Physical Sciences and Mathematics Commons^™

Functional Bottlebrush Polymer Additives For Thin Films And Coatings, Travis S. Laws

Doctoral Dissertations

Bottlebrush polymers are a class of highly branched polymers consisting of polymeric side chains that are densely grafted to a linear backbone. Their highly branched architecture results in surface enrichment across a broad range of materials. The goal of my research has been centered around the design of functional bottlebrush polymers and their use as surface active additives in blend films and coatings.

In the first chapter, we examine the segregation behavior of polystyrene bottlebrushes that are blended with linear polystyrene. We systematically vary the lengths of the bottlebrush backbone (Nb), side-chain (Nsc), and the linear matrix (Nm) in order …

Topological States In Matter, Hasitha W. Suriya Arachchige

Doctoral Dissertations

Topologically nontrivial spin textures, mesoscopic spin configurations that cannot be continuously transformed to an elementary magnetic configuration such as a ferromagnet or antiferromagnet, are of interest due to their ability to exhibit magnetic solitons, with topological protection. Such properties have the potential for applications in future data storage and communication devices. For example, spin textures found in materials such as MnSi, Cu2OSe3, Co-Zn-Mn alloys, and GaV4S8, commonly known as skyrmions, are driven by the interplay of atomic-scale exchange interactions, single-ion anisotropy, and an applied magnetic field. Of particular importance to this class of materials is the presence of a Dyaloshinski …

Overcoming Atmospheric Effects In Quantum Cryptography, Brian Joseph Rollick

Doctoral Dissertations

Quantum Computers will have the potential to greatly assist us in problems such as searching, optimization and even drug discovery. Unfortunately, among these newfound capabilities is one which allows one to break RSA encryption in orders of magnitude less time. One promising countermeasure to secure our communication today and in the future is the one time pad, although it is very difficult to generate and distribute. Quantum Key Distribution offers a practical method for two authenticated parties to generate a key. Whereby the parties, Alice and Bob, share quantum states and use physical laws to place an upper bound on …

Semi-Classical Theories Of Quantum Magnets, Hao Zhang

Doctoral Dissertations

Recent progress in magnetism has been driven by embracing the complexity associated with entangled spin, orbital, and lattice degrees of freedom and by understanding the emergent quantum behaviors of magnetic systems. Over the past decades, intense efforts have been devoted to “extreme quantum materials” comprising low-dimensional lattices of spin S = 1/2 degrees of freedom, that are candidates to host quantum spin liquid phases with no classical counterpart. Finite-spin (S ≥ 1) systems that exhibit ground states with short-ranged entanglement have not been the center of much attention because they are expected to behave semi-classically. However, as we will demonstrate …

Science Education To The Rescue? Assessing The Relationship Between Scientific Literacy And Carbon Emissions, Anthony Schmidt

Doctoral Dissertations

Human activities have radically changed the climate, negatively impacting all life on earth. The technical means to address this climate crisis exist, but there are major social and political hurdles that stand in the way. Education has been touted as one possible means for helping to move forward necessary action on climate change. A hybrid model of planned behavior and human capital helps explain how education can affect climate change. The current dissertation sought to assess what relationship may exist between changes in per capita carbon emissions and science education as measured by the Programme for International Student Achievement (PISA). …

How Dynamic Bond Results In The Unique Viscoelastic Behavior Of The Associating Polymers, Sirui Ge

Doctoral Dissertations

Associating polymer is a special kind of polymer possessing transient reversible bonds in addition to the conventional covalent bonds. The reversible bonds provide unique dynamics and fascinating viscoelastic properties, resulting in attractive applications for these polymers, such as self-healing and shape memory materials. Despite many years of studies, the understanding of dynamics of polymers with reversible bonds, especially on molecular level, is still in the rudimentary stage, preventing the rational design of the potential novel functional materials based on associating polymers. In this dissertation, we provide a detailed and quantitative understanding of the dynamics and viscoelastic properties of associating polymers. …

Precambrian Molar-Tooth Structure: Unraveling The Diagenesis Of Ancient Carbonates, Agustin Kriscautzky

Doctoral Dissertations

Molar-tooth structure (MTS) is an enigmatic carbonate fabric that occurs mainly within Proterozoic carbonate host rocks. It is composed of two distinct features: cracks of various morphologies and crack-filling calcite microspar. Although the origins of MTS remain unknown, most previous investigation has focused on the formation of the cracks and mechanisms involved in the void space generation, with less emphasis on the intriguing carbonate fill. In this study I have investigated molar-tooth bearing carbonates from regions that span both paleogeography and geologic time. Analysis at the microscopic scale, including traditional petrography, cathodoluminescence petrography, scanning electron microscopy, and micrometer-scale geochemical analyses …

Elucidating Molecular Structure And Interactions Of Disease-Related Noncovalent Assemblies Through Ion Mobility Spectrometry - Mass Spectrometry, Amber Leann Hope Gray

Doctoral Dissertations

Ion mobility spectrometry – mass spectrometry (IMS-MS) is a powerful gas-phase technique that is routinely employed in the investigations of amyloid oligomers and conformational studies due to its ability to separate isobaric and isomeric species with the same mass-to-charge ratio (m/z). The goal of this dissertation is to use IMS-MS as a primary platform to probe the conformational landscape of a macrocyclic protein, Cyclosporin A (CycA), and to characterize interactions of amyloidogenic assemblies. Through five independent studies presented within document, the limits of IMS-MS are pushed by employing conditions which mimic biological environments.

Chapter 2 focused on how …

Improving Sensitivities In 0𝒗ββ Decay Searches By Utilizing Pen As A Structural Scintillating Material, Brennan Theresa Hackett

Doctoral Dissertations

Neutrinoless double beta decay, 0nbb is currently the only experimental test to unambiguously determine the majorana nature of the neutrino. There is a large international effort to measure 0nbb decay, with several detector technologies being pursued. This dissertation will consider the LEGEND experiment (Large Enriched Germanium Experiment for Neutrinoless bb Decay), an international effort to measure 0nbb decay with ⁷⁶Ge as both the target isotope and the detecting material.

LEGEND has a 200 kg stage and a 1000 kg stage, each requiring extremely low levels of background radiation at Q_bb (E = 2.039 MeV). These ultra-low background levels …

Measurement Of Jet Constituent Yields In Pb-Pb Collisions At √Snn = 5.02 Tev Using The Alice Detector, Charles P. Hughes

Doctoral Dissertations

Hard partonic scatterings serve as an important probe of quark-gluon-plasma (QGP) properties. The properties of jets and their constituents can provide a tool for understanding the partonic energy loss mechanisms. Low momentum jets offer a unique window into partonic energy loss because they reconstruct the partons which have lost a significant amount of energy to the QGP medium. The main difficulty in studying low momentum jets in heavy ion collisions is the presence of a significant uncorrelated background of low momentum hadrons from soft processes. One way to deal with this background is to use jet- hadron correlations to fit …

Weighted Incremental–Decremental Support Vector Machines For Concept Drift With Shifting Window, Honorius Gâlmeanu, Răzvan Andonie

Computer Science Faculty Scholarship

We study the problem of learning the data samples’ distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Machines (SVMs) can learn weighted input instances and that they can also be trained online (incremental–decremental learning). Combining these two SVM properties, the open problem is to define an online SVM concept drift model with shifting weighted window. The classic SVM model should be retrained from scratch after each window shift. We introduce the Weighted Incremental–Decremental SVM (WIDSVM), …

Rock Climbing And Conservation In Land Management: Can They Coexist?, Marissa Heller

Geography ETDs

As participation in outdoor recreation is growing in the U.S., a dilemma is presented for conservation planners and land area managers who must manage the increasing demand for recreation while simultaneously working to protect species. Rock climbers and other outdoor recreationalists have asserted that a relationship exists between recreation and the conservation of public lands. However, mounting evidence suggests that rock climbing continues to cause a multitude of negative impacts to ecosystems. Here, I investigate the extent to which land management practices allow for rock climbing and conservation to coexist, and how it is that well developed plans protect ecologically …

Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying Covid-19 Patients, Aaron Christopher Segura

Mathematics & Statistics ETDs

This study compared the performance of machine learning models in classifying COVID-19 patients using exhaled breath signals and simulated datasets. Ground truth classification was determined by the gold standard Polymerase Chain Reaction (PCR) test results. A residual bootstrapped method generated the simulated datasets by fitting signal data to Autoregressive Moving Average (ARMA) models. Classification models included neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines. A Recursive Feature Elimination (RFE) study was performed to determine if reducing signal features would improve the classification models performance using Gini Importance scoring for the two classes. The top 25% of …

Robust Uncertainty Quantification With Analysis Of Error In Standard And Non-Standard Quantities Of Interest, Zachary Stevens

Mathematics & Statistics ETDs

This thesis derives two Uncertainty Quantification (UQ) methods for differential equations that depend on random parameters: (\textbf{i}) error bounds for a computed cumulative distribution function (\textbf{ii}) a multi-level Monte Carlo (MLMC) algorithm with adaptively refined meshes and accurately computed stopping-criteria. Both UQ approaches utilize adjoint-based \textit{a posteriori} error analysis in order to accurately estimate the error in samples of numerically approximated quantities of interest. The adaptive MLMC algorithm developed in this thesis relies on the adjoint-based error analysis to adaptively create meshes and accurately monitor a stopping criteria. This is in contrast to classical MLMC algorithms which employ either a …

Quantification Of Hydrologic Response To Forest Disturbance In Western U.S. Watersheds, Sara A. Goeking '92

Doctoral Dissertations

Forested watersheds produce more than half of the water supply in the United States. Forests affect how precipitation is partitioned into available water versus evapotranspiration. This dissertation investigated how water yield and snowpack responded to forest disturbance following recent disturbances in western U.S. forests during the period 2000-2019.

Chapter 2 systematically reviewed 78 recent studies that examined how water yield or snowpack changed after forest disturbances. Water yield and snowpack often increased after disturbance, but decreased in some circumstances. Decreased water yield was most likely to occur following disturbances that did not remove the entire forest canopy. It was also …

Farmer Perspectives On Administrative Burdens And Potential Compensation Structures: A Short Summary Report Of Farmer Interviews From Spring 2022. Vermont Payment For Ecosystem Services Technical Research Report # 3c, Ellen Friedrich, Nour El-Naboulsi, Alissa C. White, Heather M. Darby

UVM Extension Faculty Publications

Interviews with 35 Vermont farmers explored their perspectives on compensation associated with a soil health payment for ecosystem services (PES) program in 2022. This report summarizes thematic analysis of those interviews. Farmers’ willingness to participate in a soil health PES is linked to both the burden of enrollment paperwork and the payment level, among other factors.

If deciding whether to participate in a soil health PES program, nearly all farmers said they would weigh the time and energy put into the administrative workload against the perceived benefits and value of the program, i.e., the payment level or technical assistance provided. …

Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero

Doctoral Dissertations

With the continuous improvements in biological data collection, new techniques are needed to better understand the complex relationships in genomic and other biological data sets. Explainable Artificial Intelligence (X-AI) techniques like Iterative Random Forest (iRF) excel at finding interactions within data, such as genomic epistasis. Here, the introduction of new methods to mine for these complex interactions is shown in a variety of scenarios. The application of iRF as a method for Genomic Wide Epistasis Studies shows that the method is robust in finding interacting sets of features in synthetic data, without requiring the exponentially increasing computation time of many …

Machine Learning For Earth Systems Modeling, Analysis And Predictability, Linsey Passarella

Doctoral Dissertations

Artificial intelligence (AI) and machine learning (ML) methods and applications have been continuously explored in many areas of scientific research. While these methods have lead to many advances in climate science, there remains room for growth especially in Earth System Modeling, analysis and predictability. Due to their high computational expense and large volumes of complex data they produce, earth system models (ESMs) provide an abundance of potential for enhancing both our understanding of the climate system as well as improving performance of ESMs themselves using ML techniques. Here I demonstrate 3 specific areas of development using ML: statistical downscaling, predictability …

Fire And Human Management Of Late Holocene Ecosystems In Southern Africa, Benjamin Davies, Mitchell J. Power, David R. Braun, Matthew J. Douglass, Stella G. Mosher, Lynne J. Quick, Irene Esteban, Judith Sealy, John Parkington, J. Tyler Faith

School of Natural Resources: Faculty Publications

Globally, fire is a primary agent for modifying environments through the long-term coupling of human and natural systems. In southern Africa, control of fire by humans has been documented since the late Middle Pleistocene, though it is unclear when or if anthropogenic burning led to fundamental shifts in the region's fire regimes. To identify potential periods of broad-scale anthropogenic burning, we analyze aggregated Holocene charcoal sequences across southern Africa, which we compare to paleoclimate records and archaeological data. We show climate-concordant variability in mid-Holocene fire across much of the subcontinent. However, increased regional fire activity during the late Holocene (~2000 …

Pipeline For Calculating Calories For Print Recipes With Minimal User Intervention, Karl W. Holten

Theses and Dissertations

The thesis will provide a pipeline to estimate calorie counts from print recipes. The pipeline takes scanned recipes from cookbooks and uses Optical Character Recognition (OCR) to convert the scanned images of recipes to text. Several OCR tools were tested for their accuracy on fractions using a sample of the data, and the most accurate tool is used on the data. Next, a specially trained named entity recognition model is used to identify ingredients, quantities and units. These ingredients are used to search a database of values from the FDA to compute a calorie count for the recipe. The thesis …

A Positivity Preserving, Energy Stable Finite Difference Scheme For The Flory-Huggins-Cahn-Hilliard-Navier-Stokes System, Wenbin Chen, Jianyu Jing, Cheng Wang, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we propose and analyze a finite difference numerical scheme for the Cahn-Hilliard-Navier-Stokes system, with logarithmic Flory-Huggins energy potential. in the numerical approximation to the singular chemical potential, the logarithmic term and the surface diffusion term are implicitly updated, while an explicit computation is applied to the concave expansive term. Moreover, the convective term in the phase field evolutionary equation is approximated in a semi-implicit manner. Similarly, the fluid momentum equation is computed by a semi-implicit algorithm: implicit treatment for the kinematic diffusion term, explicit update for the pressure gradient, combined with semi-implicit approximations to the fluid convection …

College Of Natural Sciences Newsletter, July & August 2022, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Volume 3, Issue 5

Page 1 Dean's Message
Page 2 Awards & Recognition
Page 3 Resources for Student Success
Page 4 Welcome to New Faculty & Staff
Page 5 Summer Activities in CNS
Page 9 Celebrating the lives of those who touched the College
Page 10 Media Coverage of CNS
Page 12 Open PRAIRIE Data
Page 13 Snaps from he start of the semester
Page 14 Science as Art Competition

How To Make Inflation Optimal And Fair, Sean Aguilar, Vladik Kreinovich

Departmental Technical Reports (CS)

A reasonably small inflation helps economy as a whole -- by encouraging spending, but it also hurts people by decreasing the value of their savings. It is therefore reasonably to come up with an optimal (and fair) level of inflation, that would stimulate economy without hurting people too much. In this paper, we describe how this can be potentially done.

Why Decision Paralysis, Sean Aguilar, Vladik Kreinovich

Departmental Technical Reports (CS)

If a person has a small number of good alternatives, this person can usually make a good decision, i.e., select one of the given alternatives. However, when we have a large number of good alternatives, people take much longer to make a decision -- sometimes so long that, as a result, no decision is made. How can we explain this seemingly no-optimal behavior? In this paper, we show that this "decision paralysis" can be naturally explained by using the usual decision making ideas.

Modern Pyromes: Biogeographical Patterns Of Fire Characteristics Across The Contiguous United States, Megan E. Cattau, Adam Mahood, Jennifer K. Balch, Carol Wessman

Human-Environment Systems Research Center Faculty Publications and Presentations

In recent decades, wildfires in many areas of the United States (U.S.) have become larger and more frequent with increasing anthropogenic pressure, including interactions between climate, land-use change, and human ignitions. We aimed to characterize the spatiotemporal patterns of contemporary fire characteristics across the contiguous United States (CONUS). We derived fire variables based on frequency, fire radiative power (FRP), event size, burned area, and season length from satellite-derived fire products and a government records database on a 50 km grid (1984–2020). We used k-means clustering to create a hierarchical classification scheme of areas with relatively homogeneous fire characteristics, or modern …

Land Use/ Land Cover Change Patterns And Trends In Two Dryland Regions, Omar Sulaiman Belhaj

Open Access Theses & Dissertations

Development and climate change affect the environment in numerous ways and to varyingdegrees. This effect appears prominent and more influential in arid regions. Therefore, land use/ land cover in these regions experience profound changes such as plant cover decrease and extinction of some plant species. Also, land use/ land cover in these regions has critical impacts on the environment and its components, such as shrublands shrinking and losing the habitat of animal species. The degree of changes and effects reaches severe levels that become urgent to figure out the impacts and find solutions to stop or mitigate the negative consequences. …

A Computationally Efficient Wald Test In M-Estimation, Denisse Urenda Castañeda

Open Access Theses & Dissertations

Under the maximum likelihood framework, three asymptotic overall tests have been well developed in generalized linear models (GLM) for testing the single null hypothesis H0 : θ = θ0, namely, the Wald test, Likelihood Ratio Test (LRT) and Score test also known as the Lagrange Multiplier test (LM). Modified versions of Wald, LR and LM tests can also be found for testing the significance of a portion of the parameter θ, i.e., if θ = (θ T 1 , θ T 2 ) T it is of interest to test H0 : θ2 = 0. However, with the constant increase …

Cyberbullying Detection Using Weakly Supervised And Fully Supervised Learning, Abhinav Abhishek

ETD Archive

Machine learning is a very useful tool to solve issues in multiple domains such as sentiment analysis, fake news detection, facial recognition, and cyberbullying. In this work, we have leveraged its ability to understand the nuances of natural language to detect cyberbullying. We have further utilized it to detect the subject of cyberbullying such as age, gender, ethnicity, and religion. Further, we have built another layer to detect the cases of misogyny in cyberbullying. In one of our experiments, we created a three-layered architecture to detect cyberbullying , then to detect if it is gender based and finally if it …

A Natural Deep Eutectic Solvent - Protonated L-Proline-Xylitol - Based Stationary Phase For Gas Chromatography, Malwina Momotko, Justyna Łuczak, Andrzej Przyjazny, Grzegorz Boczkaj

Natural Sciences Publications

The paper presents a new kind of stationary phase for gas chromatography based on deep eutectic solvents (DES) in the form of a mixture of L-proline (protonated with hydrochloric acid) as a hydrogen bond acceptor (HBA) and xylitol as a hydrogen bond donor (HBD) in a molar ratio of HBA:HBD 5:1. DES immobilized on a silanized chromatographic support was tested by gas chromatography (GC) in order to determine its resolving power for volatile organic compounds. Studies have demonstrated the suitability of this type of DES as a stationary phase for GC. The Rohrschneider-McReynolds constants were determined for the synthesized DES, …

Fake News Detection On Social Media: A Word Embedding-Based Approach, Muammer Eren Sahin, Chunyang Tang, Mohammad A. Al-Ramahi

Computer Information Systems Faculty Publications

The rapid development of social media, together with the large number of user-generated content on them, has not only connected an unprecedented number of people together to do good stuff, but also has provided convenient platforms to spread misleading pieces of information such as fake news. Existing research has attempted to leverage machine learning to automatically classify fake news. In this paper, we extend such literature by proposing an approach that utilize word embedding and Long Short-Term Memory (LSTM) neural network algorithm. Unlike existing studies, we used two publicly available datasets of news articles to evaluate the proposed model. The …