Matching Items (576)
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Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java

Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
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As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily

As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
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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

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.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description

The concept of Nature Made Candles was to “educate candle lovers on the importance of knowing what is in the candle. Everyone should know what they are inhaling...no matter how nice (or not) it smells. Earth needed a candle for enjoying scents and sights without hindering health, so we made

The concept of Nature Made Candles was to “educate candle lovers on the importance of knowing what is in the candle. Everyone should know what they are inhaling...no matter how nice (or not) it smells. Earth needed a candle for enjoying scents and sights without hindering health, so we made one.” The objective evolved into educating the student population of Arizona State University (ASU) about what ingredients go into commercial candles, with a particular focus on the wax and scent, as well as giving students a free candle that emulated the holistic ingredients they were educated on. This project was designed to be a quality improvement and health promotion project with an emphasis on the ASU student population. The purpose of the project was to find a type of candle that was friendly to the lungs of all individuals who wanted candles in their household.

ContributorsMuenchen, Cassandra (Co-author) / Waterman, Grace (Co-author) / Jaurigue, Lisa (Thesis director) / Kenny, Katherine (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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The following paper explores the various effects of stress on the endocrine system. Many understand that being stressed can jeopardize maintaining adequate health, but what specifically happens when humans are stressed? Why does stress affect human health? This paper delves into background information, previous research, and the depths to which

The following paper explores the various effects of stress on the endocrine system. Many understand that being stressed can jeopardize maintaining adequate health, but what specifically happens when humans are stressed? Why does stress affect human health? This paper delves into background information, previous research, and the depths to which stress negatively affects the body. The effects stress has on the endocrine system, specifically on the hypothalamic-pituitary-thyroid axis (HPT) and hypothalamic-pituitary-adrenal axis (HPA), is discussed, and additionally, at home de-stressing methods are researched. The study included a set of participants at Arizona State University. The method took place over the course of 2 weeks: one normal week, and the other with the implementation of a de-stressing method. The normal week involved the participants living their daily lives with the addition of a stress-measuring survey, while the second week involved implementing a de-stressing method and stress-measuring survey. The purpose of this study was to discover if there was a correlation between performing these relaxation activities and decreasing stress levels in ASU students. The results found that students reported they felt more relaxed and calm after the activities. Overall, this thesis provides information and first hand research on the effects of stress and stress-reducing activities and discusses the importance of maintaining lower stress levels throughout everyday life.

ContributorsWeissmann, Megan Diane (Co-author) / Gebara, Nayla (Co-author) / Don, Rachael (Thesis director) / Irving, Andrea (Committee member) / Kizer, Elizabeth (Committee member) / College of Health Solutions (Contributor) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Purpose: This qualitative research aimed to create a developmentally and gender-appropriate game-based intervention to promote Human Papillomavirus (HPV) vaccination in adolescents. <br/>Background: Ranking as the most common sexually transmitted infection, about 80 million Americans are currently infected by HPV, and it continues to increase with an estimated 14 million new

Purpose: This qualitative research aimed to create a developmentally and gender-appropriate game-based intervention to promote Human Papillomavirus (HPV) vaccination in adolescents. <br/>Background: Ranking as the most common sexually transmitted infection, about 80 million Americans are currently infected by HPV, and it continues to increase with an estimated 14 million new cases yearly. Certain types of HPV have been significantly associated with cervical, vaginal, and vulvar cancers in women; penile cancers in men; and oropharyngeal and anal cancers in both men and women. Despite HPV vaccination being one of the most effective methods in preventing HPV-associated cancers, vaccination rates remain suboptimal in adolescents. Game-based intervention, a novel medium that is popular with adolescents, has been shown to be effective in promoting health behaviors. <br/>Methods: Sample/Sampling. We used purposeful sampling to recruit eight adolescent-parent dyads (N = 16) which represented both sexes (4 boys, 4 girls) and different racial/ethnic groups (White, Black, Latino, Asian American) in the United States. The inclusion criteria for the dyads were: (1) a child aged 11-14 years and his/her parent, and (2) ability to speak, read, write, and understand English. Procedure. After eligible families consented to their participation, semi-structured interviews (each 60-90 minutes long) were conducted with each adolescent-parent dyad in a quiet and private room. Each dyad received $50 to acknowledge their time and effort. Measure. The interview questions consisted of two parts: (a) those related to game design, functioning, and feasibility of implementation; (b) those related to theoretical constructs of the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB). Data analysis. The interviews were audio-recorded with permission and manually transcribed into textual data. Two researchers confirmed the verbatim transcription. We use pre-developed codes to identify each participant’s responses and organize data and develop themes based on the HBM and TPB constructs. After the analysis was completed, three researchers in the team reviewed the results and discussed the discrepancies until a consensus is reached.<br/>Results: The findings suggested that the most common motivating factors for adolescents’ HPV vaccination were its effectiveness, benefits, convenience, affordable cost, reminders via text, and recommendation by a health care provider. Regarding the content included in the HPV game, participants suggested including information about who and when should receive the vaccine, what is HPV and the vaccination, what are the consequences if infected, the side effects of the vaccine, and where to receive the vaccine. The preferred game design elements were: 15 minutes long, stories about fighting or action, option to choose characters/avatars, motivating factors (i.e., rewards such as allowing users to advance levels and receive coins when correctly answering questions), use of a portable electronic device (e.g., tablet) to deliver the education. Participants were open to multiplayer function which assists in a facilitated conversation about HPV and the HPV vaccine. Overall, the participants concluded enthusiasm for an interactive yet engaging game-based intervention to learn about the HPV vaccine with the goal to increase HPV vaccination in adolescents. <br/>Implications: Tailored educational games have the potential to decrease the stigma of HPV and HPV vaccination, increasing communication between the adolescent, parent, and healthcare provider, as well as increase the overall HPV vaccination rate.

ContributorsBeaman, Abigail Marie (Author) / Chen, Angela Chia-Chen (Thesis director) / Amresh, Ashish (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

Objective: This study looked at three key variables of fear of COVID-19, preventative behaviors, and vaccination intent among college students in the United Sates. In addition, the three key variables were compared between genders, age groups, race groups, and over time to see if there were any significant findings. <br/>Method:

Objective: This study looked at three key variables of fear of COVID-19, preventative behaviors, and vaccination intent among college students in the United Sates. In addition, the three key variables were compared between genders, age groups, race groups, and over time to see if there were any significant findings. <br/>Method: This longitudinal study consisted of two anonymous online surveys administered on REDCap before and after a COVID-19 vaccine became available. <br/>Results: The findings suggested positive correlations between students’ fear of COVID-19 and their preventative behaviors with the passing of time. Hispanic/Latino participants had significantly higher fear of COVID-19 scores compared to Non-Hispanic Whites and other races at Wave I and II. Participants between 25 and 30 years old had a marginally greater difference fear of COVID-19 score compared to those less than 25. Females had significantly higher mean preventative behavior score than males at Wave II. There was a significant association between race/ethnicity groups and vaccination intent. <br/>Conclusion: Knowing why different groups do not engage in recommended preventative behaviors or receive vaccinations can tell us more about what tailored interventions may need to be developed and implemented to promote health and wellbeing in this population. Further research needs to be done regarding race, gender, and age and how these different groups of college students are responding to COVID-19 and why.

ContributorsFones, Shaelyn Kaye (Author) / Chen, Angela (Thesis director) / Han, SeungYong (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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In a healthcare system already struggling with burnout among its professionals, the COVID-19 pandemic presented a barrage of personal and occupational strife to US healthcare workers. Structural and everyday discrimination contributed to the health inequities of people of color in the US, exacerbated by COVID-19-related racism and xenophobia. There is

In a healthcare system already struggling with burnout among its professionals, the COVID-19 pandemic presented a barrage of personal and occupational strife to US healthcare workers. Structural and everyday discrimination contributed to the health inequities of people of color in the US, exacerbated by COVID-19-related racism and xenophobia. There is little research regarding the effects of COVID-19 and related and/or concurring discrimination upon minority nursing staff, despite their importance in supporting the diverse American patient population with culturally competent, tireless care amid the pandemic. This cross-sectional survey study aimed to examine 1) the relationships between discrimination, social support, resilience, and quality of life among minority nursing staff in the US during COVID-19, and 2) the differences of discrimination, social support resilience, and quality of life among minority nursing staff between different racial/ethnic groups during COVID-19. The sample (n = 514) included Black/African American (n = 161, 31.4%), Latinx/Hispanic (n = 131, 25.5%), Asian (n = 87, 17%), Native American/Alaskan Native (n = 69, 13.5%), and Pacific Islander (n = 65, 12.7%) nursing staff from 47 US states. The multiple regression results showed that witnessing discrimination was associated with a lower quality of life score, while higher social support and resilience scores were associated with higher quality of life scores across all racial groups. Furthermore, while participants from all racial groups witnessed and experienced discrimination, Hispanic/Latinx nursing staff experienced discrimination most commonly, alongside having lowest quality of life and highest resilience scores. Native American/Alaskan Native nursing staff had similarly high discrimination and low quality of life, although low resilience scores. Our findings suggest that minority nursing staff who have higher COVID-19 morbidity and mortality rates (Hispanic/Latinx, Native American/Alaskan Native) were left more vulnerable to negative effects from discrimination. Hispanic/Latinx nursing staff reported a relatively higher resilience score than all other groups, potentially attributed to the positive effects of biculturality in the workplace, however, the low average quality of life score suggests a simultaneous erosion of well-being. Compared to all other groups, Native American and Alaskan Native nursing staff’s low resilience and quality of life scores suggest a potential compounding effect of historical trauma affecting their well-being, especially in contrast to Hispanic/Latinx nursing staff. This study has broader implications for research on the lasting effects of COVID-19 on minority healthcare workers’ and communities’ well-being, especially regarding Hispanic/Latinx and Native American/Alaskan Native nursing staff.

ContributorsLaufer, Annika Noreen (Author) / Chen, Angela (Thesis director) / Fries, Kathleen (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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“College Students' Perceived Risk of COVID-19 Infection, Protective Behaviors, and Vaccination Intent” is a thesis project based on research conducted from the end of 2020 to the beginning of 2021. This project investigated various protective behavior factors against the Coronavirus (COVID-19) based on gender, race/ethnicity, and financial difficulty of college

“College Students' Perceived Risk of COVID-19 Infection, Protective Behaviors, and Vaccination Intent” is a thesis project based on research conducted from the end of 2020 to the beginning of 2021. This project investigated various protective behavior factors against the Coronavirus (COVID-19) based on gender, race/ethnicity, and financial difficulty of college students in the United States. The plan for this thesis project was to send out two surveys through Amazon Mturk to a group of 500 college students. The first survey further narrowed down the sample size to include only the participants who met the eligibility factors. A second larger survey was sent to this sample which included the data for this research project. This paper will explore the topics of perceived risk of becoming infected with COVID-19, preventive behaviors, vaccination intent based on gender, race/ethnicity, and financial difficulty.

ContributorsMattingly, Haley Nicole (Author) / Chen, Angela (Thesis director) / Han, SeungYong (Committee member) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05