Researchers from The Hong Kong University of Science and Technology and the Southern University of Science and Technology have developed a novel deep learning neural network, Electrode Net. By introducing signed distance fields and three-dimensional convolutional neural networks, this method can significantly accelerate electrode design while maintaining high accuracy. It is widely applicable to fuel cells, water electrolyzers, flow batteries, etc.

Delta.g has raised £4.6M to commercialise the world’s first field-tested quantum sensor enabling real-time spatial intelligence.  The funding will enable Delta.g to deliver field systems through pilot deployments across key industry and government partners, including the UK Department for Transport.

Researchers at Osaka Metropolitan University have developed SORA-DET, a lightweight and high-performance deep learning model for UAV-based remote sensing. By introducing innovative feature extraction and fusion techniques, SORA-DET achieves state-of-the-art detection accuracy while being up to 88% smaller and faster than conventional models. Its compact design enables real-time deployment directly on UAVs, making it highly effective for applications such as disaster response, search-and-rescue, and environmental monitoring.