Search Results (2,740)

This study compares the performance of XGBoost and LightGBM models for predicting live weights of Yucatecan Criollo pigs from biometric measurements and examines the structural and algorithmic differences that affect model fit. Detailed analysis of the models’ hyperparameter optimization and variable importance revealed how each model approaches the data and prioritizes features. This study was conducted on 182 Yucatecan Criollo pigs (134 females and 48 males). When model performances were evaluated, the XGBoost model showed superior prediction performance with acceptable accuracy and lower error rates in the test dataset, with R² = 0.905, RMSE = 5.704, and MAE = 3.636. In contrast, the LightGBM model produced acceptable results under certain hyperparameter combinations with R² = 0.824, RMSE = 7.772, and MAE = 5.505. While the robust performance of both models requires strategic decisions in model selection and optimization, it is recommended to consider the dataset’s nature in feature selection and hyperparameter settings. This study provides important insights for simplifying the model and improving its efficiency in machine learning applications, and serves as a reference for more effective model use. Full article

This study addressed a critical gap in understanding the drivers of movie consumption during digital transformation and streaming platform proliferation. It examined the direct effects of three core dimensions—social value, functional value, and emotional value—on movie consumption behavioral intention, alongside the mediating mechanism of satisfaction. Data were collected via questionnaire surveys administered to cinema audiences in Eastern China and through Wenjuanxing online platform, yielding 1089 valid responses. Statistical analysis was conducted using SPSS 26.0, and Structural Equation Modeling (SEM) was performed employing AMOS 26.0. Findings indicate significant positive direct effects of social value and emotional value on movie consumption behavioral intention. Furthermore, these value dimensions indirectly enhance movie consumption behavioral intention through the mediating influence of satisfaction. In contrast, functional value demonstrates no significant direct effect on either movie consumption behavioral intention or satisfaction. Satisfaction serves as a significant mediator in the relationships between both social value and emotional value, and movie consumption behavioral intention. This study elaborated the distinct pathways through which varied perceived value dimensions operate and empirically validates the mediating role of satisfaction within movie consumption decision-making. For the movie industry, these insights suggest prioritizing social engagement and emotional resonance to optimize offerings, establishing dynamic satisfaction monitoring, and designing member incentives targeting these values to foster sustained behavioral activation. This provides empirically grounded guidance for refining marketing strategies and experiential enhancements. Full article

This paper proposes a power-based optimization procedure to identify the optimal number and vertical placement of buckling restrained brace (BRB) outrigger systems for enhancing the seismic performance of core-wall-dominated benchmark model. The proposed method is validated using a nine-zone numerical model subjected to nonlinear time-history analysis implemented in MATLAB R2025.a (25.1.0.2943329). The optimization variables include the number and locations of outriggers as well as the stiffness of the BRBs, while the objective function is defined as the minimization of the maximum inter-story drift response. Outriggers are installed between zones 2 and 9, with each zone subdivided into five potential outrigger levels located 150 mm above the floor level, resulting in 40 potential outrigger placement scenarios. The total number of outriggers is constrained to range from one to eight, with at most one outrigger allowed per zone. Optimal outrigger–BRB configurations are identified by incrementally distributing BRB stiffness at the perimeter column-outrigger connection regions using a power-based allocation strategy. At each optimization step, the proposed framework evaluates only one candidate configuration per eligible story and outrigger level, resulting in several nonlinear time-history analysis grows linearly with the number of candidate locations. This contrasts with the combinatorial growth in computational demand typically associated with exhaustive or evolutionary optimization methods and leads to a significant reduction in overall computational efforts. Full article

Objective: Ultrasound Resolution Microscopy (URM) is an emerging technique that provides superior delineation of tumor microvasculature. This prospective study aimed to evaluate the diagnostic value of URM in differentiating benign from malignant breast lesions. Methods: From September 2024 to October 2025, 55 patients with 57 breast masses underwent conventional ultrasound and contrast-enhanced URM. Microvascular parameters were quantitatively analyzed and cross-referenced with histopathology. To mitigate overfitting, LASSO regression was employed to screen 14 URM indices. A combined predictive model integrating core URM features with BI-RADS categorization (dichotomized at 4A) was developed and evaluated using ROC and decision curve analysis (DCA). Results: Thirty-four malignant and 23 benign masses were confirmed. Malignant lesions exhibited comprehensively elevated microvascular abundance and architectural chaos. LASSO regression distilled these features down to two core independent predictors: Vessel Count and Max Curvature. The BI-RADS-alone model yielded 100% sensitivity but extremely low specificity (30.43%). Crucially, the Combined model significantly outperformed the single-modality approaches, achieving an excellent AUC of 0.896 (vs. 0.652 for BI-RADS alone, p < 0.001). By integrating URM parameters, the Combined model maintained adequate sensitivity (73.53%) while drastically boosting specificity to 91.30%. DCA confirmed superior net clinical benefit for the combined strategy. Conclusions: Quantitative URM imaging effectively characterizes the distinct microvascular features of breast cancers. Integrating URM functional parameters with conventional BI-RADS categorization significantly improves diagnostic specificity. Consequently, this combined approach provides a reliable non-invasive strategy to optimize risk stratification, effectively minimizing false-positive diagnoses and averting unnecessary invasive biopsies in routine clinical practice. Full article

To investigate the influence of working fluid composition on the thermo-hydraulic performance of plate heat exchangers (PHEs) under single-phase sensible heat transfer conditions, a three-dimensional steady-state numerical model was developed for a transverse corrugated channel with a chevron angle of 60°. The governing equations were solved using the finite volume method implemented in ANSYS Fluent, in conjunction with the standard k–ε turbulence model. The analysis considered pure refrigerants R134a and R245fa, as well as their mixtures with mass ratios of 0.2, 0.5, and 0.8, with thermophysical properties assumed to be temperature-independent constants. The results indicate that as the mass fraction of R134a decreases from 1.0 to 0, the heat transfer coefficient (h) decreases from 1025 to 815 W/(m²·K), primarily attributed to the combined effects of reduced thermal conductivity and increased viscosity. Among the investigated cases, the R134a/R245fa mixture with a mass ratio of 0.8 provides the most favorable performance trade-off, exhibiting a heat transfer coefficient only 3.0% lower than that of pure R134a while achieving a 12.5% reduction in flow resistance compared with pure R245fa. Furthermore, the heat transfer coefficient is found to be weakly affected by heat flux in the range of 8000–20,000 W/m²; in contrast, increasing the mass flow rate from 0.001 to 0.005 kg/s enhances heat transfer coefficient by 65.1%, accompanied by a significant increase in pressure drop. Comparisons with established single-phase correlations for corrugated channels show average deviations of 6.5% for the Nusselt number and 3.8% for the friction factor. The present study provides useful guidance for working fluid selection and operational optimization of PHEs in applications dominated by sensible heat transfer, such as specific stages of heat pump cycles and medium-temperature waste heat recovery. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

Phase change materials (PCMs) offer potential for passive thermal regulation in building envelopes through latent heat storage; however, their effectiveness remains strongly climate-dependent, and configurations optimized for one region often underperform in others. Existing evaluation approaches rely largely on location-specific simulations or surrogate models with limited climate transferability. This study develops a physics-informed, climate-aware machine-learning framework to assess PCM-integrated wall assemblies across diverse climates. A structured dataset of 720 EnergyPlus simulations was generated across nine PCM materials, ten ASHRAE climate zones, two placement configurations, and four thickness levels using automated model generation and batch simulation through Eppy-based workflows. Ensemble-based models (XGBoost, LightGBM, CatBoost, Random Forest) were trained under climate-grouped validation to predict total annual energy consumption, peak cooling demand, and peak heating demand. The models achieved high predictive accuracy for total annual energy use (R² ≈ 0.98–0.99) and peak cooling demand (R² ≈ 0.93–0.96), outperforming statistical, climate-only, and PCM-agnostic baselines. In contrast, peak heating demand showed low predictability (R² ≤ 0.26), indicating limited sensitivity to PCM parameters under the studied configuration. These results demonstrate that climate-aware validation enables defensible cross-climate PCM assessment, supporting energy demand reduction and sustainable envelope design decisions aligned with global building decarbonization goals. Full article

Elucidating the trade-offs and synergistic relationships between different ecosystem services is essential to optimize the benefits of ecosystem services and ensure their proper management for human well-being and ecosystem health. However, previous studies have focused only on quantitative analysis based on statistical relationships to explore ecosystem service trade-offs and synergistic relationships as a whole; additionally, some of them lack scientific expression of spatial and temporal differences within regions. Therefore, here, we explored the trade-offs and synergies among ecosystem services in the Taihang Mountains region and conducted ecological service zoning based on the findings to support ecological conservation and high-quality development in the Taihang Mountains and North China Plain. We employed yield spatialization, the InVEST model, and ArcGIS kernel density analysis to assess the interactions among ecosystem services: provisioning (food supply), regulating (water yield and carbon density), supporting (soil retention and habitat quality), and cultural services (leisure and recreation) in the study area. Linear Pearson correlation coefficients and non-linear bagplots were utilized to analyze the interrelationships among these services. Based on the bagplot results, the geographic patterns of ecosystem service trade-offs/synergies and the distribution of dominant services were identified. The results revealed considerable trade-offs between food supply and both regulating and supporting services, with most of the latter exhibiting synergistic relationships with one another. In contrast, leisure and recreation services showed a neutral relationship with other services. Among ecosystem services, carbon density services demonstrated the highest synergistic effects, whereas food supply services exhibited the most conflicts. The various ecosystem trade-off/synergy zones and dominant service distributions generated through bagplot mappings may optimize management methods for multiple ecosystem services. Overall, these findings provide significant insights for improving ecological service zoning and natural resource management. Full article

Person re-identification (ReID) under diverse weather conditions remains a critical yet insufficiently explored problem. Most existing ReID approaches are developed and benchmarked on clear-weather datasets, resulting in significant performance degradation when deployed in rainy, snowy, or hazy environments. Conventional image restoration methods, typically optimized for low-level image quality metrics, are often misaligned with the objectives of high-level identity discrimination and thus fail to improve the person ReID performance. To address these limitations, we propose DW-ReID, a unified framework that integrates weather-degraded image restoration with person re-identification tasks. The proposed DW-ReID is built upon a large-scale Contrastive Language-Image Pre-training (CLIP) model and achieved by a two-stage training paradigm. In the first stage, a set of learnable text prompts is optimized to construct identity-specific ambiguous descriptions for each person’s identity. In the second stage, the optimized text descriptions, together with a frozen text encoder, provide language supervision to jointly train a weather encoder, an image restorer, and a ReID encoder in an end-to-end manner. The experimental results on two our contributed synthetic datasets consistently demonstrate the effectiveness and superior performance of the proposed DW-ReID method. Full article

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we propose Direction-Aware U-Net (DA-UNet), an improved U-Net architecture that incorporates a Direction-Aware Context Pooling (DACPool) module and Topology-aware Segmentation loss (TopoSeg loss). The DACPool module explicitly exploits vein orientation to aggregate directional contextual information, while the TopoSeg loss jointly optimizes pixel-level accuracy and topological continuity. DA-UNet achieves efficient leaf vein segmentation with improved continuity and structural integrity, according to evaluations on the self-constructed Tissue-Cultured Plantlet Vein Dataset 2025 (TCPVD2025). Comparative experiment results show that the improved model outperforms PSPNet, DeepLabV3+, U-Net, TransUNet, Swin-UNet, CCNet, and SegNeXt, as evidenced by Recall, Dice, and CONNECT scores of 71.35%, 69.08%, and −2.25, while maintaining competitive Precision of 66.98%. Ablation experiment results provide further evidence for the efficacy of the TopoSeg loss and the DACPool module. The results demonstrate the effectiveness of the proposed vein segmentation framework for generating outputs that are both accurate and structurally consistent, thus enabling reliable automated processes for plant genetic transformation. Full article

This study addresses the instability of statistical modeling for small-sample maximum friction torque data under multiple temperature conditions. Within the Weibull distribution framework, a sample-aggregation method is proposed, and a unified modeling scheme separating central tendency from dispersion structure is established. This approach enables equivalent aggregation of data across different temperature levels while preserving structural consistency, thereby improving parameter estimation stability and statistical efficiency. To overcome the tendency of single-criterion optimization to fall into local optima under small-sample conditions, a secondary identification criterion combining residual minimization with a Levene-based statistical consistency test is introduced, and a dual-level search strategy is used to obtain a more robust global optimal solution. The parameter estimation results indicate that direct estimation based on small samples produces unstable parameters, with the coefficient of variation of the shape parameter reaching approximately 7.4%. In contrast, the sample-aggregation method shows that the scale parameter increases with temperature, while the location parameter first decreases and then increases due to the combined influence of central tendency and dispersion. The parameters obtained by the aggregation method exhibit more stable and regular variation trends with temperature. The results demonstrate that the proposed method significantly improves parameter stability and statistical efficiency for small-sample maximum friction torque data and provides a practical statistical modeling approach for multi-condition small-sample engineering data. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

The rheological behavior of chitosan–vanillin crosslinked olive oil-in-water emulsions (Φ = 0.52) was investigated to identify formulation and processing conditions suitable for designing oleogel precursors. The effects of homogenization conditions, reaction temperature, chitosan concentration, vanillin-to-chitosan molar ratio, and non-ionic surfactants were systematically evaluated. Surfactant-free emulsions exhibited a structured, gel-like response and non-thixotropic shear-thinning flow, which was well described by the Herschel–Bulkley model within the investigated shear-rate range. Optimal homogenization (4 min, ≥9500 rpm) refined the microstructure without compromising stability. Increasing the reaction temperature to 55 °C, the chitosan concentration to ~0.9% (w/w), and the vanillin-to-chitosan molar ratio to 0.7 maximized yield stress, consistency, and thermal robustness, consistent with enhanced network formation. In contrast, Tween^® surfactants produced divergent responses, increasing small-amplitude oscillatory stiffness while markedly reducing resistance under steady shear, likely due to surfactant-driven interfacial displacement. Among the tested surfactants, Tween^® 20 provided the highest thermal stability. Overall, these results define processing and formulation windows to obtain surfactant-free, structured emulsions with improved structuring performance, supporting their use as effective templates for olive oil oleogel development. Full article

Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement amount. This study introduces a hybrid computational process that integrates metaheuristic optimization with symbolic regression to generate direct analytical equations for the estimation of the required CFRP area. First, a comprehensive database containing 1300 optimal strengthening scenarios was generated using the Jaya optimization algorithm under the constraints specified in ACI 440.7R and ACI 530. The resulting dataset was subsequently processed through symbolic regression using the PySR platform to identify explicit mathematical relationships between structural parameters and the optimum CFRP area. Most traditional machine learning approaches operate as black-box predictors. In contrast, the proposed approach generates interpretable closed-form expressions that can be used directly in engineering calculations. Two models were derived from the Pareto-optimal solution set. The first model is a simplified equation emphasizing algebraic simplicity. The second model prioritizes prediction accuracy. The simplified formulation achieved a coefficient of determination of approximately 0.992. The accuracy-focused model achieved a value above 0.997 with very low prediction errors. Validation studies with independent test samples showed that the obtained equations are reliable. The average error for the simplified model is below 4%, and for the high-accuracy model, it is approximately 2%. The results demonstrate that combining the optimization-generated datasets with symbolic regression makes it possible to obtain transparent design equations. These equations eliminate iterative design processes and provide a fast and reliable estimation tool for CFRP strengthening of masonry walls. Full article

Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Although current deep learning approaches have improved efficiency, most remain confined to single tasks and lack a pathway to translate image recognition into quantifiable geological parameters. Moreover, these methods struggle with cross-scale feature extraction and accurate grain boundary localization in complex textures. To overcome these limitations, this study proposes a three-stage automated analysis framework integrating intelligent lithology identification, sandstone grain segmentation, and quantitative analysis of fabric parameters. To address scale discrepancies in lithology discrimination, Rock-PLionNet integrates a Partial-to-Whole Context Fusion (PWC-Fusion) module and the Lion optimizer, which mitigates cross-scale feature inconsistencies and enables accurate screening of target sandstone samples. Subsequently, to correct boundary deviations caused by low contrast and grain adhesion, the PetroSAM-CRF strategy integrates polarization-aware enhancement with dense conditional random field (DenseCRF)-based probabilistic refinement to extract precise grain contours. Based on these outputs, the framework automatically calculates key fabric parameters, including grain size and roundness. Experiments on 3290 original multi-source thin-section images show that Rock-PLionNet achieves a classification accuracy of 96.57% on the test set. Furthermore, PetroSAM-CRF reduces segmentation bias observed in general-purpose models under complex texture conditions, enabling accurate parameter estimation with a roundness error of 2.83%. Overall, this study presents an intelligent workflow linking microscopic image recognition with quantitative analysis of geological fabric parameters, providing a practical pathway for digital petrographic evaluation in hydrocarbon exploration. Full article