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This study uses migrant household survey data from 2008 to 2009 to examine how parental migration decisions are associated with the nutritional status of children in rural and urban China. Results from instrumental variables regressions show a substantial adverse effect of children’s exposure to parental migration on height-for-age Z scores of left-behind children relative to children who migrate with their parents. Additional results from a standard Blinder–Oaxaca decomposition, a quantile decomposition, and a counterfactual distribution analysis all confirm that children who are left behind in rural villages – usually because of the oppressive hukou system – have poorer nutritional status than children who migrate with their parents, and the gaps are biggest at lower portions of the distribution.

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. Besides, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management, and so on. In recent years, knowledge graph techniques in China are also developing rapidly and different Chinese knowledge graphs have been built to support various applications. Under the background of “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China on developing knowledge graph is also a good reference. Thus, in this chapter, the authors mainly introduce the development of Chinese knowledge graphs and their applications. The authors first describe the background of OBOR, and then introduce the concept of knowledge graph and three typical Chinese knowledge graphs, including Zhishi.me, CN-DBpedia, and XLORE. Finally, the authors demonstrate several applications of Chinese knowledge graphs.

The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods to remove the interactive effects. The authors show that the quasi-difference MLE (QDMLE) over time is inconsistent when N→∞ whether T is fixed or goes to infinity. On the other hand, the QDMLE is consistent and asymptotically unbiased if the difference is taken over individuals when T is large whether N is fixed or large. Monte Carlo studies are conducted to compare the performance of the QDMLE using different quasi-difference methods.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

Purpose: There have been continuous developments in the production industry to meet the increasing customer demand from the past to the present. At this point, supply chain management (SCM) systems emerge as an important topic. SCM is a set of systems that manages the entire process from the production of a product to its delivery to the end user. Industry 4.0 aims to improve the production industry by increasing the quality, efficiency, and performance of the production process. Therefore, in this chapter, the authors highlight the challenges, benefits, and future trends of the combination of Industry 4.0 and SCM systems.

Methodology: In this chapter, the integration of Industry 4.0 and SCM systems was investigated. For this purpose, the Industry 4.0 position of the countries and the current status of SCM systems have been examined. In addition, the key technologies in the Industry 4.0 transformation, the possible problems encountered in the transformation, the deficiencies encountered in SCM systems, and how these deficiencies can be solved with Industry 4.0 were investigated.

Findings: The results of this study show that companies that use an SCM system can separate themselves from their competitors by using Industry 4.0 technologies.

Significance: This can allow them to achieve their strategic goals and to ensure the maintenance of their competitive advantage.

Purpose: Consumer acceptance is considered the most important factor in functional food success and is given top priority in new product development. This study focussed on finding factors that influence consumer perceptions about active food and the pattern of active food consumption. This chapter aims to provide a deeper understanding of taste trading based on a consumer decision-making framework.

Research Methodology: Cross-sectional consumer data were collected by floating G-Doc containing the questionnaire which was supposed to be answered by people according to their behaviours, preferences, knowledge regarding functional foods etc. It contained questions about their income, gender, preference towards functional foods, factors affecting decision-making while purchasing functional foods etc.

Results of the Study: The findings suggest that the consumers' attitude towards functional foods was mainly influenced by the quality and suitability of the product. Purchase intent was found based on age, literacy of population, income and health benefits of active foods. Awareness of functional foods and their price significantly affected the purchase of functional foods. Most people were willing to spend more on functional foods in the future regardless of the taste and provided high quality and product suitability.