Search Results (18,977)

Using waste materials in the manufacture of concrete has many environmental advantages. However, it can be difficult to estimate structural performance, especially when beams are reinforced with fiber-reinforced polymers (FRP). In order to provide a data-driven approach to sustainable structural design, this work explores the use of machine learning (ML) approaches to forecast the flexural strength of FRP-strengthened waste aggregate concrete beams. A total number of 92 experimental datasets were used to develop and assess four ML algorithms: Random Forest (RF), Decision Tree (DT), Neural Network (NN), and Extreme Gradient Boosting (XGBoost). Regression plots, Taylor diagrams, statistical measures (R2R^2R2, RMSE, MAE, MSE), and explainable AI (XAI) tools, including SHAP, LIME, and partial dependence plots (PDPs), were used to evaluate the model’s performance. RF outperformed NN in terms of predictive accuracy, while XGBoost exhibited similar performance to RF. The most significant predictors, according to a SHAP analysis, were beam length and fiber length, with the lower followed by steel tensile strength, fiber width, and concrete compressive strength. LIME offered local interpretability for individual predictions, but PDPs demonstrated optimal parameter ranges and a nonlinear feature strength relationship. The findings provide engineers with a strong decision-support tool for designing green infrastructure, since they show that ensemble-based models can accurately represent the intricate, nonlinear dynamics controlling flexural behavior in sustainable FRP-strengthened waste aggregate concrete beams. Full article

This work introduces the MassSeg-Framework, a fully automatic two-stage pipeline for breast mass analysis in mammography that integrates YOLOv11-based detection with Chan–Vese ACM refinement to achieve accurate mass localization and segmentation with a lightweight computational footprint. The framework was trained and evaluated on two publicly available datasets using consistent experimental protocols. In the detection stage, YOLOv11-nano was the most effective architecture, with a confidence threshold of 0.4, achieving statistically significant mAP50 values of 0.862 and 0.709 on the dINbreast and dCBIS datasets, respectively. These results confirm that a moderate threshold preserves clinically relevant true-positive candidates, which is particularly important for screening-oriented settings where missed lesions are costly. In the segmentation stage, the proposed framework achieved mean DICE scores of 0.721 and 0.700 on the test sets of the same datasets, demonstrating consistent overlap with expert annotations. Compared with state-of-the-art approaches that commonly assume lesion-centered ROIs or rely on heavier backbones, the proposed pipeline addresses a more realistic scenario by performing automatic detection followed by segmentation while maintaining substantially lower computational requirements. This balance between performance and efficiency makes the MassSeg-Framework a promising tool for scalable mammography analysis, particularly in resource-constrained environments or high-throughput screening workflows that require rapid processing. Full article

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

The manuscript aims to assess the scale of shadow economic processes within the non-observed economy, focusing on the self-employment sector, which is insufficiently reflected in national statistics. The research methodology includes an analysis of the conceptual foundations of the shadow economy, decomposition of its components, identification of factors negatively affecting the economy, development of an algorithm for sociological research, and the selection of appropriate models for evaluating the non-observed economy. The study formulates the concept of the shadow economy and shows that shadow business activity in Kazakhstan contributes to income inequality, hidden unemployment, and the exclusion of certain goods and services from official GDP statistics. Using statistical data from 2005 to 2024 and applying methods such as system and statistical analysis, modeling approaches, and the MIMIC (Multiple Indicator Multiple Cause) and DGE (Dynamic General Equilibrium) models, the study estimates the size of the shadow sector. The results reveal insufficient statistical data on shadow activities within self-employment and SMEs. The study concludes that the most reliable assessment of the shadow economy requires an integrated methodological approach, including targeted sociological research and models that account for the influence of multiple factors on informal self-employment. Full article

Guaranteeing equitable access to computational thinking (CT) remains a persistent challenge in computing education, particularly across socioeconomically diverse school contexts. Although prior research has demonstrated the effectiveness of block-based and physical computing environments, limited empirical evidence has examined whether structured instructional mediation can compensate for contextual disparities. This quasi-experimental pre–post study addresses this gap by analyzing CT development in three socioeconomically diverse primary schools in Chile ($N=88$, third grade), including private urban, public urban, and rural public institutions. Students engaged in scaffolded Scratch programming and Arduino simulation activities designed to explicitly support abstraction, sequencing, and debugging processes. These activities were framed within a broader STEAM learning approach, integrating computational thinking with problem-solving, experimentation, and interdisciplinary reasoning. Statistical analysis revealed significant differences in instructional time across contexts ($F(2,85)=14.62$, $p<0.001$, ${\eta }^2=0.26$), indicating structural disparities in pacing. However, no statistically significant differences were observed in CT gains ($F(2,85)=0.31$, $p=0.74$), suggesting that structured pedagogical scaffolding buffered contextual inequalities. These findings provide empirical evidence from a Latin American non-WEIRD context and advance the conceptualization of instructional mediation as a compensatory mechanism for equity in early computing education. This study contributes to digital equity research by demonstrating that instructional design quality may play a more decisive role than infrastructural availability in enabling computational thinking development for all learners. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

Background/Objective: The parotid gland is the largest salivary gland, and tumors arising from it exhibit wide histopathological diversity. Management approaches vary according to tumor characteristics and carry a risk of postoperative complications, particularly facial nerve injury. However, local data remain limited. This study aimed to describe the clinicopathological characteristics, surgical approaches, and postoperative outcomes of patients undergoing parotidectomy. Method: A retrospective cohort study was conducted at a high-volume tertiary center in Saudi Arabia. All consecutive patients who underwent parotidectomy between June 2015 and January 2025 were included. Demographic data, histopathological diagnoses, surgical procedures and postoperative complications were extracted from electronic medical records. Statistical analyses were performed using SPSS version 26, with A p-value of <0.05 considered statistically significant. Results: A total of 154 patients were included, with a mean age of 45.2 ± 12.6 years; 61% were male. Benign lesions constituted 87% of cases, with pleomorphic adenoma being the most common histopathological diagnosis. Malignancies accounted for 13% of cases, most frequently mucoepidermoid carcinoma. The most common postoperative complications were facial nerve palsy, followed by sensory numbness. Conclusions: The majority of parotid gland tumors in this cohort were benign, with pleomorphic adenoma as the most common histological subtype. Facial nerve palsy and sensory disturbances were the most common postoperative complications. These findings provide valuable local data on parotid gland lesions in Saudi Arabia and support current surgical management practices. Full article

Background/Objectives: Chemically induced hepatotoxicity is widely used in experimental research to model liver disease pathophysiology and to support preclinical studies. Thioacetamide (TAA) is a well-established hepatotoxic agent in conventional laboratory rodents; however, its effects in synanthropic rats—characterized by genetic heterogeneity and chronic environmental exposure—remain poorly defined. This study aimed to establish and characterize a preclinical model of TAA-induced hepatotoxicity in synanthropic rats and to assess its relevance for experimental liver disease research. Methods: Female synanthropic rats representing four phenotypic variants (albino, mottled, black, and brown; total n = 132) were housed under controlled conditions and assigned to control or TAA-treated groups. TAA was administered intraperitoneally at doses ranging from 200 to 300 mg/kg. Clinical parameters, including body weight and vital signs, were periodically monitored. Hematological profiles and serum biochemical markers of liver function were analyzed. Hepatic injury was evaluated by histopathological examination using hematoxylin–eosin staining. Statistical analyses were performed using R software, with p ≤ 0.05 considered statistically significant. Results: TAA-treated rats developed consistent clinical manifestations of hepatotoxicity, including progressive weight loss and reduced activity. Biochemical analyses revealed significant increases in serum transaminases, gamma-glutamyl transferase, and alkaline phosphatase, accompanied by alterations in hematological parameters. Histological evaluation demonstrated dose-dependent liver injury characterized by centrilobular necrosis, inflammatory infiltration, hepatocellular degeneration, and architectural disruption across all synanthropic rat variants. Conclusions: Synanthropic rats exhibit reproducible biochemical, hematological, and histopathological features of TAA-induced liver injury comparable to those reported in conventional laboratory strains. This model represents a robust preclinical approach for studying chemically induced hepatotoxicity and may provide enhanced translational relevance due to its genetic and environmental heterogeneity. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

This study explores the impact of renewable energy consumption on environmental quality in ten OECD economies over the period 1990–2024, aiming to assess its contribution as a structural driver of decarbonization in advanced economies. Given the presence of strong cross-sectional dependence and heterogeneous country dynamics, the analysis employs second-generation panel econometric techniques. Stationarity is assessed using the CIPS unit root test. Long-run relationships are examined using the Westerlund error-correction-based cointegration approach. Long-run elasticities are estimated using the Common Correlated Effects Mean Group (CCE-MG) and Augmented Mean Group (AMG) estimators. Short-run dynamics are analyzed within a panel error-correction framework. The results confirm the existence of a stable long-run equilibrium relationship among the variables. Renewable energy consumption is associated with a negative effect on CO₂ emissions, with the CCE-MG estimate indicating that a 1% increase in renewable energy reduces emissions by approximately 0.067%, although the long-run statistical significance remains marginal. In the short run, renewable energy is also associated with lower emissions, indicating both structural and immediate mitigation dynamics. By contrast, energy consumption and financial development increase emissions, while economic growth does not exhibit a robust long-run effect, providing no support for the Environmental Kuznets Curve hypothesis. The error-correction term confirms rapid convergence toward long-run equilibrium. Robustness analysis using carbon intensity as an alternative environmental indicator yields consistent findings. In sum, the results suggest that renewable energy expansion should be complemented by energy efficiency policies and the reorientation of financial systems toward green investments to achieve effective decarbonization. From a policy perspective, coordinated strategies integrating renewable deployment, efficiency improvements, and sustainable finance are essential for achieving long-term environmental sustainability in OECD economies. Full article

Non-invasive assessment of oocyte quality remains a challenge in assisted reproductive technology (ART). Through their bidirectional communication with the gamete, cumulus cells (CCs) act as a functional mirror of oocyte competence; however, the specific angiogenic signature within this microenvironment is still poorly understood. In the present study, we performed RNA-seq on CCs from healthy oocyte donors and infertile patients, utilizing a multi-pipeline bioinformatic approach (STAR-Cufflinks, TopHat-HTSeq, and HISAT2-StringTie) to establish a high-confidence, exploratory transcriptomic profile. A set of 234 differentially expressed genes (DEGs) consistently identified across pipelines was obtained, with functional enrichment highlighting blood vessel morphogenesis and angiogenesis as primary drivers of transcriptomic divergence between groups. RT-qPCR validation in individual samples confirmed statistically significant differences for ANKRD22 (upregulated) and E2F7 (downregulated) in infertile patients, while other angiogenesis-related genes, including ANGPT1, ANGPT2 and THBS1, showed consistent but non-significant expression trends, suggesting alterations in angiogenesis-related processes within the follicular microenvironment. These findings support the presence of coordinated angiogenesis-related alterations in cumulus cells and provide a basis for future studies exploring their potential relevance in oocyte competence and ART outcomes. Full article

Against the backdrop of uneven educational development and structural constraints in rural Mainland China, teacher agency is critical for driving professional growth and instructional improvement. Rural educators face distinct challenges—limited resources, isolated work contexts, and systemic pressures—that shape their capacity to enact change. While scholarship has documented the roles of contextual resources and individual beliefs in shaping teacher agency, less is known about the mediating mechanisms linking job resources and self-efficacy to agency within China’s rural educational landscape. This study examines how perceived job resources (teaching resources, administrative support, colleague support, parental support) and teaching self-efficacy collectively shape rural teachers’ agency, to inform policy and practice for strengthening their professional capacity. Drawing on a quantitative survey of 625 rural teachers, we employ a two-stage analytical approach: first, descriptive statistics, t-tests, ANOVA, and Pearson correlations to map baseline variable relationships; second, Hayes’ PROCESS macro (Model 4) with bootstrapping to test the mediating role of teaching self-efficacy between job resources and teacher agency. Findings reveal the following: (1) Rural teachers report moderate agency (M = 3.53/5), indicating room for growth; (2) All four job resource dimensions significantly and positively predict agency (β = 0.099–0.163); (3) Teaching self-efficacy is a robust predictor of agency (β = 0.785–0.822, p < 0.001) after controlling for resources; (4) Self-efficacy partially mediates the links between each job resource and agency, with indirect effects ranging from 0.269 (teaching resources) to 0.451 (colleague support), highlighting its central role in translating contextual resources into agentic action. We conclude that fostering rural teacher agency requires a holistic approach addressing both external job resources and internal self-efficacy. Policymakers and administrators should prioritize investments in teaching resources, collaborative support structures, and professional development to build educators’ confidence and competence. Limitations include self-report bias, cross-sectional design constraints on causal inference, and limited generalizability. Future research should use longitudinal designs and broader samples to deepen understandings of agency in structurally constrained educational settings. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

During the excavation of the Qinling Water Conveyance Tunnel, rockbursts influenced by structural planes with varying dip angles occurred frequently, posing a significant threat to personnel and construction safety. This study combines statistical analysis of rockburst cases, numerical simulation, and microseismic monitoring to systematically reveal the influence mechanism of structural plane dip angle on rockbursts. Statistical results indicate that intense rockbursts occurring in the dip angle interval of 0–30° account for 60%. Numerical simulations further demonstrate that dip angles of less than 90° (especially near 10°) induce continuous stress accumulation, leading to large-scale instability. Specifically, the peak acoustic emission (AE) count at a dip angle of 10° is significantly higher than in other configurations, further indicating the highest rockburst risk. Incorporating the influence mechanism of structural plane dip angle into microseismic monitoring analysis significantly improved prediction accuracy. This approach successfully predicted an intense rockburst. Based on these findings, engineering suggestions regarding excavation direction and rockburst early warning optimization are proposed, offering a valuable reference for rockburst mitigation in deep-buried tunnels under similar geological conditions. Full article

Urban neighborhood parks are essential infrastructure for sustainable cities, supporting physical and mental health, social cohesion, and climate adaptation. Equity-oriented park planning, however, requires accurate identification of residents who can access parks within network-constrained travel time thresholds. Many accessibility studies estimate served populations using coarse administrative zones and areal-weighting assumptions, which can bias results in heterogeneous, vertically developed districts. This study develops a building-based population allocation framework (implemented via a building centroid overlay) that integrates Statistics Korea’s census output areas (2023 Q4 release) with the Ministry of Land, Infrastructure and Transport (MOLIT)’s GIS Integrated Building Information database (2023 Q4 release) and applies it to Yongsan-gu (Yongsan District), Seoul. Park entrances were verified and digitized using street-view imagery available on multiple web map platforms, and walkable service areas (5 and 10 min) were delineated via network analysis. Potential service coverage and unserved population were then estimated under three spatial configurations—administrative dong (neighborhood-level administrative unit in Seoul; hereafter administrative unit), census output area, and building-based allocation—and compared. Under the 10 min scenario, the unserved share reached 24.6% at the administrative unit level but decreased to 5.9% and 4.3% when using census output areas and building-based allocation, respectively. The building-based approach additionally revealed micro-scale clusters of unserved residents near localized pedestrian constraints and boundary-crossing areas that are obscured by zone-based methods. These findings demonstrate the sensitivity of access-based potential service coverage diagnostics to spatial unit choice and population disaggregation and suggest that building-based population allocation can improve the targeting of park pro-vision policies and promote spatial equity in dense, vertically developed cities. Full article