A note on the symmetrization postulate and cluster property

1969 ◽  
Vol 1 (17) ◽  
pp. 911-914 ◽  
Author(s):  
M. E. Arons
Keyword(s):  
Cluster Property

Cluster Property based Data Transfer for Efficient Energy Consumption in IoT

2017 ◽  
Vol 44 (9) ◽  
pp. 966-975
Author(s):  
Chungsan Lee ◽  
Soobin Jeon ◽  
Inbum Jung
Keyword(s):  
Energy Consumption ◽  
Data Transfer ◽  
Cluster Property ◽  
Efficient Energy

scholarly journals Redes Colaborativas nos Estudos Métricos de Ciência e Tecnologia │ Collaborative Networks in Metric Studies of Science and Technology

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Samile Andréa de Souza Vanz
Keyword(s):  
Network Theory ◽  
Scientific Collaboration ◽  
Preferential Attachment ◽  
Collaborative Networks ◽  
Centrality Measures ◽  
Cluster Property ◽  
Scale Free ◽  
Free Model ◽  
History Of ◽  
The Subject

Resumo A teoria de redes passou a ser muito utilizada pela Bibliometria e Cientometria porque auxilia na interpretação e no entendimento dos dados resultantes das pesquisas realizadas na área. O artigo aborda um breve histórico das redes comentando desde o modelo aleatório de Erdós e Rényi aos modelos mais atuais. Apresenta as medidas mais importantes para redes de coautoria, como densidade e medidas de centralidade. Descreve pesquisas empíricas aplicadas em redes de coautoria e suas descobertas, como a propriedade de conexão preferencial, o nível de agrupamento e o modelo sem escala. Conclui que o entendimento da teoria de redes é fundamental para o estudo do fenômeno da coautoria e que os pesquisadores interessados na temática devem ampliar o uso da mesma em suas pesquisas.Palavras-chave Redes de coautoria, colaboração científica. Abstract The network theory became widely used for Bibliometrics and Scientometrics because it helps in the understanding and interpretation of data resulting from these studies. This article covers a brief history of networks and comments from the random model of Erdós and Rényi to most current models. It presents the most important measures for co-author networks, such as density and centrality measures. It also describes applied empirical research on networks of co-authorship and its findings, as the preferential attachment, cluster property and scale free model. The article concludes that the understanding of the network theory is crucial to the study of the phenomenon of co-authorship and that researchers interested in the subject should expand the use of this theory in their research.Keywords Co-author network, scientific collaboration.

scholarly journals An OD Flow Clustering Method Based on Vector Constraints: A Case Study for Beijing Taxi Origin-Destination Data

2020 ◽  
Vol 9 (2) ◽  
pp. 128
Author(s):  
Xiaogang Guo ◽  
Zhijie Xu ◽  
Jianqin Zhang ◽  
Jian Lu ◽  
Hao Zhang
Keyword(s):  
Coordinate System ◽  
Traffic Flow ◽  
Pattern Mining ◽  
Flow Behavior ◽  
High Dimensional ◽  
Cluster Property ◽  
Clustering Methods ◽  
Clustering Method ◽  
Flow Vector ◽  
Depth Analysis

Origin-destination (OD) flow pattern mining is an important research method of urban dynamics, in which OD flow clustering analysis discovers the activity patterns of urban residents and mine the coupling relationship of urban subspace and dynamic causes. The existing flow clustering methods are limited by the spatial constraints of OD points, rely on the spatial similarity of geographical points, and lack in-depth analysis of high-dimensional flow characteristics, and therefore it is difficult to find irregular flow clusters. In this paper, we propose an OD flow clustering method based on vector constraints (ODFCVC), which defines OD flow event point and OD flow vector to express the spatial location relationship and geometric flow behavior characteristics of OD flow. First, the OD flow vector coordinate system is normalized by the Euclidean distance-based OD flow event point spatial clustering, and then the OD flow clusters with similar flow patterns are mined using adjusted cosine similarity-based OD flow vector feature clustering. The transformation of OD data from point set space to vector space is realized by constraining the vector coordinate system and vector similarity through two-step clustering, which simplifies the calculation of high-dimensional similarity of OD flow and helps mining representative OD flow clusters in flow space. Due to the OD flow cluster property, the k-means algorithm is selected as the basic clustering logic in the two-step clustering method, and a sum of squared error perceptually important points algorithm considering silhouette coefficients (SSEPIP) is adopted to automatically extract the optimal cluster number without defining any parameters. Tested by origin-destination flow data in Beijing, China, new traffic flow communities based on traffic hubs are obtained by using the ODFCVC method, and irregular traffic flow clusters (including cluster mode, divergence mode, and convergence mode) with representative travel trends are found.

A molecular dynamics study of nanoscale titanium nitrides formation in ferrite

2020 ◽  
Vol 34 (10) ◽  
pp. 2050099
Author(s):  
Yanan Lv ◽  
Dong Chen
Keyword(s):  
Molecular Dynamics ◽  
Titanium Nitride ◽  
Cluster Formation ◽  
High Strength ◽  
Dynamics Simulation ◽  
Cluster Property ◽  
Titanium Nitrides ◽  
Formation Stage ◽  
Nitride Formation ◽  
Atomic Interactions

Molecular dynamics simulation was adopted to investigate the nanoscale titanium nitride formation at the early formation stage in high-strength low-alloy steel. During the cluster formation process, the nitride clusters were formed through the atom aggregation. The atomic interactions of titanium and nitride atoms were revealed and the cluster property was discussed. The nanoscale titanium nitride clusters own a wide composition, and the cluster formation mechanism was concluded.

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