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Combining the coping theory and social support theory, this study aims to reveal users' coping strategies for mobile fitness app (MFA) engagement and fitness intentions with a rigorous and comprehensive hybrid research approach.

A three-stage hybrid research design was employed in this study. In the first stage, this study utilized structural equation modeling (SEM) to investigate the associations between coping resources and coping outcomes. A post hoc analysis was conducted in the second stage to unveil the reasons behind the insignificant or weak linkages. In the third stage, the fuzzy-set qualitative comparative analysis (fsQCA) technique was applied to explore the various configurations of coping resources that lead to the coping outcomes.

The results in the three stages verify and compensate each other. The SEM results confirm the presence of two coping strategies in MFA, highlighting the importance of the intertwining of the strategies, and the post hoc analysis unveils the mediating role of positive affect. Moreover, the fsQCA results reinforce and complement the SEM findings by revealing eight alternative configurations that are sufficient for leading to users' MFA engagement and fitness intention.

This study offers a prominent methodological paradigm by demonstrating the application of multi-analysis in exploring users' coping strategies. In addition, the study also advances the understanding of the complexity of the mechanism that determines users' behavioral decisions by presenting a comprehensive interpretation.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

This study aims to review the status quo, current state of research, research hot themes and research gaps in sustainable facilities management (SFM) in the built environment (BE) through an extant literature review.

To map and analyze knowledge paths in the context of SFM research, a sequential explanatory mixed-method review involving bibliometric and content analysis was used to help identify current research trends, research hot themes and knowledge gaps. The Scopus search engine was used to find 169 relevant articles. For a better understanding of the literature accumulated, a bibliometric analysis was carried out by using VOSviewer to reveal current research themes, the status quo and current state of research as well as research gaps.

Through the literature review and content analysis, the current research themes on SFM revealed from the study include green building technologies, assessment methods of SFM, smart buildings and building information modeling. The research hot themes in SFM include smart buildings and green building technologies, green buildings (GB), architectural and building designs in the university sector, assessment methods in buildings and decision-making and the adoption of asset and facility management in the university sector. Indoor air pollution, intelligent buildings, climate change, maintenance, environmental management, facilities, historic preservation, environmental performance, energy management, etc. are the research gaps identified from the study, and these serve as potential areas for future research studies under SFM. It was recognized that facilities managers are increasingly involved with sustainability policies within their organizations and are developing sustainability agendas to keep up with the changing nature of the facilities management (FM) profession.

The findings of this study hold relevance to the FM practice, as the integration of SFM by facilities managers can lead to waste reduction, decreased operating expenses and reduced energy consumption. In addition, occupants of sustainable buildings experience improved conditions that contribute to better health and productivity, thus boosting their overall well-being. Consistent with the themes of smart buildings and green technologies, revealed to be the hot themes in the SFM research scope, properties with sustainable features can command higher rental rates and property values, appealing to a broader range of stakeholders. SFM practices in universities can aid in saving money from reduced facility operational costs and improve the image of institutions while creating better indoor environments for students and staff. The analyses of countries involved in research can open doors for the establishment of research groups and the development of collaboration between universities in different countries researching similar topics of interest.

The geographical scope of this study is not limited and, therefore, encourages broad applicability of the findings to the global sustainable BE.