Engineering Commons^™

Bht-Qaoa: The Generalization Of Quantum Approximate Optimization Algorithm To Solve Arbitrary Boolean Problems As Hamiltonians, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the “Boolean-Hamiltonians Transform for QAOA” (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial optimization problems using QAOA, the BHTQAOA adds an additional capability to QAOA to find all optimized approximated solutions for Boolean problems, by transforming such problems from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a transformation, we noticed that the total utilized numbers of qubits and quantum gates …

Simultaneous Modulation Of Pulse Charge And Burst Period Elicits Two Differentiable Referred Sensations, T. R. Benigni, A. E. Pena, S. S. Kuntaegowdanahalli, J. J. Abbas, R. Jung

Biomedical Engineering Faculty Publications and Presentations

Objective. To investigate the feasibility of delivering multidimensional feedback using a single channel of peripheral nerve stimulation by complementing intensity percepts with flutter frequency percepts controlled by burst period modulation. Approach. Two dimensions of a distally referred sensation were provided simultaneously: intensity was conveyed by the modulation of the pulse charge rate inside short discrete periods of stimulation referred to as bursts and frequency was conveyed by the modulation of the period between bursts. For this approach to be feasible, intensity percepts must be perceived independently of frequency percepts. Two experiments investigated these interactions. A series of two alternative forced …

An Overview Of Additively Manufactured Metal Matrix Composites: Preparation, Performance, And Challenge, Liang Yu Chen, Peng Qin, Lina Zhang, Lai Chang Zhang

Research outputs 2022 to 2026

Highlight Recent progresses in additive manufacturing on metal matrix composites are reviewed. Additive manufacturing technologies for metal matrix composites are summarized. The characteristics of feedstocks and reinforcements are introduced. Mechanical property of additively manufactured metal matrix composites is reviewed. Challenges of additively manufactured metal matrix composites are discussed.

Sub-Surface Geospatial Intelligence In Carbon Capture, Utilization And Storage: A Machine Learning Approach For Offshore Storage Site Selection, Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer

Research outputs 2022 to 2026

This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a high-resolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expert-weighted …

Carbon Fiber And Carbon Fiber Composites—Creating Defects For Superior Material Properties, Ashis Sutradhar Nitai, Tonny Chowdhury, Md Nafis Inam, Md Saifur Rahman, Md Ibrahim H. Mondal, M. A.H. Johir, Volker Hessel, Islam Md Rizwanul Fattah, Md Abul Kalam, Wafa Ali Suwaileh, John L. Zhou, Masoumeh Zargar, Mohammad Boshir Ahmed

Research outputs 2022 to 2026

Recent years have seen a rise in the use of carbon fiber (CF) and its composite applications in several high-tech industries, such as the design of biomedical sensor components, 3D virtual process networks in automotive and aerospace parts, and artificial materials or electrodes for energy storage batteries. Since pristine CF have limited properties, their properties are often modified through a range of technologies, such as laser surface treatment, electron-beam irradiation grafting, plasma or chemical treatments, electrophoretic deposition, carbonization, spinning-solution or melt, electrospinning, and sol–gel, to greatly improve their properties and performance. These procedures cause faulty structures to emerge in CF. …

Sex Differences In Active Avoidance And Neural Circuit Mechanisms In Contextual Fear Generalization, Carly Vincent

Theses and Dissertations

The current studies were aimed to investigate two behavioral hallmarks of anxiety and stress-related disorders, avoidance responses and the over-generalization of fear. In the first set of studies, active avoidance and extinction learning, that parallels exposure therapy in preclinical rodent models, were used. It is known that stress can influence aversive learning and extinction training, which can result in poor extinction retention. However, it is not well understood how the stress response is facilitating extinction resistance in active avoidance learning across sexes. Therefore, the first set of studies aimed to investigate the role of biological sex and glucocorticoid receptor (GR) …

Design And Application Of Redox-Mediated Flow Electrode Electrodialysis For Ion Removal And Recovery, Rongxuan Xie

Theses and Dissertations

To meet the growing demand for freshwater driven by population growth and rising living standards, the first desalination plants were established in the late 1950s. As energy costs have risen over time, research has increasingly focused on reducing the overall cost of water treatment. Electrodialysis (ED), which facilitates the migration of anions and cations across ion exchange membranes under the influence of an electric field, has gained significant attention as a treatment method for saline water and brine due to its simplicity, low cost, and scalability. However, its traditional batch operation mode and the potential for generating flammable gases have …

Autonomous Real-Time Model Updating Within Digital Twin Frameworks For Thermal Systems, Braden Robert Priddy

Theses and Dissertations

As engineering systems increase in scale and complexity in the era of the Fourth Industrial Revolution, data-driven solutions will become essential in enabling the next generation of these systems. One of the trending tools that can aid in this transition is digital twins. As physical systems degrade throughout their life cycles, their behavior also changes. Digital twins use data assimilation to continuously update virtual models to represent the current state of their physical counterparts. A reliable digital twin can be leveraged by a system operator to perform diagnostics, optimize, and tests without ever needing the physical system. However, implementing effective …

Advancement Of The Zinc Ion Battery: Polymer Electrolyte And Electrode Development For Aqueous Zinc-Ion Batteries, Roya Rajabi

Theses and Dissertations

Rechargeable aqueous zinc-ion batteries (ZIBs) have garnered significant attention in recent years as a promising candidate for stationary large-scale energy storage due to their distinct safety features and cost-effectiveness compared to conventional lithium-ion batteries. Despite their great potential, ZIBs are currently facing critical challenges for commercialization, including poor cycle stability at low discharge rates, lower energy density (Wh/kg) and higher self-discharge rate. These issues must be addressed for them to become a viable energy storage solution. The above challenges are fundamentally rooted in the bulk properties of electrolytes and interactions with electrodes, such as; 1) formation of insulating layered double …

Augmented Reality (Ar) And Virtual Reality (Vr)-Based Data Visualization Frameworks For The Manufacturing Industry, Nitol Saha

Theses and Dissertations

Industry 4.0 is transforming the manufacturing industry by integrating digital technologies throughout the production lifecycle, leading to the development of smart factories. The integration of Augmented Reality (AR) and Virtual Reality (VR) alongside smart manufacturing is transforming traditional industry practices by establishing a robust cyber-physical infrastructure, enhanced data visualization, and improved task execution. This thesis aims to develop AR and VR-based data visualization frameworks for the manufacturing industry, focusing on detailed implementation strategies, initial results, and key findings of these frameworks. One major focus of these frameworks is the generalization of the technological assets so that these frameworks can be …

Augmented Reality Based Maintenance Operations And Training, Victor Scott Gadow

Theses and Dissertations

Augmented Reality (AR) is the process of superimposing virtual information on top of objects or other structures in the real-world environment. It is considered one of many paradigms included in the idea of smart manufacturing and can enable a mixture of the other technologies involved. A large increase in research on AR has been seen in the past decade as technologies have begun to evolve to become more robust and reliable. It has been implemented into several key areas of manufacturing such as maintenance, assembly, training, and quality control. The rise of industry 4.0 is changing how data is used …

Characterizing Mechanical Behaviors Of Railroad Ballast Based On Large-Scale Triaxial Tests, Shihao Huang

Theses and Dissertations

Ballast, typically composed of large unbound aggregates with uniform gradation, is essential for the structural integrity of railroad tracks, facilitating load transfer, drainage, and ensuring stability and resilience. The mechanical behaviors of ballast, including shear resistance, resilient modulus, and permanent deformation, are critical for effective railroad design and maintenance, directly impacting track geometry. Degradation of these mechanical properties can lead to significant track-related issues, such as excessive settlement and train derailments, resulting in substantial property damage, injuries, and fatalities. This dissertation aims to characterize mechanical behaviors of railroad ballast from perspectives of aggregate interaction, cyclic loading pulses and climatic conditions, …

Data-Driven Discovery Of Extreme Thermal Materials By High-Throughput Computation And Machine Learning, Joshua Ojih

Theses and Dissertations

The quest for materials with extraordinary properties has been a longstanding endeavor in material science and engineering, driving future technological advancement. However, the discovery of such materials is non-trivial. Recent advancements in computational methods, particularly the integration of machine learning (ML) techniques with density functional theory (DFT), have opened new avenues for accelerating the discovery of materials with exceptional and extreme properties. This dissertation focuses on developing a synergistic approach and workflow combining ML and DFT to identify materials with properties that are pushed beyond current limits, using lattice thermal conductivity (LTC) as a case study of the workflow.

We …

Electroless Deposition And Galvanic Displacement For The Control Of Supported Nanoparticle Size And Composition, Haiying Zhou

Theses and Dissertations

Catalyst performances can be greatly affected by properties such as particle size, composition, geometry, and cleanliness of the active surface. To achieve optimal catalytic performance, appropriate synthesis methods are required to synthesize the desired structures. Supported metal particle catalysts are usually prepared by impregnation and precipitation methods in industry. Although these two methods are simple, they exhibit poor control over surface morphology, leading to a wide particle size distribution and uncontrolled distribution of different metal components. In this dissertation, three topics on controlling surface morphology are discussed.

For structure sensitive reactions, reactivity and selectivity can be strongly affected by particle …

Rational Catalyst Synthesis For Sustainable Energy Applications, Alaba Upe Ojo

Theses and Dissertations

Energy accounts for two-thirds of the global climate challenges. Effective utilization of green hydrogen or finding cleaner ways to improve energy generation is necessary to combat climate crisis and allow for a more sustainable future. This work evaluates three heterogeneous catalytic processes which contribute to a sustainable future. In chapter 2, we studied the hydrogenation of toluene to methylcyclohexane; a reaction that can potentially serve as a medium for hydrogen storage and help to overcome the safety challenges of transporting gaseous hydrogen. We found that palladium catalysts on certain carbons exhibited significantly enhanced toluene hydrogenation rates compared to activated carbons …

Real-Time Robot Pose Estimation For Industry 4.0: Enhancing Motion Validation And Monitoring With A Vision-Based Framework, Jad Samaha

Theses and Dissertations

Amidst the era of Industry 4.0, robots became integral to automated production lines by performing complex tasks with high precision and adapting to changing production needs in real-time. Therefore, ensuring their precise and reliable operation is paramount for maintaining high-quality standards and operational efficiency. This invoked the need for an automated motion validation tool that guarantees accurate program task execution, by detecting deviations or anomalies that may indicate mechanical faults or software errors. Given the exponential advancements in AI, particularly in Computer Vision, a vision-based solution is ideal for ensuring the correct positioning of a robot in real-time. Operating independently …

Redefining Gas Turbine Engine Development: A Digital Twin Framework Informed By Operational Dynamics And Numerical Analysis, Sowmya Raghu

Theses and Dissertations

Gas Turbine Engines (GTEs) serve as primary propulsion systems in aviation and are key for power generation units in various industrial applications. The conventional Gas Turbine Engine Development and Monitoring Lifecycle (EDML) typically encompasses six stages: preliminary design, numerical analysis, prototyping and testing, manufacturing, systems integration, and subsequent systematic monitoring processes. This dissertation redefines the gas turbine engine design and development process by synergistically integrating design capabilities, real-time operational data, and predictive maintenance through the implementation of digital twins. The primary objective is to establish a comprehensive framework for gas turbine engine design by utilizing thermodynamic and aerodynamic modeling, supported …

Tracking Deposition And Settling Characteristics Of Airborne Metallic Particulate, William Kahale Caspino

Theses and Dissertations

Transport and deposition of metal particles is a source of environmental and public health concern. Understanding how ambient conditions and the properties of the particles interact to influence the dispersal range can provide information on and the possible extent of environmental contamination. Controlled laboratory experiments and modeling were utilized to determine when such particles would deposit. Particle samples containing heavy metals, a Palladium-dominant alloy and a Nickel/Aluminum dominant alloy LANA, were released in a static column, and the vertical displacement was tracked utilizing a Phantom v7.3 camera and a camera lens. Electron microscopy revealed that particles are not spherical and …

Thermal Management Of Power Electronic Devices Using Single Crystal Aln Heat Spreaders, Md Abdullah-Al Mamun

Theses and Dissertations

Due to high critical electric field and saturation velocity, wide bandgap (WBG) III-Nitride semiconductor materials (AlxGa1-xN) and their heterostructures have gone through extensive research in academia and industry for applications requiring high-voltage, high-current, high-frequency, and high-temperature transistor operation. AlGaN/GaN high electron mobility transistor (HEMT) has been commercialized for 650-V power converters, smartphones, PCs, USB wall power outlets, on-board and off-board EV chargers, and numerous other applications. However, AlGaN/GaN HEMTs grown on low-cost sapphire or Si substrates face significant self-heating issues due to the poor thermal conductivity of the substrate. The high thermal resistance (RTH) and heat capacity of the substrates …

A Process Planning Software For Modeling Parameter Behavior In Automated Fiber Placement, Benjamin Jeffrey Francis

Theses and Dissertations

The manufacturing of large-scale, geometrically complex composite structures is often accomplished today using the Automated Fiber Placement (AFP) process. AFP utilizes a fiber placement end effector and a gantry or robotic kinematic system to lay up groups of composite tows, iteratively building the complete structure. The reduced width of each tow allows the deployment of AFP for builds with significant curvature, unlike other automated methods such as automated tape laying. Despite its proven track record and widespread use, the current AFP process contains inefficiencies and suffers from workflow bottlenecks that significantly increase cycle time, material wastage, and overall cost. A …

Development Of Avalanche Photo Detector For Uv-C Using Gallium Oxide Material System, Md Ghulam Ghulam Zakir

Theses and Dissertations

This work describes the metal-organic chemical vapor deposition (MOCVD) optimization process for epitaxially grown gallium oxide (Ga2O3) layers, electrical field distribution, and the design for Avalanche Photo Detector (APD). MOCVD is a well-accepted process in the semiconductor industry, as it is well-known for its precise deposition of ultra-thin, high-quality semiconductor layers with extraordinary control over composition and thickness. The choice of Ga2O3 is due to r its distinctive properties of ultra-wide bandgap and low turn-on resistance. Thus, Ga₂O₃-based materials can withstand high voltage and current with the least energy loss, making them an attractive choice for …

Unveiling Similarities In The Code Of Life: A Detailed Exploration Of Dna Sequence Matching Algorithm, Mahmoud Y. Shams, Romany M. Farag, Dalia A. Aldawody, Huda E. Khalid, Ahmed K. Essa, Hazem M. El-Bakry, A. A. Salama

Neutrosophic Systems with Applications

Identifying similar DNA sequences is crucial in various biological research endeavors. This paper delves into the intricate workings of a specific algorithm designed for this purpose. We provide a systematic explanation, exploring how the algorithm handles user input, reads stored DNA sequences, utilizes the Word2Vec model for vector representation, and calculates sequence similarity using diverse metrics like Cosine Similarity and Neutrosophic Distance. Additionally, the paper explores the incorporation of neutrosophic values to account for uncertainty in the comparisons. Finally, we discuss the extraction of results, including matched sequences, similarity scores, and accuracy measures. This in-depth exploration provides a clear understanding …

An Approach To Multi-Attribute Decision-Making Based On Single-Valued Neutrosophic Hesitant Fuzzy Aczel-Alsina Aggregation Operator, Raiha Imran, Kifayat Ullah, Zeeshan Ali, Maria Akram

Neutrosophic Systems with Applications

A single-valued Neutrosophic hesitant fuzzy set (SVNHFS) is a combination of a single-valued neutrosophic set (SVNS) and hesitant fuzzy set (HFS) that has been developed to address insufficient, unreliable, and vague environments in which each element has several possible options determined by the truthiness, indeterminacy and falsity value. By considering this, in this paper, we have proposed the Aczel-Alsina aggregation operator (AAAO) for SVNHFS, which is more flexible t-norm and t-conorm than the other and due to the flexible nature of parameters to solve Multi-Attribute decision making (MADM) problems. Further, the score function, accuracy function, and certainty function of SVNHFS …

Burn Cost Modeling For Surface Grinding Optimization, Taiwo Fasae

Dissertations (1934 -)

Surface grinding plays a pivotal role in the machining industry, constituting roughly 25% of all machining operations worldwide. Its precision and efficiency are crucial, particularly in sectors requiring high-quality surface finishes, such as aerospace and semiconductor manufacturing. For thermal damage prevention, traditional approaches to parameter selection use thresholds to exclude burn-prone parameters. However, by omitting the cost of burn, the threshold-exclusion strategy yields outcomes that fail to reflect the true costs of grinding. This dissertation introduces a novel burn cost model that transcends these limitations, offering a more nuanced and cost-effective approach to managing grinding burn. The burn cost model …

Real-Time Synthesis Of A Nonuniformly Correlated, Partially Coherent Beam Using An Optical Coordinate Tansformation, Milo W. Hyde Iv

Faculty Publications

We design, build, and validate an optical system for generating light beams with complex spatial coherence properties in real time. Beams of this type self-focus and are resistant to turbulence degradation, making them potentially useful in applications such as optical communications. We begin with a general theoretical analysis of our proposed design. Our approach starts by generating a Schell-model (uniformly correlated or shift-invariant) source by spatially filtering incoherent light. We then pass this light through an optical coordinate transformer, which converts the Schell-model source into a nonuniformly correlated field. After the general analysis, we discuss system engineering, including trade-offs among …

A Systems Biology Approach To Predict Phenotypes From Genotypes For Plants And Bacteria, Niaz Bahar Chowdhury

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

Predicting phenotypes from genotypes is a central challenge in systems biology in understanding how organisms respond to environmental and genetic changes. To address this, genome-scale metabolic models (GSMs) integrated with omics data and machine learning provide a comprehensive framework to connect genotype to phenotype. This dissertation utilizes these approaches in plants and bacterial systems, revealing key metabolic adaptations. In maize, a root-specific GSM reveals metabolic reprogramming under nitrogen stress, offering strategies for improving stress tolerance. Building on this, a multi-organ maize GSM of temperature stress identifies metabolic bottlenecks, providing targets to enhance crop resilience. Similarly, in rice, a grain-specific GSM …

Editorial: Novel Computational Fluid Dynamics Methods For Diagnosis, Monitoring, Prediction, And Personalized Treatment For Cardiovascular Disease And Cancer Metastasis, Zahra Keshavarz Motamed, Nima Maftoon, Lakshmi Prasad Dasi, John F. Ladisa Jr.

Biomedical Engineering Faculty Research and Publications

No abstract provided.

Investigating Gender Bias In Large Language Models Through Text Generation, Shweta Soundararajan, Sarah Jane Delany

Conference papers

Large Language Models (LLMs) have swiftly become essential tools across diverse text generation applications. However, LLMs also raise significant ethical and societal concerns, particularly regarding potential gender biases in the text they produce. This study investigates the presence of gender bias in four LLMs: ChatGPT 3.5, ChatGPT 4, Llama 2 7B, and Llama 2 13B. By generating a gendered language dataset using these LLMs, focusing on sentences about men and women, we analyze the extent of gender bias in their outputs. Our evaluation is two-fold: we use the generated dataset to train a gender stereotype detection task and measure gender …

Hardware Control Unit For Trusted Program Verification System, Jake Owen Alt

Master's Theses

Trust in the underlying hardware is the foundational step towards trusting the correctness and integrity of a software application. However, verifying that today's extremely complex processors work exactly as intended has not been feasible, as evidenced by several recent hardware bugs. Trustworthy, formally verified processors currently forego intricate performance enhancements such as out-of-order execution, hampering them substantially versus their less secure counterparts.

The Containment Architecture with Verified Output (CAVO) system solves this problem by isolating the host system and requiring the result of each instruction to be validated by a small, trusted hardware module called the Sentry. Any transmissions to …

A Theoretical Framework For Resilience In Complex System Governance (Csg), Susan A. Caskey

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation addresses a critical gap in resilience research within complex systems by developing a theoretical framework for governance resilience—an underexplored area despite governance's crucial role in maintaining system resilience. Using the Complex System Governance (CSG) framework (Keating, Katina, Chesterman, et al., 2022; Keating & Katina, 2019), resilience is conceptualized as a system's ability to absorb disturbances while sustaining performance and enabling adaptive transformation. This involves three main capabilities: persistence, adaptability, and transformation in response to both internal and external pressures.

Through a constructivist grounded theory approach involving iterative coding, memoing, and theory development, this study inductively constructs a governance …