The idea that membranes had different components in the inner and outer sides of the membrane was not new, but this hypothesis was taken one step further because the model provided an explanation: The high, free energy of activation necessary for the hydrophilic part of a membrane component to cross the hydrophobic membrane core prevented the random tumbling [ 1].
We used the EAC post-hoc analysis to determine the rates for the speculum examination because this model provides the most complete estimate of the underlying prevalence rate.
The probabilistic model proposed here gets a bigger life but, however, it is more precise because this model provides the mean fatigue life, the variance for this life, and a cumulative probability function as Figure 7 shows.
In what follows, we will refer to the hierarchal Bayesian learning model as the "perceptual model," because this model provides a mapping from hidden states (or environmental causes) to sensory inputs (Daunizeau, den Ouden, Pessiglione, Kiebel, Stephan et al. 2010; Daunizeau, den Ouden, Pessiglione, Kiebel, Friston et al. 2010).
Though this model is originally designed for application under normal wind condition, the originality of this paper is the study of the applicability of this model under an extreme wind condition, because this model provides a second-by-second simulation resolution and uses the coherence matrix in frequency domain to describe the coherences of power reduction among wind turbines.
If the climatic effect as a whole was of interest, the method based on two parameters elasticities would be more applicable compared to that based on multi-parameter elasticities because the simple model provides higher confidence in modelling the overall change in runoff.
The functional form of risk dependency on the distance from the AC plant (exponential decay with a threshold) was chosen because this model provided the best fit to data.
Columns and SWs are modeled as thick shell elements because this modeling provides more accurate results of the period of vibration than other modeling as concluded from the above analysis.
On the other hand, a document store can do so because its data model provides the capability to index and query the document contents.
Again, columns and SWs are modeled as thick shell elements because this modeling provides more accurate results of period of vibration than other modeling as concluded from the above analysis.
