Search Results (4,395)

Innovation-oriented productive forces are increasingly concentrated in cities, but the multiscale mechanisms through which the built environment shapes these forces remain insufficiently understood. This study develops a spatial analytical framework linking firm-level new quality productive forces (NQPF) to fine-grained urban spatial structures. Using 89 A-share listed firms in the Xiamen–Zhangzhou–Quanzhou (XZQ) urban agglomeration, we first construct an entropy-weighted NQPF index from eleven financial indicators related to R&D human capital, advanced capital stock, intangible assets, and operational efficiency. Kernel density estimation is then used to transform discrete firm-level NQPF values into a continuous 600 m × 600 m grid surface as the dependent variable. On the explanatory side, 27 built-environment variables are organized into an integrated indicator system covering urban form, natural conditions, jobs–housing structure, and service infrastructures. We combine cross-validated recursive feature elimination (RFE-CV) with multiscale geographically weighted regression (MGWR) to construct two model specifications: a 7-variable parsimonious subset and a 14-variable highest-performing subset. This dual-subset design allows us to distinguish core structural drivers from more context-dependent spatial mechanisms. The results reveal three mechanisms. First, ecological adaptation reflects the scale-dependent enabling and constraining effects of infrastructure and natural-foundation variables. Second, structural coordination shows that mature cores may experience crowding-related suppression when functional and institutional resources become spatially mismatched. Third, boundary activation indicates that transport, public-service, and leisure-related facilities can activate peripheral and cross-jurisdictional interface zones when supported by network connectivity and institutional coordination. By coupling variable-specific bandwidths with local coefficients, this study advances the analysis of spatial heterogeneity and provides evidence for differentiated, innovation-oriented urban regeneration. Full article

Scope 3.1 emissions from purchased goods and services often represent a substantial share of corporate greenhouse gas footprints, yet their quantification is characterized by high uncertainty and limited transparency. In practice, organizations frequently rely on weight-based secondary emission factors, which can vary substantially depending on underlying assumptions such as production technology or geographical origin. Existing standards and data quality approaches provide important guidance on representativeness, reliability, and data exchange, but offer limited operational support for selecting appropriate emission factors. This study addresses this practical challenge by examining emission-factor variability for selected material groups, identifying key influencing factors, and developing a qualitative decision-support framework for evaluating, selecting, and documenting secondary emission factors in Scope 3.1 accounting. The results demonstrate that emission factors for the same material can differ by more than an order of magnitude, leading to substantial deviations in carbon footprint results if selected inconsistently. The proposed framework, while not replacing supplier-specific primary data or formal data-quality assessment, reduces selection-related uncertainty, and supports more reliable carbon accounting, particularly in data-constrained supply chain contexts. By providing a transparent screening logic for early-stage Scope 3.1, the study enables more informed sustainability and procurement decisions. Full article

Photovoltaic (PV) site suitability analysis has become an essential component of renewable energy planning due to the rapid global expansion of solar energy systems and the increasing complexity of land-use, environmental, economic, and infrastructural constraints. Over the past decade, Geographic Information Systems (GIS) integrated with Multi-Criteria Decision-Making (MCDM) techniques have emerged as the dominant methodological framework for identifying optimal PV deployment locations. However, recent advancements in machine learning (ML), explainable artificial intelligence (XAI), clustering techniques, and large language models (LLMs) are beginning to reshape the field toward more data-driven and intelligent spatial decision-making systems. This review provides a comprehensive analysis of PV site suitability studies published between 2016 and 2026, focusing on methodological evolution, criteria selection patterns, reproducibility challenges, and emerging AI-driven approaches. A systematic literature review was conducted using Scopus, Web of Science, and IEEE Xplore databases, resulting in 72 final studies after multi-stage screening. The analysis reveals that AHP-based GIS-MCDM frameworks remain overwhelmingly dominant, while machine learning and hybrid AI approaches are still limited but rapidly emerging. A total of 63 unique suitability criteria were identified, with climatic, infrastructure, and topographic factors representing the most frequently used categories. The review further highlights substantial challenges related to reproducibility, regional variability, data transparency, and expert-driven subjectivity. Recent studies employing explainable ML, unsupervised clustering, and LLM-assisted weighting frameworks demonstrate significant potential for improving adaptability, interpretability, and automation within renewable energy planning. The review concludes that future PV suitability analysis is likely to evolve toward hybrid GeoAI systems integrating GIS, ML, XAI, clustering, and human-centered AI frameworks to support more robust, scalable, and transparent spatial energy planning. Full article

Based on the high relevance of climate change issues and the extent of their investigation at global and regional levels, this study aims to quantify current changes in average annual air temperature, relative humidity, and precipitation within the Chu River watershed, and to identify patterns of their spatiotemporal variability under conditions of intensifying global warming. The study is based on long-term observational data from 18 meteorological stations collected during the period 1940–2024 under diverse physical and geographical conditions across the basin. The analysis of climatic dynamics within the Chu River basin catchment during the period 1940–2024, conventionally divided into four physiographic-altitudinal zones (high-mountain, mid-mountain, low-mountain, and plain regions), and conducted using a linear trend assessment approach with the application of statistical criteria, revealed the presence of multidirectional trends in climatic variables. It was established that the spatial and temporal variability of the trend coefficients for mean annual air temperature ranged from −2.50 to 2.45, for mean annual relative humidity from 0.93 to 1.03, and for annual atmospheric precipitation from 0.59 to 1.42. The identified positive trend in mean annual air temperature, occurring simultaneously with negative trends in relative humidity and atmospheric precipitation, is characterized not only by a stochastic component but also by a pronounced deterministic component manifested in the form of persistent positive and negative trends. These observed patterns are associated with the complex nature of climatic responses occurring in the northern part of the Tien Shan mountain system and along the eastern periphery of the Turan Lowland. Full article

This study aimed to project the current and future suitable habitats of three primary malaria vectors in China—An. lesteri, An. minimus, and An. sinensis—using an ensemble modeling approach. We simulated their geographical distributions under current and future climates (SSP126, SSP245, SSP585) using the Biomod2 platform with 19 bioclimatic variables and elevation. The ensemble models achieved high predictive performance, as reflected by AUC and TSS values. Environmental drivers were species-specific: An. lesteri was primarily influenced by elevation, temperature seasonality (Bio4), and seasonal precipitation (Bio18, Bio19); An. minimus by the mean temperature of the coldest quarter (Bio11); and An. sinensis by annual precipitation (Bio12), mean temperature of the wettest quarter (Bio8), and elevation. Future projections revealed divergent responses: the habitat of An. lesteri is projected to contract and shift northeastward; An. sinensis is expected to expand northward, potentially extending climatically suitable areas into new regions; and although the overall range of An. minimus remains stable, its internal suitability shifts toward higher classes under warming. These findings demonstrate that climate change will critically reshape the distribution of major malaria vectors across China, underscoring the need to integrate climate-informed projections into adaptive surveillance and vector control strategies in the post-elimination era. Full article

We studied long-term changes in temperature extremes in Georgia against the backdrop of ongoing global warming within 1961–2020. Using observational data from 20 meteorological stations from different physical-geographic regions of the country, we analyzed the main climate temperature indices recommended by the WMO and the Expert Team on Climate Change Detection and Indices (ETCCDI). Indices characterizing extremely cold and warm conditions (ice days, frost days, summer days, and tropical nights); growing season length; as well as indices of absolute extremes and percentile temperature characteristics were considered. The results show that along the Black Sea coast and the Kolkheti Lowland, changes in temperature indices are less pronounced due to the moderating influence of the sea and humid landscapes; however, a significant increase in the number of tropical nights is observed. In areas with a more continental climate (Eastern Georgia, the South Georgian Highlands, and the mid-mountain zone of the Greater Caucasus), a general decrease in the number of ice days and frost days was observed, along with a simultaneous increase in the number of hot summer days, tropical nights, and growing season length. Tropical nights and percentile temperature indices were found to be the most sensitive to global warming. Overall, the results indicate widespread long-term tendencies consistent with regional manifestations of global warming, characterized by decreases in cold extremes and an expansion of warm indices. However, the statistical robustness and explanatory power of these trends vary substantially across physical-geographical regions and specific indices. Many significant shifts exhibit low coefficients of determination (R² < 0.1), indicating that although long-term warming trajectories are real, local topography and intense interannual natural variability remain the primary drivers of annual temperature fluctuations in Georgia. Full article

Background: Geographic peripherality in Israel is linked to poorer health outcomes and may disproportionately affect patients requiring chronic therapies such as hemodialysis (HD). Though malnutrition and inflammation are strong predictors of morbidity and mortality in HD patients, regional differences in nutritional status and dietary adherence are unclear. Objectives: To examine the association between peripherality and malnutrition risk, dietary intake, and adherence to nutrition guidelines among Jewish Israeli adults on HD. Methods: In this multi-center, cross-sectional study, 154 adult Jewish HD patients were recruited from the northern periphery (n = 66) and central regions of Israel (n = 88). Demographic, clinical, laboratory, and anthropometric data were obtained from medical records. Nutrient intake was assessed using the multi-pass 24 h dietary recall method. Malnutrition risk was classified using BMI and serum albumin; the C-reactive protein-to-albumin ratio (CAR) was also calculated. Adherence to International Society of Renal Nutrition and Metabolism (ISRNM) dietary guidelines was evaluated. Between-group comparisons and multivariable regression analyses were conducted. Results: Overall, participant characteristics were similar between groups; however, coronary heart disease prevalence and dialysis vintage were higher in the periphery. Participants from the periphery had lower serum albumin, blood urea nitrogen, hemoglobin, and blood pressure, but higher LDL cholesterol. Sodium intake was significantly higher and adherence to ISRNM sodium guidelines markedly lower in the periphery. In multivariable analysis, peripherality reduced the odds of meeting sodium recommendations by 92.8%. Adherence to energy and protein guidelines was low in both groups. Nearly half of participants had some level of elevated malnutrition risk using the categorized variable, and an overall difference in the categories of malnutrition risk was detected, driven by the increase in the moderate risk category in the periphery. The composite malnutrition risk variable (any increase in risk vs. no increase in risk) did not differ by peripherality. Peripherality was independently associated with higher percent ideal body weight (%IBW), but not with CAR. Conclusions: Peripherality among Jewish Israeli HD patients is associated with differences in nutrition biomarkers, cardiovascular burden, and dietary adherence, especially sodium intake. Interventions considering peripherality should be explored. Full article

The Yellow River Basin is a vital ecological security barrier for China, as well as a region rich in cultural and tourism resources. Tourism has emerged as a core industry underpinning both ecological conservation and sustainable, high-quality regional development within the basin. As the tourism industry transitions toward sustainable and high-quality development, tourism efficiency serves not only as a core indicator for measuring the quality of tourism development but also as a critical basis for assessing regional tourism sustainability. Taking 68 Outstanding Tourism Cities in the Yellow River Basin from 2009 to 2023 as research samples, this study employs the Super-Slack-Based Measure (Super-SBM) model to measure tourism efficiency. It depicts the spatiotemporal evolution through trend surface analysis, spatial autocorrelation analysis, hotspot analysis, and standard deviation ellipses and utilizes the Geographically and Temporally Weighted Regression (GTWR) model to identify the determinants of spatiotemporal heterogeneity. Tourism efficiency in the basin’s Outstanding Tourism Cities is generally low but has a variably increasing trend with a pronounced spatial gradient of upstream > midstream > downstream. The efficiency of tourism is highly interdependent spatially and highly clustered, as the regional high and low values are mostly situated up- and downstream, respectively. In general, the center of tourism efficiency has changed to the southwest instead of the northeast. The infrastructure, industrial structure and human capital characterize the efficiency of tourism, but the openness to the external world is the most significant factor, and the impact of these factors also varies sharply in terms of their strength. This study systematically reveals the spatiotemporal evolution patterns and heterogeneous driving mechanisms of tourism efficiency in Outstanding Tourism Cities within the Yellow River Basin. It not only expands the research perspectives and empirical analytical frameworks for sustainable tourism development at the basin scale but also provides a precise decision-making basis for the coordinated advancement of sustainable and high-quality tourism development in the region. Full article

This paper proposes a hybrid, interpretable machine learning framework for the preliminary screening of soil macronutrients using Sentinel-2 and AgroLens data. This study aims not to replace laboratory analysis, but to test the feasibility of obtaining a useful proxy signal for estimating nitrogen (N), phosphorus (P), and potassium (K) content using a limited set of remote sensing and agricultural features. The developed pipeline includes data auditing, leakage control, feature engineering, train-only normalization, group-aware partitioning, baseline/SOTA model comparison, hybrid regression modeling, SHAP interpretation, and uncertainty assessment. The experiment used 4471 AgroLens observations and 126 features derived from Sentinel-2 spectral aggregates, vegetation indices, temporal characteristics, and crop-related parameters. The evaluation indicated that the proposed approach consistently improves forecasting quality relative to baseline models under reduced-input conditions. Linear relationships between target variables ranged from 0.14 to 0.17, while nonlinear relationships reached 0.23. SHAP analysis revealed significant contributions from vegetation indices, crop-specific interactions, and Sentinel-2 spectral channels. The findings support the applicability of the proposed framework for preliminary monitoring, prioritizing field surveys, and decision support in digital agriculture. Although an additional AgroLens control segment was used to assess the robustness of the study, the study did not include independent external validation of the data collected across different geographic or agro-climatic conditions. Full article

Dendrobium crepidatum Lindl. et Paxt. is a medicinal Dendrobium species whose quality and market value may vary with geographic origin, making rapid origin traceability important for batch management, market supervision, and application promotion. This study used near-infrared spectroscopy (NIRS) combined with multivariate analysis and machine learning to discriminate the origin of D. crepidatum. Fifty batches of stem samples from Yunnan, Guangxi, and Guizhou, China, were analyzed after Savitzky-Golay smoothing, standard normal variate transformation, and first-derivative preprocessing. Principal component analysis (PCA) showed origin-related spectral variation, and a three-class partial least squares-discriminant analysis (PLS-DA) model achieved a mean cross-validated accuracy of 70.2% with a significant permutation-test result (p = 0.0020). Six machine learning algorithms, including KNN, CART, RF, NB, LDA, and ANN, were further compared using repeated nested cross-validation. KNN performed best, with an accuracy of 0.811 ± 0.029 and a macro F1-score of 0.813 ± 0.029, followed by RF (0.804 ± 0.038 and 0.805 ± 0.037, respectively). Key spectral variables were mainly located at 4231–4235 and 5523–5624 cm⁻¹ corresponding mainly to C-H-dominated overtone or combination absorptions with possible C-O/O-H-related contributions from carbohydrates, polysaccharides, phenolics, flavonoids, and other organic constituents. These results demonstrate the feasibility of NIRS combined with machine learning for preliminary origin traceability of D. crepidatum and provide spectral clues for future investigation of origin-related chemical variation and quality discrimination. Full article

Achieving climate neutrality by 2050 requires strategic shifts in national energy portfolios tailored to specific geographical and socio-political conditions. This study examines the divergent low-carbon trajectories of Portugal, characterized by variable renewable integration, and Slovakia, defined by a robust nuclear baseload, through the perspectives of STEM students who will lead the energy transition. The rationale for comparing these two countries lies in their contrasting low-carbon strategies, which provide a natural setting for examining how divergent national energy systems shape the attitudes of a technically educated cohort largely overlooked in previous research yet responsible for implementing the transition. Data were collected through an online survey (n = 133) at technical universities in Bratislava and Porto. Results indicate regional disparities in household energy mixes, notably higher wood burning in Slovakia (12%) than Portugal (<1%). Slovak respondents reported higher residential solar adoption (63.6% of solar users), possibly associated with higher homeownership (93%) and energy-crisis pressures, despite Portugal’s superior solar irradiance. Although limited by sample size, this pilot assessment shows that future engineers navigate trade-offs between sustainability and economic viability and view transition costs largely as a governmental responsibility. These findings inform the alignment of urban energy policies with the next generation’s readiness. Full article

Groundwater-potential assessment in karst aquifers is complicated by pronounced spatial heterogeneity driven by structural permeability, lithological variability, recharge redistribution, and unresolved subsurface conduit connectivity. Although machine-learning approaches have improved regional groundwater mapping, most existing models provide only deterministic predictions and offer limited information on predictive uncertainty and hydrogeological reliability. To address this limitation, we propose CT-TreeFlow. This probabilistic groundwater assessment framework goes beyond conventional machine-learning models by explicitly learning the full conditional probability distribution of groundwater favourability rather than a single deterministic estimate. The framework integrates sparse probabilistic environmental routing, conditional density estimation, hydrogeologically constrained pseudo-absence generation, geographically structured spatial validation, and explainability-driven interpretation within a unified modelling architecture, enabling simultaneous groundwater prediction, uncertainty quantification, and hydrogeological interpretation. The framework was applied to the Zagros karst system in Khuzestan Province, Iran, using remote-sensing-derived environmental predictors, Copernicus DEM-based morphometric variables, geological–structural datasets, and hydroclimatic indicators. Performance was evaluated against LightGBM and XGBoost using GroupKFold spatial cross-validation. CT-TreeFlow achieved a mean RMSE of 2.737 and a mean R² of 0.852, while also providing spatially explicit uncertainty estimates and probabilistic prediction intervals. Explainability analyses identified fracture density, lithology, drainage organisation, and terrain-controlled recharge conditions as the dominant controls on groundwater favourability. Predicted high-favourability zones showed strong spatial correspondence with major carbonate formations and independent spring–cave inventories, supporting the hydrogeological plausibility of the mapped patterns. These results demonstrate that probabilistic modelling can provide more reliable and physically interpretable groundwater assessments than deterministic approaches in structurally complex karst environments. CT-TreeFlow offers a transferable framework for uncertainty-aware groundwater exploration and regional hydrogeological decision support in heterogeneous aquifer systems. Full article

Water composition is a key factor influencing brewing performance and beer quality due to its impact on mash chemistry, fermentation kinetics, and fermentation-derived metabolites. This study evaluated the effect of four Albanian spring waters (Bogova, Germenji, Selita, and Lajthiza), each with distinct mineral compositions, on the fermentation behaviour and physicochemical characteristics of Pale Ale beer produced under standardised brewing conditions. All beers were brewed using the same malt formulation, hopping regime, yeast strain, and fermentation parameters, with water source as the sole experimental variable. The produced worts showed only moderate differences in pH, colour, extract, free amino nitrogen (FAN), bitterness, and density, whereas alcoholic fermentation proceeded efficiently in all treatments and was completed within seven days. Final alcohol contents ranged from 5.56 to 5.70% v/v, confirming comparable fermentation performance among treatments. More pronounced differences were observed in acidity-related parameters and fermentation-derived compounds. Volatile acidity ranged from 0.19 to 0.93 g/L, with the highest values in beers produced with Selita and Lajthiza waters. Glycerol concentrations varied from 0.88 to 1.24 g/L, with Germenji beer showing the highest value, whereas acetaldehyde ranged from 3.16 to 6.04 mg/L, with the lowest concentration in Germenji beer. Pearson correlation analysis and exploratory principal component analysis (PCA) identified associations between water mineralisation and selected physicochemical and fermentation-derived beer parameters. Calcium, magnesium, conductivity, and hardness were positively associated with glycerol concentration, whereas bicarbonate concentration was associated with beer pH and acidity-related parameters. The first two principal components explained 87.7% of the total variance. Overall, the results indicate that Albanian spring waters are suitable for Pale Ale production and show that differences in water mineral composition were associated with variations in the physicochemical and fermentation-derived characteristics of the final beers. These findings highlight that brewing water should not be regarded as a neutral processing medium but rather as an important technological factor associated with differences in the physicochemical characteristics of beer, while supporting the valorisation of Albanian spring waters for geographically distinctive craft brewing applications. Full article

The rapid development of airport economic zones has significantly reshaped regional land-use structures and industrial spatial organization. Taking the Nanjing Airport Economic Zone as the study area, this study integrates multi-source geospatial data, including land-use data, enterprise registration records, Points of Interest (POIs), transportation networks, nighttime light intensity, population, topography, and ecological-environmental variables for 2013, 2018, and 2023. Land-use transition matrices, spatial autocorrelation analysis, standard deviation ellipse analysis, Geodetector, and Multiscale Geographically Weighted Regression (MGWR) models were employed to examine land-use transition, industrial spatial restructuring, and their influencing factors from 2013 to 2023. The results show that: (1) Land-use change in the study area was mainly characterized by the decline of cropland, the expansion of impervious surfaces, and the shrinkage of water bodies. From 2013 to 2023, cropland decreased from 81.07 km² to 70.12 km², impervious surfaces increased from 10.98 km² to 25.65 km², and water bodies decreased from 5.50 km² to 1.79 km². The conversion from cropland to impervious surfaces was the dominant transition pathway, covering 14.67 km². (2) Industrial space exhibited significant spatial clustering, with a Moran’s I value of 0.9639 in 2023. The standard deviation ellipse results indicate that industrial space expanded during 2013–2018 and contracted during 2018–2023, suggesting a shift from extensive outward expansion to relative agglomeration around the core area and major transport corridors. (3) Nighttime light intensity and distance to major transport access points were important explanatory factors for industrial spatial distribution, with q-values of 0.396 and 0.310, respectively. The interaction between slope and metro accessibility showed the strongest explanatory power, with a q-value of 0.6967. The MGWR results further revealed the spatial heterogeneity of the effects of transportation, economic activity, population concentration, and ecological constraints. Overall, land-use transition and industrial spatial restructuring in the Nanjing Airport Economic Zone were jointly shaped by transportation accessibility, economic vitality, population agglomeration, and ecological constraints. These findings provide a reference for land-use optimization and industrial spatial governance in airport economic zones. Full article

Changes in urban form strongly affect surface thermal conditions, yet long-term quantitative assessments of this relationship, particularly the role of ventilation resistance, remain limited. To address this gap, this study integrates XGBoost, SHapley Additive explanations (SHAP), and multi-scale geographically weighted regression (MGWR) to examine how six morphological, ecological, and human-activity factors influence land surface temperature (LST) in Kaifeng City. The results indicate three main findings. First, LST increased significantly from 1986 to 2024, while interannual variability declined, indicating a gradual reduction in regional thermal fluctuations. Second, NTL was consistently the dominant indicator across the five representative years, while BF and NTL together captured the effects of urban expansion and intensified human activity. Third, FAD coefficients were more spatially heterogeneous in urban fringe areas than in the urban core. In 2020, the dispersion of FAD coefficients in fringe areas was 2.74 times greater than that in the central area, indicating stronger spatial differentiation in ventilation-related morphological constraints during urban expansion. Although FAD made only a modest contribution to overall predictive accuracy, it provided supplementary diagnostic information not captured by conventional density indicators and showed nonlinear, directional, and spatially heterogeneous responses. Compared with previous studies that mainly examined short-term or single-dimensional relationships between urban morphology and LST, this study integrates building densification, ventilation-related morphological resistance, ecological conditions, and human activity intensity into a long-term LST-driver framework, providing evidence to support heat-risk management during urban regeneration and outward expansion. Full article