Energy minimisation is used to model these properties.
An ability to precisely model these properties can improve reservoir characterization and flow modeling at different scales.
Probabilistic frameworks have been developed to model these properties, allowing them to be compared at various stages of development to determine which properties are influenced by this selection [ 100].
Simple models with these properties include the exponential model Y = eb+c·X, c <0, used for risk analysis in the EU [71]-[75], and the power model Y = a · X m with m <0, used by Aylor et al. [29], Arrit et al. [39], and Boehm et al. [44].
Explicit models for these properties are developed from analytical studies.
The parallel-serial approach models of these properties reasonably explained the experimental results.
Second paragraph of Strongly adapting model: "If these properties (f-I curve, rheobase current and adaptation) are captured, then the model will spike with similar frequencies as the physiological cell given the same amount of synaptic input".
These mathematical equations describe the expected properties of the hypothetical mitotic control system given the assumptions behind each model, and these properties can then be compared with experimental observations to assess the reliability of the model.
Finally, the book addresses the modeling of these properties in the context of evolution, with a particular emphasis on the emerging field of evolutionary developmental biology (or evo-devo).
Modelling of these properties has facilitated the identification of changes to the core structure, which should lead to improved beam stiffness, while retaining a low density.
