Narrow Results

Recently neutrino oscillation experiments have shown that it is very likely that there are one or two sterile neutrinos. In this review neutrino oscillations with one, two, three sterile and three active neutrinos, and parameters that are consistent with experiments, are reviewed.

The present paper attempts to generate visual clustering and data extraction of cell formation problem using both principal component analysis (PCA) and self-organizing map (SOM) from input of sequence based on the machine-part incidence matrix. Firstly, the focus is to utilize PCA for extracting high-dimensionality of input variables and project the dataset onto a 2D space. Secondly, the unsupervised competitive learning of SOM algorithm is used for data visualization and subsequently, to solve cell formation problem based on ordinal sequence data via the node cluster on the SOM map. Although the numerically illustrated results from dataset revealed that PCA has explained most of the cumulative variance of data but in reality, when the very large-dimensional cell formation problem based on sequence is available then, obtaining the clustering structure from PCA projection becomes very difficult. Most importantly, in the visual clustering of ordinal data, the use of U-matrix alone cannot be efficient to get the cluster structure but with color extraction, hit map, labeling via the SOM node map it becomes a powerful clustering visualization methodology and thus, the present research contribute significantly in the research of cellular manufacturing.

A new method has been developed to find similar substructures in protein binding cavities. It is based on the idea that protein function is intimately related to the recognition and subsequent response to the binding of an endogenous ligand in a well-characterized binding pocket. It can therefore be assumed that proteins having similar binding cavities also bind similar ligands and exhibit related function. For the comparison of the binding cavities, the binding-site exposed physicochemical characteristics are described by assigning generic pseudocenters to the functional groups of the amino acids flanking a particular active site. These pseudocenters are assembled into small substructures. To find substructures with spatial similarity and appropriate chemical properties, an emergent self-organizing map is used for clustering. Two substructures which are found to be similar form the basis for an expanded comparison of the complete cavities. Preliminary results with four pairs of binding cavities show that similarities are detected correctly and motivate further studies.