The incidence of suicide attempts and completions during academically gifted students' attendance at state-supported, residential high schools was the subject of this study. School personnel's perceptions of this environment, roles in identifying students at-risk for attempting suicide, and knowledge about suicide were also studied. Five schools participated in this study. Initial interviews were conducted with each school's director, measuring the incidence of suicide utilizing an adaptation of a survey developed by M. Hayes and R. Sloat (1990). Questionnaires distributed to personnel included questions about knowledge about the school's history concerning suicide, perceptions of the environment, and knowledge about suicide. The adaptation of Hayes and Stoat's survey was included in questionnaire materials, along with the School-Level Environment Questionnaire (Rentoul & Fraser, 1983) and 11 true/false statements measuring knowledge of suicide. A total of 83 questionnaires were completed. Telephone interviews were conducted with a random sample of 21 personnel who completed the questionnaire. These interviews consisted of open-ended questions measuring the environment of these schools and perceived roles of personnel in identifying students at-risk for suicide. Descriptive and qualitative analyses were conducted. Ten attempts and two completions occurred among 4899 students. Suicide attempts and completions were measured to be .20% and .04% respectively. These findings indicate that these schools need to be prepared in order to prevent suicide among this population. The environment of these schools was one of affiliation, autonomy, and innovativeness. However, affiliation predominately existed within specific departments and/or divisions. Personnel characterized their roles in suicide prevention as unofficial because of lack of time, academic priorities, and other individuals who were better equipped to respond to students. Even though personnel considered their roles unofficial, they attempted to meet students' emotional needs through support, observation, and involvement. Knowledge of suicide was observed in the areas of incidence of suicide among adolescents, importance of warning signs, factors increasing risk, and need for intervention. Respondents were less knowledgeable about the rate of suicide among adolescents, factors not increasing risk, and myths about suicide. / Department of Educational Psychology

This study revolves around operators defined by conditional expectations and operators generated by random measures. Studies of operators in function spaces defined by conditional expectations first appeared in the mid 1950's by S-T.C. Moy [22] and S. Sidak [26]. N. Kalton studied them in the setting of Lp-spaces 0 < p < 1 in [15, 131 and in L1-spaces, [14], while W. Arveson [5] studied them in L2-spaces. Their averaging properties were studied by P.G. Dodds and C.B. Huijsmans and B. de Pagter in [7] and C.B. Huijsmans and B. de Pagter in [lo]. A. Lambert [17] studied their relationship with multiplication operators in C*-modules. It was shown by J.J. Grobler and B. de Pagter [8] that partial integral operators that were studied A.S. Kalitvin et a1 in [2, 4, 3, 11, 121 and the special cases of kernel operators that were, inter alia, studied by A.R. Schep in [25] were special cases of conditional expectation operators. On the other hand, operators generated by random measures or pseudo-integral operators were studied by A. Sourour [28, 271 and L.W. Weis [29,30], building on the studies of W. Arveson [5] and N. Kalton [14, 151, in the late 1970's and early 1980's. In this thesis we extend the work of J.J. Grobler and B. de Pagter [8] on Multiplication Conditional Expectation-representable (MCE-representable) operators. We also generalize the result of A. Sourour [27] and show that order continuous linear maps between ideals of almost everywhere finite measurable functions on u-finite measure spaces are MCE-representable. This fact enables us to easily deduce that sums and compositions of MCE-representable operators are again MCE-representable operators. We also show that operators generated by random measures are MCE-representable. The first chapter gathers the definitions and introduces notions and concepts that are used throughout. In particular, we introduce Riesz spaces and operators therein, Riesz and Boolean homomorphisms, conditional expectation operators, kernel and absolute T-kernel operators. In Chapter 2 we look at MCE-operators where we give a definition different from that given by J.J. Grobler and B. de Pagter in [8], but which we show to be equivalent. Chapter 3 involves random measures and operators generated by random measures. We solve the problem (positively) that was posed by A. Sourour in [28] about the relationship of the lattice properties of operators generated by random measures and the lattice properties of their generating random measures. We show that the total variation of a random signed measure representing an order bounded operator T, it being the difference of two random measures, is again a random measure and represents ITI. We also show that the set of all operators generated by a random measure is a band in the Riesz space of all order bounded operators. In Chapter 4 we investigate the relationship between operators generated by random measures and MCE-representable operators. It was shown by A. Sourour in [28, 271 that every order bounded order continuous linear operator acting between ideals of almost everywhere measurable functions is generated by a random measure, provided that the measure spaces involved are standard measure spaces. We prove an analogue of this theorem for the general case where the underlying measure spaces are a-finite. We also, in this general setting, prove that every order continuous linear operator is MCE-representable. This rather surprising result enables us to easily show that sums, products and compositions of MCE-representable operator are again MCE-representable. Key words: Riesz spaces, conditional expectations, multiplication conditional expectation-representable operators, random measures. / Thesis (Ph.D. (Mathematics))--North-West University, Potchefstroom Campus, 2004.

Thesis (Ph.D. (Chartered Accountancy))--North-West University, Potchefstroom Campus, 2009.

Thesis (Ph.D. (Chartered Accountancy))--North-West University, Potchefstroom Campus, 2009.

This note examines the behavior of a competitive firm that faces joint price and inflation risk. Given that the price risk is negatively correlated with the inflation risk in the sense of expectation dependence, the firm optimally opts for an over-hedge if the firm's coefficient of relative risk aversion is everywhere no greater than unity. Furthermore, banning the firm from forward trading may induce the firm to produce more or less, depending on whether the price risk premium is positive or negative, respectively. While the price risk premium is unambiguously negative in the absence of the inflation risk, it is not the case when the inflation risk prevails. In contrast to the conventional wisdom, forward hedging needs not always promote production should firms take in inflation seriously.

In pattern recognition, when data has different structures in different parts of the input space, fitting one global model can be slow and inaccurate. Learning methods can quickly learn the structure of the data in local regions, consequently, offering faster and more accurate model fitting. Breaking training data set into smaller subsets may lead to curse of dimensionality problem, as a training sample subset may not be enough for estimating the required set of parameters for the submodels. Increasing the size of training data may not be at hand in many situations. Interestingly, the data in local regions becomes more correlated. Therefore, by decorrelation methods we can reduce data dimensions and hence the number of parameters. In other words, we can find uncorrelated low dimensional subspaces that capture most of the data variability. The current subspace modelling methods have proved better performance than the global modelling methods for the given type of training data structure. Nevertheless these methods still need more research work as they are suffering from two limitations 2 There is no standard method to specify the optimal number of subspaces. &sup2 / There is no standard method to specify the optimal dimensionality for each subspace. In the current models these two parameters are determined beforehand. In this dissertation we propose and test algorithms that try to find a suboptimal number of principal subspaces and a suboptimal dimensionality for each principal subspaces automatically.

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As automated technologies continue to advance, they will be perceived more as collaborative team members and less as simply helpful machines. Expectations of the likely performance of others play an important role in how their actual performance is judged (Stephan, 1985). Although user expectations have been expounded as important for human-automation interaction, this factor has not been systematically investigated. The purpose of the current study was to examine the effect older and younger adults expectations of likely automation performance have on human-automation interaction. In addition, this study investigated the effect of different automation errors (false alarms and misses) on dependence, reliance, compliance, and trust in an automated system. Findings suggest that expectancy effects are relatively short lived, significantly affecting reliance and compliance only through the first experimental block. The effects of type of automation error indicate that participants in a false alarm condition increase reliance and decrease compliance while participants in a miss condition do not change their behavior. The results are important because expectancies must be considered when designing training for human-automation interaction. In addition, understanding the effects of type of automation errors is crucial for the design of automated systems. For example, if the automation is designed for diverse and dynamic environments where automation performance may fluctuate, then a deeper understanding of automation functioning may be needed by users.

Identifying and locating specific objects amidst irrelevant, distracting items can be difficult when one is unsure of where, or even what, to look for. Priming the perceptual/cognitive system for specific features or objects is one way of helping observers to locate and identify target items (e.g., Grice&Gwynne, 1985; Laarni and Hakkinen, 1994). Past research has demonstrated that priming single features does indeed affect search performance (e.g., Hailston&Davis, 2006; Huang&Pashler, 2005). But, what happens when more than one feature is primed? Does priming two features result in better performance than priming only one? What about three features? How does feature priming compare to simply priming the entire object itself? The current research addressed these questions with a series of three visual search experiments. In the first experiment performance in simple feature search was compared against triple-conjunction search performance. Three prominent models of visual search were compared to see which best predicted actual performance. In the second and third experiments the effects of multiple feature priming on search accuracy were examined in a triple-conjunction search (Experiment 2) and a whole-object search (Experiment 3). Moreover, in Experiment 3 the effectiveness of whole-object primes were compared to multiple-features primes. Results show that none of the three models can accurately predict performance in all cases, suggesting some modification of each is necessary. Furthermore, valid primes resulted in performance benefits, and these benefits increased with the number of primed features. Finally, no performance costs of invalid priming were observed in the current experiments.

"Strong situations" have been shown to decrease behavioral variability, thereby attenuating the criterion-related validity of non-ability individual differences for criteria such as job performance (Barrick&Mount, 1993; Meyer, Dalal,&Bonaccio, 2009). However, it has been suggested that individuals, based on individual differences in implicit motives, may impute discrepant psychological meaning to social stimuli like situational strength--a process sometimes known as differential framing (James&McIntyre, 1996). If different psychological interpretations are attached to strong situation stimuli (e.g., Meyer, Dalal,&Hermida, 2010), an interesting behavioral "double-edged sword" is possible. On the one hand, behaviors pertinent to "primary criteria" (i.e., criteria for which external situational influences and pressures lead to targeted behavioral homogeneity) may occur among those who would not normally engage in them. But, at the same time, behaviors pertinent to "secondary criteria" (i.e., unintended, unforeseen, and potentially reactionary behaviors and/or attitudes) might also increase for some individuals (i.e., those with certain implicit motive characteristics). In other words, high situational strength may simultaneously constrain behavioral variability in primary criteria while serving as a stimulus for differential framing, thereby expanding variability on secondary criteria. The purpose of the present dissertation was twofold: 1) to explore the degree to which situational strength is differentially framed, and 2) to ascertain how the differential framing of situational strength may lead to unintended secondary outcomes. Study 1 findings indicate that, to a partial extent, situational strength is differentially framed by individuals with different implicit motives. Study 2 findings are largely consistent with extant situational strength theory, though partially inconsistent with study predictions.