Predicting Australian adults' sun-safe behaviour : examining the role of personal and social norms

Objectives To address the scarcity of comprehensive, theory-based research in the Australian context, this study, using a theory of planned behaviour (TPB) framework, investigated the role of personal and social norms to identify the key predictors of adult Australians' sun-safe intentions and behaviour. Design The study used a prospective design with two waves of data collection, 1 week apart. Methods Participants were 816 adults (48.2% men) aged between 18 and 88 years recruited from urban, regional, and rural areas of Australia. At baseline, participants completed a questionnaire assessing the standard TPB predictors (attitude, subjective norm, and perceived behavioural control [PBC]), past behaviour, behavioural intention, and additional measures of group norm for the referent groups of friends and family, image norm, personal norm, personal choice/responsibility, and Australian identity. Seventy-one per cent of the participants (n = 577) reported on their sun-safe behaviour in the subsequent week. Results Via path modelling, past behaviour, attitude, group norm (friends), personal norm, and personal choice/responsibility emerged as independent predictors of intentions which, in turn, predicted sun-safe behaviour prospectively. Past behaviour, but not PBC, had direct effects on sun-safe behaviour. The model explained 61.6% and 43.9% of the variance in intention and behaviour, respectively. Conclusions This study provides support for the use of a comprehensive theoretical decision-making model to explain Australian adults' sun-safe intentions and behaviours and identifies viable targets for health-promoting messages in this high-risk context.

A novel tuning method for advanced line drop compensator and its application to response coordination of distributed generation with voltage regulating devices

Nowadays, integration of small-scale electricity generators, known as Distributed Generation (DG), into distribution networks has become increasingly popular. This tendency together with the falling price of DG units has a great potential in giving the DG a better chance to participate in voltage regulation process, in parallel with other regulating devices already available in the distribution systems. The voltage control issue turns out to be a very challenging problem for distribution engineers, since existing control coordination schemes need to be reconsidered to take into account the DG operation. In this paper, a control coordination approach is proposed, which is able to utilize the ability of the DG as a voltage regulator, and at the same time minimize the interaction of DG with another DG or other active devices, such as On-load Tap Changing Transformer (OLTC). The proposed technique has been developed based on the concepts of protection principles (magnitude grading and time grading) for response coordination of DG and other regulating devices and uses Advanced Line Drop Compensators (ALDCs) for implementation. A distribution feeder with tap changing transformer and DG units has been extracted from a practical system to test the proposed control technique. The results show that the proposed method provides an effective solution for coordination of DG with another DG or voltage regulating devices and the integration of protection principles has considerably reduced the control interaction to achieve the desired voltage correction.

Adaptable interface model for intuitively learnable interfaces : an approach to address diversity in older users' capabilities

This study started with the aim to develop an approach that will help designers create interfaces that are more intuitive for older adults to use. Two objectives were set for this study: 1) to investigate one of the possible strategies for developing intuitive interfaces for older people, and; 2) to investigate factors that could interfere with intuitive use. This paper briefly presents the outcome of the two experiments and how it has lead to the development of an adaptable interface design model that will help designers develop interfaces that are intuitive to learn and, over time, intuitive to use for users with diverse technology prior experience and cognitive abilities.

Improved fabrication of melt electrospun tissue engineering scaffolds using direct writing and advanced electric field control

Direct writing melt electrospinning is an additive manufacturing technique capable of the layer-by-layer fabrication of highly ordered 3d tissue engineering scaffolds from micron-diameter fibres. The utility of these scaffolds, however, is limited by the maximum achievable height of controlled fibre deposition, beyond which the structure becomes increasingly disordered. A source of this disorder is charge build-up on the deposited polymer producing unwanted coulombic forces. In this study we introduce a novel melt electrospinning platform with dual voltage power supplies to reduce undesirable charge effects and improve fibre deposition control. We produced and characterised several 90° cross-hatched fibre scaffolds using a range of needle/collector plate voltages. Fibre thickness was found to be sensitive only to overall potential and invariant to specific tip/collector voltage. We also produced ordered scaffolds up to 200 layers thick (fibre spacing 1 mm, diameter 40 μm) and characterised structure in terms of three distinct zones; ordered, semi-ordered and disordered. Our in vitro analysis indicates successful cell attachment and distribution throughout the scaffolds, with little evidence of cell death after seven days. This study demonstrates the importance of electrostatic control for reducing destabilising polymer charge effects and enabling the fabrication of morphologically suitable scaffolds for tissue engineering.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Inter-cycle variability of ignition delay in an ethanol fumigated common rail diesel engine

An experimental study has been performed to investigate the ignition delay of a modern heavy-duty common-rail diesel engine run with fumigated ethanol substitutions up to 40% on an energy basis. The ignition delay was determined through the use of statistical modelling in a Bayesian framework this framework allows for the accurate determination of the start of combustion from single consecutive cycles and does not require any differentiation of the in-cylinder pressure signal. At full load the ignition delay has been shown to decrease with increasing ethanol substitutions and evidence of combustion with high ethanol substitutions prior to diesel injection have also been shown experimentally and by modelling. Whereas, at half load increasing ethanol substitutions have increased the ignition delay. A threshold absolute air to fuel ratio (mole basis) of above ~110 for consistent operation has been determined from the inter-cycle variability of the ignition delay, a result that agrees well with previous research of other in-cylinder parameters and further highlights the correlation between the air to fuel ratio and inter-cycle variability. Numerical modelling to investigate the sensitivity of ethanol combustion has also been performed. It has been shown that ethanol combustion is sensitive to the initial air temperature around the feasible operating conditions of the engine. Moreover, a negative temperature coefficient region of approximately 900{1050 K (the approximate temperature at fuel injection) has been shown with for n-heptane and n-heptane/ethanol blends in the numerical modelling. A consequence of this is that the dominate effect influencing the ignition delay under increasing ethanol substitutions may rather be from an increase in chemical reactions and not from in-cylinder temperature. Further investigation revealed that the chemical reactions at low ethanol substitutions are different compared to the high (> 20%) ethanol substitutions.

Comprehensive contribution of filament thickness and crosslinker failure to the rheological property of F-actin cytoskeleton

Rheological property of F-actin cytoskeleton is significant to the restructuring of cytoskeleton under a variety of cell activities. This study numerically validates the rheological property of F-actin cytoskeleton is not only a result of kinetic energy dissipation of F-actin, but also greatly depends on the configuration remodeling of networks structure. Both filament geometry and crosslinker properties can affect the remodeling of F-actin cytoskeleton. The crosslinker unbinding is found to dissipate energy and induce prominent stress relaxation in the F-actin adjacent to cross-linkages. Coupled with F-actin elasticity, the energy dissipation and stress relaxation are more significant in bundled F-actin networks than in single F-actin networks.

A biphasic response to adenosine in the coronary vasculature of the K+-arrested perfused rat heart

Biphasic vasodilatory responses to adenosine and 5'-N-ethylcarboxamidoadenosine (NECA) were observed in the coronary vasculature of K(+)-arrested perfused rat hearts. Dose-response data for both agonists were best represented by two-site models. For adenosine, two sites with negative log ED50 (pED50) values of 8.1 +/- 0.1 (mean +/- S.E.M) and 5.2 +/- 0.1 were obtained, mediating 31 +/- 2% and 69 +/- 2% of the total response. In the presence of 8-phenyltheophylline, the vasodilatory response to adenosine remained best fitted to a two-site model with pED50 values of 7.0 +/- 0.2 and 5.4 +/- 0.2. The relative contribution of each site to the total response remained unchanged. For NECA, pED50 values of 9.6 +/- 0.1 and 6.8 +/- 0.2 were obtained, representing 48 +/- 3% and 52 +/- 3% of the sites, respectively. In contrast, ATP produced a monophasic response with a pED50 value of 8.8 +/- 0.1. These results provide evidence of adenosine receptor and response heterogeneity in the in situ coronary vasculature.

Efficient minimax strategies for square loss games

We consider online prediction problems where the loss between the prediction and the outcome is measured by the squared Euclidean distance and its generalization, the squared Mahalanobis distance. We derive the minimax solutions for the case where the prediction and action spaces are the simplex (this setup is sometimes called the Brier game) and the \ell_2 ball (this setup is related to Gaussian density estimation). We show that in both cases the value of each sub-game is a quadratic function of a simple statistic of the state, with coefficients that can be efficiently computed using an explicit recurrence relation. The resulting deterministic minimax strategy and randomized maximin strategy are linear functions of the statistic.

Néstor-Guillermo Progeria Syndrome: a biochemical insight into Barrier-to-Autointegration Factor 1, alanine 12 threonine mutation

Background Premature aging syndromes recapitulate many aspects of natural aging and provide an insight into this phenomenon at a molecular and cellular level. The progeria syndromes appear to cause rapid aging through disruption of normal nuclear structure. Recently, a coding mutation (c.34G > A [p.A12T]) in the Barrier to Autointegration Factor 1 (BANF1) gene was identified as the genetic basis of Néstor-Guillermo Progeria syndrome (NGPS). This mutation was described to cause instability in the BANF1 protein, causing a disruption of the nuclear envelope structure. Results Here we demonstrate that the BANF1 A12T protein is indeed correctly folded, stable and that the observed phenotype, is likely due to the disruption of the DNA binding surface of the A12T mutant. We demonstrate, using biochemical assays, that the BANF1 A12T protein is impaired in its ability to bind DNA while its interaction with nuclear envelope proteins is unperturbed. Consistent with this, we demonstrate that ectopic expression of the mutant protein induces the NGPS cellular phenotype, while the protein localizes normally to the nuclear envelope. Conclusions Our study clarifies the role of the A12T mutation in NGPS patients, which will be of importance for understanding the development of the disease.

A semi-alternating direction method for a 2-D fractional FitzHugh–Nagumo monodomain model on an approximate irregular domain

A FitzHugh-Nagumo monodomain model has been used to describe the propagation of the electrical potential in heterogeneous cardiac tissue. In this paper, we consider a two-dimensional fractional FitzHugh-Nagumo monodomain model on an irregular domain. The model consists of a coupled Riesz space fractional nonlinear reaction-diffusion model and an ordinary differential equation, describing the ionic fluxes as a function of the membrane potential. Secondly, we use a decoupling technique and focus on solving the Riesz space fractional nonlinear reaction-diffusion model. A novel spatially second-order accurate semi-implicit alternating direction method (SIADM) for this model on an approximate irregular domain is proposed. Thirdly, stability and convergence of the SIADM are proved. Finally, some numerical examples are given to support our theoretical analysis and these numerical techniques are employed to simulate a two-dimensional fractional Fitzhugh-Nagumo model on both an approximate circular and an approximate irregular domain.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.