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With the rapid development of modern power systems, the structure and operation of distribution networks are becoming increasingly complex, demanding higher levels of intelligence and digitization. Digital twin, as a virtual cutting-edge technique, can effectively reflect the operational status of distribution networks, offering new possibilities for real-time monitoring, optimization and other functions for distribution networks. Building efficient and accurate models is the foundation of enabling a digital twin of distribution networks. This paper proposes a digital twin operating system for distribution networks with renewable energy based on robust state estimation and deep learning-based renewable energy prediction. Furthermore, the identification and correction of possible bad or missing data based on deep learning are also included to purify the input data for the digital twin system. A digital twin test platform is also proposed in the paper. A case study and evaluations based on a real-time digital simulator are carried out to verify the accuracy and real-time performance of the established digital twin system. In general, the proposed method can provide the basis and foundation for distribution network management and operation, as well as intelligent power system operation. [ABSTRACT FROM AUTHOR]

Thin-walled parts are widely used in the aerospace field. They have the characteristics of large material removal during processing and are affected by factors such as processing technology and processing environment, which can easily cause the deformation of parts. Deformation will seriously affect the machining accuracy of parts, which is one of the key factors affecting the installation accuracy and quality of parts. Aiming at the problem that the machining deformation of thin-walled parts cannot be perceived in real-time during the milling process. This paper constructs a digital twin framework for the milling process of thin-walled parts. Unity3D and Visual Studio 2010 software are used to build a digital twin intelligent processing platform to realize the interaction and integration of physical objects and virtual models. The simulation is carried out by ABAQUS finite element analysis software and verified by experiments. At the same time, based on the simulation data and historical processing data, the PSO-LSTM thin-walled part deformation prediction model is constructed, and the prediction model is embedded into the intelligent processing platform. The real-time monitoring of deformation during the machining process of thin-walled parts is realized, and the problem that the deformation of thin-walled parts cannot be perceived in real-time during the machining process is solved. By processing Al-7075-T6 thin-walled parts for experimental analysis, the prediction accuracy reaches 96.1%, which verifies the accuracy and effectiveness of the platform. [ABSTRACT FROM AUTHOR]

This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated. [ABSTRACT FROM AUTHOR]

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method. [ABSTRACT FROM AUTHOR]

Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages. [ABSTRACT FROM AUTHOR]

At present, the informatization level in the construction process of steel structures is relatively low. Meanwhile, digital twin technology, with better interactive features, provides a new development direction for the intelligent construction of steel structures. Therefore, this paper introduces the concept of a digital twin into the steel structure construction process, analyzes the connotation and characteristics of the digital twin model, and proposes the digital twin model architecture for steel structure construction processes. Furthermore, a method for establishing a digital twin model for steel structure construction processes is presented, which includes three stages: the acquisition and transmission of physical space data, the construction of a digital twin virtual model, and information exchange in the digital twin model. Based on these concepts, this paper describes a digital twin system architecture for the steel structure construction process from the perspective of data flow in the digital twin model. Finally, with the application of information technology in the steel structure construction process of the university park library project in Xiongan New Area and the reconstruction and expansion project of the Nanchong Gaoping airport, the digital twin model and its establishment method methods are analyzed practically and demonstrated effectively in this study. [ABSTRACT FROM AUTHOR]

Featured Application: Application of the digital twin concept to a specific scenario of the lift-and-transverse garage. Starting from the design phase to the final field planning and debugging. It not only verifies the feasibility of equipment solutions using virtual models of digital twins but also optimizes design solutions, identifies problems, and solves them. The operational test results of the virtual model can also fully demonstrate the effectiveness of the proposed digital twin model and related design methods, which provide an important reference for the design optimization and operation of future stereo garages. In addition, compared with the traditional development process, the virtual simulation design and debugging method can not only shorten the development cycle and debugging cost but also accelerate the development progress. This new method provides a brand new idea for the development of the enterprise and also makes a positive contribution to promoting the modernization of urban parking lots. In the face of the challenges of limited urban space and the continuous increase of vehicles, stereo garages have been widely used as a solution in cities. In order to improve the automation and intelligence level of the stereo garage, this paper applies the digital twin technology to the lift-and-transverse stereo garage. A five-dimensional model of a digital twin has been developed based on an actual stereo garage. Combined with S7-PLCSIM Advanced, Botu TIA Portal, and NX MCD to build a virtual simulation platform, realizing the virtual simulation design and debugging of the digital twin-based stereo garage. This approach allows to test and optimize operational processes without relying on physical equipment, reducing labor and field debugging costs, shortening deployment cycles, and significantly reducing development costs. In addition, MCD allows real-time monitoring and control of security risks and failures detected. Finally, the feasibility of the virtual simulation and debugging scheme based on digital twin is verified by comparing the operation data of the virtual model and the actual stereo garage, which provides new ideas for the intelligent development of stereo garage but also can be used as an important reference for the development of equipment in other areas. [ABSTRACT FROM AUTHOR]

In the workshop scheduling problem, frequent disturbances lead to continuous and frequent rescheduling. This is detrimental to the optimal utilization of production resources, the maximization of production efficiency, and the minimization of operational costs. Therefore, finding an effective method to reduce frequent rescheduling is crucial for stable and efficient workshop operations. This paper introduces digital twin (DT) technology. Serving as a digital replica of a physical system, DT establishes an interactive connection between the physical entity and its digital counterpart. It has been applied in multiple fields. A disturbance evaluation method for scheduling mechanisms based on DT technology is proposed and studied in this paper. Firstly, this method evaluates the impact of disturbances by using a causal factor chart (CFC) and convolutional neural networks (CNN). Then, corresponding scheduling mechanisms are proposed based on the degree of disturbance impact. DT technology is used to provide data and model support throughout the entire process. Through the experimental verification, workshop disturbances were accurately evaluated by this method. Two unnecessary instances of rescheduling were avoided, resulting in a 66.3% reduction in the time to handle disturbances. The experimental results show that the proposed method can enhance the adaptability of scheduling mechanisms and contribute to a more agile response to disturbances. [ABSTRACT FROM AUTHOR]

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change's impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R 2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R 2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed. [ABSTRACT FROM AUTHOR]

The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry's processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification. [ABSTRACT FROM AUTHOR]

Traditional geometry optimization of elevator fairings is only based on computational fluid dynamics simulations to find optimal structure parameters, and a large volume of data generated during the elevator operation is not utilized to optimize elevator fairings collaboratively. This paper proposes a digital twin-driven design framework to design the elevator fairing of the next generation. A digital twin model corresponding to the real elevator is first established via a computing platform, and a multi-objective optimization method like neighborhood cultivation genetic algorithms is employed to optimize the elevator fairing design. The effectiveness of the digital twin-driven design framework is demonstrated by the elevator fairing design, and the results show that compared with the unoptimized elevator fairing, the air drag and the lateral force are lowered by 18.1% and 11.2%, respectively, and the turbulence coefficient decreases from 0.478 to 0.451 after optimizing the elevator fairing. [ABSTRACT FROM AUTHOR]

To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs' contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system's efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7% The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

The vibration of the rolling mill has been a persistent issue affecting its safe and stable operation. To address the vibration problem in the F2 stand of a continuous rolling mill, this paper establishes a digital twin model of the rolling mill. Based on the digital twin model, a rolling mill virtual commissioning framework has been proposed to enhance the accuracy of real-time data and improve the utilization of production data, laying the foundation for reducing vibration during the operation of the rolling mill. In this process, we first construct a digital twin framework for the rolling mill, consisting of four levels: physical rolling mill, virtual rolling mill, twin data, and services. Next, an online data acquisition and virtual commissioning framework is established to achieve a state mapping from the physical rolling mill to the virtual rolling mill. Finally, using the digital twin model, the effects of real-time process parameter optimization are analyzed. The impact of process parameter optimization on the dynamic response of the rolling mill system is examined. The optimized rolling process parameters effectively reduce the vertical vibration of the rolling mill system. [ABSTRACT FROM AUTHOR]

The given document is the Annual Report for 2023 of AIMS Microbiology, an international Open Access journal focused on publishing high-quality, peer-reviewed papers in the field of microbiology. In 2023, the journal received over 240 manuscript submissions and published 40 papers, including research articles, review articles, editorials, and commentaries. The authors of these papers came from more than 20 countries, indicating increased international collaborations in microbiology research. The journal also established special issues and invited 18 new experts to join the Editorial Board. In terms of impact, AIMS Microbiology received its first Impact Factor of 4.8 and saw an increase in its CiteScore from 4.8 to 6.3. The report concludes by expressing the journal's commitment to publishing high-quality papers and its goal of attracting more excellent articles in the future. [Extracted from the article]

Urban areas, with their dense populations and complex infrastructures, are increasingly susceptible to various risks, including environmental challenges and infrastructural strain. This paper delves into the transformative potential of digital twins—virtual replicas of physical entities—for mitigating these risks. It specifically explores the role of digital twins in reducing disaster risks, such as those posed by earthquakes and floods, through a comprehensive bibliometric-based literature review. Digital twins could contribute to risk reduction by combining data analytics, simulation, and predictive modeling by creating virtual replicas of physical entities and integrating real-time data streams to better address and manage risks in urban environments. In detail, they can help city planners and decision-makers analyze complex urban systems, simulate potential scenarios, and predict potential outcomes. This proactive approach allows both the identification of vulnerabilities and better implementation of targeted mitigation strategies to enhance urban resilience and sustainability. More informed decisions can be made relying on simulations, and it can also be possible to optimize resource allocation and better respond to emerging challenges. This work reviews the key publications in this domain, with the aim of finding relevant papers that can be useful to urban planners and policy-makers. The paper concludes by discussing the broader implications of these findings and identifying challenges in the widespread adoption of digital twin technology, including data privacy concerns and the need for interdisciplinary collaboration. It also outlines prospective avenues for future research in this emerging field. [ABSTRACT FROM AUTHOR]

The construction industry has been trying to enhance the level of digitalization and autonomy by adopting various communication and information technologies (ICT), e.g., augmented reality (AR), virtual reality (VR), robotics, drones, or building information modeling (BIM). However, improvement of the safety and productivity in their domains is still a struggle. One of the main reasons for failing to accelerate their digital transformation is ignoring the deep understanding of the concept of digital twin, its usage, and the potential benefits of digital twins in the construction industry. Therefore, this paper investigated the impacts and potentials of digital twins on the construction industry through a quantitative systematic review assisted by the text mining method. The study presented the potential usability of digital twins, leading and core technologies, and applications, revealing their benefits and potential for optimizing project planning, execution, and management process. Through this comprehensive literature review, this study elucidated the distinctive features, advantages, and immense potential that digital twins bring to the construction field. The findings highlight the transformative impact of digital twins, providing critical insights for their broader adoption and groundbreaking applications in the industry. By addressing the challenges of adopting this technology, the article provided valuable insights for advancing research and the broad implementation of digital twins in the sector. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented. [ABSTRACT FROM AUTHOR]

In recent years, the integration of digital technologies has grown rapidly in the field of thermal comfort and energy efficiency for buildings. The concept of a digital twin, incorporating multiple digital technologies, has gained increasing attention. The literature lacks a review of the digital twin concept in thermal comfort and energy consumption for existing buildings. This paper conducts a review of the current state-of-the-art in digital twin (DT) technology for thermal comfort/ energy consumption in buildings. The review employs a scientometric approach and examines various technologies used in creating DTs and a systematic analysis of the methods, technologies, algorithms, and approaches used in digital twin experiments. The results show a growing number of studies in this area, with a focus on thermal comfort monitoring, visualization, tracking, energy management, prediction, and optimization for existing buildings. Furthermore, the prediction of energy consumption using algorithms such as Artificial Neural Networks (ANN), Artificial Intelligence (AI), deep neural networks, and YOLOv4 have been used in buildings. However, the wider adoption of a DT that can facilitate occupants, and thermal sensations, enhance human-centered solutions, and improve energy prediction levels are necessary. There is a need for further international collaboration to expand the studies on digital twins for thermal comfort and energy efficiency. The review highlights the limitations and areas of improvement, such as the limited adoption of sensors for environmental measures, the need for more focus on the subjective perception of occupants, and the need for more comparative studies of algorithms for predicting energy consumption. Further studies can be conducted in areas such as understanding occupant psychological responses/behaviors to comfort in the digital world. This will enhance a more consolidated and robust validation for building performance. [ABSTRACT FROM AUTHOR]

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain company operational capacity at the required level. A comprehensive review of the existing literature is conducted to address this, offering an up-to-date analysis of relevant content in this field. The methodology employed is a systematic literature review using the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2012 and 2024, resulting in 201 highly relevant papers. These papers were categorized into seven groups: (a) air transportation, (b) railway transportation, (c) land transportation (road), (d) in-house logistics, (e) water and intermodal transportation, (f) supply chain operation, and (g) other applications. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, revealing the evolution of DT applications over the past decade and identifying key areas such as predictive maintenance, condition monitoring, and decision-making processes. This study highlights the varied levels of adoption across different transport sectors and underscores promising areas for future development, particularly in underrepresented domains like supply chains and water transport. Additionally, this paper identifies significant research gaps, including integration challenges, real-time data processing, and standardization needs. Future research directions are proposed, focusing on enhancing predictive diagnostics, automating maintenance processes, and optimizing inventory management. This study also outlines a framework for DT in transportation systems, detailing key components and functionalities essential for effective maintenance management. The findings provide a roadmap for future innovations and improvements in DT applications within the transportation industry. This study ends with conclusions and future research directions. [ABSTRACT FROM AUTHOR]

This article reviews the development of digital twin and summarizes the evolution of the concept of digital twin. The key words and abstracts of 1187 relevant papers in recent 5 years are quantitatively analyzed, and 13 key nodes are obtained. Then it analyzes the content of 9 systematic review articles and summarizes 15 core contents. Then select 115 application examples from the articles. Through the above three steps, nine descriptive general characteristics of digital twin system are summarized: Entity object; Virtual object; Twinning degree; Communication; Data; Functionality; Human-computer interaction; systematicness; and Cost control. This paper will explain these features in detail and summarize the application examples of classifying each feature. This paper aims to summarize the development of digital twin in recent years and propose a description method of system characteristics according to the application scenario of digital twin and the increasingly complex situation of the system. [ABSTRACT FROM AUTHOR]

Fit-out construction is crucial for the functionality of completed infrastructures. However, the absence of a robust data collection system hinders efficient project management, causing delays and extra costs. This paper introduces a digital twin-enabled platform that utilizes Internet of Things (IoT) and neural network technologies for real-time visibility and traceability of prefabricated materials. The effectiveness of the proposed platform has been validated through a real-life prefabricated construction project in Hong Kong. Leveraging the IoT-captured data, visual and event-driven process models are developed to simulate materials flow and workflow executions. Hierarchical finite state machines (HFSM) facilitate the management of prefabricated construction supply chain. Results show the platform's effectiveness in identifying abnormal event states and notifying stakeholders in real-time, thus supporting advanced project management practices. This platform improves information coordination among stakeholders, providing real-time visibility and traceability, and enabling hierarchical management at strategic, tactical, and operational levels throughout the prefabrication process. • A service-oriented 5D digital twin model is proposed for real-time visibility and traceability. • Event-driven virtual and real mapping are based on AutomationML and OPC UA. • The effectiveness of the proposed model is validated in a practical case study. • Hierarchical management positively influence on the performance of prefabricated fit-out construction projects. [ABSTRACT FROM AUTHOR]

The concept of the Human Digital Twin (HDT) has recently emerged as a new research area within the domain of digital twin technology. HDT refers to the replica of a physical-world human in the digital world. Currently, research on HDT is still in its early stages, with a lack of comprehensive and in-depth analysis from the perspectives of universal frameworks, core technologies, and applications. Therefore, this paper conducts an extensive literature review on HDT research, analyzing the underlying technologies and establishing typical frameworks in which the core HDT functions or components are organized. Based on the findings from the aforementioned work, the paper proposes a generic architecture for the HDT system and describes the core function blocks and corresponding technologies. Subsequently, the paper presents the state of the art of HDT technologies and their applications in the healthcare, industry, and daily life domains. Finally, the paper discusses various issues related to the development of HDT and points out the trends and challenges of future HDT research and development. [ABSTRACT FROM AUTHOR]

Formation testing is widely used in offshore oil and gas development, and predicting the sampling time of pure fluids during this process is very important. However, existing formation testing methods have problems such as long duration and low efficiency. To address these issues, this paper proposes a hybrid-driven method based on physical models and machine learning models to predict fluid sampling time in formation testing. In this hybrid-driven model, we establish a digital twin model to simulate a large amount of experimental data (6000 cases, totaling over 1 million data points) and significantly enhance the correlation between features using physical formulas. By applying advanced machine learning algorithms, we achieve real-time predictions of fluid sampling time with an accuracy of up to 92%. Additionally, we use optimizers to improve the model's accuracy by 3%, ultimately reaching 95%. This model provides a novel approach for optimizing formation testing that is significant for the efficient development of offshore oil and gas. [ABSTRACT FROM AUTHOR]

Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship's flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training. [ABSTRACT FROM AUTHOR]

Virtual commissioning (VC), defined as the simulation and testing of systems in a virtual environment before physical implementation, plays a key role in addressing the challenges of integrating and validating complex systems efficiently and effectively. This paper focuses on the topic of virtual commissioning, summarizing and organizing existing research in the field. The paper provides a comprehensive overview of various design methods and technologies currently in use. A case study of virtual commissioning is also presented within the area of the Cyber-Physical Manufacturing Systems Laboratory of the Széchenyi István University, detailing the solution steps taken. Drawing on both research and practical experience, the paper proposes a novel framework to support virtual commissioning design, referred to as the "Virtual Commissioning House" (VCH). The methodology is evaluated through comparisons with existing virtual commissioning solutions, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

The construction of a new generation of smart cities puts forward higher requirements for the digitization and intelligence of subway tunnel engineering. Digital twin technology has shown great potential in high-fidelity modeling, virtual–real mapping, and decision support based on data analysis, but its research is still in its infancy. To this end, this paper first discusses in depth the inherent complexity and safety risks of subway tunnel construction and emphasizes the significant advantages of digital twin technology compared with traditional technology. Then, by summarizing the existing concepts, this paper proposes a specific explanation of DT applicable to subway tunnel engineering. In order to deeply analyze the potential of digital twin technology in subway tunnel engineering, this paper first conducts a bibliometric analysis and organizes the relevant research directions in recent years based on a visual map. Then, the application of DT in the field of subway tunnel engineering is discussed, including the modeling method of the subway digital twin, intelligent management of the construction process, safety guarantee, operation and maintenance, and resource optimization of traffic facilities in subway stations. Finally, this paper discusses the prospects and gaps of digital twin technology in theoretical and practical applications, aiming to promote the practical application of this technology in subway tunnel engineering. Through the summary and prospect of the existing research, this paper provides a valuable reference for future research directions and practical applications. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology, the integration and complexity of aerospace vehicles, weaponry, and large-scale chemical equipment are becoming higher and higher. PHM plays an important role in realizing reductions in equipment loss due to failures in many fields. In order to systematically sort through the research history of PHM and deeply analyze the development status of AR and DT technologies in the field of PHM, to clarify the current technical challenges and future development directions and to provide valuable references and insights for researchers, engineers, and decision-makers in the related fields, this paper summarizes the development of PHM in the engineering field. This paper summarizes the development of PHM in the field of engineering, from the initial PHM used in aerospace to the current PHM systems supported by various advanced technologies; analyzes the advantages and shortcomings of the digital twin and augmented reality technologies used for PHM; and organizes and summarizes the future directions and future research focuses of PHM research based on the existing technologies (mainly digital twins and augmented reality). After systematic research and study, we found that the integration of augmented reality and digital twin technologies will provide superb simulation capabilities and immersive operation and bring new challenges and opportunities. Therefore, it is also imperative to address the challenges and limitations that hinder the seamless integration of the new technologies. [ABSTRACT FROM AUTHOR]

The construction industry is undergoing a digital transformation, with the digital twin serving as a core system for project information. This digital twin provides an opportunity to utilize AR technology for real-time verification of on-site project information. Although many AR developments for construction sites have been attempted, they have been limited to accessing information on stationary components via Building Information Models. There have been no attempts to access information on dynamically changing resources, such as personnel and equipment. This paper addresses this gap by presenting an AR framework that enables site managers to verify real-time information on specific personnel or equipment. It introduces a matching algorithm for retrieving the necessary information from the digital twin. This algorithm is pivotal in identifying and retrieving the specific information needed from the vast dataset within the digital twin. The matching process integrates object detection and tracking algorithms applied to video frames from AR devices, along with GPS and IMU sensor data. Experimental results demonstrate the potential of this matching algorithm to streamline on-site management and reduce the effort required to interact with digital twin information. This paper highlights the transformative potential of AR and digital twin technologies in revolutionizing construction site operations. [ABSTRACT FROM AUTHOR]

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover. [ABSTRACT FROM AUTHOR]

Objective: To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field. Methods: Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword "metaverse". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China. Results: Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence. Conclusion: The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges. [ABSTRACT FROM AUTHOR]

ChatGPT uses a so called Large Language Model (LLM) to provide textual output of analyzed data. Those LLMs are one example for Generative Artificial Intelligence (AI), which focuses on creating new content, e.g., text, images, or music, based on learned patterns. Recently, applications in the food industry and agriculture started to apply Generative AI. This mini review provides an overview about applications of Generative AI in the agri-food supply chain and discusses open research challenges, also in combination with digital twins. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. [ABSTRACT FROM AUTHOR]

In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the issue of possible local inversion errors stemming from the mismatch between the traditional spaceborne ATI-SAR inversion algorithm and various dual-antenna configurations is resolved to a certain extent. A simulated spaceborne ATI-SAR system, featuring a dual-antenna configuration comprising a baseline direction perpendicular to the track and a squint angle, is presented to validate the efficacy of the Digital Twin methodology. Under the specified simulation parameters, the average inversion error for the final ocean current velocity is recorded at 0.0084 m/s, showcasing a reduction of 0.0401 m/s compared with the average inversion error prior to optimization. [ABSTRACT FROM AUTHOR]

The digital twin (DT), which involves creating a virtual replica of a physical asset or system, has emerged as a transformative set of tools across various industries. In the oil and gas (O&G) industry, the development of DTs represents a significant evolution in how companies manage complex operations, enhance safety, and optimize decision-making processes. Despite these significant advancements, the underlying tools, technologies, and frameworks for developing DTs in O&G applications remain non-standardized and unfamiliar to many O&G practitioners, highlighting the need for a systematic literature review (SLR) on the topic. Thus, this paper offers an SLR of the existing literature on DT development for O&G from 2018 onwards, utilizing Scopus and Web of Science Core Collection. We provide a comprehensive overview of this field, demonstrate how it is evolving, and highlight standard practices and research opportunities in the area. We perform broad classifications of the 98 studies, categorizing the DTs by their development methodologies, implementation objectives, data acquisition, asset digital development, data integration and preprocessing, data analysis and modeling, evaluation and validation, and deployment tools. We also include a bibliometric analysis of the selected papers, highlighting trends and key contributors. Given the increasing number of new DT developments in O&G and the many new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the development of DTs for O&G. [ABSTRACT FROM AUTHOR]

Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. [ABSTRACT FROM AUTHOR]

Virtual experiments (VEs) and digital twins (DTs), pivotal for realizing European strategic policies on sustainability and digitalization within Industry 4.0 and the European Green Deal, simulate physical systems and characteristics in a virtual environment, with DTs incorporating dynamic inputs from and outputs to the real-world counterpart. To ensure confidence in their use and outcomes, traceability and methods to evaluate measurement uncertainty are needed, topics that are hardly covered by the literature so far. This paper provides a harmonized definition of VEs and DTs and introduces a framework for evaluating measurement uncertainty. Furthermore, it discusses how to propagate the uncertainty of the contributions coming from the different parts of the DT. For the core part of the DT, the framework derived for VEs can be used. For the physical-to-virtual (P2V) connection and the virtual-to-physical (V2P) connection, additional sources of uncertainty need to be considered. This paper provides a metrological framework for taking all these uncertainty contributions into account while describing a framework to establish traceability for DTs. Two case studies are presented to demonstrate the proposed methodology considering industrially relevant measuring instruments and devices, namely, a coordinate measuring machine (CMM) and a collaborative robot arm (cobot). [ABSTRACT FROM AUTHOR]

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support 'what-if' scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period. [ABSTRACT FROM AUTHOR]

While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. [ABSTRACT FROM AUTHOR]

The integration of high-penetration renewable energy sources is playing an increasingly important role in the frequency regulation of the power system. However, due to the varying output characteristics and response speeds of different renewable distributed energy resources (DERs), the overall response from the collective output of these distributed energy resources may not meet expected requirements and could have adverse effects on the grid. One drawback of traditional distributed coordinated control is its high communication requirements. This paper proposes using digital twin (DT) technology for the coordinated control of distributed energy resources, which can minimize the communication needs between various distributed energy resources while achieving coordinated control. Verification of the frequency support coordinated control strategy based on digital twin technology shows that it can effectively enhance the individual output characteristics and overall response of each distributed energy resource, providing effective support for grid frequency. [ABSTRACT FROM AUTHOR]

A digital twin is a new trend in the development of the current smart water conservancy industry. The main research content of intelligent water conservancy is clarified. This paper first summarizes and combs the relevant system architecture of smart water conservancy, and puts forward a smart water conservancy framework based on digital twins, highlighting the characteristics of virtual and real interaction, and symbiosis of the water conservancy twin platform. Secondly, the status quo of intelligent water conservancy "sky, air, ground and water" integrated monitoring technology, big data and artificial intelligence, model platform technology, knowledge graph and security technology is analyzed. From the perspective of application, the research progress of each technology in water security, water resources and hydraulic engineering is reviewed. Although the construction of smart water conservancy has made remarkable progress, it still faces many challenges such as data governance, technology integration and innovation, and standardization. In view of these challenges, this paper puts forward a series of countermeasures, and looks forward to the future development direction of intelligent water conservancy. [ABSTRACT FROM AUTHOR]

The construction field currently suffers from low productivity, a lack of expertise among practitioners, weak innovation, and lack of predictability. The digital twin, an advanced digital technology, empowers the construction sector to advance towards intelligent construction and digital transformation. It ultimately aims for highly accurate digital simulation to achieve comprehensive optimization of all phases of a construction project. Currently, the process of digital twin applications is facing challenges such as poor data quality, the inability to harmonize types that are difficult to integrate, and insufficient data security. Further research on the application of digital twins in the construction domain is still needed to accelerate the development of digital twins and promote their practical application. This paper analyzes the commonly used architectures for digital twins in the construction domain in the literature and summarizes the commonly used technologies to implement the architectures, including artificial intelligence, machine learning, data mining, cyber–physical systems, internet of things, virtual reality, augmented reality applications, and considers their advantages and limitations. The focus of this paper is centered on the application of digital twins in the entire lifecycle of a construction project, which includes the design, construction, operation, maintenance, demolition and restoration phases. Digital twins are mainly moving towards the integration of data and information, model automation, intelligent system control, and data security and privacy. Digital twins present data management and integration challenges, privacy and security protection, technical manpower development, and transformation needs. Future research should address these challenges by improving data quality, developing robust integration methodologies, and strengthening data security measures. [ABSTRACT FROM AUTHOR]

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic. [ABSTRACT FROM AUTHOR]

The digital twin (DT) technology is currently considered a key technology for the digital representation of real-world systems. The application of DT technology in smart manufacturing can provide accurate model support for the analysis of mechatronic equipment applications based on model simulations. However, for such a mechanics-electric-hydraulic-control coupled complex system of mechatronics equipment, how to quickly and effectively construct its consistent multi-domain DT mechanism model has become the biggest obstacle to the wide application of DT technology in this field. Therefore, based on the synthesis of existing model construction methods for mechatronics equipment, this paper proposes multi-domain, multi-level, parametric, and consistent mechatronics equipment DT mechanism model construction guidelines. Based on the proposed model construction guidelines, a DT mechanism model construction process for mechatronics equipment is given. Finally, a consistent multi-domain DT mechanism model of computer numerical control machine tools (CNCMT) is constructed by Simscape, to verify the feasibility of the proposed method. The solutions in this paper provide a reliable, rapid, and consistent mechanism model along with construction guidelines and theoretical systems for engineers or researchers that use DT technology to solve specific application problems. Also, the implementation of the virtual commissioning application case in this paper provides application guidance for service analysis based on the DT model. [ABSTRACT FROM AUTHOR]

Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the "digital twin + Long-Short-Term Memory Network (LSTM) algorithm", is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops. [ABSTRACT FROM AUTHOR]

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges of implementing the CityGML standard within database environments for managing urban digital twins (UDT), with a particular focus on addressing big geodata issues. CityGML stands as a critical standard for representing and exchanging 3D urban models and thematic data, essential for effective urban planning and infrastructure management. However, integrating CityGML into databases poses challenges due to the dual requirements of geospatial and semi-structured data. While the former imposes a certain rigor in its formalism, the second benefits from more flexibility. Through a comprehensive review and benchmarking of existing database implementations, including both new distributions and those often use, 3DCityDB, CJDB, Measur3D, and Cerbere, this paper proposes a novel approach towards a multi-database CityGML environment tailored to the specific needs of UDT. The proposed solution leverages the strengths of both relational and NoSQL databases, offering a flexible and scalable architecture while ensuring data consistency, geospatial capabilities and compliance with the CityGML schema. The research hypothesis suggests integrating Cerbere, a middleware for CityGML schema compliance and transaction validation, with 3DCityDB and Measur3D (MongoDB). This approach aims to demonstrate the feasibility of managing advanced 3D data operations based on the CityGML model (3DCityDB) and scalability for big data like IoT (Measur3D) within a multi-database environment. The paper contributes to the advancement of UDT by providing a comprehensive solution for managing diverse data types, facilitating more effective urban planning, infrastructure management, and sustainable development initiatives in the context of smart cities. [ABSTRACT FROM AUTHOR]