Search Results (7,684)

Mosquitoes are recognized as the arthropod group with the greatest vectorial capacity, and the viruses they transmit constitute a significant concern in the context of global One Health. In addition, these insects act as hosts for a wide diversity of insect-specific viruses (ISVs), which exclusively infect arthropods. Expanding knowledge of ISVs is particularly relevant, given their potential influence on arbovirus replication and their role in elucidating the evolutionary processes that shape virus–vector interactions. In this study, we report the isolation and molecular analysis of three negeviruses associated with different mosquito species of the genera Culex, Coquillettidia, Mansonia, and Ochlerotatus, collected in Belém, Pará State, in the Brazilian Amazon: Loreto virus, Wallerfield virus, and a putative new species, designated Terra firme virus. Eleven pools exhibited cellular alterations consistent with cytopathic effects in invertebrate C6/36 cells but showed no evidence of replication in vertebrate Vero cells. Notably, simultaneous infections by two or three negeviruses were detected in some mosquito pools, indicating the occurrence of multiple viral infections within individual samples. Genomic analyses revealed that the isolated strains share conserved domains with previously described isolates from other countries. Phylogenetic inferences demonstrated that the investigated strains are classified within the clades Nelorpivirus and Sandewavirus. Taken together, these findings expand the currently known diversity of the negevirus group and contribute to a more comprehensive understanding of its host range and geographic distribution. Full article

Small settlements in Serbia are confronted with long-term processes of depopulation, ageing, and migration, characterised by pronounced spatial and structural heterogeneity. This raises questions about the effectiveness of uniform development policies and underscores the need for a differentiated, place-based approach. The aim of this paper is to identify the demographic heterogeneity of small settlements (with fewer than 100 inhabitants) and to analyse its implications for decentralised territorial development. The research is based on the analysis of 1302 settlements in Serbia, using 26 demographic, socio-economic, and geographical indicators. The methodological framework is based on principal component analysis and cluster analysis, complemented by nonparametric tests and logistic regression. The results indicate pronounced population ageing, low labour potential, and a clear spatial polarisation between accessible and peripheral settlements. Four clearly differentiated types of small settlements are identified. It is concluded that demographic heterogeneity represents a key determinant of development capacity, indicating the need for territorially sensitive and differentiated development policies. In this context, decentralisation and tailored development models may contribute to the revitalisation and long-term sustainability of rural areas. Full article

Financial exclusion remains a critical barrier to sustainable economic development in emerging economies, particularly among microentrepreneurs who depend on informal financial credit (IFC) to sustain their livelihoods. This study aims to examine the determinants and consequences of IFC utilization and their relationship with distinct delinquency patterns among microentrepreneurs in the Peruvian Amazon. A cross-sectional survey was administered to 310 microentrepreneurs from the central market of Yurimaguas during the first quarter of 2024 using partial least squares structural equation modeling (PLS-SEM). Four determinants of IFC—motivation, lender choice, loan conditions, and financial stress—were tested alongside their influence on three delinquency types: accidental, intentional, and negligent. The results indicate that psychological motivation and social lender choice are the primary and statistically significant drivers of IFC utilization, whereas loan conditions showed no significant association. Regarding delinquency outcomes, IFC is significantly and positively associated with accidental and intentional delinquency, yet paradoxically shows a significant negative association with negligent delinquency, suggesting that trust-based social enforcement mechanisms embedded in informal lending relationships may constrain negligent default behavior. These differentiated effects underscore the dual nature of informal credit as both a livelihood-sustaining mechanism and a source of financial vulnerability. The findings contribute to the understanding of financial sustainability in excluded populations by providing empirical evidence that effective interventions must address the psychological and relational dimensions of credit behavior, rather than focusing solely on structural loan characteristics. Key limitations include the cross-sectional design, which precludes causal inference, and the geographic focus on a single market in the Peruvian Amazon, which restricts generalizability. This study offers actionable insights for policymakers and microfinance institutions seeking to design inclusive financial strategies aligned with Sustainable Development Goals 1 (No Poverty), 8 (Decent Work and Economic Growth), and 10 (Reduced Inequalities). Full article

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination. Full article

Leveraging artificial intelligence (AI)―a cutting-edge technological tool―to drive the new quality transformation of Chinese manufacturing is a crucial foundation for China’s steady advancement of the new real economy, as well as an inevitable requirement for China to align with contemporary economic and technological trends. This study constructs a multi-sectoral equilibrium model to theoretically analyze the focal points of the new quality transformation in Chinese manufacturing and the impact AI has on it, followed by corresponding empirical tests. The results indicate that (1) AI has a positive impact on the qualitative transformation of China’s manufacturing sector; a one-unit increase in a firm’s AI level leads to a 0.171-unit increase in the sector’s qualitative transformation level. (2) This impact exhibits heterogeneity at the firm, industry, and regional levels. At the firm level, the impact varies depending on firm size, digitalization level, operational performance, internal control strength, and governance quality. At the industry level, the impact varies depending on technology intensity, industrial structure, strategic importance, and green development level. At the regional level, heterogeneity is reflected in geographical location, natural resource endowments, and the degree of urban agglomeration. (3) Artificial intelligence promotes the new quality transformation of Chinese manufacturing through the following mechanisms: reducing time lag costs and transaction costs in market penetration mechanisms; enhancing the quality of cutting-edge factor combinations and key core technologies in advanced innovation mechanisms; and improving resource utilization and operational management efficiency in lean production mechanisms. Full article

Rapid urbanization has intensified jobs–housing separation and increased commuting distances in megacities, posing challenges for sustainable urban development. Existing studies often examine commuting behavior at a single spatial scale or focus on either residential or employment locations. Using mobile phone signaling data, this study derives network-based commuting distances within the suburban ring of Shanghai and integrates multiple built environment indicators. A multiscale framework is developed using six spatial units, ranging from 2 to 4 km grids to street-level zones, to assess spatial scale effects and support the selection of an appropriate analytical unit. The 3.5 km grid was selected for subsequent analysis as a balance between spatial detail and statistical stability. Within this framework, Multiscale Geographically Weighted Regression (MGWR) examines the spatial heterogeneity and scale effects of built environment factors from both residential and employment perspectives. The results show: (1) The choice of spatial unit significantly affects model performance, with the 3.5 km grid providing a suitable balance between spatial detail and statistical stability. (2) Built environment indicators exhibit clear multiscale effects, with different variables operating at global and local spatial scales. (3) Residential and employment locations show significant asymmetric effects, as enterprise density is associated with shorter commuting distances at residential locations but longer distances at employment centers. These findings indicate the joint role of multiscale spatial structure and dual-end built environments, supporting spatially differentiated planning and transport policies. Full article

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

In light of the accelerating process of global urbanization, the quality of cultural ecosystem services (CES) in urban parks has become a core metric for efforts to promote urban livability and sustainable cities. However, previous research has failed to consider the differential impacts of the external environment across various travel scenes. In this study, 32 parks in Chengdu serve as the empirical data, and public CES perception data are extracted from social media comments via text mining. Based on a unified 15 min time threshold, we delineate the service scope for four travel scenes and employ geographically weighted regression and piecewise regression models to analyze the spatial heterogeneity, driving mechanisms and threshold effects associated with the relationship between external environmental factors and park CES. The findings indicate that the external environment’s influence on CES exhibits a “scene-factor-scale” adaptation pattern. Walking scenes are influenced primarily by land-use and population factors; in contrast, cycling scenes rely on the availability of shared bicycle facilities, and public transport and driving scenes are driven by economic vitality and traffic-support factors, respectively. Five critical thresholds are identified, including a 40% impervious surface area. This research proposes scene-based optimization strategies and helps enhance the “external environment–travel behavior–spatial characteristics” coupling framework, thereby serving as a scientific reference for efforts to improve 15 min living circles. Full article

This study reconceptualises marginality in China’s inter-provincial border regions as a dynamic, scale-sensitive spatial relationship rather than a static condition of underdevelopment. Using the Hubei–Henan–Anhui border area as a case study, we quantitatively assess marginality across three dimensions—production, livelihood, and ecology—based on panel data from 61 counties for 2000, 2010, and 2021. The entropy-weighted TOPSIS method is used to calculate comprehensive development indices, and geographic detector models identify key driving factors. The results show that production marginality is persistently shaped by economic level and industrial structure. Livelihood marginality exhibits a clear temporal shift: dominant drivers move from healthcare security to cultural amenities and finally to transport accessibility. Ecological marginality remains primarily determined by natural endowments such as habitat quality and ecosystem services. Theoretically, the study advances marginality analysis by integrating spatial, temporal and dimensional perspectives. Practically, it offers a diagnostic framework to support differentiated, cross-administrative governance strategies that can transform peripheral border regions into cooperative hubs. Full article

As a key link between short-distance urban mobility and public transport, dockless bike-sharing (DBS) systems have expanded rapidly in recent years. However, existing studies are limited by insufficient factor coverage, incomplete temporal analysis, and inadequate assessment of spatial-scale effects. To address these gaps, this study uses Shenzhen as a case study, integrating 40 million DBS trip records from August 2021 with multi-source geospatial data to develop a spatiotemporal analytical framework. First, it examines differences in riding patterns between weekdays and weekends, further segmenting trips into six time periods to capture intra-day temporal variations. Through multicollinearity and spatial autocorrelation tests, a 700-m grid was identified as the optimal analysis unit. Subsequently, a Multi-scale Geographically Weighted Regression (MGWR) model quantified how multiple sources of factors collectively shape DBS usage behavior. Results indicate that higher frequency, faster speeds, and longer distances during peak periods characterize weekday trips. Office POIs and transit accessibility positively affect DBS usage during weekday peaks, whereas Residential POIs and Convenience Service POIs have a greater influence on weekend trips. Population density and land-use mix consistently promote DBS use across all periods. Younger residents (<30 years) were the main users, especially during weekday peak and weekend no-peak periods, whereas gender and education had limited impact. These findings provide empirical evidence to optimize bike-sharing deployment, enhance multimodal transport integration, and support sustainable urban mobility planning. Full article

Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy ($R^2$ = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article

Accurately mapping agricultural land use in fragmented hilly and mountainous areas is crucial for resource management but is severely challenged by spatial heterogeneity. While high-resolution (HR) images excel at delineating fine parcel boundaries, their limited spectral and temporal information often leads to spectral confusion among diverse agricultural types. To address this limitation, this study proposes a novel spatiotemporal feature-driven geographical zoning method integrating vegetation phenology, topography, and human activity. This zoning strategy decouples the complex global classification task into relatively simple local problems, providing explicit geoscientific constraints for subsequent classification. The proposed method was validated by classifying plain open-field croplands, sloping croplands, terraces, and greenhouses in the hilly and mountainous areas of Beijing using 2 m resolution satellite images. Compared to traditional global classification methods, the proposed zoning-based method increased the overall accuracy from 84.81% to 90.81%, the Kappa coefficient from 0.74 to 0.85, and the Intersection over Union (IoU) from 77.85% to 90.85%. The advantages of geographic zoning were particularly evident in mitigating spatial heterogeneity and enhancing boundary precision. These findings indicate that integrating dynamic geographical zoning as a priori knowledge successfully bridges the gap between HR spatial details and environmental contexts, offering a robust solution for mapping fragmented agricultural landscapes. Full article

Rapid urbanization in the Yellow River Basin intensifies the conflict between urban expansion and the thermal environment, threatening ecological security and sustainable development. Utilizing multi-source data (2000–2023) including nighttime light (NTL) and land surface temperature (LST), this study applies spatial analysis and Geographically Weighted Regression (GWR) to explore the spatial associations between urban development and LST and its drivers across core cities. The results indicate significant spatiotemporal differentiation: mid-downstream cities exhibited contiguous urban expansion, whereas upstream growth remained constrained by local topography, with heat islands consistently concentrating in built-up areas. The warming rate decreased gradually from downstream (0.29–0.40 °C/year) to upstream (0.20–0.30 °C/year). The LST-NTL correlation strengthened notably in mid-downstream regions but remained moderate upstream. GWR analysis revealed that urban development intensity, represented by NTL, is the primary driver of LST increase downstream, while natural factors predominantly mitigate warming upstream. This long-term, multi-city comparison provides a scientific basis for precise urban heat island management and sustainable planning in the basin. Full article

Cymbidium eburneum Lindl. is a valuable ornamental orchid and breeding parent, but its genetic background remains unclear due to habitat destruction and germplasm mixing. This study developed specific SSR markers to evaluate the genetic diversity and structure of 96 C. eburneum Lindl. accessions from China and Vietnam. Transcriptome analysis identified 47,248 SSR loci. Sixteen polymorphic core primer pairs detected 150 alleles (mean Na = 9.375) with an average Polymorphism Information Content (PIC) of 0.444. Observed heterozygosity (Ho = 0.290) was noticeably lower than expected (He = 0.478), indicating heterozygote deficiency. UPGMA clustering identified eight groups strongly correlated with geography. Principal Coordinate Analysis (PCoA) revealed a clear geographical differentiation pattern, featuring the most genetically cohesive group from Guangxi and more differentiated geographically marginal populations from Hainan and Vietnam. STRUCTURE analysis (K = 2) indicated two main gene pools with signals of genetic admixture. Geographical isolation was suggested as a potential driver of genetic differentiation. The Guangxi population represents a genetically consistent major reservoir, while marginal populations harbor unique variations. These findings provide a scientific basis for germplasm identification, conservation, and parental selection in C. eburneum Lindl. breeding. Full article

Rural settlements serve as the core spatial carriers of rural living space, and their spatial evolution and functional transformation reflect the dynamic restructuring of human–land relationships. In karst mountainous areas, complex topography, fragmented land resources, and uneven distribution of public facilities significantly influence settlement patterns and residents’ living spaces. This study aims to quantify the relationship between settlement clustering characteristics and living-space demand and to construct a spatially explicit framework for rural settlement restructuring from a living-space perspective. Taking the Qixingguan District of Bijie City, Guizhou Province—a representative karst mountainous area in Southwest China—as a case study, we develop an integrated analytical framework encompassing spatial identification, demand measurement, and zoning optimization. Settlement clusters were identified using the Nearest Neighbor Index and Kernel Density Analysis, while accessibility to essential services—including education, healthcare, and shopping—was quantified via a Gaussian-based two-step floating catchment area method. Living-space demand was further assessed by integrating accessibility gradients with residential conditions, and restructuring types were classified based on the Living Space Index and the distance from settlements to town centers. The results indicate that (1) rural settlements in Qixingguan District exhibit significant clustering, with high-density zones concentrated around urban peripheries and along transportation corridors; (2) accessibility to living services follows a distance-decay pattern modulated by transportation networks, forming hotspots in suburban and town-center areas and cold spots in peripheral karst mountainous areas; and (3) based on the comprehensive assessment, settlements are categorized into four types—urbanizing villages, central villages, preserved villages, and relocation villages—with corresponding targeted spatial restructuring strategies proposed. This study advances the geographical understanding of rural settlement restructuring in karst mountainous areas and provides empirical evidence for optimizing human–land relationships and promoting more equitable and sustainable spatial development in mountainous regions. Full article