Porosity is one of the most important properties of textile materials that ensures their comfort and usability. The internal pore structure of the cotton fiber assembly is complex and changeable, surface pore is difficult to explain its pore structure. It is intended to develop a method to predict the pore morphology of cotton fiber assembly. Pore image of the multi-layer fiber assembly is collected by a fiber photography instrument, used the Image Pro Plus 6.0 software to analyze, and obtained the white area indicators of image which can be applied to describe void space of fiber assembly. Using multiple linear regression analysis method, the regression equation of the white area index of image and porosity index of cotton fiber assembly is established. The results indicate that the white area index can largely be explained by three pore index namely the porosity ε, mean length of fiber between the adjacent contacts B and fiber tortuosity coefficient. Appropriate regression equations can be formulated for the pore of white area index which can aid in predicting the pore texture. Comparing the data indicators, it is found that mean length of fiber between the adjacent contacts B and the porosity ε, fiber tortuosity coefficient τ, and air permeability q have good linear correlation.
The effect of trunk stabilization circuit exercise using a rubber mat on the thickness and white area index of transverse abdominis in healthy young adults
