试论企业组织结构--电子商务企业与传统企业的比较

网络时代的到来，对传统企业的组织结构、运作模式、管理方法均提出了严峻的挑战。传统的企业组织结构是适应大工业生产而形成的等级组织结构形式，它有很大的局限性；电子商务企业的组织结构是适应网络社会而形成扁平化、双向多对多、以信息为基础的新型组织结构形式。电子商务企业以其组织结构扁平化和中空化，决策分散化，运作虚拟化，灵活高效为特点，它将成为传统企业在电子商务发展阶段变革的方向和目标。     

层序-岩相古地理图及其编制

在中国岩相古地理研究历史简要回顾基础上,以层序地层学理论为指导,就如何编制层序-岩相古地理图进行了讨论,首先是编图单元的选择,一是以层序内的体系域为成图单元,二是以相关界面为成图单元.具体方法是在对所研究的层位进行精细的层序地层分析的基础上,进行层序对比,建立等时地层格架.在上述基础上系统编制不同层序内的各体系域或有关界面沉积时的古地理图.以此方法所编制的层序-岩相古地理图具有重要的理论和现实意义,主要表现为更具有等时性、成因连续性和实用性,能更好地反映一个地区在统一地质作用场中的各种地质信息和综合效应;可以揭示出一些新的地质现象,对于覆盖区相带展布及变化具更合理的预测性.进而以中国南方二叠纪为例,在层序划分、对比的基础上,系统编制了一个完整的海平面变化旋回条件下的层序-岩相古地理图,并描述了其平面展布特点.     

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.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Reliability assessment of serviceability performance of braced retaining walls using a neural network approach

In urban environments, one major concern with deep excavations in soft clay is the potentially large ground deformations in and around the excavation. Excessive movements can damage adjacent buildings and utilities. There are many uncertainties associated with the calculation of the ultimate or serviceability performance of a braced excavation system. These include the variabilities of the loadings, geotechnical soil properties, and engineering and geometrical properties of the wall. A risk‐based approach to serviceability performance failure is necessary to incorporate systematically the uncertainties associated with the various design parameters. This paper demonstrates the use of an integrated neural network–reliability method to assess the risk of serviceability failure through the calculation of the reliability index. By first performing a series of parametric studies using the finite element method and then approximating the non‐linear limit state surface (the boundary separating the safe and ‘failure’ domains) through a neural network model, the reliability index can be determined with the aid of a spreadsheet. Two illustrative examples are presented to show how the serviceability performance for braced excavation problems can be assessed using the reliability index. Copyright © 2005 John Wiley & Sons, Ltd.     

Water and power networks and urban fragmentation in Los Angeles: Rethinking assumed mechanisms

Los Angeles is often described as the epitome of urban fragmentation, a notion which in this context is frequently connected to, or even conflated with urban sprawl. At the same time, the city features integrated water and power networks which have been under public ownership for over 70 years. We thus have an apparent paradox in the context of the debate on ‘splintering urbanism’, between socio-spatial fragmentation and the integration of networks. In discussing the idea that deregulation of infrastructural networks exacerbates urban fragmentation, the authors use the case of Los Angeles in order to highlight the central role of private interests in management decisions concerning infrastructure networks. The authors carry out their analysis in an historical perspective, revealing that network integration and universal access can often serve private interests more than the public good. Urban fragmentation in Los Angeles, they conclude, is the result of a complex process of instrumentalisation of network development and management.     

Church-based social capital, networks and geographical scale: Katrina evacuation, relocation, and recovery in a New Orleans Vietnamese American community

This research examines the role of social capital and networks to explain the evacuation, relocation, and recovery experiences of a Vietnamese American community in New Orleans, Louisiana in the aftermath of Hurricane Katrina. As the single largest community institution, the parish church’s complex bonding and bridging social capital and networks proved particularly critical in part because of its historically based ontological security. The process of evacuation, but especially relocation and recovery, was dependent on deploying co-ethnic social capital and networks at a variety of geographical scales. Beyond the local or community scale, extra-local, regional, and national scales of social capital and networks reproduced a spatially redefined Vietnamese American community. Part of the recovery process included constructing discursive place-based collective-action frames to successfully contest a nearby landfill that in turn engendered social capital and networks crossing ethnic boundaries to include the extra-local African American community. Engaging social capital and networks beyond the local geographical scale cultivated a Vietnamese American community with an emergent post-Katrina cultural and political identity.     

Identifying China’s leading world city: a network approach

This paper reports our research on China’s world cities. Formal network analysis of air passenger linkages for recent years among China’s most populous cities and among many of the world’s largest cities allows us to identify the country’s leading world city from among the leading Mainland candidates, Beijing, Shanghai and Guangzhou. We theorize our findings about China’s world cities in relation to both global forces (and China’s increasing entanglement with them) and the policies and actions of the national state. We examine the national and global urban network through a longitudinal, two-level analysis of airline passenger travel for four time points between about 1990 and 2005. We show that Beijing was China’s leading world city at the beginning of the time period, a status it lost nationally in as early as 1995, and then globally 10 years later. On the other hand Shanghai became China’s leading world city, and it acquired this status first nationally in 2000, and then globally in 2005. The changing status of the Chinese capital corresponds to the country’s increasing involvement with the capitalist world economy. Shanghai’s ascendance as the leading world city in China may indicate that global forces have come to play an increasingly important role relative to that of the developmental state.

PSO-RBFNN模型及其在岩土工程非线性时间序列预测中的应用

岩土工程受力变形演化是一个典型的非线性问题,其演化的高度非线性和复杂性,很难用简单的力学、数学模型描述,但可用粒子群优化径向基神经网络对岩土工程应力、位移非线性时间序列进行动态实时预测。网络径向基层的单元数通过均值聚类法确定后,所有其它参数：中心位置、形状参数、网络权值,均通过粒子群优化算法在全局空间优化确定。工程实例应用表明,该模型预测结果准确、精度高,有良好的应用前景。