This article introduces Crack-Net, a deep learning framework that efficiently predicts crack propagation and stress–strain curves in particulate composites. It leverages implicit constraints to enhance accuracy and reduce data needs, offering a practical solution for fracture prediction in complex materials.

Discover how integrating decentralized water systems, source separation, and low-carbon technologies can transform urban water management. This comprehensive strategy aims to reduce carbon emissions while promoting resource conservation and recovery, offering a sustainable approach to meet global climate goals.

A novel real-time automated damage detection method leveraging reinforcement learning has been unveiled in a recent study published in Engineering. The research, spearheaded by Chengwen Zhang, Qing Chun, and Yijie Lin from Southeast University, China, introduces an innovative approach that significantly enhances the efficiency and accuracy of damage detection in structural health monitoring (SHM) systems, particularly for architectural heritage.

This article reviews a study on greenhouse gas emissions from combined sewer overflows and explores strategies for mitigating these emissions to achieve carbon neutrality in urban water systems. It highlights the importance of integrated, low-carbon management solutions and the potential of data-driven technologies in addressing this environmental challenge.

Plants are constantly under siege by pathogens, and their survival depends on precise internal defense strategies.