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In the present work, monolithic LPS-SiC was fabricated by hot press method with the addition of Al₂O₃, Y₂O₃ and SiO₂ and annealed at different temperatures to observe microstructure evolution. Process temperature was varied from 1760°C to 1800°C. Process pressure and maturing time are 20MPa and 1h respectively. Hot pressed samples were cut into rectangular bars. Three-point flexural strength was measured at room temperature in air with a cross-head speed of 0.1 mm/min and a lower span of 18 mm. Flexural strength and elastic modulus measurement was performed using a universal test machine (INSTRON 5581, USA). The apparent density of the sintered body was measured by the Archimedes method. The specimen dimension of the heat treatments is 4W×25L×1.5T mm. The specimens used for weight-loss measurement were set into an open carbon crucible to avoid nonuniform temperature distribution within the furnace. Post-fabrication heat treatment was performed in vacuum atmosphere (PO2 ≈ 0.01 Pa). The temperature was increased at a rate of 20 K/min to the heat-treatment temperature and maintained for 10 hours, after which the specimens were furnace cooled. After heat-treatment, weight of heat-treated specimens was carefully measured by an electronic balance. In order to reveal the microstructural change in heat-treated specimen, X-ray diffractometry and microstructure observation were performed and compared with those of the as-fabricated one.

The smart wearable devices that can track the fitness activities are getting famous these days due to their easy-to-use features. The fitness trackers can work for an individual in a promising manner, provided that the user is well familiar with the device and is committed with the timelines. Several reports have provided evidence that these smart wearable devices have not showed promising results and in most of the cases, people have stopped using them, few weeks after the purchase. There are several reasons linked with this response. During this research, we have worked on the correlations of weight loss via smart device with the age, gender, body mass index (BMI) and ideal body weight (IBW), with the aid of gradient boosted decision trees (XGBoost) and support vector machine (SVM) learning tools. XGBoost and SVM are capable of dealing with complex datasets, with higher frequencies, and for data emerging from multiple sources. These machine learning tools use kernel functions for the clustering and other classification measures, and are thus better as compared to the logistic methods. Next, the time series forecasting tools are discussed with the Bayesian hyperparametric optimization. The time series of the weight loss monitoring of each individual, depicted in this manner, provided complex fractal patterns, with reduction in amplitude, with the passage of time.

Corrosion protection of expired Atenolol drug on the Al in the 3M HCl was investigated through the weight loss, gasometric, Tafel plot, impedance, atomic absorption spectroscopy (AAS), quantum chemical, scanning electron microscopy (SEM) and atomic force microscopy (AFM) studies. The addition of different concentrations of expired Atenolol drug enhances the protection efficiency. Gasometric technique was performed in order to study the variation in the amount of hydrogen gas in the presence and absence of the inhibitor. The potentiodynamic polarization plots show that the expired Atenolol drug acts as a mixed type. Data of impedance studies show that the charge transfer process controls the corrosion of Al in the 3M HCl medium. The SEM and AFM results explore that the expired Atenolol drug is a powerful corrosion inhibitor for the Al in HCl solution.