Real-time sepsis risk alerts: New AI model improves ICU patient survival
Peer-Reviewed Publication
Updates every hour. Last Updated: 24-Jun-2025 08:10 ET (24-Jun-2025 12:10 GMT/UTC)
Josephson microwave microscopy integrating Josephson junctions onto a nanoprobe enables spectroscopic imaging of near-field microwave with a broad bandwidth, presenting a non-destructive technique to characterize microwave devices.
Full Waveform Inversion (FWI) is capable of finely characterizing the velocity structure, anisotropy, viscoelasticity, and attenuation properties of subsurface media, which provides critical constraints for scientific problems such as understanding the Earth’s internal structure and material composition, earthquake preparation and occurrence, and plate motion and dynamic processes. In recent years, with advancements in high-performance computing platforms, improvements in numerical methods, and the cross-integration of multidisciplinary, FWI has demonstrated broad application prospects in deep underground structure exploration, resource and energy exploration, engineering geophysics, and even medical imaging. In this paper, we provide a comprehensive review and analysis of the development of the FWI method, addressing its current challenges, identifying key issues, future directions, and potential research areas in the theory, methodology, and application of high-resolution FWI imaging. The related findings were published in SCIENCE CHNIA: Earth Science, 68(2): 315‒342, 2025.
In a paper published in National Science Review, a team of Chinese scientists develop an AI-powered framework designed to achieve real-time, seamless retrieval of PM10 concentrations. This breakthrough addresses the challenges of spatial gaps and nighttime observation deficiencies in current satellite-based PM10 data. It extends daily data to high-resolution, real-time hourly insights, providing strong support for precise dust storm monitoring.
In a paper published in National Science Review, a research team from Institute of Automation, Chinese Academy of Sciences and Nanjing University present an overview of the historical developments in Generative Artificial Intelligence (Generative AI). They grouped the developments of Generative AI into four categories: 1) rule-based generative systems, 2) model-based generative algorithms, 3) deep generative methodologies, and 4) foundation models. They also described potential research directions aimed at better utilizing, understanding, and harnessing Generative AI technologies.