Innovation in the bioeconomy – dynamics of biorefinery innovation networks

The bioeconomy has become a central concept in many strategies for future economic development, emphasising an increasing need for collaboration across industries and sectors for innovation. This paper unpacks aspects of collaboration in the bioeconomy by looking at the development of innovation networks for biorefinery technologies from 2004 to 2014 based on innovation project data from Swedish public funding agencies using a stochastic actor-oriented model for network analysis. The analysis shows that although the network grew significantly during the time period, indicating an increasing interest in biorefinery technology innovation, inter-sectoral collaboration is not favoured over intra-sectoral collaboration. As is known from previous work on social networks trust-building is a key driver for collaboration, as actors tend to form collaborations with previous partners or indirectly connected partners, creating clustered networks.     

采用航迹特征的智能机型识别

针对空战目标识别中机型自动识别比较困难的问题，提出了采用航迹特征的智能目标识别方法。利用卷积神经网络（Convolutional Neural Network，CNN）分层学习特征的能力，训练CNN算法模型自动地从航迹数据中学习有用的特征并分类。利用沿海实地采集的15个类别的飞机航迹数据，经一系列数据预处理后作为智能识别算法的训练和测试数据，在验证实验中描述了算法网络的相关配置,对比了CNN与其他分类器的识别结果。实验结果表明，CNN具有很好的识别性能。     

基于转移概率和网络联系的辽宁省农村居民点适宜性评价

[目的]农村居民点适宜性评价在农村居民点调控、规划和预测中具有基础作用,且农村居民点适宜性评价应顾及农村居民点之间的社会经济活动联系。[方法]以辽宁省为研究区,首先运用逐步Logistic回归模型构建农村居民点转移概率,其次运用网络分析方法确定农村居民点图斑间的网络联系强度,最终通过加权求和方法确定农村居民点转化概率,进行农村居民点空间布局适宜性评价。[结果]辽宁省农村居民点可以划分为高度适宜类、较适宜类、一般适宜类和不适宜类4类,分别占农村居民点总面积的比例为28.18%、31.32%、13.26%和27.24%;从各地貌分区来看,高度适宜类主要集中分布在中部平原地区和东部山地丘陵区中的南方沿海地区,而不适宜类整体分布则较为零散;辽宁省中部平原区位条件好,东部沿海地区区位条件也较好,西部山地丘陵区地区较差。[结论]研究方法和结论有助于全面把握农村居民点社会经济与区位适宜性特征,为农村居民点科学规划提供科学指导。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

绿色物流视角下生活垃圾回收处理研究文献综述

近些年来,我国众多城市都出现了"垃圾围城"的现象,垃圾回收处理也成为了社会广泛关注的热点,但是众多学者对相关理论的研究成果并没有被系统性的总结。文章将在绿色物流视角下,对生活垃圾回收处理的相关研究进行全面梳理,从居民垃圾分类意识、垃圾回收处理服务网络构建、垃圾回收站点及中转站的选址等三个方面进行综述,最后进行述评和研究展望。     

Circular business model implementation: A capability development case study from the manufacturing industry

Circular business models (CBMs) have huge potential to deliver economic, social, and environmental benefits, but CBMs have yet to be implemented widely in industrial settings. One reason is that they are often presented as one-size-fits-all solutions, but this is misplaced because product-specific criteria and company capabilities determine the correct choice and implementation of CBMs. Therefore, the purpose of this paper is to investigate how CBM selection and capability development facilitates the implementation of CBMs. For this purpose, we have adopted a qualitative research approach and undertaken 25 explorative interviews in three large Swedish manufacturing companies. In this paper, a CBM implementation framework consisting of two parts has been developed. The first part addresses the choice of the appropriate CBM based on tactical configurations. The second part provides a capability development path by explicating underlying routines that need to be progressively developed in order to move smoothly to more advanced CBMs.     

Factors influencing consumers' purchase decision-making in O2O business model: Evidence from consumers' overall evaluation

Online-to-offline (O2O) has become a rapidly growing e-commerce model worldwide, but the factors influencing consumers' purchase decision-making have not been examined well. After exploring the working mechanism of the O2O model, we extract eleven indexes of consumers' overall evaluation in the O2O model with Crawlzilla and R techniques. Social network analysis (SNA) is adopted to build social networks reflecting consumers' overall evaluation in the O2O model. Based on the modified social network and the extracted subgraph, this study (N = 768) reveals consumers' overall evaluation behavior patterns in the O2O model by calculating network density, central potential, edge betweenness. The results show that shoppers overall evaluate the eleven indexes, especially perceived product quality, online product price, promotion intensity, business reputation, and product brand, which reflects the significant associations between the indexes. Among all the eleven evaluation indexes, perceived product quality, online product price, and business reputation dominate consumers’ decision-making behavior. When making purchase decisions, consumers not only overall consider online product price, perceived product quality, and business reputation but also balance perceived product quality, business reputation, and promotion intensity. Finally, we make some suggestions on marketing strategy for e-commerce companies.     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

FREE VERSUS SECOND‐BEST ENTRY IN COURNOT OLIGOPOLIES WITH FIRM‐SPECIFIC NETWORKS

This paper endogenizes the number of firms in an industry with positive network effects, complete incompatibility, and firms that compete in quantity. To this end, we compare two possibilities: free entry and second‐best number of firms (the one that maximizes social welfare). We show that with business‐stealing competition, free entry yields, in general, more firms than the socially optimal solution. In addition, we find that by the nature of the industry with firm‐specific networks, total production may be greater or lower under free entry than with a regulator; moreover, some industries attain their maximum social welfare with a monopoly.     

Evolution of FDI flows in the global network: 2003–2012

As one type of international capital flow, FDI maintains its important role in globalization. This article attempts to investigate the evolution of the FDI flows from a network perspective. Based on the bilateral FDI flows data between countries from 2003 to 2012, we construct the global FDI flows network for each year and thus quantify network measures (such as flow volumes and connections); further by analysing the tendency and changes of these network measures during the past 10 years, we delineate the features and dynamics of the FDI flows in the global network. We have the following findings: (a) the flows network changes during and after the crisis, i.e. flow volume fallen down and recovered, and flow connection restructured with more diversity; (b) the global FDI flows network is getting more loosely connected; (c) individual countries vary in different patterns.