Effects of adsorbed phase on diffusion of subcritical hydrocarbons in activated carbon at low pressures

Diffusions of free and adsorbed molecules of subcritical hydrocarbons in activated carbon were investigated to study the influence of adsorbed molecules on both diffusion processes at low pressures. A collision reflection factor, defined as the fraction of molecules undergoing collision to the solid surface over reflection from the surface, is incorporated into Knudsen diffusivity and surface diffusivity in meso/macropores. Since the porous structure of activated carbon is bimodal in nature, the diffusion of adsorbed molecules is contributed by that of weakly adsorbed molecules on the meso/macropore surfaces and that of strongly adsorbed molecules in the small confinement of micropores. The mobility of adsorbed molecules on the meso/macropore surface is characterized by the surface diffusivity D-mu 2, while that in the micropore is characterized by D-mu 1. In our study with subcritical hydrocarbons, we have found that the former increases almost linearly with pressure, while the latter exhibits a sharp increase at a very low-pressure region and then decreases beyond a critical pressure. This critical pressure is identified as a pressure at which the micropores are saturated.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

A scalarization technique for computing the power and exponential moments of gaussian random matrices

We consider the problems of computing the power and exponential moments EXs and EetX of square Gaussian random matrices X=A+BWC for positive integer s and real t, where W is a standard normal random vector and A, B, C are appropriately dimensioned constant matrices. We solve the problems by a matrix product scalarization technique and interpret the solutions in system-theoretic terms. The results of the paper are applicable to Bayesian prediction in multivariate autoregressive time series and mean-reverting diffusion processes.

A diffusion approach to approximating preservation probabilities for gene duplicates

Consider a haploid population and, within its genome, a gene whose presence is vital for the survival of any individual. Each copy of this gene is subject to mutations which destroy its function. Suppose one member of the population somehow acquires a duplicate copy of the gene, where the duplicate is fully linked to the original gene's locus. Preservation is said to occur if eventually the entire population consists of individuals descended from this one which initially carried the duplicate. The system is modelled by a finite state-space Markov process which in turn is approximated by a diffusion process, whence an explicit expression for the probability of preservation is derived. The event of preservation can be compared to the fixation of a selectively neutral gene variant initially present in a single individual, the probability of which is the reciprocal of the population size. For very weak mutation, this and the probability of preservation are equal, while as mutation becomes stronger, the preservation probability tends to double this reciprocal. This is in excellent agreement with simulation studies.