Search Results (226)

Diffusers in diffuser-augmented wind turbines (DAWTs) require high-camber airfoils operating at low Reynolds numbers (Re), and their laminar separation bubbles (LSB) significantly complicate aerodynamic predictions. No prior study has experimentally validated XFOIL, k-ω SST, and γ-Re_θ models against simultaneous lift, drag, and chord-wise pressure coefficient (Cp) measurements for the customized high-camber airfoil at Re = 68,000 (68k), 118,000 (118k), and 159,000 (159k). Lift, drag, and Cp distributions were measured experimentally. The γ-Re_θ model demonstrated superior performance, achieving a lift maximum absolute percent error of 1.6–3.4%, near-zero bias, and a coefficient of determination >0.99. It accurately captured the LSB pressure plateau at mid-chord, with mean gross-averaged Cp percent errors of 8.1% and 2.1% for upper and lower surfaces, respectively. The k-ω SST model overpredicted lift by up to +9.8% at Re = 68k and underpredicted drag by up to 66%. XFOIL is unreliable specifically for separated transitional flows at Re < 118k, but improves at Re = 159k. The experimental dataset and validated transition-sensitive RANS approach provide a foundation for low-Re airfoil and DAWT diffuser design. Future work should extend measurements below Re = 50k and above 200k, including post-stall conditions, and system-level design of DAWT. Full article

Chub mackerel (Scomber japonicus) is a commercially important pelagic species in the northwest Pacific Ocean. Accurate identification of its fishing grounds can provide a more robust and targeted scientific basis for fishery management and ecological research. Based on fishing effort and five environmental factors (i.e., sea surface temperature [SST], chlorophyll-a concentration [CHL], SST gradient [GSST], sea surface height [SSH], and current speed), this study developed a Classification and Regression Tree (CART) rule-guided MaxEnt model. Specifically, rules generated by the CART model were first extracted and then incorporated as constrained feature functions into MaxEnt for model training. To select the optimal model scheme, four combinations of rule compositions and feature function outputs were designed, and model performance on the validation dataset was evaluated using ROC curves. Finally, the model was further verified with in situ environmental and fisheries data from April to November 2024. Results showed that the predicted fishing grounds were highly aligned with the actual monthly fishing grounds in 2024, and the predicted migration routes matched the movement trajectory of fishing vessels. The model also exhibited satisfactory performance, achieving an average AUC of 0.722 ± 0.033, a sensitivity of 0.604, a specificity of 0.834, and a negative predictive value (NPV) of 0.978. In conclusion, the CART rule-guided MaxEnt model, integrating the interpretability of CART and the predictive power of MaxEnt, effectively predicts the spatial distribution of chub mackerel fishing grounds in the northwest Pacific Ocean, providing technical support for fishery management and ecological research. Full article

The Meiyu season over the Yangtze–Huaihe River Basin exhibits pronounced interannual variability and directly reflects the persistence of the East Asian summer rainband. This study examined the relationship between the preceding April sea ice anomaly of the Barents–Kara seas and Meiyu length during 1979–2023 based on CN05.1 precipitation, ERA5, HadISST sea ice concentration datasets, and Indo-Pacific SST index. A statistically significant inverse relationship was identified between the interannual Meiyu Length and the preceding April Barents–Kara seas sea ice anomaly, with the strongest signal located over the core Barents–Kara seas sector and a filtered Barents–Kara seas sea ice index–Meiyu length index correlation coefficient of −0.662. Composite and regression analyses demonstrated that reduced interannual April Barents–Kara seas sea ice concentration is associated with a downstream Rossby-wave-like upper-tropospheric circulation pattern, leading to a clearer upper-level potential vorticity band and an intensified westerly jet that generates increased convergence over the Yangtze–Huaihe River Basin. Additionally, the north-low–south-high circulation contrast over the East Asian–western North Pacific sector during years with a longer Meiyu period, associated with an interannual reduction in the Barents–Kara seas sea ice index, contributes to enhanced moisture convergence and convection that drive stronger ascent over the Yangtze–Huaihe River Basin, favoring a more persistent Meiyu rainband and a longer Meiyu period. Full article

In the geographic environment, mesoscale ocean eddies and similar phenomena exhibit continuous and gradual changes. However, due to limitations in remote sensing observation technology, the obtained observational data are discrete, which contradicts the continuously evolving characteristics of these phenomena. Although spatiotemporal interpolation is a key tool for bridging this gap, existing single-model methods fail to fully consider continuous process features, making it difficult to obtain consistent high-quality datasets. To solve this problem, this paper combines deep learning and geostatistics to propose an Evolutionary Process-embedded Marine Spatiotemporal Interpolation Model (EPMSIM). EPMSIM first applies Seasonal and Trend decomposition using Loess (STL) to decompose marine time-series fields into trend, seasonal, and evolutionary components. Then, a Convolutional Bidirectional Long Short-Term Memory (ConvBiLSTM) model is adopted to interpolate the trend and seasonal components. Meanwhile, a Process-based Spatiotemporal Dynamic Tracking Interpolation Method (PSDTIM) is designed to interpolate the evolutionary component. Finally, these components are combined through additive coupling to produce the final interpolation result. In case studies of mesoscale eddy interpolation using SST and SLA data, EPMSIM outperforms traditional geostatistical and deep learning baselines in RMSE, MAE, and SSIM. Experimental results confirm that the model achieves significant interpolation effects in marine environmental element fields with evolutionary characteristics, validating its effectiveness in capturing continuous evolution features of marine phenomena and its feasibility for generating high-temporal-resolution spatiotemporal datasets. This study provides a methodological reference for data interpolation of evolutionary process phenomena in marine information science, and this method can be extended to other similar marine environmental variables, serving research on marine ecological environments and dynamic processes. Full article

Tropospheric ozone (O₃) is a critical air pollutant that poses significant risks to human health and ecosystems. While previous studies have primarily focused on O₃ changes in Eastern China, limited attention has been given to Northwest China, where fragile but ecologically important systems may be vulnerable to O₃ pollution. The temporal evolution and driving mechanisms of surface O₃ in this region remain poorly understood. Using the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) datasets and simulations from the Community Atmosphere Model with Chemistry (CAM-Chem), we identified a significant increase in summer surface O₃ concentrations across Northwest China from 1980 to 2020, with the most pronounced rise occurring during 1993–2010. This period accounts for the majority of the long-term upward trend, despite relative declines before and after. The increase in O₃ during 1993–2010 is primarily attributed to rising surface temperatures, which reduce hydroperoxyl radical (HO₂) concentrations and enhance nitrogen dioxide (NO₂) production, leading to elevated nitrogen oxides (NO_x) levels and promoting O₃ formation. The warming trend is closely associated with a concurrent decrease in low cloud cover, which increases surface shortwave radiation and further contributes to surface warming. Further investigation reveals that warming sea surface temperature (SST) in the North Pacific influence atmospheric circulation through wave train processes, amplifying the regional geopotential height field. These circulation changes reinforce the reduction in low cloud cover and the associated increases in surface temperature and O₃ concentrations over Northwest China. The decadal variability of North Pacific SST may therefore serve as an important indicator of long-term surface ozone variability in this region. Full article

As hardware Trojans (HTs) are becoming increasingly stealthy in global semiconductor supply chains, the need for both robust and explainable detection methods is pressing. The use of deep learning models (e.g., Siamese networks, Transformer models) in side-channel signals has shown promising detection accuracy. Yet, they are black-box, data-intensive, and do not expose the causal, structural, and temporal relationships that indicate the presence of HTs. In this paper, we present a causality-focused and explainable detection framework that goes beyond pattern matching. We develop a Self-Supervised Spatio-Temporal Graph Neural Network (SST-GNN) that embeds spatio-temporal side-channel information. Our approach builds a graph that models gate-level components as nodes with temporal power and electromagnetic (EM) features, and functional and physical connections as edges. To address label scarcity, a common problem in real-world applications, we leverage a self-supervised pretraining approach. In particular, a context-aware contrastive loss allows the model to differentiate valid augmentations of benign subgraphs and their side-channel signatures, thus capturing general representations of benign components without Trojan labels. This involves a Causality-Aware GNN (CA-GNN) layer, which embeds differentiable causal discovery into graph learning. This process decouples correlation from causation, identifying the pathways potentially affected by HT trigger and payload. To explain decision making, a gradient-based graph explainer localizes minimal decisive subcircuits and pivotal time windows, generating intuitive detection reports. We evaluated our method on the IEEE Hardware Trojan Side-Channel Dataset (with netlist data), achieving state-of-the-art results (F1 > 0.98). In particular, the model achieves over 60% improvement in Trojan localization precision and false-positive rate, compared to Transformer-based approaches, with high label efficiency and adversarial robustness. Full article

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

Robot swarm manufacturing is a promising direction in smart manufacturing that aggregates multiple robots to collaboratively complete production jobs; however, achieving conflict-free scheduling remains a significant challenge. Traditional methods struggle to address this issue since robot swarms are inherently prone to conflicts. This article puts forward a non-sorted metaheuristic method to solve it. First, the conflict-free robot swarm manufacturing problem—integrating a multi-objective optimization problem (MOP), a flexible job-shop scheduling problem (FJSP) for job processing, and a flow-shop scheduling problem (FSP) for robot travel—is formulated as a multi-objective job-flow-shop scheduling problem (MJFSP). The robot swarm must accomplish all manufacturing jobs while achieving high manufacturing performance, energy efficiency, and conflict-free operations. Second, a non-sorted metaheuristic algorithm based on an artificial plant community (APC) is proposed. It employs a sequential-pairwise single-elimination tournament system (SSTS) to select elites with a time complexity of $O\left(n\right)$, which scales linearly with the population size $\left(n\right)$. This surpasses the sorting-based elite selection with polynomial time complexity employed in most metaheuristic methods, such as the $O(n^2)$ of the non-dominated sorting genetic algorithm-III (NSGA-III). Third, an MJFSP benchmark dataset is built, and the experimental results uncover the complex dependencies between the FJSP for job processing and the FSP for robot traveling. The proposed method improves the makespan by up to 13.10% and reduces non-loaded energy consumption by up to 13.49%, achieving zero collision time and an average solution time 11.18% faster than NSGA-III. Full article

The aerodynamic performance of compressor stators critically affects aircraft engine efficiency, yet traditional CFD-based evaluation and optimization suffer from high computational cost. This study addresses this gap by developing deep learning surrogate models to predict total pressure loss coefficient and outlet flow angle deviation for compressor stator vanes, using two geometric parameters—stagger angle ${\beta }_y$, leading-edge radius ratio R_rle, and one operational parameter, attack angle α. A high-fidelity dataset of 1701 cases was generated via automated CFD simulations using the transitional SST k-ω model. Among evaluated models—including standard CNN, CBAM-CNN, SS-CNN, and CNN-Transformer, SS-CNN achieved the highest accuracy, reducing mean absolute percentage error from 3.56% to 2.03% for loss and from 1.49% to 1.11% for outlet angle, with substantial computational savings. These surrogate models were integrated into a multi-objective optimization framework. The optimized vane, featuring a reduced leading-edge radius ratio within a stable stagger range, reduced total pressure loss by 2.38% (from 0.0570 to 0.0556) at the design attack angle of −2.83°, while the outlet angle deviation decreased from 0.439° to 0.066° (85% reduction), with the outlet angle improvement concentrated near the design condition. This work demonstrates a systematic, data-driven pipeline combining parametric modeling, automated simulation, deep learning-based prediction, and rapid optimization, offering an efficient solution for intelligent compressor blade design. Full article

The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. Full article

In recent years, typhoon activity over the South China Sea (SCS) has intensified, and interactions between typhoons and mesoscale eddies profoundly regulate the regional oceanic environment and air–sea energy exchange. To systematically investigate the position- and polarity-dependent eddy responses to typhoon forcing, we developed a typhoon–eddy spatial matching algorithm and analyzed the global mesoscale eddy dataset (2006–2020) combined with China Meteorological Administration (CMA) best-track typhoon records. Composite and correlation analyses were employed to examine variations in the eddy surface available potential energy (SAPE) and sea-surface temperature (SST) within a 7-day window before and after typhoon passage, with the typhoon power dissipation index (PDI) used to quantify storm intensity. Composite results reveal distinct dual-asymmetric responses: (1) Energetically, eddies on the left side of typhoon tracks exhibit overall weakening, with anticyclonic eddies (ACEs) showing more pronounced energy decay; in contrast, right-side eddies undergo significant intensification, and cyclonic eddies (CEs) display stronger enhancement than ACEs. (2) Thermally, all eddy types experience net cooling after typhoon passage, with right-side eddies showing stronger SST reductions than left-side ones, and CEs exhibiting more intense cooling than ACEs. Time-scale correlation analyses further demonstrate that the eddy energy change rate (EECR) of left-side CEs, right-side CEs, and right-side ACEs is positively correlated with PDI, whereas left-side ACEs show no significant correlation. For the SST change rate (SSTCR), all types of eddy events exhibit significant negative correlations with PDI, with weaker correlations for CEs and stronger correlations for ACEs. This study demonstrates that the track-relative position of tropical cyclones and the polarity of pre-existing mesoscale eddies exert a systematic control on the observed eddy responses to tropical cyclone forcing in the SCS. These results provide observational constraints on the asymmetric oceanic responses induced by tropical cyclones and offer insights into the interpretation of typhoon–ocean interaction diagnostics in marginal seas. Full article

As autonomous driving technology progresses, efficient and accurate object detectors are able to detect pedestrians, vehicles, road signs, and obstacles in real time, thereby enhancing driving safety and serving as a part of autonomous driving. However, the performance of such object detectors is limited and cannot be leveraged to satisfy modern autonomous driving systems. To address this issue, we develop an object detection network for autonomous driving scenarios, SST-YOLO, which is based on YOLOv8. First, we propose a Sobel Convolution & Convolution (SCC) module to enhance the backbone, which incorporates a SobelConv branch to explicitly model gradient-based edge information and improve structural feature representation. In addition, we replace the original path aggregation feature pyramid network (PAFPN) with a Small Object Augmentation Pyramid Network (SOAPN), which integrates SPDConv and CSP-OmniKernel modules to strengthen multi-scale feature fusion and enhance small object representation. Finally, a Task-Adaptive Decomposition & Alignment Head (TADAHead) is designed, which employs task decomposition, dynamic deformable convolution, and classification-aware modulation to decouple tasks and achieve adaptive spatial alignment, thereby improving detection accuracy and robustness in complex scenarios. Experiments on the public autonomous driving dataset KITTI show that our proposed method outperforms the baseline YOLOv8 model. Compared with the baseline results, mAP@0.5:0.95 ranges from 65.1% to 69.2%, which indicates that the proposed SST-YOLO network can achieve object detection for autonomous cars. Full article

With the rapid exploitation of offshore wind energy in the Beibu Gulf (BG), understanding local low-level wind variability is essential for wind farm operations. This study examines the interannual relationships between the BG low-level winds in June and tropical sea surface temperature (SST) during 1993–2021 using multiple datasets. The meridional and zonal winds show negligible correlation on interannual time scales. Further analysis indicates that the meridional wind over the BG is significantly linked to the tropical Indian Ocean (TIO) and tropical Atlantic (TA) SST. The TIO warming is able to intensify the Western Pacific Subtropical High via eastward-propagating Kelvin waves, inducing southerly wind anomalies over the BG. In contrast, the TA warming modulates the Walker circulation and triggers westward-propagating Rossby wave trains, forming an anomalous Philippine anticyclone and associated southerly winds. The anomalous southerly winds associated with TIO (TA) warming are contributed by changes in both rotational and divergent wind components (primarily divergent wind component). Conversely, the zonal wind over the BG is significantly correlated with the tropical Pacific SST. The equatorial eastern Pacific warming excites westward-propagating Rossby waves, generating an anomalous anticyclone and resulting in westerly anomalies over the BG. Air–sea coupling links warm SST in the northwestern Pacific to a local anticyclonic circulation, forming easterly anomalies in the BG. Notably, the tropical SST associated zonal wind anomalies are primarily driven by rotational wind component. This study clarifies how tropical SST anomalies influence low-level winds over the Beibu Gulf and distinguishes the roles of rotational and divergent wind components, providing new insights into the predictability of local wind variability. Full article

We propose a Cognitive Load Theory (CLT)-informed attention mechanism for transformer-based text classification. The proposed attention mechanism computes a per-token cognitive-load signal—derived from attention entropy, margin-based classification uncertainty, and optional inverse document frequency—and maps this signal to a learnable attention “budget” that scales outgoing attention mass during decoding. Unlike architectural efficiency techniques such as Multi-Query or Grouped-Query Attention, the CLT mechanism requires no structural modifications and introduces only modest per-step computational overhead while preserving full compatibility with standard transformer architectures. Experiments across four datasets (IMDB, AG News, SST-2, and DBpedia) show that CLT-informed attention achieves accuracy comparable to or exceeding a fixed-budget baseline while delivering consistently lower test loss, faster convergence to the best validation checkpoint, reduced attention entropy, and strong alignment between cognitive load and attention mass. Among all variants, an entropy-only load signal yields the most stable and consistent performance across datasets. These results demonstrate that lightweight, cognitively motivated constraints can structure transformer attention while maintaining or improving downstream classification performance. Full article

Energy dissipation in stepped weirs depends on the complex interaction between geometry, flow regime, and surface aeration. The research proposes a dimensionless empirical model (RE3T) to predict the overall energy dissipation in three-section stepped weirs with variable slopes. The formulation integrates dimensional analysis based on the Vaschy–Buckingham theorem, controlled physical experimentation, and three-dimensional numerical simulations using CFD employing the RANS–SST turbulence model implemented in ANSYS CFX. Eighteen numerical simulations were performed covering seven geometric configurations and four hydraulic inlet conditions, covering slug, transitional, and skimming flow regimes. The CFD model was previously validated by comparison with a physical scale model, obtaining a discrepancy of only 0.38% in relative energy dissipation. The validated dataset was then used to calibrate an empirical multiplicative correlation composed of eight dimensionless groups associated with sectional slopes, number of steps, overall geometric ratio, and upstream Froude number. The proposed model achieved a coefficient of determination R² = 0.81, with relative errors generally less than 1% and a maximum deviation of 2.34%. The statistical indicators (RMSE, MAE, and bias) confirm the absence of significant systematic trends within the defined domain of validity. The results show that the Froude number and the slopes of the sections are the variables with the greatest influence on overall dissipation. The RE3T formulation is a physically consistent and computationally efficient predictive tool for the design and analysis of stepped weirs with variable slopes, extending the scope of traditional correlations developed for uniform slopes. Full article