The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world.

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

The primary function of noise barriers is to shield inhabitants of affected areas from excessive noise generated by road traffic. To enhance the performance of noise barriers while simultaneously adhering to height restrictions, the attachment of structures (caps) of different shapes to the tops of conventional screens can be considered. These caps can significantly impact the diffracted sound energy, thereby increasing the desired global acoustic losses. This work presents a comprehensive study of the acoustic performance of noise barriers with single and double attached caps of different shapes through a calculation of their insertion losses (IL). This study comprehensively addresses and compares different types, sizes, combinations, and numbers of noise barrier caps for different scenarios (including sloping and absorbent grounds) and sources (“car” and “ambulance”) for an extended frequency band up to 10 kHz. To the best of the authors’ knowledge, this is a range that has not previously been analyzed. A variety of different cap shapes were considered including cylinders, rectangles, trapezoids, and Y/T-shaped forms. To calculate the IL, an innovative and fast uniform theory of diffraction (UTD)-based method developed by the authors was applied in all simulations. The results showed that the Y-shaped single and double barrier caps were, in general, the most effective at increasing IL without raising the height of the barrier, thereby successfully managing the aesthetic impact. The results also showed how the consideration of sloping and absorbent floors could also contribute to improved noise abatement.

This paper examines the propagation of radio waves in so-called urban street canyons through formulations based on Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD). As this type of environment comprises a street flanked by tall buildings more or less equally spaced on both sides (creating a canyon-like morphology), estimating the attenuation that radio signals may experience in these scenarios is crucial to the planning of urban device-to-device (D2D) wireless communication. In this sense, the results obtained through the analysis based on GO/UTD (in the horizontal plane containing the transmitter and receiver) are validated by a comparison with experimental measurements, showing good agreement. This work demonstrates how the use of GO/UTD-based formulations can contribute to a simpler and computationally more efficient planning of D2D mobile communication systems in which the considered propagation environment can be modeled as an urban street canyon comprising rectangular and equispaced buildings.