Digital Commons Network^™

Evaluation Of Solar Reflective Pigments For Use In Direct-To-Metal Coatings, William J. Diment

Master's Theses

Solar reflective coatings offer a promising strategy to mitigate the urban heat island effect by reducing absorption of near-infrared solar energy. This thesis project investigates the feasibility of incorporating near-infrared reflective pigments into waterborne direct-to-metal (DTM) latex coating systems while maintaining acceptable color comparison characteristics and coating performance. An all acrylic latex DTM formulation with titanium dioxide as the sole pigment was prepared and tinted with a variety of conventional and solar reflective commercial colorants across the yellow, red, blue, and green color ranges. Coatings were evaluated for solar reflectance, CIELAB color, gloss, contrast ratio, rheological behavior, accelerated ultraviolet weathering …

What Makes A Modern Attention Implementation?, Brian H. Slonim

Master's Theses

Since the seminal assertion by Vaswani et al. in 2017 that “Attention Is All You Need,” transformer models have risen to ubiquity due to their ability to learn extremely complex patterns from sequence data, culminating in the unprecedented generative capabilities of large language models. These models’ strength lies in their scale: hundreds of millions (e.g., BERT-LARGE) to billions or trillions of learned parameters. Running inference with these models, let alone training them, would be intractable without significant innovations in the hardware and software that support them. This need has driven an enormous demand for GPU compute and associated software ecosystems, …

On M-Estimation: From Theory To Examples, Alexander Yuan

Master's Theses

M-estimation provides a unified framework for statistical procedures defined as optimizers of data-dependent criterion functions. This thesis gives an expository account of M-estimation in classical and high-dimensional settings. The classical part develops weak convergence, empirical process tools, and the argmax framework for studying consistency, rates of convergence, and weak limits. Examples including least squares, maximum likelihood, robust location estimation, change-point estimation, and empirical risk minimization illustrate regular and non-regular asymptotic behavior.

The high-dimensional part studies regularized M-estimators, where the focus shifts to finite-sample error bounds and model selection guarantees. Topics include decomposable regularizers, restricted strong convexity, non-convex penalties, and sparsistency. …

Hot Hands Or Chance Happenings? A Simulation-Based Approach For Wnba Teams, Ruben Jimenez

Master's Theses

The hot hand is a polarizing topic in basketball analytics: fans, stakeholders, and even players themselves assert confidently their belief or disbelief in the idea that players who perform well will continue to do so over an extended period of time. Statistical research has been conducted since as early as 1985 to attempt to disprove or prove the existence of this phenomenon. More recent works have refuted the earliest objections to the hot hand’s existence, with conclusions aided by robust simulation techniques. In this work, we compare hypothesis tests using multiple simulation techniques to explore the hot hand at the …

Is The Hot Hand Real? Evidence From A Permutation And Hierarchical-Based Analysis, Cameron Z. An

Master's Theses

The hot-hand phenomenon, often described as the tendency for individuals to experience prolonged streaks of success that exceed what would be expected under random performance, has been widely studied across many disciplines, particularly basketball. Early studies attempted to evaluate this effect through various techniques, often concluding that the hot-hand phenomenon was largely a myth. However, recent studies have begun to revisit previous analyses using improved statistical techniques, with some claiming evidence of a discernible hot-hand effect. This study examines the presence of the hot-hand effect in the modern NBA by testing whether observed shooting patterns deviate from those simulated under …

High-Resolution Queries, Low-Resolution Context: Scaling Vision Transformers With Asymmetric Spatial Reduction, James M. Dwyer

Master's Theses

Vision Transformers (ViTs) have demonstrated great performance on image classifica tion benchmarks, however, the quadratic complexity of the self-attention mechanism with respect to sequence length limits their scalability to higher resolution inputs. The attention score matrix grows as O(N2) in both compute and memory, where N is the number of patch tokens, making ViTs computationally expensive and memory intensive for applications that require real-time inference or operate under resource constraints.

This thesis investigates whether the key and value sequences of the self-attention mechanism can be compressed using the local spatial structure of the image — while keeping queries at full …

A Higher Order Panel Method With Vortex Particle Wake, Luca Flick-Kaiser

Master's Theses

Panel codes were some of the earliest methods for computing the flow around aircraft. Despite the availability of higher fidelity CFD methods, panel methods remain an essential tool for the development of modern aircraft, allowing engineers to quickly iterate during the conceptual design phase.

This work develops and implements a high order panel method using Python. The wake is modeled using vortex particles, resulting in an accurate wake representation with minimal user input. The integration of the higher order singularity distributions has been extended to include the calculation of the velocity gradients necessary for the vortex particle implementation. The use …

Computer Vision Methods For Detecting Counterfeit Usd Bills, Tyler W. Jones

Master's Theses

This thesis addresses the global challenge of counterfeit paper currency by proposing a classical computer vision framework for distinguishing genuine United States Dollar (USD) bills from counterfeit ones using image data. In contrast to existing approaches that rely on a large number of easily reproducible visual features, this work prioritizes the detection of a single, robust security feature: the ultraviolet (UV) reactive security strip embedded in genuine USD bills of denominations $5 and above. By focusing on a feature that is inherently difficult to replicate, the proposed method reduces the likelihood of counterfeit bills being misclassified as genuine.

The system …

Development Of A Free-Floating Space Robotic Simulator, Truman: Terrestrial Robotic Unit For Multibody Analysis And Navigation, Jackson W. Cordova

Master's Theses

This work describes the development of a 3 Degree-of-Freedom (DOF) robotic manipulator for use on a free-floating planar air-bearing vehicle. The system developed aims to serve as a foundation for future space robotics research at the California Polytechnic State University (Cal Poly)’s Space Robotics Laboratory. Systems doc- umentation describes conceptual operation and architecture of the proposed system: Terrestrial Robotic Unit for Multibody Analysis and Navigation (TRUMAN). The key operation of TRUMAN is to study a self launch maneuver of a free-floating ve- hicle including 4 distinct phases: launch off a fixed rail, coast, reorientation using manipulator motion, and capture of …

Spaceotter: A Floating Spacecraft Simulator Air Bearing Vehicle For Hardware-In-The-Loop Experiments And Research, Alexander Debartolo

Master's Theses

Growing interest in nanosatellites has increased demand for accessible ground-testing methods, which have historically been expensive and restricted. Floating Spacecraft Simulators (FSS), built around Air Bearing Vehicles (ABVs), address this gap by approximating a zero-gravity, friction-minimized environment suitable for testing spacecraft control systems, robotics, and propulsion on the ground.

This thesis presents the design, realization, and initial performance characterization of the Space Optically Tracked Testbed for Experiments and Research (SpaceOTTER) ABV, developed for the Cal Poly Space Robotics Lab. SpaceOTTER is the first step toward emulating the 3 degree of freedom (3-DOF) planar dynamics of a simulated spacecraft and is …

When Agents Spend Watts: Measuring Performance-Energy Trade-Offs In Agentic Ai Systems With Agenticbench, Logan J. Barker

Master's Theses

Agentic AI systems increasingly transform a single user request into a multi-step workflow of reasoning, tool use, retries, and framework coordination. Standard benchmark reporting emphasizes task accuracy, but this can hide the latency and energy-related cost of producing an answer. This thesis presents AgenticBench, a modular harness for evaluating agentic AI systems with task performance and execution-cost signals recorded together. AgenticBench integrates benchmark-owned scoring, framework adapters, deterministic task selection, energy profiling, SQLite persistence, and Pareto-style analysis. The main campaign evaluated an 84-cell matrix across four models, three agent frameworks, and six benchmarks on a Windows workstation with an NVIDIA RTX …

Efficient Mathematical Modeling And Synthesis Of Realistic Musical Instrument Sounds, Andrew Chookaszian

Master's Theses

This thesis develops and evaluates a compact parametric additive synthesis model for isolated musical instrument tones. The method analyzes a single-note recording, estimates its fundamental frequency, extracts harmonic amplitude and frequency behavior, and stores the sound as a reduced set of interpretable parameters. These parameters include the note duration, pitch, per-harmonic amplitude envelopes, phase information, and amplitude- and frequency-modulation vibrato parameters. The stored model is then used to resynthesize the tone without directly using the original audio waveform.

The model was evaluated using synthetic signals, real instrument samples, objective error metrics, storage comparisons, pitch and duration modification tests, and listening …

Challenges And Opportunities Of Mastery-Based Learning: A Student-Centered Analysis In An Upper Division Programming Languages Course, Parshana Sekhon

Master's Theses

Traditional grading structures in computer science courses reward performance on a few high-stakes assessments that may not accurately reflect a student’s mastery of concepts. Mastery-Based Learning (MBL) offers students multiple opportunities to demonstrate understanding through iterative feedback and reassessment, promoting their persistence and self-regulation. However, MBL can promote student behaviors like procrastination, “minimal effort” attempts, or de-prioritizing the course in favor of other courses with inflexible deadlines, which may undermine its effectiveness. This case study investigates how students navigated MBL in an upper-division computer science course at Cal Poly in San Luis Obispo. The course explores the fundamentals of programming …

Scorespeak: An Agentic System For Natural Language Control Of Musical Scores, Nathan S. Lim

Master's Theses

With the recent popularization of large language models (LLMs), natural language has become one of the most accessible and powerful ways for people to interact with creative tools. Although they have become common in mainstream domains like image and audio editing, there is currently no robust AI-based system that can reliably turn free-form language into edits for symbolic musical scores. This gap represents a missed opportunity to improve human workflows for creating and editing sheet music, but it is also a fundamental limitation for other agentic music systems; without a robust mechanism for translating free-form language into structured scores, AI …

Design And Implementation Of An Embedded Control System For The Cal Poly Spacecraft Attitude Dynamics Simulator, Neil Mahesh Bedagkar

Master's Theses

This thesis presents the development of a cascaded reaction wheel control system for Cal Poly's Spacecraft Attitude Dynamics Simulator. This work considers system architecture, empirical modeling, high-fidelity simulation, embedded firmware, hardware integration, and experimental characterization. In discrete-time simulation, it is shown that nonlinear direct model reference adaptive control (NDMRAC) enables a nonlinear, partially known plant with realistic actuator dynamics to track the response of a canonical second-order transfer function, with settling time within 4.67% and percent overshoot within order of magnitude of the second-order response. In hardware, the feasibility of a cascaded reaction wheel control architecture is experimentally demonstrated, achieving …

Empirical Comparsion Of Traveling Salesperson Approximation Algorithms, Shayan Daijavad

Master's Theses

The traveling salesperson problem deals with optimizing the route a traveling sales- person might take to visit a set of places exactly once and return back to their starting point. The problem is NP-hard, and it is hard to approximate in general, but special cases have many approximation algorithms, which come with tradeoffs. In this thesis we compare the runtime, approximation ratio, and overall implementation complexity of two approximation algorithms for the Euclidean version of the problem, a classical 2-approximation algorithm and the multifragment heuristic. We run both algorithms on randomly generated point sets and real world data from TSPLIB. …

Characterization Of The Thermally Driven Red Hue Effect In Coated And Uncoated Concrete Systems: Implications For Post-Fire Assessment In Wildland Urban Interface Communities, Juan C. Palominos Jr

Master's Theses

The increasing frequency and intensity of Wildland-Urban Interface (WUI) wildfires, such as the destructive 2025 Pacific Palisades Fire that destroyed over 6,000 structures, have underscored the urgent need for rapid post-fire safety assessment methodologies. This study sought to quantitatively characterize the Thermally Driven Red Hue Effect (TRHE), a unique, irreversible color shift observed in architectural coatings and concrete substrates, to determine its reliability as a permanent visual record of thermal exposure. Concrete substrates were formulated with three distinct coarse aggregates: gold granite (14.7% Fe), red cinder (22.4% Fe), and green rock (33.6% Fe) and coated with a 20 PVC waterborne …

Evaluating The Commercial Aquaculture Potential Of The Native Pacific Littleneck Clam (Leukoma Staminea) As A Climate Resilient Alternative To The Non-Native Manila Littleneck Clam (Ruditapes Philippinarum)., Skylar M. Wuelfing

Master's Theses

The California shellfish industry’s near-exclusive reliance on the non-native Manila littleneck clam (Ruditapes philippinarum) creates an economic vulnerability to climate-driven disturbances, which can cause mortality events that eradicate up to 80% of stocks. This singular reliance on an introduced species highlights a need for the evaluation of native alternatives like the Pacific littleneck clam (Leukoma staminea). Our goal was to assess the feasibility of using the native Pacific littleneck in commercial aquaculture by comparing their performance to the non-native Manila littlenecks across the following conditions: 1) a five-month field outplant trial at high and low intertidal …

Political Inconsistency Detection Across Legislative Speech And Public Communications, Scott M. Pramuk

Master's Theses

Political actors communicate about legislation across multiple contexts, including committee hearings, recorded votes, and public-facing press releases. Differences between these forms of communication can provide useful signals for journalists and researchers seeking to understand how legislators present policy positions to different audiences.

This thesis extends the Digital Democracy Project, a legislative transparency initiative that provides access to California state legislative hearing transcripts, voting records, and related legislative data. Specifically, this work incorporates publicly accessible, legislator-authored news releases into the Digital Democracy Database and develops a pipeline for analyzing legislative communication across multiple sources. The system collects news releases from California …

Structured Dynamic Factor Analysis Of Environmental Time Series With Application To Morro Bay Estuary, Jose Garcia

Master's Theses

This thesis develops a structured dynamic factor analysis (sDFA) framework for decomposing multivariate environmental time series into latent biological and physical components. The methodology is applied to five years of high-resolution passive monitoring data collected from two sites in Morro Bay, California from 2020 through 2024. Relative contribution indices are developed based on the structured DFA that measure how much each latent process contributes to each observed variable at any given time. Structured DFA models fit to the application data suggest site-specific patterns in how biological and physical processes affect water quality variables. At the bay mouth location, physical processes …

A2s Uplink Latency Minimization For Wildfire Monitoring Systems Using Mbse And Stochastic Modeling, Luis Giovanni Wang

Master's Theses

Wildfire response depends on how quickly a detection reaches the people who act on it, and the slowest remaining step is often the link that carries an alert from a remote sensing platform to a satellite. This thesis models the latency of that link, the Air-to-Space uplink, for a wildfire-monitoring UAV that carries a Starlink terminal and sends an ALERT packet to a serving Low Earth Orbit satellite. The uplink is difficult to predict because both the UAV and the satellite move, and because the wildfire environment degrades the channel at the moment the data matters most.

The thesis uses …

Multimodal Machine Learning For Soil Burn Severity Mapping Across California Wildfires, Sanjana Checker

Master's Theses

Accurate mapping of soil burn severity (SBS) is critical for post-fire watershed management, erosion risk assessment, and ecological recovery planning, yet traditional field-based approaches remain costly, time-intensive, and spatially limited. This thesis presents a machine learning pipeline for wall-to-wall SBS classification across California wildfires using multi-sensor satellite imagery, terrain derivatives, and bioclimatic covariates. Field-collected SBS observations (n = 2,180) from 52 wildfires occur- ring between 2013 and 2025, sourced from the U.S. Forest Service and CAL FIRE, were used to train and evaluate multiple classification architectures within a Google Earth Engine and Google Cloud-based prediction framework. After upsampling the unburned …

2026 June - Tennessee Climate Snapshot, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

No abstract provided.

2026 June - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

Hi All,

The main weather and climate stories for Tennessee in June 2026 were several rounds of heavy rainfall that produced flash flooding in different parts of the state. Early in the month slow moving storms produced heavy downpours in different parts of the state from June 7 to 14 including areas in or around Knoxville, Nashville, and Memphis, as well as portions of Henry, Weakley, and Carroll counties in northwestern Tennessee. Four different locations across the state reported daily rainfall records between these dates.

Later in the month, more widespread heavy rains impacted southern sections of West and Middle …

(Wp 2026-03) Fomc Forecasts, Constant-Gain Learning, And Optimism/Pessimism, Stephen J. Cole

Economics Working Papers

This paper uses an adaptive learning framework to study FOMC forecasts from the Summary of Economic Projections (SEP) dataset. FOMC expectations are modeled as the sum of two components: (1) an endogenous learning part and (2) a sentiment part capturing waves of optimism and/or pessimism. The results include key policy takeaways. FOMC forecasts are responsive to incoming macroeconomic information, consistent with adaptive learning, while sentiment is persistent, correlated across GDP growth and inflation forecasts, and becomes quantitatively more important during and around recessions. FOMC participants also rely more on their endogenous/learning model to form expectations, but sentiment plays a larger …

A Simulation To Compare Models For A Longitudinal Ordinal Response With A Multilevel Cross-Classified Data Structure, Rachel S. Perlin

Dissertations, Theses, and Capstone Projects

This is a simulation study that compares four models for analyzing three-level longitudinal cross-classified data with an ordinal outcome, treatment effect and a category-specific effect. The models include the cumulative model, the sequential model, the adjacent category model, and a metric model. Data were generated for each of the first three models with 27,000 generated datasets each, totaling 81,000. The datasets vary along three continuous variables: the number of level-3 units, the ratio of treatment groups to control groups, and the treatment effect size. The outcome has four ordinal response categories. Each model had two predictor variables: treatment and time. …

Technology Before, During, And After Incarceration: Current Product Landscape, Sociotechnical Concerns, And Legal Considerations In The U.S. Context, Yael Eiger, Taylor Hansen, Teanna Barrett, Jevan Hutson, Bryce Clayton Newell, Franziska Roesner

Articles

Emerging technology, including AI, is proliferating throughout the U.S. carceral system. These technologies are marketed to prisons and police departments and then procured using taxpayer money. Previous investigative reporting has exposed troubling kickback schemes, unconstitutional data collection practices, and biased algorithmic outcomes in a handful of prominent technologies (e.g., Flock, Palantir, Clearview AI, COMPAS). In this work, we consider the broader ecosystem of carceral technologies: we catalog 122 products from 53 companies selling technology to carceral institutions. In a collaboration among computer science, law, and surveillance studies scholars, we surface sociotechnical, ethical, and legal concerns related to the use and …

The Perspectives Of Parents On Barriers To Parental Involvement In Title I Schools, Beverly Pascal

Walden Dissertations and Doctoral Studies

No abstract provided.

Saag: Structured Agent Assessment And Grounding, Ritvik Garimella, Vedant Khandelwal, Anvi Kohli, Amit Sheth

Publications

Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing benchmarks collapse these distinctions into a single binary score, leaving practitioners unable to diagnose where agent calls fail. We propose SAAG a cascaded diagnostic framework that decomposes agent-calling evaluation into three sequential stages: registry conformance, structural completeness, and argument grounding, each producing interpretable stage-specific diagnostics. These diagnostics additionally enable iterative self-repair: on prediction failure, the stage-specific signal guides targeted correction without leaking ground-truth values. We evaluate this …

Update: Colorado River Basin Storage Continues Slide Toward System Crash, Anne Castle, Jack Schmidt, Eric Kuhn, Kathryn Sorensen, Katherine Tara

The Traveling Wilburys of the Colorado River

If the Colorado River Basin (Basin) experiences another dry year, similar to Water Year 2025, it is likely that reasonably accessible storage in Lake Powell and Lake Mead would be mostly depleted, even if consumptive uses and losses are at or near historic lows. Run-of-the-river operations would shortly ensue. This would be an outcome with devastating consequences. In contrast, if next year is very wet, similar to Water Year 2023, the Basin’s largest federal reservoirs would recover somewhat, but would provide only about two years of cushion before we find ourselves again in the same position we are in today, …