Analyzing multiple acoustic diffraction over a wide barrier using equivalent knife-edge geometries and Babinet's principle (L).

We present a new method for the calculation of the multiple acoustic diffraction caused by the presence of a wide barrier. Our solution decomposes the initial scenario into an equivalent sum of geometries that only consider knife-edges. Then, by applying Babinet's principle, the total acoustic field that reaches the receiving point, which can be located at an arbitrary position, can be calculated via the uniform theory of diffraction. This method is mathematically less complex and computationally more efficient than most existing techniques. The results are validated (with and without ground reflection) by the solid agreement obtained with other solutions that solve the problem by considering the wide barrier as such, with our proposed method yielding a lower computational time (except against semi-empirical formulations) and better accuracy when compared with measurements. The presented solution can be applied in urban environments where the impact of traffic noise on residential buildings located along roads or highways needs to be evaluated, as well as in scenarios in which the insertion loss caused by a rectangular obstacle, such as a noise barrier, is to be calculated.

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining.

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.