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consciousness and reinvestment scales) will be most likely to choke under pressure Trait Anxiety In the academic test anxiety literature, a number of studies have demonstrated that individuals with high levels of trait anxiety are especially vulnerable to the detrimental impact of stressful situations (Eysenck, 1992) Do the same sort of effects apply in sporting tasks under pressure Recent work by Wang et al (2004) suggests that they do Individuals who reported higher levels of trait anxiety (assessed using the Sport Anxiety Scale; R E Smith, Smoll, & Shutz, 1990) performed more poorly on a well-learned basketball task under pressure than those who did not Furthermore, this effect was magnified for highly trait-anxious athletes who were also high in selfconsciousness (see previous section) Murray and Janelle (2003) also found that individuals higher in trait anxiety are more susceptible to stressinduced performance decrements than their low-anxious counterparts.

(2.132)

q(x|x) . r(x) (10.140)

Even supposing reliable and valid assessments of phenotypic personality traits, a routine search for indicators of, for example, conscientiousness would require enormous samples just for tracing additive polygenetic effects; for interactions, the required sizes would rise exponentially (for a discussion of strategies of molecular-genetic research on personality, see Plomin & Caspi, 1998) At the turn of the century, attempts to trace genetic polymorphisms that explain personality showed the familiar picture of high initial expectations followed by failing replications (eg, Herbst, Zonderman, McCrae, & Costa, 2000) According to a possibly more feasible scenario, large principal components of personality traits may be expected to reappear as an aggregate result of studies searching for single genes to explain speci c patterns of deviant behavior (see, eg, Brunner, Nelen, Break eld, Roppers, & Van Oost, 1993).

In one study, participants performed a simulated driving task and a secondary visual search task (requiring that participants respond as quickly as possible to visual cues presented in either central or peripheral vision) under baseline and competition conditions Although driving performance did not significantly change as a function of condition, response times showed a different pattern of results In competition, response times were reduced for the low-anxious group but increased for the high-anxious group in comparison to the baseline condition Murray and Janelle suggest that those with higher dispositional levels of anxiety may not be as efficient in processing information under stress It should be noted, however, that this inefficient search strategy under stress is not necessarily limited to those high in trait anxiety Janelle (2002) suggests that anxiety in general may alter visual search and gaze behavior, resulting in inefficient and ineffective search strategies.

Quality of life (2 items); depression (HAD); anxiety (HAD); mental adjustment (MAC)

One reason individuals high in trait anxiety may perform differently under pressure in comparison to their lowanxious counterparts is that low- and high-anxious individuals appear to interpret pressure in fundamentally different ways Giacobbi and Weinberg (2000) examined the coping responses of low and high trait-anxious athletes They found that in response to stressful situations, high trait-anxious athletes used different and often nonproductive coping behaviors (eg, self-blame) in comparison to low trait-anxious athletes Thus, individuals high in trait anxiety may actually view pressure differently than low.

^ Bilinear: For all P1 2 G1 , Q1 2 G01 , and a; b 2 Z , e aP1 ; bQ1 q ^ e P1 ; Q1 ab . ^ Nondegenerate: There exist P1 2 G1 and Q1 2 G01 such that e P1 ; Q1 6 1. ^ Computable: There is an ef cient algorithm to compute e P1 ; Q1 for any P1 2 G1 , Q1 2 G01 .

has two accounts. This means that the largest relation in the database is the Account relation, with 1000 tuples. We also know that the Branch relation has 100 tuples. Based on these statistics, Site 1 stores much more information than Site 2 does. Applying the rule above, we choose to access the Customer and the Account relations at Site 1 and to access the Branch relation at Site 2. Once we have decided where each relation is going to be accessed, we use the site ID as the subscript of the relation as shown in Figure 4.32. Phase 3: Function Shipping. In this step, we will decide where each operation of the query tree will run. Since there is only one completely local operation in the query tree (the select on the Branch relation), we must perform the SL at Site 2 (where the Branch relation is stored). The results of the select are stored at Site 2 as well. Phase 4: Data Shipping for Binary Operations. Since there are three operations in the query and each operation can run at either of the two sites, we will have eight different alternatives to perform the operations of this query as indicated in Figure 4.33. In each tree, each operation has a subscript indicating the Site ID where the operation

This chapter provides only a table of the available functions (see Table 7-4). They are more fully explained as you use them in the rest of the book. Table 7-4 The Functions Within Mail::Gmail::Webmail

Figure 14.5 SOAP distributed application using a caching intermediary [24]. [Copyright # 2002 W3C (MIT, INRIA, Keio University), all rights reserved.]