Search Results (196)

The arid–hot valley regions in southwestern China are characterized by developed geological structures and frequent local heavy rainfalls, which often trigger flash floods. The mechanical properties of soil and rock masses in these regions are critical for the construction of regional projects. Field direct shear tests can accurately reflect the mechanical properties of the soil and rock masses in their natural state, but they are costly and cause significant disturbance to the surrounding environment. In contrast, laboratory direct shear tests are more straightforward and cost-effective but cannot fully replicate the complex stress conditions and structural characteristics of in situ soil and rock masses. The lack of correlation between field and laboratory direct shear strength indicators significantly hinders the accurate assessment of geotechnical properties, thereby affecting the precision of engineering applications. To this end, this paper focuses on the soil and rock layers in the arid–hot valley regions in southwestern China. This research took into account the effects of soil depth and moisture content, proposing a solution that fully correlates field and laboratory direct shear strength test indicators. Field and laboratory direct shear tests were conducted at shallow depths to investigate the relationship between the shear strength indicators of various geological formations. The results show that laboratory remolded sample tests generally yield lower shear strength values compared to field direct shear tests. The laboratory shear strength and internal friction angle of each rock and soil layer show a linear increase with depth. A mathematical relationship between soil layer depth, laboratory shear strength indicators, and field shear strength indicators can be established using a quadratic polynomial function. This resolved the “disconnect” between field and laboratory test results, significantly reducing engineering survey costs and providing important theoretical basis and reference for engineering construction in arid and hot river valley regions. Full article

(1) Objective: This study aimed to investigate the synergistic effect and underlying mechanisms of Total Flavonoids of Rhizoma drynariae (TFRD) in combination with Bone Marrow Mesenchymal Stem Cells (BMSCs) in the repair of tendon–bone injuries. (2) Methods: The effects of TFRD on the proliferation and migration of BMSCs were assessed using CCK-8 and scratch assays, and its potential to promote osteogenic and chondrogenic differentiation was evaluated. Concurrently, the pro-angiogenic effect of TFRD on Human Umbilical Vein Endothelial Cells (HUVECs) was observed. In vivo, a rat model of Achilles tendon–bone injury was established and animals were divided into four groups: SHAM, Model, BMSCs, and BMSCs + TFRD. After an 8-week intervention, the level of functional recovery was evaluated through histological analysis, immunohistochemistry, serum biochemical analysis, and biomechanical testing. (3) Results: A concentration of 5.0 μg/mL TFRD significantly promoted the proliferation, migration, and differentiation of BMSCs and enhanced the tube formation capacity of HUVECs. In the BMSCs + TFRD group, histological analysis revealed well-organized collagen fibers, increased cartilage deposition, and an optimized tendon–bone interface (TBI) structure. Immunohistochemistry showed upregulated expression of COL I, COL II, and SOX-9, alongside downregulated VEGFA. Furthermore, serum IL-6 levels were decreased, while IL-10 and TGF-β levels were elevated. The biomechanical properties were also significantly improved in this group. (4) Conclusions: TFRD promotes tendon–bone healing and functional recovery by enhancing BMSC functions, promoting angiogenesis, and improving the local microenvironment. Full article

In continuous strata engineering, such as foundations and underground caverns, the differences in shear strength between sandstone and mudstone rock mass–structural plane–structural block systems critically affect design and safety. However, the underlying mechanisms and controlling factors of these shear strength parameters remain poorly understood, leading to challenges in optimizing engineering strategies. This study investigates the differences in shear strength parameter correlations between sandstone and mudstone and develops an intelligent model for predicting rock mass displacement. We constructed multi-parameter correlation models using laboratory and field shear test data combined with a random forest algorithm. The results show that the model achieved high prediction accuracy (R² = 0.997–0.998, RMSE = 1.649–3.898, MAE = 1.110–2.991). For instance, the peak shear strength of sandstone structural planes was approximately 54% higher than that of mudstone. Sensitivity analysis revealed that for sandstone, structural plane shear stress (27.80%) and structural block stress (25.50%) are the most sensitive factors, while for mudstone, structural plane shear displacement (35.20%) and structural block strain (34.20%) dominate. These correlations are model-predicted based on empirical data from shear tests. These findings provide a mechanistic understanding of plastic instability in sandstone and slip-strain-induced fissure extension in mudstone, and they can guide shear strength prediction and stability assessment in mixed sandstone–mudstone strata. The study contributes to the field by offering a quantitative basis for stratified adaptive design in continuous strata engineering, enhancing the efficiency and safety of foundation treatment and cavern support. Full article

Under the dual pressures of climate change and rapid urbanization, extreme heat events pose growing risks to densely populated megaregions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a densely populated and economically vital region, serves as a critical hotspot for heat risk aggregation. This study develops a high-resolution multi-dimensional framework to assess the spatiotemporal evolution of its heat risk profile from 2000 to 2020. A Heat Risk Index (HRI) integrating heat hazard and vulnerability components to measure potential heat-related impacts is calculated as the product of the Heat Hazard Index (HHI) and Heat Vulnerability Index (HVI) for 1 km grids in GBA. The HHI integrates the frequency of hot days and hot nights. HVI incorporates population density, GDP, remote-sensing nighttime light data, and MODIS-based landscape indicators (e.g., NDVI, NDWI, and NDBI), with weights determined objectively using the static Entropy Weight Method to ensure spatiotemporal comparability. The findings reveal an escalation of heat risk, expanding at an average rate of 342 km² per year (p = 0.008), with the proportion of areas classified as high-risk or above increasing from 21.8% in 2000 to 33.3% in 2020. This trend was characterized by (a) a pronounced asymmetric warming pattern, with nighttime temperatures rising more rapidly than daytime temperatures; (b) high vulnerability dominated by the concentration of population and economic assets, as indicated by high EWM-based weights; and (c) isolated high-risk hotspots (Guangzhou and Hong Kong) in 2000, which have expanded into a high-risk belt across the Pearl River Delta’s industrial heartland, like Foshan seeing their high-risk area expand from 3.4% to 27.0%. By combining remote sensing and socioeconomic data, this study provides a transferable framework that moves beyond coarse-scale assessments to identify specific intra-regional risk hotspots. The resulting high-resolution risk maps offer a quantitative foundation for developing spatially explicit climate adaptation strategies in the GBA and other rapidly urbanizing megaregions. Full article

As the demand for air transportation surges, issues like flight conflicts and air-route congestion within multi-airport terminal areas have grown progressively more serious. Analyzing the evolution of air traffic situations in these areas can effectively enhance the air traffic’s early-warning capability, reduce flight conflicts, and alleviate air-route congestion. This paper proposes a method for analyzing the evolution of air traffic situations in multi-airport terminal areas based on flight segment–flight state interdependent network. First, a flight segment–flight state interdependent network model is established. This interdependent network model consists of an upper-layer flight state network, a lower-layer air-route network, and coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network takes air-routes as nodes and the connection relationships between air-routes as edges. The inter-layer coupling edges are determined by judging the relationship between aircraft and air-routes. If an aircraft is on a certain air-route, there exists a coupling edge between the aircraft node and the air-route node. On this basis, by comprehensively considering three network indicators, namely node degree, weighted clustering coefficient, and node strength, the overall air traffic situation value is obtained. Finally, experimental verification and analysis were conducted in an actual flight scenario of a multi-airport terminal area in the Guangdong–Hong Kong–Macao Greater Bay Area. The results show that the proposed method can accurately reflect the air traffic situation. The time-series analysis of the situation evolution reveals that the evolution process has chaotic characteristics. Full article

Background/Objectives: This study aims to examine the relationships between 24-h movement guideline (24HMG) adherence and macronutrient intake, as well as assess dose–response relationships between 24-h movement behaviors and macronutrient intake among students aged 6–17 years. Methods: The study included 3624 participants aged 6 to 17 years from four rounds (2004–2011) of the Chinese Health and Nutrition Survey (CHNS). Participants’ 24-h movement behaviors and dietary intakes were evaluated. Results: Adherents to physical activity (PA) guideline had higher carbohydrate, fat, and protein intake (all p < 0.05). Those following the screen time (ST) guideline had a higher percentage of dietary energy intake (E%) from carbohydrates but a lower percentage from fat (all p < 0.05). Sleep (SLP) guideline adherents demonstrated lower protein intake and E% (all p < 0.05). PA guideline adherents were less likely to exceed carbohydrate Dietary Reference Intakes (DRIs) (OR = 0.83, 95% CI: 0.69–0.99), but more likely to surpass fat DRIs (OR = 1.20, 95% CI: 1.02–1.40). ST guideline adherents were more likely to exceed carbohydrate DRIs (OR = 1.32, 95% CI: 1.11–1.56) and less likely to surpass fat DRIs (OR = 0.78, 95% CI: 0.68–0.91). Dose–response analyses showed that moderate-to-vigorous physical activity (MVPA) and ST had positive linear associations with carbohydrate intake below DRIs. ST also showed positive linear associations with fat intake above DRIs. MVPA showed a nonlinear relationship with fat intake above DRIs. Conclusions: Among Chinese children and adolescents aged 6–17 years, those who meet the PA guideline should be cautious about the risk of excessive fat intake, while those adhering to the ST guideline should be aware of the risk of excessive carbohydrate intake in their daily diet. For promoting health and maintaining balanced macronutrient intake, MVPA should range from 60 to 90 min per day. This study underscores the importance of adjusting macronutrient intake according to levels of 24-h movement behaviors, especially MVPA and ST. Full article

Rapid urbanization threatens water ecosystem services (WESs) in China’s Greater Bay Area. This study employs a Markov-FLUS land-use simulation coupled with the InVEST model to project land-use patterns for 2035 under four scenarios—Natural Development (ND), Farmland Protection (FP), Economic Priority (EP), and Ecological Protection (EcoP)—and evaluates their impacts on water yield, soil retention, and total phosphorus (TP) export. Under ND and FP scenarios, modest gains in water yield (+32.25% and +32.13%) and soil retention (+46.16% and +45.91%) are achieved, but TP control remains limited (−0.05% and +4.82%). In contrast, the EP scenario drives severe declines in water yield (−13.39%) and soil retention (−2.11%) alongside a TP surge (+5.87%), evidencing ecological degradation under high-intensity development. Conversely, the EcoP scenario yields substantial improvements, water yield +50.67%, soil retention +70.94%, and TP export −8.17%, reflecting the synergistic “multiplier effect” of combined woodland and water-body restoration. Spatially, urban cores and agricultural margins exhibit divergent service responses, underscoring the need for differentiated management. We developed a spatial priority map by integrating the predicted WES changes under the Ecological Protection scenario with indicators of urban proximity and pollution risk. This map identifies critical intervention zones. We propose targeted spatial optimization—strict protection of sensitive ecological zones, green transformation in urban expansion areas, and diffuse pollution controls in agricultural peripheries—to reconcile development with ecosystem resilience. Full article

An analysis of air traffic complexity in multi-airport terminal areas can assist air traffic controllers in accurately assessing the air traffic situation and collaboratively managing air traffic flows, thereby enhancing the utilization of airspace resources and reducing flight delays. This paper proposes an air traffic complexity analysis method for multi-airport terminal areas based on a route segment–flight state interdependent network. The interdependent network model consists of an upper-layer flight state network, a lower-layer route segment network, and inter-layer coupling edges. The upper-layer network is constructed with aircraft as nodes and flight conflicts between aircraft as edges. The lower-layer network uses route segments as nodes and the connectivity between route segments as edges. The inter-layer coupling edges are determined by evaluating the relationship between aircraft and route segments—if an aircraft is on a specific route segment, a coupling edge exists between the corresponding aircraft node and route segment node. Based on this model, node-level complexity metrics are established to analyze the importance and complexity of individual route segments. Additionally, network-level complexity metrics are introduced to assess the overall air traffic complexity in multi-airport terminal areas. Finally, the method proposed in this paper is validated using flight scenarios in the Guangdong–Hong Kong–Macao Greater Bay Area. By comparing and analyzing the results with the actual situation, it is shown that the proposed method can accurately assess the air traffic complexity in multi-airport terminal areas. Full article

This study addresses the limitations of traditional nighttime light remote sensing data in ground object feature recognition and carbon emission monitoring by proposing a fusion framework based on Nonsubsampled Contourlet Transform (NSCT) and Intensity-Hue-Saturation (IHS). This framework successfully generates a high-resolution color nighttime light remote sensing imagery (color-NLRSI) dataset. Focusing on Guangzhou, an important city in the Guangdong-Hong Kong-Macao Greater Bay Area, the study systematically analyzes the spatiotemporal coupling mechanism between urban form evolution and carbon footprint by integrating multiple remote sensing data sources and socio-economic statistical information. Key findings include: (i) The color-NLRSI dataset outperforms traditional NPP-VIIRS data in built-up area extraction, providing more accurate spatial information by refining urban boundary recognition logic. (ii) Spatial correlation analysis reveals a remarkably strong positive relationship between built-up area expansion and carbon emissions, with the correlation coefficient for numerous districts exceeding 0.9. High-density built-up areas are strongly associated with a carbon lock-in effect, hindering low-carbon transformation efficiency. (iii) Geographically Weighted Regression analysis demonstrates that in population-polarized regions, the impact coefficient of built-up area expansion on carbon emissions is notably high at 0.961. This factor’s association (22.43%) surpasses economic development (10.34%) and urbanization rate (14.91%). The established “data fusion—dynamic monitoring—mechanism analysis” technical system, which generates a novel high-resolution color-NLRSI dataset and reveals a distinct ‘core-periphery’ heterogeneity pattern in Guangzhou, demonstrating that urban expansion is the dominant driver of carbon emissions. This approach offers a scientific basis for tailored urban low-carbon development strategies, spatial optimization, and enhanced precision in carbon emission monitoring. Full article

Traffic flow prediction is a critical component of intelligent transportation systems, playing a vital role in alleviating congestion, improving road resource utilization, and supporting traffic management decisions. Although deep learning methods have made remarkable progress in this field in recent years, current studies still face challenges in modeling complex spatio-temporal dependencies, adapting to anomalous events, and generalizing to large-scale real-world scenarios. To address these issues, this paper proposes a novel traffic flow prediction model. The proposed approach simultaneously leverages temporal and frequency domain information and introduces adaptive graph convolutional layers to replace traditional graph convolutions, enabling dynamic capture of traffic network structural features. Furthermore, we design a frequency–temporal multi-head attention mechanism for effective multi-scale spatio-temporal feature extraction and develop a cross-multi-scale graph fusion strategy to enhance predictive performance. Extensive experiments on real-world datasets, PeMS and Beijing, demonstrate that our method significantly outperforms state-of-the-art (SOTA) baselines. For example, on the PeMS20 dataset, our model achieves a 53.6% lower MAE, a 12.3% lower NRMSE, and a 3.2% lower MAPE than the best existing method (STFGNN). Moreover, the proposed model achieves competitive computational efficiency and inference speed, making it well-suited for practical deployment. Full article

This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area and employs a multi-model coupling method of InVEST-Bathtub-GeoSOS-FLUS to predict and analyze the impacts of sea level rise and rapid urbanization on ecological source areas by the year 2100. The InVEST model is used to delineate areas with higher habitat quality scores as ecological source areas. The Bathtub inundation model predicts the impact ranges under three different sea level rise scenarios by 2100. The FLUS model simulates the land-use pattern of the Greater Bay Area in 2100. Finally, the raster calculator is used to conduct overlay analysis and accurately calculate the impact on ecological source areas under the combined effects of sea level rise and urban expansion. The results show that by 2100, the proportion of cultivated land in the Greater Bay Area is expected to decrease from 24.95% to 10.55%, while the proportion of urban land will increase from 7.69% to 26.84%. Under the dual impacts of the three sea level rise scenarios and urbanization, the affected areas of ecological source areas will reach 109.88 km², 125.05 km², and 255.10 km², respectively. This study provides an important basis and decision-making support for the sustainable planning and scientific management of ecological source areas in the Greater Bay Area. Full article

Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies—such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets—can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., Akkermansia muciniphila abundance, SCFA production). In parallel, digital health tools—including mobile health applications, wearable devices, and AI-supported platforms—enhance self-monitoring, adherence, and dynamic feedback in real-world settings. Mechanistic pathways such as gut–brain axis regulation, microbial fermentation, gene–diet interactions, and anti-inflammatory responses are explored to explain inter-individual differences in dietary outcomes. However, challenges such as cost, accessibility, and patient motivation remain and should be addressed to ensure the effective implementation of these integrated strategies in real-world settings. Collectively, these insights underscore the pivotal role of precision nutrition as a cornerstone for personalized, scalable, and sustainable obesity interventions. Full article

Cultivating the best business environment ecosystem is important for advancing market-oriented reforms and achieving sustainable industrial transformation. How to quantify the business environment is also a relatively complex topic. Based on urban ecology theory, this study constructs a comprehensive evaluation framework for assessing urban business environment development. Using the entropy weight method and spatial autocorrelation analysis, we examine the time series and spatial evolution of the business environment in the Guangdong–Hong Kong–Macao Greater Bay Area from 2008 to 2021. Meanwhile, we further explore the main factors that influence the development level of the business environment. Finally, some suggestions are put forward to improve the business environment. The results show that (1) the development level of the business environment has gradually improved during the sample period, with stable growth from 2008 to 2015, followed by rapid development after 2015; (2) from different dimensions, there is an imbalance in the business environment development among cities within the Greater Bay Area, with core cities performing better than others; (3) from a spatial perspective, the business environment presents a “core-periphery” pattern, with higher levels clustered around the Pearl River Estuary, indicating strong spatial agglomeration. This research provides theoretical support and policy recommendations for the Three-Year Action Plan for Creating a World-Class Business Environment in the Greater Bay Area. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

The rapid growth of construction and demolition waste (CDW) presents significant challenges to sustainable urban development, particularly in densely populated regions, such as the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Despite substantial disparities in CDW management (CDWM) performance across cities, the key influencing factors and effective strategies remain underexplored, limiting the development of localized and evidence-based CDWM solutions. Therefore, this study formulated three hypotheses concerning the relationships among CDWM performance, city attributes, and governance capacity to identify the key determinants of CDWM outcomes. These hypotheses were tested using clustering and correlation analysis based on data from 11 GBA cities. The study identified three distinct city clusters based on CDW recycling, reuse, and landfill rates. Institutional support and recycling capacity were key determinants shaping CDWM performance. CDW governance capacity acted as a mediator between city attributes and performance outcomes. In addition, the study examined effective strategies and institutional measures adopted by successful GBA cities. By highlighting the importance of institutional and capacity-related factors, this research offers novel empirical insights into CDW governance in rapidly urbanizing contexts. Full article