重分数布朗运动的列维连续模

重分数布朗运动是布朗运动的一种推广，有较强的实用背景.该过程的增量既不是独立的，也不是平稳的. 本文研究了它的列维连续模.     

Simulation of non-Gaussian multivariate stationary processes

Mathematical justifications are given for a simulation technique of multivariate non-Gaussian random processes and fields based on Rosenblatt's transformation of Gaussian processes. Different types of convergences are given for the approaching sequence. Moreover an original numerical method is proposed in order to solve the functional equation yielding the underlying Gaussian process autocorrelation function.     

Two-dimensional liquid chromatography of diblock copolymers: Simulation at various adsorption interaction conditions

Diblock copolymers, which are heterogeneous in both molar mass and composition, can be fully characterized by using two-dimensional chromatography. Since the size-exclusion, the adsorption, and the critical interaction based modes of chromatography are possible for each of the polymers A and B, this leads to a variety of options for 2D-chromatography of copolymers AB. Using the theory of chromatography of block copolymers, 2D-chromatograms are simulated that correspond to the most interesting of these options. Orthogonal 2D-chromatograms are expected, if in the 1st dimension the critical condition is created for A, while in the 2nd dimension – for B. The situations, where A and B are both adsorbable, as well as those where the conditions of adsorption for A and SEC for B are created, are also considered. In particular, it is shown that the 2nd dimension combination of the critical condition for A and SEC – for B is preferable than that with SEC condition for both A and B. The simulated 2D-chromatograms of low- and high molar mass diblock copolymers, as well as of copolymers with one short block are compared with the reported real ones; it is concluded that the corresponding virtual and real 2D-chromatograms are qualitatively very similar.     

Forward mixing model in a rotating disc contactor for kerosene–acetic acid–water system

The application of an extraction column model which takes into account the influence of the forward mixing model is brought forward by generation of values of the mass transfer and axial dispersion coefficients required by the model. Values of these coefficients were generated from solute concentration profile measurements in a rotary disc contactor (RDC). The use of the Handlos–Baron drop mass transfer model was justified. The resulting continuous phase transfer coefficients were found to be dependent largely on disc speed. The accuracy of the forward mixing model for kerosene–acetic acid–water system was adequately presented and explained by the axial dispersion coefficient.     

Similitude généralisée et modélisation géométrique à complexité réduiteGeneral similitude solutions and low-complexity geometric models

We present low complexity models for the transport of passive scalars for environmental applications. The model uses partial observations assimilation. Similitude solutions are proposed in a non symmetric metric based on travel times. The approach does not require the solution of any PDE and is mesh free. Also, the solution can be computed in one point only without computing the whole solution. To cite this article: B. Mohammadi, J.-M. Brun, C. R. Mecanique 334 (2006).     

溶液自由基ESR谱的计算机模拟

根据溶液自由基电子自旋共振(ESR)原理,用高级BASIC语言编写了溶液自由基谱的模拟程序。自由基的ESR能级用一级近似求解,谱线的线型采用洛仑兹和高斯型的混合线型函数,程序规定体系中核自旋量子数可从1/2到7/2,不等性核的数目最多可达10组,每组中等性核的数目原则上不受限制。通过二萘嵌苯阳离子自由基和硝基苯负离子自由基谱模拟二个例子加以说明,模拟结果是满意的。     

On the estimation of optimal batch sizes in the analysis of simulation output

The estimation of the variance of point estimators is a classical problem of stochastic simulation. A more specific problem addresses the estimation of the variance of a sample mean from a steady-state autocorrelated process. Many proposed estimators of the variance of the sample mean are parameterized by batch size. A critical problem is to find an appropriate batch size that provides a good tradeoff between bias and variance. This paper proposes a procedure for determining the optimal batch size to minimize the mean squared error of estimators of the variance of the sample mean. This paper also presents the results of empirical studies of the procedure. The experiments involve symmetric two-state Markov chain models, first-order autoregressive processes, seasonal autoregressive processes, and queue-waiting times for several M/M/1 queueing models. The empirical results indicate that the estimation procedure works nearly as well as it would if the parameters of the processes were known.     

A multi-objective genetic algorithm for robust flight scheduling using simulation

Traditional methods of developing flight schedules generally do not take into consideration disruptions that may arise during actual operations. Potential irregularities in airline operations such as equipment failure are not adequately considered during the planning stage of a flight schedule. As such, flight schedules cannot be met as planned and their performance is compromised, which may eventually lead to huge losses in revenue for airlines. In this paper, we seek to improve the robustness of a flight schedule by re-timing its departure times. The problem is modeled as a multi-objective optimization problem, and a multi-objective genetic algorithm (MOGA) is developed to solve the problem. To evaluate flight schedules, SIMAIR 2.0, a simulation model which simulates airline operations under operational irregularities, has been employed. The simulation results indicate that we are able to develop schedules with better operation costs and on-time performance through the application of MOGA.     

Robust neural modeling for the cross-sectional analysis of accounting information

The performance of robust artificial neural network models in learning bivariate relationships between accounting magnitudes is assessed in this paper. Predictive performances of a number of modeling paradigms (namely, linear models, log-linear structures, classical ratios and artificial neural networks) are compared with regard to the problem of modeling a number of the most outstanding accounting ratio relations. We conduct a large scale analysis, carried out on a representative Spanish data base.     

Portfolio selection under strict uncertainty: A multi-criteria methodology and its application to the Frankfurt and Vienna Stock Exchanges

In modern portfolio theory, it is common practice to first compute the risk-reward efficient frontier and then to support an individual investor in selecting a portfolio that meets his/her preferences for profitability and risk. Potential flaws include (a) the assumption that past data provide sufficient evidence for predicting the future performances of the securities under consideration and (b) the necessity to mathematically determine or approximate the investor’s utility function. In this paper, we propose a methodology whose initial phase filters portfolios that are inefficient from a historical perspective. While this is consistent with traditional approaches, the second phase differs from the standard approach as it uses a decision table constructed by considering multiple scenarios assuming strict uncertainty. The table cells measure consequences by a multi-criteria linear performance index of simulated future returns, which avoids difficulties with performance ratios. The real world applicability is illustrated through two studies based on data from the stock exchanges in Frankfurt and Vienna.