Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects

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Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria

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.

Producción Científica

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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Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments

The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.

Producción Científica

Pedro Ángel de Santos Castro mail , Carlos del Pozo Vegas mail , Leyre Teresa Pinilla Arribas mail , Daniel Zalama Sánchez mail , Ancor Sanz-García mail , Tony Giancarlo Vásquez del Águila mail , Pablo González Izquierdo mail , Sara de Santos Sánchez mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es, Irma Dominguez Azpíroz mail irma.dominguez@unini.edu.mx, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Francisco Martín-Rodríguez mail ,

de Santos Castro

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Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model

Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.

Producción Científica

Furqan Rustam mail , Ahmad Sami Al-Shamayleh mail , Rahman Shafique mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, J. Pablo Miramontes Gonzalez mail , Imran Ashraf mail ,

Rustam

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A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

Producción Científica

Mahrukh Iftikhar mail , Muhammad Shoaib mail , Ayesha Altaf mail , Faiza Iqbal mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Imran Ashraf mail ,

Iftikhar

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Exploring body composition and somatotype profiles among youth professional soccer players

OBJECTIVE: This study aimed to analyze the body composition and somatotype of professional soccer players, investigating variations across categories and playing positions. METHODS: An observational, cross-sectional, and analytical study was conducted with 51 male professional soccer players in the U-19 and U-20 categories. Data about sex, age, height, and weight were collected between March and May 2023. Body composition analysis utilized the ISAK protocol for the restricted profile, while somatotype categorization employed the Heath and Carter formula. Statistical analysis was performed using IBM SPSS Statistics V.26, which involved the application of Mann-Whitney and Kruskal-Wallis tests to discern differences in body composition variables and proportionality based on categories and playing positions. The Dunn test further identified specific positions exhibiting significant differences. RESULTS: The study encompassed 51 players, highlighting meaningful differences in body composition. The average body mass in kg was 75.8 (±6.9) for U-20 players and 70.5 (±6.1) for U-19 players. The somatotype values were 2.6-4.6-2.3 for U-20 players and 2.5-4.3-2.8 for U-19 players, with a predominance of muscle mass in all categories, characterizing them as balanced mesomorphs. CONCLUSIONS: Body composition and somatotype findings underscore distinctions in body mass across categories and playing positions, with notably higher body mass and muscle mass predominance in elevated categories. However, the prevailing skeletal muscle development establishes a significant semblance with the recognized somatotype standard for soccer.

Producción Científica

Raynier Zambrano-Villacres mail , Evelyn Frias-Toral mail , Emily Maldonado-Ponce mail , Carlos Poveda-Loor mail , Paola Leal mail , Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Alice Leonardi mail , Bruno Trovato mail , Federico Roggio mail , Alessandro Castorina mail , Xu Wenxin mail , Giuseppe Musumeci mail ,

Zambrano-Villacres