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This study aims to seek a new economic and environmental protection fuel tank inerting method.

The principle that serves as the basis for the cooling inerting process is described, the workflow of the cooling inerting system is designed, the mathematical model of the cooling inerting system is established, and the important performance changes of cooling inerting in the flight package line and the influence of key parameters on it are simulated by using Modelica software.

The results show that the cooling inerting system can be turned on to quickly reduce the vapour concentration in the gas phase in the fuel space and reduce the temperature below the flammability limit. Within a certain range of pumping flow, the inerting effect is more obvious when the pumping flow is larger. Simply running the cooling inerting system on the ground can remain the tank in an inert state throughout the flight envelope.

However, cooling inerting is suitable for models with fewer internal heat sources. An excessive number of internal heat sources will lead to inerting failure.

This study provides theoretical support for the feasibility of cooling inerting. Cooling inerting does not require engine air, and the cooling is mainly accomplished with air, which places a small load on the cooling system and has a much lower cost than the airborne hollow fibre film inerting technology widely used at present. It is a promising new inerting technology.

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

The purpose of this paper is to seek an approach to study decision making and optimization analyzing of enterprises with multi‐factors.

In this paper, a new grey decision dynamic model was set up; it integrates with modified GM model, the transfer function and response characteristic of cybernetics, and other knowledge. The building steps of this integrated model and its application method in a certain enterprise were presented.

Until recently, there have been many references studying grey decision or grey relational analysis of factors, but it was found that dynamic affecting of multi‐factors for enterprise production and their affecting levels were not studied synthetically in these references, and by this new dynamic model, these useful conclusions can be gotten.

The built time response equation and dynamic model in this paper can be only used for whole regularity analysis and not suited to daily one‐to‐one analyzing; otherwise the error of the reductive values must be tested.

This new grey decision dynamic model can be used widely in decision making and optimization analyzing of enterprises with multi‐factors. Practical applying results show that the proposed method can instruct effectively actual production.

This paper offers a new grey decision dynamic model that can be used in decision making and optimization analyzing of enterprises with multi‐factors. By applying this new dynamic model in practice, some useful conclusions are drawn; some dynamic factors affecting production capacity of enterprises and their affecting levels can be found.

This study aims to examine the impact of supplier relationship quality on curtailing opportunism and promoting cooperation between the exchange partners. It also investigates the contingent impact of contract specificity on the relationships and assesses performance implications of relationship quality for both buyer and its major supplier in the exchange.

Confirmatory factor analysis and path analysis were performed based on data collected from manufacturers in a survey. The hypotheses were tested using path analysis.

The findings of this study indicate a pivotal role of supplier relationship quality in suppressing opportunism and enhancing cooperation between exchange parties, which lead to dyadic performance. Furthermore, the effect of supplier relationship quality is strengthened by contract specificity.

This study adds value to the existing streams of studies in several ways. First, informed by the nexus of relational capital theory and transaction cost economics, the authors emphasize the pivotal role of relationship quality in curtailing opportunism and fostering cooperation and the moderating effect of contract specificity on the above linkages. Second, this study provides empirical evidence of the mechanism of the effect of contract specificity on opportunism and cooperation.

This study aims to examine the impact of the supplier's coercive and cognitive pressures on a manufacturer's green purchasing decision-making process and the resultant implications in terms of operational and environmental performances.

Path analysis is performed to test the hypothesized linkages.

This study finds that the supplier's coercive pressure, environmental focus and socio-cultural responsibility will lead a firm to more successful implementations of green purchasing, which, in turn, results in improved operational and environmental performances. The study findings reveal that the commercial values of green purchasing in addition to social and political obligations will promote the adoption of green purchasing in supply chain management practice.

This study helps business managers understand the impacts of the supplier's coercive and cognitive pressures on green purchasing and the manufacturer's resultant performances. In particular, coercive pressure is operationalized by the supplier's coercive pressure and environmental regulatory pressure, while cognitive pressure is reflected in the supplier's environmental focus and socio-cultural responsibility. This study contributes to the extant theories and enriches the literature on green purchasing.

This study investigates the possible curvilinear relationship between operational interdependency and supply chain performance as well as the contingency effect of supply chain disruptions, in terms of disruption orientation and disruption impact.

Path analysis was employed to test the hypotheses using the data collected from Chinese manufacturers.

The results confirm an inverted U-shape effect of operational interdependency. As level of buyer-supplier operational dependency increase, the supply chain performance is enhanced. However, the benefits of operational interdependency diminish beyond a certain point. Additionally, the findings of this study show the disruption orientations positively moderate the relationship between interdependency and performance, whereas the effect of disruption impact is not significant.

The findings of this study provide an explanation to the theoretical gap about the equivocal results of the effect of dependency, which provide new insights into the literature regarding buyer-supplier relationships. Furthermore, this paper identifies the moderating role of supply chain disruption in the relationship between operational interdependency and supply chain performance, which provide further explanation about the mixed results of the effect of dependency. The results confirmed that supply chain disruption orientation positively moderate the relationship between operational interdependency and supply chain performance.

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Consumer sequential online click data, collected from JD.com, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.

The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.

There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

The purpose of this paper is to examine the effectiveness of illegal insider trading enforcement in China by focusing, among other things, on the Chinese Securities Regulatory Commission's (CSRC) enforcement actions in the period 1993‐2006.

This paper discusses the CSRC's enforcement policies and practices of insider trading regulation, based upon administrative and judicial cases, face‐to‐face interviews with regulators, and policy documents.

A major finding of the study is the paucity of insider trading cases and the lack of convictions for insider trading offences in China. The campaign against securities offences did not actually come with the stricter enforcement of insider trading laws. A primary challenge in the insider trading regulation comes from the fact that most insider trading cases involve high‐ranking government and party officials. The CSRC lacks the power to directly administer discipline and penalties on government officials and party cadres for insider trading offences.

It is recommended that the CSRC be given more power, more resources and more trained regulators to detect and address insider trading activities. It is also recommended that the CSRC improve its surveillance capabilities by fully utilizing sophisticated computer surveillance software systems, by improving inter‐agency and inter‐market information‐sharing, and by cooperating with other countries' regulators and participating in the ISG's database to detect possible international insider trading.

The paper will be of interest to researchers in the field of financial crime and securities regulation. Regulators, the private sector and government departments will also benefit from an analysis of Chinese insider trading enforcement cases. This paper also suggests better strategies for dealing with insider trading offences in China. A fair and orderly market is crucial for investors in the Chinese market.

This study examines the operational and relational governances as antecedents of cooperation commitment in buyer–supplier exchanges. It also assesses the impact of cooperation commitment on operational performance.

Path analysis was performed on the data collected from manufacturers.

The results of this study show that both operational and relational governances exert impact on cooperation commitment, which, in turn, is associated with operational performance improvement.

First, this is the first study employing the reciprocity theory to theorize the conceptual framework of the governance antecedents of cooperation commitment and operations excellence effect. Second, the study highlights how the research framework can enrich the reciprocity theory in exploring the mechanisms of the operational and relational governances of buyer–supplier exchanges and their impact on the commitment to the cooperation. Third, this study extends the reciprocity theory to examine in detail how cooperation commitment exerts impact on the operational performance.