Nanocellular Polymers with a Gradient Cellular Structure Based on Poly(methyl methacrylate)/Thermoplastic Polyurethane Blends Produced by Gas Dissolution Foaming

Graded structures and nanocellular polymers are two examples of advanced cellular morphologies. In this work, a methodology to obtain low-density graded nanocellular polymers based on poly(methyl methacrylate) (PMMA)/ thermoplastic polyurethane (TPU) blends produced by gas dissolution foaming is reported. A systematic study of the effect of the processing condition is presented. Results show that the melt-blending results in a solid nanostructured material formed by nanometric TPU domains. The PMMA/ TPU foamed samples show a gradient cellular structure, with a homogeneous nanocellular core. In the core, the TPU domains act as nucleating sites, enhancing nucleation compared to pure PMMA and allowing the change from a microcellular to a nanocellular structure. Nonetheless, the outer region shows a gradient of cell sizes from nano- to micron-sized cells. This gradient structure is attributed to a non-constant pressure profile in the samples due to gas desorption before foaming. The nucleation in the PMMA/ TPU increases as the saturation pressure increases. Regarding the effect of the foaming conditions, it is proved that it is necessary to have a fine control to avoid degeneration of the cellular materials. Graded nanocellular polymers with relative densities of 0.16–0.30 and cell sizes ranging 310–480 nm (in the nanocellular core) are obtained

Formal testing of systems presenting soft and hard deadlines

We present a formal framework to specify and test systems presenting both soft and hard deadlines. While hard deadlines must be always met on time, soft deadlines can be sometimes met in a different time, usually higher, from the specified one. It is this characteristic (to formally define sometimes) what produces several reasonable alternatives to define appropriate implementation relations, that is, relations to decide wether an implementation is correct with respect to a specification. In addition to introduce these relations, we define a testing framework to test implementations

Generalization and completeness of stochastic local search algorithms

We generalize Stochastic Local Search (SLS) heuristics into a unique formal model. This model has two key components: a common structure designed to be as large as possible and a parametric structure intended to be as small as possible. Each heuristic is obtained by instantiating the parametric part in a different way. Particular instances for Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are presented. Then, we use our model to prove the Turing-completeness of SLS algorithms in general. The proof uses our framework to construct a GA able to simulate any Turing machine. This Turing-completeness implies that determining any non-trivial property concerning the relationship between the inputs and the computed outputs is undecidable for GA and, by extension, for the general set of SLS methods (although not necessarily for each particular method). Similar proofs are more informally presented for PSO and ACO

Applications of river formation dynamics

River formation dynamics is a metaheuristic where solutions are constructed by iteratively modifying the values associated to the nodes of a graph. Its gradient orientation provides interesting features such as the fast reinforcement of new shortcuts, the natural avoidance of cycles, and the focused elimination of blind alleys. Since the method was firstly proposed in 2007, several research groups have applied it to a wide variety of application domains, such as telecommunications, software testing, industrial manufacturing processes, or navigation. In this paper we review the main works of the last decade where the river formation dynamics metaheuristic has been applied to solve optimization problems

How to make a best-seller: optimal product design problems

We formalize and analyze the computational complexity of three problems which are at the keystone of anymarketingmanagement process. Given the preferences of customers over the attribute values wemay assign to our product (i.e. its possible features), the attribute values of products sold by our competitors, and which attribute values are available to each producer (due to e.g. technological limitations, legal issues, or availability of resources), we consider the following problems: (a) select the attributes of our product in such a way that the number of customers is maximized; (b) find out whether there is a feasible strategy guaranteeing that, at some point in the future before some deadline, we will reach a given average number of customers during some period of time; and (c) the same question as (b), though the number of steps before the deadline is restricted to be, at most, the number of attributes. We prove that these problems are Poly-APX-complete, EXPTIME-complete, and PSPACE-complete, respectively. After presenting these theoretical properties, heuristic methods based on genetic, swarm and minimax algorithms are proposed to suboptimally solve these problems. We report experimental results where these methods are applied to solve some artificially-designed problem instances, and next we present a case study, based on real data, where these algorithms are applied to a particular kind of product: we automatically design the political platform of a political party to maximize its numbers of votes in an election (problem (a)) and its number of supporters along time (problems (b) and (c)). The problem instances solved in this case study are constructed from publicly released polls on political tendencies in Spain

Herramienta para escoger sistemáticamente combinaciones de ejercicios con mayor capacidad formadora y/o evaluadora

Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaFALSEsubmitte

Inteligencia colectiva aplicada al aula: Realimentación docente a través de preguntas con dificultades específicas diseñadas por alumnos

Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaFALSEsubmitte

Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems

Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEpu

Voting according to one’s political stances is difficult : Problems definition, computational hardness, and approximate solutions

This paper studies the computational complexity of two voting problems where the goal is deciding how a given voter should vote to favour their personal stances. In the first problem, given (a) the voter stance towards each law that will be voted by the parliament and (b) the political stance of each party towards each law (all party members are assumed to vote according to it), the goal is finding the parliamentary seats distribution maximizing the number of laws that will be approved/rejected as desired by the voter. In the second problem no parliament is involved, but a single issue with several possible answers is voted by citizens in a presidential election with several candidates. The problem consists in deciding how a group of voters, split in different electoral districts, all of them supporting the same candidate, should vote to make their candidate president. It is assumed that (a) all delegates of each electoral district are assigned to the candidate winning in the district, (b) after the election day, candidates may ask their assigned delegates to support other candidates receiving more votes than them, and these post-electoral supporting stances are known in advance by the electorate, and (c) the group of voters that is coordinated knows the votes that will be cast by the rest of the electorate. For each problem, its NP-hardness as well as its inapproximability are proved. This implies that something as essential as exercising the democratic right to vote, in such a way that the voting choice will be the best forp the voter’s political stances, is at least NP-hard. It is also shown how genetic algorithms can be used to obtain reasonable solutions in practice despite the limitations of theoretical approximation hardness.Depto. de Sistemas Informáticos y ComputaciónFac. de InformáticaInstituto de Tecnología del Conocimiento (ITC)TRUEpu