Resultados de investigación

The dataset contains all raw data of the work "Improving the adhesion of satin XPS to sandstone with customized polyurethane adhesives for sustainable facade cladding and thermal insulation", We gratefully acknowledge the financial support provided by all funders. This work was supported by the Regional Government of Castilla y León (Junta de Castilla y León) and by the Ministry of Science and Innovation MICIN and the European Union NextGenerationEU PRTR. Author Saul Vallejos received grant BG22/00086 funded by the Spanish Ministerio de Universidades. Author ´Alvaro Miguel’s work was facilitated by the Margarita Salas grant (CA1/RSUE/2021–00409), funded by the Universidad Autónoma de Madrid. The financial support provided by Fondo Europeo de Desarrollo Regional-European Regional Development Fund (FEDER, ERDF) and Regional Government of Castilla y León -Consejería de Educación, Junta de Castilla y León (BU025P23) is gratefully acknowledged. We also acknowledge the funding provided by Grant PID2020-113264RB-I00 funded by MCIN/AEI/10.13039/ 501100011033 and by “ERDF A way of making Europe”.

This dataset contains the data generated during the execution of the project “Full-scale study of sustainable high performance hydraulic and bituminous concretes, manufactured with steelmaking and construction waste (FULLSCALE)”, code PID2020-113837RB-I00, corresponding to the 2020 Call of “Proyectos de I+D+i” of the State Research Agency (Agencia Estatal de Investigación, AEI) and the Ministry of Science and Innovation of the Government of Spain (Ministerio de Ciencia e Innovación del Gobierno de España)., The research work conducted within the framework of the FULLSCALE project (PID2020-113837RB-I00), which was funded by the Ministry of Science and Innovation of the Government of Spain (Ministerio de Ciencia e Innovación del Gobierno de España), the State Research Agency (Agencia Estatal de Investigación, AEI) and the European Regional Development Fund (Fondo Europeo de Desarollo Regional, FEDER) through the 2020 Call of "Proyectos de I+D+i".

Los participantes en el estudio fueron aquellos maestros/profesores que imparten clases en los ciclos educativos de infantil y primaria (cualquiera que sea la especialidad) que contestaron a una encuesta distribuida de manera digital entre junio de 2024 y agosto de 2024.

The dataset contains all raw data of the work "Naked-eye Detection of Legionella pneumophila Using Smart Fluorogenic Polymers Prepared as Hydrophilic Films, Coatings, and Electrospun Nanofibers", We gratefully acknowledge the financial support provided by all funders. The financial support provided by Fondo Europeo de Desarrollo Regional-European Regional Development Fund (FEDER, ERDF) and Regional Government of Castilla y León -Consejería de Educación, Junta de Castilla y León- (BU025P23) is gratefully acknowledged. This work was supported by the Regional Government of Castilla y León (Junta de Castilla y León) and the Ministry of Science and Innovation MICIN and the European Union NextGenerationEU PRTR. Author Saul Vallejos received grant BG22/00086 funded by the Spanish Ministerio de Universidades. Author Marta Guembe-García received funding from the Spanish Ministerio de Universidades-European Union under the framework of NextGeneration EU RD 289/2021 for her Post-Doc position. We also acknowledge the financial support provided by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe” (grant PID2020-113264RB-I00). Edurne González acknowledge the financial support of the Spanish Ministerio de Ciencia e Innovación (Proyectos de I+D+i, grant PID2020-117628RJ-I00). Issei Otsuka acknowledges the financial support of the French National Centre for Scientific Research (Emergence International grant).

The optimization of a production process is often based on the efficient utilization of the production facility and equipment. In particular, reducing the time to change from producing one product to another is critical to the fulfillment of demand at a minimum cost. We study the production of steel coils in the context of searching for groups of products with similar characteristics in order to create production batches that minimize the cost of fulfilling production orders originated by a known demand. We formulate the problem as mixed-integer program and develop a heuristic solution procedure. We show that a simplified version of the problem is equivalent to the clique partition problem, which in turn is equivalent to the graph-coloring problem. Computational experiments show that the heuristic procedure is effective in finding high-quality solutions to both the clique partition problem and the original grouping problem that includes additional costs., This work was partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects ECO2013-47129-C4-3-R and ECO2016-76567-C4-2-R), the Regional Government of Castilla y León, Spain (Project BU329U14 and BU071G19), and the Regional Government of Castilla y León and FEDER funds (Project BU062U16).

In this study, a model for the selection of investment portfolios is proposed with three objectives. In addition to the traditional objectives of maximizing profitability and minimizing risk, maximization of social responsibility is also considered. Moreover, with the purpose of controlling transaction costs, a limit is placed on the number of assets for selection. To the best of our knowledge, this specific model has not been considered in the literature to date. This model is difficult (NP-Hard), and therefore, only very small instances may be solved in an exact way. This paper proposes a method based on tabu search and multiobjective adaptive memory programming (MOAMP) strategies. With this method it is possible to obtain sets of nondominated solutions in short computational times. To check the performance of our method it is compared with adaptations of the nondominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization(MOPSO). The results of different computational experiments show that our tabu search-MOAMP method performed best. The quality of the sets of solutions that were obtained and the speed of execution mean that our tabu search-MOAMP can be used as a tool for financial assessment and analysis (including online services). This tool, as we can see in this work with some examples, can take into account the social concerns of many clients and their overall risk profile (very conservative, conservative, moderate, or fearless). This approach is also in line with current legal regulations that oblige financial advisors to take the client profile into account to provide greater protection and propose good financial advice., This work was partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects PID2019-104263RB-C44 and PDC2021– 121021-C22), the Regional Government of “Castilla y León”, Spain (Projects BU329U14 and BU071G19), the Regional Government of “Castilla y León” and FEDER funds (Projects BU062U16, COV2000375 and BU056P20).

Given an undirected graph, a clique is a subset of vertices in which the induced subgraph is complete; that is, all pairs of vertices of this subset are adjacent. Clique problems in graphs are very important due to their numerous applications. One of these problems is the clique partitioning problem (CPP), which consists of dividing the set of vertices of a graph into the smallest number of cliques possible. The CPP is an NP-hard problem with many application fields (timetabling, manufacturing, scheduling, telecommunications, etc.). Despite its great applicability, few recent studies have focused on proposing specific resolution methods for the CPP. This article presents a resolution method that combines multistart strategies with tabu search. The most novel characteristic of our method is that it allows unfeasible solutions to be visited, which facilitates exploration of the solution space. The computational tests show that our method performs better than previous methods proposed for this problem. In fact, our method strictly improves the results of these methods in most of the instances considered while requiring less computation time., This work was partially supported by FEDER funds and the Spanish State Research Agency (Projects PID2019-104263RB-C44 and PDC2021–121021-C22); the Regional Government of “Castilla y León”, Spain (Project BU071G19); the Regional Government of “Castilla y León”; and FEDER funds (Project BU056P20).

In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods., This work was partially supported by FEDER funds and the Spanish State Research Agency (Projects PID2019-104263RB-C44 and PDC2021–121021-C22); the Regional Government of “Castilla y León”, Spain (Project BU071G19); the Regional Government of “Castilla y León”; and FEDER funds (Project BU056P20).

In 2020, the COVID-19 pandemic and its rapid spread shook health authorities worldwide at the regional and national levels. Healthcare systems had difficulty acquiring important supplies, such as face shields, which at that time were essential for healthcare staff. The need for this material increased with the spread of the pandemic. In most areas, warehouses did not have a sufficient stock of this product. This situation has occurred in the cities and provinces of Burgos (Spain). Volunteers (citizens and small companies) owning three-dimensional printers offered themselves to manufacture face shields. These volunteers are called “makers.” Similarly, different organizations (mainly Civil Protection) took charge of transport activities (delivery of material to the makers, collection of face shields, and delivery of the latter to hospitals and other entities). In this study, we were tasked with developing a system for planning and rationalizing these activities. The problems that were solved included a vehicle routing problem with different characteristics compared with other models in the literature. A previous work described this problem, and the heuristic method used for the planning. However, it is necessary to develop tools that are as efficient as possible for similar situations. In this study, we propose a mathematical formulation of the problem and a method based on the metaheuristic strategies variable neighborhood search and greedy randomize adaptative search procedure on a multistart framework. Different tests with real instances used during the period in which these activities were conducted show that the new method improves the results obtained by the previous method as well as the commercial software., The authors are grateful to the following entities and projects: The Spanish Research Agency (Projects PID2019-104263RB-C44, PDC2021–121021-C22, and PID2022-139543OB-C44) and the Regional Government of “Castilla y León” and FEDER funds (Project BU056P20).

The variable selection problem in the context of Linear Regression for large databases is analysed. The problem consists in selecting a small subset of independent variables that can perform the prediction task optimally. This problem has a wide range of applications. One important type of application is the design of composite indicators in various areas (sociology and economics, for example). Other important applications of variable selection in linear regression can be found in fields such as chemometrics, genetics, and climate prediction, among many others. For this problem, we propose a Branch & Bound method. This is an exact method and therefore guarantees optimal solutions. We also provide strategies that enable this method to be applied in very large databases (with hundreds of thousands of cases) in a moderate computation time. A series of computational experiments shows that our method performs well compared with well-known methods in the literature and with commercial software., This work was partially supported by FEDER funds and the Spanish Ministry of Economy and Competitiveness (Projects ECO2016-76567-C4-2-R and PID2019-104263RB-C44), the Regional Government of “Castilla y León”, Spain (Project BU329U14 and BU071G19), the Regional Government of “Castilla y León” and FEDER funds (Project BU062U16 and COV2000375).