Engineering Commons^™

Modeling Synergistic Effects Of Integrin And Tgf-Beta Signaling In Epithelial Mesenchymal Transition, Prerak Thakkar

Biology and Medicine Through Mathematics Conference

No abstract provided.

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Photoluminescence Of Beryllium-Related Defects In Gallium Nitride, Mykhailo Vorobiov, Mykhailo Vorobiov

Theses and Dissertations

This study explores the potential of beryllium (Be) as an alternative dopant to magnesium (Mg) for achieving higher hole concentrations in gallium nitride (GaN). Despite Mg prominence as an acceptor in optoelectronic and high-power devices, its deep acceptor level at 0.22 eV above the valence band limits its effectiveness. By examining Be, this research aims to pave the way to overcoming these limitations and extend the findings to aluminum nitride and aluminum gallium nitride (AlGaN) alloy. Key contributions of this work include. i)Identification of three Be-related luminescence bands in GaN through photoluminescence spectroscopy, improving the understanding needed for further material …

Modeling Epithelial-Mesenchymal Transition In A 3d Multicellular Model Of Tgf-Β1 Signaling, Kristin Kim, Chris Lemmon

Biology and Medicine Through Mathematics Conference

No abstract provided.

Material Extrusion-Based Additive Manufacturing: G-Code And Firmware Attacks And Defense Frameworks, Haris Rais

Theses and Dissertations

Additive Manufacturing (AM) refers to a group of manufacturing processes that create physical objects by sequentially depositing thin layers. AM enables highly customized production with minimal material wastage, rapid and inexpensive prototyping, and the production of complex assemblies as single parts in smaller production facilities. These features make AM an essential component of Industry 4.0 or Smart Manufacturing. It is now used to print functional components for aircraft, rocket engines, automobiles, medical implants, and more. However, the increased popularity of AM also raises concerns about cybersecurity. Researchers have demonstrated strength degradation attacks on printed objects by injecting cavities in the …

Synthesis And Application Of Redox-Active Covalent Organic Frameworks In Rechargeable Batteries, Mohammad K. Shehab

Theses and Dissertations

Synthesis and Application of Redox-Active Covalent Organic Frameworks in Rechargeable Batteries

Mohammad K. Shehab

Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, United States

Abstract

In recent years, lithium-ion batteries (LIBs) have been considered the dominant energy storage devices for portable electronics and electric vehicles due to their high energy density, low self-discharge rate, and long cycle life. In LIBs, the traditional positive electrodes employed are mainly derived from metal-containing inorganic compounds composed of cobalt, iron, nickel, or manganese (LiCoO₂, LiMn₂O₄, and LiFePO₄) coupled with graphite as the negative electrode. Despite …

Mechanisms Of Emulsion Destabilization: An Investigation Of Surfactant, Stabilizer, And Detergent Based Formulations Using Diffusing Wave Spectroscopy, Jordan N. Nowaczyk

Theses and Dissertations

Conventional approaches for studying emulsions, such as microscopy and macroscopic phase tracking, present challenges when it comes to establishing detailed mechanistic descriptions of the impact of emulsifier and stabilizer additives. Additionally, while a combination of sizing methods and macroscopic phase tracking can provide insights into droplet size changes and concentration, the use of multiple measurements can be cumbersome and error-prone. It is the focus of this work, to present a new method for studying water in oil (W/O) emulsions that involves using diffusing wave spectroscopy (DWS) to examine the impact of three different surface stabilizing additives at varying concentrations. By …

Optimal Design Of Bacterial Carpets For Fluid Pumping, Minghao W. Rostami, Weifan Liu, Amy Buchmann, Eva Strawbridge, Longhua Zhao

Biology and Medicine Through Mathematics Conference

No abstract provided.

Nlp@Vcu: Crop Characteristic Extraction Framework, Cora Lewis, Bridget Mcinnes, Getiria Onsongo

Summer REU Program

We developed a crop characteristic extraction framework. Starting from a custom SpaCy named entity recognition model, we added pre-trained word embeddings and a part-of-speech based entity expansion post-processing step. Then, we implemented an evaluation framework that functioned as a 5-fold cross validation wrapper for SpaCy custom training. Preliminary results showed improvement in the extraction framework after these additions.

A Highly Conductive, Flexible, And 3d-Printable Carbon Nanotube-Elastomer Ink For Additive Bio-Manufacturing, Andy Shar, Phillip Glass, Daeha Joung Ph.D.

Undergraduate Research Posters

The synthesis of a highly conductive, flexible, 3D-printable, and biocompatible ink has been of great interest in the field of bio-based additive manufacturing. Various applications include ultra-sensitive, microscale tactile sensors, patient-customizable scaffolds for cardiac and nerve tissue regeneration, and flexible electrocardiogram (ECG) electrodes. Here, a novel elastomeric carbon nanocomposite is presented consisting of amino-functionalized carbon nanotubes (CNT-NH₂) homogenously dispersed in a one-part room-temperature vulcanizing (RTV) silicone matrix. The use of acetone as a swelling solvent aids in electrical percolation through the elastomer matrix. CNT-NH₂ ratios can be tuned to fit various needs; higher tensile strength is favored …

Molten Salt Technologies For Advanced Nuclear Fuel Cycles And Molten Salt Reactors, Dimitris Killinger

Theses and Dissertations

This dissertation provides five topics—an assessment of different monitoring and analytical techniques often cited in the literature for molten salt systems and designs for nuclear engineering applications. First, we explored commonly used materials for quasi-reference electrodes in molten chloride salts. Second, the limitations of the electrochemical analysis known as cyclic voltammetry due to the concentration of uranium(III) present were being investigated. Third, we provided an experimental assessment on the development of a spectroelectrochemical cell for interrogating various spectroelectrochemical techniques, namely chronoabsorptometry and chronofluorometry, and their limitations due to the presence of uranium(III) ions. Fourth, a study on the corrosion resistance …

Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja

Theses and Dissertations

In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …

Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel

Theses and Dissertations

This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …

Smart City Management Using Machine Learning Techniques, Mostafa Zaman

Theses and Dissertations

In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …

Detection Of 2,4,6-Trinitrotoluene Using A Colorimetric Gold Nanoparticle Air Cassette Filter, Andrea I. Ferrer Vega

Theses and Dissertations

Trinitrotoluene (TNT) is an explosive commonly used during military and terrorist activities. Current methods to identify this compound require sampling, transport and analysis at a forensic lab using analytical instrumentation. However, on-site detection is needed to assist efforts to prevent detonation. Gold nanoparticles have been used as sensors throughout the years due to their versatility and surface enhanced Raman scattering properties in the presence of an analyte and low limits of detection. By taking advantage of the Meisenheimer complex that TNT forms in the presence of amines, it is possible to determine its presence at picogram levels. Subsequently, adhering amine …

Equations Of State For Warm Dense Carbon From Quantum Espresso, Derek J. Schauss

Theses and Dissertations

Warm dense plasma is the matter that exists, roughly, in the range of 10,000 to 10,000,000 Kelvin and has solid-like densities, typically between 0.1 and 10 grams per centimeter. Warm dense fluids like hydrogen, helium, and carbon are believed to make up the interiors of many planets, white dwarfs, and other stars in our universe. The existence of warm dense matter (WDM) on Earth, however, is very rare, as it can only be created with high-energy sources like a nuclear explosion. In such an event, theoretical and computational models that accurately predict the response of certain materials are thus very …

Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon

Theses and Dissertations

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …

Detection Of 2,4,6-Trinitrotoluene Using A Colorimetric Gold Nanoparticle Air Cassette Filter, Andrea I. Ferrer Vega

Master of Science in Forensic Science Directed Research Projects

Trinitrotoluene (TNT) is an explosive commonly used during military and terrorist activities. Current methods to identify this compound require sampling, transport and analysis at a forensic lab using analytical instrumentation. However, on-site detection is needed to assist efforts to prevent detonation. Gold nanoparticles have been used as sensors throughout the years due to their versatility and surface enhanced Raman scattering properties in the presence of an analyte and low limits of detection. By taking advantage of the Meisenheimer complex that TNT forms in the presence of amines, it is possible to determine its presence at picogram levels. Subsequently, adhering amine …

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Nebulizer-Based Systems To Improve Pharmaceutical Aerosol Delivery To The Lungs, Benjamin M. Spence

Theses and Dissertations

Combining vibrating mesh nebulizers with additional new technologies leads to substantial improvements in pharmaceutical aerosol delivery to the lungs across therapeutic administration methods. In this dissertation, streamlined components, aerosol administration synchronization, and/or Excipient Enhanced Growth (EEG) technologies were utilized to develop and test several novel devices and aerosol delivery systems. The first focus of this work was to improve the poor delivery efficiency, e.g., 3.6% of nominal dose (Dugernier et al. 2017), of aerosolized medication administration to adult human subjects concurrent with high flow nasal cannula (HFNC) therapy, a form of continuous-flow non-invasive ventilation (NIV). The developed Low-Volume Mixer-Heater (LVMH) …

Tympanal Asymmetry In A Parasitoid Fly: Small Asymmetries Produce Big Gains, Max Mikel-Stites, Anne E. Staples

Biology and Medicine Through Mathematics Conference

No abstract provided.

Leveraging Peer-To-Peer Energy Sharing For Resource Optimization In Mobile Social Networks, Aashish Dhungana

Theses and Dissertations

Mobile Opportunistic Networks (MSNs) enable the interaction of mobile users in the vicinity through various short-range wireless communication technologies (e.g., Bluetooth, WiFi) and let them discover and exchange information directly or in ad hoc manner. Despite their promise to enable many exciting applications, limited battery capacity of mobile devices has become the biggest impediment to these appli- cations. The recent breakthroughs in the areas of wireless power transfer (WPT) and rechargeable lithium batteries promise the use of peer-to-peer (P2P) energy sharing (i.e., the transfer of energy from the battery of one member of the mobile network to the battery of …

Electric Field Control Of Fixed Magnetic Skyrmions For Energy Efficient Nanomagnetic Memory, Dhritiman Bhattacharya

Theses and Dissertations

To meet the ever-growing demand of faster and smaller computers, increasing number of transistors are needed in the same chip area. Unfortunately, Silicon based transistors have almost reached their miniaturization limits mainly due to excessive heat generation. Nanomagnetic devices are one of the most promising alternatives of CMOS. In nanomagnetic devices, electron spin, instead of charge, is the information carrier. Hence, these devices are non-volatile: information can be stored in these devices without needing any external power which could enable computing architectures beyond traditional von-Neumann computing. Additionally, these devices are also expected to be more energy efficient than CMOS devices …

Predicting Tgf-Β-Induced Epithelial-Mesenchymal Transition Using Data Assimilation, Mario J. Mendez, Matthew J. Hoffman, Elizabeth M. Cherry, Dr. Christopher Lemmon, Seth Weinberg

Biology and Medicine Through Mathematics Conference

No abstract provided.

Age Dependent Regulation Of Cardiac Sodium Channel Gain Of Function, Madison Nowak, David Ryan King, Steven Poelzing, Seth Weinberg

Biology and Medicine Through Mathematics Conference

No abstract provided.

Immunofluorescence Image Feature Analysis And Clustering Pipeline For Distinguishing Epithelial-Mesenchymal Transition, Shreyas Hirway, Nadiah Hassan, Dr. Christopher Lemmon, Dr. Seth Weinberg

Biology and Medicine Through Mathematics Conference

No abstract provided.

Coupled Influence Of Heart Rate Variability And Subcellular Calcium Heterogeneity On Cardiac Electromechanical Dynamics, Vrishti M. Phadumdeo, Seth H. Weinberg Ph.D

Biology and Medicine Through Mathematics Conference

No abstract provided.

Straintronic Nanomagnetic Devices For Non-Boolean Computing, Md Ahsanul Abeed

Theses and Dissertations

Nanomagnetic devices have been projected as an alternative to transistor-based switching devices due to their non-volatility and potentially superior energy-efficiency. The energy efficiency is enhanced by the use of straintronics which involves the application of a voltage to a piezoelectric layer to generate a strain which is ultimately transferred to an elastically coupled magnetostrictive nanomaget, causing magnetization rotation. The low energy dissipation and non-volatility characteristics make straintronic nanomagnets very attractive for both Boolean and non-Boolean computing applications. There was relatively little research on straintronic switching in devices built with real nanomagnets that invariably have defects and imperfections, or their adaptation …

Adhesion At Solid/Liquid Interfaces, Neda Ojaghlou

Theses and Dissertations

The adhesion at solid/liquid interface plays a fundamental role in diverse fields and helps explain the structure and physical properties of interfaces, at the atomic scale, for example in catalysis, crystal growth, lubrication, electrochemistry, colloidal system, and in many biological reactions. Unraveling the atomic structure at the solid/liquid interface is, therefore, one of the major challenges facing the surface science today to understand the physical processes in the phenomena such as surface coating, self-cleaning, and oil recovery applications. In this thesis, a variety of theory/computational methods in statistical physics and statistical mechanics are used to improve understanding of water adhesion …