RSAND: A Fine-Grained Dataset and Benchmark for AtoN Detection in River–Sea Intermodal and Complex Estuarine Environments - Qi Chen; Mingyang Pan; Zongying Liu; Ruolan Zhang; Fei Yan; Xiaofeng Pan; Yang Zhang; Chao Li (Journal of Marine Science and Engineering, 2026)
Robust visual perception of Aids to Navigation (AtoN) is essential for Maritime Autonomous Surface Ships (MASS) operating in restricted navigational waters, where estuarine clutter, fog, glare, and dense traffic can severely degrade detection reliability. Existing maritime vision datasets largely emphasize open-sea targets or coarse AtoN categories, leaving a granularity gap for IALA-compliant fine-grained understanding in river–sea transition and port-approach channels. The River–Sea AtoN Navigation Dataset (RSAND) is introduced, a large-scale benchmark collected along the Yangtze River Deepwater Channel from inland corridors to open estuarine waters. RSAND contains 39,926 images with expert-verified bounding-box annotations and a hierarchical taxonomy that jointly captures AtoN location, shape, and functional semantics across 29 categories. To support both realistic long-tailed evaluation and standardized model comparison, two protocols are provided: RSAND-Full (29 categories) and RSAND-Balanced (10 critical categories). All quantitative benchmarking results in this paper are reported on RSAND-Balanced, while RSAND-Full is released for future large-scale long-tailed robustness studies. Benchmarking experiments on 14 state-of-the-art detectors demonstrate that YOLOv12x achieves superior performance with an mAP50-95 of 80.7%, significantly outperforming previous baselines. However, the analysis reveals persistent challenges in detecting small, distant targets and distinguishing visually similar functional markers. RSAND and the accompanying evaluation toolkit are released to facilitate reproducible research toward safer and smarter marine and coastal navigation.

Comprehensive single-cell profiling uncovers TsMHC-II expression predicting response to neoadjuvant immunotherapy in gastric cancer - Zhiyuan Xu; Zhichun Tang; Simeng Hu; Litao Yang; Jingli Xu; Pengfei Yu; Yian Du; Yanqiang Zhang; Jiahui Chen; Xiaoxiao Wu; Siwei Pan; ShengJie Zhang; et al. (Science Bulletin, 2026)

Nanozyme-mediated drug ion-exchange strategy ameliorates inflammatory bowel disease by scavenging ROS and reprogramming proinflammatory macrophages - Hongyan Lu; Ying Cheng; Xiangling Pu; Xingtao Zhao; Ruolan Li; Ruiqi Sun; Xiaoman Xie; Mingyue Wu; Shuo Zhang; Qiong Pan; Jin Chai (Chemical Engineering Journal, 2025)

MicroRNA-592 suppresses breast cancer progression by targeting CYP20A1 to modulate the CHOP pathway - Ruolan Pan; Shangquan Jiang; Jinxing Su; Caiyun Zhang; Xiaoxing Li; Kan He (Journal of Physiology and Biochemistry, 2025)

Adaptive Temporal Reinforcement Learning for Mapping Complex Maritime Environmental State Spaces in Autonomous Ship Navigation - Ruolan Zhang; Xinyu Qin; Mingyang Pan; Shaoxi Li; Helong Shen (Journal of Marine Science and Engineering, 2025)
The autonomous decision-making model for ship navigation requires extensive interaction and trial-and-error in real, complex environments to ensure optimal decision-making performance and efficiency across various scenarios. However, existing approaches still encounter significant challenges in addressing the temporal features of state space and tackling complex dynamic collision avoidance tasks, primarily due to factors such as environmental uncertainty, the high dimensionality of the state space, and limited decision robustness. This paper proposes an adaptive temporal decision-making model based on reinforcement learning, which utilizes Long Short-Term Memory (LSTM) networks to capture temporal features of the state space. The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. Additionally, a simulation training environment is constructed, incorporating multi-factor coupled physical properties and ship dynamics equations. The environment maps variables such as wind speed, current velocity, and wave height, along with dynamic ship parameters, while considering the International Regulations for Preventing Collisions at Sea (COLREGs) in training the autonomous navigation decision-making model. Experimental results demonstrate that, compared to other neural network-based reinforcement learning methods, the proposed model excels in environmental adaptability, collision avoidance success rate, navigation stability, and trajectory optimization. The model’s decision resilience and state-space mapping align with real-world navigation scenarios, significantly improving the autonomous decision-making capability of ships in dynamic sea conditions and providing critical support for the advancement of intelligent shipping.

Edge-Based Dynamic Spatiotemporal Data Fusion on Smart Buoys for Intelligent Surveillance of Inland Waterways - Ruolan Zhang; Chenhui Zhao; Yu Liang; Jingfeng Hu; Mingyang Pan (Journal of Marine Science and Engineering, 2025)
Increasing vessel traffic in narrow, winding inland waterways has heightened the risk of accidents, driving the need for improved surveillance and management. This study addresses the challenge of real-time processing and synchronization of voluminous video and AIS data for effective waterway management. We developed a surveillance method utilizing smart buoys equipped with sensors and edge computing devices, enabling dynamic spatiotemporal data fusion. The integration of AIS data with advanced computer vision techniques for target detection allows for real-time traffic analysis and provides detailed navigational dynamics of vessels. The method employs an enhanced Long Short-Term Memory network for precise trajectory prediction of AIS data and a single-stage target detection model for video data analysis. Experimental results demonstrate significant improvements in ship detection accuracy and tracking precision, with an average position prediction error of approximately 1.5 m, which outperforms existing methods. Additionally, a novel regional division and a Kalman filter-based method for AIS and video data fusion were proposed, effectively resolving the issues of data sparsity and coordinate transformation robustness under complex waterway conditions. This approach substantially advances the precision and efficiency of waterway monitoring systems, providing a robust theoretical and practical framework for the intelligent supervision of inland waterways.

Identification and Removal of Light Pollution in Maritime Night-Time Images Based on Spatial Frequency Blocks - Hui Sun; Jingyang Wang; Mingyang Pan; Zongying Liu; Shaoxi Li; Ruolan Zhang; Yang Wei (Journal of Marine Science and Engineering, 2025)
The safety of ships during nighttime navigation has always been a major concern. With the widespread application of technologies such as intelligent recognition, intelligent detection, and unmanned ship navigation at night, nighttime maritime light pollution has significantly affected the effectiveness of these intelligent technologies and navigation safety. Therefore, effectively eliminating nighttime maritime light pollution has become an urgent challenge that needs to be addressed. This paper presents a model based on spatial frequency blocks (SFBs) to solve the problem of light pollution in nighttime sea images. The model includes ResNet-50, an encoder, a decoder, and a discriminator. To enable the model to better remove the influence of light pollution, this study designs a method of first detecting the light pollution area and then removing it. It extracts image information from the space–frequency domain to help eliminate light pollution and retain more image information. The experimental results show that on the nighttime light pollution dataset, the Peak Signal-to-Noise Ratio (PSNR) of the model is improved to 24.91 compared to the current state-of-the-art image restoration model, while the Frechet inception distance (FID) is reduced to 64.85. At the same time, in the real night environment, the model can better remove light pollution to recover the original nighttime information. It has excellent performance and provides a certain reference value for advancing the safety of nighttime maritime navigation.

Semi-dynamic spatial–temporal graph neural network for traffic state prediction in waterways - Le Li; Mingyang Pan; Zongying Liu; Hui Sun; Ruolan Zhang (Ocean Engineering, 2024)