EFFECTS OF FISH OIL,DHA OIL AND LECITIHIN IN MICROPARTICULATE DIETS ON STRESS TOLERANCE OF LARVAL GILTHEAD SEABREAM（SPARUS AURATA）

The effects of natural fish oil,DHA oil and soybean lecithin in microparticulate diets on stress tolerance of larval gilthead seabream(Sparus aurata)were investigated after 15 days feeding trials.The tolerance of larval gilthead seabream to various stress factors such as exposure to air(lack of dissolved oxygen),changes in water temperature(low)and salinity(high) were determined.This study showed that microparticulate diet with natural fish oil and soybean lecithin was the most effective for in-creasing the tolerance of larval gilthead seabream to various stresses,and that microparticulate diet with natural fish oil and palmitic acid(16:0)was more effective than microparticulate diet with DHA oil and soybean lecithin.     

基于蚁群算法的城市可持续发展综合评价

在可持续发展体系分类的基础上，应用蚁群算法对可持续发展各分类指数公式和综合指数公式进行优化，建立可持续发展评价模型。将该模型应用于乌鲁木齐市可持续发展水平评价，评价结果与现状分析一敛。结果表明该模型简单适用、有良好的通用性。     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

分组密码的安全性与强化设计

分组密码是数据通讯中最常用的数据加密方式，以DES为例分析现有分组加密算法的安全隐患，并提出了可变密钥加密和变长密文输出两个新思路，可应用于所有现有分组加密算法以提高安全性，并就该方法的安全性、效率、具体应用做出了分析。     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

形变大地测量学的进展、问题与地震预报

简要概括了形变大地测量学的革命性进展，研讨了它的科学特色、功能和定义以及对地球科学和防灾减灾的推动。形变大地测量学有助于从根本上击破多年来制约地震预报的“瓶颈”，但也存在不少急待解决的问题。着重研讨了在21世纪前10年，形变大地测量学如何依托多年的学科积累并充分受益于人造卫星和数字化等新技术，开展创新性研究和试验以推进地震预报。为此，对当前的研究工作提出了12条科学技术途径。最后对学科名称提出了建议。     

用遗传算法对自动控制系统数学模型寻优

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。     

优化发电能源结构的初探

从我国电力工业发展的角度，浅析了国内外发电能源结构及其变化趋势，作者认为优化我国发电能源结构、采取必要的技术措施，是实现我国电力 工业可持续发的重要途径。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于可靠度理论的黄土高边坡优化设计

对黄土高边坡进行可靠性分析，应用数学原理和优化原理，建立了黄土高边坡的优化模型，对铜黄一级公路黄土高边坡进行分析验证。结果表明，该方法计算的结果与实际较接近．应用中易操作。