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Future challenges and pathways for open space in Australian suburbs
No full textAustralian cities are low density, and consolidation policies have promoted suburban densification which reduces open space. Open space provides ecosystem and health benefits. This thesis defines and assesses open space change in Melbourne, Australia. Government planners and independent building designers significantly influence open space outcomes. Private open space loss is significant but highly variable due to differences in suburban form, planning codes, and their application. Public open space policies value open spaces for diverse benefits, however unaligned planning and management can stymie functional open space at a wider systems level. Findings have practical application to urban and open space planning.</p
Sustainability Reporting in the Banking Sector - Evidence from the socialist orientated market of Vietnam
No full textDuring the past 30 years, the Vietnamese economy has been transitioning from a centrally planned system to a socialist-oriented market. As the current challenge is to maintain strong economic growth whilst minimising social and environmental impacts, the government legislated a Green Bank development scheme to help improve banks' sustainability practises and reporting to confirm their contribution to the United Nation's Sustainable Development Goals. In 2019, the government also relaunched the Corporate Sustainability Index national guidelines for measuring the social and environmental sustainability of Vietnamese corporations. This research investigates the reporting practices of two Vietnamese state-owned banks and two privately owned banks to determine how they contribute to achievement of the sustainable development goals.</p
Inertia Emulation Control of VSC-HVDC Systems and Integrated Offshore Wind Farms
No full textIn recent years, growing concerns over climate change have driven a shift toward renewable energy, prompting significant changes in global energy systems aimed at achieving carbon neutrality. Consequently, conventional power generation systems using rotating electrical machines are being replaced by inverter-based resources. While these converters are designed to maximize energy extraction from renewables, they lack the inertia needed to support grid frequency, which may compromise system stability. Therefore, developing inertia emulation and fast-frequency response techniques is essential to ensure the stability of converter-driven power systems. This thesis provides a pathway to address such inertia issues in future power systems.</p
Unveiling the Link Between E-Participation and Corruption Control: Insights From a Co-Creation Framework
No full textControlling corruption necessitates collaboration between service providers and recipients, as emphasized by the co-creation theory. In this context, e-participation has emerged as a promising avenue for fostering collaboration through online platforms in the ongoing battle against corruption. This research relies on national-level data obtained from the World Bank, Transparency International, and the United Nations to investigate the relationship between e-participation and the control of corruption. Through a comprehensive data analysis involving 136 countries over a five biennial period from 2012 to 2020, the results reveal no statistically significant correlation between e-participation and corruption control. This finding raises pertinent questions about the effectiveness of current e-participation approaches, or the measurements employed to evaluate their impact on combating corruption. The implications of this finding are significant for policymakers and governance monitoring bodies, underscoring the need for a re-evaluation and enhancement of existing e-participation mechanisms and anti-corruption strategies within the co-creation framework.</p
Exploring the interrelationships between urban form and air quality
No full textThis thesis examines how urban form—specifically factors like the built environment, green spaces, and economic activity from the perspective of compactness—affects local air quality, focusing on particulate matter concentrations in Melbourne. Through various analytical methods, it demonstrates that pollution sources and green infrastructure exert a more significant influence on air quality than urban density or compactness alone. The findings encourage policymakers to address pollution sources comprehensively, considering their generation, dispersion, and exposure impacts, while prioritising green space enhancement. This approach supports sustainable urban development that safeguards public health and fosters environmental resilience amid rapid urbanisation and population growth.</p
Multi-Modal Characterisation of Deep Brain Stimulation in Obsessive-Compulsive Disorder: Clinical, Phenomenological and Advocacy Updates
No full textObsessive-compulsive disorder (OCD) is a complex and debilitating condition. This thesis explores deep brain stimulation (DBS), a neurostimulation therapy for OCD that targets pathological brain circuits, offering hope for people with treatment refractory OCD. Through eight publications, the thesis presents clinical, neuroimaging, psychosocial, self-report, and phenomenological outcomes, updates on the level of scientific evidence and clinical efficacy, and rationale for greater access to care. Also, a clinical guideline for managing patients and a cognitive model of recovery are proposed. The thesis represents a comprehensive multi-modal examination of DBS efficacy and mechanisms, and highlights the need for personalized and standardized care.</p
Magnetic Resonance Spectroscopy: Investigating Glutathione with Nutraceuticals to Reduce Oxidative Stress in the brain, and Improve Cognition for Healthy Ageing
No full textAgeing is linked to a progressive loss of memory, cognitive decline and increased oxidative stress. Finding effective interventions to support brain health is therefore crucial. The neuroimaging technique magnetic resonance spectroscopy was used to examine whether the nutraceuticals Bacopa-monnieri and Pycnogenol could reduce oxidative stress and improve cognitive function in older adults by increasing brain levels of glutathione, an important antioxidant. Cognitive improvement was not observed in response to intervention. Glutathione significantly increased only after Pycnogenol supplementation. It is hoped that this research contributes to establishing effective interventions facilitating improved health outcomes and cognitive support for healthy ageing.</p
Study and Characterisation of Edge Preparation & Surface Finish on Modern Precision Cutting Tools
No full textCutting tools play an irreplaceable role in today's high-tech revolution. Prolonging tool life and enhancing tool performance are critically important for the tool industry to ensure the highest quality of their products through tailored surface finishing and edge preparation. Herein, both mechanical and chemical principles are explored, accompanied by measurement data for both raw and polished tool samples. An electropolishing apparatus was designed and optimised to achieve a mirror-like surface with an average roughness below 100 nm, and a honed edge with micron-level precision of controllable rounding effects on tungsten carbides, which have limited options for surface preparation due to their dielectric nature. Other polishing methodologies have also been investigated, with testing results presented.</p
Signal Processing-Driven Graph Learning: Some Studies on Coherence, Structure, and Unrolling
No full textIn this thesis, we focus on this signal processing perspective of graph learning: we try to reconstruct the underlying graph with some desirable structure and rate of change or frequency properties. We study three distinct problems arising in Graph learning, motivated towards target applications. In the first study, we explore the importance of robustness and coherence in graph-learning algorithms. Our algorithms are similar in spirit to robust versions of well-known algorithms like PCA; however, due to the specific framework we operate in, we obtain different and much simpler solutions. We apply our robust graph learning techniques for the classification of neuroimaging data and show better performance than existing graph learning techniques.In the second study, we investigate the possibility of learning graphs with a priori known structure. Specifically, we design a module that can be incorporated into existing structure-unaware algorithms, enabling them to learn bipartite and regular graphs. Most graph learning algorithms are evaluated by employing the learned graph to perform certain signal-processing tasks. In our final study, we delve into the role of feedback from these tasks in fine-tuning the graph learning algorithm. We introduce a parameterized graph learning model, with parameters adjusted based on feedback from the signal processing task. This adjustment is achieved by interpreting the resulting computational diagram as a neural network, a concept akin to the tech-niques used in. Our findings indicate that incorporating feedback into graph learning in this manner enhances performance in tasks such as data inpainting.</p
Artificial Intelligence Based Framework for Industrial Asset Management Using Real, Time Series Data
No full textThis Research delivers a Self-Exploratory Deep Learning Hybrid Framework that uses real-time data generated by industrial sensors attached to an Industrial Asset to perform real-time predictions related to asset health. The Self-Exploratory Framework is designed to discover and explain hidden relationships between a predicted variable and its co-variates and can handle both univariate and multi-co-variate sensor data for future value prediction. The Framework is scalable and incorporates multiple models to derive the best prediction strategy. Its self-exploration ability continually monitors and adapts to changing machine behaviour, reduces manual intervention, provides deeper insights into asset health and optimises asset utilisation.</p