中国装备制造业产学研合作创新网络初探

产学研合作是区域创新的主要途径和重要来源.以中国装备制造产业为例,基于中国知识产权局1985-2012年间的合作发明专利数据,借助SPSS,UCINET,ArcGIS等定量分析工具,对中国装备制造产业合作网络的创新主体结构,空间结构及其演变,创新合作的空间尺度的影响因素进行了分析.研究发现,民营企业,高校在中国装备制造产业创新网络中的地位不断上升,数量不断增加,且已经成为重要的创新源泉;市域空间合作成为发达地区城市产学研创新合作最重要的空间单元,国家空间是欠发达地区城市产学研创新合作的主要空间载体;理工科高校等科技资源的空间集聚态势是导致创新网络层级特征的主要因子,科技资源富集的行政中心如直辖市,省会城市等发达城市成为最重要的资源集聚地,创新源泉和创新合作对象.     

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。     

城市道路网络特征对交通事故作用机理的研究进展

机动交通快速发展背景下城市道路交通安全问题日益严峻,道路网络作为影响区域交通流与居民交通出行行为的关键因素,对区域交通事故的数量、类型和空间分布具有重要影响。论文从道路布局形式与几何拓扑特征2个方面归纳总结城市道路网络特征对交通事故的作用机理,并通过梳理道路网络与交通出行行为的关系分析其对交通事故的潜在影响。研究指出,格网型、平行曲线型、回路尽端型等道路网络布局形式以及中心性、连通性等道路网络拓扑指标,对交通事故的类型、频率和严重程度的影响存在差异性和空间尺度效应;合理的交叉口密度、路网密度、街区密度等道路网络几何特征对于提升交通安全水平具有重要作用。未来研究需要进一步运用复杂网络理论刻画道路网络的结构、功能等特征,在控制居住自选择效应的基础上,从交通出行行为视角深入分析复杂道路网络对交通事故的作用机制与非线性影响,同时考虑道路网络特征与建成环境的潜在交互作用。     

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.     

Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey

This paper proposes the application of a conjunction model (neuro‐wavelet) for forecasting monthly lake levels. The neuro‐wavelet (NW) conjunction model is improved combining two methods, discrete wavelet transform and artificial neural networks. The application of the methodology is presented for the Lake Van, which is the biggest lake in Turkey, and Lake Egirdir. The accuracy of the NW model is investigated for 1‐ and 6‐month‐ahead lake level forecasting. The root mean square errors, mean absolute relative errors and determination coefficient statistics are used for evaluating the accuracy of NW models. The results of the proposed models are compared with those of the neural networks. In the 1‐month‐ahead lake level forecasting, the NW conjunction model reduced the root mean square errors and mean absolute relative errors by 87–34% and 86–31% for the Van and Egirdir lakes, respectively. In the 6‐month‐ahead lake level forecasting, the NW conjunction model reduced the root mean square errors and mean absolute relative errors by 34–48% and 30‐46% for the Van and Egirdir lakes, respectively. The comparison results indicate that the suggested model could significantly increase the short‐ and long‐term forecast accuracy. Copyright © 2009 John Wiley & Sons, Ltd.     

基于残差神经网络改进的密集人群计数方法

为避免密集人群踩踏事件发生,从监控图像中准确获取密集人群人数信息非常重要。针对密集人群计数难度大、人群目标小、场景尺度变化大等特点,本文提出一种新型神经网络结构VGG-ResNeXt。本网络使用VGG-16的前10层作粗粒度特征提取器,使用改进的残差神经网络作为细粒度特征提取器。利用改进的残差神经网络“多通道,共激活”的特点,使得单列式人群计数神经网络获得了多列式人群计数网络的优点（即从小目标、多尺度的密集人群图像中提取更多人群特征）,同时避免了多列式人群计数网络训练难度大、结构冗余等缺点。实验结果表明本模型在UCF-CC-50数据集、ShangHaiTech B数据集和UCF-QNRF数据集中取得了最高精度,MAE指标分别优于其他同期模型7.5%、18.8%和2.4%,证明了本模型的在计数精度方面的有效性。本研究成果可以有效帮助城市管理,有效缓解公安疏导压力,保障人民生命财产安全。     

全球生产网络与本土创新网络的战略耦合动态——以中国生物医药产业为例

构建显示性比较优势指数,分析"走出去"全球化下中国医药产业的国际竞争力;识别企业战略连接类型,分析"引进来"全球化下生物医药全球生产网络(GPN)与本土创新网络(IIN)间的战略耦合过程;运用超效率SBM模型,测度GPN-IIN战略耦合目标实现的达成效率,并从静、动态视角探究其时空变化的原因。发现:(1)中国医药产业通过转口、代加工进入北美市场的难度加大,东盟、金砖国家成为其重要的出口对象,与欧盟加强贸易有助于其形成高标准的自由贸易协定网络。(2) 2000—2006年,多数外商直接投资(FDI)企业形成"两头在外"的产业布局,在GPN中处于被俘虏地位,大多内资企业学习能力不足,处于IIN边缘。2007—2013年,部分FDI企业由GPN价值链末端向高端转移,部分内资企业由IIN边缘向核心转移,但转移过程中具有强松脚性。2014—2019年,GPN-IIN战略耦合克服上一阶段嵌入不足、松脚性等问题,两者间的关系通道日益增多和稳固。(3) GPN-IIN耦合绩效经历了"大分散、小集聚→简单结构孕育→局部结构构建→级联秩序分异"的演变,在此过程中技术进步对其促进效果最强,规模效率次之,纯技...     

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.     

基于MATLAB的主成分RBF神经网络降水预报模型

以前期500 hPa高度场、海温场为预报因子,采用径向基函数(RBF)神经网络与主成分分析相结合的方法,建立了广西中部5月平均降水预报模型。在5年独立样本的预测检验中,预测的平均相对误差、均方误差及平均绝对误差分别为18.12%、50.52和34.23。对比分析RBF神经网络与BP(Back Propagation)神经网络的预测结果,表明RBF神经网络预测结果更准确、精度更高。     

Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment

Estimation of runoff is a prerequisite for many applications involving conservation and management of water resources. This study is undertaken in the Upper Damodar Valley Catchment (UDVC) having a drainage area of 17513.08 km² for prediction of monthly runoff. Thirty one microwatersheds and 15 sub-watersheds were selected from a total of 716 microwatersheds in the catchment area for this study. The feasibility of using different soil attributes (particle size distribution, organic matter content and apparent density), topographic attributes (primary, secondary and compound), geomorphologic attributes (basin, relief and network indices) and vegetation attribute as Normalized Difference Vegetation Index (NDVI), on prediction of monthly runoff were explored in this study. Principal Component Analysis (PCA) was applied to minimize the data redundancy of the input variables. Ten significant input variables namely; watershed length (km), elongation ratio, bifurcation ratio, area ratio, coarse sand (%), fine sand (%), elevation (m), slope (°), profile curvature (rad/m) and NDVI were selected. The selected input variables were added in hierarchy with monthly rainfall (mm) as inputs for prediction of monthly runoff (mm) using Bootstrap based artificial neural networks (BANN). The performance of the models was tested using Spearman’s correlation coefficient (r), coefficient of efficiency (COE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Best performance was observed for model with monthly rainfall, slope, coarse sand, bifurcation ratio and Normalized Difference Vegetation Index (NDVI) as inputs (r = 0.925 and COE = 0.839). Increase in number of input variables did not necessarily yield better performances of the BANN models. Selection of relevant inputs and their combinations were found to be key elements in determining the performance of BANN models. Annual runoff map was generated for all the microwatersheds utilizing the weights of the best performing BANN model. This study reveals that the specific combinations of soil, topography, geomorphology and vegetation inputs can be utilized for better prediction of monthly runoff.