概率布尔网络的动态行为研究

概率布尔网络是布尔网络的拓展。布尔网络和概率布尔网络已经广泛应用于生物系统模拟中。运用马尔科夫链模拟概率布尔网络的动态行为及采用消除趋势波动分析（DFA）方法,分析概率布尔网络的长程相关性特性发现,概率布尔网络的动态行为具有长程相关性,大部分概率布尔网络能呈现1/f波动,且产生1/f波动的噪声值和范围比布尔网络的大。     

知识网络组织之间相互信任的影响因素及建立机制研究

采用前因变量和后果变量的模式,对知识网络中相互信任的影响因素进行了分类概括,以满足博弈论推断出的相互信任建立前提条件为出发点,从影响因素着手,推断出知识网络中组织之间相互信任的四类建立机制,并构建出对应的模型图,最后通过案例分析对文中观点加以检验.     

802.11s无线Mesh网络中协作多样性感知的路由度量和协作路由协议

提出了802.11s无线Mesh网络中协作多样性感知的路由度量,并提出了适应无线Mesh网络特点的协作式路由协议(CDARP)与协作媒体访问控制协议(CMAC).通过构建协作虚拟节点、计算协作链路度量,CDARP-CMAC能够选择最合理的路径进行协作数据传输.实验结果表明,与最优路径的协作机制相比,CDARP-CMAC协议提高了网络有效吞吐量5％～13％;同时降低了数据包丢包率5％～10％.     

Creating value in ecosystems: Crossing the chasm between knowledge and business ecosystems

Policy makers take initiatives to stimulate knowledge ecosystems in technology hotspots. It is implicitly assumed that these ecosystems will lead to value networks through which the participating companies can realize a competitive advantage. Value networks refer to business ecosystems where the value proposition is offered by a group of companies which are mutually complementary. The strategy literature suggests that business ecosystems lead to competitive advantages for each of the partners in the ecosystem. Based on a unique hand-collected database of 138 innovative start-ups in the region of Flanders, we analyze the knowledge and business ecosystem and the financial support network. We find that the knowledge ecosystem is well structured and concentrated around a number of central actors while the business ecosystem is almost non-existent at the local level. Further, we find that the financial support network is almost 100% publicly backed and fails to bridge the knowledge and business ecosystem. The implications for policy makers who tend to focus on the development of local ecosystems are discussed.     

基于核心能力的企业战略网络--网络经济时代的企业战略管理模式

本文认为战略网络是网络经济时代新的战略思想，它与传统的战略思想有本质的区别，我们必须将战略网络思想整合于战略管理之中。作者将企业能力理论与战略网络思想想结合定性推导出一个完整的企业战略管理模型，提出了网络经济时代的企业战略管理模式——基于核心能力的企业战略网络管理模式及其理论框架，以指导企业有效地实施网络战略管理。     

Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms

In this paper, we introduce a new architecture of genetic algorithms (GA)-based self-organizing polynomial neural networks (SOPNN) and discuss a comprehensive design methodology. Let us recall that the design of the “conventional” PNNs uses an extended group method of data handling (GMDH) and exploits polynomials (such as linear, quadratic, and modified quadratic functions) as well as considers a fixed number of input nodes (as being selected in advance by a network designer) at polynomial neurons (or nodes) located in each layer. The proposed GA-based SOPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional PNNs. The design procedure applied in the construction of each layer of a PNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the network. To evaluate the performance of the GA-based SOPNN, the model is experimented with using chaotic time series data. A comparative analysis reveals that the proposed GA-based SOPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.     

基于情景化偏好的移动社交网络信息服务自适应建模研究

构建了移动社交网络基于情景化用户偏好的自适应适配信息服务系统模型,提出了移动社交网络的自适应服务发现方案。解决移动社交网络信息服务的发现与推送的延迟问题,实现高效的移动社交网络信息服务推送,对移动社交网络信息服务资源、知识资源进行科学管理。     

A network under stress: using embeddedness to understand uncertainty management and resilience in campus emergencies

Persistent school shootings have generated ongoing pressure to assess and enhance crisis communication effectiveness via strategies such as resilience-building and post-crisis restoration. A network perspective offers a robust, multilevel approach for examining complex information flows among community, campus, and individual actors affected by crises. Drawing on relational and structural embeddedness and Uncertainty Management Theory, we offer nine propositions that help explain structural and relational changes in a network over time. Although theoretically driven, these propositions are primarily functional, offering practical implications for safety officials and administrators trying to understand, prepare for, and respond to crises. Thus, we suggest measures and recommendations to improve information flow, uncertainty management, and resilience before, during, and after a crisis.     

基于企业创新网络的技术路径创造

技术创新是路径依赖的也是路径创造的。本文讨论了企业技术路径依赖创新陷阱的表现形式和基于企业创新网络的技术路径创造。认为在企业创新网络中,由于网络中的企业交互学习、新成员的加入和技术创新模式的变化等,技术的路径创造是可能的。所以,企业创新网络可以形成技术路径创造。     

盲信号分离的细胞神经网络方法

本文用双层的细胞神经网络(CNN)进行盲信号分离.提出CNN的拓扑结构和内部参数.CNN的第一层功能是一只自适应渐近收敛于平衡点的滤波器.一个平稳随机的模型,是第一层收敛的细胞,用于探测的必要条件,也是对标准的平衡状态解的主要条件.第二层的功能是一个信号分离器.仿真显示,CNN盲信号分离器具有良好的鲁棒性,而且工作平稳.