Indian Institute of Technology Guwahati

About: Indian Institute of Technology Guwahati is a education organization based out in Guwahati, Assam, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

COSMO-RS-Based Screening of Ionic Liquids as Green Solvents in Denitrification Studies

Abstract: Five- and six-membered heteroaromatic nitrogen compounds play an inhibiting role in the hydrodesulfurization of diesel oil. In this work, the ionic liquids (ILs) are used as green solvents to remove such compounds by liquid−liquid extraction (LLE). Approximately 168 ILs comprising cations which include 1-ethyl-3-methylimidaozlium [EMIM], 1-ethylpyridinium [EPY], 1-ethyl-1-methyl pyrrolidinium [EPYRO], 1-ethyl-1-methylpiperidinium [EMPIP], 4-ethyl-4-methyl morpholinium [EMMOR], and 1,2,4-trimethylpyrazolium[TMPYZO] combined with 26 anions were investigated in this work. The infinite dilution activity coefficient (IDAC) was predicted through the conductor-like screening model for real solvents (COSMO-RS) model in order to screen the potential solvents. Initially the model was benchmarked via IDAC and LLE predictions. LLE was predicted for four reported ternary systems in which a nitrogen heterocycle was one of the compounds. The average root-mean-square deviation (rmsd) obtained was 10%. The IDAC values wer...

Polyphenolic compounds: an overview

Abstract: A comprehensive review on the polyphenolic compounds is presented. The emphasis is placed on the structural and functional implications on the regiochemistry as well as on the stereochemistry of oligomeric polyphenols. A correlation between the natural polyphenolic compounds and the synthetic ones is highlighted by making a general classification of polyphenolic compounds. The uses and applications of polyphenolic compounds are discussed in detail.

iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images

Abstract: Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation in aerial imagery that combines instance-level object detection and pixel-level segmentation tasks. In comparison to instance segmentation in natural scenes, aerial images present unique challenges e.g., a huge number of instances per image, large object-scale variations and abundant tiny objects. Our large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) comes with 655,451 object instances for 15 categories across 2,806 high-resolution images. Such precise per-pixel annotations for each instance ensure accurate localization that is essential for detailed scene analysis. Compared to existing small-scale aerial image based instance segmentation datasets, iSAID contains 15$\times$ the number of object categories and 5$\times$ the number of instances. We benchmark our dataset using two popular instance segmentation approaches for natural images, namely Mask R-CNN and PANet. In our experiments we show that direct application of off-the-shelf Mask R-CNN and PANet on aerial images provide suboptimal instance segmentation results, thus requiring specialized solutions from the research community. The dataset is publicly available at: this https URL