Rice University Research Repository
The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation.
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A Flexible Multimodal 3D Single-Molecule Super-Resolution Microscope for Whole Cell Imaging
(2023-07-20) Nelson, Tyler Evan; Gustavsson, Anna-Karin
Single-molecule super resolution techniques can be used to resolve subcellular structures in nanoscale detail, but they are sensitive to background signal which is common in fluorescently labeled cells. Most microscopes are limited to standard epi- illumination, which generates high background fluorescence by illuminating the entire sample at once. Specialized illumination schemes like light sheets or Total Internal Reflection Fluorescence (TIRF) are useful to improve the resolution, but the usefulness of these methods can be limited in certain regions of the cell. In this thesis, we demonstrate a flexible, multimodal super resolution imaging system which combines the optical sectioning capacity of a tilted light sheet with the excellent contrast and homogeneous illumination of a flat-field epi- and TIRF setup. This imaging platform also includes a two-channel 4f point spread function (PSF) engineering system combined with long axial range phase masks for 3D imaging. We show that our microscope greatly reduces background fluorescence throughout thick mammalian cells and improves the performance of single-molecule super-resolution imaging in cells in both 2D and 3D and has the potential to image in 3D throughout an entire cell.
Network science algorithms for the reliability and resilience of engineered infrastructure networks
(2023-08-11) Fu, Bowen; Dueñas-Osorio, Leonardo
Critical infrastructure networks are essential engineered systems for modern society, encompassing power grids, telecommunication networks, transportation systems, and water distribution networks, among others. Despite their crucial role in maintaining community normalcy, critical infrastructure networks face a variety of threats, such as aging, extreme weather, intentional attacks, chronic hazards, and catastrophic disasters. Any damage to critical infrastructure networks may be significantly amplified by their interdependencies and the increasing interactions with users and operators, ultimately leading to serious economic losses and potential loss of life.
As enhancing the reliability and resilience of critical infrastructure systems is a priority for governance and society’s wellbeing,, different fields of research and practice are needed to develop ideas, test them, and implement them. Traditionally, efforts to improve the reliability and resilience of engineered infrastructure systems have focused on individual components within the systems, such as retrofitting and hardening of bridges in highway networks or substations in power grids. However, infrastructure systems are interconnected in network form, and decisions for retrofit or hardening should consider the broader network perspective. Furthermore, infrastructure systems are interconnected among each other, forming a network of networks (NoNs) due to the existence of cross-network dependencies. Thus, it is necessary to investigate the reliability and resilience of infrastructure systems from a network-level perspective. Network science is a dynamic field that has developed sophisticated techniques and algorithms to investigate the properties of networks, structures in networks, and processes on networks. However, the development of advanced techniques inspired by network science to complex problems in engineered infrastructure systems is still limited. We leverage significant developments in algorithms and techniques from network science and understanding of infrastructure systems to benefit their reliability and resilience.
Currently, exact reliability computation methods and optimization-based methods are among the most effective strategies for reliability and resilience management of infrastructure systems, due to their guarantees on the accuracy or optimality of the solution. However, the computational complexity of these algorithms for reliability and resilience, is usually beyond affordable. The conflict between desirable models and practical limitations calls for a rational framework which allows decision-makers to choose an effective alternative option when the optimal solution is not feasible. We address this tension with Simon’s theory of satisficing. By selecting a solution that is ‘good enough’ rather than optimal, satisficing theory is applied to the reliability and resilience of engineered infrastructure systems, on problems such as reliability estimators with guarantees on interval and confidence, and close-to-optimal solutions for optimization models, among others.
Inspired by the satisficing theory, we build upon techniques and algorithms from network science and proposed algorithms on multiple computationally intractable problems related to the reliability and resilience of infrastructure systems. Funding allocation on retrofit of bridges in highway networks are usually heavily impacted by the influence of politics, which is at odds to the optimal solution from engineering perspective. Our proposed socio-technical ranking algorithm based on a framework inspired by the Katz centrality from network science can effectively integrate network topology, bridge vulnerability information and impact of politics, and provide a compromise between optimality and practicality. We also proposed a principled recovery algorithm based on network partitioning and percolation process in networks, to restore damaged infrastructure systems quickly with supply-demand balance, which is similar to solutions from mixed-integer optimization models. In addition, we introduce the dimension of resilience into the network dismantling problem in network science to identify a dismantling solution that not only breaks the network, but also delays its recovery. By developing an adversarial-dismantling-retrofit strategy, we reveal critical information for long-term resilience enhancement of infrastructure systems. Finally, we investigate the functional relationship between different components of a transportation network, by extracting its dynamical backbones from real-time traffic data, which supports congestion mitigation, traffic intervention and transportation network design.
Overall, inspired by community reliability and resilience challenges, my research builds upon network science methods to develop novel algorithms that address resource allocation to bridges in transportation networks, resilience-based restoration using distributed percolation process, resilience-informed network dismantling algorithms and adversarial-dismantling-retrofit strategies, along with mechanisms of resilience for infrastructure systems via functional decompositions to support dynamics-based design and operation. All these algorithms are implementable in practice, unlike their parent methods which remain intractable for large infrastructure problems of today. By being integrated to the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) platform developed by the National Institute of Standards and Technology (NIST), our proposed algorithms will be implemented to support community resilience. Furthermore, as network science is also at the intersection of physical systems and their information processing capabilities, rendering insights from this research useful for emerging developments in system automation, decentralized consensus, and the development of quantum algorithms implementable in noisy-intermediate scale quantum devices.
Understanding the Conditions for Detecting a Phonology to Articulation Cascade in Speech Production
(2023-08-11) Irons, Sarah T; Fischer-Baum, Simon; Martin, Randi; Niedzielski, Nancy
Phonetic distortions, subtle acoustic traces of how a competitor would be produced on the articulation of a response, have been used as evidence for cascading activation from phonological planning to articulatory implementation, that is that information flows between these levels of representation in the language production system prior to selection at the phonological planning level. Phonetic distortions are a robust finding, when focusing on speech errors, produced either in tongue twisters (Baese-Berk & Goldrick, 2009; Frisch & Wright, 2002; Goldrick, 2016; Goldrick & Blumstein, 2006; Goldrick et al., 2016; Goldstein et al., 2007; McMillan & Corley, 2010; Pouplier, 2007) or in naturalistic speech (Alderete et al., 2021). However, a recent study failed to find evidence for phonetic distortions in another context in which it would be expected, single word reading aloud of irregular words in which the lexical and sublexical routes generate phonological plans for different vowels (Irons, 2020). The goal of this dissertation is to understand why this discrepancy exists, that is why phonetic distortions are observed in some, but not all cases, in which they are predicted by cascading activation theories of speech production as there are many, potentially critical differences between the paradigms that do and do not observe phonetic distortions.
In this dissertation, I present two experiments, one using tongue twisters, and one using picture-word interference, designed specifically to control for differences in errors, scope of planning and word position, to allow us to better understand when a cascade from phonological planning to articulatory implementation can be observed phonetically. Similar to past tongue twister work (Baese-Berk & Goldrick, 2009; Frisch & Wright, 2002; Goldrick, 2016; Goldrick & Blumstein, 2006; Goldrick et al., 2016; Goldstein et al., 2007; McMillan & Corley, 2010; Pouplier, 2007) I observed phonetic distortions, evidence for cascading activation in onset tongue twisters. In nucleus tongue twisters I found that cascading activation might present differently in vowels than it does in consonants. We did not, however, observe phonetic distortions in picture-word interference, therefore open questions remain about how scope of planning and error effects may be responsible for phonetic distortion evidence for cascading activation.
Advancing Higher Level Control of Nanoparticle Assemblies Through Ligands
(2023-08-11) Marolf, David Michael; Jones, Matthew
The nanoscale size regime is full of potential for the study and development of new functional materials. Many biological materials exist on the nanoscale and can assemble into a variety of complex machinery capable of performing the functions necessary for life to continue. Human-made materials are still far behind what exists naturally, however, over the past few decades, nanoparticles have emerged as a synthetic class of material with intriguing properties making them capable of a large variety of potential applications. One such property is their ability to self-assemble under certain conditions, potentially paving the way to better mimic the high functionality of biological materials. In this thesis, I present research into self-assembling systems of particles that will bring the community closer to true biomimetic materials, primarily through careful use and control of the ligands at the nanoparticle surface.
In chapter 1, I detail how ligands affect nanoparticles on multiple levels. First, I discuss how ligands are crucial for the synthesis and growth of a variety of nanoparticles of various compositions and morphologies. Then, I detail how ligands are used to impart functionality to nanoparticles in a variety of different applications. Finally, I discuss how ligands are used to assemble particles, paying special attention to how ligands can be used to assemble nanoparticles in more complex ways such as “lifelike” dissipative self-assembly. In chapter 2, I discuss my findings applying the community’s understanding of functionalization to the functionalization of ultrathin gold nanowires, the highest aspect ratio gold nanoparticle morphology currently known. Through functionalization with a stimuli-sensitive ligand, dynamic systems of nanowires that assemble and disassemble in response to specific stimuli can be obtained. These nanowires can also be assembled into macroscopic fibers, demonstrating preliminary work that could be continued to develop fibers of nanowires as functional materials. In chapter 3, I present my work exploring the study of DNA-functionalized gold nanoprisms and their sharp transitions between an assembled and disassembled state. I explore the origins of these sharp transitions that cannot be explained by contemporary models of similar DNA-functionalized nanoparticles. Finally, in chapter 4, I discuss the challenges the community faces developing synthetic, out-of-equilibrium, dissipative nanoscale self-assembling systems. To characterize such systems even if they can be successfully designed and synthesized, careful consideration must be given to the spatial and temporal resolution available with current analytical techniques along with the inherent advantages and drawbacks of these techniques. Furthermore, as spatial and temporal resolution continues to be improved, the amount of raw data produced per experiment increases at a high rate necessitating the adoption and development of more advanced data analysis techniques.
This thesis furthers the field of self-assembly, such that future researchers will be closer to developing synthetic systems with high degrees of complexity capable of precise function that can be targeted towards a variety of applications.
On the Design of Reconfigurable Edge Devices for RF Fingerprint Identification (RED-RFFI) for IoT Systems
(2023-08-11) Keller, Thomas Aidan Flaherty; Cavallaro, Joseph R
Radio Frequency Fingerprint Identification (RFFI) classifies wireless transmitters by the signal distortions from their unique hardware impairments. RFFI capable receivers can authenticate insecure transmissions without the sender's cooperation, making them well suited for notoriously vulnerable IoT devices. Neural networks have dominated recent RFFI implementations but are prohibitively inflexible for practical use, requiring bespoke models for different transmission schemes and complete retraining for any change in authenticated devices. This along with the high computational and energy requirements for neural network training makes RFFI unfeasible for edge deployment: a primary use case of IoT.
To remedy this, we propose the Reconfigurable Edge Device for Radio Frequency Fingerprint Identification (RED-RFFI), a novel FPGA inference framework for RFFI using a programmable Deep-Learning Processing Unit (DPU) to analyze variable length signals for a mutable list of authenticated devices. This approach is uniquely capable of operating on the edge without relying on a high-performance computer for iterative FPGA redesign. Using the Xilinx Vitis AI inference development platform, we implement a state-of-the-art Transformer-based model analyzing LoRa signals as a test case.