Physical Sciences and Mathematics Commons^™

Molecular Mechanisms Of Amyloid-Like Fibril Formation, Sharareh Jalali

Dissertations

Proteins play a critical role in living systems by performing most of the functions inside cells. The latter is determined by the protein's three-dimensional structure when it is folded in its native state. However, under pathological conditions, proteins can misfold and aggregate, accounting for the formation of highly ordered insoluble assemblies known as amyloid fibrils. These assemblies are associated with diseases like Parkinson's and Alzheimer's. Strong evidence suggests that three mechanisms are critical for forming amyloid fibrils. These mechanisms are the nucleation of amyloid fibrils in solution (primary nucleation) as well as on the surface of existing fibrils (secondary nucleation) …

Exploring Topological Phonons In Different Length Scales: Microtubules And Acoustic Metamaterials, Ssu-Ying Chen

Dissertations

The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales.

Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the …

Models And Algorithms For Promoting Diverse And Fair Query Results, Md Mouinul Islam

Dissertations

Ensuring fairness and diversity in search results are two key concerns in compelling search and recommendation applications. This work explicitly studies these two aspects given multiple users' preferences as inputs, in an effort to create a single ranking or top-k result set that satisfies different fairness and diversity criteria. From group fairness standpoint, it adapts demographic parity like group fairness criteria and proposes new models that are suitable for ranking or producing top-k set of results. This dissertation also studies equitable exposure of individual search results in long tail data, a concept related to individual fairness. First, the dissertation focuses …

Quantifying Balance: Computational And Learning Frameworks For The Characterization Of Balance In Bipedal Systems, Kubra Akbas

Dissertations

In clinical practice and general healthcare settings, the lack of reliable and objective balance and stability assessment metrics hinders the tracking of patient performance progression during rehabilitation; the assessment of bipedal balance plays a crucial role in understanding stability and falls in humans and other bipeds, while providing clinicians important information regarding rehabilitation outcomes. Bipedal balance has often been examined through kinematic or kinetic quantities, such as the Zero Moment Point and Center of Pressure; however, analyzing balance specifically through the body's Center of Mass (COM) state offers a holistic and easily comprehensible view of balance and stability.

Building upon …

Learning Representations For Effective And Explainable Software Bug Detection And Fixing, Yi Li

Dissertations

Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a 'bridge,' known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning.

This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning …

Fortifying Robustness: Unveiling The Intricacies Of Training And Inference Vulnerabilities In Centralized And Federated Neural Networks, Guanxiong Liu

Dissertations

Neural network (NN) classifiers have gained significant traction in diverse domains such as natural language processing, computer vision, and cybersecurity, owing to their remarkable ability to approximate complex latent distributions from data. Nevertheless, the conventional assumption of an attack-free operating environment has been challenged by the emergence of adversarial examples. These perturbed samples, which are typically imperceptible to human observers, can lead to misclassifications by the NN classifiers. Moreover, recent studies have uncovered the ability of poisoned training data to generate Trojan backdoored classifiers that exhibit misclassification behavior triggered by predefined patterns.

In recent years, significant research efforts have been …

Bacterial Motion And Spread In Porous Environments, Yasser Almoteri

Dissertations

Micro-swimmers are ubiquitous in nature from soil and water to mammalian bodies and even many technological processes. Common known examples are microbes such as bacteria, micro-algae and micro-plankton, cells such as spermatozoa and organisms such as nematodes. These swimmers live and have evolved in multiplex environments and complex flows in the presence of other swimmers and types, inert particles and fibers, interfaces and non-trivial confinements and more. Understanding the locomotion and interactions of these individual micro-swimmers in such impure viscous fluids is crucial to understanding the emergent dynamics of such complex systems, and to further enabling us to control and …

Diversification And Fairness In Top-K Ranking Algorithms, Mahsa Asadi

Dissertations

Given a user query, the typical user interfaces, such as search engines and recommender systems, only allow a small number of results to be returned to the user. Hence, figuring out what would be the top-k results is an important task in information retrieval, as it helps to ensure that the most relevant results are presented to the user. There exists an extensive body of research that studies how to score the records and return top-k to the user. Moreover, there exists an extensive set of criteria that researchers identify to present the user with top-k results, and result diversification …

Human-Ai Complex Task Planning, Sepideh Nikookar

Dissertations

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. …

Program Analysis For Android Security And Reliability, Sydur Rahaman

Dissertations

The recent, widespread growth and adoption of mobile devices have revolutionized the way users interact with technology. As mobile apps have become increasingly prevalent, concerns regarding their security and reliability have gained significant attention. The ever-expanding mobile app ecosystem presents unique challenges in ensuring the protection of user data and maintaining app robustness. This dissertation expands the field of program analysis with techniques and abstractions tailored explicitly to enhancing Android security and reliability. This research introduces approaches for addressing critical issues related to sensitive information leakage, device and user fingerprinting, mobile medical score calculators, as well as termination-induced data loss. …

Fluid Dynamics Of Interacting Particles: Bouncing Droplets And Colloid-Polymer Mixtures, Lauren Barnes

Dissertations

Interacting particles are a common theme across various physical systems, particularly on the atomic and sub-atomic scales. While these particles cannot be seen with the human eye, insight into such systems can be gained by observing macroscopic systems whose physical behavior is similar. This dissertation consists of three different chapters, each presenting a different problem related to interacting particles, as follows:

Chapter 1 explores chaotic trajectories of a droplet bouncing on the surface of a vertically vibrating fluid bath, with a simple harmonic force acting on the droplet. The bouncing droplet system has attracted recent interest because it exhibits behaviors …

Boundary Integral Equation Methods For Superhydrophobic Flow And Integrated Photonics, Kosuke Sugita

Dissertations

This dissertation presents fast integral equation methods (FIEMs) for solving two important problems encountered in practical engineering applications.

The first problem involves the mixed boundary value problem in two-dimensional Stokes flow, which appears commonly in computational fluid mechanics. This problem is particularly relevant to the design of microfluidic devices, especially those involving superhydrophobic (SH) flows over surfaces made of composite solid materials with alternating solid portions, grooves, or air pockets, leading to enhanced slip.

The second problem addresses waveguide devices in two dimensions, governed by the Helmholtz equation with Dirichlet conditions imposed on the boundary. This problem serves as a …

Toward Smart And Efficient Scientific Data Management, Jinzhen Wang

Dissertations

Scientific research generates vast amounts of data, and the scale of data has significantly increased with advancements in scientific applications. To manage this data effectively, lossy data compression techniques are necessary to reduce storage and transmission costs. Nevertheless, the use of lossy compression introduces uncertainties related to its performance. This dissertation aims to answer key questions surrounding lossy data compression, such as how the performance changes, how much reduction can be achieved, and how to optimize these techniques for modern scientific data management workflows.

One of the major challenges in adopting lossy compression techniques is the trade-off between data accuracy …

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …

Variable Resolution Smoothed Particle Hydrodynamics Schemes For 2-D And 3-D Viscous Flows, Francesco Ricci

Dissertations

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian particle-based method for the numerical solution of the partial differential equations that govern the motion of fluids. The main aim of this thesis work is to better enable the applicability of SPH to problems involving multi-scale fluid dynamics. In the first part of the thesis, the capability of the SPH method to simulate three-dimensional isotropic turbulence is investigated with a detailed comparison of Lagrangian and Eulerian SPH formulations. The main reason for this first investigation is to provide an assessment of the error introduced by the particle disorder on the SPH discrete operators …

Computational And Experimental Investigation Of Elemental Sulfur And Polysulfide, Jyoti Sharma

Dissertations

Petroleum processing results in the generation of significant quantities of elemental sulfur (S₈), leading to a surplus of sulfur worldwide. Despite its abundance and low cost, the use of sulfur in value-added organic compound synthesis is limited due to its unpredictable and misunderstood reactivity. This dissertation aims to address this issue by tackling it from two angles. Firstly, by utilizing Density Functional Theory (DFT) calculations, the reactivity of sulfur in the presence of nucleophiles is studied. This facilitates the identification of organic polysulfide intermediates that can be generated under different conditions, as well as the corresponding reactivity for …

Atrial Fibrillation Management In Hispanic Adults, Tania Borja

Dissertations

Background: Research has found atrial fibrillation (AF) to be the primary or a contributing cause of death on 183,321 death certificates, and an underlying cause of death for 26,535 Americans in 2019. Findings indicate an increased AF diagnosis in White people compared to racial and ethnic minorities, contrasting widespread findings of increased prevalence of cardiovascular disease and ischemic strokes in minorities. Significant disparities—by race and socioeconomic status in disease distribution and access to testing and lifesaving treatments—have been documented, specifically associated with social determinants of health (SDOH); i.e., the conditions in which people are born, grow, live, work, and age. …

A Computational Kinetics Model To Quantify Radiation Induced Chemical Products Of Atmospheric Gas Mixtures, Patrick Ables

Dissertations

This research is focused on the development of a computational model which will calculate the effects of radiation on the chemical composition of the atmosphere. The approach utilizes the open-source chemical kinetics toolkit Cantera to model the creation of radiation-induced reactant species within irradiated air mixtures. Chemical solutions are iteratively stepped toward chemical equilibrium within a ‘constantly stirred’ (homogeneous) reactor of fixed volume. Three different radiation chemistry models are implemented in several different pulsed and continuous radiation schemes. The first model includes the mechanisms and rates of a pulse radiation model from the literature to test the validity of the …

Origin Of The Mega-Streamlined Morphology In Ne Africa And Arabia: Remote Sensing And Field-Based Investigations, Mohamed Samy Mohamed Elhebery

Dissertations

Mega-streamlined landforms on Earth and Mars have been attributed to aeolian, glaciogenic, fluvial, and tectonic processes. Identifying the forces that shaped these landforms is paramount for understanding landscape evolution and constraining paleo-climate models and ice sheet reconstructions. Exhumed Late Ordovician glacial deposits and landscape of the North Gondwana are reported here for the first time from SE Egypt. Using field and remote sensing (Advanced Land Observing Satellite [ALOS], Phased Array L-band Synthetic Aperture Radar (PALSAR) radar, multispectral Landsat TM datasets, and digital elevation models (DEMs) I mapped the distribution of the Late Ordovician glacial features (i.e. deposits and landforms) in …

On Phishing: Proposing A Host-Based Multi-Layer Passive/Active Anti-Phishing Approach Combating Counterfeit Websites, Wesam Harbi Fadheel

Dissertations

Phishing is the starting point of most cyberattacks, mainly categorized as Email, Websites, Social Networks, Phone calls (Vishing), and SMS messaging (Smishing). Phishing refers to an attempt to collect sensitive data, typically in the form of usernames, passwords, credit card numbers, bank account information, etc., or other crucial facts, intending to use or sell the information obtained. Similar to how a fisherman uses bait to catch a fish, an attacker will pose as a trustworthy source to attract and deceive the victim.

This study explores the efficacy of host-side APT (Anti-Phishing Techniques) based onWebsite features like Lexical, Host-Based, or Content-Based …

Simulating Strongly Coupled Many-Body Systems With Quantum Algorithms, Manqoba Qedindaba Hlatshwayo

Dissertations

The complexity of the nuclear many-body problem is a severe obstacle to finding a general and accurate numerical approach needed to simulate medium-mass and heavy nuclei. Even with the advent of exascale classical computing, the impediment of exponential growth of the Hilbert space renders the problem intractable for most classical calculations. In the last few years, quantum algorithms have become an attractive alternative for practitioners because quantum computers are more efficient in simulating quantum physics than classical computers. While a fully fault-tolerant universal quantum computer will not be realized soon, this dissertation explores quantum algorithms for simulating nuclear physics suitable …

Topological Data Analysis Of Convolutional Neural Networks Using Depthwise Separable Convolutions, Eliot Courtois

Dissertations

In this dissertation, we present our contribution to a growing body of work combining the fields of Topological Data Analysis (TDA) and machine learning. The object of our analysis is the Convolutional Neural Network, or CNN, a predictive model with a large number of parameters organized using a grid-like geometry. This geometry is engineered to resemble patches of pixels in an image, and thus CNNs are a conventional choice for an image-classifying model.

CNNs belong to a larger class of neural network models, which, starting at a random initialization state, undergo a gradual fitting (or training) process, often a …

High Resolution Intracavity Laser Absorption Spectroscopy Of Transition Metal-Containing Diatomic Molecules, Kristin Bales

Dissertations

Three transition metal-containing diatomic molecules have been studied using intracavity laser spectroscopy. Many of the transitions were recorded using a Fourier-transform spectrometer for detection, allowing collection at Doppler-limited resolution for the gas phase molecules. Several vibrational bands in two electronic transition systems of tantalum fluoride (TaF) have been analyzed, and new molecular constants provided. Transitions involving six electronic states of tungsten sulfide (WS) have been analyzed, with new and updated constants provided, including a deperturbation analysis of three vibrational bands in two of the states. Finally, a fresh perspective on two electronic states of tungsten oxide (WO) included a deperturbation …

Near-Ir Spectroscopic Analysis Of The Primary Volatile Composition Of Long And Short-Period Comets, Younas Khan

Dissertations

Comets are among the most well-preserved objects that formed in the protosolar nebula ∼4.5 Gyr ago. Hence, they are important for understanding various aspects of the formation, evolution, and habitability of the solar system. Multiple primary volatiles (molecules directly sublimating into the coma from the nucleus) emit via rovibrational transitions in the near-IR, providing opportunities to calculate their abundances. To date, only ∼50 comets have been characterized for their primary volatiles, with the short-period Jupiter-family comets (JFCs) being significantly underrepresented. In contrast, hundreds of comets have been sampled at optical/UV wavelengths, primarily for the composition of daughter species, leading to …

Learning Finite Mixture Of Ising Graphical Models, Chong Gu

Dissertations

The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure when observed data come from heterogeneous resources in high-dimensional setting. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent, minorization–maximization principles and EM algorithm. A BIC-type criterion will be utilized for the selection of the tuning parameter in the penalized likelihood approaches. The effectiveness of the proposed method is supported by simulation studies and a real-world example.

Functional Generalized Linear Mixed Models, Harmony Luce

Dissertations

With the advancements in data collection technologies, researchers in various fields such as epidemiology, chemometrics, and environmental science face the challenges of obtaining useful information from more detailed, complex, and intricately-structured data. Since the existing methods often are not suitable for such data, new statistical methods are developed to accommodate the complicated data structures.

As a part of such efforts, this dissertation proposes Functional Generalized Linear Mixed Model (FGLMM), which extends classical generalized linear mixed models to include functional covariates. Functional Data Analysis (FDA) is a rapidly developing area of statistics for data which can be naturally viewed as smooth …

The Mobility Of Long-Lived Radioisotopes And Their Burial In The Marine Environment, Neil Redmond

Dissertations

Marine sediments record chemical signals that reflect past environmental conditions. It is important to establish how these signals are created and whether they may be altered over time so that they can be useful for reconstructing ocean history. Measurements of uranium isotopes are used as a novel proxy for sedimentary diagenetic processes (Chapter 2). Because ²³⁴U can be ejected from mineral lattice during the decay of ²³⁸U, it creates a pool of U in porewater that is potentially mobilized and then deposited elsewhere in the core. We found that alpha-recoiled ²³⁴U is sensitive to differences in sediment …

Blockchain-Enabled Ehr Sharing In Healthcare Federation: Sharding And Interblockchain Communication, Faiza Hashim

Dissertations

Electronic Health Records (EHRs) are crucial components of the healthcare system, facilitating accurate and efficient diagnosis. Blockchain technology has emerged as a promising solution to improve EHRs sharing among medical practitioners while ensuring privacy and security. By leveraging its decentralized, distributed, immutable, and secure architecture, blockchain has the potential to revolutionize the healthcare system. However, due to security concerns, blockchain networks in healthcare typically operate in private or consortium modes, resulting in isolated networks within a federation. Scalability remains a significant challenge for blockchain networks, as the number of participating nodes increases within each network of the federation. Consensus mechanisms …

Synthesis, Characterization, And Photocatalytic Activity Of Photoactive Mofs And M-Hof Under Simulated Visible Light Irradiation, Lamia Ali Siddig

Dissertations

The increasing global energy demand has resulted in environmental issues, leading to a shift in research towards sustainable and renewable energy sources. Among these, solar energy is the most abundant natural resource available. One of the most profitable ways to utilize sunlight is through chemical transformation using photocatalysts. In this regard, we reported the synthesis of different stable porous materials, such as metal-organic frameworks (MOFs) and metal hydrogen-bonded organic frameworks (M-HOFs). The MOF photocatalysts are bismuth-gallate (Bi-gallate), a mixed ligand manganese-based MOF (Mn^II₃(tp)_6/2(bpy)₂. (dmf)) and a new hexagonal layer manganese MOF compound named …

Stream-Evolving Bot Detection Framework Using Graph-Based And Feature-Based Approaches For Identifying Social Bots On Twitter, Eiman Alothali

Dissertations

This dissertation focuses on the problem of evolving social bots in online social networks, particularly Twitter. Such accounts spread misinformation and inflate social network content to mislead the masses. The main objective of this dissertation is to propose a stream-based evolving bot detection framework (SEBD), which was constructed using both graph- and feature-based models. It was built using Python, a real-time streaming engine (Apache Kafka version 3.2), and our pretrained model (bot multi-view graph attention network (Bot-MGAT)). The feature-based model was used to identify predictive features for bot detection and evaluate the SEBD predictions. The graph-based model was used to …