BUSCADOR RECOLECTA

Dataset de la publicación: "Standards for the Integration of Online Laboratories with Learning Management Systems" Cite: I. Ruano, E. Estévez, J. Gámez and J. Gómez, "Standards for the Integration of Online Laboratories With Learning Management Systems," in IEEE Access, vol. 11, pp. 125267-125290, 2023, doi: 10.1109/ACCESS.2023.3330666., Contents of the folder 'InfoInvest' ************************************************************** - Fichero 'Email to attendees.docx': Content of the email sended to participants of the survey used in the investigation. - Fichero 'Online Labs and Standards (answers).xlsx': Excel file containing the answers provided by survey attendees. - Fichero 'Survey-Online Labs and Standars.pdf': PDF file containing the questions included in the survey used in the investigation. *************************************************************** Related with the work "Standards for the Integration of Online Laboratories with Learning Management Systems" Cite: I. Ruano, E. Estévez, J. Gámez and J. Gómez, "Standards for the Integration of Online Laboratories With Learning Management Systems," in IEEE Access, vol. 11, pp. 125267-125290, 2023, doi: 10.1109/ACCESS.2023.3330666. *************************************************************** Abstract: Laboratory work is essential in most applied science and engineering specific coursers. The continuous advances in Information and Communication Technologies be able the use of online laboratories (OLs). Their use is increased due to the advantages they offer compared to conventional laboratories, as unfortunately verified during Covid-19 pandemic. OLs and Learning Management Systems (LMSs) are key technologies in the learning process field. Normally, the integration of OLs with LMSs is carried out using proprietary or ad-hoc solutions. Furthermore, there are standards that can be used for this integration, such as: LTI, IEEE P92741.1 (xAPI), SCORM and IEEE1876. This work elaborates a survey which has been provided to the experts of online laboratories for them to complete. Gathered information allowed authors to diagnose the level of knowledge and use of the main standards for creating and integrating OLs. In fact, the analysis of this information confirms that there is a lack of criteria for selecting one standard against the others and how use it in the online laboratory development. This work gives guidelines for selecting the most appropriate standard according to desired characteristics of the online laboratory under development.

Plan de gestión de datos y datasets del Proyecto denominado "Producción de bioenergía a partir de residuos forestales y agrícolas: prevención de incendios y desarrollo sostenible en zonas rurales", Agencia Estatal de Investigación (por conceder)

DATOS OBTENIDOS EN ANÁLISIS DE FÁRMACOS DE LOS MICROCOSMOS, PROYECTOS ORIENTADOS A LA TRANSICIÓN ECOLÓGICA Y A LA TRANSICIÓN DIGITAL. Plan Estatal de Investigación Científica, Técnica y de Innovación para el período 2021-2023.

Planning de los experimentos conductuales realizados en el marco del Proyecto "Frustración por pérdida de reforzamiento: identificación del conectoma funcional en el cerebro de ratas machos y hembras, Ministerio de Ciencia e Innovación. ID2021-123338NB-I00

Flujo de datos de sensores de movimiento, ambientales, apertura/cierre, localización y smartbands recopilados en distintas viviendas. Incluye descripciones de los sensores y dispositivos, diagramas que ilustran la disposición de las viviendas y la ubicación de los dispositivos y descripciones de los ficheros de datos., Proyecto PID2021-127275OB-I00 financiado por MICIU/AEI/ 10.13039/501100011033 y por “FEDER Una manera de hacer Europa"

This spreadsheet includes the final databases obtained in the process described in the article "Optimizing rule weights to improve FRBS clustering in wireless sensor networks". The optimized weights for each of the analyzed scenarios can be found in the file., Wireless sensor networks (WSNs) are usually composed of tenths or hundredths of nodes powered by batteries that need efficient resource management to achieve the WSN goals. One of the techniques used to manage WSN resources is clustering, thus nodes are grouped into clusters around a cluster head (CH) which must be chosen carefully. In this article, it is presented a new centralized clustering algorithm based on a Type-1 fuzzy logic controller that infers the probability for each node of becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded after that point until the last node runs out of power. These three KBs have been obtained from the original KB designed by the authors after an optimization process. It is based on a Particle Swarm Optimization algorithm that maximizes the lifetime of the WSN in those three periods by adjusting each rule in those KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make it unaffordable. The results of the present optimized approach improve significantly upon those from other authors with similar methods. Finally, the paper presents an analysis on why some rule weights change more than others in order to design more suitable controllers in the future.