A number of available fatigue models were assessed by predicting fatigue life of magnesium spot-welds.
The best predictive performances of the models were assessed by means of various descriptive statistical indicators.
These non-linear models were assessed by some internal and external validation methods.
Probabilistic Neural Network models were assessed in terms of their classification accuracy concerning medical disease prediction.
Performances of the developed models were assessed by simulation with the validation data set.
The turbulence models were assessed by comparing the simulations with in-house annular channel experiments.
Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer.
At the end of the semester, the completed models were assessed by a jury of professionals, including two Greek Orthodox architects, one of whom had designed Greek Orthodox churches.
Based on the SQS energetics, the predicting powers of the existing empirical ternary extrapolation models were assessed.
Both models were assessed with the experimental data of Oh and Revankar [Oh, S., Revankar, S.T., 2005a.
All the regression models were assessed for their fitness'.
