Validation of a Method of Determination of Apparent Diffusivity Versus Composition in Solids

A method is presented for computing the values of apparent diffusivity in solids with respect to the concentration of the diffusing substance (water or sodium chloride). This method does not require any assumption upon the mathematical relationship between diffusivity and concentration. It can be applied to experimental measurements of local concentration versus position within the solid (profiles) with relatively few measurements (circa 10) and a mathematical smoothing of the experimental data by using an artificial neural network model. The method was first validated on simulated data obtained by using a constant diffusivity value and on experimental profiles when the relation between diffusivity and concentration was given. It was then applied to original experimental moisture profiles obtained by putting gelatin gels with different initial moisture contents into contact for up to 14 days. The method was also successfully applied to five sets of experimental moisture and sodium chloride profiles taken from the literature and obtained from different food products. Apparent diffusivities calculated by our method were found in agreement with those obtained by authors using different numerical methods to compute the diffusivity values.     

模糊规划在炼油厂生产计划优化中的应用

生产计划的编制是炼油化工企业生产经营管理中的一个关键环节，然而由于市场变化的不确定性，使得企业的长远规划制定受到限制，笔者将模糊数学应用于炼厂生产计划的编制，通过模糊规划对炼油企业生产计划进行优化，使编得的生产计划在一定程度上反映市场的变化并使企业的生产效益最优，为企业管理决策者进行长期规划给予指导，给出了实例来说明该方法的可行性。     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

离心式高炉鼓风机扩压叶片断裂事故分析及处理

通过高炉鼓风机扩压叶片断裂事故，从化学分析、物理检验，结合使用情况对扩压叶片断裂原因进行了详细分析，并提出了改进措施。     

EXPERIMENTAL INVESTIGATIONS ON LONGITUDINAL DISPERSION CHARACTERISTICS OF TIDAL RIVERS

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运用模糊数学原理估算水利水电工程的成本

为了适应水利水电工程成本快速估价的需要,提出了一种建立在模糊数学基本原理基础上的估价模型,克服了工程量清单计价方法计算繁琐的缺点。通过实例说明了该方法的科学性、合理性及实用性。     

An Interval Fuzzy Multiobjective Watershed Management Model for the Lake Qionghai Watershed, China

Integrated watershed management is required to ensure the reasonable use of resources and reconcile interactions among natural and human systems. In the present study, an interval fuzzy multiobjective programming (IFMOP) method was used to solve an integrated watershed management problem. Based on system analysis, an IFMOP model suitable for a lake watershed system {IFMOPLWS} was developed and applied to the Lake Qionghai watershed in China. Scenario analysis and an interactive approach were used in the solution process. In this manner, various system components were incorporated into one framework for holistic consideration and optimization. Integrality and uncertainty, as well as the multiobjective and dynamic characteristics of the watershed system, were well addressed. Using two scenarios, two planning schemes were generated. Agriculture, tourism, macroeconomics, cropland use, water supply, forest coverage, soil erosion, and water pollution were fully interpreted and compared to identify a preferable planning alternative for local agencies. This study showed that the IFMOPLWS is a powerful tool for integrated watershed management planning and can provide a solid base for sustainable watershed management.     

Influence of adsorption on the measurement of diffusion coefficients by Taylor dispersion

The effect of adsorption on the measurement of diffusion coefficients by the Taylor dispersion technique is investigated by modifying the governing equation to account for reversible, nonequilibrium adsorption. The resulting two-dimensional equations are solved by an explicit finite-difference technique. Experimental data for the acridine carbon dioxide system indicated that acridine adsorbs on the walls on the tubing and these data were investigated with this model. The influence of carious parameters including the number of sites and the rates of adsorption desorption was investigated by conducting a parametric sensitivity analysis on the model. It was found that adsorption of the solute on the wall of the tubing could produce an error as high as 35% on the measured diffusion coefficient compared to the actual diffusion coellicient. Examination of the influence of each of the parameters will enable Inure investigators to reduce the effect of adsorption in the measurement of diffusion coefficients by Taylor dispersion.     

Gelling of alginate fibres

Equipment was developed to measure the time for complete gelling of sodium alginate fibres in calcium chloride solution, taken as the time to achieve maximum tensile strength. The effects of fibre diameter, alginate concentration, alginate composition and calcium chloride concentration on gelling time were investigated. A diffusional model, developed to predict the gelling time, agreed with experimental results except for the effect of calcium ion concentration which was modelled empirically.