Journal of Marine Science and Engineering

19 pages, 1660 KB

Open AccessArticle

Towards Scalable Ecological Monitoring: Assessing AI-Based Annotation of Benthic Images

by Maria Zotou, Maria Sini, Vasilis Trygonis, Nicola Greggio, Antonios D. Mazaris and Stelios Katsanevakis

J. Mar. Sci. Eng. 2025, 13(9), 1721; https://doi.org/10.3390/jmse13091721 - 5 Sep 2025

Abstract

Mediterranean rocky reef habitats are ecologically valuable yet increasingly degraded due to cumulative human pressures, necessitating efficient, large-scale ecological status assessments to inform management. Macroalgal communities are widely used as indicators of rocky reef conditions and are typically assessed via photoquadrat sampling. However, [...] Read more.

Mediterranean rocky reef habitats are ecologically valuable yet increasingly degraded due to cumulative human pressures, necessitating efficient, large-scale ecological status assessments to inform management. Macroalgal communities are widely used as indicators of rocky reef conditions and are typically assessed via photoquadrat sampling. However, the manual annotation of benthic images remains time-consuming and costly. This study evaluates the performance of CoralNet (version 1.0), an AI-assisted image annotation platform, using a pre-annotated dataset of 2537 photoquadrat images from 89 rocky reef sites in the Aegean Sea, Greece, classified into 23 taxonomic and morphofunctional groups. Half of the dataset was used to iteratively train CoralNet classifiers, while the remainder was used to compute the reef-EBQI index and compare ecological status estimates with those derived from manual annotations. The classifier accuracy improved with training volume, reaching 67% using the entire dataset. Reef-EBQI scores derived from CoralNet showed 87% agreement with the manual classifications. Despite challenges and limitations, AI-assisted annotation proved effective in regional-scale ecological assessments based on broad taxonomic and morphofunctional categories. Automated tools like CoralNet can reduce post-processing bottlenecks and enable scalable, cost-effective monitoring, especially when integrated with standardized protocols and citizen science initiatives. Full article

(This article belongs to the Section Marine Ecology)

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28 pages, 5003 KB

Open AccessArticle

An Efficient Laser Point Cloud Registration Method for Autonomous Surface Vehicle

by Dongdong Guo, Qianfeng Jing, Yong Yin and Haitong Xu

J. Mar. Sci. Eng. 2025, 13(9), 1720; https://doi.org/10.3390/jmse13091720 - 5 Sep 2025

Abstract

In the field of Autonomous Surface Vehicle (ASV), research on advanced perception technologies is crucial for enhancing their intelligence and autonomy. In particular, laser point cloud registration technology serves as a foundation for improving the navigation accuracy and environmental awareness of ASV in [...] Read more.

In the field of Autonomous Surface Vehicle (ASV), research on advanced perception technologies is crucial for enhancing their intelligence and autonomy. In particular, laser point cloud registration technology serves as a foundation for improving the navigation accuracy and environmental awareness of ASV in complex environments. To address the issues of low computational efficiency, insufficient robustness, and incompatibility with low-power devices in laser point cloud registration technology for ASV, a novel point cloud matching method has been proposed. The proposed method includes laser point cloud data processing, feature extraction based on an improved Fast Point Feature Histogram (FPFH), followed by a two-step registration process using SAC-IA (Sample Consensus Initial Alignment) and Small_GICP (Small Generalized Iterative Closest Point). Registration experiments conducted on the KITTI benchmark dataset and the Pohang Canal dataset demonstrate that the relative translation error (RTE) of the proposed method is 16.41 cm, which is comparable to the performance of current state-of-the-art point cloud registration algorithms. Furthermore, deployment experiments on multiple low-power computing devices showcase the performance of the proposed method under low computational capabilities, providing reference metrics for engineering applications in the field of autonomous navigation and perception research for ASV. Full article

(This article belongs to the Special Issue Advanced Research in Guidance, Navigation, and Control for Autonomous Surface Vehicle)

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23 pages, 717 KB

Open AccessReview

AI-Based Optimization Techniques for Hydrodynamic and Structural Design in Ships: A Review

by Nay Min Htein, Panagiotis Louvros, Evangelos Stefanou, Myo Aung, Nabile Hifi and Evangelos Boulougouris

J. Mar. Sci. Eng. 2025, 13(9), 1719; https://doi.org/10.3390/jmse13091719 - 5 Sep 2025

Abstract

Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined [...] Read more.

Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined include deep neural networks (DNNs), support vector machines (SVMs), tree-based ensemble models, genetic algorithms (GAs), and surrogate modeling approaches. Comparative analyses from the literature indicate that ensemble tree methods, such as XGBoost, have achieved predictive accuracies up to

R^{2}

= 0.995 in speed–power modeling, marginally surpassing DNN performance, while GA-based structural optimization studies have reported weight reductions exceeding 10%. The findings confirm that no single method is universally superior; rather, effectiveness depends on the problem definition, data quality, and computational resources available. Hybrid strategies that combine physics-based modeling with data-driven learning have demonstrated improved generalization, reduced data requirements, and enhanced interpretability. Practical challenges remain, including limited access to open high-fidelity datasets, the computational demands of complex models, and balancing predictive accuracy with explainability. The review concludes that AI should be employed as a complementary toolkit to augment human expertise, with method selection guided by design objectives, constraints, and integration within the broader ship design process. Full article

(This article belongs to the Section Ocean Engineering)

20 pages, 46995 KB

Open AccessArticle

Upper Ocean Response to Typhoon Khanun in the South China Sea from Multiple-Satellite Observations and Numerical Simulations

by Fengcheng Guo, Xia Chai, Yongze Li and Dongyang Fu

J. Mar. Sci. Eng. 2025, 13(9), 1718; https://doi.org/10.3390/jmse13091718 - 5 Sep 2025

Abstract

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with [...] Read more.

This study examines the upper-ocean response to Typhoon Khanun, which traversed the northern South China Sea in October 2017, by integrating multi-satellite observations with numerical simulations from the Regional Ocean Modeling System (ROMS). For the ROMS simulations, an Arakawa C-grid was adopted with a 4-km horizontal resolution and 40 vertical terrain-following

σ

-layers, covering the domain of 105° E to 119° E and 15° N to 23° N. Typhoons significantly influence ocean dynamics, altering sea surface temperature (SST), sea surface salinity (SSS), and ocean currents, thereby modulating air–sea exchange processes and marine ecosystem dynamics. High-resolution satellite datasets, including GHRSSST for SST, SMAP for SSS, GPM IMERG for precipitation, and GLORYS12 for sea surface height, were combined with ROMS simulations configured at a 4-km horizontal resolution with 40 vertical layers to analyze ocean changes from 11 to 18 October 2017. The results show that Typhoon Khanun induced substantial SST cooling, with ROMS simulations indicating a maximum decrease of 1.94 °C and satellite data confirming up to 1.5 °C, primarily on the right side of the storm track due to wind-driven upwelling and vertical mixing. SSS exhibited a complex response: nearshore regions, such as the Beibu Gulf, experienced freshening of up to 0.1 psu driven by intense rainfall, while the right side of the storm track showed a salinity increase of 0.6 psu due to upwelling of saltier deep water. Ocean currents intensified significantly, reaching speeds of 0.5–1 m/s near coastal areas, with pronounced vertical mixing in the upper 70 m driven by Ekman pumping and wave-current interactions. By effectively capturing typhoon-induced oceanic responses, the integration of satellite data and the ROMS model enhances understanding of typhoon–ocean interaction mechanisms, providing a scientific basis for risk assessment and disaster management in typhoon-prone regions. Future research should focus on refining model parameterizations and advancing data assimilation techniques to improve predictions of typhoon–ocean interactions, providing valuable insights for disaster preparedness and environmental management in typhoon-prone regions. Full article

(This article belongs to the Section Physical Oceanography)

17 pages, 4289 KB

Open AccessArticle

Performance Analysis of an Ice-Based Buoy Operating from the Packed Ice Zone to the Marginal Ice Zone with an Imaging System

by Guangyu Zuo, Haocai Huang and Huifang Chen

J. Mar. Sci. Eng. 2025, 13(9), 1717; https://doi.org/10.3390/jmse13091717 - 5 Sep 2025

Abstract

Arctic sea ice can be regarded as a sensitive indicator of climate change, and it has declined dramatically in recent decades. The swift decline in Arctic sea ice coverage leads to an expansion of the marginal ice zone (MIZ). In this study, an [...] Read more.

Arctic sea ice can be regarded as a sensitive indicator of climate change, and it has declined dramatically in recent decades. The swift decline in Arctic sea ice coverage leads to an expansion of the marginal ice zone (MIZ). In this study, an ice-based buoy with an imaging system is designed for the long-term observation of the changes in sea ice from the packed ice zone to the marginal ice zone in polar regions. The system composition, main buoy, image system, and buoy load were analyzed. An underwater camera supports a 640 × 480 resolution image acquisition, RS485 communication, stable operation at –40 °C, and long-term underwater sealing protection through a titanium alloy housing. During a continuous three-month field deployment in the Arctic, the system successfully captured images of ice-bottom morphology and biological attachment, demonstrating imaging reliability and operational stability under extreme conditions. In addition, the buoy employed a battery state estimation method based on the Extreme Learning Machine (ELM). Compared with LSTM, BP, BiLSTM, SAELSTM, and RF models, the ELM achieved a test set performance of RMSE = 0.05 and MAE = 0.187, significantly outperforming the alternatives and thereby improving energy management and the reliability of long-term autonomous operation. Laboratory flume tests further verified the power generation performance of the wave energy-assisted supply system. However, due to the limited duration of Arctic deployment, full year-round performance has not yet been validated, and the imaging resolution remains insufficient for biological classification. The results indicate that the buoy demonstrates strong innovation and application potential for long-term polar observations, while further improvements are needed through extended deployments and enhanced imaging capability. Full article

(This article belongs to the Special Issue Navigation Performance and Experimental Research of Ships in Ice-Covered Areas)

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18 pages, 9002 KB

Open AccessArticle

Characterization of TGFβ Signaling Components in Large Yellow Croaker (Larimichthys crocea) and Their Role in Growth and Body Shape Regulation

by Xuyang Jiang, Lu Zhang, Xin Hu, Mingchao Cui, Jie Li, Huang Liu and Linlin Yao

J. Mar. Sci. Eng. 2025, 13(9), 1716; https://doi.org/10.3390/jmse13091716 - 5 Sep 2025

Abstract

The transforming growth factor β (TGFβ) signaling axis plays a pivotal role in orchestrating a wide array of biological functions, encompassing cellular growth, proliferation, and differentiation. The aim of the present study was to identify the members of TGFβ signaling pathway and their [...] Read more.

The transforming growth factor β (TGFβ) signaling axis plays a pivotal role in orchestrating a wide array of biological functions, encompassing cellular growth, proliferation, and differentiation. The aim of the present study was to identify the members of TGFβ signaling pathway and their expression patterns in large yellow croaker (Larimichthys crocea) under different culture modes. TGFβ signaling pathway and their expression patterns in fish reared under two different culture modes: Group N (2400 fish in a 120 m³ cage) and Group V (168,000 fish in a 5600 m³ aquaculture vessel). After 120 days, we analyzed 15 fish from each group and found that Group V exhibited faster growth and a slender body shape compared to Group N. Bioinformatics analysis identified 48 TGFβ superfamily members in L. crocea, including 21 ligands, 10 receptors, and 3 Smads. mRNA expression levels indicated that these signaling molecules influence growth rate and body shape through five distinct ligand–receptor–R-Smad pathways, with the INHBB-, Nodal-, and GDF3-ACVR2A-ALK4-Smad2 axis playing a predominant role in regulating these traits. Full article

(This article belongs to the Special Issue Marine Ecological Ranch, Fishery Remote Sensing, and Smart Fishery)

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11 pages, 1588 KB

Open AccessArticle

Landsat-5 TM Imagery for Retrieving Historical Water Temperature Records in Small Inland Water Bodies and Coastal Waters of Lithuania (Northern Europe)

by Toma Dabulevičienė and Diana Vaičiūtė

J. Mar. Sci. Eng. 2025, 13(9), 1715; https://doi.org/10.3390/jmse13091715 - 5 Sep 2025

Abstract

Water surface temperature (WST) is an important environmental variable, and its monitoring is essential for understanding and mitigating the impacts of climate change and human activities. For this, satellite remote sensing is particularly useful in providing WST data, especially in cases when in [...] Read more.

Water surface temperature (WST) is an important environmental variable, and its monitoring is essential for understanding and mitigating the impacts of climate change and human activities. For this, satellite remote sensing is particularly useful in providing WST data, especially in cases when in situ monitoring is limited or absent, as is often the case in small inland water bodies. In this study, the approach of retrieving the historical WST data from Landsat-5 Thematic Mapper (TM) was tested by analysing different cases across various water bodies in Lithuania, including two small inland lakes, an artificial reservoir, the Curonian Lagoon, and the coastal waters of the southeastern Baltic Sea. Our results demonstrate that WST can be accurately estimated from single-band Landsat-5 TM images, achieving an R² of around 0.9 in comparison with both in situ (with RMSE of 1.35–1.73 °C) and with MODIS satellite (RMSE of 1.11–1.23 °C) water temperature data, thus enabling analysis of water temperature variations in small-sized lakes and other water bodies, and contributing to the reliable monitoring of WST trends. Full article

(This article belongs to the Section Marine Environmental Science)

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23 pages, 5735 KB

Open AccessArticle

Extended Probabilistic Risk Assessment of Autonomous Underwater Vehicle Docking Scenarios Considering Battery Consumption

by Seong Hyeon Kim, Ju Won Jung, Min Young Jang and Sun Je Kim

J. Mar. Sci. Eng. 2025, 13(9), 1714; https://doi.org/10.3390/jmse13091714 - 4 Sep 2025

Abstract

Autonomous underwater vehicles (AUVs) play a crucial role in marine environments, such as in inspecting marine structures and monitoring the condition of subsea pipelines. After completing their mission, AUVs dock with recovery systems at designated locations. However, underwater docking carries a significant risk [...] Read more.

Autonomous underwater vehicles (AUVs) play a crucial role in marine environments, such as in inspecting marine structures and monitoring the condition of subsea pipelines. After completing their mission, AUVs dock with recovery systems at designated locations. However, underwater docking carries a significant risk of failure due to unpredictable maritime conditions. Considering the limitations in communication during the mission, docking failure can lead to the loss of collected data and failure of the entire AUV mission. In this study, a hypothetical AUV docking scenario was defined based on expert knowledge and without actual operational data. A Markov chain-based probabilistic model was employed to quantitatively assess the risk of the system during the mission. Environmental factors were excluded from the evaluation, and the simulation results were classified into five categories: success, timeout, internal component failure, exceeding a predefined sequence repetition limit, and spending the electrical energy under the battery SOC threshold. By analyzing the failure points of each category, strategies to improve the scenario success rate were discussed. This study quantitatively identified the interactions between constraints and risk factors that should be considered when establishing AUV docking plans through a virtual scenario-based failure analysis, thereby providing an evaluation framework that can be utilized in actual design. Full article

(This article belongs to the Section Ocean Engineering)

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21 pages, 3261 KB

Open AccessArticle

The Mechanism of the Effect of FCC Slurry Oil Blending Ratio on the Colloidal Stability and Asphaltene Aggregation Behavior of Low-Sulfur Marine Fuel Oil

by Aigang Li, Shengjun Guo, Jianwen Deng, Hong Chen, Jinxuan Wu, Ru Jiang, Jing Tan, Lihua Cheng, Libo Zhang and Qinzhen Fan

J. Mar. Sci. Eng. 2025, 13(9), 1713; https://doi.org/10.3390/jmse13091713 - 4 Sep 2025

Abstract

Addressing IMO 2020 compliance, this study investigates marine fuel oil production from hydrotreated residues, focusing on mitigating excessive total sediment potential (TSP) caused by over-hydrotreatment. This study systematically investigates the impact of blending ratios of Fluid Catalytic Cracking (FCC) slurry oil with Residue [...] Read more.

Addressing IMO 2020 compliance, this study investigates marine fuel oil production from hydrotreated residues, focusing on mitigating excessive total sediment potential (TSP) caused by over-hydrotreatment. This study systematically investigates the impact of blending ratios of Fluid Catalytic Cracking (FCC) slurry oil with Residue Desulfurization (RDS) heavy oil on TSP, colloidal stability, and asphaltene structure evolution. Techniques such as XRD, SEM, and XPS were employed to analyze the structural changes in asphaltenes during the TSP exceeding process. The results indicate that as the FCC slurry oil blending ratio increases, TSP in the blended oil initially rises and then decreases. The peak TSP value of 0.41% occurs at a 10% FCC slurry oil blending ratio, primarily due to high-saturation hydrocarbons in RDS heavy oil disrupting the colloidal stability of asphaltenes in FCC slurry oil. When the blending ratio reaches 25%, TSP significantly decreases to 0.09%, attributed to the solubilizing effect of high aromatic compounds in the FCC slurry oil on the asphaltenes. The ω(Asp)/ω(Res) ratio mirrors the TSP trend, and the colloidal solubilizing capacity of asphaltenes increases with the blending ratio. Asphaltenes in RDS heavy oil exhibit a spherical structure, whereas those in FCC slurry oil show a layered structure. The precipitated asphaltenes in the blends primarily result from the aggregation of asphaltenes in FCC slurry oil, with heteroatoms (N, S, O) mainly originating from RDS heavy oil asphaltenes. During the early stage of blending, TSP formation is dominated by FCC slurry oil asphaltenes, but increasing the aromatic content in the system can significantly reduce TSP. This work provides theoretical and technical support for optimizing marine fuel blending processes in petrochemical enterprises. Full article

(This article belongs to the Section Ocean Engineering)

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23 pages, 9439 KB

Open AccessArticle

Compressive Sensing Convolution Improves Long Short-Term Memory for Ocean Wave Spatiotemporal Prediction

by Lingxiao Zhao, Yijia Kuang, Junsheng Zhang and Bin Teng

J. Mar. Sci. Eng. 2025, 13(9), 1712; https://doi.org/10.3390/jmse13091712 - 4 Sep 2025

Abstract

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask [...] Read more.

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

(This article belongs to the Section Physical Oceanography)

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31 pages, 1850 KB

Open AccessArticle

A High-Efficiency Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Offshore Wind Power Under Energy Constraints

by Dongliang Zhang, Wankai Li, Chenyu Liu, Xuheng He and Kaiqi Li

J. Mar. Sci. Eng. 2025, 13(9), 1711; https://doi.org/10.3390/jmse13091711 - 4 Sep 2025

Abstract

As wind turbines are affected by the harsh marine environment, inspection is crucial for the continuous operation of offshore wind farms. Nowadays, the main method of inspection is manual inspection, which has significant limitations in terms of safety, economy, and labor. With the [...] Read more.

As wind turbines are affected by the harsh marine environment, inspection is crucial for the continuous operation of offshore wind farms. Nowadays, the main method of inspection is manual inspection, which has significant limitations in terms of safety, economy, and labor. With the advancement of technology, unmanned inspection systems have attracted more attention from researchers and the industry. This study proposes a novel framework to enable Unmanned Aerial Vehicles (UAVs) to improve their adaptability in autonomous inspection tasks on offshore wind farms, which includes multi-UAVs, inspection task nodes, and multiple charging stations. The main contributions of this paper are as follows: we propose an improved PSO algorithm to improve the location of charging stations; based on the multi-depot traveling salesman problem, we establish a multi-station UAV cooperative task allocation model with energy constraints, with the inspection time consumption of UAVs as the optimization objective; we also propose the Dynamic elite Double population Genetic Algorithm (DDGA) to aid in the cooperative task allocation of UAVs. The simulation results show that, compared with other algorithms, the proposed framework has higher universality and superiority. This paper provides a specific method for the application of unmanned inspection systems in the inspection of wind turbines in offshore wind farms. Full article

(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)

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22 pages, 10200 KB

Open AccessArticle

Research on Self-Noise Processing of Unmanned Surface Vehicles via DD-YOLO Recognition and Optimized Time-Frequency Denoising

by Zhichao Lv, Gang Wang, Huming Li, Xiangyu Wang, Fei Yu, Guoli Song and Qing Lan

J. Mar. Sci. Eng. 2025, 13(9), 1710; https://doi.org/10.3390/jmse13091710 - 4 Sep 2025

Abstract

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume [...] Read more.

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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16 pages, 2205 KB

Open AccessArticle

Environmental Factors Driving Carbonate Distribution in Marine Sediments in the Canary Current Upwelling System

by Hasnaa Nait-Hammou, Khalid El Khalidi, Ahmed Makaoui, Melissa Chierici, Chaimaa Jamal, Nezha Mejjad, Otmane Khalfaoui, Fouad Salhi, Mohammed Idrissi and Bendahhou Zourarah

J. Mar. Sci. Eng. 2025, 13(9), 1709; https://doi.org/10.3390/jmse13091709 - 4 Sep 2025

Abstract

This study illustrates the complex interaction between environmental parameters and carbonate distribution in marine sediments along the Tarfaya–Boujdour coastline (26–28° N) of Northwest Africa. Analysis of 21 surface sediment samples and their associated bottom water properties (salinity, temperature, dissolved oxygen, nutrients) reveals CaCO [...] Read more.

This study illustrates the complex interaction between environmental parameters and carbonate distribution in marine sediments along the Tarfaya–Boujdour coastline (26–28° N) of Northwest Africa. Analysis of 21 surface sediment samples and their associated bottom water properties (salinity, temperature, dissolved oxygen, nutrients) reveals CaCO₃ content ranging from 16.8 wt.% to 60.5 wt.%, with concentrations above 45 wt.% occurring in multiple stations, especially in nearshore deposits. Mineralogy indicates a general decrease in quartz, with an arithmetic mean and standard deviation of 52.5 wt.% ± 19.8 towards the open sea, and an increase in carbonate minerals (calcite ≤ 24%, aragonite ≤ 10%) with depth. Sediments are predominantly composed of fine sand (78–99%), poorly classified, with gravel content reaching 6.7% in energetic coastal stations. An inverse relationship between organic carbon (0.63–3.23 wt.%) and carbonates is observed in upwelling zones, correlated with nitrate concentrations exceeding 19 μmol/L. Hydrological gradients show temperatures from 12.41 °C (offshore) to 21.62 °C (inshore), salinity from 35.64 to 36.81 psu and dissolved oxygen from 2.06 to 4.21 mL/L. The weak correlation between carbonates and depth (r = 0.10) reflects the balance between three processes: biogenic production stimulated by upwelling, dilution by Saharan terrigenous inputs, and hydrodynamic sorting redistributing bioclasts. These results underline the need for models integrating hydrology, mineralogy and hydrodynamics to predict carbonate dynamics in desert margins under upwelling. Full article

(This article belongs to the Section Geological Oceanography)

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21 pages, 8646 KB

Open AccessArticle

Influence of Varying Fractal Characteristics on the Dynamic Response of a Semi-Submersible Floating Wind Turbine Platform

by Wanyong Zhang, Haoda Huang, Qingsong Liu, Yangtian Yan, Chun Li, Weipao Miao, Minnan Yue and Wanfu Zhang

J. Mar. Sci. Eng. 2025, 13(9), 1708; https://doi.org/10.3390/jmse13091708 - 4 Sep 2025

Abstract

Offshore wind turbines positioned in deepwater areas are increasingly favored due to them providing superior and stable wind resources. However, the dynamic stability of floating offshore wind turbines (FOWTs) under complex environmental loading remains challenging. This study proposes a novel semi-submersible platform featuring [...] Read more.

Offshore wind turbines positioned in deepwater areas are increasingly favored due to them providing superior and stable wind resources. However, the dynamic stability of floating offshore wind turbines (FOWTs) under complex environmental loading remains challenging. This study proposes a novel semi-submersible platform featuring a fractal structure inspired by the venation of Victoria Amazonica and investigates the effects of fractal branching level and biomimetic structural height on platform motions, with the aim of enhancing the overall system stability of FOWTs. Within a high-fidelity computational fluid dynamics (CFD) framework coupled with a dynamic fluid–body interaction (DFBI) model and a volume-of-fluid (VOF) wave model, the dynamic responses of the biomimetic platform are investigated under varying fractal dimensions (D_f) and structural heights. The results indicate that increasing fractal complexity enhances the local wall viscosity effect, significantly improving energy dissipation capabilities within the fractal cavities. Specifically, an eight-level fractal structure shows optimal performance, achieving reductions of approximately 16.94%, 23.26%, and 35.63% in heave, pitch, and rotational energy responses, respectively. Additionally, the increasing fractal height further strengthens energy dissipation, markedly enhancing stability, particularly in pitch motion. These findings underscore the substantial potential of biomimetic fractal designs in enhancing the dynamic stability of FOWTs. Full article

(This article belongs to the Section Coastal Engineering)

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20 pages, 3767 KB

Open AccessArticle

Numerical Investigation on Erosion Characteristics of Archimedes Spiral Hydrokinetic Turbine

by Ke Song, Huiting Huan, Liuchuang Wei and Yongli Wang

J. Mar. Sci. Eng. 2025, 13(9), 1707; https://doi.org/10.3390/jmse13091707 - 4 Sep 2025

Abstract

The Archimedes spiral hydrokinetic turbine (ASHT), an innovative horizontal-axis design, holds significant potential for harvesting energy from localized ocean and river currents. However, prolonged operation can result in blade erosion, which reduces efficiency and may lead to operational failures. To ensure reliability and [...] Read more.

The Archimedes spiral hydrokinetic turbine (ASHT), an innovative horizontal-axis design, holds significant potential for harvesting energy from localized ocean and river currents. However, prolonged operation can result in blade erosion, which reduces efficiency and may lead to operational failures. To ensure reliability and prevent damage, it is essential to accurately identify the locations and progression of wear caused by sand particle impacts. Using a CFD–DPM approach, this study systematically investigates the effects of sand concentration and particle size on erosion rates and distribution across nine ASHT configurations, along with the underlying physical mechanisms. The results indicate that erosion rate increases linearly with sand concentration due to higher particle impact frequency. Erosion zones expand from the blade tip edges toward mid-span regions and areas near the hub as concentration increases. Regarding particle size, the erosion rate increases rapidly and almost linearly for diameters below 0.6 mm, but this growth slows for larger particles due to a “momentum–quantity trade-off” effect. Blade angle also exerts a tiered influence on erosion, following the pattern medium angles > small angles > large angles. Medium angles enhance the synergy between normal and tangential impact components, maximizing erosion. Erosion primarily initiates at the blade tips and edges, with the most severe wear concentrated in these high-impact zones. The derived erosion patterns provide valuable guidance for predicting erosion, optimizing ASHT blade design, and developing effective anti-erosion strategies. Full article

(This article belongs to the Topic Marine Renewable Energy, 2nd Edition)

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21 pages, 8040 KB

Open AccessArticle

An Intelligent Auxiliary Decision-Making Algorithm for Hydrographic Surveying Missions

by Ning Zhang, Kailong Li and Jingwen Zong

J. Mar. Sci. Eng. 2025, 13(9), 1706; https://doi.org/10.3390/jmse13091706 - 4 Sep 2025

Abstract

In view of the problems that the track mode accuracy of the automatic steering gear on survey ships cannot meet the requirements of hydrographic survey accuracy and the workload of manual steering is large, an intelligent auxiliary decision-making algorithm based on LSTM and [...] Read more.

In view of the problems that the track mode accuracy of the automatic steering gear on survey ships cannot meet the requirements of hydrographic survey accuracy and the workload of manual steering is large, an intelligent auxiliary decision-making algorithm based on LSTM and multiple linear regression is proposed. By learning historical track information, marine environment information, historical steering data, hull state data, etc., it provides the helm with auxiliary operation prompt information, such as the command course and its adjustment timing (time range, area), so as to reduce the number of times the helm steers. The effectiveness of the algorithm is verified through sea trials. The results show that the number of steering times is reduced by 45.5% and the number of effective measuring points is increased by 1.5% through the algorithm in this paper. This result confirms that the algorithm can improve the operational efficiency of offshore survey tasks by optimizing human–computer interaction. Full article

(This article belongs to the Special Issue Applications of Sensors and Artificial Intelligence Techniques in Ships)

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26 pages, 9169 KB

Open AccessArticle

Multi-Objective Path Planning for USVs Considering Environmental Factors

by Weiqiang Liao, Feng Zhang, Xinyue Wu and Huihui Li

J. Mar. Sci. Eng. 2025, 13(9), 1705; https://doi.org/10.3390/jmse13091705 - 3 Sep 2025

Abstract

This study investigates the multi-objective path planning problem for unmanned surface vehicles (USVs), aiming to optimize both travel distance and energy consumption in maritime environments with obstacles, sea winds, and ocean currents. The proposed method accounts for practical constraints, including collision avoidance, kinematic [...] Read more.

This study investigates the multi-objective path planning problem for unmanned surface vehicles (USVs), aiming to optimize both travel distance and energy consumption in maritime environments with obstacles, sea winds, and ocean currents. The proposed method accounts for practical constraints, including collision avoidance, kinematic boundaries, and speed limitations. The problem is formulated as a nonlinear multi-objective optimization model with generalized constraints and is solved using an improved particle swarm optimization algorithm enhanced by a vector-weighted fusion strategy. The algorithm adaptively balances exploration and exploitation to obtain diverse Pareto-optimal solutions. Simulation results under varying environmental conditions, along with real-world sea trials, validate the effectiveness of the proposed approach. The outcomes demonstrate that the method enables USVs to generate energy-efficient, smooth trajectories while maintaining robustness and adaptability, offering practical value for intelligent marine navigation. Full article

(This article belongs to the Special Issue Motion Control and Path Planning of Marine Vehicles—3rd Edition)

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10 pages, 2172 KB

Open AccessArticle

Decreasing Trend of Great White Shark Carcharodon carcharias Records in the Mediterranean: A Significant Population Loss or Shifts in Migration Patterns?

by Alen Soldo and Cemal Turan

J. Mar. Sci. Eng. 2025, 13(9), 1704; https://doi.org/10.3390/jmse13091704 - 3 Sep 2025

Abstract

The Mediterranean subpopulation of great white sharks (Carcharodon carcharias) is elusive and likely in decline, though long-term trends remain uncertain due to opportunistic record-keeping, misidentifications, and changing observation effort. We investigated whether spatial changes in shark occurrences during the 21st century [...] Read more.

The Mediterranean subpopulation of great white sharks (Carcharodon carcharias) is elusive and likely in decline, though long-term trends remain uncertain due to opportunistic record-keeping, misidentifications, and changing observation effort. We investigated whether spatial changes in shark occurrences during the 21st century correspond with shifts in Atlantic bluefin tuna (Thunnus thynnus) distribution and habitat conditions. We compiled geographically validated sighting and capture records from 1900 onward, mapped 20th- and 21st-century hotspots, and overlaid these with bluefin tuna potential habitat and long-term sea surface temperature (SST) data. Results reveal a clear redistribution of great white shark hotspots: historic coastal focal areas (e.g., Balearic Islands, Maltese Islands, eastern Adriatic, Sea of Marmara) have diminished or disappeared, while offshore zones (southern Strait of Sicily–Gulf of Gabes) and the Aegean coast of Turkey have emerged as contemporary hotspots. These patterns appear to align closely with shifts in tuna feeding grounds and cooler SST (<18 °C). We highlight limitations in using opportunistic and citizen-reported data due to detection biases and misidentifications, underscoring the need for models that correct detectability. Our findings are consistent with the hypothesis of a link between predator distribution, prey dynamics, and changing ocean conditions, and point toward targeted strategies for future conservation and monitoring of this apex predator in a warming Mediterranean. Full article

(This article belongs to the Special Issue Abundance and Diversity of the Sea Fish Community)

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17 pages, 26803 KB

Open AccessArticle

High-Precision Small-Scale 3D Seismic Technology for Natural Gas Hydrate Exploration in the Northern South China Sea

by Dasen Zhou, Siqing Liu, Xianjun Zeng, Limin Gou, Jing Li, Jingjing Zhang, Xiaozhu Hao, Qingxian Zhao, Qingwang Yao, Jiafa Zhang, Jiaqi Shen, Zelin Mu and Zelin He

J. Mar. Sci. Eng. 2025, 13(9), 1703; https://doi.org/10.3390/jmse13091703 - 3 Sep 2025

Abstract

To address the demand for high-precision exploration of natural gas hydrates in the northern South China Sea, this paper presents a novel high-precision small-scale 3D seismic exploration technology. The research team independently developed a seismic acquisition system, incorporating innovative designs such as a [...] Read more.

To address the demand for high-precision exploration of natural gas hydrates in the northern South China Sea, this paper presents a novel high-precision small-scale 3D seismic exploration technology. The research team independently developed a seismic acquisition system, incorporating innovative designs such as a narrow trace spacing of 3.125 m and a short streamer length of 150 m. By integrating advanced processing techniques, including pre-stack noise suppression, spectral broadening, and refined velocity analysis, the system significantly enhances the precision and spatial resolution of shallow seismic data. During field trials in the Qiongdongnan basin, the system successfully acquired 3D seismic data over an area of 50 km², enabling fine-scale imaging of sub-seabed strata within the upper 300 m. This represents a notable improvement in resolution compared to conventional 3D seismic technologies. When benchmarked against international counterparts such as P-cable, our system demonstrates distinct advantages in terms of exploration depth (reaching 1800 m) and dominant frequency range (spanning 10~390 Hz). The research findings provide a reliable technical approach for the detailed characterization of natural gas hydrates and the inversion of reservoir parameters, thereby holding significant practical value for advancing the industrial development of natural gas hydrates in China’s offshore areas. Full article

(This article belongs to the Special Issue Advances in Marine Gas Hydrate Exploration and Discovery—2nd Edition)

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