Agglomeration Characteristics and Factors Influencing Business Office Space in Changsha by Ye Qiang, Tan Chang, Zhao Yao

“…The average degrees of influence are shopping services>hotels>public transport>main roads>parks and green areas>external transport>metro>flats>scenic locations>residential areas. 3) Each influencing factor was characterized as a "center-periphery" influence mode. …”
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Intelligent Planning of Village Roads for Rural Revitalization: A Case Study of Gangtou Village, Huadu District, Guangzhou by Liu Yina, Bao Shitai, She Meixuan, Chen Shunqing

“…According to the five elements of residential areas, cultivated land, historical monuments, public facilities, and entrances and exits of external roads, five categories, seven starting points, and 16 ending points of village planning roads were automatically extracted, and a four-level village road from multiple starting points to multiple ending points was automatically generated, including four main roads with a width of 5-8 meters, two secondary main roads with a width of 3-5 meters, nine productive branch roads with a width of 2-3 meters, 13 living branch roads, and one village road. …”
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Influence of ground-surface deformation in pipe jacking construction with parallel differential section by Jianhua Huang, Feng Bao, Yunchen Wang, Hong Li

“…The development of urban underground space and traffic promotes the wide application of large section pipe jacking technology in urban tunnel construction.The functional requirements of parallel pipe jacking tunnel are different, so shapes and sizes of the section are also different.Based on the double-line parallel pipejacking tunnel with difference large section under through the main road in the city, a three-dimensional finite element calculation model is established for systematic analysis.At the same time, combined with the measured results of the field pipe jacking project, the characteristics and influencing factors of ground-surface deformation under different sections and different construction sequence of parallel pipe jacking are studied.The results show that the surface deformation curve caused by parallel differential section pipe jacking construction is Ⅴ-curve, and the peak point of the surface deformation curve gradually shifts from the large section rectangular pipe jacking axis to the two pipe centers.The surface settlement on the left side of the axis of the small section rectangular pipe jacking is larger than that on the right side of the axis.Construction of large section rectangular pipe jacking on the left line is more beneficial to control surface deformation.The influence of section size change on surface deformation is 30%, and the influence of process on surface deformation is 8%.The influence of section size difference on surface deformation is greater than that of process difference.The study on the influence of parallel differential section pipe jacking construction on surface deformation can provide theoretical basis and experience for future related parallel large section pipe jacking projects.…”
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Effects of Exposure to Environmental Factors on Exhaled Nitric Oxide Levels in Patients with Non-acute Exacerbation of Bronchial Asthma by HU Xiaofeng, CAI Guangyun, XIAO Kun, XU Pusheng, YU Chunhong, LIN Haihuan

“…There was no significant difference among the three groups in floors of residential buildings, length of residence, frequency of washing and drying bed sheets, quilt covers/pillow cases, curtain materials at home, potted plants at home, whether the trash can in the home was cleaned regularly, whether there were cockroaches at home and whether kitchen waste was disposed of the same day, whether there were plush toys at home (P>0.05) ; There were statistically significant differences in smoking history, whether family members smoked at home, allergy history, location of residence, the distance between the residence and the main road with large traffic volume, whether there were factories around the residence, and whether there were pets at home (P<0.05) . …”
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Mapping of Spatial Distribution of Street Vendors Based on Street-View Images and Deep-Learning Technology by Liu Yuchen, Chen Xiaochun, Liu Yilun, Wu Xiaofang, Chen Feixiang

“…The accuracy of the model was satisfactory for investigating the number and location of street vendors covering the main roads in the city and then applying a kernel density distribution model to evaluate the spatial distribution pattern of street vendors. …”
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