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Who is inclined to embrace sustainable options in on-demand mobility?
No full textShared on-demand mobility can be made more sustainable, but
this often involves trade-offs in comfort and cost. For example,
electric vehicles emit less CO2 but may require higher fares to
compensate for charging time, while walking segments can
reduce vehicle-kilometres travelled by avoiding detours, yet
offer less comfort than door-to-door services. This study
examines travellers’ willingness to choose more sustainable
shared mobility options and identifies which users are most
likely to make such choices.
To do so, we apply an integrated choice and latent variable
model to data collected via a discrete choice experiment (DCE)
administrated to a sample of residents of the Greater Sydney
Area, Australia. Specifically, respondents were presented with
three shared mobility options, two of which required walking
to reach pickup or dropoff points. Each option was described
by a set of attributes, including waiting time, in-vehicle time,
emission reduction relative to conventional vehicles, walking
time and price. In addition to completing the DCE, respondents
answered a series of attitudinal questions designed to capture
key travel-related dimensions: safety of car, pro-walk
orientation, time sensitivity, and variety-seeking behaviour.
Results indicate that respondents who place high value on car
safety are less likely to choose carbon neutral trips, whereas
pro-walkers are more inclined to select shared mobility
services that involve walking to PUDO points. The results are
then used to compute willingness to pay values for five distinct
user profiles. Our main findings are that (i) Users are generally
willing to pay a higher fare to reduce emissions; (ii)
Personalized sustainability options yield greater participation
and emission reductions than uniform policies; and (iii)
Regular public transport users show higher willingness to walk,
highlighting potential synergies between on-demand mobility
and transit
Music Making and Engagement for Older Adults at Risk for Dementia: Examining Neurobiological Relationships and Co-Designing a Music-Based Trial
No full textOlder adults with mild cognitive impairment (MCI) are at higher risk of developing dementia, making them a key group for secondary prevention. Lifestyle activities that are cognitively stimulating, socially engaging, and emotionally fulfilling may help delay or mitigate decline. Music-making has been proposed as one such activity, with evidence suggesting benefits for cognition and well-being in older adults at risk for dementia. While music-making is thought to promote neuroplasticity, the brain’s ability to reorganise neural pathways, the extent of this effect in MCI remains unclear. Despite recommendations to encourage such activities, robust evidence for their efficacy in this population is lacking.
This thesis aimed to examine the effect of music-making on neuroplasticity, cognition, and psychosocial outcomes in older adults at risk for dementia. Across four studies, it revealed that: (a) evidence linking music-making interventions to neuroplasticity is inconclusive, with no studies specifically in MCI; (b) currently playing an instrument is associated with greater grey matter density in the temporal lobe, insula, and cerebellum; (c) the type of music-making has distinct effects on neuropsychological and psychosocial outcomes; and (d) baseline characteristics of participants in the NeuroMusic trial reflect the intended at-risk profile, underscoring the trial’s significance.
These findings add to the literature on music-making and brain health in ageing. Music-making shows potential to enhance neuroplasticity, delay cognitive decline, and improve psychosocial outcomes, positioning it as a promising early intervention for older adults at risk for dementia. Understanding its effects on brain structure, cognition, and well-being provides critical insights for developing targeted, evidence-based prevention strategies in MCI
Collection of Head Images during RadiotheraPy (CHIRP)
No full textThis dataset supports the research output, Deep learning-based real-time detection of head and neck tumors during radiation therapy, https://doi.org/10.1088/1361-6560/adf40e. The Collection of Head Images during RadiotheraPy (CHIRP) involves collecting conventional images for VMAT treatments for head and neck radiation therapy treatments. Data was collected for 30 patients that were treated at Blacktown Hospital between 2022-2023. This data was collected for the Remove the Mask project (https://image-x.sydney.edu.au/home/remove-the-mask/) to study patient motion between and during treatment sessions and was used to generate the results featured in the paper "Deep learning-based real-time detection of head and neck tumors during radiation therapy" (10.1088/1361-6560/ADF40E
Careers in Criminology and Criminal Justice in Australia
No full textThis book provides an overview of the many potential roles open to a criminology graduate. Working through different agencies and roles shows just some of the career opportunities awaiting criminology graduates. This illuminates the types of skills and knowledge required for diverse roles, which will help criminology students (and those in related disciplines) to make the most of their time at university and to prepare for future work
Promoting Physical Activity for Low Back Pain: Evidence-Based Strategies for Healthcare Delivery and Policy Implementation
No full textLow back pain (LBP) is the leading cause of disability worldwide and a major contributor to disability-adjusted life years (DALYs). Although physical activity is strongly recommended as a first-line management strategy, its implementation in routine practice remains limited. This thesis explored the evidence, developed and evaluated strategies to strengthen the promotion, delivery, and uptake of physical activity for individuals with LBP, with a focus on digital health, health communication, and system-level integration. A mixed-methods program of research was conducted, including systematic reviews, synthesis of guideline evidence, development and evaluation of educational interventions, and qualitative studies within community settings. Innovative tools such as a storytelling-based video and a mobile application (app) were piloted to support patient education, encourage physical activity, and test the role of social media in disseminating health information. Feasibility and acceptability were assessed with particular attention to user experience, health literacy, and content quality. Findings demonstrated that while digital strategies can broaden access and enhance self-management, barriers remain, including fragmented systems, limited digital literacy, and partial engagement with online materials. Social media engagement metrics and qualitative insights highlighted the importance of culturally relevant, credible, and user-friendly resources. This thesis contributes to implementation research by offering practical frameworks and tools for translating evidence into action
From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Deep Learning Enhancement
No full textMagnetic Resonance Imaging (MRI) is a non-invasive imaging modality widely used for highresolution
anatomical and physiological imaging. Diffusion MRI (dMRI) leverages water molecule
diffusion to probe tissue microstructure, offering insights into white matter architecture. However,
dMRI data often suffer from low angular/spatial resolution and acquisition artifacts, limiting accurate
Fiber Orientation Distribution (FOD) estimation and downstream analyses.
Deep learning methods have emerged to mitigate these limitations, but enhancing angular resolution
within clinical scanner constraints remains challenging. While approaches like FOD-Net improve
angular resolution from single-shell acquisitions, low spatial resolution still introduces blurring,
degrading FOD quality. Most existing methods have been validated mainly on healthy subjects, with
limited clinical evaluation, and no unified framework addresses both angular and spatial limitations
together.
This thesis advances deep learning-based dMRI enhancement toward clinical translation by
addressing computational inefficiency, limited clinical validation, acquisition protocol dependency,
explainability gaps, and fragmented artifact handling. Chapter 2 presents FOD-Net 2.0 for angular
resolution enhancement and acquisition protocol optimization. Chapter 3 develops FastFOD-Net, an
efficient framework with the most comprehensive clinical evaluation to date, showing improved
disease differentiation, connectome interpretability, and reduced sample size needs. Chapter 4
explores uncertainty estimation to assess enhancement reliability. Chapter 5 introduces UFREE, a
unified framework tackling angular and spatial resolution jointly via a two-stage learning strategy.
Overall, this work delivers robust, scalable, clinically validated solutions and a comprehensive
evaluation platform, bridging the gap between deep learning promise and real-world dMRI
application
The drivers of influenza vaccination in adults: Insights from a national Australian survey
No full textStudy: Influenza vaccination coverage is suboptimal in the Australian adult population. The National Vaccination Insights Project [1] was established in 2024 to annually measure the behavioural and social drivers of vaccination in the Australian population. Prior to this, while coverage data were used to monitor uptake, there was no systematic data collection to understand the reasons for coverage gaps. The inaugural survey of Australian adults used a globally standardized survey tool [2] adapted for the Australian context to measure constructs related to influenza vaccination within four domains (i) vaccination-related thoughts and feelings, (ii) social processes, (iii) motivation, and (iv) practical issues. This study provides a foundation for ongoing national monitoring of the drivers of influenza vaccination and will help tailor timely strategies to population needs.
Dataset: A de-identified dataset from an online survey conducted in March 2024 with a nationally representative sample of 2,055 Australian adults recruited through an online panel
[1] https://ncirs.org.au/our-work/national-vaccination-insights-project
[2] Behavioural and social drivers of influenza vaccination: tools and practical guidance for achieving high uptake. Geneva: World Health Organization; 2025. Licence: CC BY-NC-SA 3.0 IGO
Mixing Kinematics in Granular Materials using X-ray Rheography
No full textThis dissertation introduces a novel method for understanding granular mixing kinematics through high-speed X-ray observations. It contains three interlinked main chapters, each addressing key challenges and building upon the previous ones to enhance our understanding of mixing kinematics in cylindrical mixers.
The first chapter uses the X-ray rheography image analysis method to determine internal velocity fields in antisymmetrical granular flows, such as those in a two-bladed impeller mixer. This chapter highlights the significant role of X-ray sources and detector alignment in capturing antisymmetric flow characteristics, setting a methodological basis for subsequent chapters.
The second chapter is focused on creating an experimental setup and framework for examining granular mixing dynamics via X-ray rheography. A granular mixer was designed for X-ray imaging from three orthogonal views. The experimental results obtained using the rheography method were compared with those from the discrete element method (DEM) simulations, which replicated the experimental mixing scenarios studied to evaluate the effectiveness of X-ray rheography in the analysis of mixing kinematics. This chapter establishes the foundation for an extensive investigation of mixing kinematics for different particle shapes in the third chapter.
The third chapter examines the influence of the particle shape and impeller blade angle on mixing kinematics. We examine 3D velocity fields and fabric orientation by mixing spherical glass beads, slightly elongated barley, and long jasmine rice. Our findings indicate that elongated particles align along their main principal axis, which impacts their mixing kinematics, while spherical particles, being axisymmetric, exhibit no notable alignment. DEM simulations confirm these findings, emphasising the effectiveness of the X-ray method in studying the internal mixing kinematics of diverse particle shapes
Advancing understanding of prognosis in sarcoma through quantitative proteomic analysis
No full textSarcoma is a rare and heterogeneous group of malignancies, often associated with poor prognosis.
Advances in quantitative mass spectrometry-based technologies now enable cancer proteomes to be
studied on an unprecedented scale, offering opportunities to address unmet clinical needs in
sarcoma and uncover novel biological insights.
Aiming to identify patients at greatest risk of relapse, this thesis describes the development of a
bioinformatic method to define prognostic proteomic signatures. A method was developed using two
cohorts of localised prostate cancer tissue samples and identified a five-protein signature prognostic
for clinical relapse. This signature was independently prognostic and enhanced the predictive
performance of an established clinical risk classifier. The method was then refined and applied to
three cohorts of leiomyosarcoma samples. A four-protein signature prognostic for metastatic relapse in localised leiomyosarcoma was identified in one cohort and evaluated in two independent cohorts.
The signature complemented established prognostic clinicopathological features and provided robust
discrimination of metastatic relapse in composite models.
To determine whether proteomic analysis could uncover biological pathways associated with
prognosis that transcend histological subtype, 500 bone and soft tissue sarcoma samples across
three cohorts were analysed. Consensus clustering in each cohort identified a good prognostic
group, and pathway analyses revealed congruent biology across cohorts. These findings were
independent of histological subtype, underscoring their subtype-agnostic nature. The study suggests
that differences in the proteome may reflect the biological foundations underlying variation in disease
behaviour among and within sarcoma subtypes.
Overall, this thesis demonstrates how prognosis can be determined through examination of the
proteome and highlights the capacity of quantitative proteomics to offer novel insights into sarcoma
Unrecognised and Untreated: Improving the Identification of Mental Health Problems in Preschool-age Children through Examining Screening Measures, Multi-informant Reports and Symptom Stability
No full textDespite evidence for the high prevalence of mental health (MH) problems in children aged 3–5 years, parents of preschool-age children are less likely to recognise and access support for MH problems compared to parents of older children. This thesis evaluates screening measures for young children, which could help parents and educators identify children at risk of MH problems. Chapter 2 presents a systematic review of existing multi-informant MH measures for young children and examines their predictive and incremental validity, effectiveness and acceptability. Chapters 3–4 present a series of studies evaluating the reliability, validity and acceptability of two versions of the Pediatric Symptom Checklist (PSC), the Preschool PSC and PSC-17, reported by parents and educators, in a national, cross-sectional study. Chapters
4–5 present a longitudinal study in which a paired, community sample of parents and educators were tested at three time points to examine incremental and diagnostic validity, and symptom stability.
The systematic review concluded that several existing measures had acceptable predictive validity, but there were many shortcomings concerning incremental validity (Chapter 2). Subsequent studies reported strong psychometric properties for both PSC measures, including internal consistency, concurrent validity, and high acceptability (Chapters 3–4). Parent reports improved the prediction of functioning scores over and above educator reports supporting incremental validity. PSC measures had moderate–strong associations with diagnostic severity and functioning scores (Chapter 5). Total scores for both PSC measures, PSC-17 externalising and attention subscales had significant measurement stability across time. Together these studies demonstrate the validity, acceptability and clinical utility of the PSC measures, and contribute new evidence supporting measures that can be used for multi-informant, universal screening of MH problems in preschool children