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.

Generating fracture networks using iterated function systems

In order to model flow and transport in fractured rocks it is important to know the geometry of the fracture network. A stochastic approach is commonly used to generate a synthetic fracture network from the statistics measured at a natural fracture network. The approach presented herein is able to incorporate the structures found in a natural fracture network into the synthetic fracture network. These synthetic fracture networks are the images generated by Iterated Function Systems (IFS) as introduced by Barnsley (1988). The conditions these IFS have to fulfil to determine images resembling fracture networks and the effects of their parameters on the images are discussed. It is possible to define the parameters of the IFS in order to generate some properties of a fracture network. The image of an IFS consists of many single points and has to be suitably processed for further use.     

Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p‐wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four‐cycle slake durability index (I_d4) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. Copyright © 2011 John Wiley & Sons, Ltd.     

Technologies of agency and performance: Tasmania Together and the constitution of harmonious island identity

Baldacchino [Baldacchino, G., 2002. Jurisdictional self-reliance for small island territories: considering the partition of Cyprus, The Round Table, 365, 349-360] has argued that the ‘troika’ of smallness, insularity and peripherality may incline island peoples (rather more than mainlanders?) to question the effects of economic globalization and be especially disposed to innovative approaches to development. He views jurisdictional capacity as integral to that task. Much of the literature on such issues relates to island nations, but this work focuses on Australia’s smallest and only island state of Tasmania, and thus on a sub-national jurisdiction. In what follows I explore the effects of an attempt to enrol Tasmanians in the creation and stabilization of a ‘2020 vision’ meant to be global in its reach, to focus on the particular strengths of the island state, and be innovative in advancing sustainable development. Known as Tasmania Together, the 20-year strategic vision outlines diverse economic, social and environmental goals assembled over two years via widespread consultations with the island’s communities of place and interest. For a time Tasmania Together generated significant debate about what it means to be an island people, and whether and to what extent Tasmanians’ future will be secured through economic globalization or localized endeavours premised on sustainability principles. Important to Tasmanians as well as to island studies, these rhetorics of social and spatial engagement also have salience beyond the borders of the island state, highlighting larger questions about the technologies of governmentality, agency and the performance of identity.     

基于改进SOFM的矢量量化图像压缩

在介绍矢量量化和自组织特征映射神经网络的基础上，针对基于自组织特征映射神经网络的矢量量化算法，在初始码书生成、获胜神经元搜索策略以及调整获胜码字及其拓扑领域权值等方面进行改进。实验结果表明改进算法具有合理性和有效性。     

三种地下水位动态预测模型在吉林西部的应用与对比

准确而可靠地预测地下水埋深对生态环境保护和水资源规划管理具有重要意义。针对吉林西部浅层地下水位动态变化的复杂性和非线性,提出了基于小波分析与人工神经网络相结合的预测方法小波神经网络(WA-ANN)模型。将研究区2002年1月2009年12月当月降水量、蒸发量、人工开采量和前月平均地下水埋深4个参数作为输入,当月平均地下水埋深作为输出,建立浅层地下水埋深预测模型,并与BP神经网络(BP-ANN)模型和自回归移动平均(ARIMA)模型进行比较,对比分析了三者的建模过程及其模拟精度。结果显示:相比两种ANN模型,ARIMA模型建模过程更为简单,计算效率更高;但WA-ANN模型的拟合精度高于BP-ANN和ARIMA模型,预测效果更好。总体来看,WA-ANN模型在浅层地下水埋深预测中具有一定的应用推广价值。     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

高陡山区开采自然坡失稳分析的神经网络方法

针对山区地下开采自然边坡稳定性分析问题,根据大量工程实测资料,建立了人工神经网络预测模型。通过对人工神经网络算法的改进,选取适当的动量项系数及变步长方法,对已有的实测资料进行了训练和测试,并对丁家河磷矿自然边坡稳定性进行了具体的预测分析,理论计算结果与工程实际情况一致。分析结果表明,所建立的理论模型可用于山区磷矿开采自然边坡稳定性预测分析。     

处理DEM中闭合洼地和平坦区域的一种新方法

数字高程模型(DEM)中的闭合洼地和平坦区域影响着流域排水网络的自动提取.目前已提出很多方法来处理这两种地形,但均针对已经形成的DEM单元网格进行处理,结果往往生成伪河道及平行河道.在回顾分析了这些方法存在的问题后,提出了一种新的处理方法,该法认为DEM中的闭合洼地和平坦区域是由于低质量的资料输入、生成DEM时的内插误差等引起的.通过增加输入地形高程信息,避免了DEM中平坦区域和闭合洼地的生成,从而使由DEM生成的河网与实际河网能够精确拟合.实例分析表明,该方法效果明显.     

New friends in new places: Network formation during the migration process among Poles in the UK

This paper contributes to on-going work that seeks to understand the dynamic nature of immigrant social network formation. We explore three propositions, derived from the literature, that might be expected to characterise the ways in which migrant associational ties evolve during and immediately after arrival in their destination country. Evidence is drawn from 42 interviews conducted between January and December 2008 with predominantly Polish migrants to the UK (28) as well as domestic service providers (14). In agreement with the existing literature on immigrant social network formation we find that weak associational ties between migrants are locally dense and rapidly formed. More surprisingly, we also find that the Poles in our sample from lower socio-economic groups tended to rely heavily upon weak associational ties while higher socio-economic group Poles tended to rely on associations made through their employing institutions. This illustrates the importance of socio-economic status in framing co-ethnic migrant network formation. This is significant because we also find that weak associational ties are not unambiguously beneficial to lower socio-economic group migrants who tend to (have to be) more compromising about, and therefore more compromised by, the social ‘friendships’ that result.