Discover how space–ground fluid AI is set to revolutionize 6G networks by integrating edge AI with satellite systems, enabling global AI services and overcoming challenges of satellite mobility and communication rates. Explore the innovative techniques proposed in a new study published in Engineering that could shape the future of intelligent connectivity.

Plant polyphenol pterostilbene packed in pH-sensitive microbeads reaches the inflamed colon, calms immune attack, heals leaky lining and rebalances gut bugs—offering a promising, low-cost idea for ulcerative colitis care.

Utilizing waste cotton fabric as a dual-functional flexible substrate and carbon source, this work enabled vapor-liquid-solid growth of high-entropy carbide nanowires. The resulting material achieved exceptional EMI shielding through synergistic electrical conduction loss, dipolar polarization loss, and interfacial polarization loss.

Multiple myeloma is an incurable plasma cell malignancy despite major therapeutic advances. A recent review by Chinese researchers highlights how immunotherapy is reshaping its treatment landscape. The article summarizes immune mechanisms driving disease progression and explores emerging targets, such as BCMA, GPRC5D, and FcRL5. It also presents promising approaches—antibody–drug conjugates, CAR-T cells, and bispecific antibodies—offering new opportunities for overcoming drug resistance and achieving durable, personalized cancer immunotherapy.

Researchers at Tsinghua University developed PriorFusion, a unified framework that integrates semantic, geometric, and generative shape priors to significantly improve the accuracy and stability of road element perception in autonomous driving systems. The research addresses a long-standing challenge: existing end-to-end perception models often generate irregular shapes, fragmented boundaries, and incomplete road elements in complex urban scenarios.

Ride-pooling is widely recognized as a sustainable way to ease congestion, reduce costs and cut emissions, yet adoption remains limited.  When operators act independently, efficiency is low because requests cannot be matched across platforms.  Aggregation platforms seek to improve this by forcing all operators into a permanent coalition, but differences in size, cost and market position make such arrangements unstable.  To address this, researchers from Beihang University and Delft University of Technology developed a multi-level coalition formation game framework that enables coalitions to form dynamically in response to trip requests, allowing flexible cooperation without requiring all operators to remain in a single group at all times.

To answer this question: How to make AI truly scalable and reliable for real-time traffic assignment? A research team from KTH Royal Institute of Technology, Monash University, Technical University of Munich, Southeast University, and the University of Electro-Communications has developed a new framework—MARL-OD-DA—that offers a promising answer. The approach redesigns learning agents at the origin–destination (OD) level and utilizes Dirichlet-based continuous actions to achieve stable and high-quality solutions under dynamic travel demand.

To address the trade-off between accuracy and cross-city generalization in traffic flow estimation, a research team from The Hong Kong Polytechnic University and New York University proposes a novel framework based on global open multi-source (GOMS) data, including urban structures and population density. By developing an advanced graph neural network model that effectively fuses these static urban features with dynamic traffic data, the study achieves stable and accurate network-wide traffic estimation, as validated across 15 diverse cities in Europe and North America.

Researchers at National University of Singapore used multiple interpretable machine learning methods to predict traffic congestion in in Alameda County in the San Francisco Bay Area, USA, during the pre-lockdown, lockdown, and post-lockdown periods.