Search Results (488)

In the process of revitalizing historic districts, creating a healthy living environment requires a focus on the microclimate comfort of historic districts. Microclimate comfort refers to the comprehensive physiological perception and psychological satisfaction of climate elements such as heat, wind, and humidity under specific local environmental conditions, typically within a spatial range of horizontal scale < 100 m and vertical scale < 10 m. Among these, wind environment quality, as a key factor influencing pedestrian health experiences and cultural tourism appeal, holds particular research value. This study takes the Dabao Island Courtyard District in Qingdao as its subject, employing computational fluid dynamics (CFD) simulation methods from the artificial intelligence (AI) technology framework for modeling. CFD is a numerical method based on computer simulation, which solves fluid control equations (such as the Navier–Stokes equations) through iterative optimization to achieve high-fidelity simulation of physical environments such as airflow, turbulence, and heat transfer. A three-dimensional geometric model of the Dabao Island courtyard district was established, and boundary conditions were set based on local meteorological data. Numerical simulations were conducted to analyze the wind environment before and after the renovation of different layouts, functional spaces, and spatial scales (individual courtyards, clustered courtyards, and surrounding neighborhoods) of the courtyard district. The results indicate that factors such as building layout, street orientation, and renovation strategies significantly influence the wind environment of the Dabao Island neighborhood courtyards, thereby affecting residents’ perceptions of wind comfort. For example, unreasonable building layouts can lead to excessive local wind speeds or vortex phenomena, reducing wind comfort, whereas reasonable renovation and update strategies can facilitate the introduction of wind corridors into the historical courtyard buildings, improving wind environment quality. This study contributes to better protection and utilization of traditional neighborhoods during urban renewal processes, creating a more comfortable wind environment for residents, providing scientific decision-making support for the renovation of historical neighborhoods under the Healthy China strategy, and offering methodological references for wind environment research in other similar traditional neighborhoods. Full article

As critical infrastructure for flood control and disaster mitigation, the completeness of a dam spatial database directly impacts regional emergency disaster response. However, existing dam data in some developing countries suffer from severe gaps and outdated information, particularly concerning small- and medium-sized dams, hindering rapid response during disasters. There is an urgent need to improve the physical dam database and implement dynamic monitoring. Yet, current remote sensing identification methods face limitations, including a lack of diverse dam samples, limited analysis of geographical factors, and low efficiency in full-image processing, making it difficult to efficiently enhance dam databases. To address these issues, this study proposes a dam extraction framework integrating comprehensive geographical factor analysis with deep learning detection, validated in Sindh Province, Pakistan. Firstly, multiple geographical factors were fused using the Random Forest algorithm to generate a dam existence probability map. High-probability candidate areas were delineated using dynamic threshold segmentation (precision: 0.90, recall: 0.76, AUC: 0.86). Subsequently, OpenStreetMap (OSM) water body data excluded non-dam potential areas, further narrowing the candidate areas. Finally, a dam image dataset was constructed to train a dam identification model based on YOLOv11, achieving an mAP50 of 0.85. This trained model was then applied to high-resolution remote sensing imagery of the candidate areas for precise identification. Ultimately, 16 previously unrecorded small and medium-sized dams were identified in Sindh Province, enhancing its dam location database. Experiments demonstrate that this method, through the synergistic optimization of geographical constraints and deep learning, significantly improves the efficiency and reliability of dam identification. It provides high-precision data support for dam disaster emergency response and water resource management, exhibiting strong practical utility and regional scalability. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

Urban traffic congestion remains a major contributor to vehicle emissions and travel inefficiency, prompting the need for adaptive and intelligent traffic management systems. In response, we introduce DeepSIGNAL-ITS (Deep Learning Signal Intelligence for Adaptive Lights in Intelligent Transportation Systems), a unified framework that leverages real-time traffic perception and learning-based control to optimize signal timing and reduce congestion. The system integrates vehicle detection via the YOLOv8 architecture at roadside units (RSUs) and manages signal control using Proximal Policy Optimization (PPO), guided by global traffic indicators such as accumulated vehicle waiting time. Secure communication between RSUs and cloud infrastructure is ensured through Transport Layer Security (TLS)-encrypted data exchange. We validate the framework through extensive simulations in SUMO across diverse urban settings. Simulation results show an average 30.20% reduction in vehicle waiting time at signalized intersections compared to baseline fixed-time configurations derived from OpenStreetMap (OSM). Furthermore, emissions assessed via the HBEFA-based model in SUMO reveal measurable reductions across pollutant categories, underscoring the framework’s dual potential to improve both traffic efficiency and environmental sustainability in simulated urban environments. Full article

Xylazine (XYL), an FDA-approved veterinary tranquilizer, is being abused both as an opioid adulterant in a street-drug known as “Tranq-dope” and as a date rape drug. Given its now nearly ubiquitous use with fentanyl and fentanyl derivatives across the globe, XYL has become a primary target for researchers seeking to develop portable and cost-effective sensors for its detection. Electrochemical sensors based on the oxidation of XYL, while useful, have limitations due to certain interferents and inherent electrode fouling that render the approach less reliable, especially in certain sample matrices. In this work, modified electrode platforms incorporating layers of multi-walled carbon nanotubes for sensitivity along with semi-permeable polyurethane (PU) layers and host–guest chemistry using β-cyclodextrin for selectivity are deployed for XYL detection using complementary adsorptive cathodic stripping analysis. The modified electrode sensors are optimized to minimize high potentials and maintain fouling resistant capabilities and investigated to better understand the function of the PU layer. The use of adsorptive cathodic stripping differential pulse voltammetry indirectly indicates the presence and concentration of XYL within complex sample media (beverages and synthetic urine). When used in this manner, the modified electrodes exhibited an overall average sensitivity of ~35 (±9) nA/μM toward XYL with a limit of quantification of <10 ppm, while also offering adaptability for the analysis of XYL in different types of samples. By expanding the capability of these XYL sensors, this study represents another facet of tool development for use by medical professionals, first-responders, forensic investigators, and drug-users to limit exposure and help stem the dangerous and illegal use of XYL. Full article

Urban public space is a crucial constituent of livable city construction. A pleasant and comfortable public space is not simply spacious, bright, and accessible but also subjectively preferred by citizens who use it. Efforts to understand how citizens experience and perceive therein thus matters and would significantly aid urban design and well-being improvement. This research constructs a perception lexicon for 129 sites of public street space, a significant type of public space, in Shanghai and identifies how citizens comment on these sites through sentiment analysis based on social platform texts. A Chinese natural language processing (NLP) tool is applied to sort out the extent of citizens’ feelings on the urban street environment through a 0–1 scoring system. Six types of built environment elements and five categories of urban public spaces are identified. Pleasantly perceived sites primarily locate in the urban center and sporadically distribute in the outskirts and are normally “high-density” and “multi-function” in nature. Among the five categories of urban public spaces, sites that are commercially dynamic with culture, arts, and historical elements or that have gourmet food and good walkability generally receive the higher sentiment scores, but scores of ancient town commercial streets (many are antique streets), once popular and contributing much to tourism economy, are not satisfactory. The NLP-based text analysis also quantifies the intensity of emotional perceptions toward the six types of built environment elements and their associations with the general perception. This study not only offers insights for designers and policy makers in public space optimization but also showcases a scalable, data-driven approach for integrating public emotional and experiential dimensions into urban livability assessments. Full article

With the advancement of refined urban governance and the construction of high-quality public spaces, street furniture design and usage face multiple challenges, including insufficient public participation and a neglect of actual user experience. These issues highlight the urgent need to establish a scientifically grounded user evaluation framework to inform design practices. This study focuses on Yangzhou Ecological Science and Technology New Town and, drawing on field investigation, grounded theory, and the Delphi method, develops a street furniture design evaluation framework encompassing three core dimensions: planning and configuration, environmental coordination, and operational management. Building on this framework, the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation method are employed to conduct a holistic assessment of the street furniture and to identify critical design deficiencies. The results demonstrate that the proposed framework effectively identifies the strengths and weaknesses of street furniture and provides robust support for formulating targeted optimization strategies. The results reveal significant variations in the perceived importance of design factors among different user groups. Residents primarily emphasize practicality and convenience in daily use. Tourists value aesthetic expression and cultural resonance, whereas government officials focus on construction standardization and maintenance efficiency. In terms of satisfaction, all three groups reported relatively low scores, with the ranking as follows: “planning and configuration” > “management and operations” > “environmental coordination.” Based on these findings, the study proposes targeted design guidelines for future practice. The evaluation framework has been adopted by local authorities, incorporated into official street furniture design guidelines, and implemented in pilot projects—demonstrating its practical applicability and value. This research contributes to the theoretical advancement of street furniture design and provides empirical and methodological support for applications in other emerging urban areas and new town developments. Full article

Through their open space layout, rich green configuration and low floor area ratio (FAR), low-density commercial blocks show significant advantages in creating high-quality outdoor thermal comfort (Universal Thermal Climate Index, UTCI) environment, reducing regional energy consumption load (building energy consumption, BEC) potential, providing pleasant public space experience and enhancing environmental resilience, which are different from traditional high-density business models. This study proposes a workflow for morphological design of low-density commercial blocks based on parametric modeling via the Grasshopper platform and the NSGA-II algorithm, which aims to balance environmental benefits (UTCI, BEC) and spatial efficiency (FAR). This study employs EnergyPlus, Wallacei and other relevant tools, along with the NSGA-II algorithm, to perform numerical simulations and multi-objective optimization, thus obtaining the Pareto optimal solution set. It also clarifies the correlation between morphological parameters and target variables. The results show the following: (1) The multi-objective optimization model is effective in optimizing the three objectives for block buildings. When compared to the extreme inferior solution, the optimal solution that is closest to the ideal point brings about a 33.2% reduction in BEC and a 1.3 °C drop in UTCI, while achieving a 102.8% increase in FAR. (2) The impact of design variables varies across the three optimization objectives. Among them, the number of floors of slab buildings has the most significant impact on BEC, UTCI and FAR. (3) There is a significant correlation between urban morphological parameters–energy efficiency correlation index, and BEC, UTCI, and FAR. Full article

Optimizing tourist routes is a critical challenge in smart tourism, which aims to enhance the visitor experience while optimizing practical parameters. However, traditional routing algorithms often fail to provide personalized and efficient itineraries in complex real-world environments. This study aims to develop a hybrid framework that integrates Simulated Annealing for global route optimization with the A algorithm* for accurate local pathfinding, leveraging geographic data from OpenStreetMap. The proposed method computes the shortest paths between all Points of Interest using A*, constructing a comprehensive distance matrix, and applying Simulated Annealing to determine the most efficient visiting sequence. The framework was evaluated in the Old Medina of Fez, Morocco, demonstrating its effectiveness in generating realistic and efficient itineraries. Compared to alternative strategies such as Genetic Algorithms, the hybrid approach achieves superior computational efficiency and produces better routes in terms of travel distance. These findings highlight the practical applicability of the framework as a modular service for smart tourism applications, offering tourists and tourism platform developers a scalable solution for personalized and sustainable itinerary planning. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

Electric vehicles (EVs) have gained significant attention in recent decades for their environmental benefits. However, their widespread adoption poses challenges due to limited charging infrastructure and long charging times, often resulting in underutilized charging stations (CSs) and unnecessary queues that complicate travel planning. Therefore, selecting the appropriate CS is essential for minimizing the total travel time of EVs, as it depends on both driving time and the required charging duration. This selection process requires estimating the energy required to reach each candidate CS and then continue to the destination, while also checking if the EV’s battery level is sufficient for a direct trip. To address this gap, we propose an integrated platform that leverages two ensemble machine learning models: Bi-LSTM + XGBoost to predict energy consumption, and FFNN + XGBoost for identifying the most suitable CS by considering required energy, waiting time at CS, charging speed, and driving time based on varying traffic conditions. This integration forms the core novelty of our system to optimize CS selection to minimize the total trip duration. This approach was validated with SUMO simulations and OpenStreetMap data, demonstrating a mean absolute error (MAE) ranging from 2.29 to 4.5 min, depending on traffic conditions, outperforming conventional approaches that rely on SUMO functions and mathematical calculations, which typically yielded MAEs between 5.1 and 10 min. These findings highlight the proposed system’s effectiveness in reducing total travel time, improving charging infrastructure utilization, and enhancing the overall experience for EV drivers. Full article

The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development. Full article

Since large-flow water diversion began in the middle route of the South-to-North Water Diversion Project, inverted siphons have experienced varying degrees of local flow pattern disorder at their inlets and outlets, resulting in a significant decline in hydraulic performance. Taking the Kuhe inverted siphon as a case study, a combination of numerical simulation and on-site testing was used to explore the causes of flow pattern disorder at the outlet of the inverted siphon. Meanwhile, based on the actual engineering situation, the influence of the flow pattern optimization measure of installing a 5D (five times the diameter of the pier) diversion pier at the outlet of the inverted siphon on its hydraulic characteristics was studied. Research findings indicated that before the implementation of flow pattern optimization measures, the Karman vortex street phenomenon was found to occur when water flowed through the piers; the interaction of the vortex streets behind each pier led to flow pattern disorder and affected the flow capacity. After implementation of the flow pattern optimization measures, the diversion piers had a significant inhibitory effect on the formation and development of the Karman vortex street behind the piers under the dispatching and design flow conditions. The flow velocities in each vertical layer were adjusted, with a significant improvement in the flow pattern. The hydraulic loss of the Kuhe inverted siphon was reduced by 11.5 mm, or approximately 7.8%. Under the dispatching flow condition, the water diversion flow of the Kuhe inverted siphon increased by approximately 4.11%. The water diversion capacity of the structure could be effectively enhanced by adding diversion piers to the tails of the piers. This method can be widely applied in similar open-channel long-distance water diversion projects. Full article

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities. Full article

This study adopts a multi-scale, simulation-driven approach to evaluate the performance of different passive roof types in mitigating Urban Heat Island (UHI) in hot arid climate. A comparative analysis was performed for selected roof types; green, pond, cool, and dark roofs. At the urban scale, ENVI-met v5.7.1 was employed to simulate microclimatic impacts, including Mean Radiant Temperature (MRT) at the pedestrian street level (1.4 m) and above building canopy level (25 m). The results revealed that green roofs were the most effective in mitigating UHI on the urban scale, reducing MRT by 1.83 °C at the pedestrian level and by 3.5 °C at the above canopy level. Surprisingly, dark roofs also performed well, with MRT reductions of 1.81 °C and 3.5 °C, respectively, outperforming pond roofs, which showed reductions of 1.80 °C and 0.31 °C. While cool roofs effectively reduced MRT at the pedestrian level by 1.80 °C, they had adverse effect at the canopy level, increasing MRT by 15.58 °C. At the building scale, Design Builder v7.3.1, coupled with Energy Plus, was used to assess indoor thermal and energy performance. Pond and cool roofs reduced operative temperature by 0.08 °C and 0.07 °C, respectively, followed by green roofs, with a 0.05 °C reduction, while dark roofs increased it by 0.07 °C. In terms of energy performance, green roofs yielded the greatest benefit, reducing cooling load by 3.3%, followed by pond roofs, with a 1.32% reduction; cool roofs showed negligible reduction, while dark roofs increased it by 1.2%. Finally, a Python-based Multi criteria Decision Making (MCDM) analytical framework integrated these findings with additional factors to optimize thermal comfort, environmental impact, sustainability, and feasibility and rank strategies accordingly. The analysis identified green roofs as the optimal solution, followed by pond roofs and then cool roofs tied with the base case, leaving dark roofs as the least favorable strategy. This study’s key contribution lies in its integrated simulation–decision analysis methodology, which bridges urban climatology and building performance to provide actionable insights for sustainable urban design. By validating green roofs as the most effective passive strategy in hot arid regions, this work aids policymakers and planners in prioritizing interventions that support climate-resilient urbanization. Full article