Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by u...Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by using kernel density estimation(KDE), spatial autocorrelation analysis(SAA), and spatial error model(SEM). Results showed that: 1) the spatial distribution of cold storage in China is unbalanced, and has evolved from ‘one core’ to ‘one core and many spots’, that is, ‘one core’ refers to the Bohai Rim region mainly including Beijing, Tianjin, Hebei, Shandong and Liaoning regions, and ‘many spots’ mainly include the high-density areas such as Shanghai, Fuzhou, Guangzhou, Zhengzhou, Hefei, Wuhan, ürümqi. 2) The distribution of cold storage has significant global spatial autocorrelation and local spatial autocorrelation, and the ‘High-High’ cluster area is the most stable, mainly concentrated in the Bohai Rim;the ‘Low-Low’ cluster area is grouped in the southern China. 3) Economic development level, population density, traffic accessibility, temperature and land price, all affect the location choice of cold storage in varying degrees, while the impact of market demand on it is not explicit.展开更多
Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems...Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.展开更多
From the perspective of environmental criminology, non-gated old residential compounds are particularly vulnerable to burglary due to insufficient natural surveillance and the lack of access control. To identify the s...From the perspective of environmental criminology, non-gated old residential compounds are particularly vulnerable to burglary due to insufficient natural surveillance and the lack of access control. To identify the spatial patterns of burglary distribution in such areas and to provide scientific guidance for their renovation, this study examines ten non-gated old residential compounds in Z City. The analysis primarily employs space syntax variables, supplemented by kernel density estimation of burglary incidents, field survey, and case comparisons. Focusing on three spatial aspects(i.e., clustering, hierarchy, and connection), the study explores the relationship between residential spatial structure and burglary distribution. The findings reveal three distinct patterns:(1) polarization in spatial clustering;(2) discretization in spatial hierarchy;and(3) convenience in spatial connection. Based on these patterns, the study identifies three theoretical implications:(1) the objectivity of spatial environment shapes the correlation between residential spatial structure and burglary distribution;(2) the subjectivity of burglary behavior leads to misalignment between the two;and(3) the complex interaction between spatial environment and burglary behavior determines the spatial economy of burglary distribution. Finally, the paper offers planning and renovation strategies for security improvement.展开更多
基金Under the auspices of the National Social Science Fund of China(No.15BGL185,19XJL004)General Project of Humanities and Social Sciences Research and Planning Fund of Ministry of Education(No.19YJA790097)+1 种基金Social Science Fund of Fujian Province(No.FJ2017C080)A Key Discipline of Henan University of Animal Husbandry and Economy‘Business Enterprise Management’(No.MXK2016201)。
文摘Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by using kernel density estimation(KDE), spatial autocorrelation analysis(SAA), and spatial error model(SEM). Results showed that: 1) the spatial distribution of cold storage in China is unbalanced, and has evolved from ‘one core’ to ‘one core and many spots’, that is, ‘one core’ refers to the Bohai Rim region mainly including Beijing, Tianjin, Hebei, Shandong and Liaoning regions, and ‘many spots’ mainly include the high-density areas such as Shanghai, Fuzhou, Guangzhou, Zhengzhou, Hefei, Wuhan, ürümqi. 2) The distribution of cold storage has significant global spatial autocorrelation and local spatial autocorrelation, and the ‘High-High’ cluster area is the most stable, mainly concentrated in the Bohai Rim;the ‘Low-Low’ cluster area is grouped in the southern China. 3) Economic development level, population density, traffic accessibility, temperature and land price, all affect the location choice of cold storage in varying degrees, while the impact of market demand on it is not explicit.
基金This project was funded by the Deanship of Scientific Research(DSR)at King Abdul Aziz University,Jeddah,under Grant No.KEP-10-611-42.The authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.
基金funded by the Key Research Project of the Chinese Society of Criminology in 2022 (FZXXH2022B03)the Key Science and Technology Development Project of China Railway Sixth Institute Group Co.,Ltd. (ky202423)。
文摘From the perspective of environmental criminology, non-gated old residential compounds are particularly vulnerable to burglary due to insufficient natural surveillance and the lack of access control. To identify the spatial patterns of burglary distribution in such areas and to provide scientific guidance for their renovation, this study examines ten non-gated old residential compounds in Z City. The analysis primarily employs space syntax variables, supplemented by kernel density estimation of burglary incidents, field survey, and case comparisons. Focusing on three spatial aspects(i.e., clustering, hierarchy, and connection), the study explores the relationship between residential spatial structure and burglary distribution. The findings reveal three distinct patterns:(1) polarization in spatial clustering;(2) discretization in spatial hierarchy;and(3) convenience in spatial connection. Based on these patterns, the study identifies three theoretical implications:(1) the objectivity of spatial environment shapes the correlation between residential spatial structure and burglary distribution;(2) the subjectivity of burglary behavior leads to misalignment between the two;and(3) the complex interaction between spatial environment and burglary behavior determines the spatial economy of burglary distribution. Finally, the paper offers planning and renovation strategies for security improvement.
