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The purpose of this paper is to describe a novel implementation of a multispatial method, multitime-scheme subdomain differential algebraic equation (DAE) framework allowing a mix of different space discretization methods and different time schemes by a robust generalized single step single solve (GS4) family of linear multistep (LMS) algorithms on a single body analysis for the first-order nonlinear transient systems.

This proposed method allows the coupling of different numerical methods, such as the finite element method and particle methods, and different implicit and/or explicit algorithms in each subdomain into a single analysis with the GS4 framework. The DAE, which constrains both space and time in multi-subdomain analysis, combined with the GS4 framework ensures the second-order time accuracy in all primary variables and Lagrange multiplier. With the appropriate GS4 parameters, the algorithmic temperature rate variable shift can be matched for all time steps using the DAE. The proposed method is used to solve various combinations of spatial methods and time schemes between subdomains in a single analysis of nonlinear first-order system problems.

The proposed method is capable of coupling different spatial methods for multiple subdomains and different implicit/explicit time integration schemes in the GS4 framework while sustaining second-order time accuracy.

Traditional approaches do not permit such robust and flexible coupling features. The proposed framework encompasses most of the LMS methods that are second-order time accurate and unconditionally stable.

The purpose of this qualitative study is to explore well-being experiences of international business travellers (IBTs) and contribute to our understanding of personal and job characteristics as antecedents of ill- or well-being.

The authors’ insights are based on semi-structured in-depth interviews with 32 IBTs assigned to various destinations ranging from single-country travel to global operation. Participants in this study represent a range of traveller personas (regarding demographics, type of work, travel patterns). Thematic analysis is used to reveal new insights.

The authors’ analysis revealed trip-load (i.e. workload, control, organisational support) and intensity of travel (i.e. frequency, duration and quality) as job characteristics that sit on an energy stimulation continuum, driving work-related outcomes such as stress and burnout or health and well-being. Energy draining and boosting processes are moderated by cognitive flexibility and behavioural characteristics.

Findings represent a framework for managing IBT well-being via adjustments in job and travel characteristics, plus guidance for training and development to help IBTs self-manage.

The insights within this paper contribute to the conversation around how to enhance well-being for IBTs and frequent flyers. The study intends to offer direction as to which specific job, psychological and behavioural characteristics to focus on, introducing a novel framework for understanding and avoiding serious consequences associated with international mobility such as increased stress, burnout and ill-health.

This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.