Search Results (477)

Rapid urbanization has exacerbated urban-stormwater challenges, highlighting the critical need for coordinated surface-water and groundwater management through rainfall recharge. However, current sponge city construction methods often overlook the crucial role of underground aquifers in regulating the water cycle and mostly rely on simplified engineering approaches. To address these limitations, this study proposes a spatial optimization framework for urban-scale sponge systems that integrates a hydrodynamic model (FVCOM), geographic information systems (GIS), and Monte Carlo simulations. This framework establishes a comprehensive evaluation system that synergistically integrates surface water inundation depth, geological lithology, and groundwater depth to quantitatively assess sponge city suitability. The FVCOM was employed to simulate surface water inundation processes under extreme rainfall scenarios, while GIS facilitated spatial analysis and data integration. The Monte Carlo simulation was utilized to optimize the spatial layout by objectively determining factor weights and evaluate result uncertainty. Using Shenzhen City in China as a case study, this research combined the “matrix-corridor-patch” theory from landscape ecology to optimize the spatial structure of the sponge system. Furthermore, differentiated planning and management strategies were proposed based on regional characteristics and uncertainty analysis. The research findings provide a replicable and verifiable methodology for developing sponge city systems in high-density urban areas. The core value of this methodology lies in its creation of a scientific decision-making tool for direct application in urban planning. This tool can significantly enhance a city’s climate resilience and facilitate the coordinated, optimal management of water resources amid environmental changes. Full article

Perovskite solar cells (PSCs) have achieved remarkable efficiencies, yet further progress is limited by defect-induced nonradiative recombination and instability associated with uncontrolled crystallization. Here, we develop a protic ionic-liquid precursor engineering strategy based on methylammonium acetate (MAAc) for high-performance inverted (p–i–n) triple-cation perovskite solar cells. Systematic variation of the MAAc content reveals that a moderate concentration yields perovskite films with enlarged grains, suppressed pinholes, and strongly reduced residual PbI₂. Steady-state and time-resolved photoluminescence measurements, together with electrochemical impedance spectroscopy and light-intensity-dependent analysis, demonstrate that MAAc effectively suppresses trap-assisted nonradiative recombination, prolongs carrier lifetime, and increases recombination resistance without introducing additional transport losses. As a result, optimized inverted devices deliver a champion power conversion efficiency of 23.68% with a high open-circuit voltage of 1.21 V, a fill factor of ~0.83, negligible J–V hysteresis, and excellent device-to-device reproducibility. Moreover, the MAAc-2M devices exhibit markedly improved operational and shelf stability, retaining 73.2% of their initial efficiency after 30 days, compared to 53.2% for the control. This work establishes MAAc as an effective ionic-liquid additive that simultaneously governs crystallization and defect chemistry, offering a general route to efficient and stable inverted perovskite solar cells via protic ionic-liquid-assisted precursor engineering. Full article

Under the dual pressures of global climate change and China’s “carbon peak and carbon neutrality” targets, traditional urban development models are insufficient to support sustainable transitions. Smart cities (SCs) have emerged as key platforms for achieving low-carbon urban transformation, yet the systemic causal mechanisms and dynamic transmission pathways of carbon emissions within these cities remain underexplored. This study develops an integrated DEMATEL–ISM–SD modeling framework to systematically identify key drivers, reveal causal structures, and simulate the dynamic evolution of carbon emissions in SCs. Eighteen influencing factors were identified through a comprehensive literature review. DEMATEL analysis evaluated the causal strength and centrality of factors, ISM constructed a five-level hierarchical structure, and a system dynamics model was established for scenario simulation, using Shenzhen as a case study. The results show that green technological innovation capacity exhibits the highest centrality, while energy structure demonstrates the strongest causal influence. SC policy intensity is positioned at the deepest level of the hierarchical structure, serving as a foundational driver that exerts influence on all other factors. Scenario simulations indicate that enhancing green innovation, optimizing industrial and energy structures, and developing smart transportation systems can significantly reduce carbon emissions over time. The research findings reveal the key drivers and transmission pathways of carbon emissions in SCs, providing a reference basis for policy formulation on urban low-carbon transformation and sustainable development. Full article

To achieve effective coordination among planning, operation, and service in urban management, and based on the fundamental characteristic of urban spatial development expanding from points to areas, this paper proposes an approach for identifying high-intensity urban development zones based on seed grid growth. First, seed grids are selected using the Getis–Ord $G_i$* of grid floor area ratios as the criterion. Second, drawing on relevant image recognition methods, high-intensity development zones are derived through seed-grid-based zone growth, as well as zone merging and segmentation. Furthermore, the rationality of the geometric morphology and the independence of the spatial relationships of the identified zones are evaluated. Meanwhile, the utilization efficiency of these zones is assessed from the perspectives of population carrying capacity and industrial agglomeration, using data on population, digital brightness of nighttime lights, and points of interest (POI). Finally, the proposed identification and utilization efficiency assessment method is verified through a case study of Shenzhen City. Full article

Urban Air Mobility (UAM) is emerging as a transformative solution to urban traffic congestion and inefficient ground travel. This paper presents the UAM development of Shenzhen, a pioneering city of low-altitude economy in China. It focuses on eVTOL drones for Shenzhen UAM, systematically reviewing the technical challenges, policy support, and practical progress. Firstly, the technical status of eVTOL drone design and research is reviewed, and the multidimensional technologies and application bottlenecks faced by eVTOL drones are identified. Secondly, by combining flight safety technology and urban air mobility regulation technology, the systematic technical challenges of urban low-altitude traffic based on eVTOL drones are analyzed. Furthermore, from the perspective of coordinated promotion of infrastructure and regulation, the foundation of urban air mobility applications is clarified, among which efficient flight approval and large-scale construction of takeoff and landing sites across the entire city represent prominent advantages of Shenzhen’s future air mobility. Then, given the high correlation between the systemic technological challenges of urban air mobility and low-altitude economic policies, this paper reveals the complementary relationship between technological challenges and low-altitude policies based on the current status of Shenzhen’s policy promotion and its impact on technology and industry. Finally, the technical issues and regulatory trends faced by eVTOL drones in urban air mobility in Shenzhen are summarized, and combined with the global and Chinese commercial prospects of manned eVTOL drones, suggestions for the future development of urban air mobility in Shenzhen are proposed from the following four dimensions: technology research and development, infrastructure, industrial ecology, and regional coordination. Full article

To address the challenge of fabricating metal-based composite coatings on the inner surfaces of tubular and internal hole components, a novel cylindrical electro-spark powder deposition (CEPD) technique is introduced. Utilizing the CEPD process, Ni-based composite coatings are successfully prepared on the inner surface of 316L stainless-steel tubes. The resultant Ni-based composite coatings completely covered the inner surface, exhibiting a splattered morphology and forming a robust metallurgical bond. Microstructural analysis revealed that the composite coatings primarily consisted of submicron-sized fine dendrites, with the main phases identified as Ni, FeNi₃, and Fe₃Ni₂, in addition to Ag particles. These fine grains and reinforcing phases contributed to a substantial increase in coating hardness, with an average value of 673.33 HV, representing approximately 2.82 times the hardness of the substrate. Tribological testing indicated that the high-hardness Ni-based composite coatings nearly doubled the surface wear resistance of the substrate and exhibited a significantly lower friction coefficient. Compared to other existing inner surface coating techniques, the CEPD process offers simplicity, low cost, and the ability to produce functional composite coatings with complex compositions. The prepared coatings exhibit considerable development potential and may offer a novel approach for the advancement of coating techniques for non-line-of-sight surfaces. Full article

Understanding how long-term local climate zone (LCZ) dynamics interact with rapid urbanization and land surface temperature (LST) changes is essential for sustainable planning in megaregion-scale urban clusters. In this paper, we propose a multi-feature local sample transfer method to obtain LCZ maps from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) and then analyze spatiotemporal changes in LCZs and their impacts on surface thermal environments. Results show the following: (1) The proposed multi-feature local sample transfer approach significantly improves the efficiency of long-term LCZ mapping by greatly reducing the effort required for sample acquisition. (2) The built types (LCZ1–10) increased by 1.34% overall, with large low-rise (LCZ8) showing the greatest expansion (4.72%). The compact low-rise (LCZ3) was the only built type to decline, decreasing by 2.02%. (3) Urbanization has produced a contiguous warming core that expands outward from the central metropolitan zones, thereby promoting the UHI coalescence. (4) Dense trees (LCZA) and large low-rise (LCZ8) exerted the strongest influence on LST. Large low-rise (LCZ8) consistently exhibited the highest warming contribution in Foshan, Zhongshan, and Dongguan. In coastal cities including Shenzhen, Hong Kong, and Macao, the largest LST increases occurred when water (LCZG) areas were converted to bare rock or paved (LCZE) or cs (LCZ1–10). Overall, the results highlight the strong coupling between urbanization and surface heating, providing critical insights for urban climate adaptation and integrated land-use planning in rapidly urbanizing megaregions. Full article

Background: Early-life gut microbiota colonization plays a significant role in the neurodevelopment of infants and young children. However, the causal relationship between early-life gut microbiota colonization and neurodevelopment in preterm infants has not yet been conclusively established. Our research will initiate the PIGMAN (Premature Infants Gut Microbiota Assembly and Neurodevelopment) cohort study to systematically examine the dynamic interplay between gut microbiota developmental trajectories and neurodevelopmental processes in preterm infants. Methods: This study will employ a longitudinal cohort design and utilize data from the PIGMAN cohort, examining the interplay between gut microbiota metabolism and neurodevelopmental outcomes. The study design incorporates longitudinal stool sample collection, which will be analyzed through 16S rRNA gene sequencing and metagenomic shotgun sequencing, enabling comprehensive characterization of microbial community dynamics and functional metabolic pathways. Anticipated Results: Advanced analytical approaches incorporating causal inference methodologies will be implemented to identify significant microbial and metabolic biomarkers associated with neurodevelopmental outcomes in preterm neonates, and to establish causal pathways between these biomarkers and neurodevelopment. These analytical advancements will facilitate the construction of predictive models that utilize temporal microbial signatures and metabolite trajectories as prognostic indicators for neurodevelopmental outcomes. Causal inference method evaluations will further reveal that specific gut-derived metabolites, particularly those involved in cholesterol metabolism and neural signaling pathways—such as bile acids and GABA (gamma-aminobutyric acid)—exhibit superior predictive capacity for cognitive development trajectories. Anticipated Conclusions: The findings will collectively suggest that longitudinal metabolic profiling of the gut ecosystem, when combined with causal network analysis, provides a novel paradigm for developing clinically actionable predictive models of neurodevelopment in vulnerable preterm populations. Full article

The biosynthesis of AAAs in plants primarily relies on the shikimate pathway, with metabolic flux sustained by NADPH and E4P generated via the OPP pathway. However, how OPP enzymes coordinate to support AAA production remains unclear. Here, we investigated the direct interaction between two consecutive NADPH-producing enzymes, G6PD6 and PGD2, and its role in metabolic coupling. Using BiFC, Co-IP, pull-down assays, and domain mapping, we showed that G6PD6 and PGD2 form a cytosolic protein complex via the C-terminal domain of PGD2. Structural modeling identified potential interaction residues: PHE²⁹⁴, GLY²⁹⁷, and LEU²⁹⁸ in PGD2, and GLY³⁵¹, LYS⁴⁹⁹, and ALA⁵⁰⁰ in G6PD6. Overexpression of either enzyme partially rescued the dwarf phenotype of adh2 mutants caused by AAA deficiency. These findings indicate that the PGD2–G6PD6 complex coordinates OPP-derived reductive power and carbon flux to support downstream AAA biosynthesis. This study reveals a functional link between OPP enzyme interactions and AAA production, suggesting that metabolic flux can be regulated through direct enzyme–enzyme association. Future work will explore how this complex responds to metabolic demand and whether additional components contribute to coordinating flux between the OPP and shikimate pathways. Full article

Background: Early childhood caries (ECC) remains a critical public health challenge, yet recent prevalence data and risk factors are scarce in rapidly urbanizing regions like Shenzhen City, China. Objectives: This study aimed to assess ECC prevalence and identify risk factors among 5-year-old children in Shenzhen City. Methods: This cross-sectional survey was conducted in Shenzhen City in 2024, recruiting 5-year-old children through multistage sampling from kindergartens. Self-administered parental questionnaires were distributed to collect data such as demographic characteristics, socioeconomic background and oral health-related behaviors. One trained dentist conducted the oral examination in kindergartens using ball-ended community periodontal index probes and disposable dental mirrors with an intra-oral light-emitting diode light attached. Dental caries was assessed using diagnosis criteria recommended by World Health Organization. The decayed, missing, and filled primary teeth (dmft) were recorded. Zero-inflated negative binomial regression was applied to identify associations between risk factors and ECC. Results: Among 1462 participants (86% response rate), ECC prevalence was 58% (mean dmft: 2.5 ± 3.4), with untreated decay (dt) accounting for 92% of cases. Socioeconomic factors, including low family income (p < 0.001), non-local residency (p < 0.001), and low caregiver education level (p = 0.012), were significantly associated with higher dmft scores. Behavioral factors such as frequent sugary drink consumption (p = 0.005), lack of parental brushing assistance (p = 0.027), and non-fluoride toothpaste use (p = 0.008) also contributed to the risk of ECC. Conclusions: Over half of Shenzhen City’s 5-year-olds suffered from ECC, predominantly untreated, driven by socioeconomic disparities and modifiable behavioral factors. Public health strategies must prioritize parental education, fluoride use and early preventive practices to reduce the burden of ECC. Full article

As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission errors. This study proposes an integrated framework for façade defect detection that combines unmanned aerial vehicle (UAV)-based visible-light and thermal infrared imaging with deep learning algorithms and parametric three-dimensional (3D) visualization. Three representative residential communities constructed between 1988 and 2010 in Shenzhen were selected as case studies. The main findings are as follows: (1) the fusion of visible and thermal infrared images enables the synergistic identification of cracks and moisture intrusion defects; (2) shooting distance significantly affects mapping efficiency and accuracy—for low-rise buildings, 5–10 m close-range imaging ensures high mapping precision, whereas for high-rise structures, medium-range imaging at approximately 20–25 m achieves the optimal balance between detection efficiency, accuracy, and dual-defect recognition capability; (3) the developed Grasshopper-integrated mapping tool enables real-time 3D visualization and parametric analysis of defect information. The Knet-based model achieves an mIoU of 87.86% for crack detection and 79.05% for leakage detection. This UAV-based automated inspection framework is particularly suitable for densely populated urban districts and large-scale residential areas, providing an efficient technical solution for city-wide building safety management. This framework provides a solid foundation for the development of automated building maintenance systems and facilitates their integration into future smart city infrastructures. Full article

Rapid global urbanization has intensified the need for cities to transition from growth-oriented models to sustainable development frameworks that prioritize livability, environmental quality, and social equity, positioning urban physical examination as an essential methodology for guiding this transformation. This study analyzes the spatial–temporal evolution of Shenzhen’s sustainable urban transformation from 2020 to 2024, employing urban physical examination methodologies combined with hypernetwork analysis to evaluate livability enhancement strategies. The research develops an economic vitality index incorporating urban points of interest density, Habitat Environment Index, and land surface temperature. Through spatial optimization analysis and hypernetwork modeling, the study examines the evolution of Shenzhen’s economic vitality and sustainable development patterns, with a particular focus on the impacts of economic centralization on regional sustainability and habitability. Results show Shenzhen’s economic vitality index increased by 10.47% from 2020 to 2024. However, regional disparities persist, with western and central regions displaying higher vitality than eastern coastal areas. The hypernetwork analysis reveals clustering patterns in livable spaces, with connectivity indicators ranging from 3.75 to 3.86. The uneven distribution of public facilities in Longgang and Yantian Districts highlights the need for improved resource allocation. These findings provide evidence-based support for sustainable urban space strategies in rapidly developing cities, emphasizing the importance of equitable resource allocation and community-centered planning approaches. Full article

Rapid urbanization has intensified the shortage of school places in many developing countries, prompting the rise of compact, high-floor area ratio (FAR) school models. However, research on high-FAR school design strategies remains limited. This study systematically analyzes 67 high-FAR schools in Shenzhen, China. Using design descriptions as the sample, the analysis applied the N-gram model and identified five major design strategies: responses to regulations, functional integration of classroom spaces, functional integration of public spaces, climate adaptation and sustainability, and alleviation of psychological stress. Correlation analysis revealed that factors including FAR, total floor area, design year of the schools, regional GDP and investment in the education sector significantly influence preferences for different design strategies. Further, K-means clustering categorized four types based on strategy adoption and FAR: the comprehensive strategy type; the user-centered innovation type; the spatial integration type; the psychological well-being type. The results emphasize the need for adaptable design strategies that reflect local development stages. These findings contribute to a data-informed foundation for improving spatial efficiency in rapidly urbanizing settings, offering policy and design guidance for rapid developing cities. Full article

As climate change and urbanization accelerate, urban flooding poses an increasingly severe threat to urban residents and their properties, creating an urgent need for effective solutions to achieve sustainable urban disaster management. While physically based hydrodynamic models can accurately simulate urban floods, they are data- and computational-resource-demanding. Meanwhile, artificial intelligence models driven by data often lack generalizability across different urban areas. To address these challenges, integrating spiking neural networks, graph convolutional networks (GCNs), and particle swarm optimization (PSO), a novel PSO-enhanced spiking graph convolutional neural network (P-SGCN) is proposed. The model is trained on a self-constructed dataset based on social media data, incorporating six representative Chinese cities: Beijing, Shanghai, Shenzhen, Wuhan, Hangzhou, and Shijiazhuang. These cities were selected for their diverse urban and flood characteristics to enhance model generalizability. The P-SGCN significantly outperforms baseline models such as GCN and long short-term memory, achieving an accuracy, precision, recall, and F1 score of 0.846, 0.847, 0.846, and 0.846, respectively. These results indicate our model’s capability to effectively handle data from six cities while maintaining high accuracy. Meanwhile, the model improves single-city performance through transfer learning and offers extremely fast inference with minimal energy consumption, making it suitable for real-time applications. This study provides a scalable and generalizable solution for urban flood risk management, with potential applications in disaster preparedness and urban planning across varied geographic and socioeconomic contexts. Full article

Against the backdrop of Shenzhen’s high-density urban environment, the multifunctional design of water purification plants offers dual benefits: providing residents with urban green spaces while simultaneously mitigating NIMBY sentiments due to their inherent characteristics. Unlike traditional urban development, Shenzhen’s water purification plants integrate into residents’ daily lives. Therefore, optimizing the built environment and road network structure to enhance residents’ perceptions of proximity benefits while reducing NIMBY (Not In My Backyard effect) sentiments holds significant implications for the city’s sustainable development. To address this question, this study adopted the following three-step mixed-methods approach: (1) It examined the relationships among residents’ YIMBY (Neighboring Benefits Effect) and NIMBY perceptions, perceptions of park spaces atop water purification plants, and perceptions of accessibility through questionnaire surveys and structural equation modeling (SEM), establishing a scoring framework for comprehensive YIMBY and NIMBY perceptions. (2) Random forest models and Shapley Additive Explanations (SHAP) analysis revealed nonlinear relationships between the built environment and composite YIMBY and NIMBY perceptions. (3) Spatial syntax analysis categorized the upgraded road network around the water purification plant into grid-type, radial-type, and fragmented-type structures. Scatter plot fitting methods uncovered relationships between these road network types and resident perceptions. Finally, negative perceptions were mitigated by optimizing path enclosure and reducing visual obstructions around the water purification plant. Enhancing neighborhood benefits—through improved path safety and comfort, increased green spaces and resting areas, optimized path networks, and diversified travel options—optimized the built environment. This approach proposes design strategies to minimize NIMBY perceptions and maximize YIMBY perceptions. Full article