Search Results (5,114)

Municipal solid waste challenges (MSW) and concerns about fossil fuel dependence motivate efforts to recover energy from waste, including refuse-derived fuel (RDF). Techno-economic assessment (TEA) evaluates the feasibility of systems by quantifying investment performance. However, most RDF-TEA studies typically rely on isolated sensitivity analyses. That provides limited insight into interaction effects in emerging markets. This study maps the multivariable feasibility of RDF production from MSW in Ghana under realistic economic conditions. Using a pilot-calibrated case study, the assessment integrates discounted cash flow analysis with response surface methodology–design of experiment (RSM-DoE). A central composite design evaluates interaction effects among operational and economic variables for a system capacity of 2875 tonnes RDF/year. The results indicate economic viability with a net present value (NPV) of USD 892,556.44, a payback period (PBP) of 6.61 years and a levelised production cost (LPC) of USD 18.96/tonne. The RSM models show high explanatory power (R², R²adj, R²pred > 90%). Sensitivity results demonstrate that support mechanisms can significantly reduce LPC and PBP while preserving investment viability. The study quantifies the feasibility thresholds and the support instruments within the RDF design levers. It further provides a transferable framework for assessing deployment and upscaling in emerging markets. The findings highlight the need for structured pricing mechanisms and regulatory support for the long-term sustainability of RDF as an AF. Full article

Groundwater, a critical resource for regional water security and public health, faces escalating threats from heavy metal contamination—a pressing environmental challenge worldwide. This study focuses on the central-south Hunan region of China, a mineral-rich, densely populated area characterized predominantly by non-point-source pollution, aiming to systematically unravel the spatial patterns, source contributions, and associated health risks of heavy metals in local groundwater. Based on 717 spring and well water samples collected in 2024, we determined pH and seven heavy metals (As, Cd, Pb, Zn, Fe, Mn, and Tl). By integrating hydrogeological zoning, lithology, topography, and river networks, the study area was divided into 11 assessment units, clearly revealing the spatial heterogeneity of heavy metals. The results demonstrate that exceedances of Cd, Pb, and Zn were sporadic and point-source-influenced, whereas As, Fe, Mn, and Tl showed regional exceedance patterns (e.g., Mn exceeded the standard in 9.76% of samples), identifying them as priority control elements. The spatial distribution of heavy metals was governed the synergistic effects of lithology, water–rock interactions, and hydrological structure, showing a distinct “acidic in the northeast, alkaline in the southwest” pH gradient. Combined application of the APCS-MLR and PMF models resolved five principal pollution sources: an acid-reducing-environment-driven release source (contributing 76.1% of Fe and 58.3% of Pb); a geogenic–anthropogenic composite source (contributing 81.0% of Tl and 62.4% of Cd); a human-perturbation-triggered natural Mn release source (contributing 94.8% of Mn); an agricultural-activity-related input source (contributing 60.1% of Zn); and a primary geological source (contributing 89.9% of As). Monte Carlo simulation-based health risk assessment indicated that the average hazard index (HI) and total carcinogenic risk (TCR) for all heavy metals were below acceptable thresholds, suggesting generally manageable risk. However, As was the dominant contributor to both non-carcinogenic and carcinogenic risks, with its carcinogenic risk exceeding the threshold in up to 3.84% of the simulated adult exposures under extreme scenarios. Sensitivity analysis identified exposure duration (ED) as the most influential parameter governing risk outcomes. In conclusion, we recommend implementing spatially differentiated management strategies: prioritizing As control in red-bed and granite–metamorphic zones; enhancing Tl monitoring in the northern and northeastern granite-rich areas, particularly downstream of the Mishui River; and regulating land use in brick-factory-dense riparian zones to mitigate disturbance-induced Mn release—for instance, through the enforcement of setback requirements and targeted groundwater monitoring programs. This study provides a scientific foundation for the sustainable management and safety assurance of groundwater resources in regions with similar geological and anthropogenic settings. Full article

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects. Full article

To address the limitation of existing research on engine oil filter structural parameters—overemphasizing pressure drop while neglecting internal flow uniformity and filter media utilization—this study establishes a three-dimensional Computational Fluid Dynamics (CFD) model of a pleated oil filter for a certain type. With other structural and material parameters fixed, nine inner diameter schemes (60–84 mm) and seven pleat number schemes (50–80) were designed to systematically investigate their effects on pressure drop, flow uniformity, and media utilization via numerical simulations and experimental validation. The results show that pressure drop decreases monotonically with increasing inner diameter, with smaller diameters being more sensitive to flow rate variations; flow uniformity improves nonlinearly, with severe jets and large dead zones causing poor uniformity for smaller diameters, while uniformity is significantly enhanced with larger diameters, though marginal benefits diminish after a critical threshold. In contrast, pressure drop increases monotonically with more pleats, and higher pleat numbers are more sensitive to resistance changes; flow uniformity follows a threshold effect—deteriorating gradually without extensive dead zones for fewer pleats (maintaining high utilization) but declining sharply beyond a threshold due to narrowed inter-pleat spacing inducing intense jets and expanded dead zones. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article

Ensuring sustained driver attention is critical for intelligent transportation safety systems; however, the performance of data-driven driver distraction detection models is often limited by the high cost of large-scale manual annotation. To address this challenge, this paper proposes an adaptive semi-supervised driver distraction detection framework based on teacher–student learning and deformable pyramid feature fusion. The framework leverages a limited amount of labeled data together with abundant unlabeled samples to achieve robust and scalable distraction detection. An adaptive pseudo-label optimization strategy is introduced, incorporating category-aware pseudo-label thresholding, delayed pseudo-label scheduling, and a confidence-weighted pseudo-label loss to dynamically balance pseudo-label quality and training stability. To enhance fine-grained perception of subtle driver behaviors, a Deformable Pyramid Sparse Transformer (DPST) module is integrated into a lightweight YOLOv11 detector, enabling precise multi-scale feature alignment and efficient cross-scale semantic fusion. Furthermore, a teacher-guided feature consistency distillation mechanism is employed to promote semantic alignment between teacher and student models at the feature level, mitigating the adverse effects of noisy pseudo-labels. Extensive experiments conducted on the Roboflow Distracted Driving Dataset demonstrate that the proposed method outperforms representative fully supervised baselines in terms of mAP@0.5 and mAP@0.5:0.95 while maintaining a balanced trade-off between precision and recall. These results indicate that the proposed framework provides an effective and practical solution for real-world driver monitoring systems under limited annotation conditions. Full article

Green space equity is increasingly recognized as a critical environmental condition for healthy aging, yet existing research often overlooks how different green space attributes—accessibility and diversity—are associated with distinct dimensions of older adults’ health. Limited attention has been paid to their nonlinear threshold effects or to the social pathways through which green spaces influence health outcomes. Using the United States county-level panel data from 2020 to 2023, this study integrates fixed-effects models, Extreme Gradient Boosting (XGBoost), and mediation analysis to examine the associations between green accessibility measured by the Two-Step Floating Catchment Area (2SFCA) method, and green diversity measured by the Shannon Index, on the general, physical, and mental health of older adults. Findings indicate that (1) higher green accessibility is associated with better general health, whereas green diversity shows a stronger association with physical health, reflecting its link to more heterogeneous ecosystem service environments. (2) Green accessibility demonstrates the threshold effect, in which the strength of association with health becomes steeper once accessibility approaches higher levels. (3) Green space equity is linked to health partly through social structures. Education clustering and marital stability mediate the associations with general health, while mental health appears to depend more on the social interaction opportunities embedded within green environments than on their physical attributes alone. The study proposes an integrated “physical environment–social structure–health outcome” framework and a threshold-oriented spatial intervention strategy, highlighting the need to prioritize improvements in green accessibility in underserved areas and prioritizing green diversity and age-friendly social functions where accessibility is already high. These findings offer evidence for designing inclusive, health-oriented urban environments for aging populations. Full article

Unstable car-following behavior under truck-induced visual occlusion on mountainous two-lane roads significantly increases rear-end crash risk. However, compared with studies focusing on overtaking or curve risk prediction, the car-following-truck (CFT) risk and its influencing factors have received limited attention. Therefore, this study used unmanned aerial vehicles (UAVs) to collect high-resolution trajectory data of CFT scenarios on both straight and curved segments under truck-induced occlusion. First, the CFT risk was quantified based on an anticipated collision time (ACT) indicator, a two-dimensional surrogate safety measure that accounts for vehicle acceleration variations. Then, extreme value theory (EVT) was applied to calibrate alignment-specific risk thresholds. Finally, an XGBoost-based risk identification model was developed using vehicle dynamics-related features, and feature importance analysis combined with partial dependence interpretability was conducted to obtain key influencing factors. The results show that the calibrated ACT thresholds are approximately 3.838 s for straight segments and 4.385 s for curved segments, providing a reliable basis for risk classification. In addition, the XGBoost-based risk identification achieved accuracies of 90.63% and 95.87% for straight and curved segments, respectively. Further analysis indicates that CFT distance was the contributing factor. Moreover, risk increases markedly within a 10–20 m range on straight segments, while it rises rapidly once spacing falls below about 10 m on curved segments. Speed and acceleration differences exhibited stronger amplifying effects under short-spacing conditions. These findings provide a micro-behavioral basis for safety management and intelligent driving applications on mountainous roads with high truck mixing rates, supporting safer and more sustainable traffic operations. Full article

Objectives: Postpartum depression is a major global mental health concern, yet epidemiological evidence from the Baltic region remains limited. This study aimed to estimate the prevalence of depressive symptoms among postpartum women attending postpartum outpatient care in Latvia and identify associated sociodemographic and clinical factors. Methods: A cross-sectional study was conducted at the outpatient department of the largest maternity hospital in Latvia from May 2024 to June 2025. All women aged 18 years or older, who attended a routine postpartum gynaecological visit 4 to 6 weeks after delivery and screened positive on the Patient Health Questionnaire-9 (PHQ-9) (≥5 points), completed a sociodemographic and clinical questionnaire and the Edinburgh Postnatal Depression Scale (EPDS). Descriptive statistics were used in the study, and logistic regression was used to examine factors associated with postpartum depressive symptoms. Results: A total of 272 women aged 18 to 49 years (mean age 30.66 ± 5.59) participated. PHQ-9 results indicated that 43.02% of respondents met the threshold for a positive screen (≥5 points) and were included in the further analysis. Using a cut-off EPDS ≥11, the point prevalence of clinically significant depressive symptoms among women who screened positive on the PHQ-9 was 11.4%. In univariate analyses, postpartum depressive symptoms were most strongly associated with comorbid mental disorders (OR = 4.55; 95% CI 1.85–11.18; p = 0.001), caesarean section (OR = 3.05; 95% CI 1.18–7.92; p = 0.022), stress (OR = 2.49; 95% CI 1.04–5.94; p = 0.04) and obstetric complications (OR = 2.78; 95% CI 1.01–7.64; p = 0.048) during pregnancy. In the multivariate model, only three independent predictors remained: comorbid mental disorder (aOR = 9.54; 95% CI 2.72–33.49; p < 0.001) and caesarean section (aOR = 5.80; 95% CI 1.66–20.21; p = 0.006) were associated with higher odds of postpartum depression, while first-time motherhood was associated with a substantially lower likelihood of depressive symptoms (aOR = 0.14; 95% CI 0.04–0.49; p = 0.002). Sociodemographic characteristics, including age, education, employment, and income, were not significant predictors. Conclusions: The point prevalence of clinically significant depressive symptoms among Latvian postpartum women screening positive for depression appears similar to other European settings. Comorbid mental disorders and caesarean section were the strongest predictors of depressive symptoms, while primiparity showed a protective effect. Sociodemographic factors did not independently contribute to risk. As the first study of its kind in Latvia and conducted within a clinical setting that captures a large and diverse proportion of postpartum women, these findings highlight the context-specific nature of postpartum depression and underscore the need for further longitudinal research to inform effective screening and intervention strategies. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

To clarify the influence of gouge-induced altered layers on fracture initiation and load-bearing capacity of pipelines, X70 pipeline steel is taken as the research object. The geometry and partition of the altered layer are first determined by means of a micro-Vickers hardness array and a threshold criterion, and its mechanical parameters are then obtained from small-scale tensile tests. The altered layer is subsequently embedded into a finite element model of a gouged pipe as an independent material domain, and the Gurson–Tvergaard–Needleman (GTN) damage model is employed to simulate damage evolution and crack propagation under pure internal pressure and combined internal pressure and tensile loading. The results indicate that, compared with the base metal, the yield strength and ultimate tensile strength of the altered layer increase by about 39% and 47%, respectively, while the elongation to failure decreases from 16% to 1.8%, exhibiting a typical “high-strength–low-ductility” behavior. When the altered layer is considered, the fracture initiation location under pure internal pressure shifts from the base metal to the altered layer, and the burst pressure decreases from 19 MPa to 16.5 MPa. Under the combined internal pressure and tensile loading, the peak load changes little, whereas the ultimate displacement is reduced by about 26.5%, leading to a marked loss of pipeline ductility. These findings demonstrate that the gouge-induced altered layer has a significant effect on the fracture initiation pressure, failure mode, and load-bearing characteristics of gouged pipes. Modeling it as an independent material domain in finite element analysis can more realistically capture the failure behavior and safety margin of gouged pipelines, thereby providing a more reliable theoretical basis for improving integrity assessment criteria for externally damaged pipelines. Full article

This study aimed to evaluate four different models for the genetic evaluation of sixteen conformation traits, milking ease, and milking temperament in Dairy Gir cattle. The models vary based on whether they include only statistically significant fixed effects or all recorded effects, along with contemporary groups (CGs) treated as fixed or random effects. Categorical traits were also analyzed using a threshold model. The adjusted R-squared ($R_{adj}^2$) was used to compare the goodness-of-fit of the linear models. Spearman’s rank correlation and the average accuracy of bull estimated breeding values (EBVs) with at least 20 phenotyped daughters were compared. Models fitting CG as a random effect performed better based on their $R_{adj}^2$ values and had a greater average accuracy of EBVs for most traits. Spearman’s rank correlation coefficients indicated low to medium bull EBV re-ranking between most of the models. The linear models performed better than threshold models for almost all traits. When possible, more parsimonious linear models fitting only significant fixed effects should be used to reduce the standard error of estimation. Additionally, fitting CGs as a random effect seems more beneficial for the genetic evaluation of conformation and milking traits in Dairy Gir cattle in Brazil. Full article

This study, for the first time, investigates and simulates ballistic-transport-induced avalanche behavior in two-dimensional materials. Using a technology computer-aided design simulation platform, a device model for ballistic avalanche transport is systematically established. By accurately calibrating the material parameters of two-dimensional materials and selecting appropriate physical models, the key features of the ballistic avalanche effect are successfully reproduced, including low threshold voltage and high gain. The simulation results show good agreement with experimental data. Furthermore, mechanism-based analysis is performed to clarify the influence of critical design parameters on the avalanche threshold and multiplication gain. Finally, based on the same physical models and mechanistic understanding, the operational paradigm and performance of ballistic-transport avalanche photodetectors based on two-dimensional materials are predicted. This work provides a reliable theoretical foundation and a robust simulation framework for the optimized design of high-performance and low-power avalanche photon devices. Full article

In the era of digital transformation, organizations increasingly invest in Artificial Intelligence (AI) to enhance competitiveness, yet persistent evidence shows that AI investment does not automatically translate into superior firm performance. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study aims to explain this paradox by examining how AI-enabled dynamic capability (AIDC) is converted into performance outcomes through organizational mechanisms. Specifically, the study investigates the mediating roles of organizational data-driven culture (DDC) and organizational learning (OL). Data were collected from 254 senior managers and executives in U.S. firms actively employing AI technologies and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AIDC exerts a significant direct effect on firm performance as well as indirect effects through both DDC and OL. Serial mediation analysis reveals that AIDC enhances performance by first fostering a data-driven mindset and subsequently institutionalizing learning processes that translate AI-generated insights into actionable organizational routines. Moreover, DDC plays a contingent moderating role in the AIDC–performance relationship, revealing a nonlinear effect whereby excessive reliance on data weakens the marginal performance benefits of AIDC. Taken together, these findings demonstrate the dual role of data-driven culture: while DDC functions as an enabling mediator that facilitates AI value creation, beyond a threshold it constrains dynamic reconfiguration by limiting managerial discretion and strategic flexibility. This insight exposes the “dark side” of data-driven culture and extends the RBV and DCT by introducing a boundary condition to the performance effects of AI-enabled capabilities. From a managerial perspective, the study highlights the importance of balancing analytical discipline with adaptive learning to sustain digital efficiency and strategic agility. Full article