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In practice, the existence of a continuous extension of the difference quotient to is shown by modifying the numerator to cancel in the denominator.
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We present both bi-linear and bi-quadratic versions of this scheme as well as a fourth-order extension of the original difference scheme of Günter et al. (2005).
The extended stochastic central difference method is, however, an extension of the stochastic central difference method for the determination of the recursive mean square or covariance of responses of systems under narrow band stationary and non-stationary random disturbances.
From the number of exclusive functions associated to each pathway it is not possible to predict the true extension of the possible metabolic differences existent between these species, as they strongly depend of the genomic context (total functions coded within the genome).
Method A is a Galerkin FE approximation of the density current (written in generalized ohmic form) where the harmonic average of the electrical conductivity is used instead of the standard average while method B is a genuine 3D extension of the classic 1D Scharfetter-Gummel diformulae formula interpreted as a stabilized Galerkin FE approximation with the use of an 'optimal' artificial diffusion.
9 Still, we add an extension of the model allowing for differences in purchasing power for the sake of generality.
In Section 6, we provide an extension of the model allowing for differences in purchasing power between the host country and the origin country.
While the basic version of the model assumes for expositional simplicity that prices are the same in both locations, an extension of the model allows for differences in purchasing power between host and origin countries.
First, this model was a good candidate explaining the findings of Heldmann et al. [31] and second, an extension of the model takes individual differences into account.
There also has been a large number of studies on the extension of difference equations to time-delay systems in the literature [15 24].
The first was an extension of the DD model known as a difference-in-difference-in-differences model (DDD) which tests for the existence of factors that differentially affect screening rates in Ontario in the post-intervention period which could mistakenly be attributed as impacts of the ColonCancerCheck program.
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