Digital Commons Network^™

Study Of Electronic And Magnetic Properties Of Bilayer Graphene Nanoflakes And Bimetallic Chalcogenides Using First-Principles Density Functional Theory And Machine Learning, Dharmendra Pant

Dissertations, Master's Theses and Master's Reports

Graphene, a one-atom-thick material, has been a wonder material since its discovery because of its superlative electronic, mechanical, and optical properties. When a layer of graphene is rotated over another layer, it exhibits many intriguing behaviors, ranging from superconductivity to the anomalous Hall effect to ferromagnetism at a magic angle of 1°, and hence the twisted bilayer graphene has been the subject of intense research in recent years. The surge in interest in this moiré structure can be attributed to the emergence of electronic flat minibands near the magic angle. Here, we studied the electronic and magnetic properties of twisted …

A Case Study On The Practice Of Carbon Offset Projects In Michigan: Forest Practitioners’ Perspectives, Zachary B. Hough Solomon

Dissertations, Master's Theses and Master's Reports

This report examines the practice of carbon offset programs on private nonindustrial forestlands in the state of Michigan ass. Previously, the state of Michigan provided technical assistance for enrollment in carbon offsets through the Michigan Working Forest Carbon Offset Program, but this program has now been discontinued. Currently, private companies and nongovernmental organizations are developing projects throughout the state with nonindustrial private landowners. This descriptive analysis in this case study examines the roles of different forest practitioners who are working on carbon offsets, including private consulting foresters and foresters within Michigan public agencies. I review the academic and grey literature …

Finer Details Of Language Modeling: Text Segmentation, Working Within Resource Limits, And Watermarking, Evan Gordon Lucas

Dissertations, Master's Theses and Master's Reports

Language modeling is a vast sub-field of natural language processing and this work focuses on solving some specific problems within that field. Technically, the work falls into a number of sub-categories within natural language processing; how to segment texts, improving sparse transformer performance for summarization tasks, character level models for dialect determination, watermarking of large language models, and a general method of incorporating minimal human feedback for continual or online learning. Despite touching on many small areas, they all connect as being related to the very general problem of handling sequential data. Language and text can be thought of as …

Tracing The Most Powerful Galactic Cosmic-Ray Accelerators With The Hawc Observatory, Dezhi Huang

Dissertations, Master's Theses and Master's Reports

Since Victor Hess's groundbreaking detection of cosmic rays in the Earth's atmosphere in 1912, the origins of these charged particles have remained an enduring mystery. Recent studies suggest that these cosmic rays are accelerated beyond Peta electronvolts by powerful astrophysical sources within our own galaxy. While the cosmic rays themselves are being deflected in all directions by magnetic fields, the gamma rays produced by them, being electrically neutral, travel to the observer in a straight line. They carry crucial information, allowing us to trace cosmic-ray accelerators within our galaxy. The High Altitude Water Chrenkov (HAWC) Observatory, located on the slopes …

Neuromorphic Computing Applications In Robotics, Noah Zins

Dissertations, Master's Theses and Master's Reports

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, …

Microscopic And Laboratory Scale Characterization Methods To Evaluate Biomass Deconstruction, Meenaa Chandrasekar

Dissertations, Master's Theses and Master's Reports

Renewable fuels from lignocellulosic biomass are an appealing option because they can seamlessly integrate into the existing fuel distribution infrastructure. Lignocellulosic biomass constitutes nonedible plant material obtained from plant cell walls. The natural recalcitrance of lignocellulosic biomass poses a challenge in accessing the cell wall carbohydrates during biochemical conversion. Despite various approaches, enzymatic hydrolysis of lignocellulosic biomass remains economically impractical due to incomplete knowledge about biomass recalcitrance and the influence of environmental factors on biomass quality.

The first goal of this dissertation was to construct a microfluidic imaging reactor to better understand the tissue-specific deconstruction of plant materials. Confocal laser …

Remote Sensing Approach For Terramechanics Applications Utilizing Machine And Deep Learning, Jordan J. Ewing

Dissertations, Master's Theses and Master's Reports

Terrain traversability is critical for developing Go/No Go maps, significantly impacting a mission's success. To predict the mobility of a vehicle over a terrain, one must understand the soil characteristics. In situ measurements performed by soldiers in the field are the current method of collecting this information, which is time-consuming, are only point measurements, and can put soldiers in harm's way. Therefore, this study investigates using remote sensing as an alternative approach to characterize terrain properties.

This approach will explore the relationships between electromagnetic radiation and soil types with varying properties. Optical, thermal, and hyperspectral sensors will be used to …

Exercise Is Medicine® On Campus: A National Analysis And Assessment Of Community Impact, Isaac M. Lennox

Dissertations, Master's Theses and Master's Reports

Introduction: The American College of Sports Medicine (ACSM) founded the Exercise is Medicine® on Campus (EIM-OC) initiative, which calls upon colleges and universities to promote physical activity on their campuses. The distribution of EIM-OC programs across the U.S. has not been reported. In addition, the impact that EIM-OC programs have on community-level physical activity prevalence is unknown. The purpose of my thesis was to evaluate and enhance the EIM-OC initiative to promote physical activity and overall health in the U.S. Methods: Recognized EIM-OC programs in the U.S. (n=131) were described based on local, county, state, and regional-level variables. Local variables …

The Flight Attendants Of Academia: Liminality, Emotional Labor, And Feminization In Graduate Student Writing Program Administrators, Laura Vidal Chiesa

Dissertations, Master's Theses and Master's Reports

Writing Program Administration (WPA) is an interdisciplinary field that addresses the management and development of writing programs in educational institutions. WPA entails writing instruction pedagogy, curriculum design, assessment, and faculty development pertaining to the teaching of writing. Graduate students in Humanities and English-based programs typically fill this position which can offer a career trajectory. However, the position is often experienced as demanding, unrewarding and does not deliver on the career-enhancing experience it seems to promise. Historically, this is unsurprising given that the position has been a subordinated role occupied by women in composition. And yet, the troubles besetting contemporary graduate …

Statistical Methods For Gwas And The Impact Of Diabetic Medication Adherence On Healthcare Costs, Meida Wang

Dissertations, Master's Theses and Master's Reports

This dissertation includes three Chapters. In Chapter One, we develop a computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS. In this paper, based on the existing CLC method and ACAT strategy, we develop the ceCLC method to test association between multiple phenotypes and a genetic variant. In Chapter Two, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. In Chapter Three, we …

Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi

Dissertations, Master's Theses and Master's Reports

The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.

This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …

White-Tailed Deer In A Winter-Wonderland: Long-Term Deer Yard Use And Methodological Considerations For Ungulate Fecal Dna Metabarcoding, Melanie A. Ottino

Dissertations, Master's Theses and Master's Reports

Northern white-tailed deer (Odocoileus virginianus) mitigate the increased energetic costs of severe winter conditions through obligate migration to densely congregated areas with abundant conifer cover, a behavior referred to as yarding. “Deer yards” in the Western Upper Peninsula (UP) of Michigan are principally located within the reduced snowpack and increased ambient temperature microclimates of densely canopied eastern hemlock (Tsuga canadensis) stands, but specific drivers of long-term site fidelity and utilization are largely uncharacterized. As an important game species of high economic and cultural value and a keystone herbivore with critical impact of plant community composition and structure, identifying winter yarding …

Wavelet-Based Real-Time Harmonic Phasor Estimation In Complex Signals With Inter-Harmonics And Transient Events, M A Aziz Jahan

Dissertations, Master's Theses and Master's Reports

Addressing the evolving challenges in modern power systems induced by the integration of Inverter Based Resources (IBRs), such as solar inverters, wind turbine converters, electric vehicle charging stations etc is the main goal of this research. The study focuses on the critical task of harmonic phasor estimation in the presence of sub-harmonics, inter-harmonics, time-varying harmonics, DC offset, transient events, and other power quality disturbances. A novel wavelet-based harmonic phasor estimation method is proposed to enhance accuracy, particularly during transient events, ensuring a nuanced understanding of phase angle variations. The research emphasizes the versatility of wavelet analysis for both steady-state and …

Sequencing Antisense Transcriptome Of Populus Through Antisense/Sense Transcript Pair Enrichment, Emma V. Burke

Dissertations, Master's Theses and Master's Reports

Past transcriptome research on plants focused primarily on protein coding genes, and only recently researchers began looking into the non-protein coding regions that may play significant roles in gene regulation. Antisense RNA transcripts that are found naturally in the cell share complementary sequence with sense transcripts and have been shown to regulate expression of their sense counterparts. Since antisense RNA has been largely under-studied and difficult to sequence because of their low relative abundance, new methods are needed to target antisense RNA for efficient genome-wide profiling. To address this gap in methods to efficiently and cost effectively enrich antisense RNA …

Has Bollywood Lost The Plot? Analyzing The Influence Of Item Songs On Rape Culture In India, Donna Susan Mathew

Dissertations, Master's Theses and Master's Reports

An item song is a provocative musical song and dance sequence that appears in Bollywood films that have little to no connection to the film’s plot, performed by an item girl for the male gaze. Contemporary Bollywood item songs have become highly contested social and political texts in the discourse surrounding rape culture in India. This research explores the socio-cultural context of rape culture in India and examines what makes item songs a popular and acceptable form of entertainment in a patriarchal, conservative society where sexualized violence against women is a major cause for concern.

The study also addresses the …

Treating Contaminated Water: Predicting Reactivities Of Aqueous Organic Contaminants With Hydrated Electrons, And Development Of A Biogarden To Manage Greywater Discharge In Monteverde, Costa Rica, Rose C. Daily

Dissertations, Master's Theses and Master's Reports

Advanced reduction processes (ARPs) that generate highly reactive solvated electrons (e^-_aq) are a promising method for the destruction of conventional and emerging aqueous organic contaminants. While there is a large database of contaminant reactivity with e^-_aq in the literature, there is little information on the detailed elementary mechanisms for reduction of multifunctional group compounds and the impact of those functional groups on reactivity. As it is difficult to determine specific mechanisms through experiments and time consuming to measure reactivity, the development of computational approaches to elucidate mechanisms and predict reactivity is becoming increasingly important. In …

Two Research Perspectives On Sugar Maple Canopy Dieback In The Upper Great Lakes: A Decade Of Herbaceous Community Responses And A Survey Of Michigan Forest Managers, Manuel Eduardo Anderson

Dissertations, Master's Theses and Master's Reports

Sugar maple canopy dieback has been reported in the upper Great Lakes since the mid-2000s. This thesis builds on research conducted to determine some of the underlying causes of canopy dieback, which include invasive earthworm impacts, with a view toward adaptations in management of northern hardwood forests in our region. The first chapter reports a study of understory communities in a subsection of forest health monitoring plots located in four national forests near Lake Superior. Based on plot assessments from 2010 and 2021, responses of plant community composition and traits to earthworm impacts and sugar maple canopy dieback were explored. …

Nanomaterials For Cardiovascular Engineering, Roya Bagheri

Dissertations, Master's Theses and Master's Reports

Cardiovascular diseases and disorders (i.e., those related to heart and blood vessels) are the main reasons for mortality worldwide. Nanomaterials, with their unique morphologies and properties, have a great potential for advancing cardiovascular engineering to treat diseases and disorders. In this dissertation, several cardiovascular applications of conductive nanomaterials were investigated. First, a conductive nanomaterial was explored to fabricate biohybrid nanomaterial-cardiomyocyte (CM – heart muscle cell) systems. Using Carbon Nanotube (CNT) forest as a 3D porous and conductive scaffold was investigated. The influence of the CNT forest on the viability, attachment, and spreading of CMs and their genetic information was …

Memory Optimizations For High-Throughput Computer Systems, Zhiyuan Lu

Dissertations, Master's Theses and Master's Reports

The emergence of new non-volatile memory (NVM) technology and deep neural network (DNN) inferences bring challenges related to off-chip memory access. Ensuring crash consistency leads to additional memory operations and exposes memory update operations on the critical execution path. DNN inference execution on some accelerators suffers from intensive off-chip memory access. The focus of this dissertation is to tackle the issues related to off-chip memory in these high performance computing systems.

The logging operations, required by the crash consistency, impose a significant performance overhead due to the extra memory access. To mitigate the persistence time of log requests, we introduce …

Altering The Environment: The Effects Of Light And Music On Vigilance Performance And Affective State, Lauren Sprague

Dissertations, Master's Theses and Master's Reports

Vigilance, otherwise known as a state of alert watchfulness, is a phenomenon that occurs when someone is sustaining their attention on a particular task or environment. Largely, tasks that require a state of vigilance induce negative mood outcomes, result in loss of performance over time, and cause increased disengagement from the task. These tasks involve detecting critical signals which are buried among more frequently occurring neutral signals. While little can be done to change the base nature of the vigilance task, altering the physical environment of the operator may improve working conditions to a point where there are fewer losses …

The Impact Of Pre-Experiment Walking On Distance Perception In Vr, Soheil Sepahyar

Dissertations, Master's Theses and Master's Reports

While individuals can accurately estimate distances in the real world, this ability is often diminished in virtual reality (VR) simulations, hampering performance across training, entertainment, prototyping, and education domains. To assess distance judgments, the direct blind walking method—having participants walk blindfolded to targets—is frequently used. Typically, direct blind walking measurements are performed after an initial practice phase, where people become comfortable with walking while blindfolded. Surprisingly, little research has explored how such pre-experiment walking impacts subsequent VR distance judgments. Our initial investigation revealed increased pre-experiment blind walking reduced distance underestimations, underscoring the importance of detailing these preparatory procedures in research—details …

The Hidden Role Of The “Co-” Paradigm In The Conceptualization And Application Of Living Labs, Madelina E. Dilisi

Dissertations, Master's Theses and Master's Reports

In the past two decades, the living lab has emerged as an innovative approach for addressing a wide range of issues. Living labs challenge traditional top-down research and development approaches in an array of subjects including climate change and sustainability, healthcare, information communication technology, and urban planning. Despite their growth, the current conceptualization of living labs is incomplete. The conceptual ambiguity surrounding living labs prevents researchers and practitioners from appreciating their true value, limitations, and appropriate applications. My thesis builds on Dekker et al.’s (2020) living lab research by including key concepts from the “co” paradigm literature that includes the …

A Search For Compact Object Dark Matter In The Universe Utilizing Gravitational Millilensing Of Gamma-Ray Bursts, Oindabi Mukherjee

Dissertations, Master's Theses and Master's Reports

In this dissertation, I explore the existence of Compact Object (CO) Dark Matter (DM), examining its predicted millilensing impact on the light curves of Gamma-Ray Bursts (GRBs). The hypothesis under investigation is that if CO DM exists, it will act as a gravitational lens, influencing light emanating from GRBs and creating a measurable gravitational echo. The detection of this echo is conditional on several factors, including the spatial alignment of the observer, lens, and source and the mass of the CO DM.

Recent studies have reported the potential detection of millilensing in several GRBs, including GRB 950830, GRB 081122A, GRB …

Multilevel Computational Investigation Into The Catalytic Mechanisms Of Matrix Metalloproteinase-1 And Fat Mass And Obesity-Associated Enzyme, Ann Varghese

Dissertations, Master's Theses and Master's Reports

Enzymes are biological macromolecules, typically proteins, that efficiently accelerate the rate of chemical reactions. Their remarkable catalytic power plays a vital role in essential processes across all kingdoms of life. Nowadays, computational chemistry methods provide valuable insights into enzymatic functions aiding our understanding of biological processes and providing advancements in fields such as biomedical sciences, biomimetic catalysis, drug design and biotechnology. This dissertation employs multilevel computational chemistry methods to investigate the structure-function relationships and the catalytic mechanisms of two metalloenzymes -Zn(II)-dependent matrix metalloproteinase-1 (MMP-1) and non-heme Fe(II)/2-oxoglutarate (2OG) dependent fat-mass and obesity-associated (FTO) enzyme. Chapter 2 explores the role of …

Using Artificial Intelligence To Understand Environmental Policy Innovation Labs' Contribution To The Sustainable Development Goals, Sidney M. Mechling

Dissertations, Master's Theses and Master's Reports

Policy innovation labs (PILs) serve as centers of experimental innovation pursued by governmental, private, and non-profit entities that employ design-thinking methodology, scientific experimentation, and user-centric approaches (Wellstead et al., 2021). The multi-disciplinary expertise of policy innovation labs in social and economic topics equips them to offer policy recommendations addressing public issues, including environmental concerns. When designing innovative solutions, it is customary for policy innovation labs to provide substantial information and reports detailing their progress and methods. However, the wealth of information available becomes a challenge to fully divest in comprehending a lab's contribution to solving global issues. This report addresses …

Investigating Collaborative Explainable Ai (Cxai)/Social Forum As An Explainable Ai (Xai) Method In Autonomous Driving (Ad), Tauseef Ibne Mamun

Dissertations, Master's Theses and Master's Reports

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system's behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don't encounter or provide information that users do not seek.

In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers …

Novel Bayesian Neural Networks And Uncertainty Quantification Of Computational Mechanics Models, Ponkrshnan Thiagarajan

Dissertations, Master's Theses and Master's Reports

Computational and data-driven models suffer from a wide range of uncertainties that impact the reliability of such models. Given the exponential proliferation of machine learning models in real-world systems, establishing a degree of confidence in their predictions becomes paramount. Reliability in predictions takes on utmost significance in domains such as autonomous driving, medical image analysis, etc., where human lives are involved, and inaccuracies in predictions could lead to disastrous outcomes. For these reasons, comprehending and quantifying uncertainties in computational and data-driven models is of utmost importance. A number of techniques have been developed to quantify uncertainties in machine learning models. …

Physical, Optical, And Chemical Properties Of Light Absorbing Aerosols And Their Climatic Impacts, Susan Mathai

Dissertations, Master's Theses and Master's Reports

Aerosols are particles suspended in the atmosphere; they are emitted during natural phenomena such as dust storms, wildfires, and volcanic eruptions, and during anthropogenic activities like household wood burning, vehicles operations, and industrial productions, or they can form in the atmosphere from gas to particle partition. Aerosols impact earth’s weather and climate by absorbing and scattering the incoming solar and the outgoing earth thermal radiation and interacting with clouds. The optical properties of aerosols evolve as the chemical and physical properties vary during their residence in the atmosphere. In addition, the aerosols’ properties strongly depend on the vertical distribution in …

On Examining Solvation And Dielectric Constants Of Polar And Ionic Liquids Using The Stockmayer Fluid Model, Cameron J. Shock

Dissertations, Master's Theses and Master's Reports

We develop Stockmayer fluid (SF) models to predict and analyze dielectric properties of polar solvents, ionic liquids, and polyermized ionic liquids. A Stockmayer fluid treats molecules as simple Lennard-Jones spheres with dipole moments and point charges. With this model we calculate the solvation energies of 26 ions in 7 polar solvents and compare with experiment. There is qualitative agreement and we find that the SF model can account for the effects of dielectric saturation, which the commonly used Born solvation energy equation lacks. We also calculate the dielectric constants of these 7 solvents and find quantitative agreement with experiment. We …

Deep Learning For Medical Image Segmentation Using Prior Knowledge And Topology, Chen Zhao

Dissertations, Master's Theses and Master's Reports

Image segmentation refers to the division of a digital image into distinct segments or groups of pixels/voxels. However, most of the existing deep learning approaches lack the utilization of prior knowledge, such as shape information, which could improve segmentation accuracy. In addition, conventional image segmentation frequently falls short in preserving intricate spatial details, motivating the innovation of strategies for multi-scaled feature integration. Furthermore, traditional image segmentation methods primarily concentrate on pixel-level or region-level analysis. However, given the inherent morphological similarities among various image objects, the significance of topology information surpasses that of pixel-level data in the realm of medical image …