Digital Commons Network^™

Detecting Prescribed Fire, Haying And Grazing Events Via Remote Sensing To Create Grassland Disturbance Landcovers For The Ring-Necked Pheasant (Phasianus Colchicus), Megan Amy Baldissara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation developed disturbance detection models to fulfill the need for remote sensing landcover products describing grassland structure. The use of landcover products derived from remote sensing is increasing over time in pheasant (Phasianus colchicus) research. Such landcover, however, does not provide relevant pheasant structural habitat information (Chapter 1). Pheasants require tall, high-density grassland for nesting, tall grassland with medium density for brood rearing, and tall grassland for wintering. Time since disturbance can serve as a proxy for structure, as it shapes vegetation by removing biomass and resetting succession. Disturbance is easier to detect than structure with current …

Global, Continental, And Local Scale Paleoenvironmental Reconstruction Methods For Interpreting The History Of Miocene North America And The Great Plains, Willow Hue Nguy

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The Miocene of North America is characterized by dynamic changes in herbivore communities, habitat composition, and global climate. Long-term cooling and drying trends influenced large changes in floral and faunal communities. The Great Plains, North America shifted from closed-canopy biomes to open-canopy biomes with C3-grasses to biomes with C4-grasses. This shift affected the herbivore communities as browsing-adapted ungulates were replaced by grazing-adapted ungulates. Much focus has centered on the Great Plains, so it is not well understood how the changes in biomes compare to other regions of North America. The models used for vegetation density indicate open-canopy biomes since at …

Investigating Programming Behaviors To Understand Student Engagement And Experience In Introductory Programming Courses, Marcus Eugene Gubanyi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Introductory programming courses are foundational to developing students’ problem-solving abilities and shaping their persistence in computing pathways. Engagement with programming tasks plays a central role in student learning and experience. Many research measures, including self-reports and code submissions, offer only a limited view of student engagement with programming tasks. This dissertation leverages programming process data, consisting of keystrokes and compilation events, to capture the programming process as it unfolds and to investigate observable programming behaviors. Guided by educational theories, three studies examine how students’ programming behaviors vary across instructional and assessment contexts, how they relate to motivational profiles, and how …

Large-Scale Experimental Study On The Impact Of Air Cleaning & Ventilation On Indoor Air Quality And Student Illness-Related Absenteeism, Daud Nosham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This large-scale randomized experimental field study investigated the effect of portable air purifiers (PAPs) on classroom’s air quality and student’s illness related absenteeism. PAPs were installed in 317 classrooms from various school districts located in Eastern Nebraska. The classrooms were randomly assigned into four conditions: three treatment conditions (T1, T2 & T3) and one control condition (device without any filter). T1 was equipped with HEPA filters, T2 added Activated Carbon (AC) layer to HEPA, and T3 further integrated Germicidal Ultraviolet (GUV) alongside HEPA and AC filters. The measured variables were carbon dioxide (CO2), fine particles (PN2.5) and coarse particles (PNcoarse), …

An Integrated Modeling Framework To Evaluate Circularity Of The Corn–Water–Ethanol–Beef Nexus, Heydi Han

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation examines the transition from conventional linear agricultural and food bioenergy systems toward more circular, resilient, and resource-efficient configurations. Prior research indicates that spatial and temporal variability in soil, weather, animal biological responses, and management decisions strongly influences productivity, nutrient-use efficiency, and profitability. Studies have also shown that a diversified circular system can buffer these sources of variability, improving stability and adaptive capacity. Building on these insights, the central focus of this work is to develop an integrated modeling framework to evaluate circularity within the Corn-Water-Ethanol-Beef (CWEB) nexus and explore how climate variability and management choices shape system-wide performance. …

Making Deep Neural Networks Trustworthy: Intelligibility And Safety Through Symbolic Methods, Eleanor Catherine Quint

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The rapid adoption of deep learning has come at the cost of properties long valued in artificial intelligence: intelligibility and safety. This dissertation develops methods that restore these properties by coupling neural networks with symbolic structure.

First, for supervised classification, I propose a differentiable decision tree integrated with a supervised variational autoencoder. The resulting model maintains competitive accuracy and generative performance while exposing clear macro-features in its latent space, improving interpretability.

Second, for reinforcement learning, I extend constrained Markov decision processes by specifying constraints in formal languages. This formal language constrained MDP enables the use of automata for state augmentation, …

Data-Driven Streamflow Forecasting In The Upper Colorado River Basin Using Spatio-Temporal Graph Networks, Akhila Akkala

All Graduate Theses and Dissertations, Fall 2023 to Present

Forecasting river flow is essential for managing water supplies, reducing flood risk, and supporting healthy ecosystems. In the Upper Colorado River Basin, much of the yearly water comes from melting snow. However, many traditional models struggle to capture how snowpack and river flow interact, especially across such a large and complex region.

This study uses a modern machine learning approach called a Spatio-Temporal Graph Neural Network (STGNN) to improve streamflow prediction. The model uses Snow Water Equivalent (SWE)—a measure of how much water is stored in the snowpack—along with river flow data. By treating each river gauge as part of …

Optimization In The Synthesis Of Dual Ionic-Liquid-Functionalized Cellulosic Materials, Megan L. Franke

Honors College Theses

There is a growing urgency to develop sustainable alternatives to petroleum-based materials as concerns about environmental impact and resource depletion intensify. Among renewable biopolymers, cellulose stands out as one of the most abundant and versatile organic compounds on Earth. As the structural backbone of plant cell walls, it provides rigidity and mechanical strength, while its abundance, biodegradability, and tunable chemistry make it an attractive candidate for sustainable materials. Beyond its traditional uses in textiles, paper, and food additives, cellulose has been increasingly investigated in polymer chemistry for advanced

applications. The present study aims to optimize synthetic routes to dual ionic …

Predicting Major Solar Flares Using Convolutional Neural Networks And Multivariate Magnetic Field Time-Series Data, Arash Azizian Foumani

All Graduate Theses and Dissertations, Fall 2023 to Present

Major solar flares are sudden, intense bursts of X-ray energy from the Sun, capable of severely impacting critical technological infrastructure like satellites, communication networks, and power grids on Earth. Accurate prediction of these high-intensity events is crucial but presents a significant challenge. This is largely due to their infrequent occurrence and the complex, dynamic nature of the Sun's underlying magnetic activity which drives these events. This research focuses on improving the prediction of major solar flares by utilizing detailed historical data that tracks the evolution of magnetic properties within solar active regions over time. This time-based data, however, contains inherent …

Soil Organic Carbon Depth Profiles And Centennial And Millennial Decay Rates In Tidal Marsh, Mangrove And Seagrass Blue Carbon Ecosystems, Nerea Piñeiro-Juncal, Miguel Ángel Mateo, Carmen Leiva-Dueñas, Eduard Serrano, Karina Inostroza, Montserrat Soler, Eugenia T. Apostolaki, Paul Lavery, Carlos M. Duarte, Anna Lafratta, Oscar Serrano

Research outputs 2022 to 2026

The management of coastal blue carbon ecosystems can contribute to mitigate anthropogenic greenhouse gas emissions. However, limited information on soil organic carbon (OC) decay rates in tidal marsh, mangrove, and seagrass soils hinders their inclusion in climate strategies and carbon-crediting schemes. Here, we analyzed downcore OC trends in 3761 soil cores from blue carbon ecosystems worldwide. A decrease in OC content with soil depth was measured in 63% of the cores, whereas stable and increasing trends were observed in 22% and 15% of the cores, respectively. Based on 85 profiles where OC decay could be modelled, the OC decay rate …

Navigating Ai-Nature Frictions: Autonomous Vehicle Testing And Nature-Based Constraints, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In cities, the application of Artificial Intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modelling. …

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 …

Privacy, Identity, And Fairness: Unpacking Ethical Influences On Metaverse Adoption In University Learning, Mousa Al-Kfairy, Meera Alalawi, Saed Alrabaee, Omar Alfandi

All Works

As immersive technologies like the Metaverse continue to reshape higher education, it becomes increasingly vital to examine the ethical dimensions shaping student engagement with these platforms. This study investigates how university students perceive privacy, digital identity, informed consent, and algorithmic fairness in Metaverse-based classrooms, and how these perceptions influence their trust and behavioral intention to adopt the technology. A quantitative survey was conducted with 310 university students, all of whom had prior exposure to virtual learning platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0, the study found that Metaverse Ethical Dimensions (MED) significantly influence both Trusting …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

All Works

The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

All Works

Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Embedding-Driven Dual-Branch Approach For Accurate Breast Tumor Cellularity Classification, Hossam Magdy Balaha, Ali Mahmoud, Khadiga M. Ali, Mohammed Ghazal, Norah Saleh Alghamdi, Ashraf Khalil, Ayman El-Baz

All Works

This study proposes a dual-branch framework for precise classification of breast tumor cellularity via histopathological images where it integrates two distinct branches: the Embedding Extraction Branch (embedding-driven) and the Vision Classification Branch (vision-based). The Embedding Extraction Branch uses the Virchow2 transformation to generate dense, structured embeddings, whereas the Vision Classification Branch employs Nomic AI Embedded Vision v1.5 to process image patches and produce classification logits. Both branches’ outputs are combined to form the final classification. The framework also suggests Knowledge Block with fully connected layers, batch normalization, and dropout to improve feature extraction and reduce overfitting. The proposed approach reports …

Enhancing Ad/Adrd Management Through Ihelpcare: A Compliant And Culturally Sensitive Ai-Driven Digital Healthcare Platform, Trisha Bhowmick

Master's Theses

The digital healthcare field is expanding fast, and now it requires platforms that use advanced technology and maintain robust data security and compliance practices. In the present paper, we present the main structure, key methods, and compliance strategies of the digital healthcare system iHelpCare, which, while fully meeting the HIPAA/GDPR requirements, provides health services more accessible, efficient, and inclusive. The proposed platform is powered by AI for personalized care solutions, with the main emphasis on preventive health management and providing tools for people with disabilities.

iHelpCare achieves real-time patient monitoring while securing medical data management and easy communication between patients, …

Mountain Communities During A Time Of War: A Case Study Of The Hutsul Region In The Carpathian Mountains Of Ukraine, Pavlo Rybaruk

Electronic Theses and Dissertations

This thesis investigates the ethno-ecological technologies employed by the Hutsul people within the highland agrarian economy of the Carpathian Mountains. Over centuries, the Hutsuls have developed ethno-ecological technologies that maintain ecological integrity while supporting community livelihoods.

The study addresses three primary research questions: What are the most important ethno-ecological technologies of the Hutsul highland economy? How might these traditional ethno-ecological practices be applied to economic, agricultural, and tourism development in the Hutsul region? How might these traditional ethno-ecological practices be combined with contemporary approaches to encourage sustainable development in the region?

The research explores the devastating impact of the Russian …

Characterizing Stellar Flares On Low-Mass Stars Tic 144217628 And Proxima Centauri Using Transiting Exoplanet Space Satellite (Tess) Optical Data, Charles Edward Holloway

Graduate Theses and Dissertations

Low-mass stars account for more than 70% of stars within our galaxy. Their ob?servational interest can be attributed to their smaller radius and luminosity, making them ideal for detecting terrestrial planets inside their habitable zones. These magnetically active stars produce frequent stellar flaring events that can impact the atmospheric composition of nearby planets and interfere with the prebiotic processes for carbon-based life. Using TESS optical data, I have detected, measured astrophysical properties, and modeled the behavior of 44 flares from two low-mass stars: TIC 144217628 and Proxima Centuari. Given similar observational periods, Proxima Cen has shown to produce larger amplitudes …

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 …

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 …

Artificial Intelligence For Reliability: Predictive Health Maintenance And Geolocation In Gps-Denied Environments, Rafael Toche Pizano

Graduate Theses and Dissertations

In this dissertation, we explore the potential of machine learning and deep learning techniques to enhance the performance and robustness of applications across two major domains. By addressing the challenges within these fields, we demonstrate that we can leverage learning algorithms to obtain substantial improvements in accuracy and robustness. First, we tackle a problem in the field of predictive health maintenance. We propose a novel auto encoder and neural network based methodology to predict failure times in complex aviation systems to learn to distinguish between normal and abnormal operational behavior, and use this information to inform the neural network to …

Agricultural Practices’ Impact On Soil Health Indicators In Mid-South U.S. Crop Production Systems, Katherine Suzanne French

Graduate Theses and Dissertations

Soil health and regenerative agriculture are concepts gaining popularity across global agriculture systems. The effect of sustainable farming practices such as nutrient management, cover crops, adoption of no-tillage, and residue retention on crop yield, as well as environmental resilience, is increasingly being studied. This research aimed to evaluate the impacts of (i) cover cropping and nutrient management, and of (ii) soil sampling depth and timing on indicators of soil health in various mid-southern irrigated row crop systems. Soil health indicators like soil organic matter (SOM), carbon dioxide respiration (CDR), beta-glucosidase enzyme activity (BG), permanganate oxidizable carbon (POXC), and the soil …

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. …

Automation And Upgrading Of Custom Cold Wall Uhv-Cvd Reactor For Group Iv Material Development, Alexander Golden

Graduate Theses and Dissertations

Group IV alloys have shown promising results for infrared optoelectronic applications where III-V materials are currently dominant. The III-V material system has vacancies that Group IV materials can fill, most importantly complementary metal oxide semiconductor (CMOS) process compatibility making quality devices much cheaper and widely available compared to the current state of the art. Silicon, being an indirect band gap Group IV material, has poor performance in optoelectronic applications but is the dominant material for most of the electronic industry. However, when silicon is alloyed or substituted with other group IV materials, these optoelectronic properties can be enhanced and, in …

Evidence Of Backwater Morphodynamics In Stratigraphy On Earth And Mars: Implications For Ancient Mars, Cory Mitchell Hughes

Graduate Theses and Dissertations

Geologic evidence indicates that liquid water once flowed in abundance across the martian surface. For this reason, in the search for evidence of extraterrestrial life, Mars represents a prime target for scientific inquiry. River systems encode climatic and environmental signals in their deposits on Earth and Mars, often in fan-shaped landforms (e.g., alluvial fans, river deltas, etc.). Distal fan-shaped deposits (i.e., river deltas), in particular, act as a relatively high-fidelity record of dynamic geologic processes. As such, NASA frequently sends rovers to locations on Mars’ surface with hypothesized river deltas (e.g., Perseverance and Jezero crater). Problematically, there are many different …

Solar Powered Conservation: The Effects Of Solar Photovoltaic Facilities On Avian Community Composition, Michael Christopher Ferrara

Graduate Theses and Dissertations

The expansion of solar photovoltaic energy infrastructure is transforming landscapes worldwide. Increasingly, facilities are planted with native vegetation, yet how vegetation characteristics and landscape variables shape wildlife communities in solar facilities remains poorly understood. Because avian populations have been in decline and are sensitive to habitat changes, understanding how solar facilities influence avian communities is important. In Chapter 1, we used autonomous recording units, local vegetation measurements, and land use and land cover data to quantify how avian community occupancy is influenced by solar cover in solar facilities and comparable reference sites. We used a Bayesian multi-species occupancy model to …

Towards Vision-Brain Understanding At Scales: From Classical To Quantum Machine Learning Approaches, Xuan-Bac Nguyen

Graduate Theses and Dissertations

In recent years, large-scale learning approaches such as unsupervised and self-supervised learning have revolutionized artificial intelligence. These methods enable machines to learn high-level representations without explicit human supervision, achieving remarkable success across vision, language, and multimodal tasks. However, such advances come at a cost—they rely on massive datasets, billions of parameters, and extensive computational resources. Despite these achievements, artificial systems still fall short of the remarkable learning efficiency of the human brain, which can infer, adapt, and generalize from limited experiences. This gap motivates a deeper exploration of how biological intelligence acquires knowledge and how these principles can inspire the …

Updating The Surface Geological Maps In The Boston Mountains Region Of Arkansas Using Subsurface Well Log Correlation And Lidar Mapping, Olivia Smith

Graduate Theses and Dissertations

The Pennsylvanian Atoka Formation are the primary surface outcrops of the Boston Mountains in northwest Arkansas. Where fully preserved in the Arkoma Basin to the south, the Atoka Formation exceeds 18,000 feet of sandstone and shale. It is mapped as a single “undifferentiated” unit on the surface geological map of Arkansas. This project correlates ten subsurface subdivisions of the Atoka Formation plus the Desmoinesian to the surface outcrops of the southern Boston Mountains. This produced a higher resolution surface map beyond the current Atoka Undifferentiated scheme. Recognition of the Atoka Formation subdivisions in surface outcrops using standard lithostratigraphic and biostratigraphic …

Novel Radical Chemistry Of Electron-Rich Double Bonds, Claire Allyson Beard

Graduate Theses and Dissertations

This work delves into the reactivity of nucleophilic N-heterocyclic carbenes (NHCs) and their ability to form electron-rich alkene intermediates, which can undergo various chemical transformations depending on the nature of the electrophile. Initial reports by Hahn disclosed the NHC promoted radical nature of electron-rich N,N’-benzyltetraazafulvalenes, which led to 2,2’- biimidazoles. We found that N-aryl substituted pyridinium salts also underwent rapid a similar NHC promoted dimerization when exposed to a base, producing 2,2’-bipyridyls. This is likely due to the adept radical nature of the bipyridinylidene intermediate for C-Naryl bond scission. We have also uncovered an unprecedented C-N bond scission of Breslow-like …