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Many researchers have studied predator-prey models with delay and derived some important results [1 9].
Worm propagation models with delay have been investigated by some scholars at home or broad in recent years [11 15].
In addition, delay is a common phenomenon in the natural world; then it is interesting to consider stochastic models with delay (see e.g. [22, 23]), which can lead to further investigation of along the line of the present paper.
We expect the approach to be extensible to a wide range of field models with delay, and in particular to be highly useful for analyses of experimental data in the domain of computational neuroscience.
In order to investigate delay, we built Cox proportional hazards models with delay as time-to-event.
However, numerical results showed that these first-order reaction models with delay did not give good approximation to the detailed degradation process [ 37].
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For the study on risk models with delayed claims, we refer the interested readers to Yuen and Guo [9], Yuen et al. [10] and Xiao and Guo [11].
In the figures of biomass and product concentrations, predictions of the hybrid models with delays are practically congruent to the true process behaviour.
A discrete-time logistic model with delay is studied.
This paper is concerned with qualitative properties of the evolutionary p-Laplacian population model with delay.
In this paper, we consider a three-dimensional discrete neural network model with delay.
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