Green buildings for greying people : a case study of a retirement village in Australia

Purpose With an increasingly ageing population and widespread acceptance of the need for sustainable development in Australia, the demand for green retirement villages is increasing. This paper aims to identify the critical issues to be considered by developers and practitioners when embarking on their first green residential retirement project in Australia. Design/methodology/approach In view of the lack of adequate historical data for quantitative analysis, a case study approach is employed to examine the successful delivery of green retirement villages. Face-to-face interviews and document analysis were conducted for data collection. Findings The findings of the study indicate that one of the major obstacles to the provision of affordable green retirement villages is the higher initial costs involved. However, positive aspects were identified, the most significant of which relate to: the innovative design of site and floor plans; adoption of thermally efficient building materials; orientation of windows; installation of water harvesting and recycling systems, water conservation fittings and appliances; and waste management during the construction stage. With the adoption of these measures, it is believed that sustainable retirement development can be achieved without significant additional capital costs. Practical implications The research findings serve as a guide for developers in decision making throughout the project life-cycle when introducing green features into the provision of affordable retirement accommodation. Originality/value This paper provides insights into the means by which affordable green residential retirement projects for aged people can be successfully completed.

On selecting an optimal wavelet for detecting singularities in traffic and vehicular data

Serving as a powerful tool for extracting localized variations in non-stationary signals, applications of wavelet transforms (WTs) in traffic engineering have been introduced; however, lacking in some important theoretical fundamentals. In particular, there is little guidance provided on selecting an appropriate WT across potential transport applications. This research described in this paper contributes uniquely to the literature by first describing a numerical experiment to demonstrate the shortcomings of commonly-used data processing techniques in traffic engineering (i.e., averaging, moving averaging, second-order difference, oblique cumulative curve, and short-time Fourier transform). It then mathematically describes WT’s ability to detect singularities in traffic data. Next, selecting a suitable WT for a particular research topic in traffic engineering is discussed in detail by objectively and quantitatively comparing candidate wavelets’ performances using a numerical experiment. Finally, based on several case studies using both loop detector data and vehicle trajectories, it is shown that selecting a suitable wavelet largely depends on the specific research topic, and that the Mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data.

Analysing environmental acoustic data through collaboration and automation

Monitoring environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; online collaboration, manual, automatic and human-in-the loop analysis.

Analysis of marine conflicts

The traffic conflict technique (TCT) is a powerful technique applied in road traffic safety assessment as a surrogate of the traditional accident data analysis. It has subdued the conceptual and implemental weaknesses of the accident statistics. Although this technique has been applied effectively in road traffic, it has not been practised well in marine traffic even though this traffic system has some distinct advantages in terms of having a monitoring system. This monitoring system can provide navigational information as well as other geometric information of the ships for a larger study area over a longer time period. However, for implementing the TCT in the marine traffic system, it should be examined critically to suit the complex nature of the traffic system. This paper examines the suitability of the TCT to be applied to marine traffic and proposes a framework for a follow up comprehensive conflict study.

The emerging role of private health care provision in China : a critical analysis of the current health system

China continues to face great challenges in meeting the health needs of its large population. The challenges are not just lack of resources, but also how to use existing resources more efficiently, more effectively, and more equitably. Now a major unaddressed challenge facing China is how to reform an inefficient, poorly organized health care delivery system. The objective of this study is to analyze the role of private health care provision in China and discuss the implications of increasing private-sector development for improving health system performance. This study is based on an extensive literature review, the purpose of which was to identify, summarize, and evaluate ideas and information on private health care provision in China. In addition, the study uses secondary data analysis and the results of previous study by the authors to highlight the current situation of private health care provision in one province of China. This study found that government-owned hospitals form the backbone of the health care system and also account for most health care service provision. However, even though the public health care system is constantly trying to adapt to population needs and improve its performance, there are many problems in the system, such as limited access, low efficiency, poor quality, cost inflation, and low patient satisfaction. Currently, private hospitals are relatively rare, and private health care as an important component of the health care system in China has received little policy attention. It is argued that policymakers in China should recognize the role of private health care provision for health system performance, and then define and achieve an appropriate role for private health care provision in helping to respond to the many challenges facing the health system in present-day China.

A framework for cost-aware process management : generation of accurate and timely management accounting cost reports

Organisations are constantly seeking efficiency improvements for their business processes in terms of time and cost. Management accounting enables reporting of detailed cost of operations for decision making purpose, although significant effort is required to gather accurate operational data. Business process management is concerned with systematically documenting, managing, automating, and optimising processes. Process mining gives valuable insight into processes through analysis of events recorded by an IT system in the form of an event log with the focus on efficient utilisation of time and resources, although its primary focus is not on cost implications. In this paper, we propose a framework to support management accounting decisions on cost control by automatically incorporating cost data with historical data from event logs for monitoring, predicting and reporting process-related costs. We also illustrate how accurate, relevant and timely management accounting style cost reports can be produced on demand by extending open-source process mining framework ProM.

International differences in management of physical activity data: Can they explain some of the difference in prevalence estimates?

Background: National physical activity data suggest that there is a considerable difference in physical activity levels of US and Australian adults. Although different surveys (Active Australia and BRFSS) are used, the questions are similar. Different protocols, however, are used to estimate “activity” from the data collected. The primary aim of this study was to assess whether the 2 approaches to the management of PA data could explain some of the difference in prevalence estimates derived from the two national surveys. Methods: Secondary data analysis of the most recent AA survey (N = 2987). Results: 15% of the sample was defined as “active” using Australian criteria but as “inactive” using the BRFSS protocol, even though weekly energy expenditure was commensurate with meeting current guidelines. Younger respondents (age < 45 y) were more likely to be “misclassified” using the BRFSS criteria. Conclusions: The prevalence of activity in Australia and the US appears to be more similar than we had previously thought.

Surviving an avalanche of data

Open the sports or business section of your daily newspaper, and you are immediately bombarded with an array of graphs, tables, diagrams, and statistical reports that require interpretation. Across all walks of life, the need to understand statistics is fundamental. Given that our youngsters’ future world will be increasingly data laden, scaffolding their statistical understanding and reasoning is imperative, from the early grades on. The National Council of Teachers of Mathematics (NCTM) continues to emphasize the importance of early statistical learning; data analysis and probability was the Council’s professional development “Focus of the Year” for 2007–2008. We need such a focus, especially given the results of the statistics items from the 2003 NAEP. As Shaughnessy (2007) noted, students’ performance was weak on more complex items involving interpretation or application of items of information in graphs and tables. Furthermore, little or no gains were made between the 2000 NAEP and the 2003 NAEP studies. One approach I have taken to promote young children’s statistical reasoning is through data modeling. Having implemented in grades 3 –9 a number of model-eliciting activities involving working with data (e.g., English 2010), I observed how competently children could create their own mathematical ideas and representations—before being instructed how to do so. I thus wished to introduce data-modeling activities to younger children, confi dent that they would likewise generate their own mathematics. I recently implemented data-modeling activities in a cohort of three first-grade classrooms of six year- olds. I report on some of the children’s responses and discuss the components of data modeling the children engaged in.

Modeling and multiway analysis of chatroom tensors

This work identifies the limitations of n-way data analysis techniques in multidimensional stream data, such as Internet chat room communications data, and establishes a link between data collection and performance of these techniques. Its contributions are twofold. First, it extends data analysis to multiple dimensions by constructing n-way data arrays known as high order tensors. Chat room tensors are generated by a simulator which collects and models actual communication data. The accuracy of the model is determined by the Kolmogorov-Smirnov goodness-of-fit test which compares the simulation data with the observed (real) data. Second, a detailed computational comparison is performed to test several data analysis techniques including svd [1], and multi-way techniques including Tucker1, Tucker3 [2], and Parafac [3].

Collective sampling and analysis of high order tensors for chatroom communications

This work investigates the accuracy and efficiency tradeoffs between centralized and collective (distributed) algorithms for (i) sampling, and (ii) n-way data analysis techniques in multidimensional stream data, such as Internet chatroom communications. Its contributions are threefold. First, we use the Kolmogorov-Smirnov goodness-of-fit test to show that statistical differences between real data obtained by collective sampling in time dimension from multiple servers and that of obtained from a single server are insignificant. Second, we show using the real data that collective data analysis of 3-way data arrays (users x keywords x time) known as high order tensors is more efficient than centralized algorithms with respect to both space and computational cost. Furthermore, we show that this gain is obtained without loss of accuracy. Third, we examine the sensitivity of collective constructions and analysis of high order data tensors to the choice of server selection and sampling window size. We construct 4-way tensors (users x keywords x time x servers) and analyze them to show the impact of server and window size selections on the results.

Data requirements and graph data structures for a multi-modal, multi-objective trip planner

Traffic congestion has a significant impact on the economy and environment. Encouraging the use of multimodal transport (public transport, bicycle, park’n’ride, etc.) has been identified by traffic operators as a good strategy to tackle congestion issues and its detrimental environmental impacts. A multi-modal and multi-objective trip planner provides users with various multi-modal options optimised on objectives that they prefer (cheapest, fastest, safest, etc) and has a potential to reduce congestion on both a temporal and spatial scale. The computation of multi-modal and multi-objective trips is a complicated mathematical problem, as it must integrate and utilize a diverse range of large data sets, including both road network information and public transport schedules, as well as optimising for a number of competing objectives, where fully optimising for one objective, such as travel time, can adversely affect other objectives, such as cost. The relationship between these objectives can also be quite subjective, as their priorities will vary from user to user. This paper will first outline the various data requirements and formats that are needed for the multi-modal multi-objective trip planner to operate, including static information about the physical infrastructure within Brisbane as well as real-time and historical data to predict traffic flow on the road network and the status of public transport. It will then present information on the graph data structures representing the road and public transport networks within Brisbane that are used in the trip planner to calculate optimal routes. This will allow for an investigation into the various shortest path algorithms that have been researched over the last few decades, and provide a foundation for the construction of the Multi-modal Multi-objective Trip Planner by the development of innovative new algorithms that can operate the large diverse data sets and competing objectives.

Critical Factors for Sustainable Post Natural Disaster Reconstruction : Chinese Data

While scientists continue to explore the level of climate change impact to new weather patterns and our environment in general, there have been some devastating natural disasters worldwide in the last two decades. Indeed natural disasters are becoming a major concern in our society. Yet in many previous examples, our reconstruction efforts only focused on providing short-term necessities. How to develop resilience in the long run is now a highlight for research and industry practice. This paper introduces a research project aimed at exploring the relationship between resilience building and sustainability in order to identify key factors during reconstruction efforts. From extensive literature study, the authors considered the inherent linkage between the two issues as evidenced from past research. They found that sustainability considerations can improve the level of resilience but are not currently given due attention. Reconstruction efforts need to focus on resilience factors but as part of urban development, they must also respond to the sustainability challenge. Sustainability issues in reconstruction projects need to be amplified, identified, processed, and managed properly. On-going research through empirical study aims to establish critical factors (CFs) for stakeholders in disaster prone areas to plan for and develop new building infrastructure through holistic considerations and balanced approaches to sustainability. A questionnaire survey examined a range of potential factors and the subsequent data analysis revealed six critical factors for sustainable Post Natural Disaster Reconstruction that include: considerable building materials and construction methods, good governance, multilateral coordination, appropriate land-use planning and policies, consideration of different social needs, and balanced combination of long-term and short-term needs. Findings from this study should have an influence on policy development towards Post Natural Disaster Reconstruction and help with the achievement of sustainable objectives.

An analysis of the KEEP CLEAR pavement markings effects on queuing vehicles dynamic performance at urban signalised intersections

KEEP CLEAR pavement markings are widely used at urban signalised intersections to indicate to drivers to avoid entering blocked intersections. For example, ‘Box junctions’ are most widely used in the United Kingdom and other European countries. However, in Australia, KEEP CLEAR markings are mostly used to improve access from side roads onto a main road, especially when the side road is very close to a signalised intersection. This paper aims to reveal how the KEEP CLEAR markings affect the dynamic performance of the queuing vehicles on the main road, where the side road access is near a signalised intersection. Raw traffic field data was collected from an intersection at the Gold Coast, Australia, and the Kanade–Lucas–Tomasi (KLT) feature tracker approach was used to extract dynamic vehicle data from the raw video footage. The data analysis reveals that the KEEP CLEAR markings generate positive effects on the queuing vehicles in discharge on the main road. This finding refutes the traditional viewpoint that the KEEP CLEAR pavement markings will cause delay for the queuing vehicles’ departure due to the enlarged queue spacing. Further studies are suggested in this paper as well.

Development and validation of a ultra performance LC-ESI/MS method for analysis of metabolic phenotypes of healthy men in day and night urine samples

Ultra-performance LC coupled to quadrupole TOF/MS (UPLC-QTOF/MS) in positive and negative ESI was developed and validated to analyze metabolite profiles for urine from healthy men during the day and at night. Data analysis using principal components analysis (PCA) revealed differences between metabolic phenotypes of urine in healthy men during the day and at night. Positive ions with mass-to-charge ratio (m/z) 310.24 (5.35 min), 286.24 (4.74 min) and 310.24 (5.63 min) were elevated in the urine from healthy men at night compared to that during the day. Negative ions elevated in day urine samples of healthy men included m/z 167.02 (0.66 min), 263.12 (2.55 min) and 191.03 (0.73 min), whilst ions m/z 212.01 (4.77 min) were at a lower concentration in urine of healthy men during the day compared to that at night. The ions m/z 212.01 (4.77 min), 191.03 (0.73 min) and 310.24 (5.35 min) preliminarily correspond to indoxyl sulfate, citric acid and N-acetylneuraminic acid, providing further support for an involvement of phenotypic difference in urine of healthy men in day and night samples, which may be associated with notably different activities of gut microbiota, velocity of tricarboxylic acid cycle and activity of sialic acid biosynthesis in healthy men as regulated by circadian rhythm of the mammalian bioclock.