In order to overcome the challenges of conventional synthesis of NPs, natural sources such as plants, bacteria, fungi, biopolymers are used for the synthesis of AgNPs.
Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.
Tetrabutylammonium valinate ionic liquid [NBu 4 ][Val] supported on 3-chloropropyltriethoxysilane graftedsuperparamagnetic Fe 3 O 4 NPs (VSF) was synthesized and characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), transmission electronic microscopy (TEM), scanning electronic microscopy (SEM), thermal gravimetric analysis (TGA), and vibrating sample magnetometer (VSM). The VSF catalyst was used as an efficient "quasi-homogeneous" catalyst for the multi-component synthesis of 1,4-dihydropyridines and 2-amino-4-(indol-3-yl)-4H-chromenes at room temperature. The VSF catalyst was recovered using an external magnet and recycled six times without a significant loss in the catalytic activity. Moreover, VSF as a "quasi-homogeneous" catalyst can bridge the gap between homogeneous and heterogeneous catalyses.
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