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Digital twin, a core technology for intelligent manufacturing, has gained extensive research interest. The current research was mainly focused on digital twin based on design models representing ideal geometric features and behaviors at macroscopic scales, which is challenging to accurately represent accuracy and performance. However, a numerical representation is essential for precision microstructures whose accuracy and performance are difficult to measure. The concept of a digital twin for an accurate representation, proposed in 2015, is still in the conceptual stage without a clear construction method. Therefore, the goal of accurate representation has not been achieved. This paper defines the concept and connotation of an accuracy and performance-oriented accurate digital twin model and establishes its architecture in two levels: geometric and physical. First, a geometric digital twin model is constructed by the contact surfaces distributed error modeling and virtual assembly with nonuniform contact states. Then, based on this, a physical digital twin model is constructed by considering the linear and nonlinear response of the structural internal physical properties to the external environment and time to characterize the accuracy and performance variation. Finally, the models are evaluated. The method is validated on microtarget assembly. The estimated values of surface modeling, center offset, and stress prediction accuracy are 94.22%, 89.3%, and 83.27%. This paper provides a modeling methodology for the digital twin research to accurately represent accuracy and performance, which is critical for product quality improvements in intelligent manufacturing. Research results can be extended to larger-scale precision structures for performance prediction and optimization.

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field.

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

Over the last few decades, our digitally expanding world has experienced another significant digitalization boost because of the COVID-19 pandemic. Digital transformations are changing every aspect of this world. New technological innovations are springing up continuously, attracting increasing attention and investments. Digital twin, one of the highest trending technologies of recent years, is now joining forces with the healthcare sector, which has been under the spotlight since the outbreak of COVID-19. This paper sets out to promote a better understanding of digital twin technology, clarify some common misconceptions, and review the current trajectory of digital twin applications in healthcare. Furthermore, the functionalities of the digital twin in different life stages are summarized in the context of a digital twin model in healthcare. Following the Internet of Things as a service concept and digital twining as a service model supporting Industry 4.0, we propose a paradigm of digital twinning everything as a healthcare service, and different groups of physical entities are also clarified for clear reference of digital twin architecture in healthcare. This research discusses the value of digital twin technology in healthcare, as well as current challenges and insights for future research.

Background As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment. Objective This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions. Methods Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management. Results Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved. However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment decision-making, prognosis prediction, and surgical planning. Conclusion Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.

The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building.

Many transportation infrastructures all around the world are facing new challenges in terms of ageing and loss of performance. The infrastructural asset managers are required to perform scrupulous control of the health condition of the infrastructures over time and to execute the required maintenance works. In this context, digital twin models of the infrastructures should have a key role to simplify and speed up the procedures for proper maintenance. This paper discusses the advantages of developing digital twin models for the management of infrastructures, with a focus on bridges. In particular, the role of dynamic tests performed on bridges for the development of digital twin models is addressed, paying attention to test procedures and requirements. Issues such as the quality of instrumentation, the numerosity, and layout of sensors, and the acquisition and post-processing procedures are addressed through applications to two real bridge case studies. Both infrastructures are multi-span pre-stressed RC bridges that were dynamically tested after the restoration and seismic upgrading works. Results of ambient vibration tests and operational modal analyses are described, providing an idea of dynamic test requirements, as well as their use within the framework of the digital twin model creation.

This article presents an innovative method for predicting transformer temperature and faults in distribution substations using a digital twin model combined with deep learning techniques. By constructing a digital model of the transformer, real-time monitoring and precise simulation of its operating status are achieved. In the prediction process, convolutional neural networks (CNN) and long short-term memory networks (LSTM) are fused to mine data features deeply and predict the future state of the transformer. The results show that this method demonstrates significant advantages in transformer temperature and fault prediction, with an accuracy rate as high as 96.55%. Moreover, the error rate of this method has been significantly reduced through comparative experimental verification. In addition to ensuring high accuracy, this method achieves a false alarm rate of less than 0.12% and an average detection time of only 1.35 seconds, further highlighting its effectiveness in practical applications. Therefore, the transformer temperature and fault prediction system developed in this article for distribution substations can effectively improve the stability and safety of the electrical power system (EPS) and provide new and powerful support for the intelligent management and maintenance of transformers.

This paper proposes a digital twin (DT) modeling method for the lifecycle state evaluation of cable-stayed bridges, which cover a large time scale and simultaneously involve macroscopic and microcosmic exploration. First, the DT model at the design stage corresponds to the computer-aided design (CAD) model and then evolves to the computer-aided engineering (CAE) model during the construction stage. After that, the DT model for the operational stage starts from the completion model of the bridge and keeps evolving with the bridge physical entity in the long-term service. An information interaction media is used for the information interchange between the DT model and the bridge physical entity. And a fidelity index gives the quantitative estimation of similarity between the DT model and the bridge physical entity. Finally, the proposed method has been verified against a cable-stayed bridge model, whose DT model could reflect the actual state evolution of the physical entity.

As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage.