Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam.   

In a new paper published in Advanced Materials, a team led by Professor Tobin Filleter describes how they made nanomaterials with properties that offer a conflicting combination of exceptional strength, light weight and customizability. The approach could benefit a wide range of industries, from automotive to aerospace. 

Many people struggle to maintain a healthy weight, and choosing the optimal meals for weight loss can be challenging. A research team at the University of Illinois Urbana-Champaign has developed a weight management program that helps individuals plan meals with the assistance of a web application and support from a registered dietitian. In a new study, they discuss the app’s features, development, and implementation.

A new study shows that long-read sequencing has the potential to improve the rate of diagnosis while reducing the time to diagnosis from years to days — in a single test and at a much lower cost.

As the electricity consumption of AI is projected to double by 2026, neuromorphic computing emerges as a promising solution. Researchers at The University of Texas at San Antonio are advancing neuromorphic computing, a field that applies principles of neuroscience to computing systems to mimic the brain’s function and structure. Neuromorphic chips have the potential to outpace traditional computers in energy and space efficiency as well as performance, presenting substantial advantages across various domains, including artificial intelligence, health care and robotics. To achieve scale in neuromorphic computing, the team proposes several key features that must be optimized, including sparsity, a feature observed in the biological brains.