Engineering Commons^™

Reducing Brain Kynurenic Acid Synthesis Precludes Kynurenine-Induced Sleep Disturbances, Katherine M. Rentschler, Snezana Milosavljevic, Annalisa M. Baratta, Courtney J. Wright, Maria V. Piroli, Zachary Tentor, Homayoun Valafar, Christian O'Reilly, Ana Pocivavsek

Publications

Patients with neurocognitive disorders often battle sleep disturbances. Kynurenic acid is a tryptophan metabolite of the kynurenine pathway implicated in the pathology of these illnesses. Modest increases in kynurenic acid, an antagonist at glutamatergic and cholinergic receptors, result in cognitive impairments and sleep dysfunction. We explored the hypothesis that inhibition of the kynurenic acid synthesising enzyme, kynurenine aminotransferase II, may alleviate sleep disturbances. At the start of the light phase, adult male and female Wistar rats received systemic injections of either: (i) vehicle; (ii) kynurenine (100 mg kg−1; i.p.); (iii) the kynurenine aminotransferase II inhibitor, PF-04859989 (30 mg kg−1; s.c.); …

Computation Of High-Order Sensitivities Of Model Responses To Model Parameters—I: Underlying Motivation And Current Methods, Dan Gabriel Cacuci

Faculty Publications

The mathematical/computational model of a physical system comprises parameters and independent and dependent variables. Since the physical system is seldom known precisely and since the model’s parameters stem from experimental procedures that are also subject to uncertainties, the results predicted by a computational model are imperfect. Quantifying the reliability and accuracy of results produced by a model (called “model responses”) requires the availability of sensitivities (i.e., functional partial derivatives) of model responses with respect to model parameters. This work reviews the basic motivations for computing high-order sensitivities and illustrates their importance by means of an OECD/NEA reactor physics benchmark, which …

Computation Of High-Order Sensitivities Of Model Responses To Model Parameters—Ii: Introducing The Second-Order Adjoint Sensitivity Analysis Methodology For Computing Response Sensitivities To Functions/Features Of Parameters, Dan Gabriel Cacuci

Faculty Publications

This work introduces a new methodology, which generalizes the extant second-order adjoint sensitivity analysis methodology for computing sensitivities of model responses to primary model parameters. This new methodology enables the computation, with unparalleled efficiency, of second-order sensitivities of responses to functions of uncertain model parameters, including uncertain boundaries and internal interfaces, for linear and/or nonlinear models. Such functions of primary model parameters customarily describe characteristic “features” of the system under consideration, including correlations modeling material properties, flow regimes, etc. The number of such “feature” functions is considerably smaller than the total number of primary model parameters. By enabling the computations …

Ki-Cook: Clustering Multimodal Cooking Representations Through Knowledge-Infused Learning, Revathy Venkataramanan, Swati Padhee, Saini Rohan Rao, Ronak Kaoshik, Anirudh Sundara Rajan, Amit Sheth

Publications

Cross-modal recipe retrieval has gained prominence due to its ability to retrieve a text representation given an image representation and vice versa. Clustering these recipe representations based on similarity is essential to retrieve relevant information about unknown food images. Existing studies cluster similar recipe representations in the latent space based on class names. Due to inter-class similarity and intraclass variation, associating a recipe with a class name does not provide sufficient knowledge about recipes to determine similarity. However, recipe title, ingredients, and cooking actions provide detailed knowledge about recipes and are a better determinant of similar recipes. In this study, …

Ecg Recordings As Predictors Of Very Early Autism Likelihood: A Machine Learning Approach, Deepa Tilwani, Jessica Bradshaw, Amit Sheth, Christian O'Reilly

Publications

In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1–2 years of age, but ASD diagnoses are not typically made until ages 2–5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker …

Magnetic Softness Tuned Superparamagnetic Nanoparticles For Highly Efficient Cancer Theranostics, Jie Wang

Theses and Dissertations

Magnetic resonance imaging (MRI)-guided magnetic nanofluid hyperthermia (MNFH) using iron oxide based superparamagnetic nanoparticles (SPNPs) has recently attracted considerable attention as a treatment modality for cancer theranostics, because MRI-guided MNFH can allow for diagnosis, therapeutics, and prognosis simultaneously using the same administrated magnetic nanofluid agent. However, several primary limiting factors: (1) insufficient AC magnetic heating induction (specific loss power/intrinsic loss power, SLP/ILP) at the biologically safe and physically tolerable range of AC magnetic field (H_AC,safe: f_appl × H_appl < 3.0 ~ 5.0×10⁹ A·m^-1·s^-1), (2) low _r2- relaxivity directly related to the low resolution of …

Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez

Theses and Dissertations

Currently, heat transfer in many industries is the limiting factor for innovation, especially in the energy sector. For example, maximizing thermal conductivity of ceramic coatings in power plant devices improves the overall electrical to thermal energy ratio, whereas minimizing thermal conductivity is required for desirable heat-to-electricity conversion in thermoelectric devices. As such, rapid discovery of new materials with extreme thermal conductivity values is quintessential for the near-future deployment of current and developing energy applications.

The vibrational properties of crystalline materials are essential for their ability to conduct heat. Fundamentally, the restorative atomic forces of displaced atoms are sufficient to represent …

Developing A Vision-Based Framework For Measuring And Monitoring Water Resource Systems Using Computer Vision And Deep Learning Techniques, Seyed Mohammad Hassan Erfani

Theses and Dissertations

Increased vulnerability of water systems to extreme events and climate change is among the profound challenges facing the management of water resource systems around the world. Extreme events, including droughts, floods, and natural hazards have become more frequent and intensive, particularly in coastal regions. Floods, for instance, caused tens of billions of US dollars losses and put the lives of thousands in danger, globally. To cope with the adverse consequences of floods, a wide range of structural, non-structural, and emergency measures are studied and deployed by flood management sectors. Various flood simulation, mapping, and forecast systems have been developed to …

Analysis, Measurement, And Modeling Of Millimeter Wave Channels For Aviation Applications, Zeenat Afroze

Theses and Dissertations

Millimeter wave (mmWave) communication systems can employ a large amount of spectrum, and can consequently offer large data rates, e.g., multi-Gigabits-per-second. This technology can be used in many sectors: aviation, vehicles, public transportation, robotics, autonomous factories, etc. Yet mmWave communication systems suffer from some propagation challenges, including large free space path loss (PL), large penetration loss, and large diffraction loss. Hence, it is vital to quantify these and other channel effects to ensure link reliability. Most mmWave systems will employ directional antennas to enable acceptable link distances. In many settings this will require directional receiver antennas to rotate in azimuth …

Optimization Of Ultrawide Bandgap Semiconductor Materials For Heterostructure Field Effect Transistors (Hfets), Mohi Uddin Jewel

Theses and Dissertations

Ultra-wide bandgap (UWBG) gallium oxide (Ga2O3), and aluminum gallium oxide (Al_xGa_1-x)₂O₃ materials with bandgap E_G ≥ 4.8 eV are promising for heterostructure field effect transistors (HFETs) with high breakdown voltage and operating at high temperatures. Despite some initial breakthroughs in good quality β-Ga₂O₃ thin films growth on (001), (100), (010), and (201) β-Ga₂O₃ substrates, and promising results on β- (Al_xGa_1-x)₂O₃/β-Ga₂O₃ HFETs fabricated on (010) β-Ga₂O₃ substrates, heat dissipation in Ga2O3 materials and …

Cost-Effective Strengthening And Automated Inspection Of One-Way Precast Reinforced Concrete Flat Slab Bridges In South Carolina, Laxman Kc

Theses and Dissertations

In South Carolina, the South Carolina Department of Transportation (SCDOT) manages 90% of the state's inventory of 9,400 bridges built in the 1950s. With more than 30% of its bridges having been under-designed and experiencing decades of service deterioration, the implications for public safety, the economy, and the transportation system are significant. To prioritize safety and address these concerns, SCDOT is in the process of conducting thorough inspections of bridges, load ratings, and exploring cost-effective and practical methods to strengthen the bridges and reduce load postings. This study is a part of the multi-year research investigation supported by the SCDOT. …

First Principles Doping Analysis Of Perovskite- And Ruddlesden-Popper-Based Solid Oxide Fuel Cells, Nicholas Alexander Szaro

Theses and Dissertations

Solid oxide fuel cells (SOFCs) have shown significant promise as a high efficiency energy conversion technology. SOFCs are solid-state, high temperature (600 – 1000 °C), and electrochemical conversion devices that can operate with a wide variety of fuels such as hydrogen, syngas, and hydrocarbon feedstocks. The state-of-the-art SOFC is manufactured with a lanthanum strontium manganite (LSM) cathode, an yttria-stabilizedzirconia electrolyte (YSZ), and a nickel on yttria-stabilized-zirconia (Ni/YSZ) cermet anode. LSM || YSZ || Ni/YSZ SOFCs operate at or above 800 °C to achieve sufficient oxide mobility. The high temperatures introduce problems such as long device start-up, particle sintering, and material …

Leveraging Programmable Switches To Enhance The Performance Of Networks: Active And Passive Deployments, Elie Kfoury

Theses and Dissertations

The performance of networks today is drastically affected by: 1) switches equipped with large buffers, referred to as “bloated buffers”: due to the lack of programmability and traffic visibility in legacy switches, operators nowadays configure large buffers statically without considering the characteristics or dynamics of flows. Such buffers increase the delays on packets, causing the Quality of Service (QoS) of networked applications (e.g., voice over IP, web browsing) to degrade; 2) switches forwarding packets on a best-effort basis: traffic crossing a switch is heterogeneous in many ways. Mixing such traffic in a single queue without any QoS measures can drastically …

Revisiting The Volumetric Swing Frequency Response Method For The Determination Of Limiting Mass Transfer Mechanisms Of N2 And O2 In Carbon Molecular Sieve 3k172, Adam Marshall Burke

Theses and Dissertations

Adsorption-based separations processes, along with the adsorbents that enable them, have benefited from a greater particular focus in recent years, following a desire to improve process energy efficiencies and cost economics. One such adsorbent, carbon molecular sieves (CMS), have likewise been a greater focus. CMS materials offer several key practical uses, such as the separation of nitrogen and oxygen, and the removal of carbon dioxide from methane process streams. In order to effectively design and implement an industrial scale process using a CMS material, the behavior of these gases on the chosen material must be known, including the limiting mass …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Impact Of Dam Height And Grain Size Distribution On Breaching Of Non-Cohesive Dams Due To Overtopping, Heather O'Donal

Theses and Dissertations

The National Inventory of Dams reports 74,400 earthen dams in the United States in 2021, of these dams approximately 27% are considered at high or significant hazard risk, that is dam failure will cause widespread damage and loss of lives. The most frequent cause of dam failure is breaching caused by overtopping. Accurate predictions of breach evolution are thus crucial to determine flood hydrographs for the safety of communities and properties at risk. Laboratory experiments were conducted on non-cohesive, compacted embankments to understand the role of dam height and sediment grain size on breaching caused by overtopping. Dam heights varied …

Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song

Theses and Dissertations

Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …

A Systems Approach To Design And Plan Sustainable Antifragile Infrastructure Based On Aggregate Footprint And Satisfaction, Farboud Khatami

Theses and Dissertations

The concepts of robustness and sustainability in planning and design of water and energy infrastructures have been extensively explored in previous research, primarily focusing on system reliability, environmental considerations, and economic aspects. This study aims to broaden the understanding of these concepts by offering comprehensive frameworks that capture the essence of robustness and sustainability at two distinct levels.

The first level of investigation focuses on the performance of infrastructure networks during natural disasters. Traditionally, this has been addressed using reliability, resilience, and vulnerability metrics. However, these methods rely on static, deterministic, and non-stationary data, which is inadequate when dealing with …

Automated Fiber Placement Through Thickness Defect Stacking Optimization, Noah Christopher Swingle

Theses and Dissertations

In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to …

Digital Health Design For Improving Treatment Decisions, Akanksha Singh

Theses and Dissertations

In the age of artificial intelligence and large datasets, information retrieval by querying large databases is an impossible task for the common user due to the information overload. Recommender Systems (RS) for commercial applications like YouTube, Amazon and Netflix were designed to support users by finding items of interest based on their user profiles and various filtering techniques. Health Recommender Systems (HRS) is a category of RSs that provides immense opportunities for application across several healthcare domains and contexts including treatment decision support. Unlike RS applications that focus on analyzing consumer choices, a key differentiator for HRS applications is the …

Gpu-Enabled Genetic Algorithm Optimization And Path Planning Of Robotic Arm For Minimizing Energy Consumption, Yichuan Cao

Theses and Dissertations

The robotic arm represents a complicated mechanical system and advanced engineering principles in robotics, enabling precise and efficient manipulation and interaction with objects in a variety of applications, such as manufacturing, healthcare, space exploration, and other industries. In most cases, energy consumption of robotic tools has been given little consideration due to their perceived insignificance compared to the benefits they offer. However, the increasing capacity demands of factories and the expanding use of robotic arms necessitate careful evaluation and reduction of energy consumption. In this regard, given a task configuration, adjusting the arm's movement path emerges as one of the …

Streamwise And Vertical Dispersal Of Tracer Stones From A Continuously Supplied Source, Amanda Grace Balkus

Theses and Dissertations

Individual movement of sediment grains is oftentimes studied with the use of tracer stones. Studies performed both in the field, in the laboratory and with numerical models involve installing a patch of stones (tracers) on the bed surface and rarely deep in the deposit. Once installed, particle displacement is monitored over time scales varying from one flood to several years. These studies are useful, for example, to study bedload transport dynamics, understand contaminant fate and transport as well as to identify and define stream restoration practices (e.g. quality of fish habitat). Here we use a vertically continuous model to study …

Implementation Of A 3d Laser Profiler System Into A Non-Contacting Method For Rnt & Stress Measurements, Caio Neto Penna

Theses and Dissertations

Continuous welded rail (CWR) is susceptible to the development of internal forces in the rail due to thermal effects that can lead to track buckling. Railroads typically deal with the internal stresses in the rail through the inspection of the rail neutral temperature (RNT) which is the temperature at which the internal force is zero. The existing methods in the industry to measure RNT are contacting, destructive, disruptive to railroad operations, and ineffective. The proposed concept is a promising non-contacting, non-disruptive and reference-free technique to measure RNT and the stress state in the rail. The method is based on the …

Assessment And Development Of Collaboration Framework For Less-Than-Truckload Carriers, Bhavya Padmanabhan

Theses and Dissertations

The growth of e-commerce is changing consumer expectations for delivery services. Competition is making “faster and cheaper” a nonnegotiable part of success; hence, retailers are seeking carriers that can deliver with ever-shorter lead times and at the lowest possible cost. In this environment, large carriers have a competitive advantage due to their market power. For small-to-medium-sized less-than-truckload (LTL) carriers to stay in business, they must improve their efficiency and lower cost. A potential strategy to achieve these goals is to collaborate with other carriers. In collaborating, carriers would exchange and perform certain jobs for each other to lower their transportation …

Liquid Cooling System For A High Power, Medium Frequency, And Medium Voltage Isolated Power Converter, Hooman Taghavi

Theses and Dissertations

Controlling and transforming electrical power from one form into another is the Primary function of the power electronics systems that are employed today. These systems have a wide variety of applications and are utilized in things like industrial automation, electric cars, consumer electronics, and renewable energy systems, among other things. The functioning of power electronics systems results in the production of a substantial quantity of heat, despite the fact that these systems are very dependable and efficient. This heat must be dispersed to preserve system performance and avoid component damage. By ensuring that the working temperature of the components stays …

Conceptual Design And Preliminary Safety Analysis Of A Proposed Nuclear Microreactor For Mobile Application, A. S. M. Fakhrul Islam

Theses and Dissertations

Conceptual design and preliminary safety analysis of a proposed helium-cooled Micro Nuclear reactor In ONe megawatt (MINION) thermal output is done through three-dimensional computational analysis. Compared with the conventional high temperature gas-cooled reactors (HTGR) that use tri-structural or bi-structural isotropic (TRISO or BISO) fuel particles, MINION uses traditional clad fuel pellets. This greatly simplifies the core design and allows for a substantially more compact design thanks to the fuel’s signif-icantly higher U-235 atom density. Uranium carbide (UC) enriched up to 19.75% in U-235 is used as fuel. In the power-flattened design, U-235 enrichment varies radially between 19.75% and 10.5% in …

Model-Driven Analysis Of Ecg Using Reinforcement Learning, Christian O'Reilly, Sai Durga Rithvik Oruganti, Deepa Tilwani, Jessica Bradshaw

Publications

Modeling is essential to better understand the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We use reinforcement learning to train a deep neural network to estimate the modeling parameters from an ECG recorded in babies from 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. From the 751,510 PQRST complexes modeled, 82.7% provided a signal-to-noise ratio that …

Consolidated Chamber Design And Protocol For Olfactory Conditioning Assay With Drosophila Melanogaster, Sasha Bronovitskiy, Andres Castillo, Michael Yan, Fang Ju Lin

Journal of the South Carolina Academy of Science

The olfactory conditioning assay is widely used in Alzheimer’s disease research to quantify learning and memory in Drosophila melanogaster. The assay tests ability to recall an aversive conditioned stimulus of scent paired with electrical shock when presented a choice between shock-associated and unrelated scents. The T-maze, a commonly used apparatus for olfactory conditioning assays, employs an elevator mechanism to transfer live flies from the shock-delivering training chamber to the scent selection point. This elevator mechanism is known to cause fly casualty. T-mazes are not commercially available and often difficult to reproduce. Other existing variations of olfactory conditioning apparatuses use …

Microcanonical Thermodynamics Of Small Ideal Gas Systems, David S. Corti, Donya Ohadi, Ricardo Fariello, Mark J. Uline

Faculty Publications

We consider the thermal, mechanical, and chemical contact of two subsystems composed of ideal gases, both of which are not in the thermodynamic limit. After contact, the combined system is isolated, and the entropy is determined through the use of its standard connection to the phase space density (PSD), where only those microstates at a given energy value are counted. The various intensive properties of these small systems that follow from a derivative of the PSD, such as the temperature, pressure, and chemical potential (evaluated via a backward difference), while equal when the two subsystems are in equilibrium are nevertheless …

Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith

Theses and Dissertations

Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work …