Complex Network of Aviation E-Services in the Belt and Road Initiative: A Heuristic Study of Small Data based on Block Modeling

ABSTRACT

The aviation e-service system is an important part of support for business communication in regions covered by Belt and Road initiative. In exploring the role of airline companies in this system, we examine the structure of the network, which consists of airlines interconnections based on small data of individual attributes of aviation companies in aviation e-services, and study these interconnections across different groups using block modeling. The heuristic solution of airline companies in development of international communication and cooperation is further discussed. Among our policy implications for global managers, we suggest the enhancement of cross-regional cooperation among airlines.     

A product-centric data mining algorithm for targeted promotions

Targeted promotions in retail are becoming increasingly popular, particularly in UK grocery retail sector, where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimising items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector show that the algorithm performs well in finding the best items and customers to target whilst eliminating “false positives” (targeting customers who do not buy a product) and reducing “false negatives” (not targeting customers who could buy a product). The algorithm also shows better performance when compared to a similar published framework, particularly in handling “false positives” and “false negatives”. The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields.     

时空大数据支持的土地储备智能决策体系与应用研究

研究目的:设计土地储备智能决策体系,将土地储备决策环节进行科学化、定量化和模型化,为政府合理开展土地储备提供决策依据。研究方法:遵循科学性、实用性、先进性、开放性原则,以数据为驱动,人工智能为核心,通过基于Hadoop/Spark的大数据处理技术、多源异构数据融合技术、智能决策模型集对多源时空大数据挖掘分析。研究结果:提出了土地储备智能决策体系,研究了一套面向土地储备的决策方法集,实现对现状条件的精准刻画,对土地储备规模、结构、时序等内容的科学决策;在此基础上,研发了智能决策支持平台,将数据资源共享、现状条件评估、智能决策专题等功能集成,构建土地储备智能决策"一张图",并实现平台的自我学习和更新。研究结论:应用于宁波市实例验证,该体系有效地促进了土地储备科学智慧决策,对优化土地资源配置具有参考意义。     

What determines the efficiency of Australian mining companies?

We examine the firm‐specific determinants of technical efficiency in Australian mining companies using data envelopment analysis (DEA). To do so, we employ panel data sourced from individual mining companies listed on the Australian Securities Exchange (ASX) over the period 2010–2014. To ensure valid statistical inference in the presence of serial correlation between DEA efficiency scores, we apply Simar and Wilson's two‐stage bootstrap method. We find that ownership concentration, firm size, firm age, product portfolio, product diversification and growth status significantly contribute to efficiency gains. However, other firm‐specific factors, such as capacity utilisation, financial risk and overseas operations appear to have limited impact on the technical efficiency of mining firms.     

微波加热对叶绿素水溶液的影响

本文通过对叶绿素水溶液进行微波加热,分析微波加热对叶绿素水溶液的温度、色度、叶绿素含量的影响。结果表明,微波加热处理的水溶液温度显著高于对照沸水浴,1~2 min时,1000 W功率下的水溶液温度显著高于200 W(P<0.05)。与沸水浴相比,微波加热显著降低了叶绿素水溶液的绿色程度,且高功率微波加热显著加剧了水溶液亮度的变化。200 W微波加热使水溶液中叶绿素a、b含量降低,但1000 W微波加热则提高了水溶液中叶绿素a、b含量。     

Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation

This study employs the structural topic model to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. 22 service related topics were extracted from the corpus and four topics have not appeared in previous Airbnb studies. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by comparing its service quality attributes with the results of the topic modelling, which indicates that this MSQ can cover general Airbnb service quality attributes. This study also examines the different preferences of Malaysian and international Airbnb users and the changing patterns of the top six service quality attributes during a five-year period. The findings reveal that Malaysian Airbnb users care more about the appearance and location of the property, and international Airbnb users pay more attention to whether the property can accommodate a group of people. In addition, communication with the host is found to play an increasingly important role in Airbnb users’ lodging experiences.     

Shifts in consumer behavior towards organic products: Theory-driven data analytics

Consumer behavior is key in shifts towards organic products. A diversity of factors influences consumer preferences, driving planned, impulsive, and unplanned purchasing decisions. We study choices among organic and conventional wine using an extensive survey among Australian consumers (N = 1003). We integrate five behavioral theories in the survey design, and use supervised and unsupervised machine learning algorithms for analysis. We quantify a gap between intention and behavior, and emphasize the importance of cognitive factors. Findings go beyond correlation to the causation of behavior when combining predictive prowess with explanatory power. Results reveal that affective factors and normative cues may prompt unplanned and spontaneous purchasing behavior, causing consumers to act against their beliefs.     

NTRU格上高效的身份基线性同态签名方案

针对现有的格上身份基线性同态签名方案密钥存储量大、结构复杂导致方案实际运行效率相对偏低的问题，提出了一个NTRU（Number Theory Research Unit）格上高效的身份基线性同态签名方案。首先在密钥生成阶段利用NTRU密钥生成算法产生主密钥，接着采用格基委派算法给出身份签名私钥，最后运行NTRU格上原像抽样算法产生出线性同态签名。对方案的安全性证明与性能分析结果表明，新方案满足正确性，具有弱内容隐私性。在随机预言机模型下，该方案在小整数解问题困难性条件下满足适应性选择身份和选择消息的存在性不可伪造性。同时，由于采用NTRU格的特殊结构，新方案在密钥量与运行效率方面与已有方案相比较均具有显著的优势，这对于计算资源受限环境的同态认证中具有重要的应用价值。     

Incorporating textual information in customer churn prediction models based on a convolutional neural network

This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current best practices for analyzing textual data in CCP, and, using real life data from a European financial services provider, validates a framework that explains how textual data can be incorporated in a predictive model. First, the results confirm previous research showing that the inclusion of textual data in a CCP model improves its predictive performance. Second, CNNs outperform current best practices for text mining in CCP. Third, textual data are an important source of data for CCP, but unstructured textual data alone cannot create churn prediction models that are competitive with models that use traditional structured data. A calculation of the additional profit obtained from a customer retention campaign through the inclusion of textual information can be used by practitioners directly to help them make more informed decisions on whether to invest in text mining.