Scientists develop ‘smart pyjamas’ to monitor sleep disorders
Peer-Reviewed Publication
Updates every hour. Last Updated: 7-May-2025 23:09 ET (8-May-2025 03:09 GMT/UTC)
Researchers have developed comfortable, washable ‘smart pyjamas’ that can monitor sleep disorders such as sleep apnoea at home, without the need for sticky patches, cumbersome equipment or a visit to a specialist sleep clinic.
Machine learning is transforming the control of particle accelerators, enabling "autonomous driving" for these complex systems. Researchers from the Institute of Modern Physics and Xiamen University have developed innovative solutions to improve accelerator operation, reducing manual intervention and enhancing efficiency. By integrating reinforcement learning and virtual accelerators, they achieved seamless transfer to real-world applications, marking a significant breakthrough. This research paves the way for more efficient, intelligent control technologies in particle accelerators and sets a milestone in AI's application to advanced scientific tools.
With the rapid development of natural resource element change monitoring technology based on remote sensing imagery, improving the accuracy of change polygons and reducing false alarms have become key research topics. Professor Li Yansheng and his team at the School of Remote Sensing and Information Engineering, Wuhan University, proposed an intelligent purification method for natural resource element change polygons based on a remote sensing spatiotemporal knowledge graph. This method effectively reduces false alarm rates while ensuring a high recall rate, thus significantly improving the efficiency of natural resource monitoring. The related research results were published in the Journal of Geo-Information Science.