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This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.

The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.

The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.

To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

New information technologies such as IoT and big data analytics have reshaped the development of smart green products. These products exhibit two important features that are not seen in traditional products: environmental friendliness and data network effect. Based on these unique features, the authors investigate a firm's optimal selling strategy of smart green products from both the profitability and environmental perspectives.

The authors establish stylized models to consider the optimality of three selling strategies: (1) traditional strategy – only offering traditional products, (2) green strategy – only offering smart green products, and (3) hybrid strategy – offering both traditional and smart green products.

The authors’ analysis shows that in the absence of data network effect, there will always be a conflict between profit maximization and environmental protection. However, a strategy that benefits both the firm and the environment exists when data network effect is present. Interestingly, hybrid and traditional strategies can be win-win strategies, but the green strategy cannot. Also surprisingly, the green strategy may harm the environment more as smart products become greener.

This study examines the economic and environmental implications of selling smart green products, and contributes to existing literature on sustainable operations and green product design by incorporating the impact of both consumer environmental awareness and data network effect. The authors’ findings shed light on how to coordinate the profitability and environmental impact of selling smart green products in the era of big data and IoT.

The purpose of this paper is to propose an attention alignment method for opinion mining of massive open online course (MOOC) comments. Opinion mining is essential for MOOC applications. In this study, the authors analyze some of bidirectional encoder representations from transformers (BERT’s) attention heads and explore how to use these attention heads to extract opinions from MOOC comments.

The approach proposed is based on an attention alignment mechanism with the following three stages: first, extracting original opinions from MOOC comments with dependency parsing. Second, constructing frequent sets and using the frequent sets to prune the opinions. Third, pruning the opinions and discovering new opinions with the attention alignment mechanism.

The experiments on the MOOC comments data sets suggest that the opinion mining approach based on an attention alignment mechanism can obtain a better F1 score. Moreover, the attention alignment mechanism can discover some of the opinions filtered incorrectly by the frequent sets, which means the attention alignment mechanism can overcome the shortcomings of dependency analysis and frequent sets.

To take full advantage of pretrained language models, the authors propose an attention alignment method for opinion mining and combine this method with dependency analysis and frequent sets to improve the effectiveness. Furthermore, the authors conduct extensive experiments on different combinations of methods. The results show that the attention alignment method can effectively overcome the shortcomings of dependency analysis and frequent sets.

The simulation of the fluid–solid interaction (FSI) problem is important for both academic studies and engineering applications. However, the numerical approach for simulating the FSI problems is a great challenge owing to the large discrepancy of material properties and inconsistent description of grid motion between the fluid and solid domains. The difficulties will be further increased if there are multiple materials in the fluid region. In these complicated applications, interface reconstruction, multi-material advection and FSI must be all taken into account. This paper aims to present an effective integrated work of multi-material arbitrary Lagrangian Eulerian (MMALE) method, finite element (FE) method and the continuum analogy method to simulate the complex FSI problems involving multi-material flow. The coupled method is used to simulate the three-dimensional CONT test and the blast-plate interaction. The numerical results show good agreement with the benchmark and the experiment data, which indicates that the presented method is effective for solving the complicated FSI problems.

MMALE and FE methods are used to simulate fluid and solid regions, respectively. The interfacial nodes of fluid and solid are required to be coincident in the whole simulation so the interacted force can be easily and accurately calculated. To this end, the continuum analogy method is used in the rezoning phase.

The coupled method is used to simulate the three-dimensional CONT test and the blast-plate interaction. The numerical results show good agreement with the benchmark and the experiment data, which indicates that the presented method is effective for solving the complicated FSI problems.

To the best of the authors’ knowledge, this is the first time that the ALE method, moment of fluid interface reconstruction method, continuum analogy method and the FE method are combined to solve complicated practical problems.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

The emergence of internet-enabled technology has led to the software service model in which the software firm, instead of the consumer, maintains software ownership. This model can curtail software piracy more effectively than the traditional on-premises software model. However, software firms are not abandoning traditional on-premises software but embracing both models simultaneously. In this study, the authors consider a firm’s software bundling decision in combination with its piracy deterrence strategy. The paper aims to discuss these issues.

The authors build three stylized models to analytically compare the bundling strategies under three scenarios: no piracy, piracy is present and piracy is present while the firm applies digital rights management (DRM).

The authors find pure bundling (PB) to be the optimal strategy due to the combination of competition and cannibalization effects in mixed bundling (MB). Simultaneously, consumers may enjoy greater surplus in PB than in MB, making PB the preferred strategy for both the firm and consumers. Interestingly, the win-win outcome coexists with some degree of piracy in the market.

The results provide important insights for firms and policy-makers and contribute to the literature on piracy and product bundling. First, the authors show piracy could be another driver for product bundling, which has never been discussed in prior literature. Second, the authors suggest an alternative perspective; that PB may be a desirable outcome for both firms and consumers when considering piracy and DRM. More surprisingly, this desirable outcome occurs with some level of piracy in the market. The presence of piracy leads to competition and cannibalization effects in MB, which eventually results in the win-win outcome in the software market for both the firm and the consumers.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

In the cloud computing era, three merging developments in software industry are: cloud and on-premises software may offer complementary value to each other; cloud software service requires the support of significant information technology infrastructure; and software piracy problems can be better managed in the cloud. However, how these developments together impact a vendor’s bundling strategy has not yet been investigated. The paper aims to discuss this issue.

Drawing on the product bundling framework, this research establishes stylized models to study a software vendor’s bundling decision in the cloud-based era with special consideration on the issue of software piracy.

The authors find different key parameters associated with the cloud era exert different effects on the bundling decision. When on-premises software and cloud software generate additional value by complementing each other, software vendors can make greater profits under the pure components (PC) strategy. Regarding a low infrastructure cost, software vendors should favor pure bundling (PB). The impact of piracy deterrence effectiveness is less straightforward – it favors PC when piracy deterrence effectiveness is low, but PB when piracy deterrence effectiveness is high.

This study makes key contributions to theory and practice. First, this is the first study to examine software bundling strategies in the cloud computing era, whereby the three factors relevant to the cloud phenomenon have been considered. Second, this paper contributes to the literature of bundling and software piracy by examining the intersection of these two streams of literature. Third, this paper sheds light on a vendor’s bundling decision when facing piracy problems in the emerging cloud software era.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.