Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.

In this paper, we introduce the need for real-time temperature monitoring in battery packs used in automotive applications so to have an accurate estimation of battery life and performances. Advanced energy storage management systems should sense operating and ambient temperature of battery packs in order to implement proper strategies to improve the efficiency of charge and discharge processes and to extend battery life. The proposed evaluation technique is based on an innovative and dynamic circuital model, which allows to accurately represent the functioning of a battery pack (in charge and discharge) in the various operating conditions. Each circuital parameter of the model has a well-defined function which highlights its dependence on temperature, state of charge, state of health and number of cells. The model characterization has been made through tests on different battery sets. A climatic chamber has been used to simulate different operating conditions of ambient temperature. The analysis of the parameters evolution in time has led to the identification of an ideal ambient temperature range for the type of batteries under examination in order to improve their performances over time in terms of energy efficiency and extension of useful life.

Prognostic and health management technologies are increasingly important in many fields where reducing maintenance costs is critical. Non-destructive testing techniques and the Internet of Things (IoT) can help create accurate, two-sided digital models of specific monitored objects, enabling predictive analysis and avoiding risky situations. This study focuses on a particular application: monitoring an endodontic file during operation to develop a strategy to prevent breakage. To this end, the authors propose an innovative, non-invasive technique for early fault detection based on digital twins and infrared thermography measurements. They developed a digital twin of a NiTi alloy endodontic file that receives measurement data from the real world and generates the expected thermal map of the object under working conditions. By comparing this virtual image with the real one acquired by an IR camera, the authors were able to identify an anomalous trend and avoid breakage. The technique was calibrated and validated using both a professional IR camera and an innovative low-cost IR scanner previously developed by the authors. By using both devices, they could identify a critical condition at least 11 s before the file broke.

This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodynamic feasibility of the trajectories is guaranteed through a series of set-based arguments, which involve the solution of semi-definite programming problems. Navigation in the graph is performed through a hybrid A* algorithm whose performance metrics are improved through a properly trained classificator, which can forecast whether a candidate trajectory segment is feasible or not. The proposed solution is validated through numerical simulations, with a focus on the effects of different classificators features and by using two different kinds of artificial intelligence: a support vector machine (SVM) and a long-short term memory (LSTM). Results show up to a 28% reduction in computational effort and the importance of lowering the false negative rate in classification for achieving good planning performance outcomes.

This paper proposes an innovative IoT-based system for monitoring air quality inside protective equipment such as FFP2 masks, which have become more widespread due to the COVID-19 pandemic. The system aims to provide diagnostic elements to specialist doctors and suggest a healthier use of the mask by monitoring the concentration of pollutants. The system aggregates data over a 15-minute window and calculates average values for each measured parameter, comparing them with reference thresholds to suggest removing the mask if necessary. An innovative aspect is personalized monitoring of exhaled breath, specifically Volatile Organic Compounds (VOCs), providing a customized and reliable information framework for doctors. This is possible thanks to the integration of removable memories, inside which the user's personal information and metrological characteristics of the system are stored in a standardized form. The proposed platform is accessible to both users and doctors, enabling early diagnosis by providing a complete picture of the patient's specific condition. This solution can have a strong impact on daily life and well-being, especially for diseases that increase the presence of certain compounds in the exhaled breath.