An orthogonal double cover (ODC) of a complete graph is a collection of graphs such that every two of them share exactly one edge and every edge of the complete graph belongs to exactly two of the graphs. In this paper, we construct ODCs of Kn where all graphs are isomorphic to certain spanning subgraphs G (such as co-triangles, a star with cotriangles, and two other graph classes).

1 6 ] b y t h e a u t h o r s. T h i s a r t i c l e i s i n t e n d e d t o b e a c o u n t e r p a r t t o t h e a b s t r a c t t h e o r y o f

In order to produce any product with desired quality by machining, proper selection of process parameters is essential. This can be accomplished by Full Factorial method. The aim of the present work is to investigate the effect of process parameter on surface finish and material removal rate (MRR) to obtain the optimal setting of these process parameters and the analysis of variance is also use...

We have performed a shape optimization of the disc in an industrial double-eccentric butterfly valve using the effect analysis of design variables to enhance the valve performance. For the optimization, we select three performance quantities such as pressure drop, maximum stress, and mass (weight) as the responses and three dimensions regarding the disc shape as the design variables. Subsequent...

An orthogonal double cover (ODC) of the complete graph Kn by some graph G is a collection of n spanning subgraphs of Kn, all isomorphic to G, such that any two of the subgraphs share exactly one edge and every edge of Kn is contained in exactly two of the subgraphs. A necessary condition for such an ODC to exist is that G has exactly n− 1 edges. We show that for any given positive integer d, al...

This paper describes the application of a combined orthogonal array design and overlapping resolution mapping to the optimization of miceller electrokinetic chromatography for the separation of 10 substituted benzenes. The most important factors were first determined according to an OA16(2(15)) through 16 pre-designed experiments; a second set of experiments was carried out according to a trian...

We present an algorithm using transformation groups and their irreducible representations to generate an orthogonal basis for a signal in the vector space of the signal. It is shown that multiresolution analysis can be done with amplitudes using a transformation group. G-lets is thus not a single transform, but a group of linear transformations related by group theory. The algorithm also specif...

In uncertainty quantification, critical parameters of mathematical models are substituted by random variables. We consider dynamical systems composed of ordinary differential equations. The unknown solution is expanded into an orthogonal basis of the random space, e.g., the polynomial chaos expansions. A Galerkin method yields a numerical solution of the stochastic model. In the linear case, th...

The DDHFm package is designed to perform data-driven Haar-Fisz (DDHF) variance stabilization. The basic DDHF method itself is described in [4, 5]. The modifications to DDHF to make it work successfully for microarray (or indeed similar kinds of replicate data) are described in [10]. The basic idea of the Haar-Fisz transform is very simple. First, a Haar wavelet transform is applied to the data....