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One of the more common topologies of FDDI is the counterrotating ring Here two classes of station may be de ned: dual (attachment) and single (attachment) FDDI trunk rings may be composed only of dual-attachment stations which have two PMD (physical layer medium dependent) entities (and associated PHY entities) to accommodate the dual ring Concentrators provide additional PMD entities beyond those required for their own attachment to the FDDI network, for the attachment of single-attachment stations which have only one PMD and thus cannot directly attach to the FDDI trunk ring A dual-attachment station, or one-half of it, may be substituted for a single-attachment station in attaching to a concentrator The FDDI network consists of all attached stations The example illustrated in Figure 1118 shows the concept of multiple physical connections used to create logical rings.

As shown in the gure, the logical sequence of MAC connections is stations, 1, 3, 5, 8, 9, 10, and 11 Station 2, 3, 4, and 6 form an FDDI trunk ring Stations 1, 5, 7, 10, and 11 are attached to the ring by lobes branching out from stations that form it Stations 8 and 9, in turn, are attached by lobes branching out from station 7 Stations 2, 4, 6, and 7 are concentrators, serving as the means of attaching MAC entities and station functionality The concentrator examples of Figure 1118 do not show any MACs, although their presence is implied by the designation of these concentrators as stations Symbol set A symbol is the smallest signaling element used by the FDDI MAC Symbols can be used to convey three types of information:.

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MSIE 5.0/Win98: No NN 4.0/Win95: No NN 4.7/Win98: No MSIE 3.0/Mac OS: No MSIE 4.0/Mac OS: No NN 4.0/Mac OS: No EXAMPLE TIP

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In this procedure, participants list their beliefs about the attributes of the attitude object, and they rate the desirability of each attribute An overall index of attitude is then obtained by summing the desirability ratings The thoughtlisting procedure does not require probability ratings because it elicits attributes that participants perceive as being highly associated with the attitude object (Esses, Haddock, & Zanna, 1993; Esses & Zanna, 1995) Evidence Research has examined the utility of the expectancy-value model by testing whether people s reports of their own attitudes are correlated with the summed products of the attitude-relevant expectancies and values Results indicate that there are at least moderate correlations between attitudes and the expectancy-value products (eg, Budd, 1986; van der Pligt & de Vries, 1998), although there have been statistical and methodological criticisms of these ndings (eg, Bagozzi, 1984; Sparks, Hedderley, & Shepherd, 1991).

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This chapter has presented our overview of currently available plug-ins and add-ons that you may want to include in your MythTV system build. In subsequent chapters, we give more thorough treatment to what we believe are the most useful and popular MythTV plug-ins.

Figure 9.16. Distribution of prediction error for testing data (width is 120).

if i2 is not equal to zero for one or more blocks. This idea is illustrated in Figure 1.5, where the total sum of squares and degrees of freedom for the two designs are partitioned. The F statistic that is used to test the null hypothesis can be thought of as a ratio of error and treatment effects, F= f (error effects) + f (treatment effects) f (error effects)

To test the expectancy-value model directly, it is necessary to examine experimentally the causal impact of beliefs and evaluations on attitudes Fortunately, studies of persuasion have yielded some support for the idea that persuasive messages in uence evaluative beliefs, which in uence attitudes (eg, Maio, Bell, et al, 1996), although this effect may occur only when people are motivated and able to process persuasive messages in a systematic manner (Chaiken, Liberman, & Eagly, 1989; Petty & Cacioppo, 1986; E P Thompson, Kruglanski, & Spiegel, 2000) Reconciling the Three-Component and Belief-Based Models The comprehensiveness of the expectancy-value model is challenged by the ndings noted earlier that affect and past behavior predict attitudes independently of beliefs This.

fact that although very popular documents tend to remain popular, their relative ranking does change over time However, as we choose more documents, we may also include some less popular documents Since these documents are less likely to remain popular over time, the temporal overlap decreases This phenomenon was also observed at a popular news site for wireline clients [26] 3 We observe that the document overlap between pairs of weekdays is generally higher than the overlap between a weekend day and a weekday; the overlap between two weekend days is even higher This observation is consistent with our intuition, and suggests that we should use past weekday (weekend) workload to predict future weekday (weekend) workload We also examine the requests coming from wireless users only, and observe similar results As before, the set of popular queries remains stable over time.

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