Abstract: Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge. However, limited research exists for imbalanced and long-tailed data.

Abstract: The federated learning (FL) client selection scheme can effectively mitigate global model performance degradation caused by the random aggregation of clients with heterogeneous data.

Experience space like never before with this Python simulation of the ISS orbit – upgraded animation! 🌌 Watch the International Space Station (ISS) move along its trajectory with realistic ...

There are University of Toronto jobs that have salaries up to $139,000 a year and $42 an hour. Some positions don't require ...

CADEXSOFT announces new features and improvements in Manufacturing Toolkit 2026.1. In this new release, core MTK binaries have been renamed from CadExMTK to MTKCore. This change aligns binary naming ...

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...

This python crash course book on Amazon is great for beginners who want to learn programming. It teaches Python basics step-by-step and includes exercises to help you practice. You’ll build real ...

Data security incidents have been increasing in frequency and prevalence across industries in recent years. In the foodservice sector alone, Panera Bread, Starbucks, and Krispy Kreme all reported ...