Resultados de investigación

Se trata de una encuesta de clima laboral durante el año 2023 llevado a cabo por la Biblioteca Universidad Complutense de Madrid, más concretamente por el Servicio de Evaluación de Procesos y Centros

Se actualiza el panorama de la segregación ocupacional en España para el año 2022 y se examina su evolución y factores determinantes a lo largo del último siglo. Para ello, se emplean los índices más utilizados en la literatura: el Índice de Gini (y las curvas de Lorenz), el Índice de Desigualdad y el Índice de Desigualdad Descompuesto (que neutraliza el efecto de la estructura), utilizando datos censales para asegurar un alto nivel de desagregación. Los factores que influyen en la segregación se evalúan mediante regresiones multinomiales y se contrastan a través de efectos marginales (EPA, 2011 y 2021). Los resultados muestran que la segregación no experimenta una disminución significativa, dado que aumenta el peso de las ocupaciones con mayor grado de segregación mientras que disminuye en las ocupaciones que ya presentaban un proceso de integración. Asimismo, se verifica una relación inversa entre ingresos y nivel educativo en las ocupaciones predominantemente masculinas y femeninas

This work was supported by the I+D+I projects PID2020-118376RA-I00 (funded by MCIN/AEI/10.13039/501100011033) and AYUD/2021/51289 (from the PCTI Program of the Government of the Principality of Asturias and the FEDER Program of the EU). P.R.L. was supported by the Asturian Council for Science, Technology, and Innovation Severo Ochoa grant (BP21-029) and E.C.R. by the Spanish Ministry of Universities Beatriz Galindo grant (BG20-00027).

As detailed in the "ReadMe.txt" file of the dataset, the name of the text files which contain the CFD data has been encoded to facilitate post-processing of the data by any user., Numerical data associated with the original research paper entitled: "Multi-Objective Response Surface Optimization of a centrifugal turbine for application in Oscillating Water Column devices equipped with a Twin Turbine Configuration" authored by Aitor Vega-Valladares and co-authored by Manuel Garcia-Diaz and Bruno Pereiras, all members of FERES research group of the University of Oviedo. In this paper, a novel methodology, specifically created to optimize Oscillating Water Column (OWC) devices equipped with Twin Turbine Configuration (TTC) systems, is presented. With the information contained in this dataset, the optimization could be carried out in another working environment or following another methodology., At data collection date, Aitor Vega-Valladares was supported by the “Administración del Principado de Asturias” within the “Severo Ochoa” Program (grant number BP21-160).

The authors wish to thank the financial support of the Spanish Ministry of Science, Innovation and Universities in reference to the Project: Efficiency improvement and noise reduction of a vertical axis wind turbine for urban environments (MERTURB) – Ref. MCINN-22-TED2021–131307B-I00. Additionally, the authors would like to acknowledge the contribution of the rest of the members of the Fluids Engineering for Renewable Energy and Sustainability (FERES) research group, as well as the hard work of the laboratory technician Germán Marcos Robredo.

Dataset asociado a la publicación "A hand-drawn graphite electrode for affordable analysis: application to the enzymatic determination of tyramine in fish". DOI: https://doi.org/10.1016/j.snb.2024.136705, This work was supported by the Spanish Government (MCIN/AEI/10.13039/501100011033) through the I+D+I project PID2020-118376RA-I00, and by the PCTI Program of the Government of the Principality of Asturias and FEDER Program of the European Union (No. AYUD/2021/51289). This work also received support from FCT/MCTES (LA/P/0008/2020, doi: 10.54499/ LA/P/0008/2020, UIDP/50006/2020, doi: 10.54499/UIDP/50006/ 2020 and UIDB/50006/2020, doi: 10.54499/UIDB/50006/2020) through Portuguese funds. R. Torre thanks FCT and the European Social Fund (ESF), through the “Programa Operacional Capital Humano” (POCH), for her PhD grant (SFRH/BD/143753/2019). Maria Cerrato-Alvarez thanks the “Ministerio de Universidades” and “Universidad de Extremadura” for grant “Margarita Salas” MS-18 (Convocatoria Ayudas UNI/551/2021 – Complementaria 2022). E. Costa‐Rama thanks the support of the grant “Beatriz Galindo” (BG20/00027) funded by the “Ministerio de Universidades” of the Spanish Government.

This work was financially supported through projects PID2019-107838RB-I00 and PID2020-118376RAI00/Agencia Estatal de Investigación (AEI)/https://doi. org/10.13039/501100011033, and the PCTI Program of the Government of the Principality of Asturias and FEDER Program of the European Union (Ref. AYUD/2021/51289). M. Marina-Latorre acknowledges a grant from “Severo Ochoa Program” with Ref. PA23-BP22-183 funded by Principality of Asturias (Spain).

To visualize the measurements of the scattered field in each of the 11 planes, simply execute the program "measurement_data_representation.m" from Matlab IDE. By doing so, two figures will be generated showing the magnitude and phase of the field in each of the measurement planes. Next, the user will be prompted to input the plane number (between 1 and 11) to be used for SAR processing. Finally, a third figure will display the SAR image generated from the measurements of the selected plane. This program only intends to display an image using the dataset measurements, which is available to any other user and can be processed in any other way., The present contribution consists of two files. The first one (measurements_25GHz_11planes.mat) contains a series of measurements of the scattered electric field using two two-dimensional objects made of aluminum foil as targets. The dataset consists of S21 paramenter measurements, captured with a VNA at a frequency of 25 GHz. The field was sampled in 11 planes located at different distances from the plane containing the objects under test. In each of the planes, the scattered field was sampled forming a grid of 101x101 points, for a total of 10201 measurements along a square of 50 cm per side. The second file (measurement_data_representation.m) is a Matlab program that imports the measurements and graphically represents the magnitude and phase of the scattered field in each plane. Subsequently, the user can input the number of the plane to use to generate a SAR (Synthetic Aperture Radar) image of the objects under test. The ultimate goal of the dataset is to exploit the measurements in the different planes to explore the possibility of using various techniques to obtain a similar image from amplitude-only measurements.