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The analysis of runtime of all the techniques (LVH method) for KEGG_U data set is stated in Table 13.
Amount of biofuel sent from warehouses to demand points which are calculated through model is stated in Table 10.
Amount of biomass sent from warehouses to plants which are calculated through model is stated in Table 8 and depicted in Fig. 2 for clarifying these flows.
The performance of the two algorithms with different initial points ((u^{0}=operatorname{rand}(61,1)), (u^{0}=50^operatorname{ones}(61,1)), and (u^{0}=100^operatorname{ones}(61,1))) and optimality tolerance ((epsilon=10^{-4}), (epsilon=10^{-5}), and (epsilon=10^{-6})) for the traffic equilibrium problem with fixed demand constraints (Example 5.2) is stated in Table 4.
The prognostic power of other classical factors was also evaluated and is stated in Table 1.
The probe location of each particular gene is stated in table 3 and the Cyclophilin A (PPIA) gene (Hs99999904_m1) was used as housekeeping gene to normalize the results.
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The effect of LVH versus OVA for handling the three multi-class structured data sets over AUC values is stated in Tables 5, 6, 7, 8 and 9.
The details are stated in Table 7.
The results are stated in Table 1.
And detailed simulation settings are stated in Table 1.
The average values and the statistical significances are stated in Table 5.
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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