沉积物粒度分析在厦门市第四纪环境研究和地层划分对比中的应用

采用MS2000型激光粒度分析仪进行测试，利用计算机粒度分析软件对数据进行整理和计算，绘制出样品的频率曲线、概率累积曲线以及粒度众数位值曲线等图件并进行沉积环境分析。厦门地区第四纪环境演化经历了中更新世同安组(含泥中粗砂、含泥细中砂、砂为主)海积一冲积一更新统上部龙海组(含泥粗中砂、含泥细中砂、含泥粗砂、粗砂、细中砂、含细砾中粗砂、中细砂等)冲积一洪冲积一更新世上部东山组(砂、砂砾、含泥中粗砂、粗砂、泥质中细砂、细砂、中粗砂等)冲洪积及部分海积(粉砂、细粉砂、含碳质泥、粘土、淤泥质粘土、粉砂质粘土等)一全新世长乐组(粉砂，淤泥质粘土、粉砂质粘土、碳质粘土、含砂淤泥质粘土，粉砂质亚粘土、粉细砂亚粘土等)海湾沉积。在解释环境变化的同时，说明粒度变化曲线在一定程度上可以作为地层划分的依据之一，并以此对研究区地层进行了详细划分。     

Coronal Holes and Icosahedral Symmetry

The study of the expansion of the solar wind out of a system of coronal holes is continued. To this end, we consider the numerical integration of partial differential equations for problems with icosahedral symmetry, in general. First, employing Weyl theory, orbifold coordinates are introduced. Second, the icosahedral coordinates are discussed in detail. Third, following an analysis of the properties of these coordinates and the derivation of a few expressions useful for grid construction, various alternatives for the distribution of lattice points required for numerical integration are considered. A comparison of these numerical grids motivates the choice of a specific grid optimized for the numerical integration carried out in the accompanying paper by Kalish et al.(2002). This revised version was published online in July 2006 with corrections to the Cover Date.     

成都市冬季重污染日气象条件成因分析及预报

本文通过对成都10个重污染日进行天气学分析,将污染浓度与气象要素进行聚类、研究了重污染日形成原因及污染浓度与气象要素的关系。在此基础上,建立了SO_2、TSP日平均浓度分级预报方程。     

介质横向不均匀对震后地表位移场的影响

利用三维有限元模拟两层地球模型(包括弹性层和粘弹性层)介质的横向不均匀和断层错动的复杂性对震后地表位移场的影响，其数值结果包括弹性解和粘弹性解。结果表明，震后位移场的分布特征不仅与粘弹性层的流变特性有关，还与弹性层介质的剪切模量以及断层的同震错动有关。     

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