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.     

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.     

Central bank disclosure as a macroprudential tool for financial stability

The Basel Capital Accord (pillar 3) states that disclosure of information (transparency) is essential to financial stability. This study analyzes, through inflation reports, the disclosure of information from the Central Bank of Brazil concerning the credit market. We consider credit risk and capital buffers as measures of financial stability in this analysis. Furthermore, in order to measure the perception of the monetary authority on the credit market, we built two indices based on the central bank’s communication on credit development. We performed a panel data analysis based on a sample of 125 banks for the period from June 1999 to September 2014 (7000 observations). The findings suggest that central bank communication regarding expectations concerning the credit market contributes to financial stability. Therefore, this kind of communication of central banks (about credit development) may constitute an important macroprudential tool to improve financial stability.     

Hotel school alumni’s intentions to use communication channels – a cross-generational comparison study

ABSTRACT

Alumni communication is vital in sustaining the relationship between alumni and their alma maters. This research investigated four cohorts of alumni, and their intentions to use a range of traditional and digital communication channels, including social media. An online questionnaire was sent to 8060 alumni and resulted in 595 usable responses, yielding a 7% response rate. The research results showed that alumni have the highest intentions to use the alumni e-newsletter and the alumni Website and the lowest intentions to use social media, e.g., Twitter, Instagram, and YouTube. Among alumni cohorts, significant statistical differences in their intentions to use these communication channels were identified. Given our findings, alumni communication staff are encouraged to leverage different communication channels targeting alumni in different age groups.     

水生态系统服务供需视角下的水网乡村 适应性规划策略——以吴江张鸭荡片区为例

苏南乡村地区一直是中国乡村建设的先行区域，特殊的纵横交错的水网结构构成了一个错综复杂的系统，呈现出其他地区不具有的复杂性和生态特殊性。随着城镇的扩张，乡村的发展建设使其水网空间的平衡发展面临极大的挑战。传统单一静态的规划方法逐渐显示出无法适应经济、社会等发展要求的缺陷。苏南乡村地区以水为核心，从水生态系统服务供需关系的视角下探究水网乡村的适应性规划策略更加适应当前的乡村现状和需求。以传统水网乡村空间形态转译为基础，建立水生态系统供需服务评估体系，在评估水生态系统服务供需能力的基础上，分析供需分异模式及供需矛盾。从构建乡村水域空间生态格局、乡村水域空间功能分区规划，以及乡村水域空间多情景预判3个方面，提出苏南水网乡村的适应性规划策略，并为水网乡村的生态实践提出新思路。     

公民参与网络反腐的意愿及影响因素研究——基于武汉市调研数据的分析

文章运用Logistic回归模型,基于武汉市调研数据,从实践角度透视和思考公民参与网络反腐的意愿及影响因素。研究发现,大部分接受调研公民参与网络反腐的意愿较高;政府信任对公民参与网络反腐意愿的负向影响非常显著;年龄的负向影响比较显著;月收入的正向影响一般显著;性别、受教育程度和单位类别对其参与意愿的影响不显著。     

A practical road map for assessing cyber risk

The increase in interconnectivity and developments in technology have caused cyber security to become a universal concern. This paper highlights the dangers of the evolution of cyber risk, the challenges of quantifying the impact of cyber-attacks and the feasibility of the traditional actuarial methodologies for quantifying cyber losses. In this paper, we present a practical roadmap for assessing cyber risk, a roadmap that emphasizes the importance of developing a company and culture-specific risk and resilience model. We develop a structure for a Bayesian network to model the financial loss as a function of the key drivers of risk and resilience. We use qualitative scorecard assessment to determine the level of cyber risk exposure and evaluate the effectiveness of resilience efforts in the organization. We highlight the importance of capitalizing on the knowledge of experts within the organization and discuss methods for aggregating multiple assessments. From an enterprise risk management perspective, impact on value should be the primary concern of managers. This paper uses a value-centric/reputational approach to risk management rather than a regulatory/capital-centric approach to risk.     

The evolution of world trade from 1995 to 2014: A network approach

This paper employs network analysis to study world trade from 1995 to 2014. We focus on the main connective features of the world trade network (WTN) and their dynamics. Results suggest that countries’ efforts to attain the benefits of trade have resulted in an intertwined network that is increasingly dense, reciprocal, and clustered. However, these features do not correspond to a linear aggregation of the characteristics of its constituents (trade sectors). Trade linkages are distributed homogeneously among countries, but their intensity (i.e. their value) is highly concentrated in a small set of countries. The main connective features of the WTN were not affected by the 2007–2008 international financial crisis. However, we find that the crisis marks a turning point in the evolution of the WTN from a two-group (led by the US and Germany) to a three-group (led by the US, Germany, and China) hierarchical structure.     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

黑色叙事对旅游目的地引致形象的影响机制

"讲好故事"已成为塑造和传播国家和地方形象的重要手段。以"讲故事"为手段打造旅游目的地形象是一种国际通行的旅游目的地营销方式。现有旅游目的地叙事研究开展还很不充分,关注的叙事偏重于正面叙事材料,对负面叙事的关注还很不够。文章着眼于黑色叙事对旅游目的地引致形象的影响机制,以德国首都柏林作为研究案例,以著名历史游记《柏林:一座城市的肖像》(Berlin:Imagine a City)中关于柏林的系列黑色叙事和柏林旅游局在微信公众号发布的图片作为实验材料,通过招募志愿者参与包括4个阶段的混合研究设计,验证黑色叙事对柏林旅游目的地形象(认知、情感及意动)的复杂影响。研究的主要理论贡献在于:首先,验证了黑色叙事对旅游目的地形象具有显著影响,实证该影响主要透过"叙事主角-游客-自我形象一致性"这一主线发挥作用,明确了黑色叙事主角形象对黑色叙事引致旅游目的地形象的影响机理,丰富了叙事传输理论相关结论。其次,验证了黑色叙事引致情感形象在黑色叙事影响旅游目的地形象过程中的影响效应,发现黑色叙事引致情感形象(而非认知形象)是黑色叙事最终能够影响游客动机的关键。最后,在黑色叙事这一特定研究情境中,验证了目的地认知形象对"情感形象影响意动形象"具有"增强调节效应",丰富了现有旅游目的地形象"认知-情感-意动"三联结构理论。文章在此基础上提供了相应的管理和营销建议。