Eighty-seven male Sprague-Dawley rats (245-300 g) were randomly assigned to one of two experimental groups. The first group consumed a diet high in fat and low in carbohydrate (LCD), whereas the second group ate a normal diet (ND). After either 1 or 5 wk on the diets, rats from each group were killed either before or after an exhausting run on a rodent treadmill (35 m X min-1, 0% grade). The LCD animals ran significantly longer before exhaustion at both week 1 (44.9 +/- 5.1 vs. 41.6 +/- 4.2 min) and week 5 (47.1 +/- 3.6 vs. 35.5 +/- 3.1 min) (P less than 0.05). Adaptations to the LCD included lower muscle and liver glycogen content, decreased rate of glycogen breakdown during exercise, decreased lactate production, and elevated blood ketone levels. In addition to these substrate changes, the LCD caused increased enzyme activities of muscular 3-hydroxyacyl-CoA dehydrogenase (35-110%) and citrate synthase (15-20%). These data indicate that rats exposed to a high-fat diet are capable of prolonged intense exercise in spite of limited glycogen stores. This improved capacity for exercise appears to be partially the result of muscular adaptations to the diet, which apparently increase the ability to oxidize fat and concomitantly spare glycogen.

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Adaptive control charts allow the components of the quality-monitoring scheme to vary in order to obtain improved performance over non-adaptive control charts. Research has centered on components such as the sample size, time between samples, warning limits, and control limits and has recommended a variety of schemes, many of which are optimal in some sense. In practice, there are many other adaptive schemes that are near optimal, which will still yield considerable improvement over non-adaptive control charts. In addition, the impact of parameter estimation on adaptive control chart performance must be taken into consideration. Based on the simulation results shown here, adaptive control charts should only be used for mature processes, where a sufficient amount of Phase I data have been obtained to ensure that the estimated control limits are accurate. When evaluating control chart performance, we consider initial state performance measures for simplicity and note that the conclusions obtained here apply to steady-state performance measures. The evaluation of performance measures is easily handled by the Markov chain approach detailed in the Appendix.

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