## 2 Literature Review Methodology  
The Digital Twin concept is in a continuous process of development, and its promising development could bring benefits in various fields. The research methodology used in this study examines a comprehensive approach, starting from the Digital Twin concept, taxonomy, proposed reference models and architectures, and addressing its current applications in various fields such as manufacturing, medicine, energy production, education, and smart cities. In light of the multiple application areas and the ability to simulate various real-world scenarios, DT plays an important role in technological advancement and digital transformation. The databases used for this paper are the Web of Science (WoS), IEEE Xplore, Scopus, and Google Scholar. Figures 1-3 present the number of articles obtained following a search on Web of Science, IEEE Xplore, Scopus, and Google Scholar regarding the concept of \"Digital Twin\" over an interval of 8 years, from 2015 and until 2023. Analyzing the evolution of the number of articles published in the period 2015-2023, a significant increase in interest for research in this field was identified. In 2015, Digital Twin was found in 21 articles published by IEEE Xplore, 85 by Web of Science, 1393 by Scopus, and 28,199 by Google Scholar. Figure 1: Number of articles about Digital Twin 2015–2023. Figure 4 shows a statistic regarding the number of articles published on Google Scholar as a result of the search according to the keywords: \"Digital Twin definition\"--39,500 results, \"Digital Twin classification\"--27,100 results, \"Digital Twin architecture\"--31,100 results. The criteria that were the basis for the selection of the definitions were represented by the diversity and complementarity of the information that helps to identify the key functionalities, the relationship between the Digital Twin and the CPS, and the need for integration with various components. Also, the selection of a detailed classification helps to identify the technologies that facilitate the development and implementation of DTs. At the same time, this classification highlights the adaptation of DTs to various scenarios built on the basis of specific requirements, while ensuring effective implementation in a variety of fields and industries. The criteria on which the architectures were selected was relevance to the ways in which DTs can be structured and managed, ensuring interoperability, adaptability, scalability, and security of the systems. The focus has been on service delivery and adaptability in various fields such as the medical field. Among the exclusion criteria of the architectures is excessive complexity that can generate problems in the development, implementation, and management of Digital Twins, along with a lack or reduction of interoperability, scalability, flexibility, and security. The use of rigid architectures does not allow continuous adaptation to specific requirements. In the context of implementation, problems related to dependence on outdated technologies, resources, and high maintenance costs may occur. Figure 3: Number of Articles on Scopus and Google Scholar about DT. (**a**) Number of Articles on Scopus about DT 2015–2023. (**b**) Number of Articles on Google Scholar about DT 2015–2023. Figure 2: Number of Articles on Web of Science and IEEE Xplore about DT. (**a**) Number of Articles on Web of Science about DT 2015–2023. (**b**) Number of Articles on IEEE Xplore about DT 2015–2023. Figure 5 presents some fields of application of the DT such as manufacturing, medicine, energy production, education, and smart cities. Following a Google Scholar search, the largest number of articles was identified in the field of education--305,000 articles. The second-largest number of articles--273,000--was identified in the field of medicine, indicating a strong trend towards redefining existing paradigms and advancing personalized medicine.