Women entrepreneurship has attracted the attention of academics and practitioners with a large body of research studies in recent years. Past literature reviews on women entrepreneurship have been criticized for their limited scope, lack of interdisciplinary perspective, and the need for more objective, technology-facilitated analytical methods. Our study provides insights into the development of women entrepreneurship research, including a new analysis through the lens of sustainable development and the impact of the COVID-19 pandemic. Bibliometric indicators and a systematic literature review approach are used to analyze literature published between 1991 and 2021 to better map the development of research and related opportunities for enhancing studies on women entrepreneurship. In addition to traditional bibliometric indicators such as publications, citations, etc., we used altmetrics, a new metric to assess the engagement and impact of publications based on social media presence. The Dimensions database has been used to assemble and arrange 3157 publications on women entrepreneurship, of which 843 publications are directly aligned with the Sustainable Development Goals (SDG) and 80 publications related to COVID-19. Our findings indicate that the top three SDG of interest to researchers are: SDG 8, decent work and economic growth; SDG 10, reducing inequalities; and SDG 5, gender equality. Within each SDG, we find concentrated studies on themes relating to the socio-political and small-medium enterprises, including family business management and gender biases, and their implications for sustainable development. Further, studies on the impact of COVID-19 reveal a significant bias towards women’s empowerment in ICT, digitization, and e-commerce while exposing the need for gender-moderated policies and governmental interventions. We offer suggestions for future studies on enabling and measuring the contributions of women’s entrepreneurship to sustainable development, including capital investments and the long-term impacts of the pandemic on women-led enterprises.

Introduction: The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.Methods: This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for a comprehensive literature review, analyzing over 9,350 publications from 2004 to 2023. Utilizing BERTopic modeling, 14 key themes in AI-driven cybersecurity were identified. Topics were clustered and validated through a combination of algorithmic and expert-driven evaluations, focusing on semantic relationships and coherence scores.Results: AI applications in cybersecurity are concentrated around intrusion detection, malware classification, federated learning in privacy, IoT security, UAV systems and DDoS mitigation. Emerging fields such as adversarial machine learning, blockchain and deep learning are gaining traction. Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. Global trends indicate significant contributions from the US, India, UK, and China, highlighting geographical diversity in research priorities.Discussion: While AI enhances cybersecurity efficacy, challenges such as computational resource demands, adversarial vulnerabilities, and ethical concerns persist. More research in trustworthy AI, standardizing AI-driven methods, legislations for robust privacy protection amongst others is emphasized. The study also highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes.

Access and personalized instruction required for laboratory education can be highly compromised due to regulatory constraints in times such as COVID-19 pandemic or resource shortages at other times. This directly impacts the student engagement and immersion that are necessary for conceptual and procedural understanding for scientific experimentation. While online and remote laboratories have potential to address the aforementioned challenges, theoretical perspectives of laboratory learning outcomes are critical to enhance their impact and are sparsely examined in the literature. Using Transactional Distance Theory (TDT), this paper addresses the gap through a case study on Universal Testing Machine (UTM). By comparing physical (PL-UTM) and remotely triggerable (RT-UTM) laboratory platforms, the structure and interactions as per TDT are analysed. Characterization of interactivity between remote learners and instructors disclose indicative parameters that affect transactional distances and aid in conceptual understanding in remote laboratory learning environment. An extensive pedagogical study through development of two instruments towards assessing conceptual understanding and perception of platform effectiveness that was conducted both on physical laboratory and RT-UTM showed: (1) remote users conducted experiments 3 times more frequently (2) completed assignments in 30% less time and (3) had over 200% improvement in scores when RT-UTM platform was integrated into mainstream learning.

The increasing emphasis on sustainability in computing systems, ranging from small devices to large data centers, is fueled by environmental concerns. Moreover, ensuring cybersecurity in these interconnected networks demands the use of technologies such as resource-intensive cryptography and advanced intrusion detection systems. Our unique study investigates the integration of environmental sustainability into cybersecurity practices by identifying six pivotal themes through a textual analysis of related publications via machine learning based topic modeling. These themes highlight the convergence of cybersecurity with sustainable development goals (SDGs), particularly SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 8 (Decent Work and Economic Growth). These include the integration of sustainable cybersecurity measures in smart cities, sustainable digital protection strategies, the application of blockchain for cybersecurity in smart grids, and cybersecurity solutions for SMEs aimed at minimizing resource consumption. Additionally, the study explores multidisciplinary strategies and innovations across four perspectives: adaptive frameworks that prioritize resilience and environmental consciousness, policy shifts for coordinated protection, the power of AI in intelligent threat detection, and the impact of emerging technologies on both security and environmental efficiency. These strategies advocate for an evolved approach that incorporates advanced technologies such as AI, IoT, and blockchain into resilient, sustainable cybersecurity frameworks. This study not only provides a comprehensive overview of the intersection of cybersecurity and sustainability but also serves as a guide for future research and practical applications in creating robust, environmentally friendly cybersecurity practices.