Search Results (1,341)

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment. Full article

A design framework is presented for broad-crested weirs with exponential (power-law) head–discharge behavior and three practical control-section shapes: Rectangular, parabolic, and triangular. Unlike ideal-flow sizing, the approach explicitly accounts for real-flow effects through a velocity coefficient at the control section. Starting from the energy equation and the critical-depth condition, analytical relations are obtained for the control-section depth, the critical depth, and the velocity and discharge coefficients. These relations are coupled with geometry-specific critical-flow expressions to derive a general, dimensionless design equation that links the required contraction ratio to the approach-velocity coefficient, the control-section velocity coefficient, and the head exponent n. The core innovation of the framework is a general dimensionless design equation that directly yields the required control-section area ratio $A^{*}$/$A_o$, i.e., the geometric contraction relative to the approach section, for a specified design head and approach-velocity condition. The method provides direct design parameters for each section family: Rectangular width, parabolic parameter, and triangular head angle. A short quantitative check against representative classical experimental ratios shows very good agreement with measured values. For the applied design example based on a trapezoidal approach section and conservative lower-bound $C_v$ values, neglecting real-flow effects underpredicts the required contraction ratio by about 28–39%, depending on the selected section shape. The developed framework provides a transparent, theoretically grounded, and practical tool for the hydraulic design of broad-crested weirs. Full article

Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann–Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010–2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region. Full article

Against the backdrop of increasing global emphasis on sustainable development and ecological conservation, green seafood has emerged as a key component of sustainable marine food consumption. However, the discrepancy between consumers’ intention to consume and their consumption behavior remains a critical issue requiring in-depth investigation. Herein, based on survey data collected from 415 consumers in China in 2025, this study employs structural equation modeling (SEM) to analyze the determinants and mechanisms influencing green seafood consumption intention and behavior. The findings indicate that heightened concerns regarding dietary health, food safety, and nutrition significantly enhance consumer intention, driven primarily by ecological awareness and the pursuit of a higher quality of life. Individual and household characteristics, along with consumers’ cognitive status of green seafood, exert significant positive effects on consumption intention, with cognitive status demonstrating the strongest influence. Nevertheless, a notable gap exists between consumption intention and actual behavior. Among respondents with consumption intention, only 48.7% had ever purchased green seafood, and the consumption frequency remained generally low. SEM path coefficients further reveal that marketing factors play a dominant role in actualizing consumption behavior. Compared to marketing factors, consumption intention shows a relatively weaker effect in facilitating consumption behavior. This finding further confirms the intention–behavior gap in green seafood consumption. The intention–behavior gap in green seafood consumption is jointly driven by asymmetric information on product quality, an underdeveloped certification system, a relatively undiversified supply structure, and elevated prices. Accordingly, this study proposes an integrated strategy that includes establishing a unified certification and traceability system, optimizing supply structures and pricing mechanisms, and strengthening science communication and targeted marketing. These measures aim to bridge the intention–behavior gap and promote the transition toward sustainable consumption patterns. Full article

Argali (Ovis ammon), the largest wild sheep in Asia, are of high conservation concern and remain taxonomically and genetically debated across parts of their range. We investigated population structure, relatedness, and inbreeding within Argali sampled in Kyrgyzstan using the Illumina Ovine High-Density SNP array, with an emphasis on dense within-population sampling rather than range-wide comparisons. After quality control, 72 individuals and 135,242 markers were retained for analysis. Principal component analysis revealed subtle genetic variation within the sampled population, but no clustering consistent with discrete subspecies. In particular, we found no genomic support for separating O. a. polii and O. a. karelini within Kyrgyzstan, suggesting that they represent a single genetic unit in this region. Estimates of identity by descent indicated a high average relatedness (0.35), consistent with harem-based breeding systems typical of wild sheep, while individual inbreeding coefficients averaged near zero, with some evidence of moderate inbreeding in a subset of animals. Together, these results characterize fine-scale genetic structure and kinship within Tian Shan Argali and provide a regional genomic baseline for conservation planning in Kyrgyzstan. Our findings highlight the importance of maintaining connectivity within and among managed populations while acknowledging that broader inference will require sampling across the core Pamir range and other parts of the species’ distribution. Full article

Accurate urban expansion mapping in dryland environments is essential for sustainable planning, infrastructure management, and heritage-sensitive development, yet it remains methodologically challenging because built-up surfaces often exhibit strong spectral similarity to bright bare soils. This study comparatively evaluates three widely used urban mapping approaches in Diriyah, Saudi Arabia, a rapidly transforming heritage district of high relevance to Saudi Vision 2030: the Global Human Settlement Layer (GHSL), the Normalized Difference Built-up Index (NDBI), and unsupervised k-means classification. Built-up extent was mapped for 2015, 2020, and 2025, and method performance was assessed using 150 stratified reference points interpreted from high-resolution imagery. The results reveal substantial quantitative differences among methods. GHSL produced the most conservative estimates of urban extent (2.80, 4.94, and 5.31 km²), while NDBI and unsupervised classification generated much larger and less realistic built-up areas due to spectral confusion with bright bare soil. Accuracy assessment confirmed the superiority of GHSL, which achieved the highest overall accuracy (0.88) and Kappa coefficient (0.83), compared with NDBI (0.53; 0.41) and unsupervised classification (0.61; 0.50). To support integrative interpretation, the study also developed a Hybrid Built-up Detection Model (HBDM), which combines the three outputs into a continuous urban intensity layer that helps distinguish persistent urban cores from uncertain transition zones. The findings demonstrate that conservative global built-up products provide a more reliable baseline than index-based or unsupervised methods in bright-soil dryland settings. More broadly, the study offers practical methodological guidance for urban monitoring and sustainable land management in desert cities undergoing rapid transformation under large-scale development agendas such as Saudi Vision 2030. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems. Full article

Resource-based regions (RBRs) are vital to socio-economic development, yet intensive resource exploitation strains water, energy, and food (WEF) security and causes environmental stress. Optimizing collaborative management of the WEF nexus is crucial for their sustainable development. This study developed an integrated model (WEFN) for optimizing the WEF nexus in RBRs by combining multi-objective optimization and the efficacy coefficient method. The WEFN model incorporates internal couplings and external linkages of the WEF nexus into objectives and constraints. Using Daqing, China, as a case study, six policy scenarios were designed. S1 follows the 2030 planning scheme, while S2–S5 prioritize energy-food supply, environmental protection, water conservation, and economic gains, respectively. S6, formulated via the WEFN model, integrates the objectives of S2–S5 into a collaborative management policy. A comprehensive benefit evaluation system was established, yielding an Evaluation Index (EVI) to quantify WEF system benefits and identify the optimal scenario. Results show that collaborative policy S6 best supports coordinated socio-economic and environmental development in Daqing. The findings offer a valuable reference for WEF nexus management in other RBRs. Full article

This study employs Western Anhui as a case study, establishing a three-dimensional quantitative analytical framework comprising ‘genetic map analysis—architectural feature coding—distribution pattern analysis’ to systematically describe and measure the cross-regional dissemination characteristics of Huizhou architectural culture within Western Anhui. Through field surveys and quantitative analysis of 20 traditional buildings (10 dwellings and 10 ancestral halls), this study employs order and law to determine feature weights and uses Spearman’s correlation coefficient to analyse feature associations, revealing the selective distribution pattern of Huizhou characteristics within Western Anhui architecture. Findings indicate: (1) the frequency of Huizhou features in ancestral halls (71%) significantly exceeds that in dwellings (36%), demonstrating typological differentiation; (2) plan-related features (plan form, courtyard configuration, compositional arrangement) appear most frequently in dwellings (60%), while stone carvings achieve comprehensive coverage in ancestral halls (100%); and (3) wood carvings and stone carvings co-occurred highly (ρ = 0.90), reflecting systematic application of decorative features; doors and plan forms showed a weak negative correlation (ρ = −0.17), potentially suggesting distinct adoption pathways, though not entirely mutually exclusive. The quantitative descriptive framework and feature database constructed in this study provide a replicable methodological reference for research into cross-regional architectural cultural transmission, while also offering scientific grounds for the conservation and restoration of traditional architecture in Western Anhui. Full article

As an elite indigenous poultry breed under national protection in China, the Xupu goose is renowned for its large body size, superior fatty liver production, premium meat quality, and high tolerance to roughage. To elucidate its genomic architecture, genetic diversity, and evolutionary selection signatures, we conducted whole-genome resequencing on 15 purposively selected, unrelated male Xupu geese. An average of 6.79 Gb of high-quality sequence data was generated per individual, yielding approximately 4.27 million single-nucleotide polymorphisms (SNPs) with a transition/transversion (Ti/Tv) ratio of 2.49. Population genomic analyses revealed that while the population retains a moderate genetic reservoir (H_E = 0.298), it exhibits a distinct heterozygote deficit (H_O = 0.217) and a moderate genomic inbreeding coefficient F_ROH = 0.204). This structural pattern underscores the genetic impact of historical ex situ closed-flock conservation and the consequent formation of cryptic family lineages. Furthermore, genome-wide integrated haplotype score (iHS) scans detected distinct regions under recent positive selection. Functional annotation of these regions highlighted candidate genes tightly associated with the breed’s hallmark traits, specifically lipid metabolism and hepatic fat deposition (ACSS2, ACSS3, PECR), alongside muscle development (CMYA5, MTPN, LEPR). Conclusively, this study delineates a comprehensive genomic landscape of the Xupu goose, providing a robust foundational resource for future germplasm conservation, molecular marker development, and precision breeding programs. Full article

This article presents the results of a study on an axisymmetric gravity-driven slender free-surface flow down a cone by deriving depth-averaged conservation equations on a cone-adapted coordinate system and obtaining a backwater-type differential equation for steady, axisymmetric films with prescribed apex discharge. Analysis of this equation reveals a location-dependent critical condition separating supercritical and subcritical regimes and shows that a classical constant normal depth does not exist; instead, the flow approaches an equilibrium between gravity and resistance forces as it develops downstream. Asymptotic expansions for the flow and critical depths recover previously established results for the laminar leading-order and first-order corrections under consistent velocity shape coefficients, confirming that capillarity affects only first-order terms. The framework predicts a critical length beyond which the flow must be subcritical, Reynolds number decays inversely with the distance, leading to inevitable relaminarization on sufficiently long cones, and the potential need for hydraulic jumps to compatibilize supercritical and subcritical flow regimes, paralleling open-channel hydraulics on mild slopes. Numerical solutions of the backwater equation agree with existing measurements where the slender-film assumptions hold, providing a practical basis to compute flow depth and regime transitions on conical surfaces. Full article

Cognitive stress, also known as mental workload, constitutes a central topic within the field of psychophysiology due to its role in modulating attention, autonomic regulation, and stress reactivity. Furthermore, it bears direct relevance to practical monitoring systems that employ non-invasive sensing techniques. This study investigates whether a multimodal, non-invasive measurement setup can detect systematic physiological differences between Resting periods and short episodes of cognitive load within the same individuals. Additionally, it explores the capacity of such a system to differentiate tasks characterized by varying cognitive demands. A sequential, within-subject protocol was employed, comprising five consecutive phases (rest 1, Stroop, rest 12, subtraction, rest 3), during which five modalities were recorded concurrently: EEG, heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO₂). Beyond phase-wise inspection of time-series data, an exploratory assessment of similarity across participants was conducted using correlation coefficients. The maximum cross-participant correlations observed were 0.88 (HR), 0.90 (GSR), 0.83 (facial temperature), and 0.77 (SpO₂); however, these correlations were used only as exploratory descriptors of inter-individual similarity and did not imply a significant phase effect. For inferential analysis, phase-wise epoch means were evaluated through one-factor repeated-measures ANOVA. The heart rate exhibited a robust main effect of phase (F(4, 32) = 10.5862, p_GG = 0.01044, ηp² = 0.5696), with higher HR observed during cognitive load epochs (e.g., 77.841 ± 11.777 bpm at rest 1 versus 83.926 ± 14.532 bpm during subtraction). The relatively large standard deviation reflects variability between subjects rather than variability within epochs. Regarding processed baseline-referenced GSR, the omnibus phase effect was not statistically significant under the conservative Greenhouse–Geisser correction; therefore, GSR was interpreted as exploratory in this dataset. Facial temperature and SpO₂ likewise did not show statistically significant omnibus phase effects under Greenhouse–Geisser correction (e.g., SpO₂: p_GG = 0.1209). EEG-derived measures provide supplementary central evidence of task engagement; entropy variations within an approximate dynamic range of 0.2 to 0.8 were observed, and the α/θ ratios demonstrated nearly a twofold distinction between rest and cognitive load epochs across different leads. Full article

Offline safe reinforcement learning (RL) aims to learn policies from a fixed dataset while maximizing performance under cumulative cost constraints. In practice, deployment requirements often vary across scenarios, necessitating a single policy capable of zero-shot adaptation to different cost thresholds. However, most existing offline safe RL methods are trained under a pre-specified threshold, yielding policies with limited generalization and deployment flexibility across cost thresholds. Motivated by recent progress in conditional sequence modeling (CSM), which enables flexible goal-conditioned control by specifying target returns, we propose Return–Cost Regularized Constrained Decision Transformer (RCDT), a CSM-based method that supports zero-shot deployment across multiple cost thresholds within a single trained policy. RCDT is the first CSM-based offline safe RL algorithm that integrates a Lagrangian-style cost penalty with an auto-adaptive penalty coefficient. To avoid overly conservative behavior and achieve a more favorable return–cost trade-off, a reward–cost-aware trajectory reweighting mechanism and Q-value regularization are further incorporated. Extensive experiments on the DSRL benchmark demonstrate that RCDT consistently improves return–cost trade-offs over representative baselines. Full article