Search Results (2,077)

With the advancement in deep-sea resource development, the creep behavior of deep-sea remolded sediments under coupled temperature, confining pressure (σ₃), and stress effects has become a critical issue threatening engineering stability. The traditional Singh–Mitchell model, limited by its neglect of temperature effects and prediction of infinite strain, struggles to meet deep-sea environmental requirements. Based on low-temperature, high-pressure triaxial tests (with temperatures ranging from 4 to 40 °C and confining pressures ranging from 100 to 300 kPa), this study proposes a modified model incorporating temperature–stress–time coupling. The model introduces a hyperbolic creep strain rate decay function to achieve strain convergence, establishes a saturated strain–stress exponential relationship, and quantifies the effect of temperature on characteristic time via coupling through the Arrhenius equation. The modified model demonstrates R² values > 0.96 for full-condition creep curves. The results show several key findings: a 10 °C increase in temperature leads to a 30–50% growth in the steady-state creep rate; a 100 kPa increase in confining pressure enhances long-term strength by 20–30%. 20 °C serves as a critical temperature point. At this point, strain amplification reaches 2.1 times that of low-temperature ranges. These experimental findings provide crucial theoretical foundations and technical support for incorporating soil creep effects in deep-sea engineering design. Full article

The Three-North Shelterbelt Forest Program (TNSFP) region in northern China, a critical ecological zone, has experienced significant changes in vegetation coverage and water availability under climate change. However, a comprehensive understanding of how vegetation growth responds to both water deficit and surplus remains limited. This study systematically assessed the spatiotemporal dynamics of vegetation responses to atmospheric water constraints (represented by the Standardized Precipitation Evapotranspiration Index (SPEI)) and soil moisture constraints (represented by the Standardized Soil Moisture Index (SSMI)) across the TNSFP region from 2001 to 2022. Our results revealed a compound water constraint pattern: soil moisture deficit dominated vegetation limitation across 46.41–67.88% of the region, particularly in the middle (28–100 cm) and deep (100–289 cm) layers, while atmospheric water surplus also substantially affected 37.35% of the area. From 2001 to 2022, vegetation has shown weakening correlations with atmospheric and shallow-soil moisture, but strengthening coupling with middle- and deep-soil moisture, indicating a growing dependence on deep water resources. Furthermore, the response times of vegetation to water deficit and water surplus have been reduced, indicating that vegetation growth was increasingly restricted by water deficit while being less constrained by water surplus during the period. Attribution analysis identified that air temperature exerted a stronger influence than precipitation on vegetation–water relationships over the study period. This study improved the understanding of vegetation–water interactions under combined climate and land use change, providing critical scientific support for land use-targeted adaptive management in arid and semi-arid regions. Full article

This study investigated the influence of groundwater flux and temperature on denitrification in a steep coastal agricultural Island in western Japan. Push–pull tests (PPTs) were conducted at depths of 3 m, 15 m, and 30 m, during winter, spring, and summer to assess denitrification under varying hydrogeological and seasonal conditions. The 3 m layer is silty loam, 15 m is granitic weathered soil, and 30 m is granitic weathered rock, each with distinct hydraulic conductivities and fluxes. The objectives were to assess denitrification rates and fluxes, assess depth- and season-related variability, and determine the relative roles of hydraulic flux and temperature on denitrification. Denitrification was higher at shallow (3 m) and deep (30 m) boreholes during low-flux periods, while low at the intermediate depth (15 m) where fluxes were highest. Temperature variation had weak correlations compared to hydraulic flux, which showed a strong inverse correlation with denitrification. These findings demonstrate that residence time, controlled by groundwater flux, is the dominant factor influencing nitrate attenuation in this steep coastal aquifer. The PPTs results indicate that denitrification rates derived from PPTs decrease under higher hydraulic fluxes, as these conditions promote more oxic conditions. The study highlights the potential for natural denitrification to mitigate nitrate contamination during low-flux periods, providing insights for sustainable groundwater management in agricultural island environments. Full article

This study examines the post-war evolution of groundwater quality in Mosul by evaluating the seasonal and annual behavior of physicochemical parameters and heavy metals, while differentiating the responses of shallow and deep aquifers and determining whether groundwater conditions during the early recovery period (2022–2023) indicate natural improvement or continued deterioration. Groundwater samples from shallow (W5–W8) and deep (W1–W4) wells were collected across four sampling campaigns representing both wet and dry seasons. Shallow wells exhibited marked seasonal increases, with pH, electrical conductivity (EC), and total dissolved solids (TDS) increasing during the dry season, driven by evaporation and limited recharge. Nutrient concentrations (PO₄³⁻, NO₃⁻, SO₄²⁻) showed similar seasonal rises but declined slightly in 2023 following reduced rainfall. Heavy metals (Cd, Pb, Cr, Ni, Zn) displayed pronounced seasonal peaks in the wet season and higher annual averages in 2023, suggesting delayed mobilization from contaminated soils. In contrast, deep wells remained relatively stable, reflecting the buffering capacity of deeper geological formations. Statistical analyses supported these patterns: shallow wells demonstrated significant seasonal variability (p < 0.05) across most parameters, whereas deep wells exhibited limited seasonal differences and no significant annual variation. These findings indicate that shallow aquifers—particularly those constructed during the conflict—are more vulnerable to post-war environmental stresses, while deeper aquifers retain greater resilience. Overall, the study underscores progressive degradation of shallow groundwater linked to post-conflict conditions and highlights the need for sustained monitoring, stricter regulation of groundwater use, and targeted remediation strategies to protect drinking and irrigation resources in conflict-affected regions. These insights are crucial for developing sustainable groundwater management strategies in post-war urban environments. Full article

Ground fissures are widespread around the world and are particularly severe in the North China Plain. In order to investigate the crack propagation path and propagation mode of buried ground fissures from deep strata to the surface, physical simulation experiments and numerical simulation experiments were conducted based on the sand–clay interlayer strata in the Longyao area. The results show that during the settlement of the hanging wall strata, the propagation path of the cracks changes due to differences in soil properties. The crack propagation is interrupted in the sand layer and slowed down in the clay layer. The surface displacement is characterized by an alternating sequence of gradual and rapid growth phases. The process of crack propagation from depth to surface is divided into five stages, forming tensile cracks and causing the differential settlement of the surface. The strata are mainly under tensile stress, with the stress range of the hanging wall being 2.1 to 3.0 times that of the footwall. Under identical experimental conditions, buried ground fissures in the strata of sand–clay interlayers exhibit anti-dip crack propagation angles and surface deformation zone widths that are between those of homogeneous silty clay and sand. Based on the experimental results, an analytical formula for the hanging wall deformation zone was further proposed. The research results can provide an important reference and theoretical basis for the investigation and disaster prevention of buried ground fissures in the Longyao area of Hebei Province. Full article

Grassland-based livestock systems across Mexico’s arid and semi-arid belt are increasingly exposed to drought, degrading forage reliability, and soil function. This review synthesizes evidence on native C4 grasses and forage shrubs as complementary building blocks of drought-resilient swards. We searched Web of Science, Scopus, CAB Abstracts and key grey sources (USDA/NRCS Plant Guides, USFS FEIS, Tropical Forages, SNICS) for 1990–2025 studies in English/Spanish. Dominant native grasses (Bouteloua spp., Hilaria belangeri, Digitaria californica, Trichloris crinita, Sporobolus airoides, Panicum hallii) provide high warm-season digestibility and structural cover via C4 physiology, basal/intercalary meristems, and deep/fibrous roots. Forage shrubs (Atriplex canescens, Desmanthus bicornutus, Leucaena leucocephala, Flourensia cernua, Prosopis spp.) bridge the dry-season protein/energy gap and create “resource islands” that enhance infiltration, provided anti-nutritional risks (mimosine/DHP, tannins, salts/oxalates, terpenoids) are managed by dose and diet mixing. We integrate these findings into a Resistance–Recovery–Persistence framework and translate them into operations: (i) site-matching rules for species/layouts, (ii) PLS (pure live seed)-based seed specifications and establishment protocols, (iii) grazing TIDD (timing–intensity–distribution–duration) with a practical monitoring dashboard (CP targets, stubble/cover thresholds, NDVI/SPEI triggers). Remaining bottlenecks are seed quality/availability and uneven extension; policy alignment on PLS procurement and regional seed increase can accelerate adoption. Mixed native grass–shrub systems are a viable, scalable pathway to strengthening drought resilience in Mexican rangelands. Full article

Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models for oilseed flax, this study proposes a CNN–Informer hybrid framework that integrates convolutional neural networks (CNNs) with the Informer architecture to model multi-source spatio-temporal data. Unlike conventional Transformer-based approaches, the proposed framework combines CNN-based local temporal feature extraction with the ProbSparse attention mechanism of Informer, enabling the efficient modeling of long-range temporal dependencies across multiple years while reducing the computational burden of attention-based time-series modeling. The model incorporates multi-source inputs, including remote sensing indices (NDVI, EVI, SAVI, KNDVI), TerraClimate meteorological variables, soil properties, and historical yield records. Comprehensive experiments conducted at the county level in Gansu Province, China, demonstrate that the CNN–Informer model consistently outperforms representative machine learning and deep learning baselines (Transformer, Informer, LSTM, and XGBoost), achieving an average performance of R² = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%. Results from feature ablation and historical yield window analyses reveal that a three-year historical yield window yields optimal performance, with remote sensing features contributing most strongly to predictive accuracy, while meteorological and soil variables enhance spatial adaptability under heterogeneous environmental conditions. Model robustness was further verified through fivefold county-based spatial cross-validation, indicating stable performance and strong generalization capability in unseen regions. Overall, the proposed CNN–Informer framework provides a reliable and interpretable solution for county-level oilseed flax yield prediction and offers practical insights for precision management of specialty crops in arid and semi-arid regions. Full article

Accurately delineating soil elements from satellite imagery is fundamental for regional geological mapping and survey. However, vegetation cover and complex geomorphological conditions often obscure diagnostic surface information, weakening the visibility of key geological features. Additionally, long-term tectonic deformation and weathering processes reshape the spatial organization of soil elements, resulting in substantial within-class variability, inter-class spectral overlap, and fragmented structural patterns—all of which hinder reliable segmentation performance for conventional deep learning approaches. To mitigate these challenges, this study introduces a Reinforced Feature and Multiscale Feature Fusion Network (RFMFFNet) tailored for semantic interpretation of soil elements. The model incorporates a rectangular calibration attention (RCA) module into a ResNet101 backbone to recalibrate feature responses in critical regions, thereby improving scale adaptability and the preservation of fine geological structures. A complementary multiscale feature fusion (MFF) component is further designed by combining sparse self-attention with pyramid pooling, enabling richer context aggregation while reducing computational redundancy. Comprehensive experiments on the Landsat-8 and Sentinel-2 datasets verify the effectiveness of the proposed framework. RFMFFNet consistently achieves superior segmentation performance compared with several mainstream deep learning models. On the Landsat-8 dataset, the oPA and mIoU increase by 2.4% and 2.6%, respectively; on the Sentinel-2 dataset, the corresponding improvements reach 4.3% and 4.1%. Full article

Small-scale farms produce more than one-third of the world’s food supply, making them a crucial contributor to global food security. In this study, an artificial intelligence of things (AIoT) framework is introduced for smart small-scale farm management. For plant disease detection, the lightweight MobileViT model, which integrates vision transformer and convolutional modules, was utilized to efficiently capture both global and local image features. Data augmentation and transfer learning were employed to enhance the model’s overall performance. MobileViT resulted in a test accuracy of 99.5%, with per-class precision, recall, and f1-score ranging between 0.92 and 1.00 considering the benchmark Plant Village dataset (14 species–38 classes). MobileViT was shown to outperform several standard deep convolutional networks, including MobileNet, ResNet and Inception, by 2–12%. Additionally, an LLM-powered interactive chatbot was integrated to provide farmers with instant plant care suggestions. For plant environment management, the powerful, cost-effective ESP32 microcontroller was utilized as the core processing unit responsible for collecting sensor data (e.g., soil moisture), controlling actuators (e.g., water pump for irrigation), and maintaining connectivity with Google Firebase Cloud. Finally, a mobile application was developed to integrate the AI and IoT system capabilities, hence providing users with a reliable platform for smart plant disease detection and environment management. Each system component was each tested individually, before being incorporated into the mobile application and tested in real-world scenarios. The presented AIoT-based solution has the potential to enhance crop productivity within small-scale farms while promoting sustainable farming practices and efficient resource management. Full article

In arid regions, the ecological restoration of limestone tailings requires sustainable strategies, yet the synergistic effects of substrate optimization and native plant selection remain poorly understood. In this study, we systematically evaluated substrate amendments and native species for rehabilitating limestone tailings in Northern China’s arid zone using a controlled pot experiment. An orthogonal L9(3⁴) experimental design was employed to test three factors: the soil-to-tailings ratio (1:2, 1:1, and 2:1), moisture level (30%, 45%, and 60% of field capacity), and nitrogen addition (0, 5, and 10 g N m⁻²). Five native grass species (Pennisetum centrasiaticum, Setaria viridis, Leymus chinensis, Achnatherum splendens, and Eleusine indica) were grown under these treatment conditions, and plant biomass and key soil nutrient variables were measured. Stepwise regression, structural equation modeling, and principal component analysis were applied to assess plant growth responses and soil nutrient dynamics. The results indicated that a 2:1 soil-to-tailings substrate maintained at 60% moisture content maximized biomass production across all species. Soil total potassium consistently correlated positively with biomass (Standardized β: 0.397–0.603), whereas available potassium showed a negative relationship (Standardized β: −0.825–−0.391). Nutrient dynamics ultimately governed biomass accumulation, accounting for 57.8–84.2% of the biomass variation. P. centrasiaticum ranked as the most effective species, followed by S. viridis, L. chinensis, A. splendens, and E. indica. We concluded that successful restoration under these experimental conditions hinged on key factors: using a 2:1 soil-to-tailings substrate, maintaining 60% soil moisture, and strategically combining deep-rooted P. centrasiaticum with shallow-rooted S. viridis to exploit complementary resource use. This work provides fundamental data and a conceptual framework for rehabilitating arid limestone tailings in similar ecological settings, based on controlled experimental evidence. Full article

Satellite precipitation products serve as valuable global data sources for hydrological modeling, yet their applicability across different hydrological models remains insufficiently explored. The distributed physics-informed deep learning model (DPDL), as a representative of emerging differentiable, physics-based hydrological models, requires a systematic evaluation of the suitability of multi-source precipitation products within its modeling framework. This study focuses on the Xiangjiang River Basin in southern China, where both a DPDL model and a Soil and Water Assessment Tool (SWAT) model were constructed. In addition, two model training strategies were designed: S1 (fixed parameters) and S2 (product-specific recalibration). Multiple precipitation products were used to drive both hydrological models, and their streamflow simulation performance was evaluated under different training schemes to analyze the compatibility between precipitation products and hydrological modeling frameworks. The results show that: (1) In the Xiangjiang River Basin of southern China, GSMaP demonstrated the best overall performance with a Critical Success Index of 0.70 and a correlation coefficient (Corr) of 0.79; IMERG-F showed acceptable accuracy with a Corr of 0.75 but had a relatively high false alarm rate (FAR) of 0.32; while CMORPH exhibited the most significant systematic underestimation with a relative bias (RBIAS) of −8.48%. (2) The DPDL model more effectively captured watershed hydrological dynamics, achieving a validation period correlation coefficient of 0.82 and a Nash–Sutcliffe efficiency (NSE) of 0.79, outperforming the SWAT model. However, the DPDL model showed a higher RBIAS of +16.69% during the validation period, along with greater overestimation fluctuations during dry periods, revealing inherent limitations of differentiable hydrological models when training samples are limited. (3) The S2 strategy (product-specific recalibration) improved the streamflow simulation accuracy for most precipitation products, with the maximum increase in the NSE coefficient reaching 15.8%. (4) The hydrological utility of satellite products is jointly determined by model architecture and training strategy. For the DPDL model, IMERG-F demonstrated the best overall robustness, while GSMaP achieved the highest accuracy under the S2 strategy. This study aims to provide theoretical support for optimizing differentiable hydrological modeling and to offer new perspectives for evaluating the hydrological utility of satellite precipitation products. Full article

The Yellow River Floodplain region of Shandong Province is dominated by silty soils that challenge geotechnical construction. Drilling–Mixing–Jetting (DMJ) Deep Cement Mixing Pile groups have been adopted to improve the geotechnical properties of the soil. This study conducted field tests to evaluate column strength and numerically investigated the effects of area replacement ratio (7.10%, 10.66% and 14.21%) and column spacing. It is observed that the DMJ-integrated columns demonstrate enhanced soil–cement strength in the Yellow River Floodplain region, with sample strengths varying between 2 and 8 MPa. The electrical resistivity of soil–cement shows a strong linear correlation (Pearson’s R > 0.75) with unconfined compressive strength. Settlement reduction ratios range between 32.11% and 94.75% and increase with higher area replacement ratio (ARR) and applied stress but decrease with larger column spacing. Bearing capacity improvement factors are found to be increased with ARR, while column spacing has minimal effect, with values between 423.89 kPa and 431.61 kPa. Lateral displacement decreased with column installation and increasing area replacement ratio (ARR), while the effect of column spacing was confined to depths near the column head. Full article

Focusing on the ecotone from the Qinghai–Tibetan Plateau to the Loess Plateau (QPtoLP), this study firstly constructs a retrieval model of soil moisture in various depth layers based on multi-source remote sensing data by using the two-source energy balance (TSEB) model and soil–vegetation–atmosphere transfer (SVAT) model. And then, it uncovers how the soil moisture changes across various depths in the root zone and discusses the lagging effect of rainfall. This research indicated that the correlation between the retrieved soil moisture and field-monitored values in various depth layers ranged from 0.720 to 0.8414, demonstrating that it is suitable for the retrieval of soil moisture at various depths in the study area. During the growing season, soil moisture experienced a slight decrease from mid-May to mid-June, followed by a partial recovery in mid-June. After a dry spell in July, the soil moisture reached its lowest point, but surface and deep soil moisture levels rebounded to above 0.2 and 0.1 cm³/cm³, respectively, by mid-August. Spatially, the soil moisture was higher in the southern region, characterized by dense human activities, and lower in the northern region, which is dominated by alpine grasslands. Comparing different depths, the soil moisture at a 0–5 cm depth was generally the highest most of the time, except in July, when the 35–50 cm depth had the highest value. Additionally, the surface soil moisture at a 0–5 cm depth indicated frequent fluctuations at elevations above 4000 m. As the soil depth increases, the rainfall lag effect becomes more pronounced, and the lag effect in the 35–50 cm soil layer is three days. Full article

Structural deterioration inevitably leads to defects in buildings. It is primarily caused by environmental exposure, material ageing, and long-term service conditions, whereas defects such as poor soil compaction arise from improper construction practices rather than deterioration mechanisms. Major concrete defects include missing portions such as cracking, corrosion, dents, blemishes, and spalling. Failure to identify minor issues can lead to serious problems, which become more expensive and difficult to repair, as well as poorer overall building performance. Traditional structural assessment methods, such as visual inspections and non-destructive testing are typically used for periodic condition evaluation, whereas SHM involves continuous or long-term monitoring using sensor-based systems. However, such approaches can be manual, costly, dangerous, and biased. In order to overcome these limitations, contemporary SHM systems combine traditional approaches with building information modelling (BIM) and artificial intelligence (AI). Different AI algorithms are used, including SVM, random forest, regression, and KNN for machine learning and decision trees; random forest, K-means clustering, CNN, U-Net, ResNet, FCN, VGG16, and DeepLabv3+ for deep learning. This review will survey both the traditional and novel approaches in the field of SHM and the recent advancements. Full article

Abiotic stresses, including salt stress, drought, extreme temperature, heavy metal pollution, and waterlogging, interfere with the normal physiological activities of plants through multiple pathways. These stresses destroy the structure and function of cell membranes, inhibit enzyme activity, cause protein denaturation, and trigger oxidative stress. Such effects not only slow plant biomass accumulation but may also initiate a series of secondary metabolic reactions, increasing the metabolic burden on plants. Abiotic stress poses a serious threat to agricultural production through yield reductions, while exerting profound negative impacts on ecosystem stability, causing many adverse effects. This review focuses on how Trichoderma promotes plant growth and nutrient uptake through multiple mechanisms under abiotic stress conditions. Additionally, it produces abundant secondary metabolites to activate the antioxidant system, thereby enhancing plant tolerance to abiotic stress and their defense capabilities. It can boost soil nutrient availability, enhance agrochemical-contaminated soil, promote crop growth, and improve yield and quality, while reducing the use of chemical pesticides and lessening environmental impacts. Therefore, as a crucial soil microorganism, Trichoderma has great potential in alleviating crop abiotic stress. Through deep research and technological innovation, Trichoderma is expected to become an important tool for sustainable agricultural development. Full article