Digital Commons Network^™

Ab-Initio Investigation Of 2d Materials For Voc-Based Cancer Biomarker Detection And Hydrogen Storage Application, Ibrahim J.M. Alghoul

Thesis/ Dissertation Defenses

This dissertation is motivated by two driving forces:
(i) the need for biosensors for early cancer diagnosis, and
(ii) the need for hydrogen storage materials to support a future energy hydrogen-based economy.
Throughout this thesis, both challenges were addressed using computational methods based upon density functional theory (DFT), as implemented in the Vienna Ab-initio Simulation Package (VASP), ab-initio molecular dynamics (AIMD), and thermodynamic analysis.

On the side of cancer biomarker detection, exhaled breath containing volatile organic compounds (VOCs) have been shown to offer the analysis of a promising non-invasive diagnostic strategy. This thesis explored the functionalization of two distinct 2D …

Genai In Qualitative Data Analysis: Framework-Guided Prompt Engineering In Library Research Practice, Debby R. Wegener

FORCE 2026

As Generative AI (GenAI) tools become increasingly integrated across the research lifecycle, researchers need practical, reproducible methods for the responsible use of these technologies. This presentation will demonstrate a systematic approach to using GenAI for qualitative data analysis through a case study of thematic coding in a library website usability study at the Singapore Institute of Technology.

Drawing on prompt engineering frameworks like CLEAR, this session will illustrate how structured prompts can maintain academic rigour and enhance the reliability of GenAI-assisted analysis. The presentation will walk through the complete workflow, that is, from initial data preparation and tool selection to …

Rfc - Request For Information - Waste Rock Dump Stormwater Runoff Collection System Substitution, Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Biologically Informed Negative Samplingfor Antibody Chain Pairing Classification, Ishita Singh

Computer Science Senior Theses

Antibody heavy and light chain (H/L) pairing is fundamental to antigen recognition and stability. While single-cell sequencing preserves native pairing information, widely used bulk repertoire and spatial transcriptomics platforms do not, motivating the need for efficient ML methods to infer H/L pairing. Training a binary classifier for this task faces the methodological challenge of a lack of true biological negatives, since natural selection eliminates B cells with incompatible H/L pairs.

In this thesis, I introduce a biologically informed negative sampling strategy for H/L pairing classification, drawing on known V-gene biases in heavy and light chain pairing. Pseudo-negatives are constructed by …

Mimic: A Multimodal Dataset For Affective Incongruity In Video, Ethan M. Baird

Computer Science Senior Theses

This thesis investigates the computational challenges of constructing a database for affective incongruity, exploring the difficulties in automating the collection of contradictory affective states. Capturing these incongruities is essential for advancing Vision-Language Models (VLMs) and sentiment analysis, which struggle to interpret signals deviating from basic emotional archetypes. Curating non-congruent affect provides the data necessary for models to navigate complex social contexts, which is critical for applications like automated Audio Description (AD) for the visually impaired and nuanced Human-Computer Interaction (HCI). To capture these signals, two distinct methodologies were employed. The first utilized a tripartite decomposition of video data, isolating textual, …

Llzk-Symex: Building A Circuit Verification Framework For The Llzk Zero-Knowledge Intermediate Language, Nicolás Iair Schaievitch

Computer Science Senior Theses

The rising adoption of ZK (Zero-Knowledge) technology has led to a plethora of DSLs (Domain Specific Languages) for facilitating the development of ZK circuits. At the same time, there has also been an increase in the number of compilation targets for these languages, with different cryptographic foundations and performance optimizations. LLZK, by Veridise, aims to unify the ecosystem by providing a single IR (Intermediate Representation) for the different frontend DSLs, that would then also allow choosing between different backends. Given the high stakes (particularly financial) that a lot of ZK circuits are under, together with their high level of complexity, …

Capturing Large Language Model Similarity Through Spectral Analysis, Ishan Verma Prasad

Computer Science Senior Theses

With the rapid development of open-sourced models on Huggingface, there is a strong need for a way to systematically determine the similarity between models. More strongly, for intellectual property and organization, we need a way to determine the "lineage" of models. We borrow principles from Heavy-Tailed Self-Regularization and Random Matrix Theory to provide an inference-free method to accomplish this. We cluster a corpus of several model families by their spectral fingerprints and demonstrate that each model family occupies a distinct region in weight space. This confirms prior ideas of training setups leaving artifacts on model weights and allows us to …

Rscore - A Tool For Learning And Quantifying Software Infrastructure Resiliency, Selena Yujia Zhou

Computer Science Senior Theses

As LLMs take over writing code, infrastructure resilience has become the central challenge of software development, especially for gaming applications, where latency, availability, and scale demands are extreme. However, holistic software infrastructure is difficult to understand for casual developers because of the multiple layers of abstraction that make up an application, and the lack of structured information from proprietary companies. This thesis explores the creation of rscore, a tool that quantifies software infrastructure resiliency and subsequently educates developers about the architecture of any existing gaming application. The tool generates two graphical models of a game’s early versus current infrastructure and …

Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz

Faculty Publications

Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a …

Holistic Strategies For Optimizing Municipal It Operations And Cost Efficiency, Joseph Yazdanpanahi

Certified Public Manager® Applied Research

Municipalities work to deliver secure and impactful IT (information technology) services while often working within tight budget constraints. To address this problem, this article offers an experience-based and comprehensive framework to optimize municipal IT operations. It emphasizes leveraging automation, streamlining hardware and software management, and forging strategic partnerships to drive cost savings and efficiency gains. Designed to equip municipal IT leaders with practical tools and insights, this article aims to help modernize IT processes.

A Comprehensive Survey Of Artificial Intelligence Applications In Predicting Mining-Induced Subsidence, Deformation, And Landslides: Strengths, Limitations, And Future Trends, Long Quoc Nguyen, Dung Ba Nguyen, Minh Tuyet Dang

Journal of Sustainable Mining

Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms …

Decoding Stock Market Movements: The Role Of Unemployment And Volatility, Bipllab Roy, Suparna Bhattacharjee

Northeast Journal of Complex Systems (NEJCS)

This study investigates how unemployment and market volatility interact with stock prices in the Indian context, framing the stock–labour–volatility nexus as a complex adaptive system (CAS) rather than a set of linear, time-invariant relationships. Using verified secondary data on unemployment, India VIX, and NSE stock indices for 2013–2023, we first apply simple and multiple regression as a descriptive baseline. Results show a strong negative association between unemployment and stock prices (R ≈ 0.824, R² ≈ 0.68, p < 0.05), consistent with Keynesian demand-side channels, while the linear VIX–stock relationship is weak and statistically insignificant (R² ≈ 0.07, p > 0.05), consistent with the expectation that volatility operates through non-linear, regime-dependent mechanisms not captured by OLS.

Importantly, we document and transparently disclose critical …

Strang-Type Exponential Integrators For Stiff Reaction-Diffusion Systems, Saburi Tolulope Rasheed

Doctoral Dissertations

Reaction-diffusion systems, as examples of semilinear parabolic partial differential equations, have played significant roles in the mathematical modeling of physical, chemical, and biological processes. Several reaction-diffusion systems typically do not have exact solutions in closed form, and numerically solving them also comes with challenges due to the presence of the nonlinear local interaction/chemical reaction dynamics representing the reaction term, coupling between components, multidimensionality of the diffusion operator, and stiffness of the diffusion and/or reaction terms. Wederive and analyze several second-order accurate exponential integrators of the Strang type for the time discretization of stiff reaction-diffusion systems. We utilize the finite difference …

Building And Restoring Trust In Deep Learning: From Multimodal Sensing To Generative Synthesis And Model Integrity, Liqun Shan

Doctoral Dissertations

Deep learning has become a foundational technology for modern intelligent systems used in sensing, authentication, media generation, and automated decision-making. As these systems are increasingly deployed in security- and privacy-sensitive settings, ensuring their trustworthiness has become a critical challenge. Yet deep learning models remain vulnerable to spoofed sensory inputs, synthetic media, and malicious behaviors hidden within trained networks. These vulnerabilities undermine reliability and raise serious concerns about whether such systems can be trusted under adversarial and deceptive scenarios. This dissertation investigates how to build and restore trust in deep learning across three tightly connected dimensions: multimodal sensing, generative authenticity, and …

Learning And Predicting The Performance Of Gradual Type System, Mohammad Wahiduzzaman Khan

Doctoral Dissertations

Gradual typing reconciles the complementary strengths of static and dynamic typing by allowing programmers to incrementally introduce type annotations while preserving the flexibility of dynamically typed code. This approach improves reliability, documentation, and tooling support without sacrificing rapid prototyping. To ensure soundness, gradual type systems enforce annotations through runtime checks, typically implemented via cast insertion. Although these checks guarantee correctness, they can introduce substantial and highly variable runtime overhead, making performance prediction and optimization a central challenge for practical adoption. Prior work has largely focused on coarse-grained (macro-level) configurations, where entire modules are either fully typed or untyped. While such …

Investigating The Distribution And Origin Of Pitted Mounds On Mars Using Machine Learning-Based Mapping And Integrated Geological Analysis: Application To Isidis Planitia, Precious Batubo

Doctoral Dissertations

Pitted mounds are widespread landforms across the northern plains of Mars, yet their origin remains uncertain. These features have been interpreted as possible expressions of subsurface fluid activity, including sedimentary volcanism, magmatic processes, or other fluid-assisted mechanisms. Determining their distribution, morphology, and geology is therefore important for understanding the evolution of subsurface hydrological processes and the planet’s potential for habitability. However, the large spatial extent of mound-bearing terrains makes comprehensive manual mapping impractical. This dissertation presents an automated approach to mound detection using Faster Region-Based Convolutional Neural Network (Faster R-CNN) from high-resolution Context Camera (CTX) images including morphometric and mineralogical …

Emerging Catalysts And System Designs For High-Current-Density Co2 Electroreduction To Multicarbon Fuels And Chemicals Toward Industrial-Scale Applications, Godwin Uche Edor

Masters Theses

Electrochemical CO2 reduction (eCO2RR) offers a sustainable route to convert CO2 into value-added fuels and chemicals, particularly multicarbon (C2+) products due to their higher energy density and economic value compared to C1 counterparts. Industrial adoption of eCO2RR technology is hindered by the difficulty in stably reaching superior Faradaic efficiency (FE) for C2+ products at ≥ 200 mA cm-2 at low applied overpotential due to sluggish C–C coupling kinetics, competing reactions, and catalyst instability. Hence, we examine the recent advances in eCO2RR catalyst engineering strategies, such as nanostructural and electronic modifications of Cu-based catalysts (e.g., interfacial, surface, facet, and defect engineering, …

Classifying Storm Surges Along Louisiana Coast Using Machine Learning, Faith Ewere Okunbor

Masters Theses

Storm surges are among the most significant threats to coastal wetlands, causing flooding and saltwater intrusion that alter hydrology, vegetation structure, and long-term ecosystem resilience. Along the Louisiana coast, storm surge effects vary depending on storm behavior, shoreline geometry, and marsh hydrologic connectivity, yet are commonly characterized using broad storm metrics rather than site-specific environmental responses. This study develops a data-driven framework to classify storm surges across coastal Louisiana wetlands using observations from the Coastwide Reference Monitoring System (CRMS). Hourly water-level and salinity time series from twelve hurricanes (2007-2024) were analyzed using a 40-day storm-centered window. Hydrologic and salinity metrics …

Regioselective Tellurination Reactions And Their Utilities, Itunu Comfort Olanrewaju

Masters Theses

This thesis provides a comprehensive study of regioselective tellurination and its applications by integrating experimental and computational techniques, with an aim of synthesizing libraries of organotellurium heterocycles. This study addresses existing challenges in regioselective tellurination, such as unpredictable regioselectivity and avoidance of toxic reagents by harnessing intramolecular Te…O coordination and electrophilic aromatic tellurination. This strategy enables the development of previously poorly accessible compounds including benzotellurazinones, 2-acylaminobenzotellurazoles, and previously unknown 2,3-dihydrobenzo [b] oxatelluranes, which were prepared by antiMarkovnikov anti-addition of TeCl4 to C-C triple bonds. Experimental studies were complemented by computational DFT analyses to provide mechanistic and energetic insight into the …

Disparities In The Identification Of Vulnerable Communities Due To Design Storm Methods: A Case Study In The Vermilion River Watershed, South Louisiana, Usa, Claire Orgeron

Masters Theses

Accurately identifying socially vulnerable populations at-risk of flooding is critical for equitable disaster mitigation and planning. This study investigates how the selection of design storm method, which is fundamental to developing flood risk maps used for this identification, influences which socially vulnerable populations are recognized as being at elevated flood risk. The most widely used design storm approach relies on National Oceanic and Atmospheric Administration (NOAA) Atlas 14 point-based rainfall frequency estimates. These estimates are typically applied spatially using areal reduction factors, thereby disregarding rainfall spatial variability in real storms. Stochastic Storm Transposition (SST), an alternative design storm approach that …

Scientific Crosstalk: Natural Language Processing, Praveshika Bhandari

Theses and Dissertations

While sentiment analysis has made significant strides in domains such as social media and personal correspondence, its application to formal scientific writings remains under-explored. The crosstalk between emotional expressions in personal and professional communications has also received limited attention despite its potential to reveal insights into the emotional drivers of scientific creativity. Our research introduces a computational framework designed to detect and quantify emotional expressions across various documents over time. Leveraging state-of-the-art transformer models fine-tuned on domain-specific corpora, the framework models emotional tone distribution. Integrating emotion analysis with knowledge graph modeling enables the exploration of emotional trends alongside key scientific …

Olaf: Towards Robust Llm-Based Annotation Framework In Empirical Software Engineering, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the Operationalization for LLM-based Annotation Framework (OLAF), a conceptual framework that organizes key constructs: reliability, calibration, drift, consensus, aggregation, and transparency. The paper …

30 Db On-Chip Ultra-High Inverse Weak Value Amplification, Yuhan Mei, Meiting Song, Andrew N. Jordan, Jaime Cardenas

Mathematics, Physics, and Computer Science Faculty Articles and Research

Weak value amplification (WVA) has emerged as a powerful technique that enhances measurement precision. However, traditional table-top WVA setups and on-chip demonstrations have not achieved an amplification over 20 dB. The practical limit of the amplification is the fidelity of photon post-selection. To address this limit, we design a weak value device with an over 30 dB interferometer extinction ratio, where the stray light in the dark port is minimized with thermally tunable phase shifters. As a result, the device successfully shows 30 dB WVA, termed ultra-high WVA. This WVA optimization strategy by improving interferometer extinction is extensible to a …

A Large Language Model-Based Analysis Of Vulnerability Discovery In Windows Software, Puya Pakshad, Samson Quaye, Jamal Al-Karaki, Marwan Omar, Maurice E. Dawson

All Works

Source code security auditing is essential before software release in order to identify programming faults that may lead to vulnerabilities and functional failures. In this paper, we present a structured security assessment of the Windows App SDK by integrating multiple static analysis tools with a context-aware and disagreement-aware Large Language Model (LLM) interpretation layer. Although static analyzers are effective in reporting potential weaknesses, their raw outputs often contain redundant alerts, limited contextual explanation, and inconsistent severity assignments. To address these limitations, the proposed LLM-based interpretation layer normalizes and de-duplicates alerts, filters context-limited or nonactionable warnings, and refines severity prioritization under …

Deployment-Aware Deep Learning For Computer Vision: Efficient Architectures From 3d Segmentation To Mixed Reality, Bahar Uddin Mahmud

Dissertations

Deep learning has become the dominant approach for solving vision-centric problems; however, its successful deployment in real-world applications remains limited by high computational cost, data dependency, and insufficient integration with practical and human-centered environments. While state-of-the art deep learning models often achieve impressive performance in controlled settings, they frequently fail to generalize or operate efficiently under deployment constraints such as limited resources, complex data modalities, and real-time interaction requirements. These limitations motivate the need for a deployment-oriented deep learning framework that balances accuracy, efficiency, and practical usability.

This dissertation investigates the design and deployment of efficient deep learning architectures for …

Breadquest: Enhancing Roguelike Accessibility Through Procedural Generation And Thematic Design, Hahns Pena

Computer Science and Software Engineering

BreadQuest is a top-down roguelike dungeon crawler with a whimsical dessert theme that aims to make the genre more accessible while preserving strategic depth and replayability. Players explore procedurally generated dungeons, fight pastry-themed enemies, and collect bakery-inspired items that support a flavor-elemental combat system, with each run offering unique layouts, encounters, and rewards. Built in Unity with a modular, data-driven architecture, the game uses procedural generation techniques like Binary Space Partitioning, Voronoi diagrams, and Perlin noise to create varied and replayable levels. The project emphasizes approachable gameplay, cultural dessert inspiration, and replayability, with success evaluated through playtesting and player feedback.

Active Galaxies In A New Light: The Broad Line Region In The Near-Infrared, Sky O'Donnell

Physics

At the center of most massive galaxies, there is a supermassive black hole with a mass millions to billions of times that of the Sun. In some of these galaxies, so-called Active Galactic Nuclei (AGNs), the supermassive black hole is converting the gas that falls toward it into radiation energy, creating large amounts of luminosity. AGNs are composed of the central supermassive black hole, its accretion disk, and a region just outside, composed of fast-moving ionized gas clouds called the Broad Line Region (BLR). Using spectra obtained with NASA’s Infrared Telescope Facility, we use a technique known as reverberation mapping …

Real-Time Simulation Of Bio-Luminescent Light Propagation Using Compute Shaders Within Unreal Engine, Jaden D. Halevi

Computer Science Senior Theses

Presented in this paper is a GPU-native approach to interactive fluid simulation within Unreal Engine 5. The system, BioFluidSim, implements an incompressible Navier-Stokes solver using Unreal’s Niagara Grid2D compute shader pipeline, with a modular biological emission output stage parameterized from experimentally measured Lingulodinium polyedrum bioluminescence behavior. The system is evaluated against FluidNinja Live, a commercially available fragment shader fluid implementation, as a performance baseline. Beyond performance, BioFluidSim offers greater physical fidelity than the fragment shader baseline. Helmholtz–Hodge pressure projection enforces a divergence-free velocity field at runtime, a physical constraint approximated but not enforced by fragment shader approaches. The biological emission …

A Systematic Review Of Intrusion Detection Systems For Internet Of Medical Things: Performance, Efficiency, Explainability, And Generalization, Oswald Adohinzin, Youssef Harrath

Research & Publications

The Internet of Medical Things (IoMT) has transformed health care delivery through medical devices, remote patient monitoring, and real-time clinical decision support. However, the proliferation of IoMT devices introduces security vulnerabilities that put patient safety and data privacy at risk. Intrusion Detection Systems (IDS) have emerged as essential components for protecting IoMT networks from cyberattacks. This article presents a systematic review of IoMT-IDS research, analyzing 53 high-quality papers published between 2020 and 2025, identified through database searches spanning 2016–2025 across IEEE Xplore, Springer, ScienceDirect, and ACM Digital Library. We organize the literature through a comprehensive taxonomy spanning classical machine learning …

50,000 Spectra And Counting: Preparing For A Roman Spectral Atlas, Maya Jordan Seagraves

Master's Theses

Slitless spectroscopy with the Hubble Space Telescope's Wide Field Camera 3 \linebreak (WFC3/IR) enables simultaneous spectral coverage of all sources in a field, but large-scale single-orientation datasets remain challenging to process. We developed a pipeline that automates HSTaXe spectral extraction across hundreds of Hubble Space Telescope observations, enabling efficient processing of 84 fields and producing over 50,000 spectra from three filters (F110W, F125W, and F160W) and two grisms (G102 and G141). This was achieved by creating a companion Python package, the Module for Infrared Label Analysis (MILA), that integrates data calibration, extraction, and visualization. To assess the scientific utility of …