Journal of Marine Science and Engineering

25 pages, 8759 KB

Open AccessArticle

Safe Guidance Strategy for Affine Formation Manoeuvre of ASVs Using the Interference Vector Method

by Yiping Liu and Jianqiang Zhang

J. Mar. Sci. Eng. 2026, 14(4), 341; https://doi.org/10.3390/jmse14040341 - 10 Feb 2026

This paper presents a safe guidance strategy for affine formations based on the Interference Vector Method (IVM) to address dynamic formation guidance and collision avoidance for Autonomous Surface Vessels (ASVs) in multi-obstacle environments. An affine formation control framework is first adopted to enable [...] Read more.

This paper presents a safe guidance strategy for affine formations based on the Interference Vector Method (IVM) to address dynamic formation guidance and collision avoidance for Autonomous Surface Vessels (ASVs) in multi-obstacle environments. An affine formation control framework is first adopted to enable dynamic formation transformations for the Autonomous Surface Vessel (ASV) fleet. Building on this, an IVM-based obstacle avoidance method is developed, enabling the formation to evade both static and dynamic obstacles in real time. Furthermore, a course guidance law based on the Vector Field Method (VFM) and a speed magnitude guidance law based on Control Barrier Functions (CBFs) are proposed to simultaneously achieve formation guidance and prevent inter-vessel collisions. The proposed safe guidance strategy is rigorously validated through theoretical proofs and comprehensive numerical simulations. The simulation results further confirm the robustness of the obstacle avoidance algorithm under ideal perception conditions, as well as the practical applicability of the overall strategy in complex, obstacle-rich environments. Full article

(This article belongs to the Special Issue Advanced Studies in Marine Vessel Motion Control)

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18 pages, 3352 KB

Open AccessArticle

A Multi-Supervised Network for Real-Time and Accurate Semantic Segmentation in Underwater Scenes

by Yue Liu, Jun Ding, Mingze Xu, Zhigang Huang and Yiming Qiang

J. Mar. Sci. Eng. 2026, 14(4), 340; https://doi.org/10.3390/jmse14040340 - 10 Feb 2026

Abstract

Real-time semantic segmentation is a core perception capability for underwater robots and autonomous underwater vehicles (AUVs), yet it remains challenging because underwater imagery often exhibits low contrast, blurred boundaries, and strong appearance degradation under strict onboard computation budgets. This paper proposes MSNet, a [...] Read more.

Real-time semantic segmentation is a core perception capability for underwater robots and autonomous underwater vehicles (AUVs), yet it remains challenging because underwater imagery often exhibits low contrast, blurred boundaries, and strong appearance degradation under strict onboard computation budgets. This paper proposes MSNet, a multi-supervised two-pathway network that decouples feature learning into a semantic branch for context modeling and a detail branch for preserving high-resolution spatial information. MSNet introduces three complementary supervisory signals: (i) low-frequency semantic supervision derived from smoothed labels to encourage body semantics, (ii) high-frequency detail supervision derived from edge-enhanced labels to improve boundary localization, and (iii) category representation supervision implemented by a Category Representation Enhancement Module (CREM) to strengthen class discrimination at the deepest stage. To prevent auxiliary supervision from amplifying cross-resolution misalignment during fusion, we embed a Bilateral Flow-based Alignment Module (BFAM) into multi-stage feature fusion. Experiments on the SUIM benchmark show that MSNet achieves 79.83% mIoU and 86.57% F-score at 55 FPS with 6.2 M parameters on an RTX 3060 GPU, outperforming mainstream encoder–decoder and two-pathway algorithms. Compared with SFNet and BiSeNet V3, MSNet improves mIoU by 1.52% and 1.89%, and runs 9 FPS faster than SFNet. Ablation studies verify the effectiveness and complementarity of the proposed supervision and alignment strategies, indicating MSNet offers a practical accuracy–speed trade-off for marine engineering applications. Full article

(This article belongs to the Section Ocean Engineering)

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15 pages, 3062 KB

Open AccessArticle

Low-Cost Technologies for Marine Habitat Monitoring: A Case Study on Seagrass Meadows

by Valentina Costa and Teresa Romeo

J. Mar. Sci. Eng. 2026, 14(4), 339; https://doi.org/10.3390/jmse14040339 (registering DOI) - 10 Feb 2026

Abstract

Seagrass meadows are essential coastal ecosystems that provide key ecological services, including carbon sequestration, sediment stabilization, and shoreline protection. Increasing threats from natural and anthropogenic stressors highlight the need for efficient, reproducible, and non-invasive monitoring solutions. This study evaluates the performance of low-cost [...] Read more.

Seagrass meadows are essential coastal ecosystems that provide key ecological services, including carbon sequestration, sediment stabilization, and shoreline protection. Increasing threats from natural and anthropogenic stressors highlight the need for efficient, reproducible, and non-invasive monitoring solutions. This study evaluates the performance of low-cost commercial drones for seagrass assessment in shallow coastal waters, with an emphasis on freely accessible mission-planning and photogrammetric workflows. Field surveys were conducted along the Calabrian coast (southern Italy), where automated flight paths were generated using the software WaypointMap, and high-resolution orthophotos were generated using the WebODM software and subsequently analyzed in QGIS for seagrass patch detection, mapping, and surface estimation. The methodological pipeline is described in detail to facilitate full reproducibility. Compared with traditional diver-based methods, this workflow offers faster data collection, broader spatial coverage, and minimal environmental disturbance. Although some limitations remain, the results demonstrate that combining low-cost drones with open-source tools provides a practical and scalable solution for routine monitoring. This approach has strong potential for integration into routine coastal habitat assessment, supports early impact detection, and contributes to evidence-based conservation and management strategies. Full article

(This article belongs to the Section Marine Ecology)

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17 pages, 3074 KB

Open AccessArticle

Dual-Modal Vision–Sonar Object Detection for Underwater Robots Based on Deep Learning

by Xiaoming Wang, Zhenyu Wang and Dexue Bi

J. Mar. Sci. Eng. 2026, 14(4), 338; https://doi.org/10.3390/jmse14040338 - 10 Feb 2026

Abstract

Applying state-of-the-art RGB object detectors (e.g., YOLOv8) to underwater scenes often yields unstable performance due to scattering, absorption, illumination deficiency, and bandwidth-limited transmission that severely corrupt image contrast and details. Forward-looking sonar (FLS) remains informative in turbid or low-visibility water, yet its low [...] Read more.

Applying state-of-the-art RGB object detectors (e.g., YOLOv8) to underwater scenes often yields unstable performance due to scattering, absorption, illumination deficiency, and bandwidth-limited transmission that severely corrupt image contrast and details. Forward-looking sonar (FLS) remains informative in turbid or low-visibility water, yet its low resolution and weak semantics make conventional fusion architectures costly and difficult to deploy on resource-constrained robots. This paper proposes a paired-sample-free RGB–FLS joint training paradigm based on parameter sharing, where RGB and FLS images from different datasets are jointly used during training without any frame-level pairing or architectural modification. The resulting model preserves the original detector parameter scale and inference cost, and requires only RGB input at test time. Experiments on the SeaClear and Marine Debris FLS datasets under six representative underwater degradation factors (contrast loss, blur, resolution reduction, color cast, and JPEG compression) show consistent robustness gains over RGB-only training. In particular, under severe low-contrast corruption, the proposed training strategy improves mAP50 by more than 14 percentage points compared with the RGB-only baseline. These results indicate that sonar-domain supervision functions as an auxiliary structural constraint during optimization, rather than a conventional multi-source data enlargement. By forcing a shared-parameter detector to fit a texture-poor, geometry-dominant sonar domain, the learned representation is biased away from color/texture shortcuts and becomes more stable under adverse underwater degradations, without increasing deployment complexity. Full article

(This article belongs to the Special Issue Advances in Marine Autonomous Vehicles)

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33 pages, 1433 KB

Open AccessArticle

Distributed Multi-Agent Uplink Resource Scheduling for Space–Air–Ground–Sea Networks: A Game-Theoretic Approach

by Ruijing Zhou, Xuedou Xiao, Mozi Chen, Shengkai Zhang and Kezhong Liu

J. Mar. Sci. Eng. 2026, 14(4), 337; https://doi.org/10.3390/jmse14040337 - 9 Feb 2026

Abstract

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime [...] Read more.

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime SAGSINs remains challenging due to time-varying channels, locally bursty traffic, and intense contention, while centralized optimization is ill-suited, as global information collection is often delayed, incomplete, and inconsistent over long-haul maritime links. Therefore, this paper investigates distributed uplink scheduling in maritime SAGSINs, where maritime nodes jointly select the access tier, spectrum slice, and transmit power under interference, spectrum, deadline, and energy constraints. Specifically, we formulate the uplink resource scheduling as a cumulative value of information (VoI) maximization problem, and develop a game-theoretic distributed multi-agent reinforcement learning algorithm, termed GTMARL. Therein, maritime nodes learn transmission policies from local observations, coordinated through congestion prices broadcast by access nodes. These prices are derived from Lagrangian relaxation and act as coordination signals that align individual decisions with global objectives. To ensure stable operation, a two-timescale mechanism is adopted, where maritime nodes make fast slot-level transmission decisions, while access nodes adapt and broadcast congestion prices on a slower timescale. Extensive experiments demonstrate that GTMARL achieves up to 90% of the idealized upper bound, significantly outperforming baselines in deadline satisfaction, throughput, delay, energy efficiency and fairness under varying traffic loads and network densities. Full article

(This article belongs to the Section Ocean Engineering)

20 pages, 1630 KB

Open AccessArticle

Influence of Gas Composition on Gas Hydrate Stability Zones in the Northern South China Sea

by Qian Huang, Yong Chen, Miao Wang and Wanjun Lu

J. Mar. Sci. Eng. 2026, 14(4), 336; https://doi.org/10.3390/jmse14040336 - 9 Feb 2026

Abstract

Evaluation of gas hydrate stability in marine sediments is commonly conducted assuming pure methane systems, although increasing drilling and logging evidence indicates that natural gas hydrates frequently contain minor amounts of heavier hydrocarbons. In the northern South China Sea, the presence of ethane [...] Read more.

Evaluation of gas hydrate stability in marine sediments is commonly conducted assuming pure methane systems, although increasing drilling and logging evidence indicates that natural gas hydrates frequently contain minor amounts of heavier hydrocarbons. In the northern South China Sea, the presence of ethane has been widely reported, yet its influence on hydrate phase equilibrium and the distribution of the gas hydrate stability zone (GHSZ) remains insufficiently quantified. The results show that ethane is preferentially incorporated into large cages and promotes structure II hydrate stability, leading to lower dissociation pressures and higher stability temperatures compared with pure methane hydrates. Incorporation of as little as 1 mol% ethane systematically deepens the predicted base of the GHSZ and enlarges the hydrate-free gas coexistence interval beneath the bottom-simulating reflector (BSR). These effects indicate that conventional pure CH₄ models underestimate both the thickness of the hydrate stability zone and the potential extent of hydrate occurrence. At the regional scale, composition-dependent stability provides a coherent explanation for discrepancies between seismic BSR depths and hydrate predictions. This study establishes a composition-sensitive framework for regional GHSZ evaluation, demonstrating that even trace hydrocarbons must be considered to reliably assess hydrate occurrence, resource potential, and associated geohazards in continental margin settings. Full article

(This article belongs to the Section Geological Oceanography)

13 pages, 1716 KB

Open AccessArticle

Estimation of the Length at First Maturity of the Swimming Crab (Portunus trituberculatus) in the Yellow Sea of Korea Using Machine Learning

by Jaehyung Kim, Daehyeon Kwon and Soojeong Lee

J. Mar. Sci. Eng. 2026, 14(4), 335; https://doi.org/10.3390/jmse14040335 - 9 Feb 2026

Abstract

Swimming crab (Portunus trituberculatus) is a commercially valuable species in the Yellow Sea, where recent fluctuations in resource levels have raised concerns about sustainable management. This study aimed to improve the estimation of the carapace length at 50% maturity (L₅₀ [...] Read more.

Swimming crab (Portunus trituberculatus) is a commercially valuable species in the Yellow Sea, where recent fluctuations in resource levels have raised concerns about sustainable management. This study aimed to improve the estimation of the carapace length at 50% maturity (L₅₀) using machine learning techniques, providing a more consistent and reproducible framework for visual maturity classification by standardizing image-based decision processes. Using geometric image augmentation (e.g., rotation, flipping, brightness adjustment), Hue–Saturation–Value (HSV) color segmentation, and algorithms, such as Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and ensemble models, we classified the maturity of female crabs based on gonad color features. Model performance was evaluated using accuracy, AUC, and the TSS, with the ensemble model showing the highest predictive capability. The machine learning-based L₅₀ was estimated at 64.63 mm (±1.73 mm), yielding a narrower uncertainty range than the visually derived L₅₀ of 65.47 mm (±2.89 mm) under the same macroscopic labeling framework. These results suggest that machine learning-assisted maturity classification can enhance the precision and operational consistency of maturity estimation under a standardized framework, while biological accuracy cannot be confirmed in the absence of an independent reference, such as histological validation. Full article

(This article belongs to the Section Marine Biology)

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16 pages, 23180 KB

Open AccessArticle

Noise Characteristics and Shallow Subsurface Structure Detection in Coastal Zones: A Case Study from Dong’ao Island, Zhuhai

by Siqing Liu, Sixu Han, Yongzhi Liang, Shuji Yang, Yi Chai, Tongying Hu, Ruifeng Wu, Yu Li, Qingxian Zhao, Zengjia Li, Wei Zhang, Xianqing Wang and Rui Wang

J. Mar. Sci. Eng. 2026, 14(4), 334; https://doi.org/10.3390/jmse14040334 - 9 Feb 2026

Abstract

Shallow subsurface structure detection in coastal zones serves as a critical foundation for resource development and engineering construction. However, conventional geophysical methods exhibit significant limitations in land–sea transition zones, where pronounced “boundary effects” create substantial “exploration gaps” due to difficulties in merging terrestrial [...] Read more.

Shallow subsurface structure detection in coastal zones serves as a critical foundation for resource development and engineering construction. However, conventional geophysical methods exhibit significant limitations in land–sea transition zones, where pronounced “boundary effects” create substantial “exploration gaps” due to difficulties in merging terrestrial and marine datasets. To achieve truly seamless subsurface imaging across the coastal boundary, this study develops and implements an integrated cross-boundary survey approach utilizing nodal seismometers and seismic ambient noise. At Dong’ao Island, Zhuhai, we deployed a comprehensive seismic profile spanning hillside, sandbeach, and seafloor environments to evaluate the method’s applicability in complex coastal settings systematically. Results demonstrate substantially stronger ambient noise energy in submarine environments compared to terrestrial settings. All stations recorded abundant and stable high-frequency (>1 Hz) noise signals, which are adequate for shallow subsurface imaging. Rayleigh wave dispersion curves extracted via the advanced Frequency-Bessel transform method enabled inversion of a continuous 2D shear-wave velocity profile along the survey line. Bedrock interface depths determined using the Horizontal-to-Vertical Spectral Ratio (HVSR) method showed remarkable consistency with the bedrock morphology revealed by the shear-wave velocity structure, validating the reliability of our approach in coastal environments. This research successfully demonstrates the feasibility of seismic ambient noise imaging as a bridging technique for land–sea exploration, providing an efficient, environmentally friendly, and continuous technical solution to overcome coastal zone exploration challenges. Full article

(This article belongs to the Special Issue Advances in Sedimentology and Coastal and Marine Geology, 3rd Edition)

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30 pages, 14583 KB

Open AccessArticle

MF-CLF: Multi-Feature Chrominance–Luminance Fusion for Blind Underwater Image Quality Assessment

by Wei Chen, Yi Zhang, Damon M. Chandler and Mikolaj Leszczuk

J. Mar. Sci. Eng. 2026, 14(4), 333; https://doi.org/10.3390/jmse14040333 - 9 Feb 2026

Abstract

Underwater images commonly exhibit blurring, color casts, and low contrast due to light attenuation and scattering in water. Although numerous underwater image enhancement (UIE) algorithms have been developed to improve the usability of underwater imaging systems, evaluating the performance of these algorithms remains [...] Read more.

Underwater images commonly exhibit blurring, color casts, and low contrast due to light attenuation and scattering in water. Although numerous underwater image enhancement (UIE) algorithms have been developed to improve the usability of underwater imaging systems, evaluating the performance of these algorithms remains challenging due to the lack of reference images. Thus, blind/no-reference (NR) underwater image quality assessment (UIQA) has emerged as a key research focus. While existing NR-UIQA methods based on luminance and chrominance cues have shown effectiveness, modeling these attributes separately ignores valuable information arising from their joint behavior, since underwater degradations often induce simultaneous changes in luminance and chrominance that cannot be reliably characterized by either attribute alone. In this paper, we propose a lightweight and explainable NR-UIQA method, called multi-feature chrominance–luminance fusion (MF-CLF), based on jointly modeling the intra- and cross-attribute dependencies among chrominance and luminance statistics. Specifically, our approach constructs chrominance-attribute features across multiple color spaces, extracts luminance-attribute features using multi-kernel perceptual descriptors, and models the chrominance–luminance characteristics by explicitly capturing the interactions between the luminance and chrominance attributes. The extracted features are then mapped into a quality score using a support vector machine (SVM), enabling objective and reliable underwater image quality prediction. Experimental results tested on four public benchmark datasets demonstrate that MF-CLF significantly outperforms among lightweight, statistical-learning-based methods. Specifically, our approach achieves an SROCC value of 0.864 on the SAUD2.0 dataset, outperforming existing methods by 20.3%, and demonstrates strong robustness in cross-dataset evaluations with an SROCC value of 0.737, which is more than twice that of the traditional methods. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

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11 pages, 1694 KB

Open AccessArticle

Enhanced Resting Cyst Production in Harmful Dinoflagellate Akashiwo sanguinea Amended with Taebaek Coal Powder

by Jeong Kwon Kim, Xudong Lian, Won Seok Seo and Zhun Li

J. Mar. Sci. Eng. 2026, 14(4), 332; https://doi.org/10.3390/jmse14040332 - 9 Feb 2026

Abstract

The dinoflagellate Akashiwo sanguinea is a prominent harmful algal bloom (HAB) species responsible for significant mortalities of marine fauna. Its life cycle, which includes a benthic resting cyst stage, is fundamental to its bloom dynamics and geographic dispersal. This study investigates the effects [...] Read more.

The dinoflagellate Akashiwo sanguinea is a prominent harmful algal bloom (HAB) species responsible for significant mortalities of marine fauna. Its life cycle, which includes a benthic resting cyst stage, is fundamental to its bloom dynamics and geographic dispersal. This study investigates the effects of Taebaek coal powder, a silicate-rich mineral supplement, on the growth and life-stage transitions of A. sanguinea. Cultures were grown in standard f/2 medium (control) and f/2 medium amended with an extract of the coal powder. We monitored culture performance using fluorometry for quantitative biomass assessment and imaging flow cytometry (FlowCam) for qualitative life-stage analysis. The coal powder amendment conferred a distinct advantage, promoting both vegetative proliferation and the formation of resting cysts. Fluorescence-based measurements showed that the coal powder-amended cultures reached a density equivalent of 3851 ± 214 cells mL⁻¹ by day 4, significantly outpacing the control (2963 ± 351 cells mL⁻¹). Peak vegetative abundance in the treated cultures reached 6967 ± 423 cells mL⁻¹ on day 14, compared to 5979 ± 288 cells mL⁻¹ in the control. Critically, resting cyst production was substantially enhanced in the coal powder treatment, with densities reaching 32–37 cysts mL⁻¹ by the end of the experiment, compared to 22–26 cysts mL⁻¹ in the control. These findings demonstrate that mineral supplementation with Taebaek coal powder can significantly augment both vegetative growth and encystment in A. sanguinea, suggesting a potential link to micronutrient availability, though the underlying mechanisms remain to be elucidated. This enhanced cyst production method may prove valuable for harvesting cysts for ecophysiological research and highlights the need to explore how mineral-induced life-cycle shifts could influence bloom dynamics in a context-dependent manner. Full article

(This article belongs to the Section Marine Biology)

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23 pages, 3810 KB

Open AccessArticle

Comparative Analysis of Performance and Emissions of a Two-Stroke Marine Diesel Engine According to CPP Modes

by Jaesung Moon

J. Mar. Sci. Eng. 2026, 14(4), 331; https://doi.org/10.3390/jmse14040331 - 9 Feb 2026

Abstract

This study experimentally investigates the performance and exhaust emission characteristics of a low-speed two-stroke marine diesel engine operated with different controllable pitch propeller (CPP) modes during actual sea operation. Full-scale measurements were conducted on the training vessel T/S Baek-Kyung, equipped with a MAN [...] Read more.

This study experimentally investigates the performance and exhaust emission characteristics of a low-speed two-stroke marine diesel engine operated with different controllable pitch propeller (CPP) modes during actual sea operation. Full-scale measurements were conducted on the training vessel T/S Baek-Kyung, equipped with a MAN B&W 5S35ME-B9.5 engine, operating under IMO Tier II fallback (FB) conditions. Two CPP control strategies were compared: a constant-speed mode, in which engine speed was maintained at approximately 162 rpm and load was controlled by propeller pitch, and a combinator mode, in which engine speed and pitch were jointly controlled. In the combinator mode, the propeller pitch reached saturation (100%) at approximately 25% load, and further load variation was governed primarily by engine speed. The analysis focused on an engine-load range of approximately 25–75% SMCR and evaluated propulsion performance, including specific fuel oil consumption (SFOC) and shaft torque, together with estimated brake-specific exhaust emissions expressed in g/kWh. The combinator mode achieved superior fuel efficiency under partial-load conditions, reducing SFOC by up to 10.5 g/kWh (5.4%) at 25% load, while increasing shaft torque by up to 47%, indicating improved engine–propeller matching. However, this benefit was accompanied by higher estimated emissions at low load, with BSNO_x increasing from 13.61 to 16.95 g/kWh. As engine load increased, differences in both performance and emissions between the two modes diminished. These results reveal a clear load-dependent trade-off between fuel efficiency and exhaust emissions in CPP operation and emphasize the importance of load-based switching or optimal joint control strategies under off-design conditions. Full article

(This article belongs to the Special Issue Ship Performance and Emission Prediction)

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20 pages, 9724 KB

Open AccessArticle

Analysis and Evaluation of the Impact of Sea-Level Rise on Storm Surges in the Guangdong–Hong Kong–Macao Greater Bay Area

by Juan Zhang, Weiming Xu, Dazhi Xu, Boliang Xu, Changxia Liang, Junjie Deng and Peng Zhou

J. Mar. Sci. Eng. 2026, 14(4), 330; https://doi.org/10.3390/jmse14040330 - 9 Feb 2026

Abstract

Sea-level rise (SLR), a climate hazard driven by global warming, poses a severe threat to low-lying coastal regions when combined with strong typhoons and storm surges, endangering human lives and socio-economic development. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a core strategic [...] Read more.

Sea-level rise (SLR), a climate hazard driven by global warming, poses a severe threat to low-lying coastal regions when combined with strong typhoons and storm surges, endangering human lives and socio-economic development. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a core strategic zone for China’s economic development and is increasingly affected by such compound hazards, exacerbating its storm-related disasters amid climate change. Here, we analyze long-term observational data from the GBA using mathematical statistics and simulation methods to address these climate-related challenges. This study predicts future scenarios of extreme water levels in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), aiming to assess the hazard posed by storm surge disasters under varied sea-level rise (SLR) scenarios. The findings indicate that, under future climate projections, both the extreme water levels in the GBA and the hazard of storm surge disasters in its floodplain areas will exhibit a significant upward trend—with the degree of hazard amplification positively correlated with the magnitude of SLR. This study provides a scientific basis to improve the accuracy of extreme water-level prediction, supporting more reliable short-term early flood warnings. It also offers guidance for optimizing SLR-adapted coastal zone spatial planning, guiding the layout of storm surge control projects and land use in high-hazard areas. Additionally, our results fill a gap in the literature on the SLR’s impact in the GBA and support decision-makers in the GBA in building climate resilience and mitigating disaster hazards. Full article

(This article belongs to the Section Physical Oceanography)

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28 pages, 11528 KB

Open AccessArticle

A New Concept for Docking Vessels

by Adi Tal and Nitai Drimer

J. Mar. Sci. Eng. 2026, 14(4), 329; https://doi.org/10.3390/jmse14040329 - 8 Feb 2026

Abstract

Docking vessels are used to transport and launch landing crafts, for launching offshore platforms, and in other marine operations. This research develops a new concept for docking vessels, with the aim of optimizing landing operations. Our idea involves separating the functions of transit [...] Read more.

Docking vessels are used to transport and launch landing crafts, for launching offshore platforms, and in other marine operations. This research develops a new concept for docking vessels, with the aim of optimizing landing operations. Our idea involves separating the functions of transit and landing into two different vessels, where the transporter is the docking vessel of the lander. This generates an efficient concept, as efficient transportation craft and efficient landing craft have different properties to fulfil their functional requirements. The separation enables the design of each vessel with appropriate performance in areas such as cruising speed, range and seakeeping. These functional specifications affect the whole naval architecture of the vessels. This concept is applicable for shores with no harbor facilities, where landing may be necessary for supply or survey. The transporter provides a floating base to the landing craft, with advanced cruising performance, while the lander design has optimal features for shallow water maneuvering and for landing. The docking vessel is of a Semi-SWATH (Small Water-Plane Area Twin Hull) type. A critical aspect of the design concept is the feasibility of launching and docking operations. This research develops this new concept for docking vessels and applies hydrodynamic response analysis to the transporter’s interaction with the lander, for several operational sea states. The method used for the hydrodynamic analysis involves modeling the vessels and solving the wave–body problem for the two interacting vessels, in the frequency domain as well as in the time domain. The time domain analysis enables us to determine the motion of the vessels in real sea spectra, including the representation of the nonlinear response of fenders between the vessels. We apply the AQWA software 2021 developed by ANSYS. The results validate the suitability of this docking application up to a significant wave height of 1.5 m, which present a margin of 0.1 m above the upper limit of sea state 3: 1.4 m. This shows the feasibility of conducting launching and docking operations using this unique design; there is a significant possibility of using this technique to achieve fast and comfortable transportation to a natural shore with no terminal facilities. Full article

(This article belongs to the Section Ocean Engineering)

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12 pages, 2738 KB

Open AccessArticle

Incorporating Radioactive Decay Chains Within Lagrangian Marine Radionuclide Transport Models for Assessing the Consequences of Nuclear Accidents

by Carmen Cortés and Raúl Periáñez

J. Mar. Sci. Eng. 2026, 14(4), 328; https://doi.org/10.3390/jmse14040328 - 8 Feb 2026

Abstract

Lagrangian particle-tracking models are increasingly used to simulate radionuclide transport in marine environments, especially for assessing the consequences of accidental releases. However, existing models generally neglect radioactive decay chains, limiting their ability to reproduce the complete behavior of radionuclides and their progeny. To [...] Read more.

Lagrangian particle-tracking models are increasingly used to simulate radionuclide transport in marine environments, especially for assessing the consequences of accidental releases. However, existing models generally neglect radioactive decay chains, limiting their ability to reproduce the complete behavior of radionuclides and their progeny. To the authors’ knowledge, this work presents the first implementation of radioactive decay chains within a fully three-dimensional Lagrangian marine radionuclide transport model, explicitly coupling stochastic particle tracking with decay kinetics and dynamic sediment–water interactions, enabling a realistic simulation of parent–daughter transformations in the ocean. The approach is tested for the chain in the Western Mediterranean Sea, following a hypothetical nuclear accident. Results confirm that the stochastic treatment accurately reproduces analytical decay solutions and can be seamlessly incorporated into operational-scale transport simulations. The framework can be extended to other radionuclide series and marine domains, providing a versatile and computationally efficient tool for emergency response, environmental impact assessment, and safety analysis in nuclear engineering applications. Full article

(This article belongs to the Special Issue Dispersion of Radioactive Spills in the Marine Environment Modelling and Simulation)

23 pages, 867 KB

Open AccessArticle

Compensating Environmental Disturbances in Maritime Path Following Using Deep Reinforcement Learning

by Björn Krautwig, Dominik Wans, Till Temmen, Tobias Brinkmann, Sung-Yong Lee, Daehyuk Kim and Jakob Andert

J. Mar. Sci. Eng. 2026, 14(4), 327; https://doi.org/10.3390/jmse14040327 - 8 Feb 2026

Abstract

One of the major challenges in autonomous path following for unmanned surface vehicles (USVs) is the impact of stochastic environmental forces—primarily wind, waves and currents—which introduce nonlinearities that affect control models. Conventional strategies often rely on minimizing cross-track error, resulting in a reactive [...] Read more.

One of the major challenges in autonomous path following for unmanned surface vehicles (USVs) is the impact of stochastic environmental forces—primarily wind, waves and currents—which introduce nonlinearities that affect control models. Conventional strategies often rely on minimizing cross-track error, resulting in a reactive system that corrects heading only after a disturbance has displaced the vessel, potentially leading to oscillatory behavior and reduced precision. Deep Reinforcement Learning (DRL) is successfully used for a wide range of nonlinear control tasks. It has already been shown that robust solutions that can handle disturbances such as sensor noise or changes in system dynamics can be obtained. This study investigates whether an agent, provided it can explicitly observe disturbances, can go beyond simply correcting deviations and autonomously learn the correlation between environmental conditions and necessary counter-forces. We show that integrating the wind vector directly into the agent’s observation space allows a Proximal Policy Optimization (PPO) policy to decouple the environmental cause from the kinematic effect, facilitating drift compensation before significant errors accumulate. By systematically comparing agents trained with randomized wind scenarios, we found that agents that can observe the wind can achieve goal reaching rates of up to 99.0% and reduce the spread of path deviation and velocity in our tested scenarios. Furthermore, our results quantify a distinct Pareto frontier between navigational velocity and tracking precision, demonstrating that explicit disturbance perception improves consistency, although robust implicit training already provides substantial resilience. These findings indicate that augmenting state observations with environmental data enhances the stability of learning-based controllers. Full article

(This article belongs to the Special Issue Dynamics and Control of Marine Mechatronics)

31 pages, 11304 KB

Open AccessArticle

Effect of Density Ratio and Surface Tension on Vortex–Interface Interactions: A Numerical Study

by Xiaobin Yang, Yiding Hu, Zhihan Li, Chenghan Wu, Ping Wei, Weige Liang and Shiyan Sun

J. Mar. Sci. Eng. 2026, 14(4), 326; https://doi.org/10.3390/jmse14040326 - 7 Feb 2026

Abstract

In two-phase flow, the interaction between multi-scale vortex structures and interfaces (bubbles or free surfaces) triggers a range of complex physical phenomena. This study employs numerical simulations to investigate the interaction between a horizontal vortex and the interface separating two layers of immiscible [...] Read more.

In two-phase flow, the interaction between multi-scale vortex structures and interfaces (bubbles or free surfaces) triggers a range of complex physical phenomena. This study employs numerical simulations to investigate the interaction between a horizontal vortex and the interface separating two layers of immiscible fluids with different densities (e.g., water and air). The vortex is initialized as an internal motion within the heavier phase. We focus specifically on the impact of the phase density ratio and surface tension. Numerical simulations reveal that when the density ratio is near unity, interface rupture occurs only at high Weber numbers (We), where low surface tension enables the rupture of sharp interface points. Conversely, at high surface tension (low We), these sharp points stretch into thin liquid films, significantly increasing the surface area without causing breakage. As the density ratio increases, interface rupture at sharp points accelerates, even under high surface tension, leading to faster dissipation of the initial vortex. In high-We scenarios, an increased density ratio promotes the faster formation and greater intensity of new vortex layers at the interface. However, increasing surface tension enhances the vorticity of these layers but simultaneously slows their generation rate. The findings highlight the critical interplay between surface tension and density differences in vortex–interface interactions, with surface tension stabilizing the interface and density differences driving more intense vortex shedding and deformation. These insights offer valuable guidance for understanding two-phase flow behavior and improving the design of systems involving multiphase fluids. Full article

(This article belongs to the Section Physical Oceanography)

29 pages, 4219 KB

Open AccessArticle

Random Vibrations of Wind Turbines Mitigated by the Hourglass Transition Piece

by Alessandro Tombari, Marco Fabiani and Yucheng Peng

J. Mar. Sci. Eng. 2026, 14(4), 325; https://doi.org/10.3390/jmse14040325 - 7 Feb 2026

Abstract

Wind turbines are subjected to complex stochastic loadings generated by various environmental sources, including wind, waves, and earthquakes. Efficient mitigation of the resulting vibrations in the structural components, such as the tower and monopile, leads to more cost-effective designs and longer operational life [...] Read more.

Wind turbines are subjected to complex stochastic loadings generated by various environmental sources, including wind, waves, and earthquakes. Efficient mitigation of the resulting vibrations in the structural components, such as the tower and monopile, leads to more cost-effective designs and longer operational life by reducing fatigue accumulation. Conventional vibration control systems have primarily relied on tuned mass dampers. However, alternative and non-conflicting strategies that modify the connection between the tower and the foundation at the transition piece level have recently gained attention. This study investigates the hourglass transition piece (HGTP), a novel concept that utilises the Reduced Column Section approach. The hourglass geometry enables fine-tuning of the wind turbine’s fundamental period and introduces controlled rotational motion, both contributing to a reduction in structural stresses and improved dynamic performance. To assess the efficacy of the HGTP as a vibration control system, an analytical model of a simplified wind turbine is developed. The formulation employs frequency-dependent solutions of prismatic and tapered beam elements, assembled to capture the dynamic behaviour of the turbine equipped with the HGTP. Exact dynamic stiffness matrices are derived and assembled into a stochastic framework suitable for uniformly modulated non-stationary random processes. Modal and dynamic responses are evaluated for different reductions of the hourglass central section. A case study based on the IEA 15 MW Reference Wind Turbine demonstrates that the HGTP can mitigate stochastic mean peak bending moments induced by wind and earthquake excitations by up to 50%, confirming its potential as an effective vibration control solution. Full article

(This article belongs to the Special Issue New Era in Offshore Wind Energy)

29 pages, 8256 KB

Open AccessArticle

A Novel ROA-Optimized CNN-BiGRU Hybrid Network with an Attention Mechanism for Ship Fuel Consumption Prediction

by Zifei Wang, Kai Wang, Zhongwei Li, Hongzhi Liang, Shuo Yin, Qitai Ma, Diankang Zhang and Weijie Xiong

J. Mar. Sci. Eng. 2026, 14(4), 324; https://doi.org/10.3390/jmse14040324 - 7 Feb 2026

Abstract

Optimizing ship energy efficiency and advancing the green transition of the shipping industry depend on an accurate model for predicting ship fuel consumption (FC). This study builds a hybrid prediction model that combines a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), [...] Read more.

Optimizing ship energy efficiency and advancing the green transition of the shipping industry depend on an accurate model for predicting ship fuel consumption (FC). This study builds a hybrid prediction model that combines a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism using operational data from ships. The model is tuned using the Red Kite Optimization Algorithm (ROA). First, correlations between ship navigational environmental data and operational data are analyzed, and cluster analysis is performed to select suitable input features. Subsequently, the ship FC prediction model based on ROA-CNN-BiGRU-Attention (RCGA) is developed. A case study shows that the RCGA model reaches a root mean square error (RMSE) as low as 0.0205 and an R² value as high as 0.9330, demonstrating strong performance in dynamic shipping scenarios, with advantages in handling temporal dependencies and complex operational patterns. Moreover, the model exhibits reasonable robustness, providing some support for ship energy efficiency optimization and assisting the shipping industry in advancing low-carbon development and sustainable green transition. Full article

(This article belongs to the Topic Maritime Transportation in the Blue Economy and Green Shipping Technology)

25 pages, 7057 KB

Open AccessArticle

Reinforcement-Learning-Based Adaptive PID Depth Control for Underwater Vehicles Against Buoyancy Variations

by Jian Wang, Shuxue Yan, Honghao Bao, Cong Chen, Deyong Yu, Jixu Li, Xi Chen, Rui Dou, Yuangui Tang and Shuo Li

J. Mar. Sci. Eng. 2026, 14(4), 323; https://doi.org/10.3390/jmse14040323 - 7 Feb 2026

Abstract

Underwater vehicles performing sampling tasks often encounter significant buoyancy variations due to payload adjustments and environmental changes, which severely challenge the stability and accuracy of controllers. To address this issue, this paper proposes a hybrid control framework that integrates Proximal Policy Optimization (PPO) [...] Read more.

Underwater vehicles performing sampling tasks often encounter significant buoyancy variations due to payload adjustments and environmental changes, which severely challenge the stability and accuracy of controllers. To address this issue, this paper proposes a hybrid control framework that integrates Proximal Policy Optimization (PPO) with adaptive PID tuning. The framework employs PPO to dynamically adjust PID parameters online while incorporating output saturation, stepwise quantization, and dead zone filtering to ensure control safety and actuator longevity. A dual-error state representation—combining instantaneous error and its derivative—along with actuator command buffering is introduced to compensate for system lag and inertia. Comparative simulations and experimental tests demonstrate that the proposed method achieves faster convergence, lower steady-state error, and smoother control signals compared to both conventional PID and pure PPO-based control. The framework is validated through pool tests and field trials, confirming its robustness under realistic hydrodynamic disturbances. This work provides a practical and safe solution for adaptive depth control of sampling-capable AUVs operating in dynamic underwater environments. Full article

(This article belongs to the Section Ocean Engineering)

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