Physical Sciences and Mathematics Commons^™

Ultrafast Relaxation Dynamics In Graphene Oxide-Dye And Perovskites Nanocomposites, Abubkr A. Arzaq Abuhagr

Dissertations

Novel materials such as graphene oxide (GO), reduced graphene oxide (RGO), and Perovskites nanocomposites nanosheets have shown interesting electrical and optical properties. These materials have shown prominence in research regarding optical sensing applications. The interaction of different fluorescent molecules like dye molecules with GO, and RGO have been studied recently to develop novel optical sensors, photo-catalysts, and light-harvesting agents. In this study, we have monitored the excited state interactions of dyes covalently attached to GO and RGO nanosheets. Three amine derivatives of anthracene, pyrene, and coumarin were covalently bound to different systems via amide bonds and diazotization. Characterization of different …

Embedded Silver Nanoparticles For Metal Enhanced Photoluminescence, Shahid Iqbal

Dissertations

Imaging of biologically significant molecules using plasmons of Metal Nanoparticles (MNPs) is gaining attention in the research community. Localized Surface Plasmon Resonance (LSPR) is the coherent oscillation of conduction electrons of MNPs. The biologically significant molecule is labeled with the fluorophore molecule to get the image. This approach is widely used in clinical practices, however, low intensity light emission from the labeled molecule makes it difficult to image the biologically significant material. One way to improve the weak intensities of fluorophore is to enhance the brightness using a process called Metal Enhanced Photoluminescence (MEP). This phenomenon occurs in the close …

Smart Sensors With Dual Modes Of Signal Transduction For Monitoring Molecules Pertinent To Health And The Environment, Jared T. Wabeke

Dissertations

The dissertation focuses on the design and synthesis of smart materials for the detection of molecules pertinent to environmental protection and healthcare. The use of computational simulations is pivotal toward advancing molecular design for targeted applications. Research was conducted to investigate the use of simulations to develop novel sensors with dual modes of signal transduction. The molecular properties were determined using computational modelling, and then used to elucidate the binding mechanism of the corresponding sensor complexes. Several molecules were produced that respond to important organic analytes, such as glucose and fenthion, an organophosphorus pesticide. Glucose is an exceedingly important biological …

Scalable Algorithms And Hybrid Parallelization Strategies For Multivariate Integration With Paradapt And Cuda, Omofolakunmi Elizabeth Olagbemi

Dissertations

The evaluation of numerical integrals finds applications in fields such as High Energy Physics, Bayesian Statistics, Stochastic Geometry, Molecular Modeling and Medical Physics. The erratic behavior of some integrands due to singularities, peaks, or ridges in the integration region suggests the need for reliable algorithms and software that not only provide an estimation of the integral with a level of accuracy acceptable to the user, but also perform this task in a timely manner. We developed ParAdapt, a numerical integration software based on a classic global adaptive strategy, which employs Graphical Processing Units (GPUs) in providing integral evaluations. Specifically, ParAdapt …

Social Media Sentiment Analysis With A Deep Neural Network: An Enhanced Approach Using User Behavioral Information, Ahmed Sulaiman M. Alharbi

Dissertations

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data (including tweet length, spelling errors, abbreviations, and special characters), the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis constitutes a fundamental problem with many interesting applications, such as for Business Intelligence, Medical Monitoring, and National Security. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this research, we propose deep learning based frameworks that …

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine learning …

Towards Completely Automated Glycan Synthesis, Matteo Panza

Dissertations

Carbohydrates are ubiquitous both in nature as biologically active compounds and in medicine as pharmaceuticals. Although there has been continued interest in the synthesis of carbohydrates, chemical methods require specialized knowledge and hence remain cumbersome. The need for development of rapid, efficient and operationally simple procedures has come to the fore. This dissertation focuses on the development of a fully automated platform that will enable both experts and non-specialists to perform the synthesis of glycans. Existing automated methods for the synthesis of oligosaccharides are highly sophisticated, operationally complex, and require significant user know-how. By contrast, high performance liquid chromatography (HPLC) …

Recover Data In Sparse Expansion Forms Modeled By Special Basis Functions, Abdulmtalb Mohamed Hussen

Dissertations

In data analysis and signal processing, the recovery of structured functions (in terms of frequencies and coefficients) with respect to certain basis functions from the given sampling values is a fundamental problem. The original Prony method is the main tool to solve this problem, which requires the equispaced sampling values.

In this dissertation, we use the equispaced sampling values in the frequency domain after the short time Fourier transform in order to reconstruct some signal expansions, such as the exponential expansions and the cosine expansions. In particular, we consider the case that the phase of the cosine expansion is quadratic. …

A Framework For Personalized Content Recommendations To Support Informal Learning In Massively Diverse Information Wikis, Heba M Ismail

Dissertations

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a …

Chemical Synthesis Of Oligosaccharides From Human Milk, Mithila Dulanjalee Bandara Weerasooriya Mudiyanselage

Dissertations

Human milk oligosaccharides (HMO) are a family of structurally related glycans that are highly abundant in breast milk. Oligosaccharide fraction is the third largest solid component in human milk after lactose and lipids. There is an accumulating evidence that HMO can provide significant benefits to the breast-fed infants. However, understanding of the exact HMO functions is still incomplete due to the lack of individual compounds in sufficient quantities. Therefore, development of expeditious strategies for the chemical synthesis of HMO has been increasingly important. Among all the methods available for oligosaccharide synthesis, armed-disarmed strategy introduced by Fraser-Reid is based on chemoselective …

A Computational Study Of Sleep And The Hemispheres Of The Brain, Tera Ashley Glaze

Dissertations

Sleep and sleep cycles have been studied for over a century, and scientists have worked on modeling sleep for nearly as long as computers have existed. Despite this extensive study, sleep still holds many mysteries. Larger and more extensive sleep-wake models have been developed, and the circadian drive has been depicted in numerous fashions, as well as incorporated into scores of studies. With the ever-growing knowledge of sleep comes the need to find more ways to examine, quantify, and define it in the context of the most complex part of the human anatomy – the brain. Presented here is the …

Evaluation Of Reasons That May Affect Whether Academically Capable Females Choose To Major In Stem, Kerri Alexander Adkins

Dissertations

The purpose of this research was to study the reasons why academically capable females choose to pursue majors in STEM (science, technology, engineering, and math) fields. A mixed-methods approach using focus groups and a survey were used. Data were gathered from the focus group sessions and used to develop the survey that was then validated and checked for reliability. After some edits, the survey was administered to female freshmen attending Western Kentucky University. Unfortunately, all female students who completed the survey except one indicated they were pursuing STEM majors.

The results from this study suggest that the reasons surrounding the …

Effects Of Electro-Osmotic Consolidation Of Clays And Its Improvement Using Ion Exchange Membranes, Lucas Martin

Dissertations

Electro-osmosis is an established method of expediting consolidation of soft, saturated clayey soils compared to commonly used methods, such as preloading with wick drains. In electro-osmotic consolidation a direct current (DC) is applied via inserted electrodes. This causes hydrated ions in the interstitial fluid to migrate to oppositely charged electrodes. Because the clay particles have a negative surface charge, the majority of ions in the interstitial fluid are positively charged. Therefore, the net flow will be towards the negatively charged electrode (cathode), where the water can be removed and thus consolidation is achieved. Certain problems, such as pH changes in …

Engineering Of Escherichia Coli 2-Oxoglutarate Dehydrogenase Complex With Mechanistic And Synthetic Goals, Joydeep Chakraborty

Dissertations

The Escherichia coli 2-oxoglutarate dehydrogenase complex (OGDHc) compromises multiple copies of three enzymes - 2-oxoglutarate dehydrogenase (E1o), dihydrolipoyl succinyltransferase (E2o), and dihydrolipoyl dehydrogenase (E3). OGDHc is found in the Krebs cycle and catalyzes the formation of the all-important succinyl-Coenzyme A (succinyl-CoA). OGDHc was engineered to understand the catalytic mechanism and optimized for chemical synthetic goals.

Succinyl-CoA formation takes place within the catalytic domain of E2o via a transesterification reaction. The succinyl group from the thiol ester of S8-succinyldihydrolipoyl-E2o is transferred to the thiol group of CoA. Mechanistic studies were designed to investigate enzymatic transthioesterification. His375 and Asp374 was shown to …

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …

Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang

Dissertations

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization …

Using Novel Spectroscopy Tool To Study Organized Self-Assemblies, Mohammad Rafe M Bin Hatshan

Dissertations

Organized self-assemblies are the cornerstones for countless biological processes and are integral parts of lipids, proteins, carbohydrates, nucleic acids, and cell membranes. Several man-made organized assemblies also play a vital role in interdisciplinary sciences that include micelles, reverse micelles, polymers, polyelectrolytes etc. Weak chemical interactions such as hydrogen bonding, p-p stacking, hydrophilic-hydrophobic and electrostatics often result in interesting organized self-assemblies. Understanding organized self-assemblies provides a huge opportunity to mimic naturally occurring biological macromolecules, design materials and develop strategies for specific applications. Several techniques are routinely used to understand the organized self-assemblies, and optical techniques play an important role in most …

Morphological Study Of Voids In Ultra-Large Models Of Amorphous Silicon, Durga Prasad Paudel

Dissertations

The microstructure of voids in pure and hydrogen-rich amorphous silicon (a:Si) network was studied in ultra-large models of amorphous silicon, using classical and quantum- mechanical simulations, on the nanometer length scale. The nanostructure, particularly voids of device grade ultra-large models of a:Si was studied, in which observed three-dimensional realistic voids were extended using geometrical approach within the experimental limit of void-volume fractions. In device-grade simulated models, the effect of void morphology; size, shape, number density, and distribution on simulated scattering intensities in small- angle region were investigated. The evolution of voids on annealing below the crystallization temperature …

Investigation Of Structural, Optical And Electronic Properties Of Modified Methylammonium Lead Iodide Perovskites, Rasanjali Jayathissa

Dissertations

Owing to their high-power conversion efficiency (PCE), easy processability, and low fabrication cost, organic lead halide perovskites (OLHP) are emerging as a most promising photovoltaic technology. However, toxicity of lead (Pb) is a major concern for further development. Therefore, it is essential to explore nontoxic metals to replace lead in these materials. In the current research work, nontoxic Mn²⁺, Na⁺ and Ba²⁺ are doped at 1, 5 and 10% mole concentrations to partially substitute Pb²⁺ in methyl ammonium lead iodide (CH₃NH₃PbI₃ or MAPbI₃) perovskite systems, and the effects …

Predicting The Complexity Of Locality Patterns In Loop Nests In C Scientific Programs, Nasser M. Alsaedi

Dissertations

On modern computer systems, the performance of an application depends on its locality. Most existing locality measurements performed by compiler static analysis mainly target analyzing regular array references in loop nests. Measurements based on compiler static analysis have limited applicability when the loop bounds are unknown at compile time, when the control flow is dynamic, or when index arrays or pointer operations are used. In addition, compiler static analysis cannot adapt to input change.

Training-based locality analysis predicts the data reuse change across program inputs to provide run-time information. This analysis quantifies the number of unique memory locations accessed between …

Capabilities And Limitations Of The Spin Hamiltonian Formalism In Single Molecule Magnets, Philip Ferko

Dissertations

The rational design of molecular magnetic materials is an ongoing effort involving physics, materials science, and chemistry. A common approach to design of complexes and interpretation of magnetic data is the spin Hamiltonian formalism. In this approach, magnetic data is interpreted through constants extracted from the parameterization of data. In design, certain structural motifs are pursued, rationalized by the minimization or maximization of terms in the spin Hamiltonian. In this work, monometallic complexes were prepared to simplify magnetic behavior and allow the examination of specific factors that influence single molecule magnetism like coordination geometry, ligand identity, symmetry, and spin-orbit coupling. …

Enhancing Scalability In Genetic Programming With Adaptable Constraints, Type Constraints And Automatically Defined Functions, George Gerules

Dissertations

Genetic Programming is a type of biological inspired machine learning. It is composed of a population of stochastic individuals. Those individuals can exchange portions of themselves with others in the population through the crossover operation that draws its inspiration from biology. Other biologically inspired operations include mutation and reproduction. The form an individual takes can be many things. It, however, is represented most of the time as a computer program. Constructing correct efficient programs can be notoriously difficult. Various grammar, typing, function constraint, or counting mechanisms can guide creation and evolution of those individuals. These mechanisms can reduce search space …

Decoding The History Of The Early Solar System Using Comet Volatile Compositions, Nathan Roth

Dissertations

Understanding the evolution of the solar system, as well as its current volatile content, requires knowledge of the initial conditions present in the solar nebula. As some of the first objects to accrete in the solar nebula, cometary nuclei are among the most primitive remnants of solar system formation, and their present-day volatile composition likely reflects the composition and conditions where (and when) they formed. As such, the volatile compositions of cometary nuclei may serve as "fossils" of solar system formation. High-resolution near-infrared spectroscopy offers a valuable tool for sampling the primary volatile (i.e., ices subliming directly from the nucleus) …

Approximate Algorithms For Regulatory Motif Discovery In Dna, Hasnaa Imad Al-Shaikhli

Dissertations

Motif discovery is the problem of finding common substrings within a set of biological strings. Therefore it can be applied to finding Transcription Factor Binding Sites (TFBS) that have common patterns (motifs). A transcription factor molecule can bind to multiple binding sites in the promoter region of different genes to make these genes co-regulating. The Planted (l, d) Motif Problem (PMP) is a classic version of motif discovery where l is the motif length and d represents the maximum allowed mutation distance. The quorum Planted (l, d, q) Motif Problem (qPMP) is a version of PMP …

High-Performance Quasi-Monte Carlo Integration And Applications, Ahmed Hassan H. Almulihi

Dissertations

While adaptive integration by region partitioning is generally effective in low dimensions, quasi-Monte Carlo methods can be used for integral approximations in moderate to high dimensions. Important application areas include high-energy physics, statistics, computational finance and stochastic geometry with applications in robotics, tessellations and imaging from medical data using tetrahedral meshes.

Lattice rule integration is a class of quasi-Monte Carlo methods, implemented by an equal-weight cubature formula and suited for fairly smooth functions. Successful methods to construct these rules are the component-by-component (CBC) algorithm by Sloan and Restsov (2001) and the fast algorithm for CBC by Nuyens and Cools (2006). …

Improving Protein Analysis By Desorption Electrospray Ionization (Desi-Ms), Elahe Honarvar

Dissertations

Electrospray ionization mass spectrometry (ESI-MS) is one of the most well-known and versatile techniques for analyzing a broad range of molecules and it has become one of the leading techniques to study biomolecules, such as proteins. ESI-MS can accurately determine the molecular weight of proteins and provide information about their peptide sequence, post-translational modifications as well as their interaction with other molecules.

During ESI-MS analysis, by spraying a sample of proteins, prepared in form of a solution, charged droplets are produced using an electric field. As the solvent molecules gradually evaporate from these droplets, freely hovering bare protein ions remain. …

An Evaluation Of An I-Ready Math Program For 5th Graders In One School District, Tashanda Brown-Cannon

Dissertations

This study evaluated the impact of i-Ready Math instruction on 5^th grade students identified as performing below grade level in mathematics. The participants in this study, administrators and teachers from three Title I schools, answered survey and interview questions to provide their perception on the program’s effectiveness. Equally important, I analyzed student assessment data and online program usage data to ascertain the program’s impact on student achievement. The results of this study revealed a lack of fidelity of implementation of the i-Ready Math program. Based on these findings, I proposed an extension to the teacher contract and a revision …

Exploring The Dynamics Of Scientific Research, Shilpa Lakhanpal

Dissertations

Scientific research papers present the research endeavors of numerous scientists around the world, and are documented across multitudes of technical conference proceedings, and other such publications. Given the plethora of such research data, if we could automate the extraction of key interesting areas of research, and provide access to this new information, it would make literature searches incredibly easier for researchers. This in turn could be very useful for them in furthering their research agenda. With this goal in mind, we have endeavored to provide such solutions through our research. Specifically, the focus of our research is to design, analyze …

Sensitivity Of The Theoretical Electron Capture Shape And Comparisons To Experiment, Katrina E. Koehler

Dissertations

The direct neutrino mass is a fundamental physics quantity with far-reaching implications for the physics community. Current experimental limits put the direct neutrino mass at less than 2 eV. The neutrino mass can be explored through an end-point measurement of tritium beta decay, which is currently underway in the KArlsruhe TRItium Neutrino experiment (KATRIN). KATRIN has a lower limit of 0.2 eV, at which point there will be either a mass measurement or another upper bound. In either case, an alternative experiment with different systematics is needed to verify the results and/or push the upper bound lower. The end point …

Model-Based Deep Autoencoders For Characterizing Discrete Data With Application To Genomic Data Analysis, Tian Tian

Dissertations

Deep learning techniques have achieved tremendous successes in a wide range of real applications in recent years. For dimension reduction, deep neural networks (DNNs) provide a natural choice to parameterize a non-linear transforming function that maps the original high dimensional data to a lower dimensional latent space. Autoencoder is a kind of DNNs used to learn efficient feature representation in an unsupervised manner. Deep autoencoder has been widely explored and applied to analysis of continuous data, while it is understudied for characterizing discrete data. This dissertation focuses on developing model-based deep autoencoders for modeling discrete data. A motivating example of …