DETERMINING THE OPTIMAL LOAD FOR RESISTED SPRINT TRAINING WITH SLED TOWING. A PILOT STUDY.

An excessive load in resisted sprint training can produce changes in running patterns. Load control is essential to ensure specificity of these training methods. The most common way to control is the percentage of speed lost in relation to maximum speed. The aim of the study was establishing the load for sprint training with sled towing in the maximum velocity phase. 12 athletes, Spanish national level, participated in the study. They run 30 m flying sprints, an unloaded sprint and sprints pulling loads of 6%, 10%, and 15% of their body mass, on a synthetic track surface wearing spikes. The regression equation obtained was: % Body mass = (-0.8325 · % velocity) + 84.08. This equation is specific for the type of surface used and the sled towing characteristics. Therefore, when using different surfaces and sled towings, specific equations should be calculated

PERFORMANCE ADAPTATIONS TO SHORT-TERM SLED TOWING AND SPRINT TRAINING

The use of resisted sprinting techniques is common both in athletics and in a variety of sports (Cronin and Hansen, 2006). However, previous research has focused in studying the performance when applying these methods on untrained subjects (Zafeiridis et al., 2005; Kristensen et al., 2006). Considering that the magnitudes and time courses of the neural adaptations in the neuromuscular system in elite athletes may differ from those adaptations reported for untrained athletes (Hakkinen et al., 1987), the results from those papers may not be representative of the experienced subjects. Therefore, the aim of the study was to examine the effects of resisted and unloaded sprint training programs on acceleration, transition and maximum speed performance on experienced athletes

Using annotation and specialised electronic corpora to facilite the reading of journal aricles in the filed of Law. Póster

A comprehensive analysis of any professional genre must consider and integrate text-internal as well as text-external aspects of language use (Bhatia, 2004). Beyond the well-known difficulties of legal language (Bhatia, 1982) and its main features (Alcaraz Varó, 2002), the rhetorical structure of a RA is a reflection of the professional practices of scholars in the field of law. Linguists consider that a genre is an instance of a sociolinguistic activity through which members of certain discourse community achieve their communicative purposes (Swales, 1990). A genre is defined then by its shared communicative purposes and manifested by its particular structural and linguistic features. The Research Article (RA) is the main channel of scientific or scholarly communication, which turns it into the target genre for PhD candidates in law.Campus Mare Nostrum, Universidad Politécnica de Cartagena, Universidad de Murcia, Región de Murci

The role of corpus linguistics in developing innovation in data-driven language learning

[SPA]El data-driven learning o aprendizaje basado en datos se caracteriza por el uso de bases de datos de lengua en uso que los alumnos analizan para identificar patrones de uso. El aprendizaje a partir de datos encaja a la perfección dentro de los nuevos paradigmas de enseñanza y se ajusta a lo que se conoce como aprendizaje en el S.XXI o aprendizaje para la vida. En este nuevo escenario docentes y discentes deben adoptar nuevos roles. Los alumnos deben hacerse responsables de su propio aprendizaje y deben actuar como agentes activos en el proceso de aprendizaje y no como meros receptores de información. Por su parte, los profesores se convierten en guías o en facilitadores del proceso. Al hacer a los alumnos comportarse como investigadores, el aprendizaje a partir de datos es, por lo tanto, un ejemplo representativo de aprendizaje centrado en el alumno. Además, este enfoque promueve el aprendizaje inductivo, ya que el análisis de datos, la formulación de hipótesis y la extracción de conclusiones son los tres pilares en los que éste se sustenta. A pesar de los beneficios derivados de esta metodología de trabajo basada en corpus lingüísticos, sus aplicaciones en el aula se han basado tradicionalmente en la transferencia directa de los métodos y las herramientas que se usan para el análisis de la lengua en el ámbito investigador, lo que causa problemas para la implantación y expansión de esta metodología en contextos educativos. El tipo de aprendizaje basado en datos que proponemos en este trabajo está basado en un enfoque novedoso en lo concerniente al modo en el que los datos lingüísticos son tratados por los investigadores/profesores y por los alumnos. El uso de un nuevo modelo que favorezca una transformación de datos en información significativa para los alumnos es la clave de nuestra forma de abordar la innovación educativa en el aprendizaje de lenguas. Los proyectos europeos, SACODEYL y Corpora for Content and Language Integrated Learning, son buena muestra de experiencias de innovación en el campo del aprendizaje para la vida. [ENG]Data Driven (language) Learning (DDL) is characterized by the use of language data in the language learning classroom so that students can analyse language and identify patterns of use. DDL fits well with contemporary learning paradigms and with the so-called 21st C learning or lifelong learning, which implies a new attitude on the part of students and teachers. In this new scenario, students need to take responsibility over their own learning and become active learners, and not mere recipients of information. Teachers turn themselves into guides and facilitators of the learning process. In making students work as researchers, DDL is therefore a representative example of learner-centred teaching. Moreover, this approach fosters inductive learning, as the process of analyzing data, formulating hypotheses and deriving conclusions is at the heart of this approach. However, classroom applications of traditional corpus linguistics have relied on heavy linguistic research paradigms, which according to different authors has problematized the use of this methodology. In the context of our proposal, the data-driven culture that we want to foster is based on a totally new approach to the way in which language data are treated by researchers/teachers and learners. The use of a new data model which favours a more rapid transformation into information which is meaningful to learners is at the hub of our approach to innovation. Two European projects, SACODEYL and Corpora for Content Language Integrated Learning are examples of innovation in the field of lifelong learning.Campus Mare Nostrum, Universidad Politécnica de Cartagena, Universidad de Murcia, Región de Murci

Manual de litografía

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Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.This work was funded in part by the European Commission Horizon 2020 5G-PPP Program under Grant Agreement Number H2020-ICT-2020-2/101017226: “6G BRAINS: Bringing Reinforcement learning Into Radio Light Network for Massive Connections” and the EU Horizon INCODE project Programming Platform for Intelligent Collaborative Deployments over Heterogeneous Edge IoT Environments (HORIZON-CL4-2022-DATA-01-03/101093069)

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, k-fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.This research has received financial support from public grants PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund, SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Moreover, Daniele Padovano holds a predoctoral scholarship 2022-PRED-20642, which is cofinanced by the operating program of European Social Fund (ESF) 2014-2020 of Castilla-La Mancha.Padovano, D.; Martínez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2022). On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning. IEEE Access. 10:92710-92725. https://doi.org/10.1109/ACCESS.2022.320191192710927251

On- and off-center helium atom in a spherical multilayer quantum dot with parabolic confinement

The ground state energy of a helium atom inside a spherical multilayer quantum dot as a function of the atomic impurity location inside the quantum dot has been calculated. The multilayer quantum dot is modeled by a core/shell/well/shell structure using a parabolic confinement. The Configuration Interaction method and the Diffusion Monte Carlo have been used to solve the Schrödinger equation. Results obtained showed that the lowest energy configuration depends on the size of the different layers of the quantum dot and agreement between Configuration Interaction and Diffusion Monte Carlo results indicates that the Configuration Interaction approach used here would be suitable to compute excited states of this system