Prediction of alcohol-related harm from controlled drinking stragegies and alcohol consumption trajectories

Aims To establish predictors of age 21 alcohol-related harm from prior drinking patterns, current levels of alcohol consumption and use of controlled drinking strategies.
Participants One thousand, five hundred and ninety-six students recruited from an initial sample of 3300 during their final year of high school in 1993.
Design Longitudinal follow-up across five waves of data collection.
Setting Post high school in Victoria, Australia.
Measurements Self-administered surveys examining a range of health behaviours, including alcohol consumption patterns and related behaviour.
Findings Drinking behaviours at age 21 were found to be strongly predicted by drinking trajectories established through the transition from high school. Multivariate regression analysis revealed that alcohol-related harms at age 21 were reduced where current levels of alcohol use fell within limits recommended in Australian national guidelines. After controlling for this effect it was found that the range of strategies employed by participants to control alcohol use maintained a small protective influence. Post-high-school drinking trajectories continued to demonstrate a significant effect after controlling for current behaviours. Findings revealed that over one quarter of males and females drank alcohol, but on a less-than-weekly basis. This pattern of alcohol use demonstrated considerable stability through the post-school transition and was associated with a low level of subsequent harm at age 21.
Conclusions Future research should investigate whether encouraging more Australian adolescents to drink alcohol on a less-than-weekly basis may be a practical intervention target for reducing alcohol-related harms.

Prediction of alcohol-related harm from controlled drinking strategies and alcohol consumption trajectories

Aims To establish predictors of age 21 alcohol-related harm from prior drinking patterns, current levels of alcohol consumption and use of controlled drinking strategies.

Participants One thousand, five hundred and ninety-six students recruited from an initial sample of 3300 during their final year of high school in 1993.

Design Longitudinal follow-up across five waves of data collection.

Setting Post high school in Victoria, Australia.

Measurements Self-administered surveys examining a range of health behaviours, including alcohol consumption patterns and related behaviour.

Findings Drinking behaviours at age 21 were found to be strongly predicted by drinking trajectories established through the transition from high school. Multivariate regression analysis revealed that alcohol-related harms at age 21 were reduced where current levels of alcohol use fell within limits recommended in Australian national guidelines. After controlling for this effect it was found that the range of strategies employed by participants to control alcohol use maintained a small protective influence. Post-high-school drinking trajectories continued to demonstrate a significant effect after controlling for current behaviours. Findings revealed that over one quarter of males and females drank alcohol, but on a less-than-weekly basis. This pattern of alcohol use demonstrated considerable stability through the post-school transition and was associated with a low level of subsequent harm at age 21.

Conclusions Future research should investigate whether encouraging more Australian adolescents to drink alcohol on a less-than-weekly basis may be a practical intervention target for reducing alcohol-related harms.

Analytical model of pass-by-pass strain in rod (or bar) rolling and its applications to prediction of austenite grain size

Rod rolling is a process where the deformation state of the workpiece between the work rolls is quite different from the strip rolling process. However, in most microstructure evolution models, the simple area strains (natural logarithm of the area reduction ratio) multiplied by a constant have been used to compute pass-by-pass evolution of austenite grain size (AGS) in rod (or bar) rolling, without any verification. The strains at a given pass play a crucial role in determining the recrystallization behavior (static or dynamic). In this study, an analytical model that calculates the pass-by-pass strain and strain rate in rod rolling has been developed and verified by conducting four-pass (oval–round) bar and plate rolling experiments. Numerical simulations have then been carried out for the four-pass rolling sequence using the area strain model and the new analytical model, focusing on the effect of the method for calculating the strain on the recrystallization behavior and evolution of AGS. The AGS predicted was compared with those obtained from hot torsion tests. It is shown that the analytical model developed in this study is more appropriate in the analysis of bar (or rod) rolling. It was found that the recrystallization behavior and evolution of AGS during this process were influenced significantly by the calculation method for the deformation parameters (strain and strain rate). The pass-by-pass strain obtained from the simple area strain model is inadequate to be used as an input to the equations for recrystallization and AGS evolution under these rolling conditions.

Manualised treatment and dropout : a study of parent training

Parents who dropped out of manualised parent training programs for children with externalising behaviour disorders were interviewed retrospectively and compared prospectively with parents who continued in these programs. Parents who dropped out tended to feel more overwhelmed by their situation, because their child's behaviour problems were more severe, they had single parent status or had more than one child with difficulties.

Prediction of pedestrians routes within a built environment in normal conditions

Modelling and prediction of pedestrian routing behaviours within known built environments has recently attracted the attention of researchers across multiple disciplines, owing to the growing demand on urban resources and requirements for efficient use of public facilities. This study presents an investigation into pedestrians' routing behaviours within an indoor environment under normal, non-panic situations. A network-based method using constrained Delaunay triangulation is adopted, and a utility-based model employing dynamic programming is developed. The main contribution of this study is the formulation of an appropriate utility function that allows an effective application of dynamic programming to predict a series of consecutive waypoints within a built environment. The aim is to generate accurate sequence waypoints for the pedestrian walking path using only structural definitions of the environment as defined in a standard CAD format. The simulation results are benchmarked against those from the A* algorithm, and the outcome positively indicates the usefulness of the proposed method in predicting pedestrians' route selection activities. © 2014 Elsevier Ltd. All rights reserved.

Prediction of FLD of sheet metals based on crystal plasticity model

 In some advanced sheet metal forming processes such as the incremental forming process, a local fracture strain after necking is very important. In order to accurately predict necking and fracture phenomena, a crystal plasticity model is introduced in the finite element analysis of tensile tests. A tensile specimen is modeled by many grains that have their own crystalline orientation. And each of the grains is discretized by many elements. Using this analysis, necking behavior of a tensile specimen can be predicted without any initial imperfections. A damage model is also implemented to predict sudden drops of load carrying capacity after necking and to reflect the void nucleation and growth of the severely deformed region. From an analysis of the tensile test, the necking behavior is well predicted. Finally, analyses are carried out for various strain paths, and FLDs up to necking and fracture are predicted.

Longitudinal prediction of mid-adolescent psychosocial outcomes from early adolescent family help seeking and family support

This study examined whether family help seeking and family support represented the same or distinct constructs and prospective associations between emergent constructs and psychosocial outcomes. Data were from 1,713 school-based adolescents participating in a randomized controlled trial, in Victoria, Australia. Family help seeking emerged as a single factor, distinct from family support, and was prospectively associated with improved psychosocial outcomes. Father closeness predicted lower depressive symptoms. Family help seeking predicted higher help seeking for peers. Interactions between family help seeking and family support on psychosocial outcomes were not apparent. Findings highlight the importance of examining family help seeking and family support separately in future studies of adolescents' help-seeking behavior.

Prediction of failure in bending of an aluminium sheet alloy

This work is dedicated to numerical prediction of the bending of thin aluminium alloy sheets, with a focus on the material parameter identification and the prediction of rupture with or without pre-strains in tension prior to bending. The experimental database consists of i) mechanical tests at room temperature, such as tension and simple shear, performed at several orientations to the rolling direction and biaxial tension ii) air bending tests of rectangular samples after (or not) pre-straining in tension. The mechanical model is composed of the Yld2004-18p anisotropic yield criterion (Barlat et al. [3]) associated with a mixed hardening rule. The material parameters (altogether 21) are optimized with an inverse approach, in order to minimize the gap between experimental data and model predictions. Then, the Hosford-Coulomb rupture criterion is used in an uncoupled way, and the parameters are determined from tensile tests, both uniaxial and biaxial, with data up to rupture. In a second step, numerical simulations of the bending tests are performed, either on material in its original state or after pre-straining in tension, with pre-strain magnitudes increasing from 0.19 up to 0.3. The comparisons are performed on different outputs: load evolution, strain field and prediction of the rupture. A very good correlation is obtained over all the tests, in the identification step as well as in the validation one. Moreover, the fracture criterion proves to be successful whatever the amount of pre-strain may be. A convincing representation of the mechanical behavior at room temperature for an aluminium alloy is thus obtained.

Prediction of stress-strain behaviors in steels using an integrated constitutive, FEM and ANN model

Austenitic steels with a carbon content of 0.0037 to 0.79 wt% C are torsion tested and modeled using a physically based constitutive model and an Integrated Phenomenological and Artificial neural Network (IPANN) model. The prediction of both the constitutive and IPANN models on steel 0.017 wt% C is then evaluated using a finite element (FEM) code ABAQUS with different reduction in the thickness after rolling through one roll stand. It is found that during the rolling process, the prediction accuracy of the reaction force from FEM simulation for both constitutive and IPANN models depends on the strain achieved (average reduction in thickness). By integrating FEM into IPANN model and introducing the product of strain and stress as an input of the ANN model, the accuracy of this integrated FEM and IPANN model is higher than either the constitutive or IPANN model.

Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach

1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low   abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal.
2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat.
3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike’s information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability.
4. Negative associations between rufous bristlebird presence and  increasing elevation, 'distance to cree', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation.
5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant  independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥0·5 level ) was 16%.
6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multi-scale  approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.

Mill specific prediction of worsted yarn performance

Different spinning mills use different raw materials, processing methodologies, and equipment, all of which influence the quality of the yarns produced. Because of many variables, there is a difficulty in developing a universal empirical/theoretical model. This work presents a multilayer perceptron algorithm (MLP) model for the purpose of building a mill specific worsted spinning performance prediction tool. Sixteen inputs are used to predict key yarn properties and spinning performance, including number of fibers in cross-section, unevenness (U%), thin places, neps, yarn tenacity, elongation at break, thick places, and spinning ends-down. Validation of the model on mill specific commercial data set shows that the general fit to the target values is good. Importantly, the performance of the MLP shows a certain degree of stability to different, random selections of independent test data. Subsequent comparison against the predicted outputs of Sirolan Yarnspec™ confirms the overall performance of the artificial neural network (ANN) method to be more accuratefor mill specific predictions.

The utility of a workplace injury instrument in prediction of workplace injury

Workplace injuries are common and destructive to persons, organisations, and society. Various instruments presently exist that are designed to assess the factors underlying workplace injury. The study reports on the construct and predictive validity of a 46-item instrument, the safety perception survey (SPS), currently used to assess safety climate in industrial organisations throughout Australia. Initially, factor analysis was conducted on the data from a sample of 1238 employees from nine organisations, which indicated a one-factor solution, was the best fit. A structural equation model (SEM) linking injury rates to the safety climate measure for 16 sub-groups of six industrial organisations indicated that the measure contributed just 23% of the variance in injury rates. Interestingly, the results indicated that the number of employees was a better and more significant predictor of injury (R² = 0.48). It is proposed that the SPS as is would need to be modified significantly from its current form to produce improvements in validity, as in its current form the survey is no more predictive of injury than organisational size. Future research into safety climate measures should incorporate predictive validity analysis on injury rates, as for many organisations; this is a performance outcome measure.

The prediction of damage condition in regards to damage factor influence of light structures on expansive soils in Victoria, Australia

This paper proposes a neural network model using genetic algorithm for a model for the prediction of the damage condition of existing light structures founded in expansive soils in Victoria, Australia. It also accounts for both individual effects and interactive effects of the damage factors influencing the deterioration of light structures. A Neural Network Model was chosen because it can deal with 'noisy' data while a Genetic Algorithm was chosen because it does not get `trapped' in local optimum like other gradient descent methods. The results obtained were promising and indicate that a Neural Network Model trained using a Genetic Algorithm has the ability to develop an interactive relationship and a Predicted Damage Conditions Model.