Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate–catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

The main objective of this study was to evaluate the groundwater quality for domestic, agriculture use and to describe fluoride contamination in groundwater and their impacts on human health. 67 groundwater samples were collected and analyzed for major ions. Water Quality Index (WQI), Piper diagram and Gibbs diagrams were calculated to measure the suitability of groundwater for drinking purpose. The hazards index value was calculated to estimate the noncarcinogenic risk to adult (male, female) and children suggested by the United States Environmental Protection Agency (USEPA). The irrigation indices were calculated to evaluate the quality of water for irrigation purpose. Statistical methods such as principal component and hierarchical cluster analysis were used to analyses the interrelationship of data. Hydrochemistry of the samples shows, the major ions in the order of Ca 2+ >Mg 2+ >Na + and Cl->SO4in the study area. WQI value of groundwater, 74.62% of sample locations are good and 25.38 % of sample locations need primary treatment for drinking purpose. The results of the hazards index show that 65.67% of the sample locations exceeds the tolerable limit for non-carcinogenic risk (greater than one) for children higher than the risk level for Male and female. Statistical report of PCA and HCA reveals that Ca-Na-HCO3-F has positive loading and TDS-EC has negative loading. The study results show that rock-water interaction and anthropogenic activities are the major factors that influence the quality of groundwater. The continuous intake of excess concentration fluoride causes bone diseases and teeth problems.

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This paper deals with a new approach implemented to decrease power losses and improve voltage profile in distribution networks using Distribution STATic COMpensator (DSTATCOM). DSTATCOM location can be determined by the voltage stability index (VSI) and sizing can be identified by nature inspired, recently developed whale optimization algorithm (WOA). To check efficacy, the proposed technique is tested on two standard buses: Indian rural electrification 28-bus and IEEE 69-bus distribution systems. Obtained results show that optimal allocation of DSTATCOM effectively reduces power losses and improves voltage profile.