Static and Dynamic Scaling Properties of Single, Self-Avoiding Polymer Chains in Two Dimensions via the Bond Fluctuation Method of Monte Carlo Simulation

1994 ◽  
Vol 27 (11) ◽  
pp. 2929-2932 ◽  
Author(s):  
James Patton Downey
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Two Dimensions ◽  
Polymer Chains ◽  
Dynamic Scaling ◽  
Scaling Properties ◽  
Fluctuation Method ◽  
Bond Fluctuation

scholarly journals Bias Corrections for Probit and Logit Models with Two-way Fixed Effects

2017 ◽  
Vol 17 (3) ◽  
pp. 517-545 ◽  
Author(s):  
Mario Cruz-Gonzalez ◽  
Iván Fernández-Val ◽  
Martin Weidner
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Panel Data ◽  
Fixed Effects ◽  
Two Dimensions ◽  
Panel Data Models ◽  
Logit Models ◽  
Unobserved Effects ◽  
Parameter Problem ◽  
Bias Corrections

In this article, we present the user-written commands probitfe and logitfe, which fit probit and logit panel-data models with individual and time unobserved effects. Fixed-effects panel-data methods that estimate the unobserved effects can be severely biased because of the incidental parameter problem (Neyman and Scott, 1948, Econometrica 16: 1–32). We tackle this problem using the analytical and jackknife bias corrections derived in Fernández-Val and Weidner (2016, Journal of Econometrics 192: 291–312) for panels where the two dimensions ( N and T) are moderately large. We illustrate the commands with an empirical application to international trade and a Monte Carlo simulation calibrated to this application.

Solvation of poly(methyl acrylate) and poly(vinyl acetate) by CO2 studied via atomistic Monte Carlo simulation techniques

e-Polymers ◽  
2004 ◽  
Vol 4 (1) ◽  
Author(s):  
Sabine Beuermann ◽  
Michael Buback ◽  
Marco Drache ◽  
Dorit Nelke ◽  
Gudrun Schmidt-Naake
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vinyl Acetate ◽  
Methyl Acrylate ◽  
Polymer Chains ◽  
Phase Behaviour ◽  
Specific Interactions ◽  
Model Compounds ◽  
Simulation Techniques ◽  
Solvent Environment

Abstract The differences in solubility of poly(vinyl acetate) (PVAc) and poly(methyl acrylate) (PMA) were addressed by applying atomistic Monte Carlo simulation techniques. Polymer segments consisting of nine monomer units serve as model compounds for polymer chains. As a measure of intermolecular interactions with the solvent environment, cohesion energies of the polymer segments embedded in either the corresponding monomer or in CO2 were calculated. Only in case of PMA segments in CO2 environment, specific interactions between polymer segments were identified. This finding is in agreement with experimental results on phase behaviour and propagation kinetics.

Interaction between small colloidal particles and polymer chains in a semidilute solution: Monte Carlo simulation

1998 ◽  
Vol 276 (9) ◽  
pp. 753-768 ◽  
Author(s):  
L. V. Zherenkova ◽  
D. A. Mologin ◽  
P. G. Khalatur ◽  
A. R. Khokhlov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Colloidal Particles ◽  
Polymer Chains ◽  
Semidilute Solution

Equilibrium and dynamical properties of polymer chains in random medium filled with randomly distributed nano-sized fillers

2015 ◽  
Vol 17 (47) ◽  
pp. 31877-31886 ◽  
Author(s):  
Chao-Yang Li ◽  
Meng-Bo Luo ◽  
Jian-Hua Huang ◽  
Hong Li
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Medium ◽  
Polymer Chains ◽  
Linear Polymers ◽  
Lattice Monte Carlo ◽  
Dynamical Properties

The effect of randomly distributed nano-sized fillers on the equilibrium and dynamical properties of linear polymers is studied by using off-lattice Monte Carlo simulation.

Probability Methods for Detecting Randomness in Small Sample Spatial Distributions

1994 ◽  
Vol 78 (3) ◽  
pp. 715-720 ◽  
Author(s):  
Frank O'brien
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Methods ◽  
Two Dimensions ◽  
Small Sample ◽  
Spatial Distributions ◽  
Theoretical Assumptions ◽  
Simulation Results ◽  
Probability Methods ◽  
Spatial Randomness

Several probability and statistical methods are discussed for detecting spatial randomness in two dimensions. One method is derived and proposed for its ease of application. Monte Carlo simulation results are presented in support of the theoretical assumptions of the proposed method.

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