This research paper explores the fusion-driven dynamics of Egypt's digital economy and its socio-economic implications, with a specific focus on the growth of e-commerce, FinTech, startups, and digital skills development. It explores the transformative effects of the digital economy on entrepreneurship, job creation, and inclusive participation in Egypt's evolving digital landscape. The study examines the barriers hindering inclusive access to digital technologies, digital skills, and digital financial services, aiming to propose strategies for promoting digital inclusion. Additionally, the research explores the role of social entrepreneurship in bridging the digital divide and fostering sustainable development in Egypt's digital economy. By analyzing the impact of social entrepreneurship initiatives, the study sheds light on innovative models that empower marginalized communities and contribute to the inclusive growth of the digital economy. The findings of this research contribute to the existing body of knowledge by providing insights into the unique dynamics of Egypt's digital economy and the potential of social entrepreneurship in driving digital inclusion and sustainable development. The study concludes with recommendations for policymakers, businesses, and stakeholders to foster an enabling environment that supports equitable and inclusive participation in Egypt's digital economy.
Read MoreDoi: https://doi.org/10.54216/JSDGT.020201
Vol. Volume 2 Issue. Issue 2 PP. 08-17, (2023)
This paper proposes a novel Multi-Criteria Decision Making (MCDM) framework for sustainable supply chain management. The framework addresses the challenges of evaluating and selecting suppliers based on sustainability criteria, optimizing logistics operations, and making sustainable decisions within the supply chain. Through a comprehensive case study, the effectiveness of the proposed framework is demonstrated. The results show that the framework provides a structured and systematic approach for evaluating supplier sustainability performance and supporting decision-making. The framework integrates established MCDM techniques, such as Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), while accommodating the unique requirements of supply chain sustainability. The findings highlight the advantages of the proposed framework, including its ability to handle uncertainties, incorporate multiple criteria, and facilitate informed decision-making for sustainable supply chain management.
Read MoreDoi: https://doi.org/10.54216/JSDGT.020202
Vol. Volume 2 Issue. Issue 2 PP. 18-25, (2023)
As societies face increasing environmental challenges, the need for holistic sustainability frameworks in waste management becomes imperative. This paper presents a comprehensive approach to rethinking waste management within the context of a circular economy. The study begins by examining the limitations of current waste management practices, highlighting the urgency to transition towards sustainable solutions. Emphasizing the importance of a circular economy, the paper discusses the potential benefits of adopting circular principles in waste management systems. The primary objective of this research is to propose a holistic sustainability framework that integrates key components for effective waste management, including waste reduction, recycling, resource recovery, and stakeholder engagement. The framework incorporates established methodologies such as Life Cycle Assessment (LCA), Material Flow Analysis (MFA), and Multi-Criteria Decision Analysis (MCDA) to guide decision-making processes. The framework is validated through a case study on Tokyo, Japan, assessing the applicability and effectiveness of the proposed approach in a real-world context. The findings highlight the significance of implementing source separation programs and promoting composting to reduce the organic waste fraction in Tokyo. Such measures can divert organic waste from landfills and transform it into a valuable resource. The validated framework provides insights into developing a holistic sustainability framework for waste management, contributing to the advancement of sustainable practices in achieving a circular economy.
Read MoreDoi: https://doi.org/10.54216/JSDGT.020203
Vol. Volume 2 Issue. Issue 2 PP. 26-33, (2023)
Increases in sustainability performance and the adoption of innovative strategies for continuous improvement were driven by the need for production companies to compete in an increasingly globalized economy. Better operational performances are achieved when sustainable Production is included in industrial processes because wastes, costs, and environmental effects are reduced, and ergonomic requirements are met. To improve their performance and maintain a leading position in the market, several companies have turned to sustainable production practices. The study's goal is to improve the implementation of traditional Lean Production (LP) by creating an integrated Single valued neutrosophic Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method. The PAPRIKA is extended under a neutrosophic set. PAPRIKA is used to compute the weights of criteria by comparing the criteria. Also, the PAPRIKA method is used to rank and select the best strategy. The application is performed on the steps of the PAPRIKA method.
Read MoreDoi: https://doi.org/10.54216/JSDGT.020204
Vol. Volume 2 Issue. Issue 2 PP. 34-43, (2023)
As the Internet of Things (IoT) continues to expand, the security of connected devices becomes a paramount concern. Malicious actors exploit vulnerabilities in these devices, leading to severe consequences such as data breaches, privacy infringements, and service disruptions. Traditional security measures struggle to keep pace with the evolving threat landscape, necessitating advanced solutions. In this paper, we present a pioneering approach to fortify the security of IoT environments against malware through the integration of advanced machine intelligence techniques. Our work addresses this critical concern by introducing a comprehensive Machine Intelligence Strategy designed to detect and classify malware in IoT ecosystem. Leveraging Support Vector Machines (SVM) with different kernel choices, our strategy offers a multi-faceted defense mechanism. Through extensive experimentation and evaluation on public dataset of malware images, we demonstrate the efficacy of our strategy in fortifying the guardianship of connected devices, fostering a safer and more resilient IoT ecosystem. Beyond technical contributions, our research fosters a deeper understanding of the symbiotic relationship between machine intelligence and IoT security, propelling advancements in safeguarding the ever-expanding landscape of interconnected devices.
Read MoreDoi: https://doi.org/10.54216/JSDGT.020205
Vol. Volume 2 Issue. Issue 2 PP. 44-52, (2023)
