In short, we report an inverse phase enhancement (IPE) strategy to fabricate high-performance graphene paper (IPE-GP) exhibiting high strength (63.3 MPa) and high thermal conductivity (1325 W/m·K) at a minimal polymer loading of 5.9%. Using this conspicuous IPE-GP, we fabricated graphene composites that attained a record in-plane thermal conductivity of 802 W/m·K of polymer bulk composites.

Researchers have developed an artificial intelligence model that learns from real match data to evaluate and improve football attacking strategies against compact "low block" defenses. By modeling both on-ball decisions and off-ball movements, the model generates more coordinated and creative attacking solutions, offering practical decision support for coaches and analysts.

A recent study has constructed a disaster picture of the "April 27" tornado that struck Guangzhou, China in 2024 by integrating Unmanned Aerial Vehicle (UAV) imagery, detailed ground-based post-disaster surveys, and high-precision meteorological simulations. The research analyzed the tornado's trajectory, intensity, and its impact on building structures, providing a reference for tornado disaster assessment and the wind-resistant design of engineering structures in South China.

Artificial intelligence can dramatically speed up the painstaking work of tracking wildlife with remote cameras, cutting analysis time from months or even a year to just days while producing nearly the same scientific conclusions as humans. That’s according to a new study led by researchers at Washington State University and Google, published in the Journal of Applied Ecology. The team tested whether a fully automated AI system could replace humans in processing hundreds of thousands to millions of camera trap images collected in Washington, Montana’s Glacier National Park, and Guatemala’s Maya Biosphere Reserve.

High concentrations of free fatty acid (FFA) in ketotic dairy cows activate endoplasmic reticulum (ER) stress pathways, contributing to mammary epithelial cell apoptosis and reduced milk yield. The study sets the stage for in vivo trials to validate ER stress inhibitors like Tauroursodeoxycholate (TUDCA) as practical solutions for managing ketosis and enhancing dairy cows health.

The University of Hong Kong (HKU) has spearheaded an international research collaboration to develop a pioneering theoretical framework that deciphers the predictability of complex networks. A research team including Professor Qingpeng ZHANG’s group at the HKU Musketeers Foundation Institute of Data Science (HKU IDS) and the HKU Li Ka Shing Faculty of Medicine, together with researchers from Zhejiang University and Sapienza University of Rome, has developed a new theoretical framework to understand the predictability of complex networks.

A collaborative research team from Nanyang Technological University,  University of Queensland，and Beijing University of Technology have identified compelling signatures of the topological Hall effect in the layered van der Waals ferromagnet FePd₂Te₂ (FPT), revealing the emergence of nanoscale chiral spin textures stabilized during magnetic spin reorientation. They reported an angle‑selective Hall anomaly in the FPT, consistent with a topological Hall contribution from chiral or non‑coplanar spin textures stabilized during a spin‑reorientation process. By rotating the magnetic field between in‑plane and out‑of‑plane directions, the teams find two clear anomalous Hall plateaus and a pronounced hump‑like Hall signal that appears only within a narrow angular window near the in‑plane configuration and peaks around ~100 K. Importantly, this behavior persists across flakes with markedly different thicknesses (~100 nm and ~450 nm), placing strong constraints on extrinsic multi‑domain or multi‑component anomalous Hall scenarios. This work demonstrates that FPT represents a promising new platform for studying topological magnetism and developing next-generation spintronic devices based on low-dimensional magnetic materials

Assessing and undertaking building maintenance in densely populated cities like Hong Kong faces growing challenges as ageing buildings become more common. Manual inspections are often time-consuming and costly, leading to delays in identifying structural defects that can compromise public safety. To address these issues, a team in iLab, The University of Hong Kong (HKU), led by Professor Junjie Chen and Professor Wilson Lu from the Department of Real Estate and Construction, Faculty of Architecture, has developed eCheckGo, an innovative artificial intelligence (AI) system powered by a proprietary Large Defect Model (LdM) combined with traditional AI algorithms.