Abstract-The purpose of this paper is to analyze the influence of the metallic structures of a realistic car body frame on the specific absorption rate (SAR) produced by a cell phone when a complete human body model is placed at different locations inside the vehicle, and to identify the relevant parameters responsible for these changes. The modeling and analysis of the whole system was conducted by means of computer simulations based on the full wave finite-difference time-domain (FDTD) numerical method. The excitation considered was an 835 MHz 2 dipole located as a handsfree communication device or as a hand-held portable system. We compared the SAR at different planes on the human model, placed inside the vehicle with respect to the free space situation. The presence of the car body frame significantly changes the SAR distributions, especially when the dipole is far from the body. Although the results are not conclusive on this point, this change in SAR distribution is not likely to produce an increase above the limits in current guidelines for partial body exposure, but may be significant for whole-body exposure. The most relevant change found was the change in the impedance of the dipole, affecting the radiated power. A complementary result from the electromagnetic computations performed is the change in the electromagnetic field distribution inside a vehicle when human bodies are present. The whole vehicle model has been optimized to provide accurate results for sources placed inside the vehicle, while keeping low requirements for computer storage and simulation time.

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Wastewater-based epidemiology has shown to be an efficient tool to track the circulation of SARS-CoV-2 in communities assisted by wastewater treatment plants (WWTPs). The challenge comes when this approach is employed to help Health authorities in their decision-making. Here, we describe the roadmap for the design and deployment of SARSAIGUA, the Catalan Surveillance Network of SARS-CoV-2 in Sewage. The network monitors, weekly or biweekly, 56 WWTPs evenly distributed across the territory and serving 6 M inhabitants (80% of the Catalan population). Each week, samples from 45 WWTPs are collected, analyzed, results reported to Health authorities, and finally published within less than 72 h in an online dashboard (https://sarsaigua.icra.cat). After 20 months of monitoring (July 20–March 22), the standardized viral load (gene copies/day) in all the WWTPs monitored fairly matched the cumulative number of COVID-19 cases along the successive pandemic waves, showing a good fit with the diagnosed cases in the served municipalities (Spearman Rho = 0.69). Here we describe the roadmap of the design and deployment of SARSAIGUA while providing several open-access tools for the management and visualization of the surveillance data.

Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.