Jadavpur University

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

Development of Fast Dispersible Aceclofenac Tablets : Effect of Functionality of Superdisintegrants

Abstract: Aceclofenac, a non-steroidal antiinflammatory drug, is used for posttraumatic pain and rheumatoid arthritis. Aceclofenac fast-dispersible tablets have been prepared by direct compression method. Effect of superdisintegrants (such as, croscarmellose sodium, sodium starch glycolate and crospovidone) on wetting time, disintegration time, drug content, in vitro release and stability parameters has been studied. Disintegration time and dissolution parameters (t 50% and t 80% ) decreased with increase in the level of croscarmellose sodium. Where as, disintegration time and dissolution parameters increased with increase in the level of sodium starch glycolate in tablets. However, the disintegration time values did not reflect in the dissolution parameter values of crospovidone tablets and release was dependent on the aggregate size in the dissolution medium. Stability studies indicated that tablets containing superdisintegrants were sensitive to high humidity conditions. It is concluded that fast-dispersible aceclofenac tablets could be prepared by direct compression using superdisintegrants.

Facile Synthesis of Silver Nanoparticles Decorated Magnetic-Chitosan Microsphere for Efficient Removal of Dyes and Microbial Contaminants

Abstract: A facile and rapid synthesis of core–shell type magnetite-chitosan microsphere decorated with silver nanoparticles (MCSM) is described. The composition and structure of the as-synthesized microsphere characterized by various spectroscopic and microscopic techniques demonstrated formation of 3.63 ± 0.76 μm MCSM with decoration of silver nanoparticles (AgNPs) having 16 ± 2.5 nm size. The thermogravimetric analysis (TGA) data showed good thermal stability, whereas vibrating sample magnetometry (VSM) analysis indicated the superparamagnetic behavior of the as-synthesized microsphere. The adsorptive removal and antimicrobial property of MCSM was explored for eco-friendly and cost-effective water purification. The MCSM removed 99.99% microbial contaminants and 99.5% of dyes from single as well as multicomponent systems from water bodies efficiently. Furthermore, the dye removal capacity of MCSM (qe = 271.2 ± 14.5 mg/g) was found to be higher compared to the other nanoadsorbents attributing to the high effective...

Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes

Abstract: Recently, the problem of clustering categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, has been gaining significant attention from researchers. With the growing demand for categorical data clustering, a few clustering algorithms with focus on categorical data have recently been developed. However, most of these methods attempt to optimize a single measure of the clustering goodness. Often, such a single measure may not be appropriate for different kinds of datasets. Thus, consideration of multiple, often conflicting, objectives appears to be natural for this problem. Although we have previously addressed the problem of multiobjective fuzzy clustering for continuous data, these algorithms cannot be applied for categorical data where the cluster means are not defined. Motivated by this, in this paper a multiobjective genetic algorithm-based approach for fuzzy clustering of categorical data is proposed that encodes the cluster modes and simultaneously optimizes fuzzy compactness and fuzzy separation of the clusters. Moreover, a novel method for obtaining the final clustering solution from the set of resultant Pareto-optimal solutions in proposed. This is based on majority voting among Pareto front solutions followed by k-nn classification. The performance of the proposed fuzzy categorical data-clustering techniques has been compared with that of some other widely used algorithms, both quantitatively and qualitatively. For this purpose, various synthetic and real-life categorical datasets have been considered. Also, a statistical significance test has been conducted to establish the significant superiority of the proposed multiobjective approach.