The SF36 Version 2: critical analyses of population weights, scoring algorithms and population norms

Background: The SF36 Version 2 (SF36V2) is a revision of the SF36 Version 1, and is a widely used health status measure. It is important that guidelines for interpreting scores are available.

Method: A population sample of Australians (n = 3015) weighted to achieve representativeness was administered the SF36V2. Comparisons between published US weights and sample derived weights were made, and Australian population norms computed and presented.

Major findings: Significant differences were observed on 7/8 scales and on the mental health summary scale. Possible causes of these findings may include different sampling and data collection procedures, demographic characteristics, differences in data collection time (1998 vs. 2004), differences in health status or differences in cultural perception of the meaning of health. Australian population norms by age cohort, gender and health status are reported by T-score as recommended by the instrument developers. Additionally, the proportions of cases within T-score deciles are presented and show there are important data distribution issues.

Principal conclusions: The procedures reported here may be used by other researchers where local effects are suspected. The population norms presented may be of interest. There are statistical artefacts associated with T-scores that have implications for how SF36V2 data are analysed and interpreted.

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.

A Taylor Model Based Description of the proof stress of magnesium AZ31 during hot working

A series of hot-compression tests and Taylor-model simulations were carried out with the intention of developing a simple expression for the proof stress of magnesium alloy AZ31 during hot working. A crude approximation of wrought textures as a mixture of a single ideal texture component and a random background was employed. The shears carried by each deformation system were calculated using a full-constraint Taylor model for a selection of ideal orientations as well as for random textures. These shears, in combination with the measured proof stresses, were employed to estimate the critical resolved shear stresses for basal slip, prismatic slip, ⟨c+a⟩ second-order pyramidal slip, and { } twinning. The model thus established provides a semianalytical estimation of the proof stress (a one-off Taylor simulation is required) and also indicates whether or not twinning is expected. The approach is valid for temperatures between ∼150 °C and ∼450 °C, depending on the texture, strain rate, and strain path.

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.

Predicting the pilling tendency of wool knits

This work investigates the application of artificial neural network modeling (ANN) to model the relationships between fiber, yarn, and fabric properties and the pilling propensity of single jersey and rib pure wool knitted fabrics based on the ICI Pilling Box method. Validation of the model on an independent validation data set suggests that the accurate prediction of pilling propensity is possible with the best performing model achieving a correlation with the subjectively rated pilling grades of approximately 85%. Importantly, it is also illustrated that a larger training set can lead to a marked improvement in the accuracy of predictions.

A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts

The computational approach for identifying promoters on increasingly large genomic sequences has led to many false positives. The biological significance of promoter identification lies in the ability to locate true promoters with and without prior sequence contextual knowledge. Prior approaches to promoter modelling have involved artificial neural networks (ANNs) or hidden Markov models (HMMs), each producing adequate results on small scale identification tasks, i.e. narrow upstream regions. In this work, we present an architecture to support prokaryote promoter identification on large scale genomic sequences, i.e. not limited to narrow upstream regions. The significant contribution involved the hybrid formed via aggregation of the profile HMM with the ANN, via Viterbi scoring optimizations. The benefit obtained using this architecture includes the modelling ability of the profile HMM with the ability of the ANN to associate elements composing the promoter. We present the high effectiveness of the hybrid approach in comparison to profile HMMs and ANNs when used separately. The contribution of Viterbi optimizations is also highlighted for supporting the hybrid architecture in which gains in sensitivity (+0.3), specificity (+0.65) and precision (+0.54) are achieved over existing approaches.

Effect of storage on the biochemical and processing quality of Adzuki Bean (Vigna angularis)

Storage of adzuki beans and other pulse grains causes biochemical and physical changes that affect the hydration properties of the beans. This affects the quality of products made from the beans such as the Japanese bean paste “ann.” Storage, particularly under unfavourable conditions, leads to the “hard shell” phenomenon, where beans fail to imbibe water when soaked and remain hard, and the “hard-to-cook” phenomenon where the seeds hydrate normally, but the cotyledon fails to hydrate and soften during cooking. The hard shell phenomenon is attributable to impermeability of the seed coat to water, which is due to biochemical changes in the seed coat, such as the formation of protein-tannin complexes, and biophysical changes such as reduction in size or closure of the straphiole aperture in the hilum area—the main area for water entry into the adzuki bean. The hard-to-cook phenomenon is due to changes in the cotyledon tissue, which include formation of insoluble pectinates, lignification of the cell wall and middle lamella, interaction of condensed tannins with proteins and starch, and changes to the structure and functionality of the cellular proteins and starch.