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Background Self-regulation has been found to be an important contributor to a range of outcomes, with delay of gratification (a self-regulatory skill) predicting better academic, social and personal functioning. There is some evidence that individuals with Down syndrome have difficulty with delay of gratification. We investigated the question of whether this difficulty is common to intellectual disability irrespective of aetiology, or whether it presents a particular problem for those with Down syndrome. The latter was considered a possibility because of language difficulties in this group. Method Three groups of children with a mean MA between 36 and 60 months participated in the study: children with Down syndrome ( n = 32), children with a moderate intellectual disability from a cause other than Down syndrome ( n = 26) and typically developing children ( n = 50). Children completed a series of measures of language and cognitive functioning and participated in a delay of gratification task. Results The group of children with Down syndrome delayed for a significantly shorter time than either of the other two groups that did not differ from each other. Receptive language was associated with delay time for the children with Down syndrome but not for the typically developing group, nor for the group with moderate intellectual disability. Conclusions Children with Down syndrome appear to have a particular difficulty with delay of gratification. Language abilities would seem to be implicated in this difficulty, although further examination of this hypothesis is required. [ABSTRACT FROM AUTHOR]

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia's domestic low cost carriers’ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highestR2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities. [ABSTRACT FROM AUTHOR]

Sustainable water management is an essential aspect of all industries. This is particularly true in regional Australia, which in known for its harsh climate, with arid conditions. In this work we investigate the sustainable water management of major Australian regional airports. A specific case study of Mildura Airport, as the largest regional airport in the State of Victoria, is presented. Potable water is of particular importance, as it has the largest cost associated with its use. Sustainable means of supplying potable water can be significant to the operating costs of a regional airport. In an attempt to determine the potential for water harvesting at regional airports a novel image processing approach was taken to analyse the water capture area. This involved utilising satellite imagery and the image processing functionalities of Matlab, with some simple mathematics to estimate roofed areas. From here meteorological data gives rainfall data, in terms of depth, facilitating volume capture potentials. It was found that the average potable water harvesting potential for Mildura Airport is 3.964 megalitres per year. [ABSTRACT FROM PUBLISHER]

This study focuses on predicting Australia's low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia's real GDP, real GDP per capita, air fares, Australia's population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highestR-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively. [ABSTRACT FROM PUBLISHER]

Due to the vast distances across the country as well as between urban centres, Australia is heavily reliant upon its air transport industry. Following deregulation of Australia's domestic air travel market on the 30th October, 1990, low cost carriers have entered the market. Australia's LCC market has had three discrete phases. The first wave occurred between 1990 and 1993 and was subsequently followed by a duopoly period in 1994–1999. The second wave occurred between 2000 and 2006 and the final wave has been in the post-2006 period. This paper examines the evolution of Australia's domestic low cost carrier airline market and finds that by 2010, low cost carriers had captured around 64 per cent of the market. Following the evolution of the “Virgin Australia” business model from a low cost carrier to a full service network carrier, commencing in 2011, the low cost carrier's market share has declined significantly and is now around 31 per cent. “Jetstar” and “Tiger Airways” are the two major carriers presently operating in this market segment. [ABSTRACT FROM PUBLISHER]

Background Genome-wide linkage studies have identified the 9q22 chromosomal region as linked with colorectal cancer (CRC) predisposition. A candidate gene in this region is transforming growth factor β receptor 1 (TGFBR1). Investigation of TGFBR1 has focused on the common genetic variant rs11466445, a short exonic deletion of nine base pairs which results in truncation of a stretch of nine alanine residues to six alanine residues in the gene product. While the six alanine (*6A) allele has been reported to be associated with increased risk of CRC in some population based study groups this association remains the subject of robust debate. To date, reports have been limited to population-based case--control association studies, or case--control studies of CRC families selecting one affected individual per family. No study has yet taken advantage of all the genetic information provided by multiplex CRC families. Methods We have tested for an association between rs11466445 and risk of CRC using several family- based statistical tests in a new study group comprising members of non-syndromic high risk CRC families sourced from three familial cancer centres, two in Australia and one in Spain. Results We report a finding of a nominally significant result using the pedigree-based association test approach (PBAT; p = 0.028), while other family-based tests were non-significant, but with a p-value < 0.10 in each instance. These other tests included the Generalised Disequilibrium Test (GDT; p = 0.085), parent of origin GDT Generalised Disequilibrium Test (GDT-PO; p = 0.081) and empirical Family-Based Association Test (FBAT; p = 0.096, additive model). Related-person case--control testing using the "More Powerful" Quasi-Likelihood Score Test did not provide any evidence for association (MQLS ; p = 0.41). Conclusions After conservatively taking into account considerations for multiple hypothesis testing, we find little evidence for an association between the TGFBR1*6A allele and CRC risk in these families. The weak support for an increase in risk in CRC predisposed families is in agreement with recent meta-analyses of case--control studies, which estimate only a modest increase in sporadic CRC risk among 6*A allele carriers. [ABSTRACT FROM AUTHOR]