Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Myoglobin-catalyzed C—H functionalization of unprotected indoles

Abstract: Functionalized indoles are recurrent motifs in bioactive natural products and pharmaceuticals. While transition metal-catalyzed carbene transfer has provided an attractive route to afford C3-functionalized indoles, these protocols are viable only in the presence of N-protected indoles, owing to competition from the more facile N-H insertion reaction. Herein, a biocatalytic strategy for enabling the direct C-H functionalization of unprotected indoles is reported. Engineered variants of myoglobin provide efficient biocatalysts for this reaction, which has no precedents in the biological world, enabling the transformation of a broad range of indoles in the presence of ethyl α-diazoacetate to give the corresponding C3-functionalized derivatives in high conversion yields and excellent chemoselectivity. This strategy could be exploited to develop a concise chemoenzymatic route to afford the nonsteroidal anti-inflammatory drug indomethacin.

Application of mutation operators to flower pollination algorithm

Abstract: A new concept based on mutation operators is applied to flower pollination algorithm (FPA).Based on mutation, five new variants of FPA are proposed.Dynamic switch probability is used in all the proposed variants.Benchmarking of Variants with respect to standard FPA.Benchmarking and statistical testing of the best variant with respect to state-of-the-art algorithms. Flower pollination algorithm (FPA) is a recent addition to the field of nature inspired computing. The algorithm has been inspired from the pollination process in flowers and has been applied to a large spectra of optimization problems. But it has certain drawbacks which prevents its applications as a standard algorithm. This paper proposes new variants of FPA employing new mutation operators, dynamic switching and improved local search. A comprehensive comparison of proposed algorithms has been done for different population sizes for optimizing seventeen benchmark problems. The best variant among these is adaptive-Lvy flower pollination algorithm (ALFPA) which has been further compared with the well-known algorithms like artificial bee colony (ABC), differential evolution (DE), firefly algorithm (FA), bat algorithm (BA) and grey wolf optimizer (GWO). Numerical results show that ALFPA gives superior performance for standard benchmark functions. The algorithm has also been subjected to statistical tests and again the performance is better than the other algorithms.

A Sentiment Analysis System to Improve Teaching and Learning

Abstract: Natural language processing and machine learning can be applied to student feedback to help university administrators and teachers address problematic areas in teaching and learning. The proposed system analyzes student comments from both course surveys and online sources to identify sentiment polarity, the emotions expressed, and satisfaction versus dissatisfaction. A comparison with direct-assessment results demonstrates the system's reliability.