Antikaon condensation and kaon and antikaon production in protoneutron stars are investigated in a chiral hadronic model (also referred to as the FST model in this paper). The effects of neutrino trapping on protoneutron stars are analyzed systematically. It is shown that neutrino trapping makes the critical density of K − condensation delay to higher density and ##IMG## [http://ej.iop.org/icons/Entities/barK.gif] bar K 0 condensation not occur. The equation of state (EOS) of (proto)neutron star matter with neutrino trapping is stiffer than that without neutrino trapping. As a result, the maximum masses of (proto)neutron stars with neutrino trapping are larger than those without neutrino trapping. If hyperons are taken into account, antikaon does not form a condensate in (proto)neutron stars. Meanwhile, the corresponding EOS becomes much softer, and the maximum masses of (proto)neutron stars are smaller than those without hyprons. Finally, our results illustrate that the Q values for K + and K − production in (proto)neutron stars are not sensitive to neutrino trapping and inclusion of hyperons.
Multivariate statistical methods, i.e., cluster analysis (CA) and discriminant analysis (DA), were used to assess temporal and spatial variations in the water quality of the watercourses in the Northwestern New Territories, Hong Kong, over a period of five years (2000-2004) using 23 parameters at 23 different sites (31,740 observations). Hierarchical CA grouped the 12 months into two periods (the first and second periods) and classified the 23 monitoring sites into three groups (group A, group B, and group C) based on similarities of water quality characteristics. DA provided better results with great discriminatory ability for both temporal and spatial analysis. DA also provided an important data reduction because it only used six parameters (pH, temperature, five-day biochemical oxygen demand, fecal coliforms, Fe, and Ni) for temporal analysis, affording about 84% correct assignations, and seven parameters (pH, ammonia-nitrogen, nitrate nitrogen, fecal coliforms, Fe, Ni, and Zn) for spatial analysis, affording more than 90% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters that were responsible for most of the variations in water quality. Thus, this study demonstrated that the multivariate statistical methods are useful for interpreting complex data sets in the analysis of temporal and spatial variations in water quality and the optimization of regional water quality monitoring network.
Multivariate statistical methods, i.e., cluster analysis (CA) and discriminant analysis (DA), were used to assess temporal and spatial variations in the water quality of the watercourses in the Northwestern New Territories, Hong Kong, over a period of five years (2000-2004) using 23 parameters at 23 different sites (31,740 observations). Hierarchical CA grouped the 12 months into two periods (the first and second periods) and classified the 23 monitoring sites into three groups (group A, group B, and group C) based on similarities of water quality characteristics. DA provided better results with great discriminatory ability for both temporal and spatial analysis. DA also provided an important data reduction because it only used six parameters (pH, temperature, five-day biochemical oxygen demand, fecal coliforms, Fe, and Ni) for temporal analysis, affording about 84% correct assignations, and seven parameters (pH, ammonia-nitrogen, nitrate nitrogen, fecal coliforms, Fe, Ni, and Zn) for spatial analysis, affording more than 90% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters that were responsible for most of the variations in water quality. Thus, this study demonstrated that the multivariate statistical methods are useful for interpreting complex data sets in the analysis of temporal and spatial variations in water quality and the optimization of regional water quality monitoring network.