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The present work deals with an Ordinary Differential Equation (ODE) model specifically designed to describe the COVID-19 evolution in Italy. The model is particularised on the basis of National data about the infection status of the Italian population to obtain numerical solutions that effectively reproduce the real data. Our epidemic model is a classical SEIR model that incorporates two compartments of infected subpopulations, representing diagnosed and undiagnosed individuals respectively, and an additional quarantine compartment. Possible control actions representing social, political, and medical interventions are also included. The numerical results of the proposed model identification by least square fitting are analysed and commented with special emphasis on the estimation of the number of asymptomatic infective individuals. Our fitting results are in good agreement with the epidemiological data. Short and long-term predictions on the evolution of the disease are also given. [ABSTRACT FROM AUTHOR]

A measurement campaign was carried out at Orte and S. Oreste dc substations along a high-speed trunk of the Italian railway lines. The results consist of H-field values measured at the two substations, inside the buildings (near the rectifier groups) and outside (in the area surrounding the high-voltage input line, the HV/MV transformers, and the rectifier transformers) for different load conditions during normal service. The goal of this work is to determine the H-field intensity at low and medium frequency at dc railway substations. The measurement results are post-processed to extract statistical properties (namely, average values, dispersion, and distribution histograms). This information may contribute to updating and integrating standards regarding dc substation electromagnetic environment and specific immunity tests for housed equipment. [ABSTRACT FROM AUTHOR]