Digital Commons Network^™

When Saying "No" Is Not Enough: Cognitive-Action Decoupling And The Illusion Of Safety In Llm Agents, Shasha Yu, Fiona Carroll, Barry L. Bentley

School of Professional Studies

Current safety evaluations of large language models (LLMs) predominantly rely on textual compliance, implicitly assuming that refusal-style responses correspond to safe behavior. This assumption becomes fragile when LLMs are embedded in agentic systems with the ability to execute state-changing actions. In this paper, we present an empirical critique of text-centric safety evaluation through an action-aware study of LLM agents under controlled conditions. Across multiple state-of-the-art models, we observe a recurring cognitive–action decoupling: agents generate policy-aligned refusal language while still producing unsafe tool-mediated action proposals. This produces an illusion of safety, where conversational audits indicate compliance even as operational risk persists. …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

A Macrocognitive Design Taxonomy For Simulation-Based Training Systems: Bridging Cognitive Theory And Human-Computer Interaction, Jessica M. Johnson

Virginia Digital Maritime Center (VDMC) Faculty Publications

Simulation-based training systems are increasingly deployed to prepare learners for complex, safety-critical, and dynamic work environments. While advances in computing have enabled immersive and data-rich simulations, many systems remain optimized for procedural accuracy and surface-level task performance rather than the macrocognitive processes that underpin adaptive expertise. Macrocognition encompasses higher-order cognitive processes that are essential for performance transfer beyond controlled training conditions. When these processes are insufficiently supported, training systems risk fostering brittle strategies and negative training effects. This paper introduces a macrocognitive design taxonomy for simulation-based training systems derived from a large-scale meta-analysis examining the transfer of macrocognitive skills from …

Development And Validation Of An Analytical Method For Quantitative Determination Of Small Aldehydes In Plasma, Stephanie T. Clark

Chemistry & Biochemistry Theses - Archive

Small aldehydes such as formaldehyde and acetaldehyde are highly reactive metabolites that play important roles in biological processes related to oxidative stress, metabolism, and DNA damage. Because of their chemical reactivity, volatility, and low endogenous concentrations, accurate measurement of these compounds in biological samples remains analytically challenging. The goal was to develop and evaluate a practical analytical method for detecting small aldehydes in biologically relevant matrices using headspace gas chromatography mass spectrometry (HS-GC-MS). The method combined chemical derivatization with O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) and headspace sampling to stabilize aldehydes and enable selective detection by mass spectrometry. Method development focused on optimizing derivatization …

A Stable Isotope Comparison Of The Biogenic Carbonate Of Mammalian Tooth Enamel From The Modern Ohio River Valley And Fort Ancient Buffalo Sites, Sara Elizabeth Slack

Theses, Dissertations and Capstones

δ¹⁸O and δ¹³C values change in response to anthropogenic factors (Ghosh & Brand, 2003). Little is known about the effects of anthropogenic climate and land use change on the ecology of the Ohio River Valley. The goal of this research was to quantify the effects of European colonization on the native ecology of the region. The specific questions of this research were: (1) How have mammalian tooth enamel δ¹⁸O and δ¹³C compositions shifted over time within the Ohio River Valley in response to anthropogenic climate change and vegetation? (2) Are there differences …

Towards Multimodal Guideline-Aligned Agentic Systems, Wenliang Zhong

Computer Science and Engineering Dissertations - Archive

I present my work on building multimodal guideline-aligned agentic systems designed to enable AI agents to solve complex real-world tasks. My research addresses two critical perspectives: (1) Instruction-Aware Embedding Models for flexible and universal embedding tasks, and (2) Guideline-Driven LLM Agents that leverage domain-specific guidelines to perform expert-level tasks. These components address embedding and generation tasks, respectively, and lay the foundation for a hybrid agent capable of tackling challenging real-world applications.

From the embedding perspective, I first address the instruction-following capabilities of embedding models. While Large Language Models (LLMs) excel at instruction following, they are primarily designed for generation rather …

Accelerator Based Fusion Reactor With Magnetic Mirror, Keh-Fei Liu

Physics and Astronomy Faculty Publications

We consider an implementation of the novel accelerator-based fusion reactor scheme using a setup in which ion and electron beams are injected into the end openings of an axisymmetric magnetic mirror. While the magnetic mirror ensures transverse confinement of the plasma, the injected beams contribute to axial confinement, thereby extending the overall plasma lifetime. The ion beam energy is initially set above the resonance region of the fusion reactions, allowing fusion to occur both via resonance and thermal mechanisms as the beam slows down to the plasma temperature. Beam energy loss (stopping power) contributes to plasma heating, offsetting radiative losses …

Dynamical Phases Of Higher Dimensional Floquet Cfts, Diptarka Das, Sumit R. Das, Arnab Kundu, Krishnendu Sengupta

Physics and Astronomy Faculty Publications

This paper investigates the dynamical phases of Floquet Conformal Field Theories (CFTs) in space-time dimensions greater than two. Building upon our previous work which introduced quaternionic representations for studying Floquet dynamics in higher-dimensional CFTs, we now explore more general square pulse drive protocols that go beyond a single SU(1,1) subgroup. We demonstrate that, for multi-step drive protocols, the system exhibits distinct dynamical phases characterized by the nature of the eigenvalues of the quaternionic matrix representing time evolution in a single cycle, leading to different stroboscopic responses. Our analysis establishes a fundamental geometric interpretation where these dynamical phases directly correspond to …

Computational Clinical Judgment: Predicting Risk With Large Language Models, Hannah Laqueur, Ryan W. Copus

Faculty Works

For seventy years, research has shown actuarial methods outperform clinical judgment. Yet actuarial approaches have limitations: they generally rely on structured data; cannot exploit rare case-specific details; have limited accuracy where outcome data are scarce or incomplete; and cannot offer case-level justifications. Large language models (LLMs) offer a different approach. Like actuarial methods, they aggregate information algorithmically, but like clinicians, they bring general knowledge and can provide case-level justifications. We prompted seven LLMs to assess rearrest risk from 113 parole hearing transcripts and compared their predictions to a machine learning model trained on 4,000 cases with 91 administrative variables. GPT-5 …

Part Em: Classical Electrodynamics, Konstantin Likharev

Essential Graduate Physics

Includes: Electric Charge Interaction; Charges and Conductors; Polarization of Dielectrics; DC Currents; Magnetism; Time-Dependent Electromagnetism; Electromagnetic Wave Propagation; Radiation, Scattering, Interference, and Diffraction; Special Relativity; Radiation by Relativistic Charges

Frequency Stabilization Of An Affordable Adjustable Repetition Rate Dual Frequency Comb Spectrometer, Michael Moran

Physics, Astronomy and Geophysics Honors Papers

Two affordably home-built optical frequency combs are stabilized for use in molecular spectroscopy. Each comb is paired with a multi-tiered stabilization scheme that will enable the dual-comb spectrometer to perform precision measurements. The two degrees of freedom for an optical frequency comb, the repetition rate frequency and offset frequency, are accounted for in this stabilization scheme. The repetition rate is directly stabilized through control of the laser enclosure temperature and the fiber laser cavity length. The offset frequency is controlled indirectly via the stabilization of an optical beat note, the heterodyned signal resulting from a narrow linewidth laser and the …

Community Science For Enigmatic Ecosystems: Using Ebird To Assess Avian Biodiversity On Glaciers And Snowfields, William E. Brooks, Jordan Boersma, Neil Paprocki, Peter Wimberger, Scott Hotaling

Watershed Sciences Faculty Publications

Mountain glaciers and snowfields are rapidly receding because of climate warming. Species living in these habitats remain poorly studied, likely because of the remoteness and ruggedness of their terrain. We leveraged community science data from eBird—an online database of bird observations from around the world—to characterize bird use of mountain glaciers and snowfields. We estimated total bird biodiversity and preference for glaciers and snowfields over nearby, ice-adjacent habitats. We used field notes from eBird users and breeding codes to extend our data set to include insight into habitat usage and behavior. Finally, we compared our community-science approach to previous studies …

Do Algorithms Dream Of Electric Muses? Teaching Creative Writing In The Age Of Generative Ai, Anna Leahy

English Faculty Articles and Research

In Philip K. Dick’s novel Do Androids Dream of Electric Sheep? androids are given a psychological test to confirm they are not human before killing them. The story’s end suggests that humans will treat a seemingly harmless android as authentically as a human even when humans are aware the android is not human. Students use tools like ChatGPT, which function as autocomplete on steroids, to produce text using probabilistic relationships among words, and instructors can’t always tell the difference between average student writing and Gen AI text. In creative writing classes, instructors might use thinking for oneself as a central …

Multimodal Deep Learning For Biological Data Understanding, Saiyang Na

Computer Science and Engineering Dissertations

This dissertation presents three contributions to multimodal deep learning for biological data understanding, addressing the fundamental challenge of cross-modal alignment from two complementary perspectives: designing effective multimodal fusion methods for specific biomedical applications, and proposing a general framework for higher-order multimodal alignment that captures hierarchical structure in data.

First, we develop Cmai, a deep learning framework for B cell receptor (BCR) to antigen binding prediction that aligns BCR sequence information with antigen three-dimensional structures using contrastive learning. Cmai achieves an average AUROC of 0.907 across 17 antigens and 5 independent cohorts, and demonstrates clinical utility in predicting immune checkpoint inhibitor …

Evaluating Llms For Cpe Identification In Iot Reconnaissance, Christopher Davisson

EWU Masters Thesis Collection

Vulnerability identification during penetration testing relies on rigid string-matching to map network scan data to Common Platform Enumeration (CPE) identifiers and downstream Common Vulnerabilities and Exposures (CVEs). The approach frequently fails on physical Internet of Things (IoT) devices, which produce non-standard, irregular service banners that resist deterministic parsing. Large Language Models can reason through these fuzzy associations, but cloud-hosted models introduce cost, latency, and operational security concerns when processing reconnaissance data from live networks. This thesis asks whether locally-hosted open-weight Large Language Models (LLMs) can perform this task well enough to be useful, and how performance varies with model scale, …

Handwriting Recognition In Vr, Dominique Mosley

EWU Masters Thesis Collection

Virtual Reality (VR) is slowly becoming more popular for more than just entertainment. VR can be found in educational, office, and even healthcare settings to help discover more intuitive ways to teach, collaborate, and treat patients. Outside of the virtual world, these environments typically rely on writing for communicating or note-taking. Currently, VR input forces users to rely on clunky on-screen keyboards which disrupts the user’s immersion and breaks the flow of natural interaction. This thesis explores the potential of VR as a learning platform by combining it with artificial intelligence (AI). It aims to develop a VR-enhanced handwriting practicing …

Assessing Forest Health And Small Mammals After A Limited Suppression Wildfire In Colville National Forest, Samantha J. Kennel

EWU Masters Thesis Collection

In the face of a wildfire crisis, there is a growing need to expand the pace and scale of forest treatments, including the strategic use of wildfire as a management tool. However, ecological outcomes remain uncertain in forests shaped by more than a century of fire suppression. The 2022 Thor Fire, a managed wildfire in the Colville National Forest, Washington, provides an opportunity to evaluate forest health in a relatively understudied system. Using a control–impact design, I established 100 monitoring plots in burned and adjacent unburned forests to assess how wildfire and fire severity influence forest structure, fuels, and tree …

Legal Ethics Of Ai Snake Oil: Navigating The Hype, Harm, And Hope Of Legal Ai, Drew Simshaw

Scholarly Works

This paper argues that the legal ethics of AI extend far beyond competence and hallucinations. It shows how AI often functions as a mirror, exposing deeper ethical questions about institutional incentives, lawyer wellbeing, access to justice, and AI's broader social and environmental impacts.

Land Use And Land Cover Simulation Via Integrated Modelling With Gis Techniques For Sustainable Land Utilization Development In The Northeast Khong Subwatershed Of Thailand, ฺBanchongsak Faksomboon, Thipphaphone Keoviyavong

Applied Environmental Research

Land utilization is an important indicator of socioeconomic and environmental changes caused by both natural and man-made factors. Land use and land cover (LULC) simulation is a critical tool for monitoring and predicting LULC and is essential for sustainable development, land resource management and planning. The cellular automata (CA) Markov model is the basis for the current study’s prediction of LULC changes in the Northeast Khong Sub Watershed (NKSW). Landsat data from 2013 to 2023 were used to investigate LULC classification and determine the spatiotemporal distributions of LULC. In addition, LULC data from 2013 and 2023 were used to generate …

Climate And Environmental Controls On Forest Soil Organic Matter Cycling, Genevieve Goebel

Dartmouth College Ph.D Dissertations

Climate change factors act on the soil environment in complex ways, known to disturb soil organic carbon (C). Rising global temperatures and alterations to regional precipitation and nitrogen deposition regimes may contribute to the intensification of global change by accelerating the rate of biological decomposition and CO2 efflux to the atmosphere. The magnitude and endurance of the disturbance to soil organic C across landscapes and throughout time that climate change factors may pose to soil C cycling are not comprehensively understood. These knowledge gaps in our field make it challenging to anticipate how soil environments and the vast ecosystems that …

Cross-Temporal Statistical Approaches For Evaluating Predictor-Outcome Relationships, Jeff Joseph

Dartmouth College Ph.D Dissertations

Central auditory function is linked with cognitive deficits, but few research projects use existing statistical approaches or develop new ones to forecast cognitive deficits using the results of central auditory tests. To address this limitation, we use a series of statistical learning frameworks for predicting a child’s cognitive abilities based on his/her central auditory performances and demographic factors. Two key challenges exist. First, children may start the study at a time when they are unable to perform the central auditory tests or cognitive tests. Second, cognitive performance is age-dependent, particularly in the early formative years of childhood and adolescence. To …

Comparative Assessments Of Methane Flux Measurement Techniques In Serpentinite-Influenced Groundwater Wells, Madison J. Stetson

Open Access Master's Theses

Methane (CH₄₎ emissions from continental serpentinizing systems represent a significant component of Earth’s carbon cycling. Despite increasing recognition of serpentinite-hosted water-rock interactions as a source of trace gases, standardized field methodologies for quantifying trace gas flux from groundwater wells and similar subsurface conduits remain limited. This study addresses that gap by evaluating the reliability and replicability of three chamber-based sampling methods for measuring gas flux in serpentinite-hosted groundwater systems accessed by groundwater monitoring wells. Field measurements were conducted at the Coast Range Ophiolite Microbial Observatory (CROMO) within the McLaughlin Natural Reserve in Lower Lake, California, a site …

Assessing Forest Fragmentation And Edge Expansion In Nan Province, Thailand, Using Landsat Data (2014–2022), Kasidit Rison, Marut Fuangarworn, Chatchawan Chaisuekul

Applied Environmental Research

The increasing pressure from land-use change and agricultural expansion in Nan Province, northern Thailand, has accelerated forest fragmentation and reduced the ecosystem service value (ESV) of the landscape. This study aims to: assess land use and forest fragmentation change in Nan Province between 2014 and 2022, and evaluate the impact of land use change on ESV. Landsat 8 OLI satellite imagery from 2014 and 2022 was used to classify land cover into five types: forest, agriculture, settlements, exposed soil, and others. Forest fragmentation was evaluated using landscape metrics such as patch number, mean patch size, edge length, and core area. …

Environmental Sustainability Performance And Efficiency In Thailand’S Rajabhat Universities : A Cross-Regional Analysis Of The Impact Rankings, Konstantinos Balis

Applied Environmental Research

Higher education institutions (HEIs) are expected to increasingly communicate their contributions to the United Nations’ Sustainable Development Goals (SDGs), with the rise of alternative sustainability rankings serving as prominent reporting instruments for institutional transparency. In Thailand, Rajabhat Universities, a network of government HEIs historically associated with their mission of teacher training and local development, have been actively participating in the Times Higher Education (THE) Impact Rankings, presenting a unique case study to explore cross-regional sustainability efforts in relation to global benchmarks. To evaluate trends, regional disparities, and engagement performance across the sustainability pillars, this study uses a descriptive quantitative design …

Advancing Food Equity Through Explainable Ai (Xai): Identifying Place-Based Factors And Conditions Of Food Security, Leslie Hoglund, Hyoshin Park

Health Behavior, Policy & Management Faculty Publications

Food behaviors, food security, and their association with socioeconomic factors constitute a critical area of study with implications for public health, economic stability, and social equity. Understanding these relationships are essential for developing effective policies and interventions that promote sustainable, healthy food systems and greater food equity. This paper employs explainable artificial intelligence (XAI) methods to identify key features influencing household food behaviors. The insights gained from the XAI analysis are further utilized in conjunction with inverse reinforcement learning (IRL) to examine expert behaviors related to eating habits satisfaction. The XAI results reveal that household health conditions, spending patterns, and …

Machine Learning: Thematic Feature Grouping, And The Magnificent Seven: A Forecasting Analysis, Mirarmia Jalali, Mohammad Najand, Andrew Cohen

Finance Faculty Publications

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, …

Comparative Machine Learning Models For Disease Risk Prediction, Mercy Mawusi Agbley

Theses, Dissertations and Capstones

Accurate prediction of disease outcomes is crucial for improving clinical decision-making and enabling early intervention. This study compares the performance of various statistical and machine learning models for clinical risk prediction using two healthcare datasets: diabetic retinopathy and heart disease. The models assessed include Logistic Regression, LASSO, k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks, Random Forests, Gradient Boosting Machines (GBM), and a stacked ensemble model. Prior to modeling, datasets were split into train and test sets. Standardization was applied to numeric features whilst categorical features were one-hot encoded. These transformations were later applied to the test set. Principal …

Spatial Temporal Modeling Of Infectious Disease Patterns In Texas, Robert E. Lashbrook

Earth & Environmental Sciences Theses

The Texas Department of State Health Services monitors numerous notifiable conditions statewide, including Campylobacter, Salmonella, Shiga toxin-producing Escherichia coli (STEC), Rabies, and West Nile virus (WNV). Given the substantial health, economic, and public health burden associated with these conditions, improving prediction is an important step toward reducing their overall impact. This study evaluated whether external demographic, social, climate, and environmental data could improve prediction of county-year disease activity across Texas. County level data was analyzed using supervised machine learning models, including linear regression, ridge regression, multilayer perceptron, random forest, XGBoost, as well as K-means clustering to identify broader …

Investigating The Viability Of Fully Online Asynchronous Physics Instruction In Formal Education, Clifton W. Massey-Noel

Physics Dissertations

In this dissertation we present results from two consecutive investigations into the fully online asynchronous modality in Physics Education. We first discuss the efficacy of a fully online asynchronous section of Modern Physics held in Fall 2024. The asynchronous section is compared with four prior partially-flipped sections of the same class, two of which were held in-person, and two of which were held synchronously online. Results suggest that all three modalities can be made to be about equally as effective. We also discuss the efficacy of a fully online asynchronous section of Calculus-Based Introductory Mechanics held in Fall 2025. The …

A Hybrid Response Surface Methodology And Machine Learning Framework For Quantifying Effects Of Physicochemical Parameters On Pfas Distribution, Harsh V. Patel, Jazmin Green, Hyoshin Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Predicting PFAS adsorption across diverse adsorbents and environmental matrices remains challenging because adsorbent physicochemical properties, PFAS molecular descriptors, and operational conditions simultaneously influence adsorption. This study develops and evaluates a unified hybrid modeling framework that integrates Response Surface Model (RSM) with machine-learning algorithms to quantify how six key variables, surface area, Log K_ow, pH_pzc, pK_a, log dose, and log-initial concentration, affect PFAS distribution coefficients (Log K_d). A data set of more than 1000 adsorption observations spanning 15 PFAS compounds, multiple adsorbent types, and a broad operational range was compiled and preprocessed using …