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A leading crisis in the United States is the opioid use disorder (OUD) epidemic. Opioid overdose deaths have been increasing, with over 100,000 deaths due to overdose from April 2020 to April 2021. This dissertation presents two mathematical models to address illicit OUD (IOUD), treatment, and recovery within an epidemiological

A leading crisis in the United States is the opioid use disorder (OUD) epidemic. Opioid overdose deaths have been increasing, with over 100,000 deaths due to overdose from April 2020 to April 2021. This dissertation presents two mathematical models to address illicit OUD (IOUD), treatment, and recovery within an epidemiological framework. In the first model, individuals remain in the recovery class unless they relapse. Due to the limited availability of specialty treatment facilities for individuals with OUD, a saturation treat- ment function was incorporated. The second model is an extension of the first, where a casual user class and its corresponding specialty treatment class were added. Using U.S. population data, the data was scaled to a population of 200,000 to find parameter estimates. While the first model used the heroin-only dataset, the second model used both the heroin and all-illicit opioids datasets. Backward bifurcation was found in the first IOUD model for realistic parameter values. Additionally, bistability was observed in the second IOUD model with the heroin-only dataset. This result implies that it would be beneficial to increase the availability of treatment. An alarming effect was discovered about the high overdose death rate: by 2038, the disease-free equilibrium would be the only stable equilibrium. This consequence is concerning because although the goal is for the epidemic to end, it would be preferable to end it through treatment rather than overdose. The IOUD model with a casual user class, its sensitivity results, and the comparison of parameters for both datasets, showed the importance of not overlooking the influence that casual users have in driving the all-illicit opioid epidemic. Casual users stay in the casual user class longer and are not going to treatment as quickly as the users of the heroin epidemic. Another result was that the users of the all-illicit opioids were going to the recovered class by means other than specialty treatment. However, the relapse rates for those individuals were much more significant than in the heroin-only epidemic. The results above from analyzing these models may inform health and policy officials, leading to more effective treatment options and prevention efforts.
ContributorsCole, Sandra (Author) / Wirkus, Stephen (Thesis advisor) / Gardner, Carl (Committee member) / Lanchier, Nicolas (Committee member) / Camacho, Erika (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with a significant increase of DERs. This decentralized network requires a

The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with a significant increase of DERs. This decentralized network requires a paradigm change in modeling distribution systems in more detail to maintain the reliability and efficiency while accommodating a high level of DERs. Accurate models of distribution feeders, including the secondary network, loads, and DER components must be developed and validated for system planning and operation and to examine the distribution system performance. In this work, a detailed model of an actual feeder with high penetration of DERs from an electrical utility in Arizona is developed. For the primary circuit, distribution transformers, and cables are modeled. For the secondary circuit, actual conductors to each house, as well as loads and photovoltaic (PV) units at each premise are represented. An automated tool for secondary network topology construction for load feeder topology assignation is developed. The automated tool provides a more accurate feeder topology for power flow calculation purposes. The input data for this tool consists of parcel geographic information system (GIS) delimitation data, and utility secondary feeder topology database. Additionally, a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements is presented. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification.
ContributorsMontano-Martinez, Karen Vanessa (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Weng, Yang (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The bilingual experience is an often-studied multivariate phenomenon with a heterogeneous population that is often described using subtypes of bilingualism. “Bilingualism” as well as its subtypes lack consistent definitions and often share overlapping features, requiring researchers to measure a number of aspects of the bilingual experience. Different variables have been

The bilingual experience is an often-studied multivariate phenomenon with a heterogeneous population that is often described using subtypes of bilingualism. “Bilingualism” as well as its subtypes lack consistent definitions and often share overlapping features, requiring researchers to measure a number of aspects of the bilingual experience. Different variables have been operationalized to quantify the language proficiencies, use, and histories of bilinguals, but the combination of these variables and their contributions to these subtypes often vary between studies on bilingualism. Research supports that these variables have an influence not only on bilingual classification, but also on non-linguistic outcomes including perceptions of self-worth and bicultural identification. To date, there is a lack of research comparing the quantification of these bilingual subtypes and these non-linguistic outcomes, despite research supporting the need to address both. Person-centered approaches such as latent profile analysis (LPA) and fuzzy set qualitative comparative analysis (fsQCA) have been applied to describe other multivariate constructs with heterogeneous populations, but these applications have yet to be used with bilingualism. The present study integrates models of bilingualism with these analytic methods in order to quantitatively identify latent profiles of bilinguals, describe the sets of conditions that define these subtypes, and to characterize the subjective experiences that differentiate these subtypes. The first study uses an existing data set of participants who completed the Language and Social Background Questionnaire (LSBQ) and performs LPA and fsQCA, identifying latent profiles and the sets of conditions that these subtypes. The following studies use a second set of bilinguals who also completed the LSBQ as well as a supplementary questionnaire, characterizing their identification with biculturalism and their feelings of self-worth. The analyses are repeated with these data to describe the profiles within these data and the subjective experiences in common. Finally, all analyses are repeated with the combined datasets to develop a final model of bilingual subtypes, describing the differences in language use and history within each subtype. Results demonstrate that latent models can be used to consistently characterize bilingual subtypes, while also providing additional information about the relationship between individual bilingual history and attitudes towards cultural identification.
ContributorsMcGee, Samuel (Author) / Azuma, Tamiko (Thesis advisor) / Gray, Shelley (Committee member) / Roscoe, Rod (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Frontotemporal dementia (FTD) is a neurodegenerative disease that causes deterioration of the frontal and temporal lobe. Detection is pivotal in preventative care, but current screening methods are not sensitive enough to detect early-stage disease. Synapse loss has been implicated as an early contributor to neurodegeneration and subsequent atrophy. Fluorine-18 fluorodeoxy-glucose

Frontotemporal dementia (FTD) is a neurodegenerative disease that causes deterioration of the frontal and temporal lobe. Detection is pivotal in preventative care, but current screening methods are not sensitive enough to detect early-stage disease. Synapse loss has been implicated as an early contributor to neurodegeneration and subsequent atrophy. Fluorine-18 fluorodeoxy-glucose (18[F]-FDG) positron emission tomography (PET) is a noninvasive imaging biomarker method frequently used as a surrogate measure for synaptic activity in the brain. PET scans using 18[F]-FDG tracers were performed on progranulin (GRN) knockout mice (Grn-/-), a commonly used mouse model of FTD. Interestingly, 18[F]-FDG PET at both, 9 months and 11 months, two time points considered early symptomatic in the Grn-/- mouse model, did not detect significant changes in synaptic activity, suggesting that no synapse loss has occurred yet at these early stages of FTD in this model. After the last PET scan, the imaging data were validated via fluorescent immunostaining for pre- and post-synaptic marker proteins SV2 and PSD95, respectively. Quantifications in several brain regions, including the frontal cortex, did not reveal any significant differences in protein expression, supporting the lack of aberrant 18[F]-FDG tracer uptake measured via PET. Additional examinations for activated microglia, a known aspect of FTD pathology recently observed in end Grn-/- mice, did not reveal microglia activation as measured via CD68 immunostaining. These data suggest that Grn-/- mice at 9 and 11 months do not exhibit synaptic dysfunction in the frontal cortex when measured via 18[F]-FDG PET or immunostaining of pre- and postsynaptic marker proteins SV2 and PSD95.
ContributorsWeisman, Hannah (Author) / Sattler, Rita G (Thesis advisor) / Mastroeni, Diego (Committee member) / Velazquez, Ramon (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Polymer composite has been under rapid development with advancements in polymer chemistry, synthetic fibers, and nanoparticles. With advantages such as lightweight, corrosion resistance, and tunable functionalities, polymer composite plays a significant role in various applications such as aerospace, wearable electronics, energy storage systems, robotics, biomedicine, and microelectronics. In general, polymer

Polymer composite has been under rapid development with advancements in polymer chemistry, synthetic fibers, and nanoparticles. With advantages such as lightweight, corrosion resistance, and tunable functionalities, polymer composite plays a significant role in various applications such as aerospace, wearable electronics, energy storage systems, robotics, biomedicine, and microelectronics. In general, polymer composite can be divided into particulate-filled, fiber-filled, or network-filled types depending on the manufacturing process and internal structure. Over the years, fabrication processes on the macro- and micro-scales have been extensively explored. For example, lamination, fiber tow steering, and fiber spinning correspond to meter, millimeter, and micrometer scales, respectively. With the development of nanoparticles and their exceptional material properties, polymer nanoparticle composite has shown promising material property enhancements. However, the lack of economical solutions to achieve nanoscale nanoparticle morphology control limits the reinforcement efficiency and industrial applications. This dissertation focuses on utilizing additive manufacturing as a tooling method to achieve nanoparticle morphology control in polymer nanocomposite fibers. Chapter 1 gives a thorough background review regarding fiber composite, additive manufacturing, and the importance of nanoparticle orientation. Two types of nozzle designs, concentrical and layer-by-layer, are 3D printed and combined with the dry-jet-wet fiber spinning method to create continuous fibers with internal structures. Chapters 2 to 5 correspond to four stages of my research, namely, (2) multi-material fiber spinning, (3) interfacial-assisted nanoparticle alignment, (4) microscale patterning, and (5) nanoscale patterning. The achieved feature resolution also improves from 100 µm, 10 µm, 2 µm, to 170 nm, respectively. The process-structural-property relationship of polymer nanocomposite fibers is also investigated with applications demonstrations including sensors, electrically conductive fibers, thermally conductive fibers, and mechanically reinforced fibers. At last, Chapter 6 gives a summary and some future perspectives regarding fiber composites.
ContributorsXu, Weiheng (Author) / Song, Kenan (Thesis advisor) / Chen, Xiangfan (Committee member) / Kwon, Beomjin (Committee member) / Azeredo, Bruno (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Fine control of standing postural balance is essential for completing various tasks in daily activities, which might be compromised when interacting with dynamically challenging environments (e.g., moving ground). Among various biofeedback to improve postural balance control, vibrotactile feedback has an advantage of providing supplementary information about balance control without disturbing

Fine control of standing postural balance is essential for completing various tasks in daily activities, which might be compromised when interacting with dynamically challenging environments (e.g., moving ground). Among various biofeedback to improve postural balance control, vibrotactile feedback has an advantage of providing supplementary information about balance control without disturbing other core functions (e.g., seeing and hearing). This paper investigated the effectiveness of a waist vibrotactile feedback device to improve postural control during standing balance on a dynamically moving ground simulated by a robotic balance platform. Four vibration motors of the waist device applied vibration feedback in the anterior-posterior and medio-lateral direction based on the 2-dimensional sway angle, measured by an inertia measurement unit. Experimental results with 15 healthy participants demonstrated that the waist vibrotactile feedback is effective in improving postural control, evidenced by improvements in center-of-mass and center-of-pressure stability measures. In addition, this study confirmed the effectiveness of the waist vibrotactile feedback in improving standing balance control even under muscle fatigue induced by lower body exercise. The study further confirmed that the waist feedback is more effective in people with lower baseline balance performance in both normal and fatigue conditions.
ContributorsJo, Kwanghee (Author) / Lee, Hyunglae (Thesis advisor) / Sugar, Thomas (Committee member) / Peterson, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Two distinct aspects of synthetic biology were investigated: the development of viral structures for new methods of studying self-assembly and nanomanufacturing, and the designs of genetic controls systems based on controlling the secondary structure of nucleic acids. Viral structures have been demonstrated as building blocks for molecular self-assembly of diverse

Two distinct aspects of synthetic biology were investigated: the development of viral structures for new methods of studying self-assembly and nanomanufacturing, and the designs of genetic controls systems based on controlling the secondary structure of nucleic acids. Viral structures have been demonstrated as building blocks for molecular self-assembly of diverse structures, but the ease with which viral genomes can be modified to create specific structures depends on the mechanisms by which the viral coat proteins self-assemble. The experiments conducted demonstrate how the mechanisms that guide bacteriophage lambda’s self-assembly make it a useful and flexible platform for further research into biologically enabled self-assembly. While the viral platform investigations focus on the creation of new structures, the genetic control systems research focuses on new methods for signal interpretation in biological systems. Regulators of genetic activity that operate based on the secondary structure formation of ribonucleic acid (RNA), also known as riboswitches, are genetically compact devices for controlling protein translation. The toehold switch ribodevice can be modified to enable multiplexed logical operations with RNA inputs, requiring no additional protein transcription factors to regulate activity, but they cannot receive chemical inputs. RNA sequences generated to bind to specific chemicals, known as aptamers, can be used in riboswitches to confer genetic activity upon binding their target chemical. But attempts to use aptamers for logical operations and genetic circuits are difficult to generalize due to differences in sequence and binding strength. The experiments conducted demonstrate a ribodevice structure in which aptamers can be used semi-interchangeably to translate chemical inputs into the toehold switch paradigm, marrying the programmability and orthogonality of toehold switches with the broad sensing potential of aptamer-based ribodevices.
ContributorsMcCutcheon, Griffin Cooper (Author) / Green, Alexander (Thesis advisor) / Hariadi, Rizal (Committee member) / Stephanopoulos, Nicholas (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The development of biosensing platforms not only has an immediate lifesaving effect but also has a significant socio-economic impact. In this dissertation, three very important biomarkers with immense importance were chosen for further investigation, reducing the technological gap and improving their sensing platform.Firstly, gold nanoparticles (AuNP) aggregation and sedimentation-based assays

The development of biosensing platforms not only has an immediate lifesaving effect but also has a significant socio-economic impact. In this dissertation, three very important biomarkers with immense importance were chosen for further investigation, reducing the technological gap and improving their sensing platform.Firstly, gold nanoparticles (AuNP) aggregation and sedimentation-based assays were developed for the sensitive, specific, and rapid detection of Ebola virus secreted glycoprotein (sGP)and severe acute respiratory syndrome coronavirus 2 (SARS-COV2) receptor-binding domain (RBD) antigens. An extensive study was done to develop a complete assay workflow from critical nanobody generation to optimization of AuNP size for rapid detection. A rapid portable electronic reader costing (<$5, <100 cm3), and digital data output was developed. Together with the developed workflow, this portable electronic reader showed a high sensitivity (limit of detection of ~10 pg/mL, or 0.13 pM for sGP and ~40 pg/mL, or ~1.3 pM for RBD in diluted human serum), a high specificity, a large dynamic range (~7 logs), and accelerated readout within minutes. Secondly, A general framework was established for small molecule detection using plasmonic metal nanoparticles through wide-ranging investigation and optimization of assay parameters with demonstrated detection of Cannabidiol (CBD). An unfiltered assay suitable for personalized dosage monitoring was developed and demonstrated. A portable electronic reader demonstrated optoelectronic detection of CBD with a limit of detection (LOD) of <100 pM in urine and saliva, a large dynamic range (5 logs), and a high specificity that differentiates closely related Tetrahydrocannabinol (THC). Finally, with careful biomolecular design and expansion of the portable reader to a dual-wavelength detector the classification of antibodies based on their affinity to SARS-COV2 RBD and their ability to neutralize the RBD from binding to the human Angiotensin-Converting Enzyme 2 (ACE2) was demonstrated with the capability to detect antibody concentration as low as 1 pM and observed neutralization starting as low as 10 pM with different viral load and variant. This portable, low-cost, and versatile readout system holds great promise for rapid, digital, and portable data collection in the field of biosensing.
ContributorsIkbal, Md Ashif (Author) / Wang, Chao (Thesis advisor) / Goryll, Michael (Committee member) / Zhao, Yuji (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent

The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent can have an impact on learning outcomes and perceptions of the instructor. However, these outcomes and perceptions have yet to be fully understood in the context of a virtual human instructor. Outcome measures collected included: knowledge retention, knowledge transfer, and cognitive load. Perception measures were collected using the Agent Persona Instrument-Revised, API-R, and a speaker-rating survey. Overall, there were no significant differences between the accented conditions. However, the synthetic condition had significantly lower knowledge retention, knowledge transfer, and mental effort efficiency than the professional voices in the human condition. Participants rated the human recordings higher on speaker-rating and API-R measures. These findings demonstrate the importance of considering the quality of the voice when designing multimedia learning environments.
ContributorsSiegle, Robert Franklin (Author) / Craig, Scotty D (Thesis advisor) / Cooke, Nancy J (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Deep neural networks have been shown to be vulnerable to adversarial attacks. Typical attack strategies alter authentic data subtly so as to obtain adversarial samples that resemble the original but otherwise would cause a network's misbehavior such as a high misclassification rate. Various attack approaches have been reported, with some

Deep neural networks have been shown to be vulnerable to adversarial attacks. Typical attack strategies alter authentic data subtly so as to obtain adversarial samples that resemble the original but otherwise would cause a network's misbehavior such as a high misclassification rate. Various attack approaches have been reported, with some showing state-of-the-art performance in attacking certain networks. In the meanwhile, many defense mechanisms have been proposed in the literature, some of which are quite effective for guarding against typical attacks. Yet, most of these attacks fail when the targeted network modifies its architecture or uses another set of parameters and vice versa. Moreover, the emerging of more advanced deep neural networks, such as generative adversarial networks (GANs), has made the situation more complicated and the game between the attack and defense is continuing. This dissertation aims at exploring the venerability of the deep neural networks by investigating the mechanisms behind the success/failure of the existing attack and defense approaches. Therefore, several deep learning-based approaches have been proposed to study the problem from different perspectives. First, I developed an adversarial attack approach by exploring the unlearned region of a typical deep neural network which is often over-parameterized. Second, I proposed an end-to-end learning framework to analyze the images generated by different GAN models. Third, I developed a defense mechanism that can secure the deep neural network against adversarial attacks with a defense layer consisting of a set of orthogonal kernels. Substantial experiments are conducted to unveil the potential factors that contribute to attack/defense effectiveness. This dissertation also concludes with a discussion of possible future works of achieving a robust deep neural network.
ContributorsDing, Yuzhen (Author) / Li, Baoxin (Thesis advisor) / Davulcu, Hasan (Committee member) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022