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Based on the validation and analysis of the predictions, values of model parameters, including the electro-osmotic drag coefficient, capillary diffusion coefficient, and catalyst specific surface area are determined adjusted to fit experimental data of current density and MEA water content.
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The proposed algorithm predicts by combining the prediction values of all constituent models based on their performance.
Predictive accuracy measures and visualization of the prediction values can only show model performance in theory.
The max difference between the actual values and the prediction values is less than 0.28%.
The prediction values agree well and consistently with a large body of test data.
The comparison results showed that the prediction values had good consistencies with the experimental values.
Finally, the prediction values of the original water temperature datasets are calculated by the sum of the forecasting values of every sub-series.
The maximum productions of RNase (59.36 U/mL) and DNase (141.21 U/mL) were in agreement with the prediction values and the model was proven to be adequate.
The prediction values of the well-trained artificial neural network and adaptive neuro-fuzzy inference system models agreed well with the experimental data.
The calculated Prediction values are compared with the Prediction values included in the archive.
The prediction values can be confirmed by the learning algorithm.
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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