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.

Investigation of the properties of alkali-activated slag mixes involving the use of nanoclay and nucleation seeds for 3D printing

Abstract: This study investigated the properties of alkali activated slag (AAS) binders formulated for extrusion‐based 3D printing. The fresh properties of AAS mixes were tailored through the use of nanoclay (NC) and nucleation seeds. The printability criteria employed were the ease of extrusion (extrudability) and the stability of the layered structure (buildability). Introduction of 0.4% NC in AAS mixes led to improved thixotropic properties due to the flocculation effect, which accounted for the extrudability and shape fidelity of the binder. Inclusion of 2% hydromagnesite seeds in this mix design provided additional nucleation sites for the increased precipitation of hydrate phases, resulting in denser microstructures. This enhanced the hydration reaction and improved the structural build-up rate necessary for large-scale 3D printing. The developed AAS mix containing 0.4% NC and 2% hydromagnesite seeds was used in the printing of an actual 3D structure to demonstrate its feasibility to be used in 3D printing applications.

Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks

Abstract: The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with s sparse auto-encoders on gene expression data. While classifying any two tumor types, data of other tumor types were used in unsupervised manner to improve the feature representation. The performance of our algorithm was tested on 36 two-class benchmark datasets from the GEMLeR repository. On performing statistical tests, it is clearly ascertained that our algorithm statistically outperforms several generally used cancer classification approaches. The deep learning based molecular disease classification can be used to guide decisions made on the diagnosis and treatment of diseases, and therefore may have important applications in precision medicine.

Measurement of the γγ*→η and γγ*→η′ transition form factors

Abstract: We study the reactions e{sup +}e{sup -} {yields} e{sup +}e{sup -} {eta}{sup (/)} in the single-tag mode and measure the {gamma}{gamma}* {yields} {eta}{sup (/)} transition form factors in the momentum transfer range from 4 to 40 GeV{sup 2}. The analysis is based on 469 fb{sup -1} of integrated luminosity collected at PEP-II with the BABAR detector at e{sup +}e{sup -} center-of-mass energies near 10.6 GeV.