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When fitting a three-component model of phytoplankton size structure — designed to quantify the relationship between PSC and TCHLa to each AMT cruise, model parameters varied over the study period.
The model parameters varied depending upon the ratio of resin to liquid employed in the tests, with rates showing substantial enhancement once an excess of resin exchange sites were present.
In this process, model parameters varied until recorded flow patterns were accurately simulated.
Once the most suitable model form was identified, the second step was to determine whether the model parameters varied as a function of height within the tree.
The model parameters varied were: number of subpopulations (1, 2, 4, or 10); mutation rate (θ = 4N0μ, where N0 = diploid subpopulation size and μ = neutral mutation rate); and migration rate (M = 4N0m, where m = the fraction of each subpopulation made up of new migrant genotypes each generation).
Results of the sensitivity analysis showed that the relative contribution of variance of individual TK model parameters varied significantly across different doses of arsenite.
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Ultracapacitors are known to exhibit fractional dynamics and the model parameters vary significantly with frequency.
Modeling of sub-hourly turbidity as a function of streamflow shows that event model parameters vary on an event-by-event basis.
In many practical applications, however, certain computer model parameters vary from trial to trial over the physical experiment, in which case there is no single calibrated value for a parameter.
To represent nonlinearity of the ship motion, the rolling angle is used as the model index to make the model parameters vary with the ship sailing states considering the yaw-heel-effect.
The accuracy of SoC tracking is improved if it is jointly estimated along with either R1 or R2 given that these model parameters vary with SoC as revealed by EIS.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com