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This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories.

This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments.

The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations.

This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

This study aims to examine the role of mindful consumption in promoting rural sustainability, particularly in the context of tourism in Muang Kaen Community, Chiang Mai, Thailand, by establishing a robust circular economy.

The data were collected through in-depth interviews with 28 informants who are tourism stakeholders regarding sustainable development, i.e. government officers, business owners, community leaders and community members in Muang Kaen, to achieve the data triangulation. A thematic analysis of the interview data was employed in this data set.

The findings demonstrate three key themes for driving sustainable community development: a sense of community, leadership and embodiment. At an individual level, local community members co-create a sense of community through Thainess, which gradually forms the social commitment to caring for neighbors, the community, and the environment. Carefulness also relates to another theme, “leadership” – social capital, which drives mindful behavior among the community members. Both situational and official leaders are key persons in forming a culture of sustainability within the community. Finally, the community can achieve sustainable goals by driving from the individual to the collective level through the embodiment.

This single-case study warrants further examination across different communities to generalize the findings to broader circumstances.

This study has shed light on how rural tourism can drive sustainable development through a circular economy and mindful consumption.

This paper aims to develop the building information modeling (BIM) investment decision model (BIDM) for Hong Kong architecture, engineering, construction and operation (AECO) industry utilization in early BIM investment decision-making. The developed BIDM is designed to assist company leaders in measuring and amending their investment decisions and BIM strategy by considering estimators [features and net positivity (NP)] and results based on BIDM.

This research is conducted using a mixed methodology of qualitative and quantitative analysis. The necessary indicators were collected from literature and interviews with relevant researchers, where 545 semistructured questionnaires were distributed to selected AECO company leaders and collected by the authors. The least absolute contraction and selection operator (LASSO)-based result was conducted to help company leaders. The results of the validation test validated the model based on the LASSO method and the outcomes of the p-value test also supported the significance of BIDM.

More than 80 determinators were processed to conduct 19 main indicators for generating BIDM, and 6 significant main indicators on final BIDM. The data set of this research included 483 samples, which are categorized into 7 groups according to their role in an infrastructure project.

To the best of the authors’ knowledge, this is the first LASSO-used investment decision-making model integrated with the proposal of NP in the AECO industry. The value of current knowledge is the development of BIDM, which benefits company leaders in BIM investment decision-making and commercially benefits consulting cooperators as an investment forecasting tool. BIDM will help future users make better, more dynamic investment strategies.