Despite improvements to air filtration technology in the aftermath of the COVID-19 pandemic, some of the smallest particles — those of automobile and factory emissions — can still make their way through less efficient, but common filters. An interdisciplinary team of researchers from Drexel University’s College of Engineering has introduced a new way to improve textile-based filters by coating them with a type of two-dimensional nanomaterial called MXene.

There is a big problem with quantum technology — it’s tiny. The distinctive properties that exist at the subatomic scale usually disappear at macroscopic scales, making it difficult to harness their superior sensing and communication capabilities for real-world applications, like optical systems and advanced computing. Now, however, an international team led by physicists at Penn State and Columbia University has developed a novel approach to maintain special quantum characteristics, even in three-dimensional (3D) materials.

If we can remove clear indications that someone is being monitored, we are far more likely to get the information we seek. This is the basis for unattended testing, which is about as common as laboratory testing itself.

A research team led by Professor Jia Pan and Professor Yifan Evan Peng from the Department of Computer Science and Department of Electrical & Electronic Engineering under the Faculty of Engineering at the University of Hong Kong (HKU), in collaboration with a researcher at Australian National University, has recently developed a neuromorphic exposure control (NEC) system that revolutionizes machine vision under extreme lighting variations. Published in Nature Communications, this biologically inspired system mimics human peripheral vision to achieve unprecedented speed and robustness in dynamic perception environments.

A Ballbot is a unique kind of robot with great mobility, which possesses the ability to go in all directions. Obviously, controlling such a robotic device must be tricky. Indeed, ballbot systems pose unique challenges, particularly the difficulty of maintaining balance and stability in dynamic and uncertain environments. Traditional proportional integral derivative (PID) controllers struggle with these challenges, while other advanced methods, like sliding mode control, introduce issues like chattering. Therefore, there is a need to develop a controller that combines the simplicity and adaptability of PID with the learning capabilities of the now-popular neural networks, providing a robust solution to real-world robotic mobility problems.