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Table 3 summarises the profits as predicted by the model using Eq. (4).
Figure 3 shows the stream network generated by the model using SRTM data set.
The remaining 95 sets were compared with the results predicted by the model using the regressed rate parameters.
Fine-root distribution for four planting densities could be fitted by the model using a single value of each parameter (independently from tree density and distance from tree).
The thermal conductivities of pork under pressure conditions: 100, 150 and 200 MPa were estimated by the model using error trials.
Moreover, laminar burning velocities of a real gasoline at 353 and 500 K could be predicted by the model using a toluene reference fuel as a surrogate.
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Note that these parameter estimates are obtained by fitting the model using the entire dataset.
The research possibilities were identified and tested by validating the model using a real case study.
These wavelet functions are selected by optimizing the model using a genetic algorithm (GA).
By solving the model using genetic algorithm, it is obvious that the operation of the model is quite accurate.
Identical data were also obtained by analysing the model using the COBRA software [ 13].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com