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During COVID-19 restrictions, people spent more time in cyberspace and consuming health-related information. An increase was also observed in mediated caring messages or health-relevant information sent to one another. This study aims to explore how the information and interactions around COVID-19 can provide a good learning opportunity for public health, specifically related to eHealth literacy and eHealth promotion.

While mainstream literature has concentrated on experimental designs and a priming effect, this study inspects psychological distance related to a health threat under real-life circumstances. The article adopted a survey approach and utilized PLS-SEM techniques to examine the proposed hypotheses.

Results indicated that whereas closer social support correlates with closer psychological distance and less usage of the social media approach, more substantial COVID-19 impacts were associated with closer psychological distance but greater use of social media. Since both closer psychological distance and social media approach contribute to eHealth literacy, social support from closer and virtual social networks should be embraced but utilized through different routes and for different purposes. The timing of messages but not psychological distance affects people's social media approach, indicating that morning messages should be employed. Moreover, eHealth literacy mediates timing preferences and leads to a preference for eHealth communication earlier in the day. Overall, morning messages create a virtuous circle during a health crisis.

This paper establishes a mechanism of virtuous cycles for eHealth communication during a health threat. Additionally, it bridges existing research gaps by expanding chronopsychology and CLT in the health domain using an empirical approach, a real-life case and an extension of performance regarding information-seeking and utilization.

This paper presents an alternative approach to evaluating the overall efficiency and performance of Taiwanese container ports. Specifically, a parallel activity with series structure concept in the form of data envelopment analysis (MNDEA) is used to construct a model that applies to three different activities: harbor management, stevedoring and warehousing operations. We will further divide each activity into two process types, production processes and services processes. We will also adopt a Delphi survey approach and use the Analytic Network Process (ANP) to identify these processes’influence dependence and their degree of importance for the MNDEA model setting. An empirical application demonstrates the performance of Taiwanese container ports by using MNDEA with window analysis techniques via the directional distance functionThe results demonstrate that the application is effective in indicating and/or suggesting resource-adjustments, while considering which undesirable output levels and shared inputs were involved. The results also present directions for possible improvements in workplace efficiency.

The purpose of this paper is to investigate the influence of mentoring functions on expatriate voice in multinational enterprises and whether job security plays a mediating role in this relationship.

In total, 300 questionnaires were distributed in this study. Of the 173 responses received, 8 invalid questionnaires were excluded and 165 valid questionnaires were analysed. The effective questionnaire recovery rate was 55.00%.

The results revealed that the stronger the psychosocial support function, the role modelling function and the career development provided by the mentor, the more would be the expatriate voice behaviour. Furthermore, the psychological support, role model characteristics and career development guidance affect the expatriate voice behaviour through the mediation of job security.

Few studies have focussed on the influence of expatriate mentoring functions and job security on expatriate voice. Furthermore, whether the mentoring function affects the job security of expatriates is unknown. The objective of this study is to fill this gap in the literature.

The purpose of this paper is to improve the efficiency of loading and discharging operations in container terminals. Accounting for an increase in the size of ships, the yard truck (YT) routing and scheduling problem has become an important issue to terminal operators.

A (binary) integer programming model is developed using the time-space network technique to optimally move YTs between quay cranes (QC) and yard cranes (YC) in the time and space dimensions. The objective of the model is to minimize the total operating cost, and the model employs the M/M/S model in the queuing theory to determine the waiting time of YTs. The developed model can obtain the optimal number of YTs and their scheduling and routing plans simultaneously, as shown by the computational results.

The results also show that the model can be applied to practical operations. In this research, an experimental design of the QC and YC operation networks was considered with the import and export containers carried by YTs. The model can be used to tackle a real world problem in an international port, and the analysis results could be useful references for port operators in actual practice.

The purpose of this research only focusses on YTs routing and scheduling problem, however, the container terminal operation problems are interrelated with berth allocation and yard stacking plan. The managerial application of this study is to analyze the trade-off between truck numbers and truck waiting time can be used for terminal operators to adjust the truck assignment. This research can assist an operator to determine the optimal fleet size and schedule in advance to avoid wasted costs and congestion in the quayside and yard block.

This research solves the YT scheduling and routing problem for container discharging and loading processes with a time-space network model, which has not been previously reported, through an empirical research.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.