Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Genre: Music--African influences.
Description
The field of exoplanet science has matured over the past two decades with over 3500 confirmed exoplanets. However, many fundamental questions regarding the composition, and formation mechanism remain unanswered. Atmospheres are a window into the properties of a planet, and spectroscopic studies can help resolve many of these questions. For the first part of my dissertation, I participated in two studies of the atmospheres of brown dwarfs to search for weather variations. To understand the evolution of weather on brown dwarfs we conducted a multi-epoch study monitoring four cool brown dwarfs to search for photometric variability. These cool brown dwarfs are predicted to have salt and sulfide clouds condensing in their upper atmosphere and we detected one high amplitude variable. Combining observations for all T5 and later brown dwarfs we note a possible correlation between variability and cloud opacity.
For the second half of my thesis, I focused on characterizing the atmospheres of directly imaged exoplanets. In the first study Hubble Space Telescope data on HR8799, in wavelengths unobservable from the ground, provide constraints on the presence of clouds in the outer planets. Next, I present research done in collaboration with the Gemini Planet Imager Exoplanet Survey (GPIES) team including an exploration of the instrument contrast against environmental parameters, and an examination of the environment of the planet in the HD 106906 system. By analyzing archival HST data and examining the near-infrared colors of HD 106906b, we conclude that the companion shows weak evidence of a circumplanetary dust disk or cloud. Finally, I measure the properties of the low mass directly imaged planet 51 Eridani b. We combined published J, H spectra with updated LP photometry, new K1, K2 spectra, and MS photometry. The new data confirms that the planet has redder than similar spectral type objects, which might be due to the planet still transitioning from to L-to-T. Model atmospheres indicate a cooler effective temperature best fit by a patchy cloud atmosphere making 51 Eri b an excellent candidate for future variability studies with the James Webb Space Telescope.
For the second half of my thesis, I focused on characterizing the atmospheres of directly imaged exoplanets. In the first study Hubble Space Telescope data on HR8799, in wavelengths unobservable from the ground, provide constraints on the presence of clouds in the outer planets. Next, I present research done in collaboration with the Gemini Planet Imager Exoplanet Survey (GPIES) team including an exploration of the instrument contrast against environmental parameters, and an examination of the environment of the planet in the HD 106906 system. By analyzing archival HST data and examining the near-infrared colors of HD 106906b, we conclude that the companion shows weak evidence of a circumplanetary dust disk or cloud. Finally, I measure the properties of the low mass directly imaged planet 51 Eridani b. We combined published J, H spectra with updated LP photometry, new K1, K2 spectra, and MS photometry. The new data confirms that the planet has redder than similar spectral type objects, which might be due to the planet still transitioning from to L-to-T. Model atmospheres indicate a cooler effective temperature best fit by a patchy cloud atmosphere making 51 Eri b an excellent candidate for future variability studies with the James Webb Space Telescope.
ContributorsRajan, Abhijith (Author) / Patience, Jennifer (Thesis advisor) / Young, Patrick (Thesis advisor) / Scowen, Paul (Committee member) / Butler, Nathaniel (Committee member) / Shkolnik, Evgenya (Committee member) / Arizona State University (Publisher)
Created2017
Description
Intelligence, consisting of critical products that facilitate law enforcement decision-making, is a crucial component and tool in the criminal justice system. However, the ways in which intelligence is gathered and used has gone largely unevaluated, particularly at the local level of law enforcement. This thesis begins to address the sparsity of literature by investigating the Intelligence Officer function in the Phoenix Police Department. More specifically, this study explores their roles; perceptions on information they are gathering, namely reliability and validity; and their effectiveness in terms of both intelligence and case successes. Different aspects of roles and perceptions are also examined in terms of their ability to predict these outcomes. Data reflect a 22-month sample of officer reports from the Phoenix Police Department Intelligence Officer Program. Descriptive analyses suggest that Intelligence Officers typically work specific cases with varied and different natures of crime. Generally, officers seem to be confident in the information they collect in terms of reliability and validity, and also appear to be relatively successful in achieving both broad intelligence successes and more tangible case successes. However, the relationships between role and perception variables and results vary in terms of both impact and significance for each type of success. Future research is required to better understand these relationships and to continue building a foundation of knowledge on Intelligence Officer effectiveness, so their impact can be optimized.
ContributorsBottema, A. Johannes (Author) / Telep, Cody (Thesis advisor) / Terrill, William (Committee member) / Young, Jacob (Committee member) / Arizona State University (Publisher)
Created2017
Description
By examining the cognitive mechanisms behind human memory, the author hypothesizes that instrumental conductors can more quickly and effectively internalize music scores. With this theory, conductors could offer more informed and nuanced communications to their ensembles. Furthermore, these ideas could be incorporated into how conducting is taught to younger students by cultivating a more in-depth understanding of the music being studied.
This research paper surveys current trends in cognitive science related to the interactions of long-term memory (LTM) and short-term memory (STM) concerning score study and memorization employed by instrumental conductors. The research is divided into three sections, beginning with an examination of the key literature from the field of cognitive science. It continues with an overview of current musicological research and applications and finally concludes with a review of current instrumental conducting pedagogy that include discussions of memory. Moreover, recommended steps and a potential framework to incorporate cognitive science research into future conducting pedagogies are further outlined. The primary cognitive theory of focus is the Working Memory Theory of Alan Baddeley and Graham Hitch.
This research paper surveys current trends in cognitive science related to the interactions of long-term memory (LTM) and short-term memory (STM) concerning score study and memorization employed by instrumental conductors. The research is divided into three sections, beginning with an examination of the key literature from the field of cognitive science. It continues with an overview of current musicological research and applications and finally concludes with a review of current instrumental conducting pedagogy that include discussions of memory. Moreover, recommended steps and a potential framework to incorporate cognitive science research into future conducting pedagogies are further outlined. The primary cognitive theory of focus is the Working Memory Theory of Alan Baddeley and Graham Hitch.
ContributorsLucas, Cullan Baynes (Author) / Caslor, Jason (Thesis advisor) / Gardener, Joshua (Committee member) / Hill, Gary (Committee member) / Rogers, Rodney (Committee member) / Arizona State University (Publisher)
Created2017
Description
To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple sub- networks for dealing with robot dynamics and perceptual input from the environment. We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.
ContributorsSur, Indranil (Author) / Amor, Heni B (Thesis advisor) / Fainekos, Georgios (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
The notion that a singer’s voice is an expression of their personality serves as the catalyst for an examination of the relationship between the continuum of introversion and extraversion, and the pathologies of muscle tension dysphonia, vocal nodules, and performance anxiety. This paper begins with a brief introduction defining extraversion and introversion, followed by a review of personality studies identifying opera singers as primarily extraverted. Definitions of vocal nodules and muscle tension dysphonia are then given along with a list of recommended therapies. These elements tie in with two studies in speech pathology that suggest that behaviors of extraversion contribute to the development of vocal nodules, and behaviors of introversion contribute to muscle tension dysphonia and a higher laryngeal placement. Performance anxiety is shown to compound the behaviors that lead to vocal pathologies in singers. Additional therapies are recommended to address anxiety management in vocal lessons. Finally, since personality factors that contribute to vocal pathology are psychological, it is recommended that voice teachers refer their students to a psychotherapist for proper treatment.
ContributorsCurtis, Paul Josef (Author) / Norton, Kay (Thesis advisor) / Hawkins, Gordon (Thesis advisor) / Dreyfoos, Dale (Committee member) / Arizona State University (Publisher)
Created2017
Description
Segregation into own-gender peer groups, a common developmental pattern, has many potentially negative short- and long-term consequences. Understanding the social cognitive processes underlying intergroup processes may lead to a better understanding of, and a chance to improve, intergroup relations between boys and girls; however, until recently gender-typed cognitions have not received a lot of attention. Therefore, in two complementary studies, this dissertation examines developmental patterns and predictors of a particular type of social cognition, gender-based relationship efficacy (GBRE). The first study examines mean-level and interindividual stability patterns of GBRE longitudinally in two developmental periods: childhood and pre-adolescence. Specifically, the first study examined children’s and pre-adolescents’ GBRE toward own- (GBRE-Own) and other-gender (GBRE-Other) peers over a one-year period. Using a four factor repeated measures analysis of variance, the results indicated that GBRE-Own is significantly higher than GBRE-Other across both cohorts. GBRE-Other, however, increased from childhood to pre-adolescence. Stability and cross-lag effects were examined using a multi-group panel analysis and revealed that GBRE-Own and GBRE-Other were stable. Additionally, high levels of GBRE-Own led to lower levels of GBRE-Other one year later, but high levels of GBRE-Other led to higher levels of GBRE-Own. Implications for understanding segregation processes and suggestions for future research are discussed.
The second study examined potential affective/cognitive, behavioral, and contextual predictors of GBRE-Other in pre-adolescence. Several hypotheses were tested using panel models and regression analyses, but there was limited support. Results indicated that GBRE-Other predicted more positive attitudes toward other-gender peers and higher preferences for other-gender peer interaction and that, for boys, anxious attitudes toward other-gender peers negatively predicted GBRE-Other and, for girls, parental attitudes toward their children’s other-gender friendships negatively predicted GBRE-Other. The lack of significant findings in the second study should be interpreted cautiously. In general, GBRE is an important construct and more research is needed to fully understand the developmental progression and implications.
The second study examined potential affective/cognitive, behavioral, and contextual predictors of GBRE-Other in pre-adolescence. Several hypotheses were tested using panel models and regression analyses, but there was limited support. Results indicated that GBRE-Other predicted more positive attitudes toward other-gender peers and higher preferences for other-gender peer interaction and that, for boys, anxious attitudes toward other-gender peers negatively predicted GBRE-Other and, for girls, parental attitudes toward their children’s other-gender friendships negatively predicted GBRE-Other. The lack of significant findings in the second study should be interpreted cautiously. In general, GBRE is an important construct and more research is needed to fully understand the developmental progression and implications.
ContributorsField, Ryan David (Author) / Martin, Carol L (Thesis advisor) / DeLay, Dawn (Committee member) / Miller, Cindy F (Committee member) / Updegraff, Kimberly A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where
all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.
Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.
Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.
Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.
Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.
all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied.
Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear average consensus algorithm is used. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives guidelines towards how to choose the design parameter for the max estimate. It is also shown that if some prior knowledge of the initial measurements is available, the consensus process can be accelerated.
Secondly, a distributed system size estimation algorithm is proposed. The proposed algorithm is based on distributed average consensus and L2 norm estimation. Different sources of error are explicitly discussed, and the distribution of the final estimate is derived. The CRBs for system size estimator with average and max consensus strategies are also considered, and different consensus based system size estimation approaches are compared.
Then, a consensus-based network center and radius estimation algorithm is described. The center localization problem is formulated as a convex optimization problem with a summation form by using soft-max approximation with exponential functions. Distributed optimization methods such as stochastic gradient descent and diffusion adaptation are used to estimate the center. Then, max consensus is used to compute the radius of the network area.
Finally, two average consensus based distributed estimation algorithms are introduced: distributed degree distribution estimation algorithm and algorithm for tracking the dynamics of the desired parameter. Simulation results for all proposed algorithms are provided.
ContributorsZhang, Sai (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Kostas (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2017
Description
ABSTRACT
Results from previous studies indicated nursing students needed to further develop critical thinking (CT) especially with respect to employing it in their clinical reasoning. Thus, the study was conducted to support development of students’ CT in the areas of inference subskills that could be applied as they engaged in clinical reasoning during course simulations. Relevant studies from areas such as CT, clinical reasoning, nursing process, and inference subskills informed the study. Additionally, the power of simulation as an instructional technique along with reflection on those simulations contributed to the formulation of the study. Participants included junior nursing students in their second semester of nursing school. They completed a pre- and post-intervention Critical Thinking Survey, reflective journals during the course of the intervention, and interviews as the conclusion of the study. The intervention provided students with instruction on the use of three inference subskills (Facione, 2015). Moreover, they wrote reflective journal entries about their use of these skills. Quantitative results indicated no changes in various CT measures. By comparison, qualitative data analysis of individual interviews and reflective journals showed students: applied inference subskills in a limited way; demonstrated restricted clinical reasoning; displayed emerging reflection skills; and established a foundation on which to build additional CT in their professional roles. Limitations of the study included time—length of the intervention and limited power of the instruction—depth of the instruction with respect to teaching the inference subskills. Discussion focused on explaining the results. Implications for teaching included revision of the instruction in inference subskills to be more robust by extending it over time, perhaps across courses. Additionally, use of a ‘flipped’ instructional process was discussed in which students would learn the subskills by viewing video modules prior to class and then are ‘guided’ to apply their learning in classroom health care simulations. Implications for research included closer examination of the development of CT in clinical reasoning to devise a developmental trajectory that might be useful to understand this phenomenon and to develop teaching strategies to assist students in learning to use these skills as part of the clinical reasoning process.
Results from previous studies indicated nursing students needed to further develop critical thinking (CT) especially with respect to employing it in their clinical reasoning. Thus, the study was conducted to support development of students’ CT in the areas of inference subskills that could be applied as they engaged in clinical reasoning during course simulations. Relevant studies from areas such as CT, clinical reasoning, nursing process, and inference subskills informed the study. Additionally, the power of simulation as an instructional technique along with reflection on those simulations contributed to the formulation of the study. Participants included junior nursing students in their second semester of nursing school. They completed a pre- and post-intervention Critical Thinking Survey, reflective journals during the course of the intervention, and interviews as the conclusion of the study. The intervention provided students with instruction on the use of three inference subskills (Facione, 2015). Moreover, they wrote reflective journal entries about their use of these skills. Quantitative results indicated no changes in various CT measures. By comparison, qualitative data analysis of individual interviews and reflective journals showed students: applied inference subskills in a limited way; demonstrated restricted clinical reasoning; displayed emerging reflection skills; and established a foundation on which to build additional CT in their professional roles. Limitations of the study included time—length of the intervention and limited power of the instruction—depth of the instruction with respect to teaching the inference subskills. Discussion focused on explaining the results. Implications for teaching included revision of the instruction in inference subskills to be more robust by extending it over time, perhaps across courses. Additionally, use of a ‘flipped’ instructional process was discussed in which students would learn the subskills by viewing video modules prior to class and then are ‘guided’ to apply their learning in classroom health care simulations. Implications for research included closer examination of the development of CT in clinical reasoning to devise a developmental trajectory that might be useful to understand this phenomenon and to develop teaching strategies to assist students in learning to use these skills as part of the clinical reasoning process.
ContributorsLuPone, Kathleen A (Author) / Buss, Ray R (Thesis advisor) / Mertler, Craig A. (Committee member) / Heying-Stanley, Betty (Committee member) / Arizona State University (Publisher)
Created2017
Description
The South African Middle Stone Age (MSA), spanning the Middle to Late Pleistocene (Marine Isotope Stages (MIS) 8-3) witnessed major climatic and environmental change and dramatic change in forager technological organization including lithic raw material selection. Homo sapiens emerged during the MSA and had to make decisions about how to organize technology to cope with environmental stressors, including lithic raw material selection, which can effect tool production and application, and mobility.
This project studied the role and importance of lithic raw materials in the technological organization of foragers by focusing on why lithic raw material selection sometimes changed when the behavioral and environmental context changed. The study used the Pinnacle Point (PP) MSA record (MIS6-3) in the Mossel Bay region, South Africa as the test case. In this region, quartzite and silcrete with dramatically different properties were the two most frequently exploited raw materials, and their relative abundances change significantly through time. Several explanations intertwined with major research questions over the origins of modern humans have been proposed for this change.
Two alternative lithic raw material procurement models were considered. The first, a computational model termed the Opportunistic Acquisition Model, posits that archaeological lithic raw material frequencies are due to opportunistic encounters during random walk. The second, an analytical model termed the Active-Choice Model drawn from the principles of Optimal Foraging Theory, posits that given a choice, individuals will choose the most cost effective means of producing durable cutting tools in their environment and will strategically select those raw materials.
An evaluation of the competing models found that lithic raw material selection was a strategic behavior in the PP record. In MIS6 and MIS5, the selection of quartzite was driven by travel and search cost, while during the MIS4, the joint selection of quartzite and silcrete was facilitated by a mobility strategy that focused on longer or more frequent stays at PP coupled with place provisioning. Further, the result suggests that specific raw materials and technology were relied on to obtain food resources and perform processing tasks suggesting knowledge about raw material properties and suitability for tasks.
This project studied the role and importance of lithic raw materials in the technological organization of foragers by focusing on why lithic raw material selection sometimes changed when the behavioral and environmental context changed. The study used the Pinnacle Point (PP) MSA record (MIS6-3) in the Mossel Bay region, South Africa as the test case. In this region, quartzite and silcrete with dramatically different properties were the two most frequently exploited raw materials, and their relative abundances change significantly through time. Several explanations intertwined with major research questions over the origins of modern humans have been proposed for this change.
Two alternative lithic raw material procurement models were considered. The first, a computational model termed the Opportunistic Acquisition Model, posits that archaeological lithic raw material frequencies are due to opportunistic encounters during random walk. The second, an analytical model termed the Active-Choice Model drawn from the principles of Optimal Foraging Theory, posits that given a choice, individuals will choose the most cost effective means of producing durable cutting tools in their environment and will strategically select those raw materials.
An evaluation of the competing models found that lithic raw material selection was a strategic behavior in the PP record. In MIS6 and MIS5, the selection of quartzite was driven by travel and search cost, while during the MIS4, the joint selection of quartzite and silcrete was facilitated by a mobility strategy that focused on longer or more frequent stays at PP coupled with place provisioning. Further, the result suggests that specific raw materials and technology were relied on to obtain food resources and perform processing tasks suggesting knowledge about raw material properties and suitability for tasks.
ContributorsOestmo, Simen (Author) / Marean, Curtis W (Thesis advisor) / Barton, Michael (Committee member) / Hill, Kim R (Committee member) / Janssen, Marcus A (Committee member) / Surovell, Todd A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.
ContributorsYang, Xueli (Author) / Yang, Zhihua (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Li, Qi (Committee member) / Xu, Tianfang (Committee member) / Zeng, Ruijie (Committee member) / Arizona State University (Publisher)
Created2023