Knowledge graph-based data integration system for digital twins of built assets

Digital twins (DTs) and eXtended Reality (XR) are two core technological enablers for engineering in the Metaverse that can accelerate the human-centric Industry 5.0 transformation. The digital twin technology provides a digital representation of a physical asset with data linkages for inspection, monitoring, and prediction of complex processes or systems, while eXtended reality offers real-and-virtual combined environments for human users to interact with machines. However, the synergies between digital twins and eXtended reality remain understudied. This work addresses this research gap by introducing a novel method “TwinXR” that leverages ontology-based descriptions of Digital twins, i.e., digital twin documents, in industrial eXtended reality applications. To ease the use of the TwinXR method, we publish a Unity package that allows data flow and conversion between eXtended reality applications and digital twin documents on the server. Finally, the work applies the TwinXR method in two industrial eXtended reality applications involving overhead cranes and a robot arm to demonstrate the use and indicate the validity of the method. We conclude that the TwinXR method is a promising way to advance the synergies between digital twins and eXtended reality: For eXtended reality, TwinXR enables efficient and scalable eXtended reality development; For digital twins, TwinXR unlocks and demonstrates the potential of digital twins for data interchange and system interoperation. Future work includes introducing more detailed principles of Semantic Web and Knowledge Graph, as well as developing factory-level TwinXR-compatible applications.

Biopharma Is Going Digital … Bit by Bit

The emergence of Digital Twin (DT) technology has garnered significant attention in healthcare, providing a revolutionary approach to patient care, diagnostics, and treatment planning. Digital twins are virtual replicas of physical entities (in this case, human beings), created through the integration of real-time data collected from sensors and devices. This paper explores the potential role of Digital Twin technology in healthcare, its applications, challenges, and opportunities. We review recent studies and current implementations, focusing on the integration of digital twins for personalized care, predictive analytics, and decision-making. The research also highlights the barriers such as data privacy concerns, high implementation costs, and technological limitations. The findings point to a future where Digital Twin technology plays a central role in improving patient outcomes, operational efficiency, and the accuracy of treatments in healthcare. The paper concludes with a discussion of future research directions and applications for digital twins in healthcare. The purpose of this research study is to examine how digital twins can transform healthcare by going over the prospects, difficulties, and future course of their integration into healthcare systems. This study looks at the existing situation in an effort to offer insightful information about how digital twins can transform healthcare delivery and enhance patient care, while also identifying the challenges that need to be addressed for their widespread adoption

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber–physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.

Recently, more and more digital twin technologies are getting attention in managing and operating urban metros. The fact that integrating time-monitor data like a metro digital twin right now has chances to improve its operation and dependability. This essay is mostly about the applications of digital twins for the metro system: infrastructure monitoring and operation management. Digital twin, at the infrastructure level, can be utilized in observing the structure inside the metro tunnel to see if there is a dangerous situation. For example, a swelling on Earth. After applying our collected data to a predictive analysis, this will offer us an early warning and allow preventive maintenance. In terms of operation, a digital twin system would help people to monitor the operation of metro network, which means smoother passenger flow. Although the metro system's digital twin technology has great benefits, problems like data integration, reliability of the whole system and its security still need to be solved. More studies and practices need to take place to make sure that digital twin tech is employed safely and correctly on future metros.

This study examines the potential of digital twin (DT) technology to strengthen urban security, with a specific focus on flood risk management in smart cities. A DT is understood as a virtual representation of real-world assets and processes, continuously synchronised with data from the physical environment. Building on an analysis of the existing DT literature and maturity assessment, identified operational requirements and the authors’ expertise in crisis management, this study proposes a structured set of DT maturity levels with stage boundary conditions and illustrative measurable indications and designs a maturity-driven data content model for a flood-oriented DT. The framework identifies essential data layers, sensing requirements and integration mechanisms necessary for representing hydrological, infrastructural and environmental conditions at operationally meaningful update frequencies. This study further outlines the conceptual architecture of a flood DT and discusses its potential to support prediction, situational awareness and decision making across crisis management phases. By providing recommendations for DT implementation and highlighting opportunities for future development, this study contributes to ongoing efforts to enhance the resilience and safety of urban areas through advanced digital technologies.

Digital twin technology has been increasingly applied in healthcare and patient well-being in recent years. This paper provides an overview of the current methods and applications of digital twins in the healthcare field. One such application is digital twins in precision healthcare, where digital twins are used to create patient-specific models to assist in diagnosis and treatment planning. Digital twins are also used in hospital/clinic management, where they help to optimize resource allocation and workflow processes. In response to the COVID-19 pandemic, digital twins have been utilized to detect outbreaks and predict disease spread. In addition, digital twins have been applied in bio-manufacturing and pharmaceutical industry to improve manufacturing processes. Another application area is machine learning and modeling, where digital twins are used in machine learning, data generation, and system modeling for applications in healthcare and disease prediction. Security and ethical issues related to digital twins are also discussed in this paper, as privacy concerns and data protection remain important considerations in the application of digital twin technology in healthcare. Finally, the paper concludes by discussing the future challenges and directions of future work in this field. These include the need to develop more accurate and sophisticated digital twin models, addressing interoperability and integration issues, and further exploring the potential of digital twin technology in emerging areas such as telemedicine and personalized medicine.

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

In modern medicine, novel technologies play a key role in improving the diagnosis, treatment, and management of patient health. One of the most promising innovations is Digital Twin technology, which enables the creation of virtual models of real-world objects, including human organs. This technology opens new possibilities for personalised medicine, disease prediction, and the optimisation of medical processes. Digital Twins are already actively used in various industries, such as manufacturing, aviation, and urban planning, but their potential in medicine is only beginning to unfold. They allow for creating detailed models of an individual patient’s body, contributing to more accurate diagnostics, simulation of treatment responses, and enhanced efficiency of medical interventions. This article explores the key aspects of Digital Twin medical applications, their advantages, challenges, and development prospects. Special attention is given to the technical foundations of this technology, its use in personalised medicine, health monitoring, disease prediction, and the optimisation of healthcare facilities. For the practical part, the study shows how to create a Digital Twin of a heart based on real patient data. Additionally, the study investigates a model of the heart’s Digital Twin, demonstrates its use, and illustrates how to replicate heart functionality using the Digital Twin. The study describes how a part of a Digital Twin was created using the suggested architecture and outlines how to use and extend this functionality. More than that, the study shows how to integrate artificial intelligence into the Digital Twin architecture and how artificial intelligence algorithms help create Digital Twins. The research shows how to build the Digital Twin using machine learning and artificial intelligence technologies, and how to choose the mathematical model for the Digital Twin. To conclude, the study defines further work, problems, and difficulties in creating the Digital Twin of the heart. The study shows the perspectives on using Digital Twins, their practicality, and real-life cases where Digital Twins can save lives. Also, the study defines the next steps for the Digital Twin creation, testing, implementation, and usage in the real-life healthcare industry.

The article focuses on the project to develop a supply chain management information system using a digital twin for the maritime transport chain. Digital twins reflect real objects and processes in a virtual environment, which enables analysis, modelling and optimisation of supply chain operations. The author describes the main stages of the project, including requirements definition, system architecture development, implementation and testing. Particular attention is paid to the benefits of using digital twins in supply chain management, such as increased efficiency, reduced risk and improved forecasting. The results of the study show the potential of digital twins to optimise supply chain management and increase the competitiveness of companies. The article presents an approach to building a new model of interaction and systematic optimisation of business processes based on digital technologies at all stages of supply chain formation, which ensures the creation of a digital twin for the maritime transport chain. In order to improve supply chains, a digital twin has been proposed, which opens up great prospects for the management of logistics processes and the exchange of information between supply chain participants. This paper presents a framework for an integral Digital Supply Chain Twin (DSCT) capable of covering the entire transport chain. The proposed information system based on the use of the DSCT provides a number of improvements throughout the supply chain, as well as the ability to model and evaluate different scenarios for the supply chain. This creates opportunities to reduce the cost of organising and coordinating transport, improve the quality of transport and logistics services and make them more reliable by identifying potential problems at an early stage. The article analyses the main information technologies that facilitate the use of DSCT as a useful and reliable tool.

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

In the realm of proactive medical care, digital twin technology is rising to the forefront as a paradigm shift due to the combination of customised healthcare, 6G connectivity, and artificial intelligence (AI). This study investigates AI-driven, 6G-powered healthcare digital twin technology, focusing on patient-centric monitoring and predictive analytics. It must clearly outline the research objectives, such as real-time health data integration, adaptive decision-making, and improved treatment personalization. The proposed methodology should describe the use of 6G-enabled ultra-reliable low-latency communication, AI models for digital twin construction, and secure data management frameworks. Improved diagnostic accuracy, fast response time to clinical emergencies, and improved resource allocation in healthcare systems were main outcomes that are presented in this abstract. The technical contributions of this work with the integration of AI, 6G, and digital twin technologies are highlighted in the end of this study, and this may support the next-generation intelligent healthcare solutions. For self-learning healthcare digital twin, a model that leverages 6G-enabled medical informatics, real-time data streams, and artificial intelligence (AI) analytics is presented in this study. This presented model may support in clinical diagnosis, and classifying damages earlier. A real-time updated digital copy of the patient was created by the suggested method by integrating multimodal data from various sources including imaging, vital signs, consumer electronic devices such as smartwatches, wearable sensor metrics, and electronic health records (EHRs). The patient physiological patterns and previous injury profiles are constantly learnt by the integration of digital twin and deep learning (DL) algorithms. This integration is effective in predicting the occurrence, kind, and severity of injuries and internal trauma, falls, cardiovascular events, and neurodegenerative insults. The digital twin and healthcare providers are connected seamlessly with 6G communication infrastructure, guaranteeing ultra-low latency and great dependability. This allows for pre-hospital and critical care settings to implement instantaneous feedback loops, as well as for remote diagnostics and adaptive clinical decision-making. From home health monitoring solutions to smartphones and wearables, consumer electronics are ubiquitous and serve as common data collectors, greatly increase access and enabling the instant collection of accurate data using a digital twin. To keep data private while training AI models on dispersed healthcare data, the architecture offers federated learning. Based on severity level, the injury types are classified by a multi-layered decision engine, and this may promote a customized, context-aware responses like alerting emergency teams, modifying treatment regimens, or demanding robotic surgical support. The AI-driven, 6G-powered healthcare digital twin revolutionizes the diagnosis of injury, and it establish a path for a precise, preventive, and predictive medicine. Furthermore, scaling efficiencies were reduced by 95.81%, while reaction times decreased to 96.88%, resulting in significant gains. This technology is particularly helpful for populations who are elderly, at risk, or located far away, as it decreases reaction time, reduces diagnostic error, and provides scalable, continuous care.

With the prompt digital revolution in architecture, engineering, and construction (AEC), it is crucial to set the early practical steps for advanced digital technology namely digital twin technology. Although theoretical evidence on the potential of digital twins exists in AEC scope, the practical framework to the construction and operation of user-centred digital twin is yet to be investigated. Hence, this applied research investigates systematic methods and techniques for the construction and enablement of a user-centred digital twin paving the way to advanced digital applications in AEC centred on the user of the built environment. As a result, the framework unleashes a systematic roadmap to the creation and development of a city-scale digital twin centred on the human and capable of describing, analyzing, and assessing a built context per its users and usability manner. Further, the framework pours the foundations for a spectrum of digital twin applications in the scope of architectural design, building construction, infrastructure engineering, and facility management. The production of this research is foreseen to outline a systematic and transferable framework for the construction and enablement of a user-centred digital twin.

Digital Twin technology is rapidly emerging as a transformative force in various industries, playing a pivotal role in the advancement of Industry 4.0. As industries evolve, the implementation of Digital Twin is anticipated to drive significant technological progress across sectors such as manufacturing, smart cities, healthcare, and transportation. This paper presents a layered Digital Twin framework that synthesizes recent advances in sensing, data analytics, and machine learning to enable real-time monitoring, analysis, and predictive capabilities for modern power systems. By leveraging these advanced sensors, the Digital Twin framework enables more accurate system modeling, enhanced situational awareness, and improved decision-making processes. This approach aims to significantly transform traditional smart grid operations by offering a more responsive, resilient, and efficient energy management system, ultimately contributing to the optimization and modernization of existing power infrastructure. Furthermore, by highlighting key smart grid applications, the paper demonstrates the effectiveness of Digital Twin and sensor technology in optimizing power distribution, reducing downtime, and addressing prevalent energy challenges, thereby paving the way for resilient and intelligent power systems.

Purpose This study explores the integration of monitored data from Tunnel Boring Machine (TBM) operations into a digital twin framework to generate real-time status information for mechanized tunnelling. The study investigates the potential benefits that can be derived in terms of lean operations and reduced waste of rework. Design/methodology/approach Using a design science research approach within a case study of a tunnelling project, the researchers developed, tested and refined the Digital Twin Construction (DTC) system and observed use cases of its application. The empirical data collected was analyzed to draw conclusions concerning the feasibility of the integration of TBM excavation data into a digital twin and the impacts of its use in improving flow and avoiding rework by reducing uncertainty. Findings The study identifies key technical challenges, such as imperfect data interoperability across different systems involved in tunnelling operations and the need for standardized approaches to modelling. It also highlights the value of the digital twin in supporting lean design and construction workflows by providing the project team with information drawn from a dynamic representation of the tunnel’s physical state in short cycle times. Originality/value This study presents an innovative computational design algorithm that automates data integration, enabling digital twin generation as a functional product. Furthermore, it includes use cases illustrating how the resulting digital twin supports the improvement of specific operations, such as bracketry design and construction. By addressing both technical and operational dimensions, this case study serves as a valuable resource for researchers and industry practitioners aiming to advance digital twin technology and lean construction in tunnelling and broader construction applications.