Search Results (529)

Riparian vegetation plays a critical role in maintaining ecosystem function, ensuring drainage capacity, and enhancing disaster prevention and mitigation. However, existing ground-based survey methods are limited in both spatial coverage and temporal resolution, which increases the difficulty of meeting the growing demand for rapid, dynamic, and fine-scale monitoring of riverine vegetation. To address this challenge, this study proposes a remote sensing approach that integrates Sentinel-1 synthetic aperture radar imagery with Sentinel-2 optical data. A composite vegetation index was developed by combining the normalized difference vegetation index and synthetic aperture radar backscatter coefficients, thereby enabling the joint characterization of horizontal and vertical vegetation activity. The method was first tested in the Kuji River Basin in Japan and subsequently validated across eight representative river systems nationwide using 16 sets of satellite images acquired between 2016 and 2023. The results demonstrate that the proposed method achieves an average geometric correction error of less than three pixels and yields a spatial distribution of the composite index that closely aligns with the actual vegetation conditions. Moreover, the difference rate between sparse and dense vegetation exceeded 90% across all rivers, indicating a strong discriminative capability and temporal sensitivity. Overall, this method is well-suited for the multiregional and multitemporal monitoring of riparian vegetation and offers a reliable quantitative tool for water environment management and ecological assessment. Full article

Background/Objectives: Although methylphenidate is a first-line pharmacological agent in the treatment of Attention-Deficit/Hyperactivity Disorder (ADHD), its long-term effects on ocular tissues, particularly the corneal endothelium, remain poorly understood. Given the cornea’s metabolic sensitivity, subclinical changes may occur even in the absence of overt ophthalmologic symptoms. This study aims to evaluate the impact of six-month methylphenidate treatment on corneal endothelial morphology and intraocular pressure (IOP) in pediatric patients with ADHD. Methods: This prospective observational study included 100 treatment-naive children with ADHD and 100 age- and sex-matched healthy controls. All participants underwent comprehensive ophthalmologic assessment at baseline. In the ADHD group, follow-up evaluations were performed after six months of methylphenidate therapy. Endothelial cell density (ECD), average cell area (AVE), standard deviation (SD), coefficient of variation (CV), hexagonality index (6A), central corneal thickness (CCT), and IOP were measured using specular microscopy and corneal topography. ADHD symptom severity was evaluated using the Turgay DSM-IV-Based Rating Scale. Results: Significant reductions in ECD and increases in CCT, CV, AVE, and SD were observed following treatment (p < 0.001). IOP also showed a statistically significant increase while remaining within normal physiological limits. Weak but significant correlations were found between inattention scores and ECD (r = 0.222), and between inattention and corneal volume (r = −0.248). Conclusions: Chronic methylphenidate use may be associated with measurable changes in corneal endothelial microstructure and IOP in children with ADHD. These findings highlight the need for routine ophthalmologic monitoring during stimulant therapy and underscore the importance of further large-scale, long-term studies exploring the neuro-ophthalmologic implications of pediatric psychopharmacological treatment. Full article

Monitoring wheat traits across diverse environments requires reliable sensing tools that balance accuracy, cost, and scalability. This study compares the performance of proximal and UAV-derived NDVI sensing for predicting the key agronomic traits in winter wheat. The research was conducted at a long-term NPK field experiment on Haplic Chernozem soils in Rimski Šančevi, Serbia, using UAV multispectral imagery and a handheld proximal sensor to collect NDVI data across 400 micro-plots and six phenological stages. The UAV-derived NDVI achieved a higher mean value (0.71 vs. 0.60), lower coefficient of variation (29.2% vs. 33.0%), and stronger correlation with the POM readings (R² = 0.92). For trait prediction, the UAV-based NDVI reached R² values up to 0.95 for grain yield and 0.84 for plant height, outperforming the POM (maximum R² = 0.94 and 0.83, respectively), and it showed superior temporal consistency (average R² = 0.74 vs. 0.64). Although the POM performed comparably during mid-season under controlled conditions, its sensitivity to operator handling and limited spatial resolution reduced robustness in more variable field scenarios. A cost–benefit analysis revealed that the POM offers advantages in affordability, ease of use, and deployment in small-scale settings, while UAV systems are better suited for large-scale monitoring due to their higher spatial resolution and data richness. The findings highlight the importance of selecting sensing technologies based on biological context, operational goals, and resource constraints, and suggest that combining methods through stratified sampling may improve the efficiency and accuracy of crop monitoring in precision agriculture. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

The formation, development, and spatial distribution of CUHIs are influenced by urban spatial heterogeneity, yet the scale and seasonal dependencies of the effects of urban morphology modulation on CUHIs have not been fully explored, needing further study. Based on multi-source data for the Yangtze-Huaihe River Valley (YHRV), this study employs the XGBoost model to systematically investigate the effects of two-dimensional (2D)/three-dimensional (3D) urban morphological indicators on CUHIs and their inherent scale–seasonal dependencies. Results show significant provincial heterogeneity in YHRV’s CUHIs: Shanghai exhibits the highest CUHI intensity (CUHII) across all seasons, with a peak of 1.55 °C in winter, followed by Zhejiang and Jiangsu. Seasonally, winter CUHII averages 0.6–0.8 °C (the highest), followed by autumn, while spring and summer have lower values. The effect of the modulation of urban morphology on CUHIs exhibits distinct spatiotemporal dependence: in winter and autumn, CUHII is mainly dominated by the percentage of landscape (PLAND) and largest patch index (LPI) at the 4 km buffer scale (correlation coefficients r = 0.475 and 0.406 for winter); in spring and summer, the 2 km buffer scale shows a more balanced regulatory role of multiple urban morphological indicators. Notably, 2D indicators of urban morphology are consistently more influential in regulating CUHIs than 3D indicators. The Hefei station case effectively validates the model’s sensitivity to changes in urban morphology. This study provides a quantitative basis for season–scale collaborative regulation of urban thermal environments in the YHRV. Future research will integrate climatic factors into XGBoost via screening, reconstruction, and interaction quantification to enhance its predictability for transient heat island processes. Full article

This study explores the critical role of the flow deflector in suppressing boundary layer separation and enhancing aerodynamic efficiency through systematic geometric parameterization and computational analysis. By defining eight key design variables, this research identifies optimal configurations that significantly delay flow separation at high angles of attack. Computational Fluid Dynamics (CFD) simulations reveal that optimized deflector geometries enhance suction peaks near the airfoil leading edge, redirect separated flow toward the upper surface, and inject momentum into the boundary layer to generate a more positive lift coefficient. The numerical results demonstrate that the optimized design achieves a 58.4% increase in lift coefficient and an 83.3% improvement in the lift–drag ratio by effectively mitigating large-scale vortical structures inherent in baseline configurations. Sensitivity analyses further highlight threshold-dependent “sudden-jump” behaviors in lift coefficients for parameters such as element spacing and deflection angles, while thickness exhibits minimal influence. Additionally, pre-stall optimizations show that strategically aligned deflectors preserve baseline performance with a 0.4% lift gain, whereas misaligned configurations degrade aerodynamic efficiency by up to 9.1%. These findings establish a direct correlation between deflector-induced flow redirection and separation suppression, offering actionable insights for passive flow control in stalled regimes. This research advances fundamental understanding of flow deflector-based separation management and provides practical guidelines for enhancing aerodynamic performance in aerospace applications. Full article

Despite the emergence of eco-friendly solvents and scalable methods for polymeric membrane fabrication, studies on the impacts of solvent synthesis and manufacturing scale-up have not been conducted. To this end, a life cycle assessment (LCA) was developed with the goal of determining the global environmental and health impacts of producing polysulfone (PSf) membranes with the solvents PolarClean and γ-valerolactone (GVL) via doctor blade extrusion (DBE) and slot die coating (SDC). Along with PolarClean and GVL, dimethylacetamide (DMAc) and N-methyl-2-pyyrolidone (NMP) were included in the LCA as conventional solvents for comparison. The dope solution viscosity had a major influence on the material inventories; to produce a normalized membrane unit on a surface area basis, a larger quantity of PSf-PolarClean-GVL materials was required due to its high viscosity. The life cycle impact assessment found electricity and PolarClean to be major contributing parameters to multiple impact categories during membrane fabrication. The commercial synthesis route of PolarClean selected in this study required hazardous materials derived from petrochemicals, which increased its impact on membrane fabrication. Due to more materials being required to fabricate membranes via SDC to account for tool fluid priming, the PSf-PolarClean-GVL membrane fabricated via SDC exhibited the highest impacts. The amount of electricity and concentration of PolarClean were the most sensitive parameters according to Spearman’s rank coefficient analysis. A scenario analysis in which the regional energy grid was substituted found that using the Swedish grid, which comprises far more renewable technologies than the global and US energy grids, significantly lowered impacts in most categories. Despite the reported eco-friendly benefits of using PolarClean and GVL as alternatives to conventional organic solvents, the results in this study provide a wider perspective of membrane fabrication process impacts, highlighting that upstream impacts can counterbalance the beneficial properties of alternative materials. Full article

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements. Full article

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of scale, and the trend toward resource centralization, supply chains have increasingly evolved into centralized structures, with critical functions such as decision-making highly concentrated in a few focal firms. While this configuration may enhance coordination under normal conditions, it also significantly increases dependency on focal nodes. Once a focal node is disrupted, the intense task, information, and risk loads it carries cannot be effectively dispersed across the network, thereby amplifying load spillovers, coordination imbalances, and information delays, and ultimately triggering large-scale cascading failures. To capture this phenomenon, this study develops a coupled network model comprising a Physical Network and an Information and Decision Risk Network. The Physical Network incorporates a tri-load coordination mechanism that distinguishes among theoretical operational load (capacity), actual production load (production output), and actual delivery load (order fulfillment), using a load sensitivity coefficient to describe the asymmetric propagation among them. The Information and Decision Risk Network is further divided into a communication subnetwork, which represents transmission efficiency and delay, and a decision risk subnetwork, which reflects the diffusion of uncertainty and risk contagion caused by information delays. A discrete-event simulation approach is employed to evaluate system resilience under various failure modes and parametric conditions. The results reveal the following: (1) under a centralized structure, poorly allocated redundancy can worsen local imbalances and amplify disruptions; (2) the failure of a focal firm is more likely to cause a full network collapse; and (3) node failures in the Communication System Network have a greater destabilizing effect than those in the Physical Network. Full article

Fractal theory provides a powerful tool for quantifying complex geometric patterns such as concrete cracks. The box-counting method is widely employed for fractal dimension (FD) calculation due to its intuitive principles and compatibility with image data. However, two critical limitations persist in existing studies: (1) the selection of scale parameters (including minimum measurement scale and cutoff scale) lacks systematization and exhibits significant arbitrariness; (2) insufficient attention to the sensitivity of counting origins compromises the stability and comparability of FDs, severely limiting reliable engineering application. To address these limitations, this study first employs classical fractal images and crack samples to systematically analyze the impact of four minimum measurement scales (2, $\sqrt{2}$, 3, $\sqrt{3}$) and three cutoff scale coefficients (cutoff-to-minimum image side ratios: 1, 1/2, 1/3) on computational accuracy. Subsequently, the farthest point sampling (FPS) method is adopted to select counting origins, comparing two optimization strategies—Count-FD-Mean (mean of fits from multiple origins) and Count-Min-FD (fit using minimal box counts across scales). Finally, the optimized approach is validated through static loading tests on concrete beams. Key findings demonstrate that: the optimal scale combination (minimum scale: $\sqrt{2}$; cutoff coefficient: 1) yields a mere 0.5% average error from theoretical FDs; the Count-Min-FD strategy delivers the highest stability and closest alignment with theoretical values; FDs of beam cracks increase continuously with loading, exhibiting an exponential correlation with midspan deflection that effectively captures crack evolution; uncalibrated scale parameters and counting strategies may induce >40% errors in inferred mechanical parameters; results stabilize with 40–45 counting origins across three tested fractal patterns. This work advances standardization in fractal analysis, enhances reliability in concrete crack assessment, and provides critical support for the practical application of fractal theory in structural health monitoring and damage evaluation. Full article

Archard’s wear law is among the first and foremost wear models derived from contact mechanics that relates key operating conditions and material hardness to sliding wear through a multifaceted wear coefficient. This entry explores the development, generalization, and critique of the Archard model—a foundational model in wear prediction. It outlines the historical origins of the model, its basis in contact plasticity, and its use of a constant wear coefficient. The discussion highlights modern efforts to extend the model through variable exponents and empirical calibration. Key limitations such as the oversimplification of wear behavior, exclusion of factors like sliding velocity, and scale sensitivity are examined through both theoretical arguments and experimental evidence. The critiques reflect the model’s constrained applicability in diverse wear conditions across varied operating conditions and material phenomena. Full article

Evaluating the response of maize crops to different nitrogen fertilization rates is essential to ensure their agronomic, environmental, and economic efficiency. In this study, the spectral information of maize plants subjected to five distinct nitrogen fertilization strategies was analyzed. The fertilization strategies were based on the practices commonly used in maize fields in the study area, with the aim of ensuring the research findings’ applicability. The spectral reflectance was measured using a spectroradiometer covering the 350–2500 nm range, and the results enabled the identification of optimal spectral regions for monitoring plants’ nitrogen status, particularly in the visible and infrared ranges. A Principal Component Analysis (PCA) of the reflectance data revealed the key wavelengths most sensitive to the nitrogen availability: 555 nm and 720 nm during the vegetative stage and 680 nm during the reproductive stage. This information will support the development of drone-mounted multispectral sensor systems for large-scale monitoring, as well as the design of low-cost sensors for early nitrogen deficiency detection. Furthermore, the study demonstrated the feasibility of estimating the cornstalk nitrate content based on direct reflectance measurements of maize stems. The prediction model showed satisfactory performance, with a coefficient of determination (R²) of 0.845 and a root mean square error of prediction (RMSECV) of 2035.3 ppm, indicating its strong potential for predicting the NO₃-N concentrations in maize stems. Full article

High-fidelity finite element analysis (FEA) plays a key role in structural engineering by enabling accurate simulation of displacement, stress, and internal forces under static loads. However, its high computational cost limits applicability in real-time control, iterative design, and large-scale uncertainty quantification. Surrogate modeling provides a computationally efficient alternative by learning input–output mappings from precomputed simulations. Yet, the performance of individual surrogates is often sensitive to data distribution and model assumptions. To enhance both accuracy and robustness, we propose a model averaging framework based on Jackknife Model Averaging (JMA) that integrates six surrogate models: polynomial response surfaces (PRSs), support vector regression (SVR), radial basis function (RBF) interpolation, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Three ensembles are formed: JMA1 (classical models), JMA2 (tree-based models), and JMA3 (all models). JMA assigns optimal convex weights using cross-validated out-of-fold errors without a meta-learner. We evaluate the framework on the Static Analysis Dataset with over 300,000 FEA simulations. Results show that JMA consistently outperforms individual models in root mean squared error, mean absolute error, and the coefficient of determination, while also producing tighter, better-calibrated conformal prediction intervals. These findings support JMA as an effective tool for surrogate-based structural analysis. Full article

This study investigates the wear performance of 20CrMnTi steel, a commonly used material for spiral bevel gears, after heat treatment, with a focus on the microstructural evolution and wear behavior in both the surface and gradient direction of the carburized layer. The results show that the microstructure composition in the gradient direction of the carburized layer gradually transitions from martensite and residual austenite to a martensite–bainite mixed structure, and eventually transforms to fully bainitic in the matrix. With the extension of carburizing time, both the effective carburized layer depth and the hardened layer depth significantly increase. Wear track morphology analysis reveals that the wear track depth gradually becomes shallower and narrower, and the wear rate increases significantly with increasing load. However, the friction coefficient shows little sensitivity to changes in carburizing time and load. Further investigations show that as the carburized layer depth increases, the carbon concentration and hardness of the samples gradually decrease, resulting in an increase in the average wear rate and a progressive worsening of wear severity. After the wear tests, different depths of plowing grooves, spalling, and fish-scale-like features were observed in the wear regions. Additionally, with the increase in load and carburized layer depth, both the width and depth of the wear tracks significantly increased. The research results provide a theoretical basis for optimizing the surface carburizing process of 20CrMnTi steel and improving its wear resistance. Full article

The stability of shield tunnel segmental linings is highly sensitive to the lateral pressure coefficient, especially under weak, heterogeneous, and variable geological conditions. However, the mechanical behavior of steel plate-reinforced linings under such conditions remains insufficiently characterized. This study aims to investigate the effects of varying lateral pressures on the structural performance of reinforced tunnel linings. To achieve this, a custom-designed full-circumference loading and unloading self-balancing apparatus was developed for scaled-model testing of shield tunnels. The experimental methodology allowed for precise control of loading paths, enabling the simulation of realistic ground stress states and the assessment of internal force distribution, joint response, and load transfer mechanisms during the elastic stage of the structure. Results reveal that increased lateral pressure enhances the stiffness and bearing capacity of the reinforced lining. The presence and orientation of segment joints, as well as the bonding performance between epoxy resin and expansion bolts at the reinforcement interface, significantly influence stress redistribution in steel plate-reinforced zones. These findings not only deepen the understanding of tunnel behavior in complex geological environments but also offer practical guidance for optimizing reinforcement design and improving the durability and safety of shield tunnels. Full article