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Institution

Jadavpur University

EducationKolkata, India
About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Schiff base. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.


Papers
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Book ChapterDOI
01 Jan 2009

85 citations

Journal ArticleDOI
TL;DR: Air dispersion modelling of fugitive release of bioaerosols from static compost piles using the SCREEN3 air dispersion model in area source term mode was used to generate source depletion curves downwind of the facility for comparative purposes.

85 citations

Journal ArticleDOI
TL;DR: The structural origin of intrinsic piezoelectric characteristics of bio-assembled chitin nanofibers allows the prawn shell to make a tactile sensor that performs also as a highly durable mechanical energy harvester/nanogenerator.
Abstract: A human interactive self-powered wearable sensor is designed using waste by-product prawn shells. The structural origin of intrinsic piezoelectric characteristics of bio-assembled chitin nanofibers has been investigated. It allows the prawn shell to make a tactile sensor that performs also as a highly durable mechanical energy harvester/nanogenerator. The feasibility and fundamental physics of self-powered consumer electronics even from human perception is highlighted by prawn shells made nanogenerator (PSNG). High fidelity and non-invasive monitoring of vital signs, such as radial artery pulse wave and coughing actions, may lead to the potential use of PSNG for early intervention. It is presumed that PSNG has enormous future aspects in real-time as well as remote health care assessment.

85 citations

Journal ArticleDOI
TL;DR: New techniques for uncertainty management in expert systems for two generic class of problems using fuzzy Petri nets that represent logical connectivity among a set of imprecise propositions and an algorithm for selecting one evidence from each set of mutually inconsistent evidences, referred to as nonmonotonic reasoning are developed.
Abstract: The paper aims at developing new techniques for uncertainty management in expert systems for two generic class of problems using fuzzy Petri nets that represent logical connectivity among a set of imprecise propositions. One class of problems deals with the computation of fuzzy belief of any proposition from the fuzzy beliefs of a set of independent initiating propositions in a given network. The other class of problems is concerned with the computation of steady-state fuzzy beliefs of the propositions embedded in the network, from their initial fuzzy beliefs through a process called belief revision. During belief revision, a fuzzy Petri net with cycles may exhibit "limit cycle behavior" of fuzzy beliefs for some propositions in the network. No decisions can be arrived at from a fuzzy Petri net with such behavior. To circumvent this problem, techniques have been developed for the detection and elimination of limit cycles. Further, an algorithm for selecting one evidence from each set of mutually inconsistent evidences, referred to as nonmonotonic reasoning, has also been presented in connection with the problems of belief revision. Finally, the concepts proposed for solving the problems of belief revision have been applied successfully for tackling imprecision, uncertainty, and nonmonotonicity of evidences in an illustrative expert system for criminal investigation.

85 citations

Journal ArticleDOI
TL;DR: In this paper, the performance and emission of a single cylinder Diesel engine has been studied by using the blends of Diesel-Diethyl ether (DEE) and Diesel-diethyl e ether-ethanol.
Abstract: In this paper, the performance and emission of a single cylinder Diesel engine has been studied by using the blends of Diesel–diethyl ether (DEE) and Diesel–diethyl ether–ethanol. The used blends are D95DEE5 (5% DEE, 95% Diesel by volume), D90DEE10 (10% DEE, 90% Diesel by volume), D90DEE5E5 (5% ethanol, 5% DEE and 90% Diesel by volume), D85DEE5E10 (10% ethanol, 5% DEE and 85% Diesel by volume), D85DEE10E5 (5% ethanol, 10% DEE and 85% Diesel by volume) and D80DEE10E10 (10% ethanol, 10% DEE and 80% Diesel by volume). The thermal efficiency of the engine increased with the blend of 5% DEE blend whereas, decreased with 10% DEE blend. Ethanol addition to Diesel–DEE blends increased the efficiency of the engine for both the cases. Use of ethanol along with DEE reduced CO, NO x , hydrocarbon and particulate matters remarkably. Blend D80DEE10E10 showed the best potential of achieving the paradoxical objective of high performance with low emission among the tested fuel samples.

85 citations


Authors

Showing all 10999 results

NameH-indexPapersCitations
Subir Sarkar1491542144614
Amartya Sen149689141907
Susumu Kitagawa12580969594
Praveen Kumar88133935718
Rodolphe Clérac7850622604
Rajesh Gupta7893624158
Santanu Bhattacharya6740014039
Swagatam Das6437019153
Anupam Bishayee6223711589
Michael G. B. Drew61131524747
Soujanya Poria5717513352
Madeleine Helliwell543709898
Tapas Kumar Maji542539804
Pulok K. Mukherjee5429610873
Dipankar Chakraborti5411512078
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202385
2022332
20211,949
20201,936
20191,737
20181,807