Digital Commons Network^™

Contract Quality Feature Extraction Using Llm, Aaron C. Washington

Theses and Dissertations

This study explored the potential insights generated from linguistic complexity measurements and large language model (LLM) based assessments on the quality of contract documents. By combining structured True/False prompts with log-probability analysis and ambiguity scoring, the study introduced novel contract-quality assessment methods. Results support a feature-driven approach to contract evaluation, one that offers automated, scalable insights for triaging risk and improving drafting practices. These assessment methods contribute to the growing field of legal natural language processing by offering modular tools for effective contract analysis.

Equiangularity From Compatible Orthobiangularity, Tyler J. Myers

Theses and Dissertations

An equiangular tight frame (ETF) is an equal norm sequence of vectors in a Hilbert space whose coherence achieves equality in the Welch bound. Such sequences necessarily have minimal coherence and thus are, in some sense, as "spread out" in space as possible. ETFs have a variety of applications, such as compressed sensing and waveform design. The main problem in the study of ETFs is determining the pairs (D, N) for which an ETF with N vectors in a D-dimensional space exists. Real ETFs are moreover equivalent to a special subset of a well-studied class of graphs known as strongly …

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

Fused-Silica Microelectromechanical Systems For Relative Gravimetry, Ethan Doerstling

Theses and Dissertations

Gravimeters are devices that measure gravitational acceleration which can be used by the United States Air Force (USAF) in the areas of navigation and remote sensing. Fused-silica microelectromechanical systems (MEMS) devices offer capabilities to make inexpensive relative gravimeters with higher thermal stability than common silicon devices while maintaining good gravitational sensitivity. The fused-silica devices in this research were designed, simulated, fabricated, and tested to observe their performance as gravimeters. The devices exhibit properties of highly sensitive accelerometers but the current designs do not qualify as gravimeters. This study provides information to improve the sensitivity and stability of these fused-silica MEMS …

Leveraging Large Language Models (Llms) For Automated Generation Of Sysml V2 State Machines In Guidance, Navigation, And Control (Gnc) Systems, Andrea Louise Ames

Theses and Dissertations

Modern defense systems continue to grow in complexity, placing increasing pressure on engineering workflows to be faster and more adaptable. While Model-Based Systems Engineering (MBSE) with the emerging SysML v2 standard provides a framework for capturing system behavior, its practical use is often limited by the expertise and time required for manual modeling. This research investigates whether large language models (LLMs) can help overcome that barrier by automatically generating SysML v2 state machines from Guidance, Navigation, and Control (GNC) textual inputs. Three LLM Flowise-based models were developed and evaluated: the Structured Transformation Model (STM), which uses a structured extraction and …

Constraining Nuclear Data Uncertainty Requirements For The 19F(A, N)22Na Reaction For Non-Proliferation Applications, Tyler R. M. Smith

Theses and Dissertations

This thesis explores the requirements on nuclear data uncertainties needed for the use of the ¹⁹F(α, n)²²Na reaction for nuclear non-proliferation applications. An overview of how neutrons are produced from alpha decays in a UF₆ medium is discussed. Calculation demonstrate the role nuclear data uncertainties effect the neutron yield and energy spectra as a function of enrichment.

Injectable Ionic Hydrogel Conductors: Advancing Material Design To Transform Cardiac Pacing, Gabriel J Rodriguez-Rivera, Allison Post, Mathews John, Derek Bashe, Fei Xu, Trace Larue, Abbey Nkansah, Megan Wancura, Malgorzata Chwatko, Christina Waldron, Nikhith Kalkunte, Janet Zoldan, Mathieu Arseneault, Abdou Elgalad, Manuel K Rausch, Mehdi Razavi, Elizabeth Cosgriff-Hernandez

Faculty, Staff and Students Publications

Direct pacing of the mid myocardium where re-entry originates can be used to prevent ventricular arrhythmias and circumvent the need for painful defibrillation or cardiac ablation. However, there are no pacing electrodes small enough to navigate the coronary veins that cross these culprit scar regions. To address this need, we have developed an injectable ionically conductive hydrogel electrode that can fill the epicardial coronary veins and transform them into flexible electrodes. A new hydrogel chemistry based on a polyether urethane diacrylamide macromer was developed that matches myocardial stiffness and is resistant to hydrolysis. Conductivity was imparted using ionic precursor solutions …

Rebuilding Consent: Fear, Trust, And The Future Of Nuclear Energy, Austin Dunham

University Honors Theses

This thesis traces the cultural and emotional roots of nuclear fear in the United States and explores how those inherited anxieties continue to shape public resistance to nuclear energy in the face of a worsening climate crisis. Drawing on personal narrative, risk perception theory, and the history of nuclear discourse - from Hiroshima to Chernobyl to today - the research examines how trust, rather than technology, remains the largest barrier to progress. By analyzing the stories we tell about energy, disaster, control, and overall perceived risk, this work asks what it would take for Americans to imagine nuclear energy not …

Raising The Roof For All: Integrating Companion Planting Ecology, Policy And Community In Portland's Green Roof Future, Zoe Edelman

University Honors Theses

Green roofs, or ecoroofs, provide environmental and social benefits in urban areas. Ecoroofs manage stormwater by absorbing rainfall, reducing rooftop temperatures, supporting pollinators, and offering green space in densely developed cities. Portland, Oregon has adopted progressive policies that encourage green roof installation through incentives and building requirements. However, many ecoroofs in the city are underperforming due to poor maintenance, low public awareness, and limited access to rooftop spaces. This thesis explores whether companion planting can improve the ecological performance and long- term viability of extensive green roofs. A rooftop experiment at Portland State University tested the growth of radishes, with …

Early-Stage Detection Of Copper Ion Release From Bronze Corrosion Using Uv/Vis Spectroscopy And Hydrogel Sensing, Hibah Khan

University Honors Theses

Detecting copper(I) (Cu⁺) release at early corrosion stages is important for preserving bronze heritage materials. This study investigated the use of neocuproine (NC), a Cu⁺-specific ligand, for simple and selective colorimetric detection of Cu⁺ in solution. NC reacts with Cu⁺ to form a Cu(NC)₂ complex, which produces a distinct orange-red color with peak absorbance at 455 nm. Solutions were prepared with varying concentrations of Cu⁺ generated by reducing Cu²⁺ with excess ascorbic acid. Ultraviolet/Visible (UV/Vis) spectroscopy was used to quantify absorbance changes. The absorbance at 455 nm increased linearly across the tested Cu⁺ concentration range (6.25–200 µM) with an R² …

Clinical Trial Readiness In Limb Girdle Muscular Dystrophy R1 (Lgmdr1): A Grasp Consortium Study, Stephanie M Hunn, Amanda Clause, Conrad C Weihl, Et Al.

2020-Current year OA Pubs

OBJECTIVE: Identifying functional measures that are both valid and reliable in the limb girdle muscular dystrophy (LGMD) population is critical for quantifying the level of functional impairment related to disease progression in order to establish clinical trial readiness in the context of anticipated therapeutic trials.

METHODS: Through the Genetic Resolution and Assessments Solving Phenotypes in LGMD (GRASP-LGMD) Consortium, 42 subjects with LGMDR1 were enrolled in a 12-month natural history study across 11 international sites. Each subject completed a battery of clinical outcome assessments (COA), including the North Star Assessment for Limb Girdle-Type Dystrophies (NSAD), 10-m walk/run, and Performance of the …

Mitigating Nonlinear Impairments In High-Speed Optical Communication Systems Via Volterra Nonlinear Equalizer, Safa Jabbar Mohammed, Jalil A. Hamadamin

Polytechnic Journal

This paper presents an in-depth investigation into applying a frequency domain Volterra series nonlinear equalizer (FD-VNLE) to mitigate nonlinear impairments in optical communication systems operating at high speeds. The study aims to address the issues presented by nonlinear effects, specifically self-phase modulation (SPM), which significantly degrades system performance at higher power levels. A comprehensive mathematical model of the FD-VNLE was developed to provide theoretical insight, while its implementation was carried out using MATLAB co-simulated with OptiSystem. The performance of the 40 Gbps dual-polarization quadrature phase shift keying (DP-QPSK) system was rigorously evaluated, focusing on critical performance metrics like bit error …

Nf2 Loss-Of-Function And Hypoxia Drive Radiation Resistance In Grade 2 Meningiomas, Bhuvic Patel, Sangami Pugazenthi, Shree S Pari, Tatenda Mahlokozera, William A Leidig, Hsiang-Chih Lu, Alicia Yang, Kaleigh Roberts, Patrick Desouza, Kyle P Mcgeehan, Diane D Mao, Namita Sinha, Joseph E Ippolito, Sonika Dahiya, Hiroko Yano, Albert H Kim, Et Al.

2020-Current year OA Pubs

BACKGROUND: World Health Organization Grade 2 meningiomas (G2Ms) often recur and resist therapies. Grade 2 meningiomas with histopathological necrosis have been associated with worse local control (LC) after radiation therapy, but the drivers and biomarkers of radiation resistance in G2Ms remain unknown.

METHODS: We performed genetic sequencing and histopathological analysis of 113 G2Ms and investigated the role of genetic and microenvironmental factors on clonogenic survival after ionizing radiation. We performed transcriptional profiling of our in vitro model and 18 human G2M tumors by bulk RNA sequencing as well as 8 G2Ms by single nuclei RNA sequencing.

RESULTS: NF2 loss-of-function (LOF) …

The Silent Thread, Omer Shamil

The Mercury

No abstract provided.

Childhood Neuroanatomical Markers Of Familial And Nonfamilial Attention-Deficit/Hyperactivity Disorder, Rahman Baboli

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

The Role Of Excitatory Neuromodulation In Managing Variability Of Neural System Output, Omar Itani

Dissertations

Neural systems can generate consistent outputs across a population despite substantial variability in the underlying components of individuals. This dissertation aims to identify mechanisms through which neuromodulation influences the relationship between parametric and output variability in neural systems. Through a combination of theoretical analysis, computational modeling, and data-driven approaches, the research addresses how excitatory neuromodulation can shape population-level activity variability and identifies key patterns that govern the production of consistent neural population output despite underlying parameter variability.

The theoretical foundation is established by considering how excitatory neuromodulation affects population variability in simplified neuronal models. Two fundamental patterns of variability reduction …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

Phys 102-011: General Physics I, Keun Ahn

Physics Syllabi

No abstract provided.

Out Of Consumption Out Of Context Into Recursion, Xubai Li

Masters Theses

In a world where consumption is inevitable, our choices define us. However, the situation is never so black and white. The boundary between commodification and authentic representations has grown increasingly opaque, layered with irony, sincerity, critique, and tribute.

Within this complexity lies not a clear resistance, but an opportunity: to embrace the oscillation and locate one’s authenticity through metamodernist sensibilities.

My process begins by interrogating the accepted—may it be a product, a technique, or a medium. I deconstruct, fragment, and remove it from its original context—both physically and conceptually.

What emerges is a recursive reconstruction, where origin and iteration intertwine, …

Unframing Venus: Redefining The Female Nude, Sage Leafsong

Masters Theses

Historically women have been excluded from becoming acclaimed artists due to societal, educational, and institutional barriers. This exclusion has had many repercussions in Western art––in a large part it has made celebrated male artists the norm, while celebrated female artists are the exception.

These same barriers often precluded women from depicting themselves in art. When women did paint, they were encouraged to paint domestic scenes such as flowers or still lifes and were prohibited from studying or depicting the nude form. This gap of representation was filled by male artists who have felt empowered to sexualize women with no consequence. …

The Prospect Of Geospatial Analysis In The Prediction Of Surface Quality In Machining, Prithbey Raj Dey, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

This research underscores the prospect of geospatial analysis in machining operations to enhance precise prediction and robustness, offering a comprehensive framework of spatial modeling for advanced manufacturing processes. Geospatial analysis not only provides accurate predictions but also estimates the uncertainty associated with these predictions, offering valuable insights for process optimization. The surface quality in the machining processes is expressed by the estimation of the average surface roughness. While machining parameters are extensively analyzed for their influence on surface quality, the roughness profile parameters are inadequately explored. This work integrates these underexplored parameters into geospatial predictive models and evaluates their impact …

Commencement Of The Class Of 2025, Illinois Math And Science Academy

Ceremony

PROGRAM

POMP & CIRCUMSTANCE FROM MILITARY MARCHES Op.39, No.1 Edward Elgar

PLEDGE OF ALLEGIANCE Navya Dixit ,Class of 2025

REFLECTIONS ON GRADUATION DAY Tristen Marley Wicks-Castillo, Class of 2025

REFLECTIONS ON IMSA Carissa Chen,Class of 2025

INTRODUCTION OF COMMENCEMENT SPEAKER Eric Brown ,Board Chair Elect, Board of Trustees.

COMMENCEMENT ADDRESS Ryan Wang ‘08 ,Co-founder/CEO, Assembled

PRESENTATION OF THE CLASS OF 2025 Angela Rowley, Ed.D, Principal and Chief Academic Officer.

ACCEPTANCE OF THE CLASS OF 2025 Evan M. Glazer, Ph.D., President

PRESENTATION OF DIPLOMAS AND MEDALLIONS 

Eric Brown ,Board Chair Elect, Board of Trustees

Evan M. Glazer, Ph.D. President

Anita White, …