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The overall idea is to harmonize models, making them run with the same forcing in a step-by-step procedure, in such a way that the main agent in producing discrepancy between models can be found.
Then, shared species between models can be used to construct a model network, on which the query is executed.
By simulating a sample from the PPDs for all experimentally accessible moieties and fluxes, differences between models can be explored [ 28].
Then, shared species between models can be used to construct a model network, on which this query can be executed with possible results from multiple models.
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Using a layered approach sketched above, interoperation between data models can be simplified by an order of magnitude.
However, a considerable difference in the mean reaction rate under forced concentration oscillations between the models can be observed.
The difference between the models can be significant in dense deployments where the coverage areas are reduced and the cells serve a low number of users.
Accurate comparisons between dosimetry models can be achieved by using the same input patient data (administered activity and SPECT/CT images) into all of the VBDM.
The differences between the models can be identified by these two dimensions and identified according to the following cultural characteristics: strategy, flexibility/technology, organization, and management style (Morgan 2007).
The concordance between the models can be appreciated in Fig. 16, in particular for the first nine modes, while the gap becomes noticeable at increasing but less important frequencies.
This difference between the models can be explained as follows.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com