Platform selection by software developers: Theory and evidence

This paper analyzes platform selection (affiliation) by game developers in the Japanese home video game industry. We develop an elementary model of affiliation by software developers and propose the hypothesis that wider availability of game titles for a platform positively inclines game developers to affiliate with that platform when releasing new game titles. Then, using data for Japan, we estimate a multinomial logit model that includes both the attributes of game developers and the characteristics of platforms, and find evidence supporting this hypothesis. Overall, the findings suggest that game developers expect the indirect network effect to apply, and the prospect of future growth matters to them when deciding with which platform to affiliate.     

别让IT运维成为中小企业迈向国际化的“包袱”

随着中国改革开放的不断深入,中国加入WTO,国内中小企业如雨后春笋般的发展起来,甚至已经将公司开到了国外,为新中国的经济腾飞做出了不可磨灭的贡献,期间IT系统在规范管理、提升工作效率方面起到了应用的作用。但随着企业规模的不断扩大,分公司越来越多,地域间隔越来越远,IT系统运维正变得空前困难。鉴于此,本文将着重介绍如何利用新技术降低IT运维成本,提升运维效率。     

ROFIBUS现场总线在彩涂板生产线中的研究与实践

现今,我国的彩色涂层钢板的生产已经有了一定的发展,但是其生产水平还是比较低下,不能满足现代社会产业发展理念的需求,不能满足低碳发展、可持续发展的价值理念,PROFIBUS现场总线技术在生产中极大地满足了企业对彩涂板生产的需求。     

高职院校教学质量监督和保障体系的研究与实践

本论文通过对南京化工职业技术学院教学质量监控和保障体系的研究、构建与实施,并通过网络信息技术使体系的运行更顺利、便捷和高效,增强了教学质量监控和保障体系的针对性、可操作性,做到保障有方向、投入有重点、建设有力度、完善有策略,确保教学质量保障建设有理、有据、有序进行。     

基于融资行为的中小企业社会信任网络构建

中小企业社会信任网络的多层次性与其融资的可获得性有着密切的关系。通过社会信任网络解决中小企业内生资源不足问题，利用网络内含的默契、惯例、信任以及惩罚机制，构建以融资效率为核心的中小企业社会信任网络，有助于改善中小企业融资环境，消除中小企业融资障碍，提高中小企业融资效率。     

Revealing travel patterns and city structure with taxi trip data

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.     

高校网络管理与防护工作的优化设计与研究

随着湖北省教育信息化建设试点改革工作的深化,各院校信息化程度不断提升,也赋予了校园网络平台新的要求。这不仅在考验着现有的网络架构与设备,也对软件控制与资源共享技术提出了新的要求,其建设核心基本汇聚于庞大数量级的设备整合与终端控制。基于校园网络一体化硬件基础,通过开发可视化控管软件可基本解决这一难题,从而实现校园网络集中控制与防护。     

中国商业银行整体效率研究* ——基于具有中间投入和中间产出的综合网络DEA模型

本文基于对银行业务结构和特征的分析，将银行看做具有中间投入和中间产出的串联两阶段网络生产结构。第一阶段为负债业务和中间业务，第二阶段为资产业务。基于这一网络结构，本文首先建立具有中间投入和中间产出的综合网络DEA模型，并证明在此模型下整体网络DEA有效等价于每个子阶段DEA有效，即此模型是打开黑箱的网络DEA模型，并定义网络决策单元的投影。本文以中国16家上市商业银行为样本，测算了2006-2010年各商业银行的整体效率、子阶段效率、关联指数和每家银行的投影。分析结果显示，大型国有商业银行在负债业务和中间业务阶段具有明显的竞争优势，股份制商业银行在资产业务阶段具有效率优势，城市商业银行的两阶段业务关联性最差。本文计算的投影，给出了银行提高整体效率的改进方向。     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.