Digital Commons Network^™

Communication Modality And Activity Recognition In Smart Manufacturing, Haodong Chen

Doctoral Dissertations

"Advancements in sensors, computational intelligence, and big data have been rapidly transforming and revolutionizing the manufacturing sector, leading to robot-rich and digitally connected factories. To facilitate such a transformative process, workforce training and coordination between the human workforce and robots have remained a major challenge. This study aims to create intelligent workforce training systems for human-computer interaction and human-robot collaboration. It proposes the use of dynamic gestures and speech commands for seamless communication between a worker and a robot, leveraging Convolutional Neural Networks (CNN) and multiple threading for recognition and integration. Additionally, a fine-grained activity recognition model has been crafted …

Modeling And Control For Precision Robotic Machining, Patrick Bazzoli

Doctoral Dissertations

"Robots are used in a wide variety of manufacturing applications, but machining applications in which robots can excel are limited by their lower accuracy and stiffness relative to traditional CNC machines. This work is composed of two parts: one to evaluate a robot’s accuracy and one to compensate for the vibrations of the robot due to its lower stiffness.

In order to evaluate whether a robot has the necessary accuracy to perform a given machining task, Paper 1 discusses a novel Model Invalidation method. This methodology provides a statistical framework as well as a measurement strategy for determining if a …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …

Mission-Driven Inverse-Design Of Conventional And Unconventional Airplane Configurations Using Machine Learning, Rohan Srinivas Sharma

Doctoral Dissertations

"A key challenge in airplane design has been the long-desired ability to quickly create an airplane design with specific performance properties. To tackle this problem, researchers have traditionally leveraged design space exploration methodologies to iteratively explore a design space via surrogate models. On the other hand, inverse design is a process wherein performance objectives are fixed while a suitable design for satisfying these requirements is calculated. In recent years, artificial neural networks (ANNs) have been explored as viable surrogate models because traditional surrogate models struggle with the large number of output to input mapping needed for inverse design problems. In …

Lithospheric Drip And Mantle Thermal Upwelling Beneath Central Asia And The Southern Great Plains Of North America: Evidence From Receiver Function Imaging, Yangyang Liao

Doctoral Dissertations

"Investigating the depth of mantle transition zone (MTZ) discontinuities provides insights into mantle dynamics, including regional-scale downwelling of mantle lithosphere (i.e., lithospheric drip) and upwelling of thermal materials (i.e., mantle plume). In our study, we employ receiver functions to image the 410 km (d410) and 660 km (d660) mantle discontinuities and to ascertain thickness of the MTZ beneath Central Asia and the Great Plains in United States. The findings unveil a significantly thickened transition zone, exhibiting an average thickness of 284 km beneath East Sayan. When considered alongside geochemical observations, our results indicate that the descent of the lithosphere is …

Exploring Seismic Anisotropy In The Crust And Mantle Of The Tibetan Plateau And Analyzing Seismic Velocity Variations In Yellowstone, Cong Shen

Doctoral Dissertations

"Seismology is a scientific discipline that utilizes seismic waves to investigate the deep structures within the Earth's interior, which are otherwise inaccessible to direct observation by humans. These seismic waves can be categorized into two types: body waves and surface waves, each with distinct propagation modes and behaviors, carrying valuable information about their travel paths. The first paper concentrates on the analysis and inversion of body waves produced by large seismic events. It employs shear wave splitting and receiver function methods to explore the geodynamic structure of the south-central Tibetan Plateau, near the plate boundary. The investigation covers the depth …

Efficient Modeling And Simulation For The Aerothermal Analysis Of Particle-Laden Hypersonic Flows, Andrew Hinkle

Doctoral Dissertations

"Standard approaches for simulating hypersonic flows in particle-laden environments have key limitations in practice, such as high cost and numerical noise. In this work, efficient solution strategies for particle-laden hypersonic flows were developed using both Lagrangian and Eulerian models for the particle phase with the focus on investigating spacecraft surface erosion, as well as convective and radiative heat flux augmentation due to atmospheric particulate encountered during planetary entry.

First, an efficient integration strategy for Lagrangian methods, the Trajectory Control Volume (TCV) method, was developed for steady analysis of dilute particle phase problems on general geometries. The TCV method was shown …

Optimization Of Fire Emergency Preparedness In Underground Mining Environments: An Agent-Based Evacuation Approach, Oluwafemi Babatunde Salami

Doctoral Dissertations

"Underground mining environments present a complex interplay of thermal and non-thermal hazards, with fires constituting a significant risk due to the release of heat smoke from incomplete combustion. The propagation of these hazards through the ventilation networks underscores the critical need for understanding fire scenarios and their interaction with the underground environment. Existing knowledge regarding past mine fire disasters, and quantification of associated hazards and risks are insufficient for optimizing emergency evacuation strategies in the event of an underground fire emergency.

This research endeavors to bridge this gap by developing advanced quantitative risk assessment and evacuation models tailored specifically for …

In Situ Infrared Thermographic Inspection In Additive Manufacturing, Tao Liu

Doctoral Dissertations

"Laser Powder Bed Fusion (LPBF) is a key technique in metal additive manufacturing and has undergone extensive research over the past few decades. It enables the fabrication of complex geometries directly from digital design models. The solidification process in LPBF is heavily influenced by the characteristics of the powder layer, including its thickness and thermal transport properties. Additionally, the performance of the printed part is impacted by the temperature history during solidification and the final residual stress field. While these topics have been the focus of many studies, almost all research has concentrated on ex-situ measurement and testing. These ex-situ …

Topography Of The 410 And 660 Km Discontinuities Beneath The Tibetan Plateau And Adjacent Areas, Zexin Miao

Doctoral Dissertations

"The Tibetan Plateau represents the ongoing geotectonic aftermath of a continental collision initiated approximately 65 million years ago. Despite numerous seismological studies, controversies remain regarding the structure, evolution, and dynamics of the crust and mantle beneath Tibetan Plateau. In this study, we used teleseismic broadband data to investigate the topography of the mantle transition zone (MTZ) discontinuities. A total of ~655,000 receiver functions from 735 stations was utilized to image the 410 and 660 km discontinuities bordering the MTZ. Combining the observed and velocity corrected results, several areas in the central Tibet exhibit a typical d410 and an anomalously deep …

Critical Behavior And Dynamics Of The Superfluid-Mott Glass Transition, Jack Russell Crewse

Doctoral Dissertations

This work studies the effects of disorder on the thermodynamic critical behavior and dynamical properties of the superfluid-Mott glass quantum phase transition. After a brief introduction covering relevant fundamentals, we present the dissertation in the form of four separate but related publications. In the first two publications, we calculate the thermodynamic critical exponents of the superfluid-Mott glass quantum phase transition in both two and three spatial dimensions. The undiluted transition exhibits critical exponents that violate the Harris criterion, and thus the critical behavior is expected to change upon introducing disorder. We confirm this behavior via Monte Carlo simulation of a …

High-Resolution Spectroscopy Of Interstellar Lines And Comets, Chemeda Tadese Ejeta

Doctoral Dissertations

"The study of interstellar molecules such as CO is crucial because interstellar ices in the core of a pre-solar molecular cloud provide the starting point for volatile evolution in the protoplanetary disk. A record of the initial volatile composition of the protoplanetary disk can be obtained from the study of the chemical composition of cometary nuclei. Because of their long residence in the Oort cloud and infrequent passage through the inner solar system, long-period comets are one of the most primitive bodies in our solar system that can tell us about the composition of the early solar system. High-resolution infrared …

Adversarial Transferability And Generalization In Robust Deep Learning, Tao Wu

Doctoral Dissertations

Despite its remarkable achievements across a multitude of benchmark tasks, deep learning (DL) models exhibit significant fragility to adversarial examples, i.e., subtle modifications applied to inputs during testing yet effective in misleading DL models. These meticulously crafted perturbations possess the remarkable property of transferability: an adversarial example that effectively fools one model often retains its effectiveness against another model, even if the two models were trained independently. This research delves into the characteristics influencing the transferability of adversarial examples from three distinct and complementary perspectives: data, model, and optimization. Firstly, from the data perspective, we propose a new method of …

Deep Reinforcement Learning Based Strategies For Inverter Dominated Microgrids, Oroghene Oboreh-Snapps

Doctoral Dissertations

The integration of inverter-based distributed generators (IBDGs) in modern power systems has ushered in a new era of renewable energy utilization, prompting the rise of Microgrid (MG) architectures. Nevertheless, as IBDG penetration increases, a host of challenges surface. Foremost among these challenges are issues related to frequency stability stemming from the inherent lack of inertia in IBDGs, and inaccurate sharing of reactive and active power, particularly evident in parallel IBDG networks with mismatched feeder impedance. This research presents novel solutions to these challenges by adopting deep reinforcement learning (DRL), specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm, to …

Crystal Structure Prediction Of Metal Chalcogenides, Qi Zhang

Doctoral Dissertations

A novel crystal structure prediction (CSP) method has been developed to predict energetically favorable (stable) structures based on targeted chemical compositions. It leverages the structural characteristics of recurring motifs featured in many crystals and symmetry restrictions from space groups to effectively lower the degrees of freedom of a system when conducting CSP simulations. The proposed method is applied to predicting low-energy structures of two metal chalcogenide systems: Li₃PS₄ and Na₆Ge₂Se₆. Both systems feature rigid bodies in their structures as building blocks, making them particularly suited to the proposed method. The validity …

High Viscosity Friction Reducer Evaluation At High Temperature And High Tds Environments, Ghith Ali Ahmed Biheri

Doctoral Dissertations

"In unconventional reservoirs, proppant transport during hydraulic fracturing treatments could be successful when the proppant is placed further within the reservoir's fracture networks. This could increase the reservoir formation conductivities and improve the productivity of the wells. This job of improving the reservoir conductivity by distributing and transporting proppant can be conducted by reliable fracture fluids with excellent fluid rheology (e.g., viscosity and elasticity). However, several challenges, including the high temperatures and the presence of high TDS in produced water, may limit the capability of fracture fluids to transport proppant. Additionally, the use of fracture fluid has raised fears regarding …

Iqgo: Iterative Quantum Gate Optimiser For Quantum Data Embedding, Tautvydas Lisas, Ruairí De Fréin

Research Outputs: 2025-Present

Quantum kernel methods and Variational Quantum Classifiers (VQCs) have recently gained significant interest in the field of Machine Learning (ML). They have the potential to achieve superior generalisation whilst using smaller datasets and fewer parameters compared to their classical counterparts. However, kernel methods which leverage feature map embedding, often struggle with overfitting, which compromises their generalisation performance on unseen data. VQCs which utilise Parameterised Quantum Circuits (PQCs) to model the relationship between input data and the output, are susceptible to the Barren Plateau (BP) problem. To address these challenges, we introduce an adaptive quantum embedding optimisation algorithm, namely the Iterative …

Reinforcement Learning In Motor Skill Acquisition: Using The Reward Positivity To Understand The Mechanisms Underlying Short- And Long-Term Behavior Adaptation, Mariane F B Bacelar, Keith R Lohse, Juliana O Parma, Matthew W Miller

2020-Current year OA Pubs

INTRODUCTION: According to reinforcement learning, humans adjust their behavior based on the difference between actual and anticipated outcomes (i.e., prediction error) with the main goal of maximizing rewards through their actions. Despite offering a strong theoretical framework to understand how we acquire motor skills, very few studies have investigated reinforcement learning predictions and its underlying mechanisms in motor skill acquisition.

METHODS: In the present study, we explored a 134-person dataset consisting of learners' feedback-evoked brain activity (reward positivity; RewP) and motor accuracy during the practice phase and delayed retention test to investigate whether these variables interacted according to reinforcement learning …

Simplified Methods For Variance Estimation In Microbiome Abundance Count Data Analysis, Yiming Shi, Lili Liu, Jun Chen, Kristine M Wylie, Todd N Wylie, Molly J Stout, Chan Wang, Haixiang Zhang, Ya-Chen T Shih, Xiaoyi Xu, Ai Zhang, Sung Hee Park, Hongmei Jiang, Lei Liu

2020-Current year OA Pubs

The complex nature of microbiome data has made the differential abundance analysis challenging. Microbiome abundance counts are often skewed to the right and heteroscedastic (also known as overdispersion), potentially leading to incorrect inferences if not properly addressed. In this paper, we propose a simple yet effective framework to tackle the challenges by integrating Poisson (log-linear) regression with standard error estimation through the Bootstrap method and Sandwich robust estimation. Such standard error estimates are accurate and yield satisfactory inference even if the distributional assumption or the variance structure is incorrect. Our approach is validated through extensive simulation studies, demonstrating its effectiveness …

The Role Of Brainstem Biopsy And Targeted Therapies In Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma, Shehryar R Sheikh, Violette M R Recinos, Eric M Thompson, Ross Mangum, Mariah Wright-Nadkarni, Bradley Gampel, Neha J Patel

2020-Current year OA Pubs

Pediatric diffuse midline glioma (DMG), including diffuse intrinsic pontine glioma (DIPG), are aggressive brainstem tumors with a dire prognosis, traditionally diagnosed based on MRI characteristics. The recognition that molecular characteristics may determine prognosis and response to therapy has led to a reevaluation of biopsy necessity. This comprehensive review addresses the evolving role of brainstem biopsies in diagnosing and managing these tumors - both within the context of a clinical trial and in routine clinical care. We examine practice variability around brainstem biopsies for DMG/DIPG, revealing a global inconsistency in biopsy application and perceptions amongst providers. We show that safety profiles …

Work-Integrated Learning In The Humanities, Arts And Social Sciences: Where To From Here?, Denise Jackson, Elizabeth Cook

Research outputs 2022 to 2026

Amidst increasing pressures on Humanities, Arts, and Social Sciences (HASS) disciplines regarding graduate career readiness, this study examines HASS student participation in work-integrated learning (WIL) and its impacts on employability and short-term employment outcomes, using data from the Australian Graduate Outcomes Survey from 2020 to 2023. The research examined the extent of participation across WIL types (work-based, non-workplace and global) and its influence on graduates’ perceived employability and employment outcomes (full-time employment and perceived overqualification), while investigating demographic differences using descriptive analysis and logistic regression models. Results indicated rising WIL participation yet identified disparities by activity type and among student …

Icts Influence On Knowledge Sharing In Higher Education: A Pre-Ai Systematic Literature Review, Nirved Kumar, Elizabeth J. Cook, Sehkar Fayda-Kinik, Lela Maisuradze

Research outputs 2022 to 2026

Digital transformation, through innovation in information and communication technologies (ICTs), impacts higher education (HE) and fosters knowledge sharing (KS). While research explores the implications of digital transformation in HE, its specific influence on KS remains understudied. This systematic literature review investigates the nexus between ICTs and KS in HE by examining peer-reviewed literature prior to the AI boom. Three databases were searched, yielding 473 journal articles, which were screened, eligibility checked and assessed for quality and relevance to predetermined research questions. Eighteen eligible empirical studies were analysed, revealing ICTs influence on KS across university functions: teaching-learning, governance/operations and research. Retrospectively, …

Alzheimer's Disease Genetic Risk And Changes In Brain Atrophy And White Matter Hyperintensities In Cognitively Unimpaired Adults, Anja Soldan, Jiangxia Wang, Corinne Pettigrew, Christos Davatzikos, Guray Erus, Timothy J. Hohman, Logan Dumitrescu, Murat Bilgel, Susan M. Resnick, Leonardo A. Rivera-Rivera, Rebecca Langhough, Sterling C. Johnson, Tammie Benzinger, John C. Morris, Simon M. Laws, Jurgen Fripp, Colin L. Masters, Marilyn S. Albert

Research outputs 2022 to 2026

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE- ε4, APOE-ε2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged …

Board Commitment, Continuous Education Program And Firm Performance: A Law And Governance Context, Ahmad Saiful Azlin Puteh Salin, Zubaidah Ismail, Malcolm Smith

Research outputs 2022 to 2026

The majority of worldwide corporate governance codes mandate that directors allocate an adequate amount of time to fulfil their responsibilities and consistently enhance their knowledge and abilities through continuous education programs. These two factors are crucial in propelling the organisation towards higher levels of success and satisfying the needs of the stakeholders. This research aims to analyse the impact of board commitment and their access to education on firm performance. After analysing the annual reports of Malaysian public listed companies, this study does not find any conclusive evidence to support earlier research that highlights the advantages of having a director …

Can Frontline Employees Help Consumers Improve Their Financial Planning Behavior? Implications From Triadic Analysis, Vida Siahtiri, Welf Hermann Weiger, Christian Tetteh-Afi, Tobias Kraemer

Research outputs 2022 to 2026

Purpose: As consumer debt can substantially impair subjective well-being, it is crucial for research to gain insights into how consumers can be motivated to improve financial planning. This paper aims to investigate how frontline employees in financial services can help consumers regulate their financial planning behaviors and how financial service providers can effectively support their frontline employees in this effort through leadership and organizational climate. Design/methodology/approach: We incorporate regulatory focus theory and conservation of resource theory to develop a conceptual model that we test in a triadic study with a unique dataset collected from consumers, frontline employees, and managers in …

Design Of An Adaptive Robust Pi Controller For Dc/Dc Boost Converter Using Reinforcement-Learning Technique And Snake Optimization Algorithm, Seyyedmorteza Ghamari, Mojtaba Hajihosseini, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online adaptive mechanism based on the Reinforcement-Learning (RL) strategy. Classical PI controllers are simple and easy to build, but they need to be more robust against a wide range of disturbances and more adaptable to operational parameters. To address these issues, the RL adaptive strategy is used to optimize the performance of the PI controller. Some of the main …

Error In Jump Height Estimation Using The Flight Time Method: Simulation Of The Effect Of Ankle Position Between Takeoff And Landing, Carlos Gonçalves, Roberto Baptista, James Tufano, Anthony J. Blazevich, Amilton Vieira

Research outputs 2022 to 2026

During vertical jump evaluations in which jump height is estimated from flight time (FT), the jumper must maintain the same body posture between vertical takeoff and landing. As maintaining identical posture is rare during takeoff and landing between different jump attempts and in different individuals, we simulated the effect of changes in ankle position from takeoff to landing in vertical jumping to determine the range of errors that might occur in real-life scenarios. Our simulations account for changes in center of mass position during takeoff and landing, changes in ankle position, different subject statures (1.44–1.98 m), and poor to above-average …

Genograms, Culture, Love And Sisterhood: A Conversation With Monica Mcgoldrick, Deisy Amorin-Woods

Research outputs 2022 to 2026

Monica McGoldrick stands as a towering figure in the field of family therapy. Initially earning a Masters in Russian Studies, she then pivoted to social work and systems thinking. McGoldrick's illustrious career has been marked by significant contributions to family therapy, particularly through her work with family genograms, mapping family relationships and histories to identify patterns across generations. She expanded the use of genograms to incorporate cultural, ethnic and gender contexts, revolutionising the field. Her work emphasises understanding individuals within their historical, social and cultural contexts. She has authored multiple seminal texts, including ‘Ethnicity and family therapy’, and Genograms: Assessment …

Measuring Attitude Change In Nursing Students After Completion Of A First Nations Peoples’ Health Unit: Embedding A Validated Tool In Learning And Assessment Practices, Kylie Mccullough, Angela Genoni, Melanie Murray, Darren Garvey, Linda Coventry

Research outputs 2022 to 2026

The health inequities experienced by Aboriginal and Torres Strait Islander peoples, rooted in the historical and ongoing negative impacts of colonisation and disrupted traditional lifestyles, sees higher rates of illness and hospitalisations, increased morbidity and higher premature death rates than other Australians. Nurses represent the majority of Australia’s health workforce and nursing students must have knowledge to provide culturally safe care to Australia’s Aboriginal and Torres Strait Islander peoples. An Aboriginal and Torres Strait Islander Peoples’ Health and Wellbeing unit was developed and introduced into an undergraduate nursing curriculum. This project aimed to determine the effectiveness of the unit content …

The Impact Of Domain Name Server (Dns) Over Hypertext Transfer Protocol Secure (Https) On Cyber Security: Limitations, Challenges, And Detection Techniques, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Muhammad Haris, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow the analysis of DoH traffic …