U-AsymSeg: Asymmetric dual-stream RGB-Polarization network for underwater semantic segmentation - Yiming Gao; Qi Ling; Ruxin Han; Hongming Chen; Yafei Wang; Guoliang Yuan; Xianping Fu (Ocean Engineering, 2026)

Transfer learning-based blade structural health monitoring for floating offshore wind turbines using model-scale tests and full-scale simulations - Jingyan Fu; Huaxin Pan; Xuanhao Zhang; Yonghui Zhong; Xianping Du (Ocean Engineering, 2026)

Expression and Functional Characterisation of EhSWP 26 in Ecytonucleospora hepatopenaei : Insights From RNA Interference and Subcellular Localisation Analyses - Huiwen Jiang; Xiaohui Cao; Yi Qiao; Tingyue Liu; Jie Cheng; Ge Jiang; Xianping Fan; Haibo Wang; Kuipeng Fu; Hui Shen (Journal of Fish Diseases, 2026)
Ecytonucleospora hepatopenaei (EHP) is an obligate intracellular microsporidian that causes severe growth retardation in Penaeus vannamei (P. vannamei ), yet its pathogenic mechanisms remain incompletely clear. As the spore wall is essential for resisting environmental stress and mediating host invasion, characterising its protein components is critical for elucidating EHP biology. In this study, we identified and cloned a novel spore wall protein, EhSWP26. Sequence analysis revealed that EhSWP26 contains a 687 bp open reading frame encoding a 228‐amino‐acid polypeptide, lacking a signal peptide and transmembrane domain but harbouring a conserved MICSWaP (Microsporidia Spore Wall and Associated Proteins) domain characteristic of the microsporidian MICSWaPs family. Immunoelectron microscopy and indirect immunofluorescence assays demonstrated that EhSWP26 localises to the mature spore wall, where it is distributed in both the exospore and endospore layers. RNA interference further confirmed that silencing EhSWP26 significantly reduced its transcript level and led to a marked decrease in EHP spore load in shrimp hepatopancreas. Collectively, these findings indicate that EhSWP26 is a key structural and functional component associated with spore wall formation and host infection, likely playing an essential role in the EHP life cycle. This study enhances our understanding of EHP spore wall composition and function, provides a foundation for investigating its proliferation and infection mechanisms, and highlights EhSWP26 as a promising target for diagnostic development and therapeutic intervention in EHP disease of P. vannamei .

Underwater seafloor reconstruction via extended Gaussian-opacity ray integration with Multi-Dimensional Transform Filtering - Xuanhe Chu; Han Chen; Junwen Tian; Ximan Zhao; Minyi Xu; Xianping Fu; Yongjun Gong; Siyuan Liu (Ocean Engineering, 2026)

Multi-View 3D Reconstruction of Ship Hull via Multi-Scale Weighted Neural Radiation Field - Han Chen; Xuanhe Chu; Ming Li; Yancheng Liu; Jingchun Zhou; Xianping Fu; Siyuan Liu; Fei Yu (Journal of Marine Science and Engineering, 2026)
The 3D reconstruction of vessel hulls is crucial for enhancing safety, efficiency, and knowledge in the maritime industry. Neural Radiance Fields (NeRFs) are an alternative to 3D reconstruction and rendering from multi-view images; particularly, tensor-based methods have proven effective in improving efficiency. However, existing tensor-based methods typically suffer from a lack of spatial coherence, resulting in gaps in the reconstruction of fine-grained geometric structures. This paper proposes a spatial multi-scale weighted NeRF (MDW-NeRF) for accurate and efficient surface reconstruction of vessel hulls. The proposed method develops a novel multi-scale feature decomposition mechanism that models 3D space by leveraging multi-resolution features, facilitating the integration of high-resolution details with low-resolution regional information. We designed separate color and density weighting, using a coarse-to-fine strategy, for density and a weighted matrix for color to decouple feature vectors from appearance attributes. To boost the efficiency of 3D reconstruction and rendering, we implement a hybrid sampling point strategy for volume rendering, selecting sample points based on volumetric density. Extensive experiments on the SVH dataset confirm MDW-NeRF’s superiority: quantitatively, it outperforms TensoRF by 1.5 dB in PSNR and 6.1% in CD, and shrinks the model size by 9%, with comparable training times; qualitatively, it resolves tensor-based methods’ inherent spatial incoherence and fine-grained gaps, enabling accurate restoration of hull cavities and realistic surface texture rendering. These results validate our method’s effectiveness in achieving excellent rendering quality, high reconstruction accuracy, and timeliness.

Dynamic Mutual Adversarial Learning for Semi-Supervised Semantic Segmentation of Underwater Images with Limited and Noisy Annotations - Han Chen; Ming Li; Yancheng Liu; Jingchun Zhou; Xianping Fu; Siyuan Liu; Fei Richard Yu (Journal of Marine Science and Engineering, 2025)
Swift and accurate semantic segmentation of underwater images is key for precise object recognition in complex underwater environments. Nonetheless, the inherent complexity of these environments and the limited availability of labeled data pose significant challenges to underwater image segmentation. Traditional deep learning methods struggle to cope with limited and noisy annotations. In this paper, we delineate the formulation of a novel semi-supervised paradigm with dynamic mutual adversarial training for the semantic segmentation of underwater images. This paradigm identifies the sources of inaccuracies in pseudo-labeling by analyzing different confidence maps, generated by models with unique prior knowledge. A dynamic reweighing loss function is then employed to orchestrate the mutual instruction of two divergent models. Furthermore, the delineation of confidence map is facilitated via adversarial networks, which involves simultaneous adversarial refinement of the discrimination network and the segmentation model, using the predictions with high-confidence maps as pseudo-labels. Experimental results on public underwater datasets verify that the proposed method can effectively improve semantic segmentation performance under the condition of a small amount of labeled data.

EMR-YOLO: A Multi-Scale Benthic Organism Detection Algorithm for Degraded Underwater Visual Features and Computationally Constrained Environments - Dehua Zou; Songhao Zhao; Jingchun Zhou; Guangqiang Liu; Zhiying Jiang; Minyi Xu; Xianping Fu; Siyuan Liu (Journal of Marine Science and Engineering, 2025)
Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a deep learning based multi-scale BOD method. To handle the diverse sizes and morphologies of benthic organisms, we propose an Efficient Detection Sparse Head (EDSHead), which combines a unified attention mechanism and dynamic sparse operators to enhance spatial modeling. For robust feature extraction under resource limitations, we design a lightweight Multi-Branch Fusion Downsampling (MBFDown) module that utilizes cross-stage feature fusion and multi-branch architecture to capture rich gradient information. Additionally, a Regional Two-Level Routing Attention (RTRA) mechanism is developed to mitigate background noise and sharpen focus on target regions. The experimental results demonstrate that EMR-YOLO achieves improvements of 2.33%, 1.50%, and 4.12% in AP, AP50, and AP75, respectively, outperforming state-of-the-art methods while maintaining efficiency.

Optimization of LED Array Spatial Coverage Characteristics in Underwater Wireless Optical Communication - Anliang Liu; Yingming Yuan; Hongxi Yin; Haobo Zhao; Xianping Fu (Journal of Marine Science and Engineering, 2023)
To achieve uniform spatial coverage characteristics in optical signals in an underwater wireless optical communication (UWOC) system, and therefore reduce the requirement of the alignment between the receiver and the transmitter, we propose an optimized scheme of optical signal coverage based on a light-emitting diode (LED) array in this paper. For high-efficiency coverage of the optical signals, the pitch angle of the LED light source is first optimized on the basis of the light beam geometry. Then, the layout of the LED array and the horizontal deflection angle of the light source are jointly optimized by an improved particle swarm optimization (PSO) algorithm. Taking a 16-LED array as an example, the performances of the spatial coverage characteristics with three different LED array layouts are analyzed in detail under four typical seawater environments. The results show that the LED array with the PSO-optimized layout can achieve better uniformity in the power distribution for the received optical signals, and enhance the robustness of the UWOC system in complex seawater environments.

High-yield production of canine parvovirus virus-like particles in a baculovirus expression system - Hongli Jin; Xiaohong Xia; Bing Liu; Yu Fu; Xianping Chen; Huihui Wang; Zhenqiang Xia (Archives of Virology, 2016)

Antigen-subtracted 2-DE/MS strategy, a novel proteomic analysis platform - Peng Zhao; Lijuan Zhang; Weijian Zhong; Xianping Ying; He Huang; Biyun Yao; Zhun Yuan; Juanling Fu; Yuedan Wang; Zongcan Zhou (Archives of Toxicology, 2011)