Search Results (130)

This study compares three detectors—Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Only Look Once v11 (YOLOv11)—for detecting subgrade distress in GPR radargrams. SSD converges fastest but shows weaker detection performance, while Faster R-CNN achieves higher localization accuracy at the cost of slower convergence. YOLOv11 offers the best overall performance. To push YOLOv11 further, we introduce three enhancements: a Multi-Scale Edge Enhancement Module (MEEM), a Multi-Feature Multi-Scale Attention (MFMSA) mechanism, and a hybrid configuration that combines both. On a representative dataset, YOLOv11_MEEM yields a 0.2 percentage-point increase in precision with a 0.2 percentage-point decrease in recall and a 0.3 percentage-point gain in mean Average Precision@0.5:0.95, indicating improved generalization and efficiency. YOLOv11_MFMSA achieves precision comparable to MEEM but suffers a substantial recall drop and slower inference. The hybrid YOLOv11_MEEM+MFMSA underperforms on key metrics due to gradient conflicts. MEEM reduces electromagnetic interference through dynamic edge enhancement, preserving real-time performance and robust generalization. Overall, MEEM-enhanced YOLOv11 is suitable for real-time subgrade distress detection in GPR radargrams. The research findings can offer technical support for the intelligent detection of subgrade engineering while also promoting the resilient development and sustainable operation and maintenance of urban infrastructure. Full article

Long underground expressways have emerged as an alternative to surface highways in densely urbanized areas; however, their enclosed geometry, extended length, and steep longitudinal gradients introduce traffic-flow dynamics distinct from those of surface roads. This study investigates the combined and interaction effects of traffic volume, heavy-vehicle ratio, longitudinal gradient, lane number, and lane-changing policy on traffic performance in long underground expressways using microscopic traffic simulation. A hypothetical 20 km underground expressway network was evaluated under 72 systematically designed scenarios. Weighted average speed and throughput were analyzed using nonparametric statistics, generalized linear models with interaction terms, and machine learning-based sensitivity analysis. While traffic volume and heavy-vehicle ratio were confirmed as dominant determinants of performance, a key contribution of this study is the identification of the density-dependent role of lane-changing policies. Under moderate traffic density, permissive lane-changing improves efficiency by enabling vehicles to bypass localized disturbances caused by heavy vehicles and longitudinal gradients, thereby enhancing capacity utilization. In contrast, under high-density conditions, permissive lane-changing amplifies lane-change conflicts and shockwave propagation within the confined underground environment, accelerating traffic instability and performance breakdown. These adverse effects are further intensified by steep uphill gradients. The findings demonstrate that lane-changing policies on long underground expressways should be designed in a context-sensitive manner, balancing efficiency and stability across traffic states. Full article

To address the issues of parameter coupling and high computational demands in existing feed-forward Gaussian splatting methods, we propose Gaussian Patch-level Mixture-of-Experts Splatting (GaPMeS), a lightweight feed-forward 3D Gaussian reconstruction model based on a mixture-of-experts (MoE) multi-task decoupling framework. GaPMeS employs a dual-routing gating mechanism to replace heavy refinement networks, enabling task-adaptive feature selection at the image patch level and alleviating the gradient conflicts commonly observed in shared-backbone architectures. By decoupling Gaussian parameter prediction into four independent sub-tasks and incorporating a hybrid soft–hard expert selection strategy, the model maintains high efficiency with only 14.6 M parameters while achieving competitive performance across multiple datasets—including a Structural Similarity Index (SSIM) of 0.709 on RealEstate10K, a Peak Signal-to-Noise Ratio (PSNR) of 19.57 on DL3DV, and a 26.0% SSIM improvement on real industrial scenes. These results demonstrate the model’s superior efficiency and reconstruction quality, offering a new and effective solution for high-quality sparse-view 3D reconstruction. Full article

Socio-ecological systems in basin regions characterized by diverse cultural traditions and hierarchical village spatial structure are undergoing profound transformation driven by multifunctional demands and spatial restructuring. This study develops an analytical framework encompassing economic production, socio-cultural functions, and ecological potential to examine the spatial differentiation and socio-ecological coupling mechanisms within the Yilong Lake Basin, Yunnan Province. Through the entropy weighting method and a coupling coordination model, the framework evaluates the “lake–mountain–village” gradient of spatial differentiation. The results indicate that: (1) the overall coordination level of multifunctional systems in the region remains relatively low, exhibiting a decreasing trend from lakeshore to the mountain periphery; (2) village-level dependencies of spatial functions can be summarized into three coupling categories—associated with institutional embedding, self-organization, and value mismatch—revealing distinct socio-ecological interaction patterns; and (3) three coupling categories correspond to three differentiated governance pathways, namely coupling optimization, functional transition, and conflict mitigation. The study advances theoretical and methodological insights into the spatial differentiation and evolution of complex village systems, highlighting the nonlinear coexistence of interdependence and constraint among economic, social, and ecological functions. It further provides practical guidance for coordinated governance and sustainable spatial planning in similar rural and basin environments worldwide. Full article

Accurate cotton yield estimation in arid oasis regions faces challenges from landscape fragmentation and the conflict between monitoring precision and computational costs. To address this, we developed a robust integrated framework combining multi-source remote sensing, spatiotemporal fusion, and data assimilation. To resolve spatiotemporal data gaps, the existing Agricultural Fusion (Agri-Fuse) algorithm was validated and employed to generate high-resolution time-series data, which achieved superior spectral fidelity (Root Mean Square Error, RMSE = 0.041) compared to traditional methods like Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Subsequently, high-precision Leaf Area Index (LAI) time series retrieved via the eXtreme Gradient Boosting (XGBoost) algorithm (c = 0.97) were integrated into the Ensemble Kalman Filter (EnKF)-assimilated World Food Studies (WOFOST) model. This approach significantly corrected simulation biases, improving the yield estimation accuracy (R² = 0.86, RMSE = 171 kg/ha) compared to the open-loop model. Crucially, we systematically evaluated the trade-off between assimilation frequency and efficiency. Findings identified the 3-day fusion interval as the optimal operational strategy, maintaining high accuracy (R² = 0.83, RMSE = 181 kg/ha) while reducing computational costs by 66.5% compared to daily assimilation. This study establishes a scalable, cost-effective benchmark for precision agriculture in complex arid environments. Full article

Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the natural low temperature of cold winters. An integrated energy consumption model, coupling moisture and thermal balances, was developed to evaluate room temperature drop, dehumidification rate (DR), and the internal circulation coefficient of performance (IC-COP). The model was calibrated and validated with experimental data comprising over 150 operational cycles under varied operation conditions, including initial temperature differences (ranging from −20 to −5 °C), air flow rates (0.6–1.5 m/s), refrigerant flow rates (3–7 L/min), and high-humidity conditions (>90% RH). Correlation analysis showed that higher indoor humidity improved both DR and IC-COP. Four machine learning models—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Multilayer Perceptron (MLP)—were developed and compared with a stacking ensemble learning model. Results demonstrated that the stacking model achieved superior prediction accuracy, with the best R² reaching 0.908, significantly outperforming individual models. This work provides an energy-saving dehumidification solution for enclosed livestock housing and a case study on the application of machine learning for energy performance prediction and optimization in agricultural environmental control. Full article

The Yangtze River Economic Belt is a crucial driver of China’s economy, and its shoreline is a strategic, finite resource vital for ecological security, flood control, navigation, and socioeconomic development. However, intensive development has resulted in functional conflicts and ecological degradation, underscoring the need for accurate identification and suitability assessment of shoreline functions. Conventional methods, which predominantly rely on land use data and remote sensing imagery, are often limited in their ability to capture dynamic changes in large river systems. This study introduces an integrated framework combining macro-level “Three-Zone Space” (urban, agricultural, ecological) theory with micro-level Point of Interest (POI) data to rapidly identify shoreline functions along the Yichang section of the Yangtze River. We further developed a multi-criteria evaluation system incorporating ecological, production, developmental, and risk constraints, utilizing a combined AHP-Entropy weight method to assess suitability. The results reveal a clear upstream-downstream gradient: ecological functions dominate upstream, while agricultural and urban functions increase downstream. POI data enabled refined classification into five functional types, revealing that ecological conservation shorelines are extensively distributed upstream, port and urban development shorelines concentrate in downstream nodal zones, and agricultural production shorelines are widespread yet exhibit a spatial mismatch with suitability scores. The comprehensive evaluation identified high-suitability units, primarily in downstream urban cores with superior development conditions and lower risks, whereas low-suitability units are constrained by high geological hazards and poor infrastructure. These findings provide a scientific basis for differentiated shoreline management strategies. The proposed framework offers a transferable approach for the sustainable planning of major river corridors, offering insights applicable to similar contexts. Full article

Customizing diffusion models via Low-Rank Adaptation (LoRA) has become a standard approach for customized concept injection. However, synthesizing multiple customized concepts within a single image remains challenging due to the parameter pollution problem, where naive fusion leads to gradient conflicts and severe quality degradation. In this paper, we introduce ConWave-LoRA, a novel framework designed to achieve hierarchical disentanglement of object and style concepts in LoRAs. Supported by our empirical validation regarding frequency distribution in the latent space, we identify that object identities are predominantly encoded in high-frequency structural perturbations, while artistic styles manifest through low-frequency global layouts. Leveraging this insight, we propose a Discrete Wavelet Transform (DWT) based filtering strategy that projects these concepts into orthogonal optimization subspaces during contrastive learning, thereby isolating structural details from stylistic attributes. Extensive experiments, including expanded ablation studies on LoRA rank sensitivity and style consistency, demonstrate that ConWave-LoRA consistently outperforms strong baselines, producing high-fidelity images that successfully integrate multiple distinct concepts without interference. Full article

CO₂-enhanced oil recovery (CO₂-EOR) with water-alternating-gas (WAG) injection offers the dual benefit of boosted oil production and CO₂ storage, addressing both energy needs and climate goals. However, designing CO₂-WAG schemes is challenging; maximizing oil recovery, CO₂ storage, and economic returns (net present value, NPV) simultaneously under a limited simulation budget leads to conflicting trade-offs. We propose a novel closed-loop multi-objective framework that integrates high-fidelity reservoir simulation with stacking surrogate modeling and active learning for multi-objective CO₂-WAG optimization. A high-diversity stacking ensemble surrogate is constructed to approximate the reservoir simulator. It fuses six heterogeneous models (gradient boosting, Gaussian process regression, polynomial ridge regression, k-nearest neighbors, generalized additive model, and radial basis SVR) via a ridge-regression meta-learner, with original control variables included to improve robustness. This ensemble surrogate significantly reduces per-evaluation cost while maintaining accuracy across the parameter space. During optimization, an NSGA-II genetic algorithm searches for Pareto-optimal CO₂-WAG designs by varying key control parameters (water and CO₂ injection rates, slug length, and project duration). Crucially, a decision-space diversity-controlled active learning scheme (DCAF) iteratively refines the surrogate: it filters candidate designs by distance to existing samples and selects the most informative points for high-fidelity simulation. This closed-loop cycle of “surrogate prediction → high-fidelity correction → model update” improves surrogate fidelity and drives convergence toward the true Pareto front. We validate the framework of the SPE5 benchmark reservoir under CO₂-WAG conditions. Results show that the integrated “stacking + NSGA-II + DCAF” approach closely recovers the true tri-objective Pareto front (oil recovery, CO₂ storage, NPV) while greatly reducing the number of expensive simulator runs. The method’s novelty lies in combining diverse stacking ensembles, NSGA-II, and active learning into a unified CO₂-EOR optimization workflow. It provides practical guidance for economically aware, low-carbon reservoir management, demonstrating a data-efficient paradigm for coordinated production, storage, and value optimization in CO₂-WAG EOR. Full article

High-aspect-ratio wings improve aerodynamic efficiency but suffer from greater gust-induced loads, requiring innovative design methods for gust load alleviation (GLA). This study develops a reduced-order aeroelastic model to enable efficient sensitivity analysis and optimization of structural properties for passive GLA in the early design stage. A beam-based structural model was coupled with unsteady potential-flow aerodynamics in the frequency domain. The formulation, implemented in JAX, exploits automatic differentiation (AD) to compute gradients of gust responses with respect to spanwise mass and stiffness distributions. Validation was performed against MSC Nastran results. The model reproduced static and dynamic aeroelastic responses within ~10% error rate compared to MSC Nastran. Sensitivity analyses revealed that the influence of structural properties strongly depends on the chosen objective function, with mass and elastic axis location showing notable but sometimes conflicting trends. Gradient-based optimization demonstrated improved load alleviation but highlighted risks of overfitting to specific gust profiles. The proposed framework enables scalable, differentiable optimization of gust responses, bridging microstructural design and aeroelastic performance. These findings indicate that the proposed differentiable framework constitutes a valuable methodology for early-stage design, offering an efficient means to couple aeroelastic performance with structural optimization. Full article

Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, $R^2$, and $CV\text{\_}{R}^2$ metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected through Permutation Importance were used in subsequent analyses. To predict participants’ age from psychophysiological test results, we evaluated several regression models, including Random Forest, Extra Trees, Gradient Boosting, SVR, Linear Regression, LassoCV, RidgeCV, ElasticNetCV, AdaBoost, and Bagging. Model performance was compared using the determination coefficient ($R^2$) and mean absolute error (MAE). Cross-validated performance ($CV\text{\_}{R}^2$) was estimated via 5-fold cross-validation. To assess metric stability and uncertainty, bootstrapping (1000 resamples) was applied to the test set, yielding distributions of MAE and RMSE from which mean values and 95% confidence intervals were derived. Results: The study identified RidgeCV with winsorization and standardization as the best model for predicting cognitive age, achieving a mean absolute error of 5.7 years and an $R^2$ of 0.60. Feature importance was evaluated using SHAP values and permutation importance. SHAP analysis showed that stroop_time_color and stroop_var_attempt_time were the strongest predictors, followed by several task-timing features with moderate contributions. Permutation importance confirmed this ranking, with these two features causing the largest performance drop when permuted. Partial dependence plots further indicated clear positive relationships between these key features and predicted age. Correlation analysis stratified by sex revealed that most features were significantly associated with age, with stronger effects generally observed in men. Conclusions: Feature selection revealed Stroop timing measures and task-related metrics from math and campimetry tests as the strongest predictors, reflecting core cognitive processes linked to aging. The results underscore the value of careful outlier handling, feature selection, and interpretable regularized models for analyzing psychophysiological data. Future work should include longitudinal studies and integration with biological markers to further improve clinical relevance. Full article

For runoff-generated debris flow continuum mechanics-based early warning models, the computational unit must satisfy the homogeneity assumption of continuum mechanics. Although traditional grid cells meet the homogeneity assumption as computational units, they segment channel geomorphological functional reaches, weaken the clustered mobilization of sediment sources, and constrain efficiency due to grid-by-grid calculations. To address these limitations, we construct a Froude number ($Fr$) calculation model constrained by key factors such as the channel cross-sectional geometry and topographic parameters. The absolute deviation of $Fr$ is used as a criterion for homogeneity within the computational unit. By combining critical shear stress theory and velocity perturbation, physical thresholds for the criteria are derived. A physical model-based method for automatically delineating homogeneous channel units (${CU}_j$) is proposed, ensuring that the geometric features and hydrodynamic parameters within ${CU}_j$ are homogeneous, while ensuring heterogeneity between adjacent ${CU}_j$. Comprehensive multi-scale validation in Yeniu Gully, a typical debris flow catchment in Wenchuan County, demonstrates that parameters such as longitudinal gradient, cross-sectional area, flow depth, and shear stress remain relatively homogeneous within each ${CU}_j$ but differ significantly between adjacent ${CU}_j$. Furthermore, the proposed method can stably characterize key channel geomorphological functional units, such as bends, confluences, abrupt width changes, longitudinal gradient changes, erosion segments, and deposition segments. Sensitivity analysis demonstrates that the method satisfies both robustness and universality under various conditions of rainfall intensity, runoff coefficient, and Manning’s roughness coefficient. Even under the most unfavorable extreme conditions, the accuracy of ${CU}_j$ delineation exceeds 88.64%, indicating high reliability and suitability for deployment in various debris flow catchments. The proposed framework for defining ${CU}_j$ resolves the conflict in traditional computational units between the “continuum model homogeneity requirement” and “geomorphological functional unit continuity,” providing a more rational and efficient computational environment for runoff-generated debris flow continuum mechanics-based early warning models. Full article

Addressing two types of supply chain disruptions—frequent short-duration disruptions (e.g., minor natural disasters) and infrequent long-duration disruptions (e.g., geopolitical conflicts, public health crises)—while considering their impact on logistics capacity, this paper proposes a multi-echelon inventory management optimization framework based on the Proximal Policy Optimization (PPO) algorithm. Unlike traditional inventory control models with simplistic assumptions, this study integrates factors such as the frequency, duration, and impact of disruptions into the inventory optimization process. It is designed to coordinate replenishment decisions at the warehouse while reacting to local retailer states. Since retailers share the same cost parameters and demand dynamics, their decision problems are structurally symmetric, which allows us to use a shared policy across retailers and thus keep the learning model compact and scalable. Numerical experiments compare the PPO policy with classical inventory heuristics under various network sizes and disruption types. The results show that PPO consistently achieves lower total costs than the benchmarks, and its relative advantage becomes more pronounced under severe or longer disruptions. These findings suggest that modern policy-gradient methods, combined with simple forms of structural symmetry, can provide an effective and scalable tool for managing disrupted multi-echelon supply chains. Full article

Electro-hydraulic servo fatigue testing machines are susceptible to concurrent degradation and failure of multiple components during high-frequency, high-load, and long-duration cyclic operations, posing significant challenges for online health monitoring. To address this, this paper proposes a multi-state recognition method based on spatiotemporal feature fusion and bidirectional cross-attention. The method employs a Bidirectional Temporal Convolutional Network (BiTCN) to extract multi-scale local features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture forward and backward temporal dependencies, and Bidirectional Cross-Attention (BiCrossAttention) to achieve fine-grained bidirectional interaction and fusion of spatial and temporal features. During training, GradNorm is introduced to dynamically balance task weights and mitigate gradient conflicts. Experimental validation was conducted using a real-world multi-sensor dataset collected from an SDZ0100 electro-hydraulic servo fatigue testing machine. The results show that on the validation set, the cooler and servo valve achieved both accuracy and F1-scores of 100%, the motor-pump unit achieved an accuracy of 98.32% and an F1-score of 97.72%, and the servo actuator achieved an accuracy of 96.39% and an F1-score of 95.83%. Compared to single-task models with the same backbone, multi-task learning improved performance by approximately 3% to 4% for the hydraulic pump and servo actuator tasks, while significantly reducing overall deployment resources. Compared to single-task baselines, multi-task learning improves performance by 3–4% while reducing deployment parameters by 75%. Ablation studies further confirmed the critical contributions of the bidirectional structure and individual components, as well as the effectiveness of GradNorm in multi-task learning for testing machines, achieving an average F1-score of 98.38%. The method also demonstrated strong robustness under varying learning rates and resampling conditions. Compared to various deep learning and fusion baseline methods, the proposed approach achieved optimal performance in most tasks. This study provides an effective technical solution for high-precision, lightweight, and robust online health monitoring of electro-hydraulic servo fatigue testing machines under complex operating conditions. Full article

Traditional solid screw rotors suffer from excessive weight, structural redundancy, low material utilization, and high energy consumption, conflicting with the growing demand for efficient, sustainable manufacturing. To address these challenges, this study proposes a lightweight design method for hollow, internally supported male screw rotors that simultaneously enhances stiffness and static–dynamic performance. A parameterized structural model with four key design variables was established, and multi-physics simulations integrating fluid flow, heat transfer, and structural mechanics were conducted to obtain mass, maximum deformation, and first-order natural frequency. Based on these simulation results, a surrogate-assisted multi-objective evolutionary optimization framework was employed: an enhanced Newton–Raphson-based optimizer (SNRBO) was used to tune the extreme gradient boosting surrogate (XGBoost 1.5.2), and the tuned surrogate then guided the Nondominated Sorting Genetic Algorithm III (NSGA-III) to perform multi-objective search and construct the Pareto front. Compared with a conventional solid rotor, the optimized design reduces mass by 64.43%, decreases maximum deformation by 4.41%, and increases the first-order natural frequency by 82.14%. These findings indicate that the proposed method provides an effective pathway to balance lightweight design with structural safety and dynamic stability, offering strong potential for green manufacturing and high-performance applications in energy, aerospace, and industrial compressor systems, and providing robust support for further advances in this field. Full article