Mobile health platforms have shown promise in the management of various mental health conditions (including stress, anxiety, and depression) and cognitive behavioral strategies emerged as a popular and effective option offered by the platforms. This paper presents the protocol of a study aimed to test the effectiveness of a mobile platform that uses cognitive-behavioral strategies for stress self-management in the Tuscany region (Italy). The mobile app is adapted to the specific needs of each vulnerable population for which it is designed: young and older people, healthcare professionals, entrepreneurs. The app will be evaluated on the following outcomes: (i) perceived susceptibility and severity of the pandemic situation, perceived benefits, and costs of preventive health behaviors, (ii) knowledge about Covid-19 preventive behaviors and negative consequences of social distancing, (iii) stress and psychopathological symptoms (i.e., anxiety, depression, and post-traumatic stress symptoms) and cognitive distortions. If successful, we expect that the platform could give various groups clinical benefits by providing symptom self-monitoring and early intervention, consolidating the number of mental health programs available, and decreasing barriers to treatment-seeking. This population-level approach has the potential to improve mental health outcomes in pandemic periods for many people.

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In the framework of three projects co-funded by the European Union (EU), an X-band (8-12 GHz frequency range) polarimetric radar was installed in the Marche Region territory (East-Central Italy) at Cingoli municipality, province of Macerata. The radar site is located at about 750 m above sea level and about 30 km away from the Adriatic Sea. The radar, managed by the Marche Region Civil Protection Service, is employed for weather monitoring purposes and is in pre-operational stage. It is known that radar measurements are affected by various sources of error, to be addressed in order to improve the accuracy of final products. Among these, the most important are radar calibration, ground and sea clutter, beam blockage, rain attenuation, wet-radome attenuation, beam-broadening, non-uniform beam filling, vertical variability of precipitation and wireless local-area-network (WLAN) interferences. Nowadays quantitative rainfall estimation using X-band weather radar are essential to meet requirements for flood forecasting, water management and many hydro-meteorological applications. Besides higher resolution, X-band radars are cost-effective compared to S- or C-band radars because of smaller antenna size. On the other hand, main disadvantages of such systems are the large influence of attenuation by liquid water and a relatively short range. In this work, we will present the data-processing chain developed ad hoc in order to remove or at least reduce the sources of error affecting Cingoli radar data. The performance of the data-processing chain was evaluated in the light of case studies related to meteorological events that interested Marche region territory in the last two years. The software was developed using open source technology. The current version of the chain does not take into account the echoes from sea clutter and the attenuation due to wet radome that can be significant at X-band; such issues will be addressed in a future work.