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Since the declaration of COVID-19 as a pandemic in March 2020, higher education institutions (HEIs) across the globe have shifted – entirely or partly – to online teaching and learning; Lebanese HEIs were no exception. Such an unprecedented and “forced” transition to online teaching and learning has created a landscape for scholars and researchers to inquire into the efficiency and effectiveness of online teaching and learning, students’ satisfaction with this virtual educational experience, and instructors’ satisfaction with such an experience. This chapter presents a study about Lebanese instructors’ satisfaction with online teaching and learning during the pandemic. Instructors’ satisfaction was measured in terms of three satisfaction constructs of psychometrically validated “Online Instructor Satisfaction Measure” (OISM): instructor-to-student interaction (ISI); student-to-student interaction (SSI); and course design, development, and teaching (CDT). Data were collected through an online questionnaire from 102 Lebanese instructors across 7 Lebanese universities: 6 private universities and the national public Lebanese higher education institute (Lebanese University). This study found that there were no significant changes between the satisfaction levels in relation to each of the ISI, SSI, and CDT constructs between STEM education background instructors and their non-STEM education background counterparts. Despite their dissatisfaction with the level of interaction between them and their students (ISI), the participating Lebanese instructors were satisfied with the SSI, except for students’ collaborative and group work, and were also satisfied with the CDT, except for the preparation time required for delivering an online course.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

This study aims to emphasize the youth community’s importance in sustaining green entrepreneurial efforts. The study used the stimulus organism response framework as the theoretical base using two separate studies.

Study 1 commences the development of the student green engagement construct through a focus group, panel discussion and exploratory factor analysis, which supported five items. Study 2 measures the relationship of student green engagement with green entrepreneurial intention by mediating university entrepreneurial support and entrepreneurial motivation. Data from 448 students were gathered from five Malaysian private institutions using a purposive sampling technique.

Findings indicate a robust association of student green engagement (stimuli) with green entrepreneurial intention (organism). Furthermore, mediation analysis shows strong mediating effects of university entrepreneurial support and entrepreneurial motivation on green entrepreneurship behavior (response).

The study’s findings can help the universities and concerned governmental departments instill a sense of sustainable entrepreneurship in university students.