Physical Sciences and Mathematics Commons^™

Automated Chick Sexing Using Computer Vision, Marta Veganzones Rodriguez

Graduate Theses and Dissertations

This thesis presents two complementary approaches to chick sexing, a critical task in poultry production that demands accurate and early gender identification. By investigating both facial and vent-based modalities, we present two complementary methods that aim to improve the efficiency, scalability, and ethical standards of gender classification in day-old chicks through the use of computer vision and deep learning. The first approach draws inspiration from human facial gender recognition to introduce facial chick sexing, a minimally invasive technique that eliminates the need for expert knowledge. This system encompasses a complete pipeline that includes image acquisition, facial detection and alignment, keypoint …

Roles Of Three Conserved Active Site Residues Of Cytochrome C Nitrite Reductases During The Early Steps Of Nitrite Reduction, Shahama Alam, Bradley J. Dimock, Brian Bennett, Steven J. Reinhardt, Shahid Shahid, A. Andrew Pacheco, Jarett Wilcoxen

Physics Faculty Research and Publications

Cytochrome c nitrite reductases (ccNiRs) catalyze reduction of nitrite to ammonium in a six-electron, eight-proton process. This report explores how the behavior of three active site ccNiR variants differs from that of the wild type enzyme (ccNiR_wt) in the presence of nitrite and a variety of electron donors. While the nitrite-loaded active site of Shewanella oneidensis ccNiR_wt could be 2-electron reduced within seconds to yield a 6-coordinate ferrous nitrosyl ({Fe(L)NO}⁷) at high applied potentials, quantitative reduction of the nitrite-loaded variants R103Q, H257Q, or Y206F required substantially stronger reductants and yielded a different product over several …

Preservation Of The Bernstein Property For Sums Of Independent Random Variables, Iosif Pinelis

Michigan Tech Publications

It is shown that Bernstein-type conditions on independent random variables are preserved by their sum. Some optimality properties of such preservation are proved.

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

Characterization Of Search Spaces And Effects On Machine Learning, Leo Ghelarducci

Doctoral Dissertations and Master's Theses

The present status of the field of Machine Learning (ML) focuses on optimization of popular models. Rarely are the effects of the problem characteristics upon the solution algorithm studied. There exists no standard for knowing when to apply ML algorithms to a given problem or how to estimate the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem characteristics, represented via solution space characteristics, on …

Non-Blackbox Robust Design Of Machine Learning In Networks, Venkat Sai Suman Lamba Karanam

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

Networked systems have become increasingly complex, with newer communication technologies and standards being added every day. Machine Learning (ML) and Artificial Intelligence (AI) paradigms have been adopted in networks to not only solve many fundamental problems, but also to allow seamless integration of components comprising them. The saying “let’s not reinvent the wheel” in ML/AI adoption implies that model architecture design be left for pure ML/AI researchers, while network researchers focus on input preprocessing (e.g. formatting the packet data to be fed to a model), hyperparameter fine-tuning and a trial-and-error approach to find the “best” result. …

Authentication And Message Integrity Verification For Emerging Wireless Networks, Ebuka Philip Oguchi

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

This dissertation presents a comprehensive body of research on authentication and message integrity verification for emerging wireless networks, focusing on secret-free and physical layer security techniques across diverse, challenging, and unconventional environments.

It comprises four first-author contributions that span underground wireless systems, over-the-air (OTA) channels, vehicular communications, and nanoscale molecular networks.

The first contribution, Soil-Assisted Trust Establishment for Underground Wireless Networks (STUN), introduces a physical-layer trust bootstrapping protocol that achieves authentication and message integrity without pre-shared secrets. Leveraging underground-to-air propagation laws and trusted relay nodes, STUN resists active signal injection attacks and demonstrates security comparable to the unbalanced oil and …

Rhetorics Of Encounter: Toward A Biocentric Theory Of Discourse, Matthew D. Whitaker

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

Encounters with more-than-human identities are transformative moments that stand to affect dispositional and bodily change in individuals. From close brushes with wild animals in National Parks to everyday interactions with local ecologies, encounters invite us to feel and think differently about the nature of our realities. In Rhetorics of Encounter: Toward a Biocentric Theory of Discourse, I draw from interdisciplinary perspectives to disrupt the idea of rhetoric as a representational medium, arguing that rhetoric occurs not just through textual exchanges of words and symbols, but through embodied moments of contact with other responsive beings. Invoking George Kennedy’s theory …

Development And Evaluation Of Supported Ionic Liquid Membrane And Porphyrin Frameworks For Carbon Capture Separation, Sarang Ismail

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

The global urgency to mitigate anthropogenic CO₂ emissions has intensified the pursuit of energy-efficient separation technologies. Supported Ionic Liquid Membranes (SILMs) have emerged as promising candidates for CO₂ capture due to their tunable solubility-selectivity and low energy requirements. However, challenges such as mechanical instability, limited scalability, and trade-offs in transport performance have impeded their widespread adoption.

This thesis explores a systematic approach to designing and optimizing SILMs for enhanced CO₂ separation by tailoring polymer–ionic liquid interactions, processing conditions, and material architectures. A comprehensive set of studies were conducted using poly(vinylidene fluoride) (PVDF) with varying molecular weights, different grades of PEBAX®, …

Multivariate Mixture Regression Models With Known Group Membership And Informative Priors, Pahalapathirage Dona Kalani Hasanthika

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

We introduced couple different novel approaches to incorporate latent variable information to multivariate mixture regression models with both Gaussian and count data. We also evaluated the performance of these models with existing best approaches with simulated data from various sampling structures and also evaluated one of the models performance with rice metabolite data that provided some novel insights as well as validating existing literature about performance and behavior of these metabolites. We validated the method using extensive simulations and a real-world application. In both quantitative covariate designs and complex treatment design simulations, our method consistently outperformed established tools like limma, …

Chemical & Electromagnetic Signatures Of Binary Neutron Star Mergers, Benjamin Amend

All Dissertations

Binary neutron star (BNS) mergers are some of the most energetic events in the universe, producing both short gamma-ray bursts (GRBs) and kilonovae. GRBs are brief, bright flashes of gamma rays, powered by ultra-relativistic jets launched during the merger. These jets produce a prompt burst of gamma rays, followed by a broadband afterglow generated by external shocks as the outflow interacts with the surrounding medium. Kilonovae are optical-infrared transients from the radioactive decay of heavy, neutron-rich nuclei formed through the rapid neutron capture process (r-process) in the merger ejecta. Together, GRBs and kilonovae provide unique laboratories for probing fundamental physics …

Discovering High-Redshift Bl Lacs And Localizing Grbs For Mev Missions, Yong Sheng

All Dissertations

Blazars are active galactic nuclei (AGNs) with their jets aligned towards Earth. Gamma-ray bursts (GRBs) can be classified into long and short types. Long GRBs are produced by the collapse of massive stars, while short GRBs are formed by binary mergers (neutron star - neutron star or neutron star - black hole mergers). Both objects are the key probes of extreme physics processes and cosmic evolution, providing us an unique window to understand the Universe. My research aims to measure the redshift of blazars and localize GRBs for proposed and upcoming gamma-ray telescopes. BL Lac objects (BL Lacs) are a …

12co Ro-Vibrational Spectroscopy Of Protoplanetary Disks: Inferring Planet-Disk Interactions, Janus Kozdon

All Dissertations

Observing and studying planets around young stars during their initial formation stages is essential for understanding the physics of their formation. Protoplanetary disks are the host sites of planets, and they exhibit substructures that hint towards the planets' presence, but the planet itself has rarely been detected. Analysis of the substructures in protoplanetary disks, especially towards the inner regions where most planets seem to lie, will provide information that can be used to refine theories about planet-disk interactions and, ultimately, planet formation. To probe the high-velocity inner regions, this study utilizes high-resolution spectroscopy, which has increased sensitivity with increasing velocities. …

Experimental Design And Analysis For Decision Making: Methodology And Applications, Yezhuo Li

All Dissertations

This dissertation develops and applies advanced statistical and optimization frameworks to enhance decision-making under uncertainty, particularly in engineering and manufacturing contexts. First, we introduce an approach for the optimal design of controlled experiments that accounts for observational covariates, enabling more precise and personalized decisions. Second, we explore the application of constrained Bayesian optimization, using Gaussian process surrogate models, to optimize composite cure processes, significantly reducing computational effort while maintaining high predictive accuracy. Building on this foundation, we extend Bayesian optimization to bivariate Gaussian process models that capture correlations between objective and constraint functions, offering new insights into multidimensional decision landscapes. …

Advancing Lithium-Ion Batteries Through Exploration Of Novel Physico-Chemical Phenomena, Peshal Karki

All Dissertations

Lithium-ion batteries (LIBs) power a wide range of modern devices, from smartphones to electric vehicles. This dissertation integrates materials characterization and electrochemical testing to develop novel Si-based electrode materials, investigate separator effects, and improve electrochemical impedance spectroscopy (EIS) modeling. First, I synthesized Si@CC composites using bio-based carbon sources and discovered a novel in situ disorder reduction in the amorphous carbon cloud during cycling, attributed to Si volume fluctuations and mesoporous carbon structure, which enhanced capacity retention. A binder-free electrode (Si@CC@BP) using bucky paper further improved gravimetric and areal capacities while reducing weight and manufacturing complexity.

Next, I investigated how separator …

Three Essays In The Economics Of Disagreements: Incentives, Measurement And Persistence, Mustafa W. Alam

All Dissertations

This dissertation presents three chapters that contribute to the study of economic forces shaping disagreements in society.

In Chapter 1, I demonstrate that political polarization can intensify due to innovations in the information market even if a population's ideological distribution is fixed. Viewership-maximizing news firms cater to a diverse audience who assess source accuracy using noisy private signals that vary in precision and ideological bias. If better-informed consumers disproportionately migrate to newer platforms for news (e.g., the Internet), traditional media firms increase news slant to appeal more to less-informed partisans on both sides of the ideological spectrum. This leads to …

Progress Towards Uranium Isotope Ratio Measurements Directly From Cotton Swipes With The Liquid Sampling – Atmospheric Pressure Glow Discharge (Ls-Apgd) Ionization Source, Joseph V. Goodwin

All Dissertations

The liquid sampling – atmospheric pressure glow discharge (LS-APGD) ionization source is a novel microplasma ionization source capable of ionizing a diverse set of both inorganic and organic chemical species including, ionic metal constituents as well as ligated metallic species, polyaromatic hydrocarbons (PAH), halogens, proteins, and even per/poly fluorinated alkyl substances (PFAS). The ability to generate ions from diverse chemical species, known as combined atomic and molecular (CAM) ionization, is not found with traditional ionization sources. In addition, the LS-APGD is capable of coupling with mass spectrometer platforms typically reserved for “organic” mass spectrometry, such as the high-resolution Orbitrap mass …

Application Of Artificial Neural Network Algorithms For Irrigation Scheduling, Lisa Umutoni

All Dissertations

Neural networks have been extensively used in predicting soil water tension for improved irrigation scheduling and management. However, their lack of interpretability constrains their efficacy in grasping the nuanced patterns prevalent in soil water tension time series data. The first goal of this research was to develop interpretable deep neural network models for soil water tension prediction across multiple soil depths (0.15m, 0.3m, 0.46m and 0.6m) and prediction horizons (1h, 6h, and 12h). The Neural Hierarchical Interpolation for Time Series (N-HiTS) and Neural Basis Expansion Analysis Time Series (N-BEATS) models were used in this research. Historical soil water tension data …

Unsupervised Deep Learning For Video Restoration, Mary Damilola Aiyetigbo

All Dissertations

In today's digital era, visual data is vital across several domains such as medical diagnostics, scientific imaging, surveillance, and entertainment. However, video data often suffers from degradations like noise, blur, compression artifacts, and low resolution, which degrade quality and downstream usability. Video restoration aims to recover clean, high-fidelity video from such corrupted inputs. Unlike static images, video restoration must maintain temporal consistency across frames, making it a significantly more complex problem. While supervised deep learning methods have achieved state-of-the-art results, they typically require large datasets of paired noisy-clean video datasets that are scarce or impractical to obtain in real-world settings …

Homomesies And Toggleability Spaces, Alec Mertin

All Dissertations

We study the homomesy phenomenon under the rowmotion operator acting on order ideals of posets. We provide details for the extensions of several results in the literature concerning homomesies of toggleability statistics from finite to infinite orbits, which allows us to obtain homomesies for piecewise-linear and birational rowmotion, even in the case of infinite orbits. Integral to this is a novel generalization of a result in the literature, which relaxes the conditions needed to lift a statistic.

We completely describe the order ideal (resp. antichain) toggleability space for general fences: the space of statistics which are linear combinations of …

Recent Studies On The Reactions And Synthesis Of Nitrogen And Sulfur - Containing Heterocyclic Pharmacophores., Bernard Louis Adjei

Electronic Theses and Dissertations

Nitrogen and sulfur-containing heterocycles remain significant classes of compounds in the field of medicinal and organic chemistry due to their unique structural features and diverse biological activities which make them of considerable interest for drug discovery and development. Notable examples of the nitrogen containing heterocycles include the phthalimide and glutarimide rings present in thalidomide. Thalidomide, a potent small-molecule therapeutic agent, precipitated a significant global drug crisis due to its teratogenicity, causing congenital malformations in phocomelia infants, which remains unrivaled over half a century ago. Currently, thalidomide derivatives are extensively utilized in the therapeutic management of various hematological malignancies. Recent investigations …

Evometric: An Interactive Framework For Scalable Visual Analytics Of Time Series Data With Dynamic Changes., Jiahang Huang

Electronic Theses and Dissertations

In today's data-intensive landscape, rapid advances in digital sensing and recording technologies have enabled the acquisition of high-resolution multimodal time series data, capturing intricate real-world dynamics across various domains such as healthcare, behavioral science, and environmental monitoring. However, the complexity and scale of these datasets present significant analytical challenges, particularly in understanding dynamic changes at both individual and cohort levels. This dissertation introduces EvoMetric, a novel visual analytics framework designed to support scalable exploration and analysis of large-scale multimodal time series data with dynamic changes. EvoMetric seamlessly integrates individual-level temporal dynamics with population-level comparative insights, enabling users to visually …

Cotton Yield And Tissue-Potassium Response To Potassium Fertilization, Maria Paula Ramos Do Prado

Graduate Theses and Dissertations

Potassium (K) deficiency is a common yield-limiting factor in cotton (Gossypium hirsu tum L.) production across the U.S. Cotton Belt. Adequate fertilizer-K management is paramount to ensure optimum plant growth and development, minimizing yield losses. Currently, fertilizer K recommendations for cotton in Arkansas are derived from expected yield responses of other crops, with no calibrated fertilizer-K rates or critical tissue-K concentrations defined to maxim ize crop yield. Our research objectives were i) to investigate cotton yield response and fiber qual ity to K fertilization on soils with different K availability; ii) to correlate relative yield with tis sue-K concentrations at …

Algorithmic Recourse In Sequential Decision-Making For Long-Term Fairness, Francisco Gumucio

Graduate Theses and Dissertations

Algorithmic decision-making systems are increasingly being deployed in high-stakes domains such as criminal justice, education, and financial services. While machine learning models have demonstrated significant utility in automating complex decisions, they have also raised substantial concerns regarding fairness and equity. Much of the existing work in algorithmic fairness has focused on static, one-shot settings, where interventions are aimed at miti- gating bias in a single decision. However, many real-world systems operate sequentially, where decisions made at one point in time can influence future outcomes through dynamic feedback loops. In such scenarios, addressing fairness at only one decision point can be …

Revolutionizing Alzheimer’S Diagnosis: A Hybrid Deep Learning Approach For Enhanced Mri Analysis, Hassan A. Ahmed, Syed H. Ahmed, Kyle Nash

Business Faculty Publications

Alzheimer’s Disease (AD) is a neurodegenerative disorder that primarily affects the elderly, causing cognitive decline and memory loss. Traditional diagnostic methods, such as neuropsychological tests and cerebrospinal fluid analysis, are invasive and time-consuming. Neuroimaging techniques like MRI and PET provide valuable insights but require manual analysis by specialists. This study proposes a hybrid model combining EfficientNetB0, a deep learning architecture, with Convolutional Neural Networks (CNN) to automate AD detection in MRI scans. The model uses data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which includes over 200 MRI scans and clinical information. Our results show that the hybrid model …

Service With A Smile Or Salesperson Mirroring? Understanding The Flow Of Emotional Contagion In Sales Encounters, Vinh Quoc Trong Luong

Theses and Dissertations in Business Administration

This study examines the directionality of emotional contagion in sales interactions, addressing a critical gap in understanding whether emotions flow primarily from the salesperson to the customer, from the customer to the salesperson, or bidirectionally. While prior research emphasizes customer-driven emotional flow or bidirectional alignment, this study challenges these assumptions by employing categorical Cross-Recurrence Quantification Analysis (CRQA) to assess temporal emotional synchronization in sales dialogues. Leveraging automated sentiment analysis and multi-agent AI evaluation for performance metrics, the research analyzes 166 sales interactions to quantify emotional influence dynamics. Results reveal that salespeople predominantly lead emotional exchanges, exhibiting stronger and more stable …

The Influence Of Monsoon Variability On The Circulation Of The Near-Surface Indian Ocean And The Depth-Integrated Chlorophyll, Marufa Ishaque

OES Theses and Dissertations

The Indian Ocean experiences a strong semiannual reversal of monsoon winds, which determines the weather and climate of Asia, including freshwater fluxes between the atmosphere, land, and ocean. Studies and model projections suggest that the timing and intensity of the seasonal monsoon have already started to change, with more dramatic changes likely in the future. However, the impact of these changes on the Indian Ocean circulation system, including the inter-basin salt/freshwater transport between the Bay of Bengal and the Arabian Sea, remains unclear. To better understand how monsoon variability affects Indian Ocean circulation patterns, a Regional Ocean Modeling System simulation …

Enhancing Data Usability For People With Visual Impairments, Yash Prakash

Computer Science Theses & Dissertations

Human-Data Interaction (HDI) focuses on how individuals engage with, analyze, and extract insights from data. For blind and visually impaired (BVI) users, interacting with data, whether searching for relevant information from structured data (e.g., web data items) or interpreting visualizations to draw insights (e.g., data charts), presents significant challenges. These challenges arise from the complexity and sheer volume of data which cannot be effectively handled by assistive technologies like screen readers and screen magnifiers. Despite its importance, data usability, the ease, efficiency, and satisfaction with which BVI individuals can interact with the data, has received less attention compared to data …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …