Community-based public health: an Online Decision Support Systems : for Logan city, Australia

Introduction Among the many requirements of establishing community health, a healthy urban environment stands out as significant one. A healthy urban environment constantly changes and improves community well-being and expands community resources. The promotion efforts for such an environment, therefore, must include the creation of structures and processes that actively work to dismantle existing community inequalities. In general, these processes are hard to manage; therefore, they require reliable planning and decision support systems. Current and previous practices justify that the use of decision support systems in planning for healthy communities have significant impacts on the communities. These impacts include but are not limited to: increasing collaboration between stakeholders and the general public; improving the accuracy and quality of the decision making process; enhancing healthcare services; and improving data and information availability for health decision makers and service planners. Considering the above stated reasons, this study investigates the challenges and opportunities of planning for healthy communities with the specific aim of examining the effectiveness of participatory planning and decision systems in supporting the planning for such communities. Methods This study introduces a recently developed methodology, which is based on an online participatory decision support system. This new decision support system contributes to solve environmental and community health problems, and to plan for healthy communities. The system also provides a powerful and effective platform for stakeholders and interested members of the community to establish an empowered society and a transparent and participatory decision making environment. Results The paper discusses the preliminary findings from the literature review of this decision support system in a case study of Logan City, Queensland. Conclusion The paper concludes with future research directions and applicability of this decision support system in health service planning elsewhere.

Vulnerability of eco-environmental health to climate change : the views of government stakeholders and other specialists in Queensland, Australia

Background: There is overwhelming scientific evidence that human activities have changed and will continue to change the climate of the Earth. Eco-environmental health, which refers to the interdependencies between ecological systems and population health and well-being, is likely to be significantly influenced by climate change. The aim of this study was to examine perceptions from government stakeholders and other relevant specialists about the threat of climate change, their capacity to deal with it, and how to develop and implement a framework for assessing vulnerability of eco-environmental health to climate change.---------- Methods: Two focus groups were conducted in Brisbane, Australia with representatives from relevant government agencies, non-governmental organisations, and the industry sector (n = 15) involved in the discussions. The participants were specialists on climate change and public health from governmental agencies, industry, and nongovernmental organisations in South-East Queensland.---------- Results: The specialists perceived climate change to be a threat to eco-environmental health and had substantial knowledge about possible implications and impacts. A range of different methods for assessing vulnerability were suggested by the participants and the complexity of assessment when dealing with multiple hazards was acknowledged. Identified factors influencing vulnerability were perceived to be of a social, physical and/or economic nature. They included population growth, the ageing population with associated declines in general health and changes in the vulnerability of particular geographical areas due to for example, increased coastal development, and financial stress. Education, inter-sectoral collaboration, emergency management (e.g. development of early warning systems), and social networks were all emphasised as a basis for adapting to climate change. To develop a framework, different approaches were discussed for assessing eco-environmental health vulnerability, including literature reviews to examine the components of vulnerability such as natural hazard risk and exposure and to investigate already existing frameworks for assessing vulnerability.---------- Conclusion: The study has addressed some important questions in regard to government stakeholders and other specialists’ views on the threat of climate change and its potential impacts on eco-environmental health. These findings may have implications in climate change and public health decision-making.

Child participation in the early years : challenges for education

The view that children should have a say in and participate in the decision-making of, matters that affect them is now an accepted position when considering research and policy in the early years. This paper reviews the field of child participation in the Australian context to show that, despite growing evidence of support within policy and research arenas, young children’s participation rights in Australia have not been key agenda items for early childhood education. While a significant part of children’s daily experience takes place in classrooms, the actual practices of engaging young children as participants in everyday activities remains a challenge for early childhood education. Participation is an interactional process that involves managing relationships between children and adults. Recommendations include further research into the daily experiences of young children to show what participation might look like when translated to the everyday activities of the classroom and playground.

Sustainability : driver for decision making in infrastructure

With the growing importance of sustainability assessment in the construction industry, many green building rating schemes have been adopted in the building sector of Australia. However, there is an abnormal delay in the similar adoption in the infrastructure sector. This prolonged delay in practice poses a challenge in mapping the project objectives with sustainability outcomes. Responding to the challenge of sustainable development in infrastructure, it is critical to create a set of decision indicators for sustainability in infrastructure, which to be used in conjunction with the emerging infrastructure sustainability assessment framework of the Australian Green Infrastructure Council. The various literature sources confirm the lack of correlation between sustainability and infrastructure. This theoretical missing link signifies the crucial validation of the interrelationship and interdependency in sustainability, decision making and infrastructure. This validation is vital for the development of decision indicators for sustainability in infrastructure. Admittedly, underpinned by the serious socio-environmental vulnerability, the traditional focus on economic emphasis in infrastructure development needs to be drifted towards the appropriate decisions for sustainability enhancing the positive social and environmental outcomes. Moreover, the research findings suggest sustainability being observed as powerful socio-political and influential socio-environmental driver in deciding the infrastructure needs and its development. These newly developed sustainability decision indicators create the impetus for change leading to sustainability in infrastructure by integrating the societal cares, environmental concerns into the holistic financial consideration. Radically, this development seeks to transform principles into actions for infrastructure sustainability. Lastly, the thesis concludes with knowledge contribution in five significant areas and future research opportunities. The consolidated research outcomes suggest that the development of decision indicators has demonstrated sustainability as a pivotal driver for decision making in infrastructure.

The Canadian ‘Model Forest’ approach : a way forward for Tasmania?

Forest policy and forestry management in Tasmania have undergone a number of changes in the last thirty years, many explicitly aimed at improving industry sustainability, job security, and forest biodiversity conservation. Yet forestry remains a contentious issue in Tasmania, due to a number of interacting factors, most significant of which is the prevalence of a ‘command and control’ governance approach by policymakers and managers. New approaches such as multiple-stakeholder decision-making, adaptive management, and direct public participation in policymaking are needed. Such an approach has been attempted in Canada in the last decade, through the Canadian Model Forest Program, and may be suitable for Tasmania. This paper seeks to describe what the Canadian Model Forest approach is, how it may be implemented in Tasmania, and what role it may play in the shift to a new forestry paradigm. Until such a paradigm shift occurs contentions and confrontations are likely to continue.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.