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This study aims to examine the effects of iPad distribution on all teachers in a university and its application in teaching and student learning at home via wireless network during the coronavirus (COVID-19) pandemic. The attitude towards the use of iPads, behavioural intentions and the impact on the quality of teaching were evaluated.

This study used the technology acceptance model to explore the use of iPad smart mobile devices in multimedia teaching applications by university teachers. Furthermore, it used the structural equation modelling (SEM) for data analysis to explore the causal relationship between model variables, and it aimed to examine the causal relationship between variables to verify the theory. The SEM analysis included the following two stages: measurement model analysis and structural model analysis.

The “Internet information environment” had a significant positive impact on “perceived usefulness” and “perceived ease of use”. Amongst them, perceived usefulness had a significant positive effect on the use attitude, and use attitude had a significant positive effect on behaviour intention.

The findings confirmed that a good information network environment will directly and positively affect the perceived usefulness and the ease of use of iPad smart devices, of which the perceived usefulness will further positively affect teachers' perception of iPad smart devices. The attitude and behaviour of using such devices will in turn positively affect the quality of teaching. The results of the quality performance evaluation can be referenced further by manufacturers and scholars regarding the use of iPad smart devices for work at home during the COVID-19 pandemic.

The purpose of this research was to explore the stickiness of players' motivation in a virtual community and to explore the important factors for gamers.

In this research, motivation was the independent variable; the virtual community was the mediator; and stickiness was the dependent variable. An online questionnaire survey was conducted, with users of augmented reality (AR) as the research objects. Statistical analysis was carried out using SPSS and AMOS software to verify the research model and research hypotheses, to understand the relation between player motivation and stickiness and to determine whether there were any changes in the virtual community.

The authors found that the relation between players' motivation in AR-based games and the virtual community had a significant positive impact. Ingress had a significant positive impact on the virtual community and stickiness, and Pokémon had a significant positive impact too. The virtual community of the Ingress game played a completely mediating role in motivation and stickiness, but the virtual community in Pokémon did not have a mediating effect.

The novel approach adopted in this study enabled us to determine the causal relation between player motivation, the virtual community and stickiness, on the basis of the theoretical framework formulated, and the latter was used to construct a path analysis model diagram. The correlation between motivation and the virtual community, between the virtual community and stickiness, and the causal relation between all three was verified. The study results and conclusions may help companies understand how to use virtual communities in AR games to improve stickiness and motivate gamers to continue playing.

This study has two objectives: to explore the factors that influence student self-efficacy regarding engagement and learning outcomes in a business simulation game course and to compare the difference between hierarchical and general teaching methods.

From September 2021 to May 2022, a questionnaire was administered to 126 students in a business simulation game course at the Zhongshan Institute, University of Electronic Science and Technology of China. Data were analyzed using nonparametric paired samples tests and linear regression.

The results showed that student self-efficacy, engagement and learning outcomes were significantly higher with the hierarchical teaching method than with the general teaching method. There were also differences in the factors that influenced self-efficacy regarding learning outcomes between the two teaching methods. With the general teaching method, student self-efficacy did not directly affect learning outcomes, but did so indirectly by mediating the effect of engagement. However, with the hierarchical teaching method, self-efficacy directly and significantly affected learning outcomes, in addition to indirectly affecting learning outcomes through student engagement.

Compared with the control group experimental research method, the quasi-experimental research method can eliminate the influence of sample heterogeneity itself, but the state of the same sample may change at different times, which is not necessarily caused by the hierarchical teaching design.

Based on the results of this study, teachers can apply hierarchical teaching according to student ability levels when integrating business simulation games. The results of this study can inspire teachers to protect student self-confidence and make teaching objectives and specific requirements clear in the beginning of the course, and also provide an important practical suggestion for students on how to improve their course performance.

The research results can be extended to other courses. Teachers can improve students' self-efficacy through hierarchical teaching design, thus improving students' learning performance and also provide reference value for students to improve their learning performance.

This study built a model based on self-system model of motivational development (SSMMD) theory, comparing factors that affect student self-efficacy regarding learning outcomes under different teaching methods. The model enriches the literature on SSMMD theory as applied to business simulation game courses and adds to our understanding of hierarchical teaching methods in this field. The results provide a valuable reference for teachers that can improve teaching methods and learning outcomes.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

This study presents an ontological approach to assess the architectural outputs of generative adversarial networks. This paper aims to assess the performance of the generative adversarial network in representing building knowledge.

The proposed ontological assessment consists of five steps. These are, respectively, creating an architectural data set, developing ontology for the architectural data set, training the You Only Look Once object detection with labels within the proposed ontology, training the StyleGAN algorithm with the images in the data set and finally, detecting the ontological labels and calculating the ontological relations of StyleGAN-generated pixel-based architectural images. The authors propose and calculate ontological identity and ontological inclusion metrics to assess the StyleGAN-generated ontological labels. This study uses 300 bay window images as an architectural data set for the ontological assessment experiments.

The ontological assessment provides semantic-based queries on StyleGAN-generated architectural images by checking the validity of the building knowledge representation. Moreover, this ontological validity reveals the building element label-specific failure and success rates simultaneously.

This study contributes to the assessment process of the generative adversarial networks through ontological validity checks rather than only conducting pixel-based similarity checks; semantic-based queries can introduce the GAN-generated, pixel-based building elements into the architecture, engineering and construction industry.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.