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Over the past three decades, Transit-Oriented Development (TOD), with transit as its central tenet, has emerged as a pivotal urban policy driving sustainable and intelligent urban growth, drawing significant attention from researchers and practitioners worldwide. TOD involves creating high-density, mixed-use, pedestrian-friendly urban areas around transit stations to enhance transit accessibility, promote social cohesion, and improve housing conditions. However, the global implementation of TOD has encountered challenges across various domains including transportation, housing, and employment, thereby exacerbating inequities within the built environment. This study adopts a TOD perspective to comprehensively review the equity impacts of urban rail transit (URT) station areas on the built environment, with a particular focus on social, travel, perception, health, and spatial dimensions, and their impacts on promoting or hindering equitable outcomes among diverse societal groups. Utilizing a scoping review methodology, the study encapsulates the progress and themes in the field, employing a systematic approach to meticulously analyze the outcomes of each research theme. The findings reveal that URT station areas have positive impacts on economic growth and property values. However, they can also contribute to gentrification, exacerbating disparities between different societal groups in station and non-station areas, along with an unequal distribution of resources and opportunities. Additionally, while these station areas encourage pedestrian activity and public transportation usage, they also carry the potential for environmental pollution, raising concerns about spatial accessibility and facility convenience, thereby impacting environmental equity. This study employs comprehensive and critical theoretical analyses, utilizing intricate methods and detailed indicators, to elucidate disparities in equity outcomes of URT station areas across different societal groups. The crucial challenge in future research lies in integrating the concept of equity into TOD planning strategies. This study aims to provide standardized and harmonized criteria for guiding equitable TOD planning policies, thereby enhancing the scientific basis and effectiveness of planning strategies. Ultimately, it seeks to offer theoretical insights towards the creation of a more equitable and inclusive urban built environment in the future.

Background: Green spaces reduce the risk of multiple adverse health outcomes by encouraging physical activity. This study examined correlations between urban green space and residents’ health outcomes in hilly neighborhoods: if they are mediated by social cohesion, visual aesthetics, and safety. Methods: We used multiple green space indicators, including normalized difference vegetation index (NDVI) extracted from satellite imagery, green view index (GVI) obtained from street view data using deep learning methods, park availability, and perceived level of greenery. Hilly terrain was assessed by the standard deviation of the elevation to represent variations in slope. Resident health outcomes were quantified by their psychological and physiological health as well as physical activity. Communities were grouped by quartiles of slopes. Then a mediation model was applied, controlling for socio-demographic factors. Results: Residents who perceived higher quality greenery experienced stronger social cohesion, spent more time on physical activity and had better mental health outcomes. The objective greenery indicators were not always associated with physical activity and might have a negative influence with certain terrain. Conclusions: Perceived green space offers an alternative explanation of the effects on physical activity and mental health in hilly neighborhoods. In some circumstances, geographical environment features should be accounted for to determine the association of green space and resident health outcomes.

Location-based social media data provides a new perspective for understanding the relationship between human behavior and urban environments. However, further research is needed to determine the application of cognitive computing in urban environments and physical activities. This study proposes a cognitive computing framework for urban environments and human activities that extracts knowledge from structured and unstructured data through natural language processing and computer vision techniques. This paper utilizes a Naive Bayes Model constructed based on random reviews, as well as semantic segmentation and instant segmentation algorithms based on convolutional neural networks to obtain information about urban environments and human behavior from social media data and other geospatial resources. This study examines the relationship between the urban environment and residents' activity, including spatiotemporal behavior, public sentiment, and physical activity. The study found statistically significant results in subgroup analyses regarding the effects of urban environments on sentiment and physical activity, which also exhibited a strong social gradient consistent with traditional findings. This study validates the feasibility of using cognitive computing based on social media data to explore environmental behaviors, providing technical support for updating health promotion policies.

Location-based social media data provides a new perspective for understanding the relationship between human behavior and urban environments. However, further research is needed to determine the application of cognitive computing in urban environments and ical This study proposes a cognitive computing framework for urban environments and human activities that extracts knowledge from structured and unstructured data through natural language processing and computer vision techniques. This paper utilizes a Naive Bayes Model constructed based on random reviews, as well as semantic segmentation and instant segmentation algorithms based on convolutional neural networks to obtain information about urban environments and human behavior from social media data and other geospatial resources. This study examines the relationship between the urban environment and residents' activity, including spatiotemporal behavior, public sentiment, and physical activity. The study found statistically significant results in subgroup analyses regarding the effects of urban environments on sentiment and physical activity, which also exhibited a strong social gradient consistent with traditional findings. This study validates the feasibility of using cognitive computing based on social media data to explore environmental behaviors, providing technical support for updating health promotion policies. ·

Enhancing the walkability of urban streets is an effective means to improve public health, alleviate traffic congestion, and enhance the living environment. In China, the government has actively encouraged green travel and promoted improvements in the walk system. The walkability of the built environment is affected by many factors. In addition to the configuration of daily life facilities, street greening can have significant effects on walkability. To explore the rationality of street life facilities and understand the impact of the natural attributes of the block space (street-level greening) on the quality of the walking environment, we evaluated the walkability of Zhongshan Road in Qingdao, China and optimized the algorithm of the walk score. In this study, we selected residential areas as the starting point and modified the weight coefficients for facilities to evaluate the walkability of streets. Traditional research methods were combined with street view image capture, and the rate of the attenuation factor was used for the new optimization algorithm. We discussed the rationality of street life facilities and increased the green vision rate using a correction index. By comparing changes in walkability before and after joining, we analyzed the necessity of including new indicators. The results show that the average walking index of Zhongshan Road is 79.74, and the overall performance is good, showing a high trend in the west and a low trend in the east, and a high trend in the south and a low trend in the north. According to the general walking index, western stations and southern coastal areas have higher scores, and living facilities are well equipped; old northern and eastern residential areas have lower scores. Among them, the average weight of bookstores is 0.74, and the average weight of parks is 0.69. To meet residents’ needs for daily leisure activities, adding bookstores or similar facilities in community parks would be necessary to improve daily facilities and services. The average green viewing rate of Zhongshan Road is 20.48%, which is lower than the best visual perception value of 25.00%. Comparing the walking index changes before and after adding the green viewing rate, the high-scoring area shifted from the west to the south, and the west walking index has the most significant decline. Street greening has a certain impact on the quality of the walking environment. The results and conclusions of this study can be used as a reference in developing street walkability indicators and further improving the evaluation system.

Neighborhood built environment may influence residents’ physical activity, but evidence of non-major Chinese cities is lacking. We investigated the impact of five socio-demographic characteristics, 10 objectively assessed environment characteristics, eight perceived neighborhood attributes, and social environment on physical activity and health outcomes (sense of community, body mass index, as well as self-reported health status). We also examined (1) five conceptually comparable perceived neighborhood attributes as mediators of the relationship between objective environment attributes and physical activity; (2) other perceived indicators and social environment as moderators of those relationships, using the mediation analysis in regression. Objectively assessed residential density, land use mix, street connectivity, and accessibility were curvilinearly and/or linearly related to physical activity. The slope of terrain was inversely associated with body mass index (BMI). None of the perceived attributes were found as mediators probably due to the weak associations between subjective and objective environments. High density facilitated physical activity but hindered the sense of community. Further, the perceived aesthetic and safety were associated with physical activity. Additionally, social environment moderated the positive associations of all perceived environments (except for slope) and sense of community. The present study demonstrated that both physical and social environment attributes significantly correlated with physical activity in Dalian.

With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially. Dumpsites are hard to locate globally. Here the authors apply deep networks to satellite images to provide an effective and low-cost way to detect dumpsites with the new method saving more than 96.8% of the manual time with a strong sensitivity to dumpsites.

A unidirectional freezing experiment was performed on silty clay from the Changchun area using a custom freezing apparatus to examine temperature variations, frost deformation, and moisture migration within the soil samples. The study aimed to investigate the influence of cooling rate and water supply conditions on the soil’s frost heave properties. The results indicated that under different cooling rates and water supply conditions, the internal temperature evolution of the specimens could be categorized into four distinct stages: rapid cooling, rebound and stabilization, sustained cooling, and steady state.The final temperature of specimens under open water supply conditions was 2–3°C higher than that under closed conditions. The cryostructure exhibited a reticulated pattern, and its width increased from 3–4 cm to 6–7 cm with increasing cooling rate under the same water supply condition. The cooling rate significantly affected the frost heave ratio: under closed conditions, the ratio decreased, whereas under open water supply conditions, the vertical frost heave displacement increased with higher cooling rates.Moisture migration within the specimen was notably different under the two replenishment conditions. Under open water replenishment, the water content of the soil increased near the negative-temperature top plate and decreased in the surrounding areas.

Background Previous research has found that inter-item binding in working memory requires more executive attention than single items, and since intra-item binding in working memory requires more object-based attention to maintain than its constituent elements, it is possible that inter-item binding may also need more object-based attention than single items. Additionally, studies have found that semantic relationships between items in working memory help to facilitate working memory, but the mechanisms by which these semantic relationships enhance working memory are not clear. This study examines whether the semantic relationships between items in working memory can reduce the attentional resources required for inter-item binding. Methods During working memory task, participants were presented with memory items that either had or lacked semantic associations, and were required to complete an executive attention-consuming backward counting task or an object-based attention-consuming Duncan task while performing the working memory task. Results Experiment 1 revealed that for non-semantic pairs, the high-load backward counting task caused significantly greater impairment to inter-item binding than to single items ( t (23) = 3.47, p  =.002, d  = 0.71, BF₁₀ = 15.39). For semantically related pairs, Bayesian evidence strongly supported the null hypothesis of no differential executive attention cost between binding and single-item conditions ( p  =.787, d  = 0.06, BF₁₀ = 0.22). Experiment 2 showed a parallel but weaker pattern for object-based attention: non-semantic pairs exhibited higher binding costs under the Duncan task ( t (23) = 3.41, p  =.002, d  = 0.70, BF₁₀ = 17.27), whereas semantic pairs showed only partial attenuation of these costs ( p  =.123, d  = 0.33, BF₁₀ = 0.59). Conclusion Semantic relationships between items primarily alleviate executive attention required for inter-item binding maintenance and moderately reduced but did not fully eliminate the object-based attention demands.

The mechanisms and interactive effects of multiple operating parameters of tower cranes on load swing are not yet clear, which leads to the exacerbation of load swing during the lifting process due to improper control parameter settings. To address this issue, this paper establishes an electromechanical rigid-flexible coupling (EMRFC) model for tower cranes to accurately simulate the characteristics of load swing caused by flexible transmission and electromechanical nonlinear coupling. Furthermore, the Sobol sensitivity method is used to screen out the dominant and interactive operating parameters affecting load swing, and to reveal the patterns of their impact on load swing. The results show that the stiffness of the flexible transmission system has a significant impact on the load swing, which cannot be neglected in modeling and analysis. Among the dominant operating parameters, the lifting height has the greatest effect on load swing. Lifting height, luffing speed, and slewing speed show significant interactions on load swing, and the interactions make a significant difference to the load swing in different operating phases. Finally, this paper gives the reasonable interval of operation parameters of a hoisting operation under the composite working condition, which provides a scientific basis and theoretical guidance for intelligent control of tower crane operation.