Search Results (2,315)

As a critical ecological barrier in the arid and semi-arid regions of northwestern China, the spatio-temporal evolution of vegetation carbon sequestration in the Hexi Corridor is of great significance to the ecological security of this region. Based on multi-source remote sensing and meteorological data, this study integrated second-order partial correlation analysis, ridge regression, and other methods to reveal the spatio-temporal evolution patterns of Gross Primary Productivity (GPP) in the Hexi Corridor from 2003 to 2022, as well as the response characteristics of GPP to air temperature, precipitation, and Vapor Pressure Deficit (VPD). From 2003 to 2022, GPP in the Hexi Corridor showed an overall increasing trend, the spatial distribution of GPP showed a pattern of being higher in the east and lower in the west. In the central oasis region, intensive irrigation agriculture supported consistently high GPP values with sustained growth. Elevated air temperatures extended the growing season, further promoting GPP growth. Due to irrigation and sufficient soil moisture, the contributions of precipitation and VPD were relatively low. In contrast, desert and high-altitude permafrost areas, constrained by water and heat limitations, exhibited consistently low GPP values, which further declined due to climate fluctuations. In desert regions, high air temperatures intensified evaporation, suppressing GPP, while precipitation and VPD played more significant roles. This study provides a detailed analysis of the spatio-temporal change patterns of GPP in the Hexi Corridor and its response to climatic factors. In the future, the Hexi Corridor needs to adopt dual approaches of natural restoration and precise regulation, coordinate ecological security, food security, and economic development, and provide a scientific paradigm for carbon neutrality and ecological barrier construction in arid areas of Northwest China. Full article

Global ecosystems have undergone significant degradation and deterioration, making the identification of ecosystem changes essential for promoting sustainable development and enhancing quality of life. Hami City, a representative region characterized by the complex “desert–oasis–mountain” ecosystem in Xinjiang, China, provides a critical context for examining ecosystem changes in extremely arid environments. This study utilizes remote sensing data alongside the Revised Wind Erosion Equation and Revised Universal Soil Loss Equation models to analyze the transformations within the desert–oasis ecosystems of Hami City and their driving forces. The findings reveal that (1) over the past 24 years, there have been substantial alterations in the ecosystem patterns of Hami City, primarily marked by an expansion of cropland and grassland ecosystems and a reduction in desert ecosystems. (2) Between 2000 and 2023, there has been an upward trend in Fractional Vegetation Cover, Net Primary Productivity, and windbreak and sand fixation amount in Hami City, whereas soil retention has shown a declining trend. (3) The overall ecosystem change in Hami City is moderate, encompassing 61.85% of the area, with regions exhibiting positive change comprising 16.79% and those with negative change comprising 21.33%. (4) Temperature, precipitation, and evapotranspiration are the primary drivers of ecosystem change in Hami City. Although the overall changes in ecosystems in Hami City have shown an improving trend, significant spatial heterogeneity still exists. The natural climatic conditions of Hami City constrain the potential for further ecological improvement. This study enhances the understanding of ecosystem change processes in extremely arid regions and demonstrates that strategies for mitigating or adapting to climate change need to be implemented as soon as possible to ensure the sustainable development of ecosystems in arid areas. Full article

Background: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer satisfaction across the urban mobility network. Methods: Beyond conventional applications, the research integrates advanced analytical and process engineering tools, including capability indices, measurement system analysis (MSA), variance decomposition, and root-cause prioritization through Pareto–ANOVA integration, supported by a structured control plan aligned with ISO 9001:2015 and ISO 31000:2018 risk-management standards. Results: Quantitative diagnosis revealed severe process instability and nonconformities in information flow, workload balancing, and suboptimal resource allocation that constrained effective capacity utilization. Corrective interventions were modeled and validated through statistical control and real-time performance dashboards to institutionalize improvements and sustain process stability. The implemented actions led to a 37.5% reduction in cycle time, an 80% decrease in process errors, a 38.5% increase in customer satisfaction, and a 38.9% improvement in throughput. Conclusions: This study contributes theoretically by positioning Lean Six Sigma as a data-centric governance framework for stochastic capacity optimization and process redesign in public service systems, and practically by providing a replicable, evidence-based roadmap for operational excellence in governmental organizations within developing economies. Full article

The increasing frequency of global extreme climate events has heightened the need to understand the mechanisms through which desert ecosystems respond to altered precipitation patterns. This includes elucidating how arbuscular mycorrhizal fungi (AMF) drive these responses by regulating key soil processes and shaping microbial community dynamics. We therefore conducted an in situ experiment involving increased precipitation and AMF suppression, and phospholipid fatty acid (PLFA) was employed to reveal the changes in soil microbial community. Results showed that increased precipitation significantly promoted the growth of soil AMF and Actinobacteria (Act). Increased precipitation significantly changed soil microbial community structure and promoted soil microbial community diversity, but it posed neutral effects on soil microbial community biomass. AMF suppression clearly inhibited AM fungal growth but increased the growth of Act and Gram-positive bacteria (G⁺) and posed limited effects on Gram-negative bacteria (G⁻), leading to an increased G⁺/G⁻ ratio. Notably, AMF suppression posed slight effects on the biomass, diversity, and structure of soil microbial community. Random forest analysis revealed that soil ammonium nitrogen (NH₄⁺-N), microbial biomass nitrogen (MBN), and soil organic carbon (SOC) were the main factors influencing different soil microbes, and soil Act and G⁺ were the main factors influencing plant community diversity, but AMF were the primary factor influencing plant community biomass. More importantly, structural equation modeling (SEM) results further confirmed that increased precipitation and AMF significantly altered plant community diversity by influencing soil AM fungi and increased plant community biomass by promoting soil AM fungal growth. In conclusion, our results demonstrate that increased precipitation enhances plant community productivity and diversity in desert ecosystems primarily by stimulating the growth of arbuscular mycorrhizal fungi, which function as a key biological pathway mediating the ecosystem’s response to climate change. Full article

In the arid cultivation region of Xinjiang, China, shrinkage disease severely compromises the quality, yield, and market value of jujube. Published research has achieved high accuracy in detecting larger lesions using RGB imaging and hyperspectral imaging (HSI). However, these methods lack sensitivity in detecting early and subtle symptoms of disease. In this study, a multi-source data fusion strategy combining RGB imaging and HSI was proposed for non-destructive and high-precision detection of early-stage jujube shrinkage disease. Firstly, a total of 317 fruits of the ‘Junzao’ cultivar were collected during multiple stages of natural infection, covering early-stage shrinkage disease detection across different growth stages, including both green and mature red fruits. Secondly, morphological features were extracted from RGB images in multiple dimensions, while a three-stage feature selection strategy combining Principal Component Analysis (PCA), the Successive Projections Algorithm (SPA), and the Genetic Algorithm (GA) was implemented to identify four key wavelengths from HSI. Thirdly, a hybrid convolutional neural network-multilayer perceptron (CNN-MLP) architecture was constructed, with dynamic feature weighting employed to achieve effective multimodal fusion and optimize detection performance. Experimental results demonstrated that compared to the MLP and CNN models, the proposed method achieved approximately 8.0% and 5.4% improvements in accuracy and 38.6% and 32.4% improvements in F1 scores, respectively. It offers a robust and scalable solution for early disease detection and postharvest quality assessment in jujube production. Full article

An early and precise diagnosis is essential for successful intervention in Alzheimer’s disease (AD), a progressive neurological illness. In this study, we present a deep learning-based framework for multiclass classification of AD severity levels using MRI neuroimaging data. The framework integrates multiple convolutional and transformer-based architectures with a novel hybrid hyperparameter optimization strategy; Snake+EVO surpasses conventional optimizers like Genetic Algorithms and Particle Swarm Optimization by skillfully striking a balance between exploration and exploitation. A private clinical dataset yielded a classification accuracy of 99.81%for the optimized CNN model, while maintaining competitive performance on benchmark datasets such as OASIS and the Alzheimer’s Disease Multiclass Dataset. Ensemble learning further enhanced robustness by leveraging complementary model strengths, and Grad-CAM visualizations provided interpretable heatmaps highlighting clinically relevant brain regions. These findings confirm that hybrid optimization combined with ensemble learning substantially improves diagnostic accuracy, efficiency, and interpretability, establishing the proposed framework as a promising AI-assisted tool for AD staging. Future work will extend this approach to multimodal neuroimaging and longitudinal modeling to better capture disease progression and support clinical translation. Full article

Exploring the differentiated fallow compensation (FC) standards in different regions is of great significance for formulating and improving the mechanism of fallow compensation and ensuring the sustainability of policies. The groundwater overexploitation area in the Tarim River Basin was selected as the research area; this study breaks through the perspective of a single subject and integrates the “opportunity cost” of the compensated subject and the “ecosystem service value” of the compensating subject into a unified analysis framework to obtain the fallow compensation standard, and the logistic model is used to analyze the influencing factors of farmers’ compensation method selection. The results are as follows: (1) The FC standards exhibit significant spatial heterogeneity. The range of FC standards in various counties is 5540.40 to 7770.53 CNY/hm² (769.50 to 1079.24 USD/hm²), which is generally lower than the current standard. (2) There are three main compensation methods chosen by farmers, ranked in descending order of selection ratio: monetary compensation (72.06%) > physical compensation (19.37%) > technical compensation (8.57%). (3) The factors influencing the choice of compensation method are quite complex. The dependency ratio is the main influencing factor in the choice of monetary compensation (β = 0.738); the evaluation of economic conditions has a significant negative correlation with the choice of physical compensation (β = −0.562), and nonfarm household income is the main influencing factor for choosing technical compensation (β = 0.747). This study provides a new perspective for determining FC standards and aims to provide a theoretical basis for local governments to improve their fallow policies. Full article

The Xinjiang Uygur Autonomous Region, situated in northwest China, boasts a unique geographical position and a consequent variety of environmental characteristics. T. truncatus is prevalent throughout this region as the primary pest affecting various crops. In this study, we analyzed the microbial community structures of endosymbiotic bacteria in T. truncatus collected from 17 regions and three host plants in Xinjiang using 16S rRNA sequencing. Through composition analysis of the endosymbiotic bacteria in T. truncatus from Xinjiang, it was found that the dominant bacterial phyla were Pseudomonadota and Bacillota. At the genus level, in addition to Wolbachia, Cardinium, and Spiroplasma (common symbiotic bacteria in T. truncatus), the infection rate of Rickettsia in T. truncatus in Xinjiang was found to be 92.8%. The diversity of the endosymbiotic bacteria community in T. truncatus is shaped by both host plant species and geographical region. Specifically, the endosymbiotic bacterial diversity in T. truncatus populations on corn was significantly higher than that observed in populations on cotton and soybean (p < 0.05). Furthermore, we discovered the diversity of endosymbiotic bacteria in T. truncatus was significantly higher in southern Xinjiang than in northern Xinjiang (p < 0.05). Full article

Kernel row number (KRN) is a pivotal determinant for yield in maize breeding programs. However, the genetic basis underlying KRN remains largely elusive. To identify candidate genes regulating KRN, a population of 318 BC₄F₄ chromosomal segment substitution lines (CSSLs) was developed via backcrossing, with Baimaya (BMY) as the donor parent and B73 as the recurrent parent. Furthermore, a high-density genetic linkage map containing 2859 high-quality single-nucleotide polymorphism (SNP) markers was constructed for quantitative trait locus (QTL) mapping of KRN. Notably, 19 QTLs controlling KRN were detected across three environments and in the Best Linear Unbiased Prediction (BLUP) values; among these, a major-effect QTL (qKRN4.09-1) was consistently identified across all three environments and BLUP. Then, the integration of linkage mapping and transcriptome analysis of 5 mm immature ears from near-isogenic lines (NILs) uncovered a candidate gene, Zm00001eb205550. This gene exhibited significant downregulation in qKRN4.09-1^BMY, and two missense variants were detected between qKRN4.09-1^BMY and qKRN4.09-1^B73. Zm00001eb205550 exhibited preferential expression in developing ears. Moreover, the pyramiding of favorable alleles from the five stable QTLs significantly increased KRN in maize. These findings advance our genetic understanding of maize ear development and provide valuable genetic targets for improving KRN in maize breeding. Full article

Background/Objective: Dyspareunia negatively affects women’s lives. Up to 35% suffer from postpartum dyspareunia. Many factors may influence the occurrence of postpartum dyspareunia, but little is known about them. This study aimed to look at the frequency of dyspareunia one year postpartum in a cohort of primiparae. Which perinatal factors influence the frequency of postpartum dyspareunia? Methods: A total of 3264 primiparae were included in this observational cohort study. Perinatal factors were documented, and a specially designed questionnaire was sent to them one year postpartum. The primary outcome was the frequency of dyspareunia one year postpartum. The secondary outcomes included potential influencing factors such as birthing method (spontaneous bed delivery, spontaneous delivery other than bed, water delivery, and vacuum-assisted delivery); perineal injuries (first- and second-degree perineal tears, obstetric anal sphincter injuries (OASIs), and episiotomies); and the use of oxytocin. Results: Postpartum dyspareunia was observed in 15% of the 3264 primiparae. In multivariate analysis, there were influences found in the perineal injury group, especially for first- and second-degree perineal tears and OASIs. In the oxytocin group, a trend toward a higher rate of postpartum dyspareunia was observed. No influence of the different birthing methods was found. Conclusions: Postpartum dyspareunia, affecting 15% of women one year after vaginal delivery, is associated with perineal injuries, particularly minor perineal tears and OASIs. This highlights the importance of good preparation of the perineum and pelvic floor before delivery, efficient perineal protection during labor, and the use of a precise repair technique for all perineal injuries. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

Russian dandelion (Taraxacum kok-saghyz Rodin, TKS) is a natural rubber (NR)-producing species whose roots contain 3% to 27% NR, underscoring its considerable research and economic significance. The myeloblastosis (MYB) transcription factor family, one of the largest in plants, plays pivotal roles in metabolic regulation, stress responses, and various growth and developmental processes. To identify key MYB transcription factors involved in hormone-induced rubber biosynthesis, we conducted homology-based and bioinformatic analyses to characterize 268 MYB family proteins in the TKS genome. Utilizing transcriptome data from jasmonic acid (JA) and ethylene (ET) treatments, we screened and shortlisted 10 candidate TkMYB transcription factors. Through tissue-specific expression profiling, TkMYB7 was selected as the primary candidate. We confirmed that promoter analysis combined with yeast one-hybrid assays confirmed that TkMYB7 directly binds to and regulates the expression of acetyl-CoA acetyltransferase (TkACAT5), a key enzyme in the mevalonate (MVA) pathway. Furthermore, heterologous overexpression of TkMYB7 in Arabidopsis thaliana significantly enhanced seed germination and root development. These findings identify TkMYB7 as a novel transcriptional regulator linking JA and ET signaling pathways to rubber biosynthesis in TKS, representing a promising target for the genetic improvement of rubber yield. Full article

Understanding the interactions among ecosystem services (ESs) and their spatiotemporal dynamics is pivotal for sustainable ecosystem management, particularly in arid regions where water scarcity imposes significant constraints. This study focuses on the Ili River Valley, a representative arid region, to investigate the evolution of ESs, their trade-offs and synergies, and the underlying driving mechanisms from a water-resource-constrained perspective. We assessed five key ESs—soil retention (SR), habitat quality (HQ), water purification (WP), carbon sequestration (CS), and water yield (WY)—utilizing multi-source remote sensing and statistical data spanning 2000 to 2020. Employing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, Spearman correlation analysis, Geographically Weighted Regression (GWR), and the Geodetector method, we conducted a comprehensive analysis at both sub-watershed and 500 m grid scales. Our findings reveal that, except for SR and WP, the remaining three ESs exhibited an overall increasing trend over the two-decade period. Trade-off relationships predominantly characterize the ESs in the Ili River Valley; however, these interactions vary temporally and across spatial scales. Natural factors, including precipitation, temperature, and soil moisture, primarily drive WY, CS, and SR, whereas anthropogenic factors significantly influence HQ and WP. Moreover, the impact of these driving factors exhibits notable differences across spatial scales. The study underscores the necessity for ES management strategies tailored to specific regional characteristics, accounting for scale-dependent variations and the dual influences of natural and human factors. Such strategies are essential for formulating region-specific conservation and restoration policies, providing a scientific foundation for sustainable development in ecologically vulnerable arid regions. Full article

Rapid and uncoordinated urban expansion in arid oasis city clusters intensifies land use conflicts and ecological pressure, threatening regional sustainability. This study investigates the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains (UANSTM) in Xinjiang, northwestern China—an arid region urban cluster. A multi-source spatial data framework was established to delineate urban built-up areas and to construct land use efficiency (LUE) indicators, thereby facilitating an integrated analysis of the spatial coupling between urban expansion intensity (UEI) and LUE from 2000 to 2020. The results indicate that: (1) The urban built-up area expanded from 322 km² to 1096 km², shifting northward and northwestward, producing fragmented and decentralized patterns; (2) LUE improved but exhibited clear spatial disparities. Core cities like Urumqi showed strong synergy between rapid expansion and rising efficiency, whereas peripheral cities such as Wusu expanded quickly without corresponding efficiency gains, reflecting evident trade-offs; (3) The relationship between UEI and LUE exhibited a nonlinear evolution—trade-offs dominated during 2000–2005, synergy strengthened from 2005 to 2015, and trade-offs resurged again after 2015.These findings reveal the cyclical vulnerability of arid region urbanization and highlight the effectiveness of the proposed framework for diagnosing spatial mismatches and guiding compact, efficiency-oriented urban development toward long-term sustainability. Full article

The low water and fertiliser utilisation efficiency and soil quality degradation caused by high water and fertiliser inputs are the primary challenges facing goji berry cultivation in arid regions. A two-year field experiment was conducted from 2021 to 2022. The experiment included three irrigation rates (I₁, I₂, I₃) of 2160, 2565, and 2970 m³·hm⁻² and three nitrogen application rates (N₁, N₂, N₃) of 165, 225, and 285 kg·hm⁻² to quantify their impacts on soil nutrients, enzyme activity, and goji berry yield in the root zone. Results indicate that the indicators of soil nutrients decrease with increasing soil depth, with depths of 0–20 cm accounting for 24.80–72.48% of total content. With fertility period progression, soil organic matter at depths of 0–80 cm exhibits a “folded-line” trend, while total nitrogen, nitrate nitrogen, and available phosphorus show an “M”-type trend. At depths of 0–40 cm, the proportions of urease, sucrase, and alkaline phosphatase activities all exceeded 70%. At I₁ irrigation rate, enzyme activities gradually increased with rising nitrogen application rates. At I₂ and I₃ irrigation rates, enzyme activities first increased, then decreased with increasing nitrogen application. The highest yields of both fresh and dried fruits were achieved at I₂N₂ treatment, increasing by 14.17% and 14.78%, respectively, compared to conventional management (CK). Analysis of the random forest model indicates that the soil-driven factors influencing yield formation include SA, UA, APA, HPA, SOM, NH₄⁺-N, and TP. Analysis of SQI and yield fitted data indicates that water–nitrogen coupling significantly influences wolfberry yield by regulating soil quality. Partial least squares (PLS-PM) showed that N application and irrigation of soil nutrients did not cause a significant indirect impact on goji berry yield, but a significant positive effect on goji berry yield occurred through enzyme activity. Full article