UTD Theses and Dissertations

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/5608

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    Necessary Evils: the Role of Horror in Modern and Contemporary Literature
    (2023-12) Cox, Gavin Lynn; Towner, Theresa M.; Wright, Benjamin; Brewer, Kenneth; Smith, Erin A.; Hatfield, Charles
    In my extensive studies of horror, I have found that the genre of horror has typically not been taken seriously in its own right. This can be extended to the occurrence of horror in other types of literature. Horror, if recognized at all, is viewed as a component of the story, and not necessarily as significant or as relevant as other aspects of the book. This dissertation approaches the problem of how literary horror can, genre or otherwise, be recognized as a part of legitimate and influential academic study and why such study is important. To do so, I examine multiple works of both genre horror and literary horror, using established literary theories to analyze and understand these written works. I also examine multiple works not classified as horror yet contain instances of significant horror to show that horror exists past the genre. I utilize literary theories such as the uncanny, the monstrous, Kristeva’s theory of abjection and the Jungian shadow to show the literary merits of these works. To not read horror is to ignore aspects of life that act as a mirror reflecting society and individual fears at any point in time. Such willful evasion can be detrimental. Dismissing horror as merely entertainment avoids the social and cultural deceits it can expose. Horror is an interpretation of what is both desired and feared in our lives. Its omnipresence makes it critical to be understood as a vehicle used to acknowledge and understand our fears, and ultimately determine the best way to handle them.
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    Modern Iranian Fiction in the United States: Translation, Publication, and Promotion (1979–2022)
    (2022-05) Saeidfar, Ghazal; Llamas Rodriguez, Juan; Schulte, Rainer; Hatfield, Charles; Wright, Benjamin; Gooch, John C.
    My dissertation investigates the translation of modern Iranian writers in the United States. My particular focus is on the fictional works that have been written after the Iranian revolution in 1979. I examine what writers and what works have been translated and what is the place of these writers in the post-revolutionary literary landscape of Iran. I also examine the reception of this translated literature among the American readership through the analysis of the critical reviews in journals and magazines. In addition, I specifically explore the process of transmission and promotion of these literary works through an overview of the translators and the publishers who were involved. I investigate the criteria and motivations of these translators through a study of their educational and professional backgrounds as well as their knowledge of Persian and English. I also study the type of publishers and their editorial and publicity approaches that have played a significant role in the presentation of this literature in the United States. Additionally, I argue about the critical role of the Iranian-American community as well as academia in presenting and promoting this literature in the US. The findings of this research show that the representation of Iranian literature in the United States is an outcome of political, cultural, and economic factors. Based on the results of this study, I argue that although this literature is not under-represented in the American literary translation market, it has remained somehow invisible due to the stereotypical images of media about Iran, the financial challenges of the writers, translators, and publishers, and also the cultural and linguistic differences. However, the constant process of transmitting this literature has never been stopped thanks to the contributions of the Iranian-American community. This research has succeeded in drawing a clear picture of the challenges for the representation and promotion of the translated literature of a minor language in the American literary market.
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    Transfer Learning and Uncertainty Quantification in Natural Language Processing for Political Science and Cyber Security
    (2023-08) Hu, Yibo; Khan, Latifur; Makris, Yiorgos; Ouyang, Jessica; Brandt, Patrick T.; Du, Xinya
    Recent advancements in Natural Language Processing (NLP) driven by pretrained language models have revolutionized various fields reliant on large-scale text-based research through transfer learning. This dissertation presents efficient, reliable computational NLP applications to address real-world challenges, with a focus on political science, cyber security, and uncertainty quantification. The dissertation begins with interdisciplinary research in political science, where advanced NLP models are developed to track and analyze dynamics related to global political conflict. The creation of ConfliBERT, the first domain-specific sociological language model, enables improved performance on 18 downstream tasks, particularly in scenarios with limited data availability. Moreover, by leveraging transfer learning and existing expert knowledge, specific tasks such as political event extraction and classification are further optimized. One approach called Confli-T5 is a text generation model that augments labeled data by in- corporating achievable templates derived from political science knowledge bases. Another technique introduced is the Zero-Shot fine-grained relation classification model for PLOVER ontology (ZSP), which eliminates the need for labeled data by relying solely on an annotation codebook to classify intricate interactions between political actors. These strategies combine the power of transfer learning with domain-specific expertise to reduce the dependence on extensive labeled data, making them valuable tools in the field. In the field of cyber security, text generation techniques are employed for cyber deception, generating multiple fake versions of critical documents to deter malicious intrusion. A context-aware model called Fake Document Infilling (FDI) addresses the limitations of existing approaches by considering contextual awareness. FDI produces highly believable fake documents, protecting critical information and deceiving adversaries effectively. Finally, uncertainty quantification techniques are explored to enhance the reliability of NLP models in such interdisciplinary or cross-domain applications. A novel model, BERT-ENN, employees evidential theory to quantify multidimensional uncertainty in the data and calibrate uncertainty estimation in text classifiers. This approach achieves state-of-the-art out-of-distribution detection performance, thereby improving the reliability of NLP models.
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    Risk-based Motion Planning and Control for Robotic Systems
    (2023-12) Safaoui, Sleiman; Summers, Tyler; Kang, Gu Eon; Spong, Mark W.; Koeln, Justin; Ruths, Justin; Vinod, Abraham P.
    A robot autonomy stack usually consists of several modules that enable it to perceive the environment and decide how to interact with it to achieve a desired task. At the heart of this stack are the motion planning and control modules. The motion planning module is generally responsible for decision making and generating a plan for the robot to follow, such as determining how an autonomous car should drive around pedestrians and other vehicles. The control module computes a finer sequence of control actions that can be issued to the actuators to operate the robot. One issue that plagues robot motion planning and control is the effect of uncertainty, of which there are different types, on the system. This includes unknown and unmodeled disturbances that affect the system such as noise, aerodynamics, or simplified dynamics models. However, addressing these uncertainties is non-trivial and often requires a trade-off between accounting for the uncertainty accurately and the tractability of solving the problems. This dissertation develops risk-based solutions for a few robot motion planning and control problems. The contributions of the dissertation are categorized into four main types. The first part addresses control design with complex spatio-temporal requirements under uncertainty. An optimization-based control algorithm is designed to guarantee the completion of the requirements when the robot dynamics are affected by process noise. The second part addresses sampling-based motion planning under uncertainty. RRT*, a famous motion planning algorithm in robotics, is considered and risk-aware variants of it are developed to account for process and measurement noise affecting the robotic system. The third part addresses a limitation of learning-based planning approaches with an application to multi-agent motion planning. A reinforcement learning (RL) framework is considered for learning policies then an optimization-based module, called a safety filter, is proposed to enforce collision avoidance as hard constraints, which learning algorithms cannot do. The safety filter is designed to handle process, state, and measurement noise. Finally, the fourth part addresses data-driven planning in dynamic and uncertain environments. This assumes that the robot has access to some future predictions of the obstacles in the environment, such as where they may be in the next few seconds. A safety filter is then developed using these sample predictions to plan a safe trajectory for the robot. In several sections, uncertainties whose distribution is unknown, which is generally the case, are considered and addressed using the concept of distributionally robust optimization (DRO) to develop solutions that guarantee safety or the successful completion of the task despite the lack of knowledge of the underlying distribution. Throughout, examples are provided to emphasize and clarify core concepts, and simulations and physical experiments are performed to demonstrate the efficacy of the developed solutions.
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    Low Noise Integrated Circuits and Systems Using Nano-Scale MOSFETs and Intelligent Post-Fabrication Selection
    (December 2021) Yelleswarapu, Venkata Pavan Kumar; O, Kenneth K.; Venkatesan, Subbarayan; Henderson, Rashaunda; Ma, Donsheng Brian; Makris, Yiorgos
    Recent advances in integrated radio design have enabled many applications such as wearable healthcare, 5G communication, and beyond 5G or 6G applications for ultra-high data rate communications, high-resolution imaging, sensing, and spectroscopy. All these applications require low noise radio transceivers for achieving high performance. For example, applications requiring high data rate and higher order modulation schemes need to achieve high signal to noise ratio (SNR) and therefore a low noise figure to maintain a low bit-error rate (BER). In addition, noise phenomena like jitter and phase noise can impact the critical parameters like maximum achievable data rate and energy efficiency. This research aims to improve the noise performance of integrated circuits and systems through intelligent post-fabrication selection of an array of nanoscale transistors sized near the minimum in CMOS processes. A phase noise reduction technique in LC Voltage Controlled Oscillators (VCO’s) is demonstrated by post-fabrication selection of a subset of an array of near minimum-size cross-coupled transistor pairs with reduced low frequency noise and thermal noise. The technique reduces the phase noise by taking advantage of the fact that when transistor dimensions are reduced, the low frequency noise and thermal noise vary significantly. Applying an intelligent post-fabrication selection process using a genetic algorithm, the lowest phase noise of -122 dBc/Hz, -127 dBc/Hz, -137.5 dBc/Hz at 600-kHz, 1-MHz, and 3-MHz offsets, respectively from a 3.8-GHz carrier has been measured. The VCO prototype was fabricated in a 65-nm CMOS process and dissipates 7 mW of DC power. The maximum figure of merit (FoM) including phase noise, carrier frequency and power consumption is 191 dBc/Hz and the figure of merit including the VCO core area, FoMA is 207 dBc/Hz. A technique is demonstrated to reduce both the in-band and out-of-band phase noise of a 4-GHz Integer-N PLL by employing an array of individually selectable cross-coupled pairs formed using near minimum-size transistors in an LC VCO and intelligent post-fabrication selection. By reducing both the in-band and out-of-band phase noise, the overall integrated phase jitter in a frequency synthesizer can be minimized. Applying an intelligent post-fabrication selection process, the lowest phase noise of -72 dBc/Hz at 30-kHz offset, -106 dBc/Hz at 300-kHz offset, -121.8 dBc/Hz at 1-MHz offset, and -132.5 dBc/Hz at 3-MHz offset, respectively from a 4.01-GHz locked carrier has been measured. The integrated rms jitter from 100-kHz to 100-MHz offsets is 440 fs. A mixer-first downconverter employing an array of passive mixers formed using near minimumsize transistors and intelligent post-fabrication selection achieves a double sideband noise figure of 4.2 dB at RF of 6 GHz, which is the lowest at 6 GHz for CMOS mixer-first downconverters. The downconverter is fabricated in 65-nm CMOS and demonstrates out-of-band IIP3 and IIP2 of 25 dBm and 65 dBm, respectively at 80-MHz IF, while dissipating 11.5 mW. Post-fabrication selection is performed by a genetic algorithm which takes ~17 generations to converge to the combinations exhibiting the lowest noise.
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    Causarum Cognitio: the Architecture, Collections, and Social Agency of Three American Athenaea: Redwood, Boston, and Caltech
    (December 2021) Curry, Virginia; Schulte, Rainer; Gooch, John; Channell, David; Schlereth, Eric; Schich, Maximilian
    Is the athenaeum an adaptable concept in the twenty-first century university environment? What evidence exists to conclude that it contributes to a discursive community? This dissertation explores the legacy of the concept of the athenaeum in America and examines the organically formed social circles who share an interest in continuing discourse, often within multiple disciplines, and who contribute to their communities by modeling habits and behaviors reflecting their desire for improvement of themselves and their communities. From before and since our nation’s founding, the societies of the American Athenaeum have served as community-organized intellectual and artistic hubs, providing access to information, pursuing thought-provoking discourse, and applying their aggregate knowledge resources as agency for social change while presenting the most inspirational architecture, lectures, artistic performances, and collections to their communities. I focus on the eighteenth century Redwood Library and Athenaeum of Newport, Rhode Island, the nineteenth century Boston Athenaeum, and the twentieth century Caltech Athenaeum. The newest of these, Caltech Athenaeum, has been in service over one hundred years, and the oldest, the Redwood Library and Athenaeum, has been in service to its community continuously over 300 years.
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    ‘Send More Butter’: Finding Meaning in Civil War Food References
    (December 2021) Jacobs, Janet Kathleen; Wright, Ben; Veras, Christine; Ring, Natalie J.; Barnes, Ashley; Stewart, Whitney
    Food in the American Civil War meant more than nutrition. It served as a means of communication, status elevator, social lubricant, and bridge between home and front, and even across battle lines. This work examines how food, cooking, and references to food can be interpreted to tell us more about how the war operated on different levels. Approached thematically, the study looks at express boxes, mess bonding, cooking, social hierarchy of cooks, the blockade, and trade across lines. Central to the argument is that food references in Civil War letters acted as a subtle communications tool that give insight into how soldiers felt and responded to the historic events around them. Essentially, it seeks to decode the language of food in Civil War letters. In addition to the letter diagnostics, the study takes a food-centric look at Sherman’s actions in Georgia and the Carolinas in 1864-65, with an eye toward how his seizure and destruction of the resources can be interpreted, and why he felt so confident in his success. Another intervention involves the express boxes and how they connected the home front and the war front. By examining tax data, it becomes clear that many more boxes were sent to the front than previously estimated, which changes how we should approach these gifts and civilian contributions to the war effort. Food is also used as a lens into the blockade, women’s resistance, and the formation of bonds between soldiers. Cooking is examined for its ability to change the social status of meal preparers, both white and Black, free and enslaved. Cooking changed attitudes and lives during the war, even as it is suspected to have ended others. Food is more than calories and comfort, it is also a means of communication, identity, commerce, and social tie. Through this perspective, the Civil War takes on fresh nuances.
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    Accountability Overload and Its Consequence and Remedy
    (December 2021) Rabbi, Md Fazle; Sabharwal, Meghna; Giertz, Seth; Harrington, James R; Gorina, Evgenia; McCaskill, John R.
    Accountability overload (AO) may increase cost, lower responsiveness, and decrease productivity and service quality [103]. It creates an extra burden on employees [163], erodes their trust and morale [185], and decreases their job satisfaction [43]. Specifically, it undermines organizational mission [15, 68] and performance [140, 152, 155]. However, the examination of the phenomenon and its consequence and remedies is still in a nascent stage and predominantly qualitative. This dissertation undertakes three interrelated studies to fill the research gap by advancing the concept, empirically examining the relationship between AO and organizational outcome, and exploring remedies to AO. The first study conducts a systematic review of Public Administration literature on AO. The second study empirically examines the relationship between AO and the performance of public servants across societal cultures. The third study investigates the effect of ethical leadership (EL) on AO and the mediating role of the ethical environment (EE) on the relationship between EL and AO. The first study identifies the elements of AO and its consequence and remedy. The most common element of AO is multiple accountabilities or expectations. Besides, incompatibility between accountability criteria and organizational goals, ambiguous performance standards, and excessively high accountability or performance requirements are some of the dominant elements of AO. In addition, episodic and arbitrary accountability demand, incomplete outcome measures, emphasis on punitive actions, and lack of legitimacy of the accountholder are the factors that contribute to AO. The study suggests that AO generally produces negative consequences: it undermines performance and organizational objectives and makes the accountability system ineffective. Collaboration and dialogue, moderate accountability requirements, appropriate performance criteria, ethical practice in the organization, and an emphasis on the organizational mission may reduce AO. Contextual factors such as poor governance and lack of trust in government influence AO in the organization. However, extant studies are predominantly qualitative and concentrated in a limited number of countries. Thus, the study emphasizes empirical investigation into AO in comparative settings to appreciate the phenomenon and its consequences and remedies. The second study defines perceived AO and finds a negative association between AO and employee performance. It also proves that the relationship between performance and AO does not vary across societal cultures. Therefore, the study concludes that AO is a universal phenomenon and has a similar consequence irrespective of differences in contexts or cultures. The third study finds that EL reduces AO among employees and enhances EE in the organization. However, EE does not influence the relationship between EL and AO. Thus, the study underscores the importance of EL in reducing AO among employees irrespective of the ethical condition in the organization.
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    Investigation of Sweat Biomarkers for Real-time Reporting of Infection and Inflammation Using Wearable Sweat Sensor
    (December 2021) Jagannath, Badrinath; Prasad, Shalini; Bereg, Sergey; Muthukumar, Sriram; Sirsi, Shashank; Khoubrouy, Soudeh
    Inflammatory biomarkers are modulated due to an infection or inflammatory trigger. Cytokines are inflammatory biomarkers that orchestrate the manifestation and progression of an infection/inflammatory event. Hence, non-invasive, real-time monitoring of cytokines can be pivotal in assessing the progression of infection/inflammatory event. However, real-time monitoring of biomarkers is not feasible with the current technology as most of them rely on blood-based detection. Continuous monitoring of host immune markers in sweat can aid in realtime monitoring of the immune status. This dissertation demonstrates a wearable SWEATSENSER device that can track the levels of immune cytokine markers in real-time from passively expressed sweat. The developed device is of a watch form-factor that can be worn on the arm to reliably track the biomarker response from low volumes of sweat (~1 μL) and the biomarker levels can be monitored in real-time. The developed SWEATSENSER device was validated for reliably reporting the levels of several cytokines and chemokines. Additionally, this work presents a thorough validation on the presence of certain critical infection and inflammatory markers such as interferon-inducible protein (IP-10) and tumor necrosis factor- related apoptosis-inducing ligand (TRAIL), C-reactive protein that make it feasible for using sweat as a biofluid for actively monitoring the health status. Additionally, human subject clinical studies demonstrate the feasibility of non-invasively tracking infections such as influenza from sweat. Such a wearable device can offer significant strides in improving prognosis and provide personalized therapeutic treatment for several inflammatory/infectious diseases.
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    Nano-biothiol Interactions of Engineered Nanoparticles
    (December 2021) Zhou, Qinhan; Zheng, Jie; Kesden, Michael; Gnade, Bruce E.; Nielsen, Steven O.; Meloni, Gabriele; D'Arcy, Sheena
    Nanomedicines have been extensively studied in the past decades at the fundamental level because they could potentially make a paradigm shift in human healthcare. Nano-bio interactions play a central role in the precise control of the benefit and hazards of nanomedicines, but current studies mainly focus on how nanoparticles are taken up by cells and interact with different receptors. There is still not enough investigation of how the physiological environment transforms engineered nanoparticles through a variety of biochemical reactions. This dissertation aims to fundamentally understand the nanoparticle-biochemical interactions and the in vivo transport of engineered nanoparticles modulated by these interactions. In Chapter 1 of this dissertation, an overall review is given on the current understanding of nanobio interactions at the molecular and chemical levels, particularly. In Chapter 2, we systematically investigated how the nanoparticle size, the thiols species, and the protein binding affect the interactions between the nanoparticles and thiols at the in vitro level. In Chapter 3, we focused on unraveling the relation between the nanoparticle-biothiol interactions in vitro and the nanoparticle-biothiol interactions in vivo. In Chapter 4, we explored the nanoparticle-biothiol interactions in the diseased mice model and illustrated the application of nanoparticle-biothiol interactions in disease diagnosis. Finally, in Chapter 5, we present the summary and outlook. These new understanding on nano-biochemical interactions at both in vitro and in vivo levels will help further advance physiology at the nanoscale as well as open new pathways to early disease diagnosis and treatment.
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    A General Framework of Non-convex Models for Sparse Recovery With Applications
    (December 2021) Hu, Mengqi; Gassensmith, Jeremiah; Lou, Yifei; Cao, Yan; Rachinskiy, Dmitry; Pereira, Felipe; Ramakrishna, Viswanath
    Thanks to latest developments of science and technology, large data sets are becoming increasingly popular that lead to an emerging field, called compressive sensing (CS), which is about acquiring and processing sparse signals. In this thesis, we first propose a general framework to estimate sparse coefficients of generalized polynomial chaos (gPC) used in uncertainty quantification (UQ). In particular, we aim to identify a rotation matrix such that the gPC expansion of a set of random variables after the rotation has a sparser representation. However, this rotational approach alters the underlying linear system to be solved, which makes finding the sparse coefficients more difficult than the case without rotation. To resolve this issue, we examine several popular non-convex regularizations in CS that empirically perform better than the classic `1 approach. All these regularizations can be minimized by the alternating direction method of multipliers (ADMM). Numerical examples show superior performance of the proposed combination of rotation and non-convex sparsity promoting regularizations over the ones without rotation and with rotation but using the `1 norm. We observe through the UQ study that the `1 − `2 regularization often performs satisfactorily among the others. We then apply it to synthetic aperture radar (SAR) imaging based on a mathematical model of how electromagnetic waves are scattered in the space using Maxwell’s equations. Specifically we deduce an efficient sensing matrix for SAR and examine the efficiency of the `1 − `2 regularization to promote sparsity of scattered signals. Experimental results demonstrate that `1 − `2 can enhance the resolution of reconstructed image over the classic `1 approach. Motivated by conjugate gradient and adaptive momentum in the optimization literature, we propose a novel algorithmic improvement. The proposed algorithm works for general minimization problems, though numerical experiments are limited to `1 and `1 − `2 with a least-squares data fidelity term, showcasing faster convergence of the proposed algorithm over the traditional methods. We also establish the convergence of our algorithm for a quadratic problem.
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    Cooperative Collision Avoidance for Autonomous Vehicles Using Monte Carlo Tree Search
    (December 2021) Patel, Dhruvkumar; Zalila-Wenkstern, Rym; Liu, Jin; Bastani, Farokh B.; Hansen, John H. L.; Ouyang, Jessica
    Autonomous vehicles require an effective cooperative action planning strategy in an emergency situation. Most action planning approaches for autonomous vehicles do not scale well with the number of vehicles. In this dissertation, we present COCOA (Cooperative Collision Avoidance), an efficient cooperative action planning algorithm for autonomous vehicles in colliding situations. In COCOA, autonomous vehicles drive together in coalition formations for information sharing and cooperation. When a coalition member detects a colliding situation with a misbehaving vehicle, all coalition members explicitly cooperate to find conflict-free action plans to avoid collisions with the misbehaving vehicle. COCOA employs a hierarchical decision-making approach where action planning is achieved at two levels: at the vehicle level and at the coalition level. In emergency scenarios involving multiple coalitions, COCOA employs a sequential and hierarchical decision-making approach. Leaders of the coalitions in a coalition sequence cooperate to finalize action plans for their coalition members that are free of inter-coalition conflicts. The COCOA algorithm is validated through extensive realistic simulations in a multi-agent-based traffic simulation system.
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    Sound Source Localization for Improving Hearing Aid Studies Using Mobile Platforms
    (December 2021) Kucuk, Abdullah; Panahi, Issa M.S.; Ntafos, Simeon; Busso, Carlos; Nourani, Mehrdad; Nosratinia, Aria
    Microphone array is one of the powerful techniques that enables to apply effective signal processing algorithms to systems. One of the critical application areas of microphone array is sound source localization (SSL), which refers to identify the speaker of interest using a microphone array. SSL can be used as a preprocessing technique to boost up the entire system efficiency. Recent studies show that smartphones can be an efficient assistive device for hearing aid devices because of smartphones’ powerful hardware and software components. Also, Deep Learning (DL) has shown a considerable performance increase in audio signal processing. DL based SSL using the direction of arrival estimation (DOA) methods for two and eight microphone array structures and the distance estimation methods using a single microphone are proposed in this work. The performance of the proposed methods are evaluated in several realistic noisy conditions, reverberations using real-recorded data. Another contribution of this work is to present real-time implementations of the DL based methods on edges devices, i.e., smartphones, tablets.
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    Demonstration of POC Biosensor Toward Clinical Translation for Patient Bed-side Monitoring
    (December 2021) Tanak, Ambalika Sanjeev; Prasad, Shalini; Walker, Amy V.; Muthukumar, Sriram; Sirsi, Shashank; Ardestani Khoubrouy, Soudeh
    The research presented in this dissertation focuses on developing and characterizing a multiplexed affinity based electrochemical biosensing device toward clinical translation. The goal of this work is to establish a portable POC device for early disease detection across diverse healthcare applications using low sample volume, rapid response time and usability amongst minimally trained individual relying on ASSURED (Affordable, Sensitive, Specific, User friendly, rapid, and Robust, Equipment free and Deliverable to end users) criteria. Primarily, we designed a robust, non-faradaic electrochemical affinity biosensing platform for the rapid assessment of parathyroid hormone (PTH) as a single biosensing system. Unique high density semiconducting nanostructured arrays on a flexible sensing surface were used to create the analytical nanobiosensor. The surface modification technique was specifically designed to improve the interaction of the nanostructure–biological interface to capture the desired PTH level in HS and plasma. This was followed by evaluating the analytical performance of the developed biosensor with clinical rigor. The assay validation results were compared with laboratory standard as reference with results that demonstrated comparable performance with higher accuracy. Next, the scope of the biosensor was expanded to solve a clinically challenging problem of detecting host immune markers for life-threatening sepsis infection. Herein, we demonstrate a first-of-a-kind multiplexed POC biosensing device that simultaneously detects a panel of eight key immune response cytokine biomarkers in sample volume equivalent to two drops of plasma and whole blood within 5 minutes without sample dilution. Moreover, this work focuses on validating the developed biosensing device with LUMINEX standard reference method for clinical translation using nearly 200 patient samples. The DeTecT (Direct Electrochemical Technique Targeting) Sepsis biosensing device is surface engineered with specific capture probes that utilizes EIS to measure the capacitive impedance change reflecting binding interactions between the capture probe and target biomarker enabling multiplexed detection. Specificity of the biosensor was validated using cross-reactive studies, which displayed insignificant interference from non-specific biomarkers. The biosensor also displays stable and repeatable performance. The novelty presented in this research combines the effectiveness of choosing specific host immune response biomarkers for detection of sepsis combined with unique surface modification strategy coupled with EIS technique to enable efficient clinical decision-making process. This unique sensor technology would allow medical practitioners to facilitate targeted interventions for septic patients as a rapid prognostic approach, preventing complications arriving from sepsis.
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    Role of FimK in Mediating Host Urinary Bladder Epithelial Cell Association of Uropathogenic Klebsiella Pneumoniae and Quasipneumoniae
    (December 2021) Venkitapathi, Sundharamani; De Nisco, Nicole; Lou, Yifei; Palmer, Kelli; Delk, Nikki; Spiro, Stephen
    Klebsiella spp. commonly cause both uncomplicated urinary tract infection (UTI) and recurrent UTI (rUTI). K. quasipneumoniae, a relatively newly defined species of Klebsiella, has been shown to be metabolically distinct from K. pneumoniae, but its urovirulence mechanisms have not been defined. Type 1 and type 3 fimbriae, encoded by fim and mrk operons respectively, mediate attachment of Klebsiella spp. to host epithelial cells. fimK is a regulatory gene unique to the Klebsiella fim operon that encodes an N-terminal DNA binding domain and a C-terminal phosphodiesterase domain that has been hypothesized to cross-regulate type 3 fimbriae via modulation of cellular levels of cyclic di-GMP. Comparative genomic analysis between K. pneumoniae and K. quasipneumoniae revealed a conserved premature stop codon in K. quasipneumoniae fimK that results in loss of the C-terminal phosphodiesterase domain (PDE). We hypothesized that this truncation would ablate cross-regulation of type 3 fimbriae in K. quasipneumoniae. Here, we report that K. quasipneumoniae KqPF9 bladder epithelial cell association and invasion is dependent on type 3 but not type 1 fimbriae. Further, we show that basal expression of both type 1 and type 3 fimbrial operons as well as bladder epithelial cell association are higher in KqPF9 than in K. pneumoniae TOP52. Interestingly, complementation of KqPF9∆fimK with the TOP52 fimK allele markedly reduced type 3 fimbrial expression and bladder epithelial cell attachment, a phenotype that was rescued by mutation of the C-terminal PDE active site. Taken together these data suggest that C-terminal PDE of FimK modulates type 3 fimbrial expression in K. pneumoniae and its absence in K. quasipneumoniae leads to a loss of type 3 fimbrial cross-regulation.
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    Chemically Tuned Virus Like Particles: From Cancer Therapy to Targeted Delivery
    (December 2021) Shahrivarkevishahi, Arezoo; Gassensmith, Jeremiah J.; Slinker, Jason D.; Zheng, Jie; Ahn, Jung-Mo; Dodani, Sheel
    In recent years, nanoparticle-based therapeutics have been increasingly applied in broad range of clinical applications from diagnosis to treatment of many diseases such as cancer, diabetes and neurodegenerative disorders. A wide range of synthetic and naturally occurring materials such as polymers, metal oxides, silicate, liposome, and carbon nanotubes have been developed to overcome some of the key barriers in free therapeutics including intracellular trafficking, cell/tissue targeting, poor biodistribution, and low efficiency. However, despite all achievements in creating these nanomaterials with different chemical and physical properties such as size, shape and surface properties, developing a nanoparticle to surmount these limitations all in one is a big challenge. Virus like particle (VLP) as protein-based nanomaterials that closely mimic the highly symmetrical and polyvalent conformation of viruses while lack the viral genomes have emerged as a solution for these limitations. Their unique features such as high biocompatibility, biodegradability, monodisperisty, intrinsic immunogenicity, and safety combined with interior and exterior modification capability offer new tool to scientists for careful design and engineering of multi-component therapeutic agent with intended biological behavior and pharmacological profiles. Herein, various chemistry strategies are introduced in combination with biology and immunology to turn virus like particle to a favorable engineered biomaterial for several functions such as cancer therapy and intracellular delivery. We showed how by modifying surface of VLP Qβ with NIR organic molecule we can make a highly efficient and stable photothermal agent that can cause thermal ablation of tumor while simultaneously activating the immune response. We found this immunophotothermal agent, suppress primary tumor, control metastasis, and prolong survival time in mice bearing breast cancer. We also addressed one of the biggest challenges in biologic delivery which is direct delivery of therapeutic cargo into cell cytoplasm. Using organic chemistry we designed a cytosolic targeting linker that when attached to surface of VLP Qβ, helps to escape endosomes and be released into cytoplasm, Moreover, this proteinaceous material is shown to have a great potential in combination with other materials such as metal organic framework to construct a multimodal cancer therapeutic agent enabling delivering mulit therapeutic agents such as immunotherapeutic drugs while taking advantage of all unique features of virus like particles. These works clearly show the significant potential of VLP in design and modification of new therapeutic platform.
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    Ensuring Integrity, Privacy, and Fairness for Machine Learning Using Trusted Execution Environments
    (December 2021) Asvadishirehjini, Aref; Kantarcioglu, Murat; Wallace, Robert; Thuraisingham, Bhavani; Khan, Latifur; Iyer, Rishabh Krishnan
    In this day and age, numerous decision-making systems increasingly rely on machine learning (ML) and deep learning to deliver cutting-edge technologies to the members of society. Due to potential security, privacy and bias issues with respect to these ML methods, currently, end users cannot fully trust these systems with their private data, and their prediction outcome. For instance, in many cases, it is not clear how an individual’s medical record is being used for building tools for medical diagnosis? Is the data always encrypted at rest? When they are decrypted, is there a guarantee that only a trusted application can have access to the private data to eliminate potential misuse? Throughout this dissertation, solutions that leverage various security and integrity capabilities provided by hardware assisted Trusted Execution Environments (TEE) are proposed to make these ML based systems more reliable and trustworthy so that end users can have a greater trust in these systems. As a starting point, we first address the privacy and integrity issues in ML model learning in the cloud setting. Training of a deep learning model that only relies on a TEE is not very attractive to businesses that need to continuously train their models in a remote cloud setting. This is due to the fact that special hardware such as Graphical Processing Units (GPU) are much more efficient in training ML models compared to CPU based TEEs. In this dissertation, we propose an integrity-preserving solution that combines TEEs, and GPUs in order to provide an efficient solution. In this solution, we focus on the ML model training task using the efficient GPU while ensuring the detect any deviation from the ML model learning protocol with a high probability using the TEE capabilities. Using our solution, we can ascertain (with high probability) the model is trained with the correct training dataset using the correct training hyperparameters, and correct code execution flow. Once we provide an integrity preserving ML model training solution, we focus on how to use the learned ML model privately and securely in practice. To provide privacy-preserving inference on sensitive data, wherein ML model owner and data owner do not trust each other, the dissertation proposes a solution that the inference task is run inside a TEE and the result is sent to the data owner(s). The most important benefit of our solution is that the data owner can ensure their data will not be used for any other purposes in the future and no information other than the agreed model inference result is disclosed. Furthermore, we show the efficacy of our solution in the context of genomic data analysis. Next we focus on the bias and unfairness embedded in certain ML models. It is has been reported that the ML models can unfairly treat certain subgroups, and it is hard to test for such issues in application deployment settings where both the ML model and the input data to the ML model is sensitive (i.e., both the model and the data cannot be disclosed to public for auditing directly). This dissertation proposes a privacy-preserving solution for fairness analytics using TEEs. In this setting, the model owner and the fairness test set owner do not trust each other, therefore they do not want their input to be disclosed. The end goal is for the fairness analyst to conduct tests about the quality and fairness of the model’s outcome with respect to a set of predefined minority groups or subgroups and compare and contrast them with privileged group(s). This way, models can be analyzed, and the analyst can shed light on the potential latent biases in the ML model in a privacy-preserving manner. Even if the ML model is trained, and deployed securely, due to data poisoning, the final model may still contain hidden backdoors (which in the literature is referred to as trojan attacks). Finally, in this dissertation, we develop novel techniques to detect such attacks. We design experiments that first creates a multitude of models that carry a trojan, and another set that does not have any trojan. Then, we build classifiers to see if we can tell them apart. Our results show that ML models could be used to detect trojan attacks against other ML models.
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    Multiplexed Comprehensive Design and Characterization of an Electrochemical Interface Accessing Non-invasive Bodily Fluids Towards Quality-of-life Monitoring
    (December 2021) Bhide, Ashlesha; Prasad, Shalini; Balsara, Poras; Muthukumar, Sriram; Cogan, Stuart; Ardestani Khoubrouy, Soudeh
    The research presented in this thesis outlines the design and development of novel biosensing platforms for monitoring biomarkers by the non-invasive sampling of body fluids with emphasis on self-health and disease management. The purpose of this work is to demonstrate the efficacy of two combinatorial biosensors – Continuous awareness through sweat platform (CLASP) and Exhaled breath condensate scanning using rapid electro analytics (EBC-SURE) for the detection of metabolic and inflammatory biomarkers towards integration onto low-power internet of things (IoT) platforms for wearable and point-of-care diagnostic applications. First, this work demonstrated the technical utility of a lancet-free, label-free platform for the combinatorial, and continuous monitoring of alcohol and glucose in perspired human sweat produced without external sweat induction strategies. The motivation of this study was to develop a sweat-based wearable platform for alcohol and glucose management to monitor glucose levels on moderate consumption of alcohol of diabetic social drinkers. A nanotextured sensor stack was embedded into a flexible nanoporous substrate to achieve sensitive and specific affinity-based biomarker detection within physiologically relevant ranges in ultralow volumes of sweat. Non-faradaic EIS is employed as the signal transduction mechanism for biomarker detection to give an insight into the binding events occurring at the sensor interface. Additionally, the CLASP platform was benchmarked against commercially available hand-held devices to establish a one-to-one performance correlation. Furthermore, this platform was employed to demonstrate the epidermal functionality and sensor performance of CLASP for the on-body detection of sweat lactate to monitor restricted oxygen supply in sedentary populations. The successful implementation of CLASP in detecting metabolic biomarkers for health monitoring led to the transition of assessing the performance metrics of this platform for the detection of inflammatory biomarkers such as cortisol and TNF-α for chronic disease monitoring. The important highlight of this work was to establish the longterm temporal stability of the CLASP in detecting a simulated rise and fall in cortisol levels over a 6-hour sleep cycle. The last effort was focused on developing a point-of-care aid platform- EBCSURE for the trace detection of inflammatory biomarkers in exhaled breath condensate for monitoring respiratory disorders. Exhaled breath condensate is considered a promising source of inflammatory biomarkers that can determine the pathophysiological processes underlying lung inflammation in a simple and non-invasive manner. EBC-SURE displayed a stable performance after rigorous testing enabling its integration onto diagnostic platforms for rapid quantification of biomarkers related to a healthy and acute inflammatory disease condition.
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    Integrated GaN Power Conversion: Topology, Reliability and Implementation
    (December 2021) Yan, Dong; Ma, Dongsheng Brian; Zalila-Wenkstern, Rym; Akin, Bilal; Henderson, Rashaunda; Iyer, Mahadevan Krishna
    The explosive growth of power electronics has resulted in high power demand and more stringent requirements on power conversion systems. When bridging an increasing high input voltage and a decreasing low output voltage, high step-down ratio power converters demand high power density, high reliability, and high integration level. Compared to classic silicon power devices, new arising gallium nitride (GaN) technology presents the superior figure of merits, and it is regarded as a more promising power device candidate to overcome these challenges. However, because of the high step-down conversion ratio and unique characteristics of GaN power devices, GaN-based dc-dc power conversions face new challenges. Thus, a series of integrated GaN power conversion topologies, schemes and implementations have been explored to address power density, reliability and integration challenges. Firstly, a GaN-based double step-down (DSD) power topology is presented for direct 48V/1V power conversion. In order to realize closed-loop regulation of the DSD power converter, an adaptive ON- and OFF-time (AO2T) control with elastic ON-time modulation is developed for both steady state regulation and transient response enhancement. To reinforce the dual-phase operation reliability of the DSD converter, a master-phase mirroring technique enables adaptive master-slave phase operation, accomplishing automatic phase current balancing. Secondly, to improve the system reliability of automotive electronics, a low EMI noise high stepdown ratio GaN-based buck converter is designed for direct battery-to-load power conversion. It employs an anti-aliasing multi-rate spread-spectrum modulation (MR-SSM) technique to suppress EMI noise and an in-cycle adaptive zero-voltage switching (ZVS) technique to minimize switching losses. Compared to the classic fixed-rate SSM (FR-SSM), the MR-SSM technique adaptively spreads EMI spectra in a wider frequency range without aliasing spikes and, thus, reduces peak EMI noise more effectively. To improve efficiency, an elastic dead-time (tdead) controller facilitates in-cycle adaptive ZVS despite of a continuous switching frequency variation. For the enhancement of GaN power devices driving reliability, a pulse-reinforced level shifting technique is proposed to immune high switching node voltage dv/dt transition. Thirdly, to enhance the GaN power device reliability of GaN-based power conversion system, an on-chip self-calibrated full-profile dynamic on-resistance sensing strategy is proposed to monitor the online healthy state of power devices. It achieves instant dynamic on-resistance sensing beyond megahertz. Moreover, complicated high-speed current sensing circuits are avoided to reduce implementation cost, and the random sensing errors are calibrated automatically for high sensing accuracy. The online state-of-health condition of GaN-based power converter is thus monitored comprehensively, precisely, and efficiently. Finally, one monolithic integrated e-mode GaN asymmetrical half-bridge (AHB) power converter is implemented for direct 48V/1V power conversion, which minimizes non-ideal parasitics, enhances power conversion reliability, and reduces system complexity significantly. In the AHB converter, an auto-lock auto-break (A2 ) level shifting technique is developed to address the challenges of pull-up performance, device breakdown risk and dv/dt immunity at switching node voltage. The self-bootstrapped hybrid (SBH) gate driving technique adaptively achieves rail-torail dynamic gate driving in normal operation and robust static gate driving during large transients. The on-die temperature sensing facilitates hot spot monitoring and thermal management for high reliability. In this dissertation, all the proposed GaN dc-dc power converters have been fabricated and tested to demonstrate the proposed system topologies, control schemes, circuit techniques. The measurement results successfully validate the effectiveness of the designs. The high switching frequency, low EMI noise, high reliability, and monolithic integration have been verified to enable GaN dc-dc power conversions.
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    Ferroelectric HfxZr1-xO2 for Next Generation Non-volatile Memory Applications and Its Reliability
    (December 2021) Mohan, Jaidah; Kim, Jiyoung; Harabagiu, Sanda; Wallace, Robert M; Summerfelt, Scott R; Young, Chadwin D; Gnade, Bruce
    To keep up with the increasing memory demands, developing memories with higher densities, speed and energy efficiency is necessary at different levels of the memory hierarchy. Ferroelectric materials have been considered alternative memory components; however, the conventional perovskite-based ferroelectric materials pose several challenges due to CMOS integration, high thermal budget, and scaling to sub 70 nm thicknesses. In this regard, the discovery of ferroelectricity in doped HfO2 thin films was revolutionary as HfO2 is already employed in front end CMOS as a high-k dielectric material for scaled thicknesses (<10 nm) . Additionally, doping HfO2 films with ZrO2, i.e., Hf0.5Zr0.5O2 (HZO) showed stable ferroelectric phase crystallization at back-end of line compatible temperatures (<450 °C). This dissertation addresses some critical issues on the stress-induced crystallization of the ferroelectric phase in HZO films, the reliability properties of metal-ferroelectric-metal (MFM) structures, scaling ferroelectric HZO films on silicon substrates, and their reliability. First, the driving forces for the crystallization of pure ferroelectric phase in HZO thin films were addressed and the role of the TiN top electrode in phase crystallization at low process temperatures (400 °C) is studied. Then, the reliability of 10 nm thick HZO films was studied, and the various ferroelectric device reliability properties and mechanisms were evaluated for metal-ferroelectricmetal structures. Finally, the ferroelectric HZO films were integrated directly on silicon for FeFET applications and the effect of ferroelectric device reliability based on scaling HZO films on silicon structures was studied.