Engineering Commons^™

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Ferroelectricity And Multiferroicity In Ultrathin Films Of Unconventional Transition Metal Oxide, Xin Li

Department of Physics and Astronomy: Dissertations, Theses, and Student Research

Ferroic orders of transition metal oxides, such as ferroelectricity and ferromagnetism, are determined by the intercoupling between spin, charge, lattice. Consequently, ferroic epitaxial thin films have attracted wide interest due to the profuse novel phenomena and the great application potentials for modern electronics. However, the mainstream of the study for transition metal oxides has focused on perovskite structure, limiting the understanding and discovery of novel phenomena associate with ferroic orders. In this thesis, the epitaxial films of hexagonal rare-earth ferrites (h-RFeO₃) and fluorite-structure hafnia oxide (HfO₂) are studied comprehensively, providing new experimental and theoretic …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

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

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

Evaluation Of Performance Characteristics And Groundwater Contamination Risks Associated With On-Farm Swine Carcass Disposal Via Composting And Shallow Burial With Carbon, Gustavo Castro Garcia

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

This dissertation evaluates the performance of three on-farm swine carcass disposal methods: whole carcass composting (WCC), ground carcass composting (GCC), and shallow burial with carbon (SBC), focusing on biosecurity factors and groundwater contamination risks. Foreign animal diseases (FADs), such as African swine fever and classical swine fever, pose severe economic and animal well-being risks if introduced to the United States. With confirmation of an FAD, swine movement will be halted, creating an urgent need for practical, biosecure, and environmentally responsible on-farm disposal strategies for swine carcasses. Nebraska, as a leading swine-producing state, exemplifies the vulnerability of high-density livestock regions to …

Mass Effects On Energy Transfer Paths In Nonlinear Vibrating Systems, Manal Mustafa

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

This dissertation examines the role of mass in nonlinear systems, uncovering its role in enabling passive energy redistribution and robust vibration control in both idealized and real-world structures. Focusing on a strongly nonlinear two-degree-of-freedom system, it investigates how changes in mass ratio influence the dynamics of energy transfer, nonlinear normal modes (NNMs), and dissipation behavior.

A number of significant contributions are introduced in this work beginning with the introduction of the frequency-energy-peaks (FE-pks) plot, a novel tool that visualizes how energy flows through the system, revealing transient resonance orbits, internal resonance effects, and effectively capturing the different nonlinear phenomena with …

Resilience Of Interdependent Transportation And Healthcare Systems: A Simulation-Driven Framework Incorporating Social Equity And Facility Optimization, S. Yasaman Ahmadi

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

The overarching goal of this dissertation is to develop an integrated, simulation-driven framework to assess and enhance the resilience of interdependent transportation and healthcare systems before, during, and after natural hazards. By examining how spatial disruptions, social vulnerability, operational constraints, and infrastructure interdependencies affect access to and functionality of hospitals, this research offers a multifaceted evaluation of system performance under stress. This dissertation examines the impact of natural hazards, particularly floods, on the interdependent transportation and healthcare systems. The proposed approach combines and advances GIS-based network analysis, graph-theoretical modeling, and discrete-event simulation to evaluate system performance under different scenarios. The …

Decoding Anisotropic Porous Medium: A Synergy Of Lattice Boltzmann Modelling And Operator Learning To Predict Permeability As A Function Of Orientation, Soumya Shouvik Bhattacharjee

Open Access Theses & Dissertations

Understanding the directional properties of porous media is essential for accurately predicting flow behavior, reactive transport, and fluid-solid interactions in systems ranging from geothermal reservoirs to energy storage devices and biological tissues. Directional variations in permeability - reflecting a medium's response to flow at different angular orientations - are particularly important for complex, inherently anisotropic geometries. In this study, we employ a Lattice Boltzmann (LBM) model to calculate directional permeabilities from porous media images subjected to varying flow inlet angles. Three classes of porous media were investigated: (1) synthetic media with circular grains, serving as isotropic baselines; (2) synthetic media …

A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au

Open Access Theses & Dissertations

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then …

Laser Scan Path Design For Controlled Microstructure In Additive Manufacturing With Integrated Reduced-Order Phase-Field Modeling And Deep Reinforcement Learning, Augustine Twumasi

Open Access Theses & Dissertations

Laser Powder Bed Fusion (L-PBF) is a well-established additive manufacturing technique for fabricating intricate metal components with exceptional precision. A significant challenge in L-PBF is the formation of complex microstructures that influence final material properties. We propose a physics-guided, machine learning-aided approach to optimize scan paths for desired microstructure outcomes, such as equiaxed grains. We employed a phase-field method (PFM) to model the evolution of the crystalline grain structure. To reduce computational costs, we trained a surrogate machine learning model, a 3D U-Net convolutional neural network, using single-track phase-field simulations with varying laser powers to predict crystalline grain orientations based …

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 …

Low-Level Memory Attacks On Edge Assisted Robotic Applications, William Arnold

Master of Engineering Theses

This thesis investigates how low-level memory faults can undermine edge-assisted robotic systems that rely on memory optimization. As robots are utilized in real world applications, the ability to operate safely and successfully in mission critical deployment becomes important. To help achieve these goals, developers are increasingly starting to place computation nodes at network edges to meet latency and reliability requirements. Edge nodes, however, are resource-constrained and resources conservation techniques such as Kernel Same-page Merging (KSM) are enabled to deduplicate identical pages across processes or virtual machines. This thesis shows that this optimization technique quietly widens the attack surface and can …

Machine Learning Based Medical Ultrasound Image Classification And Grad-Cam Interpretation, Victoria C. Hemphill

All Theses

This work takes a step in creating a diagnostic tool for the classification decision process of Achilles tendinopathy using ultrasound images. An attention-based multiple instance learning model is developed to classify the images. Typically, doctors capture multiple ultrasound images of the Achilles tendon during a study to determine a complete diagnosis. Multiple instance models adopt this behavior by providing a single label for a set of instances (images). The images are grouped into ”bags” at the study level and passed into the model. The MIL model then uses its attention property to assign an importance score to each image to …

Autoregressive Temporal Modeling For Advanced Tracking-By-Diffusion, Pha Nguyen, Rishi Madhok, Bhiksha Raj, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Object tracking is a widely studied computer vision task with video and instance analysis applications. While paradigms such as tracking-by-regression,-detection,-attention have advanced the field, generative modeling offers new potential. Although some studies explore the generative process in instance-based understanding tasks, they rely on prediction refinement in the coordinate space rather than the visual domain. Instead, this paper presents Tracking-by-Diffusion, a novel paradigm for object tracking in video, leveraging visual generative models via the perspective of autoregressive models. This paradigm demonstrates broad applicability across point, box, and mask modalities while uniquely enabling textual guidance. We present DIFTracker, a framework that utilizes …

A Study On The Propagation And Exploitation Of Structured Light In Underwater Turbulence, Jaxon P. Wiley

All Dissertations

The development and optimization of optical systems will play a pivotal role in the continued exploration and exploitation of the world’s underwater environments. These systems offer advantages in many sectors, and includes applications in areas such as high-speed communication, advanced sensing and imaging, and environmental characterization and monitoring. Underwater environments offer a plethora of challenges, however, and mitigating these obstacles remains an arduous task. In this work, the inherent advantages of structured light are leveraged to optimize optical system performance through non-ideal underwater conditions. Additionally, fundamental relationships between the generation of specified structured modes and their interactions with complex environments …

Synthesis, Structural Characterization And Optical Studies Of Silver-Indium-(Zinc)-Chalcogenide Fluorescent Quantum Dots, Sujal Acharya

Graduate Theses and Dissertations

Developing a non-toxic, high-performance fluorescent nanomaterial is crucial for overcoming the environmental and health restrictions of current cadmium, and lead based quantum dots (QDs), which limit the application of quantum dots in optoelectronics and bioimaging. In this thesis, we synthesized environmentally friendly AgInS2 QDs by a colloidal method, systematically altering the In/Ag precursor ratio from 2 to 6 to study the impact on their optical and photophysical properties. Our goals were to find the optimal stoichiometry for maximum quantum efficiency and stability. We also investigated further improving optical and photophysical properties through shelling with ZnS. The emission spectra appeared broad, …

Assessing The Robustness Of Test Selection Methods For Deep Neural Networks, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Lei Ma, Yves Le Traon

Research Collection School Of Computing and Information Systems

Regularly testing deep learning-powered systems on newly collected data is critical to ensure their reliability, robustness, and efficacy in real-world applications. This process is demanding due to the significant time and human effort required for labeling new data. While test selection methods alleviate manual labor by labeling and evaluating only a subset of data while meeting testing criteria, we observe that such methods with reported promising results are simply evaluated, e.g., testing on original test data. The question arises: are they always reliable? In this article, we explore when and to what extent test selection methods fail. First, we identify …

Drift Dynamics Of Early Life-Stage Invasive Carps, Saurav Karki

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Asian carp species pose significant ecological threats to North American freshwater systems due to their invasive nature and prolific reproduction. Their semi buoyant eggs develop while drifting downstream with the river current. Understanding early life-stage transport is therefore critical for predicting recruitment potential and guiding management efforts. Existing fluvial egg drift models, including those relying on 1-D hydraulics and fully 3-D CFD-based models, are either inadequate for complex braided rivers or too computationally demanding for large-scale application. The Platte River in Nebraska, characterized by wide, shallow, multi-threaded channels, is dominated by two-dimensional flow conditions, making a depth-averaged 2-D modeling approach …

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®, …

Two Analytic Issues Arising From Models In Probability And Materials Science, Giang Vu Thanh Nguyen

Mathematics & Statistics Theses & Dissertations

This dissertation explores two distinct topics centered on mathematical models in probability theory and materials science. The first part investigates a series of functions derived from an adaptive algorithm designed to address the score-based secretary problem, a classic challenge in probability theory. This problem involves making immediate decisions to select the best candidate from a sequence of interviews. The algorithm aims to maximize the probability of selecting the optimal candidate based on observed scores. We prove two fundamental analytic properties of this sequence of functions as a theoretic support of the algorithm: first, the functions in the sequence each possess …

Advanced Analytical Biosensing For Cancer Detection And Neural Diagnostics Using Tapered Optical Fiber (Tof), Protonic, And High Throughput Microplate –Based Technologie, Bayan Hassan Alharbi

Chemistry & Biochemistry Theses & Dissertations

This dissertation investigates the development and application of advanced biosensing technologies to enhance early disease detection, neurological diagnostics, and bioactive compound evaluation. The research spans four key areas. First, it introduces tapered optical fiber (TOF)-based plasmonic biosensors for the non-invasive detection of prostate cancer, demonstrating high sensitivity and specificity compared to conventional diagnostic methods.

Second, it explores the use of fluorescent biosensors to test the Transmembrane Electrostatically Localized Proton (TELP) theory, shedding light on the role of localized protons in neuronal signaling and energy transfer. Third, the work presents a high-throughput, microplate-based biosensing platform for analyzing mitochondrial function under nanosecond …

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 …

Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters

Electrical & Computer Engineering Theses & Dissertations

Particle accelerators play a crucial role in our understanding of matter and the universe and have numerous practical applications in various fields. These devices enable scientists to examine the smallest components of matter, study the forces that govern their interactions, and probe conditions from the early universe. Moreover, accelerators are valuable in medicine, industry, and research, enhancing imaging methods, cancer therapies, and manufacturing techniques. As the experiments conducted at these facilities evolve and require higher precision, improved particle sources must continue to advance to keep up with their requirements. To do that, we enhanced the design of spin polarized electron …

A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez

Mathematics & Statistics Theses & Dissertations

Nematic liquid crystals are a state of matter that exhibit properties between those of conventional liquids and solid crystals. Their unique ability to align molecules in specific directions makes them essential in various applications, including display technologies and advanced materials. To model their complex behavior, mathematical frameworks such as the Q-tensor model are used to describe the orientation and degree of molecular order. In this work, we introduce a numerical scheme for a two-dimensional (2D) dynamic Q-tensor model, which is formulated as an L²-gradient flow driven by the liquid crystal free energy and incorporates a singular potential to …

Mathematical Modeling Of Effects Of Tumor Location On Lung Function., Lamargaret Temukisa Johnson

Master of Engineering Theses

Lung cancer has the highest rates of incidence and mortality of all cancers. Most lung cancer tumors are Non-Small Cell Lung Cancer (NSCLC). NSCLC patients with lesions in the upper lobes are found to have better prognosis compared to those with lesions in the middle and lower lobes. Previous studies have suggested various causes for this discrepancy at both the organ-scale and tissue-scale. To model NSCLC growth in different locations within the lung, an organ scale lung model and tissue scale tumor model were coupled through the tissue pressure, and oxygen and carbon dioxide partial pressures. The coupling was used …

A Three-Stage Matheuristic For The Blood Stochastic Inventory Routing Problem, Vincent F. Yu, Nabila Salsabila, Aldy Gunawan, Aldy Gunawan, Nurhadi Siswanto

Research Collection School Of Computing and Information Systems

This research introduces a blood distribution system under vendor-managed inventory that considers uncertain supply and demand. We present it as the Blood Stochastic Inventory Routing Problem, formulating it as a two-stage stochastic programming model. To solve this problem, this study proposes a three-stage matheuristic that combines a perturbation heuristic, Adaptive Large Neighborhood Search, and an exact approach. From historical data of Surabaya Blood Center in Indonesia, six sets of new instances are generated under different settings. Computational results show that our proposed three-stage matheuristic outperforms CPLEX and a two-stage matheuristic by gaining optimal or better solutions within a significantly shorter …

Discovering And Designing Novel Perovskite Photovoltaic Materials Via Machine Learning, Junyeong Ahn

Discovery Undergraduate Interdisciplinary Research Internship

Perovskite semiconductors are promising materials for high-efficiency photovoltaics due to their outstanding optoelectronic properties, emerging as a sustainable energy source through solar cell applications. Perovskites with the ABX₃ composition (A, B = metal or organic cations with varying oxidation states; X = chalcogen or halogen anions) have gained interest for their excellent phase stability and compositional tunability. However, combinatorial possibilities arising from the many choices of A, B, and X site species, and their respective mixing fractions, a large number of possible ABX₃ perovskites remain undiscovered. In this work, we used machine learning (ML) methods to design new stable and …

Phase Nanoscopy With Correlated Frequency Combs, Xiaobing Zhu

Optical Science and Engineering ETDs

In this dissertation a sensing method applying to any physical quantity that modifies optical phase is developed. Two pulses are produced inside a synchronously pumped Optical Parametric Oscillator, generating two identical, undistinguishable frequency combs. The physical quantity to be measured applies a small phase shift/round trip to one of the pulses, resulting in a frequency shift of the corresponding comb. The latter frequency is measured as a beat by interfering the two combs on a detector. A world record resolution, close to the quantum limit, of 0.033 nanoradian (corresponding to 0.006 fm in displacement) is achieved. A detailed analysis of …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Numerical Simulation Of The Pitting Corrosion Behavior Of Stainless Steel Bellows Influenced By Varying Liquid Film Thicknesses, Lu-Jun Ren, Guo-Min Li, Zhen-Xiao Zhu, Hai-Yan Xiong, Bing Li

Journal of Electrochemistry

To advance the understanding of the corrosion behavior of stainless steel bellows in marine atmospheric environments and enhance the precision of service life predictions, this study employs finite element simulations to investigate the pitting corrosion rates and pit morphologies of bellows peaks and troughs under varying electrolyte film thicknesses. The model incorporates localized electrochemical reactions, oxygen concentration, and homogeneous solution reactions. For improved computational accuracy, the fitted polarization curve data were directly applied as nonlinear boundary conditions on the electrode surface via interpolation functions. Simulation results reveal that the peak regions exhibit faster corrosion rates than the trough regions. With …

Research Progress On Thermal Management Of Lithium-Ion Batteries, Hong-Da Li, Qiu-Wan Shen, Zhao-Yang Zhang, Xin-Yue Zhao, Yuan Wei, Shi-An Li

Journal of Electrochemistry

Nowadays, new energy technologies are developing rapidly, energy storage systems are widely used, and lithium-ion batteries occupy a dominant position among them. Therefore, it is also very important to ensure their performance, safety and service life through thermal management technology. In this paper, the causes of thermal runaway of lithium batteries are reviewed firstly, and three commonly used thermal management technologies, namely, air cooling, liquid cooling and phase change material cooling, are compared according to relevant literature in recent years. Air cooling technology has been widely studied because of its simple structure and low cost, but its temperature control effect …