Norma DIN 476, su uso para desarrollar algunos temas de matemática de un programa de segundo año

Se presenta una propuesta de enseñanza, utilizando un material concreto que nos permite desarrollar algunos temas del programa de Matemática correspondiente al 2do año de la Escuela Industrial Superior de la ciudad de Santa Fe. La selección del material tiene que ver con una búsqueda de relaciones con otras asignaturas del mismo nivel (u otros) porque creemos que la enseñanza y aprendizaje de los contenidos de nuestra área tienen mejor recepción en los alumnos cuando se la contextualiza, cuando se evidencia su necesidad, valor o colaboración en otras áreas de estudio. El abordaje transdisciplinario requiere de mentes creativas, abiertas y capaces de resolver situaciones problemáticas específicas desde muchas perspectivas. Esto indica que el docente debe diseñar estrategias de enseñanza basadas en una concepción cognitiva del aprendizaje, favoreciendo el tratamiento de los contenidos disciplinares desde una perspectiva crítica y reflexiva; en la cual el joven pueda poner en juego sus propias capacidades y posibilidades para participar activamente del proceso y construir el conocimiento.Facultad de Humanidades y Ciencias de la Educació

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-5

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>ar model corresponding to = 1.8, as a function of the of the noise. = 0 corresponds to the noiseless case

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-1

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>, obtained using the nonlinear models corresponding to = 0.6 (a) and = 2 (b). Time is expressed in minutes

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-6

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>model, as a function of the sampling interval (in minutes)

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-8

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> function at increasingly high values of

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-0

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> of the parameter of the hyperbolic tangent function (continuous blue curves); the dashed red curves refer to the RMSE and average number of parents for the linear regression model. The dash-dotted green curve in (b) represents the average number of parents in the differential equation model (i.e. the average number of true parents). Further analyses showed that, for → + ∞, the RMSE saturates at 0.247, and the average number of parents saturates at 3.4

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-7

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>ar model corresponding to = 0.8, as a function of the sampling interval (in minutes)

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-2

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> of the hyperbolic tangent function (continuous blue curves). The dashed red curves refer to the recall, precision and F-measure of the linear regression model. Further analyses showed that, for → + ∞, recall saturates at 0.27, precision at 0.26, and the F-measure at 0.26

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-3

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p> to the number of parent-child relationships in the true model)

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks-4

<p><b>Copyright information:</b></p><p>Taken from "Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S2</p><p>BMC Bioinformatics 2007;8(Suppl 5):S2-S2.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892090.</p><p></p>model as a function of the of the noise. = 0 corresponds to the noiseless case