A novel synthesis of spiro[5H-1,4-diazepine-5,3'-[3H]indol]-2'(1'H)-ones and spiro[2H-1,5-benzodiazepine-2,3'-[3H]indol]-2'(1'H)-ones

1005-100

Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID Prediction

Abstract Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models’ pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease

Investigation of the effect of FeCl3 on combustion and emission of diesel engine with thermal barrier coating

In the present investigation, the engine performance and emission characteristics of a single cylinder diesel engine with yttria stabilized zirconia (YSZ) coating on piston crown and valves were studied. The 0.2 g L−1 of ferric chloride (FeCl3) as catalyst was added into the diesel fuel in both coated and uncoated engines. The results indicated that FeCl3 with diesel in a YSZ coated engine increased the brake thermal efficiency by 2.7%, and reduced brake specific fuel consumption by 8.3% as compared to standard diesel mode in uncoated engine. The selected thermal barrier coating improved the combustion in afterburning stage leading to effective use of intake air. Emissions such as carbon monoxide, hydrocarbons and smoke opacity were reduced with an increase in emissions of nitrogen oxide and carbon dioxide

Weak Base Dispiro-1,2,4-Trioxolanes: Potent Antimalarial Ozonides

Thirty weak base 1,2,4-dispiro trioxolanes (secondary ozonides) were synthesized. Amino amide trioxolanes had the best combination of antimalarial and biopharmaceutical properties. Guanidine, aminoxy, and amino acid trioxolanes had poor antimalarial activity. Lipophilic trioxolanes were less stable metabolically than their more polar counterparts