Physical Sciences and Mathematics Commons^™

Discriminative And Generative Video Modeling, Anh Pha Nguyen

Graduate Theses and Dissertations

Video modeling stands at the core of modern computer vision, enabling progress in domains such as surveillance, autonomous driving, and instructional assistance. Yet the complexity of spatiotemporal dynamics, multimodal integration, and the need for scalable and generalizable models present significant challenges. This dissertation addresses these issues from three complementary perspectives: discriminative modeling, multimodal (vision + language) alignment, and generative approaches, contributing new methods, datasets, and paradigms for advancing video understanding. In the discriminative setting, we propose a domain-adaptive framework for crowd counting that employs entropy minimization and adversarial learning to improve cross-domain generalization, and introduce a single-stage global association method …

Increasing Soil Health In Zero-Grade, Flood-Irrigated Rice In Eastern Arkansas, Hannah Elizabeth Vickmark

Graduate Theses and Dissertations

In an effort to build and maintain soil health and sequester soil C, many producers and researchers are turning towards regenerative management practices in agricultural systems, such as reduced tillage or no-tillage (NT), the implementation of winter cover crops (CC), and the use of organic or green manures, such as biochar. However, little research is being conducted on the long-term soil health and agronomic benefits of these practices in a flood-irrigated rice (Oryza sativa L.) production system. Three replications of seven treatment combinations were applied to 6.1 x 30.5 m rice plots. Baseline soil health properties were quantified at six …

Predicting Stock Price Movement With Llm-Enhanced Tweet Emotion Analysis, An Vuong

Graduate Theses and Dissertations

Accurately predicting short-term stock price movement remains a challenging task due to the market’s inherent volatility and sensitivity to investor sentiment. In this thesis, we present a published paper that discusses a deep learning framework integrating emo- tion features extracted from tweet data with historical stock price information to forecast significant price changes on the following day. We utilize Meta’s LLaMA 3.1-8B-Instruct model to preprocess tweet data, thereby enhancing the quality of emotion features derived from three emotion analysis approaches: a transformer-based DistilRoBERTa classifier from the Hugging Face library and two lexicon-based methods using National Research Council Canada (NRC) resources. …

Privacy Protection In Cloud-Based Biometric Systems, Yatish Reddy Dubasi

Graduate Theses and Dissertations

The widespread adoption of server-based biometric authentication systems, often hosted in the cloud, has introduced significant privacy risks. While these systems offer convenience, they require storing sensitive biometric templates on remote servers, creating a high-value target for adversaries. Unlike passwords, compromised biometric data is immutable and cannot be reissued, leading to an irreversible loss of privacy. This threat is exacerbated by template inversion attacks, which can reconstruct a user's original biometric trait (e.g., a face image) from its stored feature vector. This dissertation addresses these critical privacy challenges by designing, implementing, and evaluating a suite of novel frameworks for privacy-preserving …

Auction Consensus Algorithm With Loss Mechanism For Decentralized Task Allocation, Jose Rodriguez, Wenjie Dong, Constantine Tarawneh, Qi Lu

Electrical and Computer Engineering Faculty Publications

This paper presents an Auction-Consensus Algorithm with a Loss Mechanism (ACALM), a decentralized task allocation method for multi-robot systems that enhances the existing Consensus-Based Auction Algorithm (CBAA) by incorporating a novel loss propagation mechanism. In contrast to purely greedy bidding strategies, it enables agents to dynamically update task priorities based on the accumulated loss from previously unsuccessful bids. This extended work reduces globally inefficient allocations caused by early suboptimal decisions. The proposed approach is evaluated through large-scale simulations in thousands of randomized scenarios and swarm sizes ranging from 5 to 120 robots. Compared to existing CBAA and GCAA algorithms, ACALM …

Estimation Of 3d Facial Dynamics With Nonlinear Filters For Position Tracking, Thoa Thieu, Roderick Melnik

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comparative evaluation of three nonlinear state estimation filters, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF), for the task of 3D facial landmark tracking. Using a publicly available dataset, we assess each filter's performance under both deterministic (noise-free) and stochastic (noisy) conditions. Metrics such as mean squared error (MSE), convergence rates of state and covariance estimates, and consistency over time are used to quantify tracking performance. Results show that the EKF consistently outperforms the UKF and PF, achieving faster convergence and lower estimation error, particularly in scenarios characterized by mild nonlinearity. …

Iowa Waste Reduction Center Newsletter, December 2025, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Season of Joy
--- A Thirty-Seven Year Career Deserves a Celebration
--- Energy Efficiency Assistance Program for Iowa Small Businesses
--- Reporting Deadline: Grain Facility PM10 PTE
--- Reporting Deadline: 6X Certification and Compliance Report
--- Our Team is Growing! Meet our new Environmental Interns
--- IWRC hosts the IACC Strategic Planning Session
--- Industry News

Statistical Overview Of Long-Lived Active Regions Observed Across Multiple Carrington Rotations, Emily I. Mason, Kara L. Kniezewski

Student Publications

The study of solar active regions (ARs) is of central importance to a range of fundamental science, as well as the practical applications of space weather. Active region emergence and life cycles are two areas of particular interest, yet the lack of consistent full-Sun observations has made long-term studies of active regions difficult. Here, we present results from a study to identify and characterize long-lived active regions (LLARs), defined as those which were observed during at least two consecutive Carrington rotations and which did not undergo significant successive flux emergence once the decay phase began. Such active regions accounted for …

Precision-Weighted Federated Learning, Jonatan Reyes, Lisa Di Jorio, Cecile Low-Kam, Marta Kersten-Oertel

Computer Science Faculty Publications

Federated learning (FL) using the federated averaging (FedAvg) algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that FedAvg underestimates the full extent of heterogeneity of data when the aggregation is performed. We propose Precision-Weighted Federated Learning (PW) a novel algorithm that takes into account the second raw moment (uncentered variance) of the stochastic gradient when computing the weighted average of the parameters of independent models trained in a FL setting. With PW, we address the communication and statistical challenges …

From Inside The Recording Studio, Mckayla J. Adkison

Capstone Projects and Master's Theses

This project documents the historical, technical, and creative significance of recording studios through both industry examples and the design and construction of a personal backyard studio. By examining iconic studios such as Abbey Road and Capitol Records, the paper highlights how technological innovation, acoustic engineering, and architectural design have shaped the evolution of recorded music. These concepts are then applied to the planning and construction of a custom-built recording studio, emphasizing room dimensions, sound isolation techniques, acoustic treatment, lighting, and sustainable design choices. In addition, the project addresses the importance of aesthetics and emotional comfort in creating a space that …

Exploiting The In-Distribution Embedding Space With Deep Learning And Gaussian Discriminant Analysis For An Out-Of-Distribution Malware Attach Detection, Tosin Olusola Ige

Open Access Theses & Dissertations

State-of-the-art machine and deep learning models generally perform well on previously seen data, albeit with wrong close world assumption that all real-world data are from previously seen train and validation samples, hence there poor performance when exposed to data which deviates from previously seen training and validation set. This is clearly evident in the domain of cybersecurity where the world continues to experience several high profile malware attacks despite advancement in state-of-the-art research. The reason being that the constant evolvement of innovation in the development of tools and method deployed to carry out various attacks had given hackers and other …

Hubert-Based Models And Evaluation Strategies For Pragmatically-Faithful Speech To Speech Translation, Javier Vazquez

Open Access Theses & Dissertations

Pragmatic fidelity in speech-to-speech translation (S2ST) has largely been understudied, leading to communication tools inadequate to support non-superficial dialog. We aim to improve pragmatic faithfulness in English-Spanish translation through the development of machine learning models that are able to predict a corresponding pragmatic representation in the other language. To evaluate performance, we developed a pipeline that utilizes a recently-developed pragmatic similarity evaluation metric to compare models. Further, we developed models that exploit HuBERT features as these have been found suitable for various prosody and pragmatics related tasks. Our models outperformed human and state-of-the-art predictions, albeit the methodology being limited to …

Dolphins ‘Orient-Against-Current’: Foraging In Dredged Channels, Eliza M.M. Mills, Sarah Piwetz, Dara N. Orbach

Marine Science

Bottlenose dolphins (Tursiops sp.) are opportunistic foragers with global distributions that utilize diverse feeding tactics based on environmental factors, habitat features, prey behavior, group dynamics, and genetics. We describe a unique foraging tactic regularly observed in the confluence of dredged shipping channels with high anthropogenic disturbance, and explore potential abiotic (temporal, tidal, habitat) drivers of the behavior. A shore-based digital theodolite was used from 2021 to 2022 to observe common bottlenose dolphins (T. truncatus) foraging within a current in a technique we term Orient-Against-Current (OAC). During OAC, dolphins position themselves facing into the flow of a current, swimming at a …

Fractional Order Hierarchical Decompositions Using Multigrid Components, Panayot S. Vassilevski

Mathematics and Statistics Faculty Publications and Presentations

Motivated by the fractional order multilevel decompositions of finite element spaces developed previously, we exploit additive representations of popular multigrid (MG) cycles to design fractional order MG decompositions. The additive representations enable us to scale the individual hierarchical components thus ending up with fractional order hierarchical decompositions that are based on the readily available MG components. This results in a highly efficient and scalable (in terms of high-performance) fractional order hierarchical MG decompositions that we tested in the setting of finite element white noise sampling as an alternative to PDE-based white noise sampling using fractional order shifted Laplacians.

Visionglow: Evaluating Minimal-Disruption Smart-Home Control In Apple Vision Pro, Hongxiao Zheng

Dartmouth College Master’s Theses

Smart-home control in mixed-reality environments like Apple Vision Pro often relies on disruptive, application-based paradigms, such as using a smartphone or a windowed virtual interface. These methods create a “mode switch” that imposes cognitive load and pulls users from their primary tasks. We present VisionGlow, a minimal-disruption spatial interaction technique for Vision Pro. VisionGlow represents devices as spatially-anchored “orbs.” To control a device, the user looks at its orb and performs a pinch gesture, which invokes a compact, contextual control panel. We conducted a within-subjects study (N=18) comparing VisionGlow against two baselines: the standard Apple Home app on a smartphone …

Catalytic Synthesis Of Iron-Doped Graphitic Aerogels From Poly(Phloroglucinol-Terephthalaldehyde – Urethane) Precursors, Stephen Yaw Owusu, Rushi U. Soni, Chariklia Sotiriou-Leventis

Chemistry Faculty Research & Creative Works

We report a new class of graphitic carbon aerogel precursors based on iron oxide-doped poly(phloroglucinol-terephthalaldehyde–urethane) (T-POL/PU-FeOx) networks. The hybrid polymeric network incorporates a rigid aromatic triisocyanate, tris(4-isocyanatophenyl)methane, which reacts in situ with the hydroxyl groups of phloroglucinol to form a polyurethane-containing framework. Monolithic aerogels derived from this system undergo catalytic graphitization at significantly reduced temperatures (800–1500 °C) compared to conventional graphitization (2500–3300 °C). An oxidative ring fusion aromatization step (240 °C, air) prior to pyrolysis enhanced the degree of graphitization. The resulting graphitic aerogels were characterized by XRD, Raman spectroscopy, TGA, TEM, SEM, XPS, and N₂ sorption porosimetry. Compared to …

Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Gaber, Mohamed Abdelraheem

Faculty Publications

As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …

Dancing With Logic: The Impact Of Integrating Dance In Teaching Introductory Computer Science Concepts On Student Understanding And Engagement, April Monk

Master's Theses

The purpose of this study was to investigate the effectiveness of dance-integrated pedagogical methods in enhancing the learning experiences of students in an introductory computer science course. In particular, the research aimed to measure the impact of creative movement on student comprehension, engagement, and perceptions of computer science. To guide this investigation, the study explored three research questions: Does integrating dance into lessons impact students’ comprehension of fundamental computer science concepts? How does dance integration affect student perceptions of both dance and computer science? And what elements of dance contribute to differences in comprehension between dance-integrated and traditional lessons? A …

N-Aryl Phenoxazines As Polymer Supported Photocatalysts, Mary Kathryn Hall

Master's Theses

In photosynthesis, the energy of sunlight is perfectly harnessed to facilitate the production of small molecules that are essential for plant life on earth. Inspired by this model of energy efficiency and simplicity, this work seeks to mimic that ability to use the energy in visible light to perform new and more efficient chemistries. Just like in photosynthesis, photocatalysts can act as chromophores absorbing light to excite electrons opening new pathways for previously untapped reactivity. Recent research has provided a wide range of small molecule reactions that are made possible or easier by the use of a photocatalyst. However, these …

Contextual Embedding Using Machine Learning For Cybersecurity: Access Control And Application, Thanh Bui

Graduate Theses and Dissertations

Access control is a well-established challenge in cybersecurity, with significant research focused on enhancing system autonomy and accuracy across various scenarios. Access control rules can be designed based on users’ roles, attributes, or relationships requesting access to specific resources. However, despite their benefits, these models still require human oversight. As systems expand and grow, it becomes increasingly complex for administrators to maintain precise access control rules, often necessitating extensive system updates or even a complete overhaul. This dissertation introduces a novel approach that leverages contextual embedding for user information to enable the system to autonomously authorize user requests for resources. …

A Study Of Configuration Management Database (Cmdb) Adoption In It Service Management (Itsm) Implementations Within Nj Community Colleges, Fredrick Dande

All-Inclusive List of Electronic Theses and Dissertations

This study examines the adoption of Configuration Management Databases (CMDBs) in IT Service Management (ITSM) implementations within New Jersey (NJ) community colleges. Despite the well-documented benefits of CMDBs—such as faster issue resolution, improved compliance, and greater visibility across IT infrastructures—implementation success rates remain low. As technology continues to enhance production capabilities and expand access to information, the need for centralized configuration visibility has become critical. A CMDB provides a single system of record for IT assets and services, helping organizations manage outages, assess changes, maintain compliance, and improve asset tracking. This research used an online survey to collect data from …

Curvilinear Image Segmentation Using Multiscale Variational U-Net, Rebekah Fortes

LSU New Orleans Theses and Dissertations

Segmentation of curvilinear structures such as water contours, cracks in cement, and vascular networks in biomedical imaging, poses unique challenges due to extreme class imbalance, irregular morphology, low contrast against complex backgrounds, and the need to preserve global connectivity while detecting fine-scale details. We propose a Multiscale Variational U-Net (MSVU-Net) architecture designed specifically to address these challenges. The model integrates multiscale convolutional filters to capture both global context and local detail, while embedding a variational model in the bottleneck layer to enhance structural representation. To mitigate class imbalance and improve fidelity, the network optimizes a hybrid loss function that combines …

Coupled Machine Learning Models: Combining Observations And Numerical Analysis In A Physics-Regularized Approach, Austin B. Schmidt

LSU New Orleans Theses and Dissertations

This dissertation investigates surrogate modeling for fixed-location environmental forecasting using novel data-combination techniques. The work surveys the landscape of observational measurements and numerically generated data, identifying similar research and gaps in current methodologies. The ratio-coupled training framework is introduced to combine two data sources per predicted feature through a tunable parameter that weights training signal strength. An optimization scheme is developed to simultaneously tune surrogate weights and the coupled signal ratio, allowing relative influence between signals to act as an explicit regularizer. Three case studies demonstrate the methodology and approach in a variety of contexts. The first study is based …

Peptoid-Based Molecular Analysis Utilizing High-Speed Molecular Dynamics Methods, In Chul Hwang

LSU New Orleans Theses and Dissertations

Polypeptoids are N-substituted glycine polymers, which differ from peptides in the placement of the side chain being present on the amide nitrogen rather than the backbone C_α. While both peptoids and peptides are composed of linked amino acids, the structural changes resulting from the differing origin of the side chain leads to distinct backbone structure, as well as remove the chirality found in the peptide structure. These differences lead peptoids to have different physiochemical and biological properties, such as being resistant to proteolysis and diverse three-dimensional structures that are not observed in peptides. With the shifting of the …

Private Sand Mining In The Mississippi River: Sediment Budget And Morphology Implications Between Baton Rouge And Belle Chasse, Louisiana, Brett Mcmann

LSU New Orleans Theses and Dissertations

This analysis examined how private sand mining within the Mississippi River between Baton Rouge, LA and Belle Chasse, LA affected its sand budget from 2004-2012.

Records assembled indicated that private mining removed an annual average of 2.8 million cubic yards of sand. A HEC-RAS one-dimensional sediment transport model was utilized to assess this practice. Three scenarios were simulated: a baseline without mining, conditions reflecting documented mining rates, and a hypothetical case with excessive extraction.

Results suggest that private mining constituted approximately 26 percent of the total sand deficit between Baton Rouge and Belle Chasse. Mining appeared to cause localized changes …

Flexible Spatial Priors In Bayesian Neuroimaging: Gmrf, Nngp, And Deep Gmrf, Boyoung Hur

All Dissertations

Structural neuroimaging is essential for understanding neurological disorders such as Alzheimer’s disease, enabling accurate delineation of brain regions through image segmentation. Among various segmentation methods, multi-atlas-based approaches like label fusion have become leading techniques. In statistics, Bayesian hierarchical models for label fusion are increasingly favored for their ability to incorporate uncertainty and prior knowledge. Also, a key challenge in modeling neuroimaging data is spatial dependence among image voxels, making the choice of spatial prior critical—particularly in high-resolution settings where segmentation accuracy and computational efficiency are both essential.

This dissertation proposes fully Bayesian spatial hierarchical models that explore two flex- ible …

Role Of Competition In Avoiding Social Collapse, Gabriel M. Tonks

All Graduate Reports and Creative Projects, Fall 2023 to Present

Historical examples suggest that isolated, resource-scarce societies are prone to increased hostility and social disasters. The research in this report explores the role of intrasocietal competition in avoiding resource collapse. Two resource-consumer models are proposed with competition which depends on the level of available resources. One of these models is selected for in-depth analysis, and the region in the parameter space where saddle-node bifurcations emerge is computed numerically. The effects of environmental noise on resource growth are simulated, showing that the increased noise usually has negative long-term effects which might be mitigated via increased consumer competition.

The Depositional Environment Of The Triassic Cow Branch Formation, Dan River Basin Of Virginia And North Carolina Through Petrographic Study And Geochemical Analysis, And In Relation To Taphonomic Processes And Vertebrate Morphology, Gina M. Workman

All Graduate Reports and Creative Projects, Fall 2023 to Present

The Upper Triassic Cow Branch Formation of the Dan River Basin, located in southern Virginia and northern North Carolina, has long captivated generations of geoscientists. Since the 1970s, researchers have sought to unravel the mystery surrounding the depositional environment of this ancient lake system. This world-class lagerstätte hosts a diverse assemblage of fossil fauna and flora, with a notable preservation bias toward fossil vertebrates, most prominently the archosauromorph, Tanytrachelos ahynis. The geology of the Cow Branch strata continues to evoke a persistent question: Are these sediments representative of a shallow or deep lake?

I undertook a petrographic and geochemical study …

Programmable Network Approaches To Resilience And Security In Phasor Measurement Unit Networks, Zhiyao He

Graduate Theses and Dissertations

The security and resilience of smart grids are critical for ensuring reliable and stable power delivery. As modern power systems evolve to incorporate more advanced sensing and control capabilities, Phasor Measurement Units (PMUs) have become an important source of high-frequency, time-synchronized measurements that support wide-area monitoring, control, and protection. However, the growing complexity of smart grids and their reliance on real-time communication expose them to a range of cyber threats, including data loss, tampering, and coordinated attacks. This dissertation explores the use of programmable network technologies, particularly P4-based programmable switches, to provide in-network solutions that enhance the reliability and security …

Emotion Analysis And Neural Language Models For Classification, Andrew Mackey

Graduate Theses and Dissertations

Emotion analysis is a branch of artificial intelligence and natural language processing focused on recognizing emotions hidden throughout various forms of digital data, including text, images, and multi-modal representations. In this dissertation, we present four published and planned works that investigate different methodologies for natural language analysis tasks using deep learning techniques. The first published work we present considers the task of identifying fake news using various text and emotion representations. We demonstrate that emotion representations combined with word embedding techniques can improve the accuracy of fake news classification. Our second published work further investigates the fake news classification task …