Digital Commons Network^™

Rhetorics Of Encounter: Toward A Biocentric Theory Of Discourse, Matthew D. Whitaker

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Encounters with more-than-human identities are transformative moments that stand to affect dispositional and bodily change in individuals. From close brushes with wild animals in National Parks to everyday interactions with local ecologies, encounters invite us to feel and think differently about the nature of our realities. In Rhetorics of Encounter: Toward a Biocentric Theory of Discourse, I draw from interdisciplinary perspectives to disrupt the idea of rhetoric as a representational medium, arguing that rhetoric occurs not just through textual exchanges of words and symbols, but through embodied moments of contact with other responsive beings. Invoking George Kennedy’s theory …

Development And Evaluation Of Supported Ionic Liquid Membrane And Porphyrin Frameworks For Carbon Capture Separation, Sarang Ismail

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The global urgency to mitigate anthropogenic CO₂ emissions has intensified the pursuit of energy-efficient separation technologies. Supported Ionic Liquid Membranes (SILMs) have emerged as promising candidates for CO₂ capture due to their tunable solubility-selectivity and low energy requirements. However, challenges such as mechanical instability, limited scalability, and trade-offs in transport performance have impeded their widespread adoption.

This thesis explores a systematic approach to designing and optimizing SILMs for enhanced CO₂ separation by tailoring polymer–ionic liquid interactions, processing conditions, and material architectures. A comprehensive set of studies were conducted using poly(vinylidene fluoride) (PVDF) with varying molecular weights, different grades of PEBAX®, …

Multivariate Mixture Regression Models With Known Group Membership And Informative Priors, Pahalapathirage Dona Kalani Hasanthika

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

We introduced couple different novel approaches to incorporate latent variable information to multivariate mixture regression models with both Gaussian and count data. We also evaluated the performance of these models with existing best approaches with simulated data from various sampling structures and also evaluated one of the models performance with rice metabolite data that provided some novel insights as well as validating existing literature about performance and behavior of these metabolites. We validated the method using extensive simulations and a real-world application. In both quantitative covariate designs and complex treatment design simulations, our method consistently outperformed established tools like limma, …

Utilizing The Horseshoe Prior In Exploratory Factor Analysis And Gaussian Graphical Networks, James Thomas Roddy

Graduate Theses and Dissertations

High-dimensional data analysis frequently involves extracting meaningful structure from noisy, sparse signals. In recent years, Bayesian shrinkage priors—particularly global-local shrinkage priors—have emerged as powerful tools for inducing sparsity while preserving signal fidelity. Among these, the Horseshoe prior has gained notable attention for its capacity to simultaneously shrink irrelevant parameters and retain substantial signals. This dissertation explores the Horseshoe prior as a unified framework for sparse Bayesian inference across theory, simulation, and real-world application. The first component develops new theoretical results establishing the asymptotic Bayes optimality of the Horseshoe prior in Gaussian graphical models (GGMs). We consider sparse precision matrix estimation …

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez

Mathematics & Statistics Theses & Dissertations

Nematic liquid crystals are a state of matter that exhibit properties between those of conventional liquids and solid crystals. Their unique ability to align molecules in specific directions makes them essential in various applications, including display technologies and advanced materials. To model their complex behavior, mathematical frameworks such as the Q-tensor model are used to describe the orientation and degree of molecular order. In this work, we introduce a numerical scheme for a two-dimensional (2D) dynamic Q-tensor model, which is formulated as an L²-gradient flow driven by the liquid crystal free energy and incorporates a singular potential to …

Two Analytic Issues Arising From Models In Probability And Materials Science, Giang Vu Thanh Nguyen

Mathematics & Statistics Theses & Dissertations

This dissertation explores two distinct topics centered on mathematical models in probability theory and materials science. The first part investigates a series of functions derived from an adaptive algorithm designed to address the score-based secretary problem, a classic challenge in probability theory. This problem involves making immediate decisions to select the best candidate from a sequence of interviews. The algorithm aims to maximize the probability of selecting the optimal candidate based on observed scores. We prove two fundamental analytic properties of this sequence of functions as a theoretic support of the algorithm: first, the functions in the sequence each possess …

The Influence Of Monsoon Variability On The Circulation Of The Near-Surface Indian Ocean And The Depth-Integrated Chlorophyll, Marufa Ishaque

OES Theses and Dissertations

The Indian Ocean experiences a strong semiannual reversal of monsoon winds, which determines the weather and climate of Asia, including freshwater fluxes between the atmosphere, land, and ocean. Studies and model projections suggest that the timing and intensity of the seasonal monsoon have already started to change, with more dramatic changes likely in the future. However, the impact of these changes on the Indian Ocean circulation system, including the inter-basin salt/freshwater transport between the Bay of Bengal and the Arabian Sea, remains unclear. To better understand how monsoon variability affects Indian Ocean circulation patterns, a Regional Ocean Modeling System simulation …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Advanced Analytical Biosensing For Cancer Detection And Neural Diagnostics Using Tapered Optical Fiber (Tof), Protonic, And High Throughput Microplate –Based Technologie, Bayan Hassan Alharbi

Chemistry & Biochemistry Theses & Dissertations

This dissertation investigates the development and application of advanced biosensing technologies to enhance early disease detection, neurological diagnostics, and bioactive compound evaluation. The research spans four key areas. First, it introduces tapered optical fiber (TOF)-based plasmonic biosensors for the non-invasive detection of prostate cancer, demonstrating high sensitivity and specificity compared to conventional diagnostic methods.

Second, it explores the use of fluorescent biosensors to test the Transmembrane Electrostatically Localized Proton (TELP) theory, shedding light on the role of localized protons in neuronal signaling and energy transfer. Third, the work presents a high-throughput, microplate-based biosensing platform for analyzing mitochondrial function under nanosecond …

Mechanistic And Evolutionary Insights Into A Group Of Nicotine And Hydroxynicotine Degrading Flavoenzymes, Zhiyao Zhang

Dissertations

Flavoprotein amine oxidases (FAOs) are key enzymes in various kinds of metabolic pathways, mediating redox reactions via flavin cofactors. While most FAOs function as oxidases that readily reduce oxygen to hydrogen peroxide, a few outliers of the FAO family act as dehydrogenases that suppress the reaction with oxygen. The molecular basis for this divergence in function remains poorly understood.

The work presented in this study investigates the fundamental mechanisms of how flavincontaining enzymes activate or suppress their reaction with different electron acceptors to achieve high turnover rates. The overall body of work is divided into three different sections that (1) …

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

A Review: The Beauty Of Serendipity Between Integrated Circuit Security And Artificial Intelligence, Chen Dong, Decheng Qiu, Bolun Li, Yang Yang, Chenxi Lyu, Dong Cheng, Hao Zhang, Zhenyi. Chen

Research Collection School Of Computing and Information Systems

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and …

Use Of A Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework For Low-Resource Settings: Development And Validation Study, Shao Wei Sean Lam, Min Hun Lee, Michael Dorosan, Samuel Altonji, Hiang Khoon Tan, Walter T. Lee

Research Collection School Of Computing and Information Systems

Background: Early-stage diagnosis of laryngeal cancer significantly improves patient survival and quality of life. However, the scarcity of specialists in low-resource settings hinders the timely review of flexible nasopharyngoscopy (FNS) videos, which are essential for accurate triage of at-risk patients.Objective: We introduce a preliminary AI-based screening framework to address this challenge for the triaging of at-risk patients in low-resource settings. This formative research addresses multiple challenges common in high-dimensional FNS videos: (1) selecting clear, informative images; (2) deriving regions within frames that show an anatomical landmark of interest; and (3) classifying patients into referral grades based on the FNS video …

Debate, Reflect, And Distill: Multi-Agent Feedback With Tree-Structured Preference Optimization For Efficient Language Model Enhancement, Xiaofeng Zhou, Heyan Huang, Lizi Liao

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) continue to set new standards in knowledge-intensive and complex reasoning tasks, yet their high computational demands limit widespread adoption. While distilling large models into smaller ones offers a sustainable solution, current techniques—such as static knowledge distillation, resource-intensive reinforcement learning from human feedback, or limited self-reflection—struggle to yield substantial and lasting performance gains. In this paper, we present a novel Debate and Reflect (D&R) framework that orchestrates multi-turn debates between smaller models and stronger teacher models, eliciting actionable feedback (e.g., error analysis, corrective strategies) to guide student models. Further, we introduce Tree-structured Direct Preference Optimization (T-DPO) to …

Advancing Molecular Graph-Text Pre-Training Via Fine-Grained Alignment, Yibo Li, Yuan Fang, Mengmei Zhang, Chuan Shi

Research Collection School Of Computing and Information Systems

Understanding molecular structure and related knowledge is crucialfor scientific research. Recent studies integrate molecular graphswith their textual descriptions to enhance molecular representationlearning. However, they focus on the whole molecular graph andneglect frequently occurring subgraphs, known as motifs, whichare essential for determining molecular properties. Without suchfine-grained knowledge, these models struggle to generalize to un-seen molecules and tasks that require motif-level insights. To bridgethis gap, we propose FineMolTex, a novel Fine-grained Moleculargraph-Text pre-training framework to jointly learn coarse-grainedmolecule-level knowledge and fine-grained motif-level knowledge.Specifically, FineMolTex consists of two pre-training tasks: a con-trastive alignment task for coarse-grained matching and a maskedmulti-modal modeling task for …

Progress Towards Uranium Isotope Ratio Measurements Directly From Cotton Swipes With The Liquid Sampling – Atmospheric Pressure Glow Discharge (Ls-Apgd) Ionization Source, Joseph V. Goodwin

All Dissertations

The liquid sampling – atmospheric pressure glow discharge (LS-APGD) ionization source is a novel microplasma ionization source capable of ionizing a diverse set of both inorganic and organic chemical species including, ionic metal constituents as well as ligated metallic species, polyaromatic hydrocarbons (PAH), halogens, proteins, and even per/poly fluorinated alkyl substances (PFAS). The ability to generate ions from diverse chemical species, known as combined atomic and molecular (CAM) ionization, is not found with traditional ionization sources. In addition, the LS-APGD is capable of coupling with mass spectrometer platforms typically reserved for “organic” mass spectrometry, such as the high-resolution Orbitrap mass …

Unsupervised Deep Learning For Video Restoration, Mary Damilola Aiyetigbo

All Dissertations

In today's digital era, visual data is vital across several domains such as medical diagnostics, scientific imaging, surveillance, and entertainment. However, video data often suffers from degradations like noise, blur, compression artifacts, and low resolution, which degrade quality and downstream usability. Video restoration aims to recover clean, high-fidelity video from such corrupted inputs. Unlike static images, video restoration must maintain temporal consistency across frames, making it a significantly more complex problem. While supervised deep learning methods have achieved state-of-the-art results, they typically require large datasets of paired noisy-clean video datasets that are scarce or impractical to obtain in real-world settings …

Quantum Dot Light Emitting Diodes As A Light Source For Photodynamic Therapy, Hamid El Hamidi

Graduate Doctoral Dissertations

ABSTRACT:

QUANTUM DOT LIGHT EMITTING DIODES AS A LIGHT SOURCE FOR PHOTODYNAMIC THERAPY

Photodynamic therapy is a cancer treatment modality that involves the accumulation of a photosensitizer, exposure to visible light, and the presence of oxygen, resulting in the formation of highly reactive and cytotoxic singlet oxygen. PDT has been shown to be more effective as the fluence rate decreases due to oxygen availability and the absence of photobleaching. On the other hand, Quantum Dot Light Emitting Diodes QLEDs- a new form of light source based on nanoparticles (quantum dots)- emerge as a potentially advantageous light source for certain types …

Artificial Intelligence Integration And Teachers' Self-Efficacy In Physics Classrooms, Fouad Yehya, Areej Elsayary, Ghadah Al Murshidi, Ahmed Al Zaabi

All Works

The United Arab Emirates (UAE), in its vision 2021 and the UAE centennial 2071 plan, highlights the essential role of artificial intelligence (AI) and technology in shaping a knowledge-based, future-ready society. This study explores the integration of AI in physics classrooms, focusing on secondary education in the UAE. It also investigates the perceptions and self-efficacy of physics teachers regarding the use of AI tools in classroom settings. A qualitative research design was employed to gather in-depth insights from 15 physics teachers across schools in Sharjah, assessing their confidence and readiness for AI integration through the lens of the attitude and …

What's The Value Of A State? Accounting For The Ecosystem Services And Natural Capital Of Oregon, Kathryn Liebrecht

University Honors Theses

Ecosystem services are vital to human welfare and society, but often are overlooked when it comes to planning and setting policy. By neglecting to incorporate the benefits received from natural resources, policy makers may unknowingly be making decisions that harm the long term sustainability and use of these services. To highlight the importance of Oregon's ecosystems, the present study aims to provide a full accounting of the ecosystem services and natural capital of the state. Using data from the Institute for Natural Resources' updated Oregon statewide habitat map, Oregon's ecosystems were matched to biome categories from the Ecosystem Services Valuation …

Mathematical Modeling Of Effects Of Tumor Location On Lung Function., Lamargaret Temukisa Johnson

Master of Engineering Theses

Lung cancer has the highest rates of incidence and mortality of all cancers. Most lung cancer tumors are Non-Small Cell Lung Cancer (NSCLC). NSCLC patients with lesions in the upper lobes are found to have better prognosis compared to those with lesions in the middle and lower lobes. Previous studies have suggested various causes for this discrepancy at both the organ-scale and tissue-scale. To model NSCLC growth in different locations within the lung, an organ scale lung model and tissue scale tumor model were coupled through the tissue pressure, and oxygen and carbon dioxide partial pressures. The coupling was used …

High-Resolution Event Stratigraphy Of Ostracods In The Ozan Formation Of Texas And Arkansas, Denali L. John

Master's Theses

This study focuses on the high-resolution event stratigraphic framework of the lower Campanian Ozan Formation in Northeast (NE) Texas and Southwest AR which is richly fossiliferous in ostracods. Little research has been published on the Ozan Formation and even less dealing with Ozan ostracods. Additionally, correlation of Upper Cretaceous units between Texas and AR is controversial due to the general lack of good exposures in both regions and extensive faulting in Northeast Texas despite similar depositional environment across the region. This study calibrates the ranges of ostracods and creates an event stratigraphic composite that is orders of magnitude higher in …

Assessment Of Estuarine Respiration And Benthic Nutrient Fluxes In Mississippi Sound, Nur Pasha Sufian

Master's Theses

This study quantified seasonal and spatial variability of both water column and benthic respiration rates as well as benthic nutrient fluxes along the Mississippi Sound. Respiration was measured in recirculating incubation chambers using Firesting optical DO sensors (PyroScience GmbH), and changes in nutrient concentrations were assessed. Mean water column respiration rates ranged from 0.35 and 2.76 µM O₂ hr⁻¹, with the lowest rates in the winter and the highest in the summer while detectable nutrient concentrations (ammonium, (NH₄⁺), and soluble reactive phosphate (PO₄³⁻)) showed only slight decreases (< 1 µM) over all seasons. Sediment incubation chambers consistently had much greater oxygen declines than the water incubations and showed seasonal trends with the highest mean respiration rates measured in the summer and the lowest mean in the winter, with a range from 107.4 to 1514.9 µmol O₂ m⁻² hr⁻¹ (2.6 to 36.3 mmol O₂ m⁻² d⁻¹). Sediment incubations consistently showed increases in NH₄⁺ concentrations in the overlying water, resulting in calculated NH₄⁺ benthic flux rates of 0.73 to 379.01 µM N m⁻² hr⁻¹ with the lowest rates in the winter and highest rates in the summer. Although PO₄³⁻ concentrations were often below detection in cooler seasons, PO₄³⁻ flux also peaked during the summer (< 28.8 µmol P m⁻² hr⁻¹). In this study, temperature was a key regulator for estuarine respiration and benthic nutrient flux rates, and these rates were positively correlated with porewater nutrient concentrations and sedimentary organic matter. These findings illustrate that benthic respiration can contribute to water column hypoxia and that sediments serve as a source of nutrients to the water column. Overall, these findings provide us with valuable insight about the seasonal coupling of oxygen demand and nutrient flux in the Mississippi Sound and provide valuable baseline data for future biogeochemical modelling of hypoxia, eutrophication, and productivity.

The Development Of High Throughput Assays For Identification And Evaluation Of Small Molecule Modulators Of The Pri-Microrna-18a—Hnrnp A1 Interaction, Emile N. Van Meter

Dissertations

Therapeutics targeting RNA is a rapidly growing field. While only 1.5% of the human genome encodes genes for protein, over 70% o=f the human genome contains the genes for noncoding RNAs that regulate protein expression and function. Expanding the paradigm of small molecule drug discovery from traditional protein targets to include RNA would broaden the therapeutic landscape and provide new methods to modulate previously undruggable targets. miRNAs (miRs) have been found to be aberrantly expressed in many disease states, including neurodegenerative diseases and cancers making them promising therapeutic targets. The production of functional, mature miRs requires multiple proteins in a …

Synthesis, Structural Characterization And Optical Studies Of Silver-Indium-(Zinc)-Chalcogenide Fluorescent Quantum Dots, Sujal Acharya

Graduate Theses and Dissertations

Developing a non-toxic, high-performance fluorescent nanomaterial is crucial for overcoming the environmental and health restrictions of current cadmium, and lead based quantum dots (QDs), which limit the application of quantum dots in optoelectronics and bioimaging. In this thesis, we synthesized environmentally friendly AgInS2 QDs by a colloidal method, systematically altering the In/Ag precursor ratio from 2 to 6 to study the impact on their optical and photophysical properties. Our goals were to find the optimal stoichiometry for maximum quantum efficiency and stability. We also investigated further improving optical and photophysical properties through shelling with ZnS. The emission spectra appeared broad, …

Cotton Yield And Tissue-Potassium Response To Potassium Fertilization, Maria Paula Ramos Do Prado

Graduate Theses and Dissertations

Potassium (K) deficiency is a common yield-limiting factor in cotton (Gossypium hirsu tum L.) production across the U.S. Cotton Belt. Adequate fertilizer-K management is paramount to ensure optimum plant growth and development, minimizing yield losses. Currently, fertilizer K recommendations for cotton in Arkansas are derived from expected yield responses of other crops, with no calibrated fertilizer-K rates or critical tissue-K concentrations defined to maxim ize crop yield. Our research objectives were i) to investigate cotton yield response and fiber qual ity to K fertilization on soils with different K availability; ii) to correlate relative yield with tis sue-K concentrations at …

Bayes In The Brain: A Review Of Everything Is Predictable: How Bayesian Statistics Explain Our World, (2024) By Tom Chivers., Michael T. Catalano

Numeracy

Tom Chivers’ Everything is Predictable: How Bayesian Statistics Explain Our World, is an interesting and wide-ranging narrative on Bayesian thinking, its history, and its applicability to both our everyday lives and the pursuit of scientific truth. Although appropriate for the non-expert, afficionados and teachers of quantitative literacy should find the plethora of examples, links to psychology as it applies to how people reason about probabilities, and even Chivers’ philosophical musings informative and thought-provoking.

Highly Contaminated Sediments From The Gowanus Canal (New York) Superfund Site: An Environmental Forensic Approach, Michael A. Kruge, Kevin K. Olsen, Eric A Stern, Maria Mastalerz, Albert Permanyer

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

The 2.5 km long Gowanus Canal (Brooklyn, NY, USA) is a severely contaminated urban waterway dating from the mid-19th century, listed as a US Environmental Protection Agency (USEPA) Superfund site since 2010. Applying an environmental forensics approach to the extensive USEPA data set, we detect systematic variations in parent PAH ring number distributions in the canal sediments, as well as an extraordinary enrichment in organic carbon. We subjected a supplemental sample set to a more detailed analysis by quantitative pyrolysis-GC-MS, Rock-Eval pyrolysis, and organic petrology. With these sensitive methods, we can confirm the alkyl-PAH fingerprint of coal tar (a legacy …