Physical Sciences and Mathematics Commons^™

Boundary Conditions For A Late Triassic Climate Simulation Using Cesm1.2-Som, James Alden Breen

Earth & Environmental Sciences Theses

Climate models have been widely adapted to reconstruct the environmental changes and associated mass extinction events across geologic times, such as the Younger Dryas (~12.9 Kyr), the Paleocene-Eocene Thermal Maxima (~55 Ma), and the Permian-Triassic Mass Extinction Event (~251 Ma). However, few studies have utilized climate models to investigate the potential climatic triggers of the large biological decline at the end-Triassic Mass Extinction (ETE; ~201 Ma). In this study, global boundary conditions for the end-Triassic hothouse climate are developed in order to allow a common framework for paleoclimate simulation and to study to internal climate variability in order to better …

Replication Data For: A Closer Look At Underground Natural Gas Pipeline Leaks Across The United States, Younki Cho, Kathleen M. Smits, Nathaniel L. Steadman, Bridget A. Ulrich, Clay S. Bell, Daniel J. Zimmerle

Earth & Environmental Sciences Datasets

Underground natural gas (NG) pipeline leakage can result in methane (CH₄) buildup and migration through the soil. What is not well understood in such scenarios is how the soil conditions affect the gas migration behavior, particularly in regard to the relative contributions of specific soil properties such as soil moisture content. The objective of this study was to investigate the effects of soil properties on CH₄ concentration and migration from leaking underground NG pipelines. Site characteristics such as surface cover and spatial dimensions, soil samples, and gas concentration data were collected from over 70 gas leakage sites …

Replication Data For: Investigating Detection Probability Of Mobile Survey Solutions For Natural Gas Pipeline Leaks Under Different Atmospheric Conditions, Shanru Tian, Stuart Riddick, Younki Cho, Clay Bell, Daniel Zimmerle, Kathleen Smits

Earth & Environmental Sciences Datasets

The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH4 emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5–8.5 g min−1. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill–Gifford stability …

Gan-Based Domain Translation For Hand Pose Estimation And Face Reconstruction, Farnaz Farahanipad

Computer Science and Engineering Dissertations

Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations. Since, acquiring such datasets is a challenging task that may be infeasible for many novel applications, several studies aim to develop semi/self supervised learning methods, that learn to estimate hand pose from a few labeled/unlabeled data. Therefore, in this dissertation, we investigate new advances in semi/self supervised learning which will remove the bottleneck of obtaining time-consuming frameby- frame manual annotations through generative adversarial networks (GANs). To handle above mentioned challenges, this thesis makes the following contributions. …

Development Of Graphical Models And Statistical Physics Motivated Approaches To Genomic Investigations, Yashwanth Lagisetty

Dissertations & Theses (Open Access)

Identifying genes involved in disease pathology has been a goal of genomic research since the early days of the field. However, as technology improves and the body of research grows, we are faced with more questions than answers. Among these is the pressing matter of our incomplete understanding of the genetic underpinnings of complex diseases. Many hypotheses offer explanations as to why direct and independent analyses of variants, as done in genome-wide association studies (GWAS), may not fully elucidate disease genetics. These range from pointing out flaws in statistical testing to invoking the complex dynamics of epigenetic processes. In the …

Synthetic Studies On Oxidative Dearomatization Reactions And Applications In Synthesis Of The Kb343 Core Structure, And Investigation Of Bioactive Thiazolidines, Marian Awadalla

Chemistry & Biochemistry Dissertations

Natural products have inspired researchers over the years due to their complexity and biological activity. Organic chemists have made great efforts to investigate new unambiguous methodologies toward the synthesis of these complex structures. Moreover, the synergy between total chemical synthesis and medicinal applications to evaluate the biological activity of the isolated compounds significantly raised the desire for discovery of novel synthetic approaches. The research described in this dissertation focuses on oxidative dearomatization reactions as a key reaction to construct natural products. The three specific aims are as follows: Aim 1: investigation of the applications of oxidative dearomatization reactions for different …

Computational Materials Chemistry: From Polymer Precursors To Ceramics And High-Pressure Materials, Shariq Haseen

Chemistry & Biochemistry Dissertations

With advances in computing power, the field of computational chemistry has flourished. Using current computational resources, atomistic simulations of billion atoms and trillions of atoms have been performed. Computational techniques can be used to support experimental investigations into new materials as well as independently predict the existence of new materials. Furthermore, computational techniques are becoming instrumental in investigations. This work is split into two parts. Part I focuses on high-pressure and materials chemistry. We investigate the cristobalite-rutile transformation for SiO2, GeO2, and TiO2, and predict conditions at which cristobalite-GeO2 and cristobalite-TiO2 may be synthesized (Chapter 1), we provide supporting calculations …

Exposure Of Candyland Red Tomatoes (¬Solanum Lycopersicum L.) To Various Metal Oxide Nanomaterials: Full Life Cycle And Assessment Of Fruit Quality, Jesus Manuel Cantu

Open Access Theses & Dissertations

The use of nanotechnology in agriculture has attained high interest to enhance crop production due to their unique properties. Moreover, metal oxide nanomaterials can potentially supplement nutrients, enhance plant growth, and crop production. When investigating fruits' nutritional quality, carbohydrates and phytonutrients (bioactive compounds) play important roles. Therefore, it is important to understand how nanomaterials will affect the quality of tomato fruits. In this dissertation, various metal oxides were synthesized and applied to Candyland Red tomato seedlings to assess the plant growth and tomato production. In the first investigation, Candyland Red tomatoes (Solanum lycopersicum) were exposed to synthesized pristine CuO and …

Continuous Field Sensor Authentication And Process Integrity Assurance Mechanisms In Critical National Infrastructures, Abel Osvaldo Gomez Rivera

Open Access Theses & Dissertations

The growing modernization of traditional Industrial Cyber-Physical Systems (ICPSs) has increased the probability and effectiveness of cyber attacks by integrating modern communication technologies that expose security vulnerabilities like lack of access control policies to adversaries. The exponential growth of cyber attacks has caught the attention of stakeholders that have proposed cybersecurity initiatives to protect ICPSs. ICPSs are part of the Critical National Infrastructures (CNIs) supporting the society's sustainability and national security. The cybersecurity initiatives aiming to address cyber attacks proposed by stakeholders must continuously authenticate constrained devices. Operational thresholds and process integrity assurance must also be maintained. A centralized ICPS …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …

Population Genetics Of Two Sympatric Species, The Round-Tailed Horned Lizard (Phrynosoma Modestum) And The Greater Earless Lizard (Cophosaurus Texanus), In The Indio Mountains Of West Texas, Logan Miles Horne

Open Access Theses & Dissertations

Population genetics of two sympatric species, the round-tailed horned lizard (phrynosoma modestum) and the greater earless lizard (cophosaurus texanus), in the Indio mountains of West Texas.

Chandra Ejecta Kinematics Studies And An Iso Spectroscopic Survey Of Supernova Remnants, Matthew James Millard

Physics Dissertations

The structure of metal-rich ejecta created from a supernova (SN) reveals vital information about the explosion of the progenitor star and the dynamical evolution of the supernova remnant (SNR). This research focuses on the ejecta kinematics of SNRs in two projects. In the first project, we use High Energy Transmission Grating (HETG) spectroscopy and archival Advanced CCD Imaging Spectrometer (ACIS) imaging from the Chandra X-ray Observatory to study the 3-D ejecta structure of the remnants of the historical Type Ia SNe, SN 1572 (Tycho’s SNR) and SN 1604 (Kepler’s SNR). We use our 150 ks high-resolution Chandra HETG spectroscopic observation …

Charge Transfer And Built-In Fields At A Semiconductor-Crystalline Oxide Interface: Effects Of The Interface Dipole And Surface Termination, Matthew Dennis Chrysler

Physics Dissertations

Charge transfer and built-in fields across semiconductor heterojunctions underpin the functionality of virtually all device technologies. In this regard, interfaces between insulating oxides and semiconductors have been extensively studied due to the success of metal-oxide-semiconductor (MOS) technology. Perovskite oxides were originally proposed as potential replacements as a gate dielectric for MOS field-effect transistors because device scaling effects make SiO2 gate leakage too high. However, perovskite oxides containing transition metals display many other properties not found in semiconductors such as ferroelectricity, high-T superconductivity and metal-insulator transitions. Recent advancements in growth technology allow for the creation of structurally coherent interfaces between crystalline …

Cardiac Single Photon Emission Computed Tomography (Spect) Simulation Using The Xcat Phantom And The Simind Package, Akolade Adebayo

Physics Theses

Clinical data indicates a prevalence of transmural perfusion defects in the apical, mid, and basal segment of the left ventricle (LV) wall in patients with coronary artery diseases (CAD). To administer therapeutic regimes on myocardial function, detection of lesions in various region of the LV wall is essential. Cardiac single photon emission computed tomography (SPECT) is one of the most prevalent diagnostic tools to detect perfusion defects. Computer simulation using digital phantoms based on real patient data obtained from National Library of Medicine (NLM) offers a cost-effective approach to generate realistic imaging data to test novel imaging hardware and software …

Chirality In Quantum Field Theory And Its Role In The Standard Model And Beyond, Timothy Blake Watson

Physics Dissertations

Chirality as a symmetry of particle physics occupies a unique role in the standard model. It arises from general space-time principles yet remains central to the formulation of local gauge theories. This work explores the complexities arising from a physically comprehensive treatment of this topic. We present the origins of the property from first principles by deriving the chiral Dirac equation (CDE). We demonstrate how the resulting chiral degrees of freedom for spin-1/2 objects may be employed in constructing composite chiral objects through the Bargmann-Wigner formalism, leading to novel couplings. We then consider means by which the degrees of chiral …

Multi-Technique Characterization Of Superconducting Materials For Particle Accelerator Applications, Junki Makita

Physics Theses & Dissertations

We investigated the performance limitations of superconducting radio-frequency (SRF) cavities and materials using multiple experimental techniques. In particular, this study focuses on understanding the surface properties of nitrogen-doped Nb cavities and superconducting thin films with higher T_c such as Nb₃Sn. The main goal of this work is to use different techniques to better understand each aspect of the complex loss mechanism in superconductors to further improve the already highly efficient SRF cavities.

Nitrogen doping applied to a Nb SRF cavity significantly improves the quality factor Q₀ compared to a conventional Nb cavity, at an expense of …

Secrep : A Framework For Automating The Extraction And Prioritization Of Security Requirements Using Machine Learning And Nlp Techniques, Shada Khanneh

Theses, Dissertations and Culminating Projects

Gathering and extracting security requirements adequately requires extensive effort, experience, and time, as large amounts of data need to be analyzed. While many manual and academic approaches have been developed to tackle the discipline of Security Requirements Engineering (SRE), a need still exists for automating the SRE process. This need stems mainly from the difficult, error-prone, and time-consuming nature of traditional and manual frameworks. Machine learning techniques have been widely used to facilitate and automate the extraction of useful information from software requirements documents and artifacts. Such approaches can be utilized to yield beneficial results in automating the process of …

Investigation Of Transport Behavior In Two-Dimensional Ferroelectric Heterostructures, Pradeep Chaudhary

Department of Physics and Astronomy: Dissertations, Theses, and Student Research

This dissertation summarizes an investigation of the polarization-related electronic transport behavior in the ferroelectric thin films and two-dimensional (2D) materials heterostructures using Scanning Probe Microscopy (SPM) techniques.

The polarization-related resistive switching in hafnium oxide thin films-based ferroelectric tunnel junction has been demonstrated by employing semiconducting MoS₂ as a top electrode. We explored a coupling between the semiconducting properties of MoS₂ and the polarization of Hf₀.₅Zr₀.₅O₂ resulted in an enhanced tunneling electroresistance effect of up to 3 orders of magnitude. These results provide a possible pathway for the fabrication of …

Nanoscale Studies Of The Ferroelectric And Electromechanical Properties Of Hafnia-Based Capacitors, Pratyush Buragohain

Department of Physics and Astronomy: Dissertations, Theses, and Student Research

The work presented in this dissertation aims to provide nanoscopic insights into the electrical and electromechanical behavior of the recently discovered ferroelectric HfO₂ or hafnia-based capacitors. Hafnia-based ferroelectrics are highly promising for technological applications due to compatibility with the existing Si technology. To realize the full potential of hafnia, however, requires comprehensive understanding of its properties. In this regard, this dissertation hopes to bridge a gap between an understanding of the nanoscopic and macroscopic properties of hafnia by performing combined high-resolution piezoresponse force microscopy (PFM) and pulse switching studies.

More specifically, the dynamics of domain nucleation and wall motion …

Fermion-Induced Electroweak Symmetry Non-Restoration Via Temperature-Dependent Masses, Yu Hang Ng

Department of Physics and Astronomy: Dissertations, Theses, and Student Research

Standard Model (SM) and many extensions of SM predict that the electroweak (EW) symmetry was restored in the early universe when the temperature was around 160 GeV. However, recent studies showed that the interactions between some new scalars and SU(2)_L Higgs doublet(s) can cause the EW symmetry to remain broken at temperatures well above the EW scale in certain renormalizable extensions of SM. In this study, we found that new fermions from renormalizable models can also induce this EW symmetry non-restoration effect, provided that they have the appropriate temperature-dependent masses. These masses can arise naturally from the interactions between the …

2022 August - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

Perturbation Modeling For Molecular Design Of Protein Tyrosine Kinase Inhibitors Using Unsupervised Machine Learning, Keerthi Krishnan

Computational and Data Sciences (MS) Theses

The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental …

Antler Foreland Basin Evolution Recorded In Detrital Zircon U-Pb Geochronology, James Duncan

UNLV Theses, Dissertations, Professional Papers, and Capstones

The Antler orogeny was a Devonian-Mississippian (375-320 Ma) mountain building event that occurred along the western Laurentian margin. The initiation of the Antler orogeny is marked by the emplacement of the Roberts Mountain Allochthon (RMA) that thrust Cambrian-Devonian deep water marine siliciclastic rocks 100 km eastward onto the western Laurentian margin. Shortening related to the Antler orogeny deformed RMA strata and caused a flexural response in the continental crust that resulted in the formation of the adjacent Antler Foreland basin. The paleogeographic history of the RMA strata and tectonic drivers for the Antler orogeny are still debated. Proposed models suggest …

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …

Andea: Anomaly And Novelty Detection, Explanation, And Accommodation, Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbin Cao, Thomas G. Dietterich

Research Collection School Of Computing and Information Systems

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, …

Investigating Toxicity Changes Of Cross-Community Redditors From 2 Billion Posts And Comments, Hind Almerekhi, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

This research investigates changes in online behavior of users who publish in multiple communities on Reddit by measuring their toxicity at two levels. With the aid of crowdsourcing, we built a labeled dataset of 10,083 Reddit comments, then used the dataset to train and fine-tune a Bidirectional Encoder Representations from Transformers (BERT) neural network model. The model predicted the toxicity levels of 87,376,912 posts from 577,835 users and 2,205,581,786 comments from 890,913 users on Reddit over 16 years, from 2005 to 2020. This study utilized the toxicity levels of user content to identify toxicity changes by the user within the …

Aligning Dual Disentangled User Representations From Ratings And Textual Content, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Classical recommendation methods typically render user representation as a single vector in latent space. Oftentimes, a user's interactions with items are influenced by several hidden factors. To better uncover these hidden factors, we seek disentangled representations. Existing disentanglement methods for recommendations are mainly concerned with user-item interactions alone. To further improve not only the effectiveness of recommendations but also the interpretability of the representations, we propose to learn a second set of disentangled user representations from textual content and to align the two sets of representations with one another. The purpose of this coupling is two-fold. For one benefit, we …

Legion: Massively Composing Rankers For Improved Bug Localization At Adobe, Darryl Jarman, Jeffrey Berry, Riley Smith, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Studies have estimated that, in industrial settings, developers spend between 30 and 90 percent of their time fixing bugs. As such, tools that assist in identifying the location of bugs provide value by reducing debugging costs. One such tool is BugLocator. This study initially aimed to determine if developers working on the Adobe Analytics product could use BugLocator. The initial results show that BugLocator achieves a similar accuracy on five of seven Adobe Analytics repositories and on open-source projects. However, these results do not meet the minimum applicability requirement deemed necessary by Adobe Analytics developers prior to possible adoption. Thus, …

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

Research Collection School Of Computing and Information Systems

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …

Neural-Progressive Hedging: Enforcing Constraints In Reinforcement Learning With Stochastic Programming, Supriyo Ghosh, Laura Wynter, Shiau Hong Lim, Duc Thien Nguyen

Research Collection School Of Computing and Information Systems

We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of the policy, using probabilistic models of the state transitions to guide policy adjustments. The framework is particularly amenable to the class of sequential resource allocation problems since feasibility with respect to typical resource constraints cannot be enforced in a scalable manner. The NP framework provides an alternative that adds modest overhead during the online phase. …