Mass and isotopic concentrations of water-insoluble refractory carbon in total suspended particulates at Mt.Waliguan Observatory(China)

Mass concentration and isotopic values δ¹³C and ¹⁴C are presented for the water-insoluble refractory carbon（WIRC） component of total suspended particulates（TSP）,collected weekly during 2003,as well as from October 2005 to May 2006 at the WMO-GAW Mt.Waliguan（WLG） site.The overall average WIRC mass concentration was（1183±120）ng/m³（n = 79）,while seasonal averages were 2081 ±1707（spring）,454±205（summer）,650±411（autumn）,and 1019±703（winter） ng/m3.Seasonal variations in WIRC mass concentrations were consistent with black carbon measurements from an aethalometer,although WIRC concentrations were typically higher,especially in winter and spring.The δ¹³C PDB value（-25.3 ± 0.8）%.determined for WIRC suggests that its sources are C₃ biomass or fossil fuel combustion.No seasonal change in δ¹³C PDB was evident.The average percent Modern Carbon（pMC） for ¹⁴C in WIRC for winter and spring was（67.2 ± 7.7）%（n = 29）.Lower pMC values were associated with air masses transported from the area east of WLG,while higher pMC values were associated with air masses from the Tibetan Plateau,southwest of WLG.Elevated pMC values with abnormally high mass concentrations of TSP and WIRC were measured during a dust storm event.     

Parametric estimation of discretely sampled Gamma-OU processes

The stationary Gamma-OU processes are recommended to be the volatility of the financial assets. A parametric estimation for the Gamma-OU processes based on the discrete observations is considered in this paper. The estimator of an intensity parameter A and its convergence result are given, and the simulations show that the estimation is quite accurate. Assuming that the parameter A is estimated, the maximum likelihood estimation of shape parameter c and scale parameter a, whose likelihood function is not explicitly computable, is considered. By means of the Gaver-Stehfest algorithm, we construct an explicit sequence of approximations to the likelihood function and show that it converges the true (but unkown) one. Maximizing the sequence results in an estimator that converges to the true maximum likelihood estimator and the approximation shares the asymptotic properties of the true maximum likelihood estimator. Some simulation experiments reveal that this method is still quite accurate in most of rational situations for the background of volatility.     

从 ${\cal B}^0$ 到 $E(p,q)$ 和 $E_0(p,q)$ 空间的复合算子

设 $\varphi$ 是单位园盘 $D$ 到自身的解析映射, $X$ 是 $D$ 上解析函数的 Banach 空间, 对 $f\in X$, 定义复合算子$C_\varphi $ : $C_\varphi (f)=f\circ \varphi$. 我们利用从 ${\cal B}^0$到 $E(p,q)$ 和 $E_0(p,q)$ 空间的复合算子研究了空间 $E(p,q)$ 和 $E_0(p,q)$, 给出了一个新的特征.     

THE -PROBLEM FOR HOLOMORPHIC (0,2)-FORMS ON PSEUDOCONVEX DOMAINS IN SEPARABLE HILBERT SPACES AND D.F.N. SPACES

51. IntroductionL. H5.m.nd..l3] solved the 0problem by using the L2-estimates for partial differentialoperators in C'.. J. Kajiwara[4] studied infinite dimensional generalizations of the poten-tial kernel. Concerning the 0-problem in infinite dimensional spaces, P. ffeb.i.lll] investi-gated the a-equation for coc (o, 1)-forms in arbitrary pseudoconvex open subsets of separableHilbert spaces without growth condition. J. F. Colombeau and B. Perr.t[l1 showed that aCoc solution u of 0u = w ca…     

弱(L_1,L_2)-BLD映射的正则性

本文首先将文［１］中的ＢＬＤ映射推广为弱（Ｌ１，Ｌ２）－ＢＬＤ映射，并证明了如下正则性结果：存在两个可积指数 Ｐ１＝Ｐ１（ｎ，Ｌ１，Ｌ２）＜ｎ＜ｑ１＝ｑ１（ｎ，Ｌ１，Ｌ２），使得对任意弱（Ｌ１，Ｌ２）－ＢＬＤ映射ｆ∈（Ω，Ｒｎ），都有ｆ∈（Ω，Ｒｎ），即ｆ为（Ｌ１，Ｌ２）－ＢＬＤ映射．     

SPME-GC-MS快速分析炼油厂废水中痕量VOCs

采用固相微萃取-气相色谱-质谱联用(SPME-GC-MS)对炼油厂废水中痕量挥发性有机化合物(VOCs)进行了定性分析,简化了预处理过程,总分析时间为1h左右.对比处理前后的废水样品的MS定性结果,为废水处理工艺的评价以及有机物降解机理提供了依据.     

基于BDF的无约束优化方法的收敛性分析

1.介 绍 在上个世纪的七十年代末、八十年代初,基于常微分方程的优化方法或者说同伦方法是一类与拟牛顿法和共轭梯度法等我们所熟知的优化方法相竞争的重要方法[1-6,8,13,14,16].由于这类方法只是简单地利用现成的数值求解常微分方程的软件包,如CVODE[7]、LSODE[12],对同伦方程(一般是一个常微分方程的初值问题)进行计算,除了一些特殊的病态问题     

一类非线性抛物方程的L∞估计

本文给出了一种统一的方法来得到一类非线性抛物方程的有界解的先验的L∞估计.这类方程的基本类型是其中v=v(x,t)是一个满足某些条件的非负可测权函数.由此可以去掉文[5]中的一个本质性的条件p≥2.     

带惩罚项与随机输入的BP神经网络在线梯度学习算法的收敛性

本文对三层BP神经网络中带有惩罚项的在线梯度学习算法的收敛性问题进行了研究,在网络训练每一轮开始执行之前,对训练样本随机进行重排,以使网络学习更容易跳出局部极小,文中给出了误差函数的单调性定理以及该算法的弱收敛和强收敛性定理。