Researchers have developed an ultrathin yet ultrastrong hydrogel membrane that enables robust and conformal integration between electronic devices and biological tissues. The ~10 μm-thick material combines high strength, exceptional toughness, and tissue-like softness through a biomimetic microfibrillar network design. It supports multimodal sensing and stimulation while maintaining stable contact with complex 3D tissues, offering new opportunities for wearable and implantable bioelectronics.

With the development of quantum chips, quantum communication is becoming essential for quantum computing and quantum networks. Flying Qubits, quantum information carried by photons, play a vital role in transferring data between nodes. Prof. Guofeng ZHANG, Professor of Department of Applied Mathematics of The Hong Kong Polytechnic University is dedicated to developing precise control methods for flying qubits, aiming to significantly improve the reliability and fidelity of quantum information transfer in future technologies.

After nearly 10 years, three flight experiments and a flying rocket lab, scientists map out the chaotic forces of ultra-fast flight, redefining the future of aerospace travel.

When robots perform complex tasks, the pose accuracy of the end-effector is critical. However, errors from individual joints tend to accumulate along the kinematic chain, making it challenging to guarantee high pose precision at the end-effector. To address this issue, this study proposes a virtual-constraints-based end-effector pose compensator (VEPC). The method treats the actual angles of specific joints as known inputs and automatically adjusts the remaining joint angles in real time, effectively eliminating the pose errors of the end-effector caused by the joints. Experimental results demonstrate that the method can reduce the maximum end-effector position error by over 75%. Moreover, the method requires no additional sensors, offering low cost and high compatibility.

A recent study published in Automation in Construction by researchers from Incheon National University exploits a novel approach to improving excavator tracking performance under real-world conditions. By integrating deep learning-based instance segmentation with an automated, reliability-based multi-camera strategy, this study addresses one of the most persistent challenges in construction monitoring—frequent occlusions caused by dynamic site activities. In addition, the researchers propose a frame-level reliability estimation process that automatically identifies unreliable tracking results.

Climate change has been driving the rise of extreme weather conditions in recent years, placing immense strain on urban infrastructure such as sewer systems. Compromised sewer systems can lead to leakage, overflow and even flash flooding, threatening public health and safety. To address these vulnerabilities, a research team at The Hong Kong Polytechnic University (PolyU) has developed a multi-tiered model integrating artificial intelligence (AI) and the Internet of Things (IoT), facilitating more cost-effective and intelligent sewer system management, ranging from predicting exfiltration severity and pinpointing leakage-prone zones to monitoring and forecasting overflow occurrences in high-risk areas. 

By introducing fairness from the beginning with ‘fuzzy’ systems that understand ambiguity and shades of correctness, the evolved AIs balanced fairness and accuracy even when tasked with coming up with solutions for complicated financial and ethical issues.