Simulation study of the sheath region of a processing plasma with two-temperature electrons and charged nanoparticles

2012 ◽  
Vol 376 (33) ◽  
pp. 2244-2251 ◽  
Author(s):  
G. Foroutan ◽  
A. Akhoundi
Keyword(s):  
Simulation Study ◽  
Sheath Region ◽  
Charged Nanoparticles ◽  
Two Temperature

Influence of Charged Nanoparticles on Colloidal Forces: A Molecular Simulation Study†

2009 ◽  
Vol 113 (42) ◽  
pp. 13860-13865 ◽  
Author(s):  
Babak Fazelabdolabadi ◽  
John Y. Walz ◽  
Paul R. Van Tassel
Keyword(s):  
Molecular Simulation ◽  
Simulation Study ◽  
Colloidal Forces ◽  
Charged Nanoparticles

An Alternative to Cohen's κ

2006 ◽  
Vol 11 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Alexander von Eye
Keyword(s):  
Simulation Study ◽  
Null Hypothesis ◽  
Categorical Variables ◽  
Alternative Measure ◽  
Rater Agreement ◽  
Verbal Processing ◽  
Heavy Tailed ◽  
Applicant Selection

At the level of manifest categorical variables, a large number of coefficients and models for the examination of rater agreement has been proposed and used. The most popular of these is Cohen's κ. In this article, a new coefficient, κ s , is proposed as an alternative measure of rater agreement. Both κ and κ s allow researchers to determine whether agreement in groups of two or more raters is significantly beyond chance. Stouffer's z is used to test the null hypothesis that κ s = 0. The coefficient κ s allows one, in addition to evaluating rater agreement in a fashion parallel to κ, to (1) examine subsets of cells in agreement tables, (2) examine cells that indicate disagreement, (3) consider alternative chance models, (4) take covariates into account, and (5) compare independent samples. Results from a simulation study are reported, which suggest that (a) the four measures of rater agreement, Cohen's κ, Brennan and Prediger's κ n , raw agreement, and κ s are sensitive to the same data characteristics when evaluating rater agreement and (b) both the z-statistic for Cohen's κ and Stouffer's z for κ s are unimodally and symmetrically distributed, but slightly heavy-tailed. Examples use data from verbal processing and applicant selection.

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