Search Results (1,230)

Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. Full article

A multiphase model of hydrothermal liquefaction (HTL) using the computational fluid dynamics coupling discrete element method (CFD-DEM) is used to simulate biocrude oil production from sludge flocs in a continuous stirred tank reactor (CSTR). Additionally, the influence of the agitator speed and the slurry flow rate on dynamic biocrude oil production is investigated through full transient CFD analysis in a scaled-up CSTR study. The kinetics of the HTL mechanism as a function of temperature, pressure, and residence time distribution were employed in the model through a user-defined function (UDF). The multiphysics simulation of the HTL process in a stirred tank reactor using the Lagrangian–Eulerian (LE) approach, along with a standard k-ε turbulence model, integrated HTL kinetics. The simulation accounts for particle–fluid interactions by coupling CFD-derived hydrodynamic fields with discrete particle motion, enabling prediction of individual particle trajectories based on drag, buoyancy, and interphase momentum exchange. The three-phase flow using a compressible non-ideal gas model and multiphase interaction as design requirements increased process efficiency in high-pressure and high-temperature model conditions. Full article

The mechanical properties of corn stalks play a crucial role in the design of packing and harvesting equipment. Complete and damaged stalks were used to simulate stalk mixtures during the collection process. This study measured the mechanical characteristics of complete stalks and damaged stalks through experiments. A discrete element method (DEM) model was established which incorporated both the skin and core tissues of the samples. The compression behavior of the stalks was analyzed with the EDEM 2022 software. The results indicate that the complete stalks exhibited both a plastic and second plastic stage, while the damaged stalks fractured immediately upon reaching peak stress. The models of the complete and damaged stalks were validated through a radial compression test. An analysis of the relative errors and particle velocities enabled the quantification of experimental accuracy, ensured the reliability of the experimental data, and revealed the dynamic behavior mechanism of the materials under mechanical loading. The simulation results show that the maximum compression force is 254.11 N and 33.1 N, with a 1.5% and 12.3% relative error compared to the experiment. The particle velocity in the core part is the largest, which is 9.83 × 10⁴ mm/s and 3.51 × 10⁵ mm/s. This study can provide a theoretical reference for researching the mechanical behavior and compressive failure of stalks. Full article

Improving sowing quality is crucial for ensuring wheat emergence and healthy growth. To address issues of poor wheat sowing quality, such as uneven sowing depth and inadequate soil coverage, in the Yangtze River Delta region of China, this study systematically analyzed the effects of the implement’s structural and operational parameters on sowing quality. Based on this analysis, a double-shaft rotary tillage and double-press seeder was designed. Protrusions on the grooving press roller are used to form seed furrows, rotary tiller blades cover the seeds with soil, and the rear press roller compacts the soil. DEM-MBD (discrete element method–multibody dynamics) coupled simulations, combined with single-factor and central composite design (CCD) experiments, were conducted with seeding depth as the evaluation index and four experimental factors: the protrusion height on the press grooving roller, forward speed, seed mass in the seed box, and straw mulching amount. The optimal protrusion height was 29 mm. The effects of rotary tiller blade working depth, rotational speed, and forward speed on soil-covering mass and its coefficient of variation were evaluated through discrete element method (DEM) simulations. The optimal working depth and rotational speed were found to be 55 mm and 350 r·min⁻¹, respectively, based on single-factor and Box–Behnken Design experiments. Field experiments based on optimized parameters showed results consistent with the simulations. The qualified rate of seeding depth decreased as forward speed increased. The optimal forward speed was 4.5 km·h⁻¹, at which the average seeding depth was 25.7 mm, the qualified seeding depth rate was 90%, the soil-covering mass within a 50 cm² area was 143.2 g, and the coefficient of variation was 13.21%, meeting the requirements for wheat sowing operations. Full article

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region. Full article

Exosomes have been shown to play an important role in embryo implantation, but the mechanism is still unclear. This study aimed to investigate the functional roles of lncRNAs in intrauterine exosomes in goat pregnancy. We used RNA-seq to identify the lncRNA profiles of exosomes obtained from goat uterine rinsing fluid at 5, 15, and 18 days of gestation. In addition, we performed weighted gene co-expression network analysis based on differentially expressed mRNAs (DEMs) and lncRNAs (DELs). Functional enrichment analyses of gene modules were conducted using Gene Ontology classification (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. A lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) regulatory network was constructed based on predictive interaction derived from miRTarBase, miRDB and RNAhybrid databases. Altogether, 831 DELs were identified. GO and KEGG analysis showed that the target genes were enriched in processes associated with embryo implantation, such as signaling receptor activity, binding and immune response. Nine functional co-expression modules were enriched in various biological processes, such as metabolic pathways, protein transport, cell cycle and VEGF signaling pathway. Additionally, 12 lncRNA-mediated ceRNA networks were constructed. Our results demonstrate that exosomal lncRNAs in uterine flushing fluid exhibit dynamic changes across gestational stages and play an important role in regulating the uterine microenvironment during embryo implantation. These findings provide a foundational basis for screening exosome-derived lncRNAs that influence embryo implantation and contribute to elucidating the mechanistic roles of lncRNAs in exosome-mediated processes during early pregnancy. Full article

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) describes urban landscapes by representing the watershed relief at any given location. While, in concept, finer DEMs and land use classification (LUC) are yielding better hydrological models, it is suggested that over-accuracy overestimates minor tributaries that might be redundant. Optimal DEM resolution with integrated spectral and feature-based LUC was found to reflect the hydrological network’s significant tributaries. To cope with the karstic urban watershed complexity, ModClark Transform and SCS Curve Number methods were integrated over a GIS-HEC-HMS platform to a nominal urban watershed sub-basin analysis procedure, allowing for detailed urban runoff modeling. This precise urban karstic terrain modeling procedure can predict runoff volume and discharge in urban, mountainous karstic watersheds, and may be used for water-sensitive design or in such cities to control runoff and prevent its negative impacts. Full article

Seepage erosion around sheet pile walls represents a critical failure mechanism in geotechnical engineering, yet the underlying mechanisms governing the onset of erosion remain poorly understood. This study presents a comprehensive multi-scale investigation employing a coupled computational fluid dynamics (CFD)-discrete element method (DEM) to elucidate the onset mechanisms of seepage erosion in gap-graded soils with varying the fines content under different hydraulic gradients. The results demonstrate that increasing the fines content enhances the overall erosion resistance, as evidenced by reduced particle mobilization and eroded mass ratio. Particle tracking analysis reveals that the fines content fundamentally influences the spatial distribution of the erosion. Specimens with low fines content exhibit distributed erosion throughout the domain, while specimens with higher fines content show concentrated erosion around the sheet pile wall and downstream regions. Micromechanical analysis of local contact fabric and contact forces indicates that this spatial heterogeneity stems from the mechanical coordination number and mechanical redundancy, characterized by the reduced magnitudes of these parameters for the region with lower erosion resistance. These findings establish that the fines content governs both global erosion resistance and spatial erosion patterns, providing essential insights for optimizing soil gradation design and advancing fundamental understanding of seepage erosion mechanisms. Full article

Focusing on the Lingshan section of Guangxi’s Pinglu Canal, this study addresses frequent waterlogging during construction under subtropical monsoon rainfall. Human disturbances alter hydrological processes, causing project delays and economic losses. We developed a coupled Storm Water Management Model (SWMM 1D hydrological) and Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D hydrodynamic) model. High-resolution Unmanned Aerial Vehicle—Light Detection and Ranging (UAV-LiDAR) Digital Elevation Model (DEM) delineated sub-catchments, while the Green-Ampt model quantified soil conductivity decay. Synchronized runoff data drove high-resolution HEC-RAS 2D simulations of waterlogging evolution under design storms (1–100-year return periods) and a real event (10 May 2025). Key results: Water depth exhibits nonlinear growth with return period—slow at low intensities but accelerating beyond 50-year events, particularly at temporary road junctions where embankments impede flow. Additionally, intensive intermittent rainfall causes significant ponding at excavation pit-road intersections, and optimized drainage drastically shortens recession time. The study reveals a “rapid runoff generation–restricted convergence–prolonged ponding” mechanism under construction disturbance, validates the model’s capability for complex scenarios, and provides critical data for real-time waterlogging risk prediction and drainage optimization during the canal’s construction. Full article

Debris flows are rapid mass movements of water-laden debris that flow down mountainsides into valley channels and eventually settle on valley floors. The risk of debris flows can be significantly increased after wildfires. Following the destructive 2021 debris flows in Glenwood Canyon, the Colorado Geological Survey (CGS) initiated a LiDAR-Based Alluvial Fan Mapping Project to improve geologic hazard delineation of alluvial and high-angle fans in response to developing wildfire-ready watersheds. These landforms, shaped by episodic sediment-laden flows, pose significant risks and are often misrepresented on conventional geologic maps. CGS delineated fan-shaped landforms with improved precision using 1-m resolution LiDAR-based DEMs, DEM-derived terrain metrics, hydrologic analysis, and geospatial analysis tools within the ArcGIS Pro platform. Our results reveal previously unmapped or misclassified alluvial or high-angle fans in areas undergoing increasing development pressure, where low-gradient terrain indicates a high hazard potential. Through this study, over 3200 alluvial and high-angle fan polygons were delineated across six Colorado counties, encompassing approximately 81 km² of alluvial fans and 54 km² of high-angle fans. High-resolution LiDAR data, geospatial analytical techniques, and systematic QA/QC protocols were used to support refined hazard awareness. The resulting dataset enhances proactive land-use planning and wildfire resilience by identifying areas prone to debris flow and flood hazards. These maps are intended for regional screening and planning purposes and are not intended for site-specific design. These maps also serve as a critical resource for prioritizing geologic evaluations and guiding mitigation planning across Colorado’s wildfire-affected landscapes. Full article

To improve the mechanical stability and service durability of solar road structures, this study systematically investigates the mechanical response characteristics of photovoltaic panels with different geometric shapes—including triangles, rectangles, squares, regular pentagons, and regular hexagons—under consistent boundary and loading conditions using the discrete element method (DEM). All panels have a uniform thickness of 10 cm and equivalent surface areas to ensure shape comparability. Side lengths vary among the shapes: square panels with sides of 0.707 m, 1.0 m, and 1.5 m; triangle 1.155 m; rectangle (aspect ratio 1:2) 0.707 m; pentagon 1.175 m; and hexagon 0.577 m. Results show that panel geometry significantly influences stress distribution and deformation behavior. Although triangular panels exhibit higher ultimate bearing capacity and failure energy, they suffer from severe stress concentration and low stiffness. Regular hexagonal panels, due to their geometric symmetry, enable more uniform stress and displacement distributions, offering better stability and crack resistance. Size effect analysis reveals that larger panels improve load-bearing and energy dissipation capacity but exacerbate edge stress concentration and reduce overall stiffness, leading to more pronounced “thinning” deformation and premature failure. Failure mode analysis further indicates that shape governs crack initiation and path, while size determines crack propagation rate and failure extent—revealing a coupled shape–size mechanical mechanism. Regarding assembly, honeycomb arrangements demonstrate superior mechanical performance due to higher compactness and better load-sharing characteristics. The study ultimately recommends the use of small-sized regular hexagonal units and optimized splicing structures to balance strength, stiffness, and durability. These findings provide theoretical guidance and parameter references for the structural design of solar roads. Full article

The discrete element method (DEM), based on particle dynamics, is used to simulate the technological process of soil tillage using agricultural machinery. A key aspect of the DEM for obtaining accurate agrotechnical and energy indicators of soil cultivation is the formulation of particle contact rules, determined by normal and tangential interactions as well as cohesion forces. This study presents a comprehensive analysis of discrete element method (DEM) contact models used to simulate soil cultivation processes. This study addresses a key issue—the absence of a systematic approach to selecting adequate contact models, which limits the accuracy of predicting soil behavior during interaction with agricultural machinery. A detailed classification of 17 combinations of contact models implemented in the commercial software Rocky DEM was performed, grouped into three categories: normal force models (Linear Spring [LSP], Hysteresis [HLS], Hertzian [HSD]), tangential force models (Coulomb, linear spring limit [linear], Mindlin–Deresiewicz), and cohesive force models (linear cohesion [linear], constant force [constant], Johnson–Kendall–Roberts [JKR]). Experimental validation was conducted by analyzing the angle of repose for various soil types (sandy loam, light loam, medium loam, and heavy clay) with moisture contents ranging from 11 to 31%. This analysis identified the nine most effective combinations of contact models to describe normal, tangential, and cohesive forces (LSP–Coulomb–linear, HLS–linear–linear, HLS–Coulomb–linear, HSD–linear–linear, HSD–linear–JKR, HSD–Coulomb–linear, HSD–Coulomb–JKR, HSD–Mindlin–Deresiewicz–linear, HSD–Mindlin–Deresiewicz–JKR), which showed reliable agreement with experimental angle of repose measurements at approximately 85% accuracy. This study significantly contributes to advancing computer modeling methods in agriculture by providing a scientifically grounded approach for selecting DEM contact models. Full article

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

To address the issue that existing raisin foreign object removal equipment cannot eliminate surface contaminants adhered to raisins through non-washing methods, this paper proposes an adhesive foreign object removal method based on “rapid freezing–rolling extrusion separation-airflow screening”. A raisin adhesive foreign object removal device was designed based on this method. The separation and removal processes of adhesive foreign objects were analyzed and optimized through simulation, followed by device fabrication and performance testing. Starting from the separation process of raisins and adhesive foreign objects, we conducted experimental studies on quick-freezing separation, determined the most suitable separation method based on experimental results, and performed structural design of the equipment accordingly. To conduct simulation analysis and optimization, material parameters were calibrated. The working process of foreign object separation was simulated and optimized using discrete element method (DEM) simulation, verifying the equipment’s separation capability for different adhesive foreign objects while determining the optimal rotational speed of 600 r/min. Through EDEM-Fluent coupled simulation, the working process of foreign object removal was analyzed and optimized, validating the influence of flow field on foreign object removal and determining the optimal air velocity of 11 m/s. The equipment was ultimately fabricated, with further parameter optimization and comprehensive performance testing conducted. The final optimal rotational speed and air velocity were determined as 650 r/min and 11 m/s, respectively. In terms of comprehensive performance, the equipment achieved a separation rate of 93.76%, damage rate of 3.05%, residue rate of 4.28%, removal rate of 94.52%, carry-over ratio of 71:1, and processing capacity of 120 kg/h. Full article

High semen quality is vital for reproductive success in the swine industry; however, seasonal fluctuations often compromise this quality. The molecular mechanism underlying these seasonal effects on semen quality remains largely unclear. This study employed untargeted metabolomic profiling of boar seminal plasma (SP) to identify metabolites and metabolic pathways associated with semen quality during the summer and winter months. Semen samples were collected from mature Duroc boars at a commercial boar stud and classified as Passed or Failed based on motility and morphology. SP from five samples per group was analyzed using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS). In total, 373 metabolites were detected in positive ion mode and 478 in negative ion mode. Several differentially expressed metabolites (DEMs) were identified, including ergothioneine, indole-3-methyl acetate, and avocadyne in the summer, as well as LysoPC, dopamine, and betaine in the winter. These metabolites are associated with key sperm functions, including energy metabolism, antioxidant defense, and capacitation. KEGG pathway analysis indicated enrichment in starch and sucrose metabolism, pyrimidine metabolism, and amino acid metabolism across the seasons. Overall, the results reveal that SP metabolomic profiles vary with the season, thereby influencing semen quality. The identified metabolites may serve as potential biomarkers for assessing semen quality and enhancing reproductive efficiency in swine production. Full article