Digital Commons Network^™

Becoming A Shape, Becoming Yourself: Geometry, Identity, And Belonging In An Early Childhood Classroom, Eva Thanheiser, Molly Robinson, Simon Byeonguk Han, Amanda T. Sugimoto, Courtney Koestler, Mathew D. Felton-Koestler

Mathematics and Statistics Faculty Publications and Presentations

This paper examines how a first-grade geometry lesson became a site for exploring shape, selfhood, and belonging in an early childhood classroom. Drawing on interaction analysis of a lesson built around the picture book The Greedy Triangle , we show how children named personal strengths, identified shapes, followed a triangle through changing categories, reflected on why they liked being themselves, and used their bodies to form polygons. We ask what counts as mathematical activity in this lesson and argue that mathematics is not limited to naming shapes. It also involves noticing attributes, negotiating criteria, tracking transformation, coordinating bodies, and reasoning …

A Preliminary Exploratory Assessment Of Chatgpt To Generating Stride Data Flow Diagrams, Hassan Alsayegh, Mohamed El-Attar

All Works

Threat modeling is a core activity in security-by-design practices, enabling early identification of architectural weaknesses before system implementation. The drawings used during STRIDE analysis are typically Data Flow Diagrams (DFDs), referred to as “STRIDE diagrams” in this paper. STRIDE diagrams provide a visual approach for categorizing security threats; however, constructing accurate STRIDE diagrams require experience and is often time-consuming. Recent advances in Large Language Models (LLMs), such as ChatGPT, raise important questions about their suitability for supporting structured security modeling tasks. This study presents a preliminary exploratory assessment of ChatGPT’s ability to generate, analyse, and iteratively refine STRIDE diagrams from …

Ai And Courts: An International Human Rights Perspective, Veronika Fikfak, Laurence R. Helfer

Faculty Scholarship

International human rights courts and treaty bodies are increasingly turning to automated decision-making (ADM) technologies to expedite and improve their review of individual complaints. These tribunals have yet to consider many of the legal, normative, and practical issues raised by the use of different types of automation technologies for these purposes. This chapter offers an initial assessment of the benefits and challenges of introducing ADM into international human rights adjudication. We weigh up the benefits of introducing these tools to improve international human rights adjudication – which include greater speed and efficiency in processing and sorting cases, identifying patterns in …

Llm-Assisted Legal Propositions Identification From Party Arguments In The U.S. Supreme Court Briefs, Heng Zheng, Alex Zhang

Faculty Scholarship

Merits briefs are central to U.S. litigation, serving as the primary means for parties to present arguments and persuade judges. Legal propositions in these merits briefs are the atomic units of arguments, whose relationships evolve throughout litigation and inform court decisions and precedent. Large language models (LLMs) have been applied to legal document review, but there is limited evidence on their ability to identify legal propositions in merits briefs. Given the labor-intensive nature of the task, we evaluate a human-AI collaborative approach to identifying legal propositions in the U.S. Supreme Court merits briefs, in which legal annotators review and revise …

Oompa 2025.08: A First Cut Of The Toolkit For Object-Oriented Modeling For Planning And Acting, Mark Roberts, David H. Chan, Dana S. Nau, Jamie C. Macbeth

Computer Science: Faculty Publications

OOMPA is a partially implemented Python 3.13+ toolkit for modeling hierarchical planning domains as annotated Python classes, without writing a separate PDDL or HDDL domain file. State properties, actions, and hierarchical methods attach to domain classes via decorator syntax; OOMPA projects the resulting model into a flat dictionary of dictionaries, like the Pyhop family of planners. We describe OOMPA’s motivation and architecture as we demonstrate its use in a restaurant planning domain. There are many unrealized features, so we end with a discussion of limitations and future work.

The Forensic Implications Of Hydrochloric Acid (Hcl) Exposure On Dna Preservation In Human Teeth, Shaelyn Lee Zimmerman

Graduate Student Theses, Dissertations, & Professional Papers

Identity is a human right, and forensic anthropological methods are often aimed at returning identity to decedents. Three main pathways are used for the identification of human remains: fingerprints, dental records, and DNA analysis. Identification efforts may be hindered when perpetrators attempt to obscure the victim’s identity. In cases where corrosive substances, such as hydrochloric acid (HCl) are used, dental comparison and fingerprinting often fail and genetic analysis becomes the best chance of achieving personal identification.

Previous studies have shown teeth are an excellent source of DNA. Large, multi-rooted teeth, such as molars, are often preferred because they contain the …

High Algal Biomass Is Decoupled From Metabolism In The Gallatin River, Cora Mae Steinbach

Graduate Student Theses, Dissertations, & Professional Papers

Assessing eutrophication in rivers is difficult compared to lakes and coastal waters, because most algal biomass occurs on the riverbed and flows interact and co-vary with production (Biggs and Close, 1989; Bernhardt et al., 2018). Riverine eutrophication is typically assessed using algal biomass and water column nutrients (U.S. Environmental Protection Agency, 2000), but biomass is highly variable and labor-intensive to measure, while nutrient concentrations often underestimate enrichment due to rapid biological uptake (Dodds and Smith, 2016). Reach-scale river metabolism can help evaluate long-term functional change in rivers recovering from nutrient enrichment (Arroita et al., 2019; Jankowski et al., 2021; Diamond …

Real-Time Immersive Wildfire Simulation: A Game Engine Approach, Jake Bova

Graduate Student Theses, Dissertations, & Professional Papers

Wildfires are growing in frequency, size, and severity, placing escalating demands on firefighting resources and putting lives, property, and ecosystems at risk. Accurate prediction of fire behavior is critical to suppression planning, evacuation decisions, and firefighter training, yet the operational models that fire managers rely upon face a persistent tradeoff. High-fidelity physics-based models capture fire–atmosphere interactions faithfully but are computationally expensive, often running far slower than real time on CPU hardware, while faster empirical models sacrifice the dynamic coupling between a fire and the wind field it generates. Furthermore, most existing tools produce abstract, two-dimensional outputs that offer limited visual …

Spatiotemporal Dynamics Of Mountain Front Recharge Flow Paths And Their Contribution To Alluvial Aquifer Water Supply, Mikaela Bollag-Miller

Graduate Student Theses, Dissertations, & Professional Papers

Mountain Front Recharge (MFR) is a critical recharge source for alluvial basin aquifers throughout the Intermountain West, yet the spatial and temporal dynamics of distinct recharge pathways remain poorly constrained. This study investigates the seasonal and spatial dynamics of MFR pathways and evaluates their impact on the Missoula Aquifer, a sole-source alluvial aquifer in western Montana, USA. MFR includes both surface-water inflows from mountain areas and Mountain Block Recharge (MBR), the groundwater contribution to the basin aquifer. A valley-scale water balance was employed to estimate first-order contributions and temporal fluctuations of MBR flow paths. A three-layer steady-state MODFLOW groundwater model …

Bridging The Gap: Evaluating Gnss-Derived Total Water Storage As A Groundwater Monitoring Tool In The Colorado River Basin, Michelle Lee Barton

Graduate Student Theses, Dissertations, & Professional Papers

Groundwater resources in the Colorado River Basin are critically stressed yet remain under-monitored and under-regulated, creating urgent need for reliable, continuous observations at multiple spatial scales. This study evaluates the performance of the University of Montana Global Navigation Satellite Systems (GNSS) terrestrial water storage dataset as a tool for bridging groundwater monitoring gaps across the Upper and Lower Colorado River Basins between 2006 and 2024. We compare GNSS-derived total water storage and groundwater storage estimates against NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite observations and in-situ groundwater well measurements at regional and sub-regional scales. We employ a Bayesian autoregressive …

Can We Read Ai’S Mind? A Quest For Transparency, Santhosh Kumar Ravindran, Estera Kot, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Although Artificial Intelligence (AI) systems are playing an increasing role in critical domains such as healthcare, finance, and autonomous systems, their decision-making processes remain largely opaque. This paper examines the challenges of AI transparency, addressing the “black box” problem using Explainable AI (XAI) techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It also examines the ethical, regulatory, and societal implications of AI opacity and proposes a Comprehensive AI Observability (CAO) Framework that integrates deep explainability, provenance tracking, and real-time monitoring to enhance AI accountability. By bridging technical solutions with governance structures, this research emphasizes the …

Learning-Based Graph Shrinking For Quantum Optimization Of Constrained Combinatorial Problems, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Graph shrinking has recently emerged as a powerful preprocessing technique for hybrid classical–quantum optimization, enabling variable and constraint reduction before quantum solving. Conventional approaches rely on Semi-Definite Programming (SDP) relaxations to compute vertex correlations, but these methods suffer from high computational overhead, instance-specific tuning, and limited generalizability. In this work, we replace the handcrafted SDP correlation stage with a reinforcement learning (RL) based correlation estimator, trained to predict merge quality directly from graph structure. We reformulate the graph shrinking process as a Markov Decision Process (MDP), design a Graph Neural Network (GNN) policy to guide vertex merging, and integrate the …

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

Cliffinsight: An Educational Web Application That Visualizes The Calculation Of Effect-Sizes Using Cliff's Delta, Mohamed El-Attar, Ahmed Shuhaiber, Rima Grati, Sarah Kohail

All Works

The purpose of calculating effect sizes in statistics is to quantify the practical significance of observed differences beyond mere statistical significance. While standardized mean difference measures such as Cohen’s d are widely used, they require normally distributed data, an assumption frequently violated in educational and social science research. Non-parametric alternatives such as Cliff’s delta (δ) are more robust under these conditions yet remain underused due to perceived computational complexity and limited accessible resources. Existing web-based tools for Cliff’s delta function primarily as numerical calculators and do not expose the underlying dominance structure that gives the statistic its meaning. This paper …

Transformer-Based Language Models For Bitcoin Market Prediction And Interpretation, Erich Gozebina

Masters Theses

"In the context of a Master's thesis in applied mathematics, this work investigates compact transformer-based language models as an instrument for Bitcoin-related predictions and decision support. The work connects three topics: the structure of the Bitcoin system and its data, the mathematical and algorithmic foundations of deep autoregressive transformers, and the design of practical training pipelines for financial applications. On this foundation, a reproducible ETL pipeline for Bitcoin data is developed and two forecasting experiments are conducted with the compact language model nanochat. The first experiment approaches the prediction of next-day price movements through autoregressive next-token generation based on structured …

Characterizing A Delay-Programmable Two-Color Femtosecond Light Source For Coherent Control Of Atomic Multi-Photon Ionization Processes, Mason Tanner Toombs

Masters Theses

"In this work a delay-programmable two-color femtosecond laser based on a chirped-seed noncollinear optical parametric amplifier was characterized. This design utilizes a titanium-sapphire crystal oscillator to generate broadband pulses of light spanning approximately 600-1200 nm, with a pulse duration of ~5 fs, repetition rate of 80 MHz, and pulse energy of ~2.5 nJ. An infrared portion of the light, spanning approximately 1020-1060 nm, is split off from the pulse and used to generate a pump beam. By dispersing the remaining seed beam and controlling the relative timing delay between the seed and the pump, tunable frequencies within the seed can …

Multivariate Quantile Autoregression-Mixed Data Sampling (Mvqar-Midas) Modeling Of Cost Of Living And Supply Chain Dynamics In Canada., Patrick Gbolonyo

Theses and Dissertations (Comprehensive)

In recent years, the rising cost of living as a result of persistent inflationary pressures, disruptions in the global supply chains, and changes in the macroeconomic landscape has become a critical topic of discussion. To address this, we move beyond a mean-based framework and employ a quantile regression approach. This allows the persistence of each series and the transmis- sion of shocks between the Consumer Price Index (CPI) (the total CPI which is a percentage change over the past 12 months), the Interest Rate (IR)(the target for the overnight rate), the New Housing Price Index (NHPI), and high-frequency supply chain …

Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision, Abdelrhman Elrawy

Theses and Dissertations (Comprehensive)

This thesis studies efficiency and stability challenges in Gaussian-splatting-based reconstruction under sparse supervision. In few-shot novel view synthesis, standard 3D Gaussian Splatting (3DGS) can overfit the limited training views and grow an unnecessarily large number of primitives due to limitations in its Adaptive Density Control (ADC) mechanism. This thesis introduces an error-driven reformulation of ADC that triggers densification using opacity gradients as a lightweight proxy for rendering error, and shows that such aggressive densification must be paired with delayed and conservative pruning to prevent destructive create--destroy cycles. When combined with depth-based geometric regularization, the resulting framework produces substantially more compact …

Optimal Quantization On Spherical Surfaces: Continuous And Discrete Models—A Beginner-Friendly Expository Study, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical curves and discrete subsets ofthe sphere, emphasizing both continuous and discrete settings. We first present a detailedgeometric and analytical foundation for intrinsic quantization on the unit sphere, includingdefinitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation,the Fréchet mean, spherical Voronoi regions, centroid conditions, and quantizationdimensions. Building upon this framework, we develop explicit continuous and discretequantization models on spherical curves, namely great circles, small circles, and greatcircular arcs—supported by rigorous derivations and pedagogical exposition. For uniformcontinuous distributions, we compute optimal …

Gauge-Invariant Gravitational Wave Polarization In Metric F(R) Gravity With Cosmological Implications, Ramesh Radhakrishnan, David Mcnutt, Delaram Mirfendereski, Alejandro Pinero, Eric Davis, William Julius, Gerald Cleaver

Physics & Astronomy Faculty Publications

We develop a fully gauge-invariant analysis of gravitational-wave polarizations in metric 𝑓⁡(𝑅) gravity with a particular focus on the modified Starobinsky model 𝑓⁡(𝑅) =𝑅 +𝛼⁢𝑅2 −2⁢Λ, whose constant-curvature solution 𝑅𝑑 =4⁢Λ provides a natural de Sitter background for both early- and late-time cosmology. Linearizing the field equations around this background, we derive the Klein–Gordon equation for the curvature perturbation 𝛿⁢𝑅 and show that the scalar propagating mode acquires a mass 𝑚2𝜓 =1/(6⁢𝛼), highlighting how the same scalar degree of freedom governs inflationary dynamics at high curvature and the propagation of gravitational waves in the current accelerating Universe. Using the scalar–vector–tensor …

31p Solution Nmr Investigation Of Abasic Dna, Clarissa R. Krimmel

Graduate Theses/Dissertations

Base excision repair (BER) mechanisms fix single base lesions in DNA, such as T:G mismatches. During the base excision repair mechanism, an abasic site (AP site) is formed as an intermediate. AP sites are unstable and highly mutagenic; they can stop DNA replication. This research investigates how the conformational properties of abasic sites in DNA affect the binding recognition of enzymes involved in DNA repair mechanisms, including BER. Three different abasic sequences are being analyzed for this research project. A second project looks at the effects of a naturally occurring purine derivative, hypoxanthine, on the DNA backbone. These hypoxanthine lesions …

Srgan-Based Deep Learning Framework For Wind Turbine Damage Detection From Sentinel-2 Imagery, Kübra Çakir, Onur Elma, Murat Kuzlu

Engineering Technology Faculty Publications

The operational reliability of wind turbines is critical for sustainable energy production in smart grids. This study proposes a remote monitoring approach using perceptually enhanced satellite imagery. Sentinel-2 multispectral data (10 m resolution) has been processed with a Super-Resolution Generative Adversarial Network (SRGAN) to improve visual quality to a perceptual resolution of 30 cm. Although true spatial refinement is not achieved, the sharper structural details enhance classification accuracy. The data set comprises 15,000 images—10,000 SRGAN-enhanced and 5000 augmented through rotation, zoom in, increasing brightness, noise addition, and blurring. A custom Convolutional Neural Network (CNN) has been trained to classify turbines …

Interpretable Machine Learning For Bridge-Pier Scour Prediction And Flood Resilience, Adil Khan, Dalya Ismael

Engineering Technology Faculty Publications

Bridge-pier scour is a leading cause of flood-induced bridge failure, yet practice still lacks transparent, physics-informed tools that link data-driven prediction with design guidance. This study develops an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure. Using the 2014 U.S. Geological Survey Pier-Scour Database (569 laboratory cases), five models: Gradient Boosting, AdaBoost (Tree), XGBoost, Gaussian Process (RBF kernel), and Kernel Ridge (polynomial), were trained and evaluated with K-fold cross-validation. Model performance was evaluated using R², RMSE, and MAE. Gradient Boosting performed best, achieving training and testing R² of 0.99 …

Limits Of Machine Learning For Predicting Stillbirth At Term: A Population-Based Cohort Of 302,192 Pregnancies In Northern Italy, M. Benuzzi, T. Kawakita, F. Monari, B. Melis, G. Guariglia, A. La Marca, I. Neri, G. Saade, F. Facchinetti, I. El Moudden

Department of Obstetrics & Gynecology Faculty Publications

Background

Objective

Study Design

Zero-Shot Segmentation Of Estuary Mudflats Using The Segment Anything Model, Jaren Unzen

Honors Theses and Capstones

Estuary mudflats are ecologically sensitive environments that require consistent monitoring. Traditional satellite-based classification workflows are often constrained by the high cost and labor-intensive nature of manual data annotation. This study evaluates the utility of Segment Anything Model 3 (SAM 3), a foundational computer vision model, to automate mudflat segmentation without domain-specific fine-tuning. By leveraging the model’s text-prompting capabilities alongside specialized pre- and post-processing techniques, we generated segmentation masks in a zero-shot framework. Our approach achieved an F1- score of 0.51, demonstrating the inherent challenges of spectrally complex coastal features. Despite this, the results highlight a promising pathway for adapting large-scale …

Blens: Biomedical Literature Extraction And Scoring System, Tyler J. Simone

Honors Theses and Capstones

Systematic reviews and meta-analyses represent the gold standard for evidence synthesis in healthcare, yet their manual execution remains labor-intensive, time-consuming, and vulnerable to human bias. With the exponential growth of biomedical literature, traditional literature screening and analysis has become increasingly unstable and noncomprehensive. This thesis presents the development and validation of an automate literature gathering and review system that integrates multiple scientific databases through a unified desktop application. The platform combines APIs from PubMed (NCBI Entrez), ClinicalTrials.gov, bioRxiv and medRxiv to enable simultaneous, standardized searching across peerreviewed and preprint sources. Built in Python with a PySide6 graphical interface, this standalone …

Investigating A Nanoparticle-Assisted Approach For Localized Strain Engineering In Two-Dimensional Materials, Skye Martichuski

Honors Theses and Capstones

This work investigated a fabrication strategy for inducing localized nanoscale deformation and strain in transferred MoS2 flakes using gold nanoparticle-coated substrates as a potential platform for strain engineering in two-dimensional materials. Substrate type, nanoparticle concentration, drying conditions, cleaning procedures, and flake transfer methods were investigated using optical microscopy and atomic force microscopy (AFM). Au-coated SiO2 substrates produced improved nanoparticle coverage compared to bare SiO2, while passive air drying yielded the most favorable nanoparticle distributions. Salt crystals introduced during passive drying were successfully removed with a gentle deionized water rinse, and a 50% diluted gold nanoparticle suspension provided improved particle spacing …

Creating A Positive Learning Environment In A Math Course For Preservice Teachers, Kien H. Lim, Hilda Y. Sotelo

Departmental Papers (Math)

This study examines how a mathematics instructor cultivated a positive learning environment in a synchronous online course for 23 preservice teachers. Guided by self-determination theory, growth mindset, and mathematical caring, the course integrated high-cognitive-demand tasks, collaborative problem solving, flexible policies, and motivational supports. Data sources included reflection logs (Likert-scale items and open-ended responses) and a focus group; the instructor’s reflexive self-study informed result interpretation. Wilcoxon signed-rank tests indicated significant gains in students’ sense of inclusion and trust, though some instructor- related ratings declined late in the semester. Qualitative findings emphasized students’ appreciation for flexibility, encouragement, and growth-oriented feedback. Despite time …

Adaptive Boundary-Aware Fact-Checker Placement For Misinformation Suppression In Social Networks, Mostafa Taghizade Firouzjaee, Ghazal Naderi, Ross Gore, Neda Moghim

School of Cybersecurity Faculty Publications

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news …

Tissue Sampling And Analysis For High Rainfall Pastures In Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Tissue testing helps identify nutrient deficiencies that impact plant health. In high rainfall areas ( > 600 mm), pastures are highly productive and have greater demand for both macro- and micronutrients. Micronutrient status is best assessed through plant tissue, as soil levels are difficult to measure accurately.

The Department of Primary Industries and Regional Development recommends tissue testing to assess the effectiveness of fertiliser and soil testing programs, and diagnose emerging macronutrient and micronutrient deficiencies that could cause production losses.