## 4 Analyses of the Applications of Digital Twin Models
### Digital Twin in Other Domains
[96] developed three modules for NTUniverse (NTU): digitalizing the NTU campus, modeling the campus train, and virtual education. Thus, the Digital Twin contributes to the digitalization of the university campus, providing users with a realistic immersive experience by integrating detailed building components and accurately simulating railway transportation and vehicle dynamics, while also allowing students to interact in safe and interactive 3D environments. Additionally, the Digital Twin enables integration with other systems for real-time display of building information and the generation of realistic simulations based on data collected from sensors. Another important aspect is the integration of STEM (Science, Technology, Engineering, and Mathematics) modules and the enhancement of user interfaces to enhance the learning experience for students. These scenarios are not only beneficial for students but also for new employees, aiding in the development of technical skills and ensuring a high level of safety in their training. In ref. [97], [PERSON] et al., presented a framework called SAMPLE (Machine-learning Predictions as Lightweight Estimates) to support decision making using the online Digital Twin, employing offline learning methods. The efficiency of this framework was demonstrated through a case study focused on disruption mitigation, highlighting its ability to improve computational efficiency and accelerate the process fourfold, using machine learning models with low predictive accuracy. The authors identified several research directions to enhance the performance of SAMPLE, such as exploring alternative models to integrate offline machine learning predictions with online simulation output and investigating other estimation methods, all aimed at improving the classification of alternatives based on their importance. Additionally, the authors suggested incorporating data from real-time simulation iterations to adjust estimates from offline-trained machine learning models in new contexts. Another field of application of DTs is energy production. Thus, in [98], the authors presented how the modeling of a wind farm can be performed either at the individual level of the windmills, treating them as subsystems, or at the level of the entire farm using a single DT. An example of the oil industry is [99], where the authors propose a DT based on machine learning algorithms, with a role in forecasting and data analysis to identify risks that may occur in an oil transportation system. Thus, an integrated and intelligent virtual automatic control system is created, highlighting the integration of transmission lines with the aid of wireless data networks. In addition, Industry 4.0 standard technologies regarding oil and gas have been updated to emphasize the shift to the digital component. In Refs. [100; 101; 102], the authors presented the integration of these technologies to increase the energy efficiency in smart enterprises, aiming to reduce both production costs and gas emissions, as illustrated in [103; 104; 105]. In ref. [106], [PERSON] et al., conducted a study on bridge health monitoring systems, which included sensor technology, computer vision technology, data preprocessing methods, noise reduction, data reconstruction, and early warning systems for abnormal data. The authors highlighted the importance of these systems in the field of civil engineering, considering the integration of satellite technologies such as InSAR (Interferometric Synthetic Aperture Radar), which supports the development of visualization platforms and risk assessment methods. In this context, by integrating the concept of Digital Twin, virtual models of bridges can be created to perform various procedures regarding their simulation, prediction, and real-time analysis of their health.