Search Results (11,637)

A holistic approach that prioritizes economic success and sustainable practices through Corporate Social Responsibility (CSR) is crucial for the long-term sustainability of organizations, including the tourism and hospitality industry, and the first step is CSR knowledge. The aim of this study is to identify the key factors influencing the level of Corporate Social Responsibility (CSR) knowledge in the hospitality industry, as a practical tool for the sustainability of the territories. For this purpose, the research was conducted using a quantitative methodological approach by applying a CSR questionnaire to hotel managers from a sample of 222 hotels in the Colombian Caribbean. Multivariate statistical techniques were applied, specifically Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). The Principal Component Analysis determined two dependent variables (Basic CSR Knowledge and Advanced CSR Knowledge) and subsequently a Multiple Linear Regression was applied to each one, determining which independent variables (treated as dummy variables) have significant effects. The results have led to the conclusion that the CSR knowledge of the hotel sector in the Colombian Caribbean is positively influenced by hotel-related factors—such as age, management contract type, financial performance, and investment in innovation—as well as by managers’ gender and educational attainment. These results have important implications for the hotel sector and academia. Future research should consider more stakeholders and other geographical areas. Full article

Juniperus communis is the juniper with the widest geographical distribution, owing to its high ecological valence. Nevertheless, there is only a limited number of studies of its phenotypic and molecular variability. In this study, we coupled leaf essential oil (EO) composition with molecular and environmental data to better understand this species’ distribution and variability in the central Balkans. EOs were obtained by simultaneous hydrodistillation and extraction, and analysed using GC coupled with MS and FID detectors. For molecular analysis, inter-simple sequence repeats (ISSR) using five primers were analysed. Three chemotypes were most abundant in the study area: sabinene, an intermediate chemotype, and α-pinene. Several additional chemotypes were also identified. In total, 118 compounds present above 0.05% were detected and identified. Monoterpene hydrocarbons dominated the EO composition (43.8–79.1%). Multivariate analyses showed separation of populations from north to south. ISSRs yielded 78 polymorphic bands. Three genetic pools could also be identified that roughly correspond to this distribution, though data is not completely congruent with chemophenetic. Results indicate high genetic diversity, with high gene flow between populations, but also certain differentiation of populations. Full article

Background/Objectives: Emergency department (ED) overcrowding burdens rural and remote areas where geographic isolation limits timely care. The Compact Emergency Unit (CEU)—a 24 h facility with remote physician oversight—has been proposed but lacks an empirical foundation. We aimed to (1) quantify CEU-eligible (final KTAS 4–5) patients in a multicenter ED cohort; (2) compare their operational metrics with non-eligible patients; (3) characterize hourly demand for facility planning; and (4) develop machine-learning models for non-discharge prediction within this low-acuity stratum. Methods: Retrospective analysis of 12 months (January–December 2025) of NEDIS data from two Korean university-affiliated EDs. Effect sizes (Cliff’s δ, Cramér’s V) were reported alongside p-values. Three classifiers (logistic regression, random forest, and XGBoost) were developed with patient-level cross-validation, comparing a 16-feature baseline and a 22-feature set augmented with arrival vital signs. Calibration and decision curve analysis were performed. Results: Of 34,544 valid triage visits (27,743 unique patients), 9871 (28.6%) were CEU-eligible. They had shorter LOS (92 vs. 171 min; Cliff’s δ = −0.51), 98.8% symptomatic home discharge, and a median of 0 specialty consultations. Nighttime visits comprised 43.7% of CEU-eligible encounters, peaking at 20:00 (1.76 visits/h/day). The non-discharge rate was 1.20% (118/9871). The vital-augmented random forest reached AUROC 0.794 (95% CI 0.758–0.829); XGBoost calibration was near-perfect (ECE 0.020). A combined ML-or-vital-sign screening rule raised non-discharge sensitivity to 94.1%. Conclusions: Approximately 29% of ED visits could be CEU-suitable. Single-modality machine learning is insufficient for safety-critical triage, but a layered ML-plus-vitals screening approach achieves operationally relevant sensitivity. Prospective implementation studies are required before clinical deployment. Full article

Power battery enterprises are a key link in the new energy vehicle (NEV) industry chain. However, studies analyzing the investment layout of power battery enterprises from a micro perspective are relatively scarce. This study takes Contemporary Amperex Technology Co. Limited (CATL) as a case and employs various spatial analysis methods and an optimal parameter-based geographical detector (OPGD) to analyze the spatiotemporal evolution and driving mechanisms of its investment layout from 2020 to 2024. The results indicate that CATL’s investment center has shifted from Jiangxi to Hubei, and the spatial expansion axis has changed from a northwest–southeast to a southwest–northeast direction. The investment layout has evolved from a “one core with two secondary cores” structure to a “provincial dual core, multi-core outside the province” structure and, ultimately, to a nationwide networked pattern. By 2024, CATL’s investment network covered the southeastern coast, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), central China, and southwestern regions. County-level spatial autocorrelation analysis shows that the investment agglomeration effect has continuously strengthened (with the global Moran’s I increasing from 0.006 to 0.025). High–high agglomeration areas gradually expanded from the southeastern coast to Xiamen and several provinces in central and western China, while high–low agglomeration areas, as early signals of investment diffusion, initially expanded and then contracted. The driving mechanism analysis reveals that fiscal support (q = 0.668), industrial structure upgrading (q = 0.585), tax burden (q = 0.543), and economic development (q = 0.536) are the primary factors driving investment layout, with significant synergistic effects between these factors. The synergy between industrial structure upgrading and clean energy supply stands out as particularly prominent. These findings contribute to optimizing the spatial layout of the NEV industry and promoting regional economic development. Full article

Cultivated land fragmentation has become a critical constraint on regional agricultural sustainable development. Revealing its spatial patterns and driving mechanisms is of great significance for optimizing the utilization and management of cultivated land resources and enhancing regional agricultural productivity. This study focuses on the main grain-producing areas in the middle and lower reaches of the Yangtze River Basin. It constructs a Cultivated Land Fragmentation Index (CLFI) using an integrated method that combines landscape index analysis with an entropy-weighted approach, based on 2023 land-use data. The optimal analytical grain size and extent were determined before employing geographic detectors to identify dominant factors influencing cultivated land fragmentation. The key findings include the following: (1) The appropriate spatial resolution for fragmentation analysis was identified as 330 m, with an optimal analysis extent of 8910 m. (2) CLFI values ranged from 0.001 to 0.973, exhibiting significant spatial heterogeneity. The central plains and northeastern regions demonstrated low fragmentation levels and better contiguous cultivated land distribution, while the western and peripheral areas showed higher fragmentation. A provincial-scale comparison revealed that Jiangxi Province had the highest fragmentation level (0.255), whereas Jiangsu Province had the lowest (0.146). The topographic gradient analysis indicated a decreasing trend from the Guizhou Plateau (0.503) to the North China Plain (0.125), with plateaus and basins showing significantly higher fragmentation than hilly and plain regions. (3) Dominant controlling factors varied among provinces: In provinces with greater topographic relief (Anhui, Hubei, Hunan, Jiangxi), natural factors like elevation, slope gradient, and NDVI primarily controlled fragmentation patterns; in contrast, socioeconomic factors such as nighttime light intensity dominated in Jiangsu Province, characterized by flat terrain and high urbanization. Multi-factor interactions generally enhanced explanatory power regarding spatial patterns, confirming that cultivated land fragmentation is a result of comprehensive multi-factor interactions. This study reveals the spatial distribution characteristics of cultivated land fragmentation at the pixel scale in the study region, providing theoretical foundations and decision-making references for the efficient utilization of cultivated land resources and rural land system reforms. Full article

As a critical link between regional economic development and ecological security, understanding the dynamics of water retention is essential for sustainable water resource management in the Huaihe River Economic Belt. This study explores the spatio-temporal evolution and spatial explanatory factors of water retention across five temporal snapshots (2003, 2008, 2013, 2018, and 2023). Based on the InVEST model, we assessed water retention capacity at both grid and spatial development levels, thereby obtaining the retention characteristics of different land-use types and their responses to land-use transitions. Furthermore, a parameter-optimized geographical detector was employed to quantify the relative contributions of climatic-environmental and social-economic factors to the spatial variance of the modeled water retention index. Results indicate that the total water retention capacity exhibited significant interannual fluctuations, with the net capacity in 2023 being lower than the initial level in 2003. Retention values displayed obvious spatial heterogeneity, with high levels concentrated in the southwest and north and low levels distributed in the central area, closely mirroring precipitation distribution. While forest land exhibited the strongest unit water retention capacity, cropland contributed the most to the total volume (50.49%) due to its predominant areal proportion (73.92%). Notably, the conversion of forest to cropland was spatially associated with the most substantial loss in the modeled retention capacity. Soil saturated hydraulic conductivity and land-use type were identified as the dominant factors explaining the spatial variance of water retention. These findings underscore the methodological utility of coupling the InVEST model with a parameter-optimized geographical detector. For practical ecosystem management, the results suggest that spatial planning policies should strictly limit the conversion of ecological lands to agricultural use and prioritize targeted soil hydrological improvements in the central plains to secure long-term water resources. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

The spatiotemporal evolution of ecosystem services and the elucidation of their driving mechanisms constitute a central scientific issue in territorial spatial optimization and regional sustainable development. Taking Gansu Province, a core area of the ecological security barrier in northwestern China, as the study area, this study integrates land-use, natural geographic, and socioeconomic data from 2000 to 2020. Using a land-use transfer matrix, the InVEST model, the Geographical Detector, and the PLUS model, we constructed a comprehensive analytical framework that combines historical evolution analysis, spatial differentiation identification, and multi-scenario simulation and prediction. The framework was used to systematically reveal the spatiotemporal dynamics of four core ecosystem services, namely carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention service (SDR), and to analyze their natural and socioeconomic driving mechanisms, while also simulating land-use change and ecosystem-service responses under the natural development, ecological protection, and urban expansion scenarios in 2030. The results show that, from 2000 to 2020, land use in Gansu Province was dominated by grassland (average proportion: 33.34%) and unused land (average proportion: 41.35%). Urban land expanded from 660.52 km² to 2227.36 km², with its share increasing from 0.15% to 0.50%, mainly through the conversion of cropland and grassland. Ecosystem services exhibited marked spatial differentiation: CS increased from east to west; WY showed an increasing pattern from northwest to southeast; HQ was lower in the central and southeastern regions and higher in the western and southern regions; and SDR was dominated by low-value areas in the northwest (average proportion: 84.81%). Driving-mechanism analysis indicated that slope was the core natural factor affecting CS, HQ, and SDR (q = 0.18–0.45), while mean annual precipitation dominated the variation in WY (q = 0.31–0.35). The influence of socioeconomic factors such as GDP increased gradually over time, showing an evolutionary trend from natural dominance to coordinated natural–socioeconomic regulation. Multi-scenario simulation further showed that, under the ecological protection scenario, grassland area increased significantly (+0.60%), the proportions of medium-value CS zones and high-value WY zones increased, and ecosystem services were optimized overall; under the urban expansion scenario, cropland and urban land expanded (+0.87% and +0.23%, respectively), imposing potential pressure on part of the ecosystem-service functions. These findings provide a scientific basis for optimizing territorial spatial planning, strengthening the ecological security barrier, and promoting regional sustainable development in Gansu Province. The methodological framework also offers a broadly applicable reference for ecologically sensitive arid and semi-arid regions in northwestern China. Full article

This study proposes a scalable, AI-driven, and Geographic Information System (GIS)-integrated framework for intelligent route-level classification in large-scale airport transportation networks to support airport operations, logistics planning, and network-level decision-making. The framework addresses the need for practical artificial intelligence applications that combine spatial network analysis with supervised machine learning to improve route assessment and resource allocation in complex air transport systems. A structured dataset was developed using operational and traffic-related attributes, including route distance, aircraft capacity, weekly frequency, annual passenger volume, demand variability, and route performance indicators, with additional normalized features to improve data representation. A Gradient Boosting ensemble classifier was trained to categorize routes into high-, medium-, and low-priority classes. The model achieved strong predictive performance, with a testing area under the ROC curve of 0.961, accuracy of 0.922, F1-score of 0.915, precision of 0.918, and a recall of 0.922. Feature importance analysis identified demand variability and route-density indicators as the main drivers of classification, enhancing interpretability and practical trust. The proposed framework demonstrates the real-world potential of AI for scalable, explainable, and efficient decision support in airport logistics and transportation network management. Full article

Neglected tropical diseases (NTDs) such as soil-transmitted helminthiasis, lymphatic filariasis, schistosomiasis, leprosy, rabies, and food-borne trematodiasis are endemic in the Philippines. Despite global and national elimination efforts, these six NTDs remain a persistent burden to the poor, those living in Geographically Isolated and Disadvantaged Areas (GIDAs), and other vulnerable groups. This narrative review synthesized data from Field Health Services Information System (FHSIS) reports of the Philippine Department of Health (DOH) from 2020 to 2024, the available literature from electronic databases, and DOH and WHO reports focusing on the challenges, barriers, and gaps in NTD control and elimination in the country. Core challenges include complex epidemiological landscapes, lapses in disease surveillance, infrastructure, and fragmented health care systems. Gaps include access to diagnostics, insufficient funding and human resource training, and scarcity of local studies focusing on endemic NTDs. With these challenges and gaps, this review highlights the need for a real-time feedback loop system in surveillance strategy, community-based interventions, full integration of NTDs in primary health care, and collaboration between government, NGOs and private entities. Addressing these challenges and gaps is key to shifting from control to elimination. Full article

Aim: The aim of this study was to explore the distribution and driving factors influencing the disparity of health professionals (HPs) at the Centers for Disease Control and Prevention (CDC) in China and to provide a reference for regional health planning and rational allocation of public health resources. Methods: The Gini coefficient was used to measure the equity of HP distribution at CDC sites at the provincial level during 2012–2023 in China. Moran’s I was used to analyze the spatial agglomeration effect, and the geographic detector model was used to explore the factors driving the allocation of HPs at CDC sites in different provinces. Results: The number of HPs at the CDC showed an increasing trend from 2012 to 2023 in China. The average Gini coefficients at the population and geographical areas were 0.16 and 0.58, respectively. The global Moran’s I statistic indicated a notable decline in spatial clustering for the population dimension, decreasing from 0.503 to 0.238; in contrast, spatial clustering for the geographical dimension remained relatively stable, ranging between 0.13 and 0.16. The local Moran’s I statistic revealed that provinces such as Qinghai in the western China consistently exhibited a “low–low” spatial clustering pattern. Six factors were found to explain the variability in the CDC HP distribution based on the 2020 data. In the context of factor interactions, the synergistic effects between education level and health expenditure share (q = 0.9781), and between population aging and per capita GDP (q = 0.9699), substantially exceed the explanatory power attributable to any single factor alone. Conclusions: A significant regional disparity was observed in the distribution of HPs among 31 provinces, with the distribution based on service area being less equitable than that based on population. The shortage of healthcare professionals in the western region is characterized by notably inadequate geographical distribution. Future policy initiatives should prioritize targeted spatial interventions and integrated, multi-factor collaborative strategies. Full article

Urban sprawl constantly reconfigures the land use pattern, and such transformations may significantly modify regional carbon stocks. Utilizing Tai’an City as the study site, this research established a comprehensive integrated Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Optimal Parameters-based Geographical Detector (OPGD) system to reconstruct carbon storage shifts from 2000 to 2020, project its reaction to four diverse development trajectories in 2030, and investigate the drivers underlying spatial disparities. The results indicate a persistent decline in carbon storage throughout the past two decades, with peak concentrations primarily gathered in mountain regions dominated by forest and grassland, whereas lesser amounts were grouped in urban and suburban areas defined by built-up land. Compared to 2020, the projected carbon stock in 2030 drops by 1,803,966 t under the natural growth trajectory and by 2,417,778 t under the high-quality economic growth pathway, whereas it rises by 47,326 t under cultivated land conservation and by 7679 t under ecological conservation. Elevation represents the most crucial driver among the selected variables in clarifying the spatial fluctuation of carbon storage (q = 0.3985), followed by slope (0.3323), mean annual temperature (0.2382), and the Normalized Difference Vegetation Index (NDVI) (0.1219). The synergy between elevation and NDVI produces the highest integrated explanatory power (q = 0.4906). These outcomes imply that constraining construction land growth while protecting agricultural and ecological land is vital for preserving and enhancing regional carbon sink potential. Full article

Rabies is a fatal zoonotic disease once clinical symptoms develop. In Europe, sustained animal rabies control programs have led to a marked decline in animal rabies and subsequently human rabies cases; however, sporadic infections continue to occur. In July 2025, a fatal case of autochthonous (locally acquired) human rabies was confirmed in Romania following a stray dog bite in a patient who did not receive post-exposure prophylaxis (PEP). Here, we report the first molecular characterization of a human rabies virus (RABV) strain isolated in Romania and place it in the context of contemporaneously circulating animal-derived RABV strains. Rabies virus infection was confirmed intra vitam by fluorescent antibody testing and both conventional and real-time RT-PCR on cerebrospinal fluid and saliva, with postmortem confirmation on skin and brain tissue. Whole-genome sequencing was performed on the human isolate and on 22 animal-derived RABV strains collected in northern Romania in 2025. Phylogenetic analyses revealed that all recent Romanian sequences clustered within the North-East European (NEE) rabies virus phylogenetic group and segregated into two geographically distinct genetic clusters: a north-western cluster, closely related to strains from Slovakia and Poland, and a larger north-eastern cluster, linked to viruses circulating in eastern Romania and the Republic of Moldova. The human-derived RABV genome was grouped within the north-eastern cluster and showed the highest genetic similarity to animal viral strains from the same geographical area, supporting a local transmission event. This demonstrates the importance of integrating human viral genomic data into the national rabies surveillance framework. Full article

Equitable access to rehabilitation services is essential for individuals living with a disability, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate the travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2255 outpatient rehabilitation clinics across 6896 census tracts. Travel times varied substantially, with rural areas experiencing the longest travel burdens. The 2SFCA analysis revealed pronounced disparities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, suggesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article