FORECASTING MINERAL SUPPLY FROM MINERAL RENT DATA

An optimal control model of exhaustible resources is used to clarify the long run relationship between mineral rent and depletion cost at the industry level. A standard first order condition of the time rate of change of rents is reformulated to reveal that rent data may be used to help forecast the rise in extraction costs resulting from resource depletion. This application of the theory of exhaustible resources is illustrated using historical mineral industry rent and extraction cost data. A forecast of U.S. coal extraction costs, following the method proposed in this paper, suggests that future rates of extraction cost increases will be similar to rates experienced in the past.     

The flow of a variable viscosity fluid between parallel plates with shear heating

A model for the flow of a fluid through a channel with parallel plates is investigated. The channel is narrow, so that the lubrication approximation may be applied. The channel walls are maintained at a constant temperature. Shear heating effects are included and the fluid viscosity decreases exponentially with temperature. When the flow is driven solely by shear stress or imposed velocity at the top, analytical progress is possible. When pressure gradient also drives the flow the problem is solved numerically.     

Quantitative Impact of a Cognitive Modeling Intelligent Tutoring System on Student Performance in Balancing Chemical Equations

The need for improved interactive tutoring capabilities in educational software for chemistry problem solving is an important one clearly articulated by teachers and students. To deliver the next generation of individualized interactive capabilities users demand, it is necessary to go beyond the conventional computer-assisted instruction methodology. The focus of this paper is the assessment with first-semester general chemistry students of a recently developed artificial intelligence (AI) tutor for balancing chemical equations. This is the first such assessment of an AI-based learning tool in chemistry. Students in CHEM 121 in the Fall 2001 semester at Duquesne University (N = 273) participated in the study. Students were divided into a test group that used the AI tutor as part of their study activities and a control group that did not use the tutor. It was found that the tutor improved the performance of the test group students to a statistically significant degree, helping the weakest students the most. This study establishes the feasibility of an AI-based approach to creating advanced new tutoring software for chemistry problem solving. Access to a Web-based demonstration of the equation-balancing tutor may be obtained by emailing the corresponding author.     

A Note on an Input Control Model of a Predictive Regression Interval

A well-known problem of prediction in linear regression models is to find a confidence interval for the random value of the dependent variable when the values of the independent variables are given. Such a situation may arise in economic quality control models when the independent variables are costly inputs and the dependent variable is some measure of quality or production. In such a circumstance, an important control objective may be to find values for the inputs that will maximize the lower limit of the prediction confidence interval for a fixed budget, or alternatively, to minimize the cost of the inputs for a fixed lower limit of the confidence interval. In this paper, we shall show that global optima can be found using known algorithms. The special case of simple linear regression is discussed and an illustrative example is provided.     

Optimal Server Acquisition in Open Queueing Networks

Several production and flexible manufacturing systems can naturally be modelled using queueing networks. In this paper, we consider the problem of acquiring servers for the nodes of an open queueing network, so as to optimize the steady-state mean virtual system parameters subject to a budget constraint. A partial enumeration scheme and a heuristic method have been proposed to solve this problem. Empirical results based on randomly generated test problems are used to identify a class of problems for which the heuristic performs well.     

Global and local optimization using radial basis function response surface models

The focus of this paper is the optimization of complex multi-parameter systems. We consider systems in which the objective function is not known explicitly, and can only be evaluated through computationally intensive numerical simulation or through costly physical experiments. The objective function may also contain many local extrema which may be of interest. Given objective function values at a scattered set of parameter values, we develop a response surface model that can dramatically reduce the required computation time for parameter optimization runs. The response surface model is developed using radial basis functions, producing a model whose objective function values match those of the original system at all sampled data points. Interpolation to any other point is easily accomplished and generates a model which represents the system over the entire parameter space. This paper presents the details of the use of radial basis functions to transform scattered data points, obtained from a complex continuum mechanics simulation of explosive materials, into a response surface model of a function over the given parameter space. Response surface methodology and radial basis functions are discussed in general and are applied to a global optimization problem for an explosive oil well penetrator.     

Synthesis and characterization of a semiflexible liquid crystalline polyester with a broad nematic region

In this paper, we report on the synthesis and detailed characterization of a new semiflexible nematic liquid crystalline polyester which could serve as a 'model' polyester for a variety of physical and physico-chemical investigations. The polymer is a nematic liquid over a wide temperature range-from the glass transition temperature at ∼95°C to the isotropic transition at ∼240°C. We expect this polyester to be particularly useful for studying the effect of flow on the orientation of liquid crystalline polymers, as well as the production and removal of disclinations.