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- Genre: Masters Thesis
- Creators: Arizona State University
- Member of: ASU Electronic Theses and Dissertations
Description
Mental health disparities in the U.S. among racial and ethnic minorities are a serious public health issue associated with substantial ethical and economic costs as well as negative health outcomes. Compared with Whites, racial/ethnic minorities have been found to have greater mental disorder symptomatology, however, very little research exists on how this impacts functional outcomes and quality of life. Additionally, research addressing the impact of bias on symptomatology and functional outcomes, especially across racial/ethnic groups, is lacking. Using the International Classification of Functioning, Disability, and Health (ICF) Biopsychosocial Model of Disability as a conceptual framework, the current study aims to address the relationship between mental disorder symptomatology and functional impairment across racial/ethnic groups, as well as evaluate the influence of perceived bias on this association. These relationships were examined using data from the Collaborative Psychiatric Epidemiological Surveys (CPES) among White, Black, Latinx, and Asian American individuals (N = 10,276). Variables include past-30-day functional impairment, past-year mental disorder symptomatology, and lifetime perceived bias. One-way analyses of variance were conducted to compare mental disorder symptomatology and perceived bias across racial/ethnic groups. Pearson correlation analyses were conducted to assess the relationship between mental disorder symptomatology and functional impairment across racial/ethnic groups. Zero-inflated negative binomial regressions were conducted to evaluate the moderating effect of perceived bias on the relationship between mental disorder symptomatology and functional impairment across racial/ethnic groups. Additional exploratory analyses were conducted to assess the relationships between mental disorder symptomatology, perceived bias, and various domains of functional impairment across racial/ethnic groups. Findings speak to the need for additional research on predictors and correlates of mental health outcomes, such as social support, community, and other resiliency factors. Additionally, the need for broader conceptualizations of how bias, prejudice, stigma, and intersectional identity may impact health and wellbeing across diverse populations is illustrated in this work. Overall, findings indicate the continued existence of disparities in mental health across racial/ethnic groups and reify the need for additional work to address this public health problem.
ContributorsYu, Kimberly (Author) / Perez, Marisol (Thesis advisor) / Edwards, Michael (Committee member) / Ha, Thao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Healthy lifestyle behaviors including quality nutrition have been shown to successfully prevent chronic disease or minimize symptoms. However, many physicians lack the knowledge and skills to provide adequate nutrition counseling and education for their patients. A major component of this problem is that medical schools are not required to teach nutrition education. The purpose of this feasibility study was to compare the changes in the perceived importance of nutrition in the medical field in medical students before and after participating in a week-long interactive nutrition course in order to determine if a week-long course can positively influence students’ perceptions of nutrition. Ultimately by changing these perceptions, medical students may be able to better help patients prevent chronic disease. The participants were first year medical students at the Mayo Clinic School of Medicine (Scottsdale, AZ) who chose to participate in this medical school “Selective”. The study included a five-day curriculum of case-studies, lectures from specialized health professionals, and a cooking class led by a chef who trained in France. An anonymous pre- and post-study questionnaire with five-point Likert scale questions was used to measure changes in attitudes. The data suggest that students’ perceptions regarding the importance and relevance of nutrition in the medical shifted slightly more positive after attending this Selective, although these shifts in attitude were not statistically significant. Limitations of this study include a small sample size and selection bias, which may have decreased the potential of having significant results. Both of these factors also make the results of this study less generalizable to all medical students. This study supports the need for a larger experimental study of a similar design to verify that an interactive, evidence-based nutrition class and culinary experience increases medical students’ positive perceptions of nutrition in the medical field.
ContributorsBaum, Makenna (Author) / Johnston, Carol (Thesis advisor) / Levinson, Simin (Committee member) / Sears, Dorothy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Mobile health (mHealth) applications (apps) hold tremendous potential for addressing chronic health conditions. Smartphones are now the most popular form of computing, and the ubiquitous “always with us, always on” nature of mobile technology makes them amenable to interventions aimed and managing chronic disease. Several challenges exist, however, such as the difficulty in determining mHealth effects due to the rapidly changing nature of the technology and the challenges presented to existing methods of evaluation, and the ability to ensure end users consistently use the technology in order to achieve the desired effects. The latter challenge is in adherence, defined as the extent to which a patient conducts the activities defined in a clinical protocol (i.e. an intervention plan). Further, higher levels of adherence should lead to greater effects of the intervention (the greater fidelity to the protocol, the more benefit one should receive from the protocol). mHealth has limitations in these areas; the ability to have patients sustainably adhere to a protocol, and the ability to drive intervention effect sizes. My research considers personalized interventions, a new approach of study in the mHealth community, as a potential remedy to these limitations. Specifically, in the context of a pediatric preventative anxiety protocol, I introduce algorithms to drive greater levels of adherence and greater effect sizes by incorporating per-patient (personalized) information. These algorithms have been implemented within an existing mHealth app for middle school that has been successfully deployed in a school in the Phoenix Arizona metropolitan area. The number of users is small (n=3) so a case-by-case analysis of app usage is presented. In addition simulated user behaviors based on models of adherence and effects sizes over time are presented as a means to demonstrate the potential impact of personalized deployments on a larger scale.
ContributorsSingal, Vishakha (Author) / Gary, Kevin (Thesis advisor) / Pina, Armando (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2019
Description
An airborne, tethered, multi-rotor wind turbine, effectively a rotorcraft kite, provides one platform for accessing the energy in high altitude winds. The craft is maintained at altitude by its rotors operating in autorotation, and its equilibrium attitude and dynamic performance are affected by the aerodynamic rotor forces, which in turn are affected by the orientation and motion of the craft. The aerodynamic performance of such rotors can vary significantly depending on orientation, influencing the efficiency of the system. This thesis analyzes the aerodynamic performance of an autorotating rotor through a range of angles of attack covering those experienced by a typical autogyro through that of a horizontal-axis wind turbine. To study the behavior of such rotors, an analytical model using the blade element theory coupled with momentum theory was developed. The model uses a rigid-rotor assumption and is nominally limited to cases of small induced inflow angle and constant induced velocity. The model allows for linear twist. In order to validate the model, several rotors -- off-the-shelf model-aircraft propellers -- were tested in a low speed wind tunnel. Custom built mounts allowed rotor angles of attack from 0 to 90 degrees in the test section, providing data for lift, drag, thrust, horizontal force, and angular velocity. Experimental results showed increasing thrust and angular velocity with rising pitch angles, whereas the in-plane horizontal force peaked and dropped after a certain value. The analytical results revealed a disagreement with the experimental trends, especially at high pitch angles. The discrepancy was attributed to the rotor operating in turbulent wake and vortex ring states at high pitch angles, where momentum theory has proven to be invalid. Also, aerodynamic design constants, which are not precisely known for the test propellers, have an underlying effect on the analytical model. The developments of the thesis suggest that a different analytical model may be needed for high rotor angles of attack. However, adding a term for resisting torque to the model gives analytical results that are similar to the experimental values.
ContributorsHota, Piyush (Author) / Wells, Valana L. (Thesis advisor) / Calhoun, Ronald (Committee member) / Garrett, Frederick (Committee member) / Arizona State University (Publisher)
Created2019
Description
This thesis addresses the problem of recommending a viewpoint for aesthetic photography. Viewpoint recommendation is suggesting the best camera pose to capture a visually pleasing photograph of the subject of interest by using any end-user device such as drone, mobile robot or smartphone. Solving this problem enables to capture visually pleasing photographs autonomously in areal photography, wildlife photography, landscape photography or in personal photography.
The viewpoint recommendation problem can be divided into two stages: (a) generating a set of dense novel views based on the basis views captured about the subject. The dense novel views are useful to better understand the scene and to know how the subject looks from different viewpoints and (b) each novel is scored based on how aesthetically good it is. The viewpoint with the greatest aesthetic score is recommended for capturing a visually pleasing photograph.
The viewpoint recommendation problem can be divided into two stages: (a) generating a set of dense novel views based on the basis views captured about the subject. The dense novel views are useful to better understand the scene and to know how the subject looks from different viewpoints and (b) each novel is scored based on how aesthetically good it is. The viewpoint with the greatest aesthetic score is recommended for capturing a visually pleasing photograph.
ContributorsKatukuri, Sathish Kumar (Author) / LiKamWa, Robert (Thesis advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
The experience of language can, as any other experience, change the way that the human brain is organized and connected. Fluency in more than one language should, in turn, change the brain in the same way. Recent research has focused on the differences in processing between bilinguals and monolinguals, and has even ventured into using different neuroimaging techniques to study why these differences exist. What previous research has failed to identify is the mechanism that is responsible for the difference in processing. In an attempt to gather information about these effects, this study explores the possibility that bilingual individuals utilize lower signal strength (and by comparison less biological energy) to complete the same tasks that monolingual individuals do. Using an electroencephalograph (EEG), signal strength is retrieved during two perceptual tasks, the Landolt C and the critical flicker fusion threshold, as well as one executive task (the Stroop task). Most likely due to small sample size, bilingual participants did not perform better than monolingual participants on any of the tasks they were given, but they did show a lower EEG signal strength during the Landolt C task than monolingual participants. Monolingual participants showed a lower EEG signal strength during the Stroop task, which stands to support the idea that a linguistic processing task adds complexity to the bilingual brain. Likewise, analysis revealed a significantly lower signal strength during the critical flicker fusion task for monolingual participants than for bilingual participants. Monolingual participants also had a significantly different variability during the critical flicker fusion threshold task, suggesting that becoming bilingual creates an entirely separate population of individuals. Future research should perform analysis with the addition of a prefrontal cortex electrode to determine if less collaboration during processing is present for bilinguals, and if signal complexity in the prefrontal cortex is lower than other electrodes.
ContributorsMcLees, Sallie (Author) / Náñez Sr., José E (Thesis advisor) / Holloway, Steven (Committee member) / Duran, Nicholas (Committee member) / Arizona State University (Publisher)
Created2019
Description
Tucked peacefully into mountains just north of the City of São Paulo, the largest metropolitan area in South America, sits the Cantareira Reservoir System. This massive water catchment network received worldwide coverage in 2014 and 2015 as one of the worst droughts in a century hit the region, threatening to collapse the system. In the years since the peak of the drought, the media has changed its focus, the reservoirs have begun a slow recovery, but the people of the region have had to live with the consequences of this difficult period. Faced with an uncertain future, the people continue to grapple with the historic struggles of rural life, while being faced by new threats to the social, environmental, and technological order that has for a long time stabilized the region. My thesis explores the narrative imaginaries that individuals have pertaining to their personal future and that of the region. It delves into the identity of the Rural Producer, the battle to conserve and preserve native forest, issues surrounding the governance of common resources, and what actors perceive to be the biggest advantages and threats to the sustainable future of the region. Utilizing a set of twenty expert elicitation interviews, data was collected from a variety of actors representing a number of roles and positions within the system. My analysis connects disparate individual narratives, illuminating how they connect together with the narratives of other respondents, creating a regional narrative that illustrates a set of desired outcomes for the region. This paper does not attempt to operationalize solutions for the issues that face the region, it does however serve to provide a context for the historical and contemporary issues that exist, a means by which to consider how they may be approached, and ultimately as a tool for policy makers to make more informed decisions going forward.
ContributorsStaats, Cody B. (Author) / Parmentier, Mary Jane C. (Thesis advisor) / Haglund, LaDawn (Committee member) / Bennett, Michael G. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Blockchain technology enables peer-to-peer transactions through the elimination of the need for a centralized entity governing consensus. Rather than having a centralized database, the data is distributed across multiple computers which enables crash fault tolerance as well as makes the system difficult to tamper with due to a distributed consensus algorithm.
In this research, the potential of blockchain technology to manage energy transactions is examined. The energy production landscape is being reshaped by distributed energy resources (DERs): photo-voltaic panels, electric vehicles, smart appliances, and battery storage. Distributed energy sources such as microgrids, household solar installations, community solar installations, and plug-in hybrid vehicles enable energy consumers to act as providers of energy themselves, hence acting as 'prosumers' of energy.
Blockchain Technology facilitates managing the transactions between involved prosumers using 'Smart Contracts' by tokenizing energy into assets. Better utilization of grid assets lowers costs and also presents the opportunity to buy energy at a reasonable price while staying connected with the utility company. This technology acts as a backbone for 2 models applicable to transactional energy marketplace viz. 'Real-Time Energy Marketplace' and 'Energy Futures'. In the first model, the prosumers are given a choice to bid for a price for energy within a stipulated period of time, while the Utility Company acts as an operating entity. In the second model, the marketplace is more liberal, where the utility company is not involved as an operator. The Utility company facilitates infrastructure and manages accounts for all users, but does not endorse or govern transactions related to energy bidding. These smart contracts are not time bounded and can be suspended by the utility during periods of network instability.
In this research, the potential of blockchain technology to manage energy transactions is examined. The energy production landscape is being reshaped by distributed energy resources (DERs): photo-voltaic panels, electric vehicles, smart appliances, and battery storage. Distributed energy sources such as microgrids, household solar installations, community solar installations, and plug-in hybrid vehicles enable energy consumers to act as providers of energy themselves, hence acting as 'prosumers' of energy.
Blockchain Technology facilitates managing the transactions between involved prosumers using 'Smart Contracts' by tokenizing energy into assets. Better utilization of grid assets lowers costs and also presents the opportunity to buy energy at a reasonable price while staying connected with the utility company. This technology acts as a backbone for 2 models applicable to transactional energy marketplace viz. 'Real-Time Energy Marketplace' and 'Energy Futures'. In the first model, the prosumers are given a choice to bid for a price for energy within a stipulated period of time, while the Utility Company acts as an operating entity. In the second model, the marketplace is more liberal, where the utility company is not involved as an operator. The Utility company facilitates infrastructure and manages accounts for all users, but does not endorse or govern transactions related to energy bidding. These smart contracts are not time bounded and can be suspended by the utility during periods of network instability.
ContributorsSadaye, Raj Anil (Author) / Candan, Kasim S (Thesis advisor) / Boscovic, Dragan (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the exponential growth in video content over the period of the last few years, analysis of videos is becoming more crucial for many applications such as self-driving cars, healthcare, and traffic management. Most of these video analysis application uses deep learning algorithms such as convolution neural networks (CNN) because of their high accuracy in object detection. Thus enhancing the performance of CNN models become crucial for video analysis. CNN models are computationally-expensive operations and often require high-end graphics processing units (GPUs) for acceleration. However, for real-time applications in an energy-thermal constrained environment such as traffic management, GPUs are less preferred because of their high power consumption, limited energy efficiency. They are challenging to fit in a small place.
To enable real-time video analytics in emerging large scale Internet of things (IoT) applications, the computation must happen at the network edge (near the cameras) in a distributed fashion. Thus, edge computing must be adopted. Recent studies have shown that field-programmable gate arrays (FPGAs) are highly suitable for edge computing due to their architecture adaptiveness, high computational throughput for streaming processing, and high energy efficiency.
This thesis presents a generic OpenCL-defined CNN accelerator architecture optimized for FPGA-based real-time video analytics on edge. The proposed CNN OpenCL kernel adopts a highly pipelined and parallelized 1-D systolic array architecture, which explores both spatial and temporal parallelism for energy efficiency CNN acceleration on FPGAs. The large fan-in and fan-out of computational units to the memory interface are identified as the limiting factor in existing designs that causes scalability issues, and solutions are proposed to resolve the issue with compiler automation. The proposed CNN kernel is highly scalable and parameterized by three architecture parameters, namely pe_num, reuse_fac, and vec_fac, which can be adapted to achieve 100% utilization of the coarse-grained computation resources (e.g., DSP blocks) for a given FPGA. The proposed CNN kernel is generic and can be used to accelerate a wide range of CNN models without recompiling the FPGA kernel hardware. The performance of Alexnet, Resnet-50, Retinanet, and Light-weight Retinanet has been measured by the proposed CNN kernel on Intel Arria 10 GX1150 FPGA. The measurement result shows that the proposed CNN kernel, when mapped with 100% utilization of computation resources, can achieve a latency of 11ms, 84ms, 1614.9ms, and 990.34ms for Alexnet, Resnet-50, Retinanet, and Light-weight Retinanet respectively when the input feature maps and weights are represented using 32-bit floating-point data type.
To enable real-time video analytics in emerging large scale Internet of things (IoT) applications, the computation must happen at the network edge (near the cameras) in a distributed fashion. Thus, edge computing must be adopted. Recent studies have shown that field-programmable gate arrays (FPGAs) are highly suitable for edge computing due to their architecture adaptiveness, high computational throughput for streaming processing, and high energy efficiency.
This thesis presents a generic OpenCL-defined CNN accelerator architecture optimized for FPGA-based real-time video analytics on edge. The proposed CNN OpenCL kernel adopts a highly pipelined and parallelized 1-D systolic array architecture, which explores both spatial and temporal parallelism for energy efficiency CNN acceleration on FPGAs. The large fan-in and fan-out of computational units to the memory interface are identified as the limiting factor in existing designs that causes scalability issues, and solutions are proposed to resolve the issue with compiler automation. The proposed CNN kernel is highly scalable and parameterized by three architecture parameters, namely pe_num, reuse_fac, and vec_fac, which can be adapted to achieve 100% utilization of the coarse-grained computation resources (e.g., DSP blocks) for a given FPGA. The proposed CNN kernel is generic and can be used to accelerate a wide range of CNN models without recompiling the FPGA kernel hardware. The performance of Alexnet, Resnet-50, Retinanet, and Light-weight Retinanet has been measured by the proposed CNN kernel on Intel Arria 10 GX1150 FPGA. The measurement result shows that the proposed CNN kernel, when mapped with 100% utilization of computation resources, can achieve a latency of 11ms, 84ms, 1614.9ms, and 990.34ms for Alexnet, Resnet-50, Retinanet, and Light-weight Retinanet respectively when the input feature maps and weights are represented using 32-bit floating-point data type.
ContributorsDua, Akshay (Author) / Ren, Fengbo (Thesis advisor) / Ogras, Umit Y. (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2019
Description
Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
ContributorsPrakash, Ashok (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2019