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The observed conversions of propylene oxide are in excellent agreement with model prediction based on both a one-dimensional and a two-dimensional plug flow reactor model.

In Fig. 9 we plot the same elongations shown in Fig. 4; the ANLV model prediction (based on the AQLV model) is shown in green.

Recent advances in gene model prediction based on the application of RNA-Seq have been published in a number of research articles.

The accuracy of the model is determined by comparing the model prediction (based on average values) to the means and SDs of measured BAF.

Table 5 shows the time-series model parameter values of the significant predictors with the specified model structure and Figure 5 shows the performance of the model prediction (based on "DDT + Deltamethrin" model).

For genes with values for at least 6 of 8 time points, missing values were imputed using linear model prediction based on PCC values and accounting for mean and standard deviation differences.

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Comparison of task-based and expanded model predictions based on R values, RMS errors, relative RMS errors.

We compared the task-based and expanded model predictions based on R values, root mean squared (RMS) errors, and relative RMS errors calculated compared to direct measurements.

The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system.

In conclusion, the results of this study indicate that the expanded model predictions, based on task, questionnaire, workstation, and anthropometric parameters, perform better than the task-based predictions.

The fuel cell results are substantiated by impedance spectroscopy analysis and preliminary mathematical model predictions based on mixed potential theory.

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