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This work focuses on the heat transfer dynamics in agricultural soils when exposed to microwave fields, in order to disinfect them and to eliminate dangerous organisms without using pesticides. The work manages with the use of a horn antenna to irradiate the superficial layer of soils and so to eliminate harmful bio-agents within a fixed depth. The soil was approximated using a transmission line model. Considering the dielectric constant of the ground as a function of the soil water content, it was possible to evaluate the power absorbed by the irradiated ground and the temperature increase and its profile depending on the irradiation time. Since the latter information is strictly connected to the heat transfer process occurring in the system, we demonstrated the feasibility of the methodology here proposed to achieve difficult conditions for microbial and pathogens life, leading to an effective disinfection of the treated farmland

The use of electromagnetic fields to solve issues related to agriculture is an interesting, costeffective, and eco-friendly possibility to be explored. One of the most promising applications is the disinfection of soils obtained by microwave-induced heating. This work deals with the nonlinear computational modeling of such microwave heating of soils in metal raised bed cultivations within a greenhouse. By modeling the dielectric properties of soil, in terms of composition, moisture content, and actual temperature, and accounting for the exact thermal and electromagnetic conditions in raised beds inside the greenhouse, several realistic and nonlinear multiphysics simulations were carried out. The disinfection is directed to fungi, such as Sclerotium rolfsii, and weeds, for example, ryegrass and fleabane. The effectiveness of the proposed procedure is quantified and critically discussed exploiting analytical thermal death kinetics of soilborne plant pathogens and compared to the solarization technique.

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The field of mobility represents one of the key aspects in the study of smart cities with increasing urbanization. In this article the phenomenon related to the flows of people moving within the city has been studied. In fact, the phenomena of road congestion and excessively crowded areas are increasing. The tools used in the monitoring phase concern Wi-Fi sniffing and video camera systems for the classification of vehicles, people, bicycles, and so on. Real-time monitoring allows citizens to know in advance the road conditions with a high level of reliability, overcoming the limits of a single technological system that offers imprecise monitoring. Results obtained from the systems based on a single technology have been merged into a machine learning solution using neural networks with a high degree of accuracy.