Search Results (403)

Understanding the spatial distribution and associated factors of sustainable environmental development (SED) is crucial for effectively formulating action plans aimed at achieving the environment-related Sustainable Development Goals (SDGs). Because of spatial heterogeneity characterized by non-uniform distributions of SED within a region, the use of a single or aggregated value at the national or provincial scale in existing research obscures internal variabilities and fails to adequately reveal the spatial disparities in the progress of environment-related SDGs. Consequently, this study aims to investigate the spatially heterogeneous patterns of SED in prefecture-level cities within the Yangtze River Economic Belt (YREB), China—a critical economic zone characterized by stark intra-regional disparities and pressing environmental challenges. To achieve this objective, the SED index is first constructed using principal component weighted aggregation to quantify the SED status, local Moran’s I is then employed to identify heterogenous patters of the spatial distribution patterns of SED, and geographical random forest is utilized to explore spatially varying association patterns between SED and influences factors. In the YREB, the findings indicate the following key insights: (1) SED has generally shown a positive trend across most cities from 2013 to 2021, with approximately 30% experiencing a downward trend, particularly in Jiangxi, Hubei, and Hunan Provinces; (2) the number of high- or low-value aggregation clusters has decreased, concurrent with an increase in spatial variability; (3) human activity disturbance and population density have been identified as significant factors influencing SED, with a notable impact in cities across Sichuan, Chongqing, Guizhou, and Hubei Provinces. This research contributes technical support and a scientific foundation for evaluating and enhancing SED. Full article

The equitable allocation of health resources is fundamental to building healthy cities and advancing sustainable regional development. Using panel data for 122 county-level units in Hunan Province, China, from 2011 to 2022, this study constructs an evaluation index system for healthcare resource allocation and applies trend surface analysis, spatial autocorrelation, the Dagum Gini coefficient, and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal evolution, equity, and driving factors of health resource allocation. The results show the following. (1) The level of health resource allocation in Hunan Province rose steadily, with the composite score increasing by 74%, yet a persistent spatial pattern of higher allocation in the east and north than in the west and south remained; hot spots clustered in Changsha, while cold spots concentrated in parts of Southern Hunan and Western Hunan. (2) Regional disparities narrowed gradually, and the Dagum decomposition identified transvariation density as the dominant source of inequality, with an average contribution of 53.58%, exceeding intra-group and inter-group differences. (3) The effects of the drivers exhibited marked spatiotemporal heterogeneity. Per capita GDP promoted health resource allocation mainly in developed regions, while urbanization exerted stronger positive effects in less-developed regions. The positive effect of per capita disposable income gradually shifted from less-developed to developed regions over time, and population density generally showed a positive effect, with stronger influences concentrated in the Greater Western Hunan region. This study contributes to a deeper understanding of how regional disparities and heterogeneous driving mechanisms shape health resource allocation, providing evidence for more adaptive and equitable healthcare governance. Full article

Understanding the interactions between rural household decision-making behavior and the environment is critical to promoting sustainable rural development. Analyzing the interrelationship between multidestination urban housing purchases, homestead improvement potential, and village environments can provide a theoretical basis for formulating rural revitalization policies. This study uses full-sample household survey data, applying multiscale geographically weighted regression to assess spatial variability and K-means clustering to categorize effects. The findings reveal significant spatial heterogeneity in village homestead improvement potential, most strongly associated with locational attributes, water network density, residential quality, and social factors. The association between multidestination urban housing purchases and improvement potential varies by destination: township purchases are positively associated with it, whereas purchases in county centers and beyond show negative associations. The spatial variability of different factors is significant, allowing villages to be classified into five zones based on dominant factors. Tailored, zone-specific policy directions—developing a county-level dual-core urban system, promoting rural tourism, and encouraging concentrated local habitation—are proposed as testable hypotheses for supporting in situ urbanization and homestead land improvement. Full article

Gansu Province has abundant agricultural biomass resources, but the gap between the biological yield of biomass and available quantities, along with strong spatial heterogeneity, restricts efficient bioenergy utilization. This study quantifies four categories of agricultural biomass across 85 county-level units in Gansu, evaluates relative enrichment density via the Biomass Resource Location Quotient (BRLQ), and identifies spatial agglomeration patterns using global and local spatial autocorrelation. The results show a significant structural mismatch: total biological yield of biomass reaches 46.41 million tons, while available resources are only 12.13 million tons, with crop straw as the dominant available feedstock. The Longdong Loess Plateau has the highest enrichment density (BRLQ = 1.588). No significant global spatial agglomeration is observed, and significant clusters are confined to local small areas. This study provides quantitative support for zonal bioenergy planning in Gansu. Full article

This study evaluates the unique ecological and floristic identity of the coastal dune vegetation in the Enez delta and adjacent dune coast of Saros Bay (southern Thrace) by comparing it with diverse dune systems across the Anatolian peninsula. A comprehensive data matrix of 97 phytosociological relevés across nine representative coastal dunes spanning the East Mediterranean, Aegean, Marmara, and Black Sea coasts was analyzed. Methodologically, univariate non-parametric approaches (Friedman variance analysis and Durbin–Conover tests) were integrated with multivariate techniques, including Hierarchical Cluster Analysis and Principal Coordinates Analysis (PCoA) based on a Bray–Curtis dissimilarity matrix. Ephedra distachya ssp. monostachya was found to be the most characteristic and differentiating taxa from the clustering. Friedman test results demonstrated highly heterogeneous species abundance across localities (χ² = 27.2, p < 0.001). The multivariate synthesis revealed a profound ecological decoupling for Saros Bay dunes: while macroclimatic filtering forces a powerful functional convergence with arid Mediterranean models dominated by therophyte, strict composition-based metrics isolate Saros Bay coastal dunes into an entirely independent taxonomic clade. PCoA ordination confirmed this distinctiveness (p < 0.05 against six of the eight national localities), with the first two axes explaining 33.58% of the total variation (Axis 1: 19.66%, Axis 2: 13.92%). This isolation is driven by a high density of Irano-Turanian elements and specialized local lineages like Silene kotschyi. Conversely, a sharp latitudinal bio-climatic macro-gradient was mapped, showing a transition toward humid Black Sea systems strictly dictated by macroclimatic filtering rather than biotic competition (p > 0.05). To preserve these specialized niches, designating coastal dunes of Saros Bay as a Priority Conservation Unit and establishing international, transboundary catchment to coast monitoring frameworks are essential. Full article

Urban transportation systems face increasing sustainability and resilience challenges due to the growing frequency and intensity of weather extremes. Weather-related congestion may increase travel delays, fuel consumption, and unequal economic costs, thereby undermining urban sustainability. Although previous studies have examined the relationship between weather conditions and traffic congestion, limited attention has been paid to whether street-network design can enhance transportation resilience under extreme weather conditions. This study investigates the relationships among extreme weather, traffic congestion, and street density using daily congestion and meteorological data from 35 major Chinese cities between 2018 and 2024. Fixed-effects regressions estimate the associations between multiple weather extremes and congestion and examine the moderating role of street density. Heavy rainfall, extreme cold, and low visibility are associated with increased congestion, whereas extreme heat is associated with reduced congestion. Street density could buffer congestion under extreme cold and heavy snow cover, suggesting that denser networks may improve resilience to localized road-surface disruptions. Heterogeneity analyses reveal weaker weather-related congestion responses in megacities and clustered cities, and during the COVID-19 period. These findings highlight the potential role of street-network design in supporting sustainable and climate-resilient transportation by reducing vulnerability to weather-related congestion. Full article

This study employs panel data from 11 provinces and municipalities along the Yangtze River Economic Belt spanning 2010–2021. It measures green transport efficiency (GTE) using principal component analysis (PCA) and the undesirable Super-SBM model while constructing an economic high-quality development index (HQEDI) through an entropy-weighted CRITIC approach. The study combines coupling coordination degree modeling with spatial autocorrelation analysis (Global Moran’s I, LISA, hotspot/coldspot detection) to empirically investigate their synergistic evolution mechanism. The findings indicate the following: (1) Multidimensional policy combinations exhibit a nonlinear threshold effect on enhancing green transport efficiency, with efficiency significantly rebounding post-2015 as low-carbon policies deepened. (2) High-quality economic development displays a dual-stage ‘convergence-divergence’ pattern, where downstream regions lead in HQEDI but mid- and upstream regions show faster growth in coordination and green dimensions. (3) The coupling coordination degree exhibits pronounced spatial spillover effects, with the global Moran’s I mean reaching 0.485. High-value clusters form in downstream regions, while upstream areas predominantly exhibit low-value clusters, revealing an ‘east-high, west-low’ regional differentiation pattern. (4) The gradient divergence mechanism stems from heterogeneity in infrastructure density, industrial structure, and policy responsiveness elasticity. Accordingly, it is recommended to establish a multi-level governance mechanism to dismantle administrative barriers and to construct a tripartite policy package integrating ‘digital transport, ecological compensation, and industrial radiation’ to advance coordinated basin development. Full article

Urban green-space provision depends not only on green quantity but also on how greenery is positioned relative to buildings as urban form evolves. However, most existing assessments aggregate greenery within predefined spatial units and rely on static snapshots, thereby obscuring both its distance-sensitive distribution around buildings and its variation across urban-development trajectories. This study develops a Supply–Form framework to examine this relationship across heterogeneous development trajectories. Using multi-temporal very-high-resolution imagery from Beijing and Tianjin, we introduce Quantified Green-Space Supply (QGS), a distance-weighted indicator that accumulates greenery around buildings over nested ranges of 5–2000 m. Building-expansion trajectories are then linked to evolution-stratified XGBoost models interpreted with consensus-grouped Partition SHAP, allowing recurrent morphological groups and their within-group contributions to be compared across contexts. QGS was more strongly associated with the selected urban-form variables than conventional green coverage ($R^2=0.717$ versus $0.633$). Both cities showed higher mean QGS in 2022 than in 2012, but their internal patterns diverged: Beijing developed more continuous central low-QGS clusters, whereas Tianjin’s central cold spots contracted and fragmented. Across 15 trajectory-specific models, building density was the leading morphological group in 12, while the importance of patch size, edge structure, aggregation–adjacency, and spatial configuration varied by trajectory. Nonlinear responses showed a stable positive association for building density, diminishing returns for edge density and dispersion, adverse effects at very high adjacency, and intermediate-range benefits for patch dominance and compactness. The framework advances green-space assessment from aggregate greenness mapping to a relational, trajectory-conditioned diagnosis of potential surrounding green-space supply, providing a basis for context-sensitive urban renewal. Full article

The FMD is an endangered species under first-class national protection in China. Comparative genomic investigation of microsatellite (SSR) in CDS may provide insights into adaptive evolutionary mechanisms and may inform conservation management strategies for captive populations. Here, we analyzed the FMD genome alongside five closely related ruminants: cattle (Bos taurus), red deer (Cervus elaphus), white-tailed deer (Odocoileus virginianus), sheep (Ovis aries), and goat (Capra hircus). Through genome-wide bioinformatic identification, we systematically compared the abundance, density, structural categories, repeat motifs, chromosomal distribution, and pathway enrichment analysis of SSR-containing genes in CDS. Furthermore, we performed synteny analysis and evaluated population genetic diversity. A total of 2509 SSRs in CDS were identified in the FMD, with a relative density of 62.61 loci/Mb. Trinucleotide SSRs were overwhelmingly dominant (88.46%) in the FMD. Notably, the FMD exhibited the highest relative abundances of both tetranucleotide and pentanucleotide repeats among the six species (2.37 and 2.18 loci/Mb, respectively), with pentanucleotide abundance approximately 5.6- to 9.1-fold higher than that of the other species. Chromosomal mapping revealed the highest SSR density in CDS regions on chromosome 27, while SSR-containing genes exhibited a heterogeneous pattern characterized by localized clustering. Synteny analysis demonstrated relatively conserved syntenic relationships between the FMD and goat, sheep, and cattle, with moderate conservation also observed with red deer and white-tailed deer, suggesting that SSR-containing genes in ruminants may remain highly conserved during chromosomal rearrangements. GO and KEGG analyses indicated that SSR-containing genes across all species were predominantly enriched in transcriptional regulation, RNA processing, and signal transduction pathways. Specifically, the FMD showed enrichment patterns associated with hypoxia response, mRNA processing, and epigenetic regulation, which may reflect lineage-specific transcriptional patterns, though the functional involvement of these SSRs remains to be experimentally validated. In addition, the five primer pairs screened in this study exhibited high polymorphism, with a mean polymorphism information content (PIC) of 0.93. The observed heterozygosity (Ho) was significantly lower than the expected heterozygosity (He), and the mean inbreeding coefficient (FIS) was 0.57, indicating heterozygote deficiency and an elevated risk of inbreeding in this captive FMD population. Collectively, our findings provide preliminary insights into the conserved patterns of microsatellite evolution and lineage-specific divergence in ruminants, offering a reference framework for comparative genomics and adaptive evolution research, as well as practical molecular markers for genetic management of captive populations. Full article

Generative artificial intelligence (AI) now allows a single founder, working with a cluster of AI agents as “digital employees,” to run a venture that once required a team. We call this form the one-person company (OPC) and treat it as a human–AI socio-technical system with a “1 + N + AI” architecture, whose viability depends on the density of the surrounding AI ecosystem. Anchored in a systematic review of 2452 studies reported under PRISMA 2020, we build a task-based model in which a founder allocates tasks across her own labor, hired labor, and AI agents; once the local AI ecosystem density crosses a threshold, one person can cover the whole value chain. The model yields three propositions on the level, heterogeneity, and cost channel of OPC entry, which we test on a panel of 35 major Chinese cities (2019–2024). A one-percent increase in a city’s AI enterprise stock raises OPC entry by about 1.06 percent, an estimate robust to a Bartik shift-share instrument; the effect concentrates in initially AI-sparse, ordinary, and central–western cities and strengthens with the tertiary-sector share, as the threshold model predicts. Because OPCs are asset-light and create knowledge-intensive work, AI ecosystem building emerges as a lever for inclusive, sustainable entrepreneurship. Full article

Evaluating Sustainable Urban Built Morphology (SUBM) patterns at a metropolitan-wide scale obscures the localized trends in urban morphology across space and time. This research introduces Development Morphology Units (DMUs) as a micro-scale analytical concept for investigating the fine-grained spatio-temporal dynamics within urban morphology regimes. Based on 5844 development tracts observed between 1990 and 2023 in Mecklenburg County, North Carolina, this study utilizes within-regime Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) on five previously identified regimes. These DMUs are further described according to the four descriptors of temporal position, temporal span, spatial footprint, and spatial movement. The results detect substantial heterogeneity between DMUs in terms of spatio-temporal growth patterns. Peripheral and conventional suburban regimes account for large proportions of development tracts across the county; nonetheless, they revealed a limited DMU formation ratio, with most of their tracts left unclustered, while the identified DMUs were predominantly small, localized, and short-lived. On the other hand, the accessibility-oriented regime demonstrated a cohesive DMU structure and contains sustained and spatially stable morphological units. Within the intermediate regimes, most DMUs characterize localized and episodic growth, alongside a small number of large-scale units. Also, the highest-achieving sustainability regime in the county exhibited recent, short-lived, spatially localized, and stationary units. Across all regimes, morphological growth patterns predominantly represent limited spatial movement, suggesting that developments sharing similar morphological characteristics tend to remain anchored to previously established areas. These patterns align with evolutionary urbanism, indicating that sustainability-oriented urban morphology evolves through localized, cumulative processes rather than spatially random expansion. The identified DUMs demonstrated that localized urban morphology patterns evolve distinctively across space and time. The findings inform urban sustainability practices by tracking the dynamics of sustainability-oriented urban morphology at local levels. Full article

Biochar has been widely investigated as an amendment for mitigating soil desiccation cracking. However, variation in its effects across soil textures, application rates, and long-term drying–wetting (DW) cycles remains insufficiently resolved. Based on a global dataset comprising 278 paired observations from 44 peer-reviewed articles, this study employed a random-effects meta-analysis with article-clustered robust variance estimation to quantify the impacts of biochar on soil crack intensity factor (CIF), crack length density, and mean crack width. The pooled estimates indicated average reductions of 39.12%, 35.17%, and 22.20% in CIF, crack width, and crack length density, respectively; however, substantial heterogeneity (>98%) and prediction intervals crossing zero indicated considerable variation in both the magnitude and direction of responses among experimental conditions. Subgroup analyses revealed the following: (1) Soil-group differences were statistically supported for CIF, while the 14.20% increase in crack length density and 25.86% reduction in mean width observed in non-clay soils were not statistically significant. (2) Application-rate groups differed significantly across all three indices. The <2% group was associated with an 18.75% increase in crack length density, whereas the >5% group showed the largest average reductions. However, application rate explained only 12.60–15.80% of the observed variation. (3) Mean crack suppression was generally greater during the early DW cycles, but evidence beyond 6 cycles was too limited to determine whether the effect persisted under prolonged cycling. Overall, the evidence supported average reductions in CIF and mean crack width, whereas the effect on crack length density and the persistence of crack suppression under prolonged drying–wetting exposure remained uncertain. Field application should balance crack mitigation with agronomic feasibility, soil quality, and long-term performance under repeated drying–wetting conditions. Full article

The purpose of this narrative review was to provide a conceptual and applied framework that distinguishes resistance training methods, strategies, and systems and clarifies how these constructs organize established training variables within contemporary recommendations for exercise prescription. The historical, conceptual, and theoretical synthesis was not restricted by publication date and included seminal studies and previous evidence syntheses. In addition, a focused descriptive mapping of primary experimental studies published between January 2021 and April 2026 was conducted using PubMed, Scopus, and Web of Science. This mapping comprised 40 studies assessing acute responses or chronic adaptations to methods including drop sets, rest–pause, pyramidal loading, eccentric training, blood flow restriction, cluster sets, and velocity-based training. Several studies reported no statistically significant between-method differences in selected strength or hypertrophy outcomes; however, their generally small samples and the absence of equivalence or non-inferiority analyses preclude conclusions of true equivalence. Individual studies also reported context-specific differences in session efficiency, fatigue distribution, movement velocity, regional hypertrophy, and contraction-specific adaptations. Given the heterogeneity of the mapped studies and the absence of formal risk-of-bias or certainty-of-evidence assessments, these observations should be interpreted as descriptive and exploratory rather than as evidence of general comparative superiority. Taken together with the broader historical and synthesized literature, the findings suggest that resistance training adaptations are primarily governed by foundational prescription variables, including load, volume, frequency, effort, density, and exercise selection. The principal contribution of this review is therefore to clarify how methods, strategies, and systems can be selected and organized according to specific goals, individual characteristics, and practical constraints. Full article

With the acceleration of global urbanization, neighborhood green spaces have emerged as important carbon sinks. However, current urban carbon inventories frequently neglect the understory shrub layer owing to morphological heterogeneity and a lack of standardized allometric models. To address this limitation, we analyzed 13 major shrub species (n = 665) through whole-plant excavation. Hierarchical cluster analysis and linear discriminant analysis classified the 13 species into three functional morphological groups based on intrinsic morphological traits (basal stem density, root-to-shoot biomass allocation, and secondary radial growth capacity) (p < 0.001): small shrubs (Type I), large shrubs with high root-to-shoot allocation (Type II), and multi-stemmed sprouting shrubs (Type III). Standard models accurately estimated biomass for Type I species, whereas symbolic regression improved the prediction of the complex non-linear biomass allocation of Type II species. For Type III species, characterized by multi-stemmed growth and anthropogenic management, robust regression provided stable biomass estimates. Gompertz growth models predicted carbon sequestration trajectories, indicating that urban shrubs function as rapid carbon sinks during the early establishment stage. To facilitate practical application, we developed the Urban Forest Carbon Storage Calculator, which integrates Monte Carlo simulation and bootstrapping to generate 95% confidence intervals for species-specific biomass estimation. This study quantifies the overlooked carbon value of the urban shrub layer and provides a morphology-driven methodological approach and a practical tool for sustainable urban forest management. Full article

Dynamic vehicular integrated sensing and communication (ISAC) requires representations that remain compact, decision-relevant, and structurally stable as mobility rewires interference and sensing relations. This paper presents IC-GMRO, a conditional information-bottleneck graph-clustering framework for structured representation learning in multi-agent resource optimization. At each control epoch, vehicles, roadside units, targets, and typed interactions form a temporal heterogeneous graph. A context-conditioned variational bottleneck suppresses nuisance variation while retaining action-relevant information; balanced soft graph clusters then convert the latent space into reusable coordination codes. Feasibility-masked policies jointly select association, beam, resource block, transmit power, and sensing-time ratio. The analysis distinguishes representation-level information guarantees from the idealized potential and projected-dual arguments used only to motivate the practical neural updates. Controlled simulations and component ablations show improved utility, sensing success, latency robustness, and cross-density robustness relative to greedy, flat, and graph-only baselines. Full article