Crystallization of semiflexible polymers in melts and solutions

Soft Matter ◽  
2021 ◽  
Author(s):  
Pavel I. Kos ◽  
Viktor A. Ivanov ◽  
Alexander V. Chertovich
Keyword(s):  
Computer Simulation ◽  
Dissipative Particle Dynamics ◽  
Particle Dynamics ◽  
Simulation Technique ◽  
Polymer Chains ◽  
Semiflexible Polymers ◽  
Semiflexible Polymer ◽  
Computer Simulation Technique ◽  
Poor Solvent

We studied the crystallization of semiflexible polymer chains in melts and poor-solvent solutions with different concentrations by dissipative particle dynamics (DPD) computer simulation technique.

A Test of Downsian Voter Rationality: 1964 Presidential Voting

1978 ◽  
Vol 72 (1) ◽  
pp. 178-197 ◽  
Author(s):  
Norman Frohlich ◽  
Joe A. Oppenheimer ◽  
Jeffrey Smith ◽  
Oran R. Young
Keyword(s):  
Computer Simulation ◽  
Factor Model ◽  
Simulation Technique ◽  
Engel Curve ◽  
Systematic Testing ◽  
Percentage Improvement ◽  
Making A Difference ◽  
Unexplained Variance ◽  
Computer Simulation Technique ◽  
Random Guess

Systematic testing of Downsian voter rationality is accomplished using a computer simulation technique on the 1964 SRC voting survey. The simulation tests both the hypotheses predicting whether an individual will vote and for whom an individual will vote. To evaluate the results of the tests we develop a statistic analogous to Pearson's r. This statistic measures the percentage improvement over a random guess technique. Utilizing this statistic, Downs explains 68.5 percent of the unexplained variance in the voters' choices of party. Three alternative interpretations of the turnout decision are then considered, each premised on a different notion of how the costs of voting are distributed among the voters. Here we use an Engel Curve technique to develop the turnout decision and explain 92 percent of the variance. The importance of the various elements of the Downsian theory are evaluated and, in contrast to some recent conjectures, the probability of making a difference on the outcome of the election is shown to have an effect on the turnout decision. Finally, to determine the viability of the results, the SRC “6 factor” model is developed in an analogous fashion and used to predict both turnout and direction of vote.

A Road Map for the Validation, Verification and Testing of Discrete Event Simulation

2011 ◽  
pp. 3306-3313 ◽  
Author(s):  
Evon M. O. Abu-Taieh ◽  
Asim Abdel Rahman El Sheikh
Keyword(s):  
Computer Simulation ◽  
Service Delivery ◽  
Discrete Event ◽  
Real Life ◽  
Safety Net ◽  
Simulation Technique ◽  
Road Map ◽  
Life Problems ◽  
Computer Simulation Technique ◽  
Task Oriented

The aim of this chapter is to give an elaborate reasoning for the motivation for Validation, Verification, and Testing (VV&T) in Simulation. Thereby, defining Simulation in its broadest aspect as embodying a certain model to represent the behavior of a system, whether that may be an economic or an engineering one, with which conducting experiments is attainable. Such a technique enables the management, when studying models currently used, to take appropriate measures and make fitting decisions that would further complement today’s growth sustainability efforts, apart from cost decrease, as well as service delivery assurance. As such, the Computer Simulation technique contributed in cost decline; depicting the “cause and effect,” pinpointing task-oriented needs or service delivery assurance, exploring possible alternatives, identifying problems, as well as proposing streamlined, measurable, deliverable, solutions, providing the platform for change strategy introduction, introducing potential prudent investment opportunities, and finally, providing a safety net when conducting training courses. Yet, the simulation development process is hindered due to many reasons. Like a rose, Computer Simulation technique, does not exist without thorns, of which the length, as well as the communication during the development life cycle. Simulation reflects real-life problems; hence, it addresses numerous scenarios with handful of variables. Not only is it costly, as well as liable for human judgment, but also, the results are complicated and can be misinterpreted.

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