Physical Sciences and Mathematics Commons^™

(Non-) Recovery Of An Agricultural Stream From Straightening And Dredging, Aras Anderson Mann

Graduate Theses, Dissertations, and Problem Reports

In recent history, natural, meandering streams have been straightened and dredged to reduce flooding. While this practice can be effective in reducing flooding locally, it often results in the degradation of stream water quality and aquatic ecosystems. A straighter channel inherently increases the stream gradient, which could increase flow velocity, shear stress, and potentially downstream sediment yield. Studies have shown that straightened, channelized streams often begin to return to a meandering pattern 35-50 years post-channelization. Yet cross-sectional surveys and air photo analysis of the stream reach in this study, Deckers Creek, indicate little to no observable trend of the stream …

Modeling The Dynamics Of Radiation Belt Electrons And Ring Current Protons, Xingzhi Lyu

Graduate Theses, Dissertations, and Problem Reports

Earth’s inner magnetosphere is a highly dynamic region with various charged particle populations and current systems. The radiation belts, composed of relativistic electrons and protons, is an environment that can pose significant risks to both spacecraft and humans in space; while the fluctuations of ring current, an electric current flowing around the earth consisting of energetic electrons and ions, can lead to severe disruptions in ground-based electrical systems. In this dissertation, we first modeled the long-term evolution of ring current protons based on the measurements of Van Allen Probes. By implementing a 1D radial diffusion model with charge exchange loss, …

Exploring Crystal Polymorphism In Additive-Assisted Chemical Vapor-Deposited Transition Metal Chalcogenides And Oxides, Lawrence Kirimi Mubwika

Graduate Theses, Dissertations, and Problem Reports

Crystal polymorphism is a phenomenon in which compounds with the same chemical formula can be crystallized into different crystal structures. This phenomenon can be observed in elemental materials, such as diamond and graphite, as well as in compounds, such as the trigonal (1H) or octahedral (1T) prismatic MoS₂. Crystals can also exhibit polytypism by stacking different polymorphs in a certain order, with the stacking sequence determining the variation between polytypes. Although all polymorphs and polytypes have the same chemical composition, each polymorph and polytype possesses unique electronic and physical properties.

This study explores the additive-assisted chemical vapor deposition …

A Multi-Wavelength Determination Of The Total Luminosity And Star Formation Rate Of The Milky Way, Joshua L. Mascoop

Graduate Theses, Dissertations, and Problem Reports

The star formation rate (SFR) of the Milky Way is poorly understood in comparison to the SFR of other galaxies. In order to better find the Galaxy's place in the universe, it is imperative to understand the star formation activity occurring within it. We characterize the Galactic \hii\ region luminosity function (LF) at multiple infrared and radio wavelengths using a sample of 797 first Galactic quadrant \hii regions compiled from the WISE Catalog of Galactic \hii Regions. This sample is statistically complete for all regions powered by single stars of type O9.5V and earlier.

We find that neither a single …

C–H Alkylation Of Diol Substrates Using Bifunctional Boronic Acid Catalysts, Matthew Ryan Ross

Graduate Theses, Dissertations, and Problem Reports

Boronic acid catalysts in combination with a Lewis base have been used in previous work to perform site-selective C–H alkylation reactions targeting the α-hydroxy C–H bond of diols through a photoredox activated hydrogen atom transfer. This is done by accessing a tetracoordinate boronic acid – Lewis base complex which then mediates the hydrogen atom transfer. The research hypothesizes that combining the boronic acid and Lewis base into one molecule not only simplifies the reaction but also has the potential to unlock C–H alkylation sites previously inaccessible. This is plausible because the Lewis base portion of the synthesized bifunctional boronic acid …

Estimating Home Range Size And Density Of White-Tailed Deer (Odocoileus Virginianus) In West Virginia, Sarah M. Pesi

Graduate Theses, Dissertations, and Problem Reports

Big game hunting is an important source of revenue and recreation in the United States. Of big game species, white-tailed deer (Odocileus virginianus) are the most heavily pursued. White-tailed deer are a widespread generalist species, capable of existing at high densities. Hunting is often used as the primary method of managing deer populations, particularly in the eastern United States, where there is a lack of large predators. When populations reach high densities, there can be negative effects on the environment and conflicts with humans. Managers must consider both creating adequate hunting opportunities and reducing negative impacts when managing …

An Observational Census Of Post-Merger Galaxies And Supermassive Black Hole Pair Evolution, Gregory Walsh

Graduate Theses, Dissertations, and Problem Reports

Massive galaxy mergers are a fundamental consequence of the dynamic evolution of the Universe and play a central role in the evolution of galaxies. Because all massive galaxies harbor a central supermassive black hole (SMBH; M ≥ 10⁶ M_⊙), studying these merging systems with electromagnetic (EM) techniques constrains galaxy evolution models and provides a systematic means to examine the astrophysical mechanisms that facilitate the growth of SMBHs. These growing SMBHs are observable across the EM spectrum as Active Galactic Nuclei (AGN). Massive galaxy mergers are a natural formation mechanism for an SMBH pair, eventually evolving into an …

On Confidence And Sense Of Belonging In Cybersecurity Students: Analysis & Prediction, Sadaf Amna Sarwari

Graduate Theses, Dissertations, and Problem Reports

In recent years, there has been a rapid expansion of cybersecurity programs across higher education institutions in response to the widening skills gap in the cybersecurity job market. This study adopts quantitative and qualitative approaches to identify factors influencing West Virginia University (WVU)’s LANE Department of Computer Science and Electrical Engineering (LCSEE) students’ confidence and sense of belonging in the cybersecurity field. The results are based on data collected from surveys administered to LCSEE students in April 2022 and April 2023. The responses were analyzed using descriptive & inferential statistics and logistic regression techniques. Additionally, the 2023 data was utilized …

Synthetic Elaboration Of Β-Carbonyl Alkylboronic Esters, Mason D. Hamilton Ph.D.

Graduate Theses, Dissertations, and Problem Reports

Organoboron compounds are some of the most synthetically versatile compounds in organic chemistry due to the many valuable transformations of the C-B bond. This synthetic versatility combined with the pharmacophoric nature of carboxylic acids has led to an increased interest in the one-pot difunctionalization of vinyl arenes using CO₂ and pinacol boranes. Recently, much progress has been made to improve the scope and versatility of boracarboxylation reactions to now include electron-deficient and α-methyl substituted vinyl arenes. However, the potential transformations of boracarboxylated products have remained unexplored. Here, methodologies to transform the β-aryl alkylboronic ester into new C-C, C-N, and …

Measuring And Modeling Riparian Wetland Saturated Hydraulic Conductivity, Nutrient Concentrations And Shallow Groundwater Dynamics In An Appalachian Mixed Land Use Catchment, Bidisha Faruque Abesh

Graduate Theses, Dissertations, and Problem Reports

The paucity of research on accurate predictions of saturated hydraulic conductivity (Ksat), spatiotemporal analysis of nutrient concentrations relative to water source types (stream and shallow groundwater (SGW)), water flow directions, and land use in riparian wetlands of Appalachian mixed land use catchments underscored the need for this study. Additionally, the lack of SGW flow simulations and stream-SGW interactions using three-dimensional (3D) numerical models (i.e., MODFLOW) in these riparian wetlands further highlighted the research gap. Observed data including soil properties, Ksat, surface water (SW) and SGW levels, and nutrient concentrations, including nitrate (NO3-N), nitrite (NO2-N), ammonium (NH4-N), orthophosphate (PO43-P), total nitrogen …

Reconstructing Δ14c Production Events Using Tree Rings: Does Tree Physiology Affect Estimates Of Atmospheric Δ14c?, Meagan Rory Walker

Graduate Theses, Dissertations, and Problem Reports

Cosmic rays and solar energetic particles (SEP) bombard the Earth’s geomagnetic field, posing a threat to satellites, space stations, and human space exploration. These rays and particles also produce radiocarbon (¹⁴C) in Earth’s atmosphere, thus records of past ¹⁴C concentration in the atmosphere may be indicative of past cosmic and solar activity. Rapid increases in the concentration of atmospheric radiocarbon ¹⁴C, (Miyake events) first identified in tree rings, are thought to be a result of solar eruptive activity triggering the release of solar energetic particles, though the precise nature of past events remains unresolved. The first …

Ung: A Diagnostic Standard C Library, Jakob Knute Kaivo

Graduate Theses, Dissertations, and Problem Reports

Undefined behavior in C programs is a major source of unreliable software. Many of the most common exploitable software vulnerabilities can be traced directly to undefined behavior. In the increasingly connected world, a successful attack can cost the victim millions of dollars to recover from. While static program analysis aids in identifying undefined behavior, testing indicates that even the best static analysis tools correctly identifies about 35% of these defects. This dissertation introduces UNG’s Not GNU (UNG), a standard C library designed to mitigate undefined behavior. Where others have seen opportunities for optimization, UNG makes every effort to identify undefined …

Additive-Dependent Regioselective Boracarboxylation Of Alkenes Using Low Valent Copper And Co2, Carly H. Gordon Ph.D.

Graduate Theses, Dissertations, and Problem Reports

As a major greenhouse gas, carbon dioxide (CO₂) is an abundant and renewable C₁ feedstock, and developing methods for fixing CO₂ into high-value products is a major interest for the movement of greener chemistry. The boracarboxylation of alkenes provides access to difunctionalized products containing pharmaceutically relevant motifs, including carboxylic acids and boron-containing moieties. Significant work has been done to improve the versatility and efficiency of this methodology, with the recent expansion into a wide variety of terminal alkenes. The use of different phosphine and alkene additives have demonstrated positive effects on reaction rates and productivity, indicating …

Experimental And Chemical Modeling Of Applied Dc Electric Field Induced H2 – Air Axisymmetric Laminar Co-Flow Diffusion Flames With Low Carbon Impurities, Susith Dilshan Pathmasiri Gunamuni Halowitage

Graduate Theses, Dissertations, and Problem Reports

The effect of externally applied DC electric fields on flame structure was investigated in a stationary atmospheric axisymmetric laminar H₂–Air flame with less than 100 ppm carbon equivalent impurities. Flame OH chemiluminescence signals were recorded using a UV-sensitive CCD array as a function of voltage (+10 to -10 kV) applied to a stainless-steel ring electrode placed around the burner nozzle. Changes in chemiluminescence signal are reported as a function of electrode height above the burner, airflow, and fuel composition. Significant changes in OH* distributions were observed for voltages below -5 kV. Under optimum conditions, the height of the …

Conditional Constrained And Unconstrained Quantization For Probability Distributions, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper, we present the idea of conditional quantization for a Borel probability measure P on a normed space Rk. We introduce the concept of conditional quantization in both constrained and unconstrained scenarios, along with defining the conditional quantization errors, dimensions, and coefficients in each case. We then calculate these values for specific probability distributions. Additionally, we demonstrate that for a Borel probability measure, the lower and upper quantization dimensions and coefficients do not depend on the conditional set of the conditional quantization in both constrained and unconstrained quantization.

Exploring International Educators' Learning About Local And Global Social Justice In A Virtual Community Of Practice, Bima Sapkota, Xuwei Luo, Muna Sapkota, Murat Akarsu, Emmanuel Deogratias, Daphne Fauber, Rose Mbewe, Fidelis Mumba, Ram Krishna Panthi, Jill Newton

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this chapter, the authors report themes that emerged when a cross-cultural team of researchers involved in a virtual international community of practice (Global Social Justice in Education-GSJE) investigated reflections on activities focused on social justice in local and global contexts. The findings suggested that the activities elicited GSJE community members' understandings of the complexities of social justice associated with naming practices, privilege, and the arts within their own and across contexts. The authors discuss implications of the activities to advance diverse educators' understanding of social justice in global and local contexts. They also unpack the opportunities and challenges that …

Hierarchical Neural Networks, P-Adic Pdes, And Applications To Image Processing, Wilson A. Zuniga-Galindo, B. A. Zambrano-Luna, Baboucarr Dibba

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

The first goal of this article is to introduce a new type of p-adic reaction-diffusion cellular neural network with delay. We study the stability of these networks and provide numerical simulations of their responses. The second goal is to provide a quick review of the state of the art of p-adic cellular neural networks and their applications to image processing.

Investigating Preservice Teachers’ Conceptualizations Of Mathematical Knowledge For Teaching Through Video Analysis, Bima Sapkota

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Mathematics Preservice Teachers’ (M-PSTs) conceptions of Mathematical Knowledge for Teaching (MKT) enhance their reflective skills because they utilize such conceptions to reflect on how to contextualize content knowledge during secondary mathematics teaching. While previous studies suggested M-PSTs develop MKT, including pedagogical content knowledge by analyzing teaching in video lessons, how M-PSTs enhance their conceptions of MKT through such analysis is underexplored. I used a collective case study approach to investigate how four secondary M-PSTs conceptualized MKT when they analyzed and discussed teaching represented in a video lesson using the MKT framework. The findings indicated that the M-PSTs often described teacher …

Utility In Time Description In Priority Best-Worst Discrete Choice Models: An Empirical Evaluation Using Flynn's Data, Sasanka Adikari, Norou Diawara

Mathematics & Statistics Faculty Publications

Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best-worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn's (2007) 'Quality of Life Experiment'. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent …

Sparse Representer Theorems For Learning In Reproducing Kernel Banach Spaces, Rui Wang, Yuesheng Xu, Mingsong Yan

Mathematics & Statistics Faculty Publications

Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical learning models in an RKBS: the minimum norm interpolation (MNI) problem and the regularization problem. We first establish an explicit representer theorem for solutions of these problems, which represents the extreme points of the solution set by a linear combination of the extreme points of the subdifferential set, of the norm function, …

Weak-Strong Beam-Beam Simulation With Crab Cavity Noises For The Hadron Storage Ring Of The Electron-Ion Collider, Y. Luo, B. Gamage, C. Montag, D. Marx, D. Xu, F. Willeke, H. Huang, H. Lovelace Iii, J. Berg, M. Blaskiewicz, S. Peggs, T. Satogata, V. Ptitsyn, V. Morozov, Y. Hao

Mathematics & Statistics Faculty Publications

The Electron Ion Collider (EIC), to be constructed at Brookhaven National Laboratory, will collide polarized high-energy electron beams with hadron beams, achieving luminosities of up to 1 X 10³⁴cm⁻²s⁻¹ in the center-mass energy range of 20-140 GeV. Crab cavities are employed to compensate for the geometric luminosity loss caused by a large crossing angle of 25 mrad in the interaction region. The phase noise in crab cavities will induce a significant emittance growth for the hadron beams in the Hadron Storage Ring (HSR). Various models have been utilized to study the effects of crab cavity …

Testing Informativeness Of Covariate-Induced Group Sizes In Clustered Data, Hasika K. Wickrama Senevirathne, Sandipan Duttta

Mathematics & Statistics Faculty Publications

Clustered data are a special type of correlated data where units within a cluster are correlated while units between different clusters are independent. The number of units in a cluster can be associated with that cluster’s outcome. This is called the informative cluster size (ICS), which is known to impact clustered data inference. However, when comparing the outcomes from multiple groups of units in clustered data, investigating ICS may not be enough. This is because the number of units belonging to a particular group in a cluster can be associated with the outcome from that group in that cluster, leading …

Uniform Convergence Of Deep Neural Networks With Lipschitz Continuous Activation Functions And Variable Widths, Yuesheng Xu, Haizhang Zhang

Mathematics & Statistics Faculty Publications

We consider deep neural networks (DNNs) with a Lipschitz continuous activation function and with weight matrices of variable widths. We establish a uniform convergence analysis framework in which sufficient conditions on weight matrices and bias vectors together with the Lipschitz constant are provided to ensure uniform convergence of DNNs to a meaningful function as the number of their layers tends to infinity. In the framework, special results on uniform convergence of DNNs with a fixed width, bounded widths and unbounded widths are presented. In particular, as convolutional neural networks are special DNNs with weight matrices of increasing widths, we put …

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

The Data-Enabled Advanced Computational Training Program for Cybersecurity Research and Education (DeapSECURE) is a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. Since 2020, these lesson modules have been updated and retooled to suit fully-online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we summarize the four years of the project comparing in-person and on-line only instruction methods as well as outlining …

Disaggregating Longer-Term Trends From Seasonal Variations In Measured Pv System Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon

Articles

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …