梅州市名城文化景观的保护与开发研究

从名城文化内涵出发，着眼于作为客家人文中心和名城文化景观的集中的梅州市区，全面分析了其中丰富多样的文化景观资源的特征及其文化内蕴。揭示了梅州名城文化景观保护与开发过程中的成绩与存在问题。并从文化地理学的视觉，结合旅游开发和城市规划，论证了科学保护与开发梅州名城文化景观的下述对策与途径的必要性与可行性：提升名城办的职能并与相关部门共营名城文化氛围；强化名城轴线与标志性文化景观；抢救与保护“老字号”等一批文化景观景区；适度利用，以积极的开发促其效的保护。     

边坡立体绿化工程的稳定性

山地城市有许多大面积锚杆喷浆硬化封闭的边坡与城市景观极不协调，为改变这些生硬，单调的自然坡体现状，政府要求对其进行立体绿化。本文以修建黄花园大桥石板坡立交工程形成的石板坡为例，通过多种方法研究了绿化荷载作用对边坡稳定性的影响，结论表明绿化荷载对边坡的稳定性影响较小，只要施工顺序得当可以保证安全。     

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.     

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.     

郴州市农业结构战略性调整的思考

文章以郴州市为例，首先对市域农业结构战略调整的必要性进行了分析，然后对结构调整的基本原则作了较为深刻地论述，最后系统地阐述了产业结构要实现优化升级，产品结构要突出优质品牌，区域结构应以专业化特色化为目标等市域农业结构调整的重要内容。     

山东省省际边界地区发展研究

省际边界地区有其自身的发展特点，对于区域的发展起着特殊的作用。文章分析了山东省省际边界地区的特点，并根据康维斯（P.D.Converse)断裂点理论，对山东省及周边中心城市的引力范围进行了计算，比较分析了目前山东省省界城市和省内城市吸引范围及竞争力差异，提出了山东省发展省际边界地区的相应对策。     

数字城市建设研究

数字城市建设是一项复杂的系统工程，需要做好规划，解决管理、制度、标准以及必须的关键技术等问题。文章在分析了数字城市建设目标和内容的基础上，探讨了数字城市建设的基本原则，提出了数字城市建设的具体实施方案。