We devised a series of underlying test models based on static information which are closer to the dynamic behaviour of the program.
Of the 12 different models tested, model based on LHHW mechanism with surface reaction as rate-determining step fitted the experimental data well.
These proteins show little memory of previous cell cycles, and thus are a good starting point for testing models based on current cell properties.
We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January May.
Out of the 18 models developed and tested one model based on the LHHW formulation was adopted after evaluating the average absolute deviation and activation energy values and compared to values obtained with the empirical power law model.
Non-linear regression was used to test the model based on Friberg (1979a) with our data.
Many studies have attempted to test this model based on its predictions, such as simultaneous loss of adjacent introns, 3'-side bias of intron loss, and germline expression of intron-lost genes.
We tested the model based on electromagnetic field description via Maxwell's equations, and we also extended the monitored quantities to include various flux densities.
Among the four approaches tested, the model based on the Beer Lambert law with a single parameter (k: extinction coefficient) exhibited highest accuracy (r2 = 0.665) compared with the other models based on allometric relationships.
