Planning for Scale-Up and Sustainability: A Multi-site Process Evaluation Protocol for a Novel Intervention for Survivors of Childhood Brain Cancer

Complex interventions often fail to sustain widespread reach at a population level, despite demonstrating clinical effectiveness during piloting and trial evaluation. ‘Engage’ is a multi-disciplinary and risk-stratified intervention that is delivered remotely to childhood cancer survivors to promote equitable and improved access to survivorship care. Engage is not a standalone intervention in that it requires careful consideration of how it will be integrated into existing survivorship care pathways. Our study aims to conduct a process evaluation of the Engage intervention as applied to brain cancer survivors (‘Engage Brain’) to further contextualise trial outcomes, and understand what factors contribute to a sustainable, scalable, and successfully implementable intervention. A mixed-methods process evaluation will be conducted as part of the Engage Brain type-1 effectiveness-implementation trial. Data collection will occur across four domains of research: (1) planning, (2) implementation, (3) practice setting, and (4) ecological setting. Data sources will include semi-structured clinical stakeholder interviews, primary care practitioner interviews, transcribed implementation meetings and project log, transcribed nurse consultations, study materials, and administrative/process data. Qualitative data will be analysed using both deductive and inductive thematic analysis, guided by implementation science frameworks such as the updated Consolidated Framework for Implementation Research, which encompasses the Theoretical Domains Framework and implementation outcomes. Quantitative data will be analysed and presented using descriptive statistics where appropriate. Conducting a process evaluation underpinned by implementation science and behaviour change theories will enable the development of a national scale-up framework and improved delivery of sustainable models of care for childhood cancer survivors. Trial Registration: The Australian and New Zealand Clinical Trials Registry (ANZCTR), https://www.anzctr.org.au, ACTRN12621000590864

Optimization for Electric Vehicle Infrastructure and Route Planning in Special Services Systems

Advanced logistics systems represent a key sector in modern societies and worldwide economies. The electrification of logistics systems through the adoption of electric logistics vehicles and the corresponding charging infrastructure is a promising contributor to transportation decarbonisation, but also presents complex challenges, particularly in optimizing charging infrastructure and vehicle routing. This research addresses these challenges by developing integrated models that account for the uncertainties in charging demand and the intricacies of traffic flow prediction within logistics networks. A stochastic model is first introduced to capture the variability in ELV charging demand, utilizing the Wasserstein distance metric to effectively represent the inherent uncertainties. This is complemented by a deep learning model that combines Graph Convolutional Neural Networks (GCNN) and Long Short-Term Memory (LSTM) networks, designed to predict multi-step traffic flows essential for efficient route planning. A joint optimization framework is established to integrate ELV routing with the planning of electric vehicle charging stations (EVCS). In the context of delivery services, this framework addresses the complex interactions between delivery schedules and charging infrastructure. To solve these challenges, the framework employs a Genetic Algorithm with Best-First Search (GABFS), effectively optimizing cost efficiency and service reliability. In a separate application, the framework is adapted to optimize waste e-collection systems, where a Simulated Annealing with Greedy Idea (SAGI) solver is utilized to address the specific arc routing and infrastructure needs of waste collection logistics. A two-stage optimization process is developed to further integrate the planning of ELVs and EVCS with power distribution networks. This process considers the interdependencies between logistics operations and the power grid, ensuring that both are managed cohesively. Advanced heuristic algorithms, including the Decomposition-based Genetic Algorithm Optimizer (DGAO) and the Greedy Genetic Algorithm Optimizer (GGAO), are employed to solve the complex optimization challenges posed by this integrated system. In conclusion, this thesis provides a novel approach to the integrated planning of ELVs and EVCS within logistics systems, emphasizing the importance of coordinated optimization for cost reduction and operational efficiency enhancement. The findings contribute to the advancement of sustainable logistics planning and operations, offering integrated strategies for facilitating the transition to green logistics networks

Identity and Belonging: Intersectional experiences of Australian Muslim women with deafness in Sydney

This thesis explores the identity and belonging of deaf and hard of hearing Muslim women in Sydney. It also explores belonging to various communities, including Australian, Muslim, Deaf and Deaf Muslim communities. As an Australian Muslim woman with deafness, I used auto-ethnography and a deaf scholar approach to amplify the experiences of women from a similar demographic. Accessible and inclusive methods included interviews with 12 Muslim women with deafness, 8 other participants, and 5 participant observations. Intersectional lens was used to examine the experiences of the women, from the personal level to their community and social contexts. The thesis addresses gaps, including in deaf related research which lacks a religious focus. The findings showed the women had varying communication and access needs (including sign and cultural languages) and limited awareness of Deaf communities. Their Muslim and deafness identity manifested in certain situations, while other parts of their identity were diverse and dynamic. Context including time, place and space influenced intersectional experiences. The women expressed multiple ways of belonging. Some of the women were motivated to belong but faced many barriers, along with navigating affordances such as access negotiations, many responsibilities and COVID-19. The findings also showed access arrangements in various contexts and by stakeholders like organisers, interpreters and religious scholars. This thesis concludes Muslim women with deafness in Sydney are engaging in everyday battles to overcome challenges and asserting their agency. Implications include the responsibility of communities, organisations and governments to acknowledge these insights and remove barriers, so the women have opportunities to enhance their sense of belonging and community empowerment. The findings demonstrate benefits of adopting an intersectional lens and accessible approaches as useful strategies when working with this demographic. Various communication, language and access needs were accommodated from the inception of the thesis to dissemination. Inclusive practices such as Auslan, live captioning, online platforms and social media were used in fieldwork, recruitment, interviews and observations. These approaches highlight the value of inclusive data collection and reporting to reveal intersectional experiences. They add a deaf and hard of hearing Muslim woman’s perspective to the many related areas of research and social policy

Monstrans de droit, petition of right, and liability for Crown debt

This chapter in memory of Stephen Smith investigates the history of common law remedies for the assertion of debt obligations, notably against the Crown as a subjective person as well as before the Crown as an objective adjudicator and enforcer

Enhancing Efficiency and Precision of Static Taint Analysis via Value-Flow Refinement

Static taint analysis is a foundational technique in program analysis, widely used to detect data leaks and vulnerabilities by tracking the flow of sensitive information within programs. Its applications span critical domains, including identifying privacy leaks, preventing SQL injection attacks, and addressing memory safety issues. While vital for safeguarding data confidentiality and integrity, achieving high precision often comes at the expense of efficiency, as finer-grained analyses demand substantial computational and memory resources. Balancing precision and scalability is crucial for effectively analysing large-scale, real-world applications. This thesis aims to enhance the efficiency and precision of IFDS-based taint analysis, a high-precision static analysis technique, through the refinement of value flows. Key challenges addressed include redundant computations, spurious value flows, and over-tainting, which hinder the scalability and precision of the traditional approach. By refining value flows, the proposed techniques optimise the IFDS-based framework, enabling efficient handling of large-scale applications without compromising precision. Two innovative techniques are presented in this thesis. The first, MergeDroid, introduces a merge-and-replay strategy to consolidate equivalent value flows and prune spurious ones, significantly reducing computational overhead and enhancing precision. By leveraging context-sensitive insights derived from activation statements, it excludes infeasible value flows, ensuring precise and efficient taint propagation. The second, April, mitigates over-tainting by employing CFL-based field-sensitive pre-analysis to establish precise field relations. These relations are encoded into field-sensitivity-aware DFAs to filter spurious access paths during analysis. By exploiting field sensitivity, April achieves notable improvements in scalability and precision, enabling effective analysis of large-scale programs. Together, these value-flow refinement techniques demonstrate the potential to push the boundaries of IFDS-based taint analysis, addressing its limitations and advancing the state of the art in static analysis frameworks. Extensive evaluations on real-world applications validate the effectiveness of MergeDroid and April, highlighting their contributions to more efficient and precise program analysis

Hairpin-locker mediated CRISPR/Cas tandem system for ultrasensitive detection of DNA without pre-amplification

Achieving ultra-sensitive detection of DNA is of paramount importance in the field of molecular analytics. Conventional amplification technologies such as polymerase chain reaction (PCR) currently play a leading role in ultrasensitive DNA detection. However, amplicon contamination common in these techniques may lead to false positives. To date, CRISPR-associated nucleases (type V & VI) with their programmable cleavage have been utilised for sensitive detection of unamplified nucleic acids in complex real samples. Nevertheless, without additional amplification strategies, the pM range sensitivity of such CRISPR/Cas sensors is not sufficient for clinical applications. Here, we established a hairpin-locker (H-locker) mediated Cas12-Cas13 tandem biosensing system (Cas12-13 tandem-sensor) for ultrasensitive detection of DNA targets. Without the need for any additional amplification reaction or device, this system is capable of detecting DNA at a notable 1 aM level (<1 copy/μL) sensitivity. In addition, the system was able to distinguish cancer mutations in colorectal cancer (CRC) mice. This is a significant advance for CRISPR/Cas biosensing technology offering simple, highly sensitive, and user-friendly diagnostics for next-generation nucleic acid detection

Listening to Materials: Dialogue Between Digital and Analogue Practices in Designing Social Robots

This chapter explores the methodology of ‘listening to materials’, derived from artistic practices, as a cross-disciplinary approach that can be applied to the development of social robot morphologies. Analysis of contemporary art case studies reveals how artists have led this practice of listening, noticing, learning from and responding to various mediums to inform and shape their work. This chapter examines the application of the method in two scenarios. The first involves the design of a social robot morphology in practice-based research and the second focuses on an educational setting, where the development of both morphology and affective movement for social robots is considered. It is proposed that ‘listening to materials’ in a deliberate and purposeful manner, traversing the realms of the analogue and digital, provides a range of dynamic and generative design possibilities, resulting in distinctive morphologies. Additionally, this process offers a means of circumventing the conventional forms and movements often predetermined by design software and electronic hardware, thereby reclaiming the mark of the maker, the expression of affect, and the role of intention in the development of this social technology

Domestic violence among adult male victims in non-intimate relationships: a text mining study using NSW police narratives

Background setting: Domestic violence (DV) is a major public health problem and a violation of human rights. To date, research on DV has predominantly focused on women as victims and men as perpetrators. Male DV victims particularly in non-intimate relationships have received little attention in the literature. This study represents the first attempt to report on DV among male victims in non-intimate relationships using population-level data. Methods: This is a population-level retrospective observational study using data extracted from a large sample of police-attended narratives in New South Wales (NSW) from 2005 to 2016 using rule-based text mining. Results: From 18,611 DV events involving non-intimate relationships, most of the Persons of Interest (POIs)—individuals suspected or charged with a DV offence—were male (78%) and members of the victims’ family (26.8%, cousins, uncles and aunts). A total of 42 different types of abuse were identified in 74.3% (n = 13,832) events, the most prevalent being physical abuse with assault (unspecified) accounting for half of the cases (53.9%, n = 7462) and punching for more than one third of cases (35.4%). Almost half of DV events (46.3%, n = 8616) recorded injury type to the victim, the most common being cut(s) (43.6%, n = 3754), followed by swelling (19.9%, n = 1716), and bruising (19.5%, n = 1679). A total of 2,903 (15.6%) events had a mental illness mentions for the POIs and 857 (4.6%) for the victims, with 23 different mental illnesses recorded. Schizophrenia and dementia were the most common mental illnesses among POIs (13.6%) and victims (13.0%), respectively. Conclusions: This study provides new insights and empirical evidence on abuse types, perpetrator-victim relationships, victim injuries and mental illness on DV events involving adult male victims in non-intimate relationships. The findings form an important evidence base to trigger further research in the future

Optimized Floating-Point Arithmetic for Efficient Operations in DNN Accelerators

This thesis focuses on enhancing resource efficiency in deep neural network (DNN) training accelerators through novel approaches to optimising floating-point (FP) arithmetic. While previous work has largely addressed the efficiency of inference, this thesis targets the underexplored domain of training, aiming to reduce computational costs and energy consumption for sustainable AI development. The first contribution of this work is ApproxTrain, an open-source framework that enables efficient evaluation of DNN training and inference using simulated approximate multipliers. ApproxTrain integrates AMSim, a GPU-accelerated, LUT-based approximate FP multiplier simulator, into TensorFlow via CUDA, overcoming the lack of native hardware approximate multipliers in commercial GPUs. Experiments across image classification, object detection, and neural machine translation demonstrate that training with approximate multipliers achieves comparable convergence behaviour and negligible accuracy loss relative to FP32 and Bfloat16 (BF16) multipliers. ApproxTrain delivers over 2500× speedup compared to CPU-based simulations and achieves performance within 8× of highly optimised closed-source TensorFlow libraries. The second contribution is the Sign-Separated Accumulation (SEA) scheme, which optimises FP addition in DNN accelerators. SEA separates positive and negative terms into independent sub-accumulations, processed by resource-efficient same-signed FP adders (SS-adders). Without introducing approximations, SEA significantly improves resource efficiency, achieving up to 19.8% delay reduction, 29.1% improvement in area-delay product (ADP), and 30.7% energy savings. This scheme is implemented in weight-stationary (WS) systolic arrays (SAs) and extended to output-stationary (OS) SAs for full coverage of SA-type DNN accelerators. The third contribution introduces SEAR and SEARX, enhancements to SEA-based designs. SEAR simplifies rounding operations and minimises errors via mutual error cancellation, while SEARX combines SEAR with approximate FP multipliers to illustrate their compatibility. These enhancements are validated using an extended ApproxTrain framework, demonstrating negligible accuracy impact during DNN training. Experimental results reveal reductions of up to 42.2%, 60.0%, and 60.7% in delay, ADP, and energy consumption, respectively. All proposed designs, including SEA-based SAs, SEAR, and SEARX, are integrated into the open-source Gemmini ecosystem, ensuring accessibility to the broader research community. This thesis establishes foundational techniques for resource-efficient DNN training, paving the way for sustainable and cost-effective AI advancements

Computational Fluid Dynamics Modelling of the Cerebral Venous System with Application to Multiple Sclerosis

Multiple Sclerosis (MS) is a complex neurodegenerative disease that is influenced by a combination of genetic and environmental factors. Included in these factors is the cerebral venous system, however, the extent to which it influences the pathophysiology of MS has yet to be fully determined. This thesis was performed to characterise and investigate the influence that cerebral venous hemodynamics have on the progression of MS. A systematic review was conducted, which identified a lack of quantified hemodynamic parameters of the intracranial veins in MS. Subsequently, 40 MS participants and 20 controls were recruited for a cross-sectional study. Magnetic resonance imaging (MRI) was performed to enable 3D geometries of the anatomy and the blood flow rates at the boundaries to be computed. Computational fluid dynamics (CFD) models were created for each participant and simulated using patient-specific boundary conditions. Thirteen of the MS participants consented to having a follow-up MRI, enabling a longitudinal study to be performed. A 3D printed in vitro model was created and used in conjunction with 4D Flow MRI and pressure catheter manometry. This was compared to the CFD results to assess the accuracy of the methodologies and highlight any discrepancies between techniques. The pressure drop and vascular resistance did not significantly differ between the groups. The internal jugular vein (IJV) cross-sectional area was larger in the MS group (Right IJV: p=0.04, Left IJV: p=0.02) and the straight sinus (ST) flow rate was higher in MS across all ages (p=0.005) compared to controls in the cross-sectional study. The vascular resistance was shown to indicate regions in the cerebral veins which could correspond to increased venous pressure. The straight sinus blood flow in the initial MS group of the longitudinal study was increased compared to controls and showed a trend of decreasing at follow-up. The distal sinuses were observed to increase in size over time despite the pressure drop within them decreasing during the same period. This thesis shows that the pressure and vascular resistance of the cerebral veins are unlikely to be directly related to the pathophysiology of MS. The finding of higher ST flow could correspond to increased inflammation in the deep venous system. Resistance as a measure of vascular pathology shows promise and could be useful to holistically investigate blood flow hemodynamics in a variety of diseases of the circulatory system