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A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning.

The excavation of a deep foundation pit adjacent to an existing tunnel may lead to the large deformation and induce damages in the tunnel structure. However, the influence on existing tunnel structure from nearby excavations has not been understood clearly, since it is affected by complex influencing factors of not only the geological and topographical conditions but also the construction method and positional relationship of the adjacent structures. This paper presents a numerical investigation into an existing underground rail transit line during the excavation of an adjacent deep foundation pit, in which the behavior of the existing tunnel structure from excavating the aforementioned foundation pit is clarified, and the effectiveness of the adopted three-dimensional model is confirmed by comparison between the numerically calculated and field-measured ground settlement of the monitoring point. The results demonstrate that the deformation of the existing tunnel structure is mostly induced by the excavation of the deep foundation pit. This study can provide a reference of deep excavations adjacent to existing infrastructures.

The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection.

China’s agriculture is increasingly becoming more specialized. However, specialized production has disrupted traditional farming culture and may threaten sustainable mental health. This study takes Chinese farmers’ subjective happiness and agricultural production outsourcing as the research object, in an attempt to reveal the possible unhappy impacts of Chinese-style agricultural specialization represented by agricultural production outsourcing. First, we construct a theoretical framework of the relationship between agricultural production outsourcing and farmers’ subjective well-being. Secondly, based on more than 3800 household survey data collected by the Chinese Academy of Social Sciences in 2020, we use the classical econometrics and psychological analysis methods such as the Ordered Probit model and the instrumental variable estimation to conduct a rigorous impact assessment. The results show that for every doubling of agricultural outsourcing expenditure, the probability that farmers think they are very happy decreases by about 21%, and the probability that they think they are relatively happy decreases by about 9%. The groups affected by the negative psychological impact mainly include farmers growing rice and corn, farmers in hills and mountains, and farmers with small-scale operations. Further analysis shows that outsourcing risks, the weakening of farmers’ professional autonomy, and family split caused by agricultural outsourcing bring unhappiness, and the increase in income cannot offset the negative psychological effect of outsourcing. The findings of this study may bring inspiration to other countries with agricultural outsourcing markets and programs to improve the national subjective well-being.

Coal chemical industry (CCI) sites are characterized by complex environmental media, combined heavy metal pollution, and diverse exposure routes. However, existing human health risk assessment (HHRA) methods have multiple drawbacks, such as their small scope of application, limited assessment factors, and insufficient case applications. After 128 soil samples were collected, the contents of Be, V, Ni, Cu, Zn, As, Cd and Hg in the soils were analyzed based on general risk assessment guideline of China. Then, risk levels were calculated based on oral ingestion, skin contact and inhalation as the main exposure routes to compare and screen priority heavy metals. Furthermore, control values were identified through a contribution rate calculation model when CR > 10−6 or HQ > 1. As reference values, risk thresholds were proposed for heavy metals, and then a soil HHRA framework for the CCI site was constructed. Under the three exposure routes, the total CR was As > 10−6, and the total HQ was 1 > As > Cd; the HHRs related to As and V via the oral ingestion, dermal contact, and inhalation pathways were 76.67%, 13.13%, and 10.18% and 1.66%, 0, and 98.34%, respectively. The risk control value of As was 1.59 mg/kg and that of V was 25.1 mg/kg. Based on these results, the threshold values for priority heavy metals should be based on comprehensive considerations of the elemental background of a specific area, the contaminant criteria in different areas, the regional industrial development plan, and the most important control criterion, as well as the control value. Through the development of an HHRA framework and case verification, the authors of this article aim to guide CCI managers in screening priority heavy metals, formulating protection measures, developing improved operational procedures and improving the HHRA system for polluted CCI sites.

Accurate measurement of the shadow price of carbon dioxide (CO2) is fundamental to the scientific assessment of the carbon emission reduction cost and the formulation and execution of China’s carbon emission mitigation policies. Underpinned by the directional distance function, this research uses a parametric linear programming method and a Bayes bootstrap estimation method to estimate the marginal CO2 emission reduction cost of the industrial sector in China and to quantify the related influencing factors. The results revealed that the marginal reduction cost of industrial CO2 is CNY 4565/ton. The marginal reduction cost of CO2 varies by industry, with the textile industry being the highest and the petroleum, coking and nuclear fuel processing industries the lowest. Meanwhile, an increasing number of industries are shifting to cleaner production. Furthermore, the marginal reduction cost of industrial CO2 has an “inverted U-shaped” relation with carbon intensity. Carbon emission reduction can be accomplished effectively if the carbon intensity is kept below the threshold value of 0.41 tons/CNY 10,000.

Low-temperature preheating, fast charging, and vehicle-to-grid (V2G) capabilities are important factors for the further development of electric vehicles (EVs). However, for conventional two-stage chargers, the EV charging/discharging instructions and grid instructions cannot be addressed simultaneously for specific requirements, pulse heating and variable-current charging can cause high-frequency power fluctuations at the grid side. Therefore, it is necessary to design a bidirectional grid-friendly charger for EVs operated under pulse-current heating and variable-current charging. The DC bus, which serves as the medium connecting the bidirectional DC–DC and bidirectional DC–AC, typically employs capacitors. This paper analyzes the reasons why the use of capacitors in the DC bus cannot satisfy the grid and EV requirements, and it proposes a new DC bus configuration that utilizes energy storage batteries instead of capacitors. Due to the voltage-source characteristics of the energy storage batteries, EV instructions and grid instructions can be flexibly and smoothly scheduled by using phase-shift control and adaptive virtual synchronous generator (VSG) control, respectively. In addition, the stability of the control strategy is demonstrated using small signal modeling. Finally, typical operating conditions (such as EV pulse preheating, fast charging with variable current, and grid peak shaving and valley filling) are selected for validation. The results show that in the proposed charger, the grid scheduling instructions and EV charging/discharging instructions do not interfere with each other, and different commands between EVs also do not interfere with each other under a charging pile with dual guns. Without affecting the requirements of EVs, the grid can change the proportion of energy supply based on actual scenarios and can also obtain energy from either EVs or energy storage batteries. For the novel charger, the pulse modulation time for EVs consistently achieves a steady state within 0.1 s; thus, the pulse modulation speed is as much as two times faster than that of conventional chargers with identical parameters.

Community sociologists have examined community attachment through an almost exclusive focus on people’s social relations. Recent research efforts have noted the neglect of the physical place in traditional community sociological studies. Doing this has brought the physical environment into their discussions of community attachment. Despite this progress, we remain limited in our understanding of the physical environment’s contribution to peoples’ attachment to their communities and whether its effect on community attachment is applicable in the context of urban settings. In an effort to expand our knowledge of this topic, this study explored the contributions of the urban physical environment on community attachment. By selecting the Discovery Green Park as a typical form of physical environment in Houston, Texas, this study sought to investigate how people’s levels of community attachment could be predicted by: (1) peoples’ interactions with an urban park; (2) people’s emotional connections with such a park; and (3) peoples’ social interactions with others within the park. After conducting a series of block model regression analyses, we found that community attachment was not completely defined by social factors, but also depended upon peoples’ emotional connections with the local physical environment and the social interactions happening in those settings.