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.

Evaluation of antibacterial activity of Asparagus racemosus Willd. root

Abstract: Different concentrations (50, 100, 150 microg/mL) of the methanol extract of the roots of Asparagus racemosus Willd. showed considerable in vitro antibacterial efficacy against Escherichia coli, Shigella dysenteriae, Shigella sonnei, Shigella flexneri, Vibrio cholerae, Salmonella typhi, Salmonella typhimurium, Pseudomonas putida, Bacillus subtilis and Staphylococcus aureus. The effects produced by the methanol extract were compared with chloramphenicol.

CHEF induced highly selective and sensitive turn-on fluorogenic and colorimetric sensor for Fe3+

Abstract: A new fluorescent probe was synthesized from rhodamine-6G and 6-(hydroxymethyl) picolinohydrazide for the sensing of Fe3+ in an aqueous environment. The structure of the sensor was confirmed through its single crystal X-ray study. It exhibits a high specificity and sensitivity towards Fe3+ over other interfering heavy and transition metal ions (HTM). The turn-on greenish-yellow fluorescence and a colorimetric change from colourless to pink were observed upon addition of Fe3+ which evokes almost 116- and 23-fold enhancement in absorbance and emission intensity respectively in an acetonitrile–water (1 : 1, v/v, 25 °C) solution at a neutral pH (pH = 7.2). The Fe3+ promoted ring opening of the spirolactam framework to the open chain amide platform of the sensor is responsible for its visible colour change and turn-on fluorescence activity. It also exhibits an excellent performance in the “dip stick” method. Moreover the limit of detection of the probe is in the 10−8 M range.

A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters

Abstract: We have presented a unified model for detecting different types of video shot transitions. Based on the proposed model, we formulate frame estimation scheme using the previous and the next frames. Unlike other shot boundary detection algorithms, instead of properties of frames, frame transition parameters and frame estimation errors based on global and local features are used for boundary detection and classification. Local features include scatter matrix of edge strength and motion matrix. Finally, the frames are classified as no change (within shot frame), abrupt change, or gradual change frames using a multilayer perceptron network. The proposed method is relatively less dependent on user defined thresholds and is free from sliding window size as widely used by various schemes found in the literature. Moreover, handling both abrupt and gradual transitions along with non-transition frames under a single framework using model guided visual feature is another unique aspect of the work.