There is an increasing recognition that vascular disease risk is associated with a greater incidence of cognitive impairment and dementia. Such link is supported by the physiological observation that cerebral metabolism heavily relies on vascular supply of oxygen and energy substrates. Cerebral hypoperfusion which results from vascular dysfunction causes a mismatch between energy demand and supply and is associated with the pathological features of dementia, including the impairments of action potential generation and protein synthesis, glutamatergic excitotoxicity, and the deposition of cerebral amyloid-β proteins. In contrast, habitual aerobic exercise is an established strategy to ameliorate the risk factors for vascular disease and is increasingly recognized in improving cognitive function. Accordingly, the primary purpose of this dissertation study was to investigate whether the exercise-related improvement in cognitive function was attributable to ameliorated vascular function and risk factors for vascular disease. In order to address this as comprehensively as we could, both cross-sectional and interventional studies were conducted. The primary findings from the present study were as follows. In the cross-sectional study, a greater cognitive performance observed in endurance-trained adults was associated with higher levels of cerebral CO2 reactivity and brachial endothelium-dependent vasodilation and lower levels of central arterial stiffness and plasma insulin. In the interventional study, a 3-month aerobic exercise training intervention did not improve cognitive function although central arterial stiffness and brachial endothelium-dependent vasodilation made favorable changes. However, we found that the improvement in memory performance after aerobic exercise training was associated with the reduction in central systolic blood pressure. Taken together, a better cognitive performance observed in endurance-trained adults may not directly be attributable to greater vascular function because there were discrepant changes in cognitive and vascular functions after a 3-month aerobic exercise intervention. The correlation between the changes in memory performance and central systolic blood pressure is interesting but needs further investigation using a larger sample size. The discrepancy in the results between the cross-sectional and interventional studies could be explained by the duration of exercise training and/or the time it takes for the effect of improved vascular function to translate into cognitive function. / text

Regular inference is a research direction in machine learning. The goal of regular inference is to construct a representation of a regular language in the form of deterministic finite automaton (DFA) based on the set of positive and negative examples. DFAs take strings of symbols (words) as input, and produce a binary classification as output, indicating whether the word belongs to the language or not. There are two types of learning algorithms for DFAs: passive and active learning algorithms. In passive learning, the set of positive and negative examples is given and not chosen by inference algorithm. In contrast, in active learning, the learning algorithm chooses examples from which a model is constructed. Active learning was introduced in 1987 by Dana Angluin. She presented the L* algorithm for learning DFAs by asking membership and equivalence queries to a teacher who knows the regular language accepted by DFA to be learned. A membership query checks whether a word belongs to the language or not. An equivalence query checks whether a hypothesized model is equivalent to the DFA to be learned.The L* algorithm has been found to be useful in different areas, including black box checking, compositional verification and integration testing. There are also other algorithms similar to L* for regular inference. However, the learning of timed systems has not been studied before. This thesis presents algorithms for learning timed systems in an active learning framework. As a model of timed system we choose event-recording automata (ERAs), a determinizable subclass of the widely used timed automata. The advantages of ERA in comparison with timed automata, is that it is known priori the set of clocks of an ERA and when clocks are reset. The contribution of this thesis is four algorithms for learning deterministic event-recording automaton (DERA). Two algorithms learn a subclass of DERA, called event-deterministic ERA (EDERA) and two algorithms learn general DERA. The problem with DERAs that they do not have canonical form. Therefore we focus on subclass of DERAs that have canonical representation, EDERA, and apply the L* algorithm to learn EDERAs. The L* algorithm in timed setting requires a procedure that learns clock guards of DERAs. This approach constructs EDERAs which are exponentially bigger than automaton to be learned. Another procedure can be used to lean smaller EDERAs, but it requires to solve NP-hard problem. We also use the L* algorithm to learn general DERA. One drawback of this approach that inferred DERAs have a form of region graph and there is blow-up in the number of transitions. Therefore we introduce an algorithm for learning DERA which uses a new data structure for organising results of queries, called a timed decision tree, and avoids region graph construction. Theoretically this algorithm can construct bigger DERA than the L* algorithm, but in the average case we expect better performance.

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Previous research has suggested that there is a strong and positive relationship between being an entrepreneur and possessing a high degree of subjective well-being as well as job satisfaction. The big five personality traits have also been argued to be significantly related to both subjective well-being and job satisfaction. Little is however known if personality affects entrepreneurs and regular employees differently. In this paper the impact of personality traits on the cognitive part of subjective well-being as well as job satisfaction are investigated separately among entrepreneurs and regular employees. This is done through OLS-regressions using a Swedish nationally representative survey Employment, Material Resources, and Political Preferences (EMRAPP), where entrepreneurs were oversampled in order to be able to compare entrepreneurs (N = 2483) and regular employees (N = 2642). The findings suggest that there is no substantial difference between entrepreneurs and regular employees when looking at the relationship between personality traits and subjective well-being. Findings on job satisfaction on the other hand showed that the personality trait openness to experience had no impact on job satisfaction, and that the personality trait emotional stability (neuroticism reversed) was equally beneficial for both entrepreneurs and regular employees. Extraversion had a positive relationship with job satisfaction among both entrepreneurs and regular employees, although the relationship was twice as strong among entrepreneurs. The personality traits agreeableness and conscientiousness on the other hand were only related to job satisfaction among entrepreneurs. Personality traits are thus much more important for job satisfaction among entrepreneurs.

In this Master thesis, we use a singular and regular perturbation theory to derive an analytic approximation formula for the expected discounted penalty function. Our model is an extension of Cramer–Lundberg extended classical model because we consider a more general insurance risk model in which the compound Poisson risk process is perturbed by a Brownian motion multiplied by a stochastic volatility driven by two factors- which have mean reversion models. Moreover, unlike the classical model, our model allows a ruin to be caused either by claims or by surplus’ fluctuation. We compute explicitly the first terms of the asymptotic expansion and we show that they satisfy either an integro-differential equation or a Poisson equation. In addition, we derive the existence and uniqueness conditions of the risk model with two stochastic volatilities factors.

In 1975, Sheehan conjectured that every simple 4-regular hamiltonian graph has a second Hamilton cycle. If Sheehan's Conjecture holds, then the result can be extended to all simple d-regular hamiltonian graphs with d at least 3. First, we survey some previous results which verify the existence of a second Hamilton cycle if d is large enough. We will then demonstrate some techniques for finding a second Hamilton cycle that will be used throughout this paper. Finally, we use these techniques and show that for certain 4-regular Hamiltonian graphs whose automorphism group is large enough, a second Hamilton cycle exists.

Most crude oils contain traces of vanadyl porphyrins within their asphaltene fraction. Although these metals are only present in trace quantities, they have a significant detrimental impact on crude oil processing units; therefore, their selective removal is highly desirable. The current work studied the interaction of these vanadyl porphyrins with asphaltenes using two approaches: 1) equilibrium solubility measurements of model porphyrins and 2) membrane diffusion measurements in dilute solution. Solubility measurements with model porphyrins showed that simple model porphyrins fit the operational definition for asphaltenes, exhibiting negligible solubility in n-heptane and orders of magnitude higher solubility in toluene. Measurement of the melting point properties enabled modeling of their solubility behaviour and showed that simple models incorporating solubility parameters (Regular solution and Flory-Huggins) were not capable of describing the observed behaviour. Diffusion measurements were done using model vanadyl porphyrins, asphaltenes, and petroporphyrins in toluene using a stirred diffusion cell equipped with ultrafiltration membranes (Ultracel YM and Anopore). The pore sizes were varied between 3-20 nm to retain aggregates while allowing free molecules to diffuse. The permeate was continuously monitored using in situ UV/Visible spectroscopy. These experiments determined that the size of the asphaltene aggregates at 1 g/L in toluene at 25C were in the range of 5-9 nm. An increase in temperature results in an increase in asphaltene mobility but does not reduce the size of the asphaltene structures below 5 nm. Likewise, a decrease in concentration to 0.1 g/L did not result in a decrease in size. It was also observed that the exclusion of a large portion of the total asphaltenes by pores < 5 nm eliminates the absorbance of visible light (>600 nm) indicating the presence of Rayleigh scattering for the aggregated species in solution. The petroporphyrins are larger than the model vanadyl porphyrins as indicated by pore hindrance effects within smaller pores. An increase in temperature results in an increase in petroporphyrin mobility, although decreasing the asphaltene concentration does not. The mobility of the vanadyl petroporphyrins is affected by the origin of the sample (Safaniya, Venezuela, Athabasca) and is therefore not universal. / Chemical Engineering

Thesis (D. Min.)--Northern Baptist Theological Seminary, 2000. / Abstract. Includes bibliographical references (leaves 269-277).

Thesis (D. Min.)--Northern Baptist Theological Seminary, 2000. / Abstract. Includes bibliographical references (leaves 269-277).

Thesis (D. Min.)--Westminster Theological Seminary, Philadelphia, 1988. / This is an electronic reproduction of TREN, #036-0052. Includes bibliographical references (leaf [77]).