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.

The Faculty of Education at The University of Hong Kong (HKU) and the London Ball Foundation co-hosted the Education Summit 2026, themed “Artificial Intelligence & Cross-Cultural Intelligence”, on April 19 at Loke Yew Hall, HKU. The Summit brought together distinguished speakers and more than 400 participants, including academics, policymakers, educators, and students, for in-depth discussions on the implications of artificial intelligence (AI) for education and the importance of cross-cultural understanding.

A new study published by researchers from Wuhan University of Technology,  Wuhan University and Hubei Optical Fundamental Research Center introduced a new approach to monitoring in real-time the degradation of perovskite solar cells. using embedded dual Fiber Bragg Grating (FBG) sensors. By dynamically tracking interfacial stress evolution and identifying critical degradation thresholds during UV light exposure, this approach has enabled strategic interventions that extend the device’s recoverable window by 140%. This study paves the way for preemptive maintenance protocols and a more stable future in the construction of long-lasting, high-efficiency perovskite photovoltaics.

Artificial intelligence (AI) is transforming biomolecular and material sciences, enabling rapid predictions and generative design of proteins, drugs, and materials. However, current AI models often fail to adhere to fundamental physical laws, scientific objectives, and safety principles, leading to impractical or unsafe outcomes. A research team led by Qiang Zhang at Zhejiang University and collaborators from Tongji University, Shanghai AI Laboratory, Duke University, the National University of Singapore, The Chinese University of Hong Kong, Mingdu Tech, and University College London, has proposed a comprehensive alignment framework. Their perspective argues that AI systems for biomolecular and materials design should be aligned not only with data distributions or benchmark performance, but also with natural laws, scientific goals, and responsible research principles. By analyzing examples across protein engineering, drug discovery, and materials science, the authors show that many AI-generated candidates fail not because models are useless, but because the objectives being optimized are often disconnected from the physical, functional, and regulatory realities of the laboratory and the real world. This approach aims to make AI a trustworthy and effective partner in scientific discovery.

With the rapid advancement of the information era, the demand for device integration and intelligent sensing has grown significantly. Traditional three-dimensional (3D) materials are constrained by lattice mismatch and interfacial defects, and their limited functionalities often require bulky auxiliary components. In contrast, the rich family of two-dimensional (2D) materials eliminates lattice-matching constraints and offers unique light-matter interactions, paving the way for compact and novel intelligent sensing technologies. However, large-area fabrication and precise layer alignment in all-2D systems remain major challenges that hinder device scalability. Given that the performance and manufacturing capabilities of 2D materials cannot replace traditional semiconductors (such as Si), they are more likely to be heterogeneously integrated with conventional 3D semiconductors. 2D/3D heterojunctions combine the distinctive optoelectronic properties of 2D materials with the mature electronic functionalities of 3D semiconductors. In this work, we present recent advances in 2D/3D heterojunction photodetectors, with a particular emphasis on the underlying physical mechanisms, including band structure design, interface optimization, external-field coupling, and novel topological configurations. Meanwhile, we also explore emerging opportunities for CMOS-compatible and intelligent sensing optoelectronic systems. Finally, the challenges and future research directions toward the integrated development of 2D/3D heterojunctions are discussed.

The 23rd Carbon Research International Forum, held online on April 24, 2026, brought together researchers and professionals to explore how engineered biochar can support carbon capture, resource recovery, and sustainable industrial development. The recorded session has now been uploaded to YouTube, making the forum available to a wider international audience interested in low-carbon technologies and circular economy solutions.