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Thus, in our current analytical method, we analysed the data of each processed day independently by proposing mean values and prediction intervals for each particular processing day.
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Performance of the proposed method is assessed by comparing the mean values and coefficients of variation for components of the effective elastic tensor obtained from the present method against corresponding results calculated by using Monte Carlo simulation method for a plain-weave textile composite.
Mean values estimated for these cubic clusters are computed by adding mean values of additive components.
In order to improve the measurement precision, we propose a method by using harmonic mean value of TCR in two directions of exerting heat flux to diminish the effect.
Any missing values were replaced by a mean value.
Next, a practical real-time supervisory Energy Management strategy basing on Mean Values (EMMV) is proposed.
By mean value theorem, we have.
*Phenotypic value is indicated by mean value ± standard deviation.
# Significant difference in mean values by genotype.
We therefore used a method proposed by Levy & Siegal [ 8] to uncouple this dependency, by applying a lowess regression to condition CV on mean values.
Mean values are indicated by bars.
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CEO of Professional Science Editing for Scientists @ prosciediting.com