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In order to formulate the proposed NLP optimisation model the differential equations are discretised using a first order backward finite difference scheme.
With the help of a dynamic mathematical model, the differential pressure across the waterwall in a PFBC (pressurized fluidized bed combustion) boiler is analyzed under abnormal conditions.
In the formulation considered, the microparticle model involves a non-linear hyperbolic differential equation with source terms, and in the macroparticle model the differential equation involves a singularly perturbed parabolic diffusion problem.
In order to model the differential ionosphere, its autocorrelation function is analysed.
To obtain a space-time-discrete model, the differential operators are approximated by finite differences, see Figure 1.
According to the GM (1, 1) model, the differential equation of the new sequence can be described as follows: frac{{{text{d}}x^{left( 1 right)} left( t right)}}{{{text{d}}left( t right)}} + ax^{left( 1 right)} left( t right) = ut in [0,infty ). (8) 4.
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Distinct kinds of computational models have been applied to propagate the behaviors of GRNs; see, for instance, the Bayesian network models, the Petri net models, the Boolean models, and the differential equation models.
It is found that, among these models, the differential scheme provides the best fit of the numerical data.
In these models, the differential equations model has obvious advantages, for example, the differential equations model is more accurate than the Boolean model in describing GRNs, and it has less computational complexity than the Bayesian model.
Surrounded by the indicated models, the differential equation models describe the rate of change in the concentration of gene production, such as mRNAs and proteins, as constant values, whereas the other models do not have such a basis.
Modeling the differential coupling of dendritic Na+ spikes to AP output in CA1 vs CA2 dendrites.
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