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In this paper, we focus on measures based on variance, and therefore in the above formula, we apply the coefficient of variation as the inequality index.
In order to validate the feasibility and accuracy of this prediction formula, we apply this prediction formula to predict the ratio of injection production control area for a synthetic reservoir model.
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To illustrate the power of the proposed formula, we applied it to study the exchange of information in networks of coupled chaotic maps (Sec. Methods) and in Hindmarsh-Rose neural networks bidirectionally electrically coupled (Sec. Results).
To quantitate the degree of planarity of the normal, we applied a formula for assessing the 'flattening' ratio, F = (a − b)/ a (a is the length of the semi-major axis in the projections; b is its semi-minor axis).
First we apply formula (10) to obtain the fractional Dini derivative of the considered Lyapunov function.
We apply formula (3.3) to the region D + ∖ D ε + ⊂ D +, where D ε + is an upper half sphere centered at P ′ of radius ε.
As we apply formula (HINTER, g ) in a pairwise fashion, the distance between population i and population j for locus g is μ i μ j dpop, g (p i, p j ).
Using the analogy of the above growth processes with bubble growth in a liquid, we apply formulas for kinetics of aerosol and cluster growth in this case.
In our analytical approach, we derive the general formulas and recursions, which we apply to a case study that we believe is illustrative to the reader.
We apply this formula to the derivation of expression of.
Then we apply Green׳s formula to derive the asymptotic representation for the far field.
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CEO of Professional Science Editing for Scientists @ prosciediting.com