Optimization of silk yarn hierarchical structure by genetic algorithm to design scaffolds

A genetic algorithm model has been developed to determine the optimal parameters of mechanical aspects of a silk wire-rope scaffold with the highest predictive accuracy and generalized ability simultaneously. The study pioneered on employing a genetic algorithm (GA) to optimize the parameters of scaffold in tendon and ligament tissue engineering. Experimental results show that the GA model performs the best predictive accuracy to imply mechanical behavior with native values successfully

Classification of ring-spun yarns using cluster analysis

The aim of this study is to classify ring-spun yarns according to their unevenness, imperfections, and hairiness parameters using cluster analysis. The mentioned features of ring-spun yarns are measured for five different ranks. Five ranks of ring-spun yarns including compact and conventional as well as combed and carded types are chosen and produced. In the modeling section, the model-based clustering method was applied as a strong method based on the distribution of each variable. In order to select the best fit and to find out the final clustering, bayesian information criterion (BIC) is applied. According to the results of modeling, five ranks of selected ring-spun yarns are classified in four clusters and the acceptable agreement is measured according to Cohen’s kappa method. The highest value for Kappa represents a high agreement to match between the clustering result and the real rank.

Optimization of silk yarn hierarchical structure by genetic algorithm to design scaffolds

81-86A genetic algorithm model has been developed to determine the optimal parameters of mechanical aspects of a silk wire-rope scaffold with the highest predictive accuracy and generalized ability simultaneously. The study pioneered on employing a genetic algorithm (GA) to optimize the parameters of scaffold in tendon and ligament tissue engineering. Experimental results show that the GA model performs the best predictive accuracy to imply mechanical behavior with native values successfully

Classification of ring-spun yarns using cluster analysis

356-361The aim of this study is to classify ring-spun yarns according to their unevenness, imperfections, and hairiness parameters using cluster analysis. The mentioned features of ring-spun yarns are measured for five different ranks. Five ranks of ring-spun yarns including compact and conventional as well as combed and carded types are chosen and produced. In the modeling section, the model-based clustering method was applied as a strong method based on the distribution of each variable. In order to select the best fit and to find out the final clustering, bayesian information criterion (BIC) is applied. According to the results of modeling, five ranks of selected ring-spun yarns are classified in four clusters and the acceptable agreement is measured according to Cohen’s kappa method. The highest value for Kappa represents a high agreement to match between the clustering result and the real rank