The electrochemical oxidation of glycerol (GOR) is gaining traction as a sustainable method to convert biodiesel byproducts into valuable chemicals and fuels, aligning with global demands for renewable energy and green production. Recent advances in catalyst design, reaction mechanisms, and system integration are driving progress, though challenges in selectivity, stability, and scalability remain pivotal for industrial adoption. Researchers are tuning both noble and non-noble metal catalysts—through methods such as facet engineering and single-atom doping—to selectively steer reactions toward high-value multi-carbon products. Furthermore, coupling GOR with cathodic processes like hydrogen evolution or CO₂ reduction offers a path to lower energy use and co-produce clean fuels. Key hurdles, including mass transfer limits and feedstock compatibility, still need addressing. Proposed solutions range from advanced electrode assemblies to integrated techno-economic assessments. Moving forward, a system-level approach that balances technical performance with economic viability will be essential to accelerate GOR technology toward real-world application.

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.

Multimodal large language models have shown powerful abilities to understand and reason across text and images, but their massive size and computational cost limit real-world deployment. This research systematically examines how multimodal models can be made more efficient without severely sacrificing performance. By analyzing lightweight architectures, visual token compression strategies, efficient training methods, and compact language backbones, the study maps out the technical pathways that reduce memory demand and inference cost. The work highlights how efficiency-focused design enables multimodal intelligence to move beyond cloud-based systems toward broader, more accessible applications, including mobile devices and edge computing environments.

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.

Researchers from Southeast University have developed a compact, dual-band, dual-polarized phased array for B5G/6G millimeter-wave communication. The array integrates four independent beamforming systems on a single printed circuit board, supporting concurrent operations at 28 GHz and 38 GHz. It features scalable architecture, broad bandwidth, and high spectral efficiency, making it ideal for future high-speed wireless applications.