Surface-peaked medium effects in the interaction of nucleons with finite nuclei

We investigate the asymptotic separation of the optical model potential for nucleon-nucleus scattering in momentum space, where the potential is split into a medium-independent term and another depending exclusively on the gradient of the density-dependent g matrix. This decomposition confines the medium sensitivity of the nucleon-nucleus coupling to the surface of the nucleus. We examine this feature in the context of proton-nucleus scattering at beam energies between 30 and 100 MeV and find that the pn coupling accounts for most of this sensitivity. Additionally, based on this general structure of the optical potential we are able to treat both, the medium dependence of the effective interaction and the full mixed density as described by single-particle shell models. The calculated scattering observables agree within 10% with those obtained by Arellano, Brieva and Love in their momentum-space g-folding approach.Comment: 16 pages, 8 figures, submitted to PR

A rough set-based association rule approach implemented on exploring beverages product spectrum

[[abstract]]When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if—then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子