In this paper, authors present a thorough procedure for automatic modelling with a fair NURBS surface, having lists of points on the stations of the vessel as initial data.
Our study demonstrates that even for models with a fair discriminative performance, calibration was far from ideal.
However, it has been shown that there exists a condition under which the end effects could be neglected for modal radiation efficiencies so that the infinite model could be used with fair accuracy.
However, a series of recent experiments on corpora of recorded (read) speech and spontaneous (elicited) speech suggest that it is indeed possible to model human accent strategies with fair success (80 98% correct) for unrestricted text with only the tools for automatic text analysis currently available.
That is why we tested our model on an older infant, with fair predictions for almost all bundles.
The analytical model shows fair agreement with numerical data at modest computational cost.
Despite the restricted nature of this investigation, our model shows fair agreement with experimental results, both qualitatively and quantitatively.
This model achieved fair fit with data (RMSEA = .06); however, the RMSEA confidence interval indicated sampling instability (RMSEAC.I. =.000 -.111).
Discrimination of the SAPS II model was fair to good with areas under the ROC curve between 0.67 and 0.83 [ 20- 25, 26, 26].
Models 1, 3, 4 and 6, and Models 5, 7 and 8 generated two and three clusters, respectively, with fair cluster quality and similar profiles.
