Dr. Deanna Kennedy of the Department of Kinesiology and Sport Management and Drs. Ana Diaz Artiles and Bonnie Dunbar from the Department of Aerospace Engineering have partnered to investigate how differences in gravity influence coordination and movement control. Their latest work looks into how the body performs in altered gravity conditions to identify challenges astronauts encounter in space and how the human body adapts when environmental surroundings shift.

“On Earth, we spend our entire lives moving in a 1G environment, where the gravitational force feels natural,” Kennedy said. “But when that changes, the way our body controls movement changes too and understanding that helps us predict where performance may begin to break down.”

As demand for optical communication capacity continues to grow, the 2 µm wavelength region is emerging as a promising new window for future photonic systems. Researchers have developed a high-speed InGaAs/GaAsSb type-II superlattice photodetector that is heterogeneously integrated on silicon via micro-transfer printing. By combining a backside Au reflector with a low-index BCB cavity, the device achieves broadband operation from 0.8 to 2.5 µm and a 57.9% responsivity enhancement at 2 µm, while maintaining 17–18.7 GHz bandwidth and clear 50 Gbps eye diagrams. The work provides a practical route toward broadband, high-speed photodetectors for silicon photonics and next-generation optical interconnects.

Much of the appeal of self-driving vehicles is just that: the fact that they drive themselves. Still, there are times when a driver needs to put their hands on the wheel, and a new study from Binghamton University suggests that task could be more difficult if you have a history of head injuries.

The explosion of artificial intelligence (AI) and the era of Big Data have exposed a critical flaw in traditional computer hardware: the slow and energy-consuming data transfer between memory and processors, commonly known as the von Neumann bottleneck. Every day, when our devices process AI queries, they must move massive amounts of data back and forth, causing severe delays and battery drain.

To address this challenge, researchers at The University of Hong Kong (HKU) have created a chip with a minimalist architecture that performs complex searches directly where the data is stored, fundamentally bypassing this bottleneck. The research team, led by Professor Can Li from the Department of Electrical and Computer Engineering of the Faculty of Engineering and the Centre for Advanced Semiconductors and Integrated Circuits (CASIC), has developed a novel analogue content-addressable memory (CAM) utilising two-dimensional (2D) MoS2 flash memories.