Análisis de la técnica de salto y aterrizaje en la prevención de lesiones de LCA y su relación con la flexión dorsal: propuesta de intervención

El principal objetivo de este estudio es conocer la relación existente entre el ROM de la flexión dorsal del tobillo y la técnica de aterrizaje tras un drop jump y su importancia en las lesiones de ligamento cruzado anterior (LCA) en jugadores jóvenes de fútbol. La muestra utilizada estaba formada por 16 jugadores de un equipo de la primera división provincial de León y el instrumento utilizado fue el Landing Error Scoring System (LESS) que permite detectar los fallos cometidos durante la ejecución del salto. Una vez registrados los test se dividieron a los jugadores en dos grupos: grupo riesgo, formado por 6 jugadores con una puntuación mayor o igual a 6 puntos en el test; y el grupo no riesgo, formado por 10 jugadores con una puntuación menor o igual a 5 puntos. Los resultados mostraron una diferencia significativa y un tamaño del efecto alto en la flexión dorsal de los jugadores en función del grupo, así como en el valgo de rodilla tanto en el contacto inicial como en desplazamiento. En otras variables analizadas como el tiempo de contacto, la inclinación de tronco o la flexión de las articulaciones de tronco, cadera y rodilla no encontramos diferencias significativas. Por último, se proponen planes de actuación para cada uno de los jugadores en función de los errores cometidos durante el test y su historial de lesiones en las últimas temporada

Programa de intervención para la prevención de lesiones de isquiotibiales en futbolistas jóvenes = Intervention program for the prevention of hamstring injuries in young soccer players

El principal objetivo de este trabajo es diseñar e implementar un protocolo de prevención de lesiones de la musculatura isquiosural en jugadores jóvenes de fútbol. Se utilizó una muestra de 15 jugadores de un equipo amateur de la provincia de León de la categoría juvenil, la cual se dividió en un grupo control de 7 jugadores que realizaron únicamente los entrenamientos indicados por el entrenador y un grupo experimental de 8 jugadores que realizaron un protocolo de intervención con ejercicios de fortalecimiento de la musculatura del glúteo, de trabajo de core, de propiocepción, de pliometría, de entrenamiento excéntrico de los isquiotibiales y de flexibilidad de flexores y extensores de cadera. Ambos grupos fueron evaluados antes y después del protocolo mediante las pruebas de Elevación de la Pierna Recta (EPR), la prueba de salto de Single Leg Hop Test (SLHT), así como la asimetría en ambas piernas y la prueba de fuerza de isquiotibiales de Single Leg Hamstring Bridge (SLHB). Los resultaros mostraron que ambos grupos obtuvieron mejoras en las diferentes pruebas, siendo éstas mayores en el grupo experimental. Por lo tanto, podríamos decir que realizar un protocolo que incorpore ejercicios de los aspectos previamente dichos puede ayudar a obtener mejores resultados en pruebas que indiquen riesgo de lesión de la musculatura isquiosura

Deep Learning use in recomendation systems to reduce the desertion in Colombian High Education

El anteproyecto de monografía planteado aquí, busca encontrar cómo la implementación de una herramienta de Deep Learning puede ser empleada para mitigar de forma efectiva, la deserción en Instituciones de educación superior, al reducir el tiempo de detección y recomendación de apoyos necesarios para que los alumnos puedan continuar su proceso educativo. El gobierno colombiano es consciente de la problemática que envuelve la deserción en educación superior, y por esto, ha generado iniciativas que buscan bajar las cifras del problema, pero en la actualidad los esfuerzos no han tenido el resultado esperado. La investigación se centrará en determinar la viabilidad de implementación de la herramienta en las plataformas en la nube con reconocimiento como las de mayor capacidad y completitud de su servicio, y en determinar si las Instituciones de Educación Superior tienen la capacidad de generar la información que permita alimentarla y la posibilidad de mantener el costo de una herramienta implementada en una plataforma en la nube.The preliminary monograph proposed here seeks to find how the implementation of a Deep Learning tool can effectively mitigate attrition in Higher Education Institutions by reducing the time of detection and recommendation of supports necessary for students to can continue their educational process. The Colombian government is aware of the problems surrounding higher education dropouts. For this reason, it has generated initiatives that seek to lower the numbers of the problem, but at present, the efforts have not had the expected result. The research will focus on determining the feasibility of implementing the tool on cloud platforms recognized as those with the greatest capacity and completeness of their service and on determining whether Higher Education Institutions have the capacity to generate the information that allows power it and the possibility of maintaining the cost of a tool deployed on a cloud platform.Magíster en Inteligencia de Negocio

Blowup of certain analytic solutions of the Hall magnetohydrodynamic equations

Producción CientíficaA recent analytic solution of the Hall magnetohydrodynamics equations is analyzed. It is shown that its evolution in time depends upon a certain set of inequalities upon the initial values of the velocity and the magnetic field. For most of the cases, both magnitudes will blow up in a finite time. This shows that for keeping the original structure of the solution, energy must be introduced into the system until eventually it cannot hold any longer

Using Remote Data Mining on LIDAR and Imagery Fusion Data to Develop Land Cover Maps

Remote sensing based on imagery has traditionally been the main tool used to extract land uses and land cover (LULC) maps. However, more powerful tools are needed in order to fulfill organizations requirements. Thus, this work explores the joint use of orthophotography and LIDAR with the application of intelligent techniques for rapid and efficient LULC map generation. In particular, five types of LULC have been studied for a northern area in Spain, extracting 63 features. Subsequently, a comparison of two well-known supervised learning algorithms is performed, showing that C4.5 substantially outperforms a classical remote sensing classifier (PCA combined with Naive Bayes). This fact has also been tested by means of the non-parametric Wilcoxon statistical test. Finally, the C4.5 is applied to construct a model which, with a resolution of 1 m 2, obtained precisions between 81% and 93%

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Familia y educación [Recurso electrónico] ] : guía práctica para escuelas de padres y madres eficaces

Datos tomados de la etiqueta del disc

Optimization of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

In the scope of a broader study about wine acetification, previous works concluded that using a single bioreactor hindered simultaneously reaching high productivities with high substrate consumption and the use of two serially arranged bioreactors (TSAB) could achieve such goal. Then, the aim of this work is the optimization, using Karush–Kuhn–Tucker (KKT) conditions, of this TSAB using polynomial models previously obtained. The ranges for the operational variables leading to either maximum and minimum mean rate of acetification of 0.11 ≤ (rA)global ≤ 0.27 g acetic acid·(100 mL·h)−1 and acetic acid production of 14.7 ≤ Pm ≤ 36.6 g acetic acid·h−1 were identified; the results show that simultaneously maximizing (rA)global and Pm is not possible so, depending on the specific objective, different operational ranges must be used. Additionally, it is possible to reach a productivity close to the maximum one (34.6 ≤ Pm ≤ 35.5 g acetic acid·h−1) with an almost complete substrate use [0.2% ≤ Eu2 ≤ 1.5% (v/v)]. Finally, comparing the performance of the bioreactors operating in series and in parallel revealed that the former choice resulted in greater production

Modelling of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

In the scope of a broader study about modelling wine acetification, the use of polynomial black-box models seems to be the best choice. Additionally, the use of two serially arranged bioreactors was expected to result in increased overall acetic acid productivity. This paper describes the experiments needed to obtain enough data for modelling the process and the use of second-order polynomials for this task. A fractional experimental design with central points was used with the ethanol concentrations during loading of the bioreactors, their operation temperatures, the ethanol concentrations at unloading time, and the unloaded volume in the first one as factors. Because using two serial reactors imposed some constraints on the operating ranges for the process, an exhaustive combinatorial analysis was used to identify a working combination of such ranges. The obtained models provided highly accurate predictions of the mean overall rate of acetic acid formation, the mean total production of acetic acid of the two-reactor system, and ethanol concentration at the time the second reactor is unloaded. The operational variables associated with the first bioreactor were the more strongly influential to the process, particularly the ethanol concentration at the time the first reactor was unloaded, the unloaded volume, and the ethanol concentration when loading

Wearables and machine learning for improving runners’ motivation from an affective perspective

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training