After that, in terms of mathematically representing the flow behavior, yield stress, and plastic viscosity, mathematical models including the Bingham plastic, Power law, Casson, and Herschel Bulkley were considered to find the best fit (Agarwal et al. 2011; Hossain and Al-Majed 2015; Gupta 2000; Shah et al. 2010).
The present study is concerned with the derivation and justification of neural field equations from finite size stochastic particle models, i.e., stochastic models for the behaviour of individual neurons distributed in finitely many populations, in terms of mathematically precise probabilistic limit theorems.
Although the three different models have very different objectives focused on biological systems that can differ by orders of magnitude, mass action terms are mathematically similar, with similar biological interpretations.
These terms are mathematically related by the following equation: E = e = − Q. Galilei calculates the eccentricity e of the surface within a central diameter of 8 mm averaged over all meridians.
However, the effect of the resistive term is mathematically limited in its impact [ 17].
The modelled equation is represented in terms of resistivity mathematically as: frac{1}{{R_{text{t}} }} = frac{1}{{R_{text{ss}} }} + frac{1}{{R_{text{sh}} }} (10 where Rt is true resistivity; Rss, sandstone resistivity contribution; Rsh, shale resistivity contribution frac{1}{{R_{text{ss}} }} = frac{{S_{text{w}}^{n} }}{{F*R_{text{w}} }} (11).
This type of bifurcation is mathematically termed the "cusp bifurcation" [ 34].
Here, here we use short term data from Ethiopia to mathematically model longer term outcomes.
"... Four individual components of ionic current were formulated mathematically in terms of Hodgkin-Huxley type equations.
Perturbation forces, when expressed mathematically in terms of strength and time, are called forcing functions.
