A research team has developed a new way to measure and predict how plant leaves scatter and reflect light, revealing that leaf optical behavior can be accurately inferred from measurable phenotypic traits such as surface roughness, thickness, and pigment content.

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than relying on a single vegetation index, enables more accurate disease assessment.

A research team shows that hyperspectral imaging paired with explainable machine learning can identify early, symptomless stress caused by potato cyst nematodes.

Researchers at the MRC Laboratory of Medical Sciences (LMS) and Imperial College London in London, UK have developed a technology that integrates heart-imaging data with information from biological databases to uncover new genes and drugs linked to heart disease. The tool, called CardioKG, is explored in a paper published today in Nature Cardiovascular Research and could accelerate the discovery of new drugs for treating heart conditions.

Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework that enables accurate thermal field inversion in chiplet-based packaging systems using only limited temperature measurements. The approach addresses a critical challenge in advanced heterogeneous integration, where increasing power density and material heterogeneity complicate thermal monitoring and reliability assessment.

Chiplet-based packaging integrates multiple heterogeneous chiplets into a single system, offering improved flexibility, yield, and cost efficiency compared to traditional system-on-chip designs. However, the resulting increase in power density and deterioration of heat dissipation conditions can lead to localized overheating, threatening system stability and long-term reliability. Accurately reconstructing the temperature field of such systems from sparse sensor data is therefore essential, yet remains a difficult inverse problem in practical engineering applications.

Intestinal Stem Cells (ISCs) derived from a patient's own cells have garnered significant attention as a new alternative for treating intractable intestinal diseases due to their low risk of rejection. However, clinical application has been limited by safety and regulatory issues arising from conventional culture methods that rely on animal-derived components (xenogeneic components). A KAIST research team has developed an advanced culture technology that stably grows ISCs without animal components while simultaneously enhancing their migration to damaged tissues and regenerative capabilities.