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Biofilm, Antifungal efficacy, Non albicans Candida Candida is one of the most frequently encountered opportunistic fungi that cause severe infection in humans because of its virulence factor. The ability of Candida albicans to form biofilms and adhere to host tissues and biomaterial surfaces is an important factor in its pathogenesis. One of the main characteristics of biofilms is their resistant to broad spectrum anti-microbial drugs. The aim of the study was to know the biofilm formation by various Candida species isolated from various clinical specimens. The study was carried out over a period of 1 year from January 2016 to December 2016 at the Department of Microbiology, Government Medical College and Hospital, Jammu. A total 120 Candida spp. were isolated from various clinical specimens. Speciation of Candida was done by standard yeast identification protocol and Candida CHROMagar. Biofilm formation was detected by Congo Red Agar, Tube method and microtitre plate method. Out of total 120 Candida spp. studied, biofilm production was seen in 63/100 (52.55%) isolates. While comparing all the three methods tube method proved more reliable, easy and more efficient. Antifungal efficacy of Coconut oil and Eucalyptus oil was also tested in this study against all Candida isolates. Eucalyptus oil was observed to be a better antifungal agent than Coconut oil in the present study. When coconut oil was tested against all Candida albicans isolates, the sensitivity of biofilm non producers was higher in comparison to biofilm producers.

Summary Identification of disturbances that pushes the power system towards insecure limits can assist in timely remedial measures to be taken for improving situation awareness. The information about event's signature is primarily available in the form of frequency or voltage signals obtained from phasor measurements units (PMUs) in real‐time. Proper evaluation of power system event characteristics enhances the situational awareness and assists system operator to develop required corrective measures for secure operations. In this paper, a fast and accurate algorithm is proposed to identify, classify and locate the events using minimum synchronized data for enhancing the situational awareness of the system. An index is developed with short window synchronized bus frequency data of 18 cycles to detect an event in the network. For identifying the type of event, the same data length bus voltage magnitude and frequency synchronized measurements are utilized to develop statistical measures based novel indices. A rule‐based inference from event's signature is also developed to validate the usefulness of indices for classification and location identification of the event. These extracted statistical indices are applied as input to the Random Forest Classifier to classify and locate the events in real‐time. The proposed approach captures real‐time synchronized data and is adaptive to system topological changes. The proposed comprehensive approach for situational awareness is applied to standard IEEE 39 Bus test system and IEEE 118 Bus test system. The results highlight the performance of the composite method for event identification, classification and location identification with less computational burden and high accuracy.