Matt Pharr, assistant professor in the J. Mike Walker ’66 Department of Mechanical Engineering, has published findings on the mechanics of tears in elastic materials that may help develop more tear-resistant materials in the future.
Flood prediction becomes increasingly challenging during events of extreme rainfall, like during hurricanes. Texas A&M researchers have developed a new probability-based tool that considers the network of drainage systems to accurately predict the flow of flood water in near actual time.
Texas A&M University-San Antonio will advance cyber research through a newly established Cyber Engineering Technology/Cyber Security Research Center with a $1 million grant from The Texas A&M University System Chancellor’s Research Initiative (CRI). The center will be housed in the Department of Computing and Cyber Security within the College of Business.
Dr. Jaime Grunlan and his team are helping lead the effort to pursue safer, more effective ways to protect flammable objects through the development of flame-retardant surface treatments.
The separation between sky and sea is only one millimeter at its thickest and, yet, this sea-surface microlayer plays a major role in global phenomena. Dr. Aarthi Sekaran is taking a deeper look into how flow instabilities in this microlayer affects weather patterns and prediction.
Updates to software can sometimes create inadvertent glitches, slowing down performance. Texas A&M researchers have developed a new algorithm based on machine learning that can locate and diagnose the bug.
Squishy, jelly-like materials called microporous annealed particle hydrogels are emerging as ideal candidates for delivering stem cells to the site of tissue injury. Texas A&M researchers now show that only when these hydrogels degrade over time, stems cells grow, spread and form dense networks.
Machine learning algorithms and computational models can provide insight into the mental demand placed on individuals using prosthetics. Dr. Maryam Zahabi and her team are using these models to improve the current interface in prosthetic devices by studying prosthetics that use an electromyography-based human-machine interface.
