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Assessing Multilevel Environmental and Air Quality Changes in Australia Pre- and Post-COVID-19 Lockdown: A Spatial Machine Learning Approach Utilizing Earth Observation Data
No full textPrior research has highlighted substantial shifts in environmental and air quality indicators preceding and following COVID-19 lockdowns, with implications for human well-being, climate, and air pollution. This groundbreaking study pioneers the quantification of multilevel changes—spanning national, regional, and local scales—in six pivotal satellite-derived land surface and air quality parameters before (November 2019–March 2020) and after lockdown (November 2020–March 2021) in Australia. Leveraging Google Earth Engine (GEE) and GIS capabilities, three land surface environmental parameters (Land Surface Temperature, Normalized Difference Vegetation Index, Normalized Difference Built-up Index), and three air quality parameters (Aerosol Optical Depth, Carbon Monoxide, and Nitrogen Dioxide) were derived. Environmental and air quality shifts between these periods were rigorously examined employing spatial and machine learning methodologies. Nationally, the lockdown led to a reduction in average Land Surface Temperature, likely due to increased vegetation and enhanced air quality. Similar trends were observed at regional and local scales, though intriguing exceptions emerged, such as elevated Land Surface Temperature in Melbourne and Darwin. This study showcases a robust analytical framework capable of comprehensively exploring multiscale environmental and air quality alterations, enabled by harnessing extensive Earth Observation datasets and spatial machine learning techniques. It offers valuable insights into understanding the profound environmental impacts associated with significant events like the COVID-19 pandemic.</p
Standardizing the evaluation framework for ECG-based authentication in IoT devices
No full textDevices on the Internet of Things (IoT) often have constrained resources and operate in diverse environments, making them vulnerable to unauthorized access and cyber threats. Electrocardiogram (ECG) signals have emerged as a promising biometric for authenticating users in such settings. However, current ECG-based authentication studies lack a standardized evaluation framework tailored to resource-limited IoT contexts and long-term usage, making it difficult to assess their practical reliability. In this paper, we introduce a new evaluation framework for ECG-based authentication on IoT devices and construct a standardized dataset to facilitate rigorous testing. We categorize performance metrics into four key dimensions: scalability, adaptability, efficiency, and cancelability. Using this framework, we evaluate four representative ECG authentication algorithms for IoT devices. The results show that these algorithms struggle to maintain consistent performance under cross-session authentication scenarios. These findings highlight the critical importance of addressing the temporal variability of ECG signals and the current gap in robust ECG-based authentication for IoT devices. We believe the proposed framework will guide future research toward more resilient and secure ECG authentication systems for the IoT.</p
Community Initiative Flyers
No full textThis set of three Community Initiative Flyers reflects some of the passionate organisations, community activities and solo practices we learnt about throughout the ‘Wear & Care’ Research Project. We hope this will inspire you to create and participate in your own local community initiatives.</p
Cosy Crime Fiction, Australian Regions and Climate Crisis: Reading Sue Williams’s Rusty Bore Series in the Victorian Mallee
No full textAustralian crime fiction engaged with regional and rural places, communities and histories is flourishing. The most prominent face of this is “rural noir”, but the “cosy crime” genre has also made a mark in Australian rural crime fiction. These subgenres are often positioned at different ends of the crime spectrum, with cosy crime commonly positioned as a “lighter”, domestically oriented alternative to the hard-boiled nature of rural noir. As a result, cosy crime is not credited with the same critical power as its rural noir bedfellow. This article argues that the Australian cosy crime novel deserves consideration as a subgenre entirely capable of engagement with social critique which, in an Australian context, tend to focus on settler colonial and environmental crimes. The article explores this through a discussion of the Rusty Bore Mysteries series of cosy crime novels by Melbourne-based author Sue Williams and set largely in the Victorian Mallee region, in the tiny fictional town of Rusty Bore. We argue that these four novels exemplify the critical capacities of Australian cosy crime fiction, in particular its attention to what has been termed “eco-crime”, alongside a commentary on the economic and social impacts of global capitalism on local communities. Drawing on our conversations about Williams’s novels with Mallee-based book groups, we further consider the role of genre fiction in this regional reading community, especially genre fiction’s capacity to generate discussion around colonial violence and climate crisis in rural Australia, topics that can often otherwise be avoided or actively denied.</p
Response to Productivity Commission Interim Report
No full text1. Synopsis
The purpose of this submission is to respond to the call for textiles-specific information, drawing
from our expertise as researchers from the School of Fashion & Textiles, RMIT University.
1.1. Key Recommendations
Recommendation 1: Reframe definition to ‘A circular economy is an economic strategy that
maintains the value of materials for as long as possible and ensures materials are used effectively
across all phases of their life cycle, to meet human needs.’
Recommendation 2: Rephrase definition to ‘Circular activities include designing products and
services to reduce overall material throughput’.
Recommendation3: The government to publish guidance on use of certifications, ideally in
alignment with other governments. Certification bodies can support policy implementation should
government decide to legislate.
Recommendation 4: Care, product, and ecolabels/Certifications should be included and be part of adopting a Digital Product Passport System or similar system.
Recommendation 5: Introduce a co-regulatory or mandatory product stewardship scheme for
textiles and clothing that shifts responsibility to producers, ensures financial accountability, and
establishes a coordinated national framework for environmental and social impact.
Recommendation 6: Implement minimum sustainability standards on clothing imports and
introduce import caps that align with the principle of sufficiency—ensuring Australia imports enough
to meet the needs of its population without incentivising perpetual growth in textile consumption.
Recommendation 7: Transition to a co-regulatory or mandatory model to ensure broad business
participation and embed circular design requirements, including fibre recyclability and material reduction, to address textile waste at its source.</p
How Good are Parklets?_Colour Figures from Chapter 4
No full textColour Figures from Chapter 4 of 'How Good are Parklets?'</p
HoF_Trip 1
No full textThis collection Heritage of Flow - Trip 1, records a motorbike journey from District 1, the center of HCMC [Saigon], to District 7. This trip was taken at night. There are two files the raw file and an edited version.</p
Optimizing Elevator Performance with SARL Multi-Agent Systems: A Distributed Approach for Enhanced Responsiveness and Efficiency
No full textElevators play a pivotal role in modern urban living, boosting productivity and convenience efficiently. In elevator systems, the optimization of Multi-Agent Systems (MAS) is indispensable as it enhances agent coordination, adaptability, delay reduction, client satisfaction, and resource use. In this paper, we introduce an algorithm based on SARL MAS designed to enhance elevator controller performance. Our approach compares Centralized and Distributed Agent Systems, demonstrating the superiority of Distributed Agent Systems due to their improved responsiveness, efficiency, and adaptability. Our findings provide valuable insight into the use of SARL MAS not only for elevator control but also for other applications such as queue management systems and resource allocation in computing, highlighting the benefits of a distributed approach.</p
MW-UNet: Multi-Scale Weighted Connection UNet for Identification and Classification of Non-Meteorological Clutter over Big Radar Data
No full textThe field of weather forecasting makes extensive use of radar big data to extract information about precipitation, storms, lightning, and other weather phenomena to aid in the prediction and monitoring of weather changes. To improve the quality of radar data, machine learning and fuzzy logic algorithms are often used to identify and classify non-meteorological clutter in weather data. However, these methods often require dozens of texture features as inputs and need to manually adjust the thresholds to cope with different clutter types, which leads to significant time costs. In this paper, we propose a multi-scale weighted connected UNet to address these challenges by combining the channel attention feature fusion module and the UNet structure model. The task of recognizing non-meteorological clutter is regarded as a semantic segmentation problem, which eliminates the need to manually set thresholds for clutter pixel-level classification. Additionally, the channel-focused feature fusion mechanism is able to analyze the deep latent features of the input parameters and suppress the useless features, so that only six polarization parameters are required as inputs. Furthermore, the model incorporates full-scale deep supervision to improve the edge segmentation accuracy of clutter and meteorological echoes. Experiments confirm that our proposed model outperforms the compared models in clutter identification with Critical Success Index (CSI) of 0.808.</p
Insights into Synthesis and Optimization Features of Reverse Osmosis Membrane Using Machine Learning.
No full textReverse osmosis membranes have been predominantly made from aromatic polyamide composite thin-films, although significant research efforts have been dedicated to discovering new materials and synthesis technologies to enhance the water-salt selectivity of membranes in the past decades. The lack of significant breakthroughs is partly attributed to the limited comprehensive understanding of the relationships between membrane features and their performance. Insights into the intrinsic features of reverse osmosis (RO) membranes based on metadata were obtained using explainable artificial intelligence to understand the relationships and unify the research efforts. The features related to the chemistry, membrane structure, modification methods, and membrane performance of RO membranes were derived from the dataset of more than 1000 RO membranes. Seven machine learning (ML) models were constructed to evaluate the membrane performances, and their applicability for the tasks was assessed using the metadata. The contribution of the features to RO performance was analyzed, and the ranking of their importance was revealed. This work holds promise for metadata analysis, evaluating the RO membrane against the state of the art and developing an inverse design strategy for the discovery of high-performance RO membranes.</p