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In the theoretical model, the fractional mass loss is given as a linear function of the particle size.
Given the uncertainties in the FE model, the fractional factorial design reduced the number of simulations required in the DS method by 82%.
With knowledge acquired from the optical observations as inputs of the kinetic model, the fractional conversion of the dehydration reaction was calculated and compared with experimental results from thermogravimetric analysis (TGA).
In this paper, we propose to model the fractional transform mapping the real world space onto the camera plane and to integrate the projection in the particle filter through the importance density.
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The magnitudes of estimated biases are similar for the linear regression model, variable-dispersion beta regression model and the fractional logit model.
For example, Nieto and Pimentel [6] extended a second-order thermostat model to the fractional model; Ding and Jiang [7] used waveform relaxation methods to study some fractional functional differential equation models.
Moreover, the power and type-1 error profiles are very similar for the linear model, variable-dispersion beta regression model and the fractional logit regression model.
Subsequently, we estimate covariate effects on the response variable using one of four regression models: the linear regression model, the beta regression model, the (double-index) variable-dispersion beta regression model and the fractional logit regression model.
The linear regression model, the variable-dispersion beta regression model and the fractional logit regression model all perform well across the simulation experiments under consideration.
For comparison between ordinary model and the fractional models, it is important to present solutions corresponding to the case (gamma = 1).
When considering the power experiments estimates of average bias across the 20,000 replicate experiments were small for the linear regression model, the variable-dispersion beta regression model and the fractional logit regression model (of magnitude 1E-04 through 1E-06 respectively).
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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