Although literature has extensively documented the stereotypes of developing learning communities in schools through book clubs— especially to improve literacy— little is revealed about varied indicators of improvements, such as student self-identification, authentic dialogues, and transforming small groups into learning communities. In this respect, formal efforts on adopting book clubs to improve literacy in Saudi Arabia are simply absent. This thesis presents research findings that seek to explore the effect of book clubs on improving literacy and building a learning community among seventh-grade, under-performing students in Canada. This thesis is contextualized through a thorough review of related literature and discussion of findings from classroom observations, and students’ interviews. The completion of this thesis indicates positive, causal relationships between using a book club as a learning tool and building a learning community, thus improving literacy. The research concludes with implications for using book clubs in Saudi Arabia.

Equi-caloric bouts of interval (INT: 5x 2:2 min at 85 and 40% VO2max) and continuous (21 minutes at 60% VO2max) exercise were performed by 13 older prehypertensive males on separate days, at equivalent times of day, to assess the influence of exercise mode on post-exercise hypotension (PEH). Cardiovascular measures were collected for 30 min pre and 60 min post-exercise. PEH as measured by mean post-exercise systolic blood pressure (SBP) decrease, area under the SBP curve, and minimum SBP achieved, was equivalent after both conditions. SV was significantly reduced and HR was significantly elevated post-exercise after both conditions. No significant reductions in CO or TPR were observed. INT exercise provided a larger perturbation to the autonomic nervous system as measured by Baroreflex sensitivity and Heart Rate Variability. The responses elicited by acute INT exercise, with repeated exposure, may lead to greater improvements in blood pressure regulation than those associated with continuous aerobic training.

This study examines the effectiveness of a community of learning, through a book club, on student performance for students underachieving in literacy. This first chapter introduces the study, the researcher and how they are situated within the research, and the context and rationale of the study. The second and third chapters detail current research in literacy, learning, and communities of learning. They detail the methodological approach and rationale. The fourth and fifth chapters explain what took place during the study, what it means, and why this is important for teachers and teacher practice. Despite its limitations, this study finds that communities of learning, established through a book club, positively affect both academic and social performance. Book clubs create interconnectedness between its members that increases student engagement, which increases the amount of authentic dialogue. From this, book club members collaboratively co-construct knowledge resulting in general improvement, both academically and socially.

Although literature has extensively documented the stereotypes of developing learning communities in schools through book clubs— especially to improve literacy— little is revealed about varied indicators of improvements, such as student self-identification, authentic dialogues, and transforming small groups into learning communities. In this respect, formal efforts on adopting book clubs to improve literacy in Saudi Arabia are simply absent. This thesis presents research findings that seek to explore the effect of book clubs on improving literacy and building a learning community among seventh-grade, under-performing students in Canada. This thesis is contextualized through a thorough review of related literature and discussion of findings from classroom observations, and students’ interviews. The completion of this thesis indicates positive, causal relationships between using a book club as a learning tool and building a learning community, thus improving literacy. The research concludes with implications for using book clubs in Saudi Arabia.

Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for sequential decision-making under uncertainty. MDPs provide a generic framework that can be applied in various domains to compute optimal policies. This thesis presents techniques that offer explanations of optimal policies for MDPs and then refine decision theoretic models (Bayesian networks and MDPs) based on feedback from experts. Explaining policies for sequential decision-making problems is difficult due to the presence of stochastic effects, multiple possibly competing objectives and long-range effects of actions. However, explanations are needed to assist experts in validating that the policy is correct and to help users in developing trust in the choices recommended by the policy. A set of domain-independent templates to justify a policy recommendation is presented along with a process to identify the minimum possible number of templates that need to be populated to completely justify the policy. The rejection of an explanation by a domain expert indicates a deficiency in the model which led to the generation of the rejected policy. Techniques to refine the model parameters such that the optimal policy calculated using the refined parameters would conform with the expert feedback are presented in this thesis. The expert feedback is translated into constraints on the model parameters that are used during refinement. These constraints are non-convex for both Bayesian networks and MDPs. For Bayesian networks, the refinement approach is based on Gibbs sampling and stochastic hill climbing, and it learns a model that obeys expert constraints. For MDPs, the parameter space is partitioned such that alternating linear optimization can be applied to learn model parameters that lead to a policy in accordance with expert feedback. In practice, the state space of MDPs can often be very large, which can be an issue for real-world problems. Factored MDPs are often used to deal with this issue. In Factored MDPs, state variables represent the state space and dynamic Bayesian networks model the transition functions. This helps to avoid the exponential growth in the state space associated with large and complex problems. The approaches for explanation and refinement presented in this thesis are also extended for the factored case to demonstrate their use in real-world applications. The domains of course advising to undergraduate students, assisted hand-washing for people with dementia and diagnostics for manufacturing are used to present empirical evaluations.

Planning in partially observable domains is a notoriously difficult problem. However, in many real-world scenarios, planning can be simplified by decomposing the task into a hierarchy of smaller planning problems which, can then be solved independently of one another. Several approaches, mainly dealing with fully observable domains, have been proposed to optimize a plan that decomposes according to a hierarchy specified a priori. Some researchers have also proposed to discover hierarchies in fully observable domains. In this thesis, we investigate the problem of automatically discovering planning hierarchies in partially observable domains. The main advantage of discovering hierarchies is that, for a plan of a fixed size, hierarchical plans can be more expressive than non-hierarchical ones. Our solution frames the discovery and optimization of a hierarchical policy as a non-convex optimization problem. By encoding the hierarchical structure as variables of the optimization problem, we can automatically discover a hierarchy. Successfully solving the optimization problem therefore yields an optimal hierarchy and an optimal policy. We describe several techniques to solve the optimization problem. Namely, we provide results using general non-linear solvers, mixed-integer linear and non-linear solvers or a form of bounded hierarchical policy iteration. Our method is flexible enough to allow any parts of the hierarchy to be specified based on prior knowledge while letting the optimization discover the unknown parts. It can also discover hierarchical policies, including recursive policies, that are more compact (potentially infinitely fewer parameters).

This paper investigates whether greater investor distraction on the Swedish stock market during the summer months of June, July and August leads to a more pronounced post earnings announcement drift (PEAD) effect, during the ten year period between 2000 and 2009. PEAD is an anomaly whereby the information contained in earnings announcements is not immediately or completely incorporated into stock prices, in the cases where the announcement contains an ‘earnings surprise’. The methodology involves using the standardised unexpected earnings (SUE) metric to measure the level of ‘earnings surprise’ and a buy and hold abnormal returns (BHAR) trading strategy to measure return. The study tests and confirms the existence of greater investor distraction during summer months on the Swedish market. For a holding period of 12 months, a BHAR trading strategy generates a greater abnormal return for summer months (11.3%) compared with the abnormal return for non-summer months (10.5%). These results are also interesting in a broader context, as they confirm the existence of the PEAD effect, one of the strongest counter-arguments to the efficient markets hypothesis (EMH); the foundation of many financial models used for stock market valuation. This is because, according to the EMH, in an efficient market it should not be possible to generate abnormal returns based on available information. However, it may be noted that these results do not take into account transaction costs. This means that while it can be demonstrated that there is greater investor distraction during the Swedish summer, in order to implement a successful trading strategy based on this finding, further testing would be required. Therefore, based on the findings of this paper, a number of areas for future research have been identified.

After the complete liberalization of the airline industry during the 1990s the industry has faced a rapid growth in passenger numbers. This has mainly been caused by the emergence of so-called Low Cost Carrier (LCC) that offer a simplified product (i.e. point-to-point flights without any frills) at a lower cost than traditional Network Carriers. Furthermore LCC also introduced a less differentiated pricing structure (Restriction Free Pricing) which forced competing network carriers to reduce the degree of price discrimination which they were able to practice until then in order to defend their market shares. This has led to a decrease of average yields, which resulted in difficulties for (smaller) Network Carriers to cover their fixed costs, related to the operation of a hub & spoke network. In this environment network airlines are looking for new revenue sources as well as further sources of cost reduction. This development has amplified the consolidation trend of the airline industry and led to the emergence of several multi-hub networks (e.g. Lufthansa runs hub-operation in Frankfurt, Munich, Zurich and Vienna). One way to leverage the fact that multi-hub networks allow several routings for one origin-destination city pair would be the introduction of flexible tickets, where the actual routing of the passenger is not defined at the moment of purchase but only a certain time prior to departure. This allows airlines to raise the load factor on their network by increasing the degree of overbooking which they currently practice by pooling the risk that more passengers arrive than there is capacity among several flights. Furthermore these tickets might allow network carriers to compete in the low-cost-airline segment without having to further reduce the price level of their regular product (with specified routing). The present dissertation examined possible designs of such a ticket and their impact on the acceptance by passengers by means of a choice based conjoint study among 356 travelers. The findings suggest that while 77.5% of leisure travelers are willing to accept flexible time-range tickets in their relevant set, only 56% of business travelers are considering using this kind of ticket. More particular the results also showed that business travelers are not willing to compromise on travel duration and departure times, and are subsequently willing to pay a premium for specified tickets. A market share simulation showed that depending on the selected product layout flexible time-range tickets are able to gain up to 60% market share when offered at a discount of up to 33% relative to traditional tickets. When it comes to the actual layout, the largest lever to increase the acceptance is to exclude connection flights from the potential set of flights. The results contribute to the young research area on flexible products by assessing the disutility which is experienced by customers with regard to particular product characteristics of flexible products. Furthermore the results aim at providing airline managers with a comprehensive overview of the possibilities which flexible time-range tickets bring along when it comes to increasing the load factor and thereby the revenues in a multi-hub network. (author's abstract)

Planning in partially observable domains is a notoriously difficult problem. However, in many real-world scenarios, planning can be simplified by decomposing the task into a hierarchy of smaller planning problems which, can then be solved independently of one another. Several approaches, mainly dealing with fully observable domains, have been proposed to optimize a plan that decomposes according to a hierarchy specified a priori. Some researchers have also proposed to discover hierarchies in fully observable domains. In this thesis, we investigate the problem of automatically discovering planning hierarchies in partially observable domains. The main advantage of discovering hierarchies is that, for a plan of a fixed size, hierarchical plans can be more expressive than non-hierarchical ones. Our solution frames the discovery and optimization of a hierarchical policy as a non-convex optimization problem. By encoding the hierarchical structure as variables of the optimization problem, we can automatically discover a hierarchy. Successfully solving the optimization problem therefore yields an optimal hierarchy and an optimal policy. We describe several techniques to solve the optimization problem. Namely, we provide results using general non-linear solvers, mixed-integer linear and non-linear solvers or a form of bounded hierarchical policy iteration. Our method is flexible enough to allow any parts of the hierarchy to be specified based on prior knowledge while letting the optimization discover the unknown parts. It can also discover hierarchical policies, including recursive policies, that are more compact (potentially infinitely fewer parameters).

In view of the current situation of fluctuating high crude oil prices, it is now more important than ever for petroleum refineries to operate at an optimal level in the present dynamic global economy. Acknowledging the shortcomings of deterministic models, this work proposes a hybrid of stochastic programming formulations for an optimal midterm refinery planning that addresses three factors of uncertainties, namely price of crude oil and saleable products, product demand, and production yields. An explicit stochastic programming technique is utilized by employing compensating slack variables to account for violations of constraints in order to increase model tractability. Four approaches are considered to ensure both solution and model robustness: (1) the Markowitz’s mean–variance (MV) model to handle randomness in the objective coefficients of prices by minimizing variance of the expected value of the random coefficients; (2) the two-stage stochastic programming with fixed recourse approach via scenario analysis to model randomness in the right-hand side and left-hand side coefficients by minimizing the expected recourse penalty costs due to constraints’ violations; (3) incorporation of the MV model within the framework developed in Approach 2 to minimize both the expectation and variance of the recourse costs; and (4) reformulation of the model in Approach 3 by adopting mean-absolute deviation (MAD) as the risk metric imposed by the recourse costs for a novel application to the petroleum refining industry. A representative numerical example is illustrated with the resulting outcome of higher net profits and increased robustness in solutions proposed by the stochastic models.