Search Results (3,257)

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

For offshore renewable energy planning and intelligent power management, access to long-term, high-resolution, and physically consistent meteorological and power generation records is essential. Such data supports a wide range of tasks, including resource assessment, hybrid system capacity sizing, grid operation planning, and data-driven forecasting model development. This article presents the construction of a 10-year continuous hourly dataset for 16 deep-sea grid sites in the Beibu Gulf, China, spanning from January 2016 to December 2025. The raw meteorological variables, including 10 m wind speed, wind direction, solar irradiance, and 2 m air temperature, were retrieved from the NASA POWER satellite database and subsequently cleaned using a 24 h periodic substitution algorithm designed to preserve the physical integrity of daily weather cycles. The dataset is organized into two sub-datasets, the Historical Weather Dataset and the Normalized Power Yield Dataset, with the latter providing normalized wind and solar power outputs on a 1.0 per-unit (p.u.) basis derived from a wind turbine power curve model and a PV thermodynamic model. All 32 CSV files are freely accessible online with UTF-8 encoding. The utility of the dataset is illustrated through two representative application cases including offshore site selection with hybrid capacity sizing and physics-informed deep learning forecasting, demonstrating its suitability for both engineering analysis and machine learning model development. Full article

Given the energy-intensive operations and considerable carbon emissions of offshore oil and gas platforms (OOGPs) in deep-sea regions, adopting floating offshore wind farms (FOWFs) as power sources offers substantial benefits. However, the expenses associated with dynamic submarine cables constitute a substantial portion of the capital expenditure (CAPEX) for this hybrid system, highlighting the crucial need for optimization in the power collection system design. In this study, we present a mixed-integer quadratic programming (MIQP) model designed to reduce both the costs of investment and power losses associated with dynamic submarine cables, taking into account the influence of the wake effect in local wind conditions. Due to the complexity of this problem, we employ the Benders’ decomposition method to reformulate it into a master problem and a slave problem. Additionally, two valid inequalities are specifically incorporated into the master problem to accelerate the solution process. These constraints are derived from a heuristic combination of various cable connection configurations and a greedy-based spanning tree structure. Through multiple case studies, we first demonstrate the accuracy and rapid convergence of our method. Furthermore, we reveal that as the wind farm grows in size, the influence of the wake effect becomes increasingly pronounced. Full article

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

Seafloor hydrothermal systems at mid-ocean ridges are focal points for heat and matter exchange between the seawater and lithosphere. While seafloor seismographs (OBS) and pressure recorders (BPR) are standard for regional monitoring, achieving high-precision, vertical sub-surface data in complex hydrothermal terrains remains a significant technical objective. This study presents a novel in situ penetration probe designed for multi-parameter monitoring of marine hydrothermal vent areas. A key innovation of this work is its operational versatility and engineering efficiency: the probe is specifically designed for post-drilling deployment in boreholes, effectively utilizing existing coring sites to achieve direct coupling with the deep-seated crust, or for targeted placement via Remotely Operated Vehicles (ROVs). The device integrates a titanium-alloy conical tip and cylindrical chamber, housing tri-axial accelerometers and dual temperature-pressure sensors. Numerical simulations using the SST k-ω turbulence model and finite element analysis optimized the cone aperture and assessed fluid–structure stability under deep-sea conditions. Laboratory vibration tests and shallow-water sea trials validated the probe’s basic dynamic response, electromechanical integrity, and capability to acquire coupled environmental parameters. This compact, modular design provides a scalable and cost-effective framework for precise three-dimensional observation of sub-surface hydrothermal processes and deep-sea resource exploration. Full article

Deep-sea mining systems are a critical pathway for acquiring key strategic resources such as nickel and cobalt. The core conveying component, the mining rigid pipe, is susceptible to transverse vibrations under complex wave excitation, which threaten system safety, necessitating the development of efficient and reliable vibration control solutions. This paper proposes an improved Triple-spring nonlinear energy sink (TS-NES). An integrated dynamic model coupling the mining rigid pipe and the TS-NES is established using the vector form intrinsic finite element method and solved via the central difference method. The effectiveness and superiority of the TS-NES are verified through displacement, time–frequency, energy, and phase analyses. Subsequently, a systematic parameter sensitivity study is conducted. The results indicate that under both single-frequency and multi-frequency wave excitations, the TS-NES exhibits broadband, high-efficiency vibration suppression performance superior to that of the conventional tuned mass damper (TMD). It can substantially and uniformly dissipate vibration energy and maintain an approximately 90° phase lag with the primary structure. Parameter studies reveal that installing the TS-NES in the upper section of the pipe yields significant vibration reduction. The device is insensitive to stiffness variations, and appropriately increasing its mass, damping, and inclination angle can further enhance the vibration suppression effect. Full article

Wind energy is an important form of clean energy, and its rational utilization represents a crucial solution for mitigating the energy crisis and global warming. In this study, wind energy potential and its long-term changes in the South China Sea (SCS) are evaluated using ERA5 100 m wind data from 1944 to 2023, validated against ASCAT observations. High wind speeds and high wind power density (WPD) are concentrated southwest of Taiwan and southeast of Vietnam. Annual wind availability exceeds 6457 h across most regions, reaching up to 8283 h in optimal locations. WPD and capacity factor peak in winter (up to 2.4 × 10⁸ Wh·m⁻² and >50% capacity factor), with the most stable conditions occurring in the southwestern Taiwan Strait, southeast of the Pearl River Delta, and the Beibu Gulf. Empirical orthogonal function analysis reveals that the first mode of winter WPD accounts for 65.7% of the total variance, with a statistically significant increasing trend since 1990. The interannual variation in wind energy resources in the SCS during winter is controlled by the combined effects of sea surface temperature (SST) anomalies in the tropical Pacific and the Arctic Barents Sea. Specifically, in the years with strong wind anomalies in the SCS, mega-La Niña-type SST patterns in the tropical Pacific trigger anomalous cyclonic circulation in the SCS and the eastern Philippine Sea, while warm anomalies in the Arctic Barents Sea surface drive a wave-like structure of “anticyclone–cyclone–anticyclone” from Siberia to South China. The coupling of the two systems jointly promotes the strengthening of the South China Sea monsoon, leading to increased wind speeds and elevated WPD in the northern SCS. These findings provide a scientific basis for wind farm siting and long-term operational planning in the region. Full article

Deep-sea mining operations demand continuous, drift-free positioning over multi-day missions—a requirement that traditional acoustic dead-reckoning systems struggle to meet due to cumulative error accumulation and frequent DVL bottom-lock loss in sediment plume environments. Inspired by Google Cartographer’s 2D grid mapping paradigm, we present a prior map-based visual localization framework that decouples offline mapping from real-time localization, fundamentally eliminating drift through absolute image registration against pre-built seabed mosaics. By integrating adaptive keyframe selection, Multi-Scale Retinex (MSR) enhancement, and the AD-LG deep feature matching architecture, our system constructs globally consistent seabed maps for absolute positioning. The framework leverages deformable convolutions and LightGlue to effectively mitigate challenges such as low texture and non-rigid distortion. Quantitative validation on tank simulation datasets demonstrates significant superiority over IMU-only and standard fusion schemes; qualitative deployment on real Pacific CCZ imagery confirms near-real-time operational feasibility on an embedded Jetson Orin NX platform. This system establishes visual navigation as a viable backup to acoustic systems, addressing a critical gap in deep-sea mining vehicle autonomy. Full article

The scarcity and incompleteness of well-log data pose a critical challenge to deep learning-based reservoir prediction. To address this small-sample problem and improve prediction quality, we propose a novel semi-supervised asymmetric deep co-training framework integrated with a shape context descriptor. This method leverages abundant unlabeled seismic data as well as complementary information on related physical properties. Specifically, we introduce a shape context descriptor to encode seismic waveform morphology and spatial context, thereby improving the lateral continuity and interpretability of predictions while mitigating issues inherent in the sequence-to-point paradigm, wherein three-dimensional seismic data are used as input and a single target point is predicted. To overcome data limitations, a sliding-window resampling strategy is employed to expand the training samples. For co-training, we design an asymmetric dual-task architecture wherein one model performs porosity regression while the other conducts reservoir type classification, thereby enabling synergistic learning. The proposed framework is validated using real three-dimensional seismic data from the Yinggehai Basin in the South China Sea through ablation experiments. The results demonstrate superior performance in prediction accuracy, spatial consistency, and training stability compared to baseline methods. Full article

The Rio Grande Rise (RGR) is the largest oceanic plateau in the South Atlantic and represents a key natural laboratory for understanding oceanic plateau formation, deep-sea circulation, ecosystem functioning, and ferromanganese crust development. This study presents a critical synthesis of current scientific knowledge on the RGR, integrating geological, geophysical, oceanographic, biological, and geochemical evidence published over the last two decades. Geophysical data reveal a complex tectono-magmatic evolution involving Late Cretaceous plume-related volcanism, crustal thickening, rifting, and subsequent subsidence. The structural framework of the plateau is dominated by the Cruzeiro do Sul Rift, which plays a central role in controlling sedimentation, magmatism, and seawater circulation. Oceanographic studies demonstrate that the interaction between the southern branch of the South Equatorial Current and the complex topography of the RGR generates intense internal tides and bottom currents, strongly influencing sediment transport and benthic habitats. Biological investigations indicate that the RGR hosts diverse deep-sea communities, including sponge grounds, cold-water corals, and associated fauna, whose distribution is tightly linked to geomorphology and hydrodynamics. Ferromanganese crusts occurring on the plateau preserve valuable geochemical records of oceanographic and redox conditions, although their spatial distribution, thickness, and metal budgets remain incompletely constrained. Despite major advances, significant knowledge gaps persist regarding crustal structure, sedimentary evolution, ecosystem functioning, and mineral formation processes. This review highlights these uncertainties and outlines research priorities necessary to improve understanding of oceanic plateaus and deep-sea systems in the South Atlantic. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

This paper proposes a robust navigation method based on a robust square-root cubature Kalman filter (RSRCKF) to address the accuracy divergence of integrated navigation systems caused by drilling-induced slippage and the mismatch between the tail-cable encoder and the robot motion during operations of a seafloor drilling robot in deep-sea soft sedimentary layers. Considering the large-deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model incorporating a time-varying odometer scale-factor error is first established. To alleviate the numerical instability of the nonlinear system in the presence of non-Gaussian noise, a square-root cubature Kalman filter (SRCKF) framework is employed, in which the positive definiteness of the error covariance matrix is dynamically preserved via QR decomposition. Subsequently, an online fault detection mechanism based on a modified chi-square test is developed. By introducing a two-segment IGG (a classical robust weighting scheme) weighting function, an adaptive variance inflation factor is constructed to enable real-time identification and down-weighting of abnormal observations induced by slippage. Field experiments, including drilling and turning tests conducted on tidal mudflats off the coast of Zhoushan, demonstrate that the proposed method can effectively mitigate the impact of “false displacement” disturbances caused by typical soft clay slippage conditions through enhanced statistical robustness. Taking the conventional SINS/OD integration scheme as the baseline, the proposed method achieves an approximate 82.4% reduction in positioning error. These results verify the robustness and engineering applicability of the proposed algorithm in complex seabed environments. Full article

Offshore wind energy has developed rapidly in recent years as a crucial component of renewable energy. However, offshore wind turbines (OWTs) face significant challenges in operations under complex marine environmental conditions, such as multimodal nonlinear vibrations, reliable structural monitoring, efficient maintenance, and sustainable long-term operations. The model-updating-based parameter identification takes advantages of structural vibration measurements, assisting in structural health monitoring. However, the traditional methods have not fully accounted for the parameter uncertainties and the need for real-time state updating, making them insufficient to meet the long-term online monitoring requirements for OWTs. This study introduces an innovative structural parameter identification framework that integrates modal parameter identification with Bayesian recursive updating. The proposed framework enables more efficient updates and uncertainty quantification of critical physical parameters for OWTs. It combines the covariance-driven stochastic subspace identification (COV-SSI) method for automatic modal parameter identification with the unscented Kalman filter (UKF) for parameter estimation. A 10 MW jacket-type offshore wind turbine was used as a case study. First, the numerical simulations were conducted to generate synthetic measurements for method validation and demonstration, enabling stepwise updating of the tower material’s elastic modulus across different sea conditions. A comparison of update speed and the convergence rate with the traditional time-step-based UKF method demonstrated the superiority of the proposed sea-condition-based approach in terms of computational efficiency and stability. Finally, the proposed framework was systematically validated using scaled model experimental data of a jacket-type OWT with a 4.2% identification error, confirming its engineering applicability. This research provides reliable technical support for the safety assessment of offshore wind turbine structures. Full article

Understanding human’s responses to extreme environments holds significant importance for space exploration, deep-sea research, and environmental adaptation. Traditionally, human subjects were used to study humans’ responses to extreme environments. The main limitations of this approach include the inability to independently investigate specific cellular mechanisms, ethical and safety constraints, limited experimental controllability, and inter-individual variability that complicates mechanistic interpretation. Another approach is to study humans’ responses at the cellular level using 2D culture. This approach often exhibits limited reproducibility due to its inability to recapitulate physiologically relevant microenvironments. Bioprinting can enable studies on human’s responses at the cellular level and within 3D environments. One way is to study human cells’ responses to localized and transient extreme environments created during printing. Another way is to expose 3D printed samples (embedded with human cells) to extreme environments. However, the literature does not contain comprehensive review papers to discuss the prospects and limitations of bioprinting for investigating human cells’ responses to extreme environments. This review paper aims to fill this gap in the literature. It begins with a brief description of the effects of extreme environments on human health and summarizes reported studies on cells’ responses to extreme environments. Afterward, it discusses the prospects and limitations of the two ways of using bioprinting to investigate cells’ responses to extreme environments. Finally, it concludes with identifying knowledge gaps and proposing research directions in the application of bioprinting to study human cells’ responses to extreme environments. Full article

Medical images commonly exhibit low contrast, weak boundaries, and complex textures. In addition, significant semantic differences exist between deep-level semantic features and shallow-level detail features, posing challenges for multi-scale feature fusion in terms of detail preservation and structural consistency. To address these issues, a frequency-enhanced and bidirectional feature-guided segmentation network (FBNet) is proposed. The network comprises two core components. The frequency-based enhancement (FBE) module employs the Fast Fourier Transform and applies adaptive modulation to the amplitude spectrum through a content-aware gating mechanism, enhancing detail expression and inter-structural contrast. The Bidirectional Guided Feature Fusion module (BGF) enables bidirectional interaction between shallow and deep features. Additionally, the Structure and Edge Awareness (SEA) module is constructed using directional and variance attention mechanisms to achieve collaborative optimization of structural modeling and edge perception. Experiments on four medical image segmentation datasets show that, compared to the second-best method, FBNet achieves improvements of 2.12, 1.57, 1.37, and 1.56 percentage points on the mIoU metric and 1.54, 1.11, 0.84, and 1.03 percentage points on the mDice metric. Full article