Digital Commons Network^™

Methodology For Parameter Determination For Simulation Software, Aaron Flood

Doctoral Dissertations

"This research aims to present a methodology for optimizing input datasets to increase the accuracy of mathematical models and accelerate their application to engineering problems. To accomplish this goal, this work focused on the application of mathematical models to metal additive manufacturing (AM), specifically the thermal history of laser directed energy deposition (DED) of aluminum alloys. The initial steps of this body of work were to develop a mathematical model that is capable of simulating the metal AM process and applying it to the laser DED of aluminum. It was validated using the well characterized material Ti-64 and shown to …

Development And Testing Of High Strength Scalable High Entropy Alloys, Hans Jakob Pommerenke

Doctoral Dissertations

"High entropy alloys have shown promise through their ability to be tailored to specific scenarios. From highly corrosive environments to precipitation strengthened alloys to radiation environments high entropy alloys allow tailor made alloys for every solution. New compositions are developed and tested but little work focuses on the processing effect choosing rather than using processing as a steppingstone to achieve properties without investigating the processing effects. This work focuses on single phase face-centered cubic alloys strengthened by processing and precipitation strengthening. In conjunction with the development of these alloys, the ability to scale production will be investigated and characterized to …

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 …

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 …

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 …

The Stability Of The Deadlift Three Repetition Maximum, Stuart N. Guppy, Tsuyoshi Nagatani, Wayne C. K. Poon, Kristina L. Kendall, Jason P. Lake, G. Gregory Haff

Research outputs 2022 to 2026

This study investigated the stability of three repetition maximum (3RM) strength during the deadlift. Eleven participants performed four testing sessions comprising a one repetition maximum test and 3RM tests separated by 48 h. Preparedness was assessed before each testing session using countermovement jumps and by measuring barbell velocity during each set of deadlifts. Trivial statistically significant differences were determined for the 3RM between T1 and both T2 (p = 0.012; ES [95% CI] = −0.1 [−0.58, 0.41]) and T3 (p = 0.027; ES [95% CI] = −0.09 [−0.57, −0.43]). No significant differences were noted between T2 and T3 …

Scat Dna As A Non-Invasive Method For Estimating The Abundance Of The Vulnerable Mala (Lagorchestes Hirsutus), Shannon Treloar, Cheryl Lohr, Anna J. Hopkins, Kym Ottewell, Shelley Mcarthur, Robert A. Davis

Research outputs 2022 to 2026

Context: Population-monitoring programs often use direct (e.g. live capture or spotlighting) or indirect (e.g. scats sightings) observations to estimate population abundance. Such methods, however, are often inadequate for rare, elusive, or cryptic species due to the difficulty in achieving sufficient encounters or detection rates. The mala (Lagorchestes hirsutus), a small native Australian macropod, listed as Vulnerable by the IUCN, is difficult to capture, susceptible to capture myopathy, and not easily sighted in their dense habitat; consequently, the population size cannot always be estimated. The use of molecular markers to identify individual genotypes from non-invasively collected samples is increasingly being used …

Shall We Dance? How Arbitral Tribunals And National Judiciaries Can Move In Tandem To Stop Corruption While Not Stepping On The Other's Toes, Jaimee Salgado

Fordham International Law Journal

No abstract provided.

Book Review: Nuclear Weapons And International Law, Existential Risks Of Nuclear War And Deterrence Through A Legal Lense, 2 Volumes, Second Edition, By Charles J. Moxley Jr. (2024), Roger S. Clark

Fordham International Law Journal

No abstract provided.

Qualitative Interviews, Abraham Kenin

Research Datasets

Background: Within the context of a growing sub-Saharan Africa (SSA) population in Australia, the problem of alcohol and other drug (AOD) use by SSA youth has been reported in recent studies. Although evidence from other sociocultural contexts indicate that SSA cultural values could be protective against youth AOD use, not much is known about what pertains in the Australian context. The core justification for conducting this study is grounded on the awareness that people may perceive and conceptualize their heritage cultural values based on the sociocultural context in which they find themselves (Akkuş et al., 2017).

Objectives: To explore SSA …

Eastern Australian Farmers Managing And Thinking Differently: Innovative Adaptation Cycles, David K. Mckenzie, Janine Joyce, Kerstin K. Zander, Penelope A.S. Wurm, Kim M. Caudwell

Research outputs 2022 to 2026

The uncertainty of climate change is a significant challenge prompting Australian farmers to create different thinking and different management systems that ensure sustained farm business viability and continuity, particularly in extreme environments. The purpose of this study was to explore the conditions and adaptive processes for managing farm resilience and cyclic adaptation pathways, in response to climate change. A positive deviance sample of farmers was interviewed, and data was collected from a cohort of twenty-two climate change innovators across Eastern Australia. Grounded theory analysis of data identified three processes and two transactional maps of climate change adaptation, in this under …

Parental Assessment Of Postsurgical Pain In Infants At Home Using Artificial Intelligence–Enabled And Observer-Based Tools: Construct Validity And Clinical Utility Evaluation Study, Fatos Sada, Paola Chivers, Sokol Cecelia, Sejdi Statovci, Kujtim Ukperaj, Jeffery Hughes, Kreshnik Hoti

Research outputs 2022 to 2026

Background: Pain assessment in the infant population is challenging owing to their inability to verbalize and hence self-report pain. Currently, there is a paucity of data on how parents identify and manage this pain at home using standardized pain assessment tools. Objective: This study aimed to explore parents’ assessment and intervention of pain in their infants at home following same-day surgery, using standardized pain assessment tools. Methods: This prospective study initially recruited 109 infant boys undergoing circumcision (same-day surgery). To assess pain at home over 3 days after surgery, parents using iOS devices were assigned to use the PainChek Infant …

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 …

Digital Technologies For Environmental Sustainability, Raimundo Macêdo, Marcelo Pias, Liwan Liyanage, David M. Cook, Yuko Murayama, Otthein Herzog, Gülgün Kayakutlu, David Kreps

Research outputs 2022 to 2026

Recognizing the immense challenges posed by climate change and aligning with the UN Sustainable Development Goals (SDGs), particularly SDG 13, which focuses on climate action, the International Federation for Information Processing (IFIP) established the Digital Technology and Climate Change Task Force in December 2022. The Task Force aims to identify opportunities to leverage digital technologies effectively in combating climate change and to provide recommendations for professionals and relevant institutions. Since its inception, the Task Force has met regularly. After conducting a scoping exercise and defining a taxonomy, a comprehensive questionnaire was distributed twice among IFIP stakeholders, namely, technical committees and …

Two Decades Of Seagrass Monitoring Data Show Drivers Include Enso, Climate Warming And Local Stressors, Chanelle Webster, Kathryn Mcmahon, Claire Ross, Eben Afrifa-Yamoah, Nicole Said, Renae Hovey, Belinda Martin, Simone Strydom

Research outputs 2022 to 2026

The drivers contributing to the trajectories of seagrass ecosystems is a key knowledge gap which limits our effectiveness in preventing their decline. Here, we present the key findings from the regional assessment of seagrass condition (shoot density) over time, which was undertaken to inform environmental impact assessment and monitoring as well as mitigation for the Westport program. To assess regional trends and drivers of these trends, data was collated from five regional seagrass monitoring programs, including the Cockburn Sound State Environmental Policy Seagrass Monitoring Program and marine park monitoring programs conducted by the Department of Biodiversity, Conservation and Attractions. These …

Secondary Mathematics Teachers’ Mathematical Competence, Vesife Hatisaru, Julia Collins, Steven Richardson, Constantine Lozanovski

Research outputs 2022 to 2026

Whilst educational goals in recent years for mathematics education are foregrounded the development of mathematical competencies, little is known about mathematics teachers’ competencies. In this study, a group of practising teachers were asked to solve an algebra problem, and their solutions were analysed to determine the competencies apparent (or, equally importantly, absent) in these solutions. The most demonstrated competencies were devising strategies and mathematising, whilst communication and reasoning were mostly missed. The research provides a detailed methodology on how mathematical competencies can be assessed in the context of problem solving.

Differentiating Origins Of Outflow Tract Ventricular Arrhythmias: The Correction Of Transitional Zone Index Is Not Superior To The Original One, Ming Liu, Zhuoqiao He, Pengxiang Ying, Dong Lin, Min Yu, Xuerui Tan

Research outputs 2022 to 2026

Introduction: Our team once proposed a correction of transitional zone index (CTZI) based on the transitional zone index (TZI) in view of achieving a more precise prediction of outflow tract ventricular arrhythmia (OTVA). The predictive accuracy of these two electrocardiogram (ECG) algorithms has not been validated and compared. The purpose of this study was to compare the predictive accuracy of TZI and CTZI in a much larger population with idiopathic OTVA. Methods: The predictive accuracy of TZI and CTZI was compared in 695 individuals with idiopathic premature ventricular complex or ventricular tachycardia which exhibited a left bundle branch block pattern …

Similar Fact Evidence In Contractual Interpretation: Bhoomatidevi D/O Kishinchand Chugani Mrs Kavita Gope Mirwani V Nantakumar S/O V Ramachandra And Another [2023] Sghc 37, Calvin John Kaiwen Chirnside

Research Collection Yong Pung How School Of Law

In the recent Singapore High Court case of Bhoomatidevi d/o Kishinchand Chugani Mrs Kavita Gope Mirwani v Nantakumar s/o v Ramachandra and another [2023] SGHC 37, the claimant argued, inter alia, that evidence of a prior contract between the first defendant and a third party should be admitted to prove that the defendant had entered into a loan agreement with her in his personal capacity. Justice Lee Seiu Kin dismissed her claim, applying s. 14 of the Evidence Act.

Expropriation Of Shares Via The Corporate Constitution, Stephen Bull

Research Collection Yong Pung How School Of Law

Company constitutions sometimes include powers to effect compulsory share acquisitions from members. Where these are introduced into the constitution after incorporation, the amendment, like all constitutional alterations, must be able to satisfy the common law “bona fide test” in order to be valid. The content of this test has been much debated since the first cases a century ago, and differences in view have emerged from the English and Australian courts. While there is no local case law on such expropriations per se, the High Court recently confirmed for the first time the applicability in Singapore of the common law …

Statistical Eye Diagrams For High-Speed Interconnects Of Packages: A Review, Junyong Park, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

An eye diagram, a critical metric in signal integrity analysis for high-speed interconnects such as packages, interposer, and printed circuit boards (PCBs), is generated by superposition of the received waveform. Obtaining an eye diagram is time-consuming, thus signal integrity analysis is inefficient. This article reviews that have been proposed to overcome this limitation. The statistical eye diagram provides a probability distribution depending on a sampling time and voltage, therefore it can be expanded to other metrics, such as the bit-error rate and shmoo plot. This article introduces previous research on statistical eye diagrams applied to complementary metal-oxide-semiconductors (CMOSs), noise, and …

A Segmentation Approach For Predicting Plane Wave Coupling To Pcb Structures, Shengxuan Xia, James Hunter, Aaron Harmon, Ahmed M. Hassan, Victor Khilkevich, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Evaluating the far-field radio frequency (RF) susceptibility of electronic devices often depends on extensive testing or full wave simulations. These methods are effective when complete system information is available but require substantial time and resources to evaluate a large number of variations in system configurations, where trace routings, integrated circuit (IC) package styles, trace terminations, arrival angle, and polarization of incoming wave, etc., are varied from one configuration to another. The goal of the following article is to develop simulation techniques for studying the statistical characteristics of coupling to typical printed circuit board (PCB) structures. Simulation time can be reduced …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …