Physical Sciences and Mathematics Commons^™

Examining Evolutionary Rate In Xiphosura, Samantha B. Ocon

Graduate Theses, Dissertations, and Problem Reports

Horseshoe crabs, a group of aquatic chelicerate arthropods of the class Xiphosura, are strongly linked with the concept of “living fossils” – a term colloquially used to refer to clades that display a consistently low rate of morphological evolution through time. The concept of living fossils has been hotly debated, as it is considered to simplify or obscure millennia of evolutionary change. Recent methodological and computational advances in the paleobiological sciences have allowed for the investigation of these claims. Xiphosura are a model taxon for this type of investigative study because they exhibit a complex evolutionary history, despite their reputation …

Deep Radio Observations And The Role Of The Cosmic Web In Galaxy Evolution, Nicholas M. Luber

Graduate Theses, Dissertations, and Problem Reports

A current open question in the evolution of galaxies, is what are the physical mechanisms that cut off galaxies from their primordial gas reservoirs, resulting in the end of their star-formation capabilities? Recent observational programs have shown that the properties of galaxies show dependencies on their placement within the large-scale structure (LSS) of the universe. These observations have motivated recent developments in theoretical work that have shown how a galaxy's interaction with the LSS may impact its connection to primordial gas supply, and ability to continue to accrete gas, the fundamental ingredient in star-formation.

In order to investigate the role …

Chemical Characterization Of Clastic Cave Sediments And Insights Into Particle Transport And Storage In Karst Aquifers, Jill L. Riddell

Graduate Theses, Dissertations, and Problem Reports

Abstract

Chemical characterization of clastic cave sediments and insights into particle transport and storage in karst aquifers

Jill L. Riddell

Cave sediments can be divided into two groups: precipitates and clastics. Precipitates are speleothems, or lithologic or mineral features that are chemically precipitated in the cave environment. Clastic cave sediments are frequently described by depositional facies, sorting, and particle size (Bosch and White, 2004). Robust analytical chemical analyses of these sediments to quantify their physical and chemical components is rarely performed although some chemical characterization of mineralogy and paleomagnetism has become prevalent in recent years (Chess et al., 2010; Sasowsky …

A Monte Carlo Simulation Of Rat Choice Behavior With Interdependent Outcomes, Michelle A. Frankot

Graduate Theses, Dissertations, and Problem Reports

Preclinical behavioral neuroscience often uses choice paradigms to capture psychiatric symptoms. In particular, the subfield of operant research produces nested datasets with many discrete choices in a session. The standard analytic practice is to aggregate choice into a continuous variable and analyze using ANOVA or linear regression. However, choice data often have multiple interdependent outcomes of interest, violating an assumption of general linear models. The aim of the current study was to quantify the accuracy of linear mixed-effects regression (LMER) for analyzing data from a 4-choice operant task called the Rodent Gambling Task (RGT), which measures decision-making in the context …

Analysis Of Forensically Relevant Evidence Using Electrochemistry, Spectroscopy, And Mass Spectrometry Tools, Colby Edward Ott

Graduate Theses, Dissertations, and Problem Reports

Forensic science relies on the use of multiple techniques in the assessment of evidence to increase the accuracy and reliability of the results. However, with the rapidly changing drug landscape due to the introduction of novel psychoactive substances, many traditional screening methods are no longer sensitive or selective enough for use. Additionally, many screening methods such as chemical color tests are prone to false positive and negative results and are subjective. Therefore, the goal of this dissertation was to develop a novel analytical scheme that can provide a more efficient, rapid, and sensitive method that will facilitate adoption in the …

Multifunctional Organoboron Compounds And Boralactonate Salts, Randika T. Abeysinghe

Graduate Theses, Dissertations, and Problem Reports

Organoboron compounds have gathered an important significance within the chemistry community on account of their wide range of applications in synthesis, catalysis, and medicinal chemistry. Even though the uses of boron compounds in drug discovery have been overlooked until the last several decades, boronic acid containing molecules have garnered increased attention due to the unique chemical properties of the boron center. Boron-functionalized ��-aryl propionic acid non-steroidal anti-inflammatory drug derivatives (bora-NSAIDs) can be accessed via copper(I)-catalyzed alkene boracarboxylation, using CO2 and B2pin2. To explore and expand the current synthetic and future medicinal chemistry applications of these bora-NSAIDs, methods …

Impact Of Radio Frequency Interference And Real-Time Spectral Kurtosis Mitigation, Evan T. Smith

Graduate Theses, Dissertations, and Problem Reports

We catalog the ubiquity of Radio Frequency Interference (RFI) plaguing every modern radio telescope and investigate several ways to mitigate it in order to create better science-ready data products for astronomers. There are a myriad of possible RFI sources, including satellite uplinks and downlinks, cellular communications, air traffic radar, and natural sources such as lightning. Real-time RFI mitigation strategies must take these RFI characteristics into account, as the interfering signals can look significantly different at very high time and frequency resolutions.

We examine Spectral Kurtosis (SK) as a real-time statistical RFI detection method, and compare its flagging efficacy against simulated …

Calcite Depression In Bastnaesite-Calcite Flotation System Using Organic Acids, Emmy Muhoza

Graduate Theses, Dissertations, and Problem Reports

Bastnaesite is the primary source of light REEs, namely cerium (Ce), lanthanum (La), praseodymium (Pr), neodymium (Nd), to name a few. Bastnaesite is typically concentrated using the froth flotation beneficiation method. Flotation of bastnaesite suffers from high reagent consumption due to the similar surface characteristics of bastnaesite and associated gangue minerals, including calcite. Additionally, complex stages of high-temperature conditioning are often required to suppress the detrimental impact of dissolved calcium ions on the flotation of bastnaesite. This research seeks to investigate the capabilities of organic acids in the bastnaesite-calcite flotation systems to selectively depress calcite minerals and effectively chelate calcium …

Microwave Enhanced Electron Energy Distribution Functions, John Samuel Mckee

Graduate Theses, Dissertations, and Problem Reports

The use of two (or more) radio frequency (RF) sources at different frequencies is a common technique in the plasma processing industry to control ion energy characteristics separately from plasma generation. A similar approach is presented here with the focus on modifying the electron population in argon and helium plasmas. The plasma is generated by a helicon source at a frequency f0 = 13.56 MHz. Microwaves of frequency f1 = 2.45 GHz are then injected into the helicon source chamber perpendicular to the background magnetic field. The microwaves damp on the electrons via R-mode (anti-parallel to the background magnetic field …

The Burning Bush: Linking Lidar-Derived Shrub Architecture To Flammability, Michelle S. Bester

Graduate Theses, Dissertations, and Problem Reports

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, …

On The Geometry Of The Multiplier Space Of ℓPA, Christopher Felder, Raymond Cheng

Mathematics & Statistics Faculty Publications

For p ∊ (1, ∞)\ {2}, some properties of the space M_p of multipliers on ℓ^p_A are derived. In particular, the failure of the weak parallelogram laws and the Pythagorean inequalities is demonstrated for M_p. It is also shown that extremal multipliers on the ℓ^p_A spaces are exactly the monomials, in stark contrast to the p = 2 case.

Horizontal Air Mass: Solutions For Fermi Questions, November 2022, John Adam

Mathematics & Statistics Faculty Publications

No abstract provided.

Rock Paintings, John Adam

Mathematics & Statistics Faculty Publications

No abstract provided.

Robust Testing Of Paired Outcomes Incorporating Covariate Effects In Clustered Data With Informative Cluster Size, Sandipan Dutta

Mathematics & Statistics Faculty Publications

Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

A Channel State Information Based Virtual Mac Spoofing Detector, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. …

Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Electrical & Computer Engineering Faculty Publications

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. …

Nb₃Sn Coating Of A 2.6 Ghz Srf Cavity By Sputter Deposition Technique, M. S. Shakel, Wei Cao, H. Elsayed-Ali, G. V. Eremeev, U. Pudasaini, A. M. Valente-Feliciano

Electrical & Computer Engineering Faculty Publications

Nb₃Sn is of interest as a coating for SRF cavities due to its higher transition temperature Tc ~18.3 K and superheating field Hsh ~400 mT, both are twice that of Nb. Nb₃Sn coated cavities can achieve high-quality factors at 4 K and can replace the bulk Nb cavities operated at 2 K. A cylindrical magnetron sputtering system was built, commissioned, and used to deposit Nb₃Sn on the inner surface of a 2.6 GHz single-cell Nb cavity. With two identical cylindrical magnetrons, this system can coat a cavity with high symmetry and uniform thickness. Using Nb-Sn multilayer sequential sputtering followed by …

The Effect Of Touch Simulation In Virtual Reality Shopping, Ha Kyung Lee, Namhee Yoon, Dooyoung Choi

STEMPS Faculty Publications

This study aims to explore the effect of touch simulation on virtual reality (VR) store satisfaction mediated by VR shopping self-efficacy and VR shopping pleasure. The moderation effects of the autotelic and instrumental need for touch between touch simulation and VR store satisfaction are also explored. Participants wear a head-mounted display VR device (Oculus Go) in a controlled laboratory environment, and their VR store experience is recorded as data. All participants’ responses (n = 58) are analyzed using SPSS 20.0 for descriptive statistics, reliability analysis, exploratory factor analysis, and the Process macro model analysis. The results show that touch simulation …

Bitcoin Selfish Mining Modeling And Dependability Analysis, Chencheng Zhou, Liudong Xing, Jun Guo, Qisi Liu

Electrical & Computer Engineering Faculty Publications

Blockchain technology has gained prominence over the last decade. Numerous achievements have been made regarding how this technology can be utilized in different aspects of the industry, market, and governmental departments. Due to the safety-critical and security-critical nature of their uses, it is pivotal to model the dependability of blockchain-based systems. In this study, we focus on Bitcoin, a blockchain-based peer-to-peer cryptocurrency system. A continuous-time Markov chain-based analytical method is put forward to model and quantify the dependability of the Bitcoin system under selfish mining attacks. Numerical results are provided to examine the influences of several key parameters related to …

Hybridization From Guest-Host Interactions Reduces The Thermal Conductivity Of Metal-Organic Frameworks, Mallory E. Decoster, Hasan Babaei, Sangeun S. Jung, Zeinab M. Hassan, John T. Gaskins, Ashutosh Giri, Emma M. Tiernan, John A. Tomko, Helmut Baumgart, Pamela M. Norris, Alan J.H. Mcgaughey, Christopher E. Wilmer, Engelbert Redel, Gaurav Giri, Patrick E. Hopkins

Electrical & Computer Engineering Faculty Publications

We experimentally and theoretically investigate the thermal conductivity and mechanical properties of polycrystalline HKUST-1 metal–organic frameworks (MOFs) infiltrated with three guest molecules: tetracyanoquinodimethane (TCNQ), 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4-TCNQ), and (cyclohexane-1,4-diylidene)dimalononitrile (H4-TCNQ). This allows for modification of the interaction strength between the guest and host, presenting an opportunity to study the fundamental atomic scale mechanisms of how guest molecules impact the thermal conductivity of large unit cell porous crystals. The thermal conductivities of the guest@MOF systems decrease significantly, by on average a factor of 4, for all infiltrated samples as compared to the uninfiltrated, pristine HKUST-1. This reduction in thermal conductivity goes in …

Runtime Power Allocation Based On Multi-Gpu Utilization In Gamess, Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy …

Efficient Removal Of Lead Ions From Aqueous Media Using Sustainable Sources On Marine Algae, Hannah Namkoong, Erik Biehler, Gon Namkoong, Tarek M. Abdel-Fattah

Electrical & Computer Engineering Faculty Publications

The goal of this project is to explore a new method to efficiently remove Pb(II) ions from water by processing Undaria pinnatifida into immobilized beads using sodium alginate and calcium chloride. The resulting biosorbent was characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Using immobilized U. pinnatifida, we investigated the effect of various factors on Pb(II) ion removal efficiency such as temperature, pH, ionic strength, time, and underlying biosorption mechanisms. For Pb(II) ion biosorption studies, Pb(II) ion biosorption data were obtained and analyzed using Langmuir and Freundlich adsorption models. It …

Grand Challenges In Low Temperature Plasmas, Xinpei Lu, Peter J. Bruggeman, Stephan Reuter, George Naidis, Annemie Bogaerts, Mounir Laroussi, Michael Keidar, Eric Robert, Jean-Michel Pouvesle, Dawei Liu, Kostya (Ken) Ostrikov

Electrical & Computer Engineering Faculty Publications

Low temperature plasmas (LTPs) enable to create a highly reactive environment at near ambient temperatures due to the energetic electrons with typical kinetic energies in the range of 1 to 10 eV (1 eV = 11600K), which are being used in applications ranging from plasma etching of electronic chips and additive manufacturing to plasma-assisted combustion. LTPs are at the core of many advanced technologies. Without LTPs, many of the conveniences of modern society would simply not exist. New applications of LTPs are continuously being proposed. Researchers are facing many grand challenges before these new applications can be translated to practice. …

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t₁ together with low-resolution images at a …

Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.)

Electrical & Computer Engineering Faculty Publications

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply …

Biometric Security: A Novel Ear Recognition Approach Using A 3d Morphable Ear Model, Md Mursalin, Mohiuddin Ahmed, Paul Haskell-Dowland

Research outputs 2022 to 2026

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a …

Revisiting The Interval And Fuzzy Topsis Methods: Is Euclidean Distance A Suitable Tool To Measure The Differences Between Fuzzy Numbers?, Hosein Arman, Abdollah Hadi-Vencheh, Reza Kiani Mavi, Mehdi Khodadadipour, Ali Jamshidi

Research outputs 2022 to 2026

Euclidean distance (ED) calculates the distance between n-coordinate points that n equals the dimension of the space these points are located. Some studies extended its application to measure the difference between fuzzy numbers (FNs).This study shows that this extension is not logical because although an n-coordinate point and an FN are denoted the same, they are conceptually different. An FN is defined by n components; however, n is not equal to the dimension of the space where the FN is located. This study illustrates this misapplication and shows that the ED between FNs does not necessarily reflect their difference. We …

Tuning The Fluid Wetting Dynamics On Gold Microstructures And Conductivity Of Polyaniline Using Photoactive Compounds, Ali Haghighat Mesbahi

Electronic Theses and Dissertations, 2020-2023

Organic photochromic compounds (OPCs) are considered as a class of light sensitive compounds that undergo reversible structural transformation of their physicochemical properties (i.e., polarity and charge distribution) upon light irradiation as an external stimulus. These compounds have been extensively studied for decades, and are used in various applications such as biomedicine, chemical sensors and harvesting solar energy. In this thesis, applications of OPCs were investigated under two different projects: 1) controlling wettability of flat and microstructured gold surfaces and 2) controlling the electrical conductivity of polyaniline. The first project demonstrates how photoactive compounds can be used to tune the surface …

Extensions Of The General Solution To The Inverse Problem Of The Calculus Of Variations, And Variational, Perturbative And Reversible Systems Approaches To Regular And Embedded Solitary Waves, Ranses Alfonso Rodriguez

Electronic Theses and Dissertations, 2020-2023

In the first part of this Dissertation, hierarchies of Lagrangians of degree two, three or four, each only partly determined by the choice of leading terms and with some coefficients remaining free, are derived. These have significantly greater freedom than the most general differential geometric criterion currently known for the existence of a Lagrangian and variational formulation since our existence conditions are for individual coefficients in the Lagrangian. For different choices of leading coefficients, the resulting variational equations could also represent traveling waves of various nonlinear evolution equations. Families of regular and embedded solitary waves are derived for some of …