Matching Items (127)
Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
Description
Bioindicators of wildlife health are useful tools for studying the viability of various organisms and populations, and can include a range of phenotypic variables, such as behavior, body size, and physiological parameters, such as circulating hormones and nutrients. Few studies have investigated the utility of total plasma protein as a predictor of environmental or nutritional variation among birds, as well as variation across different seasons and life-history stages. Here I examined relationships between plasma protein and season, urbanization, sex, body condition, molt status, and disease state in house finches (Haemorhous mexicanus). I sampled blood from house finches across three seasons (winter, summer and fall 2021) and measured plasma protein levels using a Bradford assay. I also collected data including condition, sex, and poxvirus infection state at capture, as well as fecal samples to assess gut parasitism (coccidiosis). During the fall season I also estimated molt status, as number of actively growing feathers. I found circulating plasma protein concentration to be lower in the fall during molt than during winter or summer. I also found a significant relationship between circulating protein levels and capture site, as well as novel links to molt state and pox presence, with urban birds, those infected with pox, and those in more intense molt having higher protein levels. My results support the hypotheses that plasma protein concentration can be indicative of a bird’s body molt (which demands considerable protein for feather synthesis) and degree of habitat urbanization, although future work is needed to determine why protein levels were higher in virus-infected birds.
ContributorsDrake, Dean (Author) / McGraw, Kevin (Thesis director) / Sweazea, Karen (Committee member) / Jackson, Daniel (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2022-05
Description
The consequences of failures from large-diameter water pipelines can be severe. Results can include significant property damage, damage to adjacent infrastructure such as roads and bridges resulting in transportation delays or shutdowns, adjacent structural damage to buildings resulting in loss of business, service disruption to a significant number of customers, loss of water, costly emergency repairs, and even loss of life. The American Water Works Association’s (AWWA) 2020 “State of the Water Industry” report states the top issue facing the water industry since 2016 is aging infrastructure, with the second being financing for improvements. The industry must find innovative ways to extend asset life and reduce maintenance expenditures. While are many different assets comprise the drinking water industry, pipelines are a major component and often neglected because they are typically buried. Reliability Centered Maintenance (RCM) is a process used to determine the most effective maintenance strategy for an asset, with the ultimate goal being to establish the required function of the asset with the required reliability at the lowest operations and maintenance costs. The RCM philosophy considers Preventive Maintenance, Predictive Maintenance, Condition Based Monitoring, Reactive Maintenance, and Proactive Maintenance techniques in an integrated manner to increase the probability an asset will perform its designed function throughout its design life with minimal maintenance. In addition to determining maintenance tasks, the timely performance of those tasks is crucial. If performed too late an asset may fail; if performed too early, resources that may be used better elsewhere are expended. Utility agencies can save time and money by using RCM analysis for their drinking water infrastructure. This dissertation reviews industries using RCM, discusses the benefits of an RCM analysis, and goes through a case study of an RCM at a large aqueduct in the United States. The dissertation further discusses the consequence of failure of large diameter water pipelines and proposes a regression model to help agencies determine the optimum time to perform maintenance tasks on large diameter prestressed concrete pipelines using RCM analysis.
ContributorsGeisbush, James R (Author) / Ariaratnam, Samuel T (Thesis advisor) / Grau, David (Committee member) / Chong, Oswald (Committee member) / Arizona State University (Publisher)
Created2024
Description
The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project is ASU VIPLE (Visual IoT/Robotics Programming Language Environment). It is a visual programming environment for Computer Science education. VIPLE supports a number of devices and platforms, including a traffic simulator developed using Unity game engine. This thesis focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithm in VIPLE. The traffic data is generated from the recorded real traffic data published at Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation based on dynamic changing traffic data.
ContributorsZhang, Zhemin (Author) / Chen, Yinong (Thesis advisor) / Wang, Yalin (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2022
Description
Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
The world faces significant environmental and social challenges due to high economic development, population growth, industrialization, rapid urbanization, and unsustainable consumption. Global communities are taking the necessary measures to confront these international challenges and applying sustainable development principles across all sectors. Construction is a critical driving instrument of economic activity, and to achieve sustainable development, it is vital to transform conventional construction into a more sustainable model. The research investigated sustainable construction perceptions in Kuwait, a rapidly growing country with a high volume of construction activities. Kuwait has ambitious plans to transition into a more sustainable economic development model, and the construction industry needs to align with these plans. This research aims to identify the characteristics of sustainable construction applications in the Kuwaiti construction market, such as awareness, current perceptions, drivers and barriers, and the construction regulations' impact. The research utilized a qualitative approach to answer research questions and deliver research objectives by conducting eleven Semi-structured interviews with experienced professionals in the Kuwaiti construction market to collect rich data that reflects insights and understandings of the Kuwaiti construction industry. The Thematic analysis of the data resulted in six themes and one sub-theme that presented reflections, insights, and perspectives on sustainable construction perceptions in the Kuwaiti construction market. The research findings reflected poor sustainable construction awareness and poor environmental and social application in the construction industry, the determinant role of construction regulations in promoting sustainable construction. and barriers and drivers to sustainable construction applications. The research concluded with answers to research questions, delivery of research objectives, and an explanation of sustainable construction perceptions in the Kuwaiti construction market.
Contributorsalsalem, mohammad salem (Author) / Duran, Melanie (Thesis advisor) / Chong, Oswald (Committee member) / Sullivan, Kenneth (Committee member) / Grau, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
During the rapid growth of infrastructure projects globally, countries pay high environmental and social costs as a result of the impacts caused from utilizing the traditional open-cut utility installation method that still widely being used in Egypt. For that, it was essential to have alternatives to reduce these environmental impacts and social costs; however, there are some obstacles that prevent the implementation and the realization of these alternatives.This research is conducted mainly to evaluate the environmental impacts of open-cut excavation vs. trenchless technology in Egypt, through two main methodologies. Firstly, a field survey that aims to measure knowledge of people working in the Egyptian construction industry of trenchless technology, and the harms caused from keeping utilizing open-cut for installing all kinds of underground utilities. In addition to investigating the reasons behind not relying on trenchless technology as a safe alternative for open-cut in Egypt.
Furthermore, in order to compare the greenhouse gases emissions resulted from both open-cut vs trenchless technology, a real case study is applied quantifying the amounts of the resulted greenhouse gases from each method. The results show that greenhouse gases emissions generated from open-cut were extremely higher than that of horizontal directional drilling as a trenchless installation method.
ContributorsKhedr, Ahmed Mossad Saeed Hafez (Author) / Ariaratnam, Samuel (Thesis advisor) / El Asmar, Mounir (Committee member) / Chong, Oswald (Committee member) / Arizona State University (Publisher)
Created2023
Description
As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024