Search Results (10,927)

Background/Objectives: Incomplete cut marks produced during dismemberment are often interpreted as indicative of saw class characteristics. However, empirical validation of these associations remains limited, with prior studies examining six or fewer saws. Considering the wide variety of saws available, it is critical to assess the reliability of reported relationships between kerf features and saw classification using a larger sample, particularly in light of the serious legal consequences of erroneous conclusions. This study examines the statistical relationships between five incomplete cut traits—kerf profile shape (KPS), kerf length shape (KLS), floor dip (FD), kerf flare (KF), and floor striae (FS)—and saw class characteristics, including tooth set, tooth shape, teeth-per-inch, power, handle orientation, and cut direction. Methods: Kerf features were scored on a sample of 472 incomplete cuts made with 34 power and hand saws. Results: In reciprocating saws, W-shaped KPS was exclusively associated with crosscut, alternating saws (100%; p < 0.001), with hourglass-shaped KLS also primarily made by alternating sets (95.6%). Necked KLS was linked to wavy sets (76.8%; p < 0.001). FD, though rare, could be correctly assigned to teeth-per-inch groups (86.4%), and was also predominantly associated with alternating saws (90.9%; p < 0.001). Undulating FS were indicative of alternating saws with less than 20 teeth-per-inch (100%, p < 0.001). In contrast, KF showed no strong relationship with saw class characteristics, including handle side. Conclusions: The results of this large-scale analysis support most reported relationships in the saw mark literature but challenge assumptions that KF reliably indicates handle orientation or cut direction, suggesting instead that its location may reflect sawyer technique. Full article

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in complex, multi-scale scenarios. To overcome this limitation, this study proposes a fully automated segmentation framework that synergistically integrates an enhanced U-Net model with a novel hybrid evolutionary optimization strategy. Extensive experiments on public Kaggle and Sentinel-2 datasets demonstrate the superior performance of our method, which achieves a Pixel Accuracy of 96.79% and an F1-Score of 94.75, outperforming various mainstream baseline models by over 10% in key metrics. The framework effectively addresses the class imbalance problem and enhances feature representation without human intervention. This work provides a viable and efficient path toward fully automated remote sensing image analysis, with significant potential for application in large-scale water resource monitoring, dynamic environmental assessment, and emergency disaster management. Full article

Background: The early detection of insulin resistance (IR) is critical for the prevention of type 2 diabetes and cardiometabolic diseases. The ECORE-BF index is a simple anthropometric tool for estimating body fat percentage and overweight. However, its potential utility as a predictor of IR risk has not been previously evaluated in large populations using validated IR indices. Methods: This cross-sectional study included 418,343 Spanish workers (172,282 women and 246,061 men) who underwent occupational health evaluations. The ECORE-BF index was calculated for all participants, and its association with four validated surrogate markers of IR was analyzed: the triglyceride–glucose index (TyG), TyG-BMI, METS-IR, and SPISE. Subjects were classified into normal or high-risk IR groups based on established cut-off values. We evaluated the mean ECORE-BF values across groups, the prevalence of ECORE-BF-defined obesity, and the diagnostic performance of ECORE-BF using receiver operating characteristic (ROC) curve analysis. Results: Participants with elevated IR index values had significantly higher mean ECORE-BF scores than those with normal values (p < 0.001). The prevalence of ECORE-BF-defined obesity was substantially higher in all high-risk IR groups, exceeding 99% for METS-IR and SPISE in both sexes. ROC analysis demonstrated the high diagnostic accuracy of ECORE-BF in predicting elevated IR risk, with area under the curve (AUC) values ranging from 0.698 (TyG in men) to 0.992 (METS-IR in women). Sensitivity and specificity were also high, particularly for TyG-BMI, SPISE, and METS-IR, with optimal Youden indices above 0.75. Conclusions: ECORE-BF demonstrated high accuracy as a non-invasive tool for identifying individuals at increased insulin resistance risk; however, due to the cross-sectional design, predictive value for incident disease cannot be inferred. Its simplicity, cost-effectiveness, and high diagnostic accuracy support its potential utility in large-scale screening programs for early detection of metabolic risk. Full article

Rising atmospheric CO₂ is expected to fertilize forest photosynthesis; yet, ecosystem-scale observations often reveal muted responses, creating a critical knowledge gap in global climate projections. In this review, we explore this paradox by moving beyond the traditional ‘CO₂ fertilization’ paradigm. We propose an integrated framework that positions elevated CO₂ as a complex modulator whose net effect is determined by a hierarchy of cross-scale constraints. At the plant level, photosynthetic acclimation acts as a universal first brake on the initial biochemical potential. At the ecosystem level, nutrient availability—primarily nitrogen in temperate/boreal systems and phosphorus in the tropics—emerges as the dominant bottleneck limiting long-term productivity gains. Furthermore, interactions with the water cycle, such as increased water-use efficiency, create state-dependent dynamic responses. By synthesizing evidence from pivotal Free-Air CO₂ Enrichment (FACE) experiments, we systematically evaluate these constraining factors. We conclude that accurately predicting the future of the forest carbon sink necessitates a paradigm shift: from single-factor analysis to multi-stressor approaches, and from ecosystem-scale observations to an integrated understanding that links these phenomena to their underlying molecular and genetic mechanisms. This review provides a roadmap for future research and informs more realistic strategies for forest management and climate mitigation in a high-CO₂ world. Full article

This paper addresses the critical challenge of energy management for autonomous robots in the context of large-scale photovoltaic parks. The dynamic and vast nature of these environments, characterized by dense, structured rows of solar panels, introduces unique complexities, including uneven terrain, varied operational demands, and the need for equitable resource allocation among diverse robot fleets. The presented framework adapts and significantly extends the Affinity Propagation algorithm for strategic charging station placement within photovoltaic parks. The key contributions include: (1) a multi-attribute grid-based environment model that quantifies terrain difficulty and panel-specific obstacles; (2) an extended multi-factor scoring function that incorporates penalties for terrain inaccessibility and proximity to sensitive photovoltaic infrastructure; (3) a sophisticated, energy-aware consumption model that accounts for terrain friction, slope, and rolling resistance; and (4) a novel multi-agent fairness constraint that ensures equitable access to charging resources across heterogeneous robot sub-fleets. Through extensive simulations on synthesized photovoltaic park environments, it is demonstrated that the enhanced algorithm not only significantly reduces travel distance and energy consumption but also promotes a fairer, more efficient operational ecosystem, paving the way for scalable and sustainable robotic maintenance and inspection. Full article

Carbon emissions from dairy farms have significantly hindered the advancement of sustainable agriculture, and improving carbon efficiency is a key pathway to mitigate these emissions. As a critical technological innovation, smart farming technology exerts a substantial impact on boosting carbon efficiency in dairy farms. Based on field survey data collected from Chinese dairy farms, this study employs an integrated empirical approach, including endogenous switching regression, two-stage least squares, and propensity score matching, to rigorously evaluate the impact of smart farming technology on economies of scale. A mediation analysis is further conducted to examine the interrelationships among smart farming technology, economies of scale, and carbon efficiency, while the moderating role of government regulation is also empirically tested. The findings reveal three key results: (1) Smart farming technology exerts a direct and positive influence on the economies of scale in dairy farms, with this effect becoming more pronounced as farm size increases. (2) Economies of scale serve as a partial mediator in the relationship between smart farming technology and carbon efficiency. This indicates that smart farming technology not only directly enhances carbon efficiency but also does so indirectly by facilitating the expansion of production scale and reducing unit costs. (3) Government regulation positively moderates this mediating pathway. Specifically, through standardizing production practices, offering policy incentives, and guiding the application of technology, government interventions strengthen the ability of smart farming technology to foster economies of scale. These insights underscore the importance of steering dairy farms toward the adoption of smart farming technologies to simultaneously improve scale efficiency and carbon performance, thereby supporting the transition toward low-carbon and sustainable agricultural development. Finally, this study proposes three policy implications: strengthening institutional support for the adoption of smart farming technologies in dairy production systems, significantly enhancing training programs related to these technologies, and systematically guiding dairy farms toward smart technology adoption to simultaneously improve economies of scale and carbon efficiency. Full article

Particle histories critically influence product quality in spray drying processes, encompassing statistical data on particle dynamics and behavior inside the chamber, including temperatures, moisture levels, wall impacts, and residence times. This study presents the first systematic parametric assessment of how chamber geometry influences particle histories in spray drying, extending previous work on airflow dynamics. A design of experiments (DOE) methodology combined with cost-efficient CFD simulations was employed to establish quantitative parameter–response relationships. The results reveal two distinct classes of particle responses: (i) residence time, moisture content, and wall temperature, which are primarily governed by chamber aspect ratio and drying air flow rate, and (ii) particle–wall impact behavior, which is dominated by chamber topology. Inlet swirl modulates all particle histories, differentially impacting final product quality and energy efficiency. These findings provide predictive guidelines for chamber design and operation, while the methodology offers a general framework for scale-up analyses and parametric CFD studies of particle-laden multiphase processes. Full article

The increasing prevalence of Autism Spectrum Disorder (ASD) is a significant health concern influenced by both genetic and environmental factors. However, limited data exist on the socio-demographic and clinical characteristics associated with ASD in our region. This cross-sectional study assessed 200 children (155 boys and 45 girls) diagnosed with ASD at Hawler Psychiatric Hospital in Erbil city between January and December 2023. The Childhood Autism Rating Scale-Second Edition (CARS-2) was used for diagnosis and severity assessment. The mean age of participants was 4.6 ± 1.8 years, with males representing 77.5% of the sample. Cesarean section was the most common mode of delivery. The average parental ages were 34.8 years for mothers and 38.5 years for fathers. The first signs of autism were noticed at a mean age of 25.7 ± 9.7 months, with the first medical consultation at 34.6 ± 15.4 months and diagnosis at 42.4 ± 15.5 months. Delayed speech was the most common reason for seeking medical help. Statistically significant associations were found between severe autism symptoms and several factors, including older child age, younger age at first assessment, delayed speech, parental consanguinity, paternal age over 40, lower paternal education, and lower socioeconomic status. These findings emphasize the critical role of early detection and the influence of both socio-demographic and clinical factors on ASD symptom severity, highlighting the need for targeted early intervention strategies to improve outcomes in affected children. Full article

Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation. Full article

The scalability of coordination policies is a critical challenge in swarm robotics, where agent numbers may vary substantially between deployment scenarios. Reinforcement learning (RL) offers a promising avenue for learning decentralized policies from local interactions, yet a fundamental question remains: can policies trained on one swarm size transfer to different population scales without retraining? This zero-shot transfer problem is particularly challenging because the traditional RL approaches learn fixed-dimensional representations tied to specific agent counts, making them brittle to population changes at deployment time. While existing work addresses scalability through population-aware training (e.g., mean-field methods) or multi-size curricula (e.g., population transfer learning), these approaches either impose restrictive assumptions or require explicit exposure to varied team sizes during training. Graph Neural Networks (GNNs) offer a fundamentally different path. Their permutation invariance and ability to process variable-sized graphs suggest potential for zero-shot generalization across swarm sizes, where policies trained on a single population scale could deploy directly to larger or smaller teams. However, this capability remains largely unexplored in the context of swarm coordination. For this reason, we empirically investigate this question by combining GNNs with deep Q-learning in cooperative swarms. We focused on well-established 2D navigation tasks that are commonly used in the swarm robotics literature to study coordination and scalability, providing a controlled yet meaningful setting for our analysis. To address this, we introduce Deep Graph Q-Learning (DGQL), which embeds agent-neighbor graphs into Q-learning and trains on fixed-size swarms. Across two benchmarks (goal reaching and obstacle avoidance), we deploy up to three times larger teams. The DGQL preserves a functional coordination without retraining, but efficiency degrades with size. The ultimate goal distance grows monotonically (15–29 agents) and worsens beyond roughly twice the training size ($\approx 20$ agents), with task-dependent trade-offs. Our results quantify scalability limits of GNN-enhanced DQL and suggest architectural and training strategies to better sustain performance across scales. Full article

An integrated evaluation framework merging the analytic hierarchy process (AHP) and an improved matter–element extension model based on asymmetric proximity is developed to classify large deformation risk levels in soft-rock tunnel construction. From geological surveys and real-time monitoring, ten core indicators spanning three dimensions—geology (surrounding rock grade, groundwater condition, strength–stress ratio, adverse geological condition), design (excavation cross-sectional shape, excavation span, excavation cross-sectional area), and support (support stiffness, support installation timing, construction step length)—are selected. AHP constructs and validates a judgment matrix to derive subjective weights for each indicator. Within a three-tier hierarchy (indicator, criterion, and target layers), the asymmetric proximity quantifies each tunnel’s proximity to the matter–element representing predefined risk levels. Risk levels are then automatically assigned by selecting the maximum composite proximity. Application to representative soft-rock tunnel cases confirms the method’s high accuracy, stability, and operational feasibility, closely matching field observations. This framework enables precise risk stratification and intuitive visualization, offering critical technical support for optimizing tunnel design and operations, and ultimately enhancing the safety, resilience, and sustainability of large-scale infrastructure. Full article

Drought is a critical hazard to agricultural productivity in semi-arid regions such as the Antalya Agricultural Basin of Türkiye. This study assessed agricultural drought from 2001 to 2023 using multiple remote sensing-based indices processed in Google Earth Engine (GEE). Vegetation indicators (Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Difference Drought Index, Vegetation Condition Index, Temperature Condition Index, and Vegetation Health Index) were derived from MODIS datasets, while the Precipitation Condition Index was calculated from CHIRPS precipitation data. Composite indicators included the Scaled Drought Composite Index, integrating vegetation, temperature, and precipitation factors, and the Soil Moisture Condition Index derived from reanalysis soil moisture data. Results revealed recurrent moderate drought with strong seasonal and interannual variability, with 2008 identified as the driest year and 2009 and 2012 as wet years. Summer was the most drought-prone season, with precipitation averaging 5.5 mm, PCI 1.1, SDCI 15.6, and SMCI 38.4, while winter exhibited recharge conditions (precipitation 197 mm, PCI 40.9, SDCI 57.3, SMCI 89.6). Interannual extremes were detected in 2008 (severe drought) and wetter conditions in 2009 and 2012. Vegetation stress was also notable in 2016 and 2018. The integration of multi-source datasets ensured consistency and robustness across indices. Overall, the findings improve understanding of agricultural drought dynamics and provide practical insights for irrigation modernization, efficient water allocation, and drought-resilient planning in line with Türkiye’s National Water Efficiency Strategy (2023–2033). Full article

The increasing integration of High-Voltage Direct Current (HVDC) systems and renewable energy challenges traditional grid strength assessment. This paper proposes a comprehensive framework that combines a composite strength index with an interpretable importance analysis to address this issue. First, a composite index is developed using the AHP-CRITIC method to fuse structural and fault withstand metrics. Then, to identify the factors influencing this index, SHapley Additive exPlanations (SHAP) is employed, accelerated by a high-fidelity Gaussian Process Regression (GPR) surrogate model that overcomes the computational burden of large-scale simulations. This GPR-SHAP approach provides both global parameter rankings and local, scenario-specific explanations, overcoming the limitations of conventional sensitivity analysis. Validated on a detailed model of the Southwest Power Grid in China, the framework successfully quantifies grid strength and pinpoints key vulnerabilities. Verification through a typical scenario demonstrates that implementing coordinated increases in both generation and load (each by 1000 MW) in the Chengdu area, as guided by local SHAP explanations, significantly improves the grid strength index from 33.73 to 47.61. It provides operators with a dependable tool to transition from experience-based practices to targeted, proactive stability management. Full article

Power line sag monitoring is critical for ensuring transmission system reliability and optimizing grid capacity utilization. Traditional sag detection methods rely on hyperbolic cosine models that assume ideal catenary behavior under uniform loading conditions. However, these models impose restrictive assumptions about weight distribution and suspension conditions that limit accuracy under real-world scenarios involving wind loading, ice accumulation, and non-uniform environmental forces. This study introduces a novel Bézier curve-based mathematical framework for transmission line sag detection and monitoring. Unlike traditional hyperbolic cosine approaches, the proposed methodology eliminates idealized assumptions and provides enhanced flexibility for modeling actual conductor behavior under variable environmental conditions. The Bézier curve approach offers enhanced precision and computational efficiency through intuitive control point manipulation, making it well suited for Dynamic Line Rating (DLR) applications. Experimental validation was performed using a controlled laboratory setup with a 1:100 scaled transmission line model. Results demonstrate improvement in sag measurement accuracy, achieving an average error of 1.1% compared to 6.15% with traditional hyperbolic cosine methods—representing an 82% improvement in measurement precision. Statistical analysis over 30 independent experiments confirms measurement consistency with a 95% confidence interval of [0.93%, 1.27%]. The framework also demonstrates a 1.5 to 2 times increase in computational efficiency improvement over conventional template matching approaches. This mathematical framework establishes a robust foundation for advanced transmission line monitoring systems, with demonstrated advantages for power grid applications where traditional catenary models fail due to non-ideal environmental conditions. The enhanced accuracy and efficiency support improved Dynamic Line Rating implementations and grid modernization efforts. Full article

Accurate terrain modeling from high-resolution digital surface models (DSM) is critical for geosciences, geology, geomorphology, earthquake studies, and applied geology. However, existing filtering methods such as progressive morphological filtering (PMF), cloth simulation filtering (CSF), and progressive TIN densification (TIN) often struggle with complex topography and urban structures, leading to either excessive ground loss or incomplete object removal. Furthermore, some of these algorithms are only specialized for point cloud data and are not optimized for grid data. To address these limitations, we propose an iterative morphological filtering (IMF) algorithm that introduces a binary surface edge-segmentation strategy. The method refines object–ground separation by combining iterative morphological operations with block-based graph-cut stitching, thus enhancing continuity and accuracy in challenging terrain. Validation on UAV-derived DSM over the Haihe Basin in China and the ISPRS Vaihingen dataset shows that IMF achieves notable accuracy improvements: the Vaihingen test areas yielded an average Type I error of 8.93%, Type II error of 3.09%, overall accuracy of 80.85%, and Kappa coefficient of 0.7524, while the Haihe Basin test areas achieved Type I and II errors of 2.22% and 1.87%, overall accuracy of 89.32%, and a Kappa coefficient of 0.8706. These results demonstrate that IMF outperforms conventional methods by reducing both Type I and Type II errors, producing terrains highly consistent with real conditions. This innovation provides a robust and scalable solution for digital elevation models (DEM) generation from gridded DSM, offering significant value for large-scale environmental monitoring and flood risk assessment. Full article