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The mechanical response of steel auxetic structures manufactured using laser-powder bed fusion was explored. The level of control exerted by the key design parameters of vertical strut length (H), re-entrant strut length (L), strut thickness (t) and re-entrant angle (ϴ) on the mechanical response was examined through a design of experiment approach with ANOVA statistical analysis methods applied. The elastic modulus in directions normal to (Ex) and parallel to (Ey) the vertical strut was found to be primarily dependent upon t and L, respectively, whereas yield strength in both test directions (σx and σy) was strongly dependent on t and L. A large variation in modulus was found between the two test directions (Ex / Ey – 1.02 ± 0.07 GPa/ 4.4 ± 0.1 GPa), whereas, yield strength showed little anisotropy (σx / σy–45 ± 6 MPa/ 45 ± 9 MPa). Poisson’s ratio parallel to the vertical strut varied considerably with geometry but not in a direction normal to the vertical strut. Deformation mechanisms were found to be different of compression in the x and y directions, being a combination of stretching of the vertical strut; compression, bending and hinging of the re-entrant strut (x); and vertical strut compression and re-entrant strut stretching and bending (y).

Background and Aim: It is a well-known fact that many health indices have a relationship with anthropometric indices. With this in mind, the correlation of dyslipidemia with anthropometric indices was done to bring to light the best anthropometric index in predicting the same. Materials and Methods: A retrospective analysis of the data obtained from The Community Outreach Program (COP) at Holalu village conducted by the Department of Biochemistry, Mandya Institute of Medical Sciences Mandya was done. 460 participants above the age of 30 years, both males and females were a part of this COP. During the COP, physical measurements such as height, weight, waist circumference (WC), hip circumference (HC) were measured and various biochemical parameters such as total cholesterol, triglycerides, high-density lipoprotein and low-density lipoproteins were investigated. Anthropometric indices such as body mass index (BMI), waist hip ratio, WC, and waist height ratio were calculated from the data. The data analysis was done using statistical software Epi data analysis V.2.2.2.178. Results: Regression analysis and receiver operating characteristic analysis was done to determine the best anthropometric index. Of all the anthropometric indices, the area under the curve of BMI was the highest for most of the lipid profile parameters (0.6 and above). Regression analysis showed BMI as the best anthropometric index for dyslipidemia (0.078). In this study, we found that BMI is the single best anthropometric index for predicting dyslipidemia. Conclusion: Anthropometric indices can be used to predict dyslipidemia which in turn can be used as a predictor of cardiovascular diseases. BMI can be considered as a better tool to determine dyslipidemia compared to other anthropometric indices.