This research concentrated on material characteristics such as tensile property (TS) and hardness (HV) for AA-5083 manufactured using the stir casting (SC) process. The reinforcing elements silicon carbide (SiC-7.5%) and flyash (FA-5%) in the form of powders will be added to Al alloy to improve the characteristics of composites. Response surface methodology (RSM) was a scientific technique to make optimizing task at stir casting parameters. As per central composite design (CCD), 20 samples (L1-L20) were fabricated at a variation of factors such as stirrer speed (A) 350-550 rpm, stir time (B) 15-35 min, and stir temperature (C) 750-950°C. The result presented that best TS and HV exhibited at experiments L5 (A2-450 rpm, B1-15min, and C1-750°C) and L6 (A1-350 rpm, B1-15min, and C1-750°C). Design expert software (DES) is one of the optimization tools that employed to determine analysis of variance (ANOVA) and the best optimal parameter levels of SC. ANOVA helped to check contribution of SC factors on TS and HV, and it was noticed that mechanical properties were improved with increasing stir speed and stir time but it was reduced with rising of temperature.

This article presents a flexible wearable KIT monopole antenna for biomedical application. A metamaterial unit cell is proposed to improve the antenna performance. The proposed antenna and the metamaterial are fabricated on 1 mm-thick polydimethylsiloxane substrate to operate in the ISM frequency band of 2.45 GHz. Integration of the metamaterial improves the gain and reduces the specific absorption rate (SAR) of the antenna. The overall dimension of the antenna with the metamaterial is 49 × 49 × 19 mm3. The designed antenna is investigated for the loading effect of the body by placing on the hand phantom model. Bending tolerances are also analyzed for x and y direction with various bend radii. Gain and SAR of the proposed antenna are 4.61 dBi and 0.868 W/kg. The results of the fabricated prototype show that the proposed wearable antenna is safe for biomedical applications.

In this article, an attempt was made to improve the efficiency of coated solar panels by using artificial neural networks (ANNs) and response surface methodology (RSM). Using the spray coating technique, the glass surface of the photovoltaic solar panel was coated with silicon dioxide nanoparticles incorporated with polytetrafluoroethylene-modified silica sols. Multilayer perceptron with feed-forward back-propagation algorithm was used to develop ANN models for improving the efficiency of the coated solar panels. Out of the 200 sets of data collected, 75% were used for training and 25% were used for testing. On evaluating the models using performance indicators, a four-input technological parameter model (silicon dioxide nanoparticle quantity, coating thickness, surface temperature and solar insolation) with eight neurons in a single hidden layer combination was observed to be the best. The prediction accuracy indicator values of the ANN model were 0.9612 for the coefficient of determination, 0.1971 for the mean absolute percentage error, 0.2317 for the relative root mean square error and 0.00741 for the mean bias error. Using a central composite design model, empirical relationships were developed between input and output responses. The significance of the developed model was ascertained by using analysis of variance, up to a 95% confidence level. For optimization, the RSM was used, and a high efficiency of 17.1% was predicted for the coated solar panel with optimized factors; it was validated to a very high level of predictability. Using interaction and perturbation plots, a ranking of the parameters was done.