Revolutionizing heart disease prediction with quantum-enhanced machine learning.

The recent developments in quantum technology have opened up new opportunities for machine learning algorithms to assist the healthcare industry in diagnosing complex health disorders, such as heart disease. In this work, we summarize the effectiveness of QuEML in heart disease prediction. To evaluate the performance of QuEML against traditional machine learning algorithms, the Kaggle heart disease dataset was used which contains 1190 samples out of which 53% of samples are labeled as positive samples and rest 47% samples are labeled as negative samples. The performance of QuEML was evaluated in terms of accuracy, precision, recall, specificity, F1 score, and training time against traditional machine learning algorithms. From the experimental results, it has been observed that proposed quantum approaches predicted around 50.03% of positive samples as positive and an average of 44.65% of negative samples are predicted as negative whereas traditional machine learning approaches could predict around 49.78% of positive samples as positive and 44.31% of negative samples as negative. Furthermore, the computational complexity of QuEML was measured which consumed average of 670 µs for its training whereas traditional machine learning algorithms could consume an average 862.5 µs for training. Hence, QuEL was found to be a promising approach in heart disease prediction with an accuracy rate of 0.6% higher and training time of 192.5 µs faster than that of traditional machine learning approaches.

Application of a polymer-magnetic-algae based nano-composite for the removal of methylene blue - Characterization, parametric and kinetic studies.

The potential ability of synthesized PPy-Fe3O4-SW nano-composite to remove Methylene Blue (MB) from synthetic textile dye solution was investigated under batch conditions. Through parametric studies, the influence of process parameters namely solution pH, on the effective performance of nano-composite was studied. PPy - Fe3O4- SW nano-composite removed 99.14% of MB at the optimized conditions of pH-10, temperature - 25 °C, initial MB concentration - 50 mg/L, nano-composite dosage - 20 mg and contact time - 20 min. PPy - Fe3O4- SW nano-composite has a maximum sorption capacity of 666.66 mg/g. The kinetics and isotherm study revealed that the chromium adsorption obeys pseudo second order (PSO) model (R2 = 0.9941) and Freundlich isotherm (R2 = 0.9910) respectively. The PSO kinetic constant (K2) was found to be 0.000442 (g/mg) min. The thermodynamic feasibility was confirmed through negative values of standard free energy at all tested conditions. The characteristics of adsorption study were analyzed and the results of FTIR, SEM and EDS confirmed the uptake of MB by PPy-Fe3O4-SW nano-composite.

Optimization of media composition for Nattokinase production by Bacillus subtilis using response surface methodology.

Response surface methodology and central composite rotary design (CCRD) was employed to optimize a fermentation medium for the production of Nattokinase by Bacillus subtilis at pH 7.5. The four variables involved in this study were Glucose, Peptone, CaCl2, and MgSO4. The statistical analysis of the results showed that, in the range studied; only peptone had a significant effect on Nattokinase production. The optimized medium containing (%) Glucose: 1, Peptone: 5.5, MgSO4: 0.2 and CaCl2: 0.5 resulted in 2-fold increased level of Nattokinase (3194.25U/ml) production compared to initial level (1599.09U/ml) after 10h of fermentation. Nattokinase production was checked with fibrinolytic activity.