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

The Relationship Between Pharmacogenomics and Pharmacokinetics and Its Impact on Drug Choice and Dosing Regimens in Pediatrics

The concept of precision or personalized medicine in pediatrics is still in its infancy, and due to ethical and logistical constraints, it is difficult to conduct clinical studies in pediatric to obtain meaningful correlations between ontogeny and drug disposition. However, as a result of initiatives by the Food and Drug Administration (FDA) aimed toward incentivizing companies for conducting pediatric trials, knowledge on pediatric pharmacogenomics is slowly increasing. The information on pediatric pharmacogenomics is utilized to implement pharmacogenomic testing in pediatrics to allow clinicians to make an informed decision on selection and dosing of drugs in pediatrics. The ontogeny of drug-metabolizing enzymes (DMEs), transporters, and target proteins is the most crucial factor in pediatric pharmacogenomics. Based on in vitro and in vivo studies on the ontogeny of DMEs, various pharmacogenomic tests in pediatrics were evaluated concerning the pharmacokinetics of drugs utilized in pediatric pharmacotherapy. Needing to obtain clinically relevant advantages of incorporating pharmacogenomics in pediatric drug therapy, clinicians must be informed on pharmacogenomic terms by appropriate educational programs. Furthermore, a comprehensive database that can bank all pediatric pharmacogenomic information that can seamlessly collaborate with other international databases must be established