Search

The paper describes the digital twin of a Li-ion battery cell based on the MATLAB/Simulink generic model. The digital twin is based on measured data for constant current/constant voltage charging and discharging cycles with State of Health (SoH) up to 79%, also including fast charging. Mathematical equations used for the digital twin are obtained by 3D data fitting of measured SoH, battery capacity, and battery cell current. The input to the proposed digital twin is only the measured battery cell current, and its output includes State of Charge (SoC), SoH, and battery cell voltage. The designed digital twin is tested and compared with MATLAB/Simulink generic model and battery cell measurements for constant discharging current and dynamically generated discharging current profile. The results show significant improvement in the generic MATLAB/Simulink model. [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]

The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human–robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product. [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]

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]

Managing innovation in firms is reshaped by the rapidly developing technology - digital twins (DTs). Considering DTs' substitution for humans reorganises the firm's whole innovation process. This paper discusses and summarises the applications, challenges, and prospects of DTs in innovation management through a systematic literature review. Based on 48 articles and two major theories, this paper describes a new framework to reveal how DTs can compensate for human deficiencies. The results show that DTs can effectively break through the constraints humans face in innovation from four aspects: information searching, information processing, innovation idea measurement, and monitoring. This paper outlines DTs applications and difficulties for companies' innovation management and makes recommendations. [ABSTRACT FROM AUTHOR]

Digital twin is the key technology to promote the life cycle digitalization and intelligent management of municipal pipe networks. To achieve intelligent closed‐loop management for the life cycle of municipal pipe networks, this paper first analyzed the meanings of “process‐oriented” and “object‐oriented” of digital twin and gave the connotation for the life cycle management of the digital‐twin‐driven municipal pipe networks. Second, on the basis of the application paradigms in typical scenarios of design, construction, operation, and maintenance of the life cycle management of the municipal pipe networks, this paper proposed the technical framework of digital twin‐driven municipal pipe networks. Finally, the existing challenges were discussed for the digital twin in the life cycle management of municipal pipe networks. The research shows that digital twin is the technical guarantee for the life cycle management of municipal pipe networks, and high‐fidelity digital “twinning” is an important prerequisite for realizing the function of digital twin. [ABSTRACT FROM AUTHOR]

Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper. [ABSTRACT FROM AUTHOR]

In stationary power generation units such as distributed remote site power systems and ship power systems, diesel engine generator systems are essential for supplying electricity. This paper proposes a digital twin diesel generator system for teaching and research purposes. A five-layer resilient architecture, including a web interface layer, server cluster layer, real-time data layer, controller layer, and equipment layer, is proposed in this paper. Based on the resilient architecture, users are able to build, implement and monitor the digital twin through web interfaces. Apart from MATLAB/Simulink, a modeling tool called M2PLink is developed to allow users to create mathematical models using a block diagram editor similar to Simulink. Various basic blocks for control systems are provided for users to form sophisticated models. These models are converted into executable codes which are downloaded to the simulator in the controller layer, where the real-time simulations are implemented. A web-based real-time monitoring interface with many widgets such as charts, oscilloscopes, and three-dimensional (3D) animation is also provided for users to customize their monitoring interface. All the signals can be traced and all the parameters can be tuned in the monitoring interface. The users are able to interact with the digital twin just like they do with the real system. The proposed system can not only be used for research such as digital twin-assisted real-time online monitoring but also for educational purposes, which is not only cost-effective but can also ensure the safety of the user as well as the equipment. [ABSTRACT FROM AUTHOR]

The complexity of industrial challenges is emerging together with the exponentially developing information and communication technologies (ICT) that provide several implementation approaches for systems engineering. It is difficult for a single company to follow technological development and remain a pioneer in every topic. Still, the industrial challenges require experts in each technology. Therefore, collaboration among technology providers is an opportunity to gather all resources and competences needed for full delivery. A joint offering (JO) is a solution, that is, co‐created in collaboration between two or more actors that usually have complementary technological skills or value‐creation logics. A JO has several doubts relating to the use case (UC) in hand, the feasibility of the joint solution to be co‐created, the resources and skills needed to deliver the joint solution, the partners, the business value creation, the elements of the contract and the ownership of the outcome, just to mention a few. The Joint Offering Evaluation Framework (JOEF) uncovers these issues and supports decision‐making before the development of a JO starts. The JOEF comprises the Joint Offering Playbook and the Business Value Toolset (BVT). The Playbook offers seven viewpoints with checklists and tools for IT solution providers considering collaboration and co‐creation for a solution that they cannot deliver or sell alone. The BVT evaluates and illustrates the business value of a JO from the viewpoints of both creation and delivery, together with value capture and assessment. The JOEF was piloted with a digital twin (DT) UC from the pulp and paper industry. [ABSTRACT FROM AUTHOR]

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector. [ABSTRACT FROM AUTHOR]

Predictive process monitoring can provide timely information during the operation of business processes, in order to take measures to address potential risks. How to improve the accuracy of process prediction has always been highly concerned. Most of the existing research methods focus on process prediction in static environments, with few combining digital twin technology for process prediction in dynamic environments. To this end, this paper proposed a method based on concept drift detection and constructed a digital twin process prediction model to predict the next activity. Firstly, this method used behavioral relationship between event streams and weight divergence to extract features from activities in the process and obtained the feature sets of data flows. Secondly, this method performed drift detection. It dynamically selected feature sets and input them into the artificial intelligence model for training and predicting the next activity. Then, it used advanced technologies such as the Internet of Things and cloud computing to create a digital twin virtual environment. Finally, this paper obtained a digital twin model based on concept drift. It carried out evaluation and analysis on publicly available datasets, and the experimental results show that the proposed method can improve the effectiveness of prediction. [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]

The pointing accuracy of large radio telescopes is affected by antenna structure errors, installation errors, servo errors, gravity, and varying environmental factors. Although an on-line pointing model had been developed in the past for several large radio telescopes to correct these effects, it is also valuable to explore new ways to correct these effects. Therefore, in order to estimate the dynamic pointing error of large radio telescopes under solar radiation, a new way based on digital twin (DT) is used in this paper. Digital models of the Wuqing 70-m radio telescope (WRT70) are created based on the DT operational framework. Finally, the reliability of the WRT70's DT system is verified, and the dynamic pointing error of WRT70 (staying at the position of the elevation angle of 90° and the azimuth angle of 180°) on two sunny days in different seasons is estimated by the created DT system. The research results show that the pointing error of large radio telescopes under solar radiation fluctuates greatly and the seasonal differences are very striking. The maximum elevation pointing error of WRT70 under solar radiation around the winter solstice is over 40 arcsec, which is equal to 4/11 of the beam width for WRT70 operating at 8 GHz. In this paper, it is feasible to estimate the time-varying pointing errors of large radio telescopes at rest under solar radiation by constructing a DT system, but it is not effective to estimate the dynamic pointing errors of large radio telescopes in operation. It is expected to establish a dynamic pointing error model and calibrate the dynamic pointing errors of large radio telescopes in operation by DT technology in the future. [ABSTRACT FROM AUTHOR]

The heat dissipation capacity prediction and cooling adjustment of the space station are rarely reported and are currently very urgent. Therefore, this paper does the following work. First, a twin system equivalent to the actual thermal system is built based on the digital twin technology, and the net heat dissipation capacity that can reflect the energy storage characteristics of the radiator and the total heat that can reflect the load changes in the cabin are identified. Then, the optimum operating state of the thermal system is planned by using the changing characteristics of the two, to predict the maximum heat dissipation capacity and heat dissipation margin of the space station. Finally, the cooling adjustment method is given. The results show that the heat dissipation capacity of the shadow area of the space station is about 16kW, which is about 1.5 times the sunlit area. The series control reduces the time difference between hot and cold source from 677 s to about 77 s. The radiator absorptivity is only increased by 0.15, and its accumulated heat dissipation will be reduced by 7.48 kWh in one orbital period. The radiator emissivity decreased by 0.05, and the accumulated heat dissipation decreased by 1.6 kWh. When the solar azimuth is 20° and 160°, the heat dissipation capacity ratio of the whole module in the sunlit zone is about 0.9 of a single orbital period. It is worth mentioning that the idea of heat dissipation capacity prediction and cooling adjustment proposed in this paper has important reference significance for both fluid network and non-fluid network structures. [ABSTRACT FROM AUTHOR]

The integration of Digital Twin technology in small and medium‐sized enterprises (SMEs) has brought forth a transformative paradigm in engineering practices. This paper presents a comprehensive overview of the use of Digital Twin in SMEs and its implementation of advanced digital technologies in an engineering context. The paper highlights key benefits, such as the ability to simulate, analyze, and predict the behavior of products or processes before their physical instantiation. By leveraging advanced modeling and simulation techniques, SMEs can efficiently explore multiple design iterations, identify potential issues, and optimize engineering solutions without costly physical prototyping. Furthermore, the paper discusses integrating other advanced digital technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), with Digital Twin to create intelligent and interconnected systems. The implementation of Digital Twin technology in SMEs extends beyond product design to encompass various aspects of the product lifecycle, fostering efficiency, accuracy, and innovation. However, challenges such as data integration, cybersecurity, and skill requirements are addressed. With proper planning and investment, SMEs can unlock the full potential of Digital Twin technology to gain a competitive edge in the dynamic engineering domain. In conclusion, this paper demonstrates that the use of Digital Twin in SMEs and its integration with advanced digital technologies revolutionizes engineering practices. By offering virtual representations of physical assets and processes, Digital Twin enables SMEs to make informed decisions, optimize performance, and drive innovation. As the technology evolves, it becomes an indispensable tool for SMEs seeking to thrive in the ever‐evolving engineering landscape. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

Green hydrogen is a potential contributor to a more sustainable transportation system. In this paper, we present our experiences and insights gained from an industrial-scale green hydrogen refilling station, marked by the innovative hybrid Edge–Cloud electrolyser system (EMS) employed in this context. Hybrid EMS can improve system performance, adapt to dynamic conditions, and continuously improve efficiency, even under unpredictable load and low storage situations. It also allows the centralized monitoring of future hydrogen production sites to facilitate the operation and maintenance and, in the long-term vision, to enable the joint coordination of the plants flexibilities in the service market as a virtual plant, thus increasing the cost efficiency. Through a real-time digital-model based assessment of the EMS, this study offers valuable lessons learned and findings, contributing to a deeper understanding of EMS in green hydrogen infrastructure. • Description of a commercial Energy Management System (EMS) developed by the company ABB. • Demonstration of the benefits of the hybrid edge–cloud EMS architecture. • Application of an EMS to a hydrogen fueling station with on-site electrolysis. • Real-time digital-model assessment, offering valuable lessons learned contributing to a deeper understanding of EMS for H2. [ABSTRACT FROM AUTHOR]

In Industry 4.0 and advanced manufacturing, producing high-precision, complex products such as aero-engine blades involves sophisticated processes. Digital twin technology enables the creation of high-precision, real-time 3D models, optimizing manufacturing processes and improving product qualification rates. Establishing geometric models is crucial for effective digital twins. Traditional methods often fail to meet precision and efficiency demands. This paper proposes a fitting method based on an improved sparrow search algorithm (SSA) for high-precision curve fitting with minimal control points. This enhances modeling precision and efficiency, creating models suitable for digital twin environments and improving machining qualification rates. The SSA’s position update function is enhanced, and an internal node vector update range prevents premature convergence and improves global search capabilities. Through automatic iterations, optimal control points are calculated using the least squares method. Fitness values, based on local and global errors, are iteratively calculated to achieve target accuracy. Validation with aero-engine blade data showed fitting accuracies of 1e−3 mm and 1e−5 mm. Efficiency in searching for minimal control points improved by 34.7%–49.6% compared to traditional methods. This SSA-based fitting method significantly advances geometric modeling precision and efficiency, addressing modern manufacturing challenges with high-quality, real-time production capabilities. [ABSTRACT FROM AUTHOR]

In the machining process of computer numerical control (CNC) machine tools, both dynamic and static errors significantly impact the machining accuracy. The ability to accurately estimate the machining quality of products in real time is of great significance for the closed-loop control of machining accuracy of CNC machine tools. For this purpose, this paper presents a digital twin synchronous evolution method of CNC machine tools by associating dynamic and static errors. Initially, a digital twin intrinsic model of CNC machine tools with static geometric errors is constructed in the virtual space, and the high-fidelity mapping of digital twin of CNC machine tools oriented to product processing path is realized. Subsequently, an acquisition method of multi-scene perception data of CNC machine tools is described, and the unified representation of multi-source heterogeneous data is achieved based on semantic mapping. Finally, dynamic errors such as spindle thermal deformation and tool wear are analyzed in real time using integrated multi-scene sensing data. These errors, along with the static geometric errors of the tool, are reflected to the workpiece through the tool machining trajectory, which provides a basis for the closed-loop control of digital twin oriented to product quality. The proposed method is integrated into the digital twin platform of CNC machine tools, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital–model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task. [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]

Nowadays, smart systems require the use of Digital Twins (DTs) for their engineering and management. Self-updating capability is a key feature in the DT technology. This raises the challenge of model inference from data collected on the system and requires a formal framework be defined, in which a system representation can be coupled with inference methods to achieve automatic model updating. While data-based process model inference is a well-known technique, the inference of a simulation model from existing knowledge and collected data is yet an unexplored area. In this paper, we first clarify and explicitly define some key elements of the terminology related to the DT concept, including model update and model inference, and we propose a framework of inference capabilities based on a formal DT specification, which formally captures the conditions for different updating capabilities and relates them. That way, on one hand, the framework enables the use of symbolic approaches to build a DT with the desired inference capability, and on the other hand, it establishes a partial order relation between inference capabilities. Through a case study, we show how the framework helps formally specifying the DT model of a mobility system toward realizing a fully capable DT. [ABSTRACT FROM AUTHOR]

In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber–physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation. [ABSTRACT FROM AUTHOR]

The economic growth of developed or emerging countries through globalization has prompted them to increase their supply chain performance. A large number of concepts, tools, and methodologies have been proposed in support of this performance improvement. They are mainly based on the use of classical optimization or enterprise modeling methods. However, environmental and social issues, not to mention digital transformation, are often ignored or not sufficiently integrated. Indeed, the world geopolitical situation, the increase in oil prices, and the commitment to protect our earth require the integration of sustainability aspects and Industry 4.0 concepts like digital twin and artificial intelligence in transforming the supply chain. This paper focuses on defining a conceptual framework to support sustainable supply chain management and digital transformation. It aims to exploit the sustainability and digital maturity of companies to transform their supply chains and enhance their performance to meet the challenges of Industry 5.0. Several practices related to sustainability, as well as two use cases on optimization and digital twin, are presented to illustrate this framework. Finally, based on the previous practices and use cases, an adapted framework for the supply chain manager to support the transition from Industry 4.0 to Industry 5.0 has been developed, as well as a performance dashboard. [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]

In the age of Industry 4.0 and smart manufacturing, traditional production processes are undergoing significant changes due to the integration of advanced technologies like automation, robotics, big data analytics, and machine learning, which are enhancing productivity and manufacturing efficiency. The center of this evolution is the introduction of the digital twin (DT), the digital replicas of physical assets that blend real-time data with advanced analytics and simulations. Ensuring synchronization between the digital replica and its physical counterpart is crucial for successfully implementing digital twins. This paper addresses the challenge of achieving and quantifying synchronization within DTs, focusing on replicating physical system behavior and measuring deviations or delays. The study delves into the critical aspects of synchronization within digital twin applications, focusing on its implications for a robotic assembly system. The research successfully harnessed YOLOv8 to facilitate real-time event tracking and synchronization characterization, highlighting the potential of object detection deep-learning models in enhancing synchronization accuracy and, consequently, the efficiency and reliability of manufacturing processes. [ABSTRACT FROM AUTHOR]

Reverse logistics network design is a complex decision-making problem that involves the reuse, repair, remanufacturing, and recycling of end-of-life (EOL) under the tradeoff among conflicting objectives. The cutting-edge technologies in Industry 4.0 are now leading to an unprecedented and dynamic transformation of reverse logistics systems, which, however, further complicates the initial network design. In this paper, a two-level decision-support framework combined with both optimization and dynamic simulation is proposed to balance the cost, environmental impact, and service level in smart and sustainable reverse logistics network design under a dynamically evolving and stochastic environment. The results of a real-world case study in Norway show that the method can better support robust strategic decisions, eliminate dominated/near-dominated solutions, and yield holistic performance analyses considering smart reverse logistics transformation. The proposed two-level decision-support framework can better analyze the impact of the technology transformation of Industry 4.0 on reverse logistics systems, while it also provides a fundamental structure for digital reverse logistics twin. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, integrating Digital Twin (DT) technology stands out as a critical challenge for enhancing manufacturing processes and productivity. The combination of DT and Artificial Intelligence (AI) provides a significant benefit for improving processes in real-time. Industries are actively researching these technologies to keep pace with the rapid evolution of technology, utilizing virtual representations for efficient real-time monitoring and control. The present paper proposes a new approach that relies on DT technology for monitoring, optimizing manufacturing processes and enabling quality control through Deep learning (DL). The proposed methodology involves creating a digital replica of the physical system and utilizing DL models for quality control purposes. This approach improves automation and productivity while maintaining high levels of quality assurance in factories. DL is deployed within the DT for gathering data from the physical system and making predictions regarding product quality. The approach is illustrated by considering an experimental industrial prototype. The results obtained are particularly intriguing, demonstrating heightened predictive accuracy in assessing product quality and real-time issue resolution. Overall, the findings underscore the significant and interesting impact of DT technology with DL on manufacturing processes in the context of Industry 4.0. [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]

AbstractIn the field of autonomous driving research, the use of immersive virtual reality (VR) techniques is widespread to enable a variety of studies under safe and controlled conditions. However, this methodology is only valid and consistent if the conduct of participants in the simulated setting mirrors their actions in an actual environment. In this paper, we present a first and innovative approach to evaluating what we term the behavioural gap, a concept that captures the disparity in a participant’s conduct when engaging in a VR experiment compared to an equivalent real-world situation. To this end, we developed a digital twin of a pre-existed crosswalk and carried out a field experiment (N = 18) to investigate pedestrian-autonomous vehicle interaction in both real and simulated driving conditions. In the experiment, the pedestrian attempts to cross the road in the presence of different driving styles and an external Human-Machine Interface (eHMI). By combining survey-based and behavioural analysis methodologies, we develop a quantitative approach to empirically assess the behavioural gap, as a mechanism to validate data obtained from real subjects interacting in a simulated VR-based environment. Results show that participants are more cautious and curious in VR, affecting their speed and decisions, and that VR interfaces significantly influence their actions. [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]

Digital Twins (DTs) are emerging as a fundamental brick of engineering Cyber-Physical Systems (CPSs), but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or applications (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and CPSs, and the former could both fill in some gaps in agent-oriented software engineering and benefit from an agent-oriented interpretation—in a cross-fertilisation journey. [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]

As an important part of the real economy, manufacturing industry plays a major role in the whole human society. Smart manufacturing has become a strategic issue for many countries. Smart manufacturing puts forward higher requirements for the intelligence of shop-floor production process, product operation and maintenance, logistics and supply chain, which are inseparable from the support of advanced communication technology. As a new generation of mobile communication technology, 5G plays an important role in many areas of smart manufacturing with the characteristics of high bandwidth, low latency, and massive connectivity. This paper first analyzed the communication requirements for machine-to-machine, manufacturing Internet of Things, cyber-physical system-based manufacturing, logistics and supply chain, industrial Internet platform and digital twin–driven manufacturing. Based on the requirements, the research and application progress of 5G in manufacturing are investigated from the above six aspects. In addition, this paper proposed relevant future research hotspots for the further integration of 5G and the above-mentioned six areas of smart manufacturing. [ABSTRACT FROM AUTHOR]

The increase in port scale and business complexity has led to an increased demand for comprehensive and lean control on ports. The current operation mode is facing the bottleneck of the increasingly significant production efficiency and performance. Digital twin (DT) technology realizes holographic visual management and control patterns using cyber-physical fusion and promotes the transformation of a port to an intelligent operation mode. In this paper, the framework of a digital twin application system is proposed based on the analysis of business characteristics of large-scale comprehensive ports. Construction methods and technologies such as digital twin modeling, global ubiquitous perception, data mapping, and model fusion are analyzed. With regard to the construction needs of Qingdao Port's digital twin system, this paper presents a case study and illustrates the overall design process and function of the digital twin system for typical terminals. The system realizes the intelligent operation of the port with the core functions of three-dimensional visual monitoring and optimal dispatching based on real-time perception data. This paper serves as a feasible reference for future intelligent development of large ports and the application of digital twin technology. [ABSTRACT FROM AUTHOR]

Led by the manufacturing industry, virtual replicas of production systems also known as digital twins (DTs) are gradually moving into all areas of industry. Their advantages are characterized by the possibility of product optimization, simulations, improved monitoring and prediction of downtimes and optimized maintenance, to name just a few. The engineering, procurement and construction (EPC) of process plants as mechatronic systems is characterized by a high degree of project-specific modifications and interdisciplinary engineering effort with low reusability, in contrast to unit-production-driven areas such as automotive. This results in a high cost-benefit ratio for the creation of DTs over the life cycle of process plants, especially when suppliers are integrated into the value chain. The objective of this paper is to analyze the state of plant lifecycle management, data exchange and the possibilities of optimized supplier integration during the planning and EPC of process plants regarding DT creation and usage. Three research questions (RQs) were used to narrow down a total of 356 identified publications to 54, which were then examined. The papers covered a variety of topics, including combining discipline-specific models, plant management approaches and the combination of both. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on the application of digital twins in the energy sector. Initially, we generated an overview through a survey of prior reviews, independent of market vertical, then followed by a more detailed review concentrating on the power production and distribution domains, as per the NIST (National Institute of Standards and Technology) smart grid standard. We implemented a rigorous method, which included seven stages, beginning with the collection of 2238 articles. We observed that the energy sector range was too broad and filtered by generation and distribution during the practical screening, resulting in 275 for further screening. This amount was then condensed to 81 papers that matched the quality screening criteria for synthesis and examination. In summary, digital twin architectures and frameworks include five components: the physical entity, bidirectional communication, the virtual entity (with modeling and simulation), data management, and services. Our study contributed by determining that distribution management is the most pertinent application of digital twins in the distribution domain and fault diagnosis in the generation domain. Furthermore, we found that digital twins involve multiple stakeholders whose role is rarely discussed in studies, and we identified a similar absence of emphasis for security. Research on security often presents the digital twin as an additional layer of protection, yet rarely investigates the security of the digital twin by itself. The potential limitations of our study to answer some of the technical research questions may be because of the criteria for the selection of papers. However, as the emphasis of this study is on the energy sector, it enabled domain-specific findings for generation and distribution. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity. [ABSTRACT FROM AUTHOR]

The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors' knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform. [ABSTRACT FROM AUTHOR]

Roll forming is a plastic processing technology that gradually bends metal strips and sheets laterally using sequentially arranged forming dies. Due to its low cost, high efficiency, and flexibility, it has become one of the important technologies for light weighting, energy saving, emission reduction, and safety improvement in various fields such as new energy, aerospace, and rail transportation in China. However, the complexity and discreteness of the process, uneven material thickness properties, and low level of equipment informatization and flexibility automation have made it similar to a “black box”, making it difficult to predict product quality, highly dependent on manual experience for production debugging, limited in formable cross sections, and unstable in yield. Therefore, this paper proposes a data-driven intelligent roll forming equipment (system) and introduces in detail its technical architecture and characteristics. By building a data architecture based on artificial intelligence, this system collects, screens, integrates, stores, and analyzes discrete data in traditional roll forming. Meanwhile, it integrates digital twins, artificial intelligence, contour detection technology, and multi-agent collaborative control to construct a self-correcting production mode that can replace manual experience. For the new energy vehicle industry, this paper provides a case study of using this system to solve springback control of structural components of roll-formed battery packs. Finally, suggestions and prospects for the development of this system are given. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the disturbance factors affect the smooth development of berth hoisting, a hoisting planning architecture based on digital twin is proposed. The berth hoisting sequence planning model and its implementation technology are studied. The model includes three parts: the digital space for formulating the hoisting scheme, the physical space for executing the block hoisting, and the information interaction between the two parts. This paper discusses the berth hoisting planning method based on digital twin, constructs the information exchange platform of digital space and physical space, develops the berth hoisting information exchange platform based on digital twin, and simulates the block hoisting planning by using DELMIA software. An example is given to simulate the operation of the hoisting planning platform. The results show that the work of this paper has a certain reference significance to solve the influence of disturbance factors on berth hoisting sequence planning. [ABSTRACT FROM AUTHOR]

With the development of collaborative edge computing (CEC), the manufacturing market is gradually moving toward large-scale, multi-scenario, and dynamic directions. The existing scheduling strategies based on machine learning or deep learning are only applicable to specific scenarios, which is difficult to meet the requirements of dynamic real-time scheduling in multiple scenarios. The proposed digital twin technology provides a new solution for real-time scheduling of multiple scenarios. In this paper, a digital twin-oriented multi-scene real-time scheduler (GNN-RL) is proposed. This scheduler converts task sequences into node trees and sets up two learning layers. The first layer is an online learning representation layer, which uses GNN to learn node features of embedded structures in real time to boost large instances without additional training. The second layer is the online learning policy layer, which introduces imitation learning mappings into optimal scheduling behavior policies adapted to multiple scenarios. Finally, our approach is validated in several scenarios in 3D digital twin factories, such as computationally intensive, delay-sensitive, and task-urgent scenarios. Since the scheduler proposed in this paper learns general features of the embedding graph rather than instance-specific features, it has good generality and scalability, with good generalization and scalability, outperforming other scheduling rules and schedulers on various benchmarks. [ABSTRACT FROM AUTHOR]

For the problem of long modeling time and large workload of multiview bill of material (XBOM) reconstruction process in digital twin shop, this paper proposes a knowledge-driven XBOM reconstruction method for complex products. Through the research of knowledge base construction, the modeling and simulation analysis of XBOM reconstruction process in digital twin workshop are supported, so as to shorten the cycle and improve efficiency and quality. Taking the maintenance history data of the typical representative electric multiple unit (EMU) bogies in complex products as the research object, the bidirectional long short-term memory neural network with conditional random field (BiLSTM-CRF) algorithm is used to complete the entity recognition of maintenance BOM (WBOM) reconstructed parts. Finally, taking the XBOM reconstruction process of the bogie of an enterprise as an example, the XBOM reconstruction knowledge base interaction system of the EMU bogie is built. It verifies the feasibility of the method proposed in this paper, and provides knowledge support for the XBOM reconstruction process of complex products in the digital twin workshop. [ABSTRACT FROM AUTHOR]

The emerging concept of digital twins outlines the pathway towards intelligent buildings. Although abundant building data carries an overwhelming amount of information, if not well exploited, the redundant and irrelevant data dimensions result in the overfitting problem and heavy computational load. Taking the fault detection and diagnosis process for building HVAC systems as the case, this paper adopts a symbolic artificial intelligence technique to identify informative sensory dimensions for building-specific faults by exploring the symbolic representation of labelled time-series. To preserve this ad-hoc temporal knowledge in the digital twin ecosystem, machine-readable fault tags are defined to label corresponding sensor entities. A digital twin data platform is developed to annotate the real-time data with fault tags and produce filtered low-latency data streams associated with a specified tag to automate this process. This paper describes a digital twin-based approach to automatically identify and pick up informative data to support dynamic asset management. • Adopt tagging to represent and preserve knowledge learned through AI techniques. • Use Bag-of-Words to identify informative sensory dimensions for case-specific faults. • Define machine-readable fault tags to label the sensors and annotate real-time data. • Produce low-latency data streams appended with the specified tags to feed the FDD. • Informed a way to enable asset management functionalities through digital twin. [ABSTRACT FROM AUTHOR]

AbstractThis paper presents a method to create a high-fidelity gearbox thermal model. A hybrid analytical-empirical model is presented, it is based on the thermal network approach and experiments are used to determine some unknown parameters of the model. A test campaign was led on a four stages helical gear unit and consisted of cooling measurements, no-load tests and loaded tests. These experiments were performed under different rotational speeds and loads. The results show that the developed thermal model provides an accurate prediction of the thermal behavior of different components of the gearbox (namely rolling element bearings and oil) by only using few thermal sensors. These results are corroborated by power losses measurements which demonstrate that the model can estimate the gear unit efficiency. [ABSTRACT FROM AUTHOR]

Construction of wireless infrastructure using unmanned aerial vehicle (UAV) can effectively expand the coverage and support high-density traffic of next-generation communication systems. Designing wireless systems including UAVs as aerial base stations (ABSs) is a challenging task, due to the mobility of ABSs causing time-varying nature of environmental surroundings and relative propagation paths to user equipment (UE) devices. Therefore, it is essential to have an accurate estimate of the channel for varying positioning of the UAVs. In this paper, we propose to adopt a digital twin based performance evaluation procedure for wireless systems including ABSs, providing enhanced accuracy of channel modeling for specific target deployment areas. Using ray-tracing channel models reflecting detailed building and terrain information of the transmission environment, an UAV position optimization algorithm based on reinforcement learning is presented. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm calculates the overall throughput in the digital twin and determines the desired states of the UAV. Performance evaluation results demonstrate the trajectory training ability of the algorithm and the performance advantage of the system with a reduced amount of shadow area compared to those with ground base stations (GBSs). [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]

Simple Summary: This study looks at how combining real-world/time data/evidence (RWTD/E) with traditional clinical studies (known as randomized controlled trials) can improve healthcare research and ultimately patient care. Unlike past methods that often rely on outdated information, RWTD/E provides up-to-the-minute data on patient health, making it a powerful tool for doctors and researchers. This approach is especially useful in areas like sarcoma, a type of cancer, where understanding each patient's unique situation can lead to improved treatment plans. We discuss how this mix of real-world/time data and traditional research can lead to more personalized medicine, helping to create treatments tailored to individual patient needs. It also aids in managing health at a community level by spotting trends and improving health policies, making care more efficient and focused on what patients truly benefit from. Furthermore, we explore how applying new methods, like creating digital twins of patients' health profiles to RWTD/E can help in predicting individualized treatment effects. This study aims to elaborate that using prospective RWTD/E alongside traditional clinical trials can make healthcare more effective, patient-focused, and ready to adapt to new discoveries and technologies. This manuscript examines the synergistic potential of prospective real-world/time data/evidence (RWTD/E) and randomized controlled trials (RCTs) to enrich healthcare research and operational insights, with a particular focus on its impact within the sarcoma field. Through exploring RWTD/E's capability to provide real-world/time, granular patient data, it offers an enriched perspective on healthcare outcomes and delivery, notably in the complex arena of sarcoma care. Highlighting the complementarity between RWTD/E's expansive real-world/time scope and the structured environment of RCTs, this paper showcases their combined strength, which can help to foster advancements in personalized medicine and population health management, exemplified through the lens of sarcoma treatment. The manuscript further outlines methodological innovations such as target trial emulation and their significance in enhancing the precision and applicability of RWTD/E, underscoring the transformative potential of these advancements in sarcoma care and beyond. By advocating for the strategic incorporation of prospective RWTD/E into healthcare frameworks, it aims to create an evidence-driven ecosystem that significantly improves patient outcomes and healthcare efficiency, with sarcoma care serving as a pivotal domain for these developments. [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]