KN and KbarN Elastic Scattering in the Quark Potential Model

The KN and KbarN low-energy elastic scattering is consistently studied in the framework of the QCD-inspired quark potential model. The model is composed of the t-channel one-gluon exchange potential, the s-channel one-gluon exchange potential and the harmonic oscillator confinement potential. By means of the resonating group method, nonlocal effective interaction potentials for the KN and KbarN systems are derived and used to calculate the KN and KbarN elastic scattering phase shifts. By considering the effect of QCD renormalization, the contribution of the color octet of the clusters (qqbar) and (qqq) and the suppression of the spin-orbital coupling, the numerical results are in fairly good agreement with the experimental data.Comment: 20 pages, 8 figure

Closed expression of the interaction kernel in the Bethe-Salpeter equation for quark-antiquark bound states

The interaction kernel in the Bethe-Salpeter equation for quark-antiquark bound states is derived from the Bethe-Salpeter equations satisfied by the quark-antiquark four-point Green's function. The latter equations are established based on the equations of motion obeyed by the quark and antiquark propagators, the four-point Green's function and some other kinds of Green's functions which follow directly from the QCD generating functional. The B-S kernel derived is given an exact and explicit expression which contains only a few types of Green's functions. This expression is not only convenient for perturbative calculations, but also suitable for nonperturbative investigations.Comment: 27 pages,no figure

A simple approach for monitoring business service time variation.

Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from processes having nonnormal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. In this paper, we propose a new asymmetric EWMA variance chart (EWMA-AV chart) and an asymmetric EWMA mean chart (EWMA-AM chart) based on two simple statistics to monitor process variance and mean shifts simultaneously. Further, we explore the sampling properties of the new monitoring statistics and calculate the average run lengths when using both the EWMA-AV chart and the EWMA-AM chart. The performance of the EWMA-AV and EWMA-AM charts and that of some existing variance and mean charts are compared. A numerical example involving nonnormal service times from the service system of a bank branch in Taiwan is used to illustrate the applications of the EWMA-AV and EWMA-AM charts and to compare them with the existing variance (or standard deviation) and mean charts. The proposed EWMA-AV chart and EWMA-AM charts show superior detection performance compared to the existing variance and mean charts. The EWMA-AV chart and EWMA-AM chart are thus recommended

Hierarchical incremental class learning with reduced pattern training

Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement. This paper proposes a theoretical model to evaluate the performance of HICL and presents an approach to improve the classification accuracy of HICL by applying the concept of Reduced Pattern Training (RPT). The theoretical analysis shows that HICL can achieve better classification accuracy than Output Parallelism [1]. The procedure for RPT is described and compared with the original training procedure. RPT reduces systematically the size of the training data set based on the order of sub-networks built. The results from four benchmark classification problems show much promise for the improved model

An incremental approach to MSE-based feature selection

Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a Minimum Squared Error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost

Renormalization of the Sigma-Omega model within the framework of U(1) gauge symmetry

It is shown that the Sigma-Omega model which is widely used in the study of nuclear relativistic many-body problem can exactly be treated as an Abelian massive gauge field theory. The quantization of this theory can perfectly be performed by means of the general methods described in the quantum gauge field theory. Especially, the local U(1) gauge symmetry of the theory leads to a series of Ward-Takahashi identities satisfied by Green's functions and proper vertices. These identities form an uniquely correct basis for the renormalization of the theory. The renormalization is carried out in the mass-dependent momentum space subtraction scheme and by the renormalization group approach. With the aid of the renormalization boundary conditions, the solutions to the renormalization group equations are given in definite expressions without any ambiguity and renormalized S-matrix elememts are exactly formulated in forms as given in a series of tree diagrams provided that the physical parameters are replaced by the running ones. As an illustration of the renormalization procedure, the one-loop renormalization is concretely carried out and the results are given in rigorous forms which are suitable in the whole energy region. The effect of the one-loop renormalization is examined by the two-nucleon elastic scattering.Comment: 32 pages, 17 figure

Heat transfer characteristics within an array of impinging jets. Effects of crossflow temperature relative to jet temperature

Spanwise average heat fluxes, resolved in the streamwise direction to one stream-wise hole spacing were measured for two-dimensional arrays of circular air jets impinging on a heat transfer surface parallel to the jet orifice plate. The jet flow, after impingement, was constrained to exit in a single direction along the channel formed by the jet orifice plate and heat transfer surface. The crossflow originated from the jets following impingement and an initial crossflow was present that approached the array through an upstream extension of the channel. The regional average heat fluxes are considered as a function of parameters associated with corresponding individual spanwise rows within the array. A linear superposition model was employed to formulate appropriate governing parameters for the individual row domain. The effects of flow history upstream of an individual row domain are also considered. The results are formulated in terms of individual spanwise row parameters. A corresponding set of streamwise resolved heat transfer characteristics formulated in terms of flow and geometric parameters characterizing the overall arrays is described