The Synthesis, Resolution and Configuration of Beta-hydroxyvaline

Synthesis, resolution, and configuration of alpha-hydroxyvalin

Optical Resolution and Configuration of Trans-2,3-epoxybutyric Acid

Optical resolution of epoxy derivative of butyric acid with brucine and configuration determination by treatment with ammoni

Betweenness centrality correlation in social networks

Scale-free (SF) networks exhibiting a power-law degree distribution can be grouped into the assortative, dissortative and neutral networks according to the behavior of the degree-degree correlation coefficient. Here we investigate the betweenness centrality (BC) correlation for each type of SF networks. While the BC-BC correlation coefficients behave similarly to the degree-degree correlation coefficients for the dissortative and neutral networks, the BC correlation is nontrivial for the assortative ones found mainly in social networks. The mean BC of neighbors of a vertex with BC $g_i$ is almost independent of $g_i$, implying that each person is surrounded by almost the same influential environments of people no matter how influential the person is.Comment: 4 pages, 4 figures, 1 tabl

Higher Derivative CP(N) Model and Quantization of the Induced Chern-Simons Term

We consider higher derivative CP(N) model in 2+1 dimensions with the Wess-Zumino-Witten term and the topological current density squared term. We quantize the theory by using the auxiliary gauge field formulation in the path integral method and prove that the extended model remains renormalizable in the large N limit. We find that the Maxwell-Chern-Simons theory is dynamically induced in the large N effective action at a nontrivial UV fixed point. The quantization of the Chern-Simons term is also discussed.Comment: 8 pages, no figure, a minor change in abstract, added Comments on the quantization of the Chern-Simons term whose coefficient is also corrected, and some references are added. Some typos are corrected. Added a new paragraph checking the equivalence between (3) and (5), and a related referenc

A study of the almost sequential mechanism of true ternary fission

We consider the collinear ternary fission which is a sequential ternary decay with a very short time between the ruptures of two necks connecting the middle cluster of the ternary nuclear system and outer fragments. In particular, we consider the case where the Coulomb field of the first massive fragment separated during the first step of the fission produces a lower pre-scission barrier in the second step of the residual part of the ternary system. In this case, we obtain a probability of about $10^{-3}$ for the yield of massive clusters such as \nuclide[70]{Ni}, \nuclide[80-82]{Ge}, \nuclide[86]{Se}, and \nuclide[94]{Kr} in the ternary fission of \nuclide[252]{Cf}. These products appear together with the clusters having mass numbers of $A = 132$--$140$. The results show that the yield of a heavy cluster such as \nuclide[68-70]{Ni} would be followed by a product of $A = 138$--$148$ with a large probability as observed in the experimental data obtained with the FOBOS spectrometer at the Joint Institute for Nuclear Research. The third product is not observed. The landscape of the potential energy surface shows that the configuration of the Ni + Ca + Sn decay channel is lower about 12 MeV than that of the Ca + Ni + Sn channel. This leads to the fact, that the yield of Ni and Sn is large. The analysis on the dependence of the velocity of the middle fragment on mass numbers of the outer products leads to the conclusion that, in the collinear tripartition channel of \nuclide[252]{Cf}, the middle cluster has a very small velocity, which does not allow it to be found in experiments.Comment: 11 pages, 9 figure

GOES I/M image navigation and registration

Image Navigation and Registration (INR) is the system that will be used on future Geostationary Operational Environmental Satellite (GOES) missions to locate and register radiometric imagery data. It consists of a semiclosed loop system with a ground-based segment that generates coefficients to perform image motion compensation (IMC). The IMC coefficients are uplinked to the satellite-based segment, where they are used to adjust the displacement of the imagery data due to movement of the imaging instrument line-of-sight. The flight dynamics aspects of the INR system is discussed in terms of the attitude and orbit determination, attitude pointing, and attitude and orbit control needed to perform INR. The modeling used in the determination of orbit and attitude is discussed, along with the method of on-orbit control used in the INR system, and various factors that affect stability. Also discussed are potential error sources inherent in the INR system and the operational methods of compensating for these errors

Constraints and Period Relations in Bosonic Strings at Genus-g

We examine some of the implications of implementing the usual boundary conditions on the closed bosonic string in the hamiltonian framework. Using the KN formalism, it is shown that at the quantum level, the resulting constraints lead to relations among the periods of the basis 1-forms. These are compared with those of Riemanns' which arise from a different consideration.Comment: 16 pages, (Plain Tex), NUS/HEP/9320

Classification of scale-free networks

While the emergence of a power law degree distribution in complex networks is intriguing, the degree exponent is not universal. Here we show that the betweenness centrality displays a power-law distribution with an exponent \eta which is robust and use it to classify the scale-free networks. We have observed two universality classes with \eta \approx 2.2(1) and 2.0, respectively. Real world networks for the former are the protein interaction networks, the metabolic networks for eukaryotes and bacteria, and the co-authorship network, and those for the latter one are the Internet, the world-wide web, and the metabolic networks for archaea. Distinct features of the mass-distance relation, generic topology of geodesics and resilience under attack of the two classes are identified. Various model networks also belong to either of the two classes while their degree exponents are tunable.Comment: 6 Pages, 6 Figures, 1 tabl