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Nevill, A, 203 Newburg, D, 290, 293 Newcomer, R, 409, 669 Newell, A, 185, 190, 193, 214, 256 Newell, K M, 171, 172, 224, 227, 231, 248, 333, 435 Newman, A B, 599 Newman, B, 584 Newman, D, 353 Newsholme, E, 624 Newsom, J, 415, 513, 517 Newton, M, 7, 8, 76, 129 Newton, R L J, 528 Neyer, M, 317 Niaura, R S, 524, 548, 549 Nicholls, A R, 290, 603 Nicholls, J G, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 20, 21, 22, 23, 76, 567, 568, 647, 670, 675, 676, 686, 689, 706, 743, 767 Nichols, J F, 514 Nideffer, R M, 288, 294, 381, 394 Niefer, C B, 651 Nieman, D C, 499 Nietfeld, J L, 169 Nigg, C.

In support of this nding, an examination of the uctuation of symptoms and outcome of CFS over time (Hill, Tiersky, Scavalla, & Natelson, 1999) found that mode of illness onset was not predictive of positive or negative illness outcomes Contrary to these ndings, however, Levine (1997) has found that individuals with sudden onset have a better prognosis than those with gradual onset In a random community sample, Jason, Taylor, Kennedy, Song, et al (2000) found that individuals with sudden CFS onset were signi cantly more likely to experience more severe sore throat pain and more severe fatigue following exercise One possible explanation for increased severity of these two symptoms in the sudden onset group may involve an increased likelihood of viral etiology in this subgroup of participants Individuals with sudden onset were also more likely to experience lifetime psychiatric diagnosis.

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The presence of a stressful life event preceding or precipitating onset of CFS is another factor that has been.

In this case, for example in the transition from state S2 to state S3 , the probability that b3 = 1 is given by the probability that this state transition is one of the four possible trellis transitions for which b3 = 1. Then the probability that b3 = 1 is the addition of all the probabilities associated with the information bit 1. In general the estimation of the information bit bi is done over the trellis transition with which the bit is associated, de ned from state Si 1 to state Si . Equation (60) is then written as L(bi /Y) = ln = ln

, 320, 355, 514, 524, 538, 551, 553, 599 Nigorikawa, T, 387 Niino, N, 498 Ninot, G, 5, 567, 720, 722, 729 Nisbett, R E, 76, 426 Nissen, M J, 255 Nissinen, A, 495 Nitsch, J R, 332, 333, 334 Nitschke, J B, 103 Nixon, H L, 850, 853 Noble, B, 560, 561, 562, 564, 565, 567, 570 Noble, J M, 541, 560 Noblet, A J, 848 Noh, Y E, 383, 391 Nohre, L, 865 Nolen, S B, 6, 7 Nolen-Hoeksema, S, 369 Norcross, J C, 320, 548 Nordell, K, 296 Nordheim, L, 484 Nordin, K, 587, 588 Noreen, E W, 749 Norlander, T, 299 Norman, P, 353, 354, 542 Norman, R M G, 514 Norris, D M, 324 Norris, R, 600 Norsworthy, G.

So how do you take advantage of client processing First you must understand what you can and can t do using client processing, as detailed in Table 11-1.

Usage Matrix For a single site system, these applications are local and will have the usage matrix depicted in Figure 2.11. As can be seen, the usage matrix is a twodimensional matrix that indicates whether or not an attribute (column) is used by an application. The cell in position (APi, Cj) is set to 1 if application APi accesses column Cj, otherwise it is set to 0. Observation 1: The usage matrix only indicates if a column is used by an application. However, the matrix does not show how many times an application accesses the table columns during a given time period. The access frequency is a term that represents how many times an application runs in a given period of time. The period of this measurement can be an hour, a day, a week, a month, and so on as decided by the designer. Figure 2.12 depicts the expansion of the usage matrix to include the access frequency of each application.

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