Digital Commons Network^™

The Energy And Environmental Footprint Of Ai, Michael P. Vandenbergh, Ethan I. Thorpe, Jonathan M. Gilligan

Michigan Journal of Environmental & Administrative Law

Artificial intelligence (AI) has the potential to create major economic and social benefits, but also to rapidly escalate electricity demand and its associated environmental impacts. Information availability has been a cornerstone of environmental law for half a century, and this Article argues that providing information to individual, corporate, and other users about the electricity demand and environmental impacts of AI can reduce those impacts without delaying development of the technology. Little is known about how different large language models (LLMs) compare on these metrics, though. To address whether users have access to the information necessary to address this shortcoming, the …

Machine Learning In Fundamental Physics: From Early Universe Signatures To Qcd Evolution, Brandon Stevenson

Physics Theses and Dissertations

In both cosmology and small-x quantum chromodynamics, the relevant theoretical predictions are well-defined but expensive to evaluate repeatedly over broad parameter spaces or large ensembles of simulated data. The central question of this thesis is when machine learning can accelerate those calculations without weakening the reliability of the final physics conclusions. The thesis develops and tests this idea in three settings. First, it studies the Derivative Approximation for Likelihoods (DALI) as a means of diagnosing when Fisher-matrix forecasts cease to be trustworthy in cosmological parameter inference. Second, it investigates graph-based spherical neural networks for estimating primordial non-Gaussianity directly from simulated …

Deep Learning Frameworks For Biological Data Integration And Generation, Alexa Beachum

Statistical Science Theses and Dissertations

Data integration represents a key area of research for analyzing the rapidly growing volume of high-dimensional biological data across sources, stages, and modalities. To model and understand these complex, often non-linear relationships, deep learning has become an increasingly powerful tool. Here, we present two novel deep learning frameworks that address distinct but complementary integration challenges. The first framework aligns single-cell omics data across temporal stages, and the second bridges imaging and omics modalities to generate patient-level molecular profiles.

In Chapter 1, we briefly summarize existing approaches---both statistical and deep learning-based---for single-cell omics data integration and discuss their limitations for handling …

Next-Generation Democratic Cyber Statecraft - Balancing The Signal: Shutdown Shocks And Democratic Digital Governance, Scott M. Di Panni

School of Public Policy Capstones

This paper develops Next-Generation Democratic Cyber Statecraft (NG-DCS), a unified strategic doctrine for democratic governments to contest the cognitive domain against authoritarian adversaries. Drawing on twenty-six years of cross-national panel data (1999–2024) spanning 213 countries, game-theoretic modeling, and qualitative case analysis, the paper establishes three interconnected empirical and theoretical foundations. First, cross-national OLS regression across 160+ countries demonstrates that regime type is the dominant structural determinant of internet freedom (R²=0.615, β=2.513, p< 0.001), explaining more than twice the variance attributable to per-capita wealth (R²=0.268). Democratic governance, not economic development, produces open digital environments. Second, a two-way fixed effects (TWFE) difference-in-differences study exploiting government-ordered internet shutdowns as discrete policy interventions finds that digital restrictions causally degrade V-Dem governance quality by 0.21–0.38 standard deviations (p< 0.001 across all specifications). Treatment effects are immediate (β=−0.302 at k=0) and persist through five post-treatment years (β=−0.246 at k=+5), indicating structural rather than transitory governance damage. Parallel trends validation (p=0.352) and Callaway–Sant’Anna heterogeneity-robust estimation (ATT=−0.230, SE=0.077) support causal identification. Instrumental variable triangulation (2SLS β=−0.949, p=0.005) confirms that simultaneity was attenuating, not inflating, the primary estimates. Third, formal game-theoretic analysis reveals that the current U.S.–adversary equilibrium is (Restrain, Escalate)—the risk-dominant but Pareto-inferior outcome of a Stag Hunt structure. China, Russia, North Korea, and Venezuela each occupy structurally distinct positions (Stackelberg commitment, asymmetric two-level, autarky, and reactive trigger, respectively), requiring differentiated doctrinal responses rather than a uniform strategic playbook. Generative AI and algorithmic governance are shown to accelerate cognitive vulnerability by collapsing influence operation costs and exploiting engagement-optimized platform architectures that systematically degrade deliberative capacity in democratic populations.

Watch Me Watch: Reaction Videos As A Social Form Of Online Video Engagement, Rodney Okyere, Carlos Augusto Bautista Isaza, Jaehoon Pyon, Ihudiya Finda Ogbonnaya-Ogburu, Shuo Niu, Sang Wong Lee

Computer Science

Reaction videos (RVs) are surging in popularity, emerging as a distinctive facet of participatory culture on modern social video-sharing platforms such as YouTube, TikTok, and Twitch. This study aims to explore not only the motivations and engagement patterns of viewers towards RVs, but also the underlying nature of the virality and community-building phenomena that this sub-genre of video content fosters. We conducted 16 semi-structured interviews with individuals who identified as regular viewers of reaction videos to gain a deeper understanding of how they discover reaction videos (RQ1), the values that drive their motivations for viewing RVs (RQ2), and the ways …

Efficacy Analysis In Clinical Trials: A Comprehensive Review Of Statistical And Machine Learning Approaches, Dhrubajyoti Ghosh, Samhita Pal

Faculty Articles

Efficacy testing is a cornerstone of clinical trials, ensuring that medical interventions achieve their intended therapeutic effects. Over the decades, a wide range of statistical methodologies have been developed to address the complexities of clinical trial data, including parametric, nonparametric, Bayesian, and machine learning approaches. Parametric methods, such as t-tests, ANOVA, and LMMs, have traditionally been the foundation of efficacy testing due to their efficiency under well-defined assumptions. Nonparametric techniques, including the Friedman test, Brunner-Munzel test, and modern extensions like nparLD, have emerged as robust alternatives, particularly for skewed, ordinal, or non-normal data. Bayesian methodologies have enabled the incorporation of …

Adverse Childhood Experiences Effect On Resting State Eeg, Emily R. Stripling

SPARK Symposium Presentations

In this experiment, we processed the resting state EEG data. The data was split into 4 groups: a. Eyes closed with Adverse Childhood Experiences (ACES), b. Eyes open with ACES, c. Eyes closed without ACEs, d. Eyes open without ACEs. The goal is to determine whether those with ACEs have an elevated Alpha band during the eyes closed recording compared to those without ACEs

Be Responsible In Your Answers! Monitoring Out-Of-Domain Behaviors In Domain-Specific Llms, Boquan Li, Chenzhe Lou, Zhe Ren, Peixin Zhang, Zirui Fu, Jun Sun, Yaowen Zheng

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have accelerated the rapid development of chatbot web applications in various domains, such as coding, biomedicine and psychology. Compared to general LLMs like ChatGPT, domain-specific LLMs require a greater sense of responsibility. For instance, if a programming LLM casually answers medical or psychological questions, it not only misleads the public but also poses legal risks. This highlights new demands for monitoring and preventing such irresponsible behaviors. Existing efforts attempt to monitor LLMs from multiple aspects, such as lying, jailbreaks, and toxic content, while overlooking out-of-domain behaviors. In this work, we propose an innovative LLM domain monitoring …

Wright State University's Celebration Of Student Research, Scholarship & Creative Activities From Wednesday, April 1, 2026, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

The student abstract booklet is a compilation of abstracts from students' oral and poster presentations at Wright State University's Celebration of Student Research, Scholarship & Creative Activities on April 1, 2026.

Architecture-Agnostic Test-Time Adaptation Via Backprop-Free Embedding Alignment, Xiao Ma, Young D. Kwon, Pan Zhou, Dong Ma

PhD Student’s Publications Collection

Test-Time Adaptation (TTA) adapts a deployed model during online inference to mitigate the impact of domain shift. While achieving strong accuracy, most existing methods rely on backpropagation, which is memory and computation intensive, making them unsuitable for resource-constrained devices. Recent attempts to reduce this overhead often suffer from high latency or are tied to specific architectures such as ViT-only or CNN-only. In this work, we revisit domain shift from an embedding perspective. Our analysis reveals that domain shift induces three distinct structural changes in the embedding space: translation (mean shift), scaling (variance shift), and rotation (covariance shift). Based on this …

Competitive Reaction Pathways In The Fully Green Synthesis And Surface Modification Of Silver Nanowires Under L-Ascorbic Acid-Limited Conditions, Finley Neal

Honors Theses

Silver nanowires (AgNWs) are critical building blocks for transparent conductive films (TCFs) in flexible electronics, yet their commercial viability is hindered by environmentally hazardous, energy-intensive synthesis methods and limited intrinsic chemical stability. This study investigated a fully green, room-temperature (25℃) batch synthesis of pristine AgNWs using tannic acid as a biodegradable dual reducing and capping agent under strict kinetic pH control (pH 1.25). To enhance oxidative resistance, a subsequent surface modification protocol was evaluated to deposit a protective palladium (Pd) shell using L-ascorbic acid (LAA) as a secondary reducing agent. The pristine green synthesis yielded one-dimensional Ag nanostructures with a …

Ethical Issues Of The Advancement Of Artificial Intelligence, Primo G. Moreno

Augustana Center for the Study of Ethics Essay Contest

Artificial intelligence has rapidly become integrated into our modern society, allowing for new streamlined methods to reign supreme. These methods raise concerns about control, privacy, and the shifting boundaries between human and machine decision-making. This paper examines how AI has developed, how it is currently being used, and the ethical dilemmas that arise as societies integrate it into systems such as healthcare, law enforcement, and businesses. Through the expansion of historical, technical, and philosophical perspectives, this paper argues that society must prioritize having set guidelines and systems for monitoring newly created AI systems. Without them, the implementation of AI models …

Enhancing Urban Garden Capacity By Linking Biodiversity And Ecosystem Function, Mary T. Nguyen, Carleen Rauh, Shelby Gresch, Charlotte Correiro, Heidi Elmendorf, Adriane Fugh-Berman, Gina Wimp, Alexandra L. Decandia

Virginia Journal of Science

Urban agriculture broadly describes food and plant cultivation in metropolitan areas. While biodiversity is thought to support ecosystem health and resilience, the relationship between above-ground and below-ground diversity has been understudied in urban green spaces. This study examines plant, animal, bacterial, and fungal diversity across three distinct urban habitats: a turf lawn, a forb garden, and a farming space. Using bacterial 16S rRNA and fungal ITS2 sequencing, soil nutrient analyses, and surveys of vegetation and wildlife, we connect diverse philosophies in urban agriculture to their role in advancing community diversity and ecosystem function. We found that different urban gardens support …

Searching For Temporal Variations In The Fine-Structure Constant Using Espresso Spectrograph Data, David Hoffman Iv

Senior Theses

The fine-structure constant α is a dimensionless constant, defined as e²/(4πε₀ℏc) ≈ 1/137. On the order of human timescales, its value is very much unchanging, however, in recent years there has been extensive debate over whether or not the value of α has remained constant over cosmological timescales. With the natural corollary to a changing α being that either one or multiple of its constituent physical constants have also changed in value, observing a variation in the fine-structure constant would have vast ramifications for our understanding of the nature of the early universe and how it has evolved. While past …

Semantic Entanglement In Vector-Based Retrieval: A Formal Framework And Context-Conditioned Disentanglement Pipeline For Agentic Rag Systems, Nick Loghmani

iSchool - All Scholarship

Retrieval-Augmented Generation (RAG) systems deployed in agentic environments depend on the geometric properties of vector representations to retrieve contextually appropriate evidence for autonomous reasoning. When source documents conflate multiple topics within contiguous text regions, standard vectorization pipelines produce embedding spaces in which semantically distinct content occupies overlapping geometric neighborhoods — a condition we term semantic entanglement. This paper formalizes semantic entanglement as a model-relative measure of cross-topic overlap, defines an Entanglement Index (EI) as a quantitative proxy, and argues that higher EI is associated with reduced attainable Top-K retrieval precision under cosine similarity retrieval. We introduce the Semantic Disentanglement …

Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Research Collection School Of Computing and Information Systems

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …

Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution, Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, analyzing TEE applications remains challenging due to the high cost and complexity of configuring complete TEE build and runtime environments, as well as the limited observability imposed by hardware isolation. This paper presents SymTEE, a novel large language model (LLM)-assisted symbolic execution framework for detecting missing input validation issues in TEE applications without requiring real TEE setups. SymTEE begins by leveraging Abstract Syntax Tree (AST) analysis to extract TEE code slices that may lack sufficient input validation, and then …

Storycomposerai: Supporting Human-Ai Story Co-Creation Through Decomposition And Linking, Shuo Niu, Dylan Clements, Marina Margalit Nemanov, Hyungsin Kim

Computer Science

GenAI's ability to produce text and images is increasingly incorporated into human-AI co-creation tasks such as storytelling and video editing. However, integrating GenAI into these tasks requires enabling users to retain control over editing individual story elements while ensuring that generated visuals remain coherent with the storyline and consistent across multiple AI-generated outputs. This work examines a paradigm of creative decomposition and linking, which allows creators to clearly communicate creative intent by prompting GenAI to tailor specific story elements, such as storylines, personas, locations, and scenes, while maintaining coherence among them. We implement and evaluate StoryComposerAI, a system that exemplifies …

Numerical Study Of Nutrient Mixing In Trabecular Bone In Microgravity, Disuse And Normogravity, Sagar Gharti

Doctoral Dissertations and Master's Theses

Mechanical loading is known to regulate bone remodeling by driving interstitial fluid flow which stimulates cells and drives nutrient transport within the trabecular network. In microgravity, the absence of mechanical stimulation or loading suppresses convective flow processes, causing diffusion-driven nutrient mixing and renewal, and accelerated bone loss. This thesis investigates how oscillation frequency and trabecular bone density jointly control nutrient mixing and wall shear stress within trabecular cavities.

A computational fluid dynamics (CFD) framework is developed in STAR-CCM+ using a soft-cap oscillation model that mimics cyclic compression. Three idealized trabecular morphologies are simulated across different frequencies to represent microgravity or …

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Doctoral Dissertations and Master's Theses

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Data Tracking And Analytics Within Inventory Management: Coffee Shop And Retail Store Optimization, Mateo J. Moyon

Senior Theses

This thesis examines the application of inventory management theory in the small and medium-sized business context, with a specific focus on the food and beverage industry. Drawing on the foundational academic literature spanning from Harris’s EOQ formula in 1913 through stochastic inventory theory, ABC analysis, and just-in-time strategy, this paper establishes the mathematical and operational bases for modern inventory management practice. Although there are proven value to these frameworks, research demonstrates that small to medium sized businesses adopt inventory management systems at lower rates citing cost and implementation as barriers. This thesis argues that the emergence of low-cost inventory and …

Conservation Communication In Web 2.0: A Mixed Methods Analysis Of Alveus Sanctuary's Strategies On Twitch.Tv, Hope Denison

Senior Theses

This thesis examines how environmental creators can effectively transmit conservation messages in the evolving Web 2.0 media environment. The case of Alveus Sanctuary, a virtual sanctuary and education center on Twitch.TV, is used to explore the strategies currently used to engage audiences and raise ecological awareness online. Thematic analysis of interviews with the sanctuary’s founder revealed that Alveus combines traditional environmental education with an influencer business model to engage online audiences, leveraging parasocial behavior, interactivity, and collaboration to garner support. A quantitative content analysis of social media posts revealed that these strategies effectively induce parasocial engagement in online spaces. These …

Fallen Light (Video Game), Joshua Do

Presentations - 2026

Problem:

•People misunderstand Lucifer’s deception

•(shown through Temptation of Jesus in Matthew 4) •Lack of engaging ways to teach theological concepts   •Background: Lucifer corrupts not through force, but subtle self-elevation and doubt   Motivation:   •Just initially wanted to make a game of how sin entered the world for fun   •Just thought it was a cool idea in general.

Solution:

•Dialogue-driven narrative game   •6+ story chapters   •10+ regions   •Focused on spiritual conflict, deception, and discernment   •Uses event-based progression   •Presents Lucifer through subtle manipulation, by being a false light by choosing self-pleasure over what is right.

Automation Of Discovery And Classification Of Compact X-Ray Binaries In Globular Clusters, Justin Brooks

Theses and Dissertations from DePaul University

The discovery and classification of Compact X-ray Binaries (CXRBs) within globular clusters improves our understanding of stellar encounters, the evolution of globular clusters, accretion physics, and the high-energy physics of the compact objects involved in these systems. For this reason, scientists compare X-ray observations from the Chandra X-ray Observa?tory with visible and UV observations from the Hubble Space Telescope to find objects of unusual colors and magnitudes which might be CXRBs. However, this process is time-consuming and requires significant amounts of data and expertise to achieve reliable classifications. This paper de?scribes this process, as well as a proposed automated process …

Thermal Vacuum Technical Report, Taiye Ibukun Fatima Ougundare, Ryan Bade

Senior Project

This system enables ground-based testing of components prior to space deployment. It replicates key Low-Earth Orbit (LEO) conditions, including both pressure and temperature. The chamber reduces pressure from atmospheric conditions (~760 Torr) to ~7.6 × 10⁻⁶ Torr. The thermal vacuum system achieves temperatures ranging from −60 °C to +125 °C for realistic environmental simulation.

Skyforge: Payload Isolation, Denzel Ezekiel, Brayden Gogis

Senior Project

As humanity looks to expand its reach into space, there is an increasing need for large space structures. The existence of these structures would enable large-scale infrastructure for further exploration and long-term habitation of humans beyond earth. However, creating such large structures in space is extremely challenging. Extreme environmental conditions (temperature, vacuum, microgravity) make construction and material handling difficult. Manual construction is dangerous and expensive; it is unrealistic for astronauts to complete this work. The “SkyForge” project, inspired by “Skyworker” (2003), attempts to make large space structure construction possible. SkyForge is envisioned as a fully autonomous construction robot designed for …

From Attention To Reasoning: Beyond Accuracy In Multimodal Ai, Wayner Barrios

Dartmouth College Ph.D Dissertations

Multimodal large language models have achieved impressive performance on vision-language benchmarks by integrating visual encoders with large language models. Yet a critical gap persists between benchmark accuracy and genuine multimodal understanding: current evaluation frameworks assess performance by final answers alone, rewarding confident predictions while leaving systematic reasoning failures undetected.

This thesis addresses this gap through a unified framework that progresses from understanding to reasoning, using video as the most comprehensive multimodal testbed. Video inherently combines vision, audio, and language with temporal dynamics and massive token redundancy; techniques developed for video's comprehensive challenges transfer naturally to simpler multimodal tasks.

On understanding …

Time-Correlated Photons From Spontaneous Parametric Down-Conversion (Spdc), Aaron Dubois , ‘26, Tanvir Islam , ‘26, Prashan Thapa Magar , ‘26

Senior Theses, Projects, and Awards

Time-correlated photons can be used to demonstrate emerging technical applications such as ghost imaging, and they can demonstrate quantum-based phenomena in order to interest undergraduate students in engineering applications involving optics and quantum mechanics. In this work, we use spontaneous parametric down-conversion to generate time-correlated photons. To achieve this, we simulated laboratory parameters with MATLAB and designed optical and electrical setups. Specifically, we used a 405 nm diode laser, various common optical components, and a nonlinear crystal designed to generate time-correlated photons, and we used single-photon detectors, time-to-amplitude converters, custom flash-conversion analog-to-digital converters, a Teensy 4.1 microcontroller, and the Arduino …