VictorJuiz/thesis huggingface dataset:
On the Inherently Nonlinear Dynamics of Unilateral Contact in Elastic Structures

Engineering and everyday experience alike provide countless examples of deformable objects coming into contact with effectively rigid objects, from a belt stretched between pulleys in an automobile engine to a ball bouncing on a blacktop. But despite the ubiquity of such systems, we have little in the way of a fundamental understanding of the dynamics they can be expected to display. The main obstacle to deeper understanding is mathematical: the unilateral constraint of contact creates free boundaries, and free-boundary-value problems are inherently nonlinear, even if the underlying differential equations are themselves linear. This dissertation explores the dynamics of contact in several prototypical problems inspired by engineering applications such as indentation testing, offshore oil exploration and production, and microelectromechanical systems (MEMS) design./nThe first major thrust of the present work is the linear vibration analysis of a beam contacting a flat surface due to combined gravity and adhesion. In analyzing this problem we develop a technique for appropriately linearizing the conditions at a free boundary. We then extend the technique to study the nonlinear vibrations of the gravity-only problem using a second-order perturbation analysis, which shows that contact generates a rich array of nonlinear resonances. The results show good agreement with numerical solutions from a special finite element method that precisely tracks the free boundary. We conclude by using the gap function formalism common in computational contact mechanics to demonstrate that the same pattern of resonances can be expected to arise in a more general class of problems./nHaving studied a problem involving a one-dimensional continuum, we shift attention to one with a two-dimensional continuum: an electrostatically actuated plate contacting a charged, dielectric-coated substrate. We use a variational method to derive one model in which the dielectric thickness is finite, and the other in which the thickness is small. The latter corresponds to a common type of surface adhesion. We formulate the linear stability equations for both models and characterize their equilibria in the case of a circular actuator before adopting an approximation from dynamic fracture mechanics to study the dynamics of the thin-dielectric model, which display some unique mathematical properties, including finite-time blow-up. Finally, we discuss application of the model to the problem of vibration-assisted stiction repair./nThis work incorporates many analytical and computational tools, from configurational mechanics, to perturbation theory, to arbitrary Lagrangian-Eulerian finite element methods, and represents a first step toward the development of rules-of-thumb for the design of vibration-critical mechanical systems with contact constraints.

Advisor: O'Reilly, Oliver M.
Assessment of public health interventions to mitigate SARS-CoV-2 transmission and risk factors for infection in California

Vaccination and non-pharmaceutical interventions (NPIs) have been of central importance to mitigate the ongoing COVID-19 pandemic. In collaboration with California Department of Public Health, I designed and managed a test-negative design case-control study to assess risk factors for SARS-CoV-2 infection and the effectiveness of various mitigation strategies including vaccination and the use of face masks. Throughout the study period, a team of trained interviewers placed over 40,000 telephone calls and enrolled over 4,000 total cases and controls, testing positive and negative for SARS-CoV-2, respectively. In Chapter 1, I present COVID-19 vaccine effectiveness estimates using data collected between February ¬– April 2021 when Epsilon (B.1.427 and B.1.429) and Alpha (B.1.1.7) were the dominant SARS-CoV-2 variants of concern circulating in California. I demonstrate mRNA-based vaccines (BNT-162b2 [Pfizer] and mRNA-1273 [Moderna]) conferred substantial protection against documented SARS-CoV-2 infection. However, nearly one-third of participants expressed reluctance to initiate the COVID-19 vaccination series, primarily due to concerns about vaccine side effects or safety. In Chapter 2, I evaluate predictors of SARS-CoV-2 infection amongst a subset of participants who reported history of social contact with a person confirmed or suspected to have been infected with SARS-CoV-2 within 14 days of testing. Among the unvaccinated, participants had a significantly higher odds of testing SARS-CoV-2 positive when social contact was long-lasting (>3 hours), involved a household member, or occurred indoors. Additionally, in the context of these high-risk exposures, I demonstrate mask wearing was most protective among the unvaccinated or partially unvaccinated. Last, in Chapter 3, I estimate the real-world effectiveness of face masks in reducing the risk of acquiring a SARS-CoV-2 infection in public indoors settings. Participants self-reported their frequency of mask use and the type of mask typically worn in public indoor settings. I found that any mask use in public indoor settings reduced the odds the wearer acquired a SARS-CoV-2 infection and mask use exhibited additional protection with increasing frequency of use. Upgrading face masks to masks with higher filtration capacity (N95/KN95s) conferred additional protection. This work has demonstrated the value of case-control studies to rapidly collect real-time data to address and monitor emerging issues in the COVID-19 pandemic and advance public health policy.

Advisor: Lewnard, Joseph A.
The Astronomical Realists: The Social Mechanics of Visual Documentation, Art, and the American Space Age, 1944 – 1987

This dissertation examines how the practice of astronomical illustration changed and expanded over the course of the twentieth century Space Age. In the United States, the post-war period transformed outer space into a geopolitically significant environment, reorienting the authoritative picturing of space from a practice housed primarily within European astronomical observatories to one with commercial viability in American science and popular culture. In the absence of cameras capable of rendering the space environment, individual illustrators filled in pictorial gaps by hand. Between the years of 1944 and 1987, illustrators developed an aesthetic of neutrality that visually signaled the scientific accuracy of their work. This aesthetic privileged a style of representation that mirrored the technical impartiality of cameras, collapsing distinctions between “most realistic” looking with “most photographic.” The visual clarity of photographic resolution became the standard for the most successful illustrations, even though most subjects depicted required a degree of artistic license to be made visible at all. This dissertation examines the visual techniques developed to reproduce photographic-looking illustrations of unphotographable places. The status of these images as utilitarian was negotiated via a complex web of group consensus and proximity to places like NASA, educational programming at planetariums, and public television. Examining midcentury astronomical illustration as a cultural product instead of neutral technical output offers a new entry point into the visual culture of the Space Age in the United States. This study underscores the way in which socially constructed expectations about the space environment were coded into objective-looking images.

Advisor: Carson, Cathryn
An Efficient, Tolerance-Based Algorithm for the Truncated SVD

The truncated singular value decomposition (TSVD) is an important low-rank matrix approximation technique used in data science, machine learning, numerical linear algebra, and many other scientific fields. However, it is quite expensive for large matrices when only a very low-rank approximation is needed. Existing algorithms for TSVD typically assume the user knows the proper rank to use, but in practice, they may not know this rank beforehand. Thus, it is important to have versions of these algorithms that depend on a tolerance parameter, allowing the user to directly control the approximation error. We develop one such algorithm that runs quickly for matrices with rapidly decaying singular values, provide approximation error bounds that are within a constant factor away from optimal, and demonstrate its utility with matrices from a variety of applications.

Advisor: Gu, Ming
Preschoolers' acquisition of functional metaphors

Metaphors are ubiquitous in both everyday speech (e.g., “I was lost in a sea of people”) and creative literature (e.g, “A word is elegy to what it signifies”). Metaphors provide frameworks for reasoning about abstract concepts, influencing how humans attend to, remember, and process information. Metaphors are also a force for creative change across various disparate domains, for example by spurring the creation of new explanatory theories in science and new word meanings in language. While metaphors are a powerful tool for thinking and reasoning in adulthood, previous research suggests that children do not understand metaphors until quite late in development, by some accounts not until adolescence. However, using novel experimental paradigms, the current dissertation provides new data suggesting that children as young as four years of age can not only understand, but also learn from, metaphors./nChapter 1 outlines previous research on the development of metaphor comprehension. Chapter 2 uses novel paradigms to demonstrate that preschoolers can understand metaphors based on shared abstract, functional similarities (e.g., “Clouds are sponges”; “Roofs are hats”). Chapter 3 explores whether preschoolers can select the most relevant metaphor to learn from, and finds that providing preschoolers with explanations can shift their metaphor preferences in a manner that is conducive to further learning. Chapter 4 shows that preschoolers can successfully use metaphors to make additional inferences about novel concepts. Finally, Chapter 5 discusses the implications of this work for research in linguistic and cognitive development, as well as future directions./nOverall, this series of experiments suggest that children possess an early-emerging capacity to understand and use complex non-literal language. Moreover, metaphors may be a powerful cognitive mechanism that facilitates learning, even early in ontogenesis.

Advisor: Gopnik, Alison
Investigating Microwave-Activated Entangling Gates on Superconducting Quantum Processors

Superconducting quantum circuits are a leading technology in the quest toward building a quantum computer, which promises to outperform conventional, or “classical” computers, in solving a variety of tasks. To be useful for computation, a quantum processing unit (QPU) architecture must be scalable and have low error rates. Microwave activated entangling interactions, while often being slower than interactions that use flux-tunable components, are a scalable approach to realizing entanglement in that they are compatible with a minimally complex QPU design: single-junction transmon qubits with fixed qubit-qubit coupling. A prominent microwave-activated gate in this architecture, the cross resonance (CR) gate, is experimentally investigated, including mitigation of leakage errors, and budgeting of coherent and incoherent errors. Importantly, the fidelity of the CR gate is limited in general by the static cross-Kerr, or ZZ, interaction between the qubits. A novel cross-Kerr entangling interaction that commutes with the static cross-Kerr interaction is presented, based on simultaneous off-resonant drives. This cross-Kerr interaction is tunable, enabling the complete cancellation or enhancement of qubit-qubit coupling, a first for this fixed-frequency, fixed-coupling architecture. We show the tunability of the sign of the interaction, and use it to implement a two qubit controlled-phase (CZ) gate. This gate is observed to have lower coherent error than the CR gate. Designing a QPU with this as the native entangling inter- action could enable faster microwave-activated gates, improving overall performance while maintaining minimal hardware complexity.

Advisor: Siddiqi, Irfan
Genomics-guided specialized metabolite discovery from cariogenic Streptococcus mutans

Streptococcus mutans is a major constituent of dental plaque and a major etiological agent of dental caries, one of the most prevalent medical conditions with an annual disease burden in the billions, including several hundred million children. Canonically, three attributes of S. mutans comprise its ability to cause dental cavities, namely, acidogenicity (acid production), acidurity (endurance of acidic conditions), and biofilm formation. Although these traits explain much of caries etiology, and their molecular underpinnings are often well-established, the complete picture is increasingly appreciated to involve specialized metabolism.In this dissertation, I discuss the discovery, biological activity, and biosynthesis of two novel specialized metabolite families originating from distinct biosynthetic gene clusters in S. mutans. I begin by introducing the reader to specialized metabolites isolated from nasal/oral microbiota in Chapter 1. I argue these specialized metabolites play key roles in microbial physiology and ecology, but that they remain an understudied facet of oral microbiology. In Chapter 2, I present the discovery of mutanoclumpin-583, a macrocyclic lipopeptide produced by a biosynthetic gene cluster (mcg) widespread in lactic acid bacteria. I further discuss the contribution of mutanoclumpin to autoaggregation and biofilm formation, phenotypes with potential clinical relevance. In Chapter 3, I discuss studies on the biosynthesis of mutanoclumpin family metabolites. In Chapter 4, I present preliminary studies on the regulatory circuit governing mutanoclumpin production and transporter activity in mutanoclumpin localization. I highlight that a complete model of mutanoclumpin biological activity requires further study on McgR, a transcriptional regulator, and the many transporters encoded in mcg. Finally, in Chapter 5, I discuss the successful design and implementation of a synthetic biology strategy to unveil the specialized metabolite product of BGC6, a hybrid NRPS/PKS gene cluster widespread in lactic acid bacteria. In summary, my studies have revealed two new specialized metabolite families produced by S. mutans clinical isolates and present avenues for additional research in the chemical ecology of S. mutans.

Advisor: Zhang, Wenjun
Towards Mutual Understanding between Mortals and Machines in Motion for Safety

Every good collaboration is built on solid mutual understanding. Without understanding their machines' behavior, human operators cannot plan around them. Yet increasing automation is distancing them from active understanding. This dissertation will apply cognitive science to build automation that boosts human understanding./nThe need for transparency is urgent for safety supervising tasks. Humans' environmental awareness and expansive understanding of safety can save robots from unforeseen edge cases. But only if those humans can also think through the robot's ongoing activity. Actions can be optimized to evidence safety or clearly anticipate faults, enabling supervisors to develop evidence-based appropriate trust. This work explores how observing action allows both humans and robots to construct better working models of the other./nIn research on assured autonomy we focus on how machines can autonomously guarantee safety. Yet there will always remain a modeling gap that we require human collaborators to help fill: that's why after decades of autopilot experience and improvements, we still require two human pilots to validate ongoing safe operation. This thesis contends that safe robotics must work to inform these safety collaborators; that choices don't only function to complete objectives but are also evidence that other agents ultimately judge./nCharacterizing how agents judge can empower our machines to choose actions to win correct judgements. First we will exposit how to learn humans' safety concerns from data despite noisy dynamics and demonstrations. After learning humans' concerns, we typify how they perceive and forecast danger. Building on cognitive science we present a model of human safety forecasting structured by reachability analysis. This structure induces data-efficient learning on small datasets so we can learn each supervisor's idiosyncratic ways of thinking -- enabling designers to conform their intelligent systems like a glove to a hand./nWe build on these models of human safety judgement to support that judgement through machine choices. After learning each supervisor's unique alarms, respecting that safe set lets robot teams decrease supervisory false positives. Extending this approach to anticipate safety concerns ahead of the decision point, we optimize motion as evidence to reject the null hypothesis of danger./nThe approaches in this dissertation contributes a mathematical lens for further inquiries into human risk-taking, safety negotiation, and technology learning. By employing the formalisms of intelligent safety to sketch human safety behavior, we imbue machines with a ``theory of mind'' that is essential to fluent collaboration for our societal systems.

Advisor: Sastry, S Shankar
Structure-Property Relationships in Disordered Complex Oxide Perovskites

This dissertation explores the lattice degree of freedom in several heavily-substituted, highly disordered, and technologically relevant perovskite systems utilizing epitaxial thin-film synthesis and characterization. By creating controlled changes to the boundary conditions imposed on thin films, effecting different surface orientations, strain states, film thicknesses, and elastic stiffness, connections can be drawn between the resulting structural, chemical, and electronic changes that are driven by the film’s boundary conditions, and the resulting properties that can be achieved in these films. First, I focus on the impact such changes have on the surface properties of La1-xSrxCo1-yFeyO3-δ transition metal perovskites that are being considered for cathodes for oxygen electrocatalysis. There, growth-mediated control of the surface orientation gives rise to distinct evolution of local non-stoichiometry and increased oxygen reactivity in (111)-oriented films. Application of epitaxial strain on the films is then found to impact the electronic structure of these films, modulating the occupancy of transition-metal orbitals at the surface, which ultimately impacts the ability of these materials to perform oxygen exchange. The focus is then changed to relaxor ferroelectrics, and understanding how changes to elastic and electrostatic boundary conditions impact the response of the disordered nanoscale polar structure. In PbSc0.5Ta0.5O3, reducing the thickness of films results in suppression of the polarization response that undergoes a rapid collapse when film thickness is reduced below the polar correlation length. It is then demonstrated that the polarization and piezoelectric response of 0.68PbMg1/3Nb2/3O3-0.32PbTiO3 is suppressed by the mechanical clamping of the substrate, and methods for removing films from the substrate allow for enhanced responses. Together, these studies point to the connection between lattice constraints and the achievable responses in perovskites, as well as point to new engineering approaches for enhancing desirable properties across a range of applications.

Advisor: Martin, Lane W
Defect Characterization of Metals in Radiation and Oxidizing Environments using Positron Annihilation Spectroscopy

Coupled extremes such as radiation and corrosion in nuclear environments induce a state in matter that is far from equilibrium. The addition of corrosion increases the complexity of the nonequilibrium problem. Thus, there is a continual need to advance understanding of these coupled effects to ultimately predict material response and enable the design of new materials that can better withstand these combined extreme conditions. The work in this dissertation advances toward a predictive understanding for how point defect transport can drive radiation damage and corrosion./nThis dissertation seeks to advance fundamental understanding of defects from radiation damage and oxidation phenomena in structural materials for nuclear reactor applications. The first objective of this research is to validate rate theory predications of radiation damage defect populations in-situ using positron annihilation spectroscopy. This hypothesis is explored using simulation, execution, and discussion of novel positron annihilation spectroscopy experiments. Ultimately, radiation-induced nonequilibrium monovacancy defects were simulated and experimentally observable with positron spectroscopy, and in-situ vacancy concentration showed an increase with increasing dpa. The second objective of this research is to validate theoretical predictions of predominant defect identities within passivating systems. Positron spectroscopy was again used to experimentally connect to previous results from simulations of oxide layer defect behavior, electrochemical studies on oxides, and post-mortem irradiation microscopy studies. Oxidation defect behavior in iron and chromium was quantified as a function of oxidation time, temperature, and irradiation./nPositron annihilation spectroscopy is a monovacancy-sensitive, depth-dependent, nondestructive technique to understand defect behavior, which ultimately governs materials’ radiation and corrosion performance for safety and corrosion protection of new and existing nuclear structural materials. It is hoped that this research will open new doors to advanced characterization and modelling opportunities in the study of nonequilibrium radiation and oxidation defects.

Advisor: Hosemann, Peter
Ab Initio Methods for Modeling the Thermodynamics of Molecules Adsorbed in Zeolites

Zeolites are crystalline microporous solids composed of corner-sharing, tetrahedrally- coordinated silicate (SiO4) units. The isomorphic substitution of a framework Si atom by an Al atom is charge-compensated by a proton, introducing Brønsted-acidic bridging-hydroxy groups. These proton-exchanged zeolites are used in a large number of processes, including hydrocarbon cracking, isomerization, and alkylation, and conversion of petroleum to trans- portation fuel. In addition to catalytic processes, zeolites can be used as adsorbents for carbon capture, molecular sieving, and pollution control technologies. It is therefore of great interest to predict the impact of zeolite structure and composition on its functional proper- ties to screen new catalysts and improve existing catalysts. Quantum chemical calculations can provide molecular-scale information on zeolite-adsorbate interactions, as well as model the energetic changes and dynamics of important reactions that occur within the channels and pores of zeolite catalysts. However, the application of quantum chemical calculations for the study of chemical reactions occurring in zeolites is made difficult by the lack of reliable methods to generalize the theory beyond zero Kelvin. Modeling adsorption and desorption free energies is particularly troublesome, relying on a subtle balance between enthalpic and entropic terms. While the enthalpic term is becoming ever more accurate through density functional development, the much more temperature-sensitive entropic term remains gener- ally underquantified by frequently-assumed harmonic approximations. The consequence is an inability to computationally replicate experimental observations, such as rate coefficients or equilibrium constants, with chemical accuracy. This work is concerned with a re-examination of harmonic approximations, including quantifying its failures in reaction kinetics of aldol condensation on isolated metal sites, exploring alternative approximation methods for anhar- monic intramolecular motions, and developing new methods for approximating anharmonic external molecular motions for species adsorbed in zeolites.

Advisor: Bell, Alexis T
Head-Gordon, Martin