Osaka Metropolitan University researchers conducted a preliminary study of the relevant challenges, divergence, and instabilities of an e-beam in an ionospheric atmosphere and identified them quantitatively through numerical simulations.

Integrated photothermal-photocatalytic approach offers safer, more efficient route to solar hydrogen production

Analysis reveals critical insights into catalyst design, reaction mechanisms, and economic challenges for aviation biofuel production

New system eliminates throttle valve losses, extending discharge duration and enhancing grid-scale renewable energy storage capabilities

Research demonstrates 5% revenue increase for energy stations while reducing emissions through smart demand response and biogas integration

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionising the modelling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.

A research paper by scientists at University of Electro-Communications explored the long-term clinical outcomes of multi-degree-of-freedom myoelectric prosthetic hands.

The research paper, published on Mar 18, 2025 in the journal Cyborg and Bionic Systems.

Chronic pain is frequently accompanied by anxiety and depression.