Search Results (181)

Urban green spaces (UGSs) and forests play a vital role in shaping sustainable and livable cities, offering not only ecological benefits but also spaces that are essential for human well-being, social interactions, and everyday life. Understanding the landscape features that resonate most with public preferences is essential for enhancing the appeal, accessibility, and functionality of these environments. However, traditional approaches—such as surveys or single-data analyses—often lack the nuance needed to capture the complex and multisensory nature of human responses to green spaces. This study explores a cross-modal methodology that integrates natural language processing (NLP) and deep learning techniques to analyze text and image data collected from public reviews of 19 urban parks in Nanjing. By capturing both subjective emotional expressions and objective visual impressions, this study reveals a consistent public preference for natural landscapes, particularly those featuring evergreen trees, shrubs, and floral elements. Text-based data reflect users’ lived experiences and nuanced perceptions, while image data offers insights into visual appeal and spatial composition. By bridging human-centered insights with data-driven analysis, this research provides a robust framework for evaluating landscape preferences. It also underscores the importance of designing green spaces that are not only ecologically sound but also emotionally resonant and socially inclusive. The findings offer valuable guidance for the planning, design, and adaptive management of urban green infrastructure in ways that support healthier, more responsive, and smarter urban environments. Full article

Urban parks are an important component of public urban spaces, which directly impact the living experiences of residents and the urban image. High-quality urban parks are crucial for enhancing the well-being of residents. This study selected Fukuoka, Japan, as the study site. Five urban parks were chosen to evaluate landscape visual quality by using the Scenic Beauty Estimation (SBE) method. The Semantic Differential (SD) method was used to get sample subjective landscape features. Meanwhile, sample objective landscape features were obtained by using semantic segmentation techniques in deep learning and combined with spatial analysis to understand their distribution. A regression model was established, which used the SBE values as the dependent variable and subjective landscape features as the independent variables to analyze the relationship between urban park landscape visual quality and subjective landscape features. The regression analysis revealed that sense of layering, harmony, interestingness, sense of order, and vitality were the core factors influencing visual quality. All five features had a significant positive impact on landscape visual quality. The sense of order was the most influential factor, which would be the key to enhancing the landscape perception experience. Moreover, the XGBoost model and SHAP value from machine learning were used to reveal the nonlinear relationships and significant threshold effects between urban park visual quality and five objective landscape features: openness, greenness, enclosure, vegetation diversity, and Shannon–Wiener diversity index. This study showed that when openness exceeded 0.27, the positive effect was significant. The optimal threshold for the greenness was 0.38. Vegetation diversity and enclosure had to be below 0.82 and 0.58, respectively, to have a positive impact. Meanwhile, the positive influence of the Shannon–Wiener diversity index reached its maximum at a value of 1.37. This study not only establishes a systematic method for diagnosing landscape problems and evaluating landscape visual quality but also provides both theoretical support and practical guidance for urban park landscape optimization. Full article

Chemical leakage accidents in chemical industrial parks pose significant threats to personnel safety, particularly during evacuation processes, where individual behavior and evacuation strategies have a considerable impact on overall efficiency. This study takes a leakage incident at an alkylation unit as a case study. First, ALOHA5.4.7 software was used to simulate the influence of meteorological conditions across different seasons on the dispersion range of toxic gases, thereby generating an annual comprehensive risk zone distribution map. Subsequently, different evacuation scenarios were constructed in Pathfinder2024.1.0605, with the integration of trigger mechanisms to simulate individual behaviors during evacuation, such as variations in risk perception and peer influence. Furthermore, this study expanded the conventional application scope of Pathfinder—typically limited to small-scale building evacuations—by successfully adapting it for large-scale evacuation simulations in chemical industrial parks. The feasibility of such simulations was thereby demonstrated, highlighting the software’s potential. According to the simulation results, exit configuration, shelter placement, and individual behavior modeling significantly affect the total evacuation time. This study provides both theoretical insights and practical guidance for emergency response planning in chemical industrial parks. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

As research on national cultural parks advances, the significance of conducting multi-dimensional perception evaluations of their cultural ecosystem services (CESs) becomes increasingly apparent. This study examines the eight dimensions of CESs within the Grand Canal National Cultural Park from the perspective of soundscape preference. Using Tongzhou Grand Canal Forest Park as a case study, five categories of soundscapes comprising 19 sound sources were identified through the analysis of online textual data. This study then collected public preferences and perceptions of these five soundscapes via on-site questionnaires and analyzed the data using SPSS26 for correlation and IPA analyses. The results indicate that the overall evaluation of the park’s CESs is positive. There is a significant mutual influence between soundscape preference and CES perception. Specifically, the preference for natural soundscape significantly impacts the evaluation of each CES dimension, while satisfaction with leisure and entertainment is positively correlated with preferences for all types of soundscapes. Additionally, there are notable differences in soundscape preference among different age groups. These findings not only enhance our understanding of soundscape planning in national cultural parks but also provide valuable guidance for their management and design. Full article

The increasing development of coal mining subsidence wetland parks has led to a growing focus on assessing their ecological, economic, and social benefits following ecological restoration. This study establishes an assessment framework for the social landscape performance of coal mining subsidence wetland parks based on the landscape performance series (LPS), cultural ecosystem services (CES), and the unique characteristics of coal mining subsidence wetland parks. The framework integrates expert opinions and field research to select indicators, resulting in a comprehensive evaluation system comprising 28 indicators across five dimensions. Taking the Pan’an Lake National Wetland Park (PLNWP) in Xuzhou, China, as an example, we conducted empirical research by collecting data through questionnaires and on-site interviews. Using the fuzzy comprehensive evaluation method, the social landscape performance score of PLNWP was 3.511, which is rated as “good.” The importance–performance analysis (IPA) was applied to identify differences in the perceptions of visitors and local residents regarding the social landscape performance of the PLNWP. Local residents highlighted the need to enhance the amenity of waterside spaces, while visitors focused on the accessibility. Finally, based on the performance score and the perceptions from different stakeholders, optimization strategies were proposed in four aspects: enhancing waterside space amenity, optimizing accessibility, improving educational facilities, and addressing diverse user needs. This study could provide a feasible assessment framework and optimization guidance for other coal mining subsidence wetland parks. Full article

Cultural ecosystem services (CES) in urban parks, as a vital component of urban ecosystem services (ES), are increasingly recognized as an important tool for advancing urban sustainability and implementing nature-based solutions (NbS). The supply–demand relationship of CES in urban parks is strongly shaped by sociocultural and spatial geographic factors, playing a crucial role in optimizing urban landscape structures and enhancing residents’ well-being. However, current research generally lacks adaptive evaluation frameworks and quantitative methods, particularly for cities with significant spatial and cultural diversity. To address this gap, this study examines the central district of Lhasa as a case study to develop a CES supply–demand evaluation framework suitable for plateau river valley cities. The study adopts the spatial integration analysis method to establish an indicator system centered on “recreational potential–recreational opportunities” and “social needs–material needs,” mapping the spatial distribution and matching characteristics of supply and demand at the community scale. The results reveal that: (1) in terms of supply–demand balance, 25.67% of communities experience undersupply, predominantly in the old city cluster, while 16.22% experience oversupply, mainly in key development zones, indicating a notable supply–demand imbalance; (2) in terms of supply–demand coupling coordination, 55.11% and 38.14% of communities are in declining and transitional stages, respectively. These communities are primarily distributed in near-mountainous and peripheral urban areas. Based on these findings, four urban landscape optimization strategies are proposed: culturally driven urban park development, demand-oriented park planning, expanding countryside parks along mountain ridges, and revitalizing existing parks. These results provide theoretical support and decision-making guidance for optimizing urban park green space systems in plateau river valley cities. Full article

Urban green spaces are integral components of city ecosystems, supporting essential belowground microbial communities such as arbuscular mycorrhizal fungi (AMF). Understanding how green space types influence AMF communities is key to promoting urban ecological function. This study examines AMF diversity, community assembly, and co-occurrence network structures in two urban green space types—park and roadside—in Kaifeng, Henan Province, China. Soil samples were collected from both sites, and AMF community composition was assessed using high-throughput sequencing. Environmental variables, including total nitrogen (TN), available phosphorus (AP), available potassium (AK), water content, and pH, were measured to evaluate their influence on AMF communities. The results indicate marked differences between the two green space types. Park soils support significantly greater AMF species richness and more complex co-occurrence networks than roadside soils. These differences are correlated with higher nutrient levels in park soils. By contrast, AMF communities in roadside soils are more strongly associated with soil water content and pH, resulting in reduced diversity and more homogeneous community structures. Stochastic processes predominantly govern community assembly in both green space types, with roadside green spaces being more influenced by stochastic processes than park green spaces. These findings highlight the influence of urban landscape type on AMF communities and provide guidance for enhancing urban biodiversity through targeted landscape planning and soil management. In future work, we will implement long-term AMF monitoring across different green-space types and evaluate specific management practices to optimize soil health and ecosystem resilience. Full article

Passengers often struggle to identify intended pickup locations when autonomous taxis (ATs) arrive, leading to confusion and delays. While prior external human–machine interface (eHMI) studies have focused on pedestrian crossings, few have systematically compared feedback modes and display positions for AT pickup guidance at varying distances. This study investigates the effectiveness of three eHMI feedback modes (Eye, Arrow, and Number) displayed at two positions (Body and Top) for communicating AT pickup locations. Through a controlled virtual reality experiment, we examined how these design variations impact user performance across key metrics including selection time, error rates, and decision confidence across varied parking distances. The results revealed distinct advantages for each feedback mode: Number feedback provided the fastest response times, particularly when displayed at the top position; Arrow feedback facilitated more confident decisions with lower error rates in close-range scenarios; and Eye feedback demonstrated superior performance in distant conditions by preventing severe identification errors. Body position displays consistently outperformed top-mounted ones, improving users’ understanding of the vehicle’s intended actions. These findings highlight the importance of context-aware eHMI systems that dynamically adapt to interaction distances and operational requirements. Based on our evidence, we propose practical design strategies for implementing these feedback modes in real-world AT services to optimize both system efficiency and user experience in urban mobility environments. Future work should address user learning challenges and validate these findings across diverse environmental conditions and implementation frameworks. Full article

Driven by rapid growth in the new-energy vehicle (NEV) market and advances in automation, mobile charging robots are increasingly deployed in parking facilities. In complex environments featuring both static and dynamic obstacles, conventional trajectory plans often exhibit insufficient safety margins and poor smoothness. This paper proposes a hybrid path-planning strategy that combines an improved A* algorithm with an enhanced dynamic window approach (DWA). The enhanced A* algorithm incorporates obstacle influence factors and adaptive weighting during global search, enabling proactive avoidance of obstacle-dense regions and employing segmented Bezier curves for path smoothing. In local planning, the modified DWA integrates a global guidance term and distance-dependent heading weights to mitigate issues of local minima and target loss. Simulation results indicate that the proposed method substantially improves path safety, continuity, and adaptability to complex scenarios while maintaining computational efficiency. Specifically, under high-obstacle-density conditions (e.g., a 20 × 20 grid map), the collision rate is reduced by 66.7% compared to the standard A* algorithm (from 30% to 10%), and the minimum safety distance increases to 0.5 m. Current validation is conducted in simulations; future work will involve real-robot experiments to evaluate real-time performance and robustness in practical environments. Full article

Urban green spaces are essential for public health and well-being, emphasizing the importance of their equitable distribution in urban development. Despite efforts to expand green spaces, however, significant disparities persist between their spatial and social allocation. This study classified urban green spaces into community parks, urban parks, and country parks, and examined the relationship of their green coverage and park accessibility to neighborhood property prices in Hangzhou. We then assessed the urban green space equity using Gini coefficients. We found that (1) urban green space inequities occurred in both green coverage and accessibility; (2) high-priced neighborhoods occupied more green resources, especially green coverage and community park accessibility, but exhibited less green equity; and (3) low-priced neighborhoods and urban villages had the lowest green resources but more equity for country parks. This study highlights the relationship between property price (as a proxy for income) and urban green space equity at the neighborhood scale. The results offer guidance for policymakers and planners aiming to promote green equity and sustainable development in cities. Full article

While extensive research has examined the contribution of urban parks to well-being, empirical evidence on the role of cultural attributes in historical urban parks and their impact on visitors’ well-being remains limited. This study explores the impact of physical characteristics of historical urban parks on well-being from the perspective of human settlement environment. Quantitative data were collected from 11 urban parks in Wuhan, China, combining online crowdsourcing for physical characteristic assessments and questionnaire surveys for psychological evaluations. Machine learning techniques, spatial analysis, and statistical methods including multistep regression and Bootstrap sampling were employed to test our hypotheses. Our results demonstrate that objective physical features—including park area, green coverage rate, green space shape index, and the proportion of heritage landmarks—positively influence well-being, whereas road density exhibits a negative association. Cultural perception and place attachment serve as significant mediators between physical characteristics and well-being outcomes, with the proportion of heritage landmarks influencing well-being through a dual mediation path. Additionally, we found interaction effects between physical and psychological factors, with education level moderating the relationship between cultural perception and well-being. These findings advance environmental psychology theory by elucidating how historical elements foster unique pathways to well-being, distinct from those offered by conventional green spaces. Our research provides evidence-based guidance for historical urban park design and renovation in the context of urban renewal, where balancing preservation and modernization presents significant challenges. Full article

Establishing a resilient bird habitat network (BHN) and identifying ecological strategic areas for protection are critical for conserving biodiversity and maintaining ecosystem stability in wetland cities. However, existing ecological network studies often overlook dynamic resilience that incorporates explicit species information, and their scenario-based assessments lack systematic evaluation metrics. This study, using Wuhan—an international wetland city—as a case study, integrates Maximum Entropy (MaxEnt), remote sensing ecological index (RSEI) and circuit theory to identify a high-quality BHN. A comprehensive resilience assessment and optimization framework is developed, grounded in structure–function–quality indicators and informed by resilience and complex network theory. Key findings include: (1) The network comprises 147 habitat patches and 284 ecological corridors, demonstrating marked spatial heterogeneity. Habitats are predominantly located in the southern and southwestern regions of Wuhan, concentrated in contiguous green spaces. In contrast, habitats in the urban core are fragmented and small. Corridors are mainly distributed in the southwestern and central metropolitan areas. (2) Under deliberate attack, considering resilience centrality, the network’s resilience declined more slowly than in scenarios based on traditional centrality measures. Across combined node and corridor attack simulations, two critical resilience thresholds were identified at 30% and 50%. (3) The ecological strategic space is primarily composed of key habitat patches (58, 108, 117, and 27) and corridors (119–128, 9–12, 122–147, 128–138, 76–85, and 20–29), mainly located in the southern region of Wuhan, particularly around Liangzi Lake and Anshan National Wetland Park. This study advances a dynamic framework for BHN resilience assessment, planning, and restoration, providing scientific guidance for enhancing ecological security and biodiversity conservation in urban wetland environments. Full article

The microbial community is one of the key indicators for evaluating the health of alpine marsh wetlands, and understanding the composition and health of alpine wetland communities provides a scientific rationale for conservation and restoration efforts. Taking the alpine marsh wetlands in Qilian Mountain National Park, Qinghai Province, as the research object, 27 soil samples (0–30 cm depth) were collected in July 2024 from three types of wetlands: non-degraded (ND), low-level degraded (LD), and heavily degraded (HD). Using high-throughput sequencing, PICRUSt2 functional prediction, nonmetric multidimensional scaling (NMDS), and redundancy analysis (RDA), we analyzed the bacterial community structure and functional characteristics as well as the soil physicochemical properties across different degradation levels and soil depths. Pearson correlation analysis and RDA were used to identify key soil indicators influencing microbial community characteristics. The results showed that (1) compared to ND, the relative abundance of Acidobacteriota increased from 12.3% to 23.7%, and that of Pseudomonadota increased from 28.5% to 35.1% in HD wetlands. Meanwhile, the Shannon index rose from 5.31 in ND to 6.52 in HD, indicating significantly increased microbial community diversity and complexity with wetland degradation (p < 0.05). (2) Vertically, the six major primary metabolic functions gradually weakened with increasing soil depth in all three types of wetlands, the relative abundance of Proteobacteria decreased from 0 to 30 cm, and the α-diversity indices of soil bacteria also declined with depth. (3) Compared to ND, LD and HD showed significantly lower soil moisture content, organic matter, and total organic carbon (p < 0.05), while total potassium and pH increased significantly (p < 0.05). With increasing depth, total nitrogen significantly decreased across all degradation types (p < 0.05). Bacterial diversity, as measured by the Shannon and Simpson indices, showed a significant correlation with several soil properties (moisture, organic matter, total nitrogen, total potassium, cation exchange capacity, and total organic carbon; p < 0.05). Furthermore, pH emerged as a primary environmental driver shaping microbial community structure across different soil depths. These findings offer technical guidance and a theoretical framework for comprehending the degradation and restoration dynamics of alpine marsh wetland ecosystems in the Qilian Mountains. Full article

The value network of the high-tech park constitutes a value co-creation system where multiple entities facilitate knowledge transformation through interaction, thereby achieving collaborative innovation. The reasonable distribution of collaborative innovation benefits among various innovation entities is a critical factor in maintaining the motivation for innovation within the value network. This study examines the co-creation mechanism of the value network in high-tech parks from the perspective of knowledge innovation, with the aim of enhancing the efficiency of knowledge transfer and spillover among entities. Additionally, it seeks to establish a fairer and more rational benefit distribution framework to promote collaborative innovation and ensure the stable operation of the value network. Firstly, we identify the entities involved in value co-creation within the high-tech park. Subsequently, we analyze the roles and interrelationships of these entities within the value co-creation network. We determine the knowledge flow pathways by employing the shortest path method, and innovatively construct an MMPP/M/C queuing model to depict the processes of knowledge transfer and spillover among the entities engaged in value co-creation. We optimize and solve the queuing model using the matrix geometric method, deriving metrics such as the average queue length, average arrival rate, average waiting time, and service intensity under the steady state of the system, and verify the applicability and effectiveness of the model in the application of the high-tech park through empirical data. Finally, by integrating the improved Shapley value method, a benefit distribution model is constructed that incorporates five types of factors: contribution level, resource input, knowledge spillover effect, effort level, and risk undertaking. The rationality and operability of this model are validated through computational examples. Research findings indicate that the optimized queuing model enhances the efficiency of knowledge transfer and spillover among entities, while the refined benefit distribution mechanism effectively compensates entities with high contribution levels, substantial resource inputs, significant knowledge spillover effects, elevated effort levels, and high risk assumption levels. This provides both theoretical support and practical guidance for sustaining the long-term stable operation of the value network. Full article