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The effect of this is that we look for solutions in an ordered way, starting at the top Once an appropriate solution is found, we have no need to look at the lower levels This type of structure very naturally allows for the inclusion of default rules, which can reside at the lowest levels of the structure It also has an inherent mechanism for evolving by adding levels above the information we want to discard An important issue related to the use of the HPS structure is the learning of the model itself This involves determination of the content of the rules as well the determination of the level at which a rule shall appear As in all knowledge-based systems, learning can occur in many different ways One extreme is that of being told the knowledge by some (usually human) expert.

Combined with medical and surgical treatment, comprehensive cardiac rehabilitation is shown to improve outcomes for coronary heart disease patients including the elderly and women (Eagle et al, 1999) The American Heart Association s recommendations for comprehensive risk reduction involve complete cessation of smoking, lipid management through drug treatment, and a diet low in saturated fats, physical activity a minimum of 30 minutes three times a week, weight management, blood pressure control through diet, reduced alcohol intake, sodium restriction, and estrogen replacement therapy for postmenopausal women (Smith et al, 1995) Evidence supports that these more moderate lifestyle changes correlate with less disease progression (Gibbons et al, 1999) Exercise training is often the core of a cardiac rehabilitation program, since physical inactivity is an independent risk factor for CHD Aerobic exercise increases exercise.

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At the other extreme is the situation in which we are provided only with input output observations and we must use these to generate the rules Many cases lie between these extremes Here we shall discuss one type of learning mechanism associated with the HPS structure that lies between these two extremes, called the DELTA method In this we initialize the HPS with expert-provided default rules and then use input output.

deprivation (Rechtschaffen et al, 1983) In humans, there is little evidence that total sleep loss, even for several days, produces any permanent or severe physical or psychological dysfunction (Horne, 1986) The most prominent effect of sleep deprivation is an increased feeling of sleepiness and desire for sleep After one or two nights without sleep, most individuals will show microsleep episodes intruding into wakefulness, which will produce lapses of attention These cognitive impairments are found mainly on tasks requiring sustained attention and rapid reaction time Processes involved in safe and vigilant driving are particularly sensitive to sleep deprivation Executive functions such as judgment, creativity, and mental exibility are also altered after prolonged sleep loss (Horne, 1988; Johnson, 1982; Parkes, 1985) Changes in mood have been noted after as little as one night of total sleep deprivation Individuals tend to be more irritable, and show less motivation, interest, and initiative.

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26.6 PERFORMANCE ANALYSIS We develop a simple model to analyze the performance of the proposed cache management scheme. Specifically, we want to estimate the miss probability and mean query delay for the proposed scheme. For the purposes of analysis, we consider the performance in a single cell (as mobility is assumed to be transparently handled) with one MSS and N mobile hosts. We make the following assumptions: Total number of data items is M, each of size ba bits. The queries generated by a sleeping MH (i.e., when the MH is disconnected from the MSS) are lost. A single wireless channel of bandwidth W is assumed for all transmissions taking place in the cell. All messages are queued to access the wireless channel and serviced according to the FCFS (first come first served) scheduling policy. Queries are of size bq bits and invalidations are of size bi bits. Software overheads are ignored. Modeling Query-Update Pattern. The time between updates to any data item is assumed to follow an exponential distribution with mean 1/ . Each MH generates a query according to a Poisson distribution with mean rate of . These queries are uniformly distributed over all data items in the database. The query-update model is shown in Figure 26.6.

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