Application of Dangerous Probability Function in Risk Estimation of Surface Rupture Earthquake：Taking the Lingwu Fault in Ningxi1 Region as on Example

Based on the fitting on paleoearthquake data of intra-plate regions in the northern part of Chi-na and giving a statistical model of time interdependence,the potential damage earthquakes in a definite future period and characteristics of present shocks along the Lingwu fault have been analyzed by using dangerous probability function and some new data concerned.We have in-ferred that the fault has entered a period that earthquakes will probably occur.There exists a potential danger that a strong earthquake with Ms7.0-7.5 will occur in 10-100a.     

中国耕地有机质含量变化对土地生产力影响的定量研究

在土地持续利用与管理研究中，土地质量变化对土地生产力影响的定量研究是核心内容之一。借助新古典经济学的生产函数法，构建了我国耕地的生产函数模型，定量模拟了我国耕地有机质含量变化的边际产出率性和边际生产力。初步结果表明，由于我国长期存在耕过度利用导致有机质含量及肥力下降问题，当前提高耕地质量的增产潜力十分可观。     

基于神经网络的单元自动机CA及真实和忧化的城市模拟

提出了一种基于神经网络的单元自动机（ＣＡ）。ＣＡ已被越来越多地应用在城市及其它地理现象的模拟中。ＣＡ模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。     

A study on the early-warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.     

Competitive Adjustment and Advancement in Global Commodity Chains II: The Case of the Singapore Garment Industry

This is the second part of a contribution to the debate on the possibilities of leveraging economic globalisation — through incorporation in international production networks and global commodity chains (GCCs) — as a strategy for developing countries to industrialise and advance their position in the world economy. In the first part, we analysed the experience of the East Asian apparel industry and addressed the issues of sustaining positions, upgrading to more rewarding roles, and advancing to less dependent positions within production networks and GCCs. We developed an analytical framework at both the firm and industry levels, and subsequently identified alternative firm- and industry-level strategies and trajectories. The present paper deals with these issues in the context of the Singapore apparel industry. Based on extensive empirical research, we demonstrate that although the East Asian experience of upgrading and repositioning within the GCC is to some extent emulated in the case of the Singapore apparel industry, the outcomes have been less favourable in terms of the depth, extent and strength of these trajectories. The differing outcomes can be explained in terms of different (systemic) conditions in the Singapore business environment, including the agency of local players, the geography of sourcing networks, and the role of the state and prevailing business attitudes. Our conclusions merit continued attention in both research and policy circles on the development of capabilities at the firm level, and the role of local business and institutional environments in local industry development processes under globalisation.     

Using Spatial Statistics to Examine the Pattern of Urban Land Development in Halifax–Dartmouth

This article examines the locational patterns of population and employment and the resultant changes in urban form in the Halifax–Dartmouth region between 1970 and 1996. The article employs the univariate and bivariate K function to measure spatial dependence or clustering within and between the classes of residential and commercial land parcels. The results of the K function estimates suggest that residential land parcels cluster together, commercial land parcels cluster together, and residential and commercial land parcels have become more clustered over time. Evidence of clustering provides insight into the changing urban form of the region and possible multinucleation.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

城市功能区划的数量化方法研究及其应用--以深圳市南山区物流中心选址为例

文章总结了以往国内学者对城市功能区域的数量化方法研究，认为在层次分析法等方法中对指标权重的确定存在主观因素过多等不足，严重影响了研究的科学性和准确性，文章为了克服上述不足，将多属性效用理论的代理价值法与博弈论的方法应用于城市功能区的布局与研究，并以深圳市南山区物流中心的选址为例，阐述了代理价值法与博弈论在城市功能区域中的具体实践操作。     

企业网络发育程度与区域创新能力研究

本文从分析企业网络的发育和完善程度出发，探讨影响网络创新能力的因素。文章指出，网络发育程度与创新能力(创新数量、创新发生的频率)是正相关的。从宏观上讲网络越密集、各种正式和非正式联系越有效、网络联系越稳定、网络自我更新能力越强、网络的开放性越强、网络越能根植于当地的良好的区域环境，越容易激发创新；从微观上讲，网络中行为主体的学习能力越强，其创新发生频率越高，创新能力越强。