Hydrodynamics of binary mixtures of granular gases with stochastic coefficient of restitution

A hydrodynamic description of dilute binary gas mixtures comprising smooth inelastic spheres interacting by binary collisions with a random coefficient of restitution is presented. Constitutive relations are derived using the Chapman–Enskog perturbative method, associated with a computer-aided method to allow high-order Sonine polynomial expansions. The transport coefficients obtained are checked against DSMC simulations. The resulting equations are applied to the analysis of a vertically vibrated system. It is shown that differences in the shape of the distributions of the coefficient of restitution are sufficient to produce partial segregation.

A Multiperiod Equilibrium Pricing Model

We propose an equilibrium pricing model in a dynamic multiperiod stochastic framework with uncertain income. There are one tradable risky asset (stock/commodity), one nontradable underlying (temperature), and also a contingent claim (weather derivative) written on the tradable risky asset and the nontradable underlying in the market. The price of the contingent claim is priced in equilibrium by optimal strategies of representative agent and market clearing condition. The risk preferences are of exponential type with a stochastic coefficient of risk aversion. Both subgame perfect strategy and naive strategy are considered and the corresponding equilibrium prices are derived. From the numerical result we examine how the equilibrium prices vary in response to changes in model parameters and highlight the importance of our equilibrium pricing principle.

SOLVING STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS BASED ON THE EXPERIMENTAL DATA

We consider a stochastic linear elliptic boundary value problem whose stochastic coefficient a(x, ω) is expressed by a finite number NKL of mutually independent random variables, and transform this problem into a deterministic one. We show how to choose a suitable NKL which should be as low as possible for practical reasons, and we give the a priori estimates for modeling error when a(x, ω) is completely known. When a random function a(x, ω) is selected to fit the experimental data, we address the estimation of the error in this selection due to insufficient experimental data. We present a simple model problem, simulate the experiments, and give the numerical results and error estimates.

MEASURING PRODUCTIVE CAPACITY REALIZATION OF BANGLADESH FOOD MANUFACTURING: A RANDOM COEFFICIENT FRONTIER APPROACH

The aim of this paper is to empirically estimate the firm level productive capacity realization (PCR) by using the stochastic coefficient frontier production function. Empirical estimations have been done using firm level data from Bangladesh food manufacturing covering the inter-temporal periods 1988, 1992 and 1996. The results suggest that there are considerable levels of unrealized production capacity in the food processing sector and very little improvement occurred in realization of productive capacity after the implementation of economic reforms. So, there is enough room for increasing output by realizing substantial unrealized capacity.