Propuesta de un modelo de regulación ex ante para la mejora de las condiciones de competencia en los nuevos mercados distribución de contenidos digitales

La difusión de la Televisión Digital Terrestre es un tema de plena actualidad, así como la problemática de la efectividad de la regulación audiovisual en un sector para el que actualmente no existe una Ley General de Audiovisuales, que se espera para la próxima primavera de 2010. En la tesis se analizan los hitos más importantes que impulsaron el desarrollo del sector de la Radiodifusión, y el impacto que supone su migración al ámbito Digital, así como un repaso cronológico de todas las leyes que se implantaron para regir la instauración/difusión de la Televisión Digital Terrestre (TDT). Realizado este repaso cronológico de la anterior situación jurídica de los medios de telecomunicación, y en concreto de la TDT, se analiza la legislación vigente y se presta atención a la legislación que ha regido a los medios de televisión por satélite y por cable. Los objetivos del presente trabajo son por lo tanto realizar un estudio del arte de la situación actual que afecta al sector audiovisual, y aportar un modelo de regulación ex ante para mejorar las condiciones de competencia en los nuevos mercados de distribución de contenidos digitales, atendiendo a las tecnologías subyacentes que permiten el desarrollo y el impulso de este sector. Se realiza una defensa en pro del monopolio en la producción de "ciertos" contenidos audiovisuales, por ser la opción más adecuada para alcanzar el óptimo social, exponiendo algunos ejemplos y estudios que así lo avalan. Se definirá también en este punto un nuevo concepto, "ociolillo", que facilitará la determinación del precio de un contenido audiovisual al distribuidor de contenidos, que podrá valorar si el coste del producto que compra cubre sus gastos de distribución

Adaptation of Applications to Compare Development Frameworks in Deep Learning for Decentralized Android Applications

Not all frameworks used in machine learning and deep learning integrate with Android, which requires some prerequisites. The primary objective of this paper is to present the results of the analysis and a comparison of deep learning development frameworks, which can be adapted into fully decentralized Android apps from a cloud server. As a work methodology, we develop and/or modify the test applications that these frameworks offer us a priori in such a way that it allows an equitable comparison of the analysed characteristics of interest. These parameters are related to attributes that a user would consider, such as (1) percentage of success; (2) battery consumption; and (3) power consumption of the processor. After analysing numerical results, the proposed framework that best behaves in relation to the analysed characteristics for the development of an Android application is TensorFlow, which obtained the best score against Caffe2 and Snapdragon NPE in the percentage of correct answers, battery consumption, and device CPU power consumption. Data consumption was not considered because we focus on decentralized cloud storage applications in this study

Impact of COVID-19 on the psychological health of university students in Spain and their attitudes toward Mobile mental health solutions

Producción CientíficaAntecedentes: La pandemia originada por el COVID-19 ha tenido un impacto en varios aspectos de la vida, incluida la salud mental de los estudiantes universitarios. Las aplicaciones móviles de atención mental (apps) permiten una forma de atención mental en línea que permite la prestación de atención mental a distancia. Objetivos: Este estudio tuvo como objetivo principal explorar el impacto de COVID-19 en la salud mental de estudiantes universitarios en España y explorar sus actitudes hacia el uso de aplicaciones móviles de atención mental. Metodología: Los encuestados respondieron una encuesta, que constaba de dos secciones. El primero incluía el Cuestionario de Salud General de 12 ítems (GHQ-12) que se empleó para evaluar la salud mental de los estudiantes. La segunda sección incluía seis preguntas desarrolladas por los autores para explorar las actitudes de los estudiantes hacia las aplicaciones de atención mental. Resultados: Los resultados mostraron que los estudiantes padecían ansiedad y depresión, así como disfunción social. Además, el 91,3 % de los estudiantes nunca había usado una aplicación móvil para la salud mental, el 36,3 % desconocía dichas aplicaciones y el 79,2 % estaba dispuesto a usarlas en el futuro. Conclusiones: La pandemia de COVID-19 tuvo un impacto significativo en la salud psicológica de los estudiantes universitarios. Las aplicaciones móviles de atención mental pueden ser una forma eficaz y eficiente de acceder a la atención mental, especialmente durante una pandemia

Health care management models for the evolution of hospitalization in acute inpatient psychiatry units: comparative quantitative study

Producción CientíficaBackground: Mental health disorders are a problem that affects patients, their families, and the professionals who treat them. Hospital admissions play an important role in caring for people with these diseases due to their effect on quality of life and the high associated costs. In Spain, at the Healthcare Complex of Zamora, a new disease management model is being implemented, consisting of not admitting patients with mental diseases to the hospital. Instead, they are supervised in sheltered apartments or centers for patients with these types of disorders. Objective: The main goal of this research is to evaluate the evolution of hospital days of stay of patients with mental disorders in different hospitals in a region of Spain, to analyze the impact of the new hospital management model. Methods: For the development of this study, a database of patients with mental disorders was used, taking into account the acute inpatient psychiatry unit of 11 hospitals in a region of Spain. SPSS Statistics for Windows, version 23.0 (IBM Corp), was used to calculate statistical values related to hospital days of stay of patients. The data included are from the periods of 2005-2011 and 2012-2015. Results: After analyzing the results, regarding the days of stay in the different health care complexes for the period between 2005 and 2015, we observed that since 2012 at the Healthcare Complex of Zamora, the total number of days of stay were reduced by 64.69%. This trend is due to the implementation of a new hospital management model in this health complex. Conclusions: With the application of a new hospital management model at the Healthcare Complex of Zamora, the number of days of stay of patients with mental diseases as well as the associated hospital costs were considerably reduced.Ha sido financiado por la Gerencia Regional de Salud (GRS 1801/A/18) de la Junta de Castilla y León, a través del proyecto "Análisis de Eficiencia y Sostenibilidad del Método Reticular de Atención en Salud Mental y Estrategia de Mejora

Energy-aware and reliability-based localization-free cooperative acoustic wireless sensor networks

Producción CientíficaIn underwater wireless sensor networks (UWSNs), protocols with efficient energy and reliable communication are challenging, due to the unpredictable aqueous environment. The sensor nodes deployed in the specific region can not last for a long time communicating with each other because of limited energy. Also, the low speed of the acoustic waves and the small available bandwidth produce high latency as well as high transmission loss, which affects the network reliability. To address such problems, several protocols exist in literature. However, these protocols lose energy efficiency and reliability, as they calculate the geographical coordinates of the node or they do not avoid unfavorable channel conditions. To tackle these challenges, this article presents the two novel routing protocol for UWSNs. The first one energy path and channel aware (EPACA) protocol transmits data from a bottom of the water to the surface sink by taking node’s residual energy (Re), packet history (Hp), distance (d) and bit error rate (BER). In EPACA protocol, a source node computes a function value for every neighbor node. The most prior node in terms of calculated function is considered as the target destination. However, the EPACA protocol may not always guarantee packet reliability, as it delivers packets over a single path. To maintain the packet reliability in the network, the cooperative-energy path and channel aware (CoEPACA) routing scheme is added which uses relay nodes in packet advancement. In the CoEPACA protocol, the destination node receives various copies from the source and relay(s). The received data at the destination from multiple routes make the network more reliable due to avoiding the erroneous data. The MATLAB simulations results validated the performance of the proposed algorithms. The EPACA protocol consumed 29.01% and the CoEPACA protocol 19.04% less energy than the counterpart scheme. In addition, the overall 12.40% improvement is achieved in the packet’s reliability. Also, the EPACA protocol outperforms for packets’ latency and network lifetime

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation

Sentence-level classification using parallel fuzzy deep learning classifier

Producción CientíficaAt present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier’s effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.Este trabajo ha sido financiado por el grupo de investigación eVida, de la Universidad de Deusto, como parte del proyecto de investigación: Grant IT 905-16

Analysis of mental health disease trends using BeGraph software in spanish health care centers: case study

Producción CientíficaBackground: In the era of big data, networks are becoming a popular factor in the field of data analysis. Networks are part of the main structure of BeGraph software, which is a 3D visualization application dedicated to the analysis of complex networks. Objective: The main objective of this research was to visually analyze tendencies of mental health diseases in a region of Spain, using the BeGraph software, in order to make the most appropriate health-related decisions in each case. Methods: For the study, a database was used with 13,531 records of patients with mental health disorders in three acute medical units from different health care complexes in a region of Spain. For the analysis, BeGraph software was applied. It is a web-based 3D visualization tool that allows the exploration and analysis of data through complex networks. Results: The results obtained with the BeGraph software allowed us to determine the main disease in each of the health care complexes evaluated. We noted 6.50% (463/7118) of admissions involving unspecified paranoid schizophrenia at the University Clinic of Valladolid, 9.62% (397/4128) of admissions involving chronic paranoid schizophrenia with acute exacerbation at the Zamora Hospital, and 8.84% (202/2285) of admissions involving dysthymic disorder at the Rio Hortega Hospital in Valladolid. Conclusions: The data analysis allowed us to focus on the main diseases detected in the health care complexes evaluated in order to analyze the behavior of disorders and help in diagnosis and treatment.Este trabajo fue financiado por el Servicio Regional de Salud de Castilla y León (GRS 1801/A/18

FoodScan: Food Monitoring App by Scanning the Groceries Receipts

Producción CientíficaIn the mobile device market there is a large number of applications to help people monitor intake or provide suggestions to lose weight and manage a healthy diet. However, the vast majority of these apps consume a lot of time by having to introduce food one by one. This paper presents the work to develop and pilot test a new Android application, FoodScan, aimed at people over 70, specially those from rural environments or with limited technical knowledge, to manage their food from the items that appear on their grocery receipts, avoiding the obligation to introduce one by one those foods, and generating recommendations. To achieve this final objective, specific objectives have been completed as indicated in the methods section. We conducted a review of current calorie control applications to learn about their weaknesses and strengths. Different algorithms were tested to expedite the introduction of food into the application and the most suitable for the FoodScan application was selected. Likewise, several options were taken into account to create the knowledge base of food, taking into account dietary recommendations for people over 70 years. Once developed, a pilot evaluation was carried out with a convenience sample of 109 volunteers in rural areas of Caceres and Valladolid (Spain) and Alentejo (Portugal). They tested FoodScan for a month after which they completed a user satisfaction survey. 93 % (101/109) believed that the app was easy to download and install, 66 % (72/109) thought that it was easy to use, 47 % (51/109) noted that the charts with the recommendations helped them with diet control and 49 % (53/109) indicated that FoodScan helped them improve healthy eating habits. One-month pilot evaluation data suggested that most users found the app somewhat helpful for monitoring food intake, easy to download and easy to use.Este trabajo forma parte del proyecto 0499_4IE_PLUS_4_

Incorporating meteorological data and pesticide information to forecast crop yields using machine learning

Producción CientíficaThe agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information for sustainable agricultural practices. This research presents a novel crop yield prediction system utilizing a year’s worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model’s performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination (R2) of 99.99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future regarding food availability and robustness to climate change.European University of Atlanti