Digital Commons Network^™

Educating The Next Generation Of Engineers: Spatial Thinking Is Key To Their Success, Sheryl Sorby, Gavin Duffy, Jack Parkinson, Gibin Raju, John Lynch, Muhammad Asghar

Research Outputs: 2025-Present

This paper attempts to answer questions regarding the link between engineering success and spatial skills through three themes. The previous research of the authors in examining the questions surrounding each theme is the focus of this paper; we acknowledge that others around the world may have conducted similar research, but the specific findings of the authors are the main focus of this paper. In the first theme, we examine data from several author-led studies where positive correlations have been found between spatial skill levels and proficiency in tasks that are a part of what it means to be an engineer. …

Advancing Sustainable Tourism Behavioural Research: A Metacognitive Perspective, Arghavan Hadinejad, Kourosh Esfandiar, Liubov Skavronskaya

Research outputs 2022 to 2026

The current scholarly inquiry on sustainable tourism behaviour research predominantly relies on cognitive behavioural approaches, particularly exemplified by the widespread application of the theory of planned behaviour among other theories. Given recent advancements in psychology underscoring the necessity to expand beyond conventional cognitive frameworks, the current research aims to integrate metacognitive perspectives in sustainable tourism behaviour scholarship. This perspective explores the reflective awareness and control individuals exert over their cognitive processes which can uncover nuanced factors shaping sustainable tourism behaviour that may be overlooked in traditional cognitive models. Using a critical review approach, this research proposes the adoption of alternative …

Are Australian Schools Becoming Less Inclusive? Changes To Class Ability Grouping Practices In Australian Secondary Schools, Olivia Johnston, Peter Palamara

Research outputs 2022 to 2026

Class ability grouping does not improve overall academic results, and is inequitable because it segregates students according to their disability, race, class, gender, and other factors that already make students at risk in education. The practice is not inclusive and previous research has shown that it exacerbates social disadvantage. Despite the evidence, historical efforts to change practices in Australia have been unsuccessful and many schools still use class ability grouping. This paper presents analysis of data from the Class Ability Grouping Survey about how often schools reported changing their class ability grouping practices in Year 7–9 Mathematics, English, Science, and …

Stakeholder Co-Design Of Sustainable Urban Pest Management Strategies, Elizabeth C. Lowe, Nathan J. Butterworth, Alexander Austin, Cameron Webb, Tanya Latty

Research outputs 2022 to 2026

The high levels of broad-spectrum insecticides used to manage invertebrates in many cities around the world has significant environmental and health impacts. Integrated pest management (IPM) is a framework for sustainable pest control practice, but uptake of IPM remains low in most cities. We used participatory action research with pest management industry stakeholders (practitioners, industry representatives, researchers and government employees) to identify key issues in urban pest management in Australia, set priorities for change and discuss collaborative solutions. Via an online survey and a face-to-face workshop, the participants identified the key themes of education and training, public awareness, environmental impacts …

Sexisms And Gender-Washing In Australian Academia: Public Front, Private Realities, Jacqueline Ullman, Emily Gray, Mindy Blaise, Lucy Hobby

Research outputs 2022 to 2026

This paper offers an analysis of selected survey findings from a mixed methods research project, entitled Understanding and Addressing Everyday Sexisms in Australian Universities. The project is situated within the context of the negative impact of gender-based discrimination upon the higher education sector in Australia and internationally. The findings discussed in this paper relate to quantitative analyses of selected items from a national survey of over 1200 Australian academics, recruited through 12 public universities. In this paper, the ways in which different participant groups responded to items designed to explore their attitudes towards sexism in the academic workplace, both broadly …

Attacks And Detections In Recommender Systems: A Comprehensive Analysis For Models, Progresses, And Trends, Yan Feng, Zhihai Yang, Kexin Li, Jianxin Li, Pinghui Wang, Zhiquan Liu

Research outputs 2022 to 2026

Recommender systems (RSs), as crucial components of online services, can help users efficiently obtain information they may like. In reality, RSs face long-term threats. Attackers manipulate recommendation results by injecting malicious data in order to obtain benefits. At present, research on the security of RSs lacks a comprehensive understanding of attack capabilities. Moreover, existing defense strategies have not yet been systematically associated with attack characteristics. More importantly, existing defense methods rarely focus on real unlabeled data in practical application scenarios for anomaly detection and forensics. Therefore, this survey systematically analyzes the security of RSs and provides new insights. Specifically, we …

Lack Of Increase In Mercury Contamination In Coastal Western Australia Since European Settlement, Martin Dahl, Larissa Schneider, Harald Biester, Richard Bindler, Antonio Martinez Cortizas, Paul S. Lavery, Miguel A. Mateo, Oscar Serrano

Research outputs 2022 to 2026

Current knowledge of long-term mercury (Hg) deposition is predominantly based on studies from the Northern Hemisphere, leading to a geographical bias in the comprehension of the global Hg cycle. Aiming to contribute to fill this knowledge gap, our study presents a high-resolution Hg record of a seagrass Posidonia australis sedimentary archive encompassing the last 3300 years in the Waychinicup estuary (Western Australia, WA). This setting is an ideal site for studying the natural Hg cycle, as it is located in the southwest of the state, outside the prevailing wind patterns that transport emissions from major Hg sources. Our results show …

The Impact Of Congenital Heart Disease On The Timing Of Alzheimer's Disease In Down Syndrome, Julianne G Clina, Beau Ances, Et Al.

2020-Current year OA Pubs

INTRODUCTION: The incidence of Alzheimer's disease (AD) in Down syndrome (DS) exceeds 90%. Approximately 50% of people with DS have congenital heart disease (CHD). Having CHD increases risk for early-onset AD in populations without DS, but it is unclear if CHD influences AD in DS.

METHODS: Data from the Alzheimer Biomarker Consortium-Down Syndrome (ABC-DS) were used. Participants with CHD (

RESULTS: People with CHD scored lower for visuospatial ability (

DISCUSSION: Presence of CHD may influence AD progression in DS.

HIGHLIGHTS: In adults with Down syndrome (DS), those with congenital heart disease (CHD) had higher amyloid beta and reached the …

Concerted Proton Transfer In Homogeneous And Heterogeneous Cyclic Hydrogen-Bonded Clusters Of H2o, Hf, And Hcl, Max R. Tucker, Yuan Xue, Nathan R. Speake, Eden Nickolson, Jeremy M. Carr, Gregory S. Tschumper

Chemistry Faculty Research & Creative Works

This work examines various homogeneous and heterogeneous clusters of hydrogen-bonded (HF)x(H₂O)y(HCl)z complexes, where x + y + z = 3 or 4. For each unique cyclic structure associated with the xyz permutations, the geometries of the reactant, product, and transition state (TS) for concerted proton transfer (CPT) were fully optimized using second-order Møller-Plesset perturbation theory (MP2) with a mixed basis set consisting of cc-pVTZ for H atoms and aug-cc-pVTZ for O, Cl, and F atoms (denoted haTZ). Harmonic vibrational frequencies were also computed at the same level of theory to verify the nature of the stationary points. Intrinsic …

Managing And Accelerating The Circular Economy Transitions Within The Construction Value Chain Using Network Governance And Game Theory Systems Perspectives, Radwa Eissa, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The fragmented nature of the construction industry has hindered its transition toward circular economy (CE) practices. While game theory (GT) has been successfully applied to support the strategic decisions of CE transitions in other sectors, CE-GT models in the construction domain remain extremely limited. This paper addresses two research questions: (1) how are CE network governance activities currently modeled using GT across domains? (2) How can such models be transferred and adapted to solve strategic interactions within the construction value chain in a manner that enables CE practices to emerge as equilibrium outcomes? These questions are driven by the need …

Multidimensional Analysis Of Human Outdoor Comfort: Integrating Just-In-Time Adaptive Interventions (Jitais) In Urban Digital Twins, Binyu Lei, Pengyuan Liu, Kunihiko Fujiwara, Mario Frei, Clayton Miller, Xuan Yun Chua, Filip Biljecki

Research Collection College of Integrative Studies

How can bidirectional information exchange be enhanced in urban digital twins, and support human-centric data and processes? Their key characteristic is the nearly real-time exchange of information, allowing adjustments to physical environments based on simulations and analytics within virtual models. Yet, achieving such interaction remains challenging, particularly regarding device deployment and infrastructure development. Embracing the concept of humans as sensors, this work develops a two-way framework based on the emerging concept of just-in-time adaptive interventions (JITAIs), exploring how urban digital twins can play a role in understanding and enhancing human comfort outdoors. Human comfort outdoors is inherently spatio-temporal and personalised, …

Curriculum Approaches To Strengthening Student Employability And Improving Graduate Outcomes In The Stm Disciplines, Denise Jackson, Michael D. O’Connor

Research outputs 2022 to 2026

In Australia and elsewhere, curriculum improvements are needed to improve the development of Science, Technology and Mathematics (STM) graduates for future work and careers. Several studies document significant STM student attrition, poor graduate preparedness for work, relatively weak employment rates post-graduation, and mismatched expectations of graduate capabilities by employers. The inclusion of work-integrated learning (WIL) activities within curricula can better prepare students for relevant employment by bridging discipline-specific teaching with work practices. Traditionally, access to WIL has been limited for STM Bachelor students and a better understanding of WIL’s benefits and impact is needed to refine and extend STM WIL …

Baseline Body Mass Index Category And Incident Treated Hypertension In Australian Men: A Linked Prospective Cohort Study Using Pbs/Mbs Data, Ebenezer Afrifa-Yamoah

Research outputs 2022 to 2026

Background: Excess adiposity is a major modifiable risk factor for hypertension; however, the incremental prognostic value of body mass index (BMI) beyond standard predictors remains unclear. We examined the associations between baseline BMI category and incident treated hypertension, and assessed whether BMI improved risk prediction.

Methods: Data were obtained from the Ten to Men longitudinal study (2013–2024) linked to Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) records. The cohort comprised 12,742 men aged ≥ 18 years without treated hypertension at baseline, followed for 133,038 person-years. Adjusted Cox proportional hazards models were fitted. Discrimination, calibration, decision curve analysis, E-values, …

Rrto: A High-Performance Transparent Offloading System For Model Inference In Mobile Edge Computing, Zekai Sun, Xiuxian Guan, Zheng Lin, Yuhao Qing, Haoze Song, Zihan Fang, Zhe Chen, Fangming Liu, Heming Cui, Wei Ni, Jun Luo

Research outputs 2022 to 2026

Deploying Machine Learning (ML) applications on resource-constrained mobile devices remains challenging due to limited computational resources and poor platform compatibility. While Mobile Edge Computing (MEC) offers an offloading-based inference paradigm using GPU servers, existing approaches can be divided into non-transparent and transparent methods, with the former necessitating source-code modifications. Non-transparent offloading achieves high performance but requires intrusive code changes, limiting compatibility with diverse applications. Transparent offloading, in contrast, offers wide compatibility but introduces two challenges: performance degradation under MEC's low and fluctuating bandwidth, where per-operator remote procedure calls (RPCs) inflate end-to-end latency and energy consumption; and black-box logic reconstruction, which …

Exploring New Poly-Anion Based Materials For Lithium – Ion Battery Cathodes, Sutapa Bhattacharya

Doctoral Dissertations

The escalating global demand for sustainable energy storage solutions has driven intensive research into novel electrode materials. Polyanion-based cathode materials have emerged as promising candidates due to their structural stability, safety, and voltage tunability enabled by the inductive effect of polyanionic groups. This research explores the synthesis, crystal structure, and electrochemical performance of new polyanion-based cathode materials featuring vanadium, molybdenum, and iron within phosphate and selenite frameworks.

Novel selenite-based materials, such as LiFe(SeO₃)₂ and Li₀.₂₅V₂O₃(SeO₃)₂, were synthesized and characterized, revealing stable electrochemical cycling associated with Fe2+/Fe3+ and V⁴⁺/V⁵⁺ transitions. Additionally, a systematic investigation was conducted on molybdenum phosphate compounds, including …

Trust-Aware Algorithms For Stackelberg Ground Vehicle Routing, Doris Evelyn Meredith Brown

Doctoral Dissertations

Despite decades of research focused on reducing ground vehicle traffic congestion, urban traffic networks worldwide continue to experience traffic flows that lead to increased network travel times, largely resulting from the routing decisions of individual vehicles. To address this challenge, this work leverages a Stackelberg game framework to model the interaction between a vehicle agent and a routing authority as a leader–follower game, in which the routing authority proposes routing interventions to which the agent responds. This research contributes to existing traffic mitigation literature by exploring the role of trust in route decision-making and providing trust-aware algorithms that influence vehicle …

Essays On Accelerated Failure Time Models For Recurrent Event Data, Emmanuel Masavo Djegou

Doctoral Dissertations

Recurrent event data arise in many fields such as medicine, reliability, insurance, and economics, where the same event may occur repeatedly for a subject. Accelerated Failure Time (AFT) models provide an intuitive framework for relating covariates to event times and offer a useful alternative to proportional hazards models, allowing direct prediction of event timing under right censoring. However, existing AFT extensions for recurrent events, such as accelerated gap time (AGT) models, often fail to account for interventions between events and may not capture complex temporal patterns.

In this work, we first propose a class of semiparametric AGT models incorporating an …

Encryption And Globalization 15 Years Later: End-To-End Encryption And The Third Round Of The "Going Dark" Debate, Peter Swire, Kenesa Ahmad, Michael Specter

Fordham International Law Journal

No abstract provided.

Evaluating The Use Of Machine Learning For Road Maintenance Cost Estimation, Shahla Shirinzad, David Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Accurate estimation of highway maintenance costs is essential for efficient resource allocation and long-term infrastructure sustainability. This study evaluates the effectiveness of advanced machine learning models, including ResNet, Transformer, and other neural network architectures, for forecasting maintenance costs using historical data from the Highway Maintenance Improvement Program (HMIP) provided by the North Carolina Department of Transportation (NC DOT). A comprehensive comparative analysis is conducted across multiple models using standard performance metrics, including R², MAE, RMSE, and MSE. The results demonstrate that advanced architectures, particularly ResNet and Transformer, consistently outperform traditional statistical approaches and baseline machine learning models, achieving …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Deep Learning Based High-Resolution Electromagnetic Inversion Imaging Using Deep Convolutional Double-Module Structure, He Ming Yao, Shiji Song, Lijun Jiang, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel deep learning (DL) approach has been proposed to realize high-resolution electromagnetic (EM) inversion imaging. The newly proposed approach is based on the deep convolutional double-module structure (DCDMS), consisting of the pixel-interpolating module and the corresponding quality-improving module. While the pixel-interpolating module roughly increases the 'resolution' of the initial input, the following quality-improving module realizes quantitative EM imaging in high resolution. The input of the proposed DCDMS adopts the mixed input scheme, consisting of the received EM scattered field and the initial reconstruction in much low resolution computed from Gauss-Newton method. The output of the proposed …

Volatile Organic Compound Analysis Of Humboldt Penguin (Spheniscus Humboldti) Preen Oil: A Pilot Study, Dante E. Rojas, Mitchell M. Mccartney, Eva Borras, Michael O. Eze, Abigail Pietrow, Jennifer J. Valvo, Cristina E. Davis

Chemistry Faculty Research & Creative Works

Avian olfaction has gained prominence in recent decades for its roles in social communication and behavior. In penguins, the chemical characterization of preen oil remains limited. In this exploratory study, we characterized the volatile organic compound (VOC) profile of preen oil from Humboldt Penguins (Spheniscus humboldti). Preen oil from 12 captive individuals was analyzed using headspace sorptive extraction (HSSE) coupled to gas chromatography–mass spectrometry (GC–MS). Chemometric analyses examined variation in VOC profiles by sex, age class, and breeding activity. We detected 54 compounds (20 tentatively identified), including linear alcohols, methyl ketones, carboxylic acids, saturated hydrocarbons, oxygenated and nitrogen-containing organics, prenolipids, …

Soft-Chemical Scalable One-Pot Aqueous-Medium Synthesis Of Na3(Vo)2(Po4)2f-Based Cathodes: Compositional Tuning And Electrochemical Performance In Na- And Li-Ion Batteries, Prashanth Sandineni, Subal Chandra Manna, Sutapa Bhattacharya, Santhoshkumar Sundaramoorthy, Ramesh Deokate, Milad Aghayi-Anaraki, George E. Sterbinsky, Kartik Ghosh, Amitava Choudhury

Chemistry Faculty Research & Creative Works

One of the most important cathode materials for Na-ion batteries, Na₃(VO)₂(PO₄)₂F, and its compositional variants have been synthesized using four different and facile one-step soft chemical routes. The as-synthesized compounds, Na_2.95(VO)₂(PO₄)₂F (I), Na_2.93(VO)₂(PO₄)₂F (II), and Na_2.58(VO)₂(PO₄)₂F (IV), crystallize in the tetragonal crystal system in the P4₂/mnm space group, while Na₃(VO)₂(PO₄)₂F (III) crystallizes in the orthorhombic crystal system in …

External Validation Of A Knowledge-Based Clinical Toxicology Consultant For Diagnosing Single Exposures, Hugo Lerogeron, Joel D. Schipper, Jay L. Schauben, Laurent Guéguen, Valentine Lacotte, Kim An Nguyen

Publications

Introduction

Clinical prediction models assist healthcare providers in estimating disease likelihood using multiple patient-specific factors. While these models are increasingly used, few undergo the essential process of external validation, which limits their clinical utility. In toxicology, timely diagnosis is crucial and expertise scarce, making knowledge-based systems an excellent choice to support medical decision making. A knowledge-based system developed using data from the Florida Poison Information Center Network has shown promise but lacks external validation. This study aims to evaluate its performance using data from the Lyon Poison Information Center to assess its generalizability

Methods

To externally validate the Florida diagnostic …

The Impact Of Synchronous And Asynchronous Preference On The Performance Of Distance Learning Students, Christopher A. Wilson

Electronic Theses and Dissertations

Distance learning is a growing part of education. The transition to distance learning was underway at higher education institutions, but the COVID-19 pandemic forced institutions of all types to rapidly transition to distance learning. The technology that was adapted during this emergency distance learning remained in the classroom once normal instruction resumed. This quantitative study examined two aspects of distance learning technology in education. The study examined student preference for either synchronous or asynchronous instruction through the framework of expectancy theory. Students who were more familiar with the course material were hypothesized to have a higher expectation of success and …

Do Comments And Expertise Still Matter? An Experiment On Programmers’ Adoption Of Ai-Generated Javascript Code, Changwen Li, Christoph Treude, Ofir Turel

Research Collection School Of Computing and Information Systems

This paper investigates the factors influencing programmers’ adoption of AI-generated JavaScript code recommendations within the context of lightweight, function-level programming tasks. It extends prior research by (1) utilizing objective (as opposed to the typically self-reported) measurements for programmers’ adoption of AI-generated code and (2) examining whether AI-generated comments added to code recommendations and development expertise drive AI-generated code adoption. We tested these potential drivers in an online experiment with 173 programmers. Participants were asked to answer some questions to demonstrate their level of development expertise. Then, they were asked to solve a LeetCode problem without AI support. After attempting to …

Adaptive Machine Learning Framework For Microstructural Optimization And Mechanical Performance Prediction In Steels, Henry Adekola Haffner

Doctoral Dissertations

Evolution of microstructures, such as polygonal ferrite, acicular ferrite, bainite, and martensite, plays a pivotal role in determining the final microstructural and mechanical properties of steel products. Given the established inter-relationship between processing parameters, microstructure, properties, and performance, precise control of phase transformation is essential to achieve pre-determined properties. To understand transformation routes in different steel grades, time-temperature-transformation (TTT) and continuous-cooling-transformation (CCT) diagrams are necessary and can be described using the Johnson-Mehl-Avrami-Kolmogorov equation and Scheil’s additivity rule. This study presents a comprehensive computational framework for predicting and optimizing microstructure and mechanical properties in advanced high-strength steels (AHSS) using adaptive machine …

Incremental Cluster Validity Indices And Their Role In Interpreting Lifelong Learning Systems, Niklas Max Melton

Doctoral Dissertations

Clustering and supervised learning are often treated as distinct paradigms, yet both rely on structure in feature space. This dissertation investigates the relationship between cluster validity indices (CVIs) and supervised learning in real-time and lifelong learning settings where data arrive incrementally and cannot be revisited. Across four studies, it develops methods for online cluster validation, uses supervised learning to improve their interpretability, and applies these ideas to evaluating performance degradation in continual learning.

The first study extends incremental cluster validity indices (iCVIs), enabling widely used validation metrics to operate in streaming environments. Experiments on synthetic and real-world datasets show systematic …

Milankovitch Paleoclimatic Signals In Upper Permian–Lower Triassic Fluvial-Lacustrine Records, Bogda Mountains, Greater Turpan-Junggar Intracontinental Rift Basin, Nw China, Wentao Zhang

Doctoral Dissertations

"The objective of this study is to understand whether Milankovitch climatic and/or tectonic processes influenced the sedimentation of the upper Permian–Lower Triassic fluvial-lacustrine cyclic deposits in Bogda Mountains, greater Turpan-Junggar intracontinental rift basin, Xinjiang Uygur Autonomous Region, NW China. This study carried out gamma analysis, gamma and astronomical tuning, and spectral analysis on the Wutonggou, Jiucaiyuan, and Shaofanggou low-order cycles of the South and Central Taodonggou, South and North Tarlong, Dalongkou, and Zhaobishan sections. Stable and positive gamma values support the assumption of facies-dependent sedimentation rates for non-marine facies. The sedimentation rates range from 0.04 to 2.43 m/kyr, and 0.21 …

An Energy-Stable Mixed Cg-Dg Scheme For A Full Shliomis Phase Field Model Of Two-Phase Ferrofluid Flows, Mahdi Gharehbaygloo

Doctoral Dissertations

"Ferrofluids are magnetic nanoparticle suspensions whose motion couples surface tension, flow field, magnetostatics, and magnetization dynamics. This dissertation develops, analyzes, and validates an energy-stable finite element method for a two-phase ferrofluid model that couples the Cahn-Hilliard equations with the full Shliomis model of single-phase ferrofluids, retaining its damping torque term, magnetic torque term, and magnetic stress term.

The spatial discretization is a mixed continuous Galerkin (CG) and discontinuous Galerkin (DG) formulation. It uses continuous ��2 elements for the phase field, chemical potential, velocity, and magnetostatic potential, discontinuous ��2 elements for the magnetization, and discontinuous ��1 elements for the pressure. The …