Smmarize the research studys recommendations? Explain the impact of the research on risk managment and quality managment? Summarize the relationship between quality improvment and health care research in relation to this study? This study analyzed the research trends in the field of digital twins by examining metadata from 9639 peer-reviewed articles published between 2000 and 2023. We processed the metadata using an NLP-based toolkit and manually labeled each article with its most relevant application field. Using the KCN methodology, we performed temporal research trend analysis, mapping popular sensing technologies to six application fields and identifying representative examples of digital twins in each field. For researchers, this analysis provides a comprehensive view of the field's development, identifying key areas for future exploration. For architects, the findings highlight technological applications and examples essential for informed decision making in digital twin system design. This study found that the field of digital twins is rapidly growing and diversifying. We used network metrics to analyze the temporal changes in the field and identified emerging and declining keywords over time. We also identified emerging application fields, functions, and enabling sensing technologies. The findings suggest that digital twins are moving toward predictive tasks while ensuring system integrity and security across many sectors beyond manufacturing. We used a Sankey chart to visualize the mapping from popular sensing technologies to six application fields. We found that real-time data, point cloud data, and human–robot interaction are increasing trends. Additionally, we noticed an extension of the traditional sensor definition to include novel sensors such as medical tests and social media posts. We identified neural networks and reinforcement learning as crucial for autonomous decision making. The emergence of federated learning marks a shift toward distributed computation, emphasizing data privacy.Following the mapping, we reviewed specific examples of digital twins in each field. For each application, we analyzed its physical assets, sensors, physical–digital data flow, the form of digital assets, and research objectives. From these examples, we observed a connection between sensor selection and the functionality level of digital twins. We raised concerns over the mismatch between sensor capacity and digital twins' functionality and possible brittle digital twins if they are too dependent on empirical prior knowledge.
Smmarize the research studys recommendations?
Explain the impact of the research on risk managment and quality managment?
Summarize the relationship between quality improvment and health care research in relation to this study?
This study analyzed the research trends in the field of digital twins by examining metadata from 9639 peer-reviewed articles published between 2000 and 2023. We processed the metadata using an NLP-based toolkit and manually labeled each article with its most relevant application field. Using the KCN methodology, we performed temporal research trend analysis, mapping popular sensing technologies to six application fields and identifying representative examples of digital twins in each field. For researchers, this analysis provides a comprehensive view of the field's development, identifying key areas for future exploration. For architects, the findings highlight technological applications and examples essential for informed decision making in digital twin system design. This study found that the field of digital twins is rapidly growing and diversifying. We used network metrics to analyze the temporal changes in the field and identified emerging and declining keywords over time. We also identified emerging application fields, functions, and enabling sensing technologies. The findings suggest that digital twins are moving toward predictive tasks while ensuring system integrity and security across many sectors beyond manufacturing. We used a Sankey chart to visualize the mapping from popular sensing technologies to six application fields. We found that real-time data, point cloud data, and human–robot interaction are increasing trends. Additionally, we noticed an extension of the traditional sensor definition to include novel sensors such as medical tests and social media posts. We identified neural networks and reinforcement learning as crucial for autonomous decision making. The emergence of federated learning marks a shift toward distributed computation, emphasizing data privacy.Following the mapping, we reviewed specific examples of digital twins in each field. For each application, we analyzed its physical assets, sensors, physical–digital data flow, the form of digital assets, and research objectives. From these examples, we observed a connection between sensor selection and the functionality level of digital twins. We raised concerns over the mismatch between sensor capacity and digital twins' functionality and possible brittle digital twins if they are too dependent on empirical prior knowledge.
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