An assistant professor at Binghamton University, State University of New York has won an NSF CAREER award for cognitive modeling to help robots assist nurses.

A sounding rocket flight test is giving engineers at Sandia National Laboratories real-world reentry vibration data they can use to improve modeling and help drive nonnuclear component designs for the W93.

The next generation of microelectronics relies on radical improvements in transistor switching performance to advance computing power. However, conventional semiconductor technology has hit a physical limit known as the "Boltzmann tyranny," which restricts the energy efficiency of traditional transistors and stalls progress in high-performance electronics. To address this key bottleneck, a research team at The Hong Kong Polytechnic University (PolyU) has engineered a novel tunnelling field-effect transistor (TFET) utilising two-dimensional (2D) nanomaterials. The breakthrough brings this long-awaited experimental technology closer to commercial reality, offering a fundamental building block for energy-efficient computing and next-generation artificial intelligence (AI) chips.

First-of-its-kind research from Finland’s Aalto University’s School of Business reveals the modus operandi and operating model of the pro-Russian hacking group responsible for thousands of Distributed Denial of Service (DDoS) attacks against NATO and European targets.

Proteins with exposed arginine can selectively accumulate even on protein-resistant coatings, as reported by researchers from Science Tokyo. Through an in-depth analysis of which and how proteins adhere to various anti-fouling surfaces, the team found arginine disrupts the protective water layer that normally blocks adsorption. Their results will help scientists develop safer and more effective biocompatible materials for medical devices, such as stents and advanced biosensors.

A new artificial intelligence (AI)-based system shows how natural damage to traffic signs can mislead deep neural network (DNN)-based vision systems. The framework, AdvWT, uses a generative model to learn realistic damage patterns and then optimize them to trigger misclassification. Tests across multiple AI architectures showed strong attack success, transferability and physical-world robustness. The approach can identify vulnerabilities and improve AI generalization to real-world damage.