Search Results (557)

As global Marine resource development continues to expand into deep-sea and ultra-deep-sea domains, the intelligent and green transformation of deep-sea aquaculture equipment has become a key direction for high-quality development of the Marine economy. Large deep-sea cages are considered essential equipment for deep-sea aquaculture. However, there are significant challenges associated with ensuring their structural integrity and long-term monitoring capabilities in the complex Marine environments characteristic of deep-sea aquaculture. The present study focuses on large deep-sea cages, addressing their dynamic response challenges and long-term monitoring power supply needs in complex Marine environments. The present study investigates the nonlinear vibration characteristics of flexible net structures under complex fluid loads. To this end, a multi-field coupled dynamic model is constructed to reveal vibration response patterns and instability mechanisms. A self-powered sensing system based on triboelectric nanogenerator (TENG) technology has been developed, featuring a curved surface adaptive TENG array for the real-time monitoring of net vibration states. This review aims to focus on the research of optimizing the design of curved surface adaptive TENG arrays and deep-sea cage monitoring. The present study will investigate the mechanisms of energy transfer and cooperative capture within multi-body coupled cage systems. In addition, the biomechanics of fish–cage flow field interactions and micro-energy capture technologies will be examined. By integrating different disciplinary perspectives and adopting innovative approaches, this work aims to break through key technical bottlenecks, thereby laying the necessary theoretical and technical foundations for optimizing the design and safe operation of large deep-sea cages. Full article

Fungi of the genus Parengyodontium (Ascomycota, Hypocreales, Cordycipitaceae) are emerging as promising sources of secondary metabolites with significant biotechnological potential. While traditionally understudied, species such as Parengyodontium album, Parengyodontium torokii and Parengyodontium americanum have been isolated from diverse and sometimes extreme environments—including deep-sea sediments, mangroves, and NASA clean rooms—suggesting remarkable ecological adaptability. This review presents a comprehensive synthesis of current knowledge on the chemical diversity, biological activities, and potential industrial applications of secondary metabolites produced by fungi belonging to the genus. A wide variety of compounds have been identified, including polyketides (e.g., engyodontiumones, alternaphenol B2), terpenoids (e.g., cytochalasin K), alkaloids, and torrubielline derivatives. These metabolites exhibit cytotoxic, antibacterial, and antifouling properties, with promising anticancer and antimicrobial activities. In addition, recent evidence points to the genus’s role in bioremediation, particularly through the degradation of polyethylene by P. album. Despite the advances highlighted here, challenges remain in scaling production, elucidating biosynthetic pathways, and confirming in vivo efficacy. This review underscores the value of integrating chemical, genomic, and metabolomic approaches to fully unlock the biotechnological potential of Parengyodontium species. Additionally, we broaden the perspective by comparing trends in secondary metabolites among Cordycipitaceae, highlighting lifestyle-related chemical compounds that serve as a reference for the Parengyodontium profile. Full article

This study presents a deep learning–assisted integrated navigation scheme implemented on an autonomous underwater vehicle carrying a Chinese domestically developed strapdown inertial navigation system, designed for operation in surface and littoral environments. The system integrates measurements from SINS, the global positioning system, and a Doppler velocity log, while integrating a Decoder-based covariance estimator into the error state-extended Kalman filter. This hybrid architecture adaptively models time-varying processes and measurement noise from raw sensor inputs, greatly improving robustness for surface navigation in dynamic marine environments. To improve learning efficiency, we design a compact and informative feature representation that can be adapted to navigation error dynamics. The novel structure captures temporal dependencies and the evolution of nonlinear error more effectively than typical sequence models, achieving faster convergence and superior accuracy compared to GRU and Transformer baselines. The experimental results based on real sea trial data show that our method significantly outperforms model-based and learning-based methods in terms of navigation solution accuracy and stability, and the adaptive estimation of noise covariance. Specifically, it achieves the lowest RMSE of 0.0274, reducing errors by 94.6–34.6%, compared to conventional ES-EKF-integrated navigation, Transformer, GRU, and a DCE variant. These findings underscore the practical significance of integrating domain-informed filtering methodologies with deep noise modeling frameworks to achieve robust and accurate AUV surface navigation. Full article

Rubber bearings play a critical role as core components within the transmission systems of marine equipment. Investigating the evolution of their water-film stiffness coefficient under deep-sea conditions can provide deeper insights into the dynamic characteristics of water-lubricated transmission systems. Employing a viscoelastic mixed-lubrication framework designed for water lubricated rubber bearings, this paper examines the necessity of accounting for rubber hyperelasticity and extreme subsea conditions (high pressure and low temperature) when analyzing the water-film stiffness coefficient of such bearings (at a depth of 1000 m, the relative error in the k_xz component between the linear viscoelastic model and the visco-hyperelastic model reaches as high as 18.41%.). On this basis, the influence of subsea environments together with rotational velocity on the water-film stiffness coefficient is further investigated, and the dependence of the dimensionless critical mass on the eccentricity ratio for water-lubricated rubber bearings operating under deep-ocean conditions is explored. The results provide a theoretical analysis tool for evaluating the water-film stiffness coefficient of subsea rubber bearings, and offer guidance for the forward design of water-lubricated rubber bearings applied in deep-sea service. Full article

Seismic facies recognition constitutes a fundamental task in seismic data interpretation, playing an essential role in characterizing subsurface geological structures, sedimentary environments, and hydrocarbon reservoir distributions. Conventional approaches primarily depend on expert interpretation, which often introduces substantial subjectivity and operational inefficiency. Although deep learning-based methods have been introduced, most rely solely on unimodal data—namely, seismic images—and encounter challenges such as limited annotated samples and inadequate generalization capability. To overcome these limitations, this study proposes a multimodal seismic facies recognition framework named GAT-UKAN, which integrates a U-shaped Kolmogorov–Arnold Network (U-KAN) with a Graph Attention Network (GAT). This model is designed to accept dual-modality inputs. By fusing visual features with knowledge embeddings at intermediate network layers, the model achieves knowledge-guided feature refinement. This approach effectively mitigates issues related to limited samples and poor generalization inherent in single-modality frameworks. Experiments were conducted on the F3 block dataset from the North Sea. A knowledge graph comprising 47 entities and 12 relation types was constructed to incorporate expert knowledge. The results indicate that GAT-UKAN achieved a Pixel Accuracy of 89.7% and a Mean Intersection over Union of 70.6%, surpassing the performance of both U-Net and U-KAN. Furthermore, the model was transferred to the Parihaka field in New Zealand via transfer learning. After fine-tuning, the predictions exhibited strong alignment with seismic profiles, demonstrating the model’s robustness under complex geological conditions. Although the proposed model demonstrates excellent performance in accuracy and robustness, it has so far been validated only on 2D seismic profiles. Its capability to characterize continuous 3D geological features therefore remains limited. Full article

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

The large environmental and economic impact of sea lice infestation in the salmon industry has encouraged the development of non-medical methods and preventive strategies to combat sea lice infestation. Sea lice (Lepeophtheirus salmonis and Caligus elongatus) are sensitive to low salinities, and using fresh water as protection against infection may thus significantly reduce sea lice infestation of salmon while reducing the costs and impacts of traditional delousing methods. A new concept presented here is based on the manipulation of salinity within cages by adding fresh water to create an unfavourable environment for sea lice infestation. A full-scale set-up was tested in a salmon farm in northern Norway: two commercial-size cages with Atlantic salmon (Salmo salar) were enclosed with a 2 m deep tarpaulin skirt and supplied with fresh water at the centre to establish a surface layer with reduced salinity. Two reference cages had no skirt or fresh water supply. Time series of CTD-data showed that the fresh water supply caused a shallow and unstable salinity gradient, with salinities lower than 10 ppt measured for short periods in the upper 0.5 m. Despite these instabilities, significantly lower sea lice infestation in cages supplied with fresh water was observed, as infestation rates for pre-adult and adult stages of L. salmonis were reduced by 48% and 57%, respectively, in the treatment cages compared to controls. This preventive strategy is therefore very promising and deserves further development under more stable and controlled conditions. Future studies should focus on improving freshwater regulation, ensuring higher spatial resolution of salinity data in surface layers and documenting the effect on the more salinity-sensitive planktonic stages of L. salmonis. In addition, there is a need to examine the effectiveness of the technique at multiple sites and under a wide range of site conditions, especially various current rates through the site. Full article

Conventional acoustic positioning systems are typically confined to regions where direct-path measurements are available. However, in long-range underwater environments, acoustic rays undergo multiple reflections at the sea surface and seafloor, complicating the modeling of sound speed and introducing uncertainty due to seafloor bathymetric errors. To address these challenges, a high-precision positioning technology suitable for long-range deep-sea scenarios is proposed. This technology constructs an effective sound speed model based on ray-tracing principles to accommodate multipath propagation. To mitigate model errors caused by inaccurate seafloor bathymetry, a sound speed compensation mechanism is introduced to enhance the precision of reflected-path measurements. The experimental results demonstrate that, with an array baseline of 8 km, the proposed method reduces the maximum ranging error over a 50 km horizontal distance from 137.9 m to 15.5 m. The root-mean-square positioning error is decreased from 157.9 m to 31.0 m, representing an improvement in positioning precision of 80.4%. These results confirm the feasibility of high-precision long-range acoustic positioning. Full article

This paper focuses on the control problems of a multi-modal underwater robot, which is designed mainly for the task of detecting the working environment in deep-sea mining. To tackle model uncertainty and external disturbances, an improved linear active disturbance rejection control scheme based on sliding mode control is proposed (SM-ADRC). Firstly, to reduce overshoot, a piecewise $fhan$ function is introduced into the tracking differentiator (TD). This design retains the system’s fast nonlinear tracking characteristics outside the boundary layer while leveraging linear damping within it to achieve effective overshoot suppression. Secondly, two key enhancements are made to the SMC: an integral sliding surface is designed to improve steady-state accuracy, and a saturation function replaces the sign function to suppress high-frequency chattering. Furthermore, the SMC integrates the total disturbance estimate from the linear extended state observer (LESO) for feedforward compensation. Finally, the simulation experiment verification is completed. The simulation results show that the SM-ADRC scheme significantly improves the dynamic response and disturbance suppression ability of the system and simultaneously suppresses the chattering problem of SMC. Full article

Unbonded flexible risers, which can experience large bending deformation, are key equipment in advancing deep-sea exploration for marine resources. However, the riser experiences coupled loading effects from ocean environment. This results in complex response characteristics, leading to potential damage or even destruction. By presenting an analytical–numerical framework, this study uncovers the mechanism underlying the coupled failure of the pressure- and tensile-armor layers, furnishes a new tension–pressure coupled failure boundary for the ultimate-limit-state design of deep-water risers, and supplies the corresponding theoretical verification. Firstly, based on the axisymmetric load assumption, a theoretical model is proposed based on principle of functionality; afterwards, the failure model is defined by considering the material elastoplasticity. Secondly, a full-layered numerical model with detailed geometric properties is established; meanwhile, a simplified 7-layer model without a carcass layer is constructed for comparison. Finally, after verified through experimental data and interactive verification of theoretical and numerical methods, the simplified numerical model is proved to have calculation accuracy and validity. The characteristics are studied by the proposed methods. The comparison results show that the pre-applied internal pressure has limited influence on the axial stiffness of unbonded flexible rise. The initial axial tension would enhance the anti-burst failure ability of unbonded flexible riser, the failure pressure increases by 35% when the tensile force is 500 kN. Full article

The endurance of autonomous underwater vehicles (AUVs) has long been constrained by limited energy replenishment. Underwater inductive wireless power transfer (UIWPT), with its contactless power transfer capability, offers an innovative solution for efficient underwater charging of AUVs. This paper provides a systematic review of the architecture of UIWPT systems, analyzes key power loss mechanisms and corresponding optimization strategies, and summarizes the latest research progress in magnetic coupler design, compensation circuit topologies, control methods, simultaneous power and data transfer, and seawater-induced eddy current losses. Representative cases of UIWPT system integration on AUV platforms are also reviewed, with particular emphasis on environmental factors such as salinity variation, biofouling, and deep-sea pressure, as well as EMC, which critically constrain engineering applications. Finally, this paper discusses development trends including high-efficiency power transfer, enhanced reliability under extreme environments, and practical deployment challenges, and it presents a forward-looking technical roadmap towards long-term, autonomous, and intelligent underwater wireless power transfer. Full article

The floating photovoltaic (FPV) power generation technology in water has made up for some of the shortcomings of traditional inland photovoltaics and has developed rapidly in the past decade, enabling truly sustainable solar energy exploitation. Multi-module hinged offshore floating photovoltaics (OFPV) are widely used in the sea. However, how to ensure the survival of OFPVs in extreme natural environments is the biggest challenge for the implementation of the project in the future. The focus of this paper is the hydrodynamic problems that multi-module OFPV structures may encounter under regular waves. The effects of column spacing and heave plates were analyzed for a single FPV platform in order to obtain the ideal single module. Furthermore, the motion responses and inter-module forces of each module are calculated within the overall OFPV system under regular waves to investigate the overall hydrodynamic characteristics. Qualitative and quantitative comparisons between single and multi-modules are made for a deep understanding of this structure to ensure its sustainability. The corresponding conclusions can provide scientific references for multi-module OFPVs and the sustainable utilization of energy. Full article

Oceans, which cover more than 70% of Earth’s surface, are home to vast biological and mineral resources. Deep-sea exploration encounters significant challenges due to harsh environmental factors, including low temperatures, high pressure, and complex hydrodynamic forces. These constraints have led to the widespread use of underwater robots as essential tools for deep-sea resource exploration and exploitation. Conventional underwater robots, whether rigid with fixed stiffness or fully flexible, fail to achieve the propulsion efficiency observed in biological fish. To overcome this limitation, researchers have developed adjustable stiffness mechanisms for robotic fish designs. This innovation strikes a balance between structural rigidity for stability and flexible adaptability to dynamic environments. By dynamically adjusting localized stiffness, these bio-inspired robots can alter their mechanical properties in real time. This capability improves propulsion efficiency, energy utilization, and resilience to external disturbances during operation. This paper begins by reviewing the evolution of underwater robots, from fixed-stiffness systems to adjustable-stiffness designs. Next, existing methods for stiffness adjustment are categorized into two approaches: offline component replacement and online real-time adaptation. The principles, implementation strategies, and comparative advantages of each approach are then analyzed. Finally, we identify the current challenges in adjustable-stiffness underwater robotics and propose future directions, such as advancements in intelligent sensing, autonomous stiffness adaptation, and enhanced performance in extreme environments. Full article

On 31 March 2025, a severe thunderstorm system affected the Cyclades region, causing extensive flash floods on the islands of Paros and Mykonos and leading to significant material damage. This study investigates the meteorological characteristics of the event and focuses on the potential role of elevated sea surface temperatures (SSTs) in intensifying the storm’s severity. The analysis is centered on the broader Aegean region (geographic extent: 41.25° N, 21.83° E to 34.30° N, 28.51° E), utilizing ERA5 reanalysis data from ECMWF. These data provide high-resolution information on the atmospheric and ocean surface conditions during the event. The primary research objective is to explore how warmer SSTs may have contributed to enhanced moisture in the lower troposphere and increased energy availability for convective storm development. The theoretical background and a preliminary data exploration suggest that elevated SSTs likely favored increased evaporation, enhanced low-level moisture transport, and greater atmospheric instability, leading to the development of deep convective clouds. This, in turn, may have intensified precipitation rates and elevated the flood risk. This study aims to contribute to a better understanding of the mechanisms behind such extreme weather events, particularly in island environments, and to explore the sea’s potential catalytic role under a changing climate. Full article

With the advancement of scientific research, understanding the physical parameters governing acoustic wave propagation in sea ice has become increasingly important. Among these parameters, shear wave velocity plays a crucial role. However, as measurements progressed, it became apparent that there was a large discrepancy between measured values of shear waves and predictions based on empirical formulas or existing models. These inconsistencies stem primarily from the complex internal structure of natural sea ice, which significantly influences its physical behavior. Research reveals that shear wave velocity is not only influenced by bulk properties such as density, temperature, and stress state but is also sensitive to microstructural features, including air bubbles, inclusions, and ice crystal orientation. Compared to longitudinal wave velocity, the characterization of shear wave velocity is far more challenging due to these inherent complexities, underscoring the need for more precise measurement and modeling techniques. To address the challenges posed by the complex internal structure of natural sea ice and improve prediction accuracy, this study introduces a novel, integrated approach combining simulation, measurement, and inversion intelligent learning model. First, a laboratory-based method for generating sea ice layers under controlled formation conditions is developed. The produced sea ice layers align closely with measured values for Poisson’s ratio, multi-year sea ice density, and uniaxial compression modulus, particularly in the high-temperature range. Second, enhancements to shear wave velocity measurement equipment have been implemented. The improved device achieves measurement accuracy exceeding 1%, offers portability, and meets the demands of high-precision experiments conducted in harsh polar environments. Finally, according to the characteristics of small sample data. The ANN neural network was improved to a deep residual neural network with the addition of Squeeze-and-Excitation Attention (SE-ResNet) to predict longitudinal and transverse wave velocities. This prediction method improves the accuracy of shear and longitudinal wave velocity prediction by 24.87% and 39.59%, respectively, compared to the ANN neural network. Full article