Calculation of hydrophobic interactions from molecular dynamics,surface areas,and experimental hydrocarbon solubilities

Using experimental solubilities and partial pressures for hydrocarbon solution in water and molecular dynamics calculations of hydrocarbon water interaction energies, hydrocarbon–water cavity potentials are obtained and then plotted vs. accessible surface area. The data used is mainly for aliphatic hydrocarbons, but benzene is included. Molecular dynamics calculations of pairs of hydrocarbon molecules together with the cavity potential curve are then used to obtain hydrophobic interaction free energies between the hydrocarbon pairs. While the cavity potential change is related to a change in surface area for hydrocarbon systems, the hydrocarbon–water interaction energy is not, so that the hydrophobic binding energy is not. The results are in agreement with previous results by a different method (R.B. Hermann, In Seventh Jerusalem Symposium on Quantum Pharmacology, E. Bergman and B. Pullman, Eds., D. Reidel, Dordrecht, 1974, p. 441) in that there is little or no solvent-induced binding free energy between small hydrocarbon molecules in a dilute aqueous solution. It is proposed that the cavity potential vs. accessible surface area curve obtained here can be used together with OPLS parameters to calculate both hydrocarbon–water solvation free energies and hydrophobic interactions. © 1993 John Wiley & Sons, Inc.     

Representations of double coset hypergroups and induced representations

The principal goal of this paper is to investigate the representation theory of double coset hypergroups. IfK=G//H is a double coset hypergroup, representations ofK can canonically be obtained from those ofG. However, not every representation ofK originates from this construction in general, i.e., extends to a representation ofG. Properties of this construction are discussed, and as the main result it turns out that extending representations ofK is compatible with the inducing process (as introduced in [7]). It follows that a representation weakly contained in the left-regular representation ofK always admits an extension toG. Furthermore, we realize the Gelfand pair (where are a local field andR its ring of integers) as a polynomial hypergroup on ℕ₀ and characterize the (proper) subset of its dual consisting of extensible representations.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.