The Skyrme Energy Functional and Naturalness

Recent studies show that successful relativistic mean-field models of nuclei are consistent with naive dimensional analysis and naturalness, as expected in low-energy effective field theories of quantum chromodynamics. The nonrelativistic Skyrme energy functional is found to have similar characteristics.Comment: 8 pages, REVTeX 3.0 with epsf.sty, plus 2 figure

Low Energy Theorems For Nucleon-Nucleon Scattering

Low energy theorems are derived for the coefficients of the effective range expansion in s-wave nucleon-nucleon scattering valid to leading order in an expansion in which both $m_\pi$ and $1/a$ (where $a$ is the scattering length) are treated as small mass scales. Comparisons with phase shift data, however, reveal a pattern of gross violations of the theorems for all coefficients in both the $^1S_0$ and $^3S_1$ channels. Analogous theorems are developed for the energy dependence $\epsilon$ parameter which describes $^3S_1 - ^3D_1$ mixing. These theorems are also violated. These failures strongly suggest that the physical value of $m_\pi$ is too large for the chiral expansion to be valid in this context. Comparisons of $m_\pi$ with phenomenological scales known to arise in the two-nucleon problem support this conjecture.Comment: 12 pages, 1 figure, 1 table; appendix added to discuss behavior in chiral limit; minor revisions including revised figure reference to recent work adde

Determination of Matter Surface Distribution of Neutron-rich Nuclei

We demonstrate that the matter density distribution in the surface region is determined well by the use of the relatively low-intensity beams that become available at the upcoming radioactive beam facilities. Following the method used in the analyses of electron scattering, we examine how well the density distribution is determined in a model-independent way by generating pseudo data and by carefully applying statistical and systematic error analyses. We also study how the determination becomes deteriorated in the central region of the density, as the quality of data decreases. Determination of the density distributions of neutron-rich nuclei is performed by fixing parameters in the basis functions to the neighboring stable nuclei. The procedure allows that the knowledge of the density distributions of stable nuclei assists to strengthen the determination of their unstable isotopes.Comment: 41 pages, latex, 27 figure

Nucleon Charge and Magnetization Densities from Sachs Form Factors

Relativistic prescriptions relating Sachs form factors to nucleon charge and magnetization densities are used to fit recent data for both the proton and the neutron. The analysis uses expansions in complete radial bases to minimize model dependence and to estimate the uncertainties in radial densities due to limitation of the range of momentum transfer. We find that the charge distribution for the proton is significantly broad than its magnetization density and that the magnetization density is slightly broader for the neutron than the proton. The neutron charge form factor is consistent with the Galster parametrization over the available range of Q^2, but relativistic inversion produces a softer radial density. Discrete ambiguities in the inversion method are analyzed in detail. The method of Mitra and Kumari ensures compatibility with pQCD and is most useful for extrapolating form factors to large Q^2.Comment: To appear in Phys. Rev. C. Two new figures and accompanying text have been added and several discussions have been clarified with no significant changes to the conclusions. Now contains 47 pages including 21 figures and 2 table

Genome Sizes and the Benford Distribution

BACKGROUND: Data on the number of Open Reading Frames (ORFs) coded by genomes from the 3 domains of Life show the presence of some notable general features. These include essential differences between the Prokaryotes and Eukaryotes, with the number of ORFs growing linearly with total genome size for the former, but only logarithmically for the latter. RESULTS: Simply by assuming that the (protein) coding and non-coding fractions of the genome must have different dynamics and that the non-coding fraction must be particularly versatile and therefore be controlled by a variety of (unspecified) probability distribution functions (pdf's), we are able to predict that the number of ORFs for Eukaryotes follows a Benford distribution and must therefore have a specific logarithmic form. Using the data for the 1000+ genomes available to us in early 2010, we find that the Benford distribution provides excellent fits to the data over several orders of magnitude. CONCLUSIONS: In its linear regime the Benford distribution produces excellent fits to the Prokaryote data, while the full non-linear form of the distribution similarly provides an excellent fit to the Eukaryote data. Furthermore, in their region of overlap the salient features are statistically congruent. This allows us to interpret the difference between Prokaryotes and Eukaryotes as the manifestation of the increased demand in the biological functions required for the larger Eukaryotes, to estimate some minimal genome sizes, and to predict a maximal Prokaryote genome size on the order of 8-12 megabasepairs. These results naturally allow a mathematical interpretation in terms of maximal entropy and, therefore, most efficient information transmission

The Number of ORFs in Each Genome vs. Genome Size for the Three Extant Domains of Life on Earth.

<p>The points are data from 1128 genomes available on the GOLD database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036624#pone.0036624-The1" target="_blank">[3]</a> in early 2010. In this log-log plot, the <i>x</i>-axis represents the genome size (<i>G</i>) in kilobasepairs. For each genome we plot on the <i>y</i>-axis the number of ORFs quoted for the genome in the above database. In order to facilitate comparisons, we have drawn a red diagonal line on a vertical/horizontal scale where 1 vertical axis unit corresponds to 1 kbp on the horizontal axis. The Prokaryotic genomes cluster around this (slope = 1) line. The fit to the Prokaryotes given by Eqn. (6) is represented here as a cyan line. The dashed line represents the best fit to the Eukaryotic ORFs and corresponds to a Benford distribution, Eqn. (11), if we neglect the statistically insignificant contribution from the combination of the first two terms, . Note the wide range of genome sizes that the fit accommodates. See the Discussion Section regarding the right-hand axis.</p

The Impact of Advancing Technology on Marketing and Academic Research

Academic research in marketing often and rightfully tends to either build on well-established past research topics or follow well-established practices in industry. However, as technology advances, it might be possible to foresee some more enduring trends and focus research on future issues rather than on past issues. One approach would be to study emerging technologies with rapidly declining costs. Each of these emerging technologies spawns myriad applications that have the potential to dramatically impact existing markets. Interesting research topics include the study of the impact of these applications on different market participants (e.g., final consumers, the seller, the seller of complementary services, intermediaries, information providers, competitors, other industries). Research topics also include the optimal structure for products and services, given these new applications, as well as which intermediary should offer particular services. Research topics also include the interactive ability to rapidly customize marketing strategy by identifying individuals at particular points in time and under particular demand conditions. Five of these technologies include enhanced search services, biometrics and smart cards, enhanced computational speed, M-commerce, and GPS tracking.research in marketing, scholarly and academic research topics, research fads