Quasiconvex Constrained Multicriteria Continuous Location Problems: Structure of Nondominated Solution Sets

In this paper, we consider constrained multicriteria continuous location problems in two-dimensional spaces. In the literature, the continuous multicriteria location problem in two-dimensional spaces has received special attention in the last years, although only particular instances of convex functions have been considered. Our approach only requires the functions to be strictly quasiconvex and inf-compact. We obtain a geometrical description that provides a unified approach to handle multicriteria location models in two-dimensional spaces which has been implemented in MATHEMATIC

Productivity growth and competition in spanish manufacturing firms: What has happened in recent years?

This paper addresses the issue of the relationship between productivity and market competition. In comparison to the economies of other European countries, the Spanish economy has been growing, while productivity growth has stagnated. Here we provide empirical evidence about the relationship between productivity and market competition from Spanish manufacturing firms at firm level between 1994 and 2004. Correcting for selection bias, our study pays special attention to the patterns of productivity growth between openness and non-openness firms. When market competition increases the effect on firms operating in domestic markets is positive but when the level of competition is high incentives to invest in innovation and productivity gains disappear. The empirical relationship between competition and productivity is an inverted U-shape, where productivity growth is highest at intermediate levels of competition. The productivity growth of firms operating in international markets is higher than that of non-openness firms, but when market competition rises they moderate their productivity growth. Our empirical results suggest that the correct competition policy in the Spanish economy should remove the barriers to competition in internal markets in order to increase the incentives for manufacturing firms to invest in innovation and productivity growth.Manufacturing industries, innovation, competitiveness, international trade, Heckman equation

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers

On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with low accuracy, and it may be too complex to understand and explain. This paper proposes and studies a novel Mathematical Optimization model to build a sparse ensemble, which trades off the accuracy of the ensemble and the number of base regressors used. The latter is controlled by means of a regularization term that penalizes regressors with a poor individual performance. Our approach is flexible to incorporate desirable properties one may have on the ensemble, such as controlling the performance of the ensemble in critical groups of records, or the costs associated with the base regressors involved in the ensemble. We illustrate our approach with real data sets arising in the COVID-19 context. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )We thank the reviewers for their thorough comments and suggestions, which have been very valuable to strengthen the quality of the paper. This research has been financed in part by research projects EC H2020 MSCA RISE NeEDS (Grant agreement ID: 822214); FQM-329 and P18-FR-2369 (Junta de Andalucia, Spain); MTM2017-89422-P (Ministerio de Economia, Industria y Competitividad, Spain); PID2019-110886RB-I00 (Ministerio de Ciencia, Innovacin y Universidades, Spain); PR2019-029 (Universidad de Cadiz, Spain); PITUFLOW-CM-UC3M (Comunidad de Madrid and Universidad Carlos III de Madrid, Spain); and EP/R00370X/1 (EPSRC, United Kingdom). This support is gratefully acknowledged

Survey on Lehmer Problems

 Survey on Lehmer Problem

Role and Regulation of the Actin-Regulatory Protein Hs1 in Tcr Signaling

Numerous aspects of T cell function, including TCR signaling, migration, and execution of effector functions, depend on the actin cytoskeleton. Cytoskeletal rearrangements are driven by the action of actin-regulatory proteins, which promote or antagonize the assembly of actin filaments in response to external cues. In this work, we have examined the regulation and function of HS1, a poorly-understood actin regulatory protein, in T cells. This protein, which becomes tyrosine phosphorylated upon T cell activation, is thought to function primarily by stabilizing existing branched actin filaments. Loss of HS1 results in unstable actin responses upon TCR engagement and defective Ca2+ responses, leading to poor activation of the IL2 promoter. TCR engagement leads to phosphorylation of HS1 at Tyr 378 and Tyr 397, creating binding sites for SH2 domain-containing proteins, including Vav1 and Itk. Phosphorylation at these residues is required for Itk-dependent recruitment of HS1 to the IS, Vav1 IS localization, and HS1-dependent actin reorganization and IL2 production. Therefore, in addition to directly interacting with branched actin filaments, HS1 regulation of Vav1 localization provides another mechanism by which HS1 signals to the actin cytoskeleton. Analysis of Ca2+ responses in HS1-/- T cells reveals that the defect in these cells lies at the level of release from intracellular stores. Phosphorylated HS1 interacts with PLCγ1, and mediates its association with the cytoskeleton and regulates microcluster dynamics. Although HS1 is clearly important for signaling downstream of the TCR, HS1-/- mice exhibit normal T cell development and normal peripheral populations. Surprisingly, HS1 is not required for T cell homing to lymphoid organs, for TCR endocytosis or for CD8+ T cell effector function. However, T cells from HS1-/- mice produce reduced amounts of Ifnγ, and are subsequently less likely to become Ifnγ-producing TH1 effector cells. These data demonstrate that HS1 functions as a cytoskeletal adaptor protein and plays specific roles downstream of TCR engagement

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers.Supervised classification, Binarization, Column generation, Support vector machines

Educación física – Cultura física. Dos modelos, dos didácticas

Physical Education is currently facing a number of problems that are rooted in the identity crisis prompted by the spread of the professional group, the confrontation of ideas from the scientific community and the competing interests of different political and social areas, compared to which physical education has failed, or unable, to react in time. The political and ideological confrontation that characterized the twentieth century gave us two forms, each with a consistent ideological position, in which the body as a subject of education was understood from two different positions: one set from the left and communism and another, from Western democratic societies. The survival of these conflicting positions and their interests and different views on education, in a lengthy space of time, as a consequence threw two teaching approaches and two different educational models, in which the objectives and content of education differ , and with them the forms and methods of teaching. The need to define the cultural and educational approach, in every time and place, is now a pressing need and challenge the processes of teacher training, as responsible for shaping an advanced physical education, adjusted to the time and place, the interests and needs of citizens and the democratic values of modern society.La educación Física, actualmente, se enfrenta a una serie de problemas que tienen su origen en la crisis de identidad del colectivo profesional, la confrontación de las ideas de la comunidad científica y los intereses contrapuestos de diferentes áreas políticas y sociales. En relación con los cuales, la educación física no ha sido capaz de reaccionar a tiempo. La confrontación política e ideológica que caracterizó el siglo XX generó dos formas distintas de entender la EF, cada una con una posición ideológica propia, en la que el cuerpo como sujeto de la educación se entiende desde dos posiciones diferentes: una desde de la izquierda y el comunismo y, por otra parte desde el otro punto de vista, la visión Occidental de las sociedades democráticas La supervivencia de estas posiciones en conflicto, sus intereses y sus diferentes puntos de vista y objetivos sobre la educación, en un largo espacio de tiempo, tuvo como consecuencia dos enfoques de enseñanza y dos modelos educativos diferentes, en los que los objetivos y contenidos de la educación difieren, y con ellos las formas y los métodos didácticos La necesidad de definir el enfoque cultural y educativo, en todo tiempo y lugar, es ahora una necesidad urgente y definir los procesos de formación de los docentes, como responsable de la formación de una educación física avanzada, ajustado a la hora y el lugar, los intereses y las necesidades de los ciudadanos, así como a los valores democráticos de la sociedad contemporánea.peerReviewe

Heliostat field cleaning scheduling for Solar Power Tower plants: a heuristic approach

Soiling of heliostat surfaces due to local climate has a direct impact on their optical efficiency and therefore a direct impact on the productivity of the Solar Power Tower plant. Cleaning techniques applied are dependent on plant construction and current schedules are normally developed considering heliostat layout patterns, providing sub-optimal results. In this paper, a method to optimise cleaning schedules is developed, with the objective of maximising energy generated by the plant. First, an algorithm finds a cleaning schedule by solving an integer program, which is then used as a starting solution in an exchange heuristic. Since the optimisation problems are of large size, a p-median type heuristic is performed to reduce the problem dimensionality by clustering heliostats into groups to be cleaned in the same period.Ministerio de Economía y Competitivida