Search Results (1,552)

To address the issue that the current research on TBM disc cutter rock breaking insufficiently considers actual stratified rock masses, this study constructs numerical models of stratified rock masses with different bedding dip angles and bedding spacings based on the discrete element method (DEM). The whole process of TBM disc cutter rock breaking is numerically simulated through the displacement loading mode. The research results show that the bedding dip angle has a significant impact on the crack propagation mode. When α = 45°, the bedding intersects with the contact point of the disc cutter, and cracks penetrate directly along the bedding without an obvious “crushed zone”, resulting in the minimum number of cracks. The bedding spacing regulates the rock-breaking effect in stages. When d = 45°, the “crushed zone” interacts with two beddings to form three branch cracks, reaching the peak number of cracks and achieving the optimal rock-breaking efficiency. The cracks generated by disc cutter rock breaking exhibit the characteristic of “slow initial growth and rapid later surge” with the increase in time steps, which is highly consistent with the actual mechanical process of rock breaking. This study reveals the influence mechanism of bedding properties on TBM disc cutter rock breaking, verifies the reliability of the DEM combined with PB and SJ models in the simulation of stratified rock mass breaking, and provides theoretical support and data references for the parameter optimization of TBM disc cutters and efficient tunneling under complex stratified geological conditions. Full article

(1) Background: This study aims to systematically identify key candidate genes and the regulatory networks governing ovarian development in sheep breeds with divergent fecundity. Focusing on elucidating the central regulatory roles of these factors during distinct ovarian developmental stages in highly prolific breeds, the research seeks to reveal the mechanism by which multilevel regulatory networks synergistically determine ewe reproductive capacity. (2) Methods: This study utilized the ovaries from the low-fecundity sheep breed Ujumqin sheep, the high-fecundity breed small-tailed Han sheep, and various developmental stages of small-tailed Han sheep as research subjects. Through whole-transcriptome sequencing analysis, differentially expressed mRNAs(DEGs) and non-coding RNAs (ncRNAs) were screened, and a ceRNA regulatory network was constructed and subjected to bioinformatic analysis. The dual-luciferase reporter gene detection system was employed to validate the targeting relationships within the obtained key circRNA-miRNA-mRNA networks. Finally, qRT-PCR was used to verify the accuracy of the sequencing results. (3) Results: Our analysis constructed two distinct ceRNA networks: one from different fecundity groups (116 DECs, 46 DEMs, 82 DEGs) and another from different ovarian stages (186 DECs, 143 DEMs, 338 DEGs). Functional enrichment revealed key reproduction-related pathways, including Mitogen-Activated Protein Kinase(MAPK), Janus Kinase-Signal Transducer and Activator of Transcription(JAK-STAT), and WNT signaling in the fecundity comparison, and MAPK, Ras, WNT, Hippo signaling in the developmental stage comparison. Integrated analysis identified a core circRNA-miRNA-mRNA network, pinpointing circRNA2058-miR-9226-5p-MET as a central regulatory axis. The dual-luciferase assay confirmed that circRNA2058 acts as a sponge for miR-9226-5p, thereby mediating MET expression. qRT-PCR validation of randomly selected RNAs confirmed the sequencing reliability. (4) Conclusions: this study deciphers a synergistic regulatory network and identifies, for the first time, the pivotal circRNA2058-miR-9226-5p-MET ceRNA axis as an potential critical molecular switch driving follicular dominance in sheep. This discovery provides a molecular foundation for targeting core regulators of ovine reproductive efficiency and offers significant insights for innovative strategies in enhancing sheep reproduction. Full article

Coal gangue manufactured sand (CGS), a sustainable substitute for natural sand, offers both resource and environmental benefits; however, the micro-mechanisms underlying performance deterioration at different replacement levels remain unclear. In this study, cube specimens with 25%, 50%, 75%, and 100% CGS were tested in uniaxial compression, and the results were integrated with PFC2D discrete-element simulations and SEM observations to establish an energy–force-chain–crack coupling framework. Experiments and simulations showed close agreement in peak stress, peak strain, and overall curve shape (errors generally <5%). With increasing replacement, the interfacial transition zone (ITZ) evolves from a dense three-phase ITZ (NS–CGS–CA; natural sand–CGS–coarse aggregate) to a degraded two-phase ITZ (CGS–CA), accompanied by more pores and microcracks; the proportion of Adhesive cracks decreases while Cohesive (intra-particle) cracks increase. Concurrently, continuous force-chain networks deteriorate into localized short-chain clusters; the peak and fraction of strain-energy decrease, whereas frictional/damping dissipation rises—together driving a macroscopic transition from ductile to brittle behavior. At 28 d, SEM images and DEM evolution of cracks/force chains/energy exhibit strong consistency, further confirming that low replacement (25% and 50%) favors stable load-transfer paths and suppresses early cracking, whereas high replacement (75% and 100%)—through ITZ degradation and force-chain instability—induces more concentrated cracking and higher energy dissipation, thereby diminishing mechanical performance. Full article

Understanding spatial variability is central to precision agriculture, yet terrain features are often overlooked in remote sensing workflows that inform agronomic decision-making. This work introduces a terrain-aware visual analytics approach that integrates landform classification with crop performance analysis to better support field-level decisions. Terrain features are an important contributor to yield variability, alongside environmental conditions, soil properties, and management practices. However, they are rarely integrated systematically into performance analysis and decision-making workflows—limiting the potential for terrain-aware insights in precision agriculture. Addressing this gap requires approaches that incorporate terrain attributes and landform classifications into agricultural performance analysis and management zone (MZ) delineation—ideally through visual analytics that offer interpretable insights beyond the constraints of purely data-driven methods. We introduce an interactive focus+context visualization tool that integrates multiple data layers—including terrain features, vegetation index–based performance metric, and management zones—into a unified, expressive view. The system leverages freely available remote sensing imagery and terrain data derived from Digital Elevation Models (DEMs) to evaluate crop performance and landform characteristics in support of agronomic analysis. The tool was applied to eleven agricultural fields across the Canadian Prairies under diverse environmental conditions. Fields were segmented into depressions, hilltops, and baseline areas, and crop performance was evaluated across these landform groups using the system’s interactive visualization and analytics. Depressions and hilltops consistently showed lower mean performance and higher variability (measured by coefficient of variation) compared to baseline regions, which covered an average of 82% of each field. We also subdivided baseline areas using slope and the Sediment Transport Index (STI) to investigate soil erosion effects, but field-level patterns were inconsistent and no systematic differences emerged across all sites. Expert evaluation confirmed the tool’s usability and its value for field-level decision support. Overall, the method enhances terrain-aware interpretation of remotely sensed data and contributes meaningfully to refining management zone delineation in precision agriculture. Full article

Sand ingestion exerts significant effects on the performance of helicopter engines, and it is imperative to investigate this phenomenon. In this study, the mechanisms of engine sand ingestion during helicopter hover in ground effect are analyzed. Firstly, a coupled computational model is established based on computational fluid dynamics (CFD) and the discrete element method (DEM). The aerodynamic calculation accuracy of this model is validated by comparing the pressure coefficient and tip vortex with wind tunnel test results. Subsequently, based on this method, a systematic simulation is carried out to investigate the flow field dynamics and sand cloud distribution for the helicopter at different ground-effect heights (GEHs, h). Simulation results indicate that helicopter engines can potentially directly ingest sand particles from the ground at low GEHs. When h > 2R (where R is the rotor radius), the height of sand clouds is insufficient for helicopter engines to ingest sand. Finally, guided by the simulation conclusions, a rotor test bench is designed to conduct research on sand ingestion by helicopter engines. It aims to further study how GEH and engine intake flowrate (Q) affect sand ingestion amount and distribution across the inlet cross-section. Experimental results demonstrate that the sand ingestion amount exhibits a nonlinear decreasing trend with the increasing GEH and a positive correlation with Q. At h = 0.5R, the engine directly ingests sand particles from the ground sand field, leading to a significant increase in sand ingestion. The increase reaches 11 times that at other GEHs. For the right-handed rotor in this study, the sand ingestion of the right engine is significantly higher than that of the left engine. Furthermore, for the cross-sectional position of the engine inlet in this study, over 60% of sand particles are ingested through the upper region. The research can provide scientific guidance for the design of particle separators and is of great significance for helicopter engine sand prevention. Full article

This study investigates the microscale mechanisms underlying the compressibility of biochar-amended soils through combined discrete element method (DEM) simulations and laboratory consolidation tests. A three-dimensional discrete element model was established based on the MatDEM platform, accounting for the particle crushing process of biochar particles and its impact on soil mechanical properties. The biochar agglomerate particles generated in the simulation exhibit irregular morphology, and particles within different size ranges were selected for investigation. According to the model and experimental results, the average relative error is about 7%. Results demonstrate that moderate biochar content effectively reduces soil compressibility by enhancing load transfer through stable force chains formed by biochar particles, which exhibit larger contact areas and higher stiffness compared to native soil particles. However, when the biochar content exceeds approximately 40%, particle crushing intensifies, particularly under high initial void ratios, leading to increased soil compressibility. Furthermore, a larger initial void ratio weakens interparticle confinement, promotes microcrack propagation, and thereby exacerbates compressive deformation. Biochar fragmentation progresses through three stress-dependent stages: initial compaction (<100 kPa), skeletal damage (100–800 kPa), and crushing saturation (>800 kPa). Increased biochar particle size correlates with higher fragmentation rates, refined particle gradation, and reduced coordination numbers, collectively weakening the soil skeleton and promoting deformation. These findings underscore the importance of optimizing biochar content and applying graded loading strategies to balance enhanced soil performance with material integrity. These findings emphasize the necessity of optimizing biochar application rates to balance enhanced soil performance with resource efficiency, providing critical insights for sustainable geotechnical practices. Full article

Conventional ridging and mulching machines struggle to perform effectively in yellow sand substrates due to their loose texture, high collapsibility, and strong fluidity, which compromise ridge stability and operational quality. To address these challenges, this study proposes the development of an integrated rotary tillage, ridging, and film-mulching machine specifically designed to meet the agronomic requirements of tomato cultivation in greenhouse environments with yellow sand substrate. Based on theoretical analysis and parameter calculations, a soil transportation model was established, and the key structural parameters—such as blade arrangement and helical shaft geometry—were determined. A discrete element method (DEM) simulation was employed to construct a contact model for the yellow sand–slag mixed substrate. A combination of single-factor experiments and Box–Behnken response surface methodology was used to investigate the effects of forward speed, shaft rotational speed, and tillage depth on ridge stability and operational performance. The simulation results indicated that a forward speed of 0.82 m·s⁻¹, shaft speed of 260 rpm, and tillage depth of 150 mm yielded the highest ridge stability, with an average of 95.7%. Field trials demonstrated that the ridge top width, base width, height, and spacing were 598.6 mm, 802.3 mm, 202.4 mm, and 1002.8 mm, respectively, with an average ridge stability of 94.3%, differing by only 1.4 percentage points from the simulated results. However, a quantitative traction/energy comparison with conventional equipment was not collected in this study, and we report this as a limitation. The energy consumption is estimated based on power usage and effective field capacity (EFC) under similar operating conditions. Soil firmness reached 152.1 kPa, fully satisfying the agronomic requirements for tomato cultivation. The proposed machine significantly improves operational adaptability and ridge stability in yellow sand substrate conditions, providing robust equipment support for efficient greenhouse farming. Full article

The Digital Elevation Model (DEM) is widely used in fields such as geoscience and environmental management. However, the existing DEMs struggle to meet the current requirements for timeliness and accuracy, especially in forested areas where vegetation cover can lead to overestimation of elevation. To address this issue, this paper proposes a PolInSAR and Spaceborne LiDAR Regression/Classification Network (PSLRC-Net) for refining external DEMs. Additionally, a forest/non-forest classification labeling method for spaceborne LiDAR footprints is introduced to provide labeled data for the classification branch during the training phase. PSLRC-Net adopts a multi-task learning framework and uses an expert selection mechanism based on a gating network to provide targeted support for the regression and classification branches. The regression branch consists of two task towers, and their outputs are weighted and fused by the output of the classification branch. This approach directs the regression branch to focus on the feature differences between forested and non-forested areas, resulting in more accurate elevation predictions. The network was trained on SAOCOM data from two sites, and the fitting results are evaluated for accuracy using an airborne LiDAR-derived DEM. Compared to different DEM datasets, the RMSE decreased by 51.7–64.6% and 51.9–63.7% at the two sites, while the MAE decreased by 55.5–66.8% and 55.5–68.6%. The experimental results confirm the validity of the model and demonstrate the potential of spaceborne LiDAR fusion with spaceborne PolInSAR to improve DEM accuracy. Full article

Coke is an essential raw material in the blast furnace (BF) ironmaking process. Its moisture content significantly impacts BF ironmaking production. This study employs a coupled Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) approach to simulate the drying process of wet coke within a coke silo (CS) dryer. Initially, the model was validated by comparing numerical results with experimental data from the literature. Subsequently, it investigated the gas flow dynamics, heat and mass transfer characteristics, and differences in drying behaviour across distinct dryer zones. Finally, the effects of inlet gas velocity and inlet gas temperature on the drying process were systematically quantified. Simulation results reveal that the bottom of the CS dryer exhibits a low-velocity laminar state, while the middle and upper regions display intense gas flow motion. Consequently, the bottom region exhibits insufficient particle drying in comparison to other zones, with the average particle moisture content decreasing by less than 20%. Under the continuous heat exchange between the hot gas and the particles, the moisture content of the particles decreases rapidly. Based on the drying rate behaviour, the drying process exhibits the following three different stages: the pre-heating period, the constant-rate period, and the falling-rate period. Compared to zones 1 and 3, zone 2 exhibits higher temperatures due to its high heat transfer efficiency, which significantly promotes a reduction in particle moisture content. An increase in inlet gas velocity enhances the particle drying rate and heat flux, accelerates moisture reduction, and raises the temperature. The impact of inlet gas velocity is most pronounced after the constant-rate period, with particle drying uniformity decreasing as the inlet gas velocity increases, consequently leading to a decline in drying quality. Increasing inlet gas temperature significantly increases particle temperature and heat flux throughout the drying period and accelerates the high-rate drying stage. These findings provide fundamental insights for further understanding and studying the coke drying process. Full article

This study reconstructs seven decades (1949–2019) of morphodynamic changes and sediment dynamics in the Diable River (Québec, Canada) using nine series of aerial photographs, a high-resolution LiDAR Digital Elevation Model (2021), and grain-size analysis. The objectives were to document long-term river evolution, quantify erosion and deposition, and evaluate sediment connectivity between eroding sandy bluffs and depositional zones. Planform analysis and sediment budgets derived from DEMs of Difference (DoD) reveal an oscillatory trajectory characterized by alternating phases of sediment export and temporary stabilization, rather than a simple trend of degradation or aggradation. The most dynamic interval (1980–2001) was marked by widespread meander migration and the largest net export (−142.5 m³/km/year), whereas the 2001–2007 interval showed net storage (+70.8 m³/km/year) and short-term geomorphic recovery. More recent floods (2017, 2019; 20–50-year return periods) induced localized but persistent sediment loss, underlining the structuring role of extreme events. Grain-size results indicate partial connectivity: coarse fractions tend to remain in local depositional features, while finer sediments are preferentially exported downstream. These findings emphasize the geomorphic value of temporary sediment sinks (bars, beaches) and highlight the need for adaptive river management strategies that integrate sediment budgets and local knowledge into floodplain governance. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

The large yellow croaker (Larimichthys crocea) is an economically important marine fish in China, whose growth rate in aquaculture has yet to meet the industry’s demands. Understanding the mechanism underlying inter-individual growth differences will create a favorable condition for selective breeding. In combined transcriptome and metabolome analyses, this study collected muscle tissues from four groups of croakers categorized based on sex and growth rate: fast-growing males, slow-growing males, fast-growing females, and slow-growing females. We identified 2344 differentially expressed genes (DEGs) and 198 differentially expressed metabolites (DEMs). Three genes, bpgm, mstnb, and mylpfb, played a crucial role in the growth regulation of large yellow croaker. The pathway enrichment analysis showed that “Aminoacyl-tRNA biosynthesis”, “Alanine, aspartate and glutamate metabolism”, “Inositol phosphate metabolism” and “Retrograde endocannabinoid signaling” pathways were involved in growth regulation. This study provides new clues for future research on the molecular mechanisms of growth regulation in large yellow croaker and builds a theoretical basis for improving the growth quality of this species. Full article

This study explores the detection and classification of aguadas (ponds) in the Bajo El Laberinto region, in the Calakmul Biosphere Reserve, Campeche, Mexico, using remote sensing techniques. Lidar-derived digital elevation models (DEMs), orthophotos and satellite imagery from multiple sources were employed to identify and characterize these water reservoirs, which played a crucial role in ancient Maya water management and continued to be vital for contemporary wildlife. By comparing different visualization techniques and imagery sources, the study demonstrates that while lidar data provides superior topographic detail, satellite imagery—particularly with nominal 3 m, or finer, spatial resolution with a near-infrared band—offers valuable complementary data including present-day hydrological and vegetative characteristics. In this study, 350 aguadas were identified in the broader region. The shapes, canopy cover, and topographic positions of these aguadas were documented, and the anthropogenic origin of most features was emphasized. The paper’s conclusion states that combining various remote sensing datasets enhances the identification and understanding of aguadas, providing insights into ancient Mayan adaptive strategies and contributing to ongoing archaeological and ecological research. Full article

The behavior of a cantilever beam equipped with a particle damper, subjected to impact loads at various locations, was investigated using the discrete element method (DEM). The flexible cantilever steel beam and the particle damper attached to the beam’s tip were modeled with bonded particles through DEM. Computational simulations were conducted to explore the influence of different particle damper porosities and positions along the beam’s length. It was observed that reducing the particle damper’s porosity decreases the beam’s displacement. The impact force was significantly influenced by the porosity, where having lower porosities resulted in higher impact forces. In addition, the time intervals between sub-impacts were also affected by the damper’s porosity, showing a reduction as the porosity of the damper decreases. The unique type of particle damper used in this study contained sand grains as fillers and was capable of pressurizing the sand within its housing. This feature was utilized to investigate the effect of different initial pressures on the beam’s response. It was revealed that an increase in initial pressure reduces the beam’s displacement. Based on the results obtained, the optimal location for the particle damper was determined to be at the point where displacement reduction is required. Full article

The presence of discontinuities in fractured reservoirs, their mechanical and physical characteristics, and fluid flow through them are important factors influencing their effective large-scale properties. In this paper, the variation of elastic moduli in a block measuring 100 × 100 × 100 m³ that hosts a discrete fracture network (DFN) is evaluated using the discrete element method (DEM). Fractures are characterised by (1) constant, (2) interlocked, and (3) mismatched stiffness properties. First, three uniaxial verification tests were performed on a block (1 × 1 × 2 m³) containing a circular finite fracture (diameter = 0.5 m) to validate the developed numerical algorithm that implements the three fracture stiffnesses mentioned above. The validated algorithms were generalised to fractures in a DFN embedded in a 100 × 100 × 100 m³ rock block that reproduces in situ conditions at various depths (4.7 km, 2.3 km, and 0.5 km) of the Soultz-sous-Forêts geothermal site. The effective elastic moduli of this large-scale rock mass were then numerically evaluated through a triaxial loading scenario by comparing to the numerically evaluated stress field using the DFN, with the stress field computed using an effective homogeneous elastic block. Based on the results obtained, we evaluate the influence of fracture interaction and stress perturbation around fractures on the effective elastic moduli and subsequently on the large-scale P-wave velocity. The numerical results differ from the elastic moduli of the rock matrix at higher fracture densities, unlike the other methods. Additionally, the effect of nonlinear fracture stiffness is reduced by increasing the depth or stress level in both the numerical and semi-analytical methods. Full article