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The Challenges of Cyber Resilience in the Maritime Sector: Addressing the Weak Awareness of the Dangers Caused by Cyber Threats
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Identification of Ship Maneuvering Behavior Using Singular Value Decomposition-Based Hydrodynamic Variations
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Ship Design Optimization for the Effect of Wind Propulsion
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Baseline Assessment of Black Sea Food Web Integrity Using a Zooplankton-Based Approach Under the Marine Strategy Framework Directive
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Textural, Mineralogical and Chromatic Characterisation of the Beach Sediments of Cuba: Management Implications
Journal Description
Journal of Marine Science and Engineering
Journal of Marine Science and Engineering
is an international, peer-reviewed, open access journal on marine science and engineering, published monthly online by MDPI. The Australia New Zealand Marine Biotechnology Society (ANZMBS) is affiliated with JMSE and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed with Scopus, SCIE (Web of Science), Ei Compendex, GeoRef, Inspec, AGRIS, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Marine) / CiteScore - Q2 (Ocean Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 1.9 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Journal Clusters of Water Resources: Water, Journal of Marine Science and Engineering, Hydrology, Resources, Oceans, Limnological Review, Coasts.
Impact Factor:
2.8 (2024);
5-Year Impact Factor:
2.8 (2024)
Latest Articles
Autonomous Tracking of Steel Lazy Wave Risers Using a Hybrid Vision–Acoustic AUV Framework
J. Mar. Sci. Eng. 2025, 13(7), 1347; https://doi.org/10.3390/jmse13071347 (registering DOI) - 15 Jul 2025
Abstract
Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental
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Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies.
Full article
(This article belongs to the Section Ocean Engineering)
Open AccessArticle
Investigation of Bolt Grade Influence on the Structural Integrity of L-Type Flange Joints Using Finite Element Analysis
by
Muhammad Waleed and Daeyong Lee
J. Mar. Sci. Eng. 2025, 13(7), 1346; https://doi.org/10.3390/jmse13071346 (registering DOI) - 15 Jul 2025
Abstract
Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt
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Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt pretension through a finite element analysis (FEA) study of its key performance indicators, including stress distribution, deformation, and force–displacement behaviors. This paper studies two high-strength bolt grades, Grade 10.9 and Grade 12.9, and two main steps—first, bolt pretension and, second, external loading (tower shell tensile load)—to investigate the influence on joint reliability and safety margins. The novelty of this study lies in its specific focus on static axial loading conditions, unlike the existing literature that emphasizes fatigue or dynamic loads. Results show that the specimen carrying a higher bolt grade (12.9) has 18% more ultimate load carrying capacity than the specimen with a lower bolt grade (10.9). Increased pretension increases the stability of the joint and reduces the micro-movements between A and B (on model specimen), but could result in material fatigue if over-pretensioned. Comparative analysis of the different bolt grades has provided practical guidance on material selection and bolt pretension in L-type flange joints for wind turbine support structures. The findings of this work offer insights into the proper design of robust flange connections for high-demand applications by highlighting a balance among material properties, bolt pretension, and operational conditions, while also proposing optimized pretension and material recommendations validated against classical analytical models.
Full article
(This article belongs to the Section Ocean Engineering)
Open AccessArticle
Performance Assessment of B-Series Marine Propellers with Cupping and Face Camber Ratio Using Machine Learning Techniques
by
Mina Tadros and Evangelos Boulougouris
J. Mar. Sci. Eng. 2025, 13(7), 1345; https://doi.org/10.3390/jmse13071345 (registering DOI) - 15 Jul 2025
Abstract
This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in
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This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in blade number, expanded area ratio (EAR), pitch-to-diameter ratio (P/D), FCR, and cupping percentage. A multi-layer artificial neural network (ANN) is trained to predict thrust, torque, and open-water efficiency (ηo) with a high coefficient of determination (R2), greater than 0.9999. The ANN is integrated into an optimization algorithm to identify optimal propeller designs for the KRISO Container Ship (KCS) using empirical constraints for cavitation and tip speed. Unlike prior studies that rely on boundary element method (BEM)-ML hybrids or multi-fidelity simulations, this study introduces a geometry-coupled analysis of FCR and cupping—parameters often treated independently—and applies empirical cavitation and acoustic (tip speed) limits to guide the design process. The results indicate that incorporating 1.0–1.5% cupping leads to a significant improvement in efficiency, up to 9.3% above the reference propeller, while maintaining cavitation safety margins and acoustic limits. Conversely, designs with non-zero FCR values (0.5–1.5%) show a modest efficiency penalty (up to 4.3%), although some configurations remain competitive when compensated by higher EAR, P/D, or blade count. The study confirms that the combination of cupping with optimized geometric parameters yields high-efficiency, cavitation-safe propellers. Furthermore, the ML-based framework demonstrates excellent potential for rapid, accurate, and scalable propeller design optimization that meets both performance and regulatory constraints.
Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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Open AccessArticle
Probabilistic Prediction of Spudcan Bearing Capacity in Stiff-over-Soft Clay Based on Bayes’ Theorem
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Zhaoyu Sun, Pan Gao, Yanling Gao, Jianze Bi and Qiang Gao
J. Mar. Sci. Eng. 2025, 13(7), 1344; https://doi.org/10.3390/jmse13071344 (registering DOI) - 14 Jul 2025
Abstract
During offshore operations of jack-up platforms, the spudcan may experience sudden punch-through failure when penetrating from an overlying stiff clay layer into the underlying soft clay, posing significant risks to platform safety. Conventional punch-through prediction methods, which rely on predetermined soil parameters, exhibit
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During offshore operations of jack-up platforms, the spudcan may experience sudden punch-through failure when penetrating from an overlying stiff clay layer into the underlying soft clay, posing significant risks to platform safety. Conventional punch-through prediction methods, which rely on predetermined soil parameters, exhibit limited accuracy as they fail to account for uncertainties in seabed stratigraphy and soil properties. To address this limitation, based on a database of centrifuge model tests, a probabilistic prediction framework for the peak resistance and corresponding depth is developed by integrating empirical prediction formulas based on Bayes’ theorem. The proposed Bayesian methodology effectively refines prediction accuracy by quantifying uncertainties in soil parameters, spudcan geometry, and computational models. Specifically, it establishes prior probability distributions of peak resistance and depth through Monte Carlo simulations, then updates these distributions in real time using field monitoring data during spudcan penetration. The results demonstrate that both the recommended method specified in ISO 19905-1 and an existing deterministic model tend to yield conservative estimates. This approach can significantly improve the predicted accuracy of the peak resistance compared with deterministic methods. Additionally, it shows that the most probable failure zone converges toward the actual punch-through point as more monitoring data is incorporated. The enhanced prediction capability provides critical decision support for mitigating punch-through potential during offshore jack-up operations, thereby advancing the safety and reliability of marine engineering practices.
Full article
(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
Methyltrimethyltridecylchromans (MTTCs) in Mature Crude Oils: Implications for Oil Family Classification and Palaeoenvironmental Diagnosis
by
Youjun Tang, Mengyue Han, Xiaoyong Yang, Ke Liu, Lian Chen, Yahao Mei, Yulu Han, Tianwu Xu and Chengfu Zhang
J. Mar. Sci. Eng. 2025, 13(7), 1343; https://doi.org/10.3390/jmse13071343 (registering DOI) - 14 Jul 2025
Abstract
Methyltrimethyltridecylchromans (MTTCs), a class of oxygen-containing aromatic derivatives, have been used as indicators of paleosalinity in source rocks and crude oils. However, the reliability of these compounds as indicators in mature organic matter remains unclear, hindering a definitive assessment of their significance for
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Methyltrimethyltridecylchromans (MTTCs), a class of oxygen-containing aromatic derivatives, have been used as indicators of paleosalinity in source rocks and crude oils. However, the reliability of these compounds as indicators in mature organic matter remains unclear, hindering a definitive assessment of their significance for oil–oil or oil–source rock correlation. In this study, a suite of mature oils and associated source rocks from the Paleogene Shahejie (E2s) Formation in the Machang area, Dongpu Depression, Bohai Bay Basin, were analyzed. The distribution of bulk compositions and biomarkers in the oils and source rock extracts suggests a genetic relationship, indicating that the oils were derived from similar organic matter (predominantly algae and aquatic macrophytes) and depositional environments (low salinity), with comparable maturity levels (within the middle oil window). The β/γ-MTTC ratio, a proposed maturity indicator, appears unreliable in mature organic matter, as evidenced by its poor correlation with established maturity proxies (e.g., C29 24-ethylcholestanes αββ/(ααα + αββ)) in the studied samples. In contrast, MTTC-based salinity paraments (α/δ, α/γ, MTTCI, and the cross-plot of MTTCI versus Pr/Ph) consistently reflect a low-salinity depositional environment for these crude oils and source rocks, except in the ternary diagram of relative alkylation abundances. These findings suggest that MTTC-derived paleosalinity indicators may serve as effective tools for oil–oil or oil–source rock correlation within the middle oil window. This study provides evidence supporting the broader applicability of MTTC-based proxies for paleosalinity reconstruction and correlation studies, particularly in mature organic matter under geological conditions. The results also offer insights for regional petroleum exploration in saline lacustrine basins.
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(This article belongs to the Section Geological Oceanography)
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Open AccessArticle
Evaluation of the Effect of Floating Treatment Wetlands Planted with Sesuvium portulacastrum on the Dynamics of Dissolved Inorganic Nitrogen, CO2, and N2O in Grouper Aquaculture Systems
by
Shenghua Zheng, Man Wu, Jian Liu, Wangwang Ye, Yongqing Lin, Miaofeng Yang, Huidong Zheng, Fang Yang, Donglian Luo and Liyang Zhan
J. Mar. Sci. Eng. 2025, 13(7), 1342; https://doi.org/10.3390/jmse13071342 - 14 Jul 2025
Abstract
Aquaculture expansion to meet global protein demand has intensified concerns over nutrient pollution and greenhouse gas (GHG) emissions. While floating treatment wetlands (FTWs) are proven for water quality improvement, their potential to mitigate GHG emissions in marine aquaculture remains poorly understood. This study
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Aquaculture expansion to meet global protein demand has intensified concerns over nutrient pollution and greenhouse gas (GHG) emissions. While floating treatment wetlands (FTWs) are proven for water quality improvement, their potential to mitigate GHG emissions in marine aquaculture remains poorly understood. This study quantitatively evaluated the dual capacity of Sesuvium portulacastrum FTWs to (a) regulate dissolved inorganic nitrogen (DIN) and (b) reduce CO2/N2O emissions in grouper aquaculture systems. DIN speciation (NH4+, NO2−, NO3−) and CO2/N2O fluxes of six controlled ponds (three FTW and three control) were monitored for 44 days. DIN in the FTW group was approximately 90 μmol/L lower than that in the control group, and the water in the plant group was more “oxidative” than that in the control group. The former groups were dominated by NO3−, with lower dissolved inorganic carbon (DIC) and N2O concentrations, whereas the latter were dominated by NH4+ during the first 20 days of the experiment and by NO2− at the end of the experiment, with higher DIC and N2O concentrations on average. Higher primary production may be the reason that the DIC concentration was lower in the plant group than in the control group, whereas efficient nitrification and uptake by plants reduced the availability of NH4+ in the plant group, thereby reducing the production of N2O. A comparison of the CO2 and N2O flux potentials in the plant group and control group revealed that, in the presence of FTWs, the CO2 and N2O emissions decreased by 14% and 36%, respectively. This showed that S. portulacastrum FTWs effectively couple DIN removal with GHG mitigation, offering a nature-based solution for sustainable aquaculture. Their low biomass requirement enhances practical scalability.
Full article
(This article belongs to the Special Issue Coastal Geochemistry: The Processes of Water–Sediment Interaction)
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Open AccessArticle
Trajectory Tracking of Unmanned Surface Vessels Based on Robust Neural Networks and Adaptive Control
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Ziming Wang, Chunliang Qiu, Zaopeng Dong, Shaobo Cheng, Long Zheng and Shunhuai Chen
J. Mar. Sci. Eng. 2025, 13(7), 1341; https://doi.org/10.3390/jmse13071341 - 13 Jul 2025
Abstract
In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a
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In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments.
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(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
HA-CP-Net: A Cross-Domain Few-Shot SAR Oil Spill Detection Network Based on Hybrid Attention and Category Perception
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Dongmei Song, Shuzhen Wang, Bin Wang, Weimin Chen and Lei Chen
J. Mar. Sci. Eng. 2025, 13(7), 1340; https://doi.org/10.3390/jmse13071340 - 13 Jul 2025
Abstract
Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is
[...] Read more.
Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents.
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(This article belongs to the Section Marine Environmental Science)
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Open AccessReview
Berth Allocation and Quay Crane Scheduling in Port Operations: A Systematic Review
by
Ndifelani Makhado, Thulane Paepae, Matthews Sejeso and Charis Harley
J. Mar. Sci. Eng. 2025, 13(7), 1339; https://doi.org/10.3390/jmse13071339 - 13 Jul 2025
Abstract
Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling
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Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024.
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(This article belongs to the Section Ocean Engineering)
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Open AccessReview
Remote Non-Destructive Testing of Port Cranes: A Review of Vibration and Acoustic Sensors with IoT Integration
by
Jan Lean Tai, Mohamed Thariq Hameed Sultan, Rafał Grzejda and Farah Syazwani Shahar
J. Mar. Sci. Eng. 2025, 13(7), 1338; https://doi.org/10.3390/jmse13071338 - 13 Jul 2025
Abstract
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of
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Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations.
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(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model
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Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu and Wenyong Yuan
J. Mar. Sci. Eng. 2025, 13(7), 1337; https://doi.org/10.3390/jmse13071337 - 13 Jul 2025
Abstract
Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection
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Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection and early warning by utilizing the in situ monitoring data from a wind farm. This comprehensive architecture involves five modules: data preprocessing, multi-dimensional spatial feature extraction, temporal dependency modeling, global relationship learning, and hyperparameter optimization. It was achieved by using real-time monitoring data to predict the GHSS temperature in 10 min, with an accuracy of 1 °C. Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. Furthermore, the architecture establishes a normal operating condition model and provides benchmark eigenvalues for subsequent fault warnings. The model was validated to issue early warnings up to seven hours before the fault alert is triggered by the supervisory control and data acquisition system of the wind turbine. By offering reliable, cost-effective prognostics without additional hardware, this approach significantly improves wind turbine health management and fault prevention.
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(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
Optimization Design of Dynamic Cable Configuration Considering Thermo-Mechanical Coupling Effects
by
Ying Li, Guanggen Zou, Suchun Yang, Dongsheng Qiao and Bin Wang
J. Mar. Sci. Eng. 2025, 13(7), 1336; https://doi.org/10.3390/jmse13071336 - 13 Jul 2025
Abstract
During operation, dynamic cables endure coupled thermo-mechanical loads (mechanical: tension/bending; thermal: power transmission) that degrade stiffness, amplifying extreme responses and impairing configuration optimization. To address this, this study pioneers a multi-objective optimization framework integrating stiffness characteristics from mechanical/thermo-mechanical analyses, with objectives to minimize
[...] Read more.
During operation, dynamic cables endure coupled thermo-mechanical loads (mechanical: tension/bending; thermal: power transmission) that degrade stiffness, amplifying extreme responses and impairing configuration optimization. To address this, this study pioneers a multi-objective optimization framework integrating stiffness characteristics from mechanical/thermo-mechanical analyses, with objectives to minimize dynamic extreme tension and curvature under constraints of global configuration variables and safety thresholds. The framework employs a Radial Basis Function (RBF) surrogate model coupled with NSGA-II algorithm, yielding validated Pareto solutions (≤6.15% max error vs. simulations). Results demonstrate universal reduction in extreme responses across optimized configurations, with the thermo-mechanically optimized solution achieving 20.24% fatigue life enhancement. This work establishes the first methodology quantifying thermo-mechanical coupling effects on offshore cable safety and fatigue performance. This configuration design scheme exhibits better safety during actual service conditions.
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(This article belongs to the Special Issue Advanced Studies in Marine Structures)
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Open AccessArticle
Research on Operational Risk for Northwest Passage Cruise Ships Using POLARIS
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Long Ma, Jiemin Fan, Xiaoguang Mou, Sihan Qian, Jin Xu, Liang Cao, Bo Xu, Boxi Yao, Xiaowen Li and Yabin Li
J. Mar. Sci. Eng. 2025, 13(7), 1335; https://doi.org/10.3390/jmse13071335 - 12 Jul 2025
Abstract
In the context of global warming, polar tourism is developing rapidly, and the demand for polar cruise travel in the Northwest Passage continues to increase, while sea ice has long been a key factor limiting the development of polar cruise tourism. This study
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In the context of global warming, polar tourism is developing rapidly, and the demand for polar cruise travel in the Northwest Passage continues to increase, while sea ice has long been a key factor limiting the development of polar cruise tourism. This study focuses on the operational risk of sea ice on cruise ships in the Northwest Passage (NWP), aiming to provide a scientific basis for ensuring the safety of cruise ship navigation and promoting the sustainable development of polar tourism. Based on ice data from 2015 to 2024, this study used the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology recommended by the International Maritime Organization (IMO) to establish three scenarios for the route of ice class IC cruise ships: light ice, normal ice, and heavy ice. The navigable windows were systematically analyzed and critical waters along the route were identified. The results indicate that the navigable windows for IC ice-class cruise ships under light ice conditions are from mid-July to early December, while the navigable period under normal ice conditions is only from mid- to late September, and navigation is not possible under heavy ice conditions. The study identified Larsen Sound, Barrow Strait, Bellot Strait and Eastern Beaufort Sea as critical waters on the NWP cruise route. Among them, Larsen Sound and Eastern Beaufort Sea have a more prominent impact on voyage scheduling because their navigation weeks overlap less with other waters. This study provides a new idea for the risk assessment of polar cruise ships in ice regions. The research results can provide an important reference for the safe operation of polar cruise ships in the NWP and the decision-making of relevant parties.
Full article
(This article belongs to the Special Issue Navigation Performance and Experimental Research of Ships in Ice-Covered Areas)
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Open AccessArticle
A Robust Cross-Band Network for Blind Source Separation of Underwater Acoustic Mixed Signals
by
Xingmei Wang, Peiran Wu, Haisu Wei, Yuezhu Xu and Siyu Wang
J. Mar. Sci. Eng. 2025, 13(7), 1334; https://doi.org/10.3390/jmse13071334 - 11 Jul 2025
Abstract
Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological
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Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological sound coexistence. Deep learning-based BSS methods have gained wide attention for their superior nonlinear modeling capabilities. However, existing approaches in underwater acoustic scenarios still face two key challenges: limited feature discrimination and inadequate robustness against non-stationary noise. To overcome these limitations, we propose a novel Robust Cross-Band Network (RCBNet) for the BSS of underwater acoustic mixed signals. To address insufficient feature discrimination, we decompose mixed signals into sub-bands aligned with ship noise harmonics. For intra-band modeling, we apply a parallel gating mechanism that strengthens long-range dependency learning so as to enhance robustness against non-stationary noise. For inter-band modeling, we design a bidirectional-frequency RNN to capture the global dependency relationships of the same signal across sub-bands. Our experiment demonstrates that RCBNet achieves a 0.779 dB improvement in the SDR compared to the advanced model. Additionally, the anti-noise experiment demonstrates that RCBNet exhibits satisfactory robustness across varying noise environments.
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(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
Effect of Nonlinear Constitutive Models on Seismic Site Response of Soft Reclaimed Soil Deposits
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Sadiq Shamsher, Myoung-Soo Won, Young-Chul Park, Yoon-Ho Park and Mohamed A. Sayed
J. Mar. Sci. Eng. 2025, 13(7), 1333; https://doi.org/10.3390/jmse13071333 - 11 Jul 2025
Abstract
This study investigates the impact of nonlinear constitutive models on one-dimensional seismic site response analysis (SRA) for soft, reclaimed soil deposits in Saemangeum, South Korea. Two widely used models, MKZ and GQ/H, were applied to three representative soil profiles using the DEEPSOIL program.
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This study investigates the impact of nonlinear constitutive models on one-dimensional seismic site response analysis (SRA) for soft, reclaimed soil deposits in Saemangeum, South Korea. Two widely used models, MKZ and GQ/H, were applied to three representative soil profiles using the DEEPSOIL program. Ground motions were scaled to bedrock peak ground accelerations (PGAs) corresponding to annual return periods (ARPs) of 1000, 2400, and 4800 years. Seismic response metrics include the ratio of GQ/H to MKZ shear strain, effective PGA (EPGA), and short- and long-term amplification factors (Fa and Fv). The results highlight the critical role of the site-to-motion period ratio (Tg/Tm) in controlling seismic behavior. Compared to the MKZ, the GQ/H model, which features strength correction and improved stiffness retention, predicts lower shear strains and higher surface spectral accelerations, particularly under strong shaking and shallow conditions. Model differences are most pronounced at low Tg/Tm values, where MKZ tends to underestimate amplification and overestimate strain due to its limited ability to reflect site-specific shear strength. Relative to code-based amplification factors, the GQ/H model yields lower short-term estimates, reflecting the disparity between stiff inland reference sites and the soft reclaimed conditions at Saemangeum. These findings emphasize the need for strength-calibrated constitutive models to improve the accuracy of site-specific seismic hazard assessments.
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(This article belongs to the Section Marine Hazards)
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Open AccessArticle
Determining Non-Dimensional Group of Parameters Governing the Prediction of Penetration Depth and Holding Capacity of Drag Embedment Anchors Using Linear Regression
by
Mojtaba Olyasani, Hamed Azimi and Hodjat Shiri
J. Mar. Sci. Eng. 2025, 13(7), 1332; https://doi.org/10.3390/jmse13071332 - 11 Jul 2025
Abstract
Drag embedment anchors (DEAs) provide reliable and cost-effective mooring solutions for floating structures, e.g., platforms, ships, offshore wind turbines, etc., in offshore engineering. Structural stability and operational safety require accurate predictions of their penetration depths and holding capacities across various seabed conditions. In
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Drag embedment anchors (DEAs) provide reliable and cost-effective mooring solutions for floating structures, e.g., platforms, ships, offshore wind turbines, etc., in offshore engineering. Structural stability and operational safety require accurate predictions of their penetration depths and holding capacities across various seabed conditions. In this study, explicit linear regression (LR) models were developed for the first time to predict the penetration depth and holding capacity of DEAs on clay and sand seabed. Buckingham’s theorem was also applied to identify dimensionless groups of parameters that influence DEA behavior, e.g., the penetration depth and holding capacity of the DEAs. LR models were developed and validated against experimental data from the literature for both clay and sand seabed. To evaluate model performance and identify the most accurate LR models to predict DEA behavior, comprehensive sensitivity, error, and uncertainty analyses were performed. Additionally, LR analysis revealed the most influential input parameters impacting penetration depth and holding capacity. Regarding offshore mooring design and geotechnical engineering applications, the proposed LR models offered a practical and efficient approach to estimating DEA performance across various seabed conditions.
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(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
Synergistic Detrital Zircon U-Pb and REE Analysis for Provenance Discrimination of the Beach-Bar System in the Oligocene Dongying Formation, HHK Depression, Bohai Bay Basin, China
by
Jing Wang, Youbin He, Hua Li, Tao Guo, Dayong Guan, Xiaobo Huang, Bin Feng, Zhongxiang Zhao and Qinghua Chen
J. Mar. Sci. Eng. 2025, 13(7), 1331; https://doi.org/10.3390/jmse13071331 - 11 Jul 2025
Abstract
The Oligocene Dongying Formation beach-bar system, widely distributed in the HHK Depression of the Bohai Bay Basin, constitutes a key target for mid-deep hydrocarbon exploration, though its provenance remains controversial due to complex peripheral source terrains. To address this, we developed an integrated
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The Oligocene Dongying Formation beach-bar system, widely distributed in the HHK Depression of the Bohai Bay Basin, constitutes a key target for mid-deep hydrocarbon exploration, though its provenance remains controversial due to complex peripheral source terrains. To address this, we developed an integrated methodology combining LA-ICP-MS zircon U-Pb dating with whole-rock rare earth element (REE) analysis, facilitating provenance studies in areas with limited drilling and heavy mineral data. Analysis of 849 high-concordance zircons (concordance >90%) from 12 samples across 5 wells revealed that Geochemical homogeneity is evidenced by strongly consistent moving-average trendlines of detrital zircon U-Pb ages among the southern/northern provenances and the central uplift zone, complemented by uniform REE patterns characterized by HREE (Gd-Lu) enrichment and LREE depletion; geochemical disparities manifest as dual dominant age peaks (500–1000 Ma and 1800–3100 Ma) in the southern provenance and central uplift samples, contrasting with three distinct peaks (65–135 Ma, 500–1000 Ma, and 1800–3100 Ma) in the northern provenance; spatial quantification via multidimensional scaling (MDS) demonstrates closer affinity between the southern provenance and central uplift (dij = 4.472) than to the northern provenance (dij = 6.708). Collectively, these results confirm a dual (north–south) provenance system for the central uplift beach-bar deposits, with the southern provenance dominant and the northern acting as a subsidiary source. This work establishes a dual-provenance beach-bar model, providing a universal theoretical and technical framework for provenance analysis in hydrocarbon exploration within analogous settings.
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(This article belongs to the Section Geological Oceanography)
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Open AccessReview
Navigational Safety Hazards Posed by Offshore Wind Farms: A Comprehensive Literature Review and Bibliometric Analysis
by
Vice Milin, Ivica Skoko, Željana Lekšić and Zlatko Boko
J. Mar. Sci. Eng. 2025, 13(7), 1330; https://doi.org/10.3390/jmse13071330 - 11 Jul 2025
Abstract
As global energy production progressively turns toward a green environment and economy, one of the safety challenges to the maritime industry that has arisen lies within offshore wind farms (OWFs). As renewable sources of energy whose numbers are rapidly expanding, their impact to
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As global energy production progressively turns toward a green environment and economy, one of the safety challenges to the maritime industry that has arisen lies within offshore wind farms (OWFs). As renewable sources of energy whose numbers are rapidly expanding, their impact to the safety of navigation of the ships that navigate in their vicinity ought to be examined further. An ever-growing number of OWFs has led to safety concerns that have never been taken into consideration before. This article gives a structured quantitative analysis and an in-depth review of the literature connected to the safety of navigation, collision probability, and risk assessment that OWFs pose to all maritime industry agents. In this article, the main concerns of the impact of OWFs to the safety of navigation are analyzed using a combination of both the PRISMA and PICOC methodologies. Various types of scientific papers such as journal articles, conference proceedings, MSc theses, PhD theses, and online works of research are collated into a detailed bibliometric analysis and categorized by the most relevant parameters providing valuable perspectives on the current state of art in the field. The findings of this research emphasize the need for a further and more thorough analysis on the theoretical installment of OWFs and their inevitable impact on increasing maritime traffic complexity. The results of this article can form a strong basis for further scientific development in the field and can give useful insights to all maritime industry stakeholders dealing with OWFs.
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(This article belongs to the Section Ocean Engineering)
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Open AccessArticle
Dynamic Stochastic Model Optimization for Underwater Acoustic Navigation via Singular Value Decomposition
by
Jialu Li, Junting Wang, Tianhe Xu, Jianxu Shu, Yangfan Liu, Yueyuan Ma and Yangyin Xu
J. Mar. Sci. Eng. 2025, 13(7), 1329; https://doi.org/10.3390/jmse13071329 - 11 Jul 2025
Abstract
The geometric distribution of seabed beacons significantly impacts the positioning accuracy of underwater acoustic navigation systems. To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). The method quantifies the contribution weights of each
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The geometric distribution of seabed beacons significantly impacts the positioning accuracy of underwater acoustic navigation systems. To address this challenge, we propose a depth-constrained adaptive stochastic model optimization method based on singular value decomposition (SVD). The method quantifies the contribution weights of each beacon to the dominant navigation direction by performing SVD on the acoustic observation matrix. The acoustic ranging covariance matrix can be dynamically adjusted based on these weights to suppress error propagation. At the same time, the prior depth with centimeter-level accuracy provided by the pressure sensor is used to establish strong constraints in the vertical direction. The experimental results demonstrate that the depth-constrained adaptive stochastic model optimization method reduces three-dimensional RMS errors by 66.65% (300 m depth) and 77.25% (2000 m depth) compared to conventional equal-weight models. Notably, the depth constraint alone achieves 95% vertical error suppression, while combined SVD optimization further enhances horizontal accuracy by 34.2–53.5%. These findings validate that coupling depth constraints with stochastic optimization effectively improves navigation accuracy in complex underwater environments.
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(This article belongs to the Section Ocean Engineering)
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Application of Machine Learning for Fuel Consumption and Emission Prediction in a Marine Diesel Engine Using Diesel and Waste Cooking Oil
by
Tadas Žvirblis, Kristina Čižiūnienė and Jonas Matijošius
J. Mar. Sci. Eng. 2025, 13(7), 1328; https://doi.org/10.3390/jmse13071328 - 11 Jul 2025
Abstract
This study creates and tests a machine learning model that can predict fuel use and emissions (NOx, CO2, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from
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This study creates and tests a machine learning model that can predict fuel use and emissions (NOx, CO2, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R2 > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NOx exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations.
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(This article belongs to the Section Ocean Engineering)
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