Phase behaviour of quasi-block copolymers: A DFT-based Monte-Carlo study

Soft Matter ◽  
2009 ◽  
Vol 5 (22) ◽  
pp. 4499 ◽  
Author(s):  
Kostas Ch. Daoulas ◽  
Anna Cavallo ◽  
Roy Shenhar ◽  
Marcus Müller
Keyword(s):  
Monte Carlo ◽  
Block Copolymers ◽  
Monte Carlo Study ◽  
Phase Behaviour

scholarly journals Collapse transitions in thermosensitive multi-block copolymers: A Monte Carlo study

2014 ◽  
Vol 140 (20) ◽  
pp. 204904 ◽  
Author(s):  
Anastassia N. Rissanou ◽  
Despoina S. Tzeli ◽  
Spiros H. Anastasiadis ◽  
Ioannis A. Bitsanis
Keyword(s):  
Monte Carlo ◽  
Block Copolymers ◽  
Monte Carlo Study ◽  
Collapse Transitions

Phase behaviour of confined associating fluid in a functionalized slit pore: a Monte Carlo study

2021 ◽  
Vol 531 ◽  
pp. 112909
Author(s):  
Sashanka Sekhar Mandal ◽  
Sudhir Kumar Singh ◽  
Sanchari Bhattacharjee ◽  
Sandip Khan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Phase Behaviour ◽  
Slit Pore

Adsorption of block copolymers on solid surfaces: A Monte Carlo study

2014 ◽  
Vol 141 (4) ◽  
pp. 044910 ◽  
Author(s):  
Edyta Słyk ◽  
Wojciech Rżysko ◽  
Paweł Bryk
Keyword(s):  
Monte Carlo ◽  
Block Copolymers ◽  
Monte Carlo Study ◽  
Solid Surfaces

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

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