Despite recent progress in the research of people’s emotional response to the environment, the built—rather than natural—environment’s emotional effects have not yet been thoroughly examined. In response to this knowledge gap, we recruited 26 participants and scrutinized their emotional response to various urban street scenes through an immersive exposure experiment using virtual reality. We utilized new physiological monitoring technologies that enable synchronized observation of the participants’ electroencephalography, electrodermal activity, and heart rate, as well as their subjective indicators. With the newly introduced measurement for the global visual patterns of the built environment, we built statistical models to examine people’s emotional response to the physical element configuration and color composition of street scenes. We found that more diverse and less fragmented scenes inspired positive emotional feelings. We also found (in)consistency among the physiological and subjective indicators, indicating a potentially interesting neural−physiological interpretation for the classic form−function dichotomy in architecture. Besides the practical implications on promoting physical environment design, this study combined objective physiology-monitoring technology and questionnaire-based research techniques to demonstrate a better approach to quantify environment−emotion relationships.

Benefits of green spaces on stress reduction have been shown in previous studies. Most existing studies to date have focused on the general population. However, there is a lack of understanding of physiological mechanisms underlying the beneficial effects of green space among special populations, such as pregnant women. To examine physiological and affective responses to green space on stress recovery among pregnant women, using simulated green space exposure through virtual reality (VR). We recruited 63 pregnant women between 8 and 14 weeks' gestational age for a laboratory experiment. Participants were randomly assigned to view one of three, 5-min, VR videos of an urban scene with different green space levels (i.e., non-green, moderate, and high) after a laboratory stressor, the Trier Social Stress Test. Physiological stress responses were measured via changes in blood pressure, heart rate, skin conductance level, salivary alpha-amylase, and salivary cortisol. Affective response was measured using the Positive and Negative Affect Scale. We found that visual exposure to a green space environment in VR was associated with both physiological and affective stress reduction among pregnant women, including lower systolic blood pressure [-4.6 mmHg, 95% confidence interval (CI): -8.8, -0.4], reduced salivary alpha-amylase concentration (-1.2 ng/ml, 95% CI: -2.2, -0.2), improved overall positive affect (score: 6.6, 95% CI: 0.3, 13.0) and decreased negative affect of anxiety (score: -2.6, 95% CI: -5.19, -0.04) compared to non-green space environment. Exposure to high green space environment in park-like setting had the strongest impacts on stress recovery. This study demonstrated that virtual green space exposure could effectively ease stress and improve mental health and well-being during pregnancy. Even a short immersion in VR-based green space environment may bring health benefits, which has significant implications for pregnant women when access to an actual nature may not be possible.

Efficient edge server placement techniques select optimal locations for edge servers to improve network and energy performance. However, without prior knowledge of users' resource demands, the possibilities of optimal location for these edge servers within a network are vast, which is a challenging problem. On the other hand, most existing techniques ignore the influence of user mobility on user's resources demand, violation of low-latency, and high energy consumption of 5 G networks. Therefore, this article addresses the edge server placement problem using network traffic to estimate the user resource demands. We first use a network traffic prediction model based on Graph Convolution Network to generate network traffic distribution. Second, the problem of edge server placement is formulated as a constraint optimization problem that places edge servers strategically to balance energy and latency. Searching randomly through many possible solutions and selecting the most descriptive optimal solutions can be time-consuming. Therefore, we used the particle swarm optimization (PSO) algorithm to optimize network delay and energy consumption, especially for high mobility areas. Experimental results are obtained to compare the performance of the proposed algorithm with existing methods. We evaluate the algorithm based on the real dataset from Shenzhen, Futian District. The results show our proposed algorithm averagely reduces edge servers' total cost and overloaded numbers by 23.98% and 52.71%, respectively.

Street-blocks, as basic geographical units for dividing urban space, are widely used in urban planning and statistics. However, the availability and quality of street-block data vary significantly across different countries or regions worldwide. While developed countries tend to have mature urban street-block division systems and corresponding public data, such data in most developing countries are often incomplete or non-existent. Even in countries with available data, the lack of consistent standards for street-block division causes difficulty in international comparative research. To address this gap, we are releasing a new open dataset: Multi-level Street-block Divisions of 985 Cities Worldwide (MSDCW), offering a logical, standardized, and user-friendly street-block division system for cities with the estimated population over 500,000 by Demographia from 142 countries or regions, with results at five spatial levels. Validation shows that compared with official datasets, MSDCW offers a reasonable division of urban street-blocks, and is therefore suitable as foundational data for related research. Additionally, researchers can use our method to generate their own street-block division datasets.

Under China’s Rural Revitalization program, it is essential to accurately determine the prospects of revival or decay for the villages alongside specific goals and paths, which existing literature lacks a systematic coverage. Based on rural typology theories, this paper proposes an analytical framework to determine the type of village revitalization from the perspective of factor endowments. Utilizing five groups of 45 indicators characterizing the natural, socio-economic, and cultural endowments of villages, this paper applies the Self-Organizing Mapping neural network to cluster 2,388,579 natural villages in 48,322 townships across the nation into the four basic types of rural revitalization as directed by China’s Strategic Plan for Rural Revitalization (2018–2022): (1) Agglomerative Promotion, (2) Suburban Annexation, (3) Special Endowment-based Development, and (4) Out-migration and Relocation. The results of cluster analysis are spatially visualized to form a national rural revitalization zoning map at the township level, the first attempt to our knowledge. We conclude the paper with discussions on the revitalization paths of the various types of villages, particularly the seemingly gloomy prospect of 2/3 of the villages falling into the fourth category, and ways to interpret the deterministic nature of the conclusion. The paper expands the understanding of rural typology to a national scale with both innovative categorization processes and strong linkages to revitalization practices.