The role of proactive coping strategies and perceived health status for Social well-being and life-project in old age.

The latter part of the 20th century was a period characterized by a fundamental demographic transition of western society. This substantial and structural demographic change proposes several challenges to contemporary society and fosters the emergence of new issues and challenges. Among these, none is more crucial than the comprehension of the mechanisms and the processes that lead people to positive aging. Rowe and Kahn’s model of successful aging highlights the interplay between social engagement with life, health, and functioning for a positive aging experience. Other systemic models of successful aging (Kahana et al., 1996; 2003; Stevernik et al., 2006) emphasize the role of internal and external resources for attaining positive aging. Among these, the proactive coping strategies are indicated as important active strategies for avoiding the depletion of resources, counterbalancing the declines and maintaining social and civic involvement. The study has analyzed the role of proactive coping strategies for two facets of positive aging, the experience of a high social well-being and the presence of personal projects in fundamental life domains. As expected, the proactive coping strategies, referred to as the active management of the environment, the accumulation of resources and the actualization of human potentials are confirmed as positive predictors of high level of social well-being and of many personal projects focused on family, culture, leisure time, civic and social participation. Perceived health status give a significant contribution only to the possession of many personal projects. Gender and level of school education give also a significant contribution to these two dimensions of positive aging, highlighting how positive aging is rooted not only in the possession of personal resources, but also in historical models of education and in positive longitudinal chains related to early development.

Big data analytics for proactive and predictive maintenance in electric car battery packs

The idea behind the project is to develop a methodology for analyzing and developing techniques for the diagnosis and the prediction of the state of charge and health of lithium-ion batteries for automotive applications. For lithium-ion batteries, residual functionality is measured in terms of state of health; however, this value cannot be directly associated with a measurable value, so it must be estimated. The development of the algorithms is based on the identification of the causes of battery degradation, in order to model and predict the trend. Therefore, models have been developed that are able to predict the electrical, thermal and aging behavior. In addition to the model, it was necessary to develop algorithms capable of monitoring the state of the battery, online and offline. This was possible with the use of algorithms based on Kalman filters, which allow the estimation of the system status in real time. Through machine learning algorithms, which allow offline analysis of battery deterioration using a statistical approach, it is possible to analyze information from the entire fleet of vehicles. Both systems work in synergy in order to achieve the best performance. Validation was performed with laboratory tests on different batteries and under different conditions. The development of the model allowed to reduce the time of the experimental tests. Some specific phenomena were tested in the laboratory, and the other cases were artificially generated.