Digital Commons Network^™

Scalable Single-Erbium Telecom Qudits With Record Room-Temperature Quantum Coherence In Silicon-Based Nanostructures, Alexander Kaloyeros

Electronic Theses & Dissertations (2024 - present)

Advancing quantum information science demands solid-state quantum systems that maintain long quantum coherence at elevated temperatures while supporting scalable, CMOS-compatible fabrication and telecom C-band operation. No existing platform has simultaneously achieved these requirements, as state-of-the-art demonstrations of coherent control of erbium ions, with an intrinsic telecom-band optical transition, have been confined to cryogenic temperatures below < 10 K under controlled vacuum conditions. This thesis introduces a new paradigm in which materials science and engineering provides the enabling pathway to quantum coherence.

A foundry-compatible nanofabrication approach, paired with targeted materials engineering, is developed to realize a new class of CMOS-scalable quantum system: arrays of spatially isolated single-erbium-ion qudits (five-level systems) embedded in silicon-based (e.g., silicon carbide (SiC) and SiC_xO_y) hollow nanopillars (HNPs). Non-lithographically …

The Chemistry Of Red Lipstick: The Impact Red Lipstick Has On Society, Lilliana Weldeslassie

Electronic Theses & Dissertations (2024 - present)

Cosmetic chemistry is deeply embedded in everyday life, shaping the products people use from morning to night. Among these, red lipstick stands out as a formulation that blends complex chemistry with a cultural meaning. This thesis examines the scientific foundations of red lipstick through its chemical composition, pigment structure, toxicological history, analytical methods, and evolving sustainability practices. Historically, red lipsticks relied on high-risk ingredients such as lead, mercury, and other toxic metals, reflecting an era before chemical safety and regulatory oversight were incorporated.²¹ Modern formulations have established safer synthetic dyes, natural waxes, plant-based oils, and stabilizers created to optimize …

Developments And Applications Of Transport Of Intensity Equation Imaging And Optical Diffraction Tomography In Bioimaging, Shane D. Carney

Electronic Theses & Dissertations (2024 - present)

Non-invasive imaging schemes are highly sought after methodologies in biology and metrology, where maintaining the physical state of a sample is important. Quantitative phase imaging (QPI) techniques non-invasively reconstruct the relative phase delay imparted onto an optical field due to transmission through a sample. Phase delay is proportional to the thickness and refractive index of the sample, but is lost in the intensity measurement process. Digital holographic microscopy (DHM) and transport of intensity equation (TIE) phase imaging are two high-resolution QPI techniques. DHM is a well-researched, accurate, and widely implemented technique, but can be difficult to implement in labs without …

Corannulene: Optimized Synthesis And Four-Fold Reduction, Guy Rozenman

Electronic Theses & Dissertations (2024 - present)

Corannulene is a bowl-shaped π-conjugated fragment of the ball-shaped C₆₀-fullerene that undergoes stepwise multielectron reduction and can accept four electrons to form the aromatic tetraanion, C₂₀H₁₀⁴⁻. Prior literature has established this charge state in donor-solvated alkali-metal assemblies: early solution studies identified the formation of the tetraanion under lithium reduction in THF, and later X-ray diffraction crystallography demonstrated a triple-decker lithium sandwich in which two corannulene tetraanion decks encapsulate five Li⁺ ions. The central objective of this thesis is to determine whether the corannulene tetraanion can instead be isolated as a bulk solvent-free alkali-metal salt containing only corannulene and …

Nanostructured Cathode Catalysts For Aem Electrolysis: From Catalyst Design To Degradation And Hydrogen Dynamics, Yamini Kumaran

Electronic Theses & Dissertations (2024 - present)

Anion exchange membrane water electrolysis (AEMWE) presents a promising pathway toward cost-effective and sustainable hydrogen production by integrating the chemical robustness of alkaline systems with the compact, zero-gap design of proton exchange membrane electrolyzers. However, the widespread implementation of AEMWE is limited by the availability of highly active and durable platinum-group-metal (PGM)-free catalysts and by an incomplete understanding of their degradation behavior under realistic operating conditions.

This dissertation focuses on the development, characterization, and mechanistic investigation of nanostructured MoNi₄–MoO₂-based electrodes for efficient and stable hydrogen generation under alkaline and membrane-integrated environments. MoNi₄–MoO₂ nanorods …

Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling CO₂ channeling in heterogeneous reservoirs remains a major challenge for both enhanced oil recovery (EOR) and secure geological storage. AMPS-HPAM copolymers exhibit high-temperature resistance and brine tolerance compared with conventional HPAM gels, making them well suited for the harsh environments associated with CO₂ injection. Chromium-based crosslinkers (CrAc and CrCl₃) were investigated because sulfonic acid groups in AMPS can coordinate with trivalent chromium ions, enabling dual ionic crosslinking and the formation of a robust gel network. While organic crosslinked AMPS-HPAM gels have been widely studied, the behavior of chromium-crosslinked AMPS-containing systems, particularly their gelation kinetics under …

Harnessing Coherent-Wave Control For Sensing Applications In Scattering Media, Pablo Xavier Jara Palacios

Doctoral Dissertations

"Diffuse electromagnetic waves are widely used for non-invasive sensing and imaging in complex scattering media such as biological tissues. A fundamental limitation of such techniques is the scarcity of detected photons: as the source-detector separation increases to access deeper regions of the medium, the signal strength decays rapidly, leading to poor signal-to-noise ratio and limited sensitivity. This dissertation addresses this challenge through a combination of theory and computation.

We show that coherent control of the incident optical wavefront can compensate for the scarcity of detected photons that limits conventional diffuse optical imaging, typically performed in the near-infrared spectral region. By …

Stakeholder Co-Design Of Sustainable Urban Pest Management Strategies, Elizabeth C. Lowe, Nathan J. Butterworth, Alexander Austin, Cameron Webb, Tanya Latty

Research outputs 2022 to 2026

The high levels of broad-spectrum insecticides used to manage invertebrates in many cities around the world has significant environmental and health impacts. Integrated pest management (IPM) is a framework for sustainable pest control practice, but uptake of IPM remains low in most cities. We used participatory action research with pest management industry stakeholders (practitioners, industry representatives, researchers and government employees) to identify key issues in urban pest management in Australia, set priorities for change and discuss collaborative solutions. Via an online survey and a face-to-face workshop, the participants identified the key themes of education and training, public awareness, environmental impacts …

Aim5b: Ai Integrated Semantic Framework For 5g And Beyond Network Management, Thanveer Sulthana, Ava Sharif Jourabchi, Venkat Rao Manavarthi, Jayadithya Nalajala, Ankitha Srirama Reddy, Baek Young Choi, Sejun Song

Computer Science Faculty Research & Creative Works

Scalable, interpretable, and intelligent network monitoring and management are critical for 5 G and future networks. This paper introduces Aim5B, an AI-integrated semantic framework for 5 G and beyond network management to address these challenges. Aim5B processes unstructured logs from key 5G core network functions, and transforms them into a knowledge graph aligned with the semantic structure of control-plane events. Leveraging a large language model (LLM), Aim5B enables natural language queries to be translated into Cypher graph queries, facilitating precise log retrieval, event analysis, temporal correlation, and statistical summarization-without relying on static parsing rules or predefined dashboards. Integrated on a …

Self-Calibrating Uav Navigation: Reinforcement Learning Approaches For Horizontal Trajectory Estimation, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate unmanned aerial vehicle (UAV) trajectory estimation is essential for autonomous navigation, particularly in GPS-denied environments. Visualodometry and simultaneous localization and mapping (SLAM) approaches require precise camera intrinsic parameters, which are typically obtained through predefined or offline calibration. Instead, in this work, we propose a reinforcement learning (RL)-based self-calibration framework that estimates camera intrinsic parameters directly from monocular video sequences, without requiring prior knowledge of the camera, environment, or calibration targets. This intrinsic parameter estimation is then leveraged to achieve robust UAV trajectory estimation using only video data. We formulate the problem as a sequential decision-making task, where an RL …

Shock-Wave Structure In A Monatomic Gas Mixture With Rydberg Atoms, Anna Markhotok

Physics Faculty Publications

The effect of atom size on the shock-wave structure in a binary monatomic gas mixture with Rydberg atoms has been investigated. The problem was solved numerically using the system of hydrodynamic equations in argon gas for the atom-size ratios between 2 and 100, T = 1500 K, and the density between 10¹⁷ and 10²⁰ m−³. It was found that the presence of larger-sized atoms in the mixture results in shock front splitting that is on the order of the mean free path for this component. The results could be of interest in supersonic plasma dynamics and in astrophysics, studying shock …

Non-Gaussian Phenomena In Light Scattering And Applications, Shubham Atul Dawda

Graduate Studies Theses and Dissertations 2026

Physical reality is rarely deterministic; which, when probed by electromagnetic fields that also fluctuate, leads to observables that are most efficiently modelled as statistical processes. At equilibrium, this is usually achieved by invoking the Gaussian statistics of underlying physical processes, however, in practice, one often encounters out-of-equilibrium conditions where Gaussian descriptions may not suffice. Such situations are plentiful in nature– from biology to astronomy. This dissertation addresses several non-Gaussian phenomena associated with light scattering, and includes specific models’ derivations, experimental techniques developments, and demonstrations of potential applications. The systematic presentation will consider circumstances that infringe upon specific assumptions of the …

Large Language Model-Assisted Research Question Development In Public Health: A Case Study In The Special Supplemental Nutrition Program For Women, Infants, And Children (Wic), Qi Zhang, Bidusha Neupane, Priyanka Patel, Futun N. Alkhalifah, Yi He, Leslie Hodges

Health Behavior, Policy & Management Faculty Publications

Objective:

To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages.

Design:

We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the USDA summary of the final public comments on the WIC revision. Five questions weekly for three weeks were generated using LLMs under two conditions: fed with or without relevant literature. The experiment generated 90 questions, which were evaluated using the FINER criteria (Feasibility, Innovation, Novelty, Ethics, …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

The Adaptive Solution Of High-Frequency Helmholtz Equations Via Multi-Grade Deep Learning, Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

Mathematics & Statistics Faculty Publications

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical “pollution effect.” We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence …

Mapping Multiclass-Targeted Hate Speech In Online Discourse: An Open Dataset, Sanaa Kaddoura, Sumaia Al-Kohlani

All Works

Online social networks have become central spaces for public discourse, where hostile and discriminatory language toward social groups can cause psychological and social consequences for marginalized communities. Although multiple public hate speech datasets are available, many rely on binary categorization practices that obscure linguistic, cultural, and contextual variation across targeted groups. As a result, minority and less visible forms of hate speech remain insufficiently documented and analyzed. This discussion paper examines methodological limitations in existing hate speech annotation schemes and presents a re-annotation framework applied to the HatEval2019 dataset. The proposed framework introduces target-specific multiclass labels that distinguish subcategories of …

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

School of Cybersecurity Faculty Publications

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

Examining Inclusive Computing Education For Blind Students In India, Akshay Kolgar Nayak, Yash Prakash, Md Javedul Ferdous, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

The growing demand for computer professionals, driven by the expanding Information Technology industry, has led to numerous inclusive computing education efforts. These efforts have even included blind or visually-impaired (BVI) students, who are being increasingly encouraged to pursue education and a career in computing, despite the visually-oriented nature of the discipline. Extant literature has predominantly focused on identifying and addressing the accessibility barriers faced by BVI students to promote more inclusive learning environments. While few studies have also investigated the accessibility of computing education from the perspectives of BVI learners and instructors, these have been primarily situated in the Global …

The Privacy Paradox Of Llms: User Perceptions And The Reality Of Pii Leakage, Shuai Cheng, Haitao Xu, Shu Meng, Shuai Hao, Chuan Yue, Zhao Li

Computer Science Faculty Publications

Large language models (LLMs) are increasingly deployed, yet they introduce significant privacy risks by disclosing personally identifiable information (PII) during interactions. Although prior work has demonstrated the feasibility of extracting PII from LLMs, no comprehensive study has evaluated the actual extent of PII leakage across mainstream LLMs or investigated user perceptions, literacy, and behavioral responses to these risks. To address these gaps, we conduct a large-scale evaluation of PII leakage in popular LLMs, demonstrating that attackers can extract email addresses and phone numbers with high success rates. Through a mixed-methods study involving 20 interviews and 204 survey participants, we identify …

An Explainable Transformer Framework For Sentiment Analysis In Aviation Workforce Data, Sovon Chakraborty, Protiva Das, Fahmid Al Farid, Fuyad Hasan Bhoyan, Farig Yousuf Sadeque, Jia Uddin, Hezerul Abdul Karim

Computer Science Faculty Publications

Aviation is one of the predominant sectors that contribute significantly to the global economy. With the advent of technology, this industry is witnessing a paradigm shift towards data-driven approaches. The morale of the airline employees is barely noticed, which causes fatigue and depression. Furthermore, these mental health issues can be active reasons for destructive accidents. In this research, the authors are focused on collecting insightful information on aviation employees from Glassdoor.com. Moreover, the authors focus on analyzing the sentiments of the employees of renowned aviation companies. Primarily, the authors scraped necessary data from Glassdoor.com and created a dataset named JetJobJoy …

Open Scholarly Information Systems: Status Quo, Challenges, Opportunities, Hannah Bast, Guillaume Cabanac, Paolo Manghi, Jian Wu, Marcel R. Ackermann

Computer Science Faculty Publications

Over the past 30 years, a rich ecosystem of scholarly information systems has developed that openly provide their services to the scientific community. These systems include aggregators of bibliographic metadata (e.g., DBLP, OpenCitations, OpenAIRE Graph, OpenAlex, ORKG, Semantic Scholar, CiteSeerX, and CORE); publication, data, and software repositories (e.g., Arxiv.org, Figshare, Zenodo, Software Heritage, and Dataverse); and PID authorities (e.g., ORCID, ROR, Crossref, and DataCite). This interdisciplinary Dagstuhl Seminar "Open Scholarly Information Systems: Status Quo, Challenges, Opportunities" (25381) was the first of its kind to bring together practitioners from this ecosystem, as well as researchers investigating related questions or relying on …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Contextual Scaffolding And Self-Efficacy: Supporting Computer Skill Development Among Blind Learners In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Inclusive computer literacy education efforts, broadening the participation of blind or visually impaired (BVI) individuals, have gained traction in recent years. Existing literature investigating these efforts primarily draws evidence from affluent Global North contexts, where accessibility resources and legal frameworks are relatively more mature. Little is known about the in-situ teaching and learning challenges faced by trainers and BVI students, respectively, in resource-constrained, multicultural Global South countries like India. To address this knowledge gap, we conducted a four-month contextual inquiry at two computer training centers catering to 94 BVI students in India. We notably observed a rigid, experience-driven training environment …

Voxvista: Enhancing Screen Reading Experience For Online User Comments, Yash Prakash, Akshay Kolgar Nayak, Mohammed Shoaib Alyaan, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

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.

The Colorado River: Searching For Solutions In The Face Of Uncertainty: Conference Summary, Emilia Endara, Douglas Kenney

Books, Reports, and Studies

The Colorado River is facing unprecedented change and uncertainty. Average annual flows continue to decline, and negotiations to craft post-2026 guidelines to manage the river have repeatedly bogged down in disagreements about delivery obligations, sharing shortages, and mandatory conservation. While some clarity may emerge in the coming months, the ability to sustain many existing municipal and agricultural water users is already being compromised. Meanwhile, there is growing recognition that a durable resolution must also address the water rights of sovereign Tribal Nations and better integrate environmental values into river management. All sovereigns, stakeholders, and sectors must work together now more …