SWIFT, and ionospheric space weather model, is discussed as an example.
Furthermore, a mononuclear cell model is discussed as an example to verify this method.
Ontology of one simulation solver type, namely companion model solver, is discussed as an example.
The significance of process parameters for the most pronounced physical phenomena is discussed as are also the consequences of modelling.
The applicability of this mode of expression and its relation to the ideal protein concept is discussed as is the application of different response models to the data.
Besides expectancy theories, classical conditioning is discussed as a major explanatory model.
Legacy Datacenter Modeling is discussed as a series of activities structured to gather information about how resources are consumed in the datacenter and the effects that those consumption patterns have on current supply.
To this line, enzyme kinetics modeling is discussed as a bioengineering practice to explore relationships between processes, structures and functions in food products, prior describing the macro- and micro-states according to Boltzmann entropy equations.
On the other hand, Bayesian hierarchical modelling is discussed as a powerful technique in decreasing the signal to noise ratio in GWAS and for handling the problems of multiple comparisons observed in several statistical applications, for example, analyzing GWAS data (Chen and Witte, 2007; Hung et al., 2004; Liu et al., 2005).
Spatiotemporal averaging of microscopic events via the kinetic Monte Carlo method for realistic reaction mechanisms is discussed as an alternative to mean-field modeling.
Examples of measurement instruments are discussed, as well as a deterministic measurement model.
