MECHANICAL CHARACTERIZATION OF ROTATING TRIANGLE SHAPED AUXETIC SKIN GRAFT SIMULANTS

The expansion of the skin grafts plays a key role in treating severe burn injuries. Split-thickness skin grafting, which is a well-known technique for stretching donor skin samples beyond its capacity, typically produces expansions which are insufficient to cover large burn areas. In this work, the expansion potential of skin grafts with novel rotating triangle (RT) shaped auxetic incision patterns were investigated extensively. A skin simulant was employed and a range of RT configurations, with internal angles varying from 0° to 135°, were tested through the development of skin graft simulants. Mechanical testing and digital image correlation (DIC) were used to characterize the Poisson’s effect, meshing ratios, and induced stresses of the skin graft simulants, up to 50% strains. The 0° model produced the highest negative Poisson’s effect and areal expansions. As the internal angle of the auxetic was increased, expansions were observed to decrease significantly. Beyond 60°, positive Poisson’s effect and contractions occurred with an increasing trend, and its peak at 105°. At 15° and 120°, the induced strains were observed to be significant, posing risks of skin rupture. Overall, the expansions were observed to be higher at lower strains. Such experimental findings on expansion potentials and estimations of mechanical properties with auxetic skin grafts simulants have not been reported to date, and would be indispensable for further research in skin graft expansion and severe burn injury treatment

Efficient and Unbiased Estimation Procedure of Population Mean in Two-Phase Sampling

In this paper, an unbiased regression-ratio type estimator has been developed for estimating the population mean using two auxiliary variables in double sampling. Its properties are studied under two different cases. Empirical studies and graphical simulation have been done to demonstrate the efficiency of the proposed estimator over other estimators

Effective Estimation Strategy of Finite Population Variance Using Multi-Auxiliary Variables in Double Sampling

Estimation of population variance in two-phase (double) sampling is considered using information on multiple auxiliary variables. An unbiased estimator is proposed and its properties are studied under two different structures. The superiority of the suggested estimator over some contemporary estimators of population variance was established through empirical studies from a natural and an artificially generated dataset