/5fzglVf/L6GKfk9PT3BwsEqlEvYyO3eSzMzrjqSlqebOFfaihHR1ddlsNun8cnqcxWIZM2aMb9Sh9Xq93uU3K1/4hkEKiovJ6tUdb7xx7R2wpITodMIskkyXYLZfpEkmK5l4gX2UDkp9fb2OtQwVDApr4TAi1NRn5tkIfB52zgCPWbXKUlBwXcMmnbosyGbJaWnslaVYtXgAbzKZ2MtsChPPWVns64APQ0KDJ+n1pL6e/YCfnk4OHhTgYrm5CGmQBG48C7AyiclEPvmkfyyCjw5oATYkNHgYnVfCam9OSRFmi2duGzprjQgAofEuu+3plUnoRR588NoRhLQ/QEKD59GQZi27QbulPZ+erMcB7kJOAMLhLrstWDyzxve5vqwNyJeXEnr58uV1dXXYetKvGAzssdz0jSYry9NXQkiDKHh3YPZ0PNOOZ9ZFvLj7BogJdWgQEG+3tNEoQIs3Qhq8jDeePbrrmsnEXpqMXG2gQjz7CSQ0CIu3W1qQedIIafAar8RzYiI7nnkfecGHIaFBcKxuaQGXBmINHENIgxB449mjT52OWrax9Ya/QUKDl9BuaWFX7qO9gAhpEI6jePZQxdZRyzazARr4FSQ0eI83lvxCSINwhI9nbss2vQJr5j/4CSQ0+ByENAhB4HgmhN2yTQgxGtHx7NeQ0OCLENLgWcLHM7l+iAb9FUbLtp9DQoOP4g1pLAsKbqCLegoczxQNabRsA4WEBt/FO9MLIQ2DsnMne81tOrFKsKZntGwDAwkNvg4hDW7buZO9oSSdkiwktGwDAwkNkuaZJUJ5Q1qQTTHBh2RlseM5LQ1TksGbkNAgXQoFMRo9tN8Gt1svPZ39/gvA4M5KNho9vuY2gHNIaJAoZnM9j+23wd1PeudOYTbFBDnjXTQEW1WAGJDQIA+eqUzn5vJsiomQBgadlYd4BmlAQoNE1dez+449U5nmbopJd64G4M7HG/JGj6wVSAAGBQkNEsXab4Phgcq0Xs+zc7VnurtBtnjXJBnacti0gYbprwEYLCQ0SJrB4LAyPaRhXtw5M1jPxJ/xbiY1hEVDaF82k/gIaXAPEhqkjr5VckfR0pUk3G/0putOcGdheWaCF8gH72ZSQ1gyjA5A5Db/AAwWEhrkIS2NvzJNG73d7O3jrgxKCDEaMXbMXzja69HdNb2Y1h3WL6RWiw5pcAcSGmTDUc807UR2s+rL25hJx47hPdW38e71SJfcdOuL0ZZyVj8JHWqGVTzBPUhokBnenmkyxLUSc3PZzejolvZtdBAXq+PZ3WHbjpq16RJkmKgFbkNCg/zQeq99R6H9tn1u4i7o6LGlUkBKWIO4KHeHbZtM5N57+Zu16RJkqDrDUCChQa7ocGxPjsehXxHd0j7MUcfz4IdtM83an3/O/mK0WRtVZxg6JDTIm8HgiQo0g75ZJydfd5B2S6PFW+5oowhrxxT6WDbIqi7NZu7TIf31QTaDpyChATgOHmS/+5pMqgcfVOfni1MeGDJ1fr7qttt4Op7d2quKm8EYEQZCQEID8OEuDkpI0JNPosVbfkwmkpUV9OST1x2kE+08UdtFszYIBwkN4ABvtzRavOWFjtnm7XjmzgcYDNq3gtHaICgkNPidQYzO5p2C7YFFR0F4vGO2CfFgYzRd6g7N2iAcJDT4HaORKBSDyWm+Fu/+RUdRmZYm3tVIPNeyTQ2tEg4wMCQ0+BdmD4PB5bRe33XiRFdc3HUHMWFagphZUNdXnbsWLOgqKHB7JwwAUSChwa+5ntNWjcZy6BDPDh4e2A4TPMRR1dlotOTkWDUacUoF4C4kNPgRk4ns28dznMnpgZfidrSDByrT4nJQdXZxhjJ9NYDUIKHBj2i15JFHHK5ERivDA+e0o+0wUZkWi6MB27m5Aw4KY5KdPqUBSAoSGvwOXYPZeU5zhwCzOalMD/xi8BCTiaSn8/zAXVvIMz2df2kwAIlAQoOfYnK6sJDnX+m05wE2tXS0HSZ9MZpNBcVUflmreA60uJfJ1L8VlULBfikhRKcToKgA7kJCg1/TaklSksP6tMnkQgXL0XaYtDLODQEYOkfbPTrtdWYmSKen8/RFDGENUAChIKEBBmj3dun1vD3TtA0WndMexCwXw23WdtzrbDKRN95Q33abymjk6X+wX7YTy4+ApCChAfoxOW2zubXzNO2Z5oY8Oqc9wtHjDhOwnF5npkFbpyPZ2UHcL6nVEpsNS2qDdCGhAa5Ds7m+3q0FHZmQ5zZ6M53TyOnBctTlTAjR6500a+t0/A3axK7KDSBlSGgAfrRK7A7a6F1czJPwtHN661bktEtMJrJ1q8OtmGm/sePHKG77h1bbv9cFX5UbQHKUYhcAwEfRrbGysnjSZflyQggxGklqKno++dF6M+84Oxqzg2yY1misqalk2TIlft4gI6hDA3jYdcliMBCbbYCZ1xhHZo/pb+bG8+C3YrbZSFoayc0l33/funZtF+IZ5AV1aABPYtalysoiaWkkIoKkpRFiMJDUVJKXxz95uqTEvXqhTzGZSEkJyctz2G/s7s+HDrFvahpS6QBEgTo0gCDoXGpaG0xMJFl5Tmd00bP9cygZMxDM6TxlU6oBc8vB3yChAYRFK4d02WddonZnhMFUPFBO+8MUajoXysnCm1otMRpNxfU7IwyJ6Vqa4AB+Ba3cAB5DE5a2Wzs6IT2dEKLVag1pRgMxZhmIkf+8xMT+pt2EBJ65W7JGW7Md14hNRFuiTSvVG3YaCe+PB8BPCFuHNpvNmZmZ0dHRkZGR8fHxH3zwQU9Pj6BXBBARnQ5NJ/MYjc6ClWa5kRgUxEZyc/mn/tCT6IobWVmyr1UzrdmJiY7iuYTo00mujtSnm/jbtLH9FPgVARO6trY2JSXls88+u/POO1NSUqxWq9FoNBgMfhvSjY2NR44cEbsUAjpy5EhjY6PYpRDKoG4fK6oHmHpLRxs7WXSUFdXC7MnxwQcfeP6LMk3ZCkV/azZfL7uJaLOIMZEUJ5LinSTN0RejbQruqaqqqqysdPPFkof3Fl8lVEL39PS8++67bW1tb7/99u7du19//fXCwsL4+PgDBw6Ul5cLdFHpE+RNUDJ8+6+oqqqqqqpqsK+iUZ2bS2w2/n2w2KfabCQ312Htu7/qbSQKBZk40bMV67y8PM98Idrxzqy3mZ7uvEE7kRTrSL2RGEqInvccrZa88EL/4w537XMXHTly5Ndff3XzxZLn8wnt2++cTgjVD11XV1deXh4XF6e/+l6jVqtXrFiRnp5+6NCh+Ph4BZqrwM/o9USvJwYDMZlIXl5/9ZJn3e+0NJKW1n/Szp0Oh3afOnUt8GntUsT1T2gqnz7trBPenlZL0tJ0xlQT0Tr6d72eJCT0fwDgn4RK6BMnTjQ3N0+fPj04OJg5qNPpNBpNTU1Na2vrjTfeKNClASSO1pYJcVojpCcxee4kqold3Zq+kIk1gTKblqSkhJSW9mezi+g3dbV4JiP7H/V6otX64Ng4APcIldC0tTM6Otr+4PDhw0NCQk6fPt3R0YGEBnDJ9VFdYizRkxJn55tM11LTvpJN/0tIIKdPk4iIa8l99QON1XrdQwD9Osw5tNZPyKDb1el16bMCJ3hRVwZwQqiEPn36NPfgyJEjb7755pqamra2NoGuC+CztFpiMCQaDVpi0hKTnpQmkIHSmsHEreMu4S8IITqdB8pJrqY+zV7Hg7sGsbMngF8SKqF5B2wrFIphwwYYm+bb48gaGhreeustsUshFLPZnJ+f/80334hdEEHQ30zRb19ICGnsmmnq0pcQPSFupbUwTETbGNRonjixYeLEa9XhtjYi9k+Mor+WDQ0NYhdEEA0NDWazWfRfTuE0NDTk5+eLXQqh0DFbvP8kVEIHBgZyD9pstr6+PievmjlzpkDlkYgVK1aIXQQBPfzww2IXQUCO/oS8zGgkhHxDCOsxKKSKJFeRZDFKJA++/cup0Wg0Go3YpRCQb79zxsXFOco+oRI6IiKCe7Czs7OxsVGlUo0ePZr3VXFxcRJ5HwQAABCXUPOhaUL/8ssv9gevXLly8eLF0aNHq1Qqga4LAADgG4RK6IkTJ4aGhpaXl1++fJk5eOrUKZPJdOutt44ZM0ag6wIAAPgGoRJao9HExMSUl5cXFRXZbDZCiMViycnJ6evre+ihh7BcCQAAgHMKm2AzHiorKzMyMi5evBgbGzt+/PiysrLz588vXLgwKyuLdxwZAAAAMARMaEJIfX39hg0bysrKrly5Mn78+CeeeGLRokWIZwAAgAEJm9AAAADgHmH3hwYAAAD3IKEBAACkCAkNAAAgRZJL6Pb29uzs7NjY2MjIyOjo6AULFhw9elSmneVWq/XFF19cvHhxR0eH8zN//vnnGTNmRHK899573imqx3V0dKSmpq5evVrsggxab28v3cKc/gYuW7asvr7e+Uvkfvt++umnhQsXTpo0KSoq6r777isoKJDdX5zZbM7MzIyOjo6MjIyPj//ggw94twaw989//pN7y+64444ffvjBO2X2rKKiopkzZ8q08Izjx4/HxsYWFRUNeKYP3D6bzXb06NFHHnlk0qRJkZGRd911V3Z2dnt7u/05Qq366R6z2ZyWlnbq1CmdTnfvvfeeOXOmoqJi6dKlmzZtmjNnjtilGxybzVZQULB///4777xzwJMbGhqam5tHjx6tVqvtj7M+lYve3t5du3aVlZXNmzdP7LIMjtVqXb9+/a5du8LCwlJSUs6ePVtaWlpVVbVt27aYmBhHr5L17SsqKlq5cmVPT09iYuKIESNKS0uffvrpNWvWZGRkyGXdgtra2vT09JaWFmZip9Fo/PHHH51P7Dx+/LhCoQgLCxs+fDhzUK1WK5XSeld0RX19fXZ2dldXl9gFGZLW1tbs7Ozm5mZXTpb77bPZbNu2bcvOzg4MDExMTBw1alRZWdnWrVtramo2b9587a3DJhl9fX1ZWVmRkZFbtmyxWq304Lfffjt9+vT4+PgzZ86IW7xBsVqtO3bsmDx5sk6nW7RokcVicX7+9u3bIyMjP//8c+8UT1CXLl1av359ZGSkTqdbtWqV2MUZnPLy8ttuu23JkiXt7e30yKeffjp58uSMjIxLly45epV8b19LS0tycvL06dOPHTtGjzQ0NCQlJc2YMePkyZPils1FV65cefrppydPnvzpp5/SI+3t7UuWLJk8efIXX3zh6FUdHR1Lliy55557zp8/762SCqWysvL3v/+9Tqe7/fbbq6urxS6Om/73v/8lJyfrdDqdTldYWOj8ZB+4fSdPnpwxY0ZSUlJdXR09cunSpbVr1+p0uk2bNjGnSaiV+8KFC6WlpZMmTZo/f35AQAA9+Lvf/W7u3Llms/nkyZPiFs91tMFt/fr1Op3OxVrUiRMnQkJCbrrpJqHLJrSffvpp8eLFO3bsmDJlSlBQkNjFGRybzVZQUNDd3f2nP/2JuXGzZs26//77v/nmG9Yi8/bke/uqq6tra2sfeOCBO+64gx4JDw9/5plnLly48Pnnn4tbNhfV1dXRzfv0V7e8VKvVK1asGD58+KFDh2wOmus7OztPnz4dHh5+ww03eK+snnb58uX333//scce6+7u1nlqb2+vo/1KycnJv/zyy9SpU115iQ/cvi+//LKpqWnJkiXMjQsODn788cdDQ0OPHj3KdIxKK6GHDRs2adIk1qrdY8eOFatI7tm0adORI0dSUlK2bNkSEhIy4PkdHR0NDQ033XTTzTff7IXiCaejo8NoNB4/fnzlypVyXDmuvb29qqpq3LhxkyZNYg4qlcoZM2ZYLJbq6mreV8n69lVUVPT09MycOdO+QTs6Onrs2LHffvttd3e3iGVz0YkTJ5qbm6dPnx4cHMwc1Ol0Go2mpqamtbWV91Xnzp1rbW2NiooaOXKkt0rqeV999dWrr74aHh7+4Ycf/va3vxW7OG6qra196aWXAgICtm7dOnfuXFde4gO3r7GxMTQ0dMqUKfYHR44cOWLECPsjEkroKVOmFBYW5uTk2HckWCyW4uJitVotowqKRqPZtWvXa6+95uJvT3t7u9lsVqvVOTk5dIhcbGwsd8i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and vertex of a parabola given 3 points","description":"Given 3 points, each defined by x and y, compute the coefficients: [a,b,c] of a parabola with equation: y = ax^2 + bx + c passing through those points.  Also solve for the location of its vertex: [xv,yv].\r\n\r\nExample:\r\n\r\n Inputs:  D = [0 3]; E = [1 6]; F = [-1 2]\r\n Outputs: C = [1 2 3]; V = [-1 2]\r\n Hence, the quadratic equation is: y = x^2 +2x + 3, with vertex at (-1,2)","description_html":"\u003cp\u003eGiven 3 points, each defined by x and y, compute the coefficients: [a,b,c] of a parabola with equation: y = ax^2 + bx + c passing through those points.  Also solve for the location of its vertex: [xv,yv].\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre\u003e Inputs:  D = [0 3]; E = [1 6]; F = [-1 2]\r\n Outputs: C = [1 2 3]; V = [-1 2]\r\n Hence, the quadratic equation is: y = x^2 +2x + 3, with vertex at (-1,2)\u003c/pre\u003e","function_template":"function [Coeff,VERT] = Para3(P,Q,R)\r\nCoeff = [P,Q,R];\r\nVERT = [P,Q,R];\r\nend","test_suite":"%%\r\nP1 = [1 0]; P2 = [2 5]; P3 = [-1 2];\r\nabc = [2 -1 -1];\r\nO1 = Para3(P1,P2,P3);\r\nassert(isequal(O1,abc))\r\n\r\n%%\r\nP1 = [1 0]; P2 = [2 5]; P3 = [-1 2];\r\nvxvy = [0.25 -1.125];\r\n[~,O2] = Para3(P1,P2,P3);\r\nassert(isequal(O2,vxvy))\r\n\r\n%%\r\ni = [0 0]; j = [1 1]; k = [-1 1];\r\nabc = [1 0 0];\r\ntip = [0 0];\r\n[Res,Ans] = Para3(i,j,k);\r\nassert(isequal(Res,abc))\r\nassert(isequal(Ans,tip))\r\n\r\n%%\r\nptA = [-1 -5]; ptB = [2 4]; ptC = [3 -5];\r\nexp1 = [-3 6 4];\r\nexp2 = [1 7];\r\n[coefficients,vertex] = Para3(ptA,ptB,ptC);\r\nassert(isequal(exp1,coefficients))\r\nassert(isequal(exp2,vertex))","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":54708,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":"2016-05-01T20:20:27.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2016-04-29T14:32:30.000Z","updated_at":"2026-01-20T14:47:31.000Z","published_at":"2016-04-29T14:58:42.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven 3 points, each defined by x and y, compute the coefficients: [a,b,c] of a parabola with equation: y = ax^2 + bx + c passing through those points. Also solve for the location of its vertex: [xv,yv].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Inputs:  D = [0 3]; E = [1 6]; F = [-1 2]\\n Outputs: C = [1 2 3]; V = [-1 2]\\n Hence, the quadratic equation is: y = x^2 +2x + 3, with vertex at (-1,2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44553,"title":"¿Es una parábola?","description":"Dados los datos de las magnitudes _*x*_ e _*y*_, crear una función que permita conocer si se trata de una parábola o no, indicando el valor del coeficiente _*a*_. En el caso de ser una parábola, se debe limitar el análisis a aquellas cuyo coeficiente _*a*_ sea un número entero tal que 0\u003ca\u003c10. Recordad para ello que la ecuación propia de una parábola es la siguiente:\r\ny = a*x^2\r\n\r\n* Ejemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 2 0 2 8]\r\nSí es una parábola con coeficiente a = 2.\r\n\r\n* Ejemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 0 1 1 6]\r\nNo es una parábola, por lo que a = 0.\r\n","description_html":"\u003cp\u003eDados los datos de las magnitudes \u003ci\u003e\u003cb\u003ex\u003c/b\u003e\u003c/i\u003e e \u003ci\u003e\u003cb\u003ey\u003c/b\u003e\u003c/i\u003e, crear una función que permita conocer si se trata de una parábola o no, indicando el valor del coeficiente \u003ci\u003e\u003cb\u003ea\u003c/b\u003e\u003c/i\u003e. En el caso de ser una parábola, se debe limitar el análisis a aquellas cuyo coeficiente \u003ci\u003e\u003cb\u003ea\u003c/b\u003e\u003c/i\u003e sea un número entero tal que 0\u0026lt;a\u0026lt;10. Recordad para ello que la ecuación propia de una parábola es la siguiente:\r\ny = a*x^2\u003c/p\u003e\u003cul\u003e\u003cli\u003eEjemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 2 0 2 8]\r\nSí es una parábola con coeficiente a = 2.\u003c/li\u003e\u003c/ul\u003e\u003cul\u003e\u003cli\u003eEjemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 0 1 1 6]\r\nNo es una parábola, por lo que a = 0.\u003c/li\u003e\u003c/ul\u003e","function_template":"function a = comprobacion_parabola(x,y)\r\n  \r\nend","test_suite":"%% TEST 1\r\nx = [-2 -1 0 1 2];\r\ny = [8 2 0 2 8];\r\na_correcta = 2;\r\nassert(isequal(comprobacion_parabola(x,y),a_correcta))\r\n\r\n%% TEST 2\r\nx = [-2 -1 0 1 2];\r\ny = [8 0 1 1 6];\r\na_correcta = 0;\r\nassert(isequal(comprobacion_parabola(x,y),a_correcta))\r\n\r\n%% TEST 3\r\nx = [-3 -1.5 0 1.5 3];\r\ny = [54 13.5 0 13.5 54];\r\na_correcta = 6;\r\nassert(isequal(comprobacion_parabola(x,y),a_correcta))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":132597,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-12T07:45:21.000Z","updated_at":"2026-03-11T12:27:36.000Z","published_at":"2018-04-17T18:00:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDados los datos de las magnitudes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, crear una función que permita conocer si se trata de una parábola o no, indicando el valor del coeficiente\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. En el caso de ser una parábola, se debe limitar el análisis a aquellas cuyo coeficiente\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sea un número entero tal que 0\u0026lt;a\u0026lt;10. Recordad para ello que la ecuación propia de una parábola es la siguiente: y = a*x^2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEjemplo: x = [-2 -1 0 1 2] y = [8 2 0 2 8]Sí es una parábola con coeficiente a = 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEjemplo: x = [-2 -1 0 1 2] y = [8 0 1 1 6]No es una parábola, por lo que a = 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61143,"title":"Translating parabola by its vertex to the origin","description":"Given a quadratic polynomial, p(x) = ax^2 + bx + c (a ~= 0), represented by the vector [a b c], consider the translation of the parabola by shifting its vertex to the origin (see figure below).\r\nFind \r\nd (d\u003e0) the shifting distance of the above translation;\r\nv the vertical shift, which stands for 'up' and 'down' if the parabola is upward or downward shifted, respectively, or simply '' if the graph does not undergo a translation;\r\nh the horizontal shift, which stands for 'right' and 'left' if the parabola is shifted to the right and to the left, respectively, or simply '' if the graph does not undergo a translation.\r\nHint: Be careful to the potential computer errors whenever the results will be integer numbers.\r\ninput: p\r\noutput: [d, v, h]\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 633.987px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 316.987px; transform-origin: 408px 316.994px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a quadratic polynomial, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ep(x) = ax^2 + bx + c (a ~= 0)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, represented by the vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[a b c]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, consider the translation of the parabola by shifting its vertex to the origin (see figure below).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 102.188px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 51.0875px; transform-origin: 391px 51.0938px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ed (d\u0026gt;0)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e the shifting distance of the above translation;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.875px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4375px; text-align: left; transform-origin: 363px 20.4375px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ev\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e the vertical shift, which stands for 'up' and 'down' if the parabola is upward or downward shifted, respectively, or simply '' if the graph does not undergo a translation;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.875px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4375px; text-align: left; transform-origin: 363px 20.4375px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eh\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e the horizontal shift, which stands for 'right' and 'left' if the parabola is shifted to the right and to the left, respectively, or simply '' if the graph does not undergo a translation.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHint: Be careful to the potential computer errors whenever the results will be integer numbers.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003einput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ep\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eoutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[d, v, h]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 339.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 169.9px; text-align: left; transform-origin: 384px 169.9px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cimg class=\"imageNode\" width=\"480\" height=\"334\" style=\"vertical-align: baseline;width: 480px;height: 334px\" 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alt=\"Shift parabola\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [d, v, h] = shift_parabola(p)\r\n  d = x;\r\n  v = x;\r\n  h = x;\r\nend","test_suite":"%%\r\np = [-1 12 -28];\r\nd_correct = 10;\r\nv_correct = 'down';\r\nh_correct = 'left';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isequal(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%% \r\np = [1 -3 0.25];\r\nd_correct = 2.5;\r\nv_correct = 'up';\r\nh_correct = 'left';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%% \r\np = [4 -12 9];\r\nd_correct = 1.5;\r\nv_correct = '';\r\nh_correct = 'left';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%%\r\nfiletext = fileread('shift_parabola.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp') || ...\r\n          contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n\r\n%%\r\np = [-5 -2 -1.6];\r\nd_correct = sqrt(2);\r\nv_correct = 'up';\r\nh_correct = 'right';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%%\r\np = [-5 -2 1.2];\r\nd_correct = sqrt(2);\r\nv_correct = 'down';\r\nh_correct = 'right';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":3,"created_by":4993982,"edited_by":4993982,"edited_at":"2026-01-03T10:31:42.000Z","deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2026-01-03T10:31:42.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-12-24T12:38:12.000Z","updated_at":"2026-02-27T10:20:46.000Z","published_at":"2026-01-02T10:06:27.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a quadratic polynomial, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep(x) = ax^2 + bx + c (a ~= 0)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, represented by the vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[a b c]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, consider the translation of the parabola by shifting its vertex to the origin (see figure below).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ed (d\u0026gt;0)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the shifting distance of the above translation;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the vertical shift, which stands for 'up' and 'down' if the parabola is upward or downward shifted, respectively, or simply '' if the graph does not undergo a translation;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eh\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the horizontal shift, which stands for 'right' and 'left' if the parabola is shifted to the right and to the left, respectively, or simply '' if the graph does not undergo a translation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint: Be careful to the potential computer errors whenever the results will be integer numbers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc 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\",\"relationship\":null}],\"relationships\":[{\"relation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el extremo!","description":"Crea una función que calcule el extremo de una parábola (máximo o mínimo absoluto) cuyos datos son proporcionados por el usuario en base a la siguiente ecuación. \r\n\r\ny = a*x^2+b\r\n\r\nEl usuario indicará los valores de *a* y *b* mientras que los valores de *x* se encontrarán en todos los casos entre -10 y +10. Se debe redondear la respuesta al número entero más cercano.\r\n\r\n* *Ejemplo*\r\n\r\nEl mínimo absoluto de la parábola y = x^2-5 es P(0,-5).","description_html":"\u003cp\u003eCrea una función que calcule el extremo de una parábola (máximo o mínimo absoluto) cuyos datos son proporcionados por el usuario en base a la siguiente ecuación.\u003c/p\u003e\u003cp\u003ey = a*x^2+b\u003c/p\u003e\u003cp\u003eEl usuario indicará los valores de \u003cb\u003ea\u003c/b\u003e y \u003cb\u003eb\u003c/b\u003e mientras que los valores de \u003cb\u003ex\u003c/b\u003e se encontrarán en todos los casos entre -10 y +10. Se debe redondear la respuesta al número entero más cercano.\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cb\u003eEjemplo\u003c/b\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eEl mínimo absoluto de la parábola y = x^2-5 es P(0,-5).\u003c/p\u003e","function_template":"function y = calculo_maxmin(a,b)\r\n  \r\nend","test_suite":"%% TEST 1\r\na = 2;\r\nb = -8;\r\nmin_correct = -8;\r\nassert(isequal(calculo_maxmin(a,b),min_correct))\r\n\r\n%% TEST 2 \r\na = -2;\r\nb = -8;\r\nmin_correct = -8;\r\nassert(isequal(calculo_maxmin(a,b),min_correct))\r\n\r\n%% TEST 3\r\na = -5;\r\nb = 0;\r\nmin_correct = 0;\r\nassert(isequal(calculo_maxmin(a,b),min_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":132597,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":37,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-16T07:17:45.000Z","updated_at":"2026-02-05T15:59:02.000Z","published_at":"2018-04-17T18:00:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCrea una función que calcule el extremo de una parábola (máximo o mínimo absoluto) cuyos datos son proporcionados por el usuario en base a la siguiente ecuación.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = a*x^2+b\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEl usuario indicará los valores de\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e y\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e mientras que los valores de\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e se encontrarán en todos los casos entre -10 y +10. Se debe redondear la respuesta al número entero más cercano.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEjemplo\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEl mínimo absoluto de la parábola y = x^2-5 es P(0,-5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61157,"title":"Scaling vertically parabola by evaluating its area over an interval","description":"Let p be a quadratic polynomial, with its axis of symmetry being the y-axis. Considering the vertical shift of its vertex to the origin, find the positive constant, k, that scales the shifted parabola such that its area over the interval [-a; a] (a\u003e0) will be equal to the area under the original parabola (see figure below).\r\ninput: (p, a)\r\noutput: k\r\n\r\n\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 447.8px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 223.9px; transform-origin: 408px 223.9px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eLet \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ep\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e be a quadratic polynomial, with its axis of symmetry being the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ey\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-axis. Considering the vertical shift of its vertex to the origin, find the positive constant, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, that scales the shifted parabola such that its area over the interval \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[-a; a] (a\u0026gt;0)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e will be equal to the area under the original parabola (see figure below).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003einput: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e(p, a)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 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\" alt=\"Scale parabola\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function k = scale_parabola(p, a)\r\n  k = x;\r\nend","test_suite":"%%\r\np = [1 0 1];\r\na = 2;\r\nk_correct = 7/4;\r\nassert(isapprox(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [1 0 -1];\r\na = 2;\r\nk_correct = 0.25;\r\nassert(isapprox(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [5 0 0];\r\na = 0.5;\r\nk_correct = 1;\r\nassert(isequal(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\nfiletext = fileread('scale_parabola.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp') || ...\r\n          contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n\r\n%%\r\np = [-3 0 1];\r\na = 1;\r\nk_correct = 0;\r\nassert(isequal(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [-3 0 2];\r\na = 1;\r\nk_correct = -1;\r\nassert(isequal(scale_parabola(p,a),k_correct))\r\n\r\n%%\r\np = [-3 0 2];\r\na = 3;\r\nk_correct = 7/9;\r\nassert(isapprox(scale_parabola(p,a),k_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":4993982,"edited_by":4993982,"edited_at":"2026-01-16T14:57:57.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2026-01-16T14:57:57.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2026-01-10T14:36:03.000Z","updated_at":"2026-03-29T13:27:42.000Z","published_at":"2026-01-15T14:52:59.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLet \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e be a quadratic polynomial, with its axis of symmetry being the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-axis. Considering the vertical shift of its vertex to the origin, find the positive constant, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, that scales the shifted parabola such that its area over the interval \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[-a; a] (a\u0026gt;0)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e will be equal to the area under the original parabola (see figure below).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003einput: 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and vertex of a parabola given 3 points","description":"Given 3 points, each defined by x and y, compute the coefficients: [a,b,c] of a parabola with equation: y = ax^2 + bx + c passing through those points.  Also solve for the location of its vertex: [xv,yv].\r\n\r\nExample:\r\n\r\n Inputs:  D = [0 3]; E = [1 6]; F = [-1 2]\r\n Outputs: C = [1 2 3]; V = [-1 2]\r\n Hence, the quadratic equation is: y = x^2 +2x + 3, with vertex at (-1,2)","description_html":"\u003cp\u003eGiven 3 points, each defined by x and y, compute the coefficients: [a,b,c] of a parabola with equation: y = ax^2 + bx + c passing through those points.  Also solve for the location of its vertex: [xv,yv].\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre\u003e Inputs:  D = [0 3]; E = [1 6]; F = [-1 2]\r\n Outputs: C = [1 2 3]; V = [-1 2]\r\n Hence, the quadratic equation is: y = x^2 +2x + 3, with vertex at (-1,2)\u003c/pre\u003e","function_template":"function [Coeff,VERT] = Para3(P,Q,R)\r\nCoeff = [P,Q,R];\r\nVERT = [P,Q,R];\r\nend","test_suite":"%%\r\nP1 = [1 0]; P2 = [2 5]; P3 = [-1 2];\r\nabc = [2 -1 -1];\r\nO1 = Para3(P1,P2,P3);\r\nassert(isequal(O1,abc))\r\n\r\n%%\r\nP1 = [1 0]; P2 = [2 5]; P3 = [-1 2];\r\nvxvy = [0.25 -1.125];\r\n[~,O2] = Para3(P1,P2,P3);\r\nassert(isequal(O2,vxvy))\r\n\r\n%%\r\ni = [0 0]; j = [1 1]; k = [-1 1];\r\nabc = [1 0 0];\r\ntip = [0 0];\r\n[Res,Ans] = Para3(i,j,k);\r\nassert(isequal(Res,abc))\r\nassert(isequal(Ans,tip))\r\n\r\n%%\r\nptA = [-1 -5]; ptB = [2 4]; ptC = [3 -5];\r\nexp1 = [-3 6 4];\r\nexp2 = [1 7];\r\n[coefficients,vertex] = Para3(ptA,ptB,ptC);\r\nassert(isequal(exp1,coefficients))\r\nassert(isequal(exp2,vertex))","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":54708,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":"2016-05-01T20:20:27.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2016-04-29T14:32:30.000Z","updated_at":"2026-01-20T14:47:31.000Z","published_at":"2016-04-29T14:58:42.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven 3 points, each defined by x and y, compute the coefficients: [a,b,c] of a parabola with equation: y = ax^2 + bx + c passing through those points. Also solve for the location of its vertex: [xv,yv].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Inputs:  D = [0 3]; E = [1 6]; F = [-1 2]\\n Outputs: C = [1 2 3]; V = [-1 2]\\n Hence, the quadratic equation is: y = x^2 +2x + 3, with vertex at (-1,2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44553,"title":"¿Es una parábola?","description":"Dados los datos de las magnitudes _*x*_ e _*y*_, crear una función que permita conocer si se trata de una parábola o no, indicando el valor del coeficiente _*a*_. En el caso de ser una parábola, se debe limitar el análisis a aquellas cuyo coeficiente _*a*_ sea un número entero tal que 0\u003ca\u003c10. Recordad para ello que la ecuación propia de una parábola es la siguiente:\r\ny = a*x^2\r\n\r\n* Ejemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 2 0 2 8]\r\nSí es una parábola con coeficiente a = 2.\r\n\r\n* Ejemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 0 1 1 6]\r\nNo es una parábola, por lo que a = 0.\r\n","description_html":"\u003cp\u003eDados los datos de las magnitudes \u003ci\u003e\u003cb\u003ex\u003c/b\u003e\u003c/i\u003e e \u003ci\u003e\u003cb\u003ey\u003c/b\u003e\u003c/i\u003e, crear una función que permita conocer si se trata de una parábola o no, indicando el valor del coeficiente \u003ci\u003e\u003cb\u003ea\u003c/b\u003e\u003c/i\u003e. En el caso de ser una parábola, se debe limitar el análisis a aquellas cuyo coeficiente \u003ci\u003e\u003cb\u003ea\u003c/b\u003e\u003c/i\u003e sea un número entero tal que 0\u0026lt;a\u0026lt;10. Recordad para ello que la ecuación propia de una parábola es la siguiente:\r\ny = a*x^2\u003c/p\u003e\u003cul\u003e\u003cli\u003eEjemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 2 0 2 8]\r\nSí es una parábola con coeficiente a = 2.\u003c/li\u003e\u003c/ul\u003e\u003cul\u003e\u003cli\u003eEjemplo:\r\n x = [-2 -1 0 1 2]\r\n y = [8 0 1 1 6]\r\nNo es una parábola, por lo que a = 0.\u003c/li\u003e\u003c/ul\u003e","function_template":"function a = comprobacion_parabola(x,y)\r\n  \r\nend","test_suite":"%% TEST 1\r\nx = [-2 -1 0 1 2];\r\ny = [8 2 0 2 8];\r\na_correcta = 2;\r\nassert(isequal(comprobacion_parabola(x,y),a_correcta))\r\n\r\n%% TEST 2\r\nx = [-2 -1 0 1 2];\r\ny = [8 0 1 1 6];\r\na_correcta = 0;\r\nassert(isequal(comprobacion_parabola(x,y),a_correcta))\r\n\r\n%% TEST 3\r\nx = [-3 -1.5 0 1.5 3];\r\ny = [54 13.5 0 13.5 54];\r\na_correcta = 6;\r\nassert(isequal(comprobacion_parabola(x,y),a_correcta))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":132597,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-04-12T07:45:21.000Z","updated_at":"2026-03-11T12:27:36.000Z","published_at":"2018-04-17T18:00:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDados los datos de las magnitudes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ey\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, crear una función que permita conocer si se trata de una parábola o no, indicando el valor del coeficiente\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. En el caso de ser una parábola, se debe limitar el análisis a aquellas cuyo coeficiente\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sea un número entero tal que 0\u0026lt;a\u0026lt;10. Recordad para ello que la ecuación propia de una parábola es la siguiente: y = a*x^2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEjemplo: x = [-2 -1 0 1 2] y = [8 2 0 2 8]Sí es una parábola con coeficiente a = 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEjemplo: x = [-2 -1 0 1 2] y = [8 0 1 1 6]No es una parábola, por lo que a = 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61143,"title":"Translating parabola by its vertex to the origin","description":"Given a quadratic polynomial, p(x) = ax^2 + bx + c (a ~= 0), represented by the vector [a b c], consider the translation of the parabola by shifting its vertex to the origin (see figure below).\r\nFind \r\nd (d\u003e0) the shifting distance of the above translation;\r\nv the vertical shift, which stands for 'up' and 'down' if the parabola is upward or downward shifted, respectively, or simply '' if the graph does not undergo a translation;\r\nh the horizontal shift, which stands for 'right' and 'left' if the parabola is shifted to the right and to the left, respectively, or simply '' if the graph does not undergo a translation.\r\nHint: Be careful to the potential computer errors whenever the results will be integer numbers.\r\ninput: p\r\noutput: [d, v, h]\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 633.987px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 316.987px; transform-origin: 408px 316.994px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a quadratic polynomial, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ep(x) = ax^2 + bx + c (a ~= 0)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, represented by the vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[a b c]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, consider the translation of the parabola by shifting its vertex to the origin (see figure below).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 102.188px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 51.0875px; transform-origin: 391px 51.0938px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ed (d\u0026gt;0)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e the shifting distance of the above translation;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.875px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4375px; text-align: left; transform-origin: 363px 20.4375px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ev\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e the vertical shift, which stands for 'up' and 'down' if the parabola is upward or downward shifted, respectively, or simply '' if the graph does not undergo a translation;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.875px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4375px; text-align: left; transform-origin: 363px 20.4375px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eh\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e the horizontal shift, which stands for 'right' and 'left' if the parabola is shifted to the right and to the left, respectively, or simply '' if the graph does not undergo a translation.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHint: Be careful to the potential computer errors whenever the results will be integer numbers.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003einput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ep\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eoutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e[d, v, h]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 339.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 169.9px; text-align: left; transform-origin: 384px 169.9px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cimg class=\"imageNode\" width=\"480\" height=\"334\" style=\"vertical-align: baseline;width: 480px;height: 334px\" 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alt=\"Shift parabola\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [d, v, h] = shift_parabola(p)\r\n  d = x;\r\n  v = x;\r\n  h = x;\r\nend","test_suite":"%%\r\np = [-1 12 -28];\r\nd_correct = 10;\r\nv_correct = 'down';\r\nh_correct = 'left';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isequal(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%% \r\np = [1 -3 0.25];\r\nd_correct = 2.5;\r\nv_correct = 'up';\r\nh_correct = 'left';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%% \r\np = [4 -12 9];\r\nd_correct = 1.5;\r\nv_correct = '';\r\nh_correct = 'left';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%%\r\nfiletext = fileread('shift_parabola.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp') || ...\r\n          contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n\r\n%%\r\np = [-5 -2 -1.6];\r\nd_correct = sqrt(2);\r\nv_correct = 'up';\r\nh_correct = 'right';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n%%\r\np = [-5 -2 1.2];\r\nd_correct = sqrt(2);\r\nv_correct = 'down';\r\nh_correct = 'right';\r\n[d, v, h] = shift_parabola(p);\r\nassert(isapprox(d, d_correct))\r\nassert(strcmp(v, v_correct))\r\nassert(strcmp(h, h_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":3,"created_by":4993982,"edited_by":4993982,"edited_at":"2026-01-03T10:31:42.000Z","deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2026-01-03T10:31:42.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-12-24T12:38:12.000Z","updated_at":"2026-02-27T10:20:46.000Z","published_at":"2026-01-02T10:06:27.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a quadratic polynomial, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep(x) = ax^2 + bx + c (a ~= 0)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, represented by the vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[a b c]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, consider the translation of the parabola by shifting its vertex to the origin (see figure below).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ed (d\u0026gt;0)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the shifting distance of the above translation;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the vertical shift, which stands for 'up' and 'down' if the parabola is upward or downward shifted, respectively, or simply '' if the graph does not undergo a translation;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eh\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e the horizontal shift, which stands for 'right' and 'left' if the parabola is shifted to the right and to the left, respectively, or simply '' if the graph does not undergo a translation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint: Be careful to the potential computer errors whenever the results will be integer numbers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc 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