Is quality more important if you’re quirky? A review of the literature on differential susceptibility to childcare environments

Evidence concerning the impact of child care on child development suggests that higher-quality environments, particularly those that are more responsive, predict more favourable social and behavioural outcomes. However, the extent of this effect is not as great as might be expected. Impacts on child outcomes are, at best, modest. One recent explanation emerging from a new theoretical perspective of development, differential susceptibility theory, is that a minority of children are more reactive to both positive and negative environments, while the majority are relatively unaffected. These 'quirky' children have temperamental traits that are more extreme, and are often described in research studies as having 'difficult temperaments'. This paper reviews the literature on such children and argues for the need for further research to identify components of childcare environments that optimise the potential of these more sensitive, quirky individuals.

A public asset management framework for Indonesian local governments

The decentralisation reform in Indonesia has mandated the Central Government to transfer some functions and responsibilities to local governments including the transfer of human resources, assets and budgets. Local governments became giant asset holders almost overnight and most were ill prepared to handle these transformations. Assets were transferred without analysing local government need, ability or capability to manage the assets and no local government was provided with an asset management framework. Therefore, the aim of this research is to develop a Public Asset Management Framework for provincial governments in Indonesia, especially for infrastructure and real property assets. This framework will enable provincial governments to develop integrated asset management procedures throughout asset‘s lifecycle. Achieving the research aim means answering the following three research questions; 1) How do provincial governments in Indonesia currently manage their public assets? 2) What factors influence the provincial governments in managing these public assets? 3) How is a Public Asset Management Framework developed that is specific for the Indonesian provincial governments‘ situation? This research applied case studies approach after a literature review; document retrieval, interviews and observations were collated. Data was collected in June 2009 (preliminary data collection) and January to July 2010 in the major eastern Indonesian provinces. Once the public asset management framework was developed, a focus group was used to verify the framework. Results are threefold and indicate that Indonesian provincial governments need to improve the effectiveness and efficiency of current practice of public asset management in order to improve public service quality. The second result shows that the 5 major concerns that influence the local government public asset management processes are asset identification and inventory systems, public asset holding, asset guidance and legal arrangements, asset management efficiency and effectiveness, and, human resources and their organisational arrangements. The framework was then applied to assets already transferred to local governments and so included a system of asset identification and a needs analysis to classify the importance of these assets to local governments, their functions and responsibilities in delivering public services. Assets that support local government functions and responsibilities will then be managed using suitable asset lifecycle processes. Those categorised as surplus assets should be disposed. Additionally functions and responsibilities that do not need an asset solution should be performed directly by local governments. These processes must be measured using performance measurement indicators. All these stages should be guided and regulated with sufficient laws and regulations. Constant improvements to the quality and quantity of human resources hold an important role in successful public asset management processes. This research focuses on developing countries, and contributes toward the knowledge of a Public Asset Management Framework at local government level, particularly Indonesia. The framework provides local governments a foundation to improve their effectiveness and efficiency in managing public assets, which could lead to improved public service quality. This framework will ensure that the best decisions are made throughout asset decision ownership and provide a better asset life cycle process, leading to selection of the most appropriate asset, improve its acquisition and delivery process, optimise asset performance, and provide an appropriate disposal program.

Towards green universities : a decision support framework for green roof and living wall implementation

This research examined why university campus development has not fully embraced green technology despite common expectations. Semi-structured interviews and a Delphi Study explored universities’ organisational issues and delivery processes for projects with a sustainability focus. Critical organisational components and their internal relationships were studied, and critical factors for project success identified. A decision-making framework was developed to provide strategic directions for universities to optimise organisational environment and overcome barriers in order to better deliver sustainable projects on campuses.

Mapping semantic knowledge for unsupervised text categorisation

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.

The next generation health intervention tool - Can smart phone applications help young people track and moderate alcohol use and potential harms?

Whilst alcohol is a common feature of many social gatherings, there are numerous immediate and long-term health and social harms associated with its abuse. Alcohol consumption is the world’s third largest risk factor for disease and disability with almost 4% of all deaths worldwide attributed to alcohol. Not surprisingly, alcohol use and binge drinking by young people is of particular concern with Australian data reporting that 39% of young people (18-19yrs) admitted drinking at least weekly and 32% drank to levels that put them at risk of alcohol-related harm. The growing market penetration and connectivity of smartphones may be an opportunities for innovation in promoting health-related self-management of substance use. However, little is known about how best to harness and optimise this technology for health-related intervention and behaviour change. This paper explores the utility and interface of smartphone technology as a health intervention tool to monitor and moderate alcohol use. A review of the psychological health applications of this technology will be presented along with the findings of a series of focus groups, surveys and behavioural field trials of several drink-monitoring applications. Qualitative and quantitative data will be presented on the perceptions, preferences and utility of the design, usability and functionality of smartphone apps to monitoring and moderate alcohol use. How these findings have shaped the development and evolution of the OnTrack app will be specifically discussed, along with future directions and applications of this technology in health intervention, prevention and promotion.

Environments for Healthy Living (EFHL) Griffith Birth Cohort Study : background and methods

The health of an individual is determined by the interaction of genetic and individual factors with wider social and environmental elements. Public health approaches to improving the health of disadvantaged populations will be most effective if they optimise influences at each of these levels, particularly in the early part of the life course. In order to better ascertain the relative contribution of these multi-level determinants there is a need for robust studies, longitudinal and prospective in nature, that examine individual, familial, social and environmental exposures. This paper describes the study background and methods, as it has been implemented in an Australian birth cohort study, Environments for Healthy Living (EFHL): The Griffith Study of Population Health. EFHL is a prospective, multi-level, multi-year longitudinal birth cohort study, designed to collect information from before birth through to adulthood across a spectrum of eco-epidemiological factors, including genetic material from cord-blood samples at birth, individual and familial factors, to spatial data on the living environment. EFHL commenced the pilot phase of recruitment in 2006 and open recruitment in 2007, with a target sample size of 4000 mother/infant dyads. Detailed information on each participant is obtained at birth, 12-months, 3-years, 5-years and subsequent three to five yearly intervals. The findings of this research will provide detailed evidence on the relative contribution of multi-level determinants of health, which can be used to inform social policy and intervention strategies that will facilitate healthy behaviours and choices across sub-populations.

The measurement of adenosine and estrogen receptor expression in rat brains following ovariectomy using quantitative PCR analysis

In our laboratory we have developed a quantitative-polymerase chain reaction (Q-PCR) strategy to examine the differential expression of adenosine receptor (ADOR), A(1), A(2A), A(2B) and A(3), and estrogen receptors (ER) alpha and beta. Brain and uterine mRNA were first used to optimise specific amplification conditions prior to SYBR Green I real time analysis of receptor subtype expression. SYBR Green I provided a convenient and sensitive means of examining specific PCR amplification product in real time, and allowed the generation of standard curves from which relative receptor abundance could be determined. Real time Q-PCR analysis was then performed, to examine changes in receptor expression levels in brains of adult female Wistar rats 3-month post ovariectomy. Comparison with sham-operated age-matched control rats demonstrated both comparative and absolute-copy number changes in receptor levels. Evaluation of both analytical methods investigated 18S rRNA as an internal reference for comparative gene expression analysis in the brain. The results of this study revealed preferential repression of ADORA(2A) (>4-fold down) and consistent (>2-fold) down-regulation of ADORA(1), ADORA(3), and ER-beta, following ovariectomy. No change was found in ADORA(2B) or ER-alpha. Analysis of absolute copy number in this study revealed a correlation between receptor expression in response to ovariectomy, and relative receptor subtype abundance in the brain.

Two-stage power network reconfiguration strategy considering node importance and restored generation capacity

Network reconfiguration after complete blackout of a power system is an essential step for power system restoration. A new node importance evaluation method is presented based on the concept of regret, and maximisation of the average importance of a path is employed as the objective of finding the optimal restoration path. Then, a two-stage method is presented to optimise the network reconfiguration strategy. Specifically, the restoration sequence of generating units is first optimised so as to maximise the restored generation capacity, then the optimal restoration path is selected to restore the generating nodes concerned and the issues of selecting a serial or parallel restoration mode and the reconnecting failure of a transmission line are next considered. Both the restoration path selection and skeleton-network determination are implemented together in the proposed method, which overcomes the shortcoming of separate decision-making in the existing methods. Finally, the New England 10-unit 39-bus power system and the Guangzhou power system in South China are employed to demonstrate the basic features of the proposed method.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Quality of life, fatigue and activity in Australians with chronic kidney disease : a longitudinal study

Aims and objectives This study sought to determine the relationship between health related quality of life (HRQoL), fatigue and activity levels of people with anaemia secondary to chronic kidney disease (CKD) over a 12 month period following the introduction of an erythropoietin stimulating agent (ESA). Background CKD occurs in five stages and it is a complex chronic illness which severely impacts on an individual’s HRQoL, and ability to perform everyday activities. Fatigue is also a common symptom experienced by people with CKD. Design and methods Using a longitudinal repeated measures design, 28 people with CKD completed the SF-36, human activity profile and fatigue severity scale at the commencement of an ESA and then at 3, 6 and 12 months. Results Over a 12 month period, people reported a significant change in HRQoL in relation to role physical, vitality, mental health/emotional well-being and overall mental health. However activity levels did not significantly improve during that time. Both the amount of breathlessness and level of fatigue were highest at baseline and declined over time. Both fatigue and breathlessness were correlated with less reported general health over time. Conclusion Renal nurses, in dialysis units and CKD outpatient clinics, have repeated and frequent contact with people with CKD over long periods of time, and are in an ideal position to routinely assess fatigue and activity levels and to institute timely interventions. Early detection would enable timely nursing interventions to optimise HRQoL and independent activity. Relevance to Clinical Practice Drawing on rehabilitation nursing interventions could assist renal nurses to minimize the burden of fatigue and its impact on simple everyday activities and a person’s quality of life. These interventions are important for people who are living at home and could assist in lowering the burden on home support services.

Vehicle energy throughput analysis as a drivetrain motor design aid

There are many variables to consider in the design of an electric motor. However, meeting the performance requirements for an electric vehicle drive may cause a designer to loose focus on its typical operation and hence fail to optimise the motor in the region where it processes the most power. This paper investigates operating requirements of electric vehicle motor drives using the University concept vehicle as an example. The paper outlines a methodology for determining primary operating region of a vehicle drive. The methodology is applied to standard driving cycles that are commonly used in the design and testing of vehicles.

Complications of perioperative warfarin therapy in total knee arthroplasty

Patients presenting for knee replacement on warfarin for medical reasons often require higher levels of anticoagulation peri-operatively than primary thromboprophylaxis and may require bridging therapy with heparin. We performed a retrospective case control study on 149 consecutive primary knee arthroplasty patients to investigate whether anti-coagulation affected short-term outcomes. Specific outcome measures indicated significant increases in prolonged wound drainage (26.8% of cases vs 7.3% of controls, p<0.001); superficial infection (16.8% vs 3.3%, p<0.001); deep infection (6.0% vs 0%, p<0.001); return-to-theatre for washout (4.7% vs 0.7%, p=0.004); and revision (4.7% vs 0.3%, p=0.001). Management of patients on long-term warfarin therapy following TKR is particularly challenging, as the surgeon must balance risk of thromboembolism against post-operative complications on an individual patient basis in order to optimise outcomes.

Smart grid-demand side response model to mitigate prices and peak impact on the electrical system

The aims of this project is to develop demand side response model which assists electricity consumers who are exposed to the market price through aggregator to manage the air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimise the energy cost caused by the air-conditioning load considering the electricity market price and network overload. The model is tested with selected characteristics of the room, Queensland electricity market data from Australian Energy Market Operator and data from the Bureau of Statistics on temperatures in Brisbane, during weekdays on hot days from 2011 - 2012.

The interface of acute and aged care : the role of the nurse in a provincial area

Objective This study was to investigate issues that arose from pre-admission to post-discharge, for people in Toowoomba, Queensland over the age of 65 admitted to an acute facility. This paper concentrates on a significant concern that emerged from the large amount of data collected during this project, that is,the role of the nurse in the continuum of health care involving elderly people. Method The study involved a multi-site, multi-agency and multi-method (qualitative and quantitative) approach. Data was collected from regional service providers, the Department of Health and Aged Care (DHAC), the Australian Bureau of Statistics (ABS), Home and Community Care (HACC), the Aged Care Assessment Team (ACAT), elderly people who had been discharged from regional hospitals and their carers, residents of regional aged care facilities, area health professionals and elderly regional hospital inpatients. Results The data indicated that nurses in this provincial area currently play a limited role in preadmission planning, being mostly concerned with elective surgery, especially joint replacements. While nurses deliver the majority of care during hospitalisation, they do not appear to be cognizant of the needs of the elderly regarding post-acute discharge. Conclusion The recent introduction of the model of nurse case management in the acute sector appears to be a positive development that will streamline and optimise the health care of the elderly across the continuum in the Toowoomba area. The paper recommends some strategies, such as discharge liaison nurses based in Emergency Departments and the expansion of the nurse case management role, which would optimise care for the elderly person at the interface of care.

EUELC project : a multi-centre, multipurpose study to investigate early stage NSCLC, and to establish a biobank for ongoing collaboration

The European Early Lung Cancer (EUELC) project aims to determine if specific genetic alterations occurring in lung carcinogenesis are detectable in the respiratory epithelium. In order to pursue this objective, nonsmall cell lung cancer (NSCLC) patients with a very high risk of developing progressive lung cancer were recruited from 12 centres in eight European countries: France, Germany, southern Ireland, Italy, the Netherlands, Poland, Spain and the UK. In addition, NSCLC patients were followed up every 6 months for 36 months. A European Bronchial Tissue Bank was set up at the University of Liverpool (Liverpool, UK) to optimise the use of biological specimens. The molecular - pathological investigations were subdivided into specific work packages that were delivered by EUELC Partners. The work packages encompassed mutational analysis, genetic instability, methylation profiling, expression profiling utilising immunohistochemistry and chip-based technologies, as well as in-depth analysis of FHIT and RARβ genes, the telomerase catalytic subunit hTERT and genotyping of susceptibility genes in specific pathways. The EUELC project engendered a tremendous collaborative effort, and it enabled the EUELC Partners to establish protocols for assessing molecular biomarkers in early lung cancer with the view to using such biomarkers for early diagnosis and as intermediate end-points in future chemopreventive programmes. Copyright©ERS Journals Ltd 2009.