Perception of risk, feelings of vulnerability and effects on driving behaviour in women drivers across the lifespan.

Sustained driving in older age has implications for quality of life and mental health. Studies have shown that despite the recognised importance of driving in maintaining health and social engagement, many women give up driving prematurely or adopt self-imposed restrictive driving practices. Emotional responses to driving have been implicated in these decisions. This research examined the effect of risk perception and feelings of vulnerability on women’s driving behaviour across the lifespan. It also developed and tested a modified theory of planned behaviour intervention to positively affect driving habits. The first two studies (N=395) used quantitative analysis to model driving behaviours affected by risk perception and feelings of vulnerability, and established that feelings of vulnerability do indeed affect women’s driving behaviour, specifically resulting in increases in driving avoidance and the adoption of maladaptive driving styles. Further, that self-regulation, conceptualised as avoidance, is used by drivers across the lifespan. Qualitative analysis of focus group data (N=48) in the third study provided a deeper understanding of the variations in coping behaviours adopted by sub-groups of drivers and extended the definition of self-regulation to incorporate adaptive coping strategies. The next study (N=64) reported the construction and preliminary validation of the novel self-regulation index (SRI) to measure wider self-regulation behaviours using an objective measure of driving behaviour, a simulated driving task. The understanding gained from the formative research was used in the final study, an extended theory of planned behaviour intervention to promote wider self-regulation behaviour, measured using the previously validated self-regulation index. The intervention achieved moderate success with changes in affective attitude and normative beliefs as well as self-reported behaviour. The results offer promise for self-regulation, incorporating a spectrum of planning and coping behaviours, to be used as a mechanism to assist drivers in achieving their personal mobility goals whilst promoting safe driving.

Watermark-only security attack on DM-QIM watermarking:vulnerability to guided key guessing

This paper addresses the security of a specific class of common watermarking methods based on Dither modulation-quantisation index modulation (DM-QIM) and focusing on watermark-only attacks (WOA). The vulnerabilities of and probable attacks on lattice structure based watermark embedding methods have been presented in the literature. DM-QIM is one of the best known lattice structure based watermarking techniques. In this paper, the authors discuss a watermark-only attack scenario (the attacker has access to a single watermarked content only). In the literature it is an assumption that DM-QIM methods are secure to WOA. However, the authors show that the DM-QIM based embedding method is vulnerable against a guided key guessing attack by exploiting subtle statistical regularities in the feature space embeddings for time series and images. Using a distribution-free algorithm, this paper presents an analysis of the attack and numerical results for multiple examples of image and time series data.

Watermark-only security attack on DM-QIM watermarking:vulnerability to guided key guessing

This paper addresses the security of a specific class of common watermarking methods based on Dither modulation-quantisation index modulation (DM-QIM) and focusing on watermark-only attacks (WOA). The vulnerabilities of and probable attacks on lattice structure based watermark embedding methods have been presented in the literature. DM-QIM is one of the best known lattice structure based watermarking techniques. In this paper, the authors discuss a watermark-only attack scenario (the attacker has access to a single watermarked content only). In the literature it is an assumption that DM-QIM methods are secure to WOA. However, the authors show that the DM-QIM based embedding method is vulnerable against a guided key guessing attack by exploiting subtle statistical regularities in the feature space embeddings for time series and images. Using a distribution-free algorithm, this paper presents an analysis of the attack and numerical results for multiple examples of image and time series data.

Feelings of vulnerability and effects on driving behaviour:a qualitative study

Feelings of vulnerability in driving can be considered an emotional response to risk perception and the coping strategies adopted could have implications for continued mobility. In a series of focus groups with 48 licensed drivers aged 18-75 years, expressions of vulnerability in driver coping behaviours were examined. Despite feelings of vulnerability appearing low, qualitative thematic analysis revealed a complex array of coping strategies in everyday driving including planning, use of 'co-pilots', self-regulation, avoidance and confrontive coping, i.e. intentional aggression toward other road users. The findings inform future intervention studies to enable appropriate coping strategy selection and prolong independent mobility in older adults. © 2014 Elsevier Ltd. All rights reserved.

Vulnerability of exporting nations to the development of a carbon label in the United Kingdom

Carbon labels inform consumers about the amount of greenhouse gases (GHGs) released during the production and consumption of goods, including food. In the future consumer and legislative responses to carbon labels may favour goods with lower emissions, and thereby change established supply chains. This may have unintended consequences. We present the carbon footprint of three horticultural goods of different origins supplied to the United Kingdom market: lettuce, broccoli and green beans. Analysis of these footprints enables the characterisation of three different classes of vulnerability which are related to: transport, national economy and supply chain specifics. There is no simple relationship between the characteristics of an exporting country and its vulnerability to the introduction of a carbon label. Geographically distant developing countries with a high level of substitutable exports to the UK are most vulnerable. However, many developing countries have low vulnerability as their main exports are tropical crops which would be hard to substitute with local produce. In the short term it is unlikely that consumers will respond to carbon labels in such a way that will have major impacts in the horticultural sector. Labels which require contractual reductions in GHG emissions may have greater impacts in the short term.

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

Is pregnancy a teachable moment for diet and physical activity behaviour change? An interpretative phenomenological analysis of the experiences of women during their first pregnancy

OBJECTIVES: Pregnancy may provide a 'teachable moment' for positive health behaviour change, as a time when women are both motivated towards health and in regular contact with health care professionals. This study aimed to investigate whether women's experiences of pregnancy indicate that they would be receptive to behaviour change during this period. DESIGN: Qualitative interview study. METHODS: Using interpretative phenomenological analysis, this study details how seven women made decisions about their physical activity and dietary behaviour during their first pregnancy. RESULTS: Two women had required fertility treatment to conceive. Their behaviour was driven by anxiety and a drive to minimize potential risks to the pregnancy. This included detailed information seeking and strict adherence to diet and physical activity recommendations. However, the majority of women described behaviour change as 'automatic', adopting a new lifestyle immediately upon discovering their pregnancy. Diet and physical activity were influenced by what these women perceived to be normal or acceptable during pregnancy (largely based on observations of others) and internal drivers, including bodily signals and a desire to retain some of their pre-pregnancy self-identity. More reasoned assessments regarding benefits for them and their baby were less prevalent and influential. CONCLUSIONS: Findings suggest that for women who conceived relatively easily, diet and physical activity behaviour during pregnancy is primarily based upon a combination of automatic judgements, physical sensations, and perceptions of what pregnant women are supposed to do. Health professionals and other credible sources appear to exert less influence. As such, pregnancy alone may not create a 'teachable moment'. Statement of contribution What is already known on this subject? Significant life events can be cues to action with relation to health behaviour change. However, much of the empirical research in this area has focused on negative health experiences such as receiving a false-positive screening result and hospitalization, and in relation to unequivocally negative behaviours such as smoking. It is often suggested that pregnancy, as a major life event, is a 'teachable moment' (TM) for lifestyle behaviour change due to an increase in motivation towards health and regular contact with health professionals. However, there is limited evidence for the utility of the TM model in predicting or promoting behaviour change. What does this study add? Two groups of women emerged from our study: the women who had experienced difficulties in conceiving and had received fertility treatment, and those who had conceived without intervention. The former group's experience of pregnancy was characterized by a sense of vulnerability and anxiety over sustaining the pregnancy which influenced every choice they made about their diet and physical activity. For the latter group, decisions about diet and physical activity were made immediately upon discovering their pregnancy, based upon a combination of automatic judgements, physical sensations, and perceptions of what is normal or 'good' for pregnancy. Among women with relatively trouble-free conception and pregnancy experiences, the necessary conditions may not be present to create a 'teachable moment'. This is due to a combination of a reliance on non-reflective decision-making, perception of low risk, and little change in affective response or self-concept.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.