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: Adsorption & Catalysis. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

A review of multi criteria decision making (MCDM) towards sustainable renewable energy development

Abstract: In the current era of sustainable development, energy planning has become complex due to the involvement of multiple benchmarks like technical, social, economic and environmental. This in turn puts major constraints for decision makers to optimize energy alternatives independently and discretely especially in case of rural communities. In addition, topographical limitations concerning renewable energy systems which are mostly distributed in nature, the energy planning becomes more complicated. In such cases, decision analysis plays a vital role for designing such systems by considering various criteria and objectives even at disintegrated levels of electrification. Multiple criteria decision making (MCDM) is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria. This tool is becoming popular in the field of energy planning due to the flexibility it provides to the decision makers to take decisions while considering all the criteria and objectives simultaneously. This article develops an insight into various MCDM techniques, progress made by considering renewable energy applications over MCDM methods and future prospects in this area. An extensive review in the sphere of sustainable energy has been performed by utilizing MCDM technique.

A review on hydrothermal liquefaction of biomass

Abstract: The rapid depletion of conventional fossil fuels and day-by-day growth of environmental pollution due to use of extensive use of fossil fuels have raised concerns over the use of the fossil fuels; and thus search for alternate renewable and sustainable sources for fuels has started in the last few decades. In this context biomass derived fuels seems to be the promising path; and various routes are available for the biomass processing such as pyrolysis, transesterification, hydrothermal liquefaction, steam reforming, etc.; and the hydrothermal liquefaction (HTL) of wet biomass seems to be the promising route. Therefore, this article briefly enlightened a few concepts of HTL such as the elemental composition of bio-crude obtained by HTL, different types of feedstock adopted for HTL, mechanism of HTL processes, possible process flow diagrams for HTL of both wet and dry biomass and energy efficiency of the process. In addition, this article also enlisted possible future research scope for concerned researchers and a few of them are setting up HTL plant suitable for both wet and dry biomass feedstock; analysing influence of parameters such as temperature, pressure, residence time, catalytic effects, etc.; deriving optimized pathways for better conversion; and development of theoretical models representing the process to the best possible accuracy depending on nature of feedstock.

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

Abstract: Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

Evidence for an excess of B̄→D( *)τ -ν ̄τ decays

Abstract: Based on the full BaBar data sample, we report improved measurements of the ratios R(D(*)) = B(B -> D(*) Tau Nu)/B(B -> D(*) l Nu), where l is either e or mu. These ratios are sensitive to new physics contributions in the form of a charged Higgs boson. We measure R(D) = 0.440 +- 0.058 +- 0.042 and R(D*) = 0.332 +- 0.024 +- 0.018, which exceed the Standard Model expectations by 2.0 sigma and 2.7 sigma, respectively. Taken together, our results disagree with these expectations at the 3.4 sigma level. This excess cannot be explained by a charged Higgs boson in the type II two-Higgs-doublet model. We also report the observation of the decay B -> D Tau Nu, with a significance of 6.8 sigma.