Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Project Scheduling Disputes: Expert Characterization and Estimate Aggregation(2017) Neely, Lauren; Baecher, Gregory; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Project schedule estimation continues to be a tricky endeavor. Stakeholders bring a wealth of experience to each project, but also biases which could affect their final estimates. This research proposes to study differences among stakeholders and develop a method to aggregate multiple estimates into a single estimate a project manager can defend. Chapter 1 provides an overview of the problem. Chapter 2 summarizes the literature on historical scheduling issues, scheduling best practices, decision analysis, and expert aggregation. Chapter 3 describes data collection/processing, while Chapter 4 provides the results. Chapter 5 provides a discussion of the results, and Chapter 6 provides a summary and recommendation for future work. The research consists of two major parts. The first part categorizes project stakeholders by three major demographics: “position”, “years of experience”, and “level of formal education”. Subjects were asked to answer several questions on risk aversion, project constraints, and general opinions on scheduling struggles. Using Design of Experiments (DOE), responses were compared to the different demographics to determine whether or not certain attitudes concentrated themselves within certain demographics. Subjects were then asked to provide activity duration and confidence estimates across several projects, as well as opinions on the activity list itself. DOE and Bernoulli trials were used to determine whether or not subjects within different demographics estimated differently from one another. Correlation coefficients among various responses were then calculated to determine if certain attitudes affected activity duration estimates. The second part of this research dealt primarily with aggregation of opinions on activity durations. The current methodology uses the Program Evaluation and Review (PERT) technique of calculating the expected value and variance of an activity duration based on three inputs and assuming the unknown duration follows a Beta distribution. This research proposes a methodology using Morris’ Bayesian belief-updating methods and unbounded distributions to aggregate multiple expert opinions. Using the same three baseline estimates, this methodology combines multiple opinions into one expected value and variance which can then be used in a network schedule. This aggregated value represents the combined knowledge of the project stakeholders which helps mitigate biases engrained in a single expert’s opinion.Item Examination of the Brain Processes Underlying Emotion Regulation within a Stress Resilient Population(2011) Costanzo, Michelle Elizabeth; Hatfield, Bradley D.; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Emotion robustly affects the quality of cognitive-motor performance under conditions of mental stress. As such, the regulation of emotion is critical to successful execution of motor skills during emotional challenge. Previous investigations of the stress-performance relationship have typically focused on behavioral outcomes, however, few have adopted a cognitive neuroscience approach to examine the involved mechanisms underlying this relationship. Furthermore, it is unclear if individuals who have a history of superior performance under stress (stress resilient population) exhibit brain responses characterized by an efficiency of neural processing and an adaptive emotion regulatory strategy. Using functional magnetic resonance imaging (fMRI), the present study examined activation in critical brain regions during affective challenge (i.e., presentation of International Affective Picture System negative images and Sport-Specific negative images) in 13 elite athletes (intercollegiate football players who have demonstrated successful execution of cognitive-motor skills under mental stress) relative to an age-matched control group (n=12). The present dissertation is organized into three main sections. The first report, entitled Brain Processes during Motor Performance under Psychological Stress, an Independent Component Analysis of EEG, is an examination of brain processes during competitive stress. This study revealed non-essential neuromotor cerebral cortical noise with a quantified increase in complexity during a cognitive-motor task. The second report is entitled Efficiency of Affective Brain Processes in Expert Cognitive-Motor Performers during Emotional Challenge. This fMRI examination of elite athletes revealed processing economy in brain regions critical to self regulation, management of emotional impulses and social cognition. The third report, entitled The Specificity of Neural Regulatory Processes during Emotional Challenge in a Stress Resilient Population, examined with fMRI if elite athletes spontaneously engage in cognitive reappraisal during the presentation of arousing sport-specific images. Results suggest that elite athletes process sports-relevant affective information in an automatic manner, congruent with a cognitive reappraisal strategy, which neutralized the negative impact of the scenes. In conclusion, the results suggest that elite performers are important models of stress resilience and respond not only in an efficient manner to stressful events, but demonstrate an adaptive regulatory response when challenged within their domain of experience.Item Data-Informed Calibration and Aggregation of Expert Judgment in a Bayesian Framework(2009) Shirazi, Calvin Homayoon; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Historically, decision-makers have used expert opinion to supplement lack of data. Expert opinion, however, is applied with much caution. This is because judgment is subjective and contains estimation error with some degree of uncertainty. The purpose of this study is to quantify the uncertainty surrounding the unknown of interest, given an expert opinion, in order to reduce the error of the estimate. This task is carried out by data-informed calibration and aggregation of expert opinion in a Bayesian framework. Additionally, this study evaluates the impact of the number of experts on the accuracy of aggregated estimate. The objective is to determine the correlation between the number of experts and the accuracy of the combined estimate in order to recommend an expert panel size.