Search Results (7,930)

Protected areas (PAs) are increasingly challenged by the need to reconcile biodiversity conservation with sustainable human use, particularly in landscapes containing underutilized or resting area (RA). This study evaluated the potential of resting forest and agricultural lands to enhance biodiversity and support sustainable land use within protected areas of Cesme, Türkiye. A Geographic Information System (GIS)-based multi-criteria evaluation approach was employed, integrating land cover data, soil group maps, topographic parameters, and protected area classifications to generate Plant Suitability Maps (PSMs). Eight thematic layers were developed, incorporating soil depth, slope, erosion risk, and land capability classes to identify suitable plant species and land-use options. The results indicate that the strategic use of resting agricultural lands could contribute up to 35.5% to ecological enhancement, while resting forest lands could contribute an additional 18%. The proposed plant assemblages include medicinal and aromatic species, erosion-control plants, and economically valuable perennial species that support ecosystem services such as pollination, beekeeping, and agro-tourism. Overall, the findings demonstrate that integrating RA management into conservation planning can simultaneously strengthen biodiversity, improve ecosystem services, and generate socio-economic benefits for local communities. The proposed GIS-based framework offered a transferable and scalable methodology for sustainable land management in Mediterranean landscapes and other protected regions worldwide. Also, in this research, the aim was to determine plant species using GIS-based suitability analyses of multi-spatial datato guide vegetation decisions in multi-criteria PA. Full article

Frequent heavy rainstorm disasters have led to the need for temporary storage of large quantities of heterogeneous disaster-related solid waste within a short period, making temporary storage an important issue in the construction and optimization of the urban comprehensive urban emergency management systems. This study takes the “23·7” catastrophic rainstorm event in Mentougou District, an area prone to rainstorm disasters in Beijing, as a case study and develops an auxiliary decision-making model for site selection that integrates estimates of construction waste and household goods waste, an “initial selection—screening—optimization” suitability evaluation, and the optimization of spatial layout optimization. By combining the spatial analysis method of the Geographic Information System (GIS), an evaluation index system covering natural geography, ecological environment, and socio-economic factors was constructed. An integrated AHP–EWM model was constructed, merging the expert-driven, subjective weighting of the Analytic Hierarchy Process with the objective, data-derived weighting of the Entropy Weight Method to determine indicator weights. The suitability distribution for site selection was studied by combining the multi-factor weighted overlay model, and the area most suitable for construction of Temporary Disaster Waste Storage Sites (TDWSSs), accounting for 4.51% of the total area, was identified. Subsequently, multiple constraints—including ecological protection redlines and minimum area requirements—were superimposed to exclude non-compliant areas. Ultimately, a combined optimization model integrating the minimum facility location model, maximum coverage model, and minimum impedance model was constructed, and the optimal site selection scheme was determined via ArcGIS. The results show that, when seven TDWSSs are considered, the coverage rate of administrative villages within the 20 km transportation service range reaches 97.38%. The results also indicate that, when the number of TDWSSs exceeds eight, the increase in the coverage rate tends to be moderate and the optimization space is limited, indicating that the layout scheme with seven TDWSSs is close to the regional optimal solution. This framework provides crucial guidance for post-rainstorm TDWSS planning and layout optimization. Full article

In ageing and built environment research, unevenness in evidence across spatial contexts constitutes a methodological condition rather than a simple coverage gap, with direct implications for how accessibility, usability and fairness are conceptualised and measured. Research on built environments for older adults has largely relied on urban evidence. Although findings from cities remain valuable, this focus influences how concepts are defined, which indicators are considered valid, and how far results can be generalised. Indicators designed for high-density urban settings often capture service availability well but have limited validity in low-density or resource-scarce environments. In such contexts, the presence of nearby services is frequently equated with accessibility, and accessibility is often assumed to imply usability. This paper synthesises review and measurement research to identify three mechanisms sustaining urban bias: urban-focused sampling, limited transferability of common indicators, and exposure definitions that assume density and reliable infrastructure. Building on this analysis, the study proposes a measurement framework that explicitly takes principles of fairness into account. This framework is organised around four analytical lenses: distribution, recognition, participation and sustainability. Matching sampling strategies, spatial classifications, measurement strategies and reporting practices to local settlement characteristics is critical to ensuring that conclusions are appropriately limited to what indicator-based evidence can validly support. Full article

This study presents a systematic critical review of 91 peer-reviewed publications published between 2000 and 2024, examining the sustainability of Mediterranean tourism destinations through indicator-based frameworks. Using the Scopus database (Elsevier) and PRISMA-based screening, the review coded studies by methodological approach, indicator type, sustainability dimension, stakeholder involvement, and data source. Quantitative and mixed-methods designs dominated the corpus, together accounting for 90.1% of the reviewed studies, while geographical coverage was highly concentrated in Spain (52.7%), Greece (14.3%), and Italy (13.2%), which jointly represented 80.2% of the corpus. The literature also expanded markedly over time, from 8 studies (8.8%) in 2003–2010 to 39 studies (42.9%) in 2021–2024. Dimensional analysis showed strong emphasis on economic and environmental sustainability assessment, addressed in 92.3% and 91.2% of studies respectively, whereas cultural sustainability received attention in only 23.1% of the corpus. These findings highlight persistent problems of geographic imbalance, limited standardisation, and insufficient multidimensional integration in Mediterranean tourism sustainability assessment. In response, the study proposes the Mediterranean Sustainability Assessment Framework (MSAF), an appraisal-oriented framework for evaluating and improving destination-level sustainability indicator systems in terms of dimensional completeness, methodological pluralism, and contextual embeddedness. Full article

Background/Objectives: The aim of this study was to systematically synthesize and quantitatively estimate the mean serum 25-hydroxyvitamin D concentrations across populations in Kazakhstan and to examine variations according to age group, health status, and geographic region. In addition, we specifically evaluated healthy subgroups to establish reference estimates that may be useful for future epidemiological surveillance and public health planning. Methods: A systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines. PubMed, Scopus, ScienceDirect, and Google Scholar were searched through March 2026 without date restrictions. Studies reporting the mean serum vitamin D concentrations among Kazakhstani populations were included. Random-effects meta-analysis was performed in R. Subgroup analyses were conducted by age group, health status, and geographic region. Meta-regression, influence diagnostics, publication bias assessment, JBI risk-of-bias evaluation, and GRADE certainty-of-evidence assessment were performed. Results: Sixteen studies comprising 28 groups and 5771 participants were included. The pooled mean serum 25(OH)D concentration in the overall cohort was 22.3 ng/mL (95% CI: 19.3–25.3), while the healthy cohort demonstrated a slightly higher pooled mean of 24.4 ng/mL (95% CI: 20.3–28.4). Adolescents had the lowest vitamin D levels among all age groups. Significant regional variability was observed, and meta-regression identified male participant proportion as a significant moderator (p = 0.03). Heterogeneity was extremely high across analyses (I² ≈ 99.9%). Conclusions: Mean serum 25(OH)D concentrations were generally within the insufficient range across the included study groups in Kazakhstan, including healthy subgroups. However, because the certainty of evidence was very low and between-study heterogeneity was extreme, the findings should be interpreted cautiously. These results support the need for standardized national surveillance and locally evaluated public health policy considerations, including targeted supplementation for high-risk groups, screening strategies where clinically indicated, and assessment of food fortification options. Full article

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources. Full article

Seagrass meadows support critical coastal ecosystems, but corresponding species-level remote sensing data remain limited, particularly in the Arabian Gulf, where field spectral data for dominant taxa are extremely limited. We present the first multi-species spectral characterisation of three dominant seagrass species in the Kingdom of Bahrain—Halophila stipulacea (n = 46 spectra, 25 stations), Halodule uninervis (n = 34, 19 stations), and Halophila ovalis (n = 17, 8 stations)—measured with an ASD FieldSpec^® 4 Hi-Res spectroradiometer (Malvern Panalytical, Malvern, UK; 350–2500 nm) from samples collected across 29 geographic stations (52 species–station sampling units). All sample counts reported here underwent quality control. Kruskal–Wallis tests with Benjamini–Hochberg (BH) correction, Jeffries–Matusita (JM) distance, Hedges’ g, and linear discriminant analysis (LDA) were used to characterise inter-species differences. H. ovalis was clearly distinguished from both co-occurring species: the Hd. uninervis–H. ovalis pair showed a discriminating window of 692–1394 nm (mean |g| = 1.31, BH q = 0.000046), and that for the H. stipulacea–H. ovalis pair was 700–1376 nm (mean |g| = 1.21, BH q = 0.000285); the JM distances were 1.60–1.67. A secondary shortwave-infrared discriminating window (1607–1755 nm; mean |g| = 0.90, BH q = 0.006) was also identified for the Hd. uninervis–H. ovalis pair. The H. stipulacea–Hd. uninervis pair showed meaningful geometric separation (JM = 0.994) but no individually significant wavelengths at the available sample size. ASentinel-2-proxy LDA achieved 85.6% overall accuracy (balanced accuracy = 87.3%; macro area under the curve = 0.917), outperforming a Landsat-proxy model by 20 percentage points. For each species, both a best-overall index and a visible-range alternative optimised for submerged satellite remote sensing are reported. The primary indices achieved balanced accuracies of 0.877–0.924; the visible-range alternatives achieved 0.818–0.907. Performance degraded substantially under noise (σ ≥ 0.002: −7.5 percentage points [pp]) and wavelength misregistration (±2–3 nm shifts caused losses of 5.5–15.7 pp), calling for stringent calibration requirements. These results constitute the first multi-species spectral library for Kingdom of Bahrain seagrasses, supporting Sentinel-2-based species mapping in the Arabian Gulf. Full article

This study presents a hybrid spatio-textual matching approach for integrating historical web-derived address datasets with a municipal reference dataset, using the 2009 administrative delineation of Şişli (Istanbul) as a case study. The proposed approach addresses challenges commonly encountered in data obtained from web archives, such as lexical differences, abbreviations, heterogeneous structures, and missing address information. The methodology consists of three main stages: (i) preprocessing and structuring of web-based address records; (ii) hybrid matching, combining deterministic rules with similarity-based methods; and (iii) post-matching geographic enrichment using an Application Programming Interface (API) to provide supplementary geographic context for matched records. The matching process is conducted exclusively between historical datasets; contemporary geographic information is used only after the completion of the matching process to provide additional contextual information. The methodology integrates token-based, vector-based, and structural similarity measures within a calibrated scoring scheme to improve the matching of ambiguous and inconsistent address records. The results indicate that 65.4% of the records were automatically accepted, 7.3% required manual review, and no suitable candidate was found for 5.4%. Deterministic matching results reveal that strict rule-based approaches are highly sensitive to data integrity and attribute consistency, especially in heterogeneous web-based datasets, highlighting the value of combining multiple similarity measures within a hybrid matching strategy. The API-based enrichment results provide supplementary geographic context regarding the contemporary surroundings of matched records, while historical interpretations remain grounded in the original archival datasets. In this context, the study may contribute to the integration of historical web-based address data with structured municipal datasets under heterogeneous archival data conditions. Full article

The urban heat island (UHI) effect has become a global environmental challenge, and quantifying the spatial heterogeneity of its driving mechanisms while developing differentiated regulation strategies remains a critical research gap. This study takes Berlin, Germany as a case study, integrating spatial autocorrelation analysis with a coupled geographically weighted regression (GWR) model to systematically investigate the spatial heterogeneity of the driving mechanisms of Local Climate Zones (LCZs) on surface urban heat island intensity (SUHII), and proposes refined regulation strategies. First, the WUDAPT method was employed to generate a LCZ map, and global and local Moran’s I were used to identify SUHII spatial clustering characteristics, dividing the study area into High–High (HH), Low–Low (LL), and Not Significant (NS) clustering zones. Second, Ordinary Least Squares (OLS) and GWR coupled models were constructed to analyze the global and local relationships between LCZ composition and SUHII. The results indicate: (1) Berlin’s SUHII exhibits significant spatial clustering characteristics (Moran’s I = 0.984), with clear differentiation between the HH zone (25.8%, mean 2.67 °C) and the LL zone (26.4%, mean −0.16 °C); (2) the GWR model (R² = 0.921, AICc = 1279.538) significantly outperforms the OLS model (R² = 0.822, AICc = 2871.608), confirming strong spatial heterogeneity in the LCZ-SUHII relationship, with more pronounced advantages of GWR in urban–rural fringe areas; (3) LCZ 5 (low-density mid-rise buildings) and LCZ 2 (high-density mid-rise buildings) are key warming factors across the entire study area, but their warming effects are stronger in suburban areas than in central urban areas; LCZ A (dense trees) and LCZ G (water bodies) are key cooling factors across the entire area, but their cooling effects are stronger in central urban areas than in the suburbs. Based on these findings, this study establishes a differentiated strategy framework of “Zoning—Identifying Heterogeneity—Regulating”, proposing that HH zones should implement “carbon sink enhancement and source reduction”, NS zones should balance “ecological expansion with growth management”, and LL zones should adopt “strict protection and development restriction”. This framework provides a quantifiable scientific basis and practical guidance for refined urban thermal environment management. Full article

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity. Full article

The global rise in multidrug-resistant (MDR) fungal pathogens has positioned Candida auris and Candida glabrata as major threats to public health. In recent years, these pathogens have increasingly been reported beyond traditional hospital settings, including neonatal intensive care units, long-term care facilities, oncology wards, and post-pandemic critical care environments. International surveillance bodies, including the Centers for Disease Control and Prevention (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO), and regional monitoring networks, have documented escalating antifungal resistance, complex outbreak dynamics, and persistent gaps in infection control implementation. C. auris has emerged as a major etiological agent of healthcare-associated outbreaks, particularly in intensive care and neonatal units. Surveillance data indicate that a high proportion of C. auris isolates exhibit resistance to azoles, often exceeding 80% in some regions, while echinocandin resistance remains variable. Resistance patterns have evolved from predominantly azole resistance to broader multidrug-resistant phenotypes, including treatment-emergent echinocandin resistance. Six genetically distinct clades (I–VI) have been identified, with Clades I, III, and IV associated with large-scale outbreaks, whereas available data suggests that Clades II, V, and VI are more geographically restricted, although evidence for the recently described clades remains limited. C. glabrata is increasingly recognized as a major cause of invasive candidiasis, with rising resistance reported across multiple regions. While reduced azole susceptibility was historically predominant, emerging evidence highlights rising dual azole–echinocandin resistance, adaptive microevolution during antifungal therapy, and biofilm-associated tolerance mechanisms. Despite these advances, significant gaps persist in global resistance surveillance and in the mechanistic understanding of virulence and antifungal adaptation. Current mitigation strategies include antifungal stewardship programs, expanded resistance testing, and strengthened surveillance systems. Advances in rapid diagnostic technologies such as matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry, polymerase chain reaction (PCR)-based assays, and genomic surveillance have improved pathogen identification and outbreak detection, although accessibility remains limited in resource-constrained settings. This review examines emerging epidemiological, genomic, and antifungal resistance trends in C. auris and C. glabrata and highlights key priorities for improving diagnosis, surveillance, stewardship, and management of multidrug-resistant Candida infections. Full article

Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. Full article

Understanding trade-offs and synergies among ecosystem services (ESs) along environmental gradients is crucial for sustainable oasis management. This study investigated four key ESs—carbon storage (CS), habitat quality (HQ), water yield (WY), and soil conservation (SC)—in three typical oases along water–heat gradients in arid northwestern China. The InVEST model was used to quantify ESs in 1990, 2005, and 2022, and Pearson correlation, geographically weighted regression, K-means clustering, and random forest models were applied to analyze service relationships, ecosystem service bundles (ESBs), and driving factors. The results showed that CS and HQ maintained strong synergies, while the WY–SC relationship shifted from weak trade-offs under drier conditions to stronger synergies under more favorable water–heat conditions. Geographically weighted regression revealed spatial heterogeneity and directional asymmetry in ES relationships. Four ESB types were identified: ecologically fragile zones, ecological transition or buffer zones, agricultural production zones, and core ecological source zones. Driving-factor analysis indicated that vegetation-related services were mainly associated with land-cover structure and vegetation growth, whereas hydrological and erosion-related services were more closely linked to precipitation, potential evapotranspiration, temperature, and topography. These findings support differentiated oasis management through ecological restoration, development regulation, water-saving agriculture, and strict ecological protection. Full article

Background: Increasing evidence suggests that breast milk and its bioactive components, including short-chain fatty acids and the milk microbiome, are influenced by maternal nutrition and body mass index (BMI). Bioactive components transferred to the infant through breast milk play a pivotal role in infant growth and development and have indications in the child’s future short- and long-term health outcomes. This study aimed to assess the impact of maternal pre-pregnancy BMI (PP-BMI) on human breast milk macronutrient composition, short- and long-chain fatty acid profiles, and breast milk microbiome profiles. Approach: This was an exploratory cohort study of forty-four lactating Caucasian women, two to fourteen weeks postpartum, divided into groups based on pre-pregnancy body mass index (BMI). Study participants signed informed consent, completed health and nutritional surveys, and provided a breast milk sample. Breast milk samples were subjected to proximate analysis, microbiome identification and short- and long-chain fatty acid extraction and analysis. Results: Maternal age, maternal physical activity, infant birth weight, and time of lactation at sample collection were not significantly different between the maternal PP-BMI groups. PP-BMI was significantly different between the two maternal groups. No significant differences were found between the maternal BMI groups concerning nutritional intake. No differences in breast milk microbiomes were observed in alpha diversity and beta diversity between the maternal PP-BMI groups. For long-chain fatty analysis in breast milk samples, myristic acid was significantly higher in the PP-BMI overweight/obese group while stearic acid was significantly higher in the PP-BMI normal-weight group. Butyric, valeric, and isocaproic acid concentrations in HBM were significantly higher in the PP-BMI normal-weight group and lower or undetectable in the PP-BMI overweight/obese group. Conclusions: Data from this exploratory cohort study indicate that maternal diet and pre-pregnancy BMI may be associated with differences in selected HBM fatty acids. There were no significant differences in microbiomes for alpha and beta diversity in breast milk between maternal PP-BMI groups; however, lower relative abundance was observed in the breast milk of the PP-BMI overweight/obese group. These findings should be interpreted in the context of the study’s limitations, including convenience recruitment from a Facebook group, the modest sample size, and restriction to Caucasian women from a single geographic region. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article