The pore-scale models classify the void space into pores, or pore bodies, and pore throats, and then investigate the interactions between the pores.
Such models classify the individuals of a population into age classes and project the abundances in these classes in discrete time.
The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively.
The rankings of each model were compared to understand whether each model classified the same patients in different risk strata.
The texture identifier model classifies the images based on Dunham classification, while the porosity analyzer model determines the percentage of each type of pore spaces in the image.
The model classifies the tar compounds in groups, according to their structure, and it is able to predict the experimental results with reasonable accuracy.
To construct the template library, the model classifies the structure into different motifs, such as helices, hairpin loops, internal/bulge loops, pseudoknots, and N-way junctions (N ≥ 3).
That is, whilst the Sydney model classifies the second non-finite verb as the main verb and, therefore, as the process, as in (58a), the CG model instead chooses to annotate the aspectual verb as the process, as in (58b).
This book arose out of a conference in honor of Braj Kachru held in Singapore in 1997; the title alludes to Kachru's well-known model classifying the [End Page 224] various English-using countries in the world as belonging to the inner circle, the outer circle, and the expanding circle.
