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In 8 malaria-endemic states in India, mixed Plasmodium spp. infections were detected by PCR in 17.4% (265/1,521) of blood samples that microscopy had shown to contain only P. falciparum. The quality of microscopy must be improved because use of PCR for detection of malaria parasites is limited in rural areas.

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In this present work, an attempt has been made to develop a more accurate surface roughness prediction model using response surface methodology based on center composite rotatable design with Box–Cox transformation in turning of AISI 1019 steel. The analysis has been carried out in three stages. In the first stage, a quadratic model has been developed in terms of feed, speed, depth of cut and nose radius. In the second stage, an improved prediction model has been developed by improving the normality, linearity and homogeneity of the data using a Box–Cox transformation. This improved model has been found to yield good prediction accuracy when compared to the previous one. In the third stage, confirmation experiments have been carried out, which clearly show that the Box–Cox transformation has a strong potential to improve the prediction capability of empirical models. An attempt has also been made to investigate the influence of cutting parameters on surface roughness. The result shows that the feed is the main influencing factor on the surface roughness while the depth of cut has no significant influence.

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The landscape of AES in India has changed in the previous decade, and both outbreak investigations and surveillance studies have increasingly reported non-JEV etiologies; because of these findings there is a need to explore additional strategies to prevent AES beyond vector control and JEV vaccination.