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It is found that the 0-1 model could properly link up with the MDP or optimal control approaches based on discrete time states.
The statistical analysis results suggested that a second-order polynomial regression model could properly interpret the experimental data with a coefficient of determination R 2 value 0.997 and Fisher F value of 281.53.
It was shown that a second-order polynominal regression model could properly interpret the experimental data with an R2-value of 0.9957 and an F-value of 178.32, based on the maximum enzyme activity examined.
Analysis of variance showed that a second-order polynomial regression model could properly interpret the experimental data with coefficient of determination for color and COD removal (R 2) value of 0.9556 and 0.9416, respectively, and an F-value of 23.93 and 17.92, respectively.
It was shown that a second-order polynomial regression model could properly interpret the experimental data with coefficient of determination (R 2) value of 0.9890, 0.9873 and 0.9938 with F value of 70.09, 60.47 and 124.24 for BPD-1, BPD-2 and BPD-3, respectively.
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However, none of the models could properly describe the desorptive tail of DPP for the high organic matter soil.
In order to find a model who could properly impersonate the long-dead artist, Coupland crowd-sourced the planet, eventually receiving over a thousand entries from 37 different countries.
This model could be implemented properly for workshop systems.
The structure was solved by molecular replacement, initially assuming that the space group was P43212, but the model could not be properly refined.
Besides the main effects the interactions were also present in the imputation model, ensuring that interactions could properly be allowed in the analysis models.
Each response had been described by a second-order polynomial regression model developed by ANOVA, which could properly determine the experimental data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com