Search Results (1,041)

Global warming poses a serious environmental challenge worldwide. Agriculture, as a significant source of greenhouse gas (GHG) emissions, exerts considerable influence on the atmospheric environment. Chengdu, renowned for its thriving agricultural sector, serves as a key grain production center in China. Reducing agricultural greenhouse gas (AGHG) emissions is essential for mitigating the impact of climate change on Chengdu. Firstly, this paper employed the IPCC (Intergovernmental Panel on Climate Change) coefficient method and the Super-SBM-Undesired model to calculate the AGHG emissions and emission efficiency in Chengdu, respectively. Then, center of gravity shift analysis, kernel density estimation and spatial autocorrelation theory were used to analyze the spatial–temporal evolution characteristics of AGHG emissions. Finally, this paper conducted an in-depth analysis based on the STIRPAT model to identify key factors affecting AGHG emissions. The results show that: (1) From 2007 to 2021, Chengdu experienced an overall decline in both AGHG emissions and emission intensity, with reductions of 22.32% and 66.20%, respectively. And the AGHG emission efficiency was largely low. (2) AGHG emissions display regional variations and spatial clustering phenomena, characterized by a pattern of “high in the east, low in the west, high outside and low inside”. (3) AGHG emissions are highly increased by the sown area (S) and pesticide and fertilizer utilization (F) and may be reduced by the agricultural industrial structure (V) and the urbanization rate (U). These findings provide valuable scientific insights into the spatial–temporal dynamics of regional agricultural emissions. Furthermore, this study offers practical references for local governments to optimize agricultural resource allocation, formulate tailored low-carbon agricultural policies, and promote sustainable rural development. Full article

Photosystem II (PSII) contains several cofactors involved in light harvesting, charge separation, electron transfer, and catalysis. The initial charge separation in the reaction center of PSII creates the strongest known redox-cofactor oxidant in biology, a cationic radical distributed over a “special pair” of chlorophyll molecules (P680^•+). Two redox-active tyrosines, Y_Z and Y_D, located at opposite sides of the special pair, are the principal residues that reduce this cationic radical. Y_Z, in turn, oxidizes the manganese cluster of the oxygen-evolving complex to drive water oxidation, whereas Y_D forms a stable radical facilitated by local water translocation. The details of this asymmetry and the role of nearby protein residues in mediating branch-specific electron/hole-transfer pathways remain incompletely understood. Here, we investigate pathways for electron transfer (ET) from Y_Z and Y_D to P680^•+ and identify specific residues that are likely responsible for mediating ET. Graph-based analysis predicts aromatic residue-assisted pathways on both branches but also reveals a distinct tryptophan (D2-Trp191) that connects Y_D with P680^•+, whereas the corresponding D1-side position is occupied by a non-aromatic D2-Ile192. This suggests a possible role of this tryptophan as an ET mediator, thereby differentiating the nature of electronic connectivity between Y_Z/Y_D and the reaction center. Residue conservation analysis indicates retention of D2-Trp191 across various organisms. Molecular dynamics show that the predicted donor–mediator and mediator–acceptor contacts remain structurally persistent over the simulation, while QM/MM calculations show appreciable spin-density localization capacity, providing strong computational support for an ET mediator role of D2-Trp191. Together, these results suggest that ET between the redox-active tyrosines and the reaction-center chlorophylls occurs via distinct mechanisms—direct vs. mediated—with D2-Trp191 being a D2-specific mediator for the branch-selective electron/hole-transfer connectivity in PSII. Full article

Spatial clustering does not show that specific built-form characteristics lead to the concentration of terrorist incidents, but it is common for incidents to be clustered in urban areas. This is an exploratory study that examines 7960 incidents of ISIS terrorism recorded in the GTD from 2012 to 19. Geocoded incident coordinates and Haversine great-circle distance were used to implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An epsilon radius of 50 km and MinPts = 15 were chosen for the primary model, and 15 parameter combinations were analysed to investigate the robustness of the results. The main cluster found was 18 clusters with 7403 incidents (93.00%), and noise was the other cluster (557 incidents, 7.00%). The bulk of incidents (6118) were in Iraq, while the second largest cluster was in Syria with 675 incidents. The results show high geographical concentration, which is mainly due to the operational geography of ISIS, the intensity of the conflicts, the levels of exposure, and reporting. The analysis does not directly measure architectural morphology, sight lines, surveillance, permeability, crowding, and emergency egress. Based on this, the study suggests an initial multi-scalar urban design assessment framework that includes hotspot analysis as a first step, followed by site-specific assessment using the urban security, CPTED and crowd safety, and evacuation principles. The contribution is methodological and involves showing how the large-scale incident data can help inform, but not supplant, detailed architectural evaluation. Full article

The rapid development of the low-altitude economy is reshaping tourism activities, infrastructure provision, and landscape resource use, yet national-scale research on low-altitude tourism bases remains limited. Using data on 1247 bases in China, this study applies average nearest-neighbor analysis, kernel density estimation, and spatial inequality measures to characterize multi-scale patterns. The study further employs an optimal parameters-based geographical detector within a geographical nature framework to identify influencing factors and interactions. The results reveal significant clustering and regional inequality, with a pronounced east–west divide along the Heihe–Tengchong Line. A diamond-shaped core bounded by Beijing, Hangzhou, Chengdu, and Sanya, together with its 200 km buffer zone, contains 89.17% of all bases. Domestic tourism revenue, the number of low-altitude industry enterprises, general aviation airports, national tourist resorts, and domestic tourist arrivals constitute the leading factors, although the dominant factor combinations vary across base types. Factor interactions are dominated by two-factor and nonlinear enhancement. The three natures form a coupled pathway: first nature provides environmental suitability, second nature transforms resource potential into marketable tourism products, and third nature regulates implementation. General aviation infrastructure acts as an operational interface linking environmental conditions, tourism demand, industrial support, and policy arrangements. The findings provide a basis for differentiated regional zoning and type-specific facility planning, thereby informing the sustainable development of low-altitude tourism. Full article

The spatial organization of public service facilities within transit-oriented development TOD blocks is closely related to residents’ daily travel conditions and travel-related carbon emissions. This study examined all 172 operating metro station areas in Qingdao using public service facility POIs, buildings, pedestrian road network and population data, field observations, and a resident travel survey. K-means clustering, spatial syntax analysis, Global Moran’s I, spatial regression, FDR-adjusted Pearson correlation analysis, and scenario simulation were jointly applied. Four facility layout types were identified: Spatially Balanced Type, Main-Road-Concentrated Type, Point-Concentrated Type, and Scattered-and-Disordered Type. The survey included 240 valid respondents distributed across all 41 station areas along Qingdao Metro Line 1. The Spatially Balanced Type had the lowest mean weekly per capita travel carbon emissions, followed by the Main-Road-Concentrated Type, whereas the Point-Concentrated and Scattered-and-Disordered types had similarly higher emission levels. The density and accessibility of Commercial and Entertainment facilities, Medical and Health facilities, and total facilities remained negatively associated with travel carbon emissions after FDR correction. Spatial syntax analysis showed that higher road network integration and connectivity were associated with stronger facility agglomeration. Facility density, road density, and population density exhibited significant positive network-based spatial autocorrelation, and the spatial error model provided the best fit, identifying positive associations of facility density with road density and population density. Based on these findings and field observations, three differentiated renewal pathways—node embedding, proximity coordination, and intensive integration—were proposed. Under the specified scenario, a 20% increase in facilities was associated with modeled reductions in aggregate weekly carbon emissions of 32.3% in Li Village, 28.1% in the University of Petroleum station area, and 33.0% in Jinggangshan Road. These results suggest that improving overall facility coverage may support lower-carbon travel across different facility layout contexts. This study connects facility layout typology, spatial structure, travel carbon emission associations, and differentiated renewal strategies at the TOD-block scale. Full article

Henan Province is one of China’s most culturally resource-rich regions, yet the spatial organization and driving mechanisms of its high-grade tourism resources remain insufficiently understood. This study investigates the spatial distribution characteristics and influencing factors of 2396 high-grade tourism resource sites across 157 county-level units in Henan Province. An integrated analytical framework combining GIS-based spatial analysis (nearest neighbor index, kernel density estimation, and spatial autocorrelation), geographic detector methods, and a spatial error model is employed to examine the roles of natural, economic, social, and cultural factors. The results indicate that high-grade tourism resources exhibit significant spatial clustering, forming a pronounced core–periphery structure with a dual-center pattern centered on the main urban area of Zhengzhou and Dengfeng. Strong positive spatial autocorrelation is observed, characterized by contiguous hot spots in central and northwestern Henan and cold spots in the eastern plains. The spatial differentiation of high-grade tourism resources is driven by multiple interacting factors, among which topography and material cultural heritage are the primary drivers, while economic development and intangible cultural heritage act as secondary facilitators. These findings provide empirical evidence for understanding inland tourism spatial structures and offer practical insights for heritage-led, cluster-based tourism planning, aimed at promoting sustainable regional development. 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

“Altering the auxiliary atoms” is an effective approach for expanding the planar tetracoordinate carbon (ptC) family. The ternary CBe₄S₃²⁻ cluster has been designed by using the “isoelectronic replacement of auxiliary bridges” strategy, based on previously reported ptC CBe₄Cl₃⁺. It possesses a fan-shaped structure, containing one ptC center, an arc-shaped Be₄ ligand chain, and three auxiliary S bridges. The extensive search and high-level quantum chemistry calculations indicate that both ptC CBe₄S₃²⁻ and its derivative CBe₄S₃⁻ are global minima structures on their potential energy surfaces. Born–Oppenheimer molecular dynamics simulations suggest that they also possess good dynamical stability. Chemical bonding analyses indicate that the ptC center in CBe₄S₃²⁻ is stabilized by one delocalized π bond and three delocalized σ bonds within the CBe₄ core, while magnetically induced current density analysis reveals localized diatropic circulations without exhibiting a ring current. The current contribution introduces two new members to the ptC family, expanding the ptC bonding modes and design strategies. Full article

Maritime transportation, handling over 80% of global trade, is critical to the world economy. Automatic Identification System (AIS) data provides extensive static and dynamic information of vessels, enabling trajectory reconstruction and vessel behavior analysis. Recently, trajectory clustering has become a key method for analyzing maritime traffic, offering valuable insights to improve traffic management and operational efficiency. This research aims to investigate how to effectively cluster ship sub-trajectories derived from AIS data by comparing two machine learning clustering algorithms, Density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering, with a focus on improving data quality, extracting key dynamic features, and evaluating the effect of dimensionality reduction on clustering performance. Clustering sub-trajectories can help reveal localized navigation patterns and movement behaviors. The study implemented the proposed methods for tankers and cargo ships (with AIS data from 2022) in a Y-shaped channel in the Sabine-Neches Waterway (SNWW) in Southeast Texas, where the busiest docks are located. Finally, clustering performance was evaluated with the silhouette coefficient (SC), Davies–Bouldin Index (DBI), and Joint Performance Index (JPI), respectively. Experimental results show that DBSCAN effectively identifies dense, overlapping trajectory clusters and labels noise, while the spectral clustering algorithm detects subtle behavioral differences but struggles with less cohesive clusters, and does not explicitly handle noise. Full article

The Ganjiang River–Poyang Lake Basin is a typical river–lake composite water-system region and a major concentration area of intangible cultural heritage (ICH) in Jiangxi Province. However, officially listed ICH does not simply represent the natural distribution of cultural practices but reflects the combined effects of historical accumulation, environmental contexts, and institutional recognition. Based on 616 national- and provincial-level ICH items, this study employs the nearest neighbor index, kernel density analysis, Lorenz curve, standard deviational ellipse, and Geodetector to examine spatial patterns and associated factors. The results reveal significant spatial clustering (NNI = 0.31, Z = −39.16), characterized by riverine concentration, lakeside distribution, and polycentric development. Traditional craftsmanship and folk customs cluster around Poyang Lake, while traditional drama, folk literature, and quyi extend along the Ganjiang River. Distance to major water systems (q = 0.821), policy support (q = 0.813), and inheritors (q = 0.806) show the highest explanatory power. The findings reveal a spatial process of lake-area accumulation, river-channel diffusion, and nodal support. Full article

High-density school design has become an important challenge in rapidly urbanizing cities, where land scarcity, increasing educational demand, and evolving pedagogical models generate multiple and interrelated design constraints. Although a wealth of design experience has accumulated through the execution of numerous school planning projects, this knowledge remains fragmented across architectural publications and project narratives. Existing studies have primarily focused on evaluating built environments or individual cases, and limited attention has been paid to how dispersed architectural design reasoning can be systematically extracted, organized, and represented. This study was conducted to explore how AI-assisted semantic modeling can support the extraction and organization of architectural design knowledge from large-scale design discourse through a human–AI collaborative interpretation framework. Using a corpus of 330 documents reporting school design in Shenzhen published between 2017 and 2024, the proposed framework integrates BERTopic-based semantic modeling, scenario–strategy coding, network analysis, and document-based architectural interpretation to establish a continuous workflow from architectural discourse to structured knowledge representation and spatial interpretation. The results reveal a density-conditioned knowledge structure consisting of six interconnected design agendas, 25 recurrent design scenarios, 44 original design strategies, 17 core strategies, four strategy clusters, and four document-supported spatial response patterns. The findings demonstrate that high-density school design knowledge is organized through recurring problem–strategy relationships rather than isolated project solutions. Through human–AI collaborative interpretation, fragmented design narratives are transformed into hierarchical representations linking design concerns, scenarios, strategies, and spatial organizations. The aim of the proposed framework is not to automate architectural decision-making or generate implementation-ready design solutions but to provide a methodological foundation for AI-assisted architectural knowledge retrieval, knowledge representation, and future multimodal design intelligence systems. Full article

Accelerated urban expansion and socioeconomic growth have led to substantial changes in territorial spatial organization and growing pressures among spatial functions. Therefore, conducting fine-scale identification, functional evaluation, and coordination analysis of production-living-ecological space (PLES) carries practical value for improving spatial allocation and fostering harmonious regional coordination. By integrating remote sensing imagery and point-of-interest (POI) data, this study employed a hierarchical identification method combining the object-oriented random forest method with the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to map the PLES spatiotemporal evolution in Fuzhou’s rapidly urbanizing territory over the 2012–2024 period. PLES functions were quantitatively assessed through a comprehensive evaluation index system, while the standard deviation ellipse, GeoDetector, and coupling coordination degree models were further employed to reveal their evolutionary patterns, driving mechanisms, and synergistic interactions. The results indicated that ecological-dominated spaces occupied the dominant share (nearly 63%). Meanwhile, living-dominated spaces expanded significantly, growing by 68.93 km² during the study period. The PLES comprehensive function increased by 15.50%, with the living function and production function rising by 57.19% and 61.84%, respectively. Distinct spatial differentiation existed in PLES functions across urban and rural territories, shaping mutually complementary functional systems. The coupling coordination level of PLES functions continuously improved, although notable regional disparities existed, with highly coordinated areas concentrated in midwestern urban areas. PLES functional evolution was jointly driven by natural and socioeconomic factors, which interacted through nonlinear or two-factor enhancement effects. This study provides a replicable methodological framework for fine-scale PLES research and offers scientific support for regional spatial governance and high-quality coordinated development. 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

Background: Former and current smokers in lung cancer screening remain at elevated risk for lung cancer despite smoking cessation. We and others have shown that curcumin (CUR) exhibits anti-inflammatory and antiproliferative effects but is limited by poor bioavailability. However, since CUR is lipophilic, co-administration with ω-3 FAs represents a mechanistically rational strategy to enhance delivery and target complementary pathways, including signal transducer and activator of transcription 3 (STAT3) and the transcription factor NF-κB (NF-κB) signaling for lung cancer chemoprevention. Methods: We conducted a randomized, single-blind, placebo-controlled Phase II pilot study evaluating CUR combined with ω-3 FAs in high-risk former and current smokers with CT-detected pulmonary nodules. Participants received intervention agents with active-dose groups (low dose = 3; high dose = 9) or a placebo (n = 7) for 6 months. Primary endpoints included radiologic changes in nodule size, number, and density. Secondary endpoints included safety, adherence to the study agent and exploratory biomarker analyses. Correlation analyses of imaging-derived metrics were performed to assess relationships among LDCT parameters. Results: Nineteen participants were enrolled (intervention, n = 12; placebo, n = 7). Eighteen (11 intervention, 7 placebo) subjects completed post-intervention imaging. One subject was unable to complete follow-up and study-related procedures. Data from the treatment arms were pooled for analysis and comparison with the placebo arm. No statistically significant between-group differences were observed in primary imaging endpoints. The intervention was well tolerated, with predominantly grade 1 adverse events. Exploratory analyses demonstrated consistent positive correlations among established imaging biomarkers, with clustering of size-based metrics (mean diameter, volume, sum of longest diameters) and density-based parameters. Multidimensional scaling supported this structure, indicating internal coherence among imaging-derived endpoints. Conclusions: Although no statistically significant treatment effect on the image biomarkers was observed, this pilot study demonstrates feasibility challenges and identifies coherent imaging biomarkers that may serve as intermediate endpoints in early-phase chemoprevention trials. These results support further investigation of strategies utilizing agent combinations with enhanced bioavailability and safety and refinement of trial design in high-risk lung cancer patient populations. 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