社会网络视角下的创客资本研究

揭示创客资本的内涵与特征，有助于深层次挖掘创客与经济社会发展之间的关系。运用关键词共现、社会网络分析和扎根理论方法，对148篇文献、21位由创客与众创空间负责人等构成研究对象的访谈资料和网络文本资料展开分析。社会网络分析发现互联网、数字经济、分享经济等是创客发展的重要驱动力，创客可借助众创空间社会网络跳出时空局限，运用网络资源参与大众创新活动，并与社会网络形成价值共创关系，推动协同创新和平台生态系统构建；扎根理论分析结果表明，创意制造、开放协同、网络共生、边际非稀缺是创客资本的4个核心特征，并印证了社会网络分析结果。基于上述分析结果，进一步基于社会网络理论界定了创客资本的内涵，提出未来需要深度挖掘创客资本与网络场景之间的交互机制及其结果效应。     

The role of network embeddedness across multiple social networks: Evidence from mobile social network games

This article examines peer influences from network relationships within a social network game (i.e., embeddedness) and across such games (i.e., multiplexity). Drawing on social influence theory, we develop a bivariate Poisson model of users’ repeated visits and latent attrition that accommodates peer interaction after controlling for homophily. We estimate the model using data from two social network games with considerable overlap among network members. We find that friends who are only multiplex across games exert greater peer influence on users’ game visits than members who are embedded within a single game. We also determined that ignoring network multiplexity across games may lead firms to mistarget users due to biased peer influences of embedded friends. This result provides an unresearched explanation—strength of peer influence—for the mixed findings in previous literature on network embeddedness. We utilized our results to conduct several scenario analyses to demonstrate how firms can effectively manage users’ engagement and target users in multiple social network games.     

网络强度、组织冗余与创新模式——对200家样本企业问卷调研数据的实证检验

通过社会网络带来资源冗余进而对创新选择产生影响，这一机制已成为当今企业可持续发展的重要路径。运用200份企业样本调研数据，对网络强度、不同类型组织冗余及两种创新模式进行实证检验，结果发现，网络强度显著正向直接影响利用式创新和探索式创新;网络强度还通过正向影响未吸收冗余对两种创新产生正向间接影响作用;未吸收冗余与创新模式间具有显著正向影响关系,而吸收冗余在三者影响路径关系中检验结果不显著，可能是受企业自身因素或条件影响所致。最后，根据研究结论提出3点重要管理启示。     

基于关键节点与关键路径的专利集群网络演进研究

专利作为技术和知识的重要载体，是研究技术演进和产业发展的重要信息源。目前缺乏针对专利集群网络中关键节点与关键路径中节点的比较分析和技术知识挖掘。构建基于关键节点和关键路径的专利集群网络演进模型，从两个层面综合分析技术演进特征。检索德温特数据库(Derwent Innovation Index)得到碳化硅肖特基势垒二极管（SiC-SBD）相关专利作为实证数据，时间跨度为1986-2017年。结果表明，SiC-SBD专利集群网络经历了萌芽期、成长期、成熟期和衰退期，其中，萌芽期专利主要涉及基础技术，成长期为器件结构，以完善器件结构、改进二极管性能为主要研究方向。近年来SiC-SBD专利年增长率下降，技术发展速度放缓，专利价值更多体现在其商业价值上。SiC-SBD专利集群网络关键节点与关键路径中节点重合度较高，核心专利识别可通过多视角分析得到。通过关键节点与关键路径分析专利集群网络演进过程，有助于更全面呈现技术演进过程，为决策者识别核心专利、预测技术发展提供参考。     

区域知识产权保护发展的空间格局及结构体系研究

以我国各省知识产权保护制度为研究对象，创新性地将探索性空间数据分析方法与社会网络分析方法相结合，基于地理邻近视角，验证了区域知识产权保护的空间相关性、空间集聚特征和空间溢出效应。同时，突破地理近邻效应的局限，解析区域知识产权保护的空间关联特征。结果表明：我国各省知识产权保护具有全局自相关性，相似地区间存在空间集聚效应，不同发展程度地区的空间关联性质不同；网络化后的区域知识产权保护各节点间联系紧密、网络结构稳定，并且可以确定核心行动者和边缘行动者角色；长三角、珠三角、环渤海等较发达地区与其它地区之间存在较多溢出关系。     

Balancing risk with virtual private networking during a pandemic

When the pandemic struck and teaching went online worldwide, universities had to make pressing decisions that balanced cybersecurity against other factors, including health and safety, usability, and cost. One such challenge Indiana University (IU) faced was how to accommodate the secure telecommunications needs of 130,000 faculty, staff, and students who would now be teaching, learning, doing research, and working from home. Some universities reflexively promoted virtual private network (VPN) use for all activities. Such an approach would have been unsustainable at IU, however, owing both to the licenses and resources needed for the sheer number of users and to the high-throughput applications on which they rely. Perhaps even worse, it would have increased the chances that the VPN would be unavailable during a critical incident or other situation in which secure communications must be guaranteed. Instead, IU launched an awareness campaign demonstrating exactly when VPN use is and isn’t needed. In addition, network staff employed a VPN feature called split tunneling to reduce the load. This article discusses the advantages and disadvantages of this approach and how IU made the decision to balance both sides of the risk equation to ensure the continued advancement of its mission throughout the pandemic.     

全国煤炭交易中心运营模式研究

全国煤炭交易中心的设立对规范我国煤炭交易市场规则、实施能源宏观调控、提升我国煤炭国际定价话语权具有重要意义。在分析全国煤炭交易中心功能定位和业务的基础上,设计中长期合同邀约、现货挂牌、现货竞价、现货招投标4种交易模式及业务流程,提出依托国家重大战略争取政策支持、加强各方沟通完善综合物流体系、建立银企合作机制与信用体系、完善煤炭交易中心协调机制、增强信息服务与风险防控能力等对策建议。研究成果对优化煤炭供给结构、规范煤炭交易市场和保障国家煤炭能源安全提供了支撑。     

基于卷积神经网络的列车检测算法研究

列车检测作为列车自动驾驶的核心技术,可以有效地降低列车追尾等事故造成的人身危险和财产损失。为实现精准的列车检测,选用改进的卷积神经网络(PVANET)对输入图像进行特征提取,在此基础上,采用候选区域网络,从生成的特征图里滑动搜索,判断出图像中可能为列车的区域位置,并进一步采用快速区域卷积神经网络对每个候选区域进行分类,计算出其所属类别的置信度,同时精确定位列车。经验证,该方法适应范围广、鲁棒性高,可以有效地检测不同环境光强及不同朝向的列车,保障列车安全,为列车自动驾驶及辅助驾驶提供安全保障。     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.