Jadavpur University

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.

Gravastars in f ( R , T ) gravity

Abstract: We propose a unique stellar model under the $f(R,\mathcal{T})$ gravity by using the conjecture of Mazur-Mottola [P. Mazur and E. Mottola, Report No. LA-UR-01-5067, P. Mazur and E. Mottola, Proc. Natl. Acad. Sci. USA 101, 9545 (2004)] which is known as gravastar and a viable alternative to the black hole as available in literature. This gravastar is described by the three different regions, viz., (I) Interior core region, (II) Intermediate thin shell, and (III) Exterior spherical region. The pressure within the interior region is equal to the constant negative matter density which provides a repulsive force over the thin spherical shell. This thin shell is assumed to be formed by a fluid of ultrarelativistic plasma and the pressure, which is directly proportional to the matter-energy density according to Zel'dovich's conjecture of stiff fluid [Y. B. Zel'dovich, Mon. Not. R. Astron. Soc. 160, 1 (1972)], does counterbalance the repulsive force exerted by the interior core region. The exterior spherical region is completely vacuum and assumed to be de Sitter spacetime which can be described by the Schwarzschild solution. Under this specification we find out a set of exact and singularity-free solution of the gravastar which presents several other physically valid features within the framework of alternative gravity.

Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey

Abstract: Mobile Opportunistic Network (MON) is characterized by intermittent connectivity where communication largely depends on the mobility pattern of the participating nodes. In MON, a node can take the custody of a packet for a long time and carry it until a new forwarding path has been established, unlike mobile adhoc network (MANET), where a node must drop the packet otherwise. Therefore, routing in MON depends on the repeated make-and-break of communication links, which again depends on the mobility of the nodes as they encounter and drift away from each other. MONs can simply be formed by humans carrying hand-held devices (like Personal Digital Assistant [PDAs] or cell phones) or on-board devices installed in vehicles. Therefore, with mobility playing a major role in the performance of MON, researchers have repeatedly tried to understand the nature of mobility with respect to humans, vehicles, and wild animals. To study the nature of mobility, researchers have collected mobility traces, proposed mobility models, and analyzed the performance of MON with respect to various mobility parameters. This article provides a detailed survey of different mobility models which have been proposed to date and how mobility largely determines the performance of opportunistic routing. We divide the article into four major sections. First, we provide a detailed survey of all the synthetic mobility models which have been developed to date. Second, we study the various mobility traces which have been collected and analyzed. Third, we study how mobility parameters affect the performance of MON. Finally, we highlight on some of the research areas and open challenges which yet remain unsolved.

A Survey of Multiobjective Evolutionary Clustering

Abstract: Data clustering is a popular unsupervised data mining tool that is used for partitioning a given dataset into homogeneous groups based on some similarity/dissimilarity metric. Traditional clustering algorithms often make prior assumptions about the cluster structure and adopt a corresponding suitable objective function that is optimized either through classical techniques or metaheuristic approaches. These algorithms are known to perform poorly when the cluster assumptions do not hold in the data. Multiobjective clustering, in which multiple objective functions are simultaneously optimized, has emerged as an attractive and robust alternative in such situations. In particular, application of multiobjective evolutionary algorithms for clustering has become popular in the past decade because of their population-based nature. Here, we provide a comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature. The techniques are classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution. The pros and cons of the different approaches are mentioned. Finally, we have discussed some real-life applications of multiobjective clustering in the domains of image segmentation, bioinformatics, web mining, and so forth.