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It is therefore critical to find a suitable compromise between different computational parameters that allows for a good description of the physics without increasing too much the computational cost.
We propose a novel log-contrast regression model with mean shift parameters that allows the identification of sample outliers and maintains sub-compositional coherence with respect to the associated phylogenetic tree.
This paper proposes a mathematical model with distributed parameters that allows to simulate the heat transfer processes in combustion chamber waterwalls of supercritical steam boilers.
We describe here an approach based on global structural parameters that allows a two-dimensional representation of the free-energy landscape from simulated atomic trajectories.
Neuro-Bayesian inversion method has been introduced in this study, which is a combination of an artificial neural network (ANN) classifier and Bayesian inversion of rock physical parameters that allows an improved facies prediction.
These descriptions of particle-particle interactions in assemblages are used to develop an a priori model, with no adjustable parameters, that allows quantitative prediction of the rate of filtration of charged colloidal dispersions as a function of zeta-potential, particle composition (through the Hamaker constant), ionic strength, applied pressure, particle radius and membrane resistance.
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Parameters that allow the surface modification without damaging the "macro" structure of scaffolds were determined.
Each of the functions has parameters that allow the result to be scaled to a particular range of values.
In this way, we found the best combination in the reaction parameters that allowed us to optimize the process.
For X! Tandem we also selected parameters that allowed a quick check for protein N-terminal residue acetylation, peptide N-terminal glutamine ammonia loss, or peptide N-terminal glutamic acid water loss.
By definition, we live in one of the universes with parameters that allow life, because otherwise we would not be here to ask the question.
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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