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This chapter examines a multifactor model for stock returns in nine Asian markets (Japan, China, South Korea, Hong Kong, Taiwan, Singapore, Indonesia, Malaysia, and Thailand). The authors develop a model using the market risk premium, size, book-to-market, profitability, investment, momentum, price-to-earnings ratio, and dividend yield factors for each market. The empirical results suggest that this eight-factor model can better explain the variations of stock returns than the original Fama–French three-factor model. Factor-based models using local data outperform those using data from US markets. In addition, the evidence suggests that the eight-factor model can better explain stock returns when the market is under stress.

The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China.

This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen.

The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications.

This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system.

In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model.

It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency.

The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

When integrating smart elements offered by emergent technologies, libraries are facing the challenges of technological renovation and maintaining their operation using emerging technology. Given the importance of smart library, new technologies are needed in building new libraries or renovation of existing libraries. The purpose of this paper is to propose a risk warning system for library construction or renovation in the aspect of risk management.

The proposed Internet of Things (IoT)-based system consists of sensors that automatically monitor the status of materials, equipment and construction activities in real time. AI techniques including case-based reasoning and fuzzy sets are applied.

The proposed system can easily track material flow and visualize construction processes. The experiment shows that the proposed system can effectively detect, monitor and manage risks in construction projects including library construction.

Compared with existing risk warning systems, the proposed IoT-based system requires less data for making dynamic predictions. The proposed system can be applied to new builds and renovation of libraries.

Academic contention occurs when research evidence is amenable to more than one interpretation. China has a long tradition of Shang Que (商榷), in which authors argue for their preferred interpretation. The modern form of this tradition is the Shang Que article, which often takes the form of research papers in Chinese-language journals and which tends to be question-oriented. Shang Que articles usually take the views of a particular author or article as the focus of independent and complete criticism by another, independent, academic. This paper explains the role of Shang Que articles in Chinese scholarship and their influence on international academia.

A bibliometric analysis was used to explore the characteristics and evolution of Chinese Shang Que articles using 30,577 articles published between 1979 and 2018. Microsoft Excel and Gephi were used for data analysis and visualization.

Findings suggest a decline in the number of Shang Que articles and an increase in the number of co-authors. Shang Que articles remained particularly prominent in Philosophy and Humanities and Social Sciences, where they focused on local issues such as classical Chinese, the Sinicization of Marxism and Chinese literature. This suggests that the number of Shang Que articles is related to the degree of internationalization of a research field.

Shang Que articles, which have been influenced by academic paradigms in English, are a fusion of China's Shang Que tradition and of the modern academic system. Through considering Shang Que articles, this paper explores the benefits of local academic traditions in non-English-speaking cultures.