An innovative information gathering and data analysis platform for railway level crossing safety data

This paper describes an innovative platform that facilitates the collection of objective safety data around occurrences at railway level crossings using data sources including forward-facing video, telemetry from trains and geo-referenced asset and survey data. This platform is being developed with support by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper provides a description of the underlying accident causation model, the development methodology and refinement process as well as a description of the data collection platform. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

How does homework 'work' for young children? Children's accounts of homework in their everyday lives

Homework is an increasing yet under-researched part of young children’s everyday lives. Framed by the international agendas of starting strong and school accountability, homework in the lives of young children has been either overlooked or considered from the perspective of adults rather than from the perspective of children themselves. This paper redresses this situation by reporting on an Australian study of 120 young children, aged four to eight years, where homework emerges as a key part of their everyday lives. Children’s own accounts of their everyday decision-making, using audio-taped conversations and concurrent paper-based timeline activities, show homework as accomplishing the institutional purposes of the school, while affording the children opportunities to demonstrate their competence in operating in an adult-generated education regime.

Rates of radiologically confirmed pneumonia as defined by the World Health Organization in Northern Territory Indigenous children

Objective To determine the burden of hospitalised, radiologically confirmed pneumonia (World Health Organization protocol) in Northern Territory Indigenous children. Design, setting and participants Historical, observational study of all hospital admissions for any diagnosis of NT resident Indigenous children, aged between >= 29 days and < 5 years, 1 April 1997 to 31 March 2005. Intervention All chest radiographs taken during these admissions, regardless of diagnosis, were assessed for pneumonia in accordance with the WHO protocol. Main outcome measure The primary outcome was endpoint consolidation (dense fluffy consolidation [alveolar infiltrate] of a portion of a lobe or the entire lung) present on a chest radiograph within 3 days of hospitalisation. Results We analysed data on 24 115 hospitalised episodes of care for 9492 children and 13 683 chest radiographs. The average annual cumulative incidence of endpoint consolidation was 26.6 per 1000 population per year (95% Cl, 25.3-27.9); 57.5 per 1000 per year in infants aged 1-11 months, 38.3 per 1000 per year in those aged 12-23 months, and 13.3 per 1000 per year in those aged 24-59 months. In all age groups, rates of endpoint consolidation in children in the arid southern region of NT were about twice that of children in the tropical northern region. Conclusion The rates of severe pneumonia in hospitalised NT Indigenous children are among the highest reported in the world. Reducing this unacceptable burden of disease should be a national health priority.

Identification of radiological alveolar pneumonia in children with high rates of hospitalized respiratory infections : Comparison of WHO-defined and pediatric pulmonologist diagnosis in the clinical context

Background A reliable standardized diagnosis of pneumonia in children has long been difficult to achieve. Clinical and radiological criteria have been developed by the World Health Organization (WHO), however, their generalizability to different populations is uncertain. We evaluated WHO defined chest radiograph (CXRs) confirmed alveolar pneumonia in the clinical context in Central Australian Aboriginal children, a high risk population, hospitalized with acute lower respiratory illness (ALRI). Methods CXRs in children (aged 1-60 months) hospitalized and treated with intravenous antibiotics for ALRI and enrolled in a randomized controlled trial (RCT) of Vitamin A/Zinc supplementation were matched with data collected during a population-based study of WHO-defined primary endpoint pneumonia (WHO-EPC). These CXRs were reread by a pediatric pulmonologist (PP) and classified as pneumonia-PP when alveolar changes were present. Sensitivities, specificities, positive and negative predictive values (PPV, NPV) for clinical presentations were compared between WHO-EPC and pneumonia-PP. Results Of the 147 episodes of hospitalized ALRI, WHO-EPC was significantly less commonly diagnosed in 40 (27.2%) compared to pneumonia-PP (difference 20.4%, 95% CI 9.6-31.2, P < 0.001). Clinical signs on admission were poor predictors for both pneumonia-PP and WHO-EPC; the sensitivities of clinical signs ranged from a high of 45% for tachypnea to 5% for fever + tachypnea + chest-indrawing. The PPV range was 40-20%, respectively. Higher PPVs were observed against the pediatric pulmonologist's diagnosis compared to WHO-EPC. Conclusions WHO-EPC underestimates alveolar consolidation in a clinical context. Its use in clinical practice or in research designed to inform clinical management in this population should be avoided. Pediatr Pulmonol. 2012; 47:386-392. (C) 2011 Wiley Periodicals, Inc.

Cough formation in viral infections in children

Using a multidisciplinary approach, Human Respiratory Viral Infections is set at the level between the definitive reference work and an essential clinical manual. Exploring recent advances in human respiratory viral research, the text builds on the basic sciences of epidemiology, virology, molecular biology, and immunology to cover clinical diagnosis, mechanism of pathogenesis, manifestations of disease, impact, treatment, and management strategies.

Online belongings : female fan experiences in online soccer forums

Introduction: The Paradox at the Heart of Online Interaction

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

An adipoinductive role of inflammation in adipose tissue engineering : key factors in the early development of engineered soft tissues

Tissue engineering and cell implantation therapies are gaining popularity because of their potential to repair and regenerate tissues and organs. To investigate the role of inflammatory cytokines in new tissue development in engineered tissues, we have characterized the nature and timing of cell populations forming new adipose tissue in a mouse tissue engineering chamber (TEC) and characterized the gene and protein expression of cytokines in the newly developing tissues. EGFP-labeled bone marrow transplant mice and MacGreen mice were implanted with TEC for periods ranging from 0.5 days to 6 weeks. Tissues were collected at various time points and assessed for cytokine expression through ELISA and mRNA analysis or labeled for specific cell populations in the TEC. Macrophage-derived factors, such as monocyte chemotactic protein-1 (MCP-1), appear to induce adipogenesis by recruiting macrophages and bone marrow-derived precursor cells to the TEC at early time points, with a second wave of nonbone marrow-derived progenitors. Gene expression analysis suggests that TNFα, LCN-2, and Interleukin 1β are important in early stages of neo-adipogenesis. Increasing platelet-derived growth factor and vascular endothelial cell growth factor expression at early time points correlates with preadipocyte proliferation and induction of angiogenesis. This study provides new information about key elements that are involved in early development of new adipose tissue.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

Saffman-Taylor fingers with kinetic undercooling

The mathematical model of a steadily propagating Saffman-Taylor finger in a Hele-Shaw channel has applications to two-dimensional interacting streamer discharges which are aligned in a periodic array. In the streamer context, the relevant regularisation on the interface is not provided by surface tension, but instead has been postulated to involve a mechanism equivalent to kinetic undercooling, which acts to penalise high velocities and prevent blow-up of the unregularised solution. Previous asymptotic results for the Hele-Shaw finger problem with kinetic undercooling suggest that for a given value of the kinetic undercooling parameter, there is a discrete set of possible finger shapes, each analytic at the nose and occupying a different fraction of the channel width. In the limit in which the kinetic undercooling parameter vanishes, the fraction for each family approaches 1/2, suggesting that this selection of 1/2 by kinetic undercooling is qualitatively similar to the well-known analogue with surface tension. We treat the numerical problem of computing these Saffman-Taylor fingers with kinetic undercooling, which turns out to be more subtle than the analogue with surface tension, since kinetic undercooling permits finger shapes which are corner-free but not analytic. We provide numerical evidence for the selection mechanism by setting up a problem with both kinetic undercooling and surface tension, and numerically taking the limit that the surface tension vanishes.

The private problem with public service: Rural teachers in educational markets

Staffing rural and remote schools is an important policy issue for the public good. This paper examines the private issues it also poses for teachers with families working in these communities, as they seek to reconcile careers with educational choices for children. The paper first considers historical responses to staffing rural and remote schools in Australia, and the emergence of neoliberal policy encouraging marketisation of the education sector. We report on interviews about considerations motivating household mobility with 11 teachers across regional, rural and remote communities in Queensland. Like other middle-class parents, these teachers prioritised their children’s educational opportunities over career opportunities. The analysis demonstrates how teachers in rural and remote communities constitute a special group of educational consumers with insider knowledge and unique dilemmas around school choice. Their heightened anxieties around school choice under neoliberal policy are shown to contribute to the public issue of staffing rural and remote schools.

Evaluative decision-making for high-quality professional development: Cultivating an evaluative stance

Unprecedented policy attention to early childhood education internationally has highlighted the crucial need for a skilled early years workforce. Consequently, professional development of early years educators has become a global policy imperative. At the same time, many maintain that professional development research has reached an impasse. In this paper, we offer a new approach to addressing this impasse. In contrast to calls for a redesign of comparative studies of professional development programs, or for the refinement of researcher-constructed professional development evaluation frameworks, we argue the need to cultivate what we refer to as an ‘evaluative stance’ amongst all involved in making decisions about professional development in the early years – from senior bureaucrats with responsibilities for funding professional development programs to individual educators with choices about which professional development opportunities to take up. Drawing on three bodies of literature -- evaluation capacity building, personal epistemology, and co-production -- that, for the most part, have been overlooked with respect to early years professional learning this paper proposes a conceptual framework to explain why cultivating an evaluative stance in professional development decision-making has rich possibilities for systemic, sustainable, and transformative change in early years education.

Forward: Which childhood? Whose deficit?

A commentary on Whiteness studies, linguistic and cultural minority and Indigenous studies in early childhood language and literacy socialization. When the literature on ‘Whiteness’ first emerged in the 1990s, I was offended and skeptical. As an Asian who has lived in White-dominant cultures most of my life, my reflex was to say something like: “Yeah – they want to be ‘special’ too. After all our struggles to get beyond an unmarked place of deficit in the fields of disciplinary knowledge and social sciences – now they want ‘Whiteness’ as their own ethnic studies”...