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This study aims to analyse and forecast the significance of input process parameters to obtain a better ENi-P-TiO2 coated surface using artificial neural networks (ANN). By varying the four process parameters with the Taguchi L9 design, forty-five numbers of AH36 steel specimens are coated with ENi-P-TiO2 composites, and their microhardness values are determined. The ANN model was formulated using the input and output data obtained from the 45 specimens. The optimal design was developed based on mean squared error (MSE) and R2 values. The experimentally measured values were compared with their predicted values to determine the ANN model's predictability. The efficiency of the ANN model is evaluated with an R2 value of 0.959 and an MSE value of 34.563 4. The authors have concluded that the developed model is suitable for designing and predicting ENi-P-TiO2 composite coatings to avoid extensive experimentation with economic production. Scanning Electron Microscope (SEM) and X-ray diffraction analysis (XRD) are also utilised to compare the base metal and optimal coated surface. [ABSTRACT FROM AUTHOR]

The present investigation was carried out at the Plant Breeding Farm, Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University, Annamalainagar during Samba. The study was carried out in an effort to assess the extent of genetic variability in 35 genotypes for 11 character viz., days to first flowering, plant height at maturity, number of tillers per plant, number of panicles per plant, panicle length, number of grains per panicle, 1000 grain weight, grain length, grain breadth, grain L/B ratio and grain yield per plant. The characters namely, grain yield per plant, number of panicles per plant, number of tillers per plant and number of grains per panicle recorded high phenotypic and genotypic coefficient variation. High heritability along with genetic advance as percent of mean was observed for grain length and plant height at maturity followed by grain breadth, grain L/B ratio, number of grains per panicle, grain yield per plant, 1000 grain weight, number of tillers per plant, number of panicles per plant and panicle length. This may be attributed to additive gene action. Selection for these characters will play both short term and long term benefit. [ABSTRACT FROM AUTHOR]