砂土地震液化预测的人工神经网络模型

在简要分析BP算法的基础上,应用BP网络的理论与方法,选取烈度、震中距、平均粒径、不均匀系数、地下水埋深、砂层埋深、标贯击数、剪应力比等8个实测指标,建立了砂土液化预测的神经网络模型。通过实例计算与模型评价、验证了该模型的科学性、高效性并较规范法、Seed简化法等传统方法具有更高的预测精度,说明人工神经网络是解决非线性问题的有效方法之一。     

“This is a Montreal Issue”: Negotiating responsibility in global production and investment networks

While geographers have increasingly focused on how global commodity and production networks create new ‘geographies of responsibility’ there has been little empirical work considering how responsibility is worked into management systems and social activism in such networks. Drawing on literature from global production networks, geographies of responsibility and other literatures, this paper explores the dynamic and contested ways in which concepts of responsibility can play a role in network regulation. Both foreign direct investment and commodity networks (here referred to as ‘global production and investment networks’) are subject to complex negotiations and compromises involving corporate social responsibility and sustainability initiatives as well as shareholder activist, human rights, labor, and environmental activism. This is illustrated by reference to conflicts in Canada over Alcan, Inc.’s investments from 1993 to 2007 in the Utkal Alumina Project in Orissa, India. The project involved significant socio-environmental conflict. In Canada, Alcan’s investment was met by civil society campaigns that tested the company’s commitments to sustainability and corporate social responsibility. The case study suggests revising theories of geographies of responsibility. While foreign direct investment can create new relationships between distant others, these are fluid and contingent and not necessarily desirable. Rather than see networks as a source of responsibility we should work to ensure that the relationships that networks foster be structured to ensure our deeper values are respected.     

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.     

Determining the seasonality of groundwater recharge using water isotopes: a case study from the upper North Han River basin,Korea

The stable isotopes of oxygen and hydrogen were used to determine the seasonal contributions of precipitation to groundwater recharge at a forested catchment area in the upper North Han River basin, Korea. A comparison of the stable isotopic signatures of groundwater and precipitation indicates that the precipitations which occurred during both the dry and rainy seasons are the important source of groundwater recharge in this region. A stable isotopic signature shown in the stream waters at the upstream reaches is similar to that of groundwaters, indicating that stream waters are mostly fed by groundwater discharge. Reservoir waters in the downstream flood control dams have lower deuterium excess values or d-values compared with those of the upstream waters, indicating a secondary evaporative enrichment. These results can provide a basis for the effective management of groundwater and stream water resources in the North Han River basin.     

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.