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This paper explores space-filling experimental designs for use with local quadratic approximations. A Mean Squared Distance (MSD) criterion is offered for optimization of the uniformity of designs. The same criterion is also called the Mean Square Error (MSE) criterion by other authors. The authors have proposed a method of optimization of designs in unit cube and spherical regions, which can be used for designs with constrained or unconstrained level values. Also, other criteria such as Entropy, Discrepancy, MaxMin and Eglaj`s criterion are compared by approximation of the test function, using several weight functions for local approximation. It is shown that the Latin Hypercube type designs, optimized according to the MSD criteria, give the best results by approximation of test function both without statistical error and with statistical error with normal distribution. The design optimization method is used also for the well-known NASA High Speed Civil Transport aircraft (HSCT) approximation challenge. For this case, the choice of a fixed number of experiments (from the given 2490) according to the MSD criteria and following local linear approximation gives good results, more accurate than with solutions proposed by other authors, using the Response surface and the Kriging methods. [ABSTRACT FROM AUTHOR]

One solution to reduce fuel consumption (FC) of vehicles is to use infinitely variable transmission (IVT) as the power train. However, inappropriate design of IVT leads to an inadequate decrease in the vehicle FC. Therefore, in order to reach a maximum fuel economy, an optimization on the power train proves to be necessary. The present article aims to optimize IVT to decrease the vehicle FC in a driving cycle. The results have revealed that the FC of the vehicle equipped with the optimized IVT is approximately 7% and 4.7% compared to the cases of using the optimized six-speed and nine-speed manual transmissions, respectively. [ABSTRACT FROM AUTHOR]

This study investigates the determinants of applications for U.S. disability benefits between 1986 and 1993 using a semiparametric discrete factor procedure separately for men and women. Approximating a dynamic optimization model, the estimation accounts for a variety of potential biases that were unaddressed in prior studies. The authors' results indicate different responses of men and women to variations in policy measures. Past labor earnings and fringe benefits as well as benefit eligibility and benefit amounts clearly affect application behavior. [ABSTRACT FROM AUTHOR]