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Relational Ecologies Laboratory
No full textThe 'Relational Ecologies Laboratory' was presented by Climate Aware Creative Practice (CACP) Network members Clare McCracken, Katie Lee, Mark Friedlander, Beth Arnold and Terri Bird together with CACP curator Bronwyn Bailey-Charteris. The lab assembled an inventory that gathered materials, books, prompts and ideas cared for and stored in the network members' collective studios. The assembled inventory was activated through workshops, discussions, and pedagogical investigations. These investigations collaboratively generated and modelled climate-aware creative methodologies and methods that included artistic circular economies, experiments of repurposing and reuse.Staged over the three months of the 'Charge that Binds' exhibition at ACCA, the lab was a sculptural work, a residency, an incubator and a publicly focused exhibition working space designed to facilitate interrogations into how to have a climate aware creative practice in an Australian context. Research Significance and Contribution: 'The Relational Ecologies Lab' was exhibited at Australian Centre of Contemporary Art (ACCA) as part of the 'Charge that Binds' Exhibition 2004 to 2025. It was supported by the Victorian Government through Creative Victoria.</p
Switching checkerboards in (0,1)-matrices
No full textIn order to study M(R,C), the set of binary matrices with fixed row and column sums R and C, we consider submatrices of the form (1001) and (0110), called positive and negative checkerboard respectively. We define an oriented graph of matrices G(R,C) with vertex set M(R,C) and an arc from A to A′ indicates you can reach A′ by switching a negative checkerboard in A to positive. We show that G(R,C) is a directed acyclic graph and identify classes of matrices which constitute unique sinks and sources of G(R,C). Given A,A′∈M(R,C), we give necessary conditions and sufficient conditions on M=A′−A for the existence of a directed path from A to A′. We then consider the special case of M(D), the set of adjacency matrices of graphs with fixed degree distribution D. We define G(D) accordingly by switching negative checkerboards in symmetric pairs. We show that Z2, an approximation of the spectral radius λ1 based on the second Zagreb index, is non-decreasing along arcs of G(D). Also, λ1 reaches its maximum in M(D) at a sink of G(D). We provide simulation results showing that applying successive positive switches to an Erdős-Rényi graph can significantly increase λ1
An international comparison of the scope and instruments of local spatial planning
No full textThis article analyses (based on multidimensional criteria) land-use plans at a local level (using selected cities as examples) and their relationships to crucial planning issues identified at both national and local levels. The planning approaches of cities from the following countries were compared: Australia, Bolivia, Brazil, Canada, China, Ghana, Iran, Mexico, Mongolia, Portugal and Poland. These countries are diverse in terms of their legal, political, cultural and geographical perspectives. Across these diverse countries, comparisons were made of local spatial plans (as enacted in specific cities), with regard to their legal features. From this, directions for a possible universal debate of local-level spatial plans as currently emerging is proposed. Based on the comparison undertaken, it is possible that the spatial plans studied designate zones of land use and detailed principles (including parameters) of development for cities. In some countries, attempts have also been made for the plans to solve spatial problems more broadly, for example by linking strategic planning objectives with the spatial development sphere
Angular wave propagation through one-dimensional phononic crystals made of functionally graded auxetic nanocomposites
No full textComposites reinforced with graphene origami nanofillers possess negative Poisson's ratio and obtain multifunctional features offering unique mechanical properties such as tunable auxeticity, high impact resistance, better indentation resistance, and improved fracture toughness. This paper introduces such novel nanocomposites into functionally graded multilayered phononic crystals and numerically investigates the propagation characteristics of normally and obliquely incident waves in the structures containing components with negative Poisson's ratio. Theoretical models of present structures are formulated and solved by the state space approach. The transfer matrix method is used to obtain dispersion relations of normally and obliquely incident waves. A comprehensive parametric study is conducted to discuss the propagation characteristics of elastic waves in the structures with auxetic and non-auxetic components which can be tuned by the weight fraction and hydrogen coverage of graphene origami fillers. The results indicate that introducing components with negative Poisson’ ratio into unit cells can significantly affect wave propagation features. Wave modes have different sensitivities to the weight fraction and hydrogen coverage of graphene origami fillers. Weigh fraction can manipulate all wave modes simultaneously, while hydrogen coverage only influences longitudinal modes and keeps transverse modes nearly undisturbed. Theoretical results shed insights into the design of high-performance multifunctional phononic crystals
Numerical studies of indoor particulate and gaseous micropollutant transport and its impact on human health in densely-occupied spaces
No full textMicropollutants (MPs) have increasingly become a matter of concern owing to potential health risks associated with human inhalation exposure, particularly in densely-occupied indoor environments. This study employed numerical simulations in a traditional built indoor workspace and a public transport cabin to elucidate the transport dynamics and health impacts of particulate and gaseous type of indoor MPs on varying groups of occupants. The risk of infection from pathogen-bearing MPs was evaluated in the workspace using the integrated Eulerian-Lagrangian and modified Wells-Riley model. In the cabin environment, the health impact of inhaled TVOC within the human nasal system was assessed via the integrated nasal-involved manikin model and cancer/non-cancer risk model. The results demonstrated that when ventilation layout was in favour of restricting particulate MPs spread, considerably high health risks (up to 17.22% infection possibility) were generally found in near-fields of emission source (< 2.25 m). Conversely, if the ventilated flow interacts robustly with emission source, every occupant has a minimum 5% infection risk. Incorporating the nasal cavity in the human model offers a nuanced understanding of gaseous MP distributions post-inhalation. Notably, the olfactory and sinus regions displayed heightened vulnerability to TVOC exposure, with a 62.5%–108% concentration increase compared to other nasal areas. Cancer risk assessment plausibly explained the rising occurrence of brain and central nervous system cancer for aircrew members. Non-cancer risk was found acceptable. This study was expected to advance the understanding of environmental pollution and the health risks tied to indoor MPs in densely-populated environments
Faster Gastrointestinal Transit, Reduced Small Intestinal Smooth Muscle Tone and Dysmotility in the Nlgn3R451C Mouse Model of Autism
No full textIndividuals with autism often experience gastrointestinal issues but the cause is unknown. Many gene mutations that modify neuronal synapse function are associated with autism and therefore may impact the enteric nervous system that regulates gastrointestinal function. A missense mutation in the Nlgn3 gene encoding the cell adhesion protein Neuroligin-3 was identified in two brothers with autism who both experienced severe gastrointestinal dysfunction. Mice expressing this mutation (Nlgn3(R451C) mice) are a well-studied preclinical model of autism and show autism-relevant characteristics, including impaired social interaction and communication, as well as repetitive behaviour. We previously showed colonic dysmotility in response to GABAergic inhibition and increased myenteric neuronal numbers in the small intestine in Nlgn3(R451C) mice bred on a mixed genetic background. Here, we show that gut dysfunction is a persistent phenotype of the Nlgn3 R451C mutation in mice backcrossed onto a C57BL/6 background. We report that Nlgn3(R451C) mice show a 30.9% faster gastrointestinal transit (p = 0.0004) in vivo and have 6% longer small intestines (p = 0.04) compared to wild-types due to a reduction in smooth muscle tone. In Nlgn3(R451C) mice, we observed a decrease in resting jejunal diameter (proximal jejunum: 10.6% decrease, p = 0.02; mid: 9.8%, p = 0.04; distal: 11.5%, p = 0.009) and neurally regulated dysmotility as well as shorter durations of contractile complexes (mid: 25.6% reduction in duration, p = 0.009; distal: 30.5%, p = 0.004) in the ileum. In Nlgn3(R451C) mouse colons, short contractions were inhibited to a greater extent (57.2% by the GABA(A) antagonist, gabazine, compared to 40.6% in wild-type mice (p = 0.007). The inhibition of nitric oxide synthesis decreased the frequency of contractile complexes in the jejunum (WT p = 0.0006, Nlgn3(R451C) p = 0.002), but not the ileum, in both wild-type and Nlgn3(R451C) mice. These findings demonstrate that changes in enteric nervous system function contribute to gastrointestinal dysmotility in mice expressing the autism-associated R451C missense mutation in the Neuroligin-3 protein
How important is the in-store environment for new brands?
No full textNew brand launches are notoriously risky, with high failure rates. Yet, most research focuses on the out-of-store factors inherent to their success/failure, overlooking strategies that can be employed in-store. The present research addresses this oversight by examining the influence of two in-store factors, distraction and shelf position, and their impact on a new brand’s visibility on the shelf. We draw on a unique data set featuring a real-life new brand entrant into the Australian market. Using an experimental design in a shopper laboratory, and mobile eye-tracking, we find that the new brand stands a greater chance of being noticed and visually attended to on the shelf when shoppers are distracted. This is attributed to shoppers dwelling longer in front of the fixture, being more open to new-to-the-consumer brands, and by negatively affecting the top-down processing of existing brands on the shelf. We also find that optimising shelf position, which is a common in-store marketing tactic for existing brands, may not produce the same return on investment for a new brand. The findings offer valuable theoretical and practical implications for improving the success rates of new brand launches, including selection of distribution channels, allocation of marketing resources, and the interplay between in-store and out-of-sore factors driving shopper behaviour
Experimental graybox quantum system identification and control
No full textUnderstanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning (ML) methods, which provide efficient control solutions but no control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a ‘graybox’ approach to construct a physical model of a quantum system and use it to design optimal control. We report superior performance over model fitting, while generating unitaries and Hamiltonians, which are quantities not available from the structure of standard supervised ML models. Our approach combines physics principles with high-accuracy ML and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation
Probabilistic steady-state and short-term voltage stability assessment considering correlated system uncertainties
No full textAs correlated uncertainties in power grids become more prevalent, modeling of stochastic dependencies is becoming increasingly necessary. Independent probability distributions disregard these correlated uncertainties, potentially leading to significant errors, underscoring the necessity of dependence structures. This paper presents a probabilistic approach for modeling uncertain system parameters with their inherent correlations. The proposed approach is validated through various aspects of system voltage measures, including voltage profile and static and dynamic voltage stability. In total, nine alternative sampling generation techniques are employed, and their accuracy is measured based on real-world data using root mean square error (RMSE) and coefficient of determination (R2) criteria. The result suggests that multivariate (Gaussian and Student's) copula techniques accurately represent the real system data, consistently achieving high accuracy rates of 98 % for voltage profiles, 97 % for static voltage stability, and 93 % for dynamic voltage stability. In contrast, the independent sampling technique failed to follow the real-world system data for the different aspects of system voltage measures
Machine learning enabled 2D photonic crystal biosensor for early cancer detection
No full textIn this paper, a novel 2D Photonic Crystal (PC)-based cancer biosensor is proposed for the detection of different types of cancer cells HeLa, PC12, MDA, MCF, and Jurkat. The sensor is designed using Silicon-on-insulator (SOI) substrate in a triangular lattice with holes in the slab. The proposed design is optimized to provide a high-quality factor of 15,000, high sensitivity and a low detection limit that are highly effective in cancer detection. Proposed biosensor uses a series of resonant cavities that slice the resonant wavelength to a high peak resonant wavelength with a spectral linewidth of 0.1 nm. The integration of 2D PC biosensors with machine learning techniques for early and accurate cancer detection is optimized for the data set. The performance analysis of Multiple Linear Regression (MLR) and Support Vector Machine (SVM) is studied by repeating training, testing, and optimization of target values (Resonant Wavelength) with dependent and independent features of a 2D PC biosensor system. The SVM model provides an R squared value of 0.99 for the biosensor, and the MLR model gave an R squared value of 0.88. The SVM model provides excellent accuracy in predicting the target values with all the trained input features of a 2D PC biosensing system