Digital Commons Network^™

A Critical Analysis Of Alternative Section 1031 Proximate-Exchange Structures, Bradley T. Borden

UC Law Business Journal

Taxpayers have six wins and zero losses in cases that grant nonrecognition to exchanges that occur in proximity to tax-free business transactions (contributions to and distributions from entities) of the exchanged property. Those six cases (the proximate-exchange cases) were decided between 1983 and 1989. From those cases emerged the proximate-exchange principle: “[a] trade of Property A for Property B, both of like kind, may be preceded by the tax-free acquisition of Property A at the front end, or succeeded by a tax-free transfer of Property B at the back end.”1 Despite the explicit declaration of the proximate-exchange principle and the …

Aged 539 Graduate Internship Report- Liberty High School, Brittany N. Johnson

Agricultural Education: Graduate Internship Reports

This document documents the AGED 539 Internship in Agricultural Education and examines the Liberty High School–Brentwood FFA and Agriculture Department’s alignment with the three-component model of agricultural education: classroom instruction, FFA leadership development, and Supervised Agricultural Experience (SAE). Grounded in experiential learning theory, the report reflects on program strengths, challenges, and growth across all twelve California Career Technical Education (CTE) Quality Criteria, supported by narrative reflection and evidence. The internship project focused on developing a formal FFA Program of Activities (POA) to improve chapter organization, goal-setting, and long-term sustainability. Overall, the project highlights intentional program planning and leadership development as …

Understanding Bias And Fairness In Large Language Models: An Empirical Study, Joshua Johnson

Electrical Engineering and Computer Science Undergraduate Honors Theses

This thesis investigates demographic bias in large language models (LLMs) through the use of evaluating outcome disparities when utilized in decision making tasks as well as underlying associations that could contribute to furthering these disparities. Using profiles from the Adult dataset, we analyze how Gemini 2.0 Flash performs in an income prediction task using zero-shot and few-shot prompting methods. Our findings show that models exhibit measurable differences in demographic parity and false positive rates, with the use of few-shot prompting reducing these disparities. Alongside this line of testing, we tested associational bias in Qwen 2.5 using probability based association tests …

The Economics Of Us Row Crop Production With Large-Scale Autonomous Machines, Joshua Strine, Chad Fiechter, James Lowenberg-Deboer

Department of Agricultural Economics Faculty Publications

Labor challenges are underpinning large multinational farm machine manufacturers' development of autonomy solutions for their large-scale machine offerings. This study simulates a linear optimization model to examine the economics of large-scale autonomous machines for a rotational maize and soybean farm in the Midwest US. Results support the hypothesis that autonomous machines can be economically viable for farms facing severe labor shortages. However, under current technology and pricing structures, conventional mechanization remains the most profitable option for farms with reliable labor. Critical factors shaping the competitiveness of autonomy include subscription fees, field efficiency, and human supervision requirements. As these factors evolve, …

Modeling The Effects Of Shed Target Receptors On The Efficacy Of Cancer Immunotherapy Agents, Bridget M. Torsey

Theses

Cancer cells often shed receptors targeted by immunotherapies. Shed receptors can reduce drug efficacy by binding to free drug, preventing its binding to membrane-bound receptors. The goal of this dissertation is to investigate the effects of shed targets on the efficacy of cancer immunotherapies. First, we study liquid tumors by extending a PK/PD model to include receptor shedding, drug-induced enhancement of shedding, drug binding to shed receptors, and drug-induced tumor lysis. We use our model to elucidate the effect of shed target receptors on the efficacy of immunotherapies through uncertainty and sensitivity analyses. Our findings support the claim that the …

Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi

Theses

The rapid evolution of AI heightens the need for learning systems that are efficient, robust, and explainable. This dissertation advances these three pillars through innovations in classification, generative modeling, domain adaptation under data-scarce conditions, and multimodal retrieval. Collectively, the methods reduce dependence on large, labeled datasets, improve adaptability under distribution shifts, enable deployment on resource-constrained platforms, and enhance interpretability. For classification, the Iterative Maximum Likelihood Classifier (IMLC) recasts regularized maximum likelihood training as a fixed-point contraction with convergence guarantees, enabling faster and more stable optimization. Results on synthetic data and MNIST validate its efficiency. For generative models, we introduce Parametric …

Physics Meets Data: Merging Physics-Based Methods With Deep Learning To Model Complex Systems, Maryam Toloubidokhti

Theses

Accurate modeling of complex systems is crucial in domains such as healthcare, where personalized diagnosis and treatment planning are essential. Traditional physics-based models provide structured, theoretically grounded insights but are often computationally intensive and constrained by simplified assumptions that limit adaptability to patient-specific conditions. In contrast, data-driven models are computationally efficient and capable of capturing complex patterns, yet they often lack interpretability and fail to incorporate essential physical principles, reducing robustness and generalization. This disconnect between mechanistic understanding and computational practicality presents significant challenges in critical applications such as healthcare, where both physical accuracy and real-time performance are vital. To …

Exploring Engineering Students' Utilization Of Resources In Calculus Using Self-Regulated Learning, Kriz George

Theses

In response to high failure rates of engineering students in introductory math courses such as calculus, a wide variety of interventions have been implemented. A common intervention is targeted at modifying the curricula. Additionally, a major initiative to improve pass rates is to provide resources to students to help them better learn the concepts and get continual support as they complete assignments and other coursework. Despite these interventions, pass rates continue to remain low. I posit that merely the availability of resources is not enough for student success in mathematics courses. Students who lack knowledge of how to use these …

Integrating Climate And Geospatial Features Into Machine Learning Models, Abdulaziz Ahmed Aljaziri

Theses

This thesis addresses a significant gap in real estate valuation models by investigating the economic impact of localized climate conditions and granular geospatial amenities. An abstract summarizes the following: - The main themes, ideas or areas of theory being investigated: This research investigates the integration of localized climate conditions and granular geospatial amenities into machine learning (ML) frameworks for residential real estate valuation. - The background and context of the research: In dynamic urban markets like Dubai, traditional valuation models often rely on broad location labels and structural attributes, overlooking the tangible economic impact of environmental comfort and micro-climates in …

Integrated Machine Learning For Smart Home Resource Optimization, Ahmed Almazrouei

Theses

This thesis explores howmachine learning can be used to support better energy management in smart homes. Many smart home systems today collect a large amount of data through sensors and smart meters, but they still depend on simple rules and do not make predictive or automatic decisions. In the academic field, energy forecasting and energy optimization are often studied separately, which creates a gap in understanding how the two components can work together in a real setting. Because of this, there is a need to test an integrated approach that uses both forecasting and optimization in one framework. In this …

Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri

Theses

Bycreating an AI-driven method using deep learning and statistical analysis tools, this study seeks to fill important security holes in conventional intrusion detection systems. Current signature-based systems miss new and complex cyberattacks, which have significant financial and operational consequences for companies. The suggested approach detects unusual network activity in real-time by combining statistical analysis with long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and statistical analysis. This study will create and test hybrid models that can identify both known and zero-day threats while reducing false positives using publicly accessible datasets like UNSW-NB15, CIC-IDS2017, and NSL-KDD. Expected results are a …

Optimizing Power Grids In Uae Using Data Analytics For Improved Efficiency And Reliability, Khalid Bukhashem

Theses

The United Arab Emirates (UAE) has aims to triple its renewable energy capacity to 14 GW to attain the objective of 30% contribution of clean energy by 2031. Energy consumption, on the other hand, will increase from 5.2 to 5.8 exajoules by 2028. The grid infrastructure for which was developed to facilitate the centralized generation of power, is not conducive to dealing with the variability of the decentralized renewable resources that lead to inefficiencies in operation and reliability problems. The Dubai power grid is analyzed using historical data of 105,120 residential, commercial and industrial supplies. In this analysis machine learning …

An Examination Of High-Entropy Alternatives Of Connectionist Temporal Classification Loss For Optical Music Recognition Using Convolutional Recurrent Neural Networks, Hritik Saynganthone

Theses

The Connectionist Temporal Classification (CTC) loss function is the most commonly used loss function in the field of Optical Music Recognition (OMR). However, OMR suffers from a massive class imbalance problem, exacerbated by the fact that CTC loss is subject to the spiky distribution problem, wherein the blank token introduced by CTC is vastly overpredicted and appears in timesteps where it would make more sense to predict a non-blank token, since CTC will collapse repeated tokens into a single token. This work posits that alternative loss functions to CTC that optimize for an increase in entropy of the prior probability …

Mixed-Integer Linear Programming (Milp) Model For Transportation Cost Optimization, Yasmen Ghonim

Theses

Motivated by a request from a real company, this study presents a mixed integer linear programming (MILP) model for labor transportation at real company located in Dubai (BSG). The study integrates routing, assignment, and environmental pricing into one model, with the focus on fixed shift worker transport and featuring several dorms and sites. It also tests the model with the real company data from the UAE service sector. The model assigns workers from dormitories to the client sites by busses in a manner that minimizes daily transport cost and monetized CO₂ emissions, subject to capacity, routing, and utilization constraints. The …

Assessing Large Language Models As An Interpretive Layer In Marketing Mix Modeling: Implications For Marketing Analytics, Mohammad Bin Haider

Theses

Marketing mix modelling (MMM) remains a core technique for guiding budget allocation, yet its outputs are often difficult for non-technical planners to interpret and govern. At the same time, large language models (LLMs) offer new possibilities for translating complex model artefacts into narrative guidance, but raise concerns about hallucination, reproducibility, and alignment with model-risk governance. This thesis examines whether an open-source MMM framework can be engineered as a repro- ducible, governance-ready pipeline and then augmented with a tightly constrained LLM interpretive layer. The empirical setting is a multi-brand, multi-country retail portfolio with several years of digital marketing and outcome data …

Geochemical Analysis Of The Eagle Ford Shale In Webb And Dimmit Counties, Tx, Natalie G. Girlinghouse

Electronic Theses and Dissertations

The Late Cretaceous Eagle Ford Shale of South Texas is a mixed carbonate–siliciclastic succession that serves as both a prolific hydrocarbon source and reservoir. This study integrates chemostratigraphy, mineralogical analysis, and elemental ratio proxies to reconstruct depositional environments and assess controls on detrital influx, paleoproductivity, and redox conditions across multiple cores in Webb and Dimmit counties. Major, trace, and redox-sensitive elements were evaluated alongside Total Organic Content (TOC) and carbonate content to identify stratigraphic trends and environmental shifts.

Results reveal distinct geochemical signatures between the Upper and Lower Eagle Ford Shale, with the Lower Eagle Ford characterized by elevated Al-rich …

Structural Geology Of The Brady Mountain Area, Ouachita Mountains, Arkansas: Integrating Field Mapping, Geospatial Techniques, Photogrammetry And 3d Modeling For Structural Analysis, Emmanuel Darko, Zachariah Fleming

Electronic Theses and Dissertations

Much of the Ouachita orogenic belt, representing the deformed Paleozoic rocks flanking the southern margin of the North American craton, is buried beneath post-orogenic Mesozoic and Tertiary sediments composing the Gulf Coastal Plain. The Ouachita Mountains of Arkansas and Oklahoma are the largest areas of outcrop for the Pennsylvanian-age Ouachita orogenic belt, making it an ideal place to study the orogeny. This study focuses on the Brady Mountain area within the Benton Uplift, the orogenic core of the Ouachita Mountains in western Arkansas. The region is comprised of numerous ridges trending nearly east-west and are for the most part densely …

Glacial Deposits, Vol. 50, 2025, Illinois State University

Glacial Deposits

Newsletter of the Illinois State University Department of Geography, Geology, and the Environment

Fractional Order Hierarchical Decompositions Using Multigrid Components, Panayot S. Vassilevski

Mathematics and Statistics Faculty Publications and Presentations

Motivated by the fractional order multilevel decompositions of finite element spaces developed previously, we exploit additive representations of popular multigrid (MG) cycles to design fractional order MG decompositions. The additive representations enable us to scale the individual hierarchical components thus ending up with fractional order hierarchical decompositions that are based on the readily available MG components. This results in a highly efficient and scalable (in terms of high-performance) fractional order hierarchical MG decompositions that we tested in the setting of finite element white noise sampling as an alternative to PDE-based white noise sampling using fractional order shifted Laplacians.

Navigating Seismic Anisotropy, With Applications To Alaska, Aakash Gupta

Geosciences

Seismic anisotropy characterizes how elastic waves travel through solid materials with different speeds, depending on the direction of the waves and the direction of particle motion of the waves. Evidence of seismic anisotropy in the Earth is widespread, from seismic waves sampling the crust, mantle and even the core, to active-source, industry-scale seismic experiments, to laboratory mea­ surements of rock samples from boreholes or at the surface. Global models of the Earth provide a highly averaged image of anisotropy. Studies based on shear wave splitting measurements from local and global earthquakes indicate that the anisotropy in the Earth is much …

Examination Of Differences Of Similar-Sized Martian Craters At The Simple-Complex Transition As Revealed By High-Resolution Imaging And Stereo-Derived Topography, Lindsey Dorn

Geosciences

Impact craters on Mars have a wide range of morphologies, and the relationship between the mechanics of their formation and their eventual morphologies is still not completely understood. By examining craters within a narrow diameter range, impact energies are constrained to similar values - enabling focused investigation of target properties and their influence on crater excavation and modification. There is a transition from simple bowl-shaped depressions to complex craters with terraced walls and central floor structures with increasing crater diameter. We analyzed target influences on morphology by examining eleven well-preserved craters of varying morphologies in the 7 to 9 km …

Ice Surface Change And Flow Dynamics Analysis Of Sít’ Tlein (Malaspina Glacier) Through Remote Sensing, Victor Devaux-Chupin

Geosciences

Sít’ Tlein, located in southeast Alaska, is the largest piedmont glacier in the world. Periglacial lake expansion, low-lying bed, and proximity to the Pacific Ocean put its glacier lobe at risk of rapid retreat. Its disappearance would equate to removing an area of land about half the size of Rhode Island. Sít’ Tlein exhibits complex ice flow responsible for its iconic folded moraines, and for its heterogeneous ice resupply in its lobe, which occasionally surges. This lobe is gradually mantled by debris and vegetation as the ice flow fades towards its margins, protecting it from melting. This the­ sis is …

Multi-Scale Insar Investigations Of Anthropogenic And Natural Processes In The American Southwest, Emily Jo Graves

Geosciences

Arid regions in the American Southwest undergo measurable surface deformation driven by variations in subsurface fluid pressure, and other natural and anthropogenic processes. These processes range from regional scale aquifer dynamics to local oil and gas wellsite activities. This dissertation produces and then utilizes InSAR-derived average velocity fields and displacement time series to reveal and understand these multi-scale processes, their interactions, and impacts for a portion of the American Southwest. Chapter 1 discusses the climate and natural and anthropogenic processes present in our study area. In Chapter 2, we generate detailed large scale average velocity maps that reveal deformation associated …

Security Vulnerabilities And Defense Tactics For Generative Ai Application Development, Kyle Klein

University Honors Theses

Generative AI (GenAI) applications such as OpenAI's ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program's responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. …

Content And Consequences: Impact Of Representation In Stem Higher Education Instructional Content On Marginalized Students, Nichole Ventura

Doctorate in Education

This qualitative study examined representation of historically marginalized students in STEM instructional content at the higher education level and its impact on their learning experiences. Despite growing diversity initiatives in STEM enrollment, curricular materials often fail to reflect the identities of underrepresented students. Using critical theory and interpretivist approaches, this research investigated how representation—or its absence—shapes students' sense of belonging, academic identity formation, and persistence. Through semi-structured interviews with undergraduate students from historically marginalized backgrounds, and purposeful sampling, this study captured the lived experiences of students engaging with STEM instructional materials. Interview protocols explored how students perceive their representation in …

Evaluation Of Distractor Model Specifications On Assessment Quality Within The Framework Of Assessment Engineering, Sunhyoung Lee

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

Responding to the high demand for new test items, model-based item development has emerged as a promising approach for efficient item creation. Item models within Assessment Engineering (AE) offer a systematic framework that effectively generates a large volume of new items. In the development of multiple-choice questions, well-constructed distractors are essential for ensuring item quality, although creating them requires significant time and resources. A distractor model is a sub-model within an item model, producing correct answers and distractors aligned with all potential items to be generated. Various types of distractor models have different characteristics and offer efficient methods for generating …

Modelling Groundwater Nitrate Transport In Bazile Creek Watershed Using Modflow, Prajwol Thapa

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Excessive application of inorganic nitrogen fertilizers has led to pervasive nitrate contamination of groundwater in agricultural regions worldwide. This study evaluates the fate and transport of nitrate beneath the Bazile Creek watershed in northeastern Nebraska by coupling groundwater‐flow (GWF) and solute‐transport (GWT) simulations within MODFLOW 6. A conceptual model was developed using hydrogeologic, topographic, recharge, and boundary‐condition data, and the domain was discretized with a quadtree-refined grid to capture surface-subsurface interactions. The steady-state flow model was calibrated against observed hydraulic heads and baseflow measurements using NSE, RMSE, and PBIAS metrics, and subsequently extended to a transient flow simulation spanning 2002-2023. …

Changepoint Detection As Model Selection: A General Framework, Michael A. Grantham

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

This dissertation presents a general framework for changepoint detection based on ℓ₀ model selection. The core method, Iteratively Reweighted Fused Lasso (IRFL), improves upon the generalized lasso by adaptively reweighting penalties to enhance support recovery and minimize criteria such as the Bayesian Information Criterion (BIC). The approach allows for flexible modeling of seasonal patterns, linear and quadratic trends, and autoregressive dependence in the presence of changepoints.

Simulation studies demonstrate that IRFL achieves accurate changepoint detection across a wide range of challenging scenarios, including those involving nuisance factors such as trends, seasonal patterns, and serially correlated errors. The framework is …

Gradient-Based Model Shortcut Detection For Time Series Classification, Salomon Ibarra, Frida Cantu, Kaixiong Zhou, Li Zhang

Computer Science Faculty Publications

Deep learning models have attracted lots of research attention in time series classification (TSC) task in the past two decades. Recently, deep neural networks (DNN) have surpassed classical distance-based methods and achieved state-of-the-art performance. Despite their promising performance, deep neural networks (DNNs) have been shown to rely on spurious correlations present in the training data, which can hinder generalization. For instance, a model might incorrectly associate the presence of grass with the label "cat" if the training set have majority of cats lying in grassy backgrounds. However, the shortcut behavior of DNNs in time series remain under-explored. Most existing shortcut …

Near-Infrared-Spectroscopy-Derived Oxygen Desaturation Rates During Post-Exercise Circulatory Occlusion As Predictors Of Cardiorespiratory Fitness Markers, Carstell Lee

Honors Theses

Cardiorespiratory fitness, measured as maximal oxygen consumption (VO₂max) is a critical predictor of all-cause mortality and overall cardiovascular health. Metabolic carts are the current gold standard for evaluation of VO₂max, but are inaccessible to most facilities and individuals, and are expensive ($20,000+). Near-Infrared spectroscopy (NIRS) has emerged as an accessible and affordable ($5,000-$10,000) alternative for evaluating skeletal muscle oxygenation kinetics and mitochondrial oxidative capacity. However, there is limited research in examining whether NIRS-derived oxygen desaturation rates can be a predictor of cardiorespiratory fitness markers (such as VO₂max and peak power output [PPO]), and whether these measurements show a superior predictive …