Family food involvement and frequency of famiy dinner meals among Australian children aged 10-12 years. Cross-sectional and longitudinal associations with dietary patterns

Involvement in meal preparation and eating meals with the family are associated with better dietary patterns in adolescents, however little research has included older children or longitudinal study designs. This 3-year longitudinal study examines cross-sectional and longitudinal associations between family food involvement, family dinner meal frequency and dietary patterns during late childhood. Questionnaires were completed by parents of 188 children from Greater Melbourne, Australia at baseline in 2002 (mean age = 11.25 years) and at follow-up in 2006 (mean age = 14.16 years). Principal components analysis (PCA) was used to identify dietary patterns. Factor analysis (FA) was used to determine the principal factors from six indicators of family food involvement. Multiple linear regression models were used to predict the dietary patterns of children and adolescents at baseline and at follow-up, 3 years later, from baseline indicators of family food involvement and frequency of family dinner meals. PCA revealed two dietary patterns, labeled a healthful pattern and an energy-dense pattern. FA revealed one factor for family food involvement. Cross-sectionally among boys, family food involvement score (β = 0.55, 95% CI: 0.02, 1.07) and eating family dinner meals daily (β = 1.11, 95% CI: 0.27, 1.96) during late childhood were positively associated with the healthful pattern. Eating family dinner meals daily was inversely associated with the energy-dense pattern, cross-sectionally among boys (β = −0.56, 95% CI: −1.06, −0.06). No significant cross-sectional associations were found among girls and no significant longitudinal associations were found for either gender. Involvement in family food and eating dinner with the family during late childhood may have a positive influence on dietary patterns of boys. No evidence was found to suggest the effects on dietary patterns persist into adolescence.

Development and validation of a new measure of everyday adolescent functioning : the multidimensional adolescent functioning scale implications and contribution

Everyday functioning is an important outcome for studies of the developmental psychopathology of adolescence. An unbiased, well-validated, and easy-to-use instrument to specifically assess normal adolescent functioning is not yet available. The current study aimed to introduce and validate the Multidimensional Adolescent Functioning Scale

Does electricity consumption panel Granger cause GDP? A new global evidence

The goal of this paper is to undertake a panel data investigation of long-run Granger causality between electricity consumption and real GDP for seven panels, which together consist of 93 countries. We use a new panel causality test and find that in the long-run both electricity consumption and real GDP have a bidirectional Granger causality relationship except for the Middle East where causality runs only from GDP to electricity consumption. Finally, for the G6 panel the estimates reveal a negative sign effect, implying that increasing electricity consumption in the six most industrialised nations will reduce GDP. © 2010 Elsevier Ltd. All rights reserved.

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

A unit root model for trending time-series energy variables

In this paper, we propose a GARCH-based unit root test that is flexible enough to account for; (a) trending variables, (b) two endogenous structural breaks, and (c) heteroskedastic data series. Our proposed model is applied to a range of time-series, trending, and heteroskedastic energy variables. Our two main findings are: first, the proposed trend-based GARCH unit root model outperforms a GARCH model without trend; and, second, allowing for a time trend and two endogenous structural breaks are important in practice, for doing so allows us to reject the unit root null hypothesis.