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Extraction, Phytochemical profile, and neuroprotective activity of Phyllanthus emblica fruit extract against sodium valproate-induced postnatal autism in BALB/c mice
No full textThe aim of the present study was to evaluate the effect of the ethyl acetate fraction of amla (EAFA) extract on valproic acid (VPA)-induced postnatal autism in BALB/c mice. Our study revealed that mice treated with VPA on postnatal day 14 (PND14) showed significant abnormal behaviours such as social interaction, social affiliation, anxiety, and motor coordination compared to the control group, while EAFA extract treatment (100 mg/kg) ameliorated these symptoms. Our study highlights the protective effect of EAFA extract on improving behavioural alterations, significantly restoring anti-oxidative enzymes such as GST and GR, and reducing MDA and NO levels. Furthermore, the EAFA-treated group significantly lowered the proinflammatory markers (IL-1β and TNF-α) and the expression of up-regulated 5-HT1D, 5-HT2A, and D2 receptor proteins. Based on histopathological studies, the percentage of neuronal injury in the EAFA-treated group as well as cellular structural changes were reduced using SEM analysis. In conclusion, the present study suggests that treatment with EAFA extract ameliorates VPA-induced autism due to its anti-oxidant and neuroprotective activity.</p
Development of Stainless Steel-Aluminium Bimetallic Transitionally Graded Structures using Directed Energy Deposition (DED) Processes
No full textCreating a high-quality stainless steel-aluminium bimetallic transitionally graded structure
through metal additive manufacturing is complex due to brittle Iron-Aluminium
intermetallic compounds at the interface. This research aimed to construct SS316-Al4043
bimetallic structures using Directed Energy Deposition (DED) techniques, focusing on Wire
Arc-based-DED (WA-DED) and Laser-powder-based-DED (DED-LB/M). Two compatible
interlayer materials, Ni and Cu, were chosen for the purpose. The Ni interlayer resulted in a
robust, thin SS-Ni interface, but the Al-Ni interface exhibited a thick layer of Al-Ni IMCs. The
Ni interlayer was suitable for smaller structures up to 30 mm in height; after that, the
interface got cracked. However, the Cu interlayer could deposit a structure of a height of 60
mm and produce better mechanical strength in comparison to the Ni interlayer. On the other
hand, the interfacial doping method using Al2O3 powders at the SS-Al interface showed
improved interfacial strength to ~24% higher than the Cu interlayer. The DED-LB/M process
was also explored with and without dopant inclusion. However, the laser-based processes
led to significant residual stresses at the interface, resulting in multiple cracks in both cases.
The research concluded that Al2O3 is the most effective dopant for enhancing structural
integrity between SS and Al in the WA-DED process.</p
Spectroscopic Characterization of the Biochemical Transformations in Cheese
No full textIn this thesis, several varieties of cheese were analysed by two complementary vibrational spectroscopic techniques, ATR-FTIR and Raman. I investigated the effects of ageing, manufacturer, and manufacturing conditions (that leads to each variety). With aid of a chemometric tool namely PCA, spectral features responsible for differentiating each sample were highlighted and discussed. Amide bands were the most significant source of variation when comparing ATR-FTIR spectra of the cheese samples with different maturity. Raman and PCA identified that the fat crystal β’-polymorph is more intense in older cheeses. Several bands sorted out the cheese samples according to manufacturer and variety, mainly due to differences in the composition of the major components (fat, protein, and moisture), but also due to other features like food additive, metabolites of secondary microflora and molecular arrangement of the secondary structure of proteins. The relevant aspects to the formation of fat crystals and polymorphic behaviour in cheese were further investigated with aid of XRD in the last chapter.</p
Perspectives on Mandatory XBRL-Based Reporting in Saudi Listed Companies: Factors Influencing Compliance Quality
No full textExtensible Business Reporting Language (XBRL) is a type of electronic financial statement based on Extensible Mark-up Language (XML). It offers several benefits for businesses, such as cost reduction, improved control, time savings, accuracy, and enables the exchange of financial and economic information internationally. Several governments worldwide have used the XBRL system, either voluntarily or mandatorily. Saudi Arabia is one of the countries that has mandated the use of XBRL. Despite the benefits of XBRL use and the Saudi government’s efforts to implement XBRL, the lack of studies on the quality of XBRL financial statements and its application in Saudi companies, and the lack of research related to the factors that impact compliance and non-compliance with XBRL implementation requirements in the organisation are evident. Consequently, this thesis aims to investigate compliance and non-compliance with XBRL implementation by Saudi-listed companies from the perspective of financial statement preparers, addressing a gap in organisational-level studies on this topic.
By utilising a sequenced mixed methods, quantitive (secondary data) and qualitative (primary data) research strategy, the major outcome of this thesis was that Saudi-listed companies demonstrate their commitment to the government-imposed deadline of disclosing financial statements. It also illustrated the three major factors that impact negatively on timeliness, namelythe procedure of preparing financial statements using XBRL; having a new investment plan; and the COVID-19 pandemic. Furthermore, this thesis observed that the age and size of the company exert a significant positive impact on the timeliness of such financial reports. Regarding relevance and completeness, this study found that the COVID-19 pandemic and the lack of Islamic elements such as Zakat in the XBRL taxonomy, negatively impact the relevance and completeness of XBRL financial statements. Moreover, this thesis finds that the Saudi banking sector recorded the lowest relevance and completeness score.
This thesis discovered that the size of the company has a significant positive impact on the relevance of financial statements, whereas the age and sector of the economy in which the company operates exert an insignificant positive effect. The age and size of the company have an insignificant negative impact on the completeness of financial statements, whereas the sectors in which companies operate have an insignificant positive impact on the completeness of financial reports. Regarding institutional pressures, the coercive, mimetic pressures played key roles in listed companies compliance with XBRL implementation requirements, while the absence of normative pressure negatively impacted the acceptance of XBRL in Saudi listed companies. This thesis also generates important information for the Saudi Capital Market Authority (CMA) and other relevant government and industry regulators, universities, policymakers, and stakeholders to use, and principally to understand the extrnal pressures and issues related to compliance with XBRL implementation. The findings lead to recommendations on how government bodies and regulators can collaborate to increase XBRL usage, thereby contributing to the objectives and intentions of the Saudi government’s Vision 2030 economic blueprint.</p
Investigating the Social-ecological Sustainability of Coffee
No full textAgricultural production systems are a primary driver of habitat degradation and biodiversity
loss; however, they are our essential food systems and provide livelihoods for millions of
people. Although our food systems are also currently inequitable in many instances, their
transformation represents an opportunity to advance biodiversity conservation and social
justice goals. It is thus critical that we find ways to balance ecological values, production values
and social values in agricultural landscapes. Sustainability considers this the triple-bottom line:
the consideration of economic, social and environmental values, whilst ensuring that we can
meet the needs of current and future generations. However, agricultural systems also face
severe inertia from existing power structures that would maintain the dominance of the
industrial-agricultural paradigm. In opposition to this global hegemony, approaches such as
regenerative agriculture and agroecology promote biodiverse farming landscapes governed
by empowered local actors. In addition to farmers themselves, other stakeholders across
agricultural supply chains are now concerned with sustainability challenges and engaged in a
range of responses. Sustainability initiatives, pursuing balanced ecological, social and
economic outcomes, from local to global scales, driven by different actors at different stages
of the supply chain, have formed the primary subject of research in this thesis. I use coffee as
a case study to explore the levers for transforming an agricultural supply chain to become
more ecologically and socially sustainable.
Starting at the base of the coffee supply chain, in Chapter two I explore how different
ecological, social and economic values can be generated from biodiverse coffee plantations.
Using a systematic review, I examine the literature regarding the coffee bean yield and
biodiversity outcomes in conventional and agroforestry farming systems. The evidence
suggests that agroforestry farming systems do not lead to consistently lower coffee yields. In
addition, agroforestry farming systems support biodiversity at multiple scales, from soil
microbes and invertebrates to different tree, bird and mammal species. Critically, agroforestry
farms also provide diverse co-benefits including product diversification, coffee quality
improvements, carbon sequestration and cultural and relational values for the farmers
managing these landscapes.
Given the increasing evidence of the benefits that diversified farming systems can provide my
research turns to how we can advance these approaches in agricultural systems. In Chapter
three I remain at the farm stage of the supply chain but shift focus to the farmers themselves.
I use a dataset of interviews with coffee farmers in Peru to explore how farmers engage with
different environmental sustainability initiatives, including through government programs, with
the local cooperative and via sustainability certifications. I also investigate the relational values
farmers hold with the landscape and the emotional sentiments they experience in response to
landscape changes. My work in this chapter calls attention to the nuanced and complex
personal connections that farmers have to their land, and how relational values are potentially
overlooked in the design of environmental sustainability initiatives.
Following the journey of coffee along its supply chain, Chapter four focuses on coffee traders,
roasters and retailers. Using semi-structured interviews with 19 coffee retailers in Melbourne
I investigate whether, and why, they engage in sustainability initiatives, the criteria that
influence their coffee purchasing decisions, and if they consider biodiversity. The findings
advance our understanding of relationship-based trading models, in which retailers and midstage
supply chain actors seek long-term, mutually beneficial partnerships with farmers. I also
find that retailers enacting these models have strong intrinsic motivations for implementing
what some described as a ‘purpose-driven business model’. The purpose-driven business
model aims to provide value for all the stakeholders with whom a business interacts, rather leverage point from which to drive transformative change in supply chains and promoting
relationship-based trading models can advance the goals of regenerative supply chains.
In Chapter five, I broaden focus to the full coffee supply chain. Using a systematic review of
the literature I investigate sustainability governance of coffee production and develop a
typology of eleven sustainability initiatives being implemented in the coffee supply chain. My
research highlights that coffee sustainability research has focused primarily on the economic
outcomes of certification schemes. The typology draws attention to novel sustainability
initiatives being led by coffee farmers themselves and provides a useful framework for both
research and practice regarding the sustainability of supply chains.
Through this work I have identified four levers that I consider important for driving
transformative change in supply chains. The levers include promoting inclusivity; addressing
power dynamics; advancing regenerative supply chains; and acknowledging biodiversity
benefits. The first two levers are inter-related: improving inclusivity in sustainability initiatives
is about acknowledging and navigating complex power dynamics that exist at various scales
among different actors in supply chains. My research has highlighted that sustainability
governed by local actors can improve outcomes by empowering local decision makers to apply
their contextual knowledge to environmental challenges. In some instances, sustainability
initiatives do apply co-design and participatory processes, and this should become the norm
for such initiatives. Regenerative supply chains are being developed that actively seek to not
only redistribute value more equitably in supply chains, but also improve the state of the
environment and the livelihoods of farmers. Biodiverse farming landscapes provide diverse
instrumental, intrinsic and relational values for society. However, it is not always clear how
sustainability and biodiversity conservation can be achieved in these landscapes due to the
diverse contexts and drivers that shape agricultural landscapes. By integrating personal
(behavioural psychology) and social structural (multi-level governance and institutions)
research my work highlights where and how best to leverage transformative change in a global
supply chain.</p
Multi-Sensor Monitoring of Directed Energy Deposition for Anomaly Detection and Part Quality Control
No full textAdditive Manufacturing (AM) technologies are currently transitioning from prototyping and research tools towards widespread manufacturing methodologies for high-value industries. Metal-based AM has begun implementation in specialised cases to produce complex, lightweight, and functionalised parts for high-value applications. Currently, the development, production, inspection, and certification of these parts is long and intensive, involving many man-hours of specialist work. This thesis investigates how to optimise this process to lower barriers for further adoption of AM through multi-sensor process monitoring, machine learning and process modelling.
Laser-beam Directed Energy Deposition (DED-LB) is a form of metal AM used to produce free-form components, surface claddings, and facilitate component repair, making it a valuable tool for industry. The layer-wise fabrication, complex thermal, material, and environmental interactions can allow for the formation of process-induced defects, or undesirable geometric features. These anomalies are not easily detected during manufacture and may remain undetectable without specialised inspection of completed components.
Process monitoring has recently been employed to understand the formation of anomalies in AM fabricated components. The current approach is to implement a single sensor or to monitor one aspect of the fabrication, which fails to capture the full range of interactions involved in the creation of complex components. This thesis implements a new multi-axis monitoring system designed to provide continuous and wholistic measurements of melt pool shape, size, and position throughout deposition. To demonstrate this system’s capabilities, intersecting thin-walled Ti-6Al-4V samples are produced at angles of 90⁰, 60⁰, and 30⁰ to mimic complex geometries. A processing algorithm is developed to interpret data from multiple sensors, drawing information regarding various anomalies from the synthesis of results.
This multi-axis monitoring allows sample height distortion to be tracked, identifying anomalies such as bulges and depressions, with the latter correlating to severe porosity formation in intersection angles of 30°. This enabled early identification of severe defects within the first 10% of the build. Internal porosity in 30° intersections is determined to result from depletion of laser energy and powder flow and is eliminated through modification of the toolpath to remove fillets. This thesis highlights the importance of multi-axis monitoring and demonstrates the benefits of data fusion in observing process-induced defects.
While anomalies can be identified from the monitoring data, to improve the quality of a component, the conditions causing that anomaly must be mitigated. Hence, this thesis employs an advanced multi-physics Finite Element Model (FEM) to inform a new processing paradigm. When producing a component, industry practice is to determine appropriate processing parameters through trial-and-error parameter search studies, testing many combinations of laser power, speed, spot size, etc. in simple depositions. After inspection, parameters are selected and applied at constant values throughout deposition, failing to account for the highly dynamic thermal conditions experienced during fabrication. To address this shortfall, this thesis employs a thermally calibrated FEM to determine adaptive processing parameters.
In this work, high laser power (2 kW) and speed (1500 mm/min) parameters are applied in conjunction with no interlayer cooling as initial conditions to produce depositions with poor quality and substantial heat accumulation. The simulation uses these parameters as a starting point, and in conjunction with an optimisation route, predicts dynamic laser power and cooling intervals that lead to successful builds. The combination of pre-determined power reduction and interval cooling enable high-quality fabrications and consistent Widmanstätten microstructure throughout the deposit.
It is noted that the simulation model is unable to account for factors such as fluid flow and oxygen ingress under localised shielding. Therefore, samples produced with localised shielding exhibited microhardness values as large as 677 ± 44 HV0.5, accompanied by brittle failure during deposition. When repeated under controlled atmosphere, microhardness values were consistent with literature, ranging from 315 ± 8 to 337 ± 9 HV0.5. This simulation-informed approach presents a novel method of process design, minimising lengthy process selection methods and mitigating defect formation prior to fabrication.
As noted, experienced conditions may differ from simulated conditions during DED-LB, and process monitoring of these conditions generates large data files. Interpretation of the monitoring data is required to be reliable, fast, and involve minimal human intervention, else the scalability of AM fabrication will always be limited by the availability of human experts.
Machine Learning (ML) algorithms demonstrate an exciting opportunity for the rapid interpretation of large and complex datasets, such as those generated by process monitoring, and have enabled breakthroughs in many fields in recent years. Here, the context-driven Long Short-Term Memory (LSTM) algorithm is demonstrated for its applicability to the prediction of multiple feature classes simultaneously for parts fabricated by DED-LB, an approach not identified in literature. The LSTM considers the context of datapoints in a time sequence, which is relevant to the evolution of the melt pool in DED-LB, where previous states influence the current state.
To determine model architectures with highest performance, 120 network variations are trained per input data scenario (single or multi-source data). Few consistent trends are identified, reinforcing the necessity for hyperparameter optimisation routines when developing an ML tool. The performance of the algorithms is examined when trained on data from any single process monitoring data source, or from a combination of sources. For single-source algorithms, LSTMs trained on data from the coaxial camera performed best, despite lower spatial resolution and increased noise, likely due to the increased sampling rate. The use of multi-source data provides greater depth of information, allowing relationships for sparse data to be better learned than for any single data source, with up to 100-fold improvements in the true-positive rates for ‘Depression’ and ‘Porosity’ classes. However, the presented architectures are shown to not yet be appropriate for real-time prediction of features.
The combination of research investigations in this thesis creates a new framework of advanced, and automated DED-LB production. This next generation framework will improve the understanding of defect and feature formation in DED-LB, enhance and accelerate the determination of process regimes for consistent part production, and refine the interpretation of monitoring data for rapid assessment of component quality.</p
Save Food Packaging Criteria Icons
No full textThese Award-Winning Icons were developed for the Save Food Packaging Criteria and Framework research project, led by RMIT University School of Design, End Food Waste CRC, and the Australian Institute of Packaging. The purpose of the Icons are to provide visually concise iconography for easy to apply on-pack communication that uses Industry Peer-reviewed graphics and Plain Language actionable statements that best describe services packaging offers to consumers to reduce food waste.</p
Funding Liquidity Risk in Decentralized Lending: An Empirical Investigation from a Financial Intermediation Perspective
No full textDecentralised Lending (DeFi lending) is a new concept in finance. Based on blockchain technology and smart contracts, the innovative design of DeFi lending allows pseudonymous participants to lend and borrow money on a large scale with less intervention from financial intermediaries. Within the framework of financial intermediation theory, DeFi lending is an evolving landscape with the potential to reshape financial services, though it remains a frontier market with numerous challenges.
DeFi lending could potentially perform functions similar to those of traditional financial intermediaries like banks in liquidity transformation; however, it also faces a significant problem known as funding liquidity risk. This risk within one DeFi protocol can lead to systemic liquidity risk contagion, potentially affecting the stability of the broader DeFi ecosystem. Unlike traditional banks, which are established centralised financial institutions (CeFi), DeFi operates in an unregulated market and must address illiquidity issues without government bailouts or deposit insurance. Furthermore, it is considered a potential source of financial instability due to its increasing integration with traditional financial products. Compared to CeFi, DeFi features a distinct financial network with greater interconnectedness, characterised by its composability. Composability refers to the ability to create a complex financial system using various components built on crypto assets, akin to ‘Money Lego.’ This feature can enhance interoperability and liquidity transformation while potentially increasing risk contagion.
Existing literature indicates that DeFi lending is an emerging and understudied area, with most research to date being conceptual or based on aggregate data. This thesis investigates the dynamics of funding liquidity risk contagion in DeFi lending by using high-frequency, transaction-level blockchain data at 5-minute and 1-hour intervals.
This research aims to explore issues related to funding liquidity risk in DeFi lending by examining two main research questions. First, it investigates the level of contagion of this risk within the distinct financial network of DeFi lending and examines the external factors that drive this contagion. The study examines Aave, Compound, and Venus protocols, which collectively account for approximately 70% of the total value locked in the DeFi lending market. Second, as the source of risk contagion often originates from one lending protocol and spreads outward, the study delves deeper into DeFi lending protocols by examining the determinants that affect funding liquidity risk within those protocols to better understand the source of this risk. The study focuses more on internal factors, which can be adjusted through governance and protocol design, to assess how they influence funding liquidity risk. Understanding these issues will deepen our comprehension of the potential challenges and opportunities in DeFi lending, thereby offering valuable insights to improve market efficiency and stability.
The findings reveal that the average level of funding liquidity risk contagion in DeFi lending is relatively low compared to CeFi, but it varies over time based on market conditions. High spillover risks tend to occur during bullish or bubble markets, while they are significantly lower in bear markets. Furthermore, crypto policy uncertainty have a substantial impact on the level of risk contagion. The thesis also finds that current algorithmic interest rate models are ineffective as self-stabilisation mechanisms in major pools such as Wrapped Bitcoin (WBTC) and Wrapped Ethereum (WETH). Additionally, lower deposit concentration in these pools may exacerbate, rather than mitigate, funding liquidity risks.</p
The nature and community-led approach to disaster resilience: Story library
No full textThis is a small selection of the fantastic work already being done in NLCR and accompanies the NLCR Toolkit.
There are many more examples to be found in Victoria, Australia and further afield. Why not create your NLCR
story?
This story library contains:
1. DJANDAK leading Connecting with Country in Bendigo
2. Nature connection walks and workshops boost individual and community wellbeing after flood
3. The fish are back and so are we
4. Creating Nature Play Trails to Grow Resilience
5. Sharing stories of nature recovery
6. River Warriors
7. The Sounds of Recovery
8. Sarsfield Snaps
9. Students leading habitat monitoring after fires
10. Friends of groups recovering with rainforests and rivers
11. Strengthening locals’ connections to native flora in the Kinglake Ranges
12. Managing for dry times in Hovell’s Creek
13. Solving the Greater Glider housing crisis
14. Biodiversity Bushfire Recovery Grants Program</p
Synchronization of Multi-Agent Systems with Microgrid Applications
No full textMany social and engineering systems in the real world can be effectively modeled as multi-agent systems (MASs). A graph representation is used where nodes represent the states of individual entities, and edges signify the interactions between them. Notable examples include power grids, transportation networks, and the Internet. With the rise of large-scale infrastructures and advancements in computational power, MASs have garnered significant scholarly interest over the past few decades. Among various network properties, synchronization is a critical dynamic behavior frequently observed in nature, such as in neuronal network interactions and animal migrations. Furthermore, synchronization controllers are increasingly utilized in microgrid management. Hence, this thesis aims to investigate the role of synchronization in MASs and its application in the regulation of real-world microgrids. The work in this thesis can be broadly divided into four parts, which are summarized below.
Initially, this study investigates the pinning synchronization problem in MASs with periodic switching topologies. Unlike previous research, the findings in this work demonstrate that synchronization can be achieved without requiring each individual topology to contain a spanning tree, instead the only requirement is that their combined graph has a spanning tree. The synchronization conditions of MASs are examined through the construction of a Lyapunov function and the application of averaging theory. An unmanned aerial vehicle (UAV) system is utilized as a practical example to validate these findings. The numerical simulations are proposed to study the influence of switching topology on the stability threshold of the switching period through numerical simulations. Based on the simulations, two observation conclusions are obtained, which are beneficial for the pinning control design.
The second part of this study addresses the synchronization of MASs with nonlinear state dynamics. In particular, the Van der Pol oscillator is taken as an example. Due to the existence of a limit cycle in the Van der Pol oscillator, the synchronization issue is transformed into a local stability analysis of the error dynamic system near the limit cycle. The necessary and sufficient conditions that guarantee convergence are derived by examining the state transition matrix over one period of the limit cycle. Simulations are then detailed that verify the theoretical finding.
In the third part, the focus is on identifying the most influential driver nodes to ensure the fastest synchronization speed in pinning control of MASs. A methodology is developed to determine the most effective pinning nodes under time-varying topologies. Firstly, synchronization conditions for MASs under pinning control are provided. Then a method is proposed to identify the optimal driver node that achieves the fastest synchronization in periodic switching topologies, demonstrating that the selection of these nodes can be independent of the system matrix under certain conditions. A method is introduced to estimate the switching period threshold. This approach ensures that the best driver nodes remain consistent with the one in the averaged system. Numerical simulations validate the practicality of these approaches.
In the final part, this study addresses the challenge of improving control performance in islanded microgrids with switching communication networks by identifying the most influential distributed generator set. A focus is given to improve frequency regulation and active power sharing performance by pinning a set of distributed generators (DGs). To enhance the microgrid’s dynamic response under limited control resources, a method is introduced to determine the pinning DG set that ensures the fastest synchronization speed. This study further evaluates the influence of the network switching period on the DGs set selection, proposing a threshold to maintain the overall system performance. The developed methodology is validated through hardware-in-the-loop simulations on a modified IEEE 34-bus system, confirming its effectiveness in improving microgrid control performance.
The findings of this thesis offer theoretical insights and practical contributions to the synchronization control of MASs, particularly in addressing pinning control strategies under periodic switching topologies and nonlinear dynamics. By demonstrating that synchronization can be achieved without requiring individual topologies to contain a spanning tree, this research relaxes conventional constraints, broadening the applicability of synchronization strategies. Additionally, the identification of optimal driver nodes for synchronization and their application to microgrid regulation presents a novel approach to improving the control efficiency in real-world power systems. The developed methodologies enhance the performance of islanded microgrids and contribute to the advancement of resilient and adaptive energy networks.</p