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Since the beginning of the 21st century, the fields of astronomy and astrophysics have experienced significant growth at observational and computational levels, leading to the acquisition of increasingly huge volumes of data. In order to process this vast quantity of information, artificial intelligence (AI) techniques are being combined with data mining to detect patterns with the aim of modeling, classifying or predicting the behavior of certain astronomical phenomena or objects. Parallel to the exponential development of the aforementioned techniques, the scientific output related to the application of AI and machine learning (ML) in astronomy and astrophysics has also experienced considerable growth in recent years. Therefore, the increasingly abundant articles make it difficult to monitor this field in terms of which research topics are the most prolific or novel, or which countries or authors are leading them. In this article, a text-mining-based scientometric analysis of scientific documents published over the last three decades on the application of AI and ML in the fields of astronomy and astrophysics is presented. The VOSviewer software and data from the Web of Science (WoS) are used to elucidate the evolution of publications in this research field, their distribution by country (including co-authorship), the most relevant topics addressed, and the most cited elements and most significant co-citations according to publication source and authorship. The obtained results demonstrate how application of AI/ML to the fields of astronomy/astrophysics represents an established and rapidly growing field of research that is crucial to obtaining scientific understanding of the universe. The authors have no competing interests to declare that are relevant to the content of this article.

Se trata de un libro con una función divulgativa, que pretende recoger y analizar las distintas tecnologías que se han aplicado hasta ahora para la gestión de la pandemia mundial por COVID-19. Este libro presenta un estudio acerca de las tecnologías digitales disruptivas (Internet of Things, Machine Learning, Blockchain y otras) que se han aplicado a la gestión de la pandemia ocasionada por la COVID-19. La investigación se ha llevado a cabo a través de un análisis cienciométrico −basado en minería de textos− de la producción científica publicada al respecto a lo largo de un período de año y medio (2020 y mitad de 2021) y, a este respecto, se ha considerado Scopus como fuente de datos principal y Web of Science como secundaria (a efectos comparativos). De esta manera, por medio de la utilización del potente software VOSviewer, se ofrecen multitud de resultados −ilustrados por los correspondientes mapas bibliométricos− como la evolución temporal del número de publicaciones, la producción y el número de coautorías por países, los temas (topics) y autores más prolíficos o un ranking de los artículos más referenciados. En definitiva, en este libro, se pretende ofrecer una visión lo más completa y actualizada posible de cómo la inteligencia artificial y ciertas tecnologías digitales emergentes han contribuido, de manera esencial, a cuestiones de predicción, seguimiento, diagnóstico, tratamiento y prevención de la COVID-19.

[EN] Agriculture 4.0 is going to represent a massive deployment of sensors, so efficient planning of radiocommunication systems in this type of environment will be necessary. In this work, the measured path loss in a LoS situation, with the transmitter and receiver heights below the trees height, at a citrus plantation in the 1800MHz, 2100MHz, 3.5GHz and 28GHz frequency bands using the FI (Floating Intercept) and the CI (Close-In reference) models has been analyzed. It has been observed that from 3.5GHz, the slope of the FI model also represents the propagation exponent (PLE). Furthermore, a guiding effect (PLE less than 2) has been observed in the 1800MHz and 2100MHz bands, not in the 3.5GHz and 28GHz bands, in which the PLE is practically equal to 2 (free space). Finally, the analysis of the polarization reveals that the standard deviation of the measured values with respect to the values obtained from the models is greater for horizontal than vertical polarization in the 1800MHz and 2100MHz frequency bands., Este trabajo ha sido financiado por la Comunidad Autónoma de la Región de Murcia a través del proyecto 20804/PI/18 de la convocatoria de Ayudas a proyectos para el desarrollo de investigación científica y técnica por grupos competitivos, incluida en el Programa Regional de Fomento de la Investigación (Plan de Actuación 2019) de la Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia y por el Ministerio de Economía y Competitividad MINECO (PID2019-107885GB-C33).

The metabolic disease Type 1 Diabetes Mellitus (DM1) is caused by a reduction in the production of pancreatic insulin, which causes chronic hyperglycemia. Patients with DM1 are required to perform multiple blood glucose measurements on a daily basis to monitor their blood glucose dynamics through the use of capillary glucometers. In more recent times, technological developments have led to the development of cutting-edge biosensors and Continuous Glucose Monitoring (CGM) systems that can monitor patients&rsquo, blood glucose levels on a real-time basis. This offers medical providers access to glucose oscillations modeling interventions that can enhance DM1 treatment and management approaches through the use of novel disruptive technologies, such as Cloud Computing (CC), big data, Intelligent Data Analysis (IDA) and the Internet of Things (IoT). This work applies some advanced modeling techniques to a complete data set of glycemia-related biomedical features&mdash, obtained through an extensive, passive monitoring campaign undertaken with 25 DM1 patients under real-world conditions&mdash, in order to model glucose level dynamics through the proper identification of patterns. Hereby, four methods, which are run through CC due to the high volume of data collected, are applied and compared within an IoT context. The results show that Bayesian Regularized Neural Networks (BRNN) offer the best performance (0.83 R2) with a reduced Root Median Squared Error (RMSE) of 14.03 mg/dL.

[EN] En el presente trabajo, se pretende analizar la percepción del profesorado respecto a la introducción de la perspectiva de género en el ámbito educativo. Para ello, se presentan los resultados de un sondeo de opinión que se llevó a cabo entre más de doscientos docentes que acudieron a una jornada de formación, en el que se plantearon diferentes cuestiones relativas a la igualdad de género en el campo de la educación. El análisis de estos resultados muestra que, aunque se atisban mejoras −a tenor de la percepción del profesorado− con respecto a ciertas cuestiones concretas, aún queda un largo camino por recorrer para lograr la igualdad real entre mujeres y hombres en el sector docente. [EN] In the present work, an analysis of the perception of professors regarding the introduction of the gender perspective in the educational field is carried out. For this purpose, the results of an opinion questionnaire that was carried out among more than two hundred teachers who attended a training session are presented, in which different questions related to gender equality in the field of education were raised. The analysis of these results shows that, although some improvements can be noticed –according to the perception of the professors– with regard to certain specific issues, there is still a long way to go to achieve real equality between women and men within the educational field.

[EN] Agriculture 4.0 represents a considerable increase in the number of sensors, as well as the appearance of new wireless technologies, which will mean the need to efficiently planning radio communication systems in agricultural environments. In this work, the applicability of the WalfischBertoni and the Ikegami propagation models that have been used to estimate propagation losses in urban environments, has been studied in citrus plantations. The similarities and differences of the profiles between the transmitter and the receiver, have been analyzed to identify the dominant propagation mechanisms in each case: buildings, in urban environments, and trees, in citrus plantations, assuming that the height of the transmitter is above the height of the trees and the height of the receiver below them. The propagation losses estimated by the models have been compared with the measurements carried out at the 3.5GHz frequency (one of the 5G bands) in a lemon plantation after the fruit was collected. Under these conditions, it has been observed how the slope of the regression line of the measurements has a value of 2.7, far from that obtained by the Walfisch-Bertoni model and closer to the one estimated by the Ikegami model. Furthermore, when analyzing the error between the theoretical and the measured losses, a standard deviation of 6.39dB is observed for the Walfisch-Bertoni model and 4.91dB for the Ikegami model., Este trabajo ha sido financiado por la Comunidad Autónoma de la Región de Murcia a través del proyecto 20804/PI/18 de la convocatoria de Ayudas a proyectos para el desarrollo de investigación científica y técnica por grupos competitivos, incluida en el Programa Regional de Fomento de la Investigación (Plan de Actuación 2019) de la Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia y por el Ministerio de Economía y Competitividad MINECO, Spain (TEC2016-78028-C3-2-P).