Placing sustainability at the core of computing practices, the industry is poised to pioneer positive changes and create a cleaner and more sustainable world for future generations. The environmentally sustainable computing (ESC) framework is introduced in this paper as an innovative solution to revolutionize sustainability practices across various computing domains and cover multiple aspects of sustainable information technology (IT). The ESC framework includes the entire lifecycle of computing systems, including critical stages such as design, development, monitoring, refactoring, and regulatory compliance. Through the adoption of the ESC framework, academia and industry stakeholders can gain a powerful tool to evaluate and measure sustainability factors across different computing domains and can integrate eco-friendly computing principles and patterns throughout their products and services. This can significantly reduce their carbon footprint while complying with environmental regulations. In addition to presenting the ESC framework, the paper showcases real-world use cases. The first involves a leading Italian bank, emphasizing the significance of monitoring and compliance in achieving sustainable solutions within carbon-aware computing. The second use case explores resource efficiency optimization in Kubernetes clusters, illustrating how the ESC framework aligns with cloud infrastructure management trends.

Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to $9\%$ on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to $11\%$). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort.

Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had $96.0$% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances.

The possibility of continuous monitoring of health conditions represents a crucial aspect for the improvement of living conditions, the prevention of potential pathologies and prompt response in critical situations. In particular, in intensive care or emergency situations, the evaluation of illness degree of a clinical risk level can be considered a predictive task in situations where streams of vital signs data are gathered by medical devices and the Internet of Medical Things (IoMT) sensors. In this framework, Early Warning Score (EWS) systems generating an aggregate score based on the measurement of a set of vital signs, such as National Early Warning Score 2 (NEWS2) and Modified Early Warning Score (MEWS), may provide a helpful decision support for the estimation of health state and triggers for critical care intervention. In the present work, we address a preliminary analysis in order to investigate the most suitable Machine Learning (ML) technique for the prediction of clinical risk classes of a continuously monitored patient in a particular condition where a limited number of vital parameters is available. This analysis is then intended to be preparatory for the final goal of designing an edge device connected to one or more wearable medical devices via IoMT, which adaptively exploits the best ML model to predict a reliable EWS.

Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME)${}^2$, the Monitoring Equipment Mask Environment: an innovative reusable 3D-printed eco-sustainable mask with an interchangeable filter. (ME)${}^2$ is equipped with multiple vital sensors on board, connected to a system-on-a-chip micro-controller with computational capabilities, Bluetooth communication, and a rechargeable battery that allows continuous monitoring of the wearer’s vital signs. It monitors body temperature, heart rate, and oxygen saturation in a non-invasive, strategically positioned way. (ME)${}^2$ is accompanied by a mobile application that provides users’ health information. Furthermore, through Edge Computing Artificial Intelligence (Edge AI) modules, it is possible to detect an abnormal and early symptoms linked to possible pathologies, possibly linked to the respiratory or cardiovascular tract, and therefore perform predictive analysis, launch alerts, and recommendations. To validate the feasibility of embedded in-app Edge AI modules, we tested a machine learning model able to distinguish COVID-19 versus seasonal influenza using only vital signs. By generating new synthetic data, we confirm the highly reliable performances of such a model, with an accuracy of $94.80\%$.