Numerical and experimental studies of multi-ply woven carbon fibre prepreg forming process

Woven carbon fibre prepreg is being increasingly used in high-performance aerospace and automotive applications, primarily because of its superior mechanical properties and formability. A wide range of forming simulation options are available for predicting material deformation during the prepreg forming process, particularly change in fibre orientation. Development of a robust validated simulation model requires comprehensive material characterisation and reliable experimental validation techniques. This paper presents experimental and numerical methods for studying the fibre orientation in multi-ply woven carbon fibre prepreg forming process, using a double-dome geometry. The numerical study is performed using the commercial forming simulation software PAM-FORM and the material input data are generated from a comprehensive experimental material characterisation. Two experimental validation methods are adopted for fibre shear angle measurement: an optical method for measuring only the surface plies, and a novel CT scan method for measuring both the surface plies and the internal plies. The simulation results are compared against the experimental results in terms of fibre shear angle and the formation of wrinkles to assess the validity of the model

Vitamin D in Australia : issues and recommendations

BACKGROUND A significant number of Australians and people from specific groups within the community are suffering from vitamin D deficiency. It is no longer acceptable to assume that all people in Australia receive adequate vitamin D from casual exposure to sunlight.OBJECTIVE This article provides information on causes, consequences, treatment and prevention of vitamin D deficiency in Australia. DISCUSSION People at high risk of vitamin D deficiency include the elderly, those with skin conditions where avoidance of sunlight is required, dark skinned people (particularly women during pregnancy or if veiled) and patients with malabsorption, eg. coeliac disease. For most people, deficiency can be prevented by 5&ndash;15 minutes exposure of face and upper limbs to sunlight 4&ndash;6 times per week. If this is not possible then a vitamin D supplement of at least 400 IU* per day is recommended. In cases of established vitamin D deficiency, supplementation with 3000-5000 IU per day for at least 1 month is required to replete body stores. Increased availability of larger dose preparations of cholecalciferol would be a useful therapy in the case of severe deficiencies. * 40 IU (international units) = 1 &micro;g<br /

Dietary intake of fish and PUFA, and clinical depressive and anxiety disorders in women

Fish and PUFA consumption are thought to play a role in mental health; however, many studies do not take into account multiple sources of PUFA. The present study analysed data from a sample of 935 randomly selected, population-based women aged 20&ndash;93 years. A validated and comprehensive dietary questionnaire ascertained the consumption of n-3 and n-6 PUFA. Another assessed fish and energy intake and provided data for a dietary quality score. The General Health Questionnaire-12 (GHQ-12) measured psychological symptoms and a clinical interview (Structured Clinical Interview for DSM-IV-TR Research Version, Non-patient edition) assessed depressive and anxiety disorders. Median dietary intakes of long-chain n-3 fatty acids (310 mg/d) were below suggested dietary target levels. The only PUFA related to categorical depressive and anxiety disorders was DHA. There was a non-linear relationship between DHA intake and depression; those in the second tertile of DHA intake were nearly 70 % less likely to report a current depressive disorder compared to those in the first tertile. The relationship of DHA to anxiety disorders was linear; for those in the highest tertile of DHA intake, the odds for anxiety disorders were reduced by nearly 50 % after adjustments, including adjustment for diet quality scores, compared to the lowest tertile. Those who ate fish less than once per week had higher GHQ-12 scores, and this relationship was particularly obvious in smokers. These are the first observational data to indicate a role for DHA in anxiety disorders, but suggest that the relationship between DHA and depressive disorders may be non-linear

Experimental investigation on interply friction properties of thermoset prepreg systems

A comprehensive novel investigation into the characterisation of interply friction behaviour of thermoset prepregs for high-volume manufacturing (HVM) was conducted. High interply slipping rate and normal pressure typically used for HVM present challenges when preforming carbon fibre reinforced plastics (CFRP). The study involved multiple reinforcement architectures (woven and unidirectional (UD) with the same rapid-cure resin system) which were characterised using a bespoke interply friction test rig used to simulate processing conditions representative to press forming and double diaphragm forming. Under prescribed conditions, woven and UD prepregs exhibit significantly different frictional behaviour. Results demonstrated the UD material obeys a hydrodynamic lubrication mode. For the woven material, a rate-dependent friction behaviour was found at low normal pressure. At higher normal pressure however, the woven material exhibited a friction behaviour similar to that of a dry reinforcement and significant tow displacement was observed. Post-characterisation analysis of test-specimens showed significant resin migration towards the outer edges of the plies, leaving a relatively resin-starved contact interface. The findings generate new knowledge on interply friction properties of thermoset prepreg for HVM applications, yet reveal a lack of understanding of the influence of tow tensions as well as the pre-impregnation level for a range of processing conditions

The impact of smoking in adolescence on early adult anxiety symptoms and the relationship between infant vulnerability factors for anxiety and early adult anxiety symptoms : The TOPP Study

Cigarette smoking is increased in people with trait anxiety and anxiety disorders, however no longitudinal data exist illuminating whether smoking in adolescence can influence the developmental trajectory of anxiety symptoms from early vulnerability in infancy to adult anxiety expression. Using The Tracing Opportunities and Problems in Childhood and Adolescence (TOPP) Study, a community-based cohort of children and adolescents from Norway who were observed from the age of 18months to age 18&ndash;19years, we explored the relationship between adolescent smoking, early vulnerability for anxiety in infancy (e.g. shyness, internalizing behaviors, emotional temperaments) and reported early adult anxiety.Structural equation modeling demonstrated that adolescent active smoking was positively associated with increased early adulthood anxiety (&beta; = 0.17, p&lt;0.05), after controlling for maternal education (proxy for socioeconomic status). Adolescent anxiety did not predict early adult smoking. Adolescent active smoking was a significant effect modifier in the relationship between some infant vulnerability factors and later anxiety; smoking during adolescence moderated the relationship between infant internalizing behaviors (total sample: active smokers: &beta; = 0.85,p&lt;0.01, non-active smokers: ns) and highly emotional temperament (total sample: active smokers: &beta; = 0.55,p&lt;0.01,non-active smokers: ns), but not shyness, and anxiety in early adulthood. The results support a model where smoking acts as an exogenous risk factor in the development of anxiety, and smoking may alter the developmental trajectory of anxiety from infant vulnerability to early adult anxiety symptom expression. Although alternative non-mutually exclusive models may explain these findings, the results suggest that adolescent smoking may be a risk factor for adult anxiety, potentially by influencing anxiety developmental trajectories. Given the known adverse health effects of cigarette smoking and significant health burden imposed by anxiety disorders, this study supports the importance of smoking prevention and cessation programs targeting children and adolescence

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p&lt;0.001). CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin