Acute effect of induced fatigue on passing ability in elite U-19 soccer players

Increased fatigue may be observed during a soccer match, with a reduction of the high intensity activities owing to physical demands. These actions lead to a decline in players’ performance. The aim of the study was to analyze the acute effects of induced fatigue on passing ability in elite U-19 soccer players. Twenty-three elite U-19 soccer players (17.9 ± 0.7 years; weight 69.7 ± 8.1 kg; height 177.2 ± 7.6 cm) performed the Loughborough Soccer Passing Test (LSPT) to evaluate passing ability: control, dribbling, accuracy and decision making. Repeated sprint ability (RSA) was used to induce fatigue, 12 x 30 m sprints followed by 30 s recovery time. Heart rate (HR), Borg’s rating of perceived exertion (RPE), time in 5 m and 30 m, sprint decrement (Sdec) and the fatigue index (FI) was collected. Student's t test was applied to compare the difference between pre-test and post-test. Differences were interpreted using Cohen’s d effect size. Fatigue led to a significant increase in the number of penalties in the LSPT (p < 0.001; d = 0.54) and in total time to perform the test (p = 0.001; d = 0.37). Of the different types of error, passing accuracy was the ability that declined most (p = 0.010 d = 0.72). Ball control was also affected, but to a lesser extent (p = 0.030; d = 0.39). The results shown that passing ability was affected by fatigue in elite U-19 soccer players. This study provides detailed information for football coaches and physical trainers on the effects of fatigue on passing ability, describing the decline in performance of this specific ability in soccer

Optimum design and performance of a solar dish microturbine using tailored component characteristics

Versión revisada. Embargo 24 mesesThe aim of the paper is to find the optimum design and performance of solar microturbines powered by parabolic dish collectors using an innovative methodology which integrates the design and off-design models of the total system. In contrast to the common practice of assigning an estimated efficiency to the engine turbomachinery (generalized performance maps), the procedure hereinafter produces the specific geometry and the characteristic maps of compressor and turbine, according to their inlet/outlet thermodynamic states and working cycle boundary conditions. With this global approach, a sensitivity analysis is performed to search for the pressure ratio that maximizes the solar-to-electric efficiency at design point for a constant air mass flow rate and turbine inlet temperature. Maximum values in the range 18.0–21.7% are obtained for a pressure ratio of 3.2 when the turbine inlet temperature changes between 800 °C (base-case system) and 900 °C. The methodology allows also to simulate the performance of the system when different design DNIs are considered with the aim to maximize the annual yield of the system. Simulations performed for Beijing, Seville and San Diego showed that quite different DNIs (610–815 W/m2) are to be chosen to get the maximum annual (average) efficiency: 11–16% for the base-case system and 14–19% for a more advanced design.Comisión Europea Grant Agreement No. 30895

Extraction of Definitional Contexts from Biomedical Corpora

En este proyecto se formula una metodología para extraer contextos definitorios desde corpus de biomedicina en español, con el fin de generar los siguientes productos: (i) un listado de candidatos a términos, (ii) un listado de candidatos a definiciones, y (iii) una taxonomía de términos biomédicos basada en relaciones de hiponimia/hiperonimia. Nuestro método permite crear un sistema capaz de extraer tales contextos, el cual puede verse como un módulo que cubriría las primeras etapas a seguir para construir una ontología basada en información textual.In this project we formulate a methodology for extracting definitional contexts from corpus of biomedicine in Spanish, in order to generate the following products: (i) a list of candidate terms, (ii) a list of candidates for definitions, and (iii) a taxonomy of biomedical terms relationships based on hyponym/hyperonym. Our methodology allows the creation of a system capable of extracting such contexts, which can be seen as a module that would cover the first steps to follow to build an ontology based on textual information

SAFECAR: A Brain–Computer Interface and intelligent framework to detect drivers’ distractions

As recently reported by the World Health Organization (WHO), the high use of intelligent devices such as smartphones, multimedia systems, or billboards causes an increase in distraction and, consequently, fatal accidents while driving. The use of EEG-based Brain–Computer Interfaces (BCIs) has been proposed as a promising way to detect distractions. However, existing solutions are not well suited for driving scenarios. They do not consider complementary data sources, such as contextual data, nor guarantee realistic scenarios with real-time communications between components. This work proposes an automatic framework for detecting distractions using BCIs and a realistic driving simulator. The framework employs different supervised Machine Learning (ML)-based models on classifying the different types of distractions using Electroencephalography (EEG) and contextual driving data collected by car sensors, such as line crossings or objects detection. This framework has been evaluated using a driving scenario without distractions and a similar one where visual and cognitive distractions are generated for ten subjects. The proposed framework achieved 83.9% -score with a binary model and 73% with a multiclass model using EEG, improving 7% in binary classification and 8% in multi-class classification by incorporating contextual driving into the training dataset. Finally, the results were confirmed by a neurophysiological study, which revealed significantly higher voltage in selective attention and multitasking

Relationship between Body Composition and Performance Profile Characteristics in Female Futsal Players

Futsal is classified as a high-intensity intermittent sport or repeated-sprint sport. Explosive and very fast movements are performed with short reaction time, interspersed with playing time of 3 to 6 min during the 40 min match, at intensities of 85–90% of maximum HR. Performance factors such as agility, sprint repetition capacity, aerobic endurance capacity, lower body power, and speed are associated with the game actions. These performance factors can be affected by the athlete’s body composition. The aim is to determine the relationship between the different physical and physiological performance parameters and body composition in top-level women’s futsal players. The subjects of the study were 12 elite female futsal players (25.17 ± 4.75 years old) competing in the First Division Spanish League. An anthropometric assessment was conducted by an ISAK level III anthropometrist for three days during the competitive period. The sum of 4, 6 and 8 skinfolds and body composition were calculated with anthropometric data. Performance tests were conducted to evaluate agility, ability to repeat sprints, velocity and the explosive power of lower extremities in the playing court with specific warm up and previous explication during 2 days in the same week as the anthropometric tests. The tests used for that purpose were: t-test, Yo-Yo test, repeat-sprint ability (RSA), speed test, and jump test (JS, CMJ and ABK). Pearson correlations were used to establish the different associations with a p-value < 0.05. The results showed a negative correlation between agility and the fat component, and a positive correlation between the muscle component and aerobic capacity, agility, speed, and ABK jump. Body composition plays a fundamental role in the development of performance-related skills in women’s futsal

Análisis de problemas de proporcionalidad compuesta en libros de texto de 2º de eso

En este trabajo realizamos un estudio detallado de los problemas de proporcionalidad compuesta de doce libros de texto españoles de segundo curso de Educación Secundaria Obligatoria (13-14 años). En concreto, se realiza un análisis de contenido textual y a priori, clasificando los problemas atendiendo a su contexto, su estructura, su posición y papel dentro de la Unidad Didáctica correspondiente y a la tipología de magnitudes utilizadas. Entre otros resultados se concluye que, aunque la presencia de problemas varía ligeramente en cuanto a número entre los distintos textos, el tratamiento es bastante homogéneo respecto a su contexto, estructura y magnitudes implicadas: la mayoría de los problemas son de contexto realista, de valor perdido y con cinco cantidades de magnitud extensivas. También se detecta poca presencia de problemas de comparación cuantitativa y de situaciones de tipo inversa - inversa, así como poca presencia y variedad de magnitudes intensivas

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model, highlighting the potential risks of DFL communications. In response to these identified risks, this work introduces a security module designed for DFL platforms to counter communication-based attacks. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform called Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario has been deployed, involving eight physical devices implementing three security configurations: (i) a baseline with no security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks. The results indicated an average F1 score of 95%, with moderate increases in CPU usage (up to 63.2% +-3.5%) and network traffic (230 MB +-15 MB) under the most secure configuration, mitigating the risks posed by eavesdropping or eclipse attacks

Performance–energy trade‑ofs of deep learning convolution algorithms on ARM processors

In this work, we assess the performance and energy efciency of high-performance codes for the convolution operator, based on the direct, explicit/implicit lowering and Winograd algorithms used for deep learning (DL) inference on a series of ARM-based processor architectures. Specifcally, we evaluate the NVIDIA Denver2 and Carmel processors, as well as the ARM Cortex-A57 and Cortex-A78AE CPUs as part of a recent set of NVIDIA Jetson platforms. The performance–energy evaluation is carried out using the ResNet-50 v1.5 convolutional neural network (CNN) on varying confgurations of convolution algorithms, number of threads/cores, and operating frequencies on the tested processor cores. The results demonstrate that the best throughput is obtained on all platforms with the Winograd convolution operator running on all the cores at their highest frequency. However, if the goal is to reduce the energy footprint, there is no rule of thumb for the optimal confguration.Funding for open access charge: CRUE-Universitat Jaume

Performance–energy trade-offs of deep learning convolution algorithms on ARM processors

In this work, we assess the performance and energy efficiency of high-performance codes for the convolution operator, based on the direct, explicit/implicit lowering and Winograd algorithms used for deep learning (DL) inference on a series of ARM-based processor architectures. Specifically, we evaluate the NVIDIA Denver2 and Carmel processors, as well as the ARM Cortex-A57 and Cortex-A78AE CPUs as part of a recent set of NVIDIA Jetson platforms. The performance–energy evaluation is carried out using the ResNet-50 v1.5 convolutional neural network (CNN) on varying configurations of convolution algorithms, number of threads/cores, and operating frequencies on the tested processor cores. The results demonstrate that the best throughput is obtained on all platforms with the Winograd convolution operator running on all the cores at their highest frequency. However, if the goal is to reduce the energy footprint, there is no rule of thumb for the optimal configuration.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was funded by Project PID2020-113656RB-C21/C22 supported by MCIN/AEI/10.13039/501100011033. Manuel F. Dolz was also supported by the Plan Gen–T grant CDEIGENT/2018/014 of the Generalitat Valenciana. Héctor Martínez is a POSTDOC_21_00025 fellow supported by Junta de Andalucía. Adrián Castelló is a FJC2019-039222-I fellow supported by MCIN/AEI/10.13039/501100011033. Antonio Maciá is a PRE2021-099284 fellow supported by MCIN/AEI/10.13039/501100011033

Estudi de l’abandonament en el primer curs de la titulació de telecomunicacions

Dins de la tasca de coordinació del primer curs del grau de telecomunicacions s'ha observat un grau molt elevat d'abandonament de la titulació. Aquest fet condiciona la tasca i metodologia docent. Amb la intenció d'incrementar la qualitat del nou grau i augmentar les taxes de eficàcia, hem buscat els motius que originen aquest alt grau d’abandonament dins la titulació i hem ideat possibles solucions que posen remei o pal·lien en certa mesura aquest problema. Per això es presenten estratègies i mecanismes per augmentar la qualitat de la docència, es comparen els resultats dels darrers cursos i s’analitzen els resultats de les estratègies posades en marxa. La tasca ha estat desenvolupada pels coordinadors de cada assignatura que conjuntament han analitzat com es va produir l'abandó durant l'avaluació continuada.Aquesta comunicació s’ha pogut realitzar gràcies als projectes: GITE-09006-UA i GITE-09014-U