Italian Cardiological Guidelines (COCIS) for Competitive Sport Eligibility in athletes with heart disease: update 2024.

Nearly 35 years after its initial publication in 1989, the Italian Society of Sports Cardiology and the Italian Federation of Sports Medicine (FMSI), in collaboration with other leading Italian Cardiological Scientific Associations (ANCE - National Association of Outpatient Cardiology, ANMCO - National Association of Inpatient Cardiology, SIC - Italian Society of Cardiology), proudly present the 2023 version of the Cardiological Guidelines for Competitive Sports Eligibility. This publication is an update of the previous guidelines, offering a comprehensive and detailed guide for the participation of athletes with heart disease in sports. This edition incorporates the latest advances in cardiology and sports medicine, providing current information and recommendations.

Anomaly Detection for Power Quality Analysis Using Smart Metering Systems.

The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the system's availability with the minimum maintenance cost. In this paper, anomaly detection algorithms based on machine learning, such as One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD), are used as a first tool for rapid and effective clustering of the measured voltage and current signals directly on-line on the sensing unit.

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries.

We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods.