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Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0–10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management. [ABSTRACT FROM AUTHOR]

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments. [ABSTRACT FROM AUTHOR]

Featured Application: using Virtual Tours (VTs) to complement Building Information Models (BIMs) and Internet of Things (IoT) systems as a solution for remote control, building automation systems, and other tasks involved in Computer-Aided Facility Management (CAFM). The aim is to provide contextual access to information and opportunities for interaction, simplifying the task of those responsible for managing and maintaining building assets. This study investigates the integration of Building Information Models (BIMs) and Virtual Tour (VT) environments in the Architecture, Engineering and Construction (AEC) industry, focusing on Computer-Aided Facility Management (CAFM), Computerized Maintenance Management Systems (CMMSs), and data Life-Cycle Assessment (LCA). The interconnected nature of tasks throughout a building's life cycle increasingly demands a seamless integration of real-time monitoring, 3D models, and building data technologies. While there are numerous examples of effective links between IoT and BIMs, as well as IoT and VTs, a research gap exists concerning VT-BIM integration. This article presents a technical solution that connects BIMs and IoT data using VTs to enhance workflow efficiency and information transfer. The VT is developed upon a pilot based on the Controlled Environments for Living Lab Studies (CELLS), a unique facility designed for flexible monitoring and remote-control processes that incorporate BIMs and IoT technologies. The findings offer valuable insights into the potential of VTs to complement and connect to BIMs from a life-cycle perspective, improving the usability of digital twins for beginner users and contributing to the advancement of the AEC and CAFM industries. Our technical solution helps complete the connectivity of BIMs-VT-IoT, providing an intuitive interface (VT) for rapid data visualisation and access to dashboards, models and building databases. The practical field of application is facility management, enhancing monitoring and asset management tasks. This includes (a) sensor data monitoring, (b) remote control of connected equipment, and (c) centralised access to asset-space information bridging BIM and visual (photographic/video) data. [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]

Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO2 per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology. [ABSTRACT FROM AUTHOR]

Given the increasing frequency and severity of floods caused by climate change, there is a pressing requirement for creative ways to improve public comprehension and control of hydrological phenomena. Contemporary technology provides unparalleled possibilities to transform this domain. This project investigates the possibilities for merging gaming engines and digital twins to enhance flood education, data visualization, and interactive monitoring. This study proposes the utilization of immersive digital twins to enhance the comprehension of hydrological and hydraulic systems. The suggested method utilizes game engines to generate dynamic and interactive models that connect raw data to practical insights, enabling a more profound understanding of flood dynamics. This study underscores the wide-ranging usefulness of digital twins in various watersheds by focusing on the development of advanced monitoring systems, the benefits of improved data visualization, and educational outreach. The incorporation of real-time data via IoT technology considerably improves the significance and precision of these virtual models. This novel approach seeks to refashion flood management approaches by cultivating well-informed stakeholders and advocating for effective environmental education, ultimately leading to more resilient and prepared communities. An immersive digital twin of the real world can assist decision-makers technically, psychologically, and mentally by making complex phenomena easier to understand and visualize, thanks to real-time data and simulations that keep the information up-to-date, consequently leading to a more precise and intuitive decision-making process. [ABSTRACT FROM AUTHOR]

The UK Government's Building Information Modelling (BIM) mandate has encouraged the utilisation of BIM within the Transportation Sector (TS), with research demonstrating positive effects of BIM. However, BIM processes are incipient to TS project implementation across the UK. This paper is carried out to understand the current BIM usage within the UK's TS and how BIM practises and workflows contribute towards the government's NZC approach. We used research questions derived from the population, intervention, comparison and outcome (PICO) system and inclusion and exclusion criteria to screen irrelevant information from a Systematic Literature review with 18 pieces of literature. We identified six key drivers: carbon reduction and BIM, BIM in transportation design, BIM uptake and usage in transportation, BIM in transportation construction and Digital Twins and BIM. It was identified that, with the integration of the Carbon Calculator Tool into Civil 3D, structural and material data can be obtained and areas of Embodied Carbon hotspots can be identified to contribute to reduce overall carbon across a project, which requires further collaboration between software providers and industry leaders for further streamlining the process. A limitation of this research is the requirement for wider study of differing disciplines within the TS, more qualitative research and a lack of information regarding other Carbon Calculator Tools and how compatible they are with Civil 3D. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. [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]

Magnetic field is one of the basic data for constructing a transformer digital twin. The finite element transient simulation takes a long time and cannot meet the real-time requirements of a digital twin. According to the nonlinear characteristics of the core and the timing characteristics of the magnetic field, this paper proposes a fast calculation method of the spatial magnetic field of the transformer, considering the nonlinear characteristics of the core. Firstly, based on the geometric and electrical parameters of the single-phase double-winding test transformer, the corresponding finite element simulation model is built. Secondly, the key parameters of the finite element model are parametrically scanned to obtain the nonlinear working condition data set of the test transformer. Finally, a deep learning network integrating a convolutional neural network (CNN) and a long short-term memory network (LSTM) is built to train the mapping relationship between winding voltage, current, and the spatial magnetic field so as to realize the rapid calculation of the transformer magnetic field. The results show that the calculation time of the deep learning model is greatly shortened compared with the finite element model, and the model calculation results are consistent with the experimental measurement results. [ABSTRACT FROM AUTHOR]

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper's contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises. [ABSTRACT FROM AUTHOR]

While digital twin networks (DTNs) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for split learning (SL) tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreported resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 9.3 % and convergence estimation accuracy by 22.4 % compared to traditional equation-based methods. [ABSTRACT FROM AUTHOR]

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growing concerns about issues such as data privacy and cybersecurity are major obstacles to improving the quality of distributed data annotation. In this paper, we propose a reputation-based asynchronous federated learning approach for digital twins. First, this paper integrates digital twins into an asynchronous federated learning framework, and utilizes a smart contract-based reputation mechanism to enhance the interconnection and internal interaction of asynchronous mobile terminals. In addition, in order to enhance security and privacy protection in the distributed smart annotation system, this paper introduces blockchain technology to optimize the data exchange, storage, and sharing process to improve system security and reliability. The data results show that the consistency of our proposed FedDTrep distributed intelligent labeling system reaches 99%. [ABSTRACT FROM AUTHOR]

Like many coastal lagoons in several countries, the "Navío Quebrado" lagoon (La Guajira, Colombia) is a very delicate and precious environment; indeed, it is a nationally recognized Flora and Fauna Sanctuary. Several factors, including climate change, are threatening its existence because of changes in the governing hydro-morphological and biological processes. Certainly, the first step to addressing this problem is to understand its hydrological behavior and to be able to replicate, via simulation, its recent history before inferring likely futures. These potential futures will be marked by changes in the water input by its tributary, the Camarones River, and by modified water exchange with the sea, according to a foreseen sea level rise pattern, as well as by a different evaporation rate from the free surface, according to temperature changes. In order to achieve the required ability to simulate future scenarios, data on the actual behavior have to be gathered, i.e., a monitoring system has to be set up, which to date is non-existent. Conceptually, designing a suitable monitoring system is not a complex issue and seems easy to implement. However, the environmental, socio-cultural, and socio-economic context makes every little step a hard climb. An extremely simple—almost "primitive"—monitoring system has been set up in this case, which is based on very basic measurements of river flow velocity and water levels (river, lagoon, and sea) and the direct participation of local stakeholders, the most important of which is the National Park unit of the Sanctuary. All this may clash with the latest groovy advances of science, such as in situ automatized sensors, remote sensing, machine learning, and digital twins, and several improvements are certainly possible and desirable. However, it has a strong positive point: it provides surprisingly reasonable data and operates at almost zero additional cost. Several technical difficulties made this exercise interesting and worthy of being shared. Its novelty lies in showing how old, simple methods may offer a working solution to new challenges. This humble experience may be of help in several other similar situations across the world. [ABSTRACT FROM AUTHOR]

The concept of digital twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber–physical systems. Despite the various foundations, standards, and platforms in systems engineering, there are ongoing challenges with verification and validation methodology. This study aims to establish a generic framework that addresses the various aspects of digital twinning. The multifaceted nature of the problem requires raising the abstraction level in both the real (actual) and virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the quintuple helix model with the problem and operational domains of a real (actual) twin, the solution and implementation domains of a virtual twin, and the execution domain as the bridge that links them. Verification and validation dimensions follow the meta object facility abstraction layers (instance, model, meta-model, and meta-meta-model) mapping over five helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios. The application of main conceptual framework mechanisms in a real-world example study aids the verification of this research's intentions. The validation is a matter of further research endeavors. [ABSTRACT FROM AUTHOR]

Digital Twins (DT) play a key role in Industry 4.0 applications, and the technology is in the process of being mature. Since its conceptualisation, it has been heavily contextualised and often misinterpreted as being merely a virtual model. Thus, it is crucial to define it clearly and have a deeper understanding of its architecture, workflow, and implementation scales. This paper reviews the notion of a Digital Twin represented in the literature and analyses different kinds of descriptions, including several definitions and architectural models. A new fit-for-all definition is proposed which describes the underlying technology without being context-specific and also overcomes the pitfalls of the existing generalised definitions. In addition, the existing three-dimensional and five-dimensional models of the DT architecture and their characteristic features are analysed. A new simplified two-branched model of DT is introduced, which retains a clear separation between the real and virtual spaces and outlines the latter based on the two key modelling approaches. This model is then extended for condition monitoring of electronic components and systems, and a hybrid approach to Prognostics and Health Management (PHM) is further elaborated on. The proposed framework, enabled by the two-branched Digital Twin model, combines the physics-of-degradation and data-driven approaches and empowers the next generation of reliability assessment methods. Finally, the benefits, challenges, and outlook of the proposed approach are also discussed. [ABSTRACT FROM AUTHOR]

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building's operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation. [ABSTRACT FROM AUTHOR]

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site's specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research. [ABSTRACT FROM AUTHOR]

Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in a digital twin) reflecting the actual physical evolution of the installation over the long term (i.e., a century), the ultimate objective being to assess that the repository components/processes are effectively following the expected trajectory towards the closure phase. Data prediction involves using historical data and statistical methods to forecast future outcomes, but it faces challenges such as data quality issues, the complexity of real-world data, and the difficulty in balancing model complexity. Feature selection, overfitting, and the interpretability of complex models further contribute to the complexity. Data reconciliation involves aligning model with in situ data, but a major challenge is to create models capturing all the complexity of the real world, encompassing dynamic variables, as well as the residual and complex near-field effects on measurements (e.g., sensors coupling). This difficulty can result in residual discrepancies between simulated and real data, highlighting the challenge of accurately estimating real-world intricacies within predictive models during the reconciliation process. The paper delves into these challenges for complex and instrumented systems (multi-scale, multi-physics, and multi-media), discussing practical applications of machine and deep learning methods in the case study of thermal loading monitoring of a high-level waste (HLW) cell demonstrator (called ALC1605) implemented at Andra's underground research laboratory. [ABSTRACT FROM AUTHOR]

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling the loads of air-conditioning systems in summer, and the urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D building data. It aims to explore the methods of geometry optimisation, limitations, and performance issues of a 3D computer-aided design (CAD) tool dedicated to small-scale solar analysis and employed at the city scale. Two cases were considered at the city and district scales, respectively. The total face count of meshes for the simulations constituted approximately 2,000,000 faces. A total of 64,379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualised in a 3D web platform. The use of a 3D CAD environment establishes a seamless process of updating 3D models and simulations, while preprocessing in Geographic Information System (GIS) ensures working with large-scale datasets. The proposed method showed a moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future. [ABSTRACT FROM AUTHOR]

This study aims to examine the use of digital twins in critical infrastructure through a literature review as well as a bibliometric and scientific mapping analysis. A total of 3414 documents from Scopus and Web of Science (WoS) are examined. According to the findings, digital twins play an important role in critical infrastructure as they can improve the security, resilience, reliability, maintenance, continuity, and functioning of critical infrastructure in all sectors. Intelligent and autonomous decision-making, process optimization, advanced traceability, interactive visualization, and real-time monitoring, analysis, and prediction emerged as some of the benefits that digital twins can yield. Finally, the findings revealed the ability of digital twins to bridge the gap between physical and virtual environments, to be used in conjunction with other technologies, and to be integrated into various settings and domains. [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]

Fused granular fabrication (FGF) is used in industrial applications to manufacture complex parts in a short time frame and with reduced costs. Recently, the overprinting of continuous fibre-reinforced laminates has been discussed to produce high-performance, functional structures. A hybrid process combining FGF with Automated Fibre Placement (AFP) was developed to implement this approach, where an additively manufactured structure is bonded in situ onto a thermoplastic laminate. However, this combination places great demands on process control, especially in the first printing layer. When 3D printing onto a laminate, the height of the first printed layer is decisive to the shear strength of the bonding. Manufacturing-induced surface defects of a laminate, like thermal warpage, gaps, and tape overlaps, can result in deviations from the ideal geometry and thus impair the bonding strength when left uncompensated. This study, therefore, proposes a novel process flow that uses a 3D scan of a laminate to adjust the geometry of the additively manufactured structure to achieve a constant layer height in the 3D print and, thus, constant mechanical properties. For the above-listed surface defects, only thermal warpage was found to have a significant effect on the bonding strength. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The diverse and multi-manufacturer equipment in weaving workshops exacerbates the complexity of multi-source heterogeneous data. Moreover, traditional data collection methods, which are mostly based on fixed frequencies, increase the network load during real-time high-frequency data reception, making stable, long-term operation difficult. Conversely, low-frequency collection might miss important state changes, thus affecting the quality of weaving big data. To address these issues, this paper proposes a service-oriented Digital Twin modeling method for weaving workshops. This method combines OPC Unified Architecture (OPC UA) with a state change-based data collection approach, utilizing a sliding time window (STW) to identify anomalous data and employing median interpolation to correct these anomalies. The goal is to enhance the representation capability of the Digital Twin in the weaving workshop by improving the data quality. For a specific service of predicting the warp-out time of 288 air-jet looms in a workshop, the average error of the predicted warp-out time using the dynamic data set proposed in this study was reduced from 0.85 h to 0.78 h compared to the static data set based on fixed frequency, an improvement of 8.2%, thereby validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper presents the design of a digital twin for a 6U CubeSat electrical power system, including the solar arrays, solar array regulators, battery, power distribution unit, and load subsystems. The digital twin is validated by comparing its real-time outputs with those of the physical system. Experimental tests confirm its feasibility, showing that the digital twin's real-time outputs closely match those of the physical system. Additionally, the digital twin can be used for control-hardware-in-the-loop and power-hardware-in-the-loop tests, allowing the real-time integration of simulated subsystems with hardware. This capability facilitates testing of new subsystems and optimization during the project's development phases. Additionally, to demonstrate the advanced capabilities of this model, the digital twin is used to simulate the CubeSat electrical power system behavior in real time throughout a complete orbital cycle in low Earth orbit conditions. This simulation provides valuable insights into the CubeSat operation by capturing the transient and steady-state responses of the EPS components under real orbital conditions. The results obtained indicate that the digital twin significantly enhances the testing and optimization process of new subsystems during the development phases of the project. Moreover, the capabilities of the digital twin can be further augmented by incorporating real-time telemetry data from the CubeSat, resulting in a highly accurate replication of the satellite's in-orbit behavior. This approach is crucial for identifying and diagnosing failures or malfunctions in the electrical power system, ensuring the robust and reliable operation of the CubeSat. [ABSTRACT FROM AUTHOR]

Large underground spaces are complex and huge, with problems such as fragmented data that cannot be shared, outdated management methods, and high operation and maintenance costs. The digitization of building information and the use of digital twin technology can effectively improve the efficiency of building operation and maintenance. Using information technology to build a digital-twin-based operation and maintenance management system, the huge and discrete data and equipment system information are effectively integrated and explored for application. First, we analyze the shortcomings of the traditional delivery and operation and maintenance methods and introduce the necessity of a new method of operation and maintenance management based on digital twin; then, we divide the operation and maintenance information of the construction project into four major categories of spatial information and eight major categories of equipment information and complete the coding work, and the categorized data serve as the data basis for operation and maintenance; second, we develop a digital twin operation and maintenance management platform based on the operation and maintenance data of BIM; finally, we carry out case validation for the three major constructions. Finally, case validation is carried out for three major buildings and a large underground space and the practical application shows that the operation and maintenance management system based on digital twin technology provides technical guarantee for decentralized information and system integration management and the information management quality and efficiency of the operation and maintenance process are effectively improved. Through the practice of summarizing the experience, which is worth learning, we aim to create a large underground space operation and maintenance management method to provide a reference. [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]

The current building industry is facing challenges of labor shortages and labor-intensive practices. Effectively collaborating with robots will be crucial for industry upgrading. This research introduces a MR iterative design and robot-assisted construction mode based on human–robot collaboration, facilitating an integrated process innovation from design to construction. The development of the ROCOS (Robot Collaboration System) comprises three key aspects: (1) Layout Stage: using MR technology to layout the site, forming a full-scale integrated virtual and physical digital twin design environment. (2) Design Stage: conducting virtual iterative design in the digital twin environment and automatically simulating assembly processes. (3) Assembly Stage: translating simulated results into assembly path commands and driving a robotic arm to perform actual assembly. In the end, this research setup two experiments to examine the feasibility of this iterative design–construction loop script. The results showed that although the presence of obstacles reduced the designer's freedom and increased the number of steps, the designer could still finish both tasks. This means that the ROCOS has value in the prototype of human–robot collaboration. In addition, some valuable findings from users' feedback showed that potential improvements can be addressed in operability, customization, and real construction scenarios. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a dramatic increase in the use of various technologies in the Architecture, Engineering, and Construction sector. Artificial intelligence has played a significant role throughout the different phases of the design and construction process. A growing body of literature recognizes the importance of artificial neural network applications in numerous areas of the construction industry and the built environment, presenting a need to explore the main research themes, attributes, benefits, and challenges. A three-step extensive research method was utilized by conducting a bibliometric search of English language articles and conducting quantitative and qualitative analyses. The bibliometric analysis aimed to identify the current research directions and gaps forming future research areas. The scientometric analysis of keywords revealed diverse areas within the construction industry linked to ANNs. The qualitative analysis of the selected literature revealed that energy management in buildings and construction cost predictions were the leading research topics in the study area. These findings recommend directions for further research in the field, for example, broadening the application ranges of ANNs in the current Construction 4.0 technologies, such as robotics, 3D printing, digital twins, and VR applications. [ABSTRACT FROM AUTHOR]

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is recognized as a pillar in the transition from traditional to digital construction, yet the risks (opportunities and threats) associated with its implementation have not been thoroughly determined in the literature. In addition, there is a scarcity of research relating the risks of DT implementation to DT maturity levels, which has hindered the optimum consideration of such risks when DT is adopted at different maturity levels. To address these gaps, this study conducted a literature review of 1889 documents from Scopus and Web of Science databases. After rigorous filtration, 72 documents were selected and comprehensively reviewed. A total of 47 risk factors (RFs) were identified and categorized into opportunities (economic, technical, environmental and sustainability, monitoring and safety, and management) and threats (economic, technical, and policy and management). Subsequently, these RFs were mapped onto the five-level DT maturity model, providing users with insights into opportunities and threats on each level. The exhaustive list of RFs and proposed integration of a DT maturity model with corresponding RFs enables stakeholders to identify the risks in their specific use cases and facilitate the decision-making and success in transition across various levels of DT in real-life construction projects. [ABSTRACT FROM AUTHOR]

In the construction of super high-rise buildings, building machines (BMs) are increasingly replacing traditional climbing frames. Building machine jacking operation (BMJO) is a high-difficulty and high-risk stage in the construction of the top mold system. To guarantee the operational safety of the BMJO and to enhance its intelligent control level, a digital twin (DT)-based monitoring method for the operation status of the BMJO is proposed. Firstly, a DT framework for monitoring the operation status of the BMJO is presented, taking into account the operational characteristics of the BM and the requirements of real-time monitoring. The functions of each part are then elaborated in detail. Secondly, the virtual twin model is created using Blender's geometric node group function; artificial neural network technology is used to enable online prediction of the structural performance of the BMJO and a motion model is established to realize a real-time state mapping of the BMJO. Finally, taking a BM project as an example, the DT system is established in conjunction with the project to verify the feasibility of the DT framework for monitoring the state of the BMJO. It is proved that the prediction results have high accuracy and fast analysis speed, thus providing a new way of thinking for monitoring and controlling the safe operation of the BMJO. [ABSTRACT FROM AUTHOR]

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users' requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi's production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. [ABSTRACT FROM AUTHOR]

This research examines the potential of digital twin (DT) technology for reformation within China's traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system's efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system's monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors. [ABSTRACT FROM AUTHOR]

The T1DCoach mobile application is designed to educate patients—children with type 1 diabetes (T1D) and their caregivers and diabetes educators. The idea behind the mobile application is that its users perform actions that the patient needs to perform in real life. These include measuring blood glucose levels, operating the insulin pump, meal calculation, bolus administration, etc. These in-application activities are performed on the patient's digital twin. To increase user engagement, gamification elements have been implemented in the application. An important element of the T1DCoach mobile application is its interface, which should be adapted to very different groups of users: children, their caregivers and educators. In addition to presenting the T1DCoach application, the paper presents the stage examining the quality of the interface using three research groups: children, their caregivers and educators. The research was conducted using the scenario method, using eye-tracking, recording activities and thinking aloud. After the application testing sessions, surveys were carried out using the System Usability Scale method and focus group interviews were conducted. The research results are presented in the article along with the most important recommendations for improving the application interface. [ABSTRACT FROM AUTHOR]

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. [ABSTRACT FROM AUTHOR]

In conventional engineering education, issues such as the discrepancy between virtual and real environments, rigid practical operations, lack of reflective support, and a disconnect between online and offline learning prevail. Digital twin technology, with its high fidelity and real-time interaction features, presents an innovative instructional aid for engineering education. This study developed a digital twin learning system to assist instructors in implementing project-based teaching models in landscaping technology courses. To assess the effectiveness of this system, a quasi-experiment was designed. Seventy students from a vocational high school majoring in landscaping technology in China were recruited as participants. These students were divided into two groups, each consisting of 35 students, with the same teaching pace. The experimental group utilized the system to supplement the instructor's teaching of landscaping courses, while the control group received instruction through traditional methods. The experiment lasted for eight weeks, comprising a total of 16 classes. Ultimately, the results indicated that students in the experimental group significantly outperformed those in the control group in critical thinking, cognitive load, learning experience, and academic performance. Additionally, this research examined the acceptance of learners toward using the digital twin learning system and its influencing factors based on the Technology Acceptance Model, aiming to provide insights into enhancing engineering education courses teaching effectiveness and targeted technological 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]

Reconstructing three-dimensional (3D) models of power equipment plays an increasingly important role in advancing digital twin power grids. To reconstruct a high-precision model, it is crucial to accurately obtain the pylon type and its necessary parameter information before modeling. This study proposes an improved method for identifying pylon types based on similarity measurement and a linearly transformed dataset. It begins by simplifying the identification of point clouds using the pylon shape curve. Subsequently, the resemblance between the curve and those curves within the dataset is evaluated using a similarity measurement to determine the pylon type. A novel method is proposed for calculating the characteristic parameters of the pylon point clouds. The horizontal and vertical distribution characteristics of the pylon point clouds are analyzed to identify key segmentation positions based on their types. Feature points are derived from key segmentation positions to calculate the characteristic parameters. Finally, the pylon 3D models are reconstructed on the basis of the calculated values. The experimental results showed that, compared with other similarity measurements, the Hausdorff distance had the best effect as a similarity measurement using the linearly transformed dataset, with an overall evaluation F-score of 86.4%. The maximum relative error of the calculated pylon parameters did not exceed 5%, affirming the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The pre-cooling of a large LNG storage tank involves complex phenomena such as heat transfer, low-temperature flow, gas displacement, and vaporization. The whole pre-cooling process could take up to 50 h. For large-scale, full-capacity storage tanks, it is particularly important to accurately control the pre-cooling temperature. Digital twin technology can characterize and predict the full life cycle parameters from the beginning of pre-cooling development to the end and even the appearance of damage in real time. The construction of a digital twin platform requires a large number of data samples in order to predict the operating state of the device. Therefore, a simulation method with high computational efficiency for the pre-cooling process of LNG tanks is of great importance. In this paper, the mixture model and discrete phase model (DPM) are applied to simulate the pre-cooling process of a large LNG full-capacity tank. Following Euler–Lagrange, the DPM greatly simplifies the solution process. Compared with the experimental results, the maximum error of the DPM simulation results is less than 11%. Such a highly efficient simulation method for the large LNG full-capacity storage tank can make it possible to build the digital twin platform that needs hundreds of data model samples. [ABSTRACT FROM AUTHOR]

Energy performance-based contracts (EPCs) offer a promising solution for enhancing the energy performance of buildings, which is an overarching step towards achieving Net Zero Carbon Buildings, addressing climate change and improving occupants' comfort. Despite their potential, their execution is constrained by difficulties that hinder their diffusion in the architecture, engineering, construction, and operation industry. Notably, the Measurement and Verification process is considered a significant impediment due to data sharing, storage, and security challenges. Nevertheless, there have been minimal efforts to analyze research conducted in this field systematically. A systematic analysis of 113 identified journal articles was conducted to fill this gap. A paucity of research tackling the utilization of digital technologies to enhance the implementation of EPCs was found. Consequently, this article proposes a framework integrating Digital Twin and Blockchain technologies to provide an enhanced EPC execution environment. Digital Twin technology leverages the system by monitoring and evaluating energy performance in real-time, predicting future performance, and facilitating informed decisions. Blockchain technology ensures the integrity, transparency, and accountability of information. Moreover, a private Blockchain infrastructure was originally introduced in the framework to eliminate high transaction costs related to on-chain storage and potential concerns regarding the confidentiality of information in open distributed ledgers. [ABSTRACT FROM AUTHOR]

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms. [ABSTRACT FROM AUTHOR]

To address the issues of low visibility, poor real-time performance, and weak interaction capabilities in the operation of magnetic levitation circular knitting needles, an online monitoring method based on digital twin technology for magnetic levitation knitting needles is proposed. This method first conducts an in-depth analysis of the digital twin five-dimensional model of the magnetic levitation knitting needle to monitor the operational status of the equipment in real time. Additionally, an online monitoring system based on the Unity3D engine and a digital twin-based architecture for magnetic levitation knitting needles are designed. On this basis, the comprehensive monitoring, operation, and maintenance of the magnetic levitation knitting needles are achieved through the design of virtual models, visual interfaces, and real-time data acquisition and driving technology. Finally, this method takes a 5-inch 32-needle magnetic levitation circular knitting machine as the application research object. By analyzing the movement trajectory and real-time current feedback data of the three working positions of the magnetic levitation knitting needles, the rationality, effectiveness, and accuracy of the proposed method and model are demonstrated, achieving online monitoring of the magnetic levitation knitting needles and reflecting the full lifecycle process of the magnetic levitation knitting needles. [ABSTRACT FROM AUTHOR]