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- Creators: Arizona State University
- Creators: Schumann, Robert, 1810-1856
Description
Synthetic biology (SB) has become an important field of science focusing on designing and engineering new biological parts and systems, or re-designing existing biological systems for useful purposes. The dramatic growth of SB throughout the past two decades has not only provided us numerous achievements, but also brought us more timely and underexplored problems. In SB's entire history, mathematical modeling has always been an indispensable approach to predict the experimental outcomes, improve experimental design and obtain mechanism-understanding of the biological systems. \textit{Escherichia coli} (\textit{E. coli}) is one of the most important experimental platforms, its growth dynamics is the major research objective in this dissertation. Chapter 2 employs a reaction-diffusion model to predict the \textit{E. coli} colony growth on a semi-solid agar plate under multiple controls. In that chapter, a density-dependent diffusion model with non-monotonic growth to capture the colony's non-linear growth profile is introduced. Findings of the new model to experimental data are compared and contrasted with those from other proposed models. In addition, the cross-sectional profile of the colony are computed and compared with experimental data. \textit{E. coli} colony is also used to perform spatial patterns driven by designed gene circuits. In Chapter 3, a gene circuit (MINPAC) and its corresponding pattern formation results are presented. Specifically, a series of partial differential equation (PDE) models are developed to describe the pattern formation driven by the MINPAC circuit. Model simulations of the patterns based on different experimental conditions and numerical analysis of the models to obtain a deeper understanding of the mechanisms are performed and discussed. Mathematical analysis of the simplified models, including traveling wave analysis and local stability analysis, is also presented and used to explore the control strategies of the pattern formation. The interaction between the gene circuit and the host \textit{E. coli} may be crucial and even greatly affect the experimental outcomes. Chapter 4 focuses on the growth feedback between the circuit and the host cell under different nutrient conditions. Two ordinary differential equation (ODE) models are developed to describe such feedback with nutrient variation. Preliminary results on data fitting using both two models and the model dynamical analysis are included.
ContributorsHe, Changhan (Author) / Kuang, Yang (Thesis advisor) / Wang, Xiao (Committee member) / Kostelich, Eric (Committee member) / Tian, Xiaojun (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2021
Description
The goal of this project was to create a quasi experimental study using an education module that teaches evidence-based practice methods. The theoretical frameworks used to create the educational content were the self-efficacy theory and the Health Belief Model. The evaluation methods used are based on the Kirkpatrick four level model. An education module was created to be culturally and regionally relevant to South Sudan and Malawi. The education module was designed to be part of the SolarSPELL Health: Nursing and Midwifery Library. This was done by performing a literature review, curating resources, creating the educational materials, creating learning scenarios, curating relevant belief scales, and integrating the content into the SolarSPELL Health: Nursing and Midwifery Library. The on ground implementation of the materials was not a part of this project, but instead is planned for future research. This project creates a foundation from which SolarSPELL Health can implement the resources at a future date. In the long term, the goal of implementing the experiment is to improve maternal mental and physical health outcomes in South Sudan and Malawi, both of which have extremely high rates of maternal mortality and morbidity.
ContributorsRaymond, Courtney (Author) / Ross, Heather (Thesis advisor) / Hosman, Laura (Committee member) / Pepin, Susan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Applications over a gesture-based human-computer interface (HCI) require a new user login method with gestures because it does not have traditional input devices. For example, a user may be asked to verify the identity to unlock a device in a mobile or wearable platform, or sign in to a virtual site over a Virtual Reality (VR) or Augmented Reality (AR) headset, where no physical keyboard or touchscreen is available. This dissertation presents a unified user login framework and an identity input method using 3D In-Air-Handwriting (IAHW), where a user can log in to a virtual site by writing a passcode in the air very fast like a signature. The presented research contains multiple tasks that span motion signal modeling, user authentication, user identification, template protection, and a thorough evaluation in both security and usability. The results of this research show around 0.1% to 3% Equal Error Rate (EER) in user authentication in different conditions as well as 93% accuracy in user identification, on a dataset with over 100 users and two types of gesture input devices. Besides, current research in this area is severely limited by the availability of the gesture input device, datasets, and software tools. This study provides an infrastructure for IAHW research with an open-source library and open datasets of more than 100K IAHW hand movement signals. Additionally, the proposed user identity input method can be extended to a general word input method for both English and Chinese using limited training data. Hence, this dissertation can help the research community in both cybersecurity and HCI to explore IAHW as a new direction, and potentially pave the way to practical adoption of such technologies in the future.
ContributorsLu, Duo (Author) / Huang, Dijiang (Thesis advisor) / Li, Baoxin (Committee member) / Zhang, Junshan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cercopithecid primates today occupy the greatest geographic and climatic range of any non-human primate group. Pliocene and Pleistocene cercopithecids are often found together in fossil deposits across East and South Africa, raising the question of how these species co-occurred with one another and survived in increasingly arid and seasonal environments. Aspects of shearing ability, molar enamel thickness, and relative incisor, premolar, and molar proportions were analyzed in principal component analysis and used to generate six potential models of the cercopithecid dental morphological niche. Resulting principal component axes distinguish between taxa with varying proportions of leaves, fruit, insects, and seeds in the diet, but lose some clarity when variable subsets are used that exclude poorly-preserved or wear-restricted variables. Resampling was used to reconstruct the aggregate dental morphological niches of cercopithecid communities (taxocenes) from Africa and Asia today and from the African Pliocene and Pleistocene. Modern Asian cercopithecid taxocenes occupy a more restricted niche than their counterparts in Africa, but in both regions variation in taxocene structure is linked with past and current climate factors related to precipitation, temperature, and seasonality. Fossil cercopithecids from the Turkana Basin occupy an expanded niche in comparison to modern African and Asian taxocenes. In contrast, South African fossil taxocenes occupy a more distinct and restricted niche, which may reflect a mix of paleoenvironmental and taphonomic factors. Overall these results are consistent with existing research on modern African and Asian primate taxocene diversity and highlight the utility of a dental metric model for examining community evolution among Plio-Pleistocene cercopithecids in Africa. Evidence for a possible niche expansion during the early Pleistocene coincides with a period of well-documented hominin co-occurrence at the same fossil sites, suggesting that these two primate groups were diversifying in response to shared environmental stimuli.
ContributorsSmail, Irene (Author) / Reed, Kaye E (Thesis advisor) / Campisano, Christopher J (Committee member) / Gilbert, Christopher C (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the legal system, the prediction of a person’s risk of committing a crime has mostly been based on expert judgment. However, newer techniques that employ machine learning (ML)—a type of artificial intelligence—are being implemented throughout the justice system. Yet, there is a lack of research on how the public perceives and uses machine learning risk assessments in legal settings. In two mock-trial vignette studies, the perception of ML-based risk assessments versus more traditional methods was assessed. Study 1 was a 2 (severity of crime: low, high) x 2 (risk assessment type: expert, machine learning) x 2 (risk outcome: low, high) between-subjects design. Participants expressed ethical concerns and discouraged the use of machine learning risk assessments in sentencing decisions, but punishment recommendations were not affected. Study 2 was a within-subjects design where participants were randomly assigned read through one of three crime scenarios (violent, white-collar, sex offense) and one of three risk assessment techniques (expert, checklist, machine learning). Consistent with Study 1, participants had ethical concerns and disagreed with the use of machine learning risk assessments in bail decisions, yet their own decisions and recommendations did not reflect these concerns. Overall, laypeople express skepticism toward these new methods, but do not appear to differentially rely on ML-based versus traditional risk assessments in their own judgments.
ContributorsFine, Anna (Author) / Schweitzer, Nicholas (Thesis advisor) / Salerno, Jessica (Committee member) / Smalarz, Laura (Committee member) / Arizona State University (Publisher)
Created2021
Description
Halogens in drinking water sources, such as bromine (Br) and iodine (I) pose no direct health risk, but are critical precursors in formation of cyto- and genotoxic brominated and iodinated (Br-/I-) DBPs. However, few spatial or historic datasets exist for bromine and iodine species in drinking water sources. This dissertation aims to quantify and understand the occurrence and speciation of Br and I in groundwater and surface water serving as source waters for drinking water treatment plants (DWTPs). Aggregation of data from >9000 non-drinking water sampling locations in USA collected from 1930-2017 on halides (bromide (Br-) and iodide (I-)) determined that Br- concentrations were 50 μg/L and 100 μg/L; and I- concentrations were 12 μg/L and 13 μg/L in surface and groundwater respectively. Although, these locations were not drinking water sources, this first of its kind analysis provides potential bounds for Br- and I-. To focus specifically on DWTP sources, a nationwide survey of >250 drinking water sources was conducted between 2018-2020. Br- ion is the only bromine specie, whereas both inorganic (iodide and iodate ions) and organic iodine occur. I- concentrations ranged from 1-250 μg/L and are 4 to 100 times lower than Br- concentrations (10-7800 μg/L, median=80 μg/L). No strong correlation exists between bromide and iodide occurrence (R<0.5, p<0.005). I- was detected in 50% of the samples (75th percentile=5 μg/L) and IO3- was detected in 40% (75th percentile=3 μg/L) of all the samples. To quantify iodine species, tandem ion chromatography and inductively coupled plasma mass spectrometry was applied for the first time in drinking water sources. I- and IO3- peaks were well resolved and have minimum detection limit of 0.4 μg/L and 0.7 μg/L respectively. Organic iodine (Org-I) peaks in select drinking water samples from the nationwide survey were partically resolved ranging from <5 to 40 μg/L. This dissertation provides updated nationwide Br- survey and first ever national I species survey. The data generated through this dissertation will be useful to further Br-/I-DBP formation and toxicity research by providing relevant drinking water sources information. Future research targeting Br- and I- removal is advocated for managing Br-/I-DBPs in watersheds.
ContributorsSharma, Naushita (Author) / Westerhoff, Paul (Thesis advisor) / Karanfil, Tanju (Committee member) / Herckes, Pierre (Committee member) / Lackner, Klaus (Committee member) / Arizona State University (Publisher)
Created2021
Description
Concussion, or mild traumatic brain injury (mTBI), is a frequent cause of brain damage among youth and, therefore, represents a major public health problem. While most youth recover from concussion within 2 to 4 weeks, some concussed children and adolescents endure prolonged symptoms, along with mood disturbance sequelae for months. Few studies have assessed mood disturbance and concussion in pediatric populations. Additional research is necessary to understand pediatric concussion recovery and mood disturbance better, to guide early intervention efforts, and to improve pediatric concussion care. The purpose of this study was to examine how symptoms of mood disturbance (i.e., anxiety, depression, anger) and somatization relate to the odds of concussion recovery in male and female youth 12 to 17 years of age, who presented for neuropsychological evaluation after head injury. Significantly fewer females were deemed recovered at initial neuropsychological evaluation compared to males. Bivariate analyses of mood disturbance and somatization predictors revealed significant group differences in symptom burden between those determined recovered from concussion and those who had not recovered. Logistic regressions of each mood disturbance variable and somatization on concussion recovery suggested a modest decline in the odds of recovery as symptoms of mood disturbance or somatization increase. A multivariable logistic regression model of mood disturbance predictors, somatization, gender, and age was significant and explained over a quarter of the variance in concussion recovery; however, after a backward variable selection procedure, only depression and somatization symptoms were significant in the final model and accounted for a modest decline in the odds of concussion recovery at initial evaluation. Results replicate and extend research findings in pediatric concussion.
ContributorsBarros, Kathleen (Author) / Kinnier, Richard (Thesis advisor) / Kurpius, Sharon (Committee member) / Lavoie, Michael (Committee member) / Arizona State University (Publisher)
Created2021
Description
The rapid increase in the volume and complexity of data lead to accelerated Artificial Intelligence (AI) applications, primarily as intelligent machines, in everyday life. Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework, which provides the rationale behind an AI agent's decision-making. Therefore, the validity of the AI models is constrained based on their ability to explain their decision-making rationale. On the other hand, AI agents cannot perceive the social situation that human experts may recognize using their background knowledge, specifically in cybersecurity and the military. Social behavior depends on situation awareness, and it relies on interpretability, transparency, and fairness when we envision efficient Human-AI collaboration. Consequently, the human remains an essential element for planning, especially when the problem's constraints are difficult to express for an agent in a dynamic setting.
This dissertation will first develop different model-based explanation generation approaches to predict where the human teammate would misunderstand the plan and, therefore, generate an explanation accordingly. The robot's generated explanation or interactive explicable behavior maintains the human teammate's cognitive workload and increases the overall team situation awareness throughout human-robot interaction. Further, it will focus on a rule-based model to preserve the collaborative engagement of the team by exploring essential aspects of the facilitator agent design. In addition to recognizing wherein the plan might be discrepancies, focusing on the decision-making process provides insight into the reason behind the conflict between the human expectation and the robot's behavior. Employing a rule-based framework will shift the focus from assisting an individual (human) teammate to helping the team interactively while maintaining collaboration. Hence, concentrating on teaming provides the opportunity to recognize some cognitive biases that skew the teammate's expectations and affect interaction behavior.
This dissertation investigates how to maintain collaboration engagement or cognitive readiness for collaborative planning tasks. Moreover, this dissertation aims to lay out a planning framework focusing on the human teammate's cognitive abilities to understand the machine-provided explanations while collaborating on a planning task. Consequently, this dissertation explored the design for AI facilitator, helping a team tasked with a challenging task to plan collaboratively, mitigating the teaming biases, and communicate effectively. This dissertation investigates the effect of some cognitive biases on the task outcome and shapes the utility function. The facilitator's role is to facilitate goal alignment, the consensus of planning strategies, utility management, effective communication, and mitigate biases.
ContributorsZakershahrak, Mehrdad (Author) / Cooke, Nancy NC (Thesis advisor) / Zhang, Yu YZ (Thesis advisor) / Ben Amor, Hani HB (Committee member) / Srivastava, Siddharth SS (Committee member) / Hsiao, Sharon SH (Committee member) / Arizona State University (Publisher)
Created2021
Description
Experiences of perceived racial discrimination are all too common for Asian Americans living in the United States. While there is research demonstrating the negative impact of discrimination on individual well-being, there is a scarcity of literature addressing the potential associations between discrimination and family relationships outcomes, particularly the relationships between Asian American emerging adults and their parents. Drawing from family and stress theories, it was hypothesized that perceived discrimination, including blatant and subtle forms of discrimination, would be negatively associated with various aspects of relationship quality and that these associations would be mediated by general stress. The present study collected data from 137 Asian American parent-adult children dyads to examine the associations between discrimination, general stress, and parent-child relationship quality. Actor and partner associations were also tested in order to account for the interdependence of dyadic data. Results showed support for the negative direct association between discrimination and relationship quality for both children and parents, as well as the mediator role of stress. Findings from this study also have important implications for counseling to promote the mental health of Asian American emerging adults and families.
ContributorsLau, Kin (Author) / Randall, Ashley K. (Thesis advisor) / Pereira, Jennifer K. (Committee member) / Tran, Alisia G.T. (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation takes up the topic of simulations in social studies education. Though simulations are taken up widely by social studies educators, and though they are described as best practice in social studies standards documents and teacher evaluation rubrics, the term lacks specificity. Additionally, design, research, and implementation efforts associated with social studies simulations often lack theoretical grounding and clarity. A major consequence of this lack of conceptual and theoretical clarity is curriculum violence perpetrated upon young people, particularly along racial and socioeconomic lines, as the result of poorly conceived simulations.This dissertation is presented as three standalone manuscripts, bookended by an Introduction and a Conclusion. In the Introduction, I present an overview of the social studies simulation literature. In Chapter Two, I propose mechanics analysis, a methodological approach to systematically analyzing social studies simulations and games. In Chapter Three, I report on an empirical study using mechanics analysis to analyze three digital social studies-themed simulation games: Offworld Trading Company, Frostpunk, and Surviving Mars. In Chapter Four, I build on the previous two chapters to coordinate the salient research and theory across three field—history and social studies education, learning sciences, and games scholarship—to propose a design theory for a particular kind of simulation game: disciplinarily integrated, consequentially engaging simulation games, or DICES. Finally, I conclude with Chapter Five, in which I highlight what I view as the implications of this work as a whole, including for teachers, teacher educators, researchers, and designers.
ContributorsKessner, Taylor Milan (Author) / Harris, Lauren M (Thesis advisor) / Gee, Elisabeth R (Thesis advisor) / Nelson, Brian C (Committee member) / Stoddard, Jeremy (Committee member) / Arizona State University (Publisher)
Created2021