Digital Commons Network^™

Ablative Study Of Large Language Model-Based Gesture Inference For Autonomous Navigation, Neil Loftus

Theses, Dissertations and Capstones

Human gesture inference has broad applications ranging from sign language interpretation to device control. Traditional methods often rely on extensive manually labeled hand datasets for deep learning. Furthermore, they are typically limited to a discrete set of gestures existing in these datasets. Large Language Models (LLMs) created by enterprise companies such as OpenAI have demonstrated positive results in many artificial intelligence tasks, with a notable strength being their adaptability. Existing literature has shown that LLM based systems can not only perform gesture inference but can propose user intent provided with a context and list of possible actions. We propose an …

A Conserved Ethylene-Triggered Cell Death Mechanism May Underlie Hollow Stem Formation Across Plant Species, Mengxiao Yan, Weijuan Fan, Yinghui Meng, Jiamin Zhao, Wei Yang, Ziyin Xu, Yusen Gao, Haiyan Zhuang, Wuyu Zhou, Yuqin Wang, Qingjun Huang, Ling Yuan, Hongxia Wang, Jun Yang

Plant and Soil Sciences Faculty Publications

Hollow stems have independently evolved multiple times across the plant kingdom and play crucial roles in plant development and various environmental adaptations. However, the mechanisms underlying stem hollowness remain poorly understood. Water spinach (Ipomoea aquatica) is one of the few hollow-stemmed plants in the Convolvulaceae family (eudicot: asterid), and its hollow stems are essential for thriving in aquatic environments. Using histochemical staining and transcriptome analysis, we found that programmed cell death (PCD) is involved in cavity formation at water spinach shoot tips. Single-cell and spatial transcriptome analyses further revealed that ethylene and reactive oxygen species (ROS) likely drive …

A Novel Lightweight Framework For Low-Light Image Enhancement Via Gaussian Denoising And Clahe, Daniel Oluwaseun Adesoji

Master's Theses or Doctor of Nursing Practice

Low-light image enhancement is a major challenge in digital imaging, especially in medical imaging, surveillance, and autonomous vision systems. Images captured under poor illumination often appear dark, noisy, and low in contrast, which makes it hard to observe important details. Traditional enhancement methods can improve brightness but usually introduce artifacts or increase noise. Although deep learning methods have shown strong performance, they usually require large datasets, and high computational resources. This creates a need for simpler and more efficient enhancement techniques. This study proposes a lightweight framework that incorporates Gaussian denoising with Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance …

Iowa Waste Reduction Center Newsletter, January 2026, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Jim's Final Farewell
--- MSEI for Central Iowa and for All Portable Plants
--- P2 Grant Ramps up to Enhance Brewery Pollution
--- Introducing Our Newest Interns
--- IStorm 26
--- Industry News

Bridging The Knowledge-Practice Gap In Breast Cancer Awareness: A Nationwide Online Cross-Sectional Kap Study Among Adult Females In Nigeria, Madinat Hassan, Taofeek Tope Adegboyega, Bilkisu Usman Farouk, Sunday Zeal Bala, Rachael Olufunmilayo Oduyemi, Kazeem Owolabi, Munir Sani, Miracle Uwa Livinus, Hannatu Usman Ayuba, Sadiq Hassan, Ifeyinwa Maureen Okeke, Abednego Shekari, Murtala Jibril

Chemistry & Biochemistry Faculty Publications

Background and Aims

Breast cancer (BCa) is a major cause of cancer morbidity and mortality among women in Nigeria, with late presentation contributing to poor outcomes. This study assessed knowledge, attitudes, and willingness to perform breast self-examination (BSE) among adult females in Nigeria and examined associated factors.

Methods

We conducted a descriptive cross-sectional online survey between February 16 and April 30, 2024. A structured, pretested questionnaire was disseminated through digital platforms, yielding 711 consenting adult female respondents from all six geopolitical zones. Knowledge, attitude, and willingness-to-perform-BSE measures were analyzed using descriptive statistics, linear regression, and binary logistic regression. Associations were …

Attention-Based Geo–Textual Fusion Network For Disaster Risk Prediction, Mohammad Shafat Ahsan, Mst Sanjida Alam, Syed Sajjad Ahmed, Md Tanzimul Islam, Hashibul Ahsan Shoaib, M.F. Mridha, Md. Jakir Hossen

Student Publications [Scholarly]

Natural disasters pose recurring threats to human life and infrastructure, demanding intelligent systems that can process heterogeneous data streams and provide actionable insights in real time. Existing approaches often treat textual signals from social media and emergency communications separately from spatial hazard attributes, limiting their effectiveness in capturing the full complexity of evolving crises. This paper proposes an AI-driven geo–textual intelligence framework that integrates disaster-related text with GIS-based hazard features for real-time risk prediction and evacuation planning. The framework employs contextual text encoders and a neural GIS encoder, fused through an attention mechanism that dynamically weights cross-modal signals. Experiments on …

When Climate Change Forces Flight: Legal Duties In The Age Of Retreat, Mark P. Nevitt

Faculty Articles

In the face of climate-driven disasters, government officials and individuals alike must decide whether to invest in climate-exposed areas or retreat. This Article analyzes emerging legal and policy issues associated with both climate retreat (when the government relocates people and infrastructure away from climate-exposed areas) and abandonment (when individuals leave climate-exposed areas, following natural disasters or otherwise). I argue that government-driven climate retreat must consider four overlapping legal duties, raising novel questions in an era of climate destabilization. First, does the government have an affirmative duty to rescue its citizens, and is this duty transformed by the government’s role in …

A Novel Simulation Framework To Estimate Dynamic Response Of A Solar Panel Array Under Stationary Stochastic Wind Loads, Hanshu Zhang, You Jia Li, Matthew J. Dejong

Physics and Engineering Science

This paper presents a novel simulation framework for estimating the dynamic response of a full-scale ground-mounted solar panel array under stationary wind loads that are spatially correlated across the length of the solar panel array. Specifically, the framework uses mean pressure coefficient distributions along the length of a 1:10 prototype scale of the solar panel array from smooth inflow derived using the computational fluid dynamics (CFD) simulation software The Wind Engineering with Uncertainty Quantification (WE-UQ). Note that for the CFD simulation, the input wind field across the length of the solar panel array is uniform along the horizontal direction, and …

Simulating Social Attitudes With Llms: Accuracy, Demographic Effects, And Refusal Behavior In The Sensitive Domain Of Suicide Prevention, Cristina J. Perez, Michael P. Vasquez Jr., Philippe J. Giabbanelli, Patrick Y. Wu

VMASC Publications

Large language models (LLMs) are increasingly used to simulate public opinion, yet their validity in sensitive policy domains remains underexplored. We evaluate whether LLMs can reproduce attitudes toward suicide prevention policies using 32 questions drawn from seven nationally representative U.S. surveys (2023-2025). We systematically vary demographic conditioning (race/ethnicity, gender, age, education, income, party), prompt framing (direct elicitation, respondent embodiment, specialist embodiment), and model architecture (GPT-5 Nano, DeepSeek V3.2, Meta Llama 3.1 8B, Mistral Small 24B). Across 811,560 prompts, the mean absolute error—the average gap between predicted and human response distributions—is 23 percentage points. We also find that LLM responses to …

Advancing Hate Speech Detection: Binary And Multiclass Approaches From Traditional Methods To Parameter-Efficient And Ontology-Guided Language Models, Mahmoud Abusaqer

Graduate Theses/Dissertations

The widespread proliferation of hate speech on social media platforms poses significant challenges for content moderation and user safety, requiring automated systems that are simultaneously accurate, efficient, and capable of fine-grained distinctions. This thesis investigates hate speech detection through five published manuscripts organized into two complementary threads: binary detection (hateful vs. non-hateful) and multiclass detection across demographic targeting categories. The binary thread progresses from a broad 38-model baseline spanning traditional machine learning, deep learning, and transformer architectures (where RoBERTa reaches 91.48% accuracy and CatBoost remains competitive at 88.60%) to parameter-efficient adaptation, in which Low-Rank Adaptation (LoRA) of large language models …

Foreword, Pedagogical Innovations In Computer Science Education, Helen Crompton

STEMPS Faculty Publications

[Introduction] Computer science education sits at a defining moment. Across schools and universities worldwide, computing is no longer a niche discipline reserved for a select few. It is a foundational literacy that shapes how learners understand the world, participate in society, and imagine their futures. At the same time, the rapid pace of technological change, particularly in artificial intelligence, data systems, and intelligent tools, has placed unprecedented pressure on educators to rethink not only what we teach, but why and how we teach it. This book arrives precisely when such reflection is most needed.

News Media Sentiment Toward Chinese Ai: A Comparative Analysis With Belt And Road Initiative Involvement And Public Opinion On China, Asya Vaisberg

Pomona Senior Theses

This study evaluates different countries' news media’s sentiment towards Chinese AI, between May 2023 and May 2024, by using Microsoft Azure NLP Sentiment Analysis. The results are then compared with the country’s public opinion on China and its involvement in the Belt and Road Initiative (BRI). For this study 12 countries have been selected which are USA, Australia, Pakistan, Peru, Russia, Romania, Italy, Greece, Portugal, Philippines, Brazil, and Egypt. For each one, GNews Application Programming Interface (API), which has access to more than 60,000 global news sources, was used to aggregate relevant news articles based on queried keywords. The collected …

Three Decades Of Classifying Threatened Species: Lessons Learned From And About The Iucn Red List Criteria For Quantifying Extinction Risk, H. Resit Akçakaya, E. J. Milner-Gulland, Mike Hoffmann, Helen M. Regan, Ilona Naujokaitis-Lewis, Andre E. Punt, Kevin J. Gaston, David A. Keith, Stuart H. M. Butchart, Simon N. Stuart, Nigel J. Collar, Moreno Di Marco, Monika Bohm, Alex J. Berryman, David P. Mallon, Axel Hochkirch, Nicholas K. Dulvy, Malin Rivers, Thomas M. Brooks, James R. S. Westrip, Jonathan Paul Rodriguez, Craig Hilton-Taylor, Janet Scott, Simon Tarr, Sophie H. Ledger, Christi Linardich

Biological Sciences Faculty Publications

The IUCN Red List of Threatened Species, the most widely used global system for assessing species' extinction risk, has become a foundational source of information for conservation management, policy and research. Since the adoption of quantitative extinction risk criteria more than three decades ago, the Red List has expanded substantially in scope and influence, informing decisions ranging from species conservation and protected area designation to international agreements, corporate risk assessments and global biodiversity indicators. Given its central role, maintaining scientific rigour, transparency and trust in the Red List system is essential. Feedback from users, emerging from evolving applications and scientific …

Characterization Of Phytoplankton-Excreted Metabolites Mediating Carbon Flux Through The Surface Ocean, Yuting Zhu, Hanna S. Anderson, Eli Salcedo, Samuel E. Miller, Krista Longnecker, Melissa C. Kido Soule, Sheean T. Haley, Gretchen J. Swarr, Rogier Braakman, Sonya T. Dyhrman, Elizabeth B. Kujawinski

Chemistry & Biochemistry Faculty Publications

The marine labile dissolved organic carbon (DOC) pool is a dynamic reservoir of thousands of molecules that cycles approximately one-quarter of Earth’s primary production within days to weeks. After excretion by phytoplankton and other microbes, metabolites are rapidly consumed, resulting in low standing concentrations (picomolar to low nanomolar). Despite the decades-long search for labile DOC sources and molecular identities, marine phytoplankton exometabolomes are not well characterized, largely due to difficulties in measuring small polar molecules in saline water. Here, we profiled the exometabolomes of six axenic phytoplankton species representing key functional groups including a diatom (Thalassiosira pseudonana CCMP1335), a …

Expanded Scorpionate And Siderophore-Inspired Ligands: From Foundational Designs To Modern Applications, Austin Winfield Medley, Trandon Allen Bender

Chemistry & Biochemistry Faculty Publications

Scorpionate ligands have been advanced significantly through systematic modifications of their apical atoms and heterocyclic arms, expanding their structural diversity and chemical reactivity. Recent biologically inspired variants now enable accurate modeling of complex bioinorganic motifs, such as the Fe₄S₄ clusters of nitrogenase, and support enzyme‐like reactivity under mild aqueous conditions. These developments have broadened the impact of scorpionate chemistry across bioinorganic modeling, homogeneous catalysis, and biorthogonal transformations. In particular, expanded tripodal scaffolds provide modular, tunable platforms for mimicking enzyme active sites and probing biological nitrogen fixation pathways. Beyond fundamental insight, these ligands present practical opportunities for sustainable catalysis by enabling …

The Hitran2024 Molecular Spectroscopic Database, I. E. Gordon, L. S. Rothman, R. J. Hargreaves, F. M. Gomez, T. Bertin, C. Hill, R. V. Kochanov, Y. Tan, P. Wcisło, V. Yu. Makhnev, P. F. Bernath, M. Birk, V. Boudon, A. Campargue, A. Coustenis, B. J. Drouin, R. R. Gamache, J. T. Hodges, D. Jacquemart, E. J. Mlawer, A. V. Nikitin, V. I. Perevalov, M. Rotger, S. Robert, J. Tennyson, G. C. Toon, H. Tran, V. G. Tyuterev, E. M. Adkins, A. Barbe, D. M. Bailey, K. Bielska, L. Bizzocchi, T. A. Blake, C. A. Bowesman, P. Cacciani, P. Čermák, A. G. Császár, L. Denis, S. C. Egbert, O. Egorov, A. Yu. Ermilov, A. J. Fleisher, H. Fleurbaey, A. Foltynowicz, T. Furtenbacher, M. Germann, E. R. Guest, J. J. Harrison, J. -M. Hartmann, A. Hjältén, S. -M. Hu, X. Huang, T. J. Johnson, H. Jóźwiak, S. Kassi, M. V. Khan, F. Kwabia-Tchana, T. J. Lee, D. Lisak, A. -W. Liu, O. M. Lyulin, N. A. Malarich, L. Manceron, A. A. Marinina, S. T. Massie, J. Mascio, E. S. Medvedev, V. V. Meshkov, G. Ch. Mellau, M. Melosso, S. N. Mikhailenko, D. Mondelain, H.S.P. Müller, M. O' Donnell, A. Owens, A. Perrin, O. L. Polyansky, P. L. Raston, Z. D. Reed, M. Rey, C. Richard, G. B. Rieker, C. Röske, S. W. Sharpe, E. Starikova, N. Stolarczyk, A. V. Stolyarov, K. Sung, F. Tamassia, J. Terragni, V. G. Ushakov, S. Vasilchenko, B. Vispoel, K. L. Vodopyanov, G. Wagner, S. Wójtewicz, S. N. Yurchenko, N. F. Zobov

Chemistry & Biochemistry Faculty Publications

The HITRAN database is a curated compilation of validated molecular spectroscopic parameters, established in the early 1970s. It is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of six major components. These components include the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimentally derived absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally …

Multidentate Surfactant-Dependent Synthesis Of Giant Iridium Superstructures, Ramjee Balasubramanian, Maeren E. Hill

Chemistry & Biochemistry Faculty Publications

Giant superstructures of iridium, comprised of smaller spherical and slightly larger anisotropic nanoparticles, were synthesized by reducing iridium chloride in the presence of resorcinarene, a class of tetrameric macrocyclic polyphenols, with sodium borohydride in ethanol in 30 min under mild conditions. These superstructures were characterized by a range of techniques including TEM, HRTEM, EDS and other spectroscopic methods. The impact of the macrocyclic surfactant, its features and reaction conditions in dictating the formation of giant iridium superstructures was evaluated. The packing density of nanoparticles in these giant iridium superstructures could be altered qualitatively by varying the duration of the reaction, …

References, Appendices & All Parts Merged, Konstantin Likharev

Essential Graduate Physics

Includes: Appendix MA: Selected Mathematical Formulas; Appendix UCA: Selected Physical Units and Constants; References; EGP merged file (all parts, appendices, and references)

From Shock To Routine: The Evolving Impact Of Shutdown-Related Sentiment On Stock Markets, Yiran Shao

Honors Theses

To address gaps in existing research, this paper selects two U.S. government shutdown periods, 2018-2019 and 2025, as research samples to explore the effect of policy uncertainty on sentiment. This paper primarily analyzes the following two research questions.

First, what is the correlation between government shutdown-related sentiment during the shutdown period and daily market fluctuations? Specifically, can the sentiment index constructed from shutdown-related news effectively predict the next-day stock return during the event period?

Second, does the market have a learning effect? That is, between 2018-2019 and 2025, has the relationship between shutdown-related emotions and market outcomes weakened, shortened the …

Learning Design To Advance Human-Ai Collaboration In K-12 Education, Wing Sha Chan, Jinhee Kim, Seongryeong Yu, Rita Kay Detrick

STEMPS Faculty Publications

This chapter explores key components for designing effective Human-AI Collaboration (HAC) in K–12 education, addressing the current lack of theoretical and conceptual frameworks for structuring and implementing HAC in teaching and learning. It examines four essential areas: curriculum design, student and teacher–AI interaction, learning environments, and the evolution of HAC over time. The chapter introduces the concept of HAC in K–12 contexts, highlighting how humans and AI can leverage each other's strengths through co-evolutionary processes that foster mutual learning and collaboration. It reviews current HAC practices in schools and discusses their contributions to both teaching and learning. Finally, it presents …

Tackling The Societal And Regulatory Challenges Of Emerging Technologies: A Case Study Of Deepfake, Jingyao Li

2026

Governing emerging technologies such as Artificial Intelligence (AI) poses enduring challenges for policymakers, industries, and societies. Early-stage governance is often hindered by limited understanding of technological implications, rapid innovation cycles, and resistance from powerful industry actors who favor minimal oversight. Yet, timely and effective governance is essential, as new technologies are most malleable in their formative stages. This dissertation examines how emerging technologies can be governed effectively by using deepfakes technology as a focal case. This dissertation comprises three interrelated studies.

The first paper reviews the literature on deepfakes and emerging technology governance, identifying the distinct characteristics of deepfake technology …

Inverse Problems For The Radiative Transport Equation In Local And Non-Convex Geometries, Faith E. Hensley

Theses and Dissertations--Mathematics

Inverse problems for the radiative transport equation (RTE) arise in a wide range of imaging applications, including optical tomography and problems motivated by non-line-of-sight imaging. Classical reconstruction methods rely heavily on ballistic, or unscattered, photons and typically require full boundary access, leading to severe instability and limited applicability in geometrically constrained settings. This dissertation investigates inverse radiative transport problems with restricted boundary data and develops reconstruction techniques based on scattered photons. The central focus of this work is the analysis and isolation of the single-collision term in the collision expansion of solutions to the RTE. By exploiting its distinct analytical …

Impacts Of Segmenting Principle On Learner Performance And Attitude In A 3d Environment: A Mixed-Method Multiple Case Study, Kristin Herman, Mohan Yang, Jim Shifflet, Noah Glaser

STEMPS Faculty Publications

This study presents a conceptual replication of Moreno’s (Appl Cogn Psychol 21:765–781. 10.1002/acp.1348, 2007) study on the benefits of adhering to the segmentation principle when utilizing multimedia learning objects. Furthermore, this study expands upon the original by taking place in a low-immersive virtual reality environment, allowing for further understanding on the extent to which multimedia principles are still relevant. Both a synchronous and an asynchronous case are presented. Results indicate benefits for both cases in far transfer of learning. Furthermore, synchronous learners indicated a significant reduction in cognitive load and increased overall attitudes towards learning due to segmented instruction.

Clique Annealing: Semi-Supervised Community Detection Under Crystallization Kinetics, Ling Cheng, Jiashu Pu, Ruicheng Liang, Qian Shao, Hezhe Qiao, Feida Zhu

Research Collection School Of Computing and Information Systems

Semi-supervised community detection seeks to find a specified community type when only few communities are labeled. Existing "select-then-refine" pipelines often start from mis-aligned cores and rely on Reinforcement-Learning or Generative Adversarial Network, increasing computational cost and limiting scalability. We address these issues with a unified energy framework under crystallization kinetics that jointly models energy, structure, and growth. Based on this perspective, we propose CLique ANNealing (CLANN), which first employs Nucleus Proposer to select candidate clique as community core under four physics-inspired criteria. A learning-free Transitive Annealer then iteratively merges neighboring cliques and repositions the nucleus, enabling spontaneous, scalable community growth. …

Understanding Phishing Susceptibility Through Expert Consensus Using Digital Marketing Parallels And A Machine Learning-Based Implementation, Mansoor Ahmad

All Graduate Theses, Dissertations, and Other Capstone Projects

Phishing remains one of the most effective attack vectors for gaining unauthorized access to organizational systems, yet defenders often lack systematic methods to assess their exposure before an attack. This study develops a framework that uses machine learning to encode the collective expertise of cybersecurity practitioners into a portable phishing susceptibility assessment tool, with the goal to help security teams proactively identify patterns, prioritize awareness training, and strengthen detection controls. The study surveyed 27 practitioners with extensive experience in social engineering, red teaming, penetration testing, and threat analysis to identify which factors most influence phishing susceptibility. Practitioners provided quantitative ratings …

Impacts Of Management Practices And Soil Properties On Free-Living Nitrogen Fixation In Eastern South Dakota, Laura Galvan Nuevo

Electronic Theses and Dissertations

Free-living nitrogen (N) fixation (FLNF) may represent a sustainable pathway to supplement crop N demand in agroecosystems, yet edaphic and management factors regulating this process under field conditions remain poorly understood in the Northern Great Plains. Using 15N2 direct method to quantify potential FLNF (pFLNF) and baseline FLNF (bFLNF), this thesis investigates how crop rotations (2-, 3-, and 4-year systems), tillage management (conventional vs. no-till), cover cropping, and soil properties affect the potential of free-living diazotrophs to supply N via FLNF in a long-term no-till corn-based system in Southeastern South Dakota. A two-year field study evaluated pFLNF across four growing …

Erosion Of Trust In Online Information, Tirth Desai

A with Honors Projects

Researching how AI spreads misinformation and impacts trust in information.

Fixed Dune Grassland Mapping, Ronica Reyes, Madison Mayenschein, Jared Cruz

Environmental Science & Management Senior Capstones

Our  study applies Geographic Information Systems (GIS) and field-based data collection to map and assess a degraded fixed dune grassland parcel in Humboldt County, CA. Our project supports restoration planning led by Friends of the Dunes by identifying site features, habitat composition, and disturbance patterns at the site. Using GPS surveys, aerial imagery, and ArcGIS Pro, we mapped infrastructure, cement footprints, debris piles, vegetation types, and habitat boundaries. We mapped infrastructure such as concrete foundations and debris, which contribute to ongoing ecological degradation and the presence of invasive species. In addition, our spatial analysis highlights variability in disturbance. From this …

Healing Our Life Source: Riparian Eco-Cultural Restoration In Blue Lake Rancheria At School Creek, A Tributary Of Baduwa't In Northern California, Rae Mcgrath, Lily Moore, Keneti Jesus Pinones, Colin Rogers, Aberdeen Spade

Environmental Science & Management Senior Capstones

School Creek, a tributary in the Baduwa’t (mad river) Watershed, is a riparian corridor that is key habitat for Coho Salmon. This project is under the management of the Blue Lake Rancheria, located on the ancestral home of the Wiyot people, and was conducted by cal poly humboldt students. The restoration of School Creek is an ongoing project aimed at repairing the health of the riparian corridor, improving the creek’s channel structure, and creating a diverse community of culturally significant plant species. Previous restoration efforts have established plantings to further develop the riparian habitat and are surveyed within this report. …

Predicting Product Returns In Online Retail, Mihaela Cristina Stefanache

ICT

Product returns are a significant challenge for online retailers, contributing to additional operational costs and affecting inventory management and profitability. This project develops and evaluates three machine learning models to predict product returns for a UK online retailer, building on the data understanding and preparation completed in the previous CA2 capstone project. Following the CRISP-DM framework, the study extends the analysis through enhanced exploratory data analysis, feature engineering, model development, hyperparameter optimisation, cross-validation, and model interpretability. Logistic Regression, a Tuned Random Forest, and XGBoost are evaluated, with the Tuned Random Forest achieving the strongest overall performance, obtaining an F1 score …