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Our direct-demand models had reasonable goodness-of-fit (bicycle R2: 0.52; pedestrian R2: 0.71).
Because three models had reasonable support from the data (Table 3), we used model averaging (Figs. 2 & 3) to estimate the maximum offtake expected to yield λ≥1.
Overall, the three models had reasonable PPV levels (all >70%); the Spitz model had a significantly higher PPV (88.2%) than those of the LLP (75.9% P<0.001) and the Bach (80.9% P=0.009) models.
All prediction models had reasonable apparent validity, although our proposed model performed slightly worse (most noticeably for AIC) than models which included deprivation (ie, models 2 4) instead of population density.
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Other models have reasonable R 2 values as well.
The comparisons between the predicted and test results showed that the predicted results have good agreement with the collected test data and that the proposed models have reasonable accuracy.
The final refined complex model had reasonable R-factors and very good stereochemistry.
The results of these comparisons showed that the proposed model had reasonable accuracy at different biases and for different values of physical parameters of the transistor.
The comparisons between the proposed model and a well-known reference model, named FETToy, show that the proposed model had reasonable accuracy in terms of different biases and physical parameters.
Besides the fact that the proposed model has reasonable correlation with MOS, it does not take into account the value of the motion intensity at the start of a freezing interval.
We have explored various kinds of asynchronous pipelines, and their corresponding results show this model has reasonable accuracy in average (below 5%) and in variance, compared to the low level Monte Carlo Hspice simulation.
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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