Long memory and chaotic models of prices on the London Metal Exchange

Many financial time series exhibit irregular behaviour. Economic theory suggests that this irregular behaviour might be due to the existence of nonlinear dependence in the markets. Thus, economic time series are governed by nonlinear dynamics.The purpose of this paper is to investigate price behaviour in the London Metal Exchange market. Thus, this study will test the two most attractive nonlinear models—long memory and chaos—on six metal commodities to ascertain which model is consistent with the observed metal price nonlinear dynamics.Application of long memory and chaos analysis provides new approaches for assessing the behaviour of metal prices. We identified, in tin, a case of chaos. Our empirical results in the case of aluminium support the long memory hypothesis. A short memory model explains the underlying processes of the nickel and lead returns series, while zinc returns reflect an anti-persistent process. To our knowledge, this is one of the first attempts to apply long memory and chaos analysis in the evaluation of the behaviour of metal prices.     

A nonlinear model for assessing multiple probabilistic risks: a case study in South five-island of Changdao National Nature Reserve in China

Several methods for estimating the potential impacts caused by multiple probabilistic risks have been suggested. These existing methods mostly rely on the weight sum algorithm to address the need for integrated risk assessment. This paper develops a nonlinear model to perform such an assessment. The joint probability algorithm has been applied to the model development. An application of the developed model in South five-island of Changdao National Nature Reserve, China, combining remote sensing data and a GIS technique, provides a reasonable risk assessment. Based on the case study, we discuss the feasibility of the model. We propose that the model has the potential for use in identifying the regional primary stressor, investigating the most vulnerable habitat, and assessing the integrated impact of multiple stressors.     

Data driven modelling of vertical atmospheric radiation

In the Czech Hydrometeorological Institute (CHMI) there exists a unique set of meteorological measurements consisting of the values of vertical atmospheric levels of beta and gamma radiation. In this paper a stochastic data-driven model based on nonlinear regression and on nonhomogeneous Poisson process is suggested. In the first part of the paper, growth curves were used to establish an appropriate nonlinear regression model. For comparison we considered a nonhomogeneous Poisson process with its intensity based on growth curves. In the second part both approaches were applied to the real data and compared. Computational aspects are briefly discussed as well. The primary goal of this paper is to present an improved understanding of the distribution of environmental radiation as obtained from the measurements of the vertical radioactivity profiles by the radioactivity sonde system.     

Development of double-variable seismic fragility functions for oil refinery piping systems

Seismic risk assessment of piping systems, as a group of vulnerable facilities in oil refineries, is mostly based on the single-variable fragility curves. However, it is well-known that the fragility curves, developed based on a single intensity measure (IM), are not much reliable. For increasing the confidence level of seismic risk assessment of piping systems, it was tried, in this study, to develop double-variable fragility functions by using peak ground acceleration (PGA) and peak ground velocity (PGV) together as the IMs. For this purpose, the piping system of the ISOMAX Unit of Tehran oil refinery was considered, and modeled by a powerful finite element analysis program under various loadings, including gravity, pressure and seismic loads. For seismic analyses 157 set of three-component earthquake records were employed, with PGA and PGV values varying respectively from around 0.1 g–0.6 g and 10 cm/s to 60 cm/s. By using the nonlinear time histories analyses results, two single-IM fragility curves and one double-IM fragility surface were developed based on the probability of exceedance of the maximum created stress, considered as the ‘damage index’, from the allowable stress. The results indicate that using PGA and PGV jointly, as the IMs in the development of fragility functions, provides more reliable vulnerability estimations. For example, the single-IM fragility function gives, for PGA = 0.2 g, a probability of exceedance of 75%, while by using the double-IM fragility function this probability may change from 30% for PGV = 10 cm/s to 95% for PGV = 60 cm/s.     

A stochastic weather generation model for Alaska

A specific problem encountered in ecosystem-level simulation of Arctic ecosystems is the depth and extent of the driving variable record. Often, climate records are of short duration, gathered at locations different from the area to be simulated, or do not contain all the variables required by a given model. This paper addresses this problem for ecosystem simulation in Alaska with the development of a weather generator. The generator, called WGENAL, is based on the WGEN climate generator developed and validated in the 48 conterminous states. Because of the extreme variability of weather in Alaska that is not accommodated by the statistical metrics in the earlier model, a new climate generator was developed. WGENAL generates daily values of precipitation, maximum temperature, minimum temperature, solar radiation, and wind run. Precipitation is generated using a Markov chain-gamma model. A two-parameter gamma distribution is used to generate wind run. Temperatures and solar radiation are generated using procedures developed in the earlier study. Validation of the generator shows it provides adequate diurnal and seasonal weather records for Fairbanks. Other comparisons of synthetic weather with observed weather for sites north of the Brooks Range in Alaska are also within the error of the original data.     

Application of cupuassu shell as biosorbent for the removal of textile dyes from aqueous solution

The cupuassu shell (Theobroma grandiflorum) which is a food residue was used in its natural form as biosorbent for the removal of C.I. Reactive Red 194 and C.I. Direct Blue 53 dyes from aqueous solutions. This biosorbent was characterized by infrared spectroscopy, scanning electron microscopy, and nitrogen adsorption/desorption curves. The effects of pH, biosorbent dosage and shaking time on biosorption capacities were studied. In acidic pH region (pH 2.0) the biosorption of the dyes were favorable. The contact time required to obtain the equilibrium was 8 and 18 h at 298 K, for Reactive Red 194 and Direct Blue 53, respectively. The Avrami fractionary-order kinetic model provided the best fit to experimental data compared with pseudo-first-order, pseudo-second-order and chemisorption kinetic adsorption models. The equilibrium data were fitted to Langmuir, Freundlich, Sips and Radke-Prausnitz isotherm models. For both dyes the equilibrium data were best fitted to the Sips isotherm model.     

瓦斯爆炸事故的混沌特性及其控制方法初探

我国煤炭地下开采占全部产量的 96 % ,含瓦斯煤层多 ,高瓦斯矿井和突出矿井占矿井总数的4 4 % ,由瓦斯引发的爆炸事故的伤亡人数占全部矿井事故伤亡总数的 6 5 %。针对我国煤矿安全生产的严峻形势 ,笔者在对瓦斯爆炸事故特性进行分析的基础上 ,应用混沌理论对瓦斯爆炸事故的风险以及控制方法进行初步探讨 ,初步建立了瓦斯爆炸事故混沌特性分析模型 ,对预防和控制瓦斯爆炸事故的发生 ,改善矿井安全生产状况具有指导意义。     

劲柔索张拉穹顶抗火反应非线性有限元分析

劲柔索张拉穹顶结构抗火工程是以结构具有一定的抗火时间为标准 ,通过模拟预测实际火灾特性 ,采用有限元双重非线性方法进行结构的整体和局部抗火反应静力分析 ,求得直到倒塌时的结构抗火全过程响应 ,并确定结构的临界温度状态和整体结构的抗火时间极限 ,从而对其实际抗火能力有一个理性的了解和认识 ,为经济、科学的结构综合防火设计提供理论依据。     

On the use of growth rate parameters for projecting population sizes: Application to aphids

This paper extends the application of the cumulative size based mechanistic model, which has previously been shown to describe diverse aphid population size data well. The mechanistic model is reviewed with a focus on the explanatory role of the birth and death rate formulation. An analysis of two data sets, one on the mustard aphid and the other on the pecan aphid, indicates that multiple linear regression equations based on the estimated birth and death rate parameters alone account for nearly all (R² > 0.95) of the variability in two key population attributes, namely the peak count and the cumulative density. This indicates that population size variables may be projected directly from the growth rate parameters using linear equations. Such linear relationships based on the birth and death rate parameters are shown to hold also for certain generalized mechanistic models for which the analytical solution is not available. The birth and death rate coefficients, therefore, constitute a new succinct set of variables that could be included in the predictive modeling of aphid populations, as well as other insect and animal populations with local collapse which follow similar growth dynamics.