In joint research with the University of Tokyo (UTokyo), the National Institute of Advanced Industrial Science and Technology (AIST), Tohoku University, and Kyoto Institute of Technology, the National Institute for Materials Science (NIMS) developed and published "elemental reactivity maps" for discovering new phases. The research team proposed maps that use machine learning to identify over 3,000 element combinations that could potentially form new phases from among a total of 85,320 combinations of up to three elements selected from the 80 elements easily handled in the laboratory. This research result was published in Chemistry of Materials on February 21, 2025.

Researchers from Nanyang Technological University have developed a novel framework that integrates worker self-reportsaZ with objective expert evaluations to address the challenge of validating human reliability models. They also proposed a new hybrid inference model combining Self-Organizing Maps (SOM) and Bayesian Networks (BN) to more accurately predict worker performance and identify potential failures. Published in Smart Construction, this research transcends the limitations of traditional expert-driven methods, offering a validated, data-driven approach to enhance workplace safety and operational efficiency in the high-risk construction industry.

Do you think you know which species are most vulnerable in an ecosystem? A novel analytical method developed by Italian physicists at the Complexity Science Hub (CSH) suggests there's more to discover. In their recent study, they found out how species like lizards and rabbits in South Florida's cypress wetlands are among their ecosystem's most at-risk species, pointing to vulnerabilities that aren't always obvious.

A team at Harvard and Vienna University of Technology have invented a new tunable laser that uses a series of rings to smoothly emit many light wavelengths from a single chip. The laser could replace many of today’s products that face tradeoffs in accuracy, range, and cost.