Two-factor Interactions Research Articles

Identification of Key Determinants Influencing Spatiotemporal Heterogeneity of Urban Resilience

The identification of spatiotemporal heterogeneity, its key determinants, and the interaction effects between the driving factors of urban resilience (UR) within and between subregions is fundamental for understanding its underlying mechanisms. A resilience evaluation model was applied to analyze the temporal and spatial differences in UR in Hunan Province, China. A hot spot analysis identified clusters of cold and hot spots. The contributions of spatiotemporal differences in UR within and between subregions were assessed using the Theil index. A geodetector analysis determined the factors influencing UR and their interactions. There was an increasing trend in UR from 0.2692 in 2014 to 0.3422 in 2022. The number of cities with a high resilience gradually increased from 2014 to 2022, while there was a decreasing gradient in UR from northeast to southwest across the province. High-resilience cities had positive spillover effects on the surrounding area. Hot spots were predominantly located in the northeast, while cold spots were concentrated in the southwest. The barycenter of UR shifted from northeast to southwest by 2018, before moving southeast by 2022. The Theil index values declined over time both within and between subregions. Per capita GDP, the average wages of on-the-job employees, per capita social consumption, and doctor density were found to enhance UR. Two-factor interactions had a greater influence on the spatiotemporal heterogeneity of UR than single factors. Two-factor and nonlinear enhancements were identified as the primary mode of influence.

Diagnostic Accuracy of Toluidine Blue Electrospun in Detecting Oral Potentially Malignant Disorders.

Oral potentially malignant disorders (OPMDs) with epithelial dysplasia can progress to oral squamous cell carcinoma (OSCC). Toluidine blue (TB) has been used in oral screening with various methods. This study aimed to evaluate the sensitivity and specificity of TB-containing electrospun fibers in detecting OPMDs with epithelial dysplasia compared to histopathology. The concentrations of polyvinylpyrrolidone (PVP) and TB were optimized for fiber diameter and puncture strength using the design of experiments method with a central composite design. These optimized fibers were tested on 36 suspected OPMD lesions in 30 patients, followed by a biopsy for histopathological examination. The quadratic and two-factor interaction models described the relationship between TB and PVP concentrations and fiber characteristics. The TB-containing electrospun fibers had 77.3% sensitivity and 35.7% specificity in detecting OPMDs with epithelial dysplasia. The TB-containing electrospun fibers offer advantages such as easy application, cost-effectiveness, and enhanced sensitivity in identifying OPMDs with epithelial dysplasia. However, their diagnostic accuracy is insufficient as a standalone diagnostic method, and they should be considered an adjunctive tool for detecting dysplastic changes in the oral epithelium. Thailand Clinical Trials Registry: TCTR20240429002.

Optimization of serum samples derivatization process by I-Optimal Split-plot design of experiments for GC–MS metabolomics of inflammatory neuropathies

The derivatization process is critical in gas chromatography coupled to mass spectrometry-based untargeted metabolomics. This process aims to increase common non-volatile compounds’ vapor pressure and thermal stability by two sequential steps: methoximation and silylation. Up to now, there are no standardized conditions for performing these reactions. In this study, we simultaneously optimized these procedures using an I-Optimal Split-plot design in serum samples of healthy individuals. The resultant two-factor interaction model performed well with R2 > 0.90 and R2-adjusted > 0.70 in the statistical analysis. The results revealed interactions between methoximation and silylation conditions. The optimized conditions were applied for the GC–MS untargeted metabolomics analysis of serum samples of patients with inflammatory neuropathies and healthy controls. The orthogonal partial least square discriminant analysis with satisfactory cross-validation results (R2 = 0.744, Q2 = 0.557) determined 32 metabolites with VIP score > 1. Short- and long-chain organic acids, fatty acids, and carbohydrates were annotated as potential discriminative molecular markers.

Optimization of Pyrolysis Parameters by Design of Experiment for the Production of Biochar from Sewage Sludge

Sewage sludge management is a key concern in today’s world. Improper disposal can lead to various environmental issues including air, water and soil pollution. Among the available technologies, thermal treatments, particularly pyrolysis, are gaining interest for their ability to reduce sewage sludge volume and to recover materials and energy from it. This study explored the influence of some relevant parameters in the thermal pyrolysis process. The design of experiment, named central composite design, was accounted to optimize temperature, heating rate and residence time in order to maximize the biochar yield and its CO2 adsorption capacity. A two-factor interaction model provided a satisfactory interpretation of the results. Within the studied ranges, maximum values of 47.8 wt% and 0.514 mol CO2/kg were obtained for the yield and CO2 adsorption capacity, respectively. Two significant experiments were repeated in a different pyrolysis system highlighting how other factors (e.g., reactor geometry, granulometry, etc.) can influence the quantity and the quality of produced biochar. The biochar obtained under the best pyrolysis conditions was characterized by a surface area of 124 m2/g and an ash content of 61 wt%. Lastly, the theoretical energy balance showed that the drying process is the main energy-intensive step in the pyrolysis of sewage sludge.

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.

Assessment of grassland carrying capacity drivers and evaluation of pasture-livestock balance: A case study of Xinjiang, China

Evaluating the spatiotemporal variations and driving mechanisms of carrying capacity (CC) is critical for optimizing grassland resource utilization and promoting sustainable development in grassland animal husbandry. This study focuses on Xinjiang, an arid and semi-arid region significantly impacted by environmental changes and human activities. To capture the dynamics of grassland CC, we employed Theil-Sen trend analysis and the Mann-Kendall test to assess spatiotemporal trends. The innovative use of the Geodetector method allowed for a detailed quantification of the influence of various environmental and human-related factors on grassland CC. Additionally, we incorporated livestock data to evaluate the overgrazing conditions of the grassland ecosystem. Our findings demonstrate that: (1) Grassland CC has shown a pronounced overall upward trajectory over the last twenty years, with notable inter-annual fluctuations and significant spatial variations, particularly between the northern and southern regions. (2) The spatial distribution of grassland CC is primarily influenced by precipitation patterns and population density, with key determining factors varying across different types of grasslands. Except for alpine steppe and alpine desert, the spatial distribution of grassland CC was primarily influenced by two-factor interactions, surpassing the impacts of single-factor effects. (3) The overgrazing rate has generally declined, peaking in 2014 and showing the mildest condition in 2017. In comparison, the overgrazing situation in Northern Xinjiang is relatively more favorable, whereas the southern and eastern regions necessitates more immediate and comprehensive ecological restoration and management measures. In summary, this study provides important scientific evidence for prioritizing grassland protection and planning for the sustainable animal husbandry development in arid and semi-arid regions.

Assessing Healthcare Equity in Orthopaedic Surgery: An Analysis of Over 24,000 Surgical Cases.

Health disparities have been widely studied in the primary care and surgical settings. The purpose of this study was to examine surgical access disparities for orthopaedic surgical cases performed at a large academic health center by comparing the relationship between patient demographic factors and surgical wait time. A total of 24,778 orthopaedic surgical cases from 2018 to 2022 at a public, tertiary care, Level I trauma center were retrospectively analyzed to assess for surgical timing disparities based on patient-specific factors, including race, sex, language, and socioeconomic status. Elective surgical cases were completed with an average surgical wait time of 28.11 ± 26.34 days. Urgent surgical cases were completed with an average surgical wait time of 1.23 ± 1.50 days. Patient race, sex, language, and socioeconomic status had no effect on surgical wait time for urgent case scheduling. Female patients had longer average wait times in elective cases, whereas race had a weak association with increased wait time. Two-factor interaction analysis showed no multifactorial effects of patient demographic factors on surgical wait time. Patient race and socioeconomic status were associated with increased distance from surgical sites, although increased distance did not correlate with increased surgical wait time. Patient demographic factors did not demonstrate clinically notable associations with surgical timing in this patient cohort, in contrast to previous studies demonstrating the effects of race and socioeconomic status on healthcare outcomes and access. Race and socioeconomic status did correlate with increased distance from surgical centers although distance from surgical sites did not correlate with surgical wait time. This contributes to previous literature on healthcare equity and indicates that surgical wait time may not contribute to the known healthcare inequalities seen in minority and marginalized patients.

Coupling coordination between digital village construction and agricultural carbon emissions in China.

Exploring the coupling coordination degree (CCD) between digital village construction (DVC) and agricultural carbon emissions (ACE) is crucial for promoting village revitalization and sustainable agricultural development. Analyzing data from 30 provinces in China in 2011-2020, this paper employs the CCD model, the Dagum Gini coefficient, and the geographic detector for in-depth analysis. The results show that the overall level of CCD gradually increases over time, but the national CCD still remains in a state of "low coordination," and there are apparent spatial differences in the CCD among provinces. In addition, the overall difference in CCD shows a decreasing trend, and the contribution of inter-regional differences has gradually become the most critical source of CCD's regional difference. Finally, the spatial differences of CCD are the result of two-factor interaction, among which the innovation level is the most core driving factor. The above findings provide important implications for China and other developing countries to fully leverage the interaction between village digitalization and agricultural decarbonization to promote sustainable agricultural development.

Continuous Remediation of Congo Red Dye Using Polyurethane-Polyaniline Nano-Composite Foam: Experiment and Optimization Study

This study employed an innovative approach, utilizing prepared dried polyurethane-polyaniline nano-composite, through in-situ polymerization, for continuous remediation of Congo red dye. Response Surface Methodology (RSM) based on the Box-Behnken design (BBD) model was utilized to optimize the processing parameters, including initial dye concentration, flow rate, and pH. The two-factor interaction (2FI) model emerged as the most significant, highlighting the influence of individual and interaction effects of the factors. Optimization of the dye remediation process yielded the optimal conditions of a flow rate of 10 mL/min, acidic pH of 5.00, and dye concentration of 20 mg/L, resulting in an impressive, predicted removal efficiency of 99.09% agreeing with the experimental value. Moreover, the maximum adsorption capacity was determined to be 329.68 mg/g. Characterization of the adsorbent material involved techniques such as Scanning electron microscopy (SEM), Fourier transforms infrared spectra (FTIR), X-ray spectroscopy (XRD), and Zeta potential analysis. This material offers a sustainable alternative in industries to treat Congo red dye before being disposed of into the environment.

From efficiency to resilience: unraveling the dynamic coupling of land use economic efficiency and urban ecological resilience in Yellow River Basin

This study investigates the Dynamic Coupling between Land Use Economic Efficiency (LUEE) and Urban Ecological Resilience (UER) in the Yellow River Basin (YRB). This exploration is pivotal for elucidating the interaction mechanisms between economic growth and ecological governance. Furthermore, understanding this relationship is essential for fostering high-quality, sustainable urban development in the YRB. Utilizing panel data from 56 cities spanning 2003 to 2020, this study employed the coupling coordination degree (CCD) model, spatial correlation analysis, Kernel density estimation, convergence model, and Geodetector to systematically analyze the spatio-temporal distribution, dynamic trend, and determinants of the CCD between LUEE and UER in the YRB. The findings indicate that: (1) A general upward trend in both LUEE and UER, accompanied by a steady improvement in their CCD. (2) Significant spatial disparities in their CCD, with higher levels in the lower reaches. (3) Marked positive spatial autocorrelation, predominantly characterized by clusters where high (low) values are surrounded by high (low) values. (4) Regarding the impact of individual factors, government fiscal budget expenditure demonstrates the most robust explanatory power for the CCD within the YRB. Concerning the effects of two-factor interactions, the interplay between industrial structure upgrading and government fiscal budget expenditure emerges as the most significant determinant in influencing the CCD between LUEE and UER. This study enhances our comprehensive understanding of the interplay between economic and ecological systems. It offers scientific insights and strategic direction for harmonizing ecological governance with urban economic growth at both the regional and global scales.

A Mixed-level Factorial Experiment to Elucidate the Impact of Inorganic Fertilizers on Crop Yield

Lack of nutrients in the soil affects soil fertility which can hinder the growth and yield of any crop. Therefore, soil amendment is important to improve soil nutrient either by organic or inorganic technique. In this paper, we considered different inorganic fertilizers to determine the main optimal effect and their interaction on the yield of crop by adoption of mixed-level factorial experiment. Three inorganic fertilizers namely Nitrogen (N), Phosphorous (P) and Potassium (K) were considered each at different levels using tomato crop for investigation. We set up a multiple linear model to match with the design. We estimated the model parameters with the error component and sums of squares. A 2×3×4 design structure for factorial combinations—of factor N at two levels, factor P at three levels, and factor K at four levels using a randomized complete block design with three replications—was developed. Normality of the data was tested and factors visualization for both main and interaction effects were illustrated by the use of graphs. The result revealed that Nitrogen (N) and Potassium (K) concentration are significant while Phosphorous (P) concentration is not, the effect of two-factor interactions for NP and PK are not significant and NK is significant, which signified that the yields do not depend on the levels irrespective of the factor. Based on the results, we concluded that phosphorus is the best inorganic fertilizer compared with Nitrogen and Potassium in terms of soil nutrient for the yield of crops.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Spatiotemporal evolution and driving factors of vegetation net ecosystem productivity in Henan Province over the past 20 years

Net ecosystem productivity (NEP) is a crucial parameter for assessing the carbon cycle dynamics in terrestrial ecosystems. This study analyzed the spatial and temporal evolution characteristics and future trends of NEP in Henan Province over the past 20 years based on MOD17A3HGF, meteorological, and land-use data, employing the frequency counting method, trend test, Hurst index, and the center of gravity model. Various areas of changes in vegetation carbon sequestration were explored, and the driving factors were quantitatively assessed through correlation analysis, Sankey diagrams, and Geodetector. The results demonstrate that: 1) Continuous temporal changes in NEP in Henan, with annual average values fluctuating between 272.84 and 451.39 gC·m-2·a1, exhibiting an overall upward trend. 2) Spatially, there is a distinct distribution of NEP, concentrating more in the south and less in the north. While the study area generally experiences a dominant gradual enhancement of vegetation carbon sequestration capacity, the middle and north of Zhengzhou City exhibit a significant decline, which is expected to persist in the future. The migration of the centers of gravity of NEP over the past 20 years is characterized by stage-specific differentiation. 3) Among the various land cover types, forests have the strongest carbon sequestration capacity; however, cropland emerges as the province’s main source of NEP due to its extensive size. 4) The driving factors for spatial differentiation in NEP exhibit some temporal variability. Overall, climate factors and atmospheric pollution exert stronger influences, with the interactive explanatory power of the two-factor interaction being higher than that of the single factor. The results of this study can serve as a scientific theoretical basis for ecological policy-making and sustainable development in Henan Province.

Optimization and Modeling of a Dual-Chamber Microbial Fuel Cell (DCMFC) for Industrial Wastewater Treatment: A Box–Behnken Design Approach

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

Cloud point and oxidation stability analysis of turpentine oil and ethanol in b40 biodiesel blend using response surface methodology

Abstract Given the availability and the potential of bioresources as blending components of diesel blends, the synergy of turpentine oil and ethanol in the B40 biodiesel blend is presented in this work. Response surface methodology (RSM) was employed to study the influence of both bioresources on the important characteristics of B40 biodiesel blends, i.e., cloud point and oxidation stability. Based on the result, the relationship between turpentine oil and ethanol on cloud point was best expressed with a two-factor interaction (2FI) model. Meanwhile, the quadratic model was more suitable for oxidation stability The most ideal cloud point was found at 10.6 °C in 8% v/v and 0.5% v/v of turpentine oil and ethanol concentration, respectively and the most ideal oxidation induction time was found at 250 minutes in 5% v/v and 0.7% v/v of turpentine oil and ethanol, respectively. It is believed that the rigid structure of turpentine oil is responsible for preventing crystal formation and the ethanol-biodiesel blend attraction also enhances their liquid-phase stability. Furthermore, the oxidation stability of B40 biodiesel blends continues to be maintained at sufficient levels.

Strong orthogonal Latin hypercubes for computer experiments

Orthogonal Latin hypercubes are widely used for computer experiments. They achieve both orthogonality and the maximum one-dimensional stratification property. When two-factor (and higher-order) interactions are active, two- and three-dimensional stratifications are also important. Unfortunately, little is known about orthogonal Latin hypercubes with good two (and higher)–dimensional stratification properties. A method is proposed for constructing a new class of orthogonal Latin hypercubes whose columns can be partitioned into groups, such that the columns from different groups maintain two- and three-dimensional stratification properties. The proposed designs perform well under almost all popular criteria (e.g., the orthogonality, stratification, and maximin distance criterion). They are the most ideal designs for computer experiments. The construction method can be straightforward to implement, and the relevant theoretical supports are well established. The proposed strong orthogonal Latin hypercubes are tabulated for practical needs.

OPTIMIZATION OF α-AMYLASE SYNTHESIS BY BACILLUS VELEZENSIS USING TAGUCHI EXPERIMENTAL DESIGN

This research employs the Taguchi experimental design to optimize the production of α-amylase, a versatile enzyme with diverse industrial applications, using Bacillus velezensis. Thirteen key parameters, including pH, temperature, agitation, inoculum size, aeration and various carbon and nitrogen sources, were systematically investigated using the Taguchi L27 (313) orthogonal array. Initial screening of six seed media formulations identified SM6 (0.5% peptone, 0.5% yeast extract, 0.1% K2 HPO4 ) as the optimal medium, producing 2.8 U mL-1 of α-amylase. Further optimization under the Taguchi framework yielded a maximum α-amylase activity of 1097.31 U mL-1 and total protein of 1230 mg mL-1 at pH 5, 34°C, 4% moong husk as carbon source, and 2% soybean cake as nitrogen source. Two-factor interaction analysis revealed that a temperature of 34°C with 2% soybean cake, and 4% moong husk with 2% soybean cake, provided the best results. Validation experiments confirmed a 31.2% enhancement in α-amylase production under the optimized conditions. This study highlights the efficacy of the Taguchi design in systematically optimizing complex bioprocesses, paving the way for improved α-amylase synthesis with B. velezensis.

Construction of 2fi-optimal row–column designs

Row–column designs that provide unconfounded estimation of all main effects and the maximum number of two-factor interactions (2fi’s) are called 2fi-optimal. This issue has been paid great attention recently for its wide application in industrial or physical experiments. The constructions of 2fi-optimal two-level and three-level full factorial and fractional factorial row–column designs have been proposed. However, the results for higher prime levels have not been achieved yet. In this paper, we give theoretical constructions of 2fi-optimal sn full factorial row–column designs for any odd prime level s and any parameter combination, and theoretical constructions of 2fi-optimal sn−1 fractional factorial row–column designs for any prime level s and any parameter combination.

The Collapse Mechanism of Slope Rill Sidewall under Composite Erosion of Freeze-Thaw Cycles and Water

The composite erosion of freeze-thaw and water flow on slope rills is characterized by periodicity and spatial superposition. When revealing the collapse mechanism of slope rill sidewalls under the composite erosion of freeze-thaw and water flow, it is necessary to fully consider the effect of water migration and its impact on the stability of the rill sidewall. In this paper, we placed the self-developed collapse test system in an environmental chamber to carry out model tests on rill sidewall collapse on slopes under the composite erosion of freeze-thaw and water flow. We utilized three-dimensional reconstruction technology and the fixed grid coordinate method to reproduce the collapse process of the rill sidewall and precisely locate the top crack. We obtained the relationship between the water content of the specimen and mechanical indexes through the straight shear test. The main conclusions are as follows: The soil structure of the rill sidewall is significantly affected by the freeze-thaw cycle, which benefits capillary action in the soil. One freeze-thaw cycle has the most serious effect on the soil structure of the rill sidewall, and the change in the moisture field is more intense after the soil temperature drops below zero. The friction angle of the soil increases with the number of freeze-thaw cycles and tends to stabilize gradually. The effect of the freeze-thaw cycle on the rate of change of the water content of the soil at each position of the wall can be accurately described by a logarithmic function. The expression of the two-factor interaction effect on the rate of change of water content of soil at each position of the rill sidewall can be accurately fitted. We propose a calculation system for locating cracks at the top of the rill sidewall and determining the critical state of instability and collapse of the rill sidewall during the process of freeze-thaw and water flow composite erosion. The results of this research can help improve the accuracy of combined freeze-thaw and water flow erosion test equipment and the development of a prediction model for the collapse of the rill sidewall under compound erosion. This is of great significance for soil and water conservation and sustainability.

Analysis of spatial and temporal changes in vegetation cover and its drivers in the Aksu River Basin, China

Exploring vegetation dynamics in arid areas and their responses to different natural and anthropogenic factors is critical for understanding ecosystems. Based on the monthly MOD13Q1 (250 m) remote sensing data from 2000 to 2019, this study analyzed spatio-temporal changes in vegetation cover in the Aksu River Basin and predicted future change trends using one-dimensional linear regression, the Mann–Kendall test, and the Hurst index. Quantitative assessment of the magnitude of anthropogenic and natural drivers was performed using the Geodetector model. Eleven natural and anthropogenic factors were quantified and analyzed within five time periods. The influence of the driving factors on the changes in the normalized difference vegetation index (NDVI) in each period was calculated and analyzed. Four main results were found. (1) The overall vegetation cover in the region significantly grew from 2000 to 2019. The vegetation cover changes were dominated by expected future improvements, with a Hurst index average of 0.45. (2) Land use type, soil moisture, surface temperature, and potential vapor dispersion were the main drivers of NDVI changes, with annual average q-values above 0.2. (3) The driving effect of two-factor interactions was significantly greater than that of single factors, especially land use type interacts with other factors to a greater extent on vegetation cover. (4) The magnitude of the interaction between soil moisture and potential vapor dispersion and the magnitude of the interaction between anthropogenic factors and other factors showed an obvious increasing trend. Current soil moisture and human activities had a positive influence on the growth of vegetation in the area. The findings of this study are important for ecological monitoring and security as well as land desertification control.