Facing a complex and ever-changing international environment, the maritime and shipping industry requires more efficient and precise data collection and analysis technologies to enhance management efficiency. A  research team at The Maritime Data and Sustainable Development Centre (PMDC) at The Hong Kong Polytechnic University (PolyU) has developed a series of innovative artificial intelligence (AI) and big data driven tools, including advanced technology for estimating the supply and demand for typhoon shelter berths in Hong Kong to improve vessel monitoring and emergency response, a shipping data analytics platform that uses the Automatic Identification System (AIS) to assess real-time port congestion index and other maritime statistical indicators, and a trajectory analysis technology to effectively detect illegal fishing activities. These innovations not only provide governments and industry stakeholders with cutting-edge management solutions but also drive the sector’s digital transformation and sustainable development.

A research article published by the Peking University presented a control framework for exoskeletons based on environmental perception, which effectively integrates environmental information and human kinematic data, improves the accuracy and lead time of transition detection, thereby enhancing smooth switching of control strategies across different terrains. Additionally, the adoption of a learning-free method eliminates the need for data collection and model training, demonstrating strong generalization capabilities across users.

A team from TU Wien, the University of Vienna and Tufts University (USA) was able to show that swimming movements are possible even without a central control unit. This not only explains the behaviour of microorganisms, it could also enable nanobots to move in a targeted manner, for example to transport drugs to the right place in the body.

Strategically arranging histidine residues inside a protein cage is a promising approach to create artificial enzymes, reports researchers from Institute of Science Tokyo. The engineered protein cage mimics natural enzymes using simple amino acids without any metal cofactors, overcoming a major limitation in artificial enzyme design. Molecular simulations confirmed that the confined environment within the protein cage enhances catalytic efficiency, offering a new route to develop sustainable biocatalysts.

Two Case Western Reserve University engineering faculty have been awarded U.S. National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) grants. Computer scientist An Wang and environmental engineer Bridget Hegarty were each awarded a five-year grant to support their research programs. Hegarty also received a $1 million grant from the Department of Housing and Urban Development.

This article examines the potential of Artificial Intelligence-driven Distributed Acoustic Sensing (AI+DAS) technology in engineering applications. Based on fiber optic monitoring, DAS enables real-time acoustic signal monitoring by detecting disturbances along the fiber, offering long measurement distances, high spatial resolution, and a large dynamic range. The article outlines the basic principles and demodulation methods of DAS using Φ-OTDR technology, highlighting AI's role in data processing and event recognition. By integrating AI algorithms, DAS systems enhance monitoring accuracy and reliability. Additionally, the article reviews AI+DAS applications across various fields, including engineering and geology, and discusses challenges such as model complexity and resource demands. Overall, it aims to foster interdisciplinary collaboration and support digital transformation in industrial scenarios.