Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Intuitionistic Fuzzy Hamacher Aggregation Operators with Entropy Weight and Their Applications to Multi-criteria Decision-Making Problems

Abstract: The theme of this work is to present some new operational laws for intuitionistic fuzzy numbers and their averaging and geometric aggregation operators under the completely unknown attribute weights. To accomplish this, firstly the shortcoming of the existing operations has been highlighted, and then, they have been mitigated by defining intuitionistic fuzzy Hamacher interaction weighted averaging and geometric aggregation operators by considering the pairs of membership functions. Some of the desirable properties of the proposed operators are stated. The attribute weight vector used for aggregating the decision maker’s preferences has been computed by using the entropy function. Finally, a decision-making approach has been presented and illustrated with a numerical example to demonstrate the superiority of the approach over the existing operators.

Systematic Review of Clustering High-Dimensional and Large Datasets

Abstract: Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.

Lithium zirconate as solid catalyst for simultaneous esterification and transesterification of low quality triglycerides

Abstract: Alkali metal (Li, Na and K) doped zirconium oxide was prepared (Li/ZrO2, Na/ZrO2 and K/ZrO2) by wet chemical route and used as active heterogeneous catalyst for the transesterification of waste cottonseed oil with ethanol and methanol to produce fatty acid ethyl and methyl esters, respectively. The catalyst characterization supports the formation of lithium zirconate single phase in the case of Li/ZrO2 and it was able to catalyze simultaneous esterification and transesterification of high free fatty acid containing vegetable oils (VOs). The reaction conditions, such as catalyst concentration, reaction temperature, the molar ratio of alcohol/oil and stirring speed, were optimized in the presence of Li/ZrO2 catalyst. The catalyst activity was found to be a function of its basic sites which in turn depends on calcination temperature and lithium content of the catalyst. A pseudo first order kinetic equation was applied to evaluate the kinetic parameters for the transesterification of waste cottonseed oil with methanol and ethanol. The activation energy (Ea) for the Li/ZrO2 catalyzed methanolysis and ethanolysis was found to be 40.8 and 43.1 kJ mol−1, respectively. The catalyst could be reused up to nine cycles without significant loss of performance as >90% fatty acid alkyl ester yield was maintained.

Nanocrystalline Lithium Ion Impregnated Calcium Oxide As Heterogeneous Catalyst for Transesterification of High Moisture Containing Cotton Seed Oil

Abstract: The present work demonstrates the application of nanocrystalline Li+ impregnated CaO as a heterogeneous catalyst for transesterification of used cottonseed oil with a higher amount of moisture Catalysts with different alkali metal ions were prepared by the wet impregnation method, and their basic strengths were measured by Hammett indicators and a maximum was found in the case of lithium carbonate impregnated CaO Further characterization of the lithium impregnated catalyst by powder X-ray diffraction and transmission electron microscopy studies supports the formation of a nanosized catalyst with a particle size of about 50 nm The same catalyst has been chosen for studying the transesterification reaction of used cotton seed oil with methanol The variables used for the transesterification were impregnated alkali metal ion, percentage of Li+ impregnation, catalyst concentration, reaction temperature (35−65 °C), oil to methanol molar ratio, reaction time (05−8 h), free fatty acid content (up to 6 wt %),