Matching Items (43,917)
Description
This thesis seeks to answer the question: "What do artistic representations add to the dialogue about the U.S.-Mexico border and immigration beyond political rhetoric and popular media portrayals?" Drawing on political communications (as put forth by Edelman and Altheide), socio-political construction (particularly the White Racial Frame put forth by Feagin), and collective memory theory (especially those of Halbwachs and Pollak), this thesis uses a dual-coding, content analysis to examine the linguistic and visual messages disseminated through news media. Then, interviews with and the work of six immigrant artists are examined for their contribution to the information put forth in the news media. This study finds that news reporting bias falls along a continuum from pro-immigration to extreme anti-immigration (labeled "fearful" reporting). The news media skew strongly toward anti-immigration to fearful in bias, and there is no opposite pro-immigration bias. Through observations of artists' work, the study concludes that artistic representations of the border can fill this strongly pro-immigration void on this bias continuum.
Contributors
McCarty, Kelly Erin (Author) / Téllez, Michelle (Thesis advisor) / Stancliff, Michael (Committee member) / Segura, Joseph (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
Contributors
Lee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Proton exchange membrane fuel cells (PEMFCs) run on pure hydrogen and oxygen (or air), producing electricity, water, and some heat. This makes PEMFC an attractive option for clean power generation. PEMFCs also operate at low temperature which makes them quick to start up and easy to handle. PEMFCs have several important limitations which must be overcome before commercial viability can be achieved. Active areas of research into making them commercially viable include reducing the cost, size and weight of fuel cells while also increasing their durability and performance. A growing and important part of this research involves the computer modeling of fuel cells. High quality computer modeling and simulation of fuel cells can help speed up the discovery of optimized fuel cell components. Computer modeling can also help improve fundamental understanding of the mechanisms and reactions that take place within the fuel cell. The work presented in this thesis describes a procedure for utilizing computer modeling to create high quality fuel cell simulations using Ansys Fluent 12.1. Methods for creating computer aided design (CAD) models of fuel cells are discussed. Detailed simulation parameters are described and emphasis is placed on establishing convergence criteria which are essential for producing consistent results. A mesh sensitivity study of the catalyst and membrane layers is presented showing the importance of adhering to strictly defined convergence criteria. A study of iteration sensitivity of the simulation at low and high current densities is performed which demonstrates the variance in the rate of convergence and the absolute difference between solution values derived at low numbers of iterations and high numbers of iterations.
Contributors
Arvay, Adam (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Peng, Xihong (Committee member) / Liang, Yong (Committee member) / Subach, James (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Maricopa County has exceeded the 24 hour National Ambient Air Quality Standard (NAAQS) for Particulate Matter 10 micrometers in diameter or smaller (PM-10) of 150 micrograms per meter cubed (μg/m3) since 1990. Construction and construction related activities have been recognized as the highest contributors to high PM-10 levels. An analysis of days exceeding 150 μg/m3 for four of Maricopa County‟s monitors that most frequently exceed this level during the years 2007, 2008, and 2009 has been performed. Noted contributors to PM-10 levels have been identified in the study, including earthmoving permits, stationary source permits, vacant lots, and agriculture on two mile radius maps around each monitor. PM-10 levels and wind speeds for each date exceeding 225 μg/m3 were reviewed to find specific weather or anthropogenic sources for the high PM-10 levels. Weather patterns for days where multiple monitors exceed 150 μg/m3 were reviewed to find correlations between daily weather and high PM-10 levels. It was found that areas with more earthmoving permits had fewer days exceeding 150 μg/m3 than areas with more stationary permits, vacant lots, or agriculture. The Higley and Buckeye monitors showed increases in PM-10 levels when winds came from areas covered by agricultural land. West 43rd Avenue and Durango monitors saw PM-10 rise when the winds came in over large stationary sources, like aggregate plants. A correlation between weather events and PM-10 exceedances was also found on multiple monitors for dates both in 2007, and 2009.
Contributors
Cook, Heloise (Author) / Olson, Larry (Thesis advisor) / Brown, Albert (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created
2011
Description
The following dissertation provides perspectives on the social, political, economic, and academic influences on language use, and particularly heritage language use, within the Filipino American community. What is the nature of language in this community? In what ways does language exist or co-exist? The hypothesis that autochthonous Filipino languages in the United States cease to be spoken in favor of English by Filipino Americans was tested through mixed methods of research. Literature and databases were reviewed which provided information concerning statistics, issues, and policies relating to language in Filipino America. Field research and interviews were conducted in which language use was of key interest. Results varied individually and contextually. Language seems to exist within the Filipino American community on a dynamic continuum. Immigrant Filipino Americans appear to be bilingual and multilingual. Second generation Filipino Americans tend to be English dominant with a range of bilingualism. The California Department of Education (CDOE) appears to foster bilingualism / multilingualism through its World Languages Departments (secondary education level), by offering language courses, such as Tagalog-based Filipino. Efforts to maintain non-English, Filipino languages in Arizona are less conspicuous, but they do exist primarily in familial and entrepreneurial ways.
Contributors
Axel, Joseph (Author) / Mccarty, Teresa (Thesis advisor) / Wiley, Terrence (Committee member) / Faltis, Christian (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Since the 1988 uprising, a transnational advocacy network has formed around the issue of democracy and human rights in Burma. Within this transnational advocacy network, personal narratives of trauma have been promulgated in both international and oppositional news media and human rights reports. My thesis critically analyzes the use of the trauma narrative for advocacy purposes by the transnational advocacy network that has emerged around Burma and reveals the degree to which these narratives adhere to a Western, individualistic meta-narrative focused on political and civil liberties. Examining the "boomerang" pattern and the concept of marketability of movements, I highlight the characteristics of the 1988 uprising and subsequent opposition movement that attracted international interest. Reflecting on the psychological aspects of constructing trauma narratives, I then review the scholarship which links trauma narratives to social and human rights movements. Using a Foucauldian approach to discourse analysis, I subsequently explain my methodology in analyzing the personal narratives I have chosen. Beyond a theoretical discussion of trauma narratives and transnational advocacy networks, I analyze the use of personal narratives of activists involved in the 1988 uprising and the emergence of Aung San Suu Kyi's life story as a compelling narrative for Western audiences. I then explore the structure of human rights reports which situate personal narratives of trauma within the framework of international human rights law. I note the differences in the construction of traumatic narratives of agency and those of victimization. Finally, using Cyclone Nargis as a case study, I uncover the discursive divide between human rights and humanitarian actors and their use of personal narratives to support different discursive constructions of the aid effort in the aftermath of the cyclone. I conclude with an appeal to a more reflexive approach to advocacy work reliant on trauma narratives and highlight feminist methodologies that have been successful in bringing marginalized narratives to the center of human rights discussions.
Contributors
Bynum, Kate Elliott (Author) / Stancliff, Michael (Thesis advisor) / Friedrich, Patricia (Committee member) / Vaughan, Suzanne (Committee member) / Arizona State University (Publisher)
Created
2011
Description
More than 30% of college entrants are placed in remedial mathematics (RM). Given that an explicit relationship exists between students' high school mathematics and college success in science, technology, engineering, and mathematical (STEM) fields, it is important to understand RM students' characteristics in high school. Using the Education Longitudinal Survey 2002/2006 data, this study evaluated more than 130 variables for statistical and practical significance. The variables included standard demographic data, prior achievement and transcript data, family and teacher perceptions, school characteristics, and student attitudinal variables, all of which are identified as influential in mathematical success. These variables were analyzed using logistic regression models to estimate the likelihood that a student would be placed into RM. As might be expected, student test scores, highest mathematics course taken, and high school grade point average were the strongest predictors of success in college mathematics courses. Attitude variables had a marginal effect on the most advantaged students, but their effect cannot be evaluated for disadvantaged students, due to a non-random pattern of missing data. Further research should concentrate on obtaining answers to the attitudinal questions and investigating their influence and interaction with academic indicators.
Contributors
Barber, Rebecca (Author) / Garcia, David R. (Thesis advisor) / Powers, Jeanne (Committee member) / Rodrigue Mcintyre, Lisa (Committee member) / Arizona State University (Publisher)
Created
2011
Description
Risk assessment instruments play a significant role in correctional intervention and guide decisions about supervision and treatment. Although advances have been made in risk assessment over the past 50 years, limited attention has been given to risk assessment for domestic violence offenders. This study investigates the use of the Domestic Violence Screening Inventory (DVSI) and the Offender Screening Tool (OST) with a sample of 573 offenders convicted of domestic violence offenses and sentenced to supervised probation in Maricopa County, Arizona. The study has two purposes. The first is to assess the predictive validity of the existing assessment tools with a sample of domestic violence offenders, using a number of probation outcomes. The second is to identify the most significant predictors of probation outcomes. Predictive validity is assessed using crosstabulations, bivariate correlations, and the Receiver Operating Characteristic (ROC) curve. Logistic regression is used to identify the most significant predictors of probation outcomes. The DVSI and the OST were found to be predictive of probation outcomes and were most predictive of the outcomes petition to revoke filed, petition to revoke filed for a violation of specialized domestic violence conditions, and unsuccessful probation status. Significant predictors include demographics, criminal history, current offense, victim characteristics, static factors, supervision variables and dynamic variables. The most consistent predictors were supervision variables and dynamic risk factors. The supervision variables include being supervised on a specialized domestic violence caseload and changes in supervision, either an increase or decrease, during the probation grant. The dynamic variables include employment and substance abuse. The overall findings provide support for the continued use of the DVSI and the OST and are consistent with the literature on evidence-based practices for correctional interventions. However, the predictive validity of the assessments varied across sub-groups and the instruments were less predictive for females and offenders with non-intimate partner victims. In addition, study variables only explained a small portion of the variation in the probation outcomes. Additional research is needed, expanding beyond the psychology of criminal conduct, to continue to improve existing risk assessment tools and identify more salient predictors of probation outcomes for domestic violence offenders.
Contributors
Ferguson, Jennifer (Author) / Hepburn, John R. (Thesis advisor) / Ashford, José B. (Committee member) / Johnson, John M. (Committee member) / Arizona State University (Publisher)
Created
2011
Description
ABSTRACT This study details the pilot of a collaborative peer-coaching model as a form of job embedded professional development, to guide teacher collaboration and planning based on benchmark assessments. The collaborative peer-coaching framework used (including reflection and collaboration about student data, and classroom instruction) was informed by the five propositions outlined by the National Board of Professional Teacher Standards (NBPTS). This intervention included teacher training, discussion (pre and post instruction), collaboration about student benchmark data, and classroom observations with further data collected through surveys and interviews. Using a mixed methods approach to data collection and analysis, I focused on how participants engaged in a collaborative peer-coaching model to guide their instruction based on the use of student data they collected from common benchmark assessments.
Contributors
Wright, Kerri M (Author) / Beardsley, Audrey (Thesis advisor) / Serafini, Frank (Committee member) / Reyes, Elba (Committee member) / Arizona State University (Publisher)
Created
2011
Description
The link between victimization and offending is well established in the literature, yet an unexplored causal pathway within this relationship is concerned with why some individuals engage in maladaptive coping in response to victimization. In particular, those with low self-control may be attracted to problematic yet immediately gratifying forms of coping post-victimization (e.g., substance use), which may increase their likelihood of violent offending in the future. Using three waves of adolescent panel data from the Gang Resistance Education and Training (GREAT) program, this research examines: (1) whether individuals with low-self control are more likely to engage in substance use coping following violent victimization, and (2) whether victims with low self-control who engage in substance use coping are more likely to commit violent offenses in the future. The results from negative binomial regressions support these hypotheses, even after controlling for prior offending, peer influences, prior substance abuse, and other forms of offending. The implications for integrating general strain and self-control theories, as well as for our understanding of the victimization-offending overlap, are discussed.
Contributors
Turanovic, Jillian (Author) / Pratt, Travis C. (Thesis advisor) / Reisig, Michael D (Committee member) / Fornango, Robert J (Committee member) / Arizona State University (Publisher)
Created
2011