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 108
Description
This study examined the role of substance use in the relationship between the working alliance and outcome symptomatology. In this study, two groups of participants were formed: the at risk for substance abuse (ARSA) group consisted of participants who indicated 'almost always,' 'frequently,' 'sometimes,' or 'rarely' on either of two items on the Outcome Questionnaire-45.2 (OQ-45.2) (i.e., the eye-opener item: "After heavy drinking, I need a drink the next morning to get going" and the annoyed item: "I feel annoyed by people who criticize my drinking (or drug use)"). The non-ARSA group consisted of participants who indicated 'never' on both of the eye-opener and annoyed screening items on the OQ-45.2. Data available from a counselor-training center for a client participant sample (n = 68) was used. As part of the usual counselor training center procedures, clients completed questionnaires after their weekly counseling session. The measures included the Working Alliance Inventory and the OQ-45.2. Results revealed no significant differences between the ARSA and non-ARSA groups in working alliance, total outcome symptomology, or in any of the three subscales of symptomatology. Working alliance was not found to be significant in predicting outcome symptomatology in this sample and no moderation effect of substance use on the relationship between working alliance and outcome symptomatology was found. This study was a start into the exploration of the role of substance use in the relationship between working alliance and outcome symptomatology in individual psychotherapy. Further research should be conducted to better understand substance use populations in individual psychotherapy.
ContributorsHachiya, Laura Y (Author) / Bernstein, Bianca (Thesis advisor) / Tran, Giac-Thao (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
Previous research indicates that difficulties in emotion regulation and greater dissociation from one's emotions are often observed among trauma survivors. Further, trauma survivors often show greater negative emotions such as anger, and diminished positive emotions such as happiness. Relatively less is known about the relationship between posttraumatic stress symptoms, dissociation, emotion regulation difficulties, and non-trauma related emotional experiences in daily life. This study examined whether greater reports of posttraumatic stress symptoms, difficulties in emotion regulation, and dissociative tendencies were associated with greater intensity of anger and lower intensity of happiness during a relived emotions task (i.e., recalling and describing autobiographical memories evoking specific emotions). Participants were 50 individuals who had experienced a traumatic event and reported a range of posttraumatic stress symptoms. Participants rated how they felt while recalling specific emotional memories, as well as how they remembered feeling at the time of the event. Results showed that dissociative tendencies was the best predictor of greater intensity of anger and, contrary to the hypothesis, dissociative tendencies was predictive of greater happiness intensity as well. These findings are consistent with previous research indicating a paradoxical effect of heightened anger reactivity among individuals with dissociative tendencies. In addition, researchers have argued that individuals with a history of traumatization do not report lower positive emotional experiences. The present findings may suggest the use of dissociation as a mechanism to avoid certain trauma related emotions (e.g, fear and anxiety), in turn creating heightened experiences of other emotions such as anger and happiness.
ContributorsTorres, Dhannia L (Author) / Robinson Kurpius, Sharon (Thesis advisor) / Roberts, Nicole A. (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT Developing new non-traditional device models is gaining popularity as the silicon-based electrical device approaches its limitation when it scales down. Membrane systems, also called P systems, are a new class of biological computation model inspired by the way cells process chemical signals. Spiking Neural P systems (SNP systems), a certain kind of membrane systems, is inspired by the way the neurons in brain interact using electrical spikes. Compared to the traditional Boolean logic, SNP systems not only perform similar functions but also provide a more promising solution for reliable computation. Two basic neuron types, Low Pass (LP) neurons and High Pass (HP) neurons, are introduced. These two basic types of neurons are capable to build an arbitrary SNP neuron. This leads to the conclusion that these two basic neuron types are Turing complete since SNP systems has been proved Turing complete. These two basic types of neurons are further used as the elements to construct general-purpose arithmetic circuits, such as adder, subtractor and comparator. In this thesis, erroneous behaviors of neurons are discussed. Transmission error (spike loss) is proved to be equivalent to threshold error, which makes threshold error discussion more universal. To improve the reliability, a new structure called motif is proposed. Compared to Triple Modular Redundancy improvement, motif design presents its efficiency and effectiveness in both single neuron and arithmetic circuit analysis. DRAM-based CMOS circuits are used to implement the two basic types of neurons. Functionality of basic type neurons is proved using the SPICE simulations. The motif improved adder and the comparator, as compared to conventional Boolean logic design, are much more reliable with lower leakage, and smaller silicon area. This leads to the conclusion that SNP system could provide a more promising solution for reliable computation than the conventional Boolean logic.
ContributorsAn, Pei (Author) / Cao, Yu (Thesis advisor) / Barnaby, Hugh (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study explored several training variables that may contribute to counseling trainees' multicultural counseling self-efficacy and multicultural case conceptualization ability. Specifically, this study aimed to examine the cognitive processes that contribute to multicultural counseling competence (MCC) outcome variables. Clinical experience, multicultural knowledge, and multicultural awareness are assumed to provide the foundation for the development of these outcome variables. The role of how a counselor trainee utilizes this knowledge and awareness in working with diverse populations has not been explored. Diversity cognitive complexity (DCC) quantifies the process by which a counselor thinks about different elements of diversity in a multidimensional manner. The current study examined the role of DCC on the relationship between training variables of direct clinical experience with diverse populations, multicultural knowledge, and multicultural awareness and the two training outcomes (multicultural counseling self-efficacy and multicultural case conceptualization ability). A total of one hundred and sixty-one graduate trainees participated in the study. A series of hypotheses were tested to examine the impact of DCC on the relationship between MCC predictors (multicultural knowledge, multicultural awareness, and direct contact hours with diverse clinical populations) and two MCC outcomes: multicultural counseling self-efficacy and multicultural case conceptualization ability. Hierarchical regression analyses were utilized to test whether DCC mediated or moderated the relationship between the predictors and the outcome variables. Multicultural knowledge and clinical hours with diverse populations were significant predictors of multicultural counseling self-efficacy. Multicultural awareness was a significant predictor of multicultural case conceptualization ability. Diversity cognitive complexity was not a significantly related to any predictor or outcome variable, thus all hypotheses tested were rejected. The results of the current study support graduate programs emphasizing counselor trainees gaining multicultural knowledge and awareness as well as direct clinical experience with diverse clinical populations in an effort to foster MCC. Although diversity cognitive complexity was not significantly related to the predictor or outcome variables in this study, further research is warranted to determine the validity of the measure used to assess DCC. The findings in this study support the need for further research exploring training variables that contribute to multicultural counseling outcomes.
ContributorsRigali-Oiler, Marybeth (Author) / Robinson Kurpius, Sharon E (Thesis advisor) / Arciniega, Guillermo M (Committee member) / Nakagawa, Kathryn (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study examined the relationship that gender in interaction with interpersonal problem type has with outcome in psychotherapy. A sample of 200 individuals, who sought psychotherapy at a counselor training facility, completed the Outcome Questionnaire-45(OQ-45) and the reduced version of the Inventory of Interpersonal Problems (IIP-32). This study was aimed at examining whether gender (male and female), was related to treatment outcome, and whether this relationship was moderated by two interpersonal distress dimensions: dominance and affiliation. A hierarchical regression analyses was performed and indicated that gender did not predict psychotherapy treatment outcome, and neither dominance nor affiliation were moderators of the relationship between gender and outcome in psychotherapy.
ContributorsHoffmann, Nicole (Author) / Tracey, Terence (Thesis advisor) / Kinnier, Richard (Committee member) / Homer, Judith (Committee member) / Arizona State University (Publisher)
Created2013
Description
With increasing transistor volume and reducing feature size, it has become a major design constraint to reduce power consumption also. This has given rise to aggressive architectural changes for on-chip power management and rapid development to energy efficient hardware accelerators. Accordingly, the objective of this research work is to facilitate software developers to leverage these hardware techniques and improve energy efficiency of the system. To achieve this, I propose two solutions for Linux kernel: Optimal use of these architectural enhancements to achieve greater energy efficiency requires accurate modeling of processor power consumption. Though there are many models available in literature to model processor power consumption, there is a lack of such models to capture power consumption at the task-level. Task-level energy models are a requirement for an operating system (OS) to perform real-time power management as OS time multiplexes tasks to enable sharing of hardware resources. I propose a detailed design methodology for constructing an architecture agnostic task-level power model and incorporating it into a modern operating system to build an online task-level power profiler. The profiler is implemented inside the latest Linux kernel and validated for Intel Sandy Bridge processor. It has a negligible overhead of less than 1\% hardware resource consumption. The profiler power prediction was demonstrated for various application benchmarks from SPEC to PARSEC with less than 4\% error. I also demonstrate the importance of the proposed profiler for emerging architectural techniques through use case scenarios, which include heterogeneous computing and fine grained per-core DVFS. Along with architectural enhancement in general purpose processors to improve energy efficiency, hardware accelerators like Coarse Grain reconfigurable architecture (CGRA) are gaining popularity. Unlike vector processors, which rely on data parallelism, CGRA can provide greater flexibility and compiler level control making it more suitable for present SoC environment. To provide streamline development environment for CGRA, I propose a flexible framework in Linux to do design space exploration for CGRA. With accurate and flexible hardware models, fine grained integration with accurate architectural simulator, and Linux memory management and DMA support, a user can carry out limitless experiments on CGRA in full system environment.
ContributorsDesai, Digant Pareshkumar (Author) / Vrudhula, Sarma (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2013
Description
School bullying is a serious problem for children and adolescents, associated with a multitude of psychological and behavioral problems. Interventions at the individual level have primarily been social skills training for victims of bullying. However, investigators have had mixed results; finding little change in victimization rates. It has been suggested victims of school bullying have the social skills necessary to be effective in a bullying situation; however they experience intense emotional arousal and negative thoughts leading to an inability to use social skills. One intervention that has been getting increasing acknowledgement for its utility in the intervention literature in psychology is mindfulness. However, there has been no research conducted examining the effects of mindfulness meditation on victims of bullying. Therefore, the purpose of this study was to develop an online intervention for victims of bullying that utilizes the cutting-edge technique of mindfulness and to determine the efficacy of this intervention in the context of bullying victimization. Participants were 32 adolescents ages 11 to 14 identified by their school facilitators as victims of bullying. Repeated measures ANOVAs were used to assess the efficacy of the NMT program versus a treatment as usual (TAU) social skills program. Results revealed significant decreases in victimization and increases in mindfulness among both treatment groups from pre-test to follow-up and post-test to follow-up assessments. There were no differences found between the two treatment groups for mean victimization or mindfulness scores. Overall, the NMT program appears to be a promising online intervention for bullied teens. Directions for future research and limitations of this study were also discussed.
ContributorsYabko, Brandon (Author) / Tracey, Terence J. G. (Thesis advisor) / Homer, Judith (Committee member) / Sebren, Ann (Committee member) / Arizona State University (Publisher)
Created2013
Description
Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a lower power consumption rate. With multi-cores, there is increased need for dynamic energy management (DEM), much more than for single-core processors, as DEM for multi-cores is no more a mechanism just to ensure that a processor is kept under specified temperature limits, but also a set of techniques that manage various processor controls like dynamic voltage and frequency scaling (DVFS), task migration, fan speed, etc. to achieve a stated objective. The objectives span a wide range from maximizing throughput, minimizing power consumption, reducing peak temperature, maximizing energy efficiency, maximizing processor reliability, and so on, along with much more wider constraints of temperature, power, timing, and reliability constraints. Thus DEM can be very complex and challenging to achieve. Since often times many DEMs operate together on a single processor, there is a need to unify various DEM techniques. This dissertation address such a need. In this work, a framework for DEM is proposed that provides a unifying processor model that includes processor power, thermal, timing, and reliability models, supports various DEM control mechanisms, many different objective functions along with equally diverse constraint specifications. Using the framework, a range of novel solutions is derived for instances of DEM problems, that include maximizing processor performance, energy efficiency, or minimizing power consumption, peak temperature under constraints of maximum temperature, memory reliability and task deadlines. Finally, a robust closed-loop controller to implement the above solutions on a real processor platform with a very low operational overhead is proposed. Along with the controller design, a model identification methodology for obtaining the required power and thermal models for the controller is also discussed. The controller is architecture independent and hence easily portable across many platforms. The controller has been successfully deployed on Intel Sandy Bridge processor and the use of the controller has increased the energy efficiency of the processor by over 30%
ContributorsHanumaiah, Vinay (Author) / Vrudhula, Sarma (Thesis advisor) / Chatha, Karamvir (Committee member) / Chakrabarti, Chaitali (Committee member) / Rodriguez, Armando (Committee member) / Askin, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
Believe It! is an animated interactive computer program that delivers cognitive restructuring to adolescent females' irrational career beliefs. It challenges the irrational belief and offers more reasonable alternatives. The current study investigated the potentially differential effects of Asian versus Caucasian animated agents in delivering the treatment to young Chinese American women. The results suggested that the Asian animated agent was not significantly superior to the Caucasian animated agent. Nor was there a significant interaction between level of acculturation and the effects of the animated agents. Ways to modify the Believe It! program for Chinese American users were recommended.
ContributorsZhang, Xue (Author) / Horan, John J (Thesis advisor) / Homer, Judith (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive framework based on a sequential Bayesian tracking method is proposed to adaptively select the cardiac parameters that minimize the estimation error, thus precluding the need for pre-processing. Simulations using real ECG data from the online Physionet database demonstrate the improvement in performance of the proposed algorithm in accurately estimating critical heart disease parameters. In addition, two new approaches to ECG modeling are presented using the interacting multiple model and the sequential Markov chain Monte Carlo technique with adaptive model selection. Both these methods can adaptively choose between different models for various ECG beat morphologies without requiring prior ECG information, as demonstrated by using real ECG signals. A supervised Bayesian maximum-likelihood (ML) based classifier uses the estimated model parameters to classify different types of cardiac arrhythmias. However, the non-availability of sufficient amounts of representative training data and the large inter-patient variability pose a challenge to the existing supervised learning algorithms, resulting in a poor classification performance. In addition, recently developed unsupervised learning methods require a priori knowledge on the number of diseases to cluster the ECG data, which often evolves over time. In order to address these issues, an adaptive learning ECG classification method that uses Dirichlet process Gaussian mixture models is proposed. This approach does not place any restriction on the number of disease classes, nor does it require any training data. This algorithm is adapted to be patient-specific by labeling or identifying the generated mixtures using the Bayesian ML method, assuming the availability of labeled training data.
ContributorsEdla, Shwetha Reddy (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2012