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  • Author: C. Prakash x
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A novel mechanism for the elimination of false selvedge in rapier looms was successfully designed and developed, which functions to grip the protruding weft ends after each pick insertion during the weaving process, with subsequent cutting and suction of the protruding ends. The task is otherwise, presently performed by the formation of dummy or false selvedges in the fabric, requiring additional yarn other than that required for the actual fabric. A significant amount of yarn is wasted to form false selvedges, as they are removed and disposed as hard waste post weaving, which consequently increases the cost of fabric and narrows the profit margins. The developed mechanism was successfully installed and commissioned on an indigenous rapier loom leading to a reduction in the amount of hard waste otherwise generated, without affecting the normal functioning of the loom and the weaving process. The amount of reduction in hard waste was estimated and the quality of fabric manufactured before and after the installation was tested and analyzed.


Chronic exposure to fluoride causes dental and skeletal fluorosis. Fluoride exposure is also detrimental to soft tissues and organs. The present study aimed at evaluation of the effect of Ginkgo biloba and ascorbic acid on learning and memory deficits caused by fluoride exposure. Male Wistar rats were divided into five groups (n=6). Group 1 control. Groups 2 to 5 received 100 ppm of sodium fluoride over 30 days. Groups 3, 4 and 5 were further treated for 15 days receiving respectively 1% gum acacia solution, 100 mg/kg body weight ascorbic acid, and 100mg/kg body weight Ginkgo biloba extract. After 45 days, all animals were subjected to behavioural tests. The results showed that fluoride affected learning and memory. Fluoride causes oxidative stress and neurodegeneration, thereby affecting learning and memory. Ascorbic acid and Ginkgo biloba were found to augment the reversal of learning and memory deficits caused by fluoride ingestion


This research article suggests a computational method for constructing fuzzy sets in absence of expert knowledge. This method uses concepts of central tendencies mean and variance. This study gives a solution to the critical issue in designing of fuzzy systems, number of fuzzy sets. Proposed computational method helps in finding intervals and thereby fuzzy sets for fuzzy time series forecasting. Proposed computational method is implemented on the authentic data for the enrolments of University of Alabama, which is considered as benchmark problem in the field of fuzzy time series. The forecasted values are compared with the results of other methods to state its supremacy. Projected computational method along with Gaussian membership function gave promising results over other methods for fuzzy time series for the above said benchmark data.