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This model helps for better understanding of two-dimensional Canonical Correlation Analysis (2DCCA), and for further extending the method into more complex probabilistic model.
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Our model helps identify potential outage timeframes for these sectors (e.g., Deligne et al. 2015).
Understanding participants' preference for each model helps define the different dimensions of the actual user experience.
A study of this model helps us present several recommendations for optimizing humidifier performance.
The model helps to lay the foundations for graduates' lifelong learning by encouraging skills development, within a discipline-specific setting.
Further, our choice of mixed models helps to account for random variability.
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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