Homogeneity Analysis of Multiairport System Based on Airport Attributed Network Representation Learning

The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.

Effect of acupuncture plus conventional treatment on brain activity in ischemic stroke patients: a regional homogeneity analysis

OBJECTIVE: To determine differences in cerebral activity evoked by acupuncture and conventional stroke treatment, and identify the treatment targets.METHODS: In total, 21 patients were randomly divided into two groups. Group A(11 patients) received both acupuncture and conventional treatment, while group B(10 patients) received conventional treatment only. Resting-state functional magnetic resonance imaging(f MRI) was performed on each participant before and after treatment. Regional homogeneity analysis was performed to investigate the potential mechanism of acupuncture treatment by comparing differences in cerebral activity between treatments.RESULTS: Group A showed higher Re Ho in the frontal lobe(BA6, BA46), supra-marginal gyrus(BA40),middle temporal gyrus(BA21), cerebellum, and insula. Group B showed higher Re Ho in the frontal lobe(BA6) and parietal lobe(BA3, BA7).CONCLUSION: Acupuncture and conventional treatment triggered relatively different clinical efficacy and brain responses. Acupuncture treatment more significantly improved the symptoms of stroke patients. More marked changes in sensory,emotional, and motor areas(including the frontal lobe, middle temporal gyrus, cerebellum, and insula) might reflect the specific acupuncture mechanism.

A New Method of Color Edge Detection Based on Local Structure Analysis

Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital image processing, image segmentation is an important section for computers to "understand" images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature （SAT） series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization （MASH） method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57℃ （100 yr）^-1, with a regional mean trend of 1.65℃ （100 yr）^-1 ; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5℃ （100 yr）^-1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data axe available online at http：//www.dx.doi.org/10.11922/sciencedb.516.