Radiative association in Li+H revisited: the role of quasi-bound states

The role in radiative association of narrow resonances arising from quasi-bound levels is examined. For the rate coefficient the contribution from such levels is readily calculated in terms of the Einstein A -coefficient from bound rovibrational levels generated using a suitable alternative boundary condition. By locating systematically all the quasi-bound levels, an enhancement by more than an order of magnitude of the rate coefficient for Li(2s) + H(1s) association above about 600 K is obtained. The effect on the LiH abundance in the early Universe is no more than an increase of a factor of 3 for limited ranges of values of the redshift. For the Li(2p) state the effect of narrow resonances appears to have been taken into account in previous calculations.     

区域GPS网实时计算可降水量的若干问题

目前地基GPS气象学测得的可降水量 (PWV )精度好于 2mm ,但在利用区域GPS网实时计算每个测站上空的PWV时 ,要涉及到很多常规GPS资料处理时所忽略的问题 ,如需考虑数据处理软件和计算方式的选择、站坐标的确定和约束、轨道的使用方法、网外辅助站最佳数量的确定、海潮对实时计算PWV的影响以及实时应用于气象服务时的端部效应等问题。利用上海GPS综合应用网获取的 2 0 0 2年 6、7月份长江三角洲地区入梅前后的数据 ,分析了利用区域性的GPS网实时计算高精度的PWV时要解决的各种问题 ,探讨了其数据处理方案     

发挥行业和地域优势开辟经济发展新领域——矿业网站的建设设想

网络经济已成为新的社会生产力的代表。广西地质矿产勘查开发局应该利用其行业和地域方面的固有优势，积极参与网络经济，以期打开矿业市场。文章提出了建立矿业网站的设想和框架。     

Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1