Dan M. Frangopol, the inaugural Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture Emeritus and professor emeritus of civil and environmental engineering at Lehigh University, has been selected to receive the 2026 Raymond C. Reese Research Prize from the Structural Engineering Institute of the American Society of Civil Engineers (ASCE).

Red soil exhibits a high susceptibility to geological disasters and engineering instability owing to its significant dispersibility and substantial strength attenuation upon exposure to water. Consequently, there is an urgent necessity to enhance its engineering properties. As an environmentally friendly and cost-effective industrial by-product, building gypsum powder demonstrates significant potential in improving the mechanical properties of red soil. It not only reduces the burden of landfilling and lowers material costs but also achieves efficient waste utilization and resource recycling, substantially cutting expenses related to soil stabilization and improvement. It is expected to provide a scientific basis for the analysis of geological hazards such as debris flows, landslides, and collapses in red soil regions, as well as offer guidance for selecting appropriate types and dosage of soil modifiers in practical engineering projects.

Los Angeles, CA – March 30, 2026 – The Terasaki Institute for Biomedical Innovation is proud to announce that Dr. Yangzhi Zhu has been received a prestigious Career Development Award from the American Heart Association (AHA). The award will provide a total of $231,000 over three years to support his research in translational biosensing and organ health assessment.

High-resolution imaging of distant, dynamic targets remains fundamentally limited by finite-aperture diffraction. Unlike radar, optical detectors record only intensity rather than phase, making synthetic aperture imaging in the optical domain particularly challenging. Yet extending macroscopic Fourier ptychography to dynamic, non-cooperative scenes remains a major challenge, as existing implementations still rely heavily on mechanical scanning or engineered illumination. In a new paper published in Laser & Photonics Reviews, a research team from the Smart Computational Imaging Laboratory (SCILab) at Nanjing University of Science and Technology (NJUST), led by Professor Chao Zuo, reported inverse synthetic aperture macroscopic Fourier ptychography (ISAMFP), a motion-driven computational imaging framework that converts the natural motion of non-cooperative targets into a synthetic aperture for super-resolution imaging from intensity-only measurements, achieving up to a four-fold resolution enhancement beyond the diffraction limit of a single aperture without mechanical scanning.

Grasping and transporting objects is one of the most critical tasks for robots in a variety of fields. This task requires accurate 3D measurement of the objects. However, transparent or specular objects make measurement challenging, reducing grasping success rate. To address this, researchers have developed HEAPGrasp, a new technique that performs 3D measurement of objects using only their silhouettes, thus avoiding dependence on their optical properties. This approach significantly improves grasping success rate of robots.

Dentists are exposed to intense lighting for long hours, but its long-term impact on eye health is often overlooked. This study evaluates whether chronic exposure to dental lighting, especially blue and white LEDs, can damage the retina and disrupt its normal function. By combining human data with advanced imaging and animal models, the research uncovers hidden occupational risks and highlights the need for safer lighting practices to help protect dentists’ vision.    

Virtual reality feels more “real” when users can touch a physical prop, but most haptic proxies follow a one-object-one-model approach that is costly and hard to deploy. A new fabric topological haptic proxy (FTHP) integrates origami-inspired constraints and embedded sensing fibers so a single cloth can switch among flat, folded and transforming states for different interactions. A lightweight neural network decodes the signals and achieves about 92.4% action recognition accuracy.