Digital Commons Network^™

Evaluating Fall And Spring Planted Cover Crops For Integrated Management Of Herbicide Resistant Palmer Amaranth And Effect On Soil Health, Vipin Kumar

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

Herbicide-resistant Palmer amaranth (Amaranthus palmeri S. Watson) pose a severe threat to agronomic crop productivity in the United States, including corn and soybean production in Nebraska. Therefore, there is an urgent need for diversified and sustainable management approaches. Integrating cover crops (CCs) with herbicide programs represents a promising strategy to achieve Palmer amaranth suppression, sustain crop yields, and contribute to long-term soil health in irrigated corn–soybean production systems in Nebraska.

A meta-analysis of 41 studies and 595 paired observations demonstrated that CCs reduced Amaranthus spp. density and biomass by 37–59% depending on the growing season, with grasses and mixtures …

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 …

In-Field Tractor Operational Load Profile Generation In Support Of Advanced Tractor Testing In Mixedmode Power, Andrew Donesky

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

This dissertation addresses the need to better characterize real-world tractor power requirements across drawbar, power take-off (PTO), and hydraulic modes to support more representative tractor testing. Conventional testing protocols, such as OECD Code 2, emphasize steady-state performance under controlled high-load conditions, which do not reflect the mixed and dynamic demands of modern field operations. To address this gap, a Tractor Instrumentation System (TIS) was developed, validated, and deployed to collect high-resolution, mixed-mode power data during planting, anhydrous ammonia application, and grain cart operations. The TIS integrates physical sensors, including custom load pins, hydraulic pressure/flow sensors, and a redesigned PTO torque …

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), …

Real-Time, Co-Regulated Design For Cyber-Physical, Multi-Rotor Uas Swarms, Grant Simon Phillips

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

Uncrewed Aerial Systems (UAS) have been integrated into a wide range of research and industrial applications, with growing interest in extending mission duration and spatial coverage through coordinated multi-UAS systems, or swarms. While swarming offers the potential for extended mission endurance and robustness through advanced path-planning, control, and estimation algorithms, significant challenges arise when implementing these methods on decentralized platforms composed of size, weight, and power-constrained (SWaP) vehicles. Limitations in onboard computational capacity and congested communication channels can break critical design-time assumptions, which at best, will degrade application quality of service, and at worst, destabilize the fleet through excessive delays …

Ai-Enabled Robotic Nitrogen Management, Ankita Kalra

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

Nitrogen management in corn agriculture faces economic and environmental challenges, motivating development of precision, site-specific solutions. This dissertation presents AIR-N (AI-Enabled Robotic Nitrogen Management), a novel system that integrates cloud-based artificial intelligence with an autonomous field robot (Flex-Ro) to optimize nitrogen fertilizer application in real time. The research is driven by the need to improve nitrogen use efficiency while reducing leaching and emissions.

Comprehensive architecture (Cloud-E) was designed using Amazon Web Services to ingest multi-modal agronomic data (soil sensors, crop reflectance, weather) and host machine learning models. A Random Forest-based model was trained on historical field data to predict spatially …

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, …

Post-Fire Soil Health Assessment Of The Grasslands And Woodlands In The Semi-Arid Nebraska Sandhills, Stephanie M. Lugo

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Grasslands in the Nebraska Sandhills are shaped by natural wildfires, which help maintain the structure and function of native grasslands. In recent decades, afforestation with ponderosa pine and eastern redcedar has introduced new woodlands that altered the region’s fire dynamics and raised important questions about the effects of wildfire on different vegetation types. As wildfires become more frequent, understanding their impacts on soil health in grasslands and woodlands becomes increasingly important. This study examines the short-term effects of the 2022 Bovee Fire on physical, chemical, and biological soil properties in native grasslands and ponderosa pine and eastern redcedar afforested woodlands. …

Same Data, Different Analysts: Variation In Effect Sizes Due To Analytical Decisions In Ecology And Evolutionary Biology, Elliot Gould, Hannah S. Fraser, Timothy H. Parker, Shinichi Nakagawa, Simon C. Griffith, Peter A. Vesk, Fiona Fidler, Daniel G. Hamilton, Robin N. Abbey-Lee, Jessica K. Abbott, Luis A. Aguirre, Carles Alcaraz, Irith Aloni, Drew Altschul, Kunal Arekar, Jeff W. Atkins, Joe Atkinson, Christopher M. Baker, Meghan Barrett

Faculty Research, Scholarly, and Creative Activity

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope …

Genetic And Environmental Determinants Of Streaming And Aggregation In Myxococcus Xanthus, Trosporsha T. Khan, Patrick Murphy, Jiangguo Zhang, Oleg A. Igoshin, Roy D. Welch

Faculty Research, Scholarly, and Creative Activity

Under starvation conditions, a spot of a few million Myxococcus xanthus cells on agar will migrate inward to form aggregates that mature into dome-shaped fruiting bodies. This migration is thought to occur within structures called ‘streams,’ which are considered crucial for initiating aggregation. The prevailing traffic jam model hypothesizes that intersections of streams cause cell crowding and ‘jamming,’ thereby initiating the process of aggregate formation. However, this hypothesis has not been rigorously tested, in part due to the lack of a standardized, quantifiable definition of streams. To address this gap, we captured time-lapse movies and conducted fluorescent cell tracking experiments …

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 …

Rising Cancer Burden Among Older Adults In Saudi Arabia: A Longitudinal Analysis Of Incidence Rates By Demographics, 2013–2022, Hissah A. Alzahrani

Saudi Medical Journal

ABSTRACT Objectives: To analyze cancer incidence trends in Saudi Arabia from 2013 to 2022 by age group, gender, and nationality, identify significant temporal patterns or shifts, and determine the most prevalent cancer types across age groups and years. Methods: A retrospective analysis was conducted using data from the Saudi Cancer Registry, which included 185,567 reported cancer cases between 2013 and 2022. Descriptive statistics, generalized estimating equation (GEE), and Joinpoint regression were used to examine the crude incidence rate (CIR), age-specific incidence rate (AIR), and age-standardized rate (ASR) across demographic subgroups. Results: Saudi nationals had higher cancer rates than non-Saudi residents, …

Evaluation Of Simplified Arabic Matrix Test In Children With Normal Hearing, Medhat F. Yousef, Alia A. Asiri, Abdulmalik M. Marzuq, Abdulaziz A. Almalki, Mohammed N. Garrada, Manal A. Khoja, Ibrahim A. Busaad, Mahmoud M. Mahrous

Saudi Medical Journal

ABSTRACT Objectives: To evaluate the simplified version of the Arabic matrix test in individuals with normal hearing to derive reference ranges for future measurements with the test material. Methods The test was conducted in 80 native Arabic individuals (20 adults and 60 children across different age groups between 5-10 years) from November 2020 to Mar 2023. Evaluation measurements were conducted in adults and children to derive the reference psychometric functions and assess the training effect. The test list equivalence was verified only in the adults group. Results The reference ranges for speech-intelligibility in noise for 80% thresholds or speech-recognition threshold …

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 …

Cognitive Dynamics In Undergraduate Engineering Education: Effects Of Need For Cognitive Closure And Achievement Goal Orientation On Cognitive Engagement, Zain Ul Abideen

All Graduate Theses and Dissertations, Fall 2023 to Present

Cognitive and motivational factors play an important role in students’ engagement during problem-solving, which can lead to academic and professional success. Among these factors, Need for cognitive closure (NFCC) and achievement goal orientation (AGO) have been studied extensively individually, but their interaction and impact on engineering students’ problem-solving is still not explored. NFCC refers to the person’s desire for quick answers, and AGO is the student’s approach to learning and success. This research investigated the impact of NFCC on gender and engineering major, and the relation between NFCC (five facets) and AGO (Mastery, Performance) at their different levels. Additionally, this …

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, …

Impacts Of Halophila Stipulacea Invasion On Organic Matter Dynamics In Thalassia Testudinum Dominated Seagrass Meadows, Puerto Rico, Jonathan D. Herald

Master's Theses

Halophila stipulacea (Forsskål & Niebuhr) is a tropical seagrass native to the Red Sea, Persian Gulf, and Indian Ocean. In 2002, invasive populations of H. stipulacea were identified in the Caribbean Sea where it now forms large meadows, displaces native seagrasses, alters invertebrate and fish communities, and potentially affects organic matter dynamics in invaded meadows. This study investigated differences in organic matter dynamics between meadows of H. stipulacea and native Thalassia testudinum within the Jobos Bay National Estuarine Research Reserve (JBNERR) on the southern coast of Puerto Rico. From March 2023-March 2024, sampling was conducted across four sites to investigate …

Effects Of Urbanization On The Population Genetics And Morphology Of The White-Footed Mouse (Peromyscus Leucopus) Across An Urban–Rural Gradient, Sydney A. Morton

Master's Theses

Urbanization alters landscapes in ways that can reshape wildlife populations by changing habitat connectivity, resource availability, and selective pressures. To investigate how urbanization influences both genetic differentiation and morphology in small mammals, I examined Peromyscus leucopus (PELE) across an urban–rural gradient in northern Georgia. Using RADseq SNP data from PELE individuals across 13 sites, I quantified pairwise genetic differentiation (F_ST) and tested how various environmental variables, including impervious surface cover, human population density, and tree canopy cover, predict genetic differentiation after accounting for geographic distance. Genetic differentiation increased with geographic distance and, after controlling for isolation by …

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 …