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The chromatographic method is much more efficient for obtaining mixture data and is considerably easier to adapt to wider pressure and temperature ranges than static methods.
The Peng Robinson equation of state with van der Waals two-parameter mixing rule was employed successfully to correlate the binary mixture data and predict the ternary mixture data.
Correlation, either partial or complete, is inherent in such mixture data and requires the use of multivariate statistical tools for visualization and identification of important relationships in the data.
We compared the resulting predictions with experimental mixture data and we explored the sensitivity of the predictions with respect to model structure.
The experimental mixture data and the fits of the empirical and additivity models to the mixture data are shown in Figure 1.
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Parameter estimates and corresponding p-values from the additivity model in Equation 2a, from the simultaneously fit single-chemical data and mixture data, are provided in Table 3.
To the experimental data from this work and mixture data from other authors temperature dependent UNIQUAC parameters were fitted.
The single-chemical and mixture data were modeled successfully using the FSCR model.
The restricted additivity model (Casey et al. 2004) included only the single-chemical dose response model parameters but used both the single-chemical and mixture data.
The restricted additivity model (Gennings et al. 2004) included only the single-chemical dose response model parameters but used both the single-chemical and mixture data.
We used the original NA12891 sequencing data as the control to compare against the in silico mixture data set described above, and calculated sensitivity as the fraction of identified simulated sites in non-repetitive regions.
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