Engineering Commons^™

Sex-Driven Alterations In Aging Alpha Motoneurons: Exploring Size, Type, Density, And Kv2.1 Channel Expression, Kalin R. Gerber

Browse all Theses and Dissertations

This research explores how motor neurons (MNs) and Kv2.1 clustering change with age, emphasizing sex differences and MN subtypes. We found that MN density decreases with age in both sexes, while soma size increases in male mice. FF MNs were the most affected, and old weak mice had smaller MNs than their stronger counterparts, underscoring FF MN vulnerability. Baseline studies revealed that FF and FI MNs have larger Kv2.1 clusters compared to FR and S MNs. Female mice had smaller, denser Kv2.1 clusters than male mice, suggesting less clustering in females. With age, Kv2.1 clusters grew larger but became less …

Graph-Centric Bot Detection: Addressing Extreme Data Imbalances, Heterophily, And Scarcity, Bradley T. Ashmore

Browse all Theses and Dissertations

The digital landscape is ever-evolving. In recent years the amount of bot traffic, traffic generated by autonomous applications over the internet has increased significantly. Many bots perform useful and needed functions, however, malicious bots are known sources of both common and emerging security threats. Denial-of-Services (DoS), information theft, and credential stuffing have all been conducted by malicious software running on unknowingly infected machines. The dichotomy of useful bots operating in the same networks as malicious bots combined with novel bot attacks and an ever-increasing number of personal devices connecting to the Internet drives the need for continued advancement of malicious …

A Trusted Adversarial Ml Countermeasure Approach For Secure And Resilient Ai-Driven Hardware Trojan Detection, Mohammed G M Alkurdi

Browse all Theses and Dissertations

Semiconductor microelectronics Integrated Circuits (ICs) are increasingly integrated into critical life applications including medical, aerospace, and Internet of things. Their increasing importance as a technology gave rise to critical concerns regarding their security. This has led to the focus of the research community on hardware Trojans, which are malicious modifications to the ICs with undesirable outcomes. Their detection is becoming increasingly critical, with many researchers proposing methods to do so such as reverse engineering, logic testing, and side-channel analysis. Many of these proposals utilize machine learning methods to detect these malicious modifications with high accuracy and confidence. However, machine learning …

Building Services Engineering January/February 2024

Building Services Engineering

No abstract provided.

Development Of Novel Hydrogel Materials For Viscoelastically Accurate Brain Phantoms, Lila G. Schandler

Theses and Dissertations

Current methods such as concussion tests and Magnetic Resonance Imaging (MRI) scans only reveal structural damage post-injury. Creating an anatomically accurate model of the brain that mimics brain structure and viscoelastic properties will further allow medical professionals to analyze concussion events and create curated and personalized plans of recovery for a patient’s unique brain injury.

Values have been established for the necessary viscoelastic properties (time constants and elasticity) to mimic the native human brain tissue such as grey matter and white matter. The mechanical properties of the hydrogel are tunable by varying the concentration of Phytagel (PHY), a protein binding …

Applying X-Ray Fluorescence With Monte Carlo Methods For Analysis Of Surface Defects On Coated Zr4, James Tyler Cahill

Theses and Dissertations

Chromium-coated Zircaloy-4 cladding represents a novel solution for creating nuclear cladding that has passive safety features. This and related developments aimed at improving nuclear safety are broadly categorized as accident-tolerant fuel and cladding. However, implementing this material presents practical challenges such as the vulnerability that cladding has to surface damage such as fretting wear. This research aims to determine if the pre-existing technique of X-ray fluorescence analysis is applicable for detecting these defects on the surface of the cladding. This was done by developing Monte Carlo N-Particle Transport models of a handheld device, the XL-5, alongside experimental measurements using this …

Drop Retention And Departure In Adiabatic Shear Flow On Structured Superhydrophobic Surfaces, Blake M. Lyons, Daniel Maynes, Julie Crockett, Brian D. Iverson

Faculty Publications

Drops are retained or held on surfaces due to a retention force exerted on the drop by the surface. This retention force is a function of the surface tension of the liquid, drop geometry, and the contact angle between the drop and the surface. When external or body forces exceed the retention force, the drop begins to move. This work explores the conditions for which drop departure occurs on structured superhydrophobic surfaces in the presence of an applied shear flow. Drop departure is explored for five microstructured superhydrophobic surfaces, one nanostructured carbon nanotube surface and one smooth hydrophobic surface. Surface …

Data To Physics: Bridging Modeling Frameworks For Metastructures, Hrishikesh Suhas Gosavi

Dissertations, Master's Theses and Master's Reports

The effective attenuation of wave propagation in specific frequency ranges, known as bandgaps, makes metastructures valuable in various engineering applications. Traditionally, bandgap estimation has relied on physics-based models, which present challenges for complex metastructures where accurate material properties and geometric complexities make precise modeling difficult. This work introduces data-driven techniques for bandgap estimation, leveraging the dynamic behavior, particularly the Frequency Response Functions (FRFs) of unit cells, to calculate dispersion relations and couple unit cells through dynamic substructuring. This approach addresses the limitations of traditional methods by directly utilizing experimental data to characterize both longitudinal and flexural bandgaps.

To evaluate data-driven …

Statically Controlled Synchronized Lane Architectures, Scott K. Pomerville

Dissertations, Master's Theses and Master's Reports

Modern superscalar processors dominate the field of computing. While dynamic execution allows for versatility in code, these processors are complex. Statically scheduled code has historically enabled simpler processor designs, but static scheduling cannot account for variables that are unknown at compile time. Furthermore, static scheduling has many inefficiencies, such as the need to insert a large number of nops for code in traditional Very Long Instruction Word (VLIW) processors. In this dissertation, we explore a novel architectural approach for statically scheduled code by breaking the code into several synchronous instruction streams. By representing code in a fundamentally new way, we …

Electrification Of A Heavy-Duty Off-Road Material Handler: Energy Savings And Emission Reductions, Bryant Goodenough

Dissertations, Master's Theses and Master's Reports

Federal regulations are driving the adoption of electrification technologies to reduce carbon dioxide equivalent (CO₂e) emissions, a metric that quantifies the global warming potential of various greenhouse gases in terms of carbon dioxide (CO₂). Although no specific CO₂ regulations exist for heavy-duty off-road machines, future reductions are likely, given stricter emissions standards for on-road vehicles. The heavy-duty off-road sector offers significant fuel-saving potential, as its focus has traditionally been on reliability and performance rather than fuel efficiency. This dissertation examines fuel and CO₂e savings opportunities on a heavy-duty off-road material handler, the Pettibone …

Recycling Waste Plastics From E-Waste And Household Sources For Sustainable Asphalt Construction, Sepehr Mohammadi

Dissertations, Master's Theses and Master's Reports

The generation of waste plastics is continuing to grow year after year which can also provide valuable opportunities for researchers in different areas to find new applications that will eliminate their potential risks to environment and human health. For instance, in 2022 alone, global plastic production was increased to a remarkable 400.3 million metric tons, showing a 1.6 percent rise from 2021. Another notable example is the waste plastics from electronic waste (e-waste) sources which constitute up to 20% of e-waste streams. The advancement of global economies and technology has resulted in a notable increment of the volume of electronics …

Assessment Of Surface Waviness In A Wire Arc Additive Manufacturing Process, Shammas Mahmood Shafi

Dissertations, Master's Theses and Master's Reports

Wire Arc Additive Manufacturing (WAAM) is increasingly recognized for its ability to produce metal components with high deposition rates and efficiency, particularly in the aerospace and automotive sectors. However, one significant challenge that WAAM faces is achieving surface quality that complies with industry standards, as well as creating geometries that closely resemble the initial design. A crucial factor contributing to this challenge is surface waviness, which is an inherent characteristic of the WAAM process. This waviness arises from the geometry of the weld beads and the overlap distance between them. It can adversely affect the fusion quality of successive layers, …

Interplay Of Discharge Rates, Slope, And Sediment Size In River Meandering Dynamics: Insights From Experimental Studies, Yifan Zhang

Dissertations, Master's Theses and Master's Reports

Sinuosity is an important concept in various fields including hydrology, geomorphology, and ecology. It not only presents the degree of curviness of a river channel but also reflects the ecological sustaining ability. By studying the sinuosity of a stream or river channel, important implications could also be found including the transportation and deposition of the sediments, the habitats of the riverbanks, and the types of species that are able to live on the watershed and river. To study the factors that could heavily affect the sinuosity of a river channel, a full set of laboratory experiments was designed to perform …

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …

Computational Analysis Of Vascular Aneurysms, Seyedmostafa Rezaeitaleshmahalleh

Dissertations, Master's Theses and Master's Reports

Vascular aneurysms, such as intracranial aneurysms (IAs) and abdominal aortic aneurysms (AAAs), pose significant health risks, particularly upon rupture. Disturbed hemodynamics, characterized by swirling flow patterns, can lead to cellular and structural changes that disrupt normal physiological processes and contribute to destructive vascular remodeling. Integrating hemodynamic analysis into clinical assessments of aneurysms has become crucial, especially through patient-specific computational fluid dynamics (CFD) simulations based on medical imaging. However, the complexity of CFD tools presents challenges in model creation and post-simulation analysis, limiting their accessibility to clinicians.
This project aimed to advance aneurysm management by exploring the role of intraluminal thrombus …

Investigation Of Operational Parameter Differences Between The Standard Ron Test Method And Knock-Limited Modern Spark-Ignition Engine Operation, Alexander Hoth

Dissertations, Master's Theses and Master's Reports

Octane numbers (ONs) are used worldwide to rate the knock propensity of gasoline-like fuels for spark-ignition engines, making ONs the leading indicator of fuel quality for commercial distribution. The ONs were established 90 years ago and have only received minor changes compared to significant advancements in modern engine operation and fuel composition. This has resulted in discrepancies between knock-limited modern engine operation and standard ON ratings. This dissertation used a standard CFR octane rating engine at Argonne National Laboratory that was instrumented with modern combustion research tools to investigate key differences between the Research Octane Number (RON) rating conditions and …

Wet-Carbonation Of Rcas For Improved Carbonation Efficiency And Mechanical Properties Of Rca And Recycled Concrete, Zhao. Xiang

Dissertations, Master's Theses and Master's Reports

RCA typically exhibits weak interfacial transition zones (ITZ), high porosity, and micro-cracks due to residual mortar layers, resulting in suboptimal performance when reused in structural applications. To address these challenges, this study developed a wet carbonation process using glycine acid as an inducer. Glycine acid, containing carboxyl and amino functional groups, forms stable complexes with Ca²⁺ ions in RCA, increasing calcium ion solubility and thereby accelerating carbonation. RCA samples underwent carbonation in a glycine acid solution, followed by characterization using X-ray diffraction and scanning electron microscopy to evaluate mineralogical transformations and microstructural enhancements. The carbonation process promoted the formation of …

Nickel Superalloy Composition And Process Optimization For Weldability, Cost, And Strength, Sophie A. Mehl

Dissertations, Master's Theses and Master's Reports

To advance sustainability efforts, electric power plants have reduced specific carbon dioxide emissions by increasing operating temperatures and pressures to improve power generation efficiency. The latest improvements are utilized in advanced ultra-supercritical power generation. To meet these operating conditions, nickel superalloys are used in the highest temperature components; however, they are expensive and present weldability challenges. This project aims to experimentally optimize a nickel superalloy to improve material weldability and decrease cost without compromising strength. Three optimized compositions were developed, and their microstructures and mechanical properties were compared to Nimonic 263, a common nickel superalloy in electric power plants. The …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

Directional Dependent Fracture Characteristics Of 3d Printed Mechanical Metamaterials, Thomas Draper

Dissertations, Master's Theses and Master's Reports

Structural metamaterials, such as lattice metamaterials, are engineered cellular structures designed to achieve properties not achievable by natural materials. They enable mechanical properties unattainable by homogeneous solid materials and offer optimal properties for these materials tunable for specific applications in industries such as automotive, medical, and aerospace. Recent findings have revealed unique tunable mechanical properties such as negative Poisson's ratio, high strength-to-weight ratio, anisotropic stiffness, and much more. While the elastic and wave propogation properties of many lattice metamaterials are well investigated, the fracture properties are not well explored. However, their low fracture toughness is a bottleneck for their use …

Stable Energy-Efficient Macro-Scale Partial Flow-Boiling Operations Using Microstructured Surfaces And Ultrasonics, Divya Kamlesh Pandya

Dissertations, Master's Theses and Master's Reports

Controlled but explosive growth in vaporization rates is made feasible by ultrasonic acoustothermal heating of the microlayers associated with micro-scale nucleating bubbles within the microstructured boiling surface/region of a millimeter-scale single-channel heat exchanger (HX) – part of a typically multi-channel heat-sink. The experiments illustrate the achievement of a remarkably high stable heat flux (10 – 80 W/cm²) for a partial flow-boiling-based cooling approach and exceptional efficiency through active/passive enhancement in the vaporization rates into the heterogeneously nucleated micro-bubbles (through acoustothermal heating of their microlayers) and their removal rates (typically within the passive microstructured region of boiling). A controlled …

Application Of Fusion Based Deep Learning Models To Improve Millimeter Wave Beamforming, Abishek Subramanian

Dissertations, Master's Theses and Master's Reports

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: …

Aluminum Critical Mineral Production Feasibility Via Landfill Mining: Preliminary Study Of Potential Project Locations And Co-Benefits, Anabel M. Needham

Dissertations, Master's Theses and Master's Reports

In 2022, aluminum was named a critical mineral by the United States Geological Survey (USGS) and the global demand for aluminum is projected to increase by 40% from 2020 to 2030 (Aleksić 2023). There are currently no large-scale bauxite mines in the United States to contribute to aluminum production, and this study aims to investigate the feasibility of aluminum landfill mining in the United States to produce secondary aluminum. The feasibility of landfill mining for the purpose of recovering materials and energy is a relatively new technology, and often co-benefits are required to make these projects economically viable. Publicly available …

Gravity Fed Hopper Flow Of Bulk Solids For Lunar Isru, Jason Bendixen Noe

Dissertations, Master's Theses and Master's Reports

With the return to the moon in the decade of the 2020s, there has been a renaissance of lunar technology development and innovation. This has been particularly true for the area of lunar ISRU (In-Situ Resource Utilization). One key area in ISRU that has been neglected is lunar regolith storage hopper research. Many lunar regolith ISRU researchers use storage hoppers in their work but do not document the properties when designing the hoppers or do not have the information at hand. Because of this, hopper design and documentation are poorly understood, and more research is needed. Hoppers are vital for …

Assessing Wave Dynamics Induced Coastal Flooding Along The Southern Shores Of Lake Superior, Saumik Mallik

Dissertations, Master's Theses and Master's Reports

This study provides a comprehensive assessment of coastal hazards along the southern shores of Lake Superior, encompassing the interplay of static water levels, surges, and the amplifying effects of wave dynamics. Employing 51 years of historical water level data from five gauge stations in the US portion of Lake Superior, the study conducts extreme value analysis to estimate return levels over various return periods (25, 50, 100, and 500 years) for static water levels and surges. In parallel, Significant Wave Heights (SWH) data generated from the Simulating Waves Nearshore (SWAN) model is calibrated through a multi-step procedure based on historical …

Transient Simulations Of Power Systems With Inverter Interfaced Resources, Gaurish Shreedhar Gokhale

Dissertations, Master's Theses and Master's Reports

Renewable energy sources are interfaced with the electrical grid using power electronic inverters. These inverter-interfaced resources have been deployed for nearly 20 years. Still, NERC only recently highlighted the vast gap between the actual behavior of these inverters during power system transients and those observed in simulations. Simulation models need significant improvements, mainly for developing accurate inverter current controls, phase-locked loops, and fault response during different power priority modes. Additionally, only time-domain electromagnetic transient simulation tools can fully represent the fault response of the inverter-interfaced resources.

The developed simulation model of the inverter-interfaced resource is based on the recommendations made …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Matrix Stiffness Sensing By Nascent Adhesions And The Role Of Riam In Adhesion Assembly, Nikhil Mittal

Dissertations, Master's Theses and Master's Reports

Mechanical stiffness of the extracellular matrix (ECM) impacts many cellular functions such as proliferation, migration, and differentiation. ECM stiffness is sensed by a cell via integrin-based focal adhesions (FAs) by changing conformation and biochemical activities of molecules within FAs by the exchange of the force between the ECM and filamentous actin (F-actin). Cells in turn respond to this stiffness by generating traction force that plays an important role in many biological events such as tissue morphogenesis, stem cell differentiation, wound healing, and cancer cell metastasis. The stiffness of the extracellular matrix induces differential tension within integrin-based adhesions. Understanding stiffness sensing …

Developing Robust Autonomous Vehicles With Ros, Dylan J. Kangas

Dissertations, Master's Theses and Master's Reports

The demand for autonomous vehicles (AVs) is rising across both military and civilian sectors. These unmanned systems offer numerous advantages, such as improved efficiency, safety, and adaptability. Addressing this demand requires the development of resilient and versatile autonomous vehicles crucial for the transport and reconnaissance markets.

The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Factors such as sensor noise, erroneous readings, and deliberate attacks should all be considered when developing a robust autonomous system. This work aims to quantify the degradation of sensor data which causes mapping …

Anaerobic Reductive Bioleaching Of Manganese Ores, Neha Sharma

Dissertations, Master's Theses and Master's Reports

Manganese extraction by biological methods is a green and economical way to produce manganese from low as well as high grade manganese ores. This is especially important for US ores, which are primarily either small in extent, low-grade with a high iron content, or both. They are therefore not suitable for conventional mining, which is very capital-intensive and requires a large up-front investment and are therefore imported at high costs. The goal is therefore to develop a process that can be applied at small scales, with minimal startup costs, and low equipment and labor requirements. It is also critical to …