ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 78
Description
In October, 2009, participants of the Arizona Special Supplemental Nutrition Program for Women, Infants and Children (WIC) began receiving monthly Cash Value Vouchers (CVV) worth between six and 10 dollars towards the purchase of fresh fruits and vegetables. Data from the Arizona Department of Health Services (ADHS) showed CVV redemption rates in the first two years of the program were lower than the national average of 77% redemption. In response, the ADHS WIC Food List was expanded to also include canned and frozen fruits and vegetables. More recent data from ADHS suggest that redemption rates are improving, but variably exist among different WIC sub-populations. The purpose of this project was to identify themes related to the ease or difficulty of WIC CVV use amongst different categories of low-redeeming WIC participants. A total of 8 focus groups were conducted, four at a clinic in each of two Valley cities: Surprise and Mesa. Each of the four focus groups comprised one of four targeted WIC participant categories: pregnant, postpartum, breastfeeding, and children with participation ranging from 3-9 participants per group. Using the general inductive approach, recordings of the focus groups were transcribed, hand-coded and uploaded into qualitative analysis software resulting in four emergent themes including: interactions and shopping strategies, maximizing WIC value, redemption issues, and effect of rule change. Researchers identified twelve different subthemes related to the emergent theme of interactions and strategies to improve their experience, including economic considerations during redemption. Barriers related to interactions existed that made their purchase difficult, most notably anger from the cashier and other shoppers. However, participants made use of a number of strategies to facilitate WIC purchases or extract more value out of WIC benefits, such as pooling their CVV. Finally, it appears that the fruit and vegetable rule change was well received by those who were aware of the change. These data suggest a number of important avenues for future research, including verifying these themes are important within a larger, representative sample of Arizona WIC participants, and exploring strategies to minimize barriers identified by participants, such as use of electronic benefits transfer-style cards (EBT).
ContributorsBertmann, Farryl M. W (Author) / Wharton, Christopher (Christopher Mack), 1977- (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Johnston, Carol (Committee member) / Hampl, Jeffrey (Committee member) / Dixit-Joshi, Sujata (Committee member) / Barroso, Cristina (Committee member) / Arizona State University (Publisher)
Created2013
Description
There are several visual dimensions of food that can affect food intake, example portion size, color, and variety. This dissertation elucidates the effect of number of pieces of food on preference and amount of food consumed in humans and motivation for food in animals. Chapter 2 Experiment 1 showed that rats preferred and also ran faster for multiple pieces (30, 10 mg pellets) than an equicaloric, single piece of food (300 mg) showing that multiple pieces of food are more rewarding than a single piece. Chapter 2 Experiment 2 showed that rats preferred a 30-pellet food portion clustered together rather than scattered. Preference and motivation for clustered food pieces may be interpreted based on the optimal foraging theory that animals prefer foods that can maximize energy gain and minimize the risk of predation. Chapter 3 Experiment 1 showed that college students preferred and ate less of a multiple-piece than a single-piece portion and also ate less in a test meal following the multiple-piece than single-piece portion. Chapter 3 Experiment 2 replicated the results in Experiment 1 and used a bagel instead of chicken. Chapter 4 showed that college students given a five-piece chicken portion scattered on a plate ate less in a meal and in a subsequent test meal than those given the same portion clustered together. This is consistent with the hypothesis that multiple pieces of food may appear like more food because they take up a larger surface area than a single-piece portion. All together, these studies show that number and surface area occupied by food pieces are important visual cues determining food choice in animals and both food choice and intake in humans.
ContributorsBajaj, Devina (Author) / Phillips, Elizabeth D. (Thesis advisor) / Cohen, Adam (Committee member) / Johnston, Carol (Committee member) / Bimonte-Nelson, Heather A. (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables.
ContributorsKoh, Derek (Author) / Runger, George C. (Thesis advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Created2013
Description
Health knowledge alone does not appear to lead to sustained healthy behavior, suggesting the need for alternative methods for improving diet. Recent research shows a possible role of moral contexts of food production on diet related behaviors; however no studies have been conducted to specifically explore the relationship between moral constructs and food consumption. This study examined the relationship between fast food consumption and two measures of morality, Moral Foundations Questionnaire (MFQ), specifically harm/care and purity/sanctity foundations, and the Ethical Concern in food choice (EC) questionnaire, which includes animal welfare, environment protection, political values, and religion subscales. The study also examined the association between the measures of morality. 739 participants, primarily female (71.4%) and non-Hispanic Whites (76.5%), completed an online survey that included the MFQ, the EC questionnaire, and a brief fast food screener. Participant's morality scores in relation to their fast food consumption were examined first using bivariate ANOVA analysis and then using logistic regression to control for covariates. The MFQ foundations were compared with the EC subscales using Pearson correlation coefficient. Significant bivariate relationships were seen between fast food consumption and the MFQ's purity/sanctity foundation and EC's religion subscales (p<0.05). However these significant bivariate relationships did not hold after controlling for gender, race, university education, and religion in the logistic regression analysis. The foundations of the MFQ were positively correlated with the subscales for the EC questionnaire (r values ranging from .233-.613 (p<0.01). MFQ's purity/sanctity foundation and EC's religion subscale were the two most highly correlated (r=.613, p<0.01) showing that moral intuitions may be associated with eating decision making. The study did not find significant associations between MFQ or EC scores and fast food consumption.
ContributorsMartinelli, Sarah (Author) / Ohri-Vachaspati, Punam (Thesis advisor) / Hekler, Eric B. (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Johnston, Carol (Committee member) / Arizona State University (Publisher)
Created2013
Description
Background: Previous research suggests a healthy eater schema (i.e., identifying yourself as a healthy eater) may be a useful concept to target in interventions. A "stealth" intervention that discussed the moral issues related to food worked better at promoting healthful eating than an intervention focused on the health benefits. No research has explored the relationship between moral foundations, a theoretical model focused on delineating core "foundations" for making a moral decision, and healthy eater self-identity or self-efficacy. Purpose: We explored the relationship between moral foundations (i.e., harm/care, fairness/reciprocity, in-group/loyalty, authority/respect, & purity/sanctity) and health eater self-identity and fruit and vegetable self-efficacy (FVSE). Methods: 542 participants completed an online cross-sectional survey, which included moral foundations (i.e., MFQ), political views, healthy eater self-identity (i.e., HESS), and FVSE measures. Logistic regression was used to assess the relationship between moral foundations between healthy eater self-identity after controlling for age, gender, major, BMI, and political beliefs. OLS regression was used to explore the relationship between self-efficacy and the moral foundations after controlling for the covariates. Results: 75.6% of the sample were college students, with a mean age of 25.27 (SD=8.61). 25.1% of students were nutrition majors. Harm/care, authority/respect, and ingroup/loyalty were significantly associated with healthy eater schema, (i.e., OR=1.7, p<.001, OR=1.5, p=.009, and OR=1.4, p=.027, respectively). Ingroup/loyalty, authority/respect, and purity/sanctity were related to FVSE (p=.006, p=.002, p=.04, respectively). Conclusion: Among college students, harm/care and authority/respect were associated with a healthy eater schema. Future research should explore possible uses of these moral foundations in interventions (e.g., a plant-based diet based on reduced harm to animals or eating fewer processed views based on "traditional" values).
ContributorsKiser, Sarah (Author) / Hekler, Eric B. (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Johnston, Carol (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT The hormone leptin is an important regulator of body weight and energy balance, while nitric oxide (NO) produced in the blood vessels is beneficial for preventing disease-induced impaired vasodilation and hypertension. Elevations in the free radical superoxide can result in impaired vasodilation through scavenging of NO. Omega 3 is a polyunsaturated fatty acid that is beneficial at reducing body weight and in lowering many cardiovascular risk factors like atherosclerosis. The present study was designed to examine the change in plasma concentrations of leptin, nitric oxide, and the antioxidant superoxide dismutase in addition to examining the association between leptin and NO in healthy normal weight adult female subjects before and following omega 3 intakes. Participants were randomly assigned to either a fish oil group (600 mg per day) or a control group (1000 mg of coconut oil per day) for 8 weeks. Results showed no significant difference in the percent change of leptin over the 8 week supplementation period for either group (15.3±31.9 for fish oil group, 7.83±27 for control group; p=0.763). The percent change in NO was similarly not significantly altered in either group (-1.97±22 decline in fish oil group, 11.8±53.9 in control group; p=0.960). Likewise, the percent change in superoxide dismutase for each group was not significant following 8 weeks of supplementation (fish oil group: 11.94±20.94; control group: 11.8±53.9; p=0.362). The Pearson correlation co-efficient comparing the percent change of both leptin and NO was r2= -0.251 demonstrating a mildly negative, albeit insignificant, relationship between these factors. Together, these findings suggest that daily supplementation with 600 mg omega 3 in healthy females is not beneficial for improving these cardiovascular risk markers. Future studies in this area should include male subjects as well as overweight subjects with larger doses of fish oil that are equivalent to three or more servings per week. The importance of gender cannot be underestimated since estrogen has protective effects in the vasculature of females that may have masked any further protective effects of the fish oil. In addition, overweight individuals are often leptin-resistant and develop impaired vasodilation resulting from superoxide-mediated scavenging of nitric oxide. Therefore, the reported antioxidant and weight loss properties of omega 3 supplementation may greatly benefit overweight individuals.
ContributorsAlanbagy, Samer (Author) / Sweazea, Karen (Thesis advisor) / Johnston, Carol (Committee member) / Shepard, Christina (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2014
Description
The unpleasant bitter taste found in many nutritious vegetables may deter their consumption. While bitterness suppression by prototypical tastants is well-studied in the chemical and pharmacological fields, mechanisms to reduce the bitterness of foods such as vegetables remain to be elucidated. Here tastants representing the taste primaries of salty and sweet were investigated as potential bitterness suppressors of three types of Brassicaceae vegetables. The secondary aim of these studies was to determine whether the bitter masking agents were differentially effective for bitter-sensitive and bitter-insensitive individuals. In all experiments, participants rated vegetables plain and with the addition of tastants. In Experiments 1-3, sucrose and NNS suppressed the bitterness of broccoli, Brussels sprouts, and cauliflower, whereas NaCl did not. Varying concentrations of NaCl and sucrose were introduced in Experiment 4 to assess the dose-dependency of the effects. While sucrose was a robust bitterness suppressor, NaCl suppressed bitterness only for participants who perceived the plain Brussels sprouts as highly bitter. Experiment 5, through the implementation of a rigorous control condition, determined that some but not all of this effect can be accounted for by regression to the mean. Individual variability in taste perception as determined by sampling of aqueous bitter, salty, and sweet solutions did not influence the degree of suppression by NaCl or sucrose. Consumption of vegetables is deterred by their bitter taste. Utilizing tastants to mask bitterness, a technique that preserves endogenous nutrients, can circumvent this issue. Sucrose is a robust bitter suppressor whereas the efficacy of NaCl is dependent upon bitterness perception of the plain vegetables.
ContributorsWilkie, Lynn Melissa (Author) / Capaldi Phillips, Elizabeth D (Thesis advisor) / Cohen, Adam (Committee member) / Johnston, Carol (Committee member) / Sanabria, Federico (Committee member) / Arizona State University (Publisher)
Created2014
Description
No-confounding designs (NC) in 16 runs for 6, 7, and 8 factors are non-regular fractional factorial designs that have been suggested as attractive alternatives to the regular minimum aberration resolution IV designs because they do not completely confound any two-factor interactions with each other. These designs allow for potential estimation of main effects and a few two-factor interactions without the need for follow-up experimentation. Analysis methods for non-regular designs is an area of ongoing research, because standard variable selection techniques such as stepwise regression may not always be the best approach. The current work investigates the use of the Dantzig selector for analyzing no-confounding designs. Through a series of examples it shows that this technique is very effective for identifying the set of active factors in no-confounding designs when there are three of four active main effects and up to two active two-factor interactions.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
To evaluate the performance of Dantzig selector, a simulation study was conducted and the results based on the percentage of type II errors are analyzed. Also, another alternative for 6 factor NC design, called the Alternate No-confounding design in six factors is introduced in this study. The performance of this Alternate NC design in 6 factors is then evaluated by using Dantzig selector as an analysis method. Lastly, a section is dedicated to comparing the performance of NC-6 and Alternate NC-6 designs.
ContributorsKrishnamoorthy, Archana (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.
The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.
ContributorsAzarnoush, Bahareh (Author) / Runger, George C. (Thesis advisor) / Bekki, Jennifer (Thesis advisor) / Pan, Rong (Committee member) / Saghafian, Soroush (Committee member) / Arizona State University (Publisher)
Created2014