Search Results (377)

Natural recruitment of Manila clam (Ruditapes philippinarum) often persists in degraded estuaries, yet the environmental thresholds enabling this resilience remain quantitatively undefined. We employed binomial generalized additive model (GAM) coupled with field surveys (n = 168) in the Xiaoqing River estuary (Laizhou Bay, China) to identify critical limits for adult occurrence, which served as a field-based proxy for recruitment potential. Sediment median grain size (D₅₀), salinity (Sal) and dissolved inorganic nitrogen (DIN) were identified as the key factors, collectively explaining 79.30% of the deviance (AUC = 0.98). The probability of occurrence decreased sharply beyond two distinct thresholds: D₅₀ > 95 μm and salinity < 17.50‰. While DIN had a positive effect, it did not offset the strong negative associations with coarse sediment or low salinity. These field-validated thresholds provide quantifiable criteria to guide habitat suitability mapping, activation of early-warning systems against salinity-driven mortality, and site prioritization for ecological restoration in the Xiaoqing River estuary. Our findings offer a framework for developing management strategies to support clam resilience under environmental stress. Full article

Declining Leydig cell steroidogenesis contributes to late-onset hypogonadism and to age-associated impairment of male reproductive health. Determinants of dysfunction extend beyond chronological aging. This review synthesizes recent experimental and translational evidence on cellular and molecular processes that compromise Leydig cell endocrine output and the interstitial niche that supports spermatogenesis. Evidence spanning environmental endocrine-disrupting chemicals (EDCs), obesity and metabolic dysfunction, and testicular aging is integrated with emphasis on oxidative stress, endoplasmic reticulum stress, mitochondrial dysregulation, apoptosis, disrupted autophagy and mitophagy, and senescence-associated remodeling. Across model systems, toxicant exposure and metabolic stress converge on impaired organelle quality control and altered redox signaling, with downstream loss of steroidogenic capacity and, in some settings, premature senescence within the Leydig compartment. Aging further reshapes the testicular microenvironment through inflammatory shifts and biomechanical remodeling and may erode stem and progenitor Leydig cell homeostasis, thereby constraining regenerative potential. Single-cell transcriptomic atlases advance the field by resolving Leydig cell heterogeneity, nominating subsets that appear more vulnerable to stress and aging, and mapping age-dependent rewiring of interstitial cell-to-cell communication with Sertoli cells, peritubular myoid cells, vascular cells, and immune cells. Many mechanistic insights derive from rodent in vivo studies and in vitro platforms that include immortalized Leydig cell lines, and validation in human tissue and human clinical cohorts remains uneven. Together, these findings frame mechanistically informed opportunities to preserve endogenous androgen production and fertility through exposure mitigation, metabolic optimization, fertility-preserving endocrine stimulation, and strategies that target inflammation, senescence, and regenerative capacity. Full article

Monitoring the stability of hydraulic structures such as dams and reservoirs in seismically active regions is essential for ensuring their safety and operational reliability. This study presents a comprehensive geospatial approach combining lineament analysis and geodynamic zoning to assess the structural stability of the Voroshilov and Priyut reservoirs located in the Almaty region, Kazakhstan. A regional lineament map was generated using ASTER GDEM data, while ALOS PALSAR data were used for detailed local analysis. Lineaments were extracted and analyzed through automated processing in PCI Geomatica. Lineament density maps and azimuthal rose diagrams were constructed to identify zones of tectonic weakness and assess regional structural patterns. Integration of lineament density, GPS velocity fields, InSAR deformation data, and probabilistic seismic hazard maps enabled the development of a detailed geodynamic zoning model. Results show that the studied sites are located within zones of low local geodynamic activity, with lineament densities of 0.8–1.2 km/km², significantly lower than regional averages of 3–4 km/km². GPS velocities in the area do not exceed 4 mm/year, and InSAR analysis indicates minimal surface deformation (<5 mm/year). Despite this apparent local stability, the 2024 Voroshilov Dam failure highlights the cumulative effect of regional seismic stresses (PGA up to 0.9 g) and localized filtration along fracture zones as critical risk factors. The proposed geodynamic zoning correctly identified the site as structurally stable under normal conditions but indicates that even low-activity zones are vulnerable under cumulative seismic loading. This demonstrates that an integrated approach combining remote sensing, geodetic, and seismic data can provide quantitative assessments for dam safety, predict potential high-risk zones, and support preventive monitoring in tectonically active regions. Full article

Background: Rainfall-induced landslides are a widespread and destructive geological hazard that resist precise prediction. They pose serious threats to human lives and property, ecological stability, and socioeconomic development. Methods: To address the challenges in mitigating rainfall-induced landslides in high-altitude mountainous regions, this study proposes a digital twin framework that couples multiple physical fields and is based on the spherical discrete element method. Results: Two-dimensional simulations identify a trapezoidal stress distribution with inward-increasing stress. The stress increases uniformly from 0 kPa at the surface to 210 kPa in the interior. The crest stress remains constant at 1.8 kPa under gravity, whereas the toe stress rises from 6.5 to 14.8 kPa with the slope gradient. While the stress pattern persists post-failure, specific magnitudes alter significantly. This study pioneers a three-dimensional close-packed spherical discrete element method, achieving enhanced computational efficiency and stability through streamlined contact mechanics. Conclusions: The proposed framework utilizes point-contact mechanics to simplify friction modeling, enhancing computational efficiency and numerical stability. By integrating stress, rainfall, and seepage fields, we establish a coupled hydro-mechanical model that enables real-time digital twin mapping of landslide evolution through dynamic parameter adjustments. Full article

To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems. Full article

This state-of-the-art review provides a comprehensive, critical synthesis of the rapidly expanding field of HECCs, emphasizing the unique scientific challenges that distinguish these materials from conventional ceramics and high-entropy alloys. Key challenges of HECCs include accurately predicting stable phases and quantifying resultant material properties, optimizing complex fabrication and processing techniques, and establishing a robust correlation between the intricate microstructural characteristics and macroscopic performance. Unlike previous reviews that focus on individual ceramic families, this article integrates the novel features, diverse applications, and recent developmental breakthroughs across carbides, nitrides, borides, and oxides to reveal the unifying principles governing configurational disorder, phase stability, and microstructure property relationships in HECCs. A key novelty of this review work is the systematic mapping of fabrication pathways, including CTR, PAS, SPS, and reactive sintering, against the underlying thermodynamic and kinetic constraints specific to multicomponent ceramic systems. The review introduces emerging ideas such as HEDFT, machine-learning-assisted phase prediction, and entropy–enthalpy competition as foundational tools for next-generation HECC design and performance analysis. Additionally, it uniquely presents densification behavior, diffusion barriers, defect chemistry, and residual stress evolution with mechanical, thermal, and tribological performance across the coating classes. By consolidating theoretical intuitions with experimental developments, this article provides a novel roadmap for predictive compositional design, development, microstructural engineering, and targeted application of HECCs in extreme environments. This work aims to support researchers and coating industries toward the rational development of high-performance HECCs and establish a unified framework for future research in high-entropy ceramic technologies. Full article

This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a physics-based Soderberg index and a one-class support vector machine (SVM) anomaly detector. The framework is implemented and evaluated using measurements from a 2 MW onshore turbine equipped with blade-root strain gauges and standard SCADA monitoring. Ten-minute operating windows are formed by synchronizing SCADA records with high-frequency strain data, converting strain to stress, and computing DELs via Rainflow counting for flapwise, edgewise, and torsional blade root directions. SCADA inputs are summarized by their 10 min statistics and augmented with yaw misalignment features; these are used to train LightGBM-based ROMs that map operating conditions to directional DELs. On an independent test set, the DEL-ROM achieves coefficients of determination of approximately 0.87, 0.99, and 0.99 for flapwise, edgewise, and torsional directions, respectively, with small absolute errors relative to the measured DELs. The Soderberg index is then used to define conservative Normal/Alert/Alarm classes based on representative material parameters, while a one-class SVM is trained on DEL- and stress-based fatigue features to learn the distribution of normal operation. A simple AND-normal/OR-abnormal rule combines the Soderberg class and SVM label into a hybrid diagnostic decision. Application to the field dataset shows that the proposed framework provides interpretable fatigue-safety margins and reliably highlights operating periods with elevated flapwise fatigue usage, demonstrating its suitability as a scalable building block for digital-twin-enabled condition monitoring and life-extension assessment of wind turbine blades. Full article

Accurate prediction of the stress–strain fields in fiber-reinforced composites is crucial for performance analysis and structural design. However, due to their complex microstructures, traditional finite element analysis (FEA) entails a very high computational cost. Therefore, this study proposes a conditional generative adversarial network (cGAN) framework, named FiberGAN, to enable rapid prediction of the microscopic stress–strain fields in fiber-reinforced composites. The method employs an adaptive representative volume element (RVE) generation algorithm to construct random fiber arrangements with fiber volume fractions ranging from 30% to 50% and uses FEA to obtain the corresponding stress and strain fields as training data. A U-Net generator, combined with a PatchGAN discriminator, captures both global distribution patterns and fine local details. Under tensile and shear loading conditions, the R² values of FiberGAN predictions range from 0.96 to 0.99, while the structural similarity index (SSIM) values range from 0.95 to 0.99. The error maps show that prediction residuals are mainly concentrated in high-gradient regions with small magnitudes. These results demonstrate that the proposed deep learning model can successfully predict stress–strain field distributions for different fiber volume fractions under various loading conditions. Full article

The laser welding of 4 mm thick Ti80 alloy under different powers was analyzed, and the weld morphology, microstructure, and mechanical properties were studied. A simulation model was established based on ABAQUS, and laser welding simulations were conducted using 2520 W and 3000 W laser welding power sources to analyze the temperature field and stress field, which were verified by experiments. The increase in power changed the weld morphology from Y-shaped to X-shaped and affected the number of pores in incomplete and complete penetration. The microstructure in the weld zone presented fine acicular α′ phase. Subsequently, grain boundary distribution maps, Kernel Average Misorientation (KAM) maps, and geometrically necessary dislocation (GND) density maps were generated through electron backscatter diffraction (EBSD) analysis. These comprehensive data visualizations enabled multi-dimensional investigation, establishing and analyzing correlations between laser welding parameters, microstructural evolution, and mechanical properties in Ti80 titanium laser welding. The hardness of the base material was 320 HV to 360 HV, and it increased from 420 HV to 460 HV in the weld zone. At 3000 W, the tensile strength reached 903.12 MPa, and the elongation was 10.40%, indicating ductile fracture. The simulation results accurately predicted the maximum longitudinal residual stress in the weld zone, with an error of 1.65% to 1.81% of the measured value. Full article

Short dry spells during the rainy season have become increasingly common in Brazil, reinforcing the need for soil water conservation practices. Plastic mulching can enhance plant water use and mitigate abiotic stress. This study evaluates water use efficiency in terms of soil physical quality, root systems, and photosynthetic performance of citrus plants grown in different Inceptisols. The field experiment, in a randomized block design with a split-plot arrangement, was conducted in Lavras, Brazil, and involved citrus (orange) plants from 2012 to 2014. Undisturbed soil samples were collected at depths of 0.00–0.05, 0.20–0.25, and 0.90–0.95 m, two years after the installation of white plastic (WP), black plastic (BP), and no plastic (NP) mulching treatments in two Inceptisol types, totaling 54 samples. The soil water-retention curve, pore size distribution, and soil physical quality indicators were determined, and root system distribution maps were generated using B-splines. Leaf gas exchange was measured under contrasting precipitation conditions. Inceptisol I showed minimal impact from mulching, except for the bulk density and total porosity, which positively correlated with transpiration under BP. In contrast, in Inceptisol II, WP increased photosynthetic rates under low- and high-precipitation conditions but reduced water use efficiency, correlating positively with macropores and negatively with micropores. Plastic mulching reduces physiological stress in citrus and improves soil physical quality, with WP being the most effective across precipitation levels, particularly in less stable soils. Full article

Urban trees provide essential environmental, health, social, and economic benefits. Consequently, researchers and stakeholders devote considerable effort to characterizing, mapping, and monitoring urban tree species. Traditional identification methods that rely on field surveys are labor-intensive and time-consuming. This study evaluated the potential of field hyperspectral spectroscopy to classify nine common urban tree species at the leaf level. Seven random forest classifiers, each using different combinations of spectral features, were compared for classification accuracy. The model that incorporated both first derivatives of spectral reflectance and vegetation indices achieved the highest overall accuracy (80.4%), whereas the model combining spectral reflectance and vegetation indices had the lowest predictive performance (70.1%). The most influential predictors were spectral bands and first derivatives in the red-edge and SWIR 1 regions; and the vegetation indices Red-edge Vegetation Stress Index (RVSI), Plant Senescence Reflectance Index (PSRI), and Blue Ratio (BR). These results support the use of hyperspectral remote sensing for identifying and classifying urban tree species. Full article

A central challenge in tight fault-depression reservoirs is understanding how three-dimensional fracture structures control gas storage and flow. This study introduces a data-driven, geologically informed framework that integrates structural-mechanical coupling to decipher fracture networks within the Shahezi Formation. Our model, based on rock failure criteria, achieves quantitative fracture prediction across one-dimensional to three-dimensional scales. This capability overcomes the limitations inherent in single-method approaches for tight, fracture-dominated reservoirs. By synthesizing sedimentary facies-controlled reservoir modeling, sweet-spot inversion, and geo-engineering integration, we establish a predictive system for accurate reservoir assessment. The continental clastic Shahezi Formation is typified by secondary fractures. This study utilizes leverage small-scale data (core, thin section, log) to quantify key parameters (fracture density, aperture), enabling a systematic analysis of fracture typology, heterogeneity, and controls. Building on this foundation, and spatially constrained by large-scale datasets (seismic interpretation, stress-field simulations), we developed a robust fracture development model for deep tight reservoirs. Stress-field modeling delineated fracture-prone zones, where a discrete fracture network (DFN) model was built to characterize 3D fracture geometry and connectivity. Integrating simulated fracture size and aperture-derived permeability allowed us to quantify fracture contribution to total permeability, ultimately mapping favorable targets. The results identify favorable zones primarily in the western sector of the study area, forming an NS-trending, belt-like distribution. They are mainly concentrated around the wells Changshen-4, Changshen-40, and Changshen-41. This distribution is clearly controlled by the Qianshenzijing Fault. Full article

Wafer-level thin-film stress measurement is essential for reliable semiconductor fabrication. However, existing techniques present limitations in practice. Interferometry achieves high precision but at a cost that becomes prohibitive for large wafers. Meanwhile laser-scanning systems are more affordable but can only provide sparse data points. This work develops a phase-measuring deflectometry (PMD) system to bridge this gap and deliver a full-field solution for wafer stress mapping. The implementation addresses three key challenges in adapting PMD. First, screen positioning and orientation are refined using an inverse bundle-adjustment approach, which performs multi-parameter optimization without re-optimizing the camera model and simultaneously uses residuals to quantify screen deformation. Second, a backward-propagation ray-tracing framework benchmarks two iterative strategies to resolve the slope-height ambiguity which is a fundamental challenge in PMD caused by the absence of a fixed optical center on the source side. The reprojection constraint strategy is selected for its superior convergence precision. Third, this strategy is integrated with regional wavefront reconstruction based on Hermite interpolation to effectively eliminate edge artifacts. Experimental results demonstrate a peak-to-valley error in the reconstructed topography of 0.48 µm for a spherical mirror with a radius of 500 mm. The practical utility of the system is confirmed through curvature mapping of a 12-inch patterned wafer and further validated by stress measurements on an 8-inch bare wafer, which show less than 5% deviation from industry-standard instrumentation. These results validate the proposed PMD method as an accurate and cost-effective approach for production-scale thin-film stress inspection. Full article

Agricultural regions in humid tropical climates are often assumed to be water secure due to high annual rainfall, yet periodic drought remains a major constraint on production. This study demonstrates the application of the Normalized Difference Drought Index (NDDI) to identify drought-affected agricultural land in West Sumatera, Indonesia. Despite mean annual rainfall exceeding 3000 mm, rice yields in the Batang Anai Subdistrict declined from 5.28 t/ha in 2018 to 4.20 t/ha in 2022, suggesting an increased drought stress. A spatial analysis integrated administrative boundaries, land use maps, monthly rainfall records (2014–2023), and MOD09A1 V6 MODIS imagery. The NDDI was derived sequentially from the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The results show that 51.65% of agricultural land (7175 ha) exhibited average NDDI values of 0.09–0.14 over 2018–2023, with the highest drought intensity in 2022, when 4441 ha were classified as moderate drought. Land use under drought conditions was dominated by plantations (58.6%), rice fields (39.5%), and dry fields (1.9%). The NDDI method can more effectively capture localized drought impacts, making it valuable for operational drought monitoring systems. These findings highlight the vulnerability of humid tropical agricultural systems to drought and underscore the need for sustainable water management and early warning strategies based on remote sensing. Full article

Helianthus annuus L. is one of the major oilseed crops worldwide, and its production is seriously affected by a highly destructive necrotrophic pathogen, Sclerotinia sclerotiorum (S. sclerotiorum). The use of resistant cultivars is the best control measure via molecular breeding; however, the gene action underlying resistance to this stress is not well-established. Here, we conducted QTL analysis for S. sclerotiorum resistance in a recombinant inbred line (RIL) population that were developed from parents with resistant (C6) and susceptible (B728) to the disease. A high-density genetic linkage map with 6059 single nucleotide polymorphism (SNP) markers and a total length of 2763 cM was developed. The lesion length (LL) and the lesion area (LA) in the field, under climate chamber conditions or greenhouse conditions, were assessed following standardized inoculation protocols. A total of 16 major QTL for LL and 12 for LA were detected across three experimental environments, explaining 1.58–32.86% of the phenotypic variation. Of these, a major-effect QTL, qSCL2.4 on chromosome 2, could explain 30.22% of phenotypic variance with alleles from parent C6 which had more increased resistance to S. sclerotiorum. Fine-mapping in the BC₁F₃ population narrowed the locus to a 226.7 kb interval. HaWRKY48, which encodes a WRKY transcription factor located in this region, was prioritized as the prime candidate gene. Polymorphism analysis of HaWRKY48 in 138 sunflower accessions revealed eight SNPs defining six haplotypes. Resistance was associated with Hap3 and susceptibility to Hap1/Hap6. These findings advance our understanding of the genetic mechanisms governing sunflower resistance to S. sclerotiorum and provide valuable genetic markers for molecular breeding of resistant cultivars. Full article