热解硫酸钡制备硫同位素分析试样二氧化硫

改进了硫同位素分析中由ＢａＳＯ４制备ＳＯ２的方法：ＢａＳＯ４和Ｖ２Ｏ５及ＳｉＯ２混合后覆盖铜丝,在真空状态９８０℃加热２０ｍｉｎ,用液氮捕集ＳＯ２。与火焰直接加热分解法相比,用高温炉方便,改善了制样环境,避免污染。经国际标样和国家标准物质分析验证,结果与标准值相符,标准偏差（１σ）在±００９‰～±０２０‰,符合分析要求。     

利用GCV优化的正则化方法对硬X射线望远镜图象重建研究

图象重建是一个求逆过程具有不适定性。本文介绍的是一种从少数投影数据进行图象重建的线性代数法－Ｐｈｉｌｌｉｐｓ－Ｔｉｋｈｏｎｏｖ正则化方法,同时选择优化工具ＧＣＶ确定最优的正则参数。为验证该方法,我们对日本Ｙｏｈｋｏｈ卫星上塔载的硬Ｘ射线望远镜（ＨＸＴ）拍摄的太阳图象进行重建。     

Adaptive hierarchical upscaling of flow in heterogeneous reservoirs based on an a posteriori error estimate

This paper treats the upscaling of the absolute permeability in a heterogeneous reservoir. By replacing the fine scale permeability tensor with an upscaled, or effective permeability tensor, a modelling error is introduced. An a posteriori error estimate on this modelling error is formulated and tested. An implementation of the theory, based on domain decomposition coupled with a hierarchical representation of the absolute permeability field, is given. As hierarchical basis functions we have chosen the Haar system, which leads to a wavelet representation of the permeability. The wavelet representation offers a natural upscaling technique which resembles the highcut filters commonly used in signal analysis. This procedure represents an adaptive upscaling method. The numerical results show that this method conserves both the dissipation and the mean velocity in the problem fairly well. The a posteriori error estimate on the modelling error coupled with domain decomposition methods constitutes a powerful modelling tool.     

A Single-Station Spectral Model of the Monthly Median foF2 and M(3000)F2

A new single-station model (SSM) for monthly median values of the ionospheric parameters foF2 and M(3000)F2 has been developed. Fourier analysis provides a tool for decomposing the time-varying ionospheric parameters. The 12–month smoothed sunspot number R ₁₂ was used as an external solar characteristic because of its availability and predictability. However, for the first time, the solar activity is described not only by R ₁₂ , but also by the linear coefficient K _R representing the tendency of the change of solar activity. A general non-linear approximation of the influence of the solar-cycle characteristics R ₁₂ and K _R and ionospheric parameters foF2 and M(3000)F2 was accepted. The new SSM is applied to several European stations and its statistical evaluation shows better results than the other two SSMs used in the paper. The approach described in the paper does not contradict the use of different synthetic ionospheric indices (as the T-index, MF2–index); the basic aim is to show only that using one additional new characteristic of the solar-cycle variations, such as K _R , improves the monthly median model.     

Some methods for statistical analysis of multimodal distributions and their application to grain-size data

Some of the methods used in the resolution of mixed normal distributions are discussed under three headings: analytical, graphical, and numerical methods. Attention is given to their applicability in the analysis of grain-size data as derived from sieving. Comparisons are made by applying several methods to published data. It is concluded that the numerical methods offer most scope, especially the method of nonlinear least squares. Some analyses of beach sediments, using this method, are presented. The adoption of a convention for the number of individuals in the sample increases ease of interpretation.     

COMMENTS ON THE NIPALS ALGORITHM

The Non-linear lterative Partial Least Squares(NIPALS)algorithm is used in principal componentanalysis to decompose a data matrix into score vectors and eigenvectors(loading vectors)plus a residualmatrix.N1PALS starts with some guessed starting vector.The principal components obtained by NIPALSdepends on the starting vector;the first principal component could not always be computed.Wold hassuggested a starting vector for NIPALS,but we have found that even if this starting vector is used,thefirst principal component cannot be obtained in all cases.The reason why such a situation occurs isexplained by the power method.A simple modification of the original NIPALS procedure to avoid gettingsmaller eigenvalues is presented.     

中国东海海平面长期变化与趋势研究

From the analyses of the satellite altimeter Maps of Sea Level Anomaly(MSLA) data, tidal gauge sea level data and historical sea level data, this paper investigates the long-term sea level variability in the East China Sea(ECS).Based on the correlation analysis, we calculate the correlation coefficient between tidal gauge and the closest MSLA grid point, then generate the map of correlation coefficient of the entire ECS. The results show that the satellite altimeter MSLA data is effective to observe coastal sea level variability. An important finding is that from map of correlation coefficient we can identify the Kuroshio. The existence of Kuroshio decreases the correlation between coastal and the Pacific sea level. Kurishio likes a barrier or a wall, which blocks the effect of the Pacific and the global change. Moreover, coastal sea level in the ECS is mainly associated with local systems rather than global change. In order to calculate the long-term sea level variability trend, the empirical mode decomposition(EMD) method is applied to derive the trend on each MSLA grid point in the entire ECS. According to the 2-D distribution of the trend and rising rate, the sea level on the right side of the axis of Kuroshio rise faster than in its left side. This result supports the barrier effect of Kuroshio in the ECS. For the entire ECS, the average sea level rose 45.0 mm between 1993 and 2010, with a rising rate of(2.5±0.4) mm/a which is slower than global average.The relatively slower sea level rising rate further proves that sea level rise in the ECS has less response to global change due to its own local system effect.     

Decomposing productivity and efficiency changes in the Alaska head and gut factory trawl fleet

Fishing fleets are subject to numerous factors that affect economic performance, making identification and attribution of such impacts difficult. This paper separately identifies the effects of changing input and output prices, fishery management, and quota allocations on total factor productivity using a Lowe Index. Indices account for technical change and decompose productivity estimates into its technical, environmental, and scale-mix components. This results in measures that reflect shifts in the production frontier, and movements by vessels toward and around the frontier, to capture economies of scale and mix after a policy shift to a catch share program that includes fishing cooperatives and a limited access fishery. The difference between cooperative and limited access vessels is exploited to compare the changes in economic performance between the groups after the introduction of the shift to catch shares and cooperative management, which allowed the vessels to improve the timing and coordination across multi-species fisheries and to decrease incidental catch of quota-limited bycatch species that had closed the target fisheries prematurely in the past. Results indicate that total factor productivity increased significantly after the move to a catch share program, largely due to increases in technical change that shifted out the production frontier of the fishery.     

MPI环境下基于边界校正方法的海洋模型的并行化

基于区域分解技术,设计了一种针对海洋模型的边界校正方法,该方法不需要对原有海洋模型进行较大改动,只需要利用消息传递接口(message passing interface,MPI)编写子区域间的数据传输模块,即可实现原有海洋模型的并行化。相对于重新开发并行系统,该方法能够较快把原有模型的串行解决方案转化为并行解决方案,并且并行化后的海洋模型能够获得较高的加速比。     

基于滑动L2优化准则的GPS坐标时间序列重构

GPS坐标时间序列中不仅包含白噪声,还包含闪烁噪声、随机漫步噪声等有色噪声,这些噪声将影响GPS应用的可靠性,甚至可能对一些地球物理现象做出错误的解释,因此降低GPS坐标时间序列中有色噪声的影响、提高GPS精度是一个重要和基本的问题。提出了一种滑动L₂优化估计方法(ML₂),通过选取合适的窗口建立L₂优化模型,再利用交替迭代乘子法求解每段时间序列的优化问题,并逐年滑动得到整段GPS坐标时间序列的估计。实验结果表明,ML₂方法与奇异谱分析、小波分解、滑动普通最小二乘法相比具有更好的重构效果。