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.

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.

A research team led by Dr. Jae-Woo Choi from the Water Resources Recycling Research Group and Dr. Jin Young Kim from the Center for Hydrogen and Fuel Cells at the Korea Institute of Science and Technology (KIST, President Sangrok Oh) has developed an eco-friendly palladium recovery technology based on titanium-based maxene material ('TiOx/Ti3C2Tx') nanosheets. Existing overseas technologies operated only in strongly acidic environments, limiting their applicability to weakly acidic wastewater commonly found in industrial settings.

Healthy aging induces parallel changes in brain functional activity and structural morphology, yet the interplay between these changes remains unclear. Prof. Yuhui Du’s team at the College of Computer and Information Technology, Shanxi University, in collaboration with Prof. Vince D. Calhoun (Georgia State University), analyzed multimodal neuroimaging data from 27,793 healthy subjects (aged 49-76 years) in the UK Biobank. They proposed a unified framework for single-modal and multimodal brain-age prediction and joint functional-structural aging analysis, systematically characterizing diverse synergistic vs. contradictory aging patterns between functional network connectivity (FNC) and gray matter volume (GMV). Importantly, these joint patterns were further linked to specific cognitive decline. The study, titled “Joint aging patterns in brain function and structure revealed using 27,793 samples” was published in Research (2025, 8:0887; DOI: 10.34133/research.0887).

Abstract

Purpose – The impact of digital transformation on banks’ systemic risk merits thorough investigation.

Design/methodology/approach – This study examines the influence of digital transformation on banks’ systemic risk based on the fixed effect model with quarterly unbalanced panel data on 36 listed commercial banks in China from 2011 to 2020.

Findings – Results show that digital transformation has a negative impact on banks’ systemic risk by reducing both bank-specific tail risk and systemic linkage to extreme market shocks. Heterogeneity analysis suggests that digital transformation can significantly reduce systemic risk in national commercial banks relative to regional commercial banks, mediated through lowered management costs. Finally, this study finds an asymmetric relationship between digital transformation and banks’ systemic risk. Particularly, a desirable level of digital transformation can reduce systemic risk, while excessive digital transformation may exacerbate it.

Originality/value – These findings provide valuable guidance for promoting digital transformation for banks and mitigating systemic risk from digitalization.