The paper explains the application of a genetic algorithm (GA) to the problem of estimating parameters for a kinetic model of a biologically reacting system. It is demonstrated that the GA is a powerful tool for quantifying the kinetic parameters using kinetic data. As the operation of the GA does not depend on the form of the model equation, it can be applied to the wide spectrum of kinetic modelling problems without any complex formulation procedure.
We study the dynamical properties of avalanche activities in the Korean Treasury Bond (KTB) futures price and the S&P 500 stock index. We apply the detrended fluctuation analysis, multiscale sample entropy and wavelet coefficient correlation to them, which revealed the scale-free dynamics of the bursting time series, avalanche size, and laminar time. We found that the laminar time and the avalanche size are anti-correlated in a short scale but in a large scale strongly correlated in KTB503, and are strongly correlated over all scales in S&P 500.
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