A new AI model is much better than doctors at identifying patients likely to experience cardiac arrest.

The linchpin is the system’s ability to analyze long-underused heart imaging, alongside a full spectrum of medical records, to reveal previously hidden information about a patient’s heart health.

Researchers have explored how  artificial intelligence and machine learning are helping scientists identify catalysts for methane pyrolysis, a reaction that produces hydrogen without emitting carbon dioxide.

The Hebrew University of Jerusalem is proud to announce its participation in RobustifAI, a groundbreaking Horizon Europe research consortium dedicated to strengthening the reliability and robustness of Generative Artificial Intelligence (GenAI) technologies. The project officially commenced on June 1, 2025, with a total budget of €9.3 million and a projected duration of 36 months.

Bimodal pressure sensors capable of simultaneously detecting static and dynamic forces are essential to medical detection and bio-robotics. However, conventional pressure sensors typically integrate multiple operating mechanisms to achieve bimodal detection, leading to complex device architectures and challenges in signal decoupling. In this work, we address these limitations by leveraging the unique piezotronic effect of Y-ion-doped ZnO to develop a bimodal piezotronic sensor (BPS) with a simplified structure and enhanced sensitivity. Through a combination of finite element simulations and experimental validation, we demonstrate that the BPS can effectively monitor both dynamic and static forces, achieving an on/off ratio of 1029, a gauge factor of 23,439 and a static force response duration of up to 600 s, significantly outperforming the performance of conventional piezoelectric sensors. As a proof-of-concept, the BPS demonstrates the continuous monitoring of Achilles tendon behavior under mixed dynamic and static loading conditions. Aided by deep learning algorithms, the system achieves 96% accuracy in identifying Achilles tendon movement patterns, thus enabling warnings for dangerous movements. This work provides a viable strategy for bimodal force monitoring, highlighting its potential in wearable electronics.

In the first study of its kind, researchers at the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) in Japan have used artificial intelligence to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the origins of our own galaxy, particularly the elements essential for life in the Milky Way.