Digital Commons Network^™

Power Supply Induced Jitter Analysis In High-Speed Drivers, Yifan Ding

Doctoral Dissertations

The Input/Output Buffer Information Specification (IBIS) model faces challenges in accurately simulating power-supply-induced jitter (PSIJ), particularly under nonlinear and time-varying power noise conditions with pre-driver stages. This work introduces advancements in IBIS model modification algorithms to enhance PSIJ simulation accuracy for high-speed drivers.

First, a correction coefficient-based approach was developed to adjust switching coefficients K_pu and K_pd using pre-driver DC jitter sensitivity, significantly improving model robustness under DC and AC power noise. However, its effectiveness diminished under large noise amplitudes due to coefficient shape dominance.

To address this, a simplified algorithm bypassed correction coefficient, directly correlating switching transitions with jitter …

Applying Human System Integration Principles To The Investigation, Design And Validation Of Interventions For Self-Escape In Underground Mines, Eugene Adubofour Gyawu

Doctoral Dissertations

This dissertation investigates the use of human systems integration (HSI) to develop technologies for miners’ self-escape during a mine emergency, addressing barriers that compromise miner safety during emergencies.

Recognizing the inherently hazardous nature of underground mining, this study has three core objectives: (1) assessing miners' perceptions of proposed self-escape interventions, (2) designing interventions based on these insights, and (3) validating their effectiveness using experimental testing. This work evaluates self-escape interventions for both coal and metal/non-metal miners using scenario-based surveys, information design reviews, label designs and experimental testing methods varying across three distinct studies.

Results reveal that miners identified improvements to …

Implication And Applications Of Machine Learning On Biomedical Images, Jason Hagerty

Doctoral Dissertations

Medical imaging ranges in modality including computer tomography imaging, x-ray imaging, digital microscopy, and macro-focus dermoscopy images. The latter two modalities are the focus of the presented work.

To perform a diagnostic evaluation on the captured dermoscopy image, it begins with what is usually a labor-intensive operation that requires an expert to perform the initial segmentation for localizing a region of interest (ROI). Once that ROI is obtained, a physician with years of training and experience will observe biological markers that can be used to visually differentiate whether a lesion is benign or malignant. A similar process is used for …

Development Of Dual-Scan Nuclear Magnetic Resonance (Nmr) Pulse Programs For Spin-Lattice Relaxation Measurements, Zachary Mayes

Doctoral Dissertations

This research develops and refines the Split Inversion Pulse and Recovery (SIP R) methodology for spin-lattice relaxation (T₁) measurements in NMR spectroscopy. SIP R introduces a two-scan difference technique using a split inversion pulse sequence, where the 180° pulse is split into two 90° pulses phase-shifted with respect to each other. This approach simplifies data fitting, requiring only two parameters to extract T₁ values, compared to the traditional inversion-recovery method, which needs three. The SIP-R-DS and SIP-R-S adaptations extend this framework by incorporating selective NMR resonance excitations, enhancing its applicability to cross-polarization experiments and enabling unobstructed NOE measurements. These adaptations …

Experimental Study Of Defects In Coaxial Wire-Based Laser Metal Deposition, Remy Mathenia

Doctoral Dissertations

This work seeks to improve the usability and capability of coaxial wire-based laser metal deposition (LMD) through the experimental study of process parameters on output geometry, directional effects, and defect formation. Wire-based LMD is a directed energy deposition (DED) strategy that uses a focused laser heat source to melt and fuse metal wire as it is deposited. This process is used to build parts layer-by-layer until a desired geometry is accomplished. LMD enables the creation of complex components at a high build rate with low material and energy waste. This work focuses on the deposition of titanium wire in the …

Evaluating And Relaxing The Limits On Flexural Reinforcement Ratio Of Masonry Shear Walls, Tousif Mahmood

Doctoral Dissertations

Reinforced masonry shear walls (RMSWs), essential for lateral and out-of-plane load-resisting systems in modern construction are constrained by TMS 402/602 code limits on reinforcement ratios ("ρ" _"max" ) and axial compressive stresses (≤10% of masonry compressive strength, f_m^'), undermining masonry’s inherent compression capacity under high axial loads. This dissertation investigates the seismic performance of reinforced masonry shear walls (RMSWs) subjected to high axial compressive stresses (10–20% of f_m^'), with a focus on walls violating the maximum reinforcement ratio ("ρ" _"max") and axial load limits of TMS 402-22. Through experimental testing of 30 large-scale fully grouted (FG) and partially grouted (PG) …

Systematic Esd Analysis And Modeling For Electronic Device, Zhekun Peng

Doctoral Dissertations

Systematic ESD analysis provides good pre-compliance to ESD robustness evaluation on the electronic device from device level to component/system level to on-chip level. The whole process involves corona discharge on display, system level ESD analysis on PCB for race condition and transient response and 3D IC package impact to on-die ESD.

ESD to the display cover glass can damage touchscreen traces by sparkless corona discharges on the glass surface. A non-linear time dependent transmission-line model is proposed to model corona streamer propagation in terms of the coupling current and propagation speed. Results are highly promising to model the corona discharge …

Mid-Field Shock And Impulse Estimation Methods For Blast Loading On Tall Targets, Ethan Allan Steward

Doctoral Dissertations

Blast resistant structural design continues to be a major research area for governments around the world due to explosive threats from both state and non-state actors. Many of the typical targets of explosive attacks, such as government buildings, commercial high rise office buildings, and apartment complexes are mid to high-occupancy buildings that present a tall profile relative to the charge size and are often clad in curtain walls. In blast resistant structural design, the origin of a shock wave is typically assumed to be in the far-field, creating a wave that is nearly planar and parallel to at least one …

Topics On Ai Fairness Preferences In Kidney Transplantation, Mukund Telukunta

Doctoral Dissertations

Modern kidney transplantation incorporates artificial intelligence (AI) decision-support systems which exhibit social discrimination due to biases inherited from training data. Although researchers have proposed various group-based fairness notions to assess biases in AI, it remains uncertain which criterion is most suitable for evaluating biases in such complex healthcare systems. This dissertation explores human perception of fairness to identify the most appropriate fairness criterion for assessing AI tools in kidney transplantation, focusing on the preferences of non-expert (e.g. public, patients) stakeholders. The study examines two distinct AI systems employed in kidney transplantation: a classification model and a regression model. Human subject …

The Real-Time Detection Infrastructure Of Ligo, Virgo, And Kagra: Data Products, Current Performance, And Future Developments, Sushant Sharma Chaudhary

Doctoral Dissertations

The discovery of the binary neutron star merger event GW170817 marked the dawn of Multi-Messenger Astronomy (MMA) with Gravitational Waves (GWs). Such multi-messenger events are of immense scientific interest due to the wealth of information they provide through joint observations across different messengers. In this rapidly evolving field, prompt identification and timely distribution of alerts is critical for follow-up observations.

This work offers a comprehensive overview of the LIGO-Virgo-KAGRA (LVK) Collaboration’s low-latency analysis pipeline for GW events, covering key stages from calibration and data analysis to the issuance of public alerts. I examine the latency and accuracy of each stage …

Design Of Real-Time And Energy-Efficient Driver Assist Systems Using Electroencephalogram And Neuromorphic Computing, Nathan Alan Lutes

Doctoral Dissertations

Despite the technological breakthroughs in advanced driver assist systems, distracted driving persists as a major challenge to roadway safety. This investigation advances the body of knowledge towards a solution by developing an individualized driver-state detection method using electroencephalogram (EEG) and neuromorphic computing to provide a less invasive and more energy efficient ADAS solution. It furthermore explores the changes in brain functional connectivity under distracted conditions to better understand brain state information that could be used for neuro-feedback intervention systems. The first contribution introduces the concept of using Convolutional Spiking Neural Networks (CSNNs) for recognition of patterns with movement-intention predictive power …

Synthesis And Analysis Of Materials For Quantum Devices, Mathew Pollard

Doctoral Dissertations

Quantum materials play a pivotal role in the advancement of next-generation technology. Superconducting quantum computing, dissipationless spintronics, or valleytronics offer promising ways forward beyond traditional chip miniaturization. Josephson Junctions (JJs) have already revolutionized quantum information and high precision detectors. Quantum systems, however, are either hard to control, produce, and/or maintain. This calls for a better understanding of microscopic properties and tuning of these quantum states.

In this work, we experimentally investigated growth methods to control the electric and magnetic properties of Topological Insulator (TI) Sb2Te3 through Cr-doping. Our results demonstrate the onset of a Magnetic Topological Insulator (MTI) and have …

Higher-Order Statistics And Normalized Decay Analysis For Detector Deadtime Characterization, Abdallah Wazzan

Doctoral Dissertations

Detector deadtime limits radiation measurement accuracy at high count rates, yet current methods rely on idealized paralyzable or non-paralyzable models. Real detectors exhibit hybrid behavior requiring advanced characterization approaches. This study explores deadtime characterization using two Monte Carlo simulation approaches: higher-order statistical analysis of inter-arrival times and simplified deadtime correction with hybrid models.

Using MATLAB (PULSE-WIZ), we analyzed full decay curves spanning ~4.5 half-lives of Cobalt-60 and Vanadium-52, examining coefficient of variation (CV), skewness, and kurtosis of inter-arrival times, plus normalized decay curves with Full Width at Half Maximum (FWHM) analysis. Hybrid models incorporated paralyzable and non-paralyzable components with dead …

Supporting Human-Ai Interaction From User Expectations To Mental Models, Harishankar Vasudevanallur Subramanian

Doctoral Dissertations

Explainable AI (XAI) aims to unravel the "black box" nature of AI systems and provide insights into the inner workings that lead to a prediction. However, the best XAI communication varies depending on the individual, task, and broader context. It is challenging to anticipate the best XAI for a particular use case. One strategy is for users to build an appropriate mental model of AI with both prediction and system level XAI. To date, little research has focused on quantitatively measuring users interacting with system-level XAI. This dissertation has three primary contributions. The first contribution is a scoping review paper …

Enhanced Optimization Of Mass Transfer For Carbon Capture And Wastewater Remediation In Algal Systems Through Algorithmic And Bioprocessing Techniques, Peter Ofuje Obidi

Doctoral Dissertations

The scalability and industrial deployment of algal cultivation systems are limited by suboptimal mass transfer, constraining their effectiveness in carbon capture and wastewater remediation. This research investigated these challenges through integrated optimization methodologies that combine algorithmic frameworks with enhanced bioprocessing techniques to enhance efficiency, economy, and scalability. A System-of-Systems (SoS) meta-architecture was developed using genetic algorithms and fuzzy assessor functions to demonstrate a pathway toward cost reduction. Rigorous mechanical and chemical characterizations were quantitatively analyzed to reveal existing optimization strategies and further evaluated the best strategies to use in enhancing mass transfer for improved biomass yield. The work also integrates …

Characterization And Deportment Of Anode Impurities In Copper Electrorefining, Charles Michael Campbell

Doctoral Dissertations

The objective of this research was to study the deportment of the group 15 elements, arsenic, antimony and bismuth during copper electrorefining. Samples were collected from six industrial copper anodes with different compositions. Specimens were physically characterized and electro refined to understand the differences in the behavior of selected impurities. Inclusions in the cast metal structures were characterized using automated scanning electron microscopy and energy dispersive spectroscopy to measure and correlate their size, shape and composition. Arsenic and lead were found to have a positive correlation between concentration and size of inclusions. Using wavelength dispersive spectroscopy, multiphase inclusions were examined, …

Data-Driven Mulitiscale Modeling Of Electrochemical Transport, Non-Linear Electrical Contact, And Additive Manufacturing Processes, Emmanuel Olugbade

Doctoral Dissertations

The growing demand for high-efficiency energy systems and advanced manufacturing technologies requires predictive frameworks that link atomic-scale physics with engineering-scale performance. This dissertation develops a unified multiscale modeling approach that integrates molecular dynamics, density functional theory, finite-element analysis, and machine learning to connect structure, transport, and performance across materials and processes. Depending on the interactions involved, the framework employs loose coupling for parameter transfer, tight coupling for two-way feedback, and hybrid coupling where machine-learning surrogates accelerate high-fidelity simulations while retaining physical interpretability. In electrochemical systems, an XGBoost-enhanced single-particle model reproduces P2D-level electrolyte potential dynamics at roughly one-hundredth the computational cost, …

Machine Learning Models For Location Prediction, Message Routing And Path Planning For Rescue Of Underground Miners, Abhay Goyal

Doctoral Dissertations

Self-rescue during underground mine disasters is vital for miner safety. Evolving hazards and post-disaster conditions demand solutions that enable navigation under severe communication and computational constraints. Centralized systems often fail in such rugged settings, while decentralized methods—particularly Delay Tolerant Networks (DTNs), proven in battlefields and space missions—offer distinct advantages for underground applications. This research addresses five core challenges: (i) predicting miners’ next locations on low-power devices using points of interest and movement sequences; (ii) delivering timely updates on safe routes, evacuation zones, and hazardous areas; (iii) evaluating energy efficiency and comparing graph-based approaches to existing methods; (iv) enabling edge-ready frameworks, …

Optical Sensor Instrumentation For Enhanced Continuous Caster Development, Hanok Wondimagegnehu Tekle

Doctoral Dissertations

Fiber-optic sensors are an emerging technology that can enhance process monitoring and control in steelmaking. They are especially valuable in the continuous caster’s harsh environment. This dissertation presents the industrial application and demonstration of three sensor types: single-mode silica fiber with Rayleigh-based Optical Frequency Domain Reflectometry (OFDR), sapphire fiber (single-crystal alumina) with Fiber Bragg Gratings (sFBG), and an in-line Raman spectroscopy probe. Each sensor served a distinct role in the continuous caster. Rayleigh OFDR sensors were embedded in tundishes for distributed thermal mapping at 7-mm spatial resolution across ≈4 m. Measurements were taken during preheating, casting, and ladle exchanges, and …

Rapid Force Generation During Unilateral Isometric Hamstring Assessment: Reliability And Relationship To Maximal Force, N. J. Ripley, J. Fahey, M. Cuthbert, J. J. Mcmahon, Paul Comfort

Research outputs 2022 to 2026

Limited research has reported the reliability of rapid force generation characteristics during isometric assessments of the hamstrings. Therefore, the purpose of the present study was to determine the between-session reliability of rapid force generating characteristics of the hamstrings and relationship to maximal force production. Twenty-three female soccer players (age: 20.7 ± 4.7 years; height: 168.7 ± 5.9 cm; body mass: 64.4 ± 6.7 kg) performed three unilateral trials of the 90–90 isometric hamstring assessment, on two separate occasions, separated by 7 days. Peak force, force at 100- and 200 ms and average rate of force development (aRFD) over 100- and …

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Adolescent Parent Perceptions On Sustainable Career Opportunities And Building Employability Capitals For Future Work, Denise Jackson, Claire Lambert

Research outputs 2022 to 2026

As primary career influencers, parents must support adolescent children in navigating evolving and increasingly challenging employment landscapes. Using a capitals lens, this study explores secondary school parents’ capacity to provide informed career advice and their perceptions on factors known to enhance youth employability and employment prospects. Survey data were collected from 301 Australian secondary school parents to examine their understanding of sustainable youth career opportunities, what can aid youth’s employment prospects, and their utilisation of available resources to support career advice for adolescents. While parents’ perceptions of opportunities by sector and industry aligned with wider thinking, many lacked awareness of …

Appearances Can Be Deceiving: How Naturalistic Changes To Target Appearance Impact On Lineup-Based Decision-Making, Dominic T. Jordan, Adrian J. Scott, Donald M. Thomson

Research outputs 2022 to 2026

The present study examined the influence of appearance, procedure and position on identification decisions, post-decisional confidence ratings and estimates of discrimination and confidence-specific accuracy. Regarding appearance, the study examined the combined influence of three naturalistic changes that occur day-to-day (i.e. a reduction in cranial hair length, the removal of stubble, and a change of clothing), two of which have not been considered before in a lineup-decision context. Participants (N = 350) completed four experimental lineups which involved: viewing a target person, completing a brief distractor task, and making an identification decision and a post-decisional confidence rating from a photographic lineup. …

Faculty Handbook, Georgia Southern University

Faculty Handbooks

Faculty Handbook for Georgia Southern University for the 2024-2025 academic year. The Faculty Handbook is published online by the Office of the Vice President for Academic Affairs and archived in Digital Commons@Georgia Southern.

Autoregressive Neural Quantum States Of Fermi Hubbard Models, Eduardo Ibarra-García-Padilla, Hannah Lange, Roger G. Melko, Richard T. Scalettar, Juan Carrasquilla, Annabelle Bohrdt, Ehsan Khatami

Faculty Research, Scholarly, and Creative Activity

Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For …

Modeling Of Precipitation Over Africa: Progress, Challenges, And Prospects, A. A. Akinsanola, C. N. Wenhaji, R. Barimalala, P. A. Monerie, R. D. Dixon, A. T. Tamoffo, M. O. Adeniyi, V. Ongoma, I. Diallo, M. Gudoshava, C. M. Wainwright, R. James, K. C. Silverio, A. Faye, S. S. Nangombe, M. W. Pokam, D. A. Vondou, N. C.G. Hart, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

In recent years, there has been an increasing need for climate information across diverse sectors of society. This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change. Likewise, this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa. By leveraging a large volume of climate model outputs, numerous studies have investigated the model representation of African precipitation as well as underlying physical processes. These studies have assessed whether the physical processes are well depicted …

On Modeling Agent Behavior Change Through Multi-Typed Information Diffusion In Online Social Networks, Masaaki Miyashita, Norihiko Shinomiya, Daisuke Kasamatsu, Genya Ishigaki

Faculty Research, Scholarly, and Creative Activity

The rapid spread of information on online social networks (OSNs) has had both beneficial and detrimental societal impacts. Notably, the propagation of misinformation during events such as the COVID-19 pandemic has driven collective behavioral responses, sometimes exacerbating crises such as panic buying and bank runs. The toilet paper shortage in Japan in 2020 caused by people's selfish behavior owing to the diffusion of information to correct misinformation, which is considered a general phenomenon based on three sociological characteristics: a collective action problem, a self-fulfilling prophecy, and pluralistic ignorance. Understanding what kind and how information can inhibit selfish behavior to mitigate …

Physiological Effects Of Research Handling On The Northern Elephant Seal (Mirounga Angustirostris), Lauren A. Cooley, Allyson G. Hindle, Cassondra L. Williams, Paul J. Ponganis, Shawn M. Hannah, Holger Klinck, Markus Horning, Daniel P. Costa, Rachel R. Holser, Daniel E. Crocker, Birgitte I. Mcdonald

Faculty Research, Scholarly, and Creative Activity

Wildlife researchers must balance the need to safely capture and handle their study animals to sample tissues, collect morphological measurements, and attach dataloggers while ensuring their results are not confounded by stress artifacts caused by handling. To determine the physiological effects of research activities including chemical immobilization, transport, instrumentation with biologgers, and overnight holding on a model marine mammal species, we collected hormone, blood chemistry, hematology, and heart rate data from 19 juvenile northern elephant seals (Mirounga angustirostris) throughout a translocation experiment. Across our six sampling timepoints, cortisol and aldosterone data revealed a moderate hormonal stress response to handling accompanied …

Data Literacy Competencies In Context: Employee Perspectives From The Workplace, Patricia Condon, Wendy G. Pothier

Faculty Publications

Data literacy is essential in today’s workplace, and professionals across roles and experience levels recognize its value. This article reports on findings from interviews with 16 supply chain and logistics professionals. The study examines how seven proposed data literacy competencies align with workplace needs, how employees address the data literacy gap, and how librarians can support workforce readiness. Findings highlight opportunities for libraries to bridge the data literacy gap through instruction and resources development that integrate technical and non-technical data skills with information literacy.

Social And Commercial Crowdfunding Through The Lens Of The Theory Of Planned Behaviour And Financial Well-Being, Joanna Adamska, Urszula Mrzygłód

Journal of Banking and Financial Economics

A unique feature of crowdfunding is that it relies on numerous individuals contributing financially to the project, which sets it apart from traditional financing channels. Unlike banks, where funding decisions are subject to strict prudential criteria and the creditworthiness of the borrowing entity, crowdfunding projects appeal to various worldviews and values held by potential backers. Therefore, the financial decision to support the crowdfunding project becomes a unique problem in personal finance. This study aims to analyse the psychological and financial factors influencing crowdfunding participation, comparing social and commercial projects using the Theory of Planned Behaviour (hereinafter: TPB) and subjective financial …