In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.

As the involvement of Chinese enterprises in cross-border mergers and acquisitions (M&A) increases, on the one hand, it can drive enterprises to integrate with the international community and accelerate their transformation and upgrading, continuously enhancing their international competitiveness; on the other hand, it will also cause enterprises to experience more setbacks and challenges, especially the “weak acquisition of the strong” reverse cross-border acquisitions, which makes enterprises face a higher risk of failure. Reasonable control rights allocation can fully utilize the competitive advantages of enterprises, achieve synergistic cooperation among shareholders, board of directors, and management, promote the realization of enterprises’ cross-border acquisition goals, and thus enhance the value creation of acquisitions. There is a positive correlation between internal legitimacy and acquisition performance; the relevant assumptions about the distribution of shareholder control rights are invalid; the control rights at the board of directors level are negatively correlated with internal legitimacy and acquisition performance, and internal legitimacy has a mediating effect between the control rights at the board of directors level and acquisition performance, but the moderating effect of the acquisition mode is not significant; the control rights at the management level are negatively correlated with internal legitimacy and acquisition performance, and internal legitimacy has a mediating effect between the control rights at the management level and acquisition performance, and the acquisition mode negatively moderates the relationship between the control rights at the management level and internal legitimacy. This study takes the post-acquisition control rights allocation as the entry point, and examines the cross-border acquisition activities of Chinese enterprises from the perspective of stakeholders. The research results not only can enrich existing acquisition theory, but also can provide theoretical guidance for Chinese enterprise managers on allocation of control of target enterprises, and provide a theoretical basis for the state to formulate and optimize the system and policies of enterprises’ cross-border acquisitions.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

The quest for quality postgraduate research productivity through education is on the increase. However, in the context of the African society, governance structures and policies seem to be impacting on the quality level of the provided education. Hence, this conceptual study explored the roles of governance structures and policies in enhancing and ensuring quality postgraduate education programmers in African institutions of higher learning. To this end, various relevant literature was reviewed. The findings showed amongst others that governance structures and policies affect the quality of education provided. Meanwhile, other factors such as curriculum, foreign influence, lack of resources, training, amongst others contribute to the quality of education provided. The study concludes that there is need for the current structures of governance and the designed and implemented policies for postgraduate education to be reviewed and adjusted towards ensuring the desired transformation.

This research article explores the relationship between psychological well-being and satisfaction with life among young, athletically talented students educated through individualised programs. The primary objective is to assess whether a safe educational environment, emphasising psychological safety and individual support, positively impacts the general satisfaction and academic performance of these students. Using Ryff and Keyes’ Psychological Well-Being Scale and Diener’s Satisfaction with Life Scale, data were collected from 188 participants—Secondary and university students engaged in rigorous athletic training while completing their studies in the Czech Republic. Key findings reveal a strong correlation between self-acceptance, autonomy, coping with the environment, and enhanced satisfaction with life, indicating that well-being in young athletes is significantly influenced by psychological resilience, emotional support, and control over one’s educational journey. Research highlights that individually tailored learning environments, which provide flexibility for training and access to mental health support, contribute to a balanced development between academic and athletic goals. Additionally, the results suggest that a positive correlation within the educational environment, both with peers and instructors, further strengthens the satisfaction with life and reduces the risk of burnout. Implications underscore the need for educational institutions to adopt holistic approaches that support psychological well-being and accommodate the unique needs of athletically talented students. Recommendations include structured mentorship, flexibility in academic scheduling, and access to professional counselling. Future research should investigate the long-term impacts of such environments on academic and athletic success, considering factors such as social inclusion and the effects of digital education.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.