The enhanced spectrum of the symbiotic variable star CH Cygni on November 2, 2006

The symbiotic variable star CH Cygni went on the deepest minimum phase in December 2006. A high-resolution spectrum of CH Cygni obtained at the Bohyunsan Optical Astronomy Observatory (BOAO) on November 2, 2006, is reported. The spectrum of CH Cygni obtained at the BOAO was calibrated on a scale of an absolute-flux density. Emission lines of H I, [O III], [N II], and [S II] were strong, with several components, and Ca II, H, and K lines had P-Cygni profiles. Removal of the spectrum of the underlying M-type star from the CH Cygni spectrum was completed to obtain the true emission profiles. The radial velocities of all emission and absorption lines were measured. Some of the observed lines were also deconvoluted using multi-Gaussian functions to find the relationship between line shapes and the corresponding sources of these lines. The results obtained are discussed in terms of an accretion disk around the hot star, and in non-spherically extended nebular regions.     

Kriging with Inequality Constraints

A Gaussian random field with an unknown linear trend for the mean is considered. Methods for obtaining the distribution of the trend coefficients given exact data and inequality constraints are established. Moreover, the conditional distribution for the random field at any location is calculated so that predictions using e.g. the expectation, the mode, or the median can be evaluated and prediction error estimates using quantiles or variance can be obtained. Conditional simulation techniques are also provided.     

Two Artifacts of Probability Field Simulation

Probability field simulation is being used increasingly to simulate geostatistical realizations. The method can be faster than conventional simulation algorithms and it is well suited to integrate prior soft information in the form of local probability distributions. The theoretical basis of probability field simulation has been established when there are no conditioning data; however, no such basis has been established in presence of conditioning data. Realizations generated by probability field simulation show two severe artifacts near conditioning data. We document these artifacts and show theoretically why they exist. The two artifacts that have been investigated are (1) local conditioning data appear as local minima or maxima of the simulated values, and (2) the variogram model in range of conditioning data is not honored; the simulated values have significantly greater continuity than they are supposed to. These two artifacts are predicted by theory. An example flow simulation study is presented to illustrate that they affect more than the visual appearance of the simulated realizations. Notwithstanding the flexibility of the probability field simulation method, these two artifacts suggest that it be used with caution in presence of conditioning data. Future research may overcome these limitations.     

22°晕的光强度和颜色分布

本文首次提出了22°晕的折射—衍射理论，计算了几种冰晶大小平均值ā和标准差σ情况下22°晕的径向光强度分布和色品坐标，给出了相对光强度曲线和色品曲线。探讨了冰晶大小及其分布对22°晕光强度和颜色分布的影响。理论计算的22°晕光强度分布与实测结果基本相符。事实上，22°晕是一种折射—衍射晕。     

Joint simulation of multiple variables with a Markov-type coregionalization model

Many applications are multivariate in character and call for stochastic images of the joint spatial variability of multiple variables conditioned by a prior model of covariances and cross- covariances. This paper presents an algorithm to perform cosimulation of such spatially intercorrelated variables. This new algorithm builds on a Markov-type hypothesis whereby collocated information screens further away data of the same type, allowing cosimulation without the burden of a full cokriging. The proposed algorithm is checked against a synthetic multi-Gaussian reference dataset, then against a multi-Gaussian cosimulation approach using full cokriging. The results indicate that the proposed algorithm perform as well as the full cokriging approach in reproducing the univariate and bivariate statistics of the reference set, yet at less cpu cost.     

Evaluation of the parameters affecting the roughness coefficient of sewer pipes with rigid and loose boundary conditions via kernel based approaches

One of the important issues in water transport and sewer systems is determining the flow resistance and roughness coefficient.An accurate estimation of the roughness coefficient is a substantial issue in the design and operation of hydraulic structures such as sewer pipes,the calculation of water depth and flow velocity,and the accurate characterization of energy losses.The current study,applies two kernel based approaches [Support Vector Machine(SVM) and Gaussian Process Regression(GPR)] to develop roughness coefficient models for sewer pipes.In the modeling process,two types of sewer bed conditions were considered:loose bed and rigid bed.In order to develop the models,different input combinations were considered under three scenarios(Scenario 1:based on hydraulic characteristics,Scenarios2 and 3:based on hydraulic and sediment characteristics with and without considering sediment concentration as input).The results proved the capability of the kernel based approaches in prediction of the roughness coefficient and it was found that for prediction of this parameter in sewer pipes Scenario 3 performed better than Scenarios 1 and 2.Also,the sensitivity analysis results showed that Dgr(Dimensionless particle number) for a rigid bed and w_b/y(ratio of deposited bed width,w_b,to flow depth,y) for a loose bed had the most significant impact on the modeling process.     

Terrain generalization with line integral convolution

ABSTRACT

Line integral convolution is a technique originally developed for visualizing vector fields, such as wind or water directions, that places densely packed lines following the direction of movement. Geisthövel and Hurni adapted line integral convolution to terrain generalization in 2018. Their method successfully removes details and retains sharp mountain ridges; it is particularly suited for creating generalized shaded relief. This paper extends line integral convolution generalization with a series of enhancements to reduce spurious artifacts, accentuate mountain ridges, control the level of detail in mountain slopes, and preserve sharp transitions to flat areas. The enhanced line integral convolution generalization effectively removes excessive terrain details without changing the position of terrain features. Sharp mountain ridgelines are accentuated, and transitions to flat waterbodies and valley bottoms are preserved. Shaded relief imagery derived from generalized elevation models is visually pleasing and resembles manually produced shaded relief.     

波前法在高层结构计算中的应用

概述了高层结构的分析方法，给出了高层结构计算的两种波前法计算过程。文末还给出了广东国际大厦（６３层）模型结构的计算结果，其实测值非常吻合。     

Semigroup solutions to stochastic unsteady groundwater flow subject to random parameters

Two methods for the solution of partial differential equations (PDE) for the general case of random in time physical parameters are presented and their application to the solution of unsteady regional groundwater flow equations are illustrated. The first method is the semigroup approach which directly offers a solution without resorting to closure approximations (hierarchy techniques), perturbation techniques, or Montecarlo simulation techniques. The semigroup approach can also handle the general stochastic problem when randomness also appears as initial conditions, boundary conditions or forcing terms. The second method is an approximation scheme to obtain the semigroup solution in complex cases and permits the solution of equations with more than one random coefficient.     

等温剩磁获得曲线的累积对数高斯模型在泥河湾盆地磁组分识别中的运用

基于饱和等温剩磁(SIRM)获得曲线遵循累积对数高斯模型(CLG)的假设，利用改进的CLG模型分析，成功地对采自泥河湾河—湖相层上部的许家窑剖面样品所含的主要载磁组分进行了分离；结合先前的岩石磁学结果，进一步验证了高矫顽力的赤铁矿和低矫顽力的磁铁矿／磁赤铁矿为许家窑样品的主要载磁矿物，并对各磁组分对SIRM的贡献进行了定量估算；对CLG模型的优缺点和实用性进行了探讨；结合剩磁矫顽力曲线特征，尝试提出了X参数的概念。