scholarly journals COMMON NEIGHBOR POLYNOMIAL OF SOME DENDRIMER STRUCTURES

2021 ◽  
Vol 28 (1) ◽  
pp. 13-24
Author(s):  
M. Shikhi
Keyword(s):  
Common Neighbor

scholarly journals Efeito da Taxa de Resfriamento na Liga Ni80Cu20 via Dinâmica Molecular

2019 ◽  
Vol 24 (1) ◽  
Author(s):  
Camila Sanches Schimidt ◽  
Julio Cesar Guimarães Tedesco ◽  
Lucas Venancio Pires de Carvalho Lima ◽  
Ivan Napoleão Bastos ◽  
Piter Gargarella ◽  
...  
Keyword(s):  
Common Neighbor ◽  
De Se

RESUMO A simulação em Dinâmica Molecular (MD) é uma poderosa ferramenta para estudo de ligas amorfas nanométricas. Neste artigo, a MD foi empregada para o estudo da liga Ni80Cu20. As simulações foram realizadas com o código livre LAMMPS, em um sistema contendo 2.000 átomos com interação atômica dada pelo potencial de Finnis-Sinclair (EAM-FS). O aquecimento foi realizado à taxa de 2 K/ps, entretanto, no resfriamento foram utilizadas as taxas de 40, 10, 4, 3, 2 e 1 K/ps, com a finalidade de se observar a evolução estrutural da liga. Taxas superiores a 3 K/ps conduzem à formação de estrutura amorfa. Por outro lado, taxas inferiores a esta permitem ao sistema formar uma estrutura cristalina Cúbica de Face Centrada (CFC). A estrutura da liga foi analisada empregando-se Funções de Distribuição Radial (RDF), os poliedros de Voronoi, e Common Neighbor Analysis (CNA). Além disto, foram realizados estudos de tratamento térmico tanto em modo dinâmico à taxa de resfriamento de 40 K/ps como isotérmico com a finalidade de observar a cristalização da liga amorfa. A curva tensão vs deformação indica que a presença de 20 % de nanocristais eleva a resistência mecânica sem afetar o módulo elástico. Com esta fração, que se forma por efeito do tratamento térmico, o limite de resistência é aumentado em mais de 30 %.

An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

2018 ◽  
pp. 203-230
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Link Prediction ◽  
Common Neighbor ◽  
Research Directions ◽  
Definition Of

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

Open packing bondage number of a graph

2019 ◽  
Vol 11 (05) ◽  
pp. 1950051
Author(s):  
S. Saravanakumar ◽  
A. Anitha ◽  
I. Sahul Hamid
Keyword(s):  
Maximum Cardinality ◽  
Common Neighbor ◽  
Packing Number ◽  
Bondage Number ◽  
Open Packing

In a graph [Formula: see text], a set [Formula: see text] is said to be an open packing set if no two vertices of [Formula: see text] have a common neighbor in [Formula: see text] The maximum cardinality of an open packing set is called the open packing number and is denoted by [Formula: see text]. The open packing bondage number of a graph [Formula: see text], denoted by [Formula: see text], is the cardinality of the smallest set of edges [Formula: see text] such that [Formula: see text]. In this paper, we initiate a study on this parameter.

scholarly journals Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Usman Akhtar ◽  
Salma Noor ◽  
Ambreen Shahnaz
Keyword(s):  
Link Prediction ◽  
Parameterized Algorithm ◽  
Common Neighbor ◽  
Missing Link

Structural variations in strained crystalline multilayers

1994 ◽  
Vol 9 (8) ◽  
pp. 2190-2197 ◽  
Author(s):  
J. Hoekstra ◽  
H. Yan ◽  
G. Kalonji ◽  
H. Jónsson
Keyword(s):  
Structural Changes ◽  
Elevated Temperatures ◽  
Lattice Mismatch ◽  
Structural Characteristics ◽  
Binding Energies ◽  
Structural Defects ◽  
Interatomic Potentials ◽  
Structural Variations ◽  
Common Neighbor ◽  
Hcp Structure

We present a computer simulation study of thin crystalline multilayers constructed from two fcc solids with differing lattice constants and binding energies. Initially the two solids have the same orientation, and the interface is perpendicular to the common [100] direction. We then minimize the energy of the system at zero temperature or equilibrate it at a finite temperature. Both materials are described by Lennard-Jones interatomic potentials. A novel technique for analyzing local atomic ordering, common neighbor analysis, is used to identify structural characteristics in these systems. As we gradually vary the lattice mismatch between the two solids, several structural changes are observed in the layers of smaller atoms after energy minimization. At a mismatch larger than 14%, the layers transform into the hep structure, while at smaller mismatches extended structural defects are generated. At elevated temperatures, the hcp structure is transformed back to fcc, and the structure defects disappear.

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650254 ◽  
Author(s):  
Shuxin Liu ◽  
Xinsheng Ji ◽  
Caixia Liu ◽  
Yi Bai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Close Attention ◽  
Common Neighbor ◽  
Link Weight ◽  
Similarity Indices ◽  
Clustered Networks ◽  
Local Path

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

Numerical Study of Fracture Behavior of Gradient Nano-Grained Metals by Molecular Dynamics Simulations

2020 ◽  
Vol 843 ◽  
pp. 99-104
Author(s):  
Ya Ping Liu ◽  
Fan Yang
Keyword(s):  
Molecular Dynamics ◽  
Grain Size ◽  
Crack Growth ◽  
Fracture Behavior ◽  
Numerical Study ◽  
High Strength ◽  
Initial Position ◽  
Common Neighbor ◽  
Gradient Distribution ◽  
Voronoi Method

Benefit from the gradient distribution of microstructure, gradient nanograined (GNG) metals have broad application prospect owing to their advantages of both high strength and good tensile ductility. Meanwhile, the fracture behavior of gradient nanograined metals is different from that of traditional homogeneous materials. Using molecular dynamics (MD) method, we simulated the propagation of a crack in a pre-cracked GNG Cu. Voronoi method was adopted to generate the polycrystalline topology with gradient grain size, and FCC copper atoms were filled into the topological structure. The crack was introduced by removing three layers of atoms. Then, the MD specimen was loaded to simulate the crack growth and/or blunting. The micro-defects were identified by the common neighbor analysis parameter. The effects of the grain size gradient and the crack tip initial position on the crack growth were also investigated.

INFLUENCE OF PRESSURE AND ATOMIC CONCENTRATION ON STRUCTURE AND MECHANICAL PROPERTIES OF CuNi ALLOY BY MOLECULAR DYNAMICS SIMULATION

2020 ◽  
Vol 65 (6) ◽  
pp. 54-60
Author(s):  
Thao Nguyen Thi ◽  
Hang Trinh Thi Thu
Keyword(s):  
Mechanical Properties ◽  
Dynamics Simulation ◽  
Embedded Atom Method ◽  
Face Centered Cubic ◽  
Atomic Concentration ◽  
Common Neighbor ◽  
Hexagonal Close Packed ◽  
Embedded Atom ◽  
The Common ◽  
Face Centered

The structure and mechanical properties of Cu80Ni20 and Cu50Ni50 alloys with the size of 4000 atoms have been investigated using molecular dynamic (MD) simulation. The interactions between atoms of the system were calculated by the Sutton-Chen type of embedded atom method. Using a cooling rate of 0.01 K\ps, we find that both Ni and Cu atoms are crystallized into face centered cubic (fcc) and the hexagonal close packed (hcp) phases when the sample was cooled down to 300 K. The atomic concentration of CuNi alloy samples have a different effect on this crystallization. The transformation to the crystalline phase is analyzed through the Common Neighbor Analysis (CNA) methods. Furthermore, we focus on the dependence of the mechanical properties of CuNi alloy on pressure and atomic concentration

Changing and unchanging Open Packing: Edge removal

2016 ◽  
Vol 08 (01) ◽  
pp. 1650016 ◽  
Author(s):  
I. Sahul Hamid ◽  
S. Saravanakumar
Keyword(s):  
Maximum Cardinality ◽  
Common Neighbor ◽  
Packing Number ◽  
Edge Removal ◽  
Open Packing

In a graph [Formula: see text], a nonempty set [Formula: see text] is said to be an open packing set if no two vertices of [Formula: see text] have a common neighbor in [Formula: see text] The maximum cardinality of an open packing set is called the open packing number and is denoted by [Formula: see text]. In this paper, we examine the effect of [Formula: see text] when [Formula: see text] is modified by deleting an edge.

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