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Items should not re ect only the most extreme manifestations of the trait If one assumes that there is meaningful dimensional variability on most constructs of interest in personality and psychopathology, then it is important to have items that make discriminations at various points on this dimension This fact was described earlier as pertaining to the depth aspect of content validity: Items should sample across the full range of phenomena associated with this dimensional variability Perhaps the best example appears on ability or intelligence tests, in which questions are typically ordered across different levels of item dif culty Assuming reasonable validity for the items, this variability of dif culty translates into items that have the capacity to make distinctions at differing levels of ability; so-called easy items distinguish those with low levels of ability from others, whereas so-called dif cult items discriminate at higher levels of ability.

how to write pdf file in asp.net c#

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The same logic can be applied to scales measuring personality or psychopathology; items should capture the less extreme as well as the more.

navigation in Flash movies by letting users use the Forward and Back buttons in their browsers to jump from anchor to anchor. See Using named anchors on page 33.

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In each test series we varied the parameters n (number of vertices) and r (number of services) as follows: n {50, 100}, r {10, 20, 50, 100, 250}. For each of the 10 resulting parameter combinations we generated 20 random problem instances with the same number of vertices and services i.e., a total of 200 instances in each series for which we computed the following values: TS: number of colors calculated for the best ensemble assignment found by TS GFF: number of colors calculated for a GFF ensemble assignment SFF: number of colors calculated for a SFF ensemble assignment BEST: min{TS, GFF, SFF} S: lower bound on the clique packing number, using the generalized Carraghan/ Pardalos algorithm [4] with the sum estimate for the packing number [10]. Both the SFF and GFF algorithms were invoked using a service order by decreasing sizes. Coloring was always done using the saturation algorithm [2]. In the case of SFF and GFF, we furthermore employed an iterated version of the saturation algorithm with 100 runs per coloring. Thus, especially for GFF, we can expect slightly better results than given in [10]. For the TS algorithm, we employed the termination criterion discussed earlier, that is, the search was terminated after a prescribed number N of iterations without an improvement on the best known solution. However, the number of iterations was chosen in relation to the size of the problem instance, using the following heuristic formula: N= where D is the density of the graph, D= 2|E| |V |(|V | 1)

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1.3 HANDOFF INITIATION A hard handoff occurs when the old connection is broken before a new connection is activated. The performance evaluation of a hard handoff is based on various initiation criteria [1, 3, 13]. It is assumed that the signal is averaged over time, so that rapid fluctuations due to the multipath nature of the radio environment can be eliminated. Numerous studies have been done to determine the shape as well as the length of the averaging window and the older measurements may be unreliable. Figure 1.2 shows a MS moving from one BS (BS1) to another (BS2). The mean signal strength of BS1 decreases as the MS moves away from it. Similarly, the mean signal strength of BS2 increases as the MS approaches it. This figure is used to explain various approaches described in the following subsection. 1.3.1 Relative Signal Strength This method selects the strongest received BS at all times. The decision is based on a mean measurement of the received signal. In Figure 1.2, the handoff would occur at position A. This method is observed to provoke too many unnecessary handoffs, even when the signal of the current BS is still at an acceptable level. 1.3.2 Relative Signal Strength with Threshold This method allows a MS to hand off only if the current signal is sufficiently weak (less than threshold) and the other is the stronger of the two. The effect of the threshold depends

9.3 Degradation of groundwater quality 9.3.1 Causes of groundwater quality degradation A bewildering array of human activities can degrade the quality of natural groundwaters (Figure 9.1). It is possible to categorize groundwater pollution sources in a number of ways. For instance the US Congress Of ce of Technology Assessment developed a complex classi cation system for groundwater pollution sources, based

Uop1(Uop2(R)) Uop2(Uop1(R))

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