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<a class="ep_document_link" href="/10290/1/Influence%20of%20E-learning%20training%20on%20the%20acquisition%20of%20competences%20in%20basketball%20coaches%20in%20Cantabria.pdf"><img class="ep_doc_icon" alt="[img]" src="/10290/1.hassmallThumbnailVersion/Influence%20of%20E-learning%20training%20on%20the%20acquisition%20of%20competences%20in%20basketball%20coaches%20in%20Cantabria.pdf" border="0"/></a>
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The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria.
Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,
Alemany Iturriaga
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Technological firms invest in R&D looking for innovative solutions but assuming high costs and great (technological) uncertainty regarding final results and returns. Additionally, they face other problems related to R&D management. This empirical study tries to determine which of the factors favour or constrain the decision of these firms to engage in R&D. The analysis uses financial data of 14,619 ICT listed companies of 22 countries from 2003 to 2018. Additionally, macroeconomic data specific for the countries and the sector were used. For the analysis of dynamic panel data, a System-GMM method is used. Among the findings, we highlight that cash flow, contrary to the known theoretical models and empirical evidences, negatively impacts on R&D investment. Debt is neither the right source for R&D funding, as the effect is also negative. This suggests that ICT companies are forced to manage their R&D activities differently, relying more on other funding sources, taking advantage of growth opportunities and benefiting from a favourable macroeconomic environment in terms of growth and increased business sector spending on R&D. These results are similar in both sub-sectors and in all countries, both bank- and market based. The exception is firms with few growth opportunities and little debt.
Inna Alexeeva-Alexeev mail inna.alexeeva@uneatlantico.es, Cristina Mazas Pérez-Oleag mail cristina.mazas@uneatlantico.es,
Alexeeva-Alexeev
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Introduction Aggressive behaviour on the road is still one of the most studied constructs within human factor due to its strong relationship with crash-related events. Objective The aim of the current research was to analyse the relationship among vital stress, emotion dysregulation, and aggressive behaviour in this specific context. Method A sample composed of 410 Spanish drivers (62.8% female, Mage = 36.12, SDage = 12.99) taken from the general population was tested. Results The results showed positive and significant bivariate correlations among almost all of the variables. The multiple mediation model showed a partial mediation effect of emotion dysregulation, with a significant effect on the whole model, and more specifically, on both the lack of control and life interference. Conclusion The relevance of developing clinical interventions to improve emotion regulation abilities in aggressive drivers is discussed.
David Herrero-Fernández mail david.herrero@uneatlantico.es, Pamela Parada-Fernández mail pamela.parada@uneatlantico.es, Irene Rodríguez-Arcos mail , Julién Brito Ballester mail julien.brito@uneatlantico.es, Carmen Lilí Rodríguez Velasco mail carmen.rodriguez@uneatlantico.es,
Herrero-Fernández
<a class="ep_document_link" href="/12706/1/remotesensing-16-02099.pdf"><img class="ep_doc_icon" alt="[img]" src="/12706/1.hassmallThumbnailVersion/remotesensing-16-02099.pdf" border="0"/></a>
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The conservation of Cultural Heritage in cave environments, especially those hosting cave art, requires comprehensive conservation strategies to mitigate degradation risks derived from climatic influences and human activities. This study, focused on the Polychrome Hall of the Cave of Altamira, highlights the importance of integrating remote sensing methodologies to carry out effective conservation actions. By coupling a georeferenced Ground Penetrating Radar (GPR) with a 1.6 GHz central-frequency antenna along with photogrammetry, we conducted non-invasive and high-resolution 3D studies to map preferential moisture pathways from the surface of the ceiling to the first 50 cm internally of the limestone structure. In parallel, we monitored the dynamics of surface water on the Ceiling and its correlation with pigment and other substance migrations. By standardizing our methodology, we aim to increase knowledge about the dynamics of infiltration water, which will enhance our understanding of the deterioration processes affecting cave paintings related to infiltration water. This will enable us to improve conservation strategies, suggesting possible indirect measures to reverse active deterioration processes. Integrating remote sensing techniques with geospatial analysis will aid in the validation and calibration of collected data, allowing for stronger interpretations of subsurface structures and conditions. All of this puts us in a position to contribute to the development of effective conservation methodologies, reduce alteration risks, and promote sustainable development practices, thus emphasizing the importance of remote sensing in safeguarding Cultural Heritage.
Vicente Bayarri Cayón mail vicente.bayarri@uneatlantico.es, Alfredo Prada mail , Francisco García mail , Carmen De Las Heras mail , Pilar Fatás mail ,
Bayarri Cayón
<a href="/12747/1/sensors-24-03754%20%281%29.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/12747/1.hassmallThumbnailVersion/sensors-24-03754%20%281%29.pdf" border="0"/></a>
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Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.
Hafeez Ur Rehman Siddiqui mail , Ambreen Akmal mail , Muhammad Iqbal mail , Adil Ali Saleem mail , Muhammad Amjad Raza mail , Kainat Zafar mail , Aqsa Zaib mail , Sandra Dudley mail , Jon Arambarri mail jon.arambarri@uneatlantico.es, Ángel Gabriel Kuc Castilla mail , Furqan Rustam mail ,
Siddiqui