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.

Extensions of ELECTRE-I and TOPSIS methods for group decision-making under complex Pythagorean fuzzy environment

Abstract: Multi-criteria group decision-making is a process in which decision makers assess the performance of alternatives on the basis of conflicting criteria to opt the most worthy alternative as solution. TOPSIS and ELECTRE are effective and commonly used methods to solve multiple criteria decision-making problems. The aim of this study is to propose two new models, namely, complex Pythagorean fuzzy TOPSIS (CPF-TOPSIS) method and complex Pythagorean fuzzy ELECTRE I (CPF-ELECTRE I) method, to tackle multiple criteria group decision-making problems comprising complex Pythagorean fuzzy data. In these methods, we compare complex Pythagorean fuzzy numbers on the basis of their score functions. We use revised closeness index for the ranking of alternatives in CPF-TOPSIS method. {In complex Pythagorean fuzzy concordance and discordance sets, we compare the alternatives on being superior and inferior to other alternatives on the basis of score degree, accuracy degree and indeterminacy. In CPF-ELECTRE I method, we use outranking decision graph to obtain the best alternative.} We illustrate the structure of both methods with the help of flow charts. To verify the accuracy of {proposed methods}, we present an explanatory example for selection of best interior designer for a hotel renovation. {We authenticate the proposed techniques by providing a brief comparative analysis of these methods with existing methods}.

A novel possibility measure to interval-valued intuitionistic fuzzy set using connection number of set pair analysis and its applications

Abstract: The aim of this paper is to give a multi-attribute decision-making (MADM) method for the interval-valued intuitionistic fuzzy (IVIF) set using the set pair analysis (SPA) theory. IVIF set can express the uncertain information in a more valuable manner, while the connection number (CN) based on the “identity,” “discrepancy” and “contrary” degrees of the SPA theory handles the uncertainties and certainties systems. Based on these features, in this paper, we develop a new possibility degree measure based on the CNs to rank the different IVIF numbers. The properties and advantages of the measure are described in details. Later on, we present a novel MADM method and illustrate with several examples to validate it. A comparative as well as superiority analysis is performed to show the feasibility and validity of the approach.

Mutational Frequencies of SARS-CoV-2 Genome during the Beginning Months of the Outbreak in USA

Abstract: SARS-CoV-2 has spread very quickly from its first reported case on 19 January 2020 in the United Stated of America, leading WHO to declare pandemic by 11 March 2020. RNA viruses accumulate mutations following replication and passage in human population, which prompted us to determine the rate and the regions (hotspots) of the viral genome with high rates of mutation. We analyzed the rate of mutation accumulation over a period of 11 weeks (submitted between 19th January to 15 April 2020) in USA SARS-CoV-2 genome. Our analysis identified that majority of the viral genes accumulated mutations, although with varying rates and these included NSP2, NSP3, RdRp, helicase, Spike, ORF3a, ORF8, and Nucleocapsid protein. Sixteen mutations accumulated in Spike protein in which four mutations are located in the receptor binding domain. Intriguingly, we identified a fair number of viral proteins (NSP7, NSP9, NSP10, NSP11, Envelop, ORF6, and ORF7b proteins), which did not accumulate any mutation. Limited changes in these proteins may suggest that they have conserved functions, which are essential for virus propagation. This provides a basis for a better understanding of the genetic variation in SARS-CoV-2 circulating in the US, which could help in identifying potential therapeutic targets for controlling COVID-19.

Novel nitrogen enriched porous carbon adsorbents for CO2 capture: Breakthrough adsorption study

Abstract: A range of novel nitrogen enriched porous carbons were obtained by varying carbonization temperature from 500 to 800 °C using nanocasting technique. Hexamethoxymethylmelamine (HMMM) and mesoporous silica were used as carbon precursor and template respectively. Prepared porous carbons were evaluated for their textural and chemical properties followed by CO 2 adsorption–desorption performance by conducting breakthrough experiments. Evolution of nitrogen functionalities in carbon materials during the carbonization process was investigated by X-ray photoelectron spectroscopy. Porous carbons obtained at 700 °C showed the best textural properties among the prepared carbons. It exhibited the specific surface area of 463 m 2 /g and total pore volume of 0.48 cm 3 /g and showed equilibrium adsorption capacity of 0.676 mmol/g at 30 °C and 12.5% CO 2 concentration. The adsorption capacity of C-700 was found to decrease with increase in adsorption temperature while it increased with increase in inlet CO 2 concentration. CO 2 adsorption kinetics on the prepared carbons was explained by pseudo-first order adsorption model. Three isotherm models were used to investigate the CO 2 adsorption equilibrium data and among the studied models Temkin isotherm model was found to explain the equilibrium data suggesting the heterogeneous nature of carbon surface.