Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.

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Transmission pathways of SARS-CoV-2 are through aerosol, droplet and touching infected material. Indoor locations are more likely environments for the diffusion of the virus contagion among people, but direct detection of SARS-CoV-2 in air or on surfaces is quite sparse, especially regarding public transport. In fact, an important demand is to know how and if it is safe to use them. To understand the possible spreading of COVID-19 inside a city bus during normal operation and the effectiveness of the protective measures adopted for transportation, we analysed the air and the surfaces most usually touched by passengers. The measurements were carried out across the last week of the lockdown and the first week when gradually all the travel restrictions were removed.