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The model assumed an electrode with two layers: gas-supply layer and reaction-active catalyst layer.
Our model assumed an attack frequency for the stimulation cohort that was 31%% lower than the baseline frequency of 2.74 attacks per day.
However, since the Bedford-Hartl model assumed an optimum value μi (i = 1,.., N) of the mRNA abundance for every gene, it is not easy to explain the Zipf's law-like distribution of mRNA abundance in cells from this model.
The experimental data deviate from this model mainly in two respects: acrB appears to be considerably easier to find (mutated in most strains), whereas our model assumed an equal split among the three target genes (implying each would be found mutated in approximately a third of the strains); conversely, acrA appears to be harder to identify (mutated in less than a fifth of the strains).
For simplicity the model assumed an "average" practitioner.
The model assumed an annual discount rate of 3%.
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But a freemium model assumed a single-sided market – one where the users became the payers.
To reduce complexity, our model assumed a fixed vaccination schedule over a 100 year period.
For simplicity, the model assumed a sample area that was featureless, flat and 1 m deep.
However, that model assumed a uniform spatial distribution of the CNG and Cl− channels.
The model assumed a binomial error distribution with regression parameters calculated by maximum likelihood.
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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