Monitoring qualitative spatiotemporal change for geosensor networks

Recent technological advances in geosensor networks demand new models of distributed computation with dynamic spatial information. This paper presents a computational model of spatial change in dynamic regions (such as may be derived from discretizations of continuous fields) founded on embeddings of graphs in orientable surfaces. Continuous change, connectedness and regularity of dynamic regions are defined and local transition rules are used to constrain region evolution and enable more efficient inference of a region's state. The model provides a framework for the detection of global high‐level events based on local low‐level ‘snapshot’ spatiotemporal data. The approach has particular relevance to environmental monitoring with geosensor networks, where technological constraints make the detection of global behaviour from local conditions highly advantageous.     

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.     

潼峪金矿区成矿断裂构造特征

潼峪金矿区发育过冲剪滑型、平移剪张型和平移剪滑型三种不同性质、不同方向成矿断裂,分布在金罗斑背形的不同部位,其形成有先后差异,但变形环境大体相同,具脆-韧性变形特征。空间上一般不超越褶皱主体。三类断裂中,前两者具石英脉型金矿化,后者形成构造蚀变岩型及小规模石英脉型金矿化。矿化差异与断裂产出的褶皱部位及其相应的应力应变条件的不同有关。     

经济地理学视角下创新网络与区域增长研究述评

创新是引领经济发展的第一动力,创新与区域增长之间的关系成为经济地理学者关注的重点领域。文章通过梳理近年来有关创新网络测度、创新网络与区域增长关系、创新网络作用于区域增长方式等方面的文献发现：网络资本可以弥补社会资本在解释企业创新结网经济价值方面的不足,区域增长呈现出网络化特征已成为学界共识,知识流与邻近性能较好地解释创新网络与区域增长的关系机理。然而,当前研究对社会资本如何促进区域增长,网络资本与区域增长关系模型如何构建,不同类型的邻近性与知识对区域增长的影响有何差异等问题尚不明确,建议重视网络资本对区域增长的作用并实证检验二者的关联,同时还应重视创新网络中企业家精神、创新个体心理行为特征等因素对区域增长的影响。     

The Coral Triangle Initiative and regional exchanges: Strengthening capacity through a regional learning network

Natural resource management and conservation programs that promote building capacity and social learning among participants often lead to the formation of learning networks: a type of social network where learning is both a goal and potential outcome of the network. Through forming relationships and sharing information, participants in a learning network build social capital that can help a network achieve social and environmental goals. In this study, we explored social capital in a learning network that emerged through a large-scale marine governance effort, the Coral Triangle Initiative on Coral Reefs, Fisheries, and Food Security. Through a mixture of social network analysis and key informant interviews, we examined the major patterns of information exchange among individuals who had participated in regional learning exchanges; evaluated whether the network's structure resulted in information sharing; and considered implications for strengthening network sustainability, capacity building, and learning. We found that the Regional Exchange network fostered information sharing among participants across national and organizational boundaries. While the network had individuals who were more central to information sharing, the network structure was generally decentralized, indicating potential resilience to changes in leadership and membership. Participants stressed the importance of the knowledge and connections they had acquired through the learning network; however, they expressed doubts regarding its sustainability and stressed the need for a strong coordinating entity. Our findings suggest that conservation learning networks have the ability to bridge cultural divides and promote social learning; however, a strong network coordinator and continuing efforts to support information sharing and learning are crucial to the network's strength and sustainability. The tangible learning and capacity development outcomes cultivated through Regional Exchange network underscore the value of and need to invest in conservation networks that support peer-to-peer learning.     

高密度电法在老山隧道勘察中的应用

应用高密度电阻率法勘察宁淮高速公路南京老山隧道工程,查明了隧道YK5 280~ZK5 540段的地层分布和构造特征,并推断出鹰嘴山断层的位置和产状,结合勘察资料证明该断层为高角度逆断层。预测出隧道和断层破碎带相交处为浅部裂隙岩溶含水带中的优良导水带,该处涌水的可能性很大,其推断结果在施工中得到证实。说明高密度电法是进行隧道勘察和构造裂隙岩溶富水带探测的一种可靠手段。     

基于深度学习的滑坡灾害易发性分析

滑坡灾害成因机理复杂、影响因素众多,深度学习作为当前人工智能领域的热点,能够更好地模拟滑坡灾害的形成并准确预测潜在的斜坡。为了挖掘深度学习在滑坡易发性的应用潜能,本文构建了一维、二维和三维的滑坡数据表达形式,并提出3种基于卷积神经网络模型（Convolutional Neural Networks, CNN）的滑坡易发性分析处理框架：基于CNN分类器、基于CNN与逻辑回归的融合和基于CNN集成,最后以江西省铅山县为研究对象进行验证,结果表明：所有基于CNN的易发性模型都能够获得准确且可靠的滑坡易发性分析结果。其中,基于二维数据的CNN模型在所有单分类器中预测精度最高,为78.95%。此外,二维CNN特征提取能够显著提升逻辑回归的预测精度,其准确率提升7.9%。最后,异质集成策略能够大幅度提升基于CNN分类器的滑坡预测精度,其准确率提升4.35%~8.78%。     

基于贝叶斯网络的水源涵养服务空间格局优化

以渭河流域关中—天水经济区段（简称“渭河流域关天段”）为研究区,基于贝叶斯网络和水量平衡原理建立了水源涵养服务网络模型;将CA-Marcov模型与贝叶斯网络模型相结合,预测了2050年不同土地利用情景及其水源涵养服务分布概率;提出了关键变量关键状态子集方法,对研究区水源涵养服务空间格局进行优化。结果表明：① 保护情景下,林地面积增长了18.12%,其主要来源为耕地;草地和城市面积增长缓慢,分别增加了0.73%和0.38%;水体和未利用地分别减少了5.08%和0.92%,该情景下的水源涵养量值偏高的概率在3种情景中最大,保护情景的设计对未来的土地利用政策制定具有一定参考价值。② 水源涵养服务的关键影响因子是降水、蒸散发和土地利用,水源涵养量最高状态对应的关键变量关键状态子集是：﹛降水= 1,蒸散发= 2,土地利用= 2﹜,该子集主要分布在年平均降雨量和蒸散发量较大,植被覆盖率高的地区。③ 研究区适宜优化水源涵养的区域主要分布在天水市麦积区南部、宝鸡市陇县西南部和渭滨区南部、咸阳市旬邑县东北部和永寿县西北部,以及铜川市耀州区西部。结合贝叶斯网络模型研究水源涵养服务的优化区域,不仅有助于提升对生态系统水源涵养服务过程的直观认识,而且增加了情景设计和格局优化的合理性。在此基础上提出的关键状态关键因子方法,对研究区水源涵养生态环境建设和政策制定都具有重要的意义。     

Configurations of community in flood risk management

ABSTRACT

Despite a notable increase in the literature on community resilience, the notion of ‘community’ remains underproblematised. This is evident within flood risk management (FRM) literature, in which the understanding and roles of communities may be acknowledged but seldom discussed in any detail. The purpose of the article is to demonstrate how community networks are configured by different actors, whose roles and responsibilities span spatial scales within the context of FRM. Accordingly, the authors analyse findings from semi-structured interviews, policy documents, and household surveys from two flood prone areas in Finnish Lapland. The analysis reveals that the ways in which authorities, civil society, and informal actors take on multiple roles are intertwined and form different types of networks. By implication, the configuration of community is fuzzy, elusive and situated, and not confined to a fixed spatiality. The authors discuss the implications of the complex nature of community for FRM specifically, and for community resilience more broadly. They conclude that an analysis of different actors across scales contributes to an understanding of the configuration of community, including community resilience, and how the meaning of community takes shape according to the differing aims of FRM in combination with differing geographical settings.     

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。