In a paper published in National Science Review, a team of Chinese scientists developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of continuous glucose monitoring (CGM) data to represent individual’s intrinsic metabolic state and enable clinical applications. It can accurately characterize individual dynamic glycemic behaviors such as maintenance of fasting blood glucose homeostasis and adaptation to postprandial hyperglycemia., It can assist in the diagnosis, disease duration assessment, and complication prediction of type 2 diabetes, subtype classification of non-diabetic populations, predict postprandial glucose responses accurately and provide personalized dietary recommendations for diabetes patients, thereby enabling lifestyle intervention recommendations.

Recently, a research team led by Professor Zhi-Guo Zhang from Beijing University of Chemical Technology, in collaboration with Professor Ye Long from Tianjin University has published a breakthrough work in the field of flexible polymer solar cells on National Science Review. Their research has revealed the inherent trade-off of efficiency, stability and stretchability via acceptors materials structural regulation, providing critical insights for the bright future of flexible organic photovoltaics.

Low-frequency (LF) wireless communication is widely used in challenging environments like underwater, underground, and ionospheric waveguides due to its strong penetration and anti-interference capabilities. However, the demand for miniaturized, high-efficiency, and sensitive antennas in portable platforms presents a significant challenge, as traditional LF antennas are limited by size and performance constraints. Recent advancements have seen optical levitation technology emerge as a promising solution. By harnessing optically levitated nanoparticle resonators, our research has demonstrated a groundbreaking approach to LF communication. These nanoparticle antennas break the conventional size-sensitivity tradeoff, offering ultra-miniaturization and enhanced sensitivity, which is crucial for communication systems. Unlike traditional antennas, the performance of these levitated resonators is independent of their size and their resonant frequencies can be further tuned by adjusting the optical trap. This breakthrough opens new possibilities for applications in IoT, miniaturized communication in extreme environments.

A newly operational model, known as the Artificial Intelligence Forecasting System (AIFS), has been launched by the European Centre for Medium-Range Weather Forecasts (ECMWF), an intergovernmental centre and leader in numerical weather prediction. For many measures including tropical cyclone tracks, the AIFS outperforms state-of-the-art physics-based models, with gains of up to 20%. This high accuracy model complements the portfolio of ECMWF's physics-based models, advancing numerical weather prediction, and leverages the opportunities made available by machine learning (ML) and artificial intelligence (AI), such as increased speed and a reduction of approximately 1,000 times in energy use for making a forecast.

University of Texas at Arlington physicist Ben Jones has received an international honor for his contributions to developing advanced instruments used in particle physics research. Dr. Jones, an associate professor of physics, was awarded the 2025 International Committee for Future Accelerators (ICFA) Early Career Researcher Instrumentation Award. Presented by the ICFA Instrumentation Innovation and Development Panel, the award recognizes significant advancements in the innovation and development of new instrumentation for future accelerator experiments.

Ethylene oxide is a “platform chemical” with a $40 billion annual worldwide market used in the production of plastics, textiles and many other common products. Tufts University chemists discovered an inexpensive way to reduce CO₂ emissions and decrease the need for chlorine to produce the chemical.