Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Electrophoretic mobility shift assays with GFP-tagged proteins (GFP-EMSA)

The electrophoretic mobility shift assay (EMSA) is commonly used for the study of nucleic acid-binding proteins. The technique can be used to demonstrate that a protein is binding to RNA or DNA through visualization of a shift in electrophoretic mobility of the nucleic acid band. A major disadvantage of the EMSA is that it does not always provide an absolute certitude that the band shift is due to the protein under scrutiny, as contaminants in the sample could also cause the band shift. Here we describe a variation of the standard EMSA allowing to visualize with added certitude, the co-localized band shifts of a GFP-tagged protein binding to its cognate nucleic acid target sequence stained with an intercalator, such as GelRed. Herein, we present an illustrative protocol of this useful technique called GFP-EMSA along with specific notes on its advantages and limitations