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Investigating the Effects of Small Peptides on Amyloid Aggregation
No full textAmyloids, a class of protein aggregates, are highly stable elongated fibril structures. They areassociated with numerous diseases, such as Alzheimer’s, Parkinson’s, and type II diabetes. Following the COVID-19 pandemic, there have been numerous cases of individuals suffering with Long-COVID, whose symptoms overlap with common neurodegenerative and amyloid associated diseases. To explore the potential interactions between SARS-CoV-2 and amyloid proteins, two amyloidogenic protein fragments from SARS-CoV-2 were investigated with amylin, the amyloid protein associated with type II diabetes, and a-synuclein, associated with Parkinson’s. These investigations were performed utilizing all-atomistic molecular dynamics simulations in explicit solvent. Additionally, presented in this dissertation is research performed to identify and evaluate the effect of novel D-retro-inverso (DRI) peptides on mouse serum amyloid A3 (SAA3) fibril stability via virtual screening and molecular dynamics simulations
Enhancing Lost Circulation Control: Synergistic Effects of Cedar Fiber, Nutshell, and Sweeps on Filtration of Water-based Drilling Fluids
No full textThe effective management of lost circulation remains a critical challenge in drilling operations, necessitating the development of advanced strategies to minimize fluid loss and enhance wellbore stability. This study investigates the synergistic effects of various Lost Circulation Materials (LCMs) on fluid loss control and sealing efficiency.The study further demonstrates the efficacy of specific LCM combinations, such as cedar fiber, nutshell, and sweeps, in reducing filtration volume over time. The baseline for the experiments was drilling mud containing water, bentonite, nutshell, sweeps, and cedar fiber. Experiments were conducted by varying the concentrations of one of LCM components while maintaining the concentration of the other LCMs to assess the individual impact on filtration rates. The filtration tests were conducted using a permeability plugger tester (PPT). When the concentrations of each LCM have been increased the filtration volume significantly dropped down. Comparatively when cedar fiber concentration increased maximum (50%) reduction in filtration was obtained while the minimum (30%) filtration reduction was observed with sweeps addition. The smaller cedar particles were more effective in plugging the disk slot compared to nutshell and sweeps particles. Additionally, the results showed that cedar fiber reduced the filtration volume more efficiently than the other LCMs at all tested concentration
Understanding multi-scalar exchange networks along the Tanzanian Swahili Coast through the geochemical examination of copper-based metals, 7th-15th centuries CE
No full textThroughout the Global Middle Ages, Swahili Coast towns were key in articulating long-distance and regional trade that linked Eastern and Southern Africa with triangular Indian Ocean networks extending to India and the Persian Gulf and Red Sea. Copper and copper-based metal alloy objects were likely imported through these trade networks and reworked locally, but the role these metals played in local Swahili economies as well as global exchange systems remains unclear. Therefore, the examination of copper-based metals can shed light on the impact of multi-scalar trade connections within Swahili communities. To that end, this thesis used a combination of solution-mode inductively coupled plasma mass spectrometry (ICP-MS) and multi-collector ICP-MS (MC-ICP-MS) to perform geochemical and lead isotopic analyses of a collection of 28 copper-based artifacts from five different sites along the Tanzanian Swahili Coast, dating from the 7th-15th centuries CE. The results of the bulk elemental analysis reveal a variety of metal types, including pure coppers, brasses, bronzes, as well as ternary, quaternary, and highly leaded alloys, attesting to the prevalence of mixing and recycling practices surrounding the production of these copper-based metals. Trace element compositions were examined alongside lead isotope ratios in order to discern possible geological provenances of the copper and added lead, revealing likely connections with ore sources in Southwest Asia, India, and possibly the African Interior as well. The results of this research demonstrate the potential of combined isotopic and geochemical analyses to aid in understanding copper exchange in the medieval African and Indian Ocean worlds, the Swahili Coast’s role in these historic multi-scalar economic networks, and the role of nonlocal trade items such as copper within Swahili communities
INTEGRATING DEEP LEARNING AND PERSISTENT HOMOLOGY FOR ENHANCED FINANCIAL RISK ASSESSMENT
No full textAccurately predicting bankruptcy is a critical challenge for financial institutions, businesses, and policymakers. Traditional bankruptcy prediction models rely on financial ratios and historical trends, but they often fail to capture complex patterns within financial data. With the increasing availability of large-scale financial datasets, there is a growing need for more advanced methodologies that can enhance predictive accuracy and provide deeper insights into financial risk assessment.The necessity for improved bankruptcy prediction stems from the economic and societal consequences of corporate financial distress. Ineffective risk assessment can lead to cascading failures in financial markets, supply chains, and employment sectors. As financial systems become more intricate, the ability to predict and mitigate bankruptcy risk is crucial for maintaining stability, reducing losses, and informing strategic decision-making. Deep learning techniques offer a promising avenue for capturing the temporal dependencies in financial data, while topological data analysis provides structural insights that go beyond conventional feature engineering. This dissertation presents a unique approach to bankruptcy prediction by integrating deep learning models with topological data analysis. While existing models rely primarily on statistical methods, this study incorporates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to analyze temporal financial trends. Additionally, persistent homology is leveraged to extract topological features, addressing gaps in traditional risk assessment models. By combining these methodologies, this research aims to improve prediction accuracy and enhance the interpretability of financial risk factors. To validate the effectiveness of the proposed approach, multiple evaluation techniques are employed. Model performance is assessed using standard machine learning metrics such as accuracy, precision, recall, and F1-score. Comparative analysis is conducted against traditional bankruptcy prediction models, highlighting the advantages of integrating deep learning and topological insights. Robustness tests are performed to ensure the reliability of extracted features, and explainability metrics are used to interpret the influence of topological data in bankruptcy forecasting. The results of this study demonstrate significant improvements in bankruptcy prediction accuracy compared to conventional models. By integrating deep learning and PH, this research provides a novel framework that enhances financial risk assessment. The findings have valuable implications for financial institutions seeking to refine predictive analytics, reduce investment risks, and improve early warning systems. Exploring PH in temporal data is crucial for advancing financial forecasting and risk assessment, especially when combined with deep learning models, enabling the development of more sophisticated, data-driven decision-making strategies
METHANE MITIGATION IN NATURAL GAS INDUSTRIAL ENGINES: HYDROGEN FUEL BLENDING AND THE INTEGRATION OF CERAMIC ELECTROCHEMICAL REACTORS
No full textMethane emissions are one of the primary emissions causing global warming. Natural gas-fed industrial engines are one of the key drivers in excessive methane emissions due to partial load operations and combustion inefficiencies that can be controlled with modern methane mitigation strategies. In this thesis engine-based and post-combustion methane mitigation strategies are analyzed including spark ignition timing, hydrogen blending, and post-combustion protonic ceramic electrochemical cells (PCERs). The spark ignition timing is used to control the ignition time of the fuel and the electrochemical cells are considered for the conversion of exhaust methane into hydrogen. Hydrogen blending into natural gas is studied at 0%, 10%, and 20% hydrogen mole fraction and an engine load of 60%. The hydrogen mole fraction is calculated using an Aspen HYSYS model of the engine. The addition of hydrogen into the natural gas stream has resulted in a methane emission reduction of 16.2% and 35.2%. Modification of the ignition spark timing is conducted for the angles of 9°BTDC, 11°BTDC, 11.5°BTDC, and 12°BTDC, with the baseline angle being 11°BTDC. A significant methane emission reduction of 56.4% and 60.9% is observed at 11.5°BTDC, and 12°BTDC respectively due to an increase in combustion efficiency. The hydrogen blending and spark ignition are combined to obtain a methane reduction of up to 73% by improving thermal efficiency, promoting flame propagation, and reducing the methane content in the feed fuel. To remove all of the exhausted methane and convert CO to CO2 while obtaining a high-value chemical, an electrochemical cell is proposed as a post-combustion catalyst. The PCER model utilized a tubular, direct pass construction without an internal support tube. The model was constructed in engineering equation solver (EES) using a mole and electrochemical balance using a Ni/CeO2 anode, Ni BCZY electrolyte, and Ni/BCZY cathode. The PCER model was able to obtain complete methane conversion when operating at 2,000-5,000 A/m2 and a temperature between 673-780K when being fueled with realistic engine emissions. The technology developed here shows promising solutions for reducing and mitigating methane emissions from industrial engines and combustion devices
Higher Education Dynamics: Assessing the Nexus of College Costs, Sports, and Amenities
No full textThis dissertation examines three distinct aspects of higher education and public policy. The first chapter investigates the differential impact of state appropriations on STEM and non-STEM degree completion at U.S. public institutions. Using a Bartik-style instrumental variables approach with panel data from 2003-2019, I find that a 10% increase in state funding leads to a 3.4% increase in STEM degrees, primarily affecting male students, science majors, and nonselective institutions, while having minimal impact on non-STEM degrees. The second chapter explores how recreational marijuana legalization (RML) affects college enrollment, finding that RML increases first-time undergraduate enrollments by 4.6-9% without compromising degree completion or graduation rates. This effect is driven by out-of-state enrollments in non-selective public colleges, suggesting marijuana functions as a consumption amenity that enhances college competitiveness. The third chapter analyzes the prevalence of student-athletes at elite private universities, documenting that these institutions allocate a disproportionate share of enrollment capacity to varsity athletes compared to larger public universities, with athletes often coming from more advantaged socioeconomic backgrounds. Together, these chapters provide insights into how public funding, state policies, and institutional priorities shape access to and outcomes in higher education
"Fantasie und Doppelfuge" for organ by Evelyn Faltis (1887-1937): Background, Analysis, and Commentary
No full textThis document consists of a comprehensive examination of Evelyn Faltis’ Fantasie und Doppelfuge (mit dem “Dies Irae”), op. 12 for organ solo. In addition to a structural analysis of the piece from a music theory perspective, the document also provides necessary musicological context by placing the work within its genre, time period, and national style, with special attention to its religious undertones. A brief biography of Evelyn Faltis, a list of her known works, primary sources relating to her career, and a performer’s guide to the piece are also included. Chapter One details the purpose of this study, a summary of existing research, and its procedures and limitations. Chapter Two is a biographical overview of Evelyn Faltis. Chapter Three traces the development of the cantus-based organ work in German musical history. Chapter Four describes the German Romantic organ and its defining characteristics. Chapter Five provides background on the Gregorian chant Dies Irae. Chapter Six details Evelyn Faltis’ contemporaries and compositional influences in this genre. Chapters Seven and Eight analyze the work’s thematic, structural, and motivic content. Chapter Nine is a performer’s guide designed to assist in studying and playing the piece. Chapter Ten is a summary and conclusion to the study. This document demonstrates the significance of Evelyn Faltis’ Fantasie und Doppelfuge as an important contribution to the organ solo repertoire by a heretofore overlooked and under-recognized woman composer
ENHANCING AUTOMATED PUBLIC OPINION ANALYSIS: A COMPARATIVE STUDY OF VADER AND LARGE LANGUAGE MODEL METHODS
No full textTraditional sentiment analysis methods such as VADER have long been employed togauge public opinion through rule-based and lexicon-driven approaches. However, the advent of large language models (LLMs) has introduced novel methods capable of capturing deeper semantic nuances and handling heterogeneous data at scale. In this paper, a comparative analysis of VADER and LLM-based sentiment analysis methodologies is done within the context of public policy research. Using nuclear fusion as a case study, the study evaluates how each approach processes and interprets sentiment in news and social media texts. The VADER approach, characterized by its reliance on curated sentiment lexicons and syntactic rules, excels in computational efficiency and straightforward implementation. In contrast, the LLM-based framework leverages parameter-efficient fine-tuning techniques to adapt pre-trained models for nuanced sentiment extraction, thereby capturing complex opinion dynamics and contextual subtleties that traditional methods often overlook. Through rigorous experimentation, the accuracy, scalability, and processing speed of these methodologies were compared, outlining their respective advantages and limitations. Our results demonstrate that while VADER provides rapid sentiment scoring suitable for high-volume datasets, the LLM-based approach delivers richer, more robust insights that are critical for dynamic public policy analysis. This thesis further discusses the trade-offs between computational overhead and analytical depth, emphasizing that the choice of technique should align with the specific requirements of policy research. Overall, this work contributes to a better understanding of how modern deep learning techniques can enhance automated sentiment analysis and improve the timeliness and accuracy of public opinion monitoring in contemporary policy contexts
MATERIAL CHARACTERIZATION OF 3D-PRINTED SPECIMENS BASED ON PRINTING ORIENTATION
No full textA PA12 nylon material blended and reinforced with chopped carbon fibers was prepared as tensile test specimens. Experimentation was performed to investigate what effects the angle (0o, 45o, or 90o) that the material is laid down in when printing has on the mechanical properties of the printed specimens. It was determined that of the three angles investigated, a 0-degree lay down angle to the longitudinal axis results in the stiffest mechanical properties, but can withstand the least strain at fracture. The 90-degree pattern results in the least stiff results, and can withstand more strain than the 0-degree specimen. The 45-degree specimen showed intermediate stiffness and the largest strain at fracture. Microscopy performed on the specimens revealed that the chopped fibers appeared to be randomly organized throughout the specimens