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Vehicle operating speed is a crucial factor for road safety, as it strictly affects occurrence and severity of crashes. Usually, 85th percentile of the operating speed distributions (i.e., V85) in free-flow traffic condition is widely accepted as a base value of consistency evaluation for homogenous portion of existing roads. Although the computation of V85 is simple, many road authorities cannot collect speed data for each road. Therefore, providing prediction models could be a useful tool to investigate the relationship between V85 and road characteristics. The literature proposed several models to account it. However, to the best of our knowledge, the effects of some road geometric characteristics, road markings and signs, traffic data, type of terrain and the simultaneous consideration of different road categories on the V85 prediction were not completely analyzed. This paper fills this gap by isolating key variables that mostly affect V85. In doing so, 60 000+ car spot speed data were collected along the county road network of the province of Brescia (Italy), and then processed by multiple regression models. The main findings show that V85 increases owing to the presence of a wider or paved shoulder, visible road median markings, a higher number of lanes and a higher percentage of cars with respect to the total traffic flow. Conversely, V85 decreases as the road axis curvature, the number of accesses and rate of forbidden overtaking increase. In addition, the presence of visible road external markings and the surrounding mountainous terrain contribute to decreasing V85. The overall findings may support road authorities to verify roads’ operating conditions and, possibly, adjust the speed limits, especially for existing roads.

Machine learning deals with creating algorithms capable of learning from the provided data. These systems have a wide range of applications and can also be a valuable tool for scientific research, which in recent years has been focused on finding new diagnostic techniques for particle accelerator beams. In this context, SPARC_LAB is a facility located at the Frascati National Laboratories of INFN, where the progress of beam diagnostics is one of the main developments of the entire project. With this in mind, we aim to present the design of two neural networks aimed at predicting the spot size of the electron beam of the plasma-based accelerator at SPARC_LAB, which powers an undulator for the generation of an X-ray free electron laser (XFEL). Data-driven algorithms use two different data preprocessing techniques, namely an autoencoder neural network and PCA. With both approaches, the predicted measurements can be obtained with an acceptable margin of error and most importantly without activating the accelerator, thus saving time, even compared to a simulator that can produce the same result but much more slowly. The goal is to lay the groundwork for creating a digital twin of linac and conducting virtualized diagnostics using an innovative approach.

Nowadays, designing and adopting sustainable and greener transport systems is of upmost interest. The European Commission and different EU countries are developing plans and programs—but also delivering resources—aimed at the decarbonization of cities and transport by 2030. In this paper, the case study of the city of Brescia, a city of about 200,000 inhabitants located in northern Italy, is addressed. Specifically, a preliminary operational and financial feasibility study is performed assuming the replacement of the entire compressed natural gas (CNG) powered bus fleet of a specific line; the two alternatives considered are battery electric buses (BEBs) and fuel cell electric buses (FCEBs). For the comparison and evaluation of the two alternatives, specific economic parameters of the three alternatives (BEB, FCEB and the current solution CNGB) were considered: CAPEX (CAPital EXpenditure) and OPEX (OPerational EXpenditure). This allowed us to determine the TCO (total cost of ownership) and TCRO (total cost and revenues of ownership) along three annuities (2022, 2025 and 2030). For the BEB alternative, the TCO and TCRO values are between EUR 0.58/km and EUR 0.91/km. In the case of the FCEB solution, the values of TCO and TCRO are between EUR 1.75/km and EUR 2.15/km. Considering the current CNGB solution, the TCO and TCRO values range between EUR 1.43/km and EUR 1.51/km.

The current COVID-19 pandemic has highlighted the importance of aerosol-based transmission of human pathogens; this therefore calls for novel medical devices which are able to sterilize contaminated aerosols. Here we describe a new laser device able to sterilize droplets containing either viruses or bacteria. Using engineered viral particles, we determined the 10,600 nm wavelength as the most efficient and exploitable laser source to be manufactured in a commercial device. Given the lack of existing working models to reproduce a human aerosol containing living microbial particles, we developed a new system mimicking human droplet formation and preserving bacterial and viral viability. This evidenced the efficacy of 10,600 nm laser light to kill two aerosol transmitted human pathogens, Legionella pneumophila and SARS-CoV-2. The minimal exposure time of