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The present paper proposes a critical comparison between two methodologies which could be applied for a near-miss event assessment: a matrix and an index based method.
This paper presents a comparative study between two methodologies which have been applied on two different data sets of SCC mixtures, which were gathered from the literature, using artificial neural network (ANN).
Overall, we can conclude that in the event that only replicated time-course experiments are available, a situation which is not uncommon, the integration between two methodologies can lead to a dynamical model with better accuracy than one solely based on a SOO procedure.
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The major difference between the two methodologies is how the residuals between the true function value and the polynomial surface approximation value at a design point are treated.
UV-crosslinking studies confirmed that each of these interactions is direct and specific, with the exception of hnRNP K. Conflicting results between the two methodologies may arise because IHC is unable to distinguish between direct and indirect interaction.
Thus, the difference in sensitivity between the two methodologies is still confirmed.
The results show that the convergence between the two methodologies still needs to be improved.
Again, the difference between the two methodologies did not exceed 13% of the images approved for any value of α.
A compromise between these two methodologies is represented by the use of data assimilation scheme to include sparse remote estimates of surface fluxes into continuous modelling.
Results show that maximum exposure is unexpectedly variable over time and that the relative error between the two methodologies depends on the measurement conditions.
Although prior studies have examined the relationships between these two methodologies through similar experiments, they have produced conflicting results in the literature for a variety of reasons.
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