Researchers at University College London have developed a new computational model of human color vision that accounts for individual physiological differences, explaining why wide-gamut modern displays often appear inconsistent to different viewers. Published in Optics Express, the study demonstrates how adjusting a display's red, green, and blue primary light spectra can help manufacturers build screens that deliver more consistent, accurate colors across a broader audience.

The Peninsula Innovation District, a collaboration between the University of Tennessee, Knoxville and UT Medical, has received the 2026 Emerging Community of Innovation award from the Association of University Research Parks. Building on the UT Research Park at Cherokee Farm, the district is growing into a place where researchers, entrepreneurs, and industry can connect with university expertise and with one another around a shared vision for innovation. The award recognizes the momentum behind that transformation and the district’s potential for continued growth. 

JMIR Publications invites submissions to a new section in JMIR Cardio titled “Generative and Multimodal AI in Digital Cardiovascular Medicine.” 

The Karlsruhe Institute of Technology (KIT) has put the HoreKa 2 Supercomputer into operation. The new system will support researchers in their efforts to address complex scientific issues, such as climate, new materials, particle physics, energy, and mobility as well as AI research, more quickly. For this purpose, it combines powerful high-performance computing and modern AI technologies. The total cost of the facility amounts to EUR 17 million.

To simulate event cameras, which can capture brightness changes at microsecond-scale speeds, conventional methods render numerous frames from 3D scenes, resulting in high computational costs. Researchers from Japan developed an efficient method using physically based path tracing to precisely determine event timings. The efficient implementation using GPUs reduces computation time to as little as one-third of a naïve implementation, enabling more efficient training-data generation for event camera applications, including healthcare monitoring, autonomous driving, and robotics.