The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers.

This paper investigates the problem of event-triggered control for the synchronization of networks of nonlinear dynamical agents; distributed model-based approaches able to guarantee the synchronization of the overall system are derived. In these control schemes all the agents use a model of their neighbourhood in order to generate triggering instants in which the local controller is updated and, if needed, local information based on the adopted control input is broadcasted to neighbouring agents. Synchronization of the network is proved and the existence of Zeno behaviour is excluded; an event-triggered strategy able to guarantee the existence of a minimum lower bound between inter-event times for broadcasted information and for control signal updating is proposed, thus allowing applications where both the communication bandwidth and the maximum updating frequency of actuators are critical. This idea is further extended in an asynchronous periodic event-triggered schemes where the agents check a trigger condition via a periodic distributed communication without requiring a model based computation

In recent years, there has been a strong activity in the so-called precision agriculture, particularly the monitoring aspect, not only to improve productivity, but also to meet demand of a growing population. At a large scale, precise monitoring of cultivated fields is a quite challenging task. Therefore, this paper aims to propose a survey on techniques, applied to precision agriculture monitoring, through the use of drones equipped with multispectral, thermal and visible cameras. For each application, the main limitations are highlighted and the parameters to be considered before to perform a flight are reported.

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a b s t r a c tThis paper presents a framework for the study of convergence in networks where the nodes' dynamics may be both piecewise smooth and/or nonidentical. Sufficient conditions are derived for global convergence of all node trajectories towards the same bounded region in the synchronization error space. The analysis is based on the use of set-valued Lyapunov functions and bounds are derived on the minimum coupling strength required to make all nodes in the network converge towards each other. We also provide an estimate of the asymptotic bound on the mismatch between the node state trajectories. The analysis is performed both for linear and nonlinear coupling protocols. The theoretical analysis is extensively illustrated and validated via its application to a set of representative numerical examples.