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The Emerge of Digital Twin (DT) makes many changes to the life quality improvement, one of it is in how to manage the transportation systems. With DT, the current transportation systems have a great probability to become smarter. This research has a purpose to describe a variation of DT usage in transportation system. To achieve the targeted result, this research is using qualitative approach with code and network methods. Based on the analysis, there are three functions of DT in this domain. First, DT can be used to model the transportation system and find the best formula for optimizing it. Second, DT also can be used for conducting several policy scenarios (changing in the parameters). Lastly, DT can be used to monitor the quality of transport data that in to the model's algorithm.

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

With the widespread deployment of advanced heterogeneous technologies and growing complexity in our modern society, there is an increasing demand for risk‐aware management and joint operation of interconnected infrastructures and lifeline networks. The coordination between Power and Water Networks (PWNs) is urgently needed as water networks are one of the most energyintensive critical infrastructures. This paper proposes a framework for day‐ahead operation optimization and coordination of the interconnected Joint Power and Water Networks (JPWNs). Unlike the state‐of‐the‐art where PWNs are individually operated in their respective domains, we present an integrated framework for PWNs that conjoins the Optimal Power Flow (OPF) mechanisms in power grids with innovative operation models of the water networks. Piece‐wise linearization is applied to the nonlinear hydraulic operating constraints to convert the proposed optimization model into a mixed‐integer linear programming (MILP) formulation. The suggested framework is applied to a 15‐node water network jointly operated with the IEEE 9‐bus and IEEE 57‐bus test power systems. The simulation results show the effectiveness of the proposed framework, resulting in cost reduction and energy‐saving when both systems’ operation is jointly optimized. The results show that the proposed methodology is scalable and computationally‐efficient when applied to larger‐scale systems.

Digital Twins (DTs) have become a pivotal technology for enhancing the understanding, monitoring, and ultimately autonomous piloting of systems across various domains, including large-scale critical infrastructures such as smart electricity networks. The development of DTs necessitates developing diverse services that utilize different models and a digital shadow, which encompasses both real-time and historical data from the physical counterpart. The extensive scale of large infrastructures presents significant challenges, including managing numerous parameters, heterogeneous data, and the complex computations required, particularly with the increased use of AI algorithms. Current technologies, built by stacking multiple databases and using general-purpose languages, are inadequate for efficiently implementing digital twin services that need runtime reactivity. This tool demonstration paper introduces GreyCat, a framework designed for the development of digital twins over large-scale digital shadows. GreyCat combines imperative object-oriented programming, database persistent indexes, and scalable memory management to facilitate the creation of comprehensive and efficient digital twins. We demonstrate the ease use of GreyCat through simple examples and showcase its effectiveness in constructing the national digital twin of Luxembourg's electricity grid, which is currently operational and managing billions of data points. Reflecting on the development of GreyCat over the past years, we discuss the main lessons learned and identify open questions for future digital twin development frameworks.

The rapid advancement of Industry 4.0 and the Internet of Things (IoT) has underscored the importance of effective diagnostics for electric motors, which are critical components in industrial, energy, and transportation systems. Traditional diagnostic methods, such as vibration, thermographic, and electromagnetic analyses, often lack adaptability and pre-dictive capabilities, limiting their ability to proactively identify faults under variable operating conditions. The digital twin concept, a virtual representation of physical assets synchronized with real-time sensor data, offers a transformative approach to technical condition monitoring. This paper reviews the state of diagnostic methods for electric mo-tors and evaluates the integration of digital twins to enhance diagnostic accuracy and pre-dictive maintenance. By leveraging platforms like Ansys Twin Builder and MATLAB/Simulink, digital twins enable real-time simulation, fault detection, and opera-tional optimization. The study proposes a novel methodology for diagnosing electric motor conditions using digital twins, incorporating mathematical modeling and real-time signal processing. Key findings include improved fault detection accuracy (MAE < 2.5%, RMSE < 3.1%) and the ability to simulate typical defects like rotor imbalance and insula-tion breakdown. The results demonstrate the potential of digital twins to revolutionize condition-based and predictive maintenance, offering scalable solutions for industrial applications. Future prospects include integrating machine learning for adaptive diagnos-tics and expanding digital twin applications to complex electromechanical systems.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

This paper aims to present a comprehensive survey on information integration (II) in space informatics. With an ever-increasing scale and dynamics of complex space systems, II has become essential in dealing with the complexity, changes, dynamics, and uncertainties of space systems. The applications of space II (SII) require addressing some distinctive functional requirements (FRs) of heterogeneity, networking, communication, security, latency, and resilience; while limited works are available to examine recent advances of SII thoroughly. This survey helps to gain the understanding of the state of the art of SII in sense that (1) technical drivers for SII are discussed and classified; (2) existing works in space system development are analyzed in terms of their contributions to space economy, divisions, activities, and missions; (3) enabling space information technologies are explored at aspects of sensing, communication, networking, data analysis, and system integration; (4) the importance of first-time right (FTR) for implementation of a space system is emphasized, the limitations of digital twin (DT-I) as technological enablers are discussed, and a concept digital-triad (DT-II) is introduced as an information platform to overcome these limitations with a list of fundamental design principles; (5) the research challenges and opportunities are discussed to promote SII and advance space informatics in future.

The cellular network standard 5G meets the networking requirements for different industrial use cases due to the advantages of low latency, high bandwidth, and high device density while providing a very good quality of service. These capabilities enable the realization of wireless digital twins (DTs), a key element of future cyber-physical production systems. DTs for prediction, monitoring, and control of machine tools need physical modeling as well as the bidirectional exchange of information between the digital and the physical world. 5G is a wireless communication technology with the potential to disruptively change industrial communication. 5G enables wireless, highly scalable, and flexible realization of even safety- and latency-critical connections. In this paper, a 5G enabled DT of a machine tool for process control, monitoring and simulation is developed and implemented. A bidirectional communication between the physical machine tool and the DT is realized via 5G. Moreover, process prediction is enabled based on physics simulation. Next to the physical modeling of the machine behavior, a 5G-capable interface between the input and output signals of the machine control system and the developed DT is implemented. Moreover, the DT is migrated in a wireless form to an edge server. Furthermore, the capabilities of the DT are demonstrated. Therefore, the architecture and implementation of the DT as well as its benefits and challenges are outlined.

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation.

The increasing urban traffic problems have made the transportation system require a large amount of data. Aiming at the current problems of data types redundancy and low coordination rate of intelligent transportation systems (ITS), this paper proposes an improved digital twin architecture applicable to ITS. Based on the improved digital twin architecture, a framework for dynamic and static data collaboration in ITS is constructed. For various collaboration methods, this paper specifically describes the collaboration methods and scopes, and designs the framework and interfaces for data mapping. Finally, the effectiveness of the framework is verified by case studies to mine the spatiotemporal distribution characteristics of data, capture human travel characteristics, and visualize intersections using digital twins. This paper provides a new data fusion idea for digital twin systems in ITS, and the framework covers all data types in digital twin systems for cross-integration analysis.

<sec> <title>BACKGROUND</title> Diabetes is a chronic metabolic condition, characterized by impaired blood glucose regulation. It is often linked to serious health complications and comorbidities that significantly affect quality of life, requiring effective management, continuous monitoring, and advanced data analytics. Notably, tailored diabetes management can be enhanced by digital twins (DTs), which serve as an adaptive digital replica of patients using clinical, physiological, and lifestyle data. </sec> <sec> <title>OBJECTIVE</title> This review explores diabetes-related DTs by examining their patient representation levels. We aim to synthetize the current state of the art and outline the foundations of a holistic multi-level, multi-functional personalized digital twin for diabetes management. </sec> <sec> <title>METHODS</title> We investigate requirements for a patient-centred holistic DT and classify existing approaches into three representation levels: 1) Data representation, involving structured, context-aware, and AI-ready data architectures, that support data analysis, enable semantic interoperability, relationship extraction, and real-time bidirectional data exchange between patient and virtual replica. 2) Process representation, primarily based on mechanistic models simulating glucose-insulin-meal, and exercise-glucose dynamics. 3) Data-driven representation, focusing on individualization through predictive modelling of disease onset, adverse events, and generation of explainable, personalized recommendations. The literature is synthetized to provide a perspective of a holistic multi-level DT and to identify research gaps. </sec> <sec> <title>RESULTS</title> Digital twins accompany patients throughout their lifecycle and span a wide range of use cases from long-term disease prediction to timely prediction of severe events. However, patient-centered DTs remain at an early developmental stage. Most existing systems function primarily as simulation tools and lack comprehensive integration of data, process, and data-driven representations. Key gaps include limited use of standardized semantic data models and ontologies, insufficient real-time bidirectional architectures, and fragmented integration of mechanistic and machine learning models, which are often treated as independent rather than complementary components. </sec> <sec> <title>CONCLUSIONS</title> Although digital twins show substantial potential for advancing personalized diabetes care, current implementations remain fragmented and incomplete. Future research should prioritize the development of holistic, multi-level digital twins that integrate interoperable data infrastructures, mechanistic simulations, and data-driven models into cohesive, patient-centered systems capable of supporting lifelong disease management. </sec>

&lt;p&gt;The maturity of Digital Twin (DT) models has evolved in the aerospace and manufacturing industries; however, the construction industry still lags behind. DT technology can be applied to achieve smart management through the entire life cycle of structures. Particularly for bridge structures, which play an essential role in any transportation system and can have high maintenance demands throughout their long life spans. In this study, a literature review on DTs was performed, from the origins of the concept until current best practice focused on bridges. Especially concerning structural analysis and facility management, few studies that employ DT for bridges were encountered. The main challenges identified are related to treatment of the large amount of data involved in the process, mostly gathered from different platforms. Finally, a framework for smart facility management of bridges using DTs was proposed to tackle potential solutions.&lt;/p&gt;

Configuring the network connections in industrial, power, and water networks to mimic the structural patterns of ecological food webs has been shown to improve the resilience of human networks. This work investigates the ability of food web inspiration to specifically guide the incorporation of renewable energy and water sources for resilience. Feasibility is tested using the water and electricity networks of the Texas A&M University main campus, demonstrating the potential of university campus case studies as analogies for other multi-use networks, such as cities or industrial-commercial regions, due to the variety of functions met within the system boundaries. Ecological robustness, the unique and characteristic behavior of ecosystems to slightly favor redundancy over efficiency, is used to correlate the incorporation and supply-levels of solar power and rainwater collection in a realistic campus model with the overall resilience of the electricity and domestic water networks. Non-obviously, the results suggest that the ecologically-similar resilience is achieved when less than 100% of utilities come from renewable sources, indicating an important potential tradeoff between efforts to shift to 100% renewable sources and network resilience concerns.

The number of spacecraft designed and built over the next century will grow exponentially as communication satellite networks proliferate and NASA continues to push towards the development of many microspacecraft to replace its traditional grand tour space vehicles. Costs in the space industry are measured in terms of weight (dollars per pound launched) and reliability. Unit costs pale in comparison to launch costs and the cost of replacing an entire vehicle in the case of a catastrophic failure. Space systems present a unique application for microelectromechanical systems (MEMS) technology which can be applied to miniaturize many of the subsystems in a space vehicle, and can improve overall reliability. This paper identifies potential applications of MEMS in a space system, describes space environmental factors, and reviews efforts to develop appropriate packaging and space qualification methodologies. Finally, a flight experiment for testing the performance of typical MEMS devices and packages in the space environment is described.

The subject of research in the article are the workspace models for flexible integrated robotic systems. The goal of the work is in development of information models to represent workspaces for following application in the automated control systems of flexible integrated manufacturing. The article solves the next tasks: to analyze the representation of workspace to decide practical problems of robotic systems of different nature, to consider the development of informational models for representation on workspaces of intelligent control systems of integrated manufacturing, to consider the practical examples of information presentation on workspaces of production systems. Research methods are set theory and predicate theory. The following results were obtained: there were analysed the main features of informational models development to solve robotic tasks of different nature and were pointed the limitations of existing approaches of formal description, the need of integration of workspace models to decision-support systems and systems of graphical and mathematical simulation of integrated systems; the set theory-based model of information representation for problem-solving processes of flexible integrated robotic systems is proposed; the information-logic model of workspace for mobile robot applications, functioning in flexible integrated systems. is developed and contains the list of objects, includes their geometrical dimensions and supplies the preservation of parameters in time and space; information presentation for automated control system of flexible integrated manufacturing, which implements proposed models, is considered. Conclusions: application of models of information type for automated control systems makes to supply logical unification of flexible integrated manufacturing elements, to provide monitoring of states of technological equipment of production systems in space and time and formation of their digital twins, to promote functioning of intelligent decision-support systems for robotic systems of different types, that improves characteristics of production control.