1/N Expansion for Exotic Baryons

The 1/N expansion for exotic baryons is developed, and applied to the masses, meson couplings and decay widths. Masses and widths of the 27 and 35 pentaquark states in the same tower as the Theta+ are related by spin-flavor symmetry. The 27 and 35 states can decay within the pentaquark tower, as well as to normal baryons, and so have larger decay widths than the lightest pentaquark Theta. The 1/N expansion also is applied to baryon exotics containing a single heavy antiquark. The decay widths of heavy pentaquarks via pion emission, and to normal baryons plus heavy D^(*),B^(*) mesons are studied, and relations following from large-N spin-flavor symmetry and from heavy quark symmetry are derived.Comment: Major additions: plots of widths and branching ratios, discussion of strong decays of heavy pentaquarks, including consequences of heavy quark symmetr

The Relationship of Beef Primal Cut Composition to Overall Carcass Composition

The amount of lean, subcutaneous fat, seam fat and bone of each of the four major primal cuts (round, rib, loin and chuck) were used in combination with yield grade to predict total side composition. The makeup of each primal is highly related to total carcass composition The decision of which primal to fabricate depends on the sex of the animal and which component (lean, subcutaneous fat, seam fat or bone) is of greatest interest

Recommender Systems: Sources of Knowledge and Evaluation Metrics

Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender sys-tem in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature