Integral equation and Monte Carlo study on hydrophobic effects: Size dependence of apolar solutes on solute–solute interactions and structures of water

1987 ◽  
Vol 86 (3) ◽  
pp. 1512-1520 ◽  
Author(s):  
Hideki Tanaka
Keyword(s):  
Integral Equation ◽  
Monte Carlo ◽  
Size Dependence ◽  
Monte Carlo Study ◽  
Hydrophobic Effects ◽  
Solute Interactions

A Monte Carlo study on the size dependence in hydrophobic hydration

1981 ◽  
Vol 74 (10) ◽  
pp. 5863-5871 ◽  
Author(s):  
Susumu Okazaki ◽  
Koichiro Nakanishi ◽  
Hidekazu Touhara ◽  
Nobuatsu Watanabe ◽  
Yoshinori Adachi
Keyword(s):  
Monte Carlo ◽  
Size Dependence ◽  
Hydrophobic Hydration ◽  
Monte Carlo Study

An Integral Equation and Monte Carlo Study of Square-Well Fluid Mixtures

1995 ◽  
Vol 99 (5) ◽  
pp. 1587-1593 ◽  
Author(s):  
Fernando del Rio ◽  
Orlando Guzman ◽  
Anatol Malijevsky
Keyword(s):  
Integral Equation ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Fluid Mixtures ◽  
Square Well

An integral equation and Monte Carlo study of homo- and hetero- nuclear square-well diatomic fluids

2000 ◽  
Vol 98 (24) ◽  
pp. 2033-2043 ◽  
Author(s):  
VÍT JIRÁSEK ◽  
STANISLAV LABÍK ◽  
ANATOL MALIJEVSKÝ ◽  
MARTIN LÍSAL
Keyword(s):  
Integral Equation ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Square Well

The correlations in a star molecule fluid. Integral equation theory and Monte Carlo study

2000 ◽  
Vol 98 (17) ◽  
pp. 1287-1293 ◽  
Author(s):  
Yurko Duda ◽  
Ian Garcia ◽  
Andrij Trokhymchuk ◽  
Douglas Henderson
Keyword(s):  
Integral Equation ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Integral Equation Theory

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.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study

Monte Carlo study of 50 nm-long single and dual-gate MODFETs: suppression of short-channel effects

1993 ◽  
Vol 3 (9) ◽  
pp. 1719-1728
Author(s):  
P. Dollfus ◽  
P. Hesto ◽  
S. Galdin ◽  
C. Brisset
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Short Channel Effects ◽  
Short Channel ◽  
Channel Effects ◽  
Dual Gate

MONTE CARLO STUDY OF EPITAXIAL OVERLAYER WITH SUBSTANTIAL LATTICE MISMATCH

1987 ◽  
Vol 48 (C5) ◽  
pp. C5-199-C5-202
Author(s):  
T. MIYASAKI ◽  
K. AIZAWA ◽  
H. AOKI ◽  
C. ITOH ◽  
M. OKAZAKI
Keyword(s):  
Monte Carlo ◽  
Lattice Mismatch ◽  
Monte Carlo Study
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