Search Results (3,541)

The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on a digital elevation model (DEM), this work combines ArcGIS with MATLAB script programs to extract geomorphic indices including slope, the relief degree of the land surface (RDLS), hypsometric integral (HI), and channel steepness index (k_sn) of 593 sub–watersheds and strip terrain profiles around the LJF. By analyzing the spatial distribution characteristics of the geomorphic indices and combining the regional lithology and precipitation conditions, the spatial distribution of the geomorphic indices around the study area was analyzed to reveal the implications of the LJF’s activity. The results of this work indicate that (1) the distribution of geomorphic indices around the LJF may not be controlled by climate and lithological conditions, and the LJF is the dominant factor controlling the geomorphic evolution of the region. (2) The spatial distribution patterns of geomorphic indices and strip terrain profiles reveal that the vertical movement of the LJF resulted in a pronounced uplift on its northwest side, with tectonic activity gradually diminishing from northeast to southwest. Furthermore, based on the spatial distribution characteristics of these geomorphic indices, the activity intensity of the LJF can be categorized into four distinct segments: Jianchuan–Lijiang, Lijiang–Ninglang, Ninglang–Muli, and Muli–Shimian. (3) The activity of the LJF obtained from tectonic geomorphology is consistent with the conclusions obtained in previous geological and geodesic studies. This work provides evidence of the activity and segmentation of the LJF in tectonic geomorphology. The results provide insight for the discussion of tectonic deformation and earthquake disaster mechanisms in the southeastern margin of the Tibet Plateau. Full article

Tunnel Boring Machines (TBMs) are integral to modern underground engineering construction, offering enhanced safety and efficiency. However, TBMs often face challenges in complex geological conditions, such as composite strata, resulting in reduced advancement speed and increased cutter wear. This study investigates the rock-breaking characteristics of TBM disc cutters in composite strata through numerical simulations using the Particle Flow Code (PFC) 5.0 software. Focusing on the Jinan Metro Line 6, the research analyzes cutter forces, rock crack propagation, and the impact of cutter edge shapes on rock-breaking efficiency. The discrete element method (DEM) is employed to simulate microscopic behaviors of rocks, providing insights into crack formation, expansion, and failure. This study’s findings reveal that cutter design and operational parameters can significantly influence cutter lifespan and efficiency. By modifying cutter spacing and penetration depth, enhancing rock-breaking efficiency, and grouting softer layers, TBMs can maintain effective excavation in composite strata. The study establishes a comprehensive understanding of the interplay between TBM cutters and complex geological conditions, offering actionable strategies to enhance TBM performance and mitigate cutter damage. Full article

This research leverages insights from both social cognitive theory (SCT) and stimulus-organism-response theory (S-O-R) to investigate how digital entrepreneurship education (DEE) influences undergraduate students’ digital entrepreneurial alertness (DEA), motivation (DEM), and intentions (DEI). The main objective of the study was to examine whether individual levels of DEA and DEM independently and sequentially mediate the relationship between DEE and DEI. To collect the data, convenience sampling was utilized, involving 221 students from a single public university in Saudi Arabia, and a theoretical model was examined utilizing structural equation modelling (SEM) techniques in SPSS AMOS (Version 27). The results found that DEE had a significant positive impact on students’ DEIs, DEA had a significant positive impact on students’ DEI, and DEM had a significant positive impact on students’ DEI. Moreover, the results of the serial mediation analysis indicated that DEA and DEM served as independent and sequential mediators in the relationship between DEE and DEI. These findings provide further insight into the association between DEE and DEI, offering valuable implications for both entrepreneurship education curriculum developers and government policymakers. This study adds substantial contributions to the existing literature on entrepreneurship education and DEI. Full article

Bands of interdendritic porosity and positive macrosegregation are commonly observed in pressure die castings, with previous studies demonstrating their close relation to dilatant shear bands in granular materials. Despite recent technological developments, the micromechanism governing dilatancy in the high-pressure die casting (HPDC) process for alloys between liquid and solid temperature regions is still not fully understood. To investigate the influence of fluid flow and the size of externally solidified crystals (ESCs) on the evolution of dilatant shear bands in HPDC, various filling velocities were trialled to produce HPDC samples of Al8SiMnMg alloys. This study demonstrates that crystal fragmentation is accompanied by a decrease in dilatational concentration, producing an indistinct shear band. Once crystal fragmentation stagnates, the enhanced deformation rate associated with a further increase in filling velocity (from 2.2 ms⁻¹ to 4.6 ms⁻¹) localizes dilatancy into a highly concentrated shear band. The optimal piston velocity is 3.6 ms⁻¹, under which the average ESC size reaches the minimum, and the average yield stress and overall product of strength and elongation reach the maximum values of 144.6 MPa and 3.664 GPa%, respectively. By adopting the concept of force chain buckling in granular media, the evolution of dilatant shear bands in equiaxed solidifying alloys can be adequately explained based on further verification with DEM-type modeling in OpenFOAM. Three mechanisms for ESC-enhanced dilation are presented, elucidating previous reports relating the presence of ESCs to the subsequent shear band characteristics. By applying the physics of granular materials to equiaxed solidifying alloys, unique opportunities are presented for process optimization and microstructural modeling in HPDC. Full article

The Andes region has an exceptional record of high-altitude settlements integrated within widespread regional chains of mobility and exchange. The Sierra de Aconquija (NW Argentina, south-central Andes) is an effective climatic barrier that has afforded an enduring indigenous approach to land use, mobility, and exchange over millennia. Despite this rich history, the Sierra has been largely considered marginal in pre-Columbian regional cultural developments. Today, the expansion of extractive industries threatens the region’s heritage and the sustainable futures of local communities. Innovative, integrative methodologies are needed for landscape characterisation, heritage assessment, and sustainable policy development. Building on earlier work, we undertook the first comprehensive mapping of archaeological features over 3800 sq. km of the Sierra using interpreter-led assessment of commercial and open-access satellite imagery and DSM data, to verify earlier assumptions and to identify previously unnoticed trends in the aggregation, distribution, and connectivity of archaeological features. The mapping identified 6794 features distributed unevenly but with clear tendencies towards maximising topographic, ecologic, and connectivity advantages expressed consistently across the study area. The outcomes confirm the important role the Sierra had in pre-Hispanic times, highlighting the significance of ancient indigenous practices for the sustainability of vulnerable upland landscapes both in the Andes and worldwide. Full article

The large yellow croaker (Larimichthys crocea) is an important economic fish in China. However, intensive farming practices, such as high stocking densities, suboptimal water quality, and imbalanced nutrition, have led to a decline in muscle quality. Muscle elasticity is a key texture property influencing muscle quality. Herein, transcriptomic and metabolomic analyses were performed on four groups: male high muscle elasticity (MEHM), female high muscle elasticity (MEHF), male low muscle elasticity (MELM), and female low muscle elasticity (MELF), to explore the molecular regulation underlying muscle elasticity in the large yellow croaker. Transcriptomics identified 2594 differentially expressed genes (DEGs) across the four groups, while metabolomics revealed 969 differentially expressed metabolites (DEMs). Association analysis indicated that the valine, leucine, and isoleucine biosynthesis pathways were significantly enriched between the MELF and MEHF groups; 2-Oxoisovalerate and L-Valine were DEMs; and the gene encoding L-threonine ammonia-lyase was a DEG. In the MELM and MEHM groups, pathways such as arginine biosynthesis; arginine and proline metabolism; and valine, leucine, and isoleucine degradation were significantly enriched. 4-guanidinobutanoate, L-aspartate, N-acetylornithine, and L-leucine were among the DEMs, while the DEGs included glul, gls, srm, hmgcs, and aacs. These findings provide insights into the molecular mechanisms controlling muscle elasticity, representing a theoretical foundation to breed high-quality large yellow croakers. Full article

Electromagnetic exposure has become increasingly widespread, and its biological effects have received extensive attention. The purpose of this study was to explore changes in the metabolism profile of the brain and serum and to identify differentially expressed proteins in the brain after exposure to the 4.9 GHz radiofrequency (RF) field. C57BL/6 mice were randomly divided into a Sham group and an RF group, which were sham-exposed and continuously exposed to a 4.9 RF field for 35 d, 1 h/d, at an average power density (PD) of 50 W/m². After exposure, untargeted metabolomics and Tandem Mass Tags (TMT) quantitative proteomics were performed. We found 104 and 153 up- and down-regulated differentially expressed metabolites (DEMs) in the RF_Brain group and RF_Serum group, and the DEMs were significantly enriched in glycerophospholipid metabolism. Moreover, 10 up-regulated and 51 down-regulated differentially expressed proteins (DEPs) were discovered in the RF group. Functional correlation analysis showed that most DEMs and DEPs showed a significant correlation. These results suggested that 4.9 GHz exposure induced disturbance of metabolism in the brain and serum, and caused deregulation of proteins in the brain. Full article

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability. Full article

The sustainable utilization of biomass, particularly troublesome waste biomass, has become one of the pathways to meet the urgent demand for providing energy safety and environmental protection. The variety of biomass hinders the design of energy devices and systems, which must be highly efficient and reliable. Along with the technological developments in this field, broad works have been carried out on the mathematical modeling of the processes to support design and optimization for decreasing the environmental impact of energy systems. This paper aims to provide an extensive review of the various approaches proposed in the field of the mathematical modeling of the thermochemical conversion of biomass. The general focus is on pyrolysis and gasification, which are considered among the most beneficial methods for waste biomass utilization. The thermal and flow issues accompanying fuel conversion, with the basic governing equations and closing relationships, are presented with regard to the micro- (single particle) and macro-scale (multi-particle) problems, including different approaches (Eulerian, Lagrangian, and mixed). The data-driven techniques utilizing artificial neural networks and machine learning, gaining increasing interest as complementary to the traditional models, are also presented. The impact of the complexity of the physicochemical processes and the upscaling problem on the variations in the modeling approaches are discussed. The advantages and limitations of the proposed models are indicated. Potential options for further development in this area are outlined. The study shows that efforts towards obtaining reliable predictions of process characteristics while preserving reasonable computational efficiency result in a variety of modeling methods. These contribute to advancing environmentally conscious energy solutions in line with the global sustainability goals. Full article

To improve the crushing efficiency and crushing pass rate of high-moisture corn ears (HMCEs), a multi-stage crushing scheme is proposed in this paper. A two-stage crushing device for HMCEs is designed, and the ear crushing process is analyzed. Firstly, a simulation model for HMCEs was established in EDEM software (2018), and the accuracy of the model was verified by the shear test. Subsequently, single-factor simulation experiments were conducted, with the crushing rate serving as the evaluation index. The optimal working parameter ranges for the HMCE device were identified as a primary crushing roller speed of 1200–1600 revolutions per minute (r/min), a secondary crushing roller clearance of 1.5–2.5 mm, and a secondary crushing roller speed of 2750–3750 r/min. A Box–Behnken experiment was conducted to establish a multiple regression equation. With the objective of maximizing the qualified crushing pass rate, the optimal combination of parameters was revealed: a primary crushing roller speed of 1500 r/min, a secondary crushing roller clearance of 2.5 mm, and a secondary crushing roller speed of 3280 r/min. The pass rate of corn cob crushing in the simulation test was 98.2%. The physical tests, using the optimized parameter combination, yielded a qualified crushing rate of 97.5%, which deviates by 0.7% from the simulation results, satisfying the requirement of a qualified crushing rate exceeding 95%. The experimental outcomes validate the rationality of the proposed crushing scheme and the accuracy of the model, providing a theoretical foundation for subsequent research endeavors. Full article

Camellia luteoflora is a rare and endangered plant endemic to China. It has high ornamental and potential economic and medicinal value, and is an important germplasm resource of Camellia. To understand the distributions and differences in metabolites from different parts of C. luteoflora, in this study, we used liquid chromatography–tandem mass spectrometry (LC–MS/MS) to examine the types and contents of chemical constituents in five organs of C. luteoflora: roots, stems, leaves, flowers, and fruits. The results showed that a total of 815 metabolites were identified in the five organs and were classified into 18 main categories, including terpenoids (17.1%), amino acids (10.4%), flavonoids (10.3%), sugars and alcohols (9.8%), organic acids (9.0%), lipids (7.1%), polyphenols (4.8%), alkaloids (4.8%), etc. A total of 684 differentially expressed metabolites (DEMs) in five organs were obtained and annotated into 217 KEGG metabolic pathways, among which metabolic pathways, ABC transporters, the biosynthesis of cofactors, and the biosynthesis of amino acids were significantly enriched. In DEMs, flowers are rich in flavonoids, polyphenols, organic acids, and steroids; fruits are rich in amino acids, alkaloids, vitamins, and xanthones; stems are rich in lignans; and leaves have the highest relative content of phenylpropanoids, ketoaldehydic acids, quinones, sugars and alcohols, terpenoids, coumarins, lipids, and others; meanwhile, the metabolite content is lower in roots. Among the dominant DEMs, 58 were in roots, including arachidonic acid, lucidone, isoliquiritigenin, etc.; 75 were in flowers, including mannose, shikimic acid, d-gluconic acid, kaempferol, etc.; 45 were in the fruit, including pterostilbene, l-ascorbic acid, riboflavin, etc.; 27 were in the stems, including salicylic acid, d-(-)-quinic acid, mannitol, (-)-catechin gallate, etc.; there was a maximum number of 119 dominant metabolites in the leaves, including oleanolic acid, l-glucose, d-arabitol, eugenol, etc. In sum, the rich chemical composition of C. luteoflora and the significant differences in the relative contents of metabolites in different organs will provide theoretical references for the study of tea, flower tea, edible oil, nutraceuticals, and the medicinal components of C. luteoflora. Full article

A submersible sewage pump is designed for conveying solid–liquid two-phase media containing sewage, waste, and fiber components, through its small and compact design and its excellent anti-winding and anti-clogging capabilities. In this paper, the computational fluid dynamics–discrete element method (CFD-DEM) coupling model is used to study the influence of different conveying conditions and particle parameters on the wear of the flow components in a submersible sewage pump. At the same time, the energy balance equation is used to explore the influence mechanism of different tip clearance sizes on the internal flow pattern, wear, and energy conversion mechanism of the pump. This study demonstrates that increasing the particle volume fraction decreases the inlet particle velocity and intensifies wear in critical areas. When enlarging the tip clearance thickness from 0.4 mm to 1.0 mm, the leakage vortex formation at the inlet is enhanced, leading to increased wear rates in terms of the blade and volute. Consequently, the total energy loss and turbulent kinetic energy generation increased by 3.57% and 2.25%, respectively, while the local loss coefficient in regard to the impeller channel cross-section increased significantly. The findings in this study offer essential knowledge for enhancing the performance and ensuring the stable operation of pumps under solid–liquid two-phase flow conditions. Full article

Soil erosion and subsequent sedimentation pose significant challenges in the Sikkim Himalayas. In this study, we conducted an assessment of the impact of rainfall-induced soil erosion and sediment loss in South Sikkim, which falls within the Teesta Basin, employing Revised Universal Soil Loss Equation (RUSLE) and Sediment Yield Index (SYI) models. Leveraging mean annual precipitation data, a detailed soil map, geomorphological landforms, Digital Elevation Models (DEMs), and LANDSAT 8 OLI data were used to prepare the factorial maps of South Sikkim. The results of the RUSLE and SYI models revealed annual soil loss >200 t ha⁻¹ yr⁻¹, whereas mean values were estimated to be 93.42 t ha⁻¹ yr⁻¹ and 70.3 t ha⁻¹ yr⁻¹, respectively. Interestingly, both models displayed similar degrees of soil loss in corresponding regions under the various severity classes. Notably, low-severity erosion <50 t ha⁻¹ yr⁻¹ was predominantly observed in the valley sides in low-elevation zones, while areas with severe erosion rates >200 t ha⁻¹ yr⁻¹were concentrated in the upper reaches, characterized by steep slopes. These findings underscore the strong correlation between erosion rates and topography, which makes the region highly vulnerable to erosion. The prioritization of such regions and potential conservation methods need to be adopted to protect such precious natural resources in mountainous regions. Full article

The powder spreading process is a fundamental element within the laser powder bed fusion (PBF-LP) framework given its pivotal role in configuring the powder bed. This configuration significantly influences subsequent processing steps and ultimately determines the quality of the final manufactured part. This research paper presents a comprehensive analysis of the impacts of varying spreading speeds, which are enabled by different roller configurations, on powder distribution in PBF-LP. By utilizing extensive Discrete Element Method (DEM) modelling, we systematically examine how spreading speed affects vital parameters within the spreading process, including packing density, mass fraction, and actual layer thickness. Our exploration of various roller configurations has revealed that increasing spreading speed generally decreases packing density and layer thickness for non-rotating, counter-rotating, and forward-rotating rollers with low clockwise rotational speeds (sub-rolling) due to powder dragging. However, a forward-rotating roller with a high clockwise rotational speed (super-rolling) balances momentum transfer, enhancing packing density and layer thickness while increasing surface roughness. This configuration significantly improves the uniformity and density of the powder bed, providing a technique to accelerate the spreading process while maintaining and not reducing packing density. Furthermore, this configuration offers crucial insights into optimizing additive manufacturing processes by considering the complex relationships between spreading speed, roller configuration, and powder spreading quality. Full article

This study focuses on Central China (CC), including Shanxi, Henan, Anhui, Hubei, Jiangxi, and Hunan provinces. The 2019 average annual precipitation (PRE), average annual temperature (TEM), average annual wind speed (WS), population density (POP), normalized difference vegetation index (NDVI), aerosol optical depth (AOD), gross domestic product (GDP), and elevation (DEM) data were used as explanatory variables to predict the average annual PM_2.5 concentrations (PM_2.5Cons) in CC. The average annual PM_2.5Cons were predicted using different models, including multiple linear regression (MLR), back propagation neural network (BPNN), and random forest (RF) models. The results showed higher prediction accuracy and stability of the RF algorithm (RFA) than those of the other models. Therefore, it was used to analyze the contributions of the explanatory factors to the PM_2.5 concentration (PM_2.5Con) prediction in CC. Subsequently, the spatiotemporal evolution of the PM_2.5Cons from 2010 to 2021 was systematically analyzed. The results indicated that (1) PRE and AOD had the most significant impacts on the PM_2.5Cons. Specifically, the PRE and AOD values exhibited negative and positive correlations with the PM_2.5Cons, respectively. The NDVI and WS were negatively correlated with the PM_2.5Cons; (2) the southern and northern parts of Shanxi and Henan provinces, respectively, experienced the highest PM_2.5Cons in the 2010–2013 period, indicating severe air pollution. However, the PM_2.5Cons in the 2014–2021 period showed spatial decreasing trends, demonstrating the effectiveness of the implemented air pollution control measures in reducing pollution and improving air quality in CC. The findings of this study provide scientific evidence for air pollution control and policy making in CC. To further advance atmospheric sustainability in CC, the study suggested that the government enhance air quality monitoring, manage pollution sources, raise public awareness about environmental protection, and promote green lifestyles. Full article