University of Las Palmas de Gran Canaria
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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.
No full text
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,
Making gestures inside and outside the booth: A comparative study
No full textAlthough studies on multimodality in interpretation are gaining momentum, no research has
been carried out comparing the multimodal behaviour of simultaneous interpreters inside and
outside the booth to date. This exploratory study aims to compare the co-speech gestures and
adaptors made by five professional conference interpreters while interpreting simultaneously
and in face-to-face communication. The starting hypotheses are that participants will make
more and larger gestures during the interview, and more adaptors during interpreting.
Participants were filmed in both situations, and sections of similar duration of the videos were
analysed and annotated with ELAN to obtain the gesture rate (number of gestures per minute),
gesture amplitude, and adaptor rate (number of adaptors per minute) for each participant in
each situation. The results obtained invalidate the first hypothesis (the gesture rate was higher
in the booth in all cases), confirm the second hypothesis (the gestures were broader during
the interview), and are inconclusive with respect to the third hypothesis. The analysis of the
adaptors presented special methodological challenges that need to be further explored. The
finding of a higher gesture rate during interpreting than during the interview might question
the categorization of simultaneous interpretation as a monologic activity288210,131Q
Glucose Levels as a Key Indicator of Neonatal Viability in Small Animals: Insights from Dystocia Cases
No full textNeonatal mortality rates in small animals can reach alarming figures, with
perinatal mortality ranging from 20% to 40%, primarily due to the abrupt transition from
intrauterine to extrauterine environments. This study investigates the critical role of glucose
levels in neonatal viability, particularly in cases of dystocia and fetal stress during cesarean
sections. A cohort of 54 mothers and their 284 neonates was analyzed, focusing on maternal
weight, litter size, and corresponding neonatal glucose levels. The results indicated a
significant relationship between glucose concentrations and Apgar scores, with a cutoff
established at 79.50 mg/dL for optimal neonatal viability. Additionally, a higher prevalence
of hypoglycemia was documented in neonates with low birth weight and those from
smaller litters. The findings underscore the importance of monitoring glucose levels in
neonates, as hypoglycemia is associated with various pathologies, including sepsis and
portosystemic shunts. Overall, this study highlights the necessity for prompt assessment of
glucose levels to improve neonatal outcomes and reduce mortality in small animals.190,6982,7Q1Q1SCIE10,