The current study aims to determine the post COVID-19 adoption rates, the variation of the adoption by regions, and the effects of communication technologies on higher education with focus on students’ engagement and faculty satisfaction. The present research uses the convergent parallel design which is a form of mixed-methods research design. First, the study searched for 18 relevant articles using key search terms including “post-COVID-19 education”, “e-learning tools”, “communication technologies” and “higher education”. The qualitative analysis, however, shows that the technological strategies have to be in line with the preparedness of the people, the need to address challenges such as the lack of face-to-face contact and how technologies such as augmented reality and simulation-based learning can be used. Quantitative analysis shows that teleconferencing tools (β = 0.45, p < 0.001) and cloud computing (β = 0.38, p < 0.003) have positive impact on engagement and satisfaction. The one-way ANOVA results show that there is a difference in the adoption rates across the regions while the MCAs score for communication challenges is 60%. From the descriptive statistics it can be seen that there is a very high adoption rate of cloud computing (Mean = 89.7%, Standard Deviation = 3.1%) and teleconferencing tools (Mean = 84.9%, Standard Deviation = 4.5%). The Structural Equation Modeling (SEM) shows the domino effect of teleconferencing on engagement (β = 0.60, p < 0.001), satisfaction (β = 0.75, p < 0.002) and collaboration efficiency (β = 0.55, p < 0.001). Thus, the current study establishes the fact that there is a need to provide equal opportunities and technology which is adaptable to improve the students’ engagement and satisfaction in various learning institutions.

This study, drawing on the Knowledge-Based View (KBV) and Contingency Theory, explores how analyzer strategic orientation, learning capability, technical innovation, administrative innovation, and SME growth and learning effectiveness are interrelated. Analyzing cross-sectional data from 407 founders, cofounders, and managers of trade and service SMEs in Vietnam’s Southeast Key Economic Region through PLS-SEM, the research demonstrates that analyzer orientation positively impacts both technical and administrative innovation, thereby bolstering SME growth and learning effectiveness. However, learning capability does not significantly impact technical innovation or growth and learning effectiveness. Instead, learning capability negatively affects administrative innovation. Notably, technical and administrative innovations act as mediators between analyzer orientation and SME growth and learning effectiveness. The study provides practical insights tailored for SMEs navigating dynamic market environments like Vietnam, enriching theoretical understanding of SME strategic management within the trade and service sector.

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.