Remote working is increasingly seen as an effective model in several countries in the last decade, mainly thanks to the development of information and communication technologies in support of common daily working tasks. The emergence of the COVID-19 pandemic has represented a pivotal moment for the adoption of remote working in multiple sectors, with positive effects on the environmental impacts caused by the daily commuting of workers. However, due to the fact that pandemic-induced remote working has represented a major forced experiment on a global scale, and that it has often been imposed rather than chosen by employees, workers’ well-being has not always been ensured. This research work presents an analysis of a wide survey of remote workers in public administrations in four different provinces in Italy, with the aim of assessing the main characteristics of the users and the related environmental benefits. Survey data refer to remote workers before COVID-19, thus representing workers who have freely chosen to work from home for different reasons. The results of this work represent a useful tool with which to support the definition of new remote work strategies that could help policy makers reduce a part of the systematic mobility demand. We have also calculated average energy and emission savings to provide useful indicators for a preliminary estimation of the potential environmental benefits of remote working. Considering the entire sample of respondents, workers who would have commuted at least partially by car have saved on average 6 kg of CO₂ per day thanks to remote working (with an average round-trip commuting distance of approximately 35 km). The current results will be supplemented by the results of a new survey underway, aimed at evaluating the differences of remote working experiences during the emergency response to COVID-19.

Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by “intelligent” machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.

In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.