电解铝投资项目风险评价方法探讨

电解铝投资项目具有投资大、周期长等特点,投资效益受电解铝价格、经营成本和建设投资的影响较大,准确识别并判断这些风险因素对投资效果的影响十分重要。本文对电解铝投资项目风险评价的几种主要方法进行比较,通过案例分析,得出蒙特卡洛模拟法能较为全面地评价电解铝投资项目的风险。     

电解铝投资项目风险评价方法探讨

电解铝投资项目具有投资大、周期长等特点,投资效益受电解铝价格、经营成本和建设投资的影响较大,准确识别并判断这些风险因素对投资效果的影响十分重要.本文对电解铝投资项目风险评价的几种主要方法进行比较,通过案例分析,得出蒙特卡洛模拟法能较为全面地评价电解铝投资项目的风险.     

蒙特卡洛模拟法在边坡可靠性分析中的应用

本文系统地叙述了应用蒙特卡洛模拟法进行边坡可靠性分析的方法,并附有算例,具有实用性和可操作性。     

基于Copula理论的黄金和石油投资组合风险研究

运用Copula理论和蒙特卡洛模拟法对黄金和石油投资组合进行了风险价值计算。其结果表明:黄金和石油投资组合是有效的;该投资组合的风险介于风险较小的黄金和风险较大的石油之间,且投资组合的风险小于两种资产风险的平均值。这说明在黄金和石油风险不一的情况下,可以将这两种资产进行组合投资,这样既能够有效地降低整体投资风险,又能够分享高风险石油资产所带来的较高收益。     

越南矿业投资分析及建议

文章从越南的政治形势、经济形势、基础设施建设、劳动力资源以及投资政策等方面分析了越南的矿业投资环境，介绍了现阶段越南的矿业情况以及矿业的投资情况，尤其是中国对越南的矿业投资方式及矿业战略，提出了中国在越南投资矿业的最佳方式，这对中国投资越南矿业具有一定的指导意义。     

海外矿业投资项目综合评价方法研究

近年来,中国海外矿业投资急剧增长,但成功运营的海外矿业投资项目为数不多。为有效降低海外矿业项目投资风险,提高海外矿业投资成功率,把握海外矿业投资机会,需要建立一套行之有效的综合评价方法。本文在总结海外矿业投资项目综合评价要素的基础上,利用定性和定量相结合的分析计算方法,建立了海外矿业投资项目综合评价体系,并通过实际案例分析证明了该综合评价的科学性和准确性。     

中企到南非开矿的机遇与风险

南非矿产资源丰富，有着巨大的开发潜力，吸引了包括中国企业在内的众多国际矿业投资者。但是由于相关法律制度的不清晰和政策的不稳定，矿业投资存在较大的不确定性，本文就目前到南非进行矿业投资的利弊作了重点分析，仅供有意赴南投资矿业者参考。     

基于延迟期权和扩张期权的矿业投资决策研究

矿业项目投资回收期长,不确定因素多,传统评价方法在评价策略性选择权时完全不考虑投资过程中实际存在的不确定性,造成短视决策。文中首先介绍了实物期权的概念、主要类型及定价方法;其次建立了基于延迟期权和扩张期权的矿业投资决策模型;最后以江西省某钼矿投资项目为例进行应用。结果表明:所建立的模型比较全面地反映了矿业投资项目的总体价值,为矿业投资决策提供了一种较好的思路与方法。     

矿业全球化形势下矿业公司的投资选择

从20世纪80年代开始．矿业全球化开始发展并取得了显著的进步。矿业全球化主要表现在：矿业投资全球化、跨国矿业公司的兼并、矿产品国际贸易全球化和矿业政策调整的全球化。在矿业全球化的大趋势下，作为矿业投资的主体——矿业公司的投资要面向全球，因此矿业公司的投资选择主要体现在勘探和开发的矿种与地区两个方面。     

黄金矿业企业估值方法探析

随着国际金价波动率持续走高，资本市场对黄金矿业的关注度持续提升，黄金矿业企业兼并重组案例明显增多，对黄金矿业企业的估值研究在投资实务中愈发重要。从资源属性、生产期限、资本支出、环保安全等方面分析了黄金矿业企业估值特殊性，并总结了估值方法及其适用性。以某黄金公司的估值案例为依托，采用储量法、市盈率法、可比交易法、收益法等不同估值方法进行综合分析，最终给出合理估值范围，以期对黄金矿业企业估值实践有所启发。     

Latin Hypercube Sampling for Stochastic Finite Element Analysis

A Latin hypercube sampling method, including a reduction of spurious correlation in input data, is suggested for stochastic finite element analysis. This sampling procedure strongly improves the representation of stochastic design parameters compared to a standard Monte Carlo sampling. As the correlation control requires the number of realizations to be larger than the number of stochastic variables in the problem, a principal component analysis is employed to reduce the number of stochastic variables. In many cases, this considerably relaxes the restriction on the number of realizations. The method presented offers the same general applicability as the standard Monte Carlo sampling method but is superior in computational efficiency.     

Stochastic Six-Degree-of-Freedom Flight Simulator for Passively Controlled High-Power Rockets

This paper presents a method for simulating the flight of a passively controlled rocket in six degrees of freedom, and the descent under parachute in three degrees of freedom. Also presented is a method for modeling the uncertainty in both the rocket dynamics and the atmospheric conditions using stochastic parameters and the Monte Carlo method. Included within this, we present a method for quantifying the uncertainty in the atmospheric conditions using historical atmospheric data. The core simulation algorithm is a numerical integration of the rocket’s equations of motion using the Runge-Kutta-Fehlberg method. The position of the rocket’s center of mass is described using three dimensional Cartesian coordinates and the rocket’s orientation is described using quaternions. Input parameters to the simulator are made stochastic by adding Gaussian noise. In the case of atmospheric parameters, the variance of the noise is a function of altitude and noise at adjacent altitudes is correlated. The core simulation algorithm, with stochastic parameters, is run within a Monte Carlo wrapper to evaluate the overall uncertainty in the rocket’s flight path. The results of a demonstration of the simulator, where it was used to predict the flight of real rocket, show the rocket landing within the 1σ area predicted by the simulation. Also lateral acceleration during weather cocking, which was measured in the test, shows a strong correlation with simulated values.     

Sensitivity Statistics of 3D Structures under Parametric Uncertainty

Determination of sensitivity gradient is a major prerequisite for structural optimization, reliability assessment, and parameter identification. As the conventional deterministic sensitivity analysis cannot provide complete information, stochastic analysis is needed to tackle the uncertainties in structural parameters. This study focuses on the utility of the stochastic finite-element method for random response sensitivity analysis. The stochastic modeling of a random parameter is based on a commonly used 2D local averaging method generalized for a 3D case. The Choleski decomposition technique is then employed for digital simulation. The Neumann expansion based finite-element simulation method is extended for stochastic sensitivity analysis. This technique leads to a considerable saving of computational time. Example problems are used to compare the accuracy of this method to the direct Monte Carlo simulation and perturbation method in terms of varying stochasticity and efficiency in CPU time.     

Extension of Weighted Integral Stochastic Finite Element Method to the Analysis of Semi-Infinite Domain

In this study, one of the nonstatistical stochastic methods, i.e., the weighted integral method, is extended to analyze the semi-infinite domain. In the semi-infinite domain the region of uncertainties is vast when compared with that of the ordinary finite domain. Accordingly, the response variability in this domain has more significance than that in the ordinary finite domain. In modeling the semi-infinite domain, the coupled use of the infinite element is adopted. The results obtained using the proposed weighted integral method is compared with those obtained by Monte Carlo simulation. It is shown that the results of proposed method and those by the Monte Carlo simulation are in good agreement with each other showing the adequacy of the proposed method. In addition, the improvement in the response statistics, when the infinite domain is included in the model, is also attained, which shows the importance of the inclusion of far field in the analysis.     

钻井水溶法开采矿区地表移动随机介质理论预计研究

钻井水溶法开采流程简单、操作方便、劳动负荷轻、投资少、见效快，是一种很有发展前途的采矿方法。钻井水溶法开采形成的地下采空区可能会使地表发生移动和变形。随机介质预计理论概率积分法是建立在上履岩层软弱、顶板跨落基础上的非连续介质理论。由于沉积成矿的特点，岩盐矿床的地质条件决定了其岩石力学性质整体表现为顶、底板强度较低，适应于采用随机介质理论预计地表移动与变形。文章结合某钻井水溶法开采矿山实例，探讨地表移动与变形预计随机介质预计方法。     

钻井水溶法开采矿区地表移动随机介质理论预计研究

钻井水溶法开采流程简单、操作方便、劳动负荷轻、投资少、见效快,是一种很有发展前途的采矿方法。钻井水溶法开采形成的地下采空区可能会使地表发生移动和变形。随机介质预计理论概率积分法是建立在上覆岩层软弱、顶板跨落基础上的非连续介质理论。由于沉积成矿的特点,岩盐矿床的地质条件决定了其岩石力学性质整体表现为顶、底板强度较低,适应于采用随机介质理论预计地表移动与变形。文章结合某钻井水溶法开采矿山实例,探讨地表移动与变形预计随机介质预计方法。     

Rapid modeling of diffuse reflectance of light in turbid slabs

An efficient and accurate hybrid model of the Monte Carlo technique and the diffusion theory was developed to simulate the diffuse reflectance of light in a turbid slab due to an infinitely narrow light beam. The narrow beam was normally incident on the top surface of the slab. The hybrid model was accurate in modeling the diffuse reflectance near the light source, where the diffusion theory was most inaccurate. The hybrid model was much faster than a pure Monte Carlo method by a factor as great as several hundred, depending on the optical properties, the thickness of the slab, and the settings of the hybrid and the Monte Carlo computations. The computation speed of the hybrid model was insensitive to the optical properties of the medium, in contrast to the pure Monte Carlo technique. The diffusion theory was accurate in modeling both the diffuse reflectance far from the source and the diffuse transmittance. The hybrid model and the diffusion theory should be used in conjunction for efficient and accurate computation of diffuse reflectance and diffuse transmittance.     

Stochastic Finite-Element Approach to Quantify and Reduce Uncertainty in Pollutant Transport Modeling

One of the important issues in simulation of contaminant transport in the subsurface is how to quantify the hydraulic properties of soil that are randomly variable in space because of soil heterogeneity. Stochastic approaches have the potential to represent spatially variable parameters, making them an appropriate tool to incorporate the effects of the spatial variability of soil hydraulic properties on contaminant fate. This paper presents development and application of a numerical model for simulation of advective and diffusive-dispersive contaminant transport using a stochastic finite-element approach. Employing the stochastic finite-element method proposed in this study, the response variability is reproduced with a high accuracy. Comparison of the results of the proposed method with the results obtained using the Monte?Carlo approach yields a pronounced reduction in the computation cost while resulting in virtually the same response variability as the Monte?Carlo technique.     

Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty

This paper proposes a neural network embedded Monte Carlo (NNMC) approach to account for uncertainty in water quality modeling. The framework of the proposed method has three major parts: a numerical water quality model, a neural network technique, and Monte Carlo simulation. The numerical model is used to generate desirable output for training and testing sets, and the neural network is used as a universal functional mapping tool to approximate the input-output response of the numerical model. The Monte Carlo simulation then uses the neural network to generate numerical realizations based on a probabilistic distribution of parameters, thus obtaining a probabilistic distribution of the simulated state variables. By embedding a neural network into the conventional Monte Carlo simulation, the proposed approach significantly improves upon the conventional method in computational efficiency. The proposed approach has been applied to uncertainty and risk analyses of a phosphorus model for Triadelphia Reservoir in Maryland. The results of this research show that the NNMC approach has potential for efficient uncertainty analysis of water quality modeling.     

A cautionary note on modeling growth trends in longitudinal data.

Random coefficient and latent growth curve modeling are currently the dominant approaches to the analysis of longitudinal data in psychology. The application of these models to longitudinal data assumes that the data-generating mechanism behind the psychological process under investigation contains only a deterministic trend. However, if a process, at least partially, contains a stochastic trend, then random coefficient regression results are likely to be spurious. This problem is demonstrated via a data example, previous research on simple regression models, and Monte Carlo simulations. A data analytic strategy is proposed to help researchers avoid making inaccurate inferences when observed trends may be due to stochastic processes. (PsycINFO Database Record (c) 2011 APA, all rights reserved)