National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Bovine serum albumin catalyzed one-pot, three-component synthesis of dihydropyrano[2,3-c]pyrazole derivatives in aqueous ethanol

Abstract: Bovine serum albumin (BSA) catalyzed synthesis of dihydropyrano[2,3-c]pyrazole derivatives via a one pot, three component reaction of an aldehyde/ketone/isatin, malononitrile and 3-methyl-1H-pyrazol-5-(4H)-one in H2O–EtOH (7 : 3) at ambient temperature was developed in this work. The catalyst was found to work efficiently for aldehydes, ketones and isatins to give the corresponding dihydropyrano[2,3-c]pyrazole and spiro[indoline-3,4′-pyrano[2,3-c]pyrazole] derivatives in high yields. BSA showed a broad range of catalytic promiscuity towards various aldehydes, aromatic/aliphatic ketones and substituted isatins. The use of an environmentally benign protocol, reusability of the catalyst, avoidance of hazardous solvents, excellent yields, easy work up and no byproduct formation make BSA an attractive candidate for further applications as a biocatalyst.

A novel sentiment analysis of social networks using supervised learning

Abstract: Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013.

Removal of Remazol Brilliant Blue Dye from Dye-Contaminated Water by Adsorption Using Red Mud: Equilibrium, Kinetic, and Thermodynamic Studies

Abstract: Utilization of industrial solid wastes for the treatment of wastewater from another industry could help environmental pollution abatement, in solving both solid waste disposal as well as liquid waste problems. Red mud (RM) is a waste product in the production of alumina and it poses serious pollution hazard. The present paper focuses on the possibility of utilization of RM as an adsorbent for removal of Remazol Brilliant Blue dye (RBB), a reactive dye from dye-contaminated water. Adsorption of RBB, from dye-contaminated water was studied by adsorption on powdered sulfuric acid-treated RM. The effect of initial dye concentration, contact time, initial pH, and adsorbent dosage were studied. Langmuir isotherm model has been found to represent the equilibrium data for RBB–RM adsorption system better than Freundlich model. The adsorption capacity of RM was found to be 27.8 mg dye/g of adsorbent at 40 °C. Thermodynamic analysis showed that adsorption of RBB on acid-treated RM is an endothermic reaction with ∆H 0 of 28.38 kJ/mol. The adsorption kinetics is represented by second-order kinetic model and the kinetic constant was estimated to be 0.0105 ± 0.005 g/mg min. Validity of intra-particle diffusion kinetic model suggested that among the mass transfer processes during the dye adsorption process, pore diffusion is the controlling step and not the film diffusion. The process can serve dual purposes of utilization of an industrial solid waste and the treatment of liquid waste.