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The model characterizes the μCHP boiler behaviour for different inlet water flow rates and temperatures.
The model characterizes the μCHP behaviour for different part load ratios (PLR) (power modulation).
The model characterizes misorientation in three-dimension with all three degrees of freedom.
The model characterizes throughput of individual two-windows TCP flow as a function of contract rate, round trip time, loss rates of In and Out packets.
The model characterizes the relationship between the nitrification rates in unamended and NH+4-amended soils as a function of pH.
The model characterizes the response of the sensor as a function of the parameters from a power-law model rheological description and the geometry of the device.
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The selection criterion is the goodness of fit of the model characterized by the R-square.
However, when a covariate effect (age) is introduced into the model, characterizing the difference in clearance between young and elderly subjects, the overall BSV in the AUC is reduced.
The Markov model characterizes the causal rate as the difference in progression rates in CT− and CT+ individuals.
The predictor variables in this model characterize the FSA and the quasi-identifiers in the quasi-identifier model.
The transient model characterizes the transport phenomena (species, momentum, and energy) that occur in the CVC.
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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