A new study shows that coral reefs don’t just provide a home for ocean life, they also help set the daily “schedule” for tiny microbes living in the water nearby. Over the course of a single day, the quantity and types of microbes present can shift dramatically. To see this in detail, researchers took frequent water samples and used a mix of genetic and ecological methods and tools, as well as advanced imaging techniques, to track what was happening hour by hour. They found that reefs can shape microbial communities through natural interactions like grazing and predation, as well as changes in the reef’s close microbial partners. These daily ups and downs offer a fresh window into how reefs work and influence the surrounding environment— and could even point to new ways to keep an eye on reef health.

Digital twin (DT) technology is emerging as a core solution for future marine development and intelligent ocean management. The review systematically reviews digital twin applications in the marine field, clarifies its concept, proposes a five-layer framework, and summarizes key technologies, including sensing, data management, modeling, simulation, and monitoring. It highlights DT’s ability to synchronize physical marine systems with virtual models in real time, enabling simulation, prediction, optimization, and decision-making. The authors further outline challenges and development prospects, showing how DT can support deep-sea resource exploitation, offshore wind energy, marine engineering, vessel autonomy, environmental monitoring, and system reliability assessment.

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to underwater cables—and is critical for safe renewable energy development. Traditional numerical simulations and experiments require enormous computational resources, yet often fail to capture multiscale turbulence and long-term system behavior. This review highlights how machine learning (ML) is emerging as a powerful solution for analyzing, predicting, and even controlling FSI systems. Key progress spans feature detection, reduced-order modeling, physics-informed neural networks, and reinforcement-based flow control. By leveraging data-driven models to extract hidden patterns and reconstruct flow fields, ML shows promise in improving efficiency, predictive accuracy, and automated control across ocean engineering applications, positioning itself as a transformative tool for next-generation design.

Underwater wireless power transfer is emerging as a key technology for enabling long-duration, maintenance-free operation of autonomous underwater vehicles (AUVs). This review provides the most comprehensive overview to date of magnetic-coupling-based underwater wireless charging, addressing challenges such as eddy current losses in seawater, misalignment caused by ocean dynamics, and the growing need for simultaneous transfer of power and data. By comparing transmitter–receiver coil structures, compensation networks, and control strategies, the research identifies design pathways that significantly enhance efficiency, stability, and tolerance to dynamic marine conditions. The work also highlights emerging simultaneous wireless power and data transfer (SWPDT) methods that could reshape the future of marine sensing and robotic operations.

Machine learning (ML) is rapidly emerging as a powerful tool to improve the safety, reliability, and long-term performance of marine structures exposed to harsh ocean environments. This study presents a comprehensive review of ML and deep learning algorithms applied to marine engineering, highlighting how they enhance structural design, construction efficiency, and real-time maintenance. The work introduces a novel modeling framework that integrates mechanical principles with data-driven algorithms, improving interpretability and prediction accuracy. It also outlines key challenges such as data scarcity, environmental uncertainty, and model transparency, offering guidance for future research. The review provides valuable insights for structural engineers seeking to adopt ML technologies for next-generation ocean infrastructure.

Blue energy—renewable power derived from the kinetic and potential forces of water—has become a key pathway for building sustainable and low-carbon energy systems. This review synthesizes global progress in hydropower, ocean energy, and hybrid offshore technologies, highlighting rapidly advancing wave-energy devices and triboelectric nanogenerators (TENGs) for blue energy. While hydropower is technologically mature, ocean-based energy systems are expanding due to vast resource potential and accelerating technological innovation. The review identifies promising designs for efficient wave-energy harvesting alongside remaining engineering challenges related to durability, cost, and large-scale deployment. Together, these insights provide a roadmap for advancing water-derived renewable energy to support global decarbonization.

A research team led by Prof. QIAN Peiyuan, Chair Professor of the Department of Ocean Science at The Hong Kong University of Science and Technology (HKUST), in collaboration with the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), and the Yellow Sea Fisheries Research Institute of the Chinese Academy of Fishery Sciences (CAFS), has achieved a significant breakthrough in understanding the adaptive strategies of the deep-sea black coral Bathypathes pseudoalternata (B. pseudoalternata) and its symbiotic microbiome. The study has been published in the top international journal Cell Host & Microbe.

A research team led by scientists at Kumamoto University has discovered a new genus of microscopic crustaceans from northern Japan, offering rare insights into how ocean currents in the North Pacific shifted during a key period of Earth’s climate history.