Search Results (2,200)

This study conducts a diagnostic analysis of an extremely heavy rainfall event and its causative factors that occurred in Libya, North Africa on 10 September 2023. The Weather Research and Forecasting (WRF) model was also employed to perform some sensitivity experiments for this heavy rainfall event and further reveal its causes. Results indicate that the primary synoptic system responsible for this extreme precipitation event was an extratropical cyclone (storm) named “Daniel”. During the formation and development of this cyclone, the circulation at the 500 hPa level from the eastern Atlantic to western Asia exhibited a stable “two troughs and one ridge” pattern, with a upper-level cold vortex over the eastern Atlantic, a high-pressure ridge over central Europe, and a cut-off low over western Asia, collectively facilitating the formation and development of this cyclone. As this cyclone moved southward, it absorbed substantial energy from the Mediterranean Sea; following landfall, the intrusion of weak cold air enabled the cyclone to continue intensifying. Meanwhile, the northwest low-level jet stream to the west of the extratropical cyclone moved alongside the cyclone to the coastal regions of northeastern Libya, where it converged with water vapor transport belts originating from the Ionian Sea, the Aegean Sea, and the coastal waters of northeastern Libya. This convergence provided abundant water vapor for the rainstorm event, and under the combined effects of convergence and orographic lifting on the windward slopes of the coastal mountains, extreme precipitation was generated. In addition, the atmosphere over the coastal regions of northeastern Libya exhibited strong stratification instability, which was conducive to the occurrence of extreme heavy precipitation. Although WRF successfully reproduced the precipitation process, the precipitation amount was underestimated. Sensitivity experiments revealed that both the topography in the precipitation area and the sea surface temperature (SST) of the Mediterranean Sea contributed to this extreme heavy precipitation event. Full article

A notable decline in the frequency of sea fog inflows and an increase in low-cloud ceiling height were observed following the construction of the Saemangeum Seawall west of the Gunsan Airport, an area traditionally prone to frequent sea fog events. To the mechanisms underlying these changes, a numerical experiment was conducted using the Weather Research and Forecasting model. An 11-m-high seawall was used as a physical barrier, and an elevated sea surface temperature (SST) was established within the enclosed area to simulate realistic post-construction conditions. The model successfully reconstructed sea fog occurrences, and the cloud–water mixing ratio effectively captured the spatial distribution of sea fog. Deviations from the control experiment showed a consistent pattern of reduced cloud–water mixing ratios near the surface and enhanced concentrations at high levels. Decreased buoyancy frequency in the surface layer enhanced atmospheric instability, inducing upward motion and intensified condensation activity. Increases in the turbulence kinetic energy within the planetary boundary layer (TKE within the PBL), vertical wind shear, and temperature further corroborated the reduction in sea fog and enhanced stratus formation. These findings indicate that the increased SST and seawall significantly influence the modification of the sea fog structure and its inflow dynamics. Full article

Background/Objectives: The aim of this study was to establish the multifactorial physiological effect of a functional curd product enriched with collagen-containing concentrate and phytochemical extracts of various natures, under conditions of in vivo experiment. Methods: Biomarkers, such as antioxidant activity (glutathione peroxidase, glutathione reductase, MDA), immune response (IgA, IgG, IgM, IL-6, TNF-α), and purine metabolism (uric acid, xanthine oxidase, 5′-nucleotidase) were selected for evaluation and their influence change. The model was white outbred rats (n = 45), randomly distributed into three groups: control (basic product), experimental group 1 (supplements of collagen-containing concentrate and extract of the composition of sea buckthorn and rosehips), and experimental group 2 (supplements of collagen-containing concentrate and extract of the composition of yarrow and sage). Results: In both experimental groups, a reliable increase in the enzymatic activity of the antioxidant system, a decrease in lipid peroxidation and the level of proinflammatory cytokines, an increase in immunoglobulins, and activation of 5′-nucleotidase were observed. The most pronounced effects were observed with the introduction of a curd product containing collagen-containing concentrate and sea buckthorn and rosehip extract. Conclusions: The scientific novelty of the study lies in the first comprehensive in vivo evaluation of the combined enrichment of a dairy product with collagen and plant extracts for a set of biomarkers. The data obtained confirm the physiological activity and functional properties of the developed product, which can be considered as a promising means of specialized and sports nutrition with proven biological action. Full article

Accurate prediction of ship motion attitude remains a significant challenge due to the inherent non-stationarity and strong stochasticity of marine environmental conditions. To address this issue, this study proposes FMD-IBKA-BTGN, a hybrid model combining Feature Mode Decomposition (FMD), Improved Black-winged Kite Algorithm (IBKA), and a Bidirectional Temporal Convolutional Network with Gated Recurrent Unit (BTGN). First, FMD decomposes motion signals into intrinsic modes. Subsequently, IBKA—enhanced with chaotic mapping and Lévy flights—optimizes BTGN hyperparameters for global search efficiency. Finally, predictions from all components are ensembled for final output. Experiments on a 240 m vessel in Sea State 4 show our model outperforms six models, reducing MAPE by 20.38%, RMSE by 7.4%, MAE by 4.2%, and MSE by 0.97% versus LSTM. The model enhances both prediction accuracy and generalization. Full article

This study combined virtual reality technology with drone aerial imagery of Smangus, a remote Atayal tribe situated 1500 m above sea level in Hsinchu County, Taiwan, to develop a virtual drone system. This study aims to investigate the learning effectiveness and operational experience associated with the application of the virtual drone system for exploring tribal natural landscapes and enhancing junior high school students’ learning of Indigenous settlement site selection. A quasi-experimental design was conducted with two seventh-grade classes from a junior high school in Hsinchu County, Taiwan. The experimental group (n = 43) engaged with the virtual drone system to perform settlement site selection tasks, while the control group (n = 42) learned using traditional materials such as PowerPoint slides and maps. The intervention consisted of two instructional sessions, with data collected via achievement tests, questionnaires, and open-ended feedback. The results indicated that students in the experimental group significantly outperformed the control group in learning outcomes. Positive responses were also observed in learning motivation, cognitive load, and system satisfaction. Students reported that the virtual drone system improved students’ understanding of terrain and enhanced their skills in selecting appropriate sites while increasing their interest and motivation in learning. Moreover, the course incorporated the Atayal people’s migration history and field interview data, enriching its cultural authenticity and contextual relevance. Full article

This study investigates wellbore stability in ultra-deep extended-reach wells (ERWs) in the East China Sea, where perforated pipes (a type of screen completion) are commonly used to support wellbore walls and prevent collapse. Cost constraints sometimes lead to the omission of this support, yet significant wellbore collapse is rarely observed. The instability is primarily attributed to variations in production pressure differences. A predictive model for critical pressure difference was developed based on immersion experiments and single-triaxial rock mechanics tests. The results from immersion tests revealed that, in water-bearing strata, the critical pressure difference decreased significantly, drop-ping by 20.07% after two days of rock core immersion and by 28.35% after seven days. Key factors influencing this difference, such as well inclination, rock cohesion, internal friction angle, Poisson’s ratio, and Biot coefficient, were identified. As production continues, pore pressure depletion reduces this difference, particularly when pore pressure falls below 23.5 MPa, leading to wellbore instability. On-site validation in three ultra-deep ERWs showed that the model’s predictions aligned well with actual conditions, with a confidence interval analysis further validating the model’s accuracy. The proposed model provides valuable guidance for future ultra-deep well development in the East China Sea. Full article

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article

To address the problems of low efficiency and poor real-time performance in underwater acoustic modeling, as well as the requirement of maximizing search coverage for underwater target search path planning, this paper proposed an efficient path planning method based on Sound Speed Profile (SSP) clustering. Firstly, the SSPs were dimensionally reduced via Empirical Orthogonal Function (EOF) decomposition, and the sea area was divided into 10 acoustic sub-areas using K-means clustering after fusing geographic coordinates and terrain information, thereby constructing a block-wise sound field model. Secondly, with the active sonar equation as the core, sonar parameters such as the noise level and target strength were solved, respectively, to generate a spatial distribution matrix of search distances. Finally, an Improved Ant Colony Optimization (IACO) algorithm was modified by dynamically setting the pheromone evaporation rate and improving the heuristic information for search path optimization. Numerical experiments showed that clustering significantly improves the efficiency of sound field modeling, reducing the time consumption of the transmission loss calculation from 24.74 h to 10.84 min. The IACO increased the average search coverage from 47.96% to 86.01%, with an improvement of 79.34%. The performance of IACO is superior to those of the compared algorithms, providing support for efficient underwater target search. Full article

In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. 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

In recent years, frequent outbreaks of Enteromorpha disasters in the Yellow Sea have caused substantial economic losses to coastal cities. In order to tackle the challenges of the low detection accuracy and high false negative rate of Enteromorpha detection in complex marine environments, this study proposes an object detection algorithm CEE-YOLOv8, improved from YOLOv8n, and establishes the Enteromorpha dataset. Firstly, this study integrates a C2f-ConvNeXtv2 module into the YOLOv8n Backbone network to augment multi-scale feature extraction capabilities. Secondly, an ECA attention mechanism is incorporated into the Neck network to enhance the perception ability of the model to different sizes of Enteromorpha. Finally, the CIoU loss function is replaced with EIoU to optimize bounding box localization precision. Experiment results on the self-made Enteromorpha dataset show that the improved CEE-YOLOv8 model achieves a 3.2% increase in precision, a 3.3% improvement in recall, and a 4.1% gain in mAP50-95 compared to the benchmark model YOLOv8n. Consequently, the proposed model provides robust technical support for future Enteromorpha monitoring initiatives. Full article

To evaluate the prospects of multi-antenna GNSS buoy and mooring array in ocean altimetry satellite calibration, experiments are conducted in the ocean around Qianliyan islet in China’s Yellow Sea. The trials aim to validate the feasibility of establishing an ocean altimetry satellite calibration site while assessing the performance of relevant calibration equipment. Utilizing one multi-antenna GNSS buoy system and one mooring array operating for over 20 days, the experiment incorporates continuous GNSS observation data from Qianliyan islet’s permanent station. Results reveal that high-frequency sea surface height (SSH) signals exhibit periods approaching or below 10 s, with the designed low-pass filter effectively attenuating these high-frequency components. Significant differences emerge in the power spectra of filtered SSH measurements between instruments: high-frequency signals detected by the mooring array demonstrate greater spectral concentration and lower signal intensity than those recorded by the GNSS buoy. Through multi-day synchronized observations, the height datum for mooring array SSH measurements is obtained, revealing average standard deviation of 2.76 cm in filtered SSH differences between platforms—validating both the system design and data processing methodology. This experiment successfully demonstrates the performance of calibration equipment, preliminarily verifies the effectiveness of ground-based calibration data processing techniques, and further confirms the technical viability of establishing an ocean altimetry satellite calibration site around Qianliyan islet. Full article

The sea area adjacent to ports features a dense network of intricate access routes. Existing route modeling methods exhibit limitations in accurately capturing these complex routes and effectively representing the diverse handling behavior patterns of ships within them. To address this issue, this paper proposes a maritime route modeling method incorporating ship handling behavior (MARSHB) to accurately identify port channels with diverse traffic flows and enabling a multi-dimensional model of heterogeneous vessel behaviors along these channels. Numerical experiments using extensive automatic identification system (AIS) data from the Bohai Sea show that the proposed method reduces the computational time by 49.75% for route extraction compared to the traditional method. For route modeling, MARSHB covers 88.31% of 95% high-density traffic areas, with safety boundaries exhibiting a higher accuracy of conformity with historical trajectory data. Full article

High-precision angular contact ball bearings (ACBBs) are critical components in advanced manufacturing equipment, where rotation accuracy directly determines system performance and stability. Considering error superposition and equipment processing capability comprehensively, this study establishes an error analysis and control model of the ACBBs, studies the error transmission law, and puts forward a rotation accuracy control strategy for batch manufacturing of precision ACBBs. The ACBBs 7020C/P4, 7020AC/P4, 7020A/P4, and 7020B/P4 (four conventional contact angles of 15°, 25°, 30°, 40°) were taken as examples to verify the experiment. The error of the calculation compared with actual test results was not more than 7.5%, which had good accuracy and practicability. The research shows that the roundness error of bearing raceway, the thickness difference in bearing ring wall, and the parallelism error of raceway to end face were the main influencing factors of bearing rotation accuracy Kia, Sia, Kea, and Sea. The influence coefficient of raceway roundness error on the axial runout of bearing (Sia, Sea) decreased rapidly with the increase in contact angle, while the influence coefficient on radial runout (Kia, Kea) remained constant. The rotation accuracy error of the outer ring was always greater than that of the inner ring, and this law was not affected by the contact angle. Moreover, with the increase in contact angle, the radial runout of the inner and outer rings of the bearing increased. During actual machining, bearings with larger contact angle place lower demands on the equipment process capability index (Cp), particularly on the parameter Cer. This reduction in required capability is equivalent to an effective Cp improvement of about 30%. Full article

Under the context of “Energy Conservation and Emission Reduction,” low-sulfur fuel has become widely adopted in maritime operations, posing significant challenges to cylinder lubrication systems. Traditional oil injection strategies, heavily reliant on manual experience, suffer from instability and high costs. To address this, a lightweight image retrieval model for cylinder lubrication is proposed, leveraging deep learning and computer vision to support oiling decisions based on visual features. The model comprises three components: a backbone network, a feature enhancement module, and a similarity retrieval module. Specifically, EfficientNetB0 serves as the backbone for efficient feature extraction under low computational overhead. MobileViT Blocks are integrated to combine local feature perception of Convolutional Neural Networks (CNNs) with the global modeling capacity of Transformers. To further improve receptive field and multi-scale representation, Receptive Field Blocks (RFB) are introduced between the components. Additionally, the Convolutional Block Attention Module (CBAM) attention mechanism enhances focus on salient regions, improving feature discrimination. A high-quality image dataset was constructed using WINNING’s large bulk carriers under various sea conditions. The experimental results demonstrate that the EfficientNetB0 + RFB + MobileViT + CBAM model achieves excellent performance with minimal computational cost: 99.71% Precision, 99.69% Recall, and 99.70% F1-score—improvements of 11.81%, 15.36%, and 13.62%, respectively, over the baseline EfficientNetB0. With only a 0.3 GFLOP and 8.3 MB increase in model size, the approach balances accuracy and inference efficiency. The model also demonstrates good robustness and application stability in real-world ship testing, with potential for further adoption in the field of intelligent ship maintenance. Full article