The digital transition is one of the biggest challenges of the new millennium. One of the key drivers of this transition is the need to adapt to the rapidly changing and heterogeneous technological landscape that is continuously evolving. Digital Twin (DT) technology can promote this transition at an urban scale due to its ability to monitor, control, and predict the behaviour of complex systems and processes. As several scientific studies have shown, DTs can be developed for infrastructure and city management, facing the challenges of global changes. DTs are based on sensor-distributed networks and can support urban management and propose intervention strategies based on future forecasts. In the present work, a three-axial operative framework is proposed for developing a DT urban management system using the city of Venice as a case study. The three axes were chosen based on sustainable urban development: energy, mobility, and resiliency. Venice is a fragile city due to its cultural heritage, which needs specific protection strategies. The methodology proposed starts from the analysis of the state-of-the-arts of DT technologies and the definition of key features. Three different axes are proposed, aggregating the key features in a list of fields of intervention for each axis. The Venice open-source database is then analysed to consider the data already available for the city. Finally, a list of DT services for urban management is proposed for each axis. The results show a need to improve the city management system by adopting DT.

Occupational solar radiation exposure is a relevant heath risk in the fishing sector. Our aim was to provide a detailed evaluation of individual UV exposure in three different fishing activities in Italy, with personal UV dosimeters and a simple formula to calculate the fraction of ambient erythemal UV dose received by the workers. The potential individual UV exposure of the fishermen was between 65 and 542 Joules/m². The percentages of the ambient exposure were estimated between 2.5% and 65.3%. Workers’ UV exposure was mainly influenced by the characteristics of the work activity, the postures adopted, and the type of boats. Overall, our data showed that 43% of the daily measurements could result largely above the occupational limits of 1–1.3 standard erythemal dose (i.e., 100 Joules/m²) per day, in case of exposure of uncovered skin areas. Measurements of individual UV exposure are important not only to assess the risk but also to increase workers’ perception and stimulate the adoption of preventive measures to reduce solar UV risk. Furthermore, the simple method proposed, linking ambient erythemal UV dose to the workers’ exposure, can be a promising tool for a reliable assessment of the UV risk, as time series of environmental UV dose are widely available.

Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

Solar Ultra-Violet (UV) radiation has positive and negative effects on human body tissues. Small doses of solar UV radiation are needed by the human skin for the activation of the vitamin D production. Overexposure to solar UV radiation can produce acute and long-term negative effects, such as sunburns and, in the worst cases, cataracts and skin cancers. For this reason, knowing the amount of UV doses received by people is essential to evaluate their risk to UV overexposure and to evaluate the adequate countermeasure to avoid the negative effects. The original contribution of the present study consists in having searched, collected, adapted and processed a series of technical information and analytical relations, developing an algorithm suitable for the calculation of the erythemal UV dose on sloped surfaces exposed to solar radiation, which at the moment is not present in the scientific literature. The results obtained by the algorithm have been compared to the results of a field measurements campaign, carried out in three different Italian sites. Results comparison indicated that measured and calculated values show a sufficient level of agreement, with a mean absolute error equal to 20%.

An optimized system management of a commercial and industrial power system must take care of the actual operational and energetic duty of the power system distribution. A procedure based on the Arrhenius model of the Thermal Aging of Insulating Materials allows introducing the relative time coefficient (RTC) and promoting a heuristic method, as a rule of thumb, to estimate the life loss and the residual useful life for insulated power cables. The paper deals with simplified models of low voltage cable systems characterized by one and two time constants to evaluate the cable temperature behavior and the influences of the ambient and installation conditions. Furthermore, the paper proposes a device to count the “life loss hours” for LV power cables of main/critical circuits.