Ense��ando a programar: un camino directo para desarrollar el pensamiento computacional

Est�� ampliamente aceptado que es fundamental desarrollar la habilidad de resolver problemas. El pensamiento computacional se basa en resolver problemas haciendo uso de conceptos fundamentales de la inform��tica. Nada mejor para desarrollar la habilidad de resolver problemas usando conceptos inform��ticos que una asignatura de introducci��n a la programaci��n. Este trabajo presenta nuestras reflexiones acerca de c��mo iniciar a un estudiante en el campo de la programaci��n de computadores. El trabajo no detalla los contenidos a impartir, sino que se centra en aspectos metodol��gicos, con la inclusi��n de experiencias y ejemplos concretos, a la vez que generales, extensibles a cualquier ense��anza de programaci��n. En general, aunque se van desarrollado lenguajes cada vez m��s cercanos al lenguaje humano, la programaci��n de ordenadores utilizando lenguajes formales no es una materia intuitiva y de f��cil comprensi��n por parte de los estudiantes. A la persona que ya sabe programar le parece una tarea sencilla, pero al ne��fito no. Es m��s, dominar el arte de la programaci��n es complejo. Por esta raz��n es indispensable utilizar todas las t��cnicas y herramientas posibles que faciliten dicha labor.

Calmness modulus of linear semi-infinite programs

Our main goal is to compute or estimate the calmness modulus of the argmin mapping of linear semi-infinite optimization problems under canonical perturbations, i.e., perturbations of the objective function together with continuous perturbations of the right-hand side of the constraint system (with respect to an index ranging in a compact Hausdorff space). Specifically, we provide a lower bound on the calmness modulus for semi-infinite programs with unique optimal solution which turns out to be the exact modulus when the problem is finitely constrained. The relationship between the calmness of the argmin mapping and the same property for the (sub)level set mapping (with respect to the objective function), for semi-infinite programs and without requiring the uniqueness of the nominal solution, is explored, too, providing an upper bound on the calmness modulus of the argmin mapping. When confined to finitely constrained problems, we also provide a computable upper bound as it only relies on the nominal data and parameters, not involving elements in a neighborhood. Illustrative examples are provided.

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

Mathematical programming model for heat exchanger design through optimization of partial objectives

Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell���Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature.

Mathematical modelling of the lower urinary tract

The lower urinary tract is one of the most complex biological systems of the human body as it involved hydrodynamic properties of urine and muscle. Moreover, its complexity is increased to be managed by voluntary and involuntary neural systems. In this paper, a mathematical model of the lower urinary tract it is proposed as a preliminary study to better understand its functioning. Furthermore, another goal of that mathematical model proposal is to provide a basis for developing artificial control systems. Lower urinary tract is comprised of two interacting systems: the mechanical system and the neural regulator. The latter has the function of controlling the mechanical system to perform the voiding process. The results of the tests reproduce experimental data with high degree of accuracy. Also, these results indicate that simulations not only with healthy patients but also of patients with dysfunctions with neurological etiology present urodynamic curves very similar to those obtained in clinical studies.

Robust Solutions of MultiObjective Linear Semi-Infinite Programs under Constraint Data Uncertainty

The multiobjective optimization model studied in this paper deals with simultaneous minimization of finitely many linear functions subject to an arbitrary number of uncertain linear constraints. We first provide a radius of robust feasibility guaranteeing the feasibility of the robust counterpart under affine data parametrization. We then establish dual characterizations of robust solutions of our model that are immunized against data uncertainty by way of characterizing corresponding solutions of robust counterpart of the model. Consequently, we present robust duality theorems relating the value of the robust model with the corresponding value of its dual problem.

Spanish Nursing Students' Practical Experience of Computer Applications in Nursing

This study aimed to determine the level of computer practical experience in a sample of Spanish nursing students. Each student was given a Spanish language questionnaire, modified from an original used previously with medical students at the Medical School of North Carolina University (USA) and also at the Education Unit of Hospital General Universitario del Mar (Spain). The 10-item self-report questionnaire probed for information about practical experience with computers. A total of 126 students made up the sample. The majority were female (80.2%; n=101). The results showed that just over half (57.1%, n=72) of the students had used a computer game (three or more times before), and that only one third (37.3%, n=47) had the experience of using a word processing package. Moreover, other applications and IT-based facilities (e.g. statistical packages, e-mail, databases, CD-ROM searches, programming languages and computer-assisted learning) had never been used by the majority of students. The student nurses' practical experience was less than that reported for medical students in previous studies.

Robust solutions to multi-objective linear programs with uncertain data

In this paper we examine multi-objective linear programming problems in the face of data uncertainty both in the objective function and the constraints. First, we derive a formula for the radius of robust feasibility guaranteeing constraint feasibility for all possible scenarios within a specified uncertainty set under affine data parametrization. We then present numerically tractable optimality conditions for minmax robust weakly efficient solutions, i.e., the weakly efficient solutions of the robust counterpart. We also consider highly robust weakly efficient solutions, i.e., robust feasible solutions which are weakly efficient for any possible instance of the objective matrix within a specified uncertainty set, providing lower bounds for the radius of highly robust efficiency guaranteeing the existence of this type of solutions under affine and rank-1 objective data uncertainty. Finally, we provide numerically tractable optimality conditions for highly robust weakly efficient solutions.

Corrigendum to ���The envelope theorem for multiobjective convex programming via contingent derivatives��� [J. Math. Anal. Appl. 372 (1) (2010) 197���207]

The aim of this note is to formulate an envelope theorem for vector convex programs. This version corrects an earlier work, ���The envelope theorem for multiobjective convex programming via contingent derivatives��� by Jim��nez Guerra et al. (2010) [3]. We first propose a necessary and sufficient condition allowing to restate the main result proved in the alluded paper. Second, we introduce a new Lagrange multiplier in order to obtain an envelope theorem avoiding the aforementioned error.