Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Degradation of amoxicillin by Fenton and Fenton-integrated hybrid oxidation processes

Abstract: The present study aims to investigate the degradation of amoxicillin (AMX) using the Fenton process and hybrid Fenton-like processes such as photo-Fenton, solar photo-Fenton, sono-Fenton, and sono-photo-Fenton. The effect of ferrous ions (Fe2+), hydrogen peroxide (H2O2), and pH was evaluated. Complete degradation was achieved within 12 min of reaction time under the optimized conditions of Fe2+- 30 mg/l, H2O2 – 375 mg/l, and pH – 3.0 during Fenton’s process. Efficiency towards degradation of AMX was enhanced when the Fenton’s process was coupled with UV- light illumination, solar light illumination, and UV light-Ultrasound treatment. Complete degradation was observed within 3.5 min and 9 min during the photo-Fenton and solar photo-Fenton process, respectively. The sono-Fenton process takes a longer time to degrade AMX than other processes while sono-photo-Fenton process degrades AMX within 6 min. The reason for a reduced rate of degradation of AMX in the sono-Fenton process may be attributed to the competition of ultrasound and Fenton for H2O2, which ultimately decreases Fenton reagent in the process. The study concluded that the Fenton process coupled with other UV/solar light is more efficient than the stand-alone Fenton process towards degradation of AMX.

Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Abstract: Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.

FlavorDB: a database of flavor molecules.

Abstract: Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.