Search Results (9,460)

Building healthy communities is crucial for creating healthy cities and improving residents’ well-being. Old residential areas, with their substantial stock and elevated health risks, require urgent environmental upgrades. However, the relationship between community health promotion factors and resident sentiment, a crucial indicator of subjective well-being, in old residential areas remains poorly understood. By integrating big data-based community health promotion factors and Weibo data within the 15-min living circle of old residential areas in Xi’an, we developed an XGBoost model and employed SHAP analyses to interpret predictive outcomes. Results show that healthy facilities were dominant influencing factors in old residential areas. Densities of parking, supermarkets, education, package stations, and scenic spots exhibit nonlinear relationships with positive sentiment, indicating clear threshold effects and saturation effects. Two dominant patterns were observed in interactions between dominant factors and their strongest interacting factors. Four environment–sentiment patterns were identified for targeted planning interventions. It is recommended that planners and policymakers account for density phases and synergistic combinations of the dominant factors to optimize community health within old residential areas. The findings offer empirical support and planning insights for fostering healthy, sentiment-sensitive retrofit in old residential areas within the 15-min living circle. Full article

Glass fiber-reinforced polymer (GFRP) composites are highly susceptible to thermal damage during machining, which can compromise their structural integrity and final quality. This study examines the efficacy of graphene and wax additives in reducing drilling temperatures in GFRP composites. Nine unique samples were manufactured with varying weight percentages of wax (0%, 1%, 2%) and graphene (0%, 0.25%, 2%). Drilling experiments were performed on a CNC milling center under a range of cutting parameters, with temperature monitoring carried out using an infrared thermal camera. A hierarchical cubic response surface model was employed to analyze thermal behavior. The results indicate that cutting speed is the dominant factor, accounting for 67.28% of temperature generation. The formulation containing 2% wax and 0% graphene achieved the lowest average drilling temperature (64.64 °C), underscoring wax’s superior performance as both a lubricant and heat sink. Although graphene alone slightly elevated median temperatures, it substantially reduced thermal variability. The optimal condition for minimizing thermal damage was identified as 2% wax combined with a high cutting speed (200 mm/min), providing actionable insights for industrial process optimization. Full article

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. Full article

Soil salinization is a major threat to agriculture and food security globally. The effectiveness of amendments on soil quality and crop production is management-dependent, and low-cost management practices are essential for developing countries. In this 3-year field study, the effects of cattle manure and gypsum amendments on the physicochemical properties of saline–alkali soil were evaluated. We found that both single gypsum and mixed amendments significantly reduced soil hardness, bulk density, pH, and soil salt content in 20–40 cm in 2015 and 2017. A more significant decrease in soil EC and density was observed with the mixed amendments compared to single gypsum after three years of reclamation. Specifically, applying mixed amendments (M-G15) led to a significant increase in Hordeum yield by 60.94%, whereas the application of single gypsum increased Hordeum yield by 25.20–53.14%. This indicated that co-application of cattle manure can reduce the amount of gypsum needed to achieve similar improvements in soil properties and Hordeum yield, with a long-term cumulative effect. Na⁺/(Ca²⁺ + Mg²⁺) showed the largest negative contribution to Hordeum yield under amendments, while soil bulk density showed the second largest number of negative effects on Hordeum yield under mixed amendments. Single gypsum improved the soil’s physical quality during the early stage of saline–alkali soil remediation, and mixed amendments improved the soil’s physicochemical properties and Hordeum yield during the late stage of remediation. Na⁺/(Ca²⁺ + Mg²⁺) in topsoil was confirmed to be the dominant factor under the mixed amendments affecting Hordeum yield, followed by the soil bulk density. These results confirm that the co-application with cattle manure achieves a similar reclamation effect with a reduced gypsum dosage, thereby lowering the reclamation costs of saline–alkali land in semi-arid areas. Full article

Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research. Full article

Background/Objectives: This study investigated how phase-specific dietary strategies and weight regulation influence gut microbiota composition and diversity in competitive weightlifters. Particular emphasis was placed on integrating energy intake, macronutrient clustering, and weight fluctuations across distinct training phases. Methods: Thirteen competitive weightlifters were recruited, with 10–12 contributing complete data per phase. Fecal and dietary samples were collected during the preparation, competition, and transition phases. Gut microbiota was profiled via 16S rRNA gene sequencing, and alpha/beta diversity was analyzed using QIIME2. K-means clustering based on caloric/macronutrient intake identified dietary patterns. Taxonomic differences were assessed using DESeq2, and microbial structures were compared across training phases, weight classes, and weight-change categories. Results: Overall phylum- and genus-level profiles and diversity indices remained stable across training phases, indicating community-level resilience. However, specific genera varied with dietary and physiological factors. Enterococcus was higher during the preparation phase, whereas Lactobacillus was enriched during the competition and transition phases as well as in the high-calorie cluster. Lightweight and heavyweight athletes also showed distinct microbial structures, and pre- and post-competition weight changes were associated with shifts in selected taxa. Notably, the low-calorie group exhibited higher Shannon diversity than the high-calorie group (p = 0.0058), with Lactobacillus dominance contributing to reduced evenness in high-energy diets. Conclusions: Despite overall microbial stability, dietary energy availability and body-weight regulation modulated specific taxa relevant to performance and recovery. By integrating dietary clustering, weight-class comparison, and pre- and post-competition weight changes, this study provides novel insight into the microbiota of resistance-trained athletes, a population underrepresented in previous research. Despite the modest sample size and single-season scope, this study offers new evidence linking dietary strategies, weight regulation, and gut microbiota in weightlifters, and highlights the need for validation in broader cohorts. Full article

SHORT INTERNODE (SHI)-Related Sequence (SRS) transcription factors play crucial roles in plant growth, development, and stress responses and have been extensively studied in various plant species. However, the molecular functions and regulatory mechanisms of SRS genes in the economically important tropical fruit crop pitaya (Hylocereus undatus) remain poorly understood. This study identified 9 HuSRS genes in pitaya via bioinformatics analysis, with subcellular localization predicting nuclear distributions for all. Gene structure analysis showed 1–4 exons, and conserved motifs (RING-type zinc finger and IXGH domains) were shared across subclasses. Phylogenetic analysis classified the HuSRS genes into three subfamilies. Subfamily I (HuSRS1–HuSRS4) is closely related to poplar and tomato homologs and subfamily III (HuSRS6–HuSRS8) contains a recently duplicated paralogous pair (HuSRS7/HuSRS8) and shows affinity to rice SRS genes. Protein structure prediction revealed dominance of random coils, α-helices, and extended strands, with spatial similarity correlating to subfamily classification. Interaction networks showed HuSRS1, HuSRS2, HuSRS7 and HuSRS8 interact with functional proteins in transcription and hormone signaling. Promoter analysis identified abundant light/hormone/stress-responsive elements, with HuSRS5 harboring the most motifs. Transcriptome and qPCR analyses revealed spatiotemporal expression patterns: HuSRS4, HuSRS5, and HuSRS7 exhibited significantly higher expression levels in callus (WG), which may be associated with dedifferentiation capacity. In seedlings, HuSRS9 exhibited extremely high transcriptional accumulation in stem segments, while HuSRS1, HuSRS5, HuSRS7 and HuSRS8 were highly active in cotyledons. This study systematically analyzed the characteristics of the SRS gene family in pitaya, revealing its evolutionary conservation and spatio-temporal expression differences. The research results have laid a foundation for in-depth exploration of the function of the SRS gene in the tissue culture and molecular breeding of pitaya. Full article

The fragile karst ecosystem in Southwest China faces severe water scarcity. Since 2000, large-scale ecological restoration programs (e.g., the “Grain for Green” Program) have substantially increased vegetation coverage. Concurrently, climate change has manifested as a distinct warming trend and heightened drought risk in recent decades. Therefore, understanding the synergistic and competing effects of climate change and vegetation restoration on regional evapotranspiration (ET) is critical for projecting water budgets and ensuring the sustainability of ecosystems and water resources within this vital ecological barrier region. This study employs a dual-scenario PT-JPL model (simulating natural vegetation dynamics versus constant coverage) integrated with Sen + MK trend analysis to quantify the spatiotemporal patterns of ET and its components—canopy transpiration (ETc), interception evaporation (ETi), and soil evaporation (ETs)—in Southwest China’s karst region (2000–2018). Furthermore, multiple regression analysis and SEM were utilized to investigate the driving mechanisms of vegetation and climatic factors (temperature, precipitation, radiation, and relative humidity) on changes in ET and its components. The key results demonstrate the following: (1) Vegetation restoration exerted a net positive effect on total ET (+0.44 mm/a) through enhanced ETi (+0.22 mm/a) and ETs (+0.37 mm/a), despite reducing ETc (−0.08 mm/a), revealing trade-offs in water allocation. (2) Radiation dominated ET variability (66.45% of the area exhibiting >50% contribution), while temperature exhibited the most extensive spatial dominance (44.02% of the region), and relative humidity exhibited drought-mediated dual effects (promoting ETi while suppressing ETc). (3) Precipitation exhibited minimal direct influence. Vegetation restoration and climate change collectively drive ET dynamics, with ETc declines indicating potential water stress. These findings elucidate the synergistic regulation of vegetation restoration and climate change on karst ecohydrology, providing critical insights for water resource management in fragile ecosystems globally. Full article

This study aims to investigate the characteristics of load changes in the hip, knee, and ankle joints of the dominant and non-dominant lower limbs of young male runners after long-distance running. Using the OpenSim public dataset (containing bilateral biomechanical data before and after long-distance running from 20 young male runners), personalized musculoskeletal models were established. Contact forces in three directions at lower limb joints during the running stance phase were calculated. Statistical analysis employed one-dimensional statistical parameter mapping (SPM1d) and two-factor repeated measures ANOVA (time × side). Results revealed significant time × side interaction effects (p < 0.05) for contact forces in the medial–lateral direction at the hip, the anterior–posterior direction at the knee, and all three directions at the ankle. Simple effects analysis showed that post-run medial–lateral hip forces significantly increased during the push-off phase, while anterior–posterior ankle forces significantly increased during the mid-to-late stance phase on both sides (d = 0.718–1.002). For the superior–inferior direction at the hip and knee, only main effects of time or side were present. Post-run joint contact forces significantly increased, with the dominant side consistently exceeding the non-dominant side across multiple stance and push-off phases (d = 0.58–1.6), indicating stable side-to-side differences. These findings indicate that long-distance running not only increases multi-joint loading in the lower limbs but also exacerbates asymmetry between the dominant and non-dominant sides during the initial stance and push-off phases. This redistribution of load, coupled with bilateral control imbalance, may further elevate the risk of injury. Full article

High energy consumption in Chinese rural residential buildings, caused by rudimentary construction methods and the poor thermal performance of building envelopes, poses a significant challenge to national sustainability and “dual carbon” goals. To address this, this study proposes a comprehensive modeling and analysis framework integrating an improved Bio-inspired Black-winged Kite Optimization Algorithm (IBKA) with Support Vector Regression (SVR). Firstly, to address the limitations of the original B-inspired BKA, such as premature convergence and low efficiency, the proposed IBKA incorporates diversification strategies, global information exchange, stochastic behavior selection, and an NGO-based random operator to enhance exploration and convergence. The improved algorithm is benchmarked against BKA and six other optimization methods. An orthogonal experimental design was employed to generate a dataset by systematically sampling combinations of influencing factors. Subsequently, the IBKA-SVR model was developed for energy consumption prediction and analysis. The model’s predictive accuracy and stability were validated by benchmarking it against six competing models, including GA-SVR, PSO-SVR, and the baseline SVR and so forth. Finally, to elucidate the model’s internal decision-making mechanism, the SHAP (SHapley Additive exPlanations) interpretability framework was employed to quantify the independent and interactive effects of each influencing factor on energy consumption. The results indicate that: (1) The IBKA demonstrates superior convergence accuracy and global search performance compared with BKA and other algorithms. (2) The proposed IBKA-SVR model exhibits exceptional predictive accuracy. Relative to the baseline SVR, the model reduces key error metrics by 37–40% and improves the R² to 0.9792. Furthermore, in a comparative analysis against models tuned by other metaheuristic algorithms such as GA and PSO, the IBKA-SVR consistently maintained optimal performance. (3) The SHAP analysis reveals a clear hierarchy in the impact of the design features. The Insulation Thickness in Outer Wall and Insulation Thickness in Roof Covering are the dominant factors, followed by the Window-wall Ratios of various orientations and the Sun space Depth. Key features predominantly exhibit a negative impact, and a significant non-linear relationship exists between the dominant factors (e.g., insulation layers) and the predicted values. (4) Interaction analysis reveals a distinct hierarchy of interaction strengths among the building design variables. Strong synergistic effects are observed among the Sun space Depth, Insulation Thickness in Roof Covering, and the Window-wall Ratios in the East, West, and North. In contrast, the interaction effects between the Window-wall Ratio in the South and other variables are generally weak, indicating that its influence is approximately independent and linear. Therefore, the proposed bio-inspired framework, integrating the improved IBKA with SVR, effectively predicts and analyzes residential building energy consumption, thereby providing a robust decision-support tool for the data-driven optimization of building design and retrofitting strategies to advance energy efficiency and sustainability in rural housing. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

To investigate the impact of PV power station construction on insect community diversity in the desert steppe of Ningxia and its response to environmental factors, insect communities were surveyed in different areas within the PV station (both under and between PV panels) and outside the station. The species composition, diversity differences, and responses to environmental factors of insect communities in these areas were analyzed. The results showed that a total of 19,833 insect specimens, belonging to 68 species and 23 families, were collected across different areas of the PV station. The dominant species within the PV station (both under and between PV panels) were Labidura riparia japonica (Dermaptera: Labiduridae), Harpalus sinicus (Coleoptera: Carabidae) and Harpalus calceatus (Coleoptera: Carabidae), while outside the station, the dominant species were L. r. japonica, H. sinicus, H. calceatus and Harpalus pallidipennis (Coleoptera: Carabidae). The number of species by feeding habit ranked as follows: phytophagous insects > predatory insects, whereas the abundance of individuals followed the order: predatory insects > phytophagous insects. The species richness, abundance, Margalef richness index, Shannon–Wiener index and Pielou evenness index of phytophagous insects were significantly higher outside the PV power station than inside (both under and between PV panels). In contrast, Simpson dominance index was significantly lower outside the PV power station compared to inside (both under and between PV panels). For predatory insects, no significant differences were observed in species richness, Margalef richness index, Shannon–Wiener diversity index, Simpson dominance index, or Pielou evenness index among different PV panel areas. However, the abundance of predatory insects was significantly higher outside the PV power station than inside (both under and between PV panels); phytophagous insects in the PV station were primarily positively driven by soil nutrients (total nitrogen, available potassium), whereas predatory insect diversity was more responsive to soil organic matter and nitrogen levels. Both predatory and phytophagous insects showed a significant negative correlation with vegetation height. This study holds significant importance for exploring biodiversity conservation within PV power stations, providing a scientific basis for the planning, design, and implementation of ecological protection measures during the operation of PV station in Ningxia’s desert steppe. Full article

One-part geopolymers (OPG) offer a low-carbon alternative to Portland cement, yet mix design remains largely empirical. This study couples machine learning with SHAP (Shapley Additive Explanations) to quantify how mix and curing factors govern performance in Ca-containing OPG. We trained six regressors—Random Forest, ExtraTrees, SVR, Ridge, KNN, and XGBoost—on a compiled dataset and selected XGBoost as the primary model based on prediction accuracy. Models were built separately for four targets: compressive strength at 3, 7, 14, and 28 days. SHAP analysis reveals four dominant variables across targets—Slag, Na₂O, Ms, and the water-to-binder ratio (w/b)—while the sand-to-binder ratio (s/b), temperature, and humidity are secondary within the tested ranges. Strength evolution follows a reaction–densification logic: at 3 days, Slag dominates as Ca accelerates C–(N)–A–S–H formation; at 7–14 days, Na₂O leads as alkalinity/soluble silicate controls dissolution–gelation; by 28 days, Slag and Na₂O jointly set the strength ceiling, with w/b continuously regulating porosity. Interactions are strongest for Slag × Na₂O (Ca–alkalinity synergy). These results provide actionable guidance: prioritize Slag and Na₂O while controlling w/b for strength. The XGBoost+SHAP workflow offers transparent, data-driven decision support for OPG mix optimization and can be extended with broader datasets and formal validation to enhance generalization. Full article

The Larimichthys crocea represents a critically important economic marine species in China’s East Yellow Sea. However, its populations have experienced significant decline due to overexploitation. Despite implemented conservation measures—including stock enhancement, spawning ground protection, and seasonal fishing moratoria—the recovery of yellow croaker resources remains markedly slow. To address this, our study employed the Maximum Entropy (MaxEnt) model to evaluate and characterize the habitat selection patterns of Larimichthys crocea, thereby providing a theoretical foundation for scientifically informed stock enhancement and resource recovery strategies. Species occurrence data were compiled from field surveys conducted during April and November (2019–2023), supplemented with records from the GBIF database and peer-reviewed literature. Concurrent environmental variables, including primary productivity, current velocity, depth, temperature, salinity, silicate, nitrate, phosphate, and pH, were obtained from the Copernicus and NOAA databases. After rigorous screening, 136 distribution points (April) and 369 points (November) were retained for analysis. The model performance was robust, with an AUC (Area Under the Curve) value of 0.935 for April (2019–2023) and 0.905 for November (2019–2023), indicating excellent predictive accuracy (AUC > 0.9). April (2019–2023): Nitrate, salinity, phosphate, and silicate were identified as the primary environmental factors influencing habitat suitability. November (2019–2023): Silicate, salinity, nitrate, and primary productivity emerged as the dominant drivers. Spatially, Larimichthys crocea exhibited high-density distributions in offshore regions of Zhejiang and Jiangsu, particularly near the Yangtze River estuary. Populations were also associated with island-reef systems, forming continuous distributions along Zhejiang’s offshore waters. In Jiangsu, aggregations were concentrated between Nantong and Yancheng. This study delineates habitat suitability zones for Larimichthys crocea, offering a scientific basis for optimizing stock enhancement programs, designing targeted conservation measures, and establishing marine protected areas. Our findings enable policymakers to develop sustainable fisheries management strategies, ensuring the long-term viability of this ecologically and economically vital species. Full article

Located on the Loess Plateau, the Yan’an New District (YND) has experienced significant geological instability due to large-scale mountain excavation and city construction (MECC). This study applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to 66 ascending Sentinel-1A SAR images acquired between January 2017 and May 2022 to investigate ground deformation patterns and influencing factors. Results show that the maximum subsidence rate reached −86 mm/year, with a maximum cumulative deformation of 400 mm. Groundwater storage was identified as the key natural driver, exhibiting a significant positive correlation (r = 0.4–0.8) with cumulative deformation with a two-month lag. Fill thickness emerged as the dominant anthropogenic factor, controlling the duration of soil consolidation and thus the deformation rate. Regulating groundwater extraction and improving recharge can effectively reduce subsidence risks. These findings provide scientific guidance for geological hazard early warning and urban planning in YND. Full article