Synthesis, Structural, and Antimicrobial Studies of Some New Coordination Compounds of Palladium(II) with Azomethines Derived from Amino Acids

Some new coordination compounds of palladium(II) have been synthesized by the reaction of palladium(II) acetate with azomethines in a 1 : 2 molar ratio using acetonitrile as a reaction medium. Azomethines used in these studies have been prepared by the condensation of 2-acetyl fluorene and 4-acetyl biphenyl with glycine, alanine, valine, and leucine in methanol. An attempt has been made to probe their bonding and structures on the basis of elemental analyses and IR, 1H, and 13C NMR spectral studies. Pd(II) compounds have been found to be more active than their uncomplexed ligands as both of them were screened for antibacterial, antifungal, and insecticidal activities

Machine learning model development for predicting aeration efficiency through Parshall flume

This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting=0.002,R2testing=0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.Validerad;2021;Nivå 2;2021-06-07 (alebob)</p