M-ary trees for combinatorial asset management decision problems

A novel m-ary tree based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.

Educational stress scale for adolescents : development, validity, and reliability with Chinese students

This article describes the development and initial validation of a new instrument to measure academic stress—the Educational Stress Scale for Adolescents (ESSA). A series of cross-sectional questionnaire surveys were conducted with more than 2,000 Chinese adolescents to examine the psychometric properties. The final 16-item ESSA contains five latent variables: Pressure from study, Workload, Worry about grades, Self-expectation, and Despondency, which together explain 64% of the total item variance. Scale scores showed adequate internal consistency, 2-week test–retest reliability, and satisfactory concurrent validity. A confirmatory factor analysis suggested the proposed factor model fits well in a different sample. For researchers who have a particular interest in academic stress among adolescents, the ESSA promises to be a useful tool.

Economic growth and suicide rate changes : a case in China from 1982 to 2005

It is to estimate the trend of suicide rate changes during the past three decades in China and try to identify its social and economic correlates. Official data of suicide rates and economic indexes during 1982–2005 from Shandong Province of China were analyzed. The suicide data were categorized for the rural / urban location and gender, and the economic indexes include GDP, GDP per capita, rural income, and urban income, all adjusted for inflation. We found a significant increase of economic development and decrease of suicide rates over the past decades under study. The suicide rate decrease is correlated with the tremendous growth of economy. The unusual decrease of Chinese suicide rates in the past decades is accounted for within the Chinese cultural contexts and maybe by the Strain Theory of Suicide.

Estimation on disease burden related to hepatitis B virus infection in Shandong province of China [山东省乙型肝炎病毒感染所致相关疾病的疾病负担研究]

Objective: To comprehensively measure the burden of hepatitis B, liver cirrhosis and liver cancer in Shandong province, using disability-adjusted life years (DALYs) to estimate the disease burden attribute to hepatitis B virus (HBV)infection. Methods: Based on the mortality data of hepatitis B, liver cirrhosis and liver cancer derived from the third National Sampling Retrospective Survey for Causes of Death during 2004 and 2005, the incidence data of hepatitis B and the prevalence and the disability weights of liver cancer gained from the Shandong Cancer Prevalence Sampling Survey in 2007, we calculated the years of life lost (YLLs), years lived with disability (YLDs) and DALYs of three diseases following the procedures developed for the global burden of disease (GBD) study to ensure the comparability. Results: The total burden for hepatitis B, liver cirrhosis and liver cancer were 211 616 (39 377 YLLs and 172 239 YLDs), 16 783 (13 497 YLLs and 3286 YLDs) and 247 795 (240 236 YLLs and 7559 YLDs) DALYs in 2005 respectively, and men were 2.19, 2.36 and 3.16 times as that for women, respectively in Shandong province. The burden for hepatitis B was mainly because of disability (81.39%). However, most burden on liver cirrhosis and liver cancer were due to premature death (80.42% and 96.95%). The burden of each patient related to hepatitis B, liver cirrhosis and liver cancer were 4.8, 13.73 and 11.11 respectively. Conclusion: Hepatitis B, liver cirrhosis and liver cancer caused considerable burden to the people living in Shandong province, indicating that the control of hepatitis B virus infection would bring huge potential benefits.

Estimation on the mortality and disease burden attributed to selected risk factors in Shandong province [山东省主要危险因素的归因死亡和疾病负担分析]

Objective: To determine the major health related risk factors and provide evidence for policy-making,using health burden analysis on selected factors among general population from Shandong province. Methods: Based on data derived from the Third Death of Cause Sampling Survey in Shandong. Years of life lcrat(YLLs),yearS Iived with disability(YLDs)and disability-adjusted life years(DALYs) were calculated according to the GBD ethodology.Deaths and DALYs attributed to the selected risk factors were than estimated together with the PAF data from GBD 2001 study.The indirect method was employed to estimate the YLDs. Results: 51.09%of the total dearlls and 31.83%of the total DALYs from the Shandong population were resulted from the 19 selected risk factors.High blood pre.ure,smoking,low fruit and vegetable intake,aleohol consumption,indoor smoke from solid fuels,high cholesterol,urban air pollution, physical inactivity,overweight and obesity and unsafe injections in health care settings were identified as the top 10 risk faetors for mortality which together caused 50.21%of the total deaths.Alcohol use,smoking,high blood pressure,Low fruit and vegetable intake, indoor smoke from solid fuels, overweight and obesity,high cholesterol, physical inactivity,urban air pollution and iron-deficiency anemia were proved as the top 10 risk factors related to disease burden and were responsible for 29.04%of the total DALYs. Conclusion: Alcohol use.smoking and high blood pressure were determined as the major risk factors which influencing the health of residents in Shandong. The mortality and burden of disease could be reduced significantly if these major factors were effectively under control.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

Multi-body modelling of wagon trains for crashworthiness analysis

Three-dimensional wagon train models have been developed for the crashworthiness analysis using multi-body dynamics approach. The contributions of the train size (number of wagon) to the frontal crash forces can be identified through the simulations. The effects of crash energy management (CEM) design and crash speed on train crashworthiness are examined. The CEM design can significantly improve the train crashworthiness and the consequential vehicle stability performance - reducing derailment risks.

Nanostructures for sensors, electronics, energy and environment

Nanoscale science is growing evermore important on a global scale and is widely seen as playing an integral part in the growth of future world economies. The daunting energy crisis we are facing could be solved not only by new and improved ways of getting energy directly from the sun, but also by saving power thanks to advancements in electronics and sensors. New, cheap dye-sensitized and polymer solar cells hold the promise of environmentally friendly and simple production methods, along with mechanical flexibility and low weight, matching the conditions for a widespread deployment of this technology. Cheap sensors based on nanomaterials can make a fundamental contribution to the reduction of greenhouse gas emissions, allowing the creation of large sensor networks to monitor countries and cities, improving our quality of life. Nanowires and nano-platelets of metal oxides are at the forefront of the research to improve sensitivity and reduce the power consumption in gas sensors. Nanoelectronics is the next step in the electronic roadmap, with many devices currently in production already containing components smaller than 100 nm. Molecules and conducting polymers are at the forefront of this research with the goal of reducing component size through the use of cheap and environmentally friendly production methods. This, and the coming steps that will eventually bring the individual circuit element close to the ultimate limit of the atomic level, are expected to deliver better devices with reduced power consumption.

Extensible software for research data capture

Projects funded by the Australian National Data Service(ANDS). The specific projects that were funded included: a) Greenhouse Gas Emissions Project (N2O) with Prof. Peter Grace from QUT’s Institute of Sustainable Resources. b) Q150 Project for the management of multimedia data collected at Festival events with Prof. Phil Graham from QUT’s Institute of Creative Industries. c) Bio-diversity environmental sensing with Prof. Paul Roe from the QUT Microsoft eResearch Centre. For the purposes of these projects the Eclipse Rich Client Platform (Eclipse RCP) was chosen as an appropriate software development framework within which to develop the respective software. This poster will present a brief overview of the requirements of the projects, an overview of the experiences of the project team in using Eclipse RCP, report on the advantages and disadvantages of using Eclipse and it’s perspective on Eclipse as an integrated tool for supporting future data management requirements.

Sunscreen application thickness amongst primary school children : the children and sunscreen study

Childhood sun exposure has been associated with increased risk of developing melanoma later in life. Sunscreen, children.s preferred method of sun protection, has been shown to reduce skin cancer risk. However, the effectiveness of sunscreen is largely dependent on user compliance, such as the thickness of application. To reach the sun protection factor (SPF) sunscreen must be applied at a thickness of 2mg/cm2. It has been demonstrated that adults tend to apply less than half of the recommended 2mg/cm2. This was the first study to measure the thickness at which children apply sunscreen. We recruited 87 primary school aged children (n=87, median age 8.7, 5-12 years) from seven state schools within one Brisbane education district (32% consent rate). The children were supplied with sunscreen in three dispenser types (pump, squeeze and roll-on) and were asked to use these for one week each. We measured the weight of the sunscreen before and after use, and calculated the children.s body surface area (based on height and weight) and area to which sunscreen was applied (based on children.s self-reported body coverage of application). Combined these measurements resulted in an average thickness of sunscreen application, which was our main outcome measure. We asked parents to complete a self-administered questionnaire which captured information about potential explanatory variables. Children applied sunscreen at a median thickness of 0.48mg/cm2, significantly less than the recommended 2mg/cm2 (p<0.001). When using the roll-on dispenser (median 0.22mg/cm2), children applied significantly less sunscreen thickness, compared to the pump (median 0.75mg.cm2, p<0.001), and squeeze (median 0.57mg/cm2, p<0.001) dispensers. School grade (1-7) was significantly associated with thickness of application (p=0.032), with children in the youngest grades applying the most. Other variables that were significantly associated with the outcome variable included: number of siblings (p=0.001), household annual income (p<0.001), and the number of lifetime sunburns the child had experienced (p=0.007). This work is the first to measure children.s sunscreen application thickness and demonstrates that regardless of their age or the type of dispenser that they use, children do not apply enough sunscreen to reach the advertised SPF. It is envisaged that this study will assist in the formulation of recommendations for future research, practice and policy aimed at improving childhood sun protection to reduce skin cancer incidence in the future.

Handling incomplete data in survival analysis with multiple covariates

This paper studies the missing covariate problem which is often encountered in survival analysis. Three covariate imputation methods are employed in the study, and the effectiveness of each method is evaluated within the hazard prediction framework. Data from a typical engineering asset is used in the case study. Covariate values in some time steps are deliberately discarded to generate an incomplete covariate set. It is found that although the mean imputation method is simpler than others for solving missing covariate problems, the results calculated by it can differ largely from the real values of the missing covariates. This study also shows that in general, results obtained from the regression method are more accurate than those of the mean imputation method but at the cost of a higher computational expensive. Gaussian Mixture Model (GMM) method is found to be the most effective method within these three in terms of both computation efficiency and predication accuracy.

Renewal decision support for linear assets

Linear (or continuous) assets are engineering infrastructure that usually spans long distances and can be divided into different segments, all of which perform the same function but may be subject to different loads and environmental factors. Typical linear assets include railway lines, roads, pipelines and cables. How and when to renew such assets are critical decisions for asset owners as they normally involves significant capital investment. Through investigating the characteristics of linear asset renewal decisions and identifying the critical requirements that are associated with renewal decisions, we present a multi-criteria decision support method to help optimise renewal decisions. A case study that concerns renewal of an economiser's tubing system is a coal-fired power station is adopted to demonstrate the application of this method. Although the paper concerns a particular linear asset decision type, the approach has broad applicability for linear asset management.

Contextualising the teaching and learning of measurement in Torres Strait Islander schools

This paper reports on a mathematics project conducted with six Torres Strait Islander schools and communities by the research team at the YuMi Deadly Centre at QUT. Data collected is from a small focus group of six teachers and two teacher aides. We investigated how measurement is taught and learned by students, their teachers and teacher aides in the community schools. A key focus of the project was that the teaching and learning of measurement be contextualised to the students’ culture, community and home languages. A significant finding from the project was that the teachers had differing levels of knowledge and understanding about how to contextualise measurement to support student learning. For example, an Indigenous teacher identified that mathematics and the environment are relational, that is, they are not discrete and in isolation from one another, rather they mesh together, thus affording the articulation and interchange among and between mathematics and Torres Strait Islander culture.

A Bayesian‐Markov process for reliability prediction

Accurate reliability prediction for large-scale, long lived engineering is a crucial foundation for effective asset risk management and optimal maintenance decision making. However, a lack of failure data for assets that fail infrequently, and changing operational conditions over long periods of time, make accurate reliability prediction for such assets very challenging. To address this issue, we present a Bayesian-Marko best approach to reliability prediction using prior knowledge and condition monitoring data. In this approach, the Bayesian theory is used to incorporate prior information about failure probabilities and current information about asset health to make statistical inferences, while Markov chains are used to update and predict the health of assets based on condition monitoring data. The prior information can be supplied by domain experts, extracted from previous comparable cases or derived from basic engineering principles. Our approach differs from existing hybrid Bayesian models which are normally used to update the parameter estimation of a given distribution such as the Weibull-Bayesian distribution or the transition probabilities of a Markov chain. Instead, our new approach can be used to update predictions of failure probabilities when failure data are sparse or nonexistent, as is often the case for large-scale long-lived engineering assets.

A software framework for risk-aware business process management

With the large diffusion of Business Process Managemen (BPM) automation suites, the possibility of managing process-related risks arises. This paper introduces an innovative framework for process-related risk management and describes a working implementation realized by extending the YAWL system. The framework covers three aspects of risk management: risk monitoring, risk prevention, and risk mitigation. Risk monitoring functionality is provided using a sensor-based architecture, where sensors are defined at design time and used at run-time for monitoring purposes. Risk prevention functionality is provided in the form of suggestions about what should be executed, by who, and how, through the use of decision trees. Finally, risk mitigation functionality is provided as a sequence of remedial actions (e.g. reallocating, skipping, rolling back of a work item) that should be executed to restore the process to a normal situation.