Search Results (425)

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

This research study investigated the composite magnetic fields of underwater vehicles in the presence of ocean waves under varying conductivity, analyzed their spatiotemporal characteristics, attenuation laws, and influence mechanism. We integrated the modeling of three types of magnetic fields to obtain a composite magnetic field: the magnetic anomaly field generated by a ferromagnetic vehicle was simulated with a hybrid ellipsoid–dipole model, the wake magnetic field generated by its motion, and the ocean wave magnetic field generated by wind-driven waves were derived from the velocity fields. Simulation results show that the magnetic anomaly and wake magnetic fields are mainly influenced by vehicle speed, course, and diving depth, while the ocean wave magnetic field is affected by wind speed and direction. The composite magnetic field’s intensity increases with vehicle and wind speed but decreases with the increase in diving depth. This study offers a comprehensive analysis of the composite magnetic fields of underwater vehicles in the presence of ocean waves, emphasizing the significant impact of vehicle motion and marine environmental parameters. These insights are essential to gaining a deeper understanding of the magnetic fields generated by underwater vehicles as they navigate ocean waves. Full article

In this paper, we propose a quantitative evaluation method for navigation safety in coastal waters based on unstructured grids. Initially, a comprehensive analysis was conducted on various factors affecting navigation safety, including natural conditions, traffic conditions, and marine hydro-meteorological conditions, to construct a multi-source fused spatiotemporal dataset. Subsequently, channel boundary extraction was performed using Constrained Delaunay Triangle–Alpha-Shapes, and the precise extraction of ship navigation areas was performed based on Constrained Delaunay Triangle–Voronoi diagrams. Additionally, temporal feature grids were constructed based on the spatiotemporal characteristics of marine hydro-meteorological data. Finally, unstructured grids for evaluating navigation safety were established through spatial overlay analysis. Based on this foundation, a quantitative analysis and evaluation model for comprehensive navigation safety assessment was developed using the fuzzy evaluation method. By calculating the fuzzy relation matrix and weight vectors, quantitative assessments were conducted for each grid cell, yielding safety risk levels from both spatial and temporal dimensions. An analysis was performed using maritime data within the geographic boundaries of lon.119.17–120.41° E and lat.34.40–35.47° N. The results indicated that the unstructured grid division and channel boundary extraction in the demonstrated sea area were closely related to parameters such as the ship traffic flow patterns and the spatiotemporal characteristics of the marine environmental factors. A quantitative evaluation and analysis of the 186 unstructured grid cells revealed that the high risk levels primarily corresponded to restricted navigation areas, the higher-risk grid cells were mainly anchorages, and the low to lower risk levels were primarily associated with channels and navigable areas for ships. Full article

To address the issues of large fatigue loads on key components and poor platform motion stability under the coupling effect of wind, waves, and internal excitations in semi-submersible wind turbines, this paper proposes a data-driven load suppression and platform motion optimization method. First, the NREL 5 MW OC4 semi-submersible wind turbine is used as the research object. Wind-wave environment and aeroelastic simulation models are constructed based on TurbSim and OpenFAST. The rainflow counting method and Palmgren–Miner rule are applied to calculate the damage equivalent load (DEL) of key components, and the platform’s maximum horizontal displacement (Smax) is defined to represent the motion range. Secondly, a systematic analysis is conducted to examine the effects of servo control variables such as generator speed, yaw angle, and active power on the DELs of the blade root, tower base, drivetrain, mooring cables, and platform Smax. It is found that the generator speed and the yaw angle have significant impacts, with the DELs of the blade root and drivetrain showing a strong positive correlation with Smax. On this basis, a fatigue load model based on random forests is established. A multi-objective optimization framework is built using the NSGA-II algorithm, with the objectives of minimizing the total DEL of key components and Smax, thereby optimizing the servo control parameters. Case studies based on actual marine environmental data from the East China Sea show that, compared to the baseline configuration (a typical unoptimized control strategy), the optimization results lead to a maximum reduction of 14.1% in the total DEL of key components and a maximum reduction of 16.95% in Smax. The study verifies the effectiveness of data-driven modeling and multi-objective optimization for coordinated control, providing technical support for improving the structural safety and operational stability of semi-submersible wind turbines. Full article

Chrysotila dentata (Haptophyta), a harmful algal bloom (HAB) species frequently occurring in coastal waters of China, is one with strong environmental adaptability that poses a serious threat to marine ecosystems and fisheries. Current molecular detection techniques and early warning systems for this species remain limited. To address this, we developed a rapid and highly sensitive detection method for C. dentata. This method integrates recombinase polymerase amplification (RPA) with CRISPR-LbaCas12a and lateral flow dipstick (LFD) technologies, enabling visual readout of results. Key parameters, including the single-stranded DNA (ssDNA) reporter concentration, reaction time, and temperature, were systematically optimized. Field water sample testing demonstrated high specificity and sensitivity, achieving a detection limit of 5 × 10⁻⁶ pg μL⁻¹ for genomic DNA under laboratory conditions and 2.82 × 10¹ cells mL⁻¹ in simulated environmental samples. The entire detection process takes only 1 h (at a constant 39 °C), and results can be directly interpreted via LFD strips. For early warning and prevention of C. dentata outbreaks, this assay provides a powerful, reliable, and field-ready monitoring tool. Full article

This primary study aims to optimize network resource utilization efficiency in marine control systems. A novel event-triggering condition is proposed to significantly reduce communication traffic, where the error norm is squared while the input norm remains linear. To simulate realistic environmental disturbances, bounded unknown parameters are incorporated. Within the networked transmission architecture, input quantization is introduced, enabling the design of a quantized feedback controller without prior knowledge of quantization parameters. By integrating the event-triggering mechanism with sliding mode control, a quantized feedback control system is developed. The closed-loop system’s stability is rigorously proven via Lyapunov theory, with guaranteed boundedness of trajectory tracking errors. Numerical simulations validate the effectiveness of the proposed method for marine vehicle trajectory control under environmental disturbances. Full article

With the increasing deployment of unmanned surface vessels (USVs) in complex marine operations such as ocean monitoring, search and rescue, and military reconnaissance, precise heading control under environmental disturbances and system delays has become a critical challenge. This paper presents an advanced robust heading control strategy for USVs operating under these demanding conditions. The proposed approach integrates three key innovations: (1) an enhanced Smith predictor for accurate time-delay compensation, (2) a variable-universe fuzzy PID controller with self-adaptive scaling domains that dynamically adjust to error magnitude and rate of change, and (3) a hybrid metaheuristic optimization algorithm combining beetle antennae search, harmony search, and genetic algorithm (BAS-HSA-GA) for optimal parameter tuning. Through comprehensive simulations using a Nomoto first-order time-delay model under combined white noise and second-order wave disturbances, the system demonstrates superior performance with over 90% reduction in steady-state heading error and ≈30% faster settling time compared to conventional PID and single-optimization fuzzy PID methods. Field trials under sea-state 4 conditions confirm 15–25% lower tracking error in realistic operating scenarios. The controller’s stability is rigorously verified through Lyapunov analysis, while comparative studies show significant improvements in S-shaped path tracking performance, achieving better IAE/ITAE metrics than DRL, ANFC, and ACO approaches. This work provides a comprehensive solution for high-precision, delay-resilient USV heading control in dynamic marine environments. Full article

Amid growing global concerns over environmental sustainability, the shipping industry is under increasing pressure to implement innovative power systems that minimize ecological impact. A promising approach is the marine gas–electric hybrid system, which combines conventional marine propulsion with electric power to offer a cleaner energy solution. Characterized by the integration of continuous and discrete variables, these systems reflect the hybrid nature of gas–electric propulsion. Despite their potential, research on marine hybridization remains limited. To address this gap, a hybrid system model has been developed to optimize energy allocation while accurately capturing the hybrid characteristics of gas–electric systems in ships. Additionally, an energy distribution strategy based on predictive control has been proposed to validate the model’s practical applicability. A weighted evaluation method was employed on a marine gas–electric hybrid test platform to verify the performance of both the model and the control strategy. Results show that different weighting configurations lead to varying torque distribution patterns, confirming the effectiveness of the hybrid system model. Moreover, tuning the weighting parameters within the energy allocation strategy yields diverse control behaviors, further demonstrating the system’s viability for marine applications. Full article

Full waveform inversion (FWI) comprehensively utilizes phase and amplitude information of seismic waves to obtain high-resolution subsurface medium parameter models, applicable to both active-source and passive-source seismic data. Passive-source seismic exploration, using natural earthquakes or ambient noise, reduces costs and environmental impact, with growing marine applications in recent years. Its rich low-frequency content makes passive-source FWI (PSFWI) a key research focus. However, PSFWI inversion quality relies heavily on accurate virtual source reconstruction. While multi-dimensional deconvolution (MDD) can handle uneven source distributions, it struggles with irregular receiver sampling. We propose a robust MDD method based on multi-domain stepwise interpolation to improve reconstruction under non-ideal source and sampling conditions. This approach, validated via an adaptive PSFWI strategy, exploits MDD’s insensitivity to source distribution and incorporates normalized correlation objective functions to reduce amplitude errors. Numerical tests on marine and complex scattering models demonstrate stable and accurate velocity inversion, even in challenging acquisition environments. Full article

The Indian Ocean is globally significant in terms of capture fisheries, and understanding the species composition of fish in the Indian Ocean is of great importance for the protection and development of its fishery resources. While coastal fish communities in the Indian Ocean are relatively well-documented, studies on pelagic zones remain sparse, especially for non-target species constituting fishery bycatch. Traditional biodiversity surveys rely on labor-intensive, inefficient trawling methods. To address these limitations, this study aims to apply environmental DNA (eDNA) metabarcoding for a species diversity survey in the Western Indian Ocean, offering a more reliable, efficient, and non-invasive alternative to traditional methods. The results will provide important insights into the region’s fish biodiversity, supporting sustainable management and conservation of fisheries resources in the area. Samples were collected from 130 stations in different water layers in the Western Indian Ocean, and species diversity was analyzed through 12S rRNA gene amplicon sequencing. The results showed that 98 fish species were detected from 176 seawater eDNA samples, belonging to two classes (Actinopteri and Chondrichthyes), 20 orders, 35 families, and 60 genera. Within a depth range of 300 m, there were no significant differences in species diversity parameters among samples from different depths. The orders with the highest relative abundance detected include Scombriformes, Aulopiformes, and Myctophiformes. The species with the highest relative abundance include Thunnus albacares, Alepisaurus ferox, Xiphias gladius, Diaphus fragilis, Decapterus macarellus, Thunnus maccoyii, and Platycephalus cultellatus. The species composition and relative abundance of economic species observed in this study showed, as expected, differences from fishery catch statistics. These results suggest that eDNA technology can not only monitor marine fish diversity more efficiently but also complement the lack of fisheries data. Integrating eDNA technology into routine monitoring in the Western Indian Ocean in the future could promote sustainable management of fisheries resources in the region. Full article

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

Marine shelled organisms exhibit diverse growth strategies shaped by species-specific traits and environmental conditions that critically influence their ecological roles, particularly within Marine Animal Forests (MAF), which are structurally complex habitats and biodiversity-rich habitats. This review compiles and compares empirical growth data for 16 bivalve and gastropod species across seven families, classified as full MAF contributors (Pinna nobilis, Flexopecten glaber, Pecten maximus, and Placopecten magellanicus), partial MAF contributors (Cerastoderma edule, C. glaucum, Chamelea gallina, Ruditapes philippinarum, Mercenaria mercenaria, Panopea generosa, Anadara kagoshimensis, A. inaequivalvis, and Tegillarca granosa), and ecologically relevant non-MAF species (Buccinum undatum, Hexaplex trunculus, and Rapana venosa). Age estimation methods included direct techniques, such as shell growth ring and opercular annulus analysis, alongside indirect approaches, such as length-frequency analysis, stable isotope profiling, and mark–recapture studies. Growth trajectories were modelled using von Bertalanffy growth function (VBGF) parameters to estimate the shell size from ages 1 to 4. Based on these estimates, species were categorised into slow, moderate, fast, and exceptional growth groups. These classifications were further explored through hierarchical clustering that grouped species according to their VBGF-derived growth values, revealing consistent and contrasting life history strategies. This comparative analysis should enhance the understanding of molluscan growth dynamics and support the conservation and management of MAF-associated ecosystems by informing restoration planning, guiding species selection, and contributing to evidence-based policy development. Full article

The application of marine algae-derived biostimulants offers a sustainable approach to improving plant performance in aromatic and medicinal crops. This study investigated the effects of four macroalgal extracts and two commercial biostimulant products on the growth, physiology, and essential oil production of Lavandula angustifolia cultivated under greenhouse conditions at CREA, Pescia (Italy). Treatments included extracts from Ascophyllum nodosum (France and Greenland), Laminaria digitata (Iceland), Sargassum muticum (Italy), two commercial formulations (a seaweed-based and an amino acid-based biostimulant), and a control receiving only standard fertilization. Over a 10-week period, plants were evaluated for multiple parameters: plant height, leaf number and area, SPAD index (chlorophyll content), above- and below-ground biomass, flower production, microbial activity in the growth substrate, and essential oil yield. Algae extracts, particularly those from A. nodosum (Greenland) and S. muticum (Venice), significantly enhanced most parameters compared to the control and commercial products. These treatments yielded higher biomass, greater chlorophyll retention, increased flower number, and improved essential oil content. Rhizosphere microbial counts were also elevated, indicating a positive interaction between algae treatments and substrate biology. The study highlights the multifunctional nature of marine algae, whose complex composition of bioactive compounds appears to promote plant growth and secondary metabolism through multiple pathways. The superior performance of cold- and temperate-climate algae suggests a relationship between environmental origin and biostimulant efficacy. Compared to commercial inputs, the tested algae extracts showed broader and more consistent effects. These findings support the integration of macroalgae-based biostimulants into sustainable lavender cultivation strategies. Further research is recommended to optimize formulations, validate field performance, and explore synergistic effects with beneficial microbes or organic inputs. Full article

Underwater debris detection plays a crucial role in marine environmental protection. However, existing object detection algorithms generally suffer from excessive model complexity and insufficient detection accuracy, making it difficult to meet the real-time detection requirements in resource-constrained underwater environments. To address this challenge, this paper proposes a novel lightweight object detection network named the Shared Pyramid Lightweight Network (SPyramidLightNet). The network adopts an improved architecture based on YOLOv11 and achieves an optimal balance between detection performance and computational efficiency by integrating three core innovative modules. First, the Split–Merge Attention Block (SMAB) employs a dynamic kernel selection mechanism and split–merge strategy, significantly enhancing feature representation capability through adaptive multi-scale feature fusion. Second, the C3 GroupNorm Detection Head (C3GNHead) introduces a shared convolution mechanism and GroupNorm normalization strategy, substantially reducing the computational complexity of the detection head while maintaining detection accuracy. Finally, the Shared Pyramid Convolution (SPyramidConv) replaces traditional pooling operations with a parameter-sharing multi-dilation-rate convolution architecture, achieving more refined and efficient multi-scale feature aggregation. Extensive experiments on underwater debris datasets demonstrate that SPyramidLightNet achieves 0.416 on the mAP@0.5:0.95 metric, significantly outperforming mainstream algorithms including Faster-RCNN, SSD, RT-DETR, and the YOLO series. Meanwhile, compared to the baseline YOLOv11, the proposed algorithm achieves an 11.8% parameter compression and a 17.5% computational complexity reduction, with an inference speed reaching 384 FPS, meeting the stringent requirements for real-time detection. Ablation experiments and visualization analyses further validate the effectiveness and synergistic effects of each core module. This research provides important theoretical guidance for the design of lightweight object detection algorithms and lays a solid foundation for the development of automated underwater debris recognition and removal technologies. Full article

Significant wave height (SWH) is a key physical parameter influencing the safety of shipping, fisheries, and marine engineering projects, and is closely related to climate change and marine disasters. Existing models struggle to balance a high prediction accuracy with low parameter counts, and are challenging to deploy on platforms such as buoys. To address these issues, this study proposes an innovative method for SWH prediction by combining Singular Spectrum Analysis (SSA) with a residual correction mechanism in a Long Short-Term Memory (LSTM) network. This method utilizes SSA to decompose SWH time series, accurately extracting its main feature modes as inputs to the LSTM network and significantly enhancing the model’s ability to capture time-series data. Additionally, a residual correction module is introduced to fine-tune the prediction results, effectively improving the model’s 12 h forecasting accuracy. The experimental results show that for 1, 3, 6, and 12 h SWH predictions, by incorporating SSA and the residual correction module, the model reduces the Mean Squared Error (MSE), Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) by 60–95%, and increases the coefficient of determination (R²) by 2–60%. The proposed model has only 10% of the parameters for LSTM based on Variational Mode Decomposition (VMD), striking an excellent balance between prediction accuracy and computational efficiency. This study provides a new methodology for deploying SWH prediction models on platforms such as buoys, and holds significant application value in marine disaster warning and environmental monitoring. Full article