Prediction of Mechanical Properties of Woven Fabrics by ANN

This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties

Analysis of Ring Spun Yarn Wickability Using the Post-Hoc Test

Yarn wickability achieves high thermo-physiological comfort. Therefore, this paper aimed to investigate yarn wickability and analyze statistically factors affecting yarn wicking performance. Methodology consists of testing wicking height for ring spun yarn produced from three levels of fibre types and twist factors at two levels of doubling. Statistical tools such as ANOVA, T-test and Post-hoc tests analyzed the impacts on wicking heights. Findings showed that the Post-hoc test represented the variation between groups more accurately than ANOVA. Furthermore, a comparison of Bonferroni Alpha with T-test p-values revealed that yarn wicking was significantly affected by interactions of fibre type, doubling, and twist level

Parent–Child Agreement Across Child Health-Related Quality of Life Instruments: a Review of the Literature

Aim: To systematically review the literature published since 1999 on paediatric health-related quality of life (HRQL) in relation to parent–child agreement. Methods: Literature searches used to identify studies which evaluated parent–child agreement for child HRQL measures. Results: Nineteen studies were identified, including four HRQL instruments. The Pediatric Quality of Life Inventory™ (PedsQL™) was most commonly used. Differences in parent–child agreement were noted between domains for different measures. The impact of child and parent characteristics were not consistently considered; however parents of children in a nonclinical sample tended to report higher child HRQL scores than children themselves, while parents of children with health conditions tended to underestimate child HRQL. Conclusion: Despite increasing numbers of studies considering children’s HRQL, information about variables contributing to parent–child agreement levels remains limited. Authors need to consistently provide evidence for reliability and validity of measures, and design studies to systematically investigate variables that impact on levels of parent–child agreement