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This study aims to evaluate cargo and eco-efficiency of global container shipping companies (CSCs) and explore the determinants of the CSCs' efficiencies. While the former is derived from the CSCs' operational perspective, the latter highlights environmental issue related to carbon emission reduction.

In the first stage, a two-stage double bootstrap approach of data envelopment analysis (DEA) is applied to derive bias-corrected cargo and eco-efficiency of the top ten global CSCs under the variable returns to scale assumption. In the second stage, ordinary least squares and truncated regression are applied to examine determinants of the CSCs' efficiencies.

The DEA results reveal that the cargo efficiency of the CSCs is higher than their eco-efficiency by about 2.6% under variable returns to scale in DEA. However, the bias-corrected results show that the difference is 2.9%. The overall average efficiencies suggest that the CSCs can improve their cargo (eco) efficiency by 6.9% (10.8%). In the second stage, the regression results show that the numbers of ship, return on assets and asset turnover ratio are significantly related to both cargo and eco-efficiencies, whereas the total fleet capacity positively affects cargo efficiency.

The results of this study can help the inefficient CSCs make strategic decisions to improve their performance. For example, their business experience and capacity may be contributing to their efficiencies. However, this study only focuses on the container market among the three main markets, namely, dry bulk, wet bulk and container.

This study highlights an environmental issue in the shipping industry. While CSCs are operating their cargo efficiently in general, they should also put green initiatives into their business operations for the long-term sustainability.

Using social networks to identify users with traits similar to those of the target user has proven highly effective in the development of personalized recommendation systems. Existing methods treat all dimensions of user data as a whole, despite the fact that most of the information related to different dimensions is discrete. This has prompted researchers to adopt the skyline query for such search functions. Unfortunately, researchers have run into problems of instability in the number of users identified using this approach.

We thus propose the m-representative skyline queries to provide control over the number of similar users that are returned. We also developed an R-tree-based algorithm to implement the m-representative skyline queries.

By using the R-tree based algorithm, the processing speed of the m-representative skyline queries can now be accelerated. Experiment results demonstrate the efficacy of the proposed approach.

Note that with this new way of finding similar users in the social network, the performance of the personalized recommendation systems is expected to be enhanced.

This study aims to focus on the Artificial Intelligence of Things (AIoT) course. As AIoT has many theoretical theories and students usually have little interest in learning the protocols, the experiments can help stimulate their curiosity. Due to the environmental factor, the teaching requires assistive videos and Problem-Based Learning (PBL) to understand students' learning conditions.

The experimental design generally follows the course theories going from easy to complex, and students can extend the acquired concepts to other project development, yet, without in-depth knowledge about the experiment, resulting in limited creativity.

The assessment analysis can reveal whether students have grown from the teaching. The final analysis at the end of the term can show learners' conditions; meanwhile, students can deliver their level of satisfaction. The click-and-mortar teaching environment provided in this research can improve learning setting and quality, solidifying learners' proficiency.

The research result has proved the feasibility of the proposed method. Apart from showing the experimental steps, the video also explains the corresponding theories, helping students reinforce experimental knowledge and boost learning willingness.

In recent years, research on children's tourism experiences has gained prominence, as children are becoming an increasingly vital market for the tourism industry. While events are a main sector of the industry and host millions of children every year, there is a lack of research specifically focussed on children's experiences in events. This chapter focusses on children's entertainment events which can provide children with a satisfying, memorable and educational experience. This study has developed a framework to facilitate deeper mixed studies on children's experiences in event tourism. The framework is composed of four pillars based on various social, tourism and event theories and models, including the Cognition–Affect–Behaviour (CAB) theoretical framework, the Orchestra Model of Experience, the Event Experience Scales (EES), the Theory of Child Well-being and the Transtheoretical Model of Behaviour Change (TTM). The framework can be used by future researchers as an analytical evaluation tool to study children's experiences in different types of events and understand the mechanisms of behaviour change in this context.