Government regulation to promote healthy food environments : a view from inside state governments

Food policy interventions are an important component of obesity-prevention strategies and can potentially drive positive changes in obesogenic environments. This study sought to identify regulatory interventions targeting the food environment, and barriers/facilitators to their implementation at the Australian state government level. In-depth interviews were conducted with senior representatives from state/territory governments, statutory authorities and non-government organizations (n = 45) to examine participants’ (i) suggestions for regulatory interventions for healthier food environments and (ii) support for pre-selected regulatory interventions derived from a literature review. Data were analysed using thematic and constant comparative analyses. Interventions commonly suggested by participants were regulating unhealthy food marketing; limiting the density of fast food outlets; pricing reforms to decrease fruit/vegetable prices and increase unhealthy food prices; and improved food labelling. The most commonly supported preselected interventions were related to food marketing and service. Primary production and retail sector interventions were least supported. The dominant themes were the need for whole-of-government and collaborative approaches; the influence of the food industry; conflicting policies/agenda; regulatory challenges; the need for evidence of effectiveness; and economic disincentives. While interventions such as public sector healthy food service policies were supported by participants, marketing restrictions and fiscal interventions face substantial barriers including a push for deregulation and private sector opposition.

Reshaping dominant stories : a poststructuralist approach to online role play

Online role play is an increasingly popular teaching/learning technique in higher education (Wills & McDougall 2009) but there has been little research into ways a poststructuralist approach may be supported in this format. This paper describes two very different means of incorporating a poststructuralist approach into role plays in higher education to problematise dominant assumptions in the language and content of the subject matter. The first method was a series of interventions in a face-to-face role play in which medical students practised consultations with adolescent school students. The consultations were interrupted repeatedly with activities designed to interrogate assumptions and the school students acted as coaches to improve the medical students' technique. Although this role play was performed face-to-face, some of its activities may be redeveloped to suit an online role-playing format. The second method was a feature of an online role play involving Middle-East politics and journalism students, in which daily online newspapers provided a reflecting and distorting mirror to the political events simulated by the politics students. Indications of ways in which the two methods produced changes in understanding were gathered using a mixture of qualitative and quantitative methods: questionnaires, focus groups, interviews, participant observation and analysis of online discussions and artefacts.

Efficient tensor based face recognition

This paper addresses the limitation of current multilinear PCA based techniques, in terms of pro- hibitive computational cost of testing and poor gen- eralisation in some scenarios, when applied to large training databases. We define person-specific eigen-modes to obtain a set of projection bases, wherein a particular basis captures variation across light- ings and viewpoints for a particular person. A new recognition approach is developed utilizing these bases. The proposed approach performs on a par with the existing multilinear approaches, whilst sig- nificantly reducing the complexity order of the testing algorithm.

Face recognition using kernel ridge regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

Application of the DCT energy histogram for face recognition

In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, In particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.

Random Subspace Two-Dimensional PCA for face recognition

The two-dimensional Principal Component Analysis (2DPCA) is a robust method in face recognition. Much recent research shows that the 2DPCA is more reliable than the well-known PCA method in recognising human face. However, in many cases, this method tends to be overfitted to sample data. In this paper, we proposed a novel method named random subspace two-dimensional PCA (RS-2DPCA), which combines the 2DPCA method with the random subspace (RS) technique. The RS-2DPCA inherits the advantages of both the 2DPCA and RS technique, thus it can avoid the overfitting problem and achieve high recognition accuracy. Experimental results in three benchmark face data sets -the ORL database, the Yale face database and the extended Yale face database B - confirm our hypothesis that the RS-2DPCA is superior to the 2DPCA itself.

Face recognition via incremental 2DPCA

Recently, the Two-Dimensional Principal Component Analysis (2DPCA) model is proposed and proved to be an efficient approach for face recognition. In this paper, we will investigate the incremental 2DPCA and develop a new constructive method for incrementally adding observation to the existing eigen-space model. An explicit formula for incremental learning is derived. In order to illustrate the effectiveness of the proposed approach, we performed some typical experiments and show that we can only keep the eigen-space of previous images and discard the raw images in the face recognition process. Furthermore, this proposed incremental approach is faster when compared to the batch method (2DPCD) and the recognition rate and reconstruction accuracy are as good as those obtained by the batch method.

Face recognition via the overlapping energy histogram

In this paper we investigate the face recognition problem via the overlapping energy histogram of the DCT coefficients. Particularly, we investigate some important issues relating to the recognition performance, such as the issue of selecting threshold and the number of bins. These selection methods utilise information obtained from the training dataset. Experimentation is conducted on the Yale face database and results indicate that the proposed parameter selection methods perform well in selecting the threshold and number of bins. Furthermore, we show that the proposed overlapping energy histogram approach outperforms the Eigenfaces, 2DPCA and energy histogram significantly.

Ecophysiology of avian migration in the face of current global hazards

Long-distance migratory birds are often considered extreme athletes, possessing a range of traits that approach the physiological limits of vertebrate design. In addition, their movements must be carefully timed to ensure that they obtain resources of sufficient quantity and quality to satisfy their high-energy needs. Migratory birds may therefore be particularly vulnerable to global change processes that are projected to alter the quality and quantity of resource availability. Because long-distance flight requires high and sustained aerobic capacity, even minor decreases in vitality can have large negative consequences for migrants. In the light of this, we assess how current global change processes may affect the ability of birds to meet the physiological demands of migration, and suggest areas where avian physiologists may help to identify potential hazards. Predicting the consequences of global change scenarios on migrant species requires (i) reconciliation of empirical and theoretical studies of avian flight physiology; (ii) an understanding of the effects of food quality, toxicants and disease on migrant performance; and (iii) mechanistic models that integrate abiotic and biotic factors to predict migratory behaviour. Critically, a multi-dimensional concept of vitality would greatly facilitate evaluation of the impact of various global change processes on the population dynamics of migratory birds.

Content validity of visual analog scales to assess symptom severity of acute angioedema attacks in adults with hereditary angioedema : an interview study

Background: Hereditary angioedema (HAE) is a rare, debilitating, potentially life-threatening condition characterized by recurrent acute attacks of edema of the skin, face/upper airway, and gastrointestinal and urogenital tracts. During a laryngeal attack, people with HAE may be at risk of suffocation, while other attacks are often associated with intense pain, disfigurement, disability, and/or vomiting. The intensity of some symptoms is known only to the person experiencing them. Thus, interview studies are needed to explore such experience and patient-reported outcome measures (PROMs) are required for systematic assessment of symptoms in the clinical setting and in clinical trials of treatments for acute HAE attacks.

Objective: The aim of this interview study was to assess the content validity and suitability of four visual analog scale (VAS) instruments for use in clinical studies. The VAS instruments were designed to assess symptoms at abdominal, oro-facial-pharyngeal-laryngeal, peripheral, and urogenital attack locations. This is the first known study to report qualitative data about the patient's experience of the rare disorder, HAE.

Methods: Semi-structured exploratory and cognitive debriefing interviews were conducted with 27 adults with a confirmed clinical/laboratory diagnosis of HAE (baseline plasma level of functional plasma protein C1 esterase inhibitor [C1INH] <50% of normal without evidence for acquired angioedema). There were 17 participants from the US and 10 from Italy, with mean age 42.5 (SD 14.5) years, range 18–72 years, mean HAE duration 21.3 (SD 14.1) years, range 1–45 years, 67% female, and 44% VAS-naïve. Experience of acute angioedema attacks was first explored, noting spontaneous mentions by participants of HAE symptomatology. Cognitive debriefing of the VAS instruments was undertaken to assess the suitability, comprehensibility, and relevance of the VAS items. Asymptomatic participants completed the VAS instruments relevant to their angioedema experience, reporting as if they were experiencing an acute angioedema attack at the time. Interviews were conducted in the clinic setting in the US and Italy over an 8-month period.

Results: Participants mentioned spontaneously almost all aspects of acute angioedema attacks covered by the four VAS instruments, thus providing strong support for inclusion of nearly all VAS items, with no important symptoms missing. Predominant symptoms found to be associated with acute angioedema attacks were edema and pain, and there was evidence of varying degrees of disruption to everyday activities supporting the inclusion of an overall severity item reflecting the disabling effects of HAE symptoms. VAS item wording was understood by participants.

Conclusion: This interview study explored and reported the patient experience of HAE attacks. It demonstrated the content validity of the four anatomical location HAE VAS instruments and their suitability for use in clinical trials of recombinant human C1INH (rhC1INH) treatment for ascertaining trial participants' assessments of the severity of acute angioedema symptoms.

Seabird and marine mammal management options in the face of climate change

Climate change is already impacting Australia’s oceans. Responses by marine life to both climate variability and change have been documented for low trophic levels, however, responses for Australia’s iconic higher trophic level marine taxa are poorly understood, including for many conservation-dependent seabirds and marine mammals. We report initial results from a national study evaluating impacts an adaptation options. Individual time series and combined analyses show consistent responses to historical climate signals, however, improved monitoring protocols are needed to maximize detection of any climate-related demographic signals. Despite difference in sampling , the development of regional multi-species-indices of environmental change provides robust climate indicators over large regions.

Learning Boltzmann distance metric for face recognition

We introduce a new method for face recognition using a versatile probabilistic model known as Restricted Boltzmann Machine (RBM). In particular, we propose to regularise the standard data likelihood learning with an information-theoretic distance metric defined on intra-personal images. This results in an effective face representation which captures the regularities in the face space and minimises the intra-personal variations. In addition, our method allows easy incorporation of multiple feature sets with controllable level of sparsity. Our experiments on a high variation dataset show that the proposed method is competitive against other metric learning rivals. We also investigated the RBM method under a variety of settings, including fusing facial parts and utilising localised feature detectors under varying resolutions. In particular, the accuracy is boosted from 71.8% with the standard whole-face pixels to 99.2% with combination of facial parts, localised feature extractors and appropriate resolutions.

Transition to motherhood in type 1 diabetes : design of the pregnancy and postnatal well-being in transition questionnaires

Background : Life transitions are associated with high levels of stress affecting health behaviours among people with Type 1 diabetes. Transition to motherhood is a major transition with potential complications accelerated by pregnancy with risks of adverse childbirth outcomes and added anxiety and worries about pregnancy outcomes. Further, preparing and going through pregnancy requires vigilant attention to a diabetes management regimen and detailed planning of everyday activities with added stress on women. Psychological and social well-being during and after pregnancy are integral for good pregnancy outcomes for both mother and baby. The aim of this study is to establish the face and content validity of two novel measures assessing the well-being of women with type 1 diabetes in their transition to motherhood, 1) during pregnancy and 2) during the postnatal period.

Methods : The approach to the development of the Pregnancy and Postnatal Well-being in T1DM Transition questionnaires was based on a four-stage pre-testing process; systematic overview of literature, items development, piloting testing of questionnaire and refinement of questionnaire. The questionnaire was reviewed at every stage by expert clinicians, researchers and representatives from consumer groups. The cognitive debriefing approach confirmed relevance of issues and identified additional items.

Results : The literature review and interviews identified three main areas impacting on the women’s postnatal self-management; (1) psychological well-being; (2) social environment, (3) physical (maternal and fetal) well-being. The cognitive debriefing in pilot testing of the questionnaire identified that immediate postnatal period was difficult, particularly when the women were breastfeeding and felt depressed.

Conclusions : The questionnaires fill an important gap by systematically assessing the psychosocial needs of women with type 1 diabetes during pregnancy and in the immediate postnatal period. The questionnaires can be used in larger data collection to establish psychometric properties. The questionnaires potentially play a key role in prospective research to determine the self-management and psychological needs of women with type 1 diabetes transitioning to motherhood and to evaluate health education interventions.

Optimal metric selection for improved multi-pose face recognition with group information

We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ2 norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓp norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets

Critical factors to the success of alliances in Australia

Governments in Australia face the challenge of meeting the growing demand for new infrastructure, which can be delivered more quickly in an increasingly complex environment. Alliance Contracting has been introduced to overcome some of the challenges by, aligning the incentives of the partners, clearly defining their rights and responsibilities, and providing the means for resolving disputes when they arise. The purpose of this research was to explore the critical success factors of Alliance Contracting in order to understand the roles of the various terms and conditions and how they fit together to create a relationship-based contract. A qualitative technique of semi-structured in-depth interviews was used to gather primary data in response to the research questions. The research aimed to develop an in-depth understanding of Alliance Contracting. The results show that the key contributor to the success or failure of Alliances is whether all the partners benefit equitably from the venture. Analysis of the data indicates that, in general, trusting attitudes/behaviour is perceived to be the most important critical success factor for Alliance Contracting in the broader construction industry. The second most popular critical success factor was shared and aligned goals. The third issue was the evidence of open behaviour, and the final issue was the presence of shared knowledge. The implication of this research is that there are several key factors that were necessary preconditions for successful Alliances.