校园网络计费系统研究与实现

在分析校园网计费需求、常用网络计费方式的不足基础上提出一种通用的校园网计费系统解决方案，并且较为详细地描述了实现该系统的主要技术。     

四川旱涝震灾害的人工神经网络外推预测

选取旱、涝、震灾害之间的一步转移概率作为Ｂ－Ｐ人工神经网络训练样本的输入信息，建立了四川旱、涝、震的人工神经网络灾型预测模型。Ｂ－Ｐ网络模型应用于实例预测结果与用主分量分析法的趋势预测结果精度接近。     

关于网络经济的经济增长特征问题的讨论

同传统的大工业经济相比较，网络经济条件下的经济增长特征问题有了新的内容。这就是，影响经济增长的主要因素有质的变化；经济资源得以在全球范围内优化配置；人力资源开发在经济增长中具有决定性作用；集约化成为经济增长的主要方式；信息技术成为经济增长的主要推动力。     

Resource Flows of Villages with Contrasting Lifestyles in Nanda Devi Biosphere Reserve, Central Himalaya, India

Resource use efficiency analyses of village ecosystem are necessary for effective and efficient planning of resource utilization. This paper deals with economic and energy input-output analyses of different components of village ecosystem in representative buffer zone villages, which are practicing transhumance and settled way of lifestyles in Nanda Devi Biosphere Reserve （NDBR） of Garhwal Himalaya. While the villages practicing transhumance used various natural resources spatially segregated,the villages practicing settled way of lifestyle have to manage resources from a limited spatial area through rotation and varied extraction intensities. Forests subsidized the production activity in both type of villages and the per capita resource extractions were found to be greater in tran~humance village than settled village. Though crops provided maximum energy, in terms of economic criteria, animal husbandry played important role in both settled and transhumance villages. As villages representing both the situations showed different ways of adjustments to the conservation oriented land use changes, management authority needs to address the eco-development plans fulfilling the aspirations of all people traditionally using the resources of the Reserve to reduce the conflicts and encourage their participation in the conservation of the area.     

GPS掩星技术和电离层反演

系统地介绍了GPS无线电掩星技术的发展现状、系统组成和一些关键技术；论述了利用GPS掩星数据进行电离层反演的理论、模型及相应的计算流程．并结合GPS／MET和CHAMP卫星的实测资料，计算了电子密度剖面，与用其他方法所得结果的对比表明．GPS掩星电离层观测具有精度高、覆盖范围大等特点；最后讨论了GPS掩星技术应用于地震前兆监测的机理和前景。     

论网络生产力的结构层次

网络生产力是一个复杂的系统，它包括构成要素层次，渗透性因素层次与组合性因素层次而有别于一般生产力的新质。     

The physical framework of the dependence between channel flow hydrographs and drainage network morphometry

The physical basis of the linkage between magnitude and timing of channel flow hydrographs and drainage network morphometry is reviewed. Small Hortonian and structurally Hortonian networks are analysed using numerical runoff simulation. For Hortonian networks the variability of the geometry of individual channels and subcatchments within each Strahler order has generally little effect upon the overall character of the hydrograph in channels of higher order. If the network is also structurally Hortonian, the analysis of the simultaneous formation, travel, and concentration of the hydrographs in all channels of the network can be simplified to a sequence of one representative hydrograph per channel order. This approach is used in this study. Three major runoff processes control the flow hydrograph characteristics: the overland flow process which determines the water supply to the drainage network; the channel flow process which translates the hydrograph in space and time; and the drainage network process which concentrates and magnifies the flow at the junctions of the drainage network. Functional relations for the hydrograph peak, timing, and flow velocity are presented. For a given uniform rainfall and infiltration rate, the peak of the channel flow hydrograph is shown to increase geometrically with channel order, and its magnitude is directly related to the bifurcation ratio. The travel time of the peak also increases geometrically with channel order, and it is directly related to the channel length ratio over velocity ratio. The flow velocity of the peak changes in a downstream direction as a function of the bifurcation and slope ratio. It was also found that for negligible channel storage the channel flow and drainage network processes do not contribute significantly to the observed nonlinear response of a watershed to precipitation.     

SOFTWARE REVIEWS

software reviews are in this article MACATLAS . Number Cruncher Statistical System (NCSS) TRANSPRO FIRE-ROUTER     

A hybrid Bayesian back‐propagation neural network approach to multivariate modelling

There is growing interest in the use of back‐propagation neural networks to model non‐linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back‐propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back‐propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back‐propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright © 2003 John Wiley & Sons, Ltd.     

Application of a neural network model in establishing a stage–discharge relationship for a tidal river

This paper presents the applicability of neural network (NN) modelling for forecasting and filtering problems. The multilayer feedforward (MLFF) network was first constructed to forecast the tidal‐level variations at the mouth of the River Chao Phraya in Thailand. Unlike the well‐known conventional harmonic analysis, the NN model uses a set of previous data for learning and then forecasting directly the time‐series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be predicted successfully using only a short‐time hourly learning data. The MLFF network was further used to establish a stage–discharge relationship for the tidal river. The results show a considerably better performance of the NN model over the conventional models. In addition, the stage–discharge relationship obtained by the NN model can indicate reasonably well the important behaviour of the tidal influences. Copyright © 2003 John Wiley & Sons, Ltd.