Search Results (487)

Groundwater in Zhengzhou has experienced substantial changes under the combined effects of long-term abstraction, water-source substitution by the South-to-North Water Diversion Project, and ecological replenishment. During the 13th Five-Year Plan period, shallow and middle-deep groundwater levels in Zhengzhou recovered by 2.83 m and 6.46 m, respectively; nevertheless, extensive groundwater depression cones remained, highlighting the need for reliable groundwater burial-depth prediction to support dynamic monitoring and water-resource management. Aiming to address the limitations of the single long short-term memory (LSTM) network in groundwater burial depth prediction, including insufficient accuracy, tendency to fall into local optima, and difficulty in adaptive hyperparameter optimization, this study constructs a hybrid deep learning model (HO-LSTM-GPR). The Hippopotamus Optimization (HO) algorithm is employed to search for an appropriate parameter configuration of the LSTM network, and Gaussian Process Regression (GPR) is subsequently introduced to correct the residual deviations of the preliminary predictions. Four groundwater monitoring wells in Zhengzhou City, including the shallow wells Q1 and Q8 and the middle-deep wells Z14 and Z30, are selected to evaluate groundwater burial-depth prediction using historical input sequence lengths ranging from 1 to 30 days. The results show that the HO-LSTM-GPR model can significantly reduce prediction errors, and the Nash–Sutcliffe Efficiency (NSE) of all monitoring points exceeds 0.92, with the most prominent improvement observed at the Q1 site. The model can accurately characterize the high-frequency fluctuations of shallow groundwater levels and the slow variation characteristics of middle-deep groundwater levels, and effectively capture extreme points and mutation nodes. Within the comparison conducted in this study, the HO-LSTM-GPR model achieves higher fitting accuracy and prediction stability than the baseline LSTM model. Under the present dataset and model configuration, the HO-LSTM-GPR model achieves the best overall predictive performance when the historical input sequence length is 19 days. Overall, the HO-LSTM-GPR model exhibits relatively stable predictive performance for the investigated aquifers under different historical input sequence lengths. Full article

Urban air mobility depends on small aerial vehicles threading through dense, wind-swept cities, yet most sampling-based planners treat the urban wind field as noise to reject rather than structure to exploit—and pay for it in flight energy. We take the opposite view. Behind every building lies a sheltered wake where the air slows and aerodynamic drag drops, and this work turns that physical fact into a planning principle. We present an energy-aware, wind-shadow-aware framework that routes a single quadrotor, at the planning level, through these low-wind corridors. The wind model couples a power-law shear profile with Ekman directional veer and a frozen-turbulence gust component, grounding the planner in realistic boundary-layer physics. A feed-forward neural energy surrogate, trained to approximate a cost field that aggregates wind exposure and obstacle clearance, then guides a two-stage refinement—energy-aware, collision-checked shortcutting followed by Laplacian and energy-guided smoothing—so that every accepted change stays collision-free. Against classical sampling-based baselines (RRT, goal-biased RRT, Informed RRT*, and BIT*) over a 50-run Monte-Carlo study, evaluated with multi-criteria metrics and Pareto-dominance analysis, the framework characterizes how wind-shadow-aware routing balances route energy against smoothness and clearance, offering a reproducible, wind-informed basis for flying robots navigation. Full article

To evaluate the level of food and key agricultural product supply security in Gansu Province, this study constructed a comprehensive evaluation index system based on five dimensions—supply quantity, quality, structure, economic security, and distribution security—and applied the entropy-weighted TOPSIS model to assess the 14 prefectures and cities in Gansu Province from 2018 to 2023. The results show that: (1) Under the “comprehensive food security” framework, the level of food security in Gansu Province has generally improved steadily, with the indices for all prefectures and cities showing an overall positive trend. (2) Geographically, there has been an overall improvement, but growth rates have varied across regions: the central and eastern parts of the Hexi Corridor and the Longdong region have seen strong growth, while growth in parts of the Longzhong and Longnan regions has been relatively slow. (3) From various perspectives, the quality and safety of supply are at the highest level; the economic security of supply exerts a strong driving force; the quantity security of supply is improving amid fluctuations, working together with distribution security to strengthen the system’s foundation; and the structural security of supply is being adjusted and optimized amid fluctuations. Based on this, while consolidating advantages in quality and growth drivers, efforts must be focused on addressing regional development imbalances to promote balanced development across the entire region. Full article

As demand for non-motorized travel continues to rise, the underdevelopment of non-motorized lane infrastructure in high-density cities has become increasingly evident, affecting cyclists’ travel experience and safety. Existing cycling environment assessment methods have developed relatively comprehensive frameworks, but they still have difficulty capturing the various disturbances encountered during actual cycling and identifying segment-level problems for targeted interventions. To address these limitations, this study proposes a cycling-data-based framework for non-motorized lane information extraction and rideability assessment. The framework integrates cycling trajectories, first-person cycling videos, urban road networks, and points of interest (POIs) to extract information on road space, facility attributes, pavement conditions, visual environment, and static and dynamic disturbances, and further transforms this information into segment-level rideability assessment indicators. On this basis, an assessment system covering safety, comfort, attractiveness, and accessibility is constructed, and Wuhan is used as an empirical case study. Fuzzy C-means (FCM) clustering is then applied to identify six typical lane types and support differentiated governance strategies. The findings provide practical references for non-motorized lane planning, slow-traffic space improvement, and the management of motorized–non-motorized traffic conflicts. Full article

Facing the intensifying challenges of global climate change, research on urban ecosystem sustainability and resilience has become increasingly urgent. This study constructs a hybrid assessment framework integrating emergy analysis, GIS, carbon accounting, and neural networks to systematically evaluate the sustainability evolution of Zhenjiang City from 2000 to 2020 and project its resilience trajectory to 2050. The results show that Zhenjiang experienced a resilience tipping point around 2015. From 2000 to 2015, the built-up area expanded by 17.5 times, droving PM_2.5 concentration to exceed 200 μg/m³ and the emergy sustainability index to decline by 59%. From 2015 to 2020, under strong policy interventions, PM_2.5 dropped sharply by 85.6%, forest area expanded by 3.5 times, and wetland emerged from none, with an accumulation–release asymmetry index greater than 2, validating the characteristics of slow accumulation and rapid recovery. Rainwater emergy accounts for more than 94% of total emergy, making it the dominant factor in the sustainability of small and medium-sized cities. Built-up areas have achieved relative carbon–emergy decoupling, while farmland faces a high carbon emission dilemma. SHAP analysis shows that the contribution of spatial ecological indicators is 3.5 times that of population density, and policy benefits exhibit a time lag of approximately 6 years. Neural network projections indicate that the comprehensive sustainability score can improve by approximately 58% by 2050, though this improvement depends on the continuous optimization of ecological space. This study provides a quantifiable assessment tool and decision-making basis for resilience management in small and medium-sized cities. Full article

Pedestrian accessibility is a key determinant of spatial experience, cultural heritage visibility, and urban integration in historical cities. This study analyzes pedestrian accessibility in Mudurnu—a UNESCO Tentative List site and Cittaslow—using a multi-scale, integrated space syntax approach. By combining axial and segment analyses, statistical validations, and Kernel Density Estimation (KDE), the research examines the relationship between urban morphology and pedestrian movement potential. Spatial data developed through open-access maps and field validations were analyzed via depthmapX 0.8 and QGIS 3.40.11 software. Syntactic indicators—integration, connectivity, choice, and intelligibility—were calculated at global and local scales, alongside synergy and points of interest (POI) correlations. The results indicate that mobility is concentrated along the north–south Bolu–Yıldırım Beyazıt–Ankara axis, while secondary networks in traditional residential areas remain poorly integrated. Low intelligibility (R² = 0.06) indicates local spatial configurations provide insufficient cognitive guidance, and moderate synergy (R² = 0.33) suggests partial harmony between scales. Segment and KDE results highlight concentrated mobility, with secondary networks emerging after logarithmic transformation. Consequently, Mudurnu exhibits fragmented accessibility and low legibility. These findings demonstrate that multi-scale space syntax analysis is an effective decision-support tool for pedestrian-oriented planning and sustainable urban design in historical cities. Full article

The escalating security risks associated with unauthorized unmanned aerial vehicles (UAVs) in advancing smart cities necessitate the development of robust active countermeasures. This work presents a novel approach centered on a UAV-borne flexible net system and provides a rigorous investigation into its complex nonlinear dynamics. This study establishes a lumped-mass, semi-spring–damper dynamic model of the flexible capture net, characterizing its key dynamic properties, including deployment performance, aerodynamic attitude, and the high-impact phenomena of collision and entanglement with the target UAV. To verify the reliability of the proposed method, numerical simulations are combined with field tests for systematic validation. Comparative analysis reveals excellent quantitative agreement, with over 80% conformity in the net’s spatial configuration between simulated and experimental results. This paper illuminates the fundamental principles governing energy dissipation and transient tension dynamics pre- and post-capture. This study provides preliminary evidence for the feasibility of the proposed method and identifies key directions for future investigation. The findings offer guidance for the design and optimization of future systems intended to neutralize low, slow, and small (LSS) aerial threats. Full article

City networks refer to the connections of physical or virtual flows among cities at different spatial scales, including population migration networks, economic networks, information networks and innovation networks. This concept has gradually evolved into an important paradigm for understanding the regional spatial structures. Based on Baidu Index data within the Yangtze River Economic Belt (YREB) in China, this paper constructs an information network and investigates its connection patterns. Using social network analysis, the structural differentiation of the information network is further investigated at both the overall and subregional scales. The results show that the connection patterns of the information network exhibit an obvious hierarchical structure, with the complexity of the spatial pattern gradually increasing from the upstream to the downstream regions. Furthermore, the structural assessment results suggest that the information network is characterized by high agglomeration, high mobility, high hierarchy and low disassortativity. These findings indicate that the information network in the YREB is dominated by several highly developed core city clusters. However, the inherently closed structure resulting from these characteristics may not be sufficiently counterbalanced by low disassortativity. Under sudden disturbances, such a structural configuration may exhibit limited adaptability, delayed response capacity, and slow reorganization and learning processes, thereby weakening structural resilience. This study provides a deeper understanding of intercity relationships within the YREB and offers policy implications for enhancing structural resilience across the Yangtze River Basin. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based Biodiversity Index (BI) and analyze its spatiotemporal evolution and underlying drivers. Six Essential Biodiversity Variables derived from satellite observations (2000–2024) were integrated using Principal Component Analysis. Spatial autocorrelation and Geodetector models were then applied to examine BI dynamics and driving factors. The regional BI declined gradually from 0.80 in 2000 to 0.72 in 2024, with the rate of decline slowing after 2020 and a partial recovery observed in Zhoushan. Marked inter-city heterogeneity exists: Huzhou retains the highest and most stable BI due to extensive forest cover, whereas Jiaxing exhibits the lowest BI and the most pronounced decline, driven by rapid expansion of construction land. Land use/cover (LULC) and fractional vegetation cover (FVC) emerge as the dominant drivers (average q-values of 0.196 and 0.208, respectively), and their interaction explains over 46% of the spatial variance in BI. Road density shows a consistently increasing influence over time. This study demonstrates the utility of remote sensing-based frameworks for monitoring urban biodiversity dynamics and provides actionable insights for evidence-based land use planning and ecological restoration. Full article

The continuous analysis of ground deformation is essential for both the assessment of natural hazards and the monitoring of human-induced activities. In this study, we present the results of a Persistent Scatterer Interferometry (PSI) analysis of ground deformations in the region of Cluj-Napoca, Romania. The PSI was performed using more than 10 years of Sentinel-1 ascending and descending Synthetic Aperture Radar data from 2014 to 2025, using a dual master approach. Results show significant displacements at many locations, including recently built-up areas at the edges of the city, often caused by the combined effect of anthropogenic activities and geological conditions. In this study, we highlight three case studies: the surroundings of a reclaimed mine, subsidence induced by dewatering, and a large-area, slow landslide, wherein we examined natural and anthropogenic influences. The accurately mapped and quantified ground deformations can be used for a better understanding of the geological processes and assessing the risk of the urban development in the area. Full article

Contemporary cities face environmental, social and economic challenges that highlight the vulnerabilities of urban structures, spatial connections, infrastructure and socio-economic systems. These critical issues have been amplified by the pandemic and the intensification of climate change, generating significant impacts on the territory. In this uncertain context, urban planning plays a crucial role in responding to the new needs of cities, promoting proximity and environmental sustainability, and encouraging adaptation and proactive responses to change. The research aims to promote strategic planning based on an integrated and multi-scale approach, capable of generating synergies between the various complex aspects that characterize urban environments. This approach allows spatial relationships and considerations to be articulated at different scales, from the territorial to the local level, translating urban challenges into multi-level actions and strategies. However, this requires a supporting structure on which to articulate multiple urban planning strategies, resulting from the overlap and interrelation of two complementary design urban backbones: the proximity one, aimed at connecting services and public spaces through wide slow mobility networks; the natural one, aimed at integrating green areas on different scales, with both ecological functions, to promote biodiversity, and social functions, to improve collective well-being and strengthen the resilience of the urban ecosystem. The complementarity of these two backbones was explored through the revision of the Territorial Government Plan of the City of Mantua, particularly within the Services Plan, as part of the research project entitled “Re-knowing Urban Complexity through New City Awareness: In Search of Urban Proximity Systems.” This research provided an opportunity to read and interpret the urban complexity of the city while guiding effective, sustainable, and resilient intervention strategies in response to its ongoing transformations. Full article

Rapid urbanization has intensified inequalities in the distribution of urban green resources, making green equity a critical concern within the framework of the United Nations Sustainable Development Goals. This study examines Zhangdian District in Zibo City, China, a representative “Whole-Area Park City” pilot area. This study integrates 1 km population density grid data with GIS network analysis, space syntax, population-weighted service pressure assessment, and a location–allocation model. Using these methods, it evaluates four categories of urban parks from the perspectives of spatial distribution, road connectivity, and social equity. The results reveal that vehicle and cycling modes achieved nearly complete 15 min coverage, whereas pedestrian accessibility remained insufficient. Walking accessibility for comprehensive parks reached 77.69%, whereas that of community parks and petty street gardens was below 33%. Population-weighted analysis further suggests that more than 78% of residents, concentrated in dense central–western neighborhoods, are served by only 21% of total park area. The Gini coefficient of per capita park area reached 0.4765, indicating substantial inequality in park green space allocation. After optimization through the addition of 76 new parks, improvements in road connectivity, and construction of a slow-traffic system, the Gini coefficient decreased to 0.4053, representing a 14.9% reduction. Meanwhile, the population below the national standard declined from 78.09% to 40.64%. These findings reflect spatial accessibility and area-based equity, while actual park service value also depends on park quality, facilities, and user behavior. This study provides quantitative evidence for equity-oriented park planning and a replicable framework for sustainable urban green space planning. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

This study examines the Xinjiang Uygur Autonomous Region as a critical case study, constructing comprehensive evaluation frameworks for both ecological environment and tourism economy. We calculate the integrated development levels of both systems from 2010 to 2024, employing entropy weighting to derive composite development indices, Coupling Coordination Degree modeling to quantify the intensity and quality of system interactions, Relative Development Degree modeling to characterize coordination typologies and developmental asymmetries, and Grey Relational Analysis to identify key driving factors. Our findings reveal that although the coupling coordination of Xinjiang’s tourism–ecological system has transitioned from “mild imbalance” to “marginal coordination”, the system exhibits pronounced vulnerability and persistent “tourism-lag” dynamics. To effectively leverage the current “strategic window” of ecological surplus, we propose a multi-dimensional transformation pathway: (1) enhancing digital resilience through intelligent monitoring systems to mitigate external mobility shocks; (2) optimizing spatial connectivity via a “fast transit, slow travel” infrastructural paradigm; (3) institutionalizing micro-scale ecological governance to position oasis cities as sustainable “ecological gateways”; and (4) catalyzing deep cultural-tourism integration, shifting from scale-driven sightseeing to value-driven Silk Road heritage experiences. These pathways furnish a clear blueprint for Xinjiang to achieve high-quality, sustainable regional tourism development while maintaining its strategic positioning as a northwestern ecological security barrier. Full article