The Korea Research Institute of Standards and Science (KRISS, President Dr. Lee Ho Seong) has developed a deep learning technology that predicts, in real time, the vibrations at multiple locations inside a nuclear power plant using the signal from just a single seismometer, and that quantifies the risk at each location to enable the prioritization of inspections. In particular, because the AI model can be flexibly designed to match the vibration characteristics of a structure, it is expected to be widely applicable to major industrial facilities such as semiconductor plants and data centers in the future.

This study designs a phosphorus-doped vacancy regulation and phase interface engineering strategy to fabricate a MOF-derived metal selenide@carbon dual-matrix material, denoted as P-NiSe₂/CoSe₂@NC (PNCS). By optimizing the phosphorus source doping ratio and calcination temperature, the optimal electromagnetic wave absorption mechanism is explored. The results demonstrate that through iterative optimization of doping concentration and thermal conditions, the material achieves a synergistic balance between defects and heterogeneous interfaces, leading to superior electromagnetic wave absorption performance. At a matching thickness of only 2.1 mm, an effective absorption bandwidth of 7.04 GHz is attained, and the entire X-band and Ku-band can be covered within a controllable thickness of 2.6 mm, showing significantly enhanced performance compared to analogous samples. The “dual-core driven” strategy, combining oxygen vacancies and selenide heterointerfaces, optimizes dielectric relaxation and polarization loss, offering new insights for the development and application of next generation high-efficiency electromagnetic wave absorbing materials.

We are pleased to present a curated collection of our recent cover articles, featuring breakthroughs ranging from enzyme–chemical interaction prediction, genome-scale modeling and novel biosensor development to metabolic engineering of industrially relevant microbial hosts and next-generation gene-editing tools. These studies highlight the field's rapid evolution from fundamental tool discovery to translational industrial applications.

Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a life-threatening complication in patients with cervical cancer that can significantly worsen survival and treatment outcomes. Although several clinical risk assessment tools are available, they primarily rely on laboratory measurements obtained at a single time point, limiting their ability to capture the evolving biological processes that precede thrombosis. While deep learning models have demonstrated strong predictive performance, their limited interpretability and high computational demands remain barriers to routine clinical implementation.