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- 1Heavy water isotopes improve weather forecast accuracy●Heavy water isotopes make weather predictions more accurate
Researchers report that tracking isotopes of heavy water can make weather predictions more accurate. By measuring variations in hydrogen and oxygen isotopes in atmospheric water, scientists can better trace moisture sources and movement, refining the models used in forecasting. The finding, reported by Physics World, suggests isotope data could become a valuable addition to meteorological observation and prediction systems.
- 2Physics-informed machine learning improves wearable sweat biosensor calibration●Physics-informed machine learning for robust calibration and physiological validation of wearable electrochemical sweat biosensors for metabolite monitoring
Researchers publishing in Nature describe a physics-informed machine learning approach for calibrating wearable electrochemical sweat biosensors used to monitor metabolites. The method combines physical models of sensor behaviour with data-driven learning to achieve robust calibration and physiological validation, aiming to make wearable biochemical monitoring more reliable in real-world conditions.
- 3Black Forest Labs releases Flux 3 Action robotics model●Flux 3 Action: A 7B open-weight world action model for robots
Black Forest Labs has released Flux 3 Action, a 7-billion-parameter open-weight world action model aimed at robotics, available via Hugging Face. The model is designed to let robots perceive and act in physical environments. Early discussion among developers is focused on the unusual choice of an image-model company entering robotics and on how a relatively compact 7B model performs on real robot tasks.