The Effect of Porosity Density and Configuration in Composite Materials on the Ultrasonic Waveform

Current practice is to accept or reject composite parts based upon ultrasonic C-scan results. Normally, this is based only on ultrasonic attenuation data. However, attenuation data alone does not account for variations in porosity distribution or type, and ignores the fact that other variables can influence attenuation besides porosity. This work was directed at determining additional parameters which can be used to define the defect structure in a composite

Design and Social Context, RMIT,

and evaluating the use of Web-delivered tools to supplement the community decision-making process. This has involved building and testing a 3D tool for Community Collaborative decisionmaking in the Jewell Station Neighbourhood (JSN). The focus of this decision-making tool provision is placed in an urban planning context. VRML (Virtual Reality Modeling Language) was used to build two tools: a ‘Sandbox ’ and a virtual world. Building the tools was done so that community members, or planning consortia, could use the tools in a group meeting or individually through home Internet computers, or even at Internet cafes. The underlying criterion for design was simplicity and accessibility. The virtual worl

Colouring Australia: a participatory open data platform

Colouring Australia is a digital platform for collecting and visualising building level information across several Australian cities. It provides a valuable resource for bringing together data on building age, material, sustainability ratings, walkability and other key metrics as we plan for net zero cities. Colouring Australia comprises part of the international Colouring Cities Research Programme, which supports the development of open-source platforms that provide open data on national building stocks. In this paper we outline the technical architecture of the platform, and the development and visualisation of a building level walkability metric based on pedestrian access to destinations. This platform provides a useful digital tool for planners to understand which parts of the city are walkable and in turn this can support future active transport programs and policies. Future research will be to validate this novel walkability index through a series of stakeholder and public workshops using the Colouring Australia platform in an interactive tabletop environment where usability testing can be undertaken

Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations

Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct applicability is limited in models where observations possess nonlinear relationships with unknown parameters. Recently, a semi-analytical Bayesian framework known as nonlinear sparse Bayesian learning (NSBL) was introduced by the authors to induce sparsity among model parameters during the Bayesian inversion of nonlinear-in-parameter models. NSBL relies on optimally selecting the hyperparameters of sparsity-inducing Gaussian priors. It is inherently an approximate method since the uncertainty in the hyperparameter posterior is disregarded as we instead seek the maximum a posteriori (MAP) estimate of the hyperparameters (type-II MAP estimate). This paper aims to investigate the hierarchical structure that forms the basis of NSBL and validate its accuracy through a comparison with a one-level hierarchical Bayesian inference as a benchmark in the context of three numerical experiments: (i) a benchmark linear regression example with Gaussian prior and Gaussian likelihood, (ii) the same regression problem with a highly non-Gaussian prior, and (iii) an example of a dynamical system with a non-Gaussian prior and a highly non-Gaussian likelihood function, to explore the performance of the algorithm in these new settings. Through these numerical examples, it can be shown that NSBL is well-suited for physics-based models as it can be readily applied to models with non-Gaussian prior distributions and non-Gaussian likelihood functions. Moreover, we illustrate the accuracy of the NSBL algorithm as an approximation to the one-level hierarchical Bayesian inference and its ability to reduce the computational cost while adequately exploring the parameter posteriors

Providing affordable housing through urban renewal projects in Australia: Expert opinions on barriers and opportunities

This paper examined the impact of providing affordable rental housing through inner-city urban renewal projects in Australia. Providing affordable rental housing for lower-income households remains a challenge for planners, builders, policymakers and residents alike. Government intervention for inclusionary zoning in Australia has enhanced affordable housing supply but has also generated negative impacts such as NIMBY-ism, decreasing house price and urban sprawl. This study conducted in-depth interviews with housing and planning experts in affordable housing projects in Australia and evaluated the barriers and opportunities of providing affordable rental housing as stand-alone projects, or as part of urban renewal projects. This study found several existing challenges such as limited longevity of related policies and limited financing sources for renewal projects. The findings inform policymakers that the existing housing affordability issue can be tackled by adopting more innovative approaches such as negative gearing

Global Night-Time Lights for Observing Human Activity

We present a concept for a small satellite mission to make systematic, global observations of night-time lights with spatial resolution suitable for discerning the extent, type and density of human settlements. The observations will also allow better understanding of fine scale fossil fuel CO2 emission distribution. The NASA Earth Science Decadal Survey recommends more focus on direct observations of human influence on the Earth system. The most dramatic and compelling observations of human presence on the Earth are the night light observations taken by the Defence Meteorological System Program (DMSP) Operational Linescan System (OLS). Beyond delineating the footprint of human presence, night light data, when assembled and evaluated with complementary data sets, can determine the fine scale spatial distribution of global fossil fuel CO2 emissions. Understanding fossil fuel carbon emissions is critical to understanding the entire carbon cycle, and especially the carbon exchange between terrestrial and oceanic systems

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19 : a study protocol

Introduction: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. Methods and analysis: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model’s predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states. Ethics and dissemination: Approved by Carleton University's Research Ethics Board-B (clearance ID: 114596). Results will be made available through future publication