Search Results (16,460)

Surface irrigation systems are among the most common yet often inefficient methods due to poor design and management. A key factor in optimizing their design is the accurate prediction of the water advance and infiltration relationships’ coefficients. This study introduces a novel model based on the Beta cumulative distribution function for predicting water advance and estimating infiltration coefficients in surface irrigation systems. Traditional methods, such as the two-point approach, rely on limited data from only the midpoint and endpoint of the field, often resulting in insufficient accuracy and non-physical outcomes under heterogeneous soil conditions. The proposed model enhances predictive flexibility by incorporating the entire advance dataset and integrating the midpoint as a constraint during optimization, thereby improving the accuracy of advance curve estimation and subsequent infiltration coefficient determination. Evaluation using field data from three distinct sites (FS, HF, WP) across 10 irrigation events demonstrated the superiority of the proposed model over the conventional power advance method. The new model achieved average RMSE, MAPE, and R² values of 0.790, 0.109, and 0.997, respectively, for advance estimation. For infiltration prediction, it yielded an average error of 12.9% in total infiltrated volume—outperforming the two-point method—and also showed higher accuracy during the advance phase, with average RMSE, MAPE, and R² values of 0.427, 0.075, and 0.990, respectively. These results confirm that the Beta-based model offers a more robust, precise, and reliable tool for optimizing the design and management of surface irrigation systems. Full article

Leaching mining of ion-adsorption rare earths degrades soil organic matter and hampers vegetation recovery. High-resolution UAV remote sensing enables large-scale monitoring of reclamation, yet vegetation detection accuracy is constrained by key challenges. Conventional three-channel detection struggles with terrain complexity, illumination variation, and shadow effects. Fixed UAV altitude and missing topographic data further cause resolution inconsistencies, posing major challenges for accurate vegetation detection in reclaimed land. To enhance multi-spectral vegetation detection, the model input is expanded from the traditional three channels to six channels, enabling full utilization of multi-spectral information. Furthermore, the Channel Attention and Global Pooling SPPF (CAGP-SPPF) module is introduced for multi-scale feature extraction, integrating global pooling and channel attention to capture multi-channel semantic information. In addition, the C2f_DynamicConv module replaces conventional convolutions in the neck network to strengthen high-dimensional feature transmission and reduce information loss, thereby improving detection accuracy. On the self-constructed reclaimed vegetation dataset, MRV-YOLO outperformed YOLOv8, with mAP@0.5 and mAP@0.5:0.95 increasing by 4.6% and 10.8%, respectively. Compared with RT-DETR, YOLOv3, YOLOv5, YOLOv6, YOLOv7, yolov7-tiny, YOLOv8-AS, YOLOv10, and YOLOv11, mAP@0.5 improved by 6.8%, 9.7%, 5.3%, 6.5%, 6.4%, 8.9%, 4.6%, 2.1%, and 5.4%, respectively. The results demonstrate that multichannel inputs incorporating near-infrared and dual red-edge bands significantly enhance detection accuracy for reclaimed vegetation in rare earth mining areas, providing technical support for ecological restoration monitoring. Full article

Rapid evaluations of farmland soil quality can provide data support for farmland protection and utilization. This study focuses on the soda–saline soil region of Da’an City, Jilin Province, covering an area of 4879 km²; it proposes a framework for evaluating farmland soil quality based on multi-source remote sensing data (Sentinel-2 MSI, GF-5 AHSI hyperspectral and field hyperspectral data). Soil organic matter content, salt content, and pH were selected as indicators of cultivated land soil quality in soda–saline soil areas. A threshold of 20% crop residue cover was set to mask high-cover areas, extracting bare soil information. The spectral indices SI1 and SI2 were utilized to predict the comprehensive grade of soil organic matter + salinity based on the cloud model ($M{E}_c$ = 0.74 and $M{E}_v$ = 0.68). The pH grade was predicted using the red-edge ratio vegetation index (RVIre) ($M{E}_c$ = 0.95 and $M{E}_v$ = 0.98). The short-board method was used to construct a soil quality evaluation system. The results indicate that 13.73% of the cultivated land in Da’an City is of high quality (grade 1), 80.63% is of medium quality (grades 2–3), and 5.65% is of poor quality (grade 4). This study provides a rapid assessment tool for the sustainable management of cultivated land in saline–alkali areas at the county level. Full article

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest in the mechanisms driving these changes and their future evolution. This study integrates the Soil and Water Assessment Tool (SWAT), simulations under future Shared Socioeconomic Pathways (SSPs) and statistical analysis methods, to assess runoff dynamics and lake level responses in the Lake Qinghai Basin over the next 30 years. The model was developed using a combination of meteorological, hydrological, topographic, land use, soil, and socio-economic datasets, and was calibrated with the sequential uncertainty fitting Ver-2 (SUFI-2) algorithm within the SWAT calibration and uncertainty procedure (SWAT–CUP) platform. Sensitivity and uncertainty analyses confirmed robust model performance, with monthly R² values of 0.78 and 0.79. Correlation analysis revealed that runoff variability is more closely associated with precipitation than temperature in the basin. Under SSP 1-2.6, SSP 3-7.0, and SSP 5-8.5 scenarios, projected annual precipitation increases by 14.4%, 18.9%, and 11.1%, respectively, accompanied by temperature rises varying with emissions scenario. Model simulations indicate a significant increase in runoff in the Buha River Basin, peaking around 2047. These findings provide scientific insight into the hydrological response of plateau lakes to future climate change and offer a valuable reference for regional water resource management and ecological conservation strategies. Full article

Annually, about 2.4 million tons of superabsorbent polymers (SAPs) used in disposable diapers are thrown away, polluting our planet. This study aims to explore the potential for reusing SAPs removed from diapers to enhance soil water retention. To this end, the swelling and water retention properties of SAP gels from three different types of diapers were compared to those of an agricultural gel, Aquasorb. Sand was used as a model for soil. When mixed with sand, diaper gels have a swelling degree of ca. 100 g per gram of dried polymer, and a swelling pressure of 12–26 kPa, which are similar to those of Aquasorb gel. Using a synthesized poly(acrylamide-co-sodium acrylate) gel as an example, the correlation between the swelling pressure and the compression modulus of the swollen gel was demonstrated. Soil-hydrological constants were estimated from water retention curves obtained by equilibrium centrifugation of gel/sand mixtures. It was observed that adding 0.3 vol% of diaper gels to sand leads to a 3–4-fold increase in water range available to plants, which is close to that provided by agricultural gel Aquasorb. The water-holding properties were shown to be maintained during several swelling/deswelling cycles in the sand medium. The addition of diaper gels to soil had a significant positive impact on mustard (Brassica juncea L.) seed germination and seedling growth, similar to the agricultural gel Aquasorb. This suggests high potential for the reuse of SAPs from diaper waste to improve soil water retention and water accessibility to plants. This would provide both economic and environmental benefits, conserving energy and raw materials to produce new agricultural gels and limiting the amount of waste. Full article

Cropland abandonment, a global challenge, necessitates comprehensive monitoring to achieve the zero hunger goal. Prior monitoring approaches to cropland abandonment often face constraints in resolution, time series, drivers, prediction, or a combination of these. Here, we proposed an artificial intelligence framework to comprehensively monitor cropland abandonment and tested the framework in Hengyang City, China. Specifically, we first mapped land cover at 30 m resolution from 1985 to 2023 using Landsat, stable sample points, and a machine learning model. Subsequently, we constructed the extent, time, and frequency of cropland abandonment from 1986 to 2022 by analyzing pixel-level land-use trajectories. Finally, we quantified the drivers of cropland abandonment using machine learning models and predicted the spatial distribution of cropland abandonment risk from 2032 to 2062. Our results indicated that the abandonment maps achieved overall accuracies of 0.88 and 0.78 for identifying abandonment locations and timing, respectively. From 1986 to 2022, the proportion of cropland abandonment ranged between 0.15% and 4.06%, with an annual average abandonment rate of 1.32%. Additionally, the duration of abandonment varied from 2 to 38 years, averaging approximately 14 years, indicating widespread cropland abandonment in the study area. Furthermore, 62.99% of the abandoned cropland experienced abandonment once, 27.17% experienced it twice, and only 0.23% experienced it five times or more. Over 50% of cropland abandonment remained unreclaimed or reused. During the study period, tree cover, soil pH, soil total phosphorus, potential crop yield, and the multiresolution index of valley bottom flatness emerged as the five most important environmental covariates, with relative importances of 0.087, 0.074, 0.068, 0.050, and 0.043, respectively. Temporally, cropland abandonment in 1992 was influenced by transportation inaccessibility and low agricultural productivity, soil quality degradation became an additional factor by 2010, and synergistic effects of all three drivers were observed from 2012 to 2022. Notably, most cropland had a low abandonment risk (mean: 0.36), with only 0.37% exceeding 0.7, primarily distributed in transitional zones between cropland and non-cropland. Future risk predictions suggested a gradual decline in both risk values and the spatial extent of cropland abandonment from 2032 to 2062. In summary, we developed a comprehensive framework for monitoring cropland abandonment using artificial intelligence technology, which can be used in national or regional land-use policies, warning systems, and food security planning. Full article

Pile-anchor foundations, serving as one of the anchoring solutions to ensure the safety and stability of floating offshore wind turbines, are primarily subjected to inclined loading induced by anchor chain forces, resulting in significantly different bearing behavior compared to conventional vertically loaded pile foundations. However, experimental research on the inclined pullout performance of anchor piles remains insufficient. To address this gap, this study employs a self-developed servo-controlled loading system to investigate the pullout bearing characteristics of anchor piles in dry and saturated sand, considering factors such as pullout angle and loading point depth. The research results show that from the load–displacement curve of the model pile, it can be found that with the increase in displacement, the load it bears first gradually increases to the peak, then decreases, and then gradually stabilizes. The loading angle has a significant impact on the bearing performance of pile-anchor foundations. As the loading angle increases, the failure mode shows pullout failure. When the loading angle increases from 30° to 60°, the bearing performance of the pile foundation decreases by approximately 63%. When the depth of the loading point increases from 0.22 times the pile length to 0.78 times the pile length, the diagonal anchor tensile bearing capacity of the model pile increases by approximately 45%. When the depth of the loading point is the same, the distribution patterns of bending moment and shear force are basically similar. However, the smaller the loading angle, the larger the value. This is because the horizontal load component plays a dominant role. The compression of the piles above and below the loading point, as well as the bending moment, shear force and axial force under saturated sand conditions, are similar to those in dry sand, but their values are reduced by about 50%. It can be seen that the soil conditions have an influence on the bearing characteristics of pile foundations. Full article

The mechanically activated transition (MAT) from linear viscoelasticity to yielding is considered an essential part of the operational behavior of ductile materials. The MAT region is restricted by proportional limit at σ₀ and ε₀ and the yield point at σ_y and ε_y, or, in terms of this paper, $E_0={\sigma }_0/{\epsilon }_0$ and ε₀ and $E_y={\sigma }_y/{\epsilon }_y$ and ε_y, respectively. This stage precedes yielding and controls the parameters of the yield point. For bulk plastic (co)polymers and cellular polymeric foams, the quantitative correlations between $E_0$, ε₀, $E_y,$ and ε_y were determined. The ratios ${\displaystyle \raisebox{1ex}{$E_0$}\!\left/ \!\raisebox{-1ex}{$E_y$}\right.}=1.55\pm 0.15$ and ${\displaystyle \raisebox{1ex}{${\epsilon }_y$}\!\left/ \!\raisebox{-1ex}{${\epsilon }_0$}\right.}=2.1\pm 0.2$ were specified as yielding criteria. For all the samples studied, their mechanical response within the MAT region was unified in terms of master curve constructed via re-calculation of the experimental “stress–strain” diagrams in the reduced coordinates ${\displaystyle \frac{\lg \mathrm{E}-\lg {\mathrm{E}}_0}{\lg {\mathrm{E}}_0-\lg {\mathrm{E}}_y}}=f\left({\displaystyle \frac{\mathit{lg}\epsilon -\mathit{lg}{\epsilon }_0}{\mathit{lg}{\epsilon }_y-\mathit{lg}{\epsilon }_0}}\right),$ where $E=\sigma /\epsilon$ and ε are the current modulus and strain, respectively. To generalize these regularities found for bulk plastics and foams, our earlier experimental results concerning the rheology of soil-based pastes and data from the literature concerning the computer simulation of plastic deformation were invoked. Master curves for (1) dispersed pastes, (2) bulk plastics, (3) polymeric foams, and (4) various virtual models were shown to be in satisfactory coincidence. For the materials analyzed, this result was considered as the unification of their mechanical response within the MAT region. An algorithm for the express analysis of the mechanical response of plastic systems within the MAT region is proposed. The limitations and advances of the proposed methodological approach based on correlation studies followed by construction of master curves are outlined. Full article

The Shizhuyuan polymetallic deposit in Hunan Province, China, is a world-class ore field rich in tungsten (W), tin (Sn), molybdenum (Mo), and bismuth (Bi), now facing resource depletion due to prolonged exploitation. This study addresses the limitations of traditional geological prediction methods in complex terrain by integrating multi-source datasets—including γ-ray spectrometry, high-precision magnetometry, induced polarization (IP), and soil radon measurements—across 5049 samples. Unsupervised factor analysis was employed to extract five key ore-indicating factors, explaining 82.78% of data variance. Based on these geological features, predictive models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were constructed and compared. SHAP values were employed to quantify the contribution of each geological feature to the prediction outcomes, thereby transforming the machine learning “black-box models” into an interpretable geological decision-making basis. The results demonstrate that machine learning, particularly when integrated with multi-source data, provides a powerful and interpretable approach for deep mineral prospectivity mapping in concealed terrains. Full article

With the continuous improvement of the prefabricated modular technology system, the prefabricated subway station structures are widely used in underground engineering projects. However, prefabricated subway stations in soft soil can suffer significant adverse effects under seismic action. In order to study the seismic performance of a prefabricated subway station, this work is based on an actual project of a subway station in soft soil. And the nonlinear static and dynamic coupling two-dimensional finite element models of cast-in-place structures (CIPs), assembly splicing structures (ASSs), and assembly monolithic structures (AMSs) are established, respectively. The soil-structure interaction is considered, and different peak ground accelerations (PGA) are selected for incremental dynamic analysis. The displacement response, internal force characteristics, and structural damage distribution for three structural forms are compared. The research results show that the inter-story displacement of the AMS is slightly greater than that of the CIP, while the inter-story displacement of the ASS is the largest. The CIP has the highest internal force in the middle column, the ASS has the lowest internal force in the middle column, and the AMS is between the two. The damage to the CIP is concentrated at the bottom of the middle column and sidewall. The AMS compression damage moves upward, but the tensile damage mode is similar to the CIP. The ASS can effectively reduce damage to the middle column and achieve redistribution of internal force. Further analysis shows that the joint splicing interface between cast-in-place and prefabricated components is the key to controlling the overall deformation and seismic performance of the structure. The research results can provide a theoretical basis for the seismic design optimization of subway stations in earthquake-prone areas. Full article

This study presents a vertically integrated Project-Based Learning (PBL) and Community-Based Learning (CBL) framework that connects postgraduate and undergraduate programs in Applied Chemistry and Agricultural Engineering. Postgraduate students synthesized zeolite-based materials for nutrient recovery, which were subsequently applied by undergraduate students in field trials conducted in collaboration with rural farming communities. The project was evaluated using rubrics, surveys, focus groups, and reflective journals. Results demonstrated substantial development of technical, communication, and critical thinking skills, with students highlighting the value of linking theory to practice. Community feedback confirmed the perceived benefits of the material for soil improvement and fertilizer efficiency, while also underscoring the need for sustained engagement. Despite challenges such as curricular coordination and resource constraints, the model effectively fostered interdisciplinary learning and social impact. These findings highlight the contribution of this sequentially instructional design to STEM education by connecting research, teaching, and outreach within a constructivist, sustainability-oriented approach. Full article

Effectively managing agricultural practices is crucial for maximizing yield, reducing investment costs, preserving soil health, ensuring sustainability, and mitigating environmental impact. This study proposes an adaptation of the Grey Wolf Optimizer (GWO) metaheuristic to operate under specific constraints, with the goal of identifying optimal agricultural practices that boost maize crop yields and enhance economic profitability for each farm. To achieve this objective, we employ a probabilistic algorithm that constructs a model based on Clusterwise Linear Regression (CLR) as the primary method for predicting crop yield. This model considers several factors, including climate, soil conditions, and agricultural practices, which can vary depending on the specific location of the crop. We compare the performance of the Grey Wolf Optimizer (GWO) algorithm with other optimization techniques, including Hill Climbing (HC) and Simulated Annealing (SA). This analysis utilizes a dataset of maize crops from the Department of Córdoba in Colombia, where agricultural practices were optimized. The results indicate that the probabilistic algorithm defines a two-group CLR model as the best approach for predicting maize yield, achieving a 5% higher fit compared to other machine learning algorithms. Furthermore, the Grey Wolf Optimizer (GWO) metaheuristic achieved the best optimization performance, recommending agricultural practices that increased farm yield and profitability by 50% relative to the original practices. Overall, these findings demonstrate that the proposed algorithm can recommend optimal practices that are both technically feasible and economically viable for implementation and replication. Full article

Hydrological modeling is critical for assessing water availability and guiding sustainable resource management, particularly in monsoon-dependent, data-scarce basins such as the Chalakudy River Basin (CRB) in Kerala, India. This study integrated the Soil Conservation Service Curve Number (SCS-CN) method within the Google Earth Engine (GEE) platform, making novel use of multi-source, open access datasets (CHIRPS precipitation, MODIS land cover and evapotranspiration, and OpenLand soil data) to estimate spatially distributed long-term runoff (2001–2023). Model calibration against observed runoff showed strong performance (NSE = 0.86, KGE = 0.81, R² = 0.83, RMSE = 29.37 mm and ME = 13.48 mm), validating the approach. Over 75% of annual runoff occurs during the southwest monsoon (June–September), with July alone contributing 220.7 mm. Seasonal assessments highlighted monsoonal excesses and dry-season deficits, while water balance correlated strongly with rainfall (r = 0.93) and runoff (r = 0.94) but negatively with evapotranspiration (r = –0.87). Time-series analysis indicated a slight rise in rainfall, a decline in evapotranspiration, and a marginal improvement in water balance, implying gradual enhancement of regional water availability. Spatial analysis revealed a west–east gradient in precipitation, evapotranspiration, and water balance, producing surpluses in lowlands and deficits in highlands. These findings underscore the potential of cloud-based hydrological modeling to capture spatiotemporal dynamics of hydrological variables and support climate-resilient water management in monsoon-driven and data-scarce river basins. Full article

In the ecologically fragile western Shanxi Loess region, stand density regulation of artificial Robinia pseudoacacia L. forests plays a crucial role in sustaining the water regulation functions of the litter-soil system, yet multi-scale mechanistic analyses remain scarce. To address this gap, we established six stand density classes (ranging from 1200 to 3200 stems/ha) and quantified litter water-holding traits and soil physicochemical properties. We then applied principal component analysis (PCA) and structural equation modeling (SEM) to examine density-litter-soil relationships. Low-density stands (≤2000 stems/ha) exhibited significantly higher litter accumulation (6.08–6.37 t/ha) and greater litter water-holding capacity (maximum 20.58 t/ha) than the high-density stands (p < 0.05). Soil capillary water-holding capacity decreased with increasing density (4702.63–4863.28 t/ha overall), while non-capillary porosity (5.26–6.21%) and soil organic carbon (~12.5 g/kg) were higher in high-density stands (≥2800 stems/ha), reflecting a structural-carbon optimization trade-off. PCA revealed a primary hydrological function axis with low-density stands clustering in the positive quadrant, while high-density stands shifted toward nutrient-conservation traits. SEM confirmed that stand density affected soil capillary water-holding capacity indirectly through litter accumulation (significant indirect path; non-significant direct path), highlighting the central role of litter quantity. When density exceeded ~2400 stems/ha, litter decomposition rate decreased by ~56%, coinciding with capillary porosity falling below ~47%, a threshold linked to impaired balance between water storage and infiltration. These findings identify 1200–1600 stems/ha as the optimal density range; in this range, soil capillary water-holding capacity reached 4788–4863 t/ha, and available phosphorus remained ≥2.1 mg/kg, providing a density-centered, near-natural management paradigm for constructing “water-conservation vegetation” on the Loess Plateau. Full article

In the process of large-scale water conservancy and hydropower station construction in the southwest region of China, obtaining the deep overburden pressuremeter modulus E_m is of great significance for the calculation of foundation bearing capacity and dam foundation settlement. However, due to the complex nature of the soil properties in deep overburden layers, conducting deep-hole pressuremeter tests is challenging, time-consuming, and costly. In order to efficiently and accurately obtain the pressuremeter modulus of deep overburden, this paper takes the deep overburden in the river valley where a large hydropower station dam is located in the southwest region as the research object. It proposes a method based on data-driven prediction of the pressuremeter modulus and combines it with the physical mechanism to carry out the reliability analysis of the prediction results. By constructing a database of soil physical and mechanical parameters, including the pressuremeter modulus, the prediction performance of Random Forest (RF), Support Vector Regression (SVR), and BP Neural Network on the pressure modulus was evaluated. The Particle Swarm Optimization (PSO) was utilized for hyperparameter optimization to enhance the reliability of prediction results. The results indicate that the RF and PSO-RF models exhibit a comprehensive advantage for accurately predicting the pressuremeter modulus. The prediction results of the model for new data have a strong correlation with the results calculated by the Menard formula, which demonstrates the reliability of the model. Therefore, establishing the relationship between the conventional physical and mechanical parameters of deep overburden and the pressuremeter modulus, and predicting the pressuremeter modulus based on data-driven methods, has significant engineering value for obtaining the pressuremeter modulus of deep overburden efficiently, economically, and reliably. It also holds significant importance for the extended application of machine learning in the field of soil parameter prediction. Full article