Digital Commons Network^™

Proof Techniques In Analysis, Francis Antwi Frimpong

Graduate Research Papers

No abstract provided.

Using Inferences From Natural Language Narratives To Improve Video Question Answering, Hoang Bao Truong

Graduate Studies Theses and Dissertations 2026

The purpose of this thesis is to examine how structured narrative representation and reasoning strategies jointly affect the performance of Video Question Answering (VideoQA). Although recent advances in large multimodal and language models have improved video description and retrieval, causal and counterfactual reasoning remain challenging. This thesis studies an inference-only pipeline in which videos are converted into natural-language narratives, and those narratives may optionally be transformed into a structured NarrativeML format before multiple-choice questions are answered without task-specific fine-tuning. NarrativeML is a machine-readable narrative representation that explicitly encodes entities, events, and their causal and temporal relations. Three representational contexts are …

Holographic Sensors For The Detection Of Volatile Organic Compounds, Graceson Antony

Doctoral

Volatile organic compound (VOC) monitoring at parts-per-million (ppm) concentration levels is essential for occupational safety, indoor air quality, and regulatory compliance. Although holographic gas sensors have been widely investigated, most operate in reflection mode as qualitative visual indicators, with limited quantitative evaluation of sensing mechanisms and scarce systematic demonstrations of repeatable multi-cycle validation for VOCs at ppm concentrations. A physics-based optimisation framework for transmission-mode holographic gas sensors which can bridge this gap has remained largely undeveloped.

This thesis presents the development and optimisation of real-time transmission-mode volume phase transmission holographic grating (VPTHG) sensors for ppm-level VOC detection. A comprehensive theoretical …

Maternal Prebiotic Supplementation Modifies Associations Between Intrapartum Antibiotics And Infant Allergy, Summer V.M. Walker, Thomas R. Sullivan, Rachelle A, Pretorius, Nina D'Vaz, Johnny Lo, Johan Garssen, Jeffrey A. Keelan, Desiree Silva, Susan L. Prescott, Claus T. Christophersen, Debra J. Palmer

Research outputs 2022 to 2026

Background: Prebiotics promote growth of beneficial gut bacteria, whereas antibiotics can disrupt the microbial balance, reduce diversity, and potentially cause gut dysbiosis. This investigation aimed to determine whether antibiotic use before, during, or after birth altered the risk of infant allergic disease outcomes within a maternal prebiotic dietary supplementation trial. Methods: Maternal participants were allocated to consume daily prebiotics (galacto-oligosaccharides and fructo-oligosaccharides) or placebo (maltodextrin) powder from 18 to 20 weeks' gestation until 6 months postnatal. All infants had a family history of allergic disease. Maternal and infant antibiotic use was prospectively collected monthly throughout the trial intervention period, and …

Text Messaging Programs To Support Family Literacy Practices: A Scoping Review, Lennie Barblett, Leanne Lavina, Nicola Johnson, Caroline Barratt-Pugh

Research outputs 2022 to 2026

Using bulk digital text messaging has been shown to be effective in many different fields such as health program interventions and supporting parents/carers. Evaluations of these interventions indicate positive changes in practice; however, it appears the potential of digital messaging through short message services (SMS) have not been fully taken advantage of in early childhood education. This scoping review using Arksey and O’Malley’s (2005) approach investigates the current practices of text messaging programs to support families in the development of preschool aged children’s language and literacy learning. Searches of five databases identified 16 articles that fitted our criteria. Results indicate: …

Baseline Body Mass Index Category And Incident Treated Hypertension In Australian Men: A Linked Prospective Cohort Study Using Pbs/Mbs Data, Ebenezer Afrifa-Yamoah

Research outputs 2022 to 2026

Background: Excess adiposity is a major modifiable risk factor for hypertension; however, the incremental prognostic value of body mass index (BMI) beyond standard predictors remains unclear. We examined the associations between baseline BMI category and incident treated hypertension, and assessed whether BMI improved risk prediction.

Methods: Data were obtained from the Ten to Men longitudinal study (2013–2024) linked to Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) records. The cohort comprised 12,742 men aged ≥ 18 years without treated hypertension at baseline, followed for 133,038 person-years. Adjusted Cox proportional hazards models were fitted. Discrimination, calibration, decision curve analysis, E-values, …

Multi-Scale Sensor-Aware Variational Autoencoders For Adaptive Iot Security: Integrating Drift Calibration And Cyberattack Detection, Md Kamal Hossain, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Existing approaches to Industrial Internet of Things (IoT) anomaly detection treat sensor drift calibration and cyberattack detection as separate problems, overlooking their strong interdependence and the compound failure modes that arise when drift dynamics are exploited by stealthy adversaries. Industrial Internet of IoT sensor networks therefore require a unified framework that jointly models both phenomena. This paper proposes the first unified multi-scale sensor-aware variational autoencoder (MS-VAE) framework that jointly models sensor drift and malicious activity within shared latent representations, enabling integrated calibration and security monitoring. The framework introduces three key components: (i) a multi-scale latent architecture that captures short-term anomalies …

Inside Out: Improving Large Model Safety, Wei Zhao

Dissertations and Theses Collection (Open Access)

While Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are at the frontier of current advancements in artificial intelligence, demonstrating remarkable capabilities across diverse applications, there are growing concerns about their reliability and security. LLMs remain vulnerable to adversarial attacks through carefully crafted prompts that circumvent safety mechanisms, while MLLMs face additional security challenges stemming from their multimodal nature. Despite considerable efforts in reinforcement learning from human feedback (RLHF) and supervised fine-tuning, existing safeguards have proven inadequate in addressing these critical vulnerabilities. This inadequacy stems from the fact that these models are inherently blackboxes that do not provide …

Western Massasauga (Sistrurus Tergeminus): A Species Conservation Assessment For The Nebraska Natural Legacy Project, Colleen Rothe-Groleau, Melissa J. Panella

Nebraska Game and Parks Commission: Publications

The Nebraska Natural Legacy Project recognizes the western massasauga (Sistrurus tergeminus) as a Tier I at-risk species. Provided are some general management recommendations regarding western massasaugas. Conservation practitioners will need to use professional judgment to make specific management decisions based on objectives, location, and a multitude of variables. This resource was designed to share available knowledge of this at-risk species that will aid in the decision-making process or in identifying research needs to benefit the species.

Criteria for selection as Tier I: State listed, G3

Estimated Population in NE: 1,000–2,500

Estimate based on: Field surveys

Trends since 2005 …

How Do Individual And Environmental Covariates Impact The Immune Response Of Deer Mice In Western Montana?, Rachel A. Throckmorton, Angela Luis

Undergraduate Theses, Professional Papers, and Capstone Artifacts

Sin Nombre Virus (SNV) is an exceptional model system for studying wildlife disease because of how simple it is to capture, take samples, and obtain disease status from the primary host. Immune response is an important factor to consider when testing for SNV because it can affect susceptibility of the host as well as transmission of the virus. We examined individual and environmental conditions that may predict immune response and therefore possibly SNV transmission in wild populations. We investigated whether SNV infection status, reproductive status, sex, and season correlate with immune response through examining total white blood cell (WBC) counts …

2026 Document For Faculty And Staff Scholarship Spring Soirée, Kara Schleck, Diana Symons

Celebrating Scholarship and Creativity Day (2018-)

No abstract provided.

Multilayer Adaptive And Cooperative Control Of Pipeline Systems: From Local Loop Adaptation To Distributed Game-Theoretic Coordination, David Brattley

Dissertations, Master's Theses and Master's Reports

This dissertation presents a unified adaptive and coordinated control architecture for multi-station centrifugal pump pipeline systems operating under dynamic hydraulic conditions. Local controller adaptability is achieved using a variable-forgetting-factor Recursive Least Squares (RLS) algorithm, which continuously estimates process parameters to adjust controller gains in real time. To prevent excessive effort and controller-induced instability, a reinforcement-learning supervisory layer based on Q-learning is implemented to determine optimal adaptation-activation policies.

For pipeline systems with multiple pump stations, a game-theoretic coordination layer is introduced to balance discharge-pressure contributions, minimize pressure oscillations, and improve energy distribution across geographically distributed assets. Cooperative and non-cooperative strategies are …

On The Vibrational Dynamics Of Wind Turbine Blades: The Oscillatory Response To Gust Pulses, And The Physical Mechanisms Behind Them, North A. Yates

Dissertations, Master's Theses and Master's Reports

An easily noticed trend in the utility-scale wind turbine industry is that the size of the machines has continued to increase over time. While this may have benefits in terms of increasing the power generation for a single turbine, a major drawback is the rate with which the weight of the turbine blades grow. To ensure these larger machines can be utilized, efforts are being put forth to create lighter blades. This may fix the weight issue, but brings on a new one; these lighter blades can also be more flexible than previously studied ones. It is, therefore, vitally important …

Designing A Research Study: Controlling For A Variable And Using Chatgpt, Fritz Umbach

Open Educational Resources

This assignment uses a hypothetical society to help students understand the idea of “controlling for a variable” and practice applying it to a real-world research question. Students design a research approach to examine whether income differences are better explained by family background or by systemic prejudice, working within clear data limits. ChatGPT is used as a research guide to help students brainstorm, test, and revise their methods, while encouraging them to critically evaluate the usefulness and limits of AI-generated suggestions. The assignment emphasizes careful reasoning, clear research design, and thoughtful use of AI as a support for learning rather than …

Review Of Bones: Structure And Mechanics, Alexander Curry

Nebraska Anthropologist

Review of Bones: Structure and Mechanics (2002) by John D. Currey.

Unsettling The West: Migration Routes And Homesteading Through Oral Histories, Sidney Wickham

Nebraska Anthropologist

Westward expansion in the nineteenth-century United States has long been framed through settler-centered historical narratives that emphasize progress and national development. This article employs oral history as an ethnographic method to examine migration routes and homesteading experiences in Nebraska and the broader Midwest, foregrounding how memory, storytelling, and lived experience function as cultural practices. Drawing on interviews with descendants of homesteaders, community members, and museum professionals, alongside diaries, newspapers, and archival materials, this study analyzes how migration was remembered, narrated, and transmitted across generations. Rather than treating overland trails and homesteads as singular pathways of movement, this research situates them …

Af-Xray: Visual Explanation And Resolution Of Ambiguity In Legal Argumentation Frameworks, Yilin Xia, Heng Zheng, Shaun Bowers, Bertram Ludäscher

Computer Science Faculty Scholarship

Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded …

Typology And Spatiotemporal Patterns Of Illegal Fishing Activities In The Philippines: Implications For Fisheries Management, Marlowe O. Acevedo, Tzu-Yun Ching, Chih-Shin Chen

Journal of Marine Science and Technology–Taiwan

Illegal, unreported, and unregulated (IUU) fishing activity is a global threat and continues to undermine the effectiveness of fisheries regulations and management measures. However, the extent of illegal fishing in Philippine waters remains unclear. This study aims to identify the types of illegal fishing in Philippine waters and analyzed their temporal and spatial patterns. Data were obtained from the records of the Philippine Coast Guards (PCG) and Maritime Law Enforcement Agencies from 2015 to 2020. A total of 602 apprehensions were recorded, resulting in 813 violations, of which 630 cases were fishery violations. Most of the watercrafts involved in the …

Soft-Constrained Variants Of T-Distributed Stochastic Neighbor Embedding For Global Structure Preservation, Joseph A. Balderas

Mathematics Dissertations

Dimensionality reduction (DR) is a fundamental tool in data science and machine learning that transforms high-dimensional data into a low-dimensional representation while preserving important structural properties of the original data. Among modern DR methods, t-distributed stochastic neighbor embedding (t-SNE) has become one of the most widely used techniques for visualization due to its strong ability to preserve local neighborhood structure and produce visually separated clusters. However, despite its popularity, t-SNE is well known to struggle with preserving global structure of data, often producing embeddings in which distances between clusters and neighborhoods do not accurately reflect relationships in the high-dimensional space. …

Court-Appointed Neutrals And Legal Education, F. Peter Phillips

NYLS Law Review

No abstract provided.

Machine-Learning- And Iot-Based Approach For Predicting Water Quality Using Data Classification And Explainable Ai Technique, M. Samir Abou El-Seoud, Omar H. Karam 2, Hosam El-Sofany 3

Computer Science

Maintaining water quality is crucial for both public health and environmental sustainability. This study proposes a machine-learning- (ML-) and Internet-of-Things- (IoT-) based approach to water quality prediction that utilizes explainable artificial intelligence (XAI) and data classification techniques. The proposed approach integrates IoT devices to enhance real-time data collection, facilitating continuous monitoring and early anomaly detection. Ten different ML classifiers—Decision Trees, K-Nearest Neighbors, XGBoost, Naïve Bayes, Logistic Regression, AdaBoost, Random Forests, Support Vector Machines, Voting, and Multi-Layer Perceptron—were evaluated to find out which one works best for predicting water quality. We used three distinct approaches for feature selection: analysis of variance …

Eeg And Imu Gait Signal Processing: A Comparative Assessment Of The "Reza" Exponential Filter And Classic Filters, Reza Pousti, Daniel M. Russell, Derek C. Monroe, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 …

The Sequence (S) Index As A Marker Of Diminished Step-To-Step Transition Efficiency In Older Adults, Elham Alijanpour, Ashwini Kulkarni, Peter G. Adamczyk, Daniel M. Russell

Rehabilitation Sciences Faculty Publications

Introduction: Age-related declines in walking efficiency are often attributed to musculoskeletal and neuromuscular changes, yet the mechanisms underlying these inefficiencies remain poorly understood. One key contributor is the step-to-step transition (STST), during which the center of mass (CoM) is redirected between limbs. The Sequence (S) index quantifies the temporal overlap between push-off and collision forces during STST, with higher values indicating greater mechanical energy loss. This study investigated whether aging affects STST efficiency, as quantified by the S index, and examined the timing and impulse characteristics underlying observed differences.

Methods: Thirteen young and eleven older healthy adults walked at five …

Balancing Inquiry Based Learning And Direct Instruction, Sharon Liu , '26

Senior Theses, Projects, and Awards

No abstract provided.

Exploring The Synergy Between Very Large Transformer And Lstm Models For Effective Medical Captioning From Videos To Text: The Impact Of Captioning In Healthcare, R. V. Aswiga, Moin Ahmed Zahir

Data Science Faculty Publications

In today’s rapidly evolving digital landscape, the demand for accurate and contextually relevant subtitles for image and video content, particularly in the medical domain, is increasingly critical. Despite the proliferation of visual data across various platforms, existing captioning systems often struggle due to variations in visual settings, complex temporal relationships, and nuanced semantics. Additionally, challenges such as limited datasets, privacy issues, and specialized annotation requirements make medical image captioning particularly difficult. To tackle these challenges, we investigate cutting-edge deep learning methodologies, specifically Transfer Learning and Transformer models, through a comparative analysis. Specifically, we focus on Transfer Learning through the MedVisionCapturer …

A Hybrid Cnn-Lstm Surrogate Model For Hyper-Resolution Spatiotemporal Flood Forecasting In Norfolk, Virginia, Yidi Wang, Jonathan L. Goodall, Chetan Kumar, Diana Mcspadden, Sergio A. Barbosa, Binata Roy, Ali Shahabi, Navid Tahvildari

Data Science Faculty Publications

Study region

Norfolk, Virginia, United States

Study focus

Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features.

New hydrologic insights for …

Blue Spring (2001):, George Dibble

BYU Asian Studies Student Journal

“Congratulations on your graduation,” broadcasts on the upper secondary school’s PA system—the first sound in the film Blue Spring (2001), directed by Toshiaki Toyoda—yet none of the students we follow for the next 83 minutes have anything to graduate for. Like a chick denied egg-shell breakage, stuck in its hard world, the film-focused boys are told to live. But where, and how? Blue Spring exists between the graffiti laden walls of high school where classes are secondary to student politics and fights are as common as words.

Annoyances lead to murders. Money is extorted. Teachers are harassed. The first image …

Teachers’ Perceptions Of Artificial Intelligence And Its Impact On Student Learning, Ranging From Understanding, Originality, Motivation, Problem-Solving, And Resilience, Joseph Whelan

Selected Full Text Dissertations, 2011-

Artificial intelligence (AI) is rapidly transforming education, challenging traditional notions of how learning should be structured in schools. Although research on educational AI remains limited, its influence on student learning is already evident. On a daily basis, educators observe the impact artificial intelligence has on five specific areas of student learning: being, understanding, originality, motivation, problem-solving. and resilience. Using a mixed-methods approach, with descriptive research using surveys with free-response questions, the study aimed to examine how the five student characteristics are affected in the classroom daily through the integration of artificial intelligence into student learning. Teachers participated in a survey …

Front Matter, Natural Resources Journal Nrj

Natural Resources Journal

No abstract provided.

Infimum Dimension Nash Embeddings For 2d Projective Shape Analysis, Robert L. Paige, Vic Patrangenaru

Mathematics and Statistics Faculty Research & Creative Works

Vector embeddings make complicated data extracted from networks, words and images, more amendable to data science applications. At the present time, the Veronese-Whitney (VW) matrix embedding of the real projective space is the state of the art for making inference about digital images from an uncalibrated camera, such as a cell phone or security camera. In this work we consider vector embeddings for the projective shape data and in particular determine the minimum dimension isometric (distance-preserving or Nash) vector embedding for a projective space. We determine such an embedding for the projective plane in closed-form. From this embedding we determine …