Physical Sciences and Mathematics Commons^™

Ultralow Velocities Of Caco3 And The Origin Of Seismic Anomalies In The Earth's Upper Mantle, Mingqiang Hou, Ming Hao, Jin Liu, Xiaowan Su, Wen-Yi Zhou, Jin S. Zhang, Rostislav Hrubiak

Earth and Planetary Sciences Faculty and Staff Publications

Calcium carbonate (CaCO₃) is a major mineral host for CO₂ in deep Earth. The physical and chemical properties of CaCO₃ at extreme pressure-temperature (P-T) conditions are important for understanding the CO₂ circulation between the Earth's surface and interior primarily via volcanism and subduction. Using the pulse-echo-overlap method combined with Paris-Edinburgh (PE) press, we measured velocities of CaCO₃ under upper mantle conditions and observed an anomalous velocity drop associated with the amorphization of aragonite. The compressional (V_P) and shear (V_S) wave velocities of the partially amorphized CaCO …

A Novel Efficient Quantum Random Access Memory, Mohammed Zidan, Abdel-Haleem Abdel-Aty, Ashraf Khalil, Mahmoud Abdel-Aty, Hichem Eleuch

All Works

Owing to the significant progress in manufacturing desktop quantum computers, the quest to achieve efficient quantum random access memory (QRAM) became inevitable. In this paper, we propose a novel efficient random access memory for quantum computers. The proposed QRAM has a fixed structure and can be used efficiently to store both known and unknown classical/quantum data. The storage capacity of the proposed QRAM is more efficient than that of the classical RAMs and can be used to store both classical and quantum information. Furthermore, the proposed model can access an arbitrary location in O(1) compared with other state-of-the-art models.

Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri

All Works

The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit …

An Empirical Investigation Of U.K. Environmental Targets Disclosure: The Role Of Environmental Governance And Performance, Tantawy Moussa, Amr Kotb, Akrum Helfaya

All Works

Although an increasing number of companies have publicly declared environmental targets (ETs), scant research has been conducted in this area. This study, therefore, investigates the extent of corporate environmental targets disclosure (ETD) and empirically examines whether environmental governance and performance influence the ETD of companies in the U.K. during the 2005–2013 period. We find that firms show a large degree of variability and inconsistency in their reporting of ETs. The results indicate that U.K. firms, particularly those with high environmental sensitivity, tend to disclose symbolic soft or semi-hard ETs to manage stakeholder perceptions and legitimize their existence. Moreover, Global Reporting …

Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet

All Works

Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain …

An Adaptive Protection Of Flooding Attacks Model For Complex Network Environments, Bashar Ahmad Khalaf, Salama A. Mostafa, Aida Mustapha, Mazin Abed Mohammed, Moamin A. Mahmoud, Bander Ali Saleh Al-Rimy, Shukor Abd Razak, Mohamed Elhoseny, Adam Marks

All Works

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate …

Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman

All Works

There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and …

Real-Time Privacy Preserving Framework For Covid-19 Contact Tracing, Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah

All Works

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated …

A Simple Background Elimination Method For Miniaturized Fiber-Optic Raman Probe, Bohong Zhang

Masters Theses

"Raman scattering is called a photonic - molecular interaction based on the kinetic model of the analytic. Due to the uniqueness of the Raman scattering technique, it can provide a unique fingerprint signal for molecular recognition. However, a serious challenge often encountered in Raman measurement comes from the requirements of fast, real-time remote sensing, background fluorescence suppression, and micro-environmental detection.

A new Miniaturized Fiber-Optic Raman Probe (MFORP) for Raman spectroscopy, used especially for eliminating background fluorescence and enhancing sampling, is presented. Its main purpose is to provide an overview of excellent research on the detection of very small substances and …

Predator Scent And Presence Alter Mammal Assemblages In The Missouri Ozarks, Usa, Cara Jean Yocom-Russell

Masters Theses

"Species that are vulnerable to predation exhibit a host of behavioral and physiological adaptations toward the avoidance of this outcome: Heightened awareness of surroundings through visual, olfactory, and auditory senses are common ways in which these species avoid detection by predators. While links between direct predator-prey relationships are well established, less is known about how predators can shape overall community structure or the populations of secondary or less frequently consumed prey items. As humans expand into rural areas, the frequency of wildlife conflicts rises. In response, humans look to prevent these events with a variety of methods. One such method …

A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu

Masters Theses

“Wavelength meters are very important for precision measurements of both pulses and continuous-wave optical sources. Conventional wavelength meters employ gratings, prisms, interferometers, and other wavelength-sensitive materials in their design. Here, we report a simple and compact wavelength meter based on a section of multimode fiber and a camera. The concept is to correlate the multimodal interference pattern (i.e., speckle pattern) at the end-face of a multimode fiber with the wavelength of the input lightsource. Through a series of experiments, specklegrams from the end face of a multimode fiber as captured by a charge-coupled device (CCD) camera were recorded; the images …

Biochemical Assay Invariant Attestation For The Security Of Cyber-Physical Digital Microfluidic Biochips, Fredrick Eugene Love Ii

Masters Theses

“Due to the devastating global impact that infectious diseases have had, especially in developing countries, the demand for access to adequate resources to combat sickness continues to be a heavy burden. Reliable and affordable diagnostics is a vital first line of defense in fighting outbreaks and providing accurate treatment. Digital microfluidics biochips capable of running multiple diagnostic tests on a single platform are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. Although these systems offer many benefits, processing errors are inherent. Therefore, cyber-physical digital biochips are being investigated that …

Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian

Masters Theses

“Automation with artificial intelligence technology is an emerging field and is widely used in various industries. With the increasing autonomy, learning, and adaptability of intelligent machines such as self-driving cars, it is difficult to regard them as simple tools in human hands. At the same time, a series of problems and challenges such as predictability, interpretability, and causality arise. Trust in self-driving technology will impact the adoption and utilization of autonomous driving technology. A qualitative research methodology, Value-Focused Thinking, is used to identify the values of trust in autonomous driving vehicles and analyze the relationship between these values”--Abstract, page iii.

Multimodal Neuroscience Data Modeling And Inference, Sima Azizi

Doctoral Dissertations

“Mathematical models can be combined with deep learning and machine learning methods to provide new insights in neuroscience. The field of neuroscience is characterized by rich datasets that include fluid biomarkers, EEG signals, and advanced neuroimages. Recent advances in natural language processing have led to the opportunity to gain additional insights from rapidly growing text databases as well as electronic health records. In this research, we focus on applying computational intelligence methods to the analysis of three different complex data sources: blood levels of disease biomarkers, EEG signals from schizophrenic patients, and disease phenotypes encoded in electronic health records. First, …

Relating Detonation Parameters To The Detonation Synthesis Of Nanomaterials, Martin Langenderfer

Doctoral Dissertations

“This research investigates the physical and chemical processes that contribute to the detonation synthesis of silicon carbide nanoparticles. Bulk production of SiC nanoparticles through detonation is possible due to pressures achieved over 20 GPa and temperatures over 2000 K as well as quenching rates in excess of 13 billion K/second. These conditions catalyze reaction and bottom-up molecular growth while retaining particles < 100 nm in diameter. In this work, detonation synthesis of SiC was demonstrated by incorporation of polycarbosilane, an SiC precursor material, into an RDX/TNT explosive matrix prior to detonation. Detonation Synthesis of SiC was also accomplished by reacting elemental silicon with carbon liberated by the detonation of negatively oxygen balanced TNT. Hydrodynamic simulation of a 60:40 mass ratio RDX/TNT detonation created conditions thermodynamically suitable to produce cubic silicon carbide within the first 500 nanoseconds after the passage of the detonation wave while carbon remains chemically reactive for molecular formation. Simulations and experimental tests indicated that loading configuration and impedance mismatch of the precursor additives used in detonation synthesis results in conditions in the additives that exceed the accepted detonation pressure of the explosive by greater than three times. Finally, a full factorial experimental design showed increasing silicon concentration, reducing silicon size, and reducing oxygen balance by adjusting the ratio of RDX to TNT decreased the explosives detonation pressure by 20% and increased the soot yield and concentration of SiC observed in the detonation products by 82% and 442% respectively”--Abstract, page iv.

Upper-Sided Ewma-Based Distribution-Specific Tolerance Limits, Owen Visser

UNF Graduate Theses and Dissertations

Tolerance limits are constructed from sample data to ascertain if a proportion of a process is within specification limits. There exists multiple methods of calculating the sample size requirements for tolerance limits under various assumptions. In this research, a distribution-specific algorithm that utilizes the exponentially weighted moving average technique (EWMA), first introduced by Sa and Razaila (2004), is reconstructed. The algorithm is used to calculate the required sample sizes for continuous construction of upper-sided tolerance limits. The sample sizes and intervals constructed from them are compared to three existing methods for various distributions. The distribution-specific algorithm was observed to reduce …

Depicting Bivariate Relationship With A Gaussian Ellipse, Mamunur Rashid, Jyotirmoy Sarkar

Mathematics Faculty Publications

For data on two continuous variables, how should one depict the summary statistics (means, SDs, correlation coefficient, coefficient of determination, regression lines) so that their values can be read off easily from the depiction and potential outliers can be flagged also? We propose the Gaussian covariance ellipse as an answer that will benefit all users of statistics.

Predicting The Impacts Of Climate Change On The Great Lakes Water Levels Using A Fully Coupled 3d Regional Modeling System, Miraj Kayastha

Dissertations, Master's Theses and Master's Reports

The Great Lakes of North America are the largest surface freshwater system in the world and many ecosystems, industries, and coastal processes are sensitive to the changes in their water levels. The recent changes in the Great Lakes climate and water levels have particularly highlighted the importance of water level prediction. The water levels of the Great Lakes are primarily governed by the net basin supplies (NBS) of each lake which are the sum of over-lake precipitation and basin runoff minus lake evaporation. Recent studies have utilized Regional Climate Models (RCMs) with a fully coupled one-dimensional (1D) lake model to …

Quantifying The Value Of Foam-Based Flexible Floating Solar Photovoltaic Systems, Koami Soulemane Hayibo

Dissertations, Master's Theses and Master's Reports

Distributed generation with solar photovoltaic (PV) technology is economically competitive if net metered in the U.S. Yet there is evidence that net metering is misrepresenting the true value of distributed solar generation so that the value of solar (VOS) is becoming the preferred method for evaluating economics of grid-tied PV. VOS calculations are challenging and there is widespread disagreement in the literature on the methods and data needed. To overcome these limitations, this thesis reviews past VOS studies to develop a generalized model that considers realistic future avoided costs and liabilities. The approach used here is bottom-up modeling where the …

A Transdisciplinary Analysis Of Just Transition Pathways To 100% Renewable Electricity, Adewale Aremu Adesanya

Dissertations, Master's Theses and Master's Reports

The transition to using clean, affordable, and reliable electrical energy is critical for enhancing human opportunities and capabilities. In the United States, many states and localities are engaging in this transition despite the lack of ambitious federal policy support. This research builds on the theoretical framework of the multilevel perspective (MLP) of sociotechnical transitions as well as the concept of energy justice to investigate potential pathways to 100 percent renewable energy (RE) for electricity provision in the U.S. This research seeks to answer the question: what are the technical, policy, and perceptual pathways, barriers, and opportunities for just transition to …

Impact Of Hemodynamic Vortex Spatial And Temporal Characteristics On Analysis Of Intracranial Aneurysms, Kevin W. Sunderland

Dissertations, Master's Theses and Master's Reports

Subarachnoid hemorrhage is a potentially devastating pathological condition in which bleeding occurs into the space surrounding the brain. One of the prominent sources of subarachnoid hemorrhage are intracranial aneurysms (IA): degenerative, irregular expansions of area(s) of the cerebral vasculature. In the event of IA rupture, the resultant subarachnoid hemorrhage ends in patient mortality occurring in ~50% of cases, with survivors enduring significant neurological damage with physical or cognitive impairment. The seriousness of IA rupture drives a degree of clinical interest in understanding these conditions that promote both the development and possible rupture of the vascular malformations. Current metrics for the …

Statistical Methods In Genetic Studies, Cheng Gao

Dissertations, Master's Theses and Master's Reports

This dissertation includes three Chapters. A brief description of each chapter is organized as follows.

In Chapter 1, we proposed a new method, called MF-TOWmuT, for genome-wide association studies with multiple genetic variants and multiple phenotypes using family samples. MF-TOWmuT uses kinship matrix to account for sample relatedness. It is worth mentioning that in simulations, we considered hidden polygenic effects and varied the proportion of variance contributed by it to generate phenotypes. Simulation studies show that MF-TOWmuT can preserve the type I error rates and is more powerful than several existing methods in different simulation scenarios, MFTOWmuT is also quite …

Mapping Michigan's Historic Coastlines, Ryan A. Williams

Dissertations, Master's Theses and Master's Reports

This five-year project, sponsored by the Michigan Department of Environment, Great Lakes, and Energy, is working to map how Michigan’s Great Lakes shorelines have changed over the past 80+ years. Products of this project include publicly available digital, georeferenced, historic aerial photography datasets, as well as map layers depicting the locations of historic shorelines and bluff lines from 1938, 1980, 2009, 2016, 2018, and 2020. Additional products include bluff retreat risk areas, shoreline rate of change map layers, and tools to assist in the development of future Coastal Vulnerability Index projects for the Great Lakes. All products are available as …

Origin And Distribution Of Diffuse Soil Co2 Gas Emissions Across Turrialba And Irazú Volcanoes, Costa Rica, Katie Nelson

Dissertations, Master's Theses and Master's Reports

We characterized large-scale volatile emissions across the summit and flanks of the actively degassing Turrialba volcano, Costa Rica, using soil gas flux measurements and ¹³C isotopes. The objectives of this study were the following: 1) to monitor changes in the magmatic activity and identify source contributions over Turrialba using measurements of CO₂ soil gas emissions since the volcano last erupted in 2014-2015, and 2) to identify the location and extent of magma at depth, and the structures that allow gas transport to the surface. Degassing at the summit is concentrated along one normal fault lineament, the trace of …

Physicochemical Properties Of Atmospheric Aerosols And Their Effect On Ice Cloud Formation, Nurun Nahar Lata

Dissertations, Master's Theses and Master's Reports

Atmospheric aerosols play a vital role in the Earth's energy budget-directly by scattering and absorbing solar radiation and indirectly by acting as cloud condensation nuclei and ice-nucleating particles [1, 2]. The cloud formation potential of aerosol is driven by multiple factors, including surface properties, size distribution, composition, mixing state, phase state, and morphology [3]. The interaction of aerosols with clouds alters the aerosol's physicochemical properties. Those properties can also evolve during transport due to atmospheric processing, in turn, affect the aerosol's ice nucleation and cloud formation activities. This thesis presents experimental studies to understand the role of physicochemical properties of …

Major Index Over Descent Distributions Of Standard Young Tableaux, Emily Anible

Dissertations, Master's Theses and Master's Reports

This thesis concerns the generating functions $f_{\lambda, k}(q)$ for standard Young tableaux of shape $\lambda$ with precisely $k$ descents, aiming to find closed formulas for a general form given by Kirillov and Reshetikhin in 1988. Throughout, we approach various methods by which further closed forms could be found. In Chapter 2 we give closed formulas for tableaux of any shape and minimal number of descents, which arise as principal specializations of Schur functions. We provide formulas for tableaux with three parts and one more than minimal number of descents, and demonstrate that the technique is extendable to any number of …

Efficient Modeling Of Random Sampling-Based Lru Cache, Junyao Yang

Dissertations, Master's Theses and Master's Reports

The Miss Ratio Curve (MRC) is an important metric and effective tool for caching system performance prediction and optimization. Since the Least Recently Used (LRU) replacement policy is the de facto policy for many existing caching systems, most previous studies on efficient MRC construction are predominantly focused on the LRU replacement policy. Recently, the random sampling-based replacement mechanism, as opposed to replacement relying on the rigid LRU data structure, gains more popularity due to its lightweight and flexibility. To approximate LRU, at replacement times, the system randomly selects K objects and replaces the least recently used object among the sample. …

Cobalt, Molybdenum, And Nickel Complexes, Natural Zeolites, Epoxidation, And Free Radical Reactions, Nicholas K. Newberry

Dissertations, Master's Theses and Master's Reports

Chapter 2 is based on the synthesis and study of the compounds of the bidentate ligand ((5-phenyl-1H-pyrazol-3-yl)methyl)phosphine oxide with molybdenum and cobalt as the transition metal. The complexes were analyzed via FTIR, NMR, UV-Vis, Fluorescence Spectroscopy, TGA, DFT, and XRD. Chapter 3 resulted in the synthesis of the complexes [Ni(II)SSRRL](PF6)2 and [Ni(II)SRSRL](Cl)(PF6) of which [Ni(II)SRSRL](Cl)(PF6) had not been previously analyzed. Both products were analyzed via FTIR, NMR, UV-Vis, CV, DFT, and XRD. Chapter 5 contains the results of the characterization and modification of 4 natural zeolites (AZLB-Na, AZLB-Ca, NM-CA, NV-Na) from the United States in an attempt to increase the …

Superresolution Enhancement With Active Convolved Illumination, Anindya Ghoshroy

Dissertations, Master's Theses and Master's Reports

The first two decades of the 21st century witnessed the emergence of “metamaterials”. The prospect of unrestricted control over light-matter interactions was a major contributing factor leading to the realization of new technologies and advancement of existing ones. While the field certainly does not lack innovative applications, widespread commercial deployment may still be several decades away. Fabrication of sophisticated 3d micro and nano structures, specially for telecommunications and optical frequencies will require a significant advancement of current technologies. More importantly, the effects of absorption and scattering losses will require a robust solution since this renders any conceivable application of metamaterials …

Explainable Feature- And Decision-Level Fusion, Siva Krishna Kakula

Dissertations, Master's Theses and Master's Reports

Information fusion is the process of aggregating knowledge from multiple data sources to produce more consistent, accurate, and useful information than any one individual source can provide. In general, there are three primary sources of data/information: humans, algorithms, and sensors. Typically, objective data---e.g., measurements---arise from sensors. Using these data sources, applications such as computer vision and remote sensing have long been applying fusion at different "levels" (signal, feature, decision, etc.). Furthermore, the daily advancement in engineering technologies like smart cars, which operate in complex and dynamic environments using multiple sensors, are raising both the demand for and complexity of fusion. …