University of Las Palmas de Gran Canaria
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Caractérisation linguistique de la classe locutionnaire verbale en relation á l’oeuvre littéraire de don Íñigo López de Mendoza (Marquis de Santillana) RÉSUMÉ : Ce travail vise à faire progresser la connaissance de la phraséologie
No full textEste trabajo pretende avanzar en el conocimiento de la fraseología histórica española. Su objetivo fundamental es el de ofrecer una caracterización lingüística de la clase locucional verbal a la luz de la obra literaria de don Íñigo López de Mendoza. La metodología empleada se articulará en torno a un estudio descriptivo sobre el estatuto de la clase locucional verbal en la teoría fraseológica del español, así como sobre la configuración gramatical y el desarrollo diacrónico de las locuciones verbales registradas en la obra de Santillana. Los resultados indican que el segmento temporal del siglo XV resultó de gran trascendencia en la generación de locuciones verbales, y certifican la consolidación de los paradigmas gramaticales descritos en su proceso de fraseologización.This paper aims to advance in the knowledge of Spanish historical phraseology. Its main objective is to offer a linguistic characterization of the verbal locutionary class in the light of the literary work of don Íñigo López de Mendoza. The methodology employed will be articulated around a descriptive study on the status of the verbal locutionary class in the phraseological theory of Spanish, as well as on the grammatical configuration and the diachronic development of the verbal locutions recorded in Santillana’s work. The results indicate that the temporal segment of the 15th century was of great importance in the generation of verbal locutions, and certify the consolidation of the grammatical paradigms described in the process of phraseologization.Ce travail vise à faire progresser la connaissance de la phraséologie historique espagnole. Son objectif principal est de proposer une caractérisation linguistique de la classe locutionnaire verbale à la lumière de l’oeuvre littéraire de don Íñigo López de Mendoza. La méthodologie employée sera basée sur une étude descriptive du statut de la classe locutionnaire verbale dans la théorie phraséologique de l’espagnol, ainsi que sur la configuration grammaticale et le développement diachronique des locutions verbales enregistrées dans l’oeuvre de Santillana. Les résultats indiquent que le segment temporel du XVe siècle a été d’une grande importance dans la génération de locutions verbales et certifient la consolidation des paradigmes grammaticaux décrits dans le processus de phraséologisation.344325200,173Q1ESCIQ232,070,0Q19,9ERIH PLU
Dataset employed in "Operational Modal Analysis from multi-setup measurements with roving references and Stochastic Subspace Identification"
No full text<p>This repository contains the dataset used in the scientific article "Operational Modal Analysis from multi-setup measurements with roving references and Stochastic Subspace Identification" by Samuel González-Jiménez, Luis A. Padrón, Guillermo M. Álamo and Jacob D. R. Bordón</p>
<p>These files were used in the paper above to validate the presented methodology with synthetically generated data and experimental meassurements of an elevated pedestrian crossing over a highway. </p>
<p>The synthetically generated data is obtained from the execution of the file "Gen_num_data.m" which uses the functions "SSI_VB.m" and "add_noise.m", all compressed in the file "Code_for_generating_synthetic_data.zip" . Both functions use the "wgn" function from the Matlab Communications Toolbox.</p>
<p>The experimental measurements acquired from the elevated pedestrian crossing are compiled within the archive titled "Experimental_data_Elevated_pedestrian_crossing.zip." This compressed file contains seven .dat files, each designated by a filename that denotes the corresponding measurement locations. Each .dat file is formatted with two columns, corresponding to the sequential measurement values order at the positions indicated in the filename. These measurements have been recorded with two Tromino® Blu devices used as velocimeters, with a chosen dynamic range of ±0.5mm/s.</p>
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Exploring Racial Issues through Character Analysis in Harper Lee's To Kill a Mockingbird.
No full textEstudio de viabilidad técnica y económica de soluciones sostenibles para la climatización de edificios en Canarias.
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Presenvigen Aldea Rural: Prevención Y Sensibilización De La Violencia De Género En El Medio Rural
No full textAre university teachers ready for generative artificial intelligence? : Unpacking faculty anxiety in the ChatGPT era
No full textThis study investigates the role of technology‐related anxiety in shaping university teachers’ behavioral intention to adopt ChatGPT. Three distinct types of anxiety are examined: (a) anxiety about the future of the academic profession, (b) anxiety regarding the personal misuse of ChatGPT, and (c) anxiety concerning negative impacts on student learning. A structured questionnaire was administered to 249 faculty members from Spanish public universities. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both the direct effects of each type of anxiety on behavioral intention and the mediating roles of effort expectancy (EE) and performance expectancy (PE). Results indicate that anxiety about student learning exhibits a significant negative direct effect on behavioral intention and an indirect effect through performance expectancy. Similarly, anxiety related to the misuse of ChatGPT is negatively associated with behavioral intention, with significant mediation through both EE and PE. In contrast, anxiety concerning the future of the academic profession does not show a statistically significant relationship with behavioral intention. The findings underscore the importance of addressing specific psychological barriers—particularly those linked to concerns over student learning and technology misuse—to facilitate ChatGPT integration in higher education. The study suggests that effective implementation strategies should combine technical training with targeted interventions aimed at managing technology-related anxiety, enhancing ethical practices, and improving perceptions of the tool’s utility and ease of use.281,3014,8Q1Q1ESCI10,9ERIH PLU
Personalized glucose forecasting for people with type 1 diabetes using large language models
No full textBackground and objective: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous insulin via Multiple Daily Injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in Continuous Glucose Monitoring (CGM), controlling glucose levels remains challenging. Large Language Models (LLMs) have produced impressive results in text processing, but their performance with other data modalities remains unexplored. The aim of this study is three-fold. First, to evaluate the effectiveness of LLM-based models for glucose forecasting. Second, to compare the performance of different models for predicting glucose in T1D individuals treated with MDIs and pumps. Lastly, to create a personalized approach based on patient-specific training and adaptive model selection. Methods: CGM data from the T1DEXI study were used for forecasting glucose levels. Different predictive models were evaluated using the mean absolute error (MAE) and the root mean squared error and considering the Prediction Horizons (PHs) of 60, 90, and 120 min. Results: For short-term PHs (60 and 90 min), the personalized approach achieved the best results, with an average MAE of 15.7 and 20.2 for MDIs, and a MAE of 15.2 and 17.2 for pumps. For long-term PH (120 min), TIDE obtained an MAE of 19.8 for MDIs, whereas Patch-TST obtained a MAE of 18.5. Conclusion: LLM-based models provided similar MAE values to state-of-the-art models but presented a reduced variability. The proposed personalized approach obtained the best results for short-term periods. Our work contributes to developing personalized glucose prediction models for enhancing glycemic control, reducing diabetes-related complications.161,1894,9Q1Q1SCIE11,