Digital Commons Network^™

An Empirical Framework Characterizing The Metallicity And Star-Formation History Dependence Of X-Ray Binary Population Formation And Emission In Galaxies, Bret D. Lehmer, Erik B. Monson, Rafael T. Eufrasio, Amirnezam Amiri, Keith Doore, Antara Basu-Zych, Kristen Garofali, Lidia Oskinova, Jeff J. Andrews, Vallia Antoniou, Robel Geda, Jenny E. Greene, Konstantinos Kovlakas, Margaret Lazzarini, Chris T. Richardson

Physics Faculty Publications and Presentations

We present a new empirical framework modeling the metallicity and star formation history (SFH) dependence of X-ray luminous (L ≳ 10³⁶ erg s⁻¹) point-source population X-ray luminosity functions (XLFs) in normal galaxies. We expect that the X-ray point-source populations are dominated by X-ray binaries (XRBs), with contributions from supernova remnants near the low luminosity end of our observations. Our framework is calibrated using the collective statistical power of 3731 X-ray detected point sources within 88 Chandra-observed galaxies at D ≲ 40 Mpc that span broad ranges of metallicity (Z ≈ 0.03–2 Z_⊙), SFH, …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) For Spatially Heterogenous Property Awared Chicken Woody Breast Classification And Hardness Regression, Chaitanya Pallerla, Yihong Feng, Casey M. Owens, Ramesh Bahadur Bist, Siavash Mahmoudi, Pouya Sohrabipour, Amirreza Davar, Dongyi Wang

Poultry Science Faculty Publications and Presentations

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In …

A Near-Wall Methodology For Large-Eddy Simulation Based On Dynamic Hybrid Rans-Les, Michael Tullis, D. Keith Walters

Mechanical Engineering Faculty Publications and Presentations

Attempts to mitigate the computational cost of fully resolved large-eddy simulation (LES) in the near-wall region include both the hybrid Reynolds-averaged Navier–Stokes/LES (HRL) and wall-modeled LES (WMLES) approaches. This paper presents an LES wall treatment method that combines key attributes of the two, in which the boundary layer mesh is sized in the streamwise and spanwise directions comparable to WMLES, and the wall-normal mesh is comparable to a RANS simulation without wall functions. A mixing length model is used to prescribe an eddy viscosity in the near-wall region, with the mixing length scale limited based on local mesh size. The …

Clusteredlog: Optimizing Log Structures For Efficient Data Recovery And Integrity Management In Database Systems, Mariha Siddika Ahmad, Brajendra Panda

Electrical Engineering and Computer Science Faculty Publications and Presentations

In modern database systems, efficient log management is crucial for ensuring data integrity and facilitating swift recovery from potential data corruption or system failures. Traditional log structures, which store operations sequentially as they occur, often lead to significant delays in accessing and recovering specific data objects due to their scattered nature across the log. ClusteredLog addresses the limitations of traditional logging methods by implementing a novel logical organization of log entries. Instead of simply storing operations sequentially, it groups related operations for each data item into clusters. As a result, ClusteredLog enables faster identification and recovery of damaged data items …

Encapsulation Of Probiotics By Cellulose Nanocrystal-Alginate Composite Beads For Ph Stimuli-Responsive Delivery, Akrip Jati

Graduate Theses and Dissertations

Microencapsulation has garnered significant interest, with oral probiotics delivery systems showing enormous potential to help restore the gut microbiome. This method holds vast potential to protect and release specific microbial strains to the desired target site (i.e., colon) within the gastrointestinal tract (GIT). However, current encapsulation methodologies, utilizing either synthetic or biological materials as carriers, encounter challenges such as suboptimal encapsulation efficiency, premature probiotic release, and inadequate control over release kinetics. In this work, hydrogel microsphere (HM) composites of different cellulose nanocrystal (CNC) forms (i.e., colloidal (c), and crosslinked (x)) and alginate (ALG) were used as advanced controlled release systems …

Universal Shape Replication Via Self-Assembly With Signal-Passing Tiles, Andrew Alseth, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Faculty Publications and Presentations

In this paper, we investigate shape-assembling power of a tile-based model of self-assembly called the Signal-Passing Tile Assembly Model (STAM). In this model, the glues that bind tiles together can be turned on and off by the binding actions of other glues via “signals”. Specifically, the problem we investigate is “shape replication” wherein, given a set of input assemblies of arbitrary shape, a system must construct an arbitrary number of assemblies with the same shapes and, with the exception of size-bounded junk assemblies that result from the process, no others. We provide the first fully universal shape replication result, namely …

Enhancing Webtas: From An Atam Simulator To A Developmental Environment, Elizabeth G. Schmidt

Electrical Engineering and Computer Science Undergraduate Honors Theses

As interest in a niche research area, like the abstract Tile Assembly Model (aTAM), grows it is crucial to ensure that simulation tools are accessible and user-friendly for a broad audience. Existing resources can pose challenges for less experienced users, highlighting the need for enhancements that improve usability and functionality. To address these issues, key upgrades to the current online simulation platform, WebTAS, were implemented, including breakpoint functionality and nondeterminism checks, alongside features such as XML file integration, image capturing, and simulation view resets. These enhancements collectively elevate the platform from a basic learning tool to a comprehensive environment for …

Comparison Of Statistical And Machine Learning Genomic Prediction Methods In Plant Breeding: Case Studies In Maize And Soybean, Igor Kuivjogi Fernandes

Graduate Theses and Dissertations

Plant breeding is essential to increase genetic gain and food production worldwide. This study was conducted to evaluate new ways to use machine learning (ML) to tackle plant breeding challenges, where two ideas were tested — the first chapter focuses on how to combine genetic and environmental data using ML to improve the prediction of maize grain yield in multi-environment trials, while the second chapter centers on how to couple feature selection of molecular markers with ML to enhance prediction of yield in soybean, and, in both cases, ML approaches were compared to well-established statistical methods greatly adopted by the …

Establishing A Robust Decision Framework To Support Transportation Planning Decisions And Accommodate Connected And Autonomous Transportation Alternatives, Bobby Cottam

Graduate Theses and Dissertations

Transportation planning involves complex decisions to provide high-quality transportation systems involving multiple stakeholders with numerous competing interests, on top of that they are trying to improve the quality and reliability of those systems on shrinking budgets. This decision is further complicated by the advent of new transportation methods e.g., ride-hailing and the promise of connected and autonomous vehicles (CAVs). While the actual short-term impact of these technologies is uncertain, they will most certainly have a significant impact within the 20-year planning horizon that transportation planners typically consider. These technologies are also blurring the lines between traditionally separate modes of transportation; …

Gate Driver Design In High-Temperature Cmos Process For Heterogeneous Integration Inside Sic Power Module, Khandoker Asif Faruque

Graduate Theses and Dissertations

The shift toward electrification in transportation, including electric and hybrid vehicles, presents challenges for power electronic converters. Silicon Carbide (SiC) power devices are promising due to their high efficiency, temperature tolerance, and reduced losses compared to traditional silicon devices. However, their use is hindered by issues such as parasitic capacitances and gate inductances, which can lead to voltage spikes and stress on the device gate oxide, hence impacting converter reliability. Increasing gate resistance can help manage these issues, but it reduces switching speed and increases losses. Snubber circuits can mitigate switching stress but add extra components, reducing efficiency. Active gate …

Towards Robust And Fair Vision Learning In Open-World Environments, Thanh-Dat Truong

Graduate Theses and Dissertations

The rapid increase of large-scale data and high-performance computational hardware has promoted the development of data-driven machine vision approaches. Advanced deep learning approaches have achieved remarkable performance in various vision problems and are closing the capability gap between artificial intelligence (AI) and humans. However, towards the ultimate goal of AI, which replicates human ability in visual perception tasks, the machine vision learning methods still need to address several ill-posed challenges. First, while the current vision learning methods often rely on large-scale annotated data, the data annotation process is a costly and time-consuming process. Second, the unfaired predictions produced by vision …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) Integrated Hyperspectral Imaging Understanding For Woody Breast In Poultry Processing, Chaitanya Kumar Reddy Pallerla

Graduate Theses and Dissertations

The development and implementation of a Wide & Deep (WD) learning model tailored for classification and regression tasks utilizing spectral data provides a robust solution to evaluate woody breast (WB) conditions in poultry fillets. This process begins with thorough data preprocessing, which includes loading spectral and classification datasets, imputing missing values with medians, and splitting the data into training and testing sets to ensure rigorous model evaluation. The WD model architecture integrates wide linear models and deep neural networks to harness the strengths of both approaches. The wide component excels at memorizing sparse feature interactions, while the deep component captures …

At-Risk And Online: Parent Perceptions Of At-Risk Learner's Supports In A Fully Online School, Dennis Beck

Curriculum and Instruction Faculty Publications and Presentations

At-risk students face a variety of challenges that encompass cultural, social and environmental contexts and identities. Full time virtual schools offer help for at-risk students through the provision of a personalized learning option where students can catch up with past work or complete school work in a non-traditional environment. The purpose of this study was to understand parent perceptions of at-risk learner’s affective, behavioral, and cognitive engagement supports in a fully online school. Although research exists on at-risk learners in blended environments, this topic has not yet been fully explored for fully online schools. We need a much fuller understanding …

Exploring The Impact Of Generative Ai Prompt Engineering In Higher Education: A Study In Undergraduate And Graduate Business Analytics Courses, Jana Gastineau

Graduate Theses and Dissertations

The rapid evolution of generative artificial intelligence (GenAI) presents a transformative opportunity in higher education, offering new pathways for learning through personalized feedback, interactive environments, and support for complex tasks. This quasi-experimental study investigated the effects of targeted GenAI prompt engineering instruction on knowledge acquisition, prompt writing skills, and confidence levels among undergraduate and graduate students in business analytics courses at a research university. A pre-test and post-test design measured changes across knowledge acquisition, prompt writing proficiency, and GenAI confidence and usage. Findings revealed a mixed impact on knowledge acquisition, with undergraduates showing minor improvements and graduate students experiencing declines …

The Impact Of Wild Pigs (Sus Scrofa) On Avian Occupancy And Vegetation Structure, Kenneth Wilson

Graduate Theses and Dissertations

Invasive species, particularly wild pigs (Sus scrofa), pose a significant threat to native ecosystems through both direct and indirect impacts. Wild pigs are large-bodied omnivorous species that can consume a wide variety of plants and animals and are known for destructive foraging behaviors, which likely threatens avian communities through habitat alteration and can impact the long term structure of ecosystems where they are present.

In chapter 1, we used a combination of avian point count surveys, vegetation assessments, and camera trap surveillance to quantify how the occupancy of breeding birds in bottomland hardwood forests changed in relation to …

Traittrainr: Accelerating Large-Scale Simulation Under Models Of Continuous Trait Evolution, Jenniffer Roa Lozano, Mataya Duncan, Duane D. Mckenna, Todd A. Castoe, Michael Degiorgio, Richard Adams

Entomology and Plant Pathology Faculty Publications and Presentations

Abstract

The scale and scope of comparative trait data are expanding at unprecedented rates, and recent advances in evolutionary modeling and simulation sometimes struggle to match this pace. Well-organized and flexible applications for conducting large-scale simulations of evolution hold promise in this context for understanding models and more so our ability to confidently estimate them with real trait data sampled from nature. Results

We introduce TraitTrainR, an R package designed to facilitate efficient, large-scale simulations under complex models of continuous trait evolution. TraitTrainR employs several output formats, supports popular trait data transformations, accommodates multi-trait evolution, and exhibits flexibility …

Decoding Fgf/Fgfr Signaling: Insights Into Biological Functions And Disease Relevance, Oshadi Edirisinghe, Gaetane Ternier, Zeina Alraawi, Thallapuranam Krishnaswamy Suresh Kumar

Chemistry & Biochemistry Faculty Publications and Presentations

Fibroblast Growth Factors (FGFs) and their cognate receptors, FGFRs, play pivotal roles in a plethora of biological processes, including cell proliferation, differentiation, tissue repair, and metabolic homeostasis. This review provides a comprehensive overview of FGF-FGFR signaling pathways while highlighting their complex regulatory mechanisms and interconnections with other signaling networks. Further, we briefly discuss the FGFs involvement in developmental, metabolic, and housekeeping functions. By complementing current knowledge and emerging research, this review aims to enhance the understanding of FGF-FGFR-mediated signaling and its implications for health and disease, which will be crucial for therapeutic development against FGF-related pathological conditions.

Cellulose–Starch Composite Aerogels As Thermal Superinsulating Materials, Safoura Ahmadzadeh, Angelina Sagardui, David Huitink, Jingyi Chen, Ali Ubeyitogullari

Chemistry & Biochemistry Faculty Publications and Presentations

The demand for sustainable packaging materials is rapidly increasing due to growing environmental concerns over the impact of plastic waste. In this study, biodegradable, porous, lightweight, and high-surface-area microcrystalline cellulose–starch (MCC-S) hybrid aerogels were synthesized via supercritical carbon dioxide (SC–CO₂) drying. The samples were generated using five different MCC-S weight ratios and characterized for their morphology, crystallinity, and structural and thermal properties. When MCC and S were used together, aerogels with superior properties were obtained compared to those made from each component individually. Specifically, the 1:2 MCC-S aerogel exhibited the highest porosity (97%), the lowest density (0.058 g/cm …

Jürgen Habermas Revisited Via Tim Cook's Wikipedia Biography: A Hermeneutic Approach To Critical Information Systems Research, Reilly Smethurst, Amber G. Young, Arial D. Wigdor

Information Systems Faculty Publications and Presentations

Critical Information Systems (IS) research is sometimes appreciated for the shades of gray it adds to sunny portraits of technology's emancipatory potential. In this article, we revisit a theory about Wikipedia’s putative freedom from the authority of corporate media's editors and authors. We present the curious example of Tim Cook's Wikipedia biography and its history of crowd-sourced editorial decisions, published on Wikipedia's talk pages. We use a hermeneutic method to subject the theory about Wikipedia's “rational discourse” and “emancipatory potential” to a soft, empirical test. When we examined Cook's Wikipedia biography and its editorial decisions, what we found pertained to …

United We Stand, Divided We Fall: An Autogenic Perspective On Empowering Cybersecurity In Organizations, Alexandra Durcikova, Shayla M. Miranda, Matthew L. Jensen, Ryan T. Wright

Information Systems Faculty Publications and Presentations

Cybersecurity groups navigate complex, challenging environments in their mission to protect their organizations. They experience uncertainty from adaptive threats from external attackers and unpredictable stakeholders. Under such volatility, business groups operate best when they are psychologically empowered. Recognizing the potential for empowerment to reduce organizational risk, we sought to learn how cybersecurity groups come to be (dis)empowered and how this (dis)empowerment is sustained. Instead of the conventional view of the empowerment process as designed, we advance an emergent view of the empowerment process. We abductively surface this process from our case analyses of 15 U.S. organizations. We offer three insights: …

Polarimetry Terahertz Imaging Of Human Breast Cancer Surgical Specimens, Nikita Gurjar, Keith Bailey, Magda O. El-Shenawee

Electrical Engineering and Computer Science Faculty Publications and Presentations

Purpose

We investigate terahertz (THz) polarimetry imaging of seven human breast cancer surgical specimens. The goal is to enhance image contrast between adjacent tissue types of cancer, healthy collagen, and fat in excised breast tumors. Based on the biological perception of random growth of cancer and invasion of surrounding healthy tissues in the breast, we hypothesize that cancerous cells interact with the THz electric field in a different manner compared with healthy cells. This difference can be best captured using multiple polarizations instead of single polarization.

Approach

Time domain pulsed signals are experimentally collected from each pixel of the specimen …

Medium-Entropy Engineering Of Magnetism In Layered Antiferromagnet CuXNi2(1-X)CrXP2S6, Dinesh Upreti, Rabindra Basnet, M. M. Sharma, Santosh Karki Chhetri, Gokul Acharya, Md Rafique Un Nabi, Joshua Sakon, Mourad Benamara, Mansour Mortazavi, Jin Hu

Physics Faculty Publications and Presentations

Antiferromagnetic van der Waals-type M₂P₂X₆ compounds provide a versatile material platform for studying 2D magnetism and relevant phenomena. Establishing ferromagnetism in 2D materials is technologically valuable. Though magnetism is generally tunable via a chemical way, it is challenging to induce ferromagnetism with isovalent chalcogen and bimetallic substitutions in M₂P₂X₆. Here, we report co-substitution of Cu¹⁺ and Cr³⁺ for Ni²⁺ in Ni₂P₂S₆, creating Cu_xNi_2(1-x)Cr_xP₂S₆ medium-entropy alloys spanning a full substitution range ( …

Exploring The Capabilities Of Classifier-Free Guidance In Recommendation Tasks, Noah Buchanan

Graduate Theses and Dissertations

This thesis addresses the problem of recommending items to users based on their ratings of items through a diffusion recommender system. Regular recommender systems are already capable of efficient recommendation through conventional methods such as collaborative or content-based filtering. Diffusion is a new type of generative AI that aims to improve our previous AI's shortcomings in the generative domain, like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We use diffusion to create a recommender system that mirrors the sequence users take when browsing and rating items. The current methods of recommendation with diffusion do not use the new innovation …

Ohmic Contacts For Fabrication Of Sic Cmos Devices, Anthony Di Mauro

Graduate Theses and Dissertations

Today’s world of electronics is dominated by semiconductor devices which utilize silicon as their substrate material. Though, silicon is not ideal for semiconductor devices in high-voltage or high-temperature applications. Additionally, the efficiency of silicon devices becomes drastically reduced in nonideal operating conditions. Therefore, finding alternatives to silicon?based devices has been a topic for decades now. A few great candidates to replace silicon devices for said applications include Silicon Carbide (SiC), Gallium Nitride (GaN), and Aluminum Arsenide (AlAs). SiC has grown its reputation as the best candidate, when compared to other potential alternatives, due to its wide bandgap, high operating frequency, …

Comparing And Evaluating Models Of Tile-Assembly Using Intrinsic Simulation, Daniel Hader

Graduate Theses and Dissertations

Tile-assembly studies abstract models of computation inspired by advancements in the emerging field of DNA-nanotechnology, where synthetic strands of DNA are used as building blocks for microscopic structures. These DNA strands can be made to combine into structural units with selectively sticky sides that abstractly resemble Wang tiles. However, unlike Wang tiles, an assembly process is modeled where tiles combine one-by-one to form larger assemblies according to matching rules based on their sticky sides. While in practice, these tile-assembly models have seen use in designing DNA nano-structures, the mathematical study of their theory has revealed an exciting interplay between geometric …

Synthesis Of Chemical Fuels And Feedstocks From Co2 For Extraterrestrial Applications, Samuel Keith Conlin

Graduate Theses and Dissertations

The reduction of CO₂ into products for either the storage of renewable electricity or to act as a source of renewable feedstock for industry, is not economically feasible given the current costs of renewable energy. Significant amounts of work have gone into attempting to optimize both electrochemical and nonthermal plasma-based CO₂ reduction systems in an attempt to reduce side reactions and improve energy efficiency in order to meet cost feasibility targets. These techniques can also be adapted for In-Situ Resource Utilization (ISRU) contexts, which are far less sensitive to the cost of renewable energy yet have more strenuous …

Real-Time Simulation And Workforce Development For Industrial Control Systems With Troy: Testbed For Resilient Operational Systems, Gideon Henry Sutterfield

Graduate Theses and Dissertations

Digital automation for critical infrastructure (CI) is largely supported by industrial control systems (ICS) and operational technology (OT). The increase in digital automation as time progresses correlates with a rise in targeted cyberattacks at these vulnerable yet precious assets. TROY: Testbed for Resilient Operational sYstems, a highly scalable hybrid IT & OT utilizing virtual machines and a Typhoon-HIL real-time simulation, contributes to cybersecurity for CI by serving as a research and workforce development platform. By using Typhoon-HIL to simulate ICS, the platform can be used to replicate a realistic network environment for a myriad of research functions. The system uses …

Towards Comprehensive And Interpretable Video Understanding, Khoa Vo

Graduate Theses and Dissertations

Video understanding is a critical domain in computer vision, focusing on analysis of sequential visual data to extract meaningful spatiotemporal information for tasks such as action recognition, video captioning, video retrieval, and temporal action localization, etc. Despite significant advancements with spatio-temporal convolutional neural networks and attention-based video models, current methods face limitations, including inadequate representation of main actors, lack of fine-grained modeling of relevant objects, and limited interpretability.
This thesis addresses these challenges by proposing novel approaches that enhance video understanding through modeling interactions among entities (actors and objects) and between entities and the environment, while improving interpretability in the …

Deep Learning Framework For Inverse Problems In Computational Imaging: A Lensless Imaging And Super-Resolution Magnetic Resonance Imaging Case, Arpan Poudel

Graduate Theses and Dissertations

Inverse problems in computer vision involve reconstructing an original scene or image from incomplete, noisy, or indirect measurements. These problems are critical in tasks such as image denoising, deblurring, super-resolution, and lensless imaging, where the goal is to recover high-quality images from degraded or partial measurements. This thesis introduces novel approaches to address two specific real-world inverse problems: (1) image super-resolution in medical imaging and (2) lensless image reconstruction . In the first part of this work, we tackle the problem of image super-resolution in Magnetic Resonance Imaging (MRI). High-resolution MRI scans are often limited by hardware constraints, patient movement, …

Addressing Cybersecurity Data & Workforce Scarcity With Troy: Testbed For Resilient Operational Systems, Henry Oliver Schmidt

Graduate Theses and Dissertations

Machine learning has seen an explosive rise in the past decade. Companies, organizations, and governments are racing to pursue the advancements and insight provided by machine learning powered tools. However, to get effective and meaningful insights from machine learning models a significant amount of detailed data is required to train them. This poses a problem in fields where data is not openly available, such as cybersecurity. Entities are often unwilling to give out network or system data to the public for machine learning and cybersecurity research since that data can contain sensitive or proprietary information. The risk simply outweighs the …