Search Results (71,793)

The functional performance and structural integrity of natural rock materials under fluctuating environmental stressors are pivotal for their advanced applications. As a non-ionizing and radiation-free technology, terahertz (THz) spectroscopy offers a safe and promising alternative for non-destructive testing (NDT), uniquely capable of being deployed in open and unshielded environments. However, limited penetration depth, exacerbated by both the dense geological matrix and the extreme sensitivity of THz waves to moisture states, has long hindered its widespread application in rock characterization. This study establishes a quantitative Terahertz Time-Domain Spectroscopy (THz-TDS) framework to characterize four lithologies under drying–wetting cycles. Exponential signal attenuation across thicknesses was quantified based on the Beer–Lambert law, with attenuation coefficients ranging from 0.15 to 0.74 per millimeter. Planar transmission imaging successfully visualizes lithologic and moisture-dependent heterogeneity: limestone exhibits a dense, homogeneous structure with stable amplitude distribution; sandstone and purple sandstone show parallel statistical trends, reflecting uniform pore networks; and granite demonstrates the most pronounced imaging contrast under varying moisture states, driven by complex grain-boundary scattering. The findings reveal that THz transmission is dictated by the synergistic effects of mineral compositions and pore structures: scattering at grain boundaries and fractures leads to significant energy dissipation, whereas clay-rich lithologies exhibit the highest sensitivity to moisture variations due to water adsorption and interfacial polarization effects. As an exploration of THz technology in the non-destructive evaluation of rock materials, these findings establish an analytical framework for the quantitative assessment of microstructure evolution. Full article

Aerostatic spindles are indispensable in the ultra-precision manufacturing field due to their high accuracy and low friction. However, rotor manufacturing errors will affect the thickness and uniformity of the air film, thereby limiting the improvement and application of the aerostatic spindle. To explore this issue, this paper presents theoretical modelling and experimental work. Rotor cylindricity errors were first evaluated based on manufacturing errors, and a calculation model of the film thickness considering rotor cylindricity errors was established. By solving the dynamic Reynolds equation considering cylindricity errors, the dynamic stiffness and damping of aerostatic spindles were obtained. The influence mechanism of rotor cylindricity errors on the dynamic stiffness and damping coefficients of the rotor–bearing system was revealed. The stiffness coefficients K_xx, K_yy, and K_xy are more sensitive to the saddle-shaped errors, and the stiffness coefficient K_yx and both damping coefficients are more closely related to bucket-shaped errors. Regarding the influence of the cylindricity errors’ extremal position, the main and cross stiffness coefficients are sensitive to saddle-shaped errors and bucket-shaped errors, respectively; the main and cross-damping coefficients are sensitive to bucket-shaped errors. Under the effect of three kinds of error shapes, when the rotor cylindricity errors value is less than 1 μm, the dynamic stiffness and damping coefficients are conducive to improving the dynamic characteristics of the rotor–bearing system. Multiple rotors were manufactured, and their cylindricity errors were measured, and then the dynamic characteristics of the assembled aerostatic spindles with these rotors were tested. It was found that the dynamic stiffness of spindles with saddle-shaped errors is larger than that of spindles with conical-shaped errors, and the greater the error values are, the worse the rotation accuracy. The experimental results are consistent with the theoretical findings, thus verifying the feasibility and validity of the established theoretical model. This study improves the error tolerance design accuracy of rotors and thereby enhances the dynamic performance of aerostatic spindles. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

Unsafe behaviors among construction workers constitute a major contributing factor to construction accidents, making it critically important to explore their underlying mechanisms and temporal dynamics from a cognitive perspective. This study employed an exploratory sequential mixed-methods approach. Initially, grounded theory was used to conduct three-level coding of in-depth interview data from 35 construction workers, resulting in the development of a cognitive theory model of unsafe behavior among construction workers comprising two main categories: ‘ perceptual recognition’ and ‘cognitive response’. Subsequently, a questionnaire was designed based on this model, and a 10-day longitudinal survey was conducted among 300 workers. Multi-group structural equation modelling was employed to analyze the temporal variation in the relationship between cognitive ability and unsafe behavior. The results indicate that: workers’ cognitive abilities can be decomposed into four dimensions—perceiving danger, identifying hazards, perceptual response, and decision-making response—and further summarized into two higher-order factors: perceptual recognition and cognitive response; (2) cognitive abilities are significantly negatively correlated with unsafe behavior; (3) this relationship exhibits significant temporal variations, with the inhibitory effect on Day 5 (path coefficient −0.95) being stronger than that on Day 1 (−0.88) and Day 10 (−0.50); furthermore, the ‘cognitive response → decision-making response’ path also shows significant differences between Day 5 and Day 10. The study reveals the pattern of fluctuations over time in the inhibitory effects of workers’ cognitive ability on unsafe behavior, providing a theoretical basis for construction companies to implement dynamic and targeted safety interventions. Full article

Competition for water resources makes it necessary to advance research focused on estimating the water needs of row crops, such as vineyards. Following the FAO-56 methodology and the A&P approach, the soil water balance model was applied to a vineyard with continuous vegetation cover in temperate climate conditions (Galicia, Spain). Basal crop coefficients adjusted to local conditions were obtained for both the vineyard and the active vegetation. After SIMDualKc model adjustment, r² values greater than 0.86 were obtained, along with goodness-of-fit indicators that demonstrate the model’s ability to predict soil water content (PBIAS_avg = 1.16; EF_avg = 0.89; dIA_avg = 0.97). A correction factor is proposed that improves the partitioning of the transpiration component in row crops with active cover. The transpiration demand of the vineyard increased by 35% in four study cases (northern Portugal, northwestern Spain, and Italy). The proposed correction factor is shown to be in line with the actual conditions and complex behaviour of a vineyard with active vegetation cover, which opens the way for improved water requirement prediction in complex management situations such as the one studied here. The proposed methodology is expected to improve the efficiency of irrigation management through more accurate determination of the real water amount required by orchards. Full article

Genetic diversity within maize inbred populations is essential for sustaining genetic gain in breeding programmes. This study evaluated 280 maize inbred lines with two checks using an augmented block design (22 × 14). At harvest, 271 lines and two checks were analysed, with nine entries excluded due to poor survival. Using both descriptive (24) and quantitative (19) traits, significant variations were observed across many traits. Descriptive traits varied among the genotypes, as revealed by graphical analysis and correlation heatmaps. The likelihood ratio test (LRT) for lines showed significant differences for several quantitative traits with moderate–high heritability, while anthesis–silking interval, tassel length, ear position, ear aspect, bad husk cover, number of plants, and number of ears per plant exhibited low heritability. High genetic advance as a percentage of the mean was observed for grain yield, plant height, grain texture, number of plants, number of kernels, and grain weight per plant. Positive associations were observed among genotypic coefficient of variation, genetic advance, and heritability. Grain yield showed significant positive correlations with yield-related traits and morphological traits, but negative correlations with flowering traits. The first 10 principal components explained 86.17% of total variation, with flowering traits contributing most to variability in PC 1. Cluster analysis grouped genotypes into 10 clusters, with substantial genetic divergence within and between cluster groups. In conclusion, the study revealed considerable genetic diversity, supporting the selection of superior parents in breeding programmes and developing improved maize varieties to enhance productivity. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

With the global emphasis on sustainable development, supply chain management is facing new challenges and opportunities. Enterprises often face a large number of suppliers when selecting suppliers, which makes the selection process complex. Considering the crucial role of supplier selection in sustainable supply chains, a sustainable supplier selection model based on multi-attribute utility analysis and a fuzzy approximation ideal solution ranking method is proposed to reduce carbon emissions and environmental pollution. This model helps companies scientifically evaluate and select suppliers by comprehensively considering three aspects: environment, economy, and society. Meanwhile, the study utilizes an optimized genetic algorithm-based order allocation model to raise the efficacy and fairness of order allocation. Reducing procurement costs often relies on improving resource utilization and reducing production waste, which directly lowers the energy consumption and carbon emission intensity per unit of product. At the same time, reducing product damage and delivery delay rates can avoid additional greenhouse gas emissions caused by rework, abandonment, and emergency transportation. By improving supplier productivity and optimizing order allocation, the developed model can not only reduce economic costs but also control environmental pollution and carbon footprints from the source of the supply chain. The outcomes indicate that technological level is a crucial factor influencing supplier selection, with a significant positive impact on supplier willingness to choose, and its standard path coefficient is 0.199, with a significance level of 0.001. Meanwhile, the optimized genetic algorithm exhibits strong stability and convergence in order allocation. This optimization model has high efficiency in handling large-scale orders. This provides strong support for the decision-making of enterprises in sustainable supply chain management and a valuable reference for China’s exploration and practice in the field of sustainable development. Full article

This study estimates daily nitrogen dioxide (NO₂) concentrations at ground level across the Marmara Region of Türkiye at 0.01° resolution. The framework integrates Sentinel-5P (S5P) TROPOspheric Monitoring Instrument (TROPOMI) and GEOS Composition Forecast (GEOS-CF) tropospheric NO₂ vertical column density (VCD) data with meteorological, topographic, land-use, socioeconomic, and temporal features through four tree-based ensemble algorithms trained on 74 ground station observations. Under a temporal split (2019–2022 training, 2023 validation, 2024 testing), S5P-Categorical Boosting (CatBoost) achieved the best performance (Pearson correlation coefficient (R) = 0.706, R² = 0.498, root mean square error (RMSE) = 14.31 µg/m³). Random splitting inflated R by +0.168 due to temporal autocorrelation, while leave-one-station-out and leave-one-province-out cross-validation reduced R to ~0.50 by removing spatial dependence, together revealing the combined effect of temporal and spatial autocorrelation. SHapley Additive exPlanations (SHAP) analysis identified TROPOMI NO₂ VCD, population density, road length, and nighttime light as dominant predictors; population density was the top predictor in the GEOS-CF model, followed by VCD. Concentration maps for 2024 showed that 95.9% of the region’s 26.74 million inhabitants were exposed above the WHO annual air quality guideline of 10 µg/m³, with a population-weighted mean of 21.08 µg/m³. Full article

Because high-precision DEMs are costly to obtain, while low-precision DEMs often fail to meet accuracy requirements for watershed flood simulation, this study proposes a multi-source DEM fusion method based on the Random Forest algorithm. This method combines K-Means slope clustering and Optuna hyperparameter optimization to realize adaptive weight allocation across eight slope zones. After multi-source DEM fusion, the fused DEM is applied to the flood simulation model of the Wei River Basin to simulate the catastrophic flood event in July 2021. The results show that the Mean Absolute Error (MAE) of the fused DEM ranges from 0.9855 to 1.7218, the Root Mean Square Error (RMSE) ranges from 1.0902 to 2.3953, and the Mean Error (ME) is close to 0 with no significant systematic bias. Compared with single-source DEM, the fused DEM reduces MAE by 21.32–85.32% and RMSE by 7.63–82.03%. In flood simulation, the peak discharge error based on the fused DEM is controlled within 0.013–0.059, and the coefficient of determination (R²) is not less than 0.9808. The simulated errors of inundation area and flood detention volume in flood detention areas are significantly lower than those using a single-source DEM. The proposed multi-source DEM fusion method can effectively improve terrain accuracy and the reliability of flood routing simulation, providing technical support for flood control scheduling in the Wei River Basin and watershed hydrological and flood simulation in data-scarce regions. Full article

Grain number is a primary agronomic trait for targeted yield improvement, with the prospect of enhanced grain production leading to greater food security. Given the complex polygenic nature of the grain number trait, large sample sizes are essential for effective QTL identification. The implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation. In this study, we trained a grain detection model using Ultralytics’ You Only Look Once (YOLOv11) framework. Training was completed on 1000 images of barley spikes, derived from a doubled haploid (DH) population descended from Hindmarsh and RGT Planet. The trained model, termed BarleyGC, achieved satisfactory accuracy metrics (mAP50-95 = 71.9%, recall = 96.7%, precision = 97.1%). Phenotypic characterisation of the DH population was completed with BarleyGC on a distinct collection of 973 images. The Pearson correlation coefficient (r) between model and manual-derived counts for the trait of grain number per spike was 0.895 (p < 0.0001), and 92.4% of all measurements fell within three grains of the manual measurement. Downstream QTL analysis on the phenotype data (n = 153 DH lines), revealed a QTL peak at position 224.959 cM on the genetic map (LOD = 3.14), named qGN-2H. The QTL region contained 20 candidate genes—including HORVU2Hr1G092290 (HORVU.MOREX.r3.2HG0184740), encoding the six-rowed spike 1 (Vrs1) gene—a well-characterised major regulator of row-type divergence and lateral spikelet development. Our study demonstrates the power of the YOLOv11 framework for grain quantification, with BarleyGC capable of grain detection directly from images of in-tact spikes in two-rowed barley varieties—thus achieving accelerated sample processing for the grain number trait. Full article

Background/Objectives: The increasing reliance on the Internet for health information has contributed to the emergence of cyberchondria, a phenomenon closely associated with health anxiety and potentially linked to sleep disturbances. Evidence remains limited in the Saudi context, particularly regarding differences between individuals with and without psychosomatic disorders. Methods: A cross-sectional observational study was conducted among 1224 Saudi adults (535 with psychosomatic disorders and 689 without). Data were collected using validated instruments, including the Cyberchondria Severity Scale (CSS-12), Short Health Anxiety Inventory (SHAI-18), and Pittsburgh Sleep Quality Index (PSQI). Statistical analyses included Pearson correlation coefficients and two-way ANOVA. Results: The prevalence of cyberchondria was 56.78%, health anxiety 38.76%, and poor sleep quality 56.9%. Significant positive correlations were observed between cyberchondria, health anxiety, and poor sleep quality across both groups, with stronger associations among individuals with psychosomatic disorders. Two-way ANOVA revealed a significant main effect of clinical status on all variables and a significant effect of sex on health anxiety, with higher levels among females. Conclusions: Findings highlight a significant interplay between cyberchondria, health anxiety, and sleep quality, particularly among individuals with psychosomatic disorders. These results underscore the need for targeted public health interventions addressing digital health behaviours and mental health. Full article

The catalytic influence of γ-aminobutyric acid (GABA) on Zn²⁺ electroreduction at a mercury electrode was investigated in an acetate buffer. Electrochemical measurements, including DC polarography and differential capacity, indicate that GABA facilitates charge transfer through the formation of “cap-pair” surface bridges. This acceleration is reflected in a systematic increase in the standard rate constant and the transfer coefficient. Molecular dynamics simulations complement these findings by characterizing the conformational properties of GABA, showing a transition toward more folded forms in concentrated environments. Moreover, MD simulations demonstrate that GABA reduces the Zn²⁺ solvation number, providing a structural pathway that lowers the dehydration barrier prior to charge transfer. These observations correlate with the measured decrease in diffusion coefficients as the neurotransmitter concentration increases. The results establish a direct link between the zwitterionic adsorption of GABA and the reduction in the energetic barrier in the zinc electroreduction process. Full article

Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM_2.5 and r = 0.86 for PM₁₀, and low error levels (RMSE ≈ 2.1–2.2 µg/m³). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article