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Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations.
A recent study by Medel and Pournaghshband (2017) in 2017 showed that gender bias is still present in computer science instructional materials.
That said, while women are present in computer science, there is no denying that they are underrepresented, and the gap is widening – with women making up only 25percentt of the computer science workforce.
Despite the increasing number of computer users and the abundance of evidence suggesting that SRMS are present in computer users, there are very few readily available and conclusive studies that have compared these prevalence with that of the non computer users.
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Failures of linear cue integration have been attributed to cue-conflict and to unmodelled cues to flatness present in computer-generated displays.
Online interaction plays an important role with respect to emotional contagion: extensive research has shown that emotional contagion is present in computer-mediated communication as much as in face to face communication [60].
Three methods have previously been presented in Computer and Industrial Engineering for the selection of a statistical distribution to describe a data-set: the weighted sum model, the weighted multiplication model and data envelopment analysis.
New modeling techniques and methods for reducing forecast errors still further are on the horizon and will require a continuation of the present acceleration in computer processing speeds.
We describe the framework and present examples in computer graphics and image processing applications, including texture synthesis, flow field visualization, and image and vector field intrinsic regularization for data defined on 3D surfaces.
Two approaches have previously been presented in Computers and Industrial Engineering.
Petri Nets (PNs) [7] are well suited to model computer architectures, since both parallelism and conflict, two important characteristics present in modern computer systems, are easily modeled using this formalism.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com