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Industries require, nowadays, to be more adaptable to unforeseen real-time events as well as to the rapid evolution of their market (e.g. multiplication of customers, increasingly personalized and unpredictable demand, etc.). To meet these challenges, manufacturers need new solutions to update their production plan when a change in the production system or its environment occurs. In this context, our research work deals with a dynamic scheduling problem of a real Hybrid Flow Shop considering the specific constraints of a perfume manufacturing company. This paper proposes a Digital Twin-driven dynamic scheduling approach based on the combination of both optimization and simulation. For the optimization, we have developed a mixed integer linear programming (MILP) scheduling model taking into account the main specific scheduling requirements of our case study. Regarding the simulation approach, a 3D shop floor model has been developed including the additional stochastic aspects and constraints which are difficult or impossible to model with a MILP approach. These two models are connected with the real shop floor to create a digital twin (DT). The developed DT allows the re-scheduling of production according to internal and external events. Finally, validation scenarios on a perfume case study have been designed and implemented in order to demonstrate the feasibility and the relevance of the proposed digital twin-driven dynamic scheduling approach. [ABSTRACT FROM AUTHOR]

Current research on Digital Twin (DT) is largely focused on the performance of built assets in their operational phases as well as on urban environment. However, Digital Twin has not been given enough attention to construction phases, for which this paper proposes a Digital Twin framework for the construction phase, develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation. The DT framework and its prototype are underpinned by the principles of versatility, scalability, usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation. Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time. The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches: (i) To predict equipment utilisation ratios and productivities; (ii) To detect the percentage of time spent on different tasks (i.e., loading, hauling, dumping, returning or idling), the distance travelled by equipment over time and the speed distribution; and (iii) To visualise certain earthwork operations. [ABSTRACT FROM AUTHOR]

Digital twin, a core technology for intelligent manufacturing, has gained extensive research interest. The current research was mainly focused on digital twin based on design models representing ideal geometric features and behaviors at macroscopic scales, which is challenging to accurately represent accuracy and performance. However, a numerical representation is essential for precision microstructures whose accuracy and performance are difficult to measure. The concept of a digital twin for an accurate representation, proposed in 2015, is still in the conceptual stage without a clear construction method. Therefore, the goal of accurate representation has not been achieved. This paper defines the concept and connotation of an accuracy and performance-oriented accurate digital twin model and establishes its architecture in two levels: geometric and physical. First, a geometric digital twin model is constructed by the contact surfaces distributed error modeling and virtual assembly with nonuniform contact states. Then, based on this, a physical digital twin model is constructed by considering the linear and nonlinear response of the structural internal physical properties to the external environment and time to characterize the accuracy and performance variation. Finally, the models are evaluated. The method is validated on microtarget assembly. The estimated values of surface modeling, center offset, and stress prediction accuracy are 94.22%, 89.3%, and 83.27%. This paper provides a modeling methodology for the digital twin research to accurately represent accuracy and performance, which is critical for product quality improvements in intelligent manufacturing. Research results can be extended to larger-scale precision structures for performance prediction and optimization. [ABSTRACT FROM AUTHOR]

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs). [ABSTRACT FROM AUTHOR]

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

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

Recently, Digital Twin (DT) has a growth revolution by increasing Artificial Intelligence (AI) techniques and relative technologies as the Internet of Things (IoT). They may be considered as the panacea for DT technology for various applications in the real world such as manufacturing, healthcare, and smart cities. The integration of DT and AI is a new avenue for open research in the upcoming days. However, for exploring the issues of developing Digital Twins, there are interesting in identifying challenges with standardization ensures future developments in this innovative theme. This paper first presents the Digital Twins concept, challenges, and applications. Afterward, it discusses the incorporation of AI and DT for developing various IoT-based applications with exploring the challenges and opportunities in this innovative arena. Then, developing tools are presented for exploring the digital twins' system implementation. Further, a review of recent DT-based AI approaches is presented. Finally, a discussion of open research directions in this innovative theme is presented. [ABSTRACT FROM AUTHOR]

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

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]

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection. [ABSTRACT FROM AUTHOR]

Efficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports. [ABSTRACT FROM AUTHOR]

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process. [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]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

This paper thoroughly investigates material characterization, reliability evaluation, fabrication, and assembly processes of additively manufactured flexible packaging and reconfigurable on-package antenna arrays for next-generation 5G/mmWave wearable and conformal applications. The objective is to bridge the technology gap in current Flexible Hybrid Electronics (FHE) designs at mmWave frequencies and address the challenges of establishing future design standards for additively manufactured flexible packages and System-on-Package (SoP) integrated modules. Multiple 3D printed flexible materials have been characterized for their electrical and mechanical properties over the 5G/mmW frequency band (26–40 GHz), and the inkjet printed interconnects on 3D printed Polypropylene (PP) substrates demonstrated excellent electrical and mechanical performance during a 10,000-time cyclic bending test over typical wearable flexible radii down to 1 inch. A proof-of-concept flexible on-package phased array with an integrated microfluidic cooling channel on 3D printed substrates was fabricated and measured, demonstrating ± 37 ∘ beam steering capability with efficient cooling. The proposed reconfigurable design and low-temperature fabrication approach using additive manufacturing can be widely applied to next-generation highly-complex on-demand FHE, flexible multi-chip-module integration, and on-package phased-array modules for 5G/mmWave wearable and conformal smart skin, digital twin and massive MIMO applications. [ABSTRACT FROM AUTHOR]

The situation awareness, especially for collaborative robots, plays a crucial role when humans and machines work together in a human-centered, dynamic environment. Only when the humans understands how well the robot is aware of its environment can they build trust and delegate tasks that the robot can complete successfully. However, the state of situation awareness has not yet been described for collaborative robots. Furthermore, the improvement of situation awareness is now only described for humans but not for robots. In this paper, the authors propose a metric to measure the state of situation awareness. Furthermore, the models are adapted to the collaborative robot domain to systematically improve the situation awareness. The proposed metric and the improvement process of the situation awareness are evaluated using the mobile robot platform Robotino. The authors conduct extensive experiments and present the results in this paper to evaluate the effectiveness of the proposed approach. The results are compared with the existing research on the situation awareness, highlighting the advantages of our approach. Therefore, the approach is expected to significantly improve the performance of cobots in human–robot collaboration and enhance the communication and understanding between humans and machines. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of fidelity on the capability of digital twins (DT) to detect two common material extrusion (MEX) failures: extrusion error and layer shift. Monitoring of the failures is first explored using an image-based DT to provide a benchmark. The fidelity-performance analysis of DTs with significantly different levels of fidelity ranging from low to high fidelity is then studied and presented. In this paper, digital twin fidelity is defined as the expression of the necessary levels of accuracy in different attributes of the twin. The basis for the DTs employed in this study is image comparison. Consequently, six attributes are selected to describe the fidelity, including image resolution, mesh number, shadow method, illumination model, light source and state threshold. The effects of varying the fidelity of the six attributes on the capability to detect printing failures are then analysed and discussed. It is shown that, for the two failure cases considered, lower-fidelity DTs can deliver comparable capability to what are considered DTs with high or ultra-high fidelity. In addition, benefits in terms of configuration cost, data storage, simulation time, and detection time are demonstrated. [ABSTRACT FROM AUTHOR]

Urban informatics appears to be a suitable area for the application of digital twins. Definitions of the term share some characteristics, but these definitions do not agree on what exactly constitutes a digital twin. The term has the potential to be misleading unless adequate attention is paid to the inherent uncertainty in any replica of a real system. The question of uncertainty is addressed, together with some of the issues that make its quantification problematic. Digital twins for urban informatics pose questions of purpose, governance, and ethics. In the final section the paper suggests some research issues that will need to be addressed if digital twins are to be successful. [ABSTRACT FROM AUTHOR]

Dynamic evolution is the most typical feature of a digital twin, making it different from a traditional digital model. Dynamic evolution is also the core technology for building equipment digital twins because it ensures consistency between physical space and virtual space. This paper proposes a dynamic evolution framework for black box equipment digital twins. The framework consists of three main parts: data acquisition and processing, an evolution triggering mechanism and an evolution algorithm. A formal description of the dynamic evolution of a black box digital twin is also given. Furthermore, by synthetically considering the computational accuracy and efficiency, we design an incremental external attention temporal convolution network (IExATCN) model to instantiate the proposed framework. Finally, the significance of digital twin dynamic evolution and the effectiveness of the IExATCN is verified by 3D equipment attitude estimation datasets. [ABSTRACT FROM AUTHOR]

When conducting relay protection research, research costs can be significantly reduced if protection principle development, protection parameter verification and debugging can be carried out without relying on actual protection devices. The concept of 'digital twin' has made this possible, but the existing research has shortcomings in real-time data interaction ability, protection logic transparency, interface standardization, human–computer interaction etc., and consequently, mirror operation of relay protection in digital space has not been fully realized. Therefore, referring to the characteristics of digital twin, and combining with the practical application requirements in relay protection, this paper proposes the concept and characteristics of relay protection mirror operation based on digital twin. Key solutions are proposed to address the difficulties that may be encountered in the implementation of relay protection mirror operation in terms of protection principles, interfaces, real-time operation of the system, and human–computer interaction function simulation. Finally, an example of 110 kV double-bus and double-branch bus protection is used to verify the feasibility and progressiveness of the scheme proposed in this paper by comparing the action behavior and external characteristics of the twin protection and the actual protection device. The presented research can provide a reference for further in-depth research and application of relay protection using digital means. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used for object recognition and grasping posture planning in intelligent robotic grasping (IRG). Compared with the traditional usage of CNNs in image recognition, IRGs require high recognition accuracy and computational efficiency. However, the existing methodologies for CNN architecture design often rely on human experience and numerous trial-and-error attempts, which make it a very challenging task to obtain an optimal CNN for IRGs. To tackle this challenge, this paper develops a new differentiable architecture search (DARTS) method considering the floating-point operations (FLOPs) of CNNs, named the DARTS-F method, which converts the discrete CNN architecture search to a gradient-based continuous optimization problem and considers both the prediction accuracy and the computational cost of the CNN during the optimization. To efficiently identify the optimal neural network, this paper adopts a bilevel optimization, which first trains the neural network weights in the inner level and then optimizes the neural network architecture by fine-tuning the operational variables in the outer level. In addition, a new digital twin (DT) of IRG is developed considering the physics of realistic robotic grasping in the DT's virtual space, which could not only improve the IRG accuracy but also avoid the expensive training time. In the experiments, the proposed DARTS-F method could generate an optimized CNN with higher prediction accuracy and lower FLOPs than those obtained by the original DARTS method. The DT framework improves the accuracy of real robotic grasping from 61 to 71%. [ABSTRACT FROM AUTHOR]

Architectural heritage is vulnerable to disasters. Digital technologies can fight destruction and can ensure integrity by monitoring, managing and protecting architectural heritage from disasters. In this paper, we clarify the relationship between disasters, digitalization and architectural heritage conservation for the sustainability of cultural heritage. This study used the PRISMA process, and bibliometric tools VOSviewer and Citespace to explore the potential of digital technologies in the protection of architectural heritage—especially during disaster cycles, from the perspectives of both universal and typicality; the results revealed that digital twins, deep learning, and preventive conservation are currently hot topics in digital preservation research (especially that research which relates to disaster cycles). On this basis, this paper summarizes the relevant technologies involved in architectural heritage preservation from the perspective of the disaster cycle and the digital phase, and proposes three future research directions: accurate prediction of multi-disasters, automatic early warning of structural damages, and intelligent monitoring of human–computer interaction. This paper constructs a new research frame for digital preservation of architectural heritage during disasters, providing theoretical reference and practical guidance for architectural heritage conversation. [ABSTRACT FROM AUTHOR]

In order to reduce damages caused by slope instability and landslide disasters, it is of great significance to find an efficient, accurate and time-saving method for slope stability analyses. This paper proposes a convolutional neural network based on digital twin models to predict the safety factor of a slope and be evaluate its stability state. In order to solve the problem of lack of the CNN training samples, the digital twin method is resorted to generate 4000 slope models from 10 real slopes by fine-tuning the geometric coordinates and material parameters of their soil layers. The finite element computation of the safety factor of these 4000 slope models were realized by using the parametric analysis of ABAQUS platform and 4000 slope datasets were obtained to serve as the CNN training samples. With the geometric coordinates and material parameters of the slopes as the CNN input and the slope safety factor as the CNN output, the slope safety factor can be effectively predicted. The results show that the prediction accuracy for the testing set reaches 96% and the root mean square error is 0.079. Compared with the finite element modeling time, the prediction time is greatly shortened. The evaluation accuracy of stability states for the 10 real slopes has reached 100%, which indicates that the CNN model has good generalization ability and prediction effect and has practical significance in engineering applications. [ABSTRACT FROM AUTHOR]

In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them. Likewise, with the development of the Blockchain, the digital twins have the potential to redefine and could be a key strategy to support the IoT-based digital twin's applications for transferring data and value onto the Internet with full transparency besides promising accessibility, trusted traceability, and immutability of transactions. Therefore, the integration of digital twins with the IoT and blockchain technologies has the potential to revolutionize various industries by providing enhanced security, transparency, and data integrity. Thus, this work presents a survey on the innovative theme of digital twins with the integration of Blockchain for various applications. Also, provides challenges and future research directions on this subject. In addition, in this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized manner. We also discuss the challenges and limitations of this integration, including issues related to data privacy, scalability, and interoperability. Finally, we provide insights into the future scope of this technology and discuss potential research directions for further improving the integration of digital twins with IoT-based blockchain archives. Overall, this paper provides a comprehensive overview of the potential benefits and challenges of integrating digital twins with IoT-based blockchain and lays the foundation for future research in this area. [ABSTRACT FROM AUTHOR]

The majority of Europe's building stock consists of facilities built before 2001, presenting a substantial opportunity for energy efficiency improvements during their operation and maintenance phase. Digitalizing these buildings with digital twin technology can significantly enhance their energy efficiency. Reviewing the applications and trends of digital twins in this context is beneficial to understand the current state of the art and the specific challenges encountered when applying this technology to older buildings. This study focuses on the application of digital twins in building operations and maintenance (O & M), emphasizing energy efficiency throughout the building lifetime. A systematic process to select 21 pertinent use-case studies was performed, complemented by an analysis of six enterprise-level digital twin solutions. This was followed by an overview of general characteristics, thematic classification, detailed individual study analyses, and a comparison of digital twin solutions with commercial tools. Five main applications of digital twins were identified and examined: component monitoring, anomaly detection, operational optimization, predictive maintenance and simulation of alternative scenarios. The paper highlights challenges like the reliance on Building Information Modeling (BIM) and the need for robust data acquisition systems. These limitations hinder the implementation of digital twins, in particular in existing buildings with no digital information available. It concludes with future research directions emphasizing the development of methods not solely reliant on BIM data, integration challenges, and potential enhancements through AI and machine learning applications. [ABSTRACT FROM AUTHOR]

This paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool's output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model's attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) have attracted great attention in the energy sector. Game engines have been suggested to model DTs of their physical counterparts because they provide realistic graphics, lighting-, fluid- and physics engines that simulate the real world. However, the application of game engines to develop DTs for photovoltaics (PVs) has not yet been discussed in the literature. Therefore, this paper assesses the built-in game engine features' ability to support the DT development of PVs with Unreal Engine 5. This paper mainly focuses on visual representation because the surrounding environment significantly impacts PV deployment, and the existing software tools do not allow the study of the environmental factors at the early planning phase of a project's lifecycle. Furthermore, this paper investigates the position of the sun, shadows and reflections from nearby objects that influence the PVs' power output, and if the built-in light engine can be used for planning the deployment of PVs. The result shows that in-game objects in the environment can be used to affect the simulated PV output estimate over a year. It also indicates that applying Unreal Engine 5 to model PV systems that rely on mirroring real-world behaviour is promising if accurate data is used in the modelling. Real data and mathematical PV models are necessary since Unreal Engine 5's Lumen subsystem cannot provide realistic solar radiance on PVs for a given location on earth. [ABSTRACT FROM AUTHOR]

Human digital twins are computational models of the human actions involved in interacting and operating technical artifacts. Such models provide a conceptual and practical tool for artificial intelligence designers when they seek to replace human work with intelligent machines. Indeed, digital twins have long served as models of technical and cyber-physical processes. Human digital twins have such models as their foundations but also include models of human actions. As a result, human digital twin models enable technology designers to model how people interact with intelligent technical artifacts. Yet, development of human digital twins is associated with certain conceptual problems. To clarify the basic idea, we constructed a human digital twin for Minsky's M-Machine. The abstract conceptual structure of this machine and its generality allowed us to analyze the general properties of human digital twins, their design, and their use as tools in designing intelligent technologies. [ABSTRACT FROM AUTHOR]

The rise of digital twin-based operational improvements poses a challenge to protecting industrial cyber-physical systems. It is crucial to safeguard digital twins while disclosing internals, which can create an increased attack surface. However, leveraging digital twins to simulate attacks on physical infrastructure becomes essential for enhancing ICPS cybersecurity resilience. This paper introduces an integrated intelligent defense framework called CyberDefender to study various attacks on digital twin-based ICPS from a four-layer perspective (i.e., digital twin-based industrial cyber-physical systems infrastructure layer, honeynet and software-defined industrial network layer, intelligent security platform layer, and smart industrial application layer). To demonstrate its feasibility, we implemented a proof-of-concept (PoC) solution using open-source tools, including AWS for cloud infrastructure, T-Pot for Honeynet, Mininet for SDN support, ELK tools for data management, and Docker for containerization. This framework utilizes an integrated intelligent approach to enhance intrusion detection and classification capabilities for digital twin-based industrial cyber-physical systems (DT-ICPS). The proposed intrusion detection system (IDS) combines two strategies to improve security. First, we present an innovative approach to identifying essential features using explainable AI and ensemble-based filter feature selection (XAI-EFFS). By using Shapley Additive Explanations (SHAP), we analyze the impact of different variables on predictive outcomes. Secondly, we propose a hybrid GRU-LSTM deep-learning model for detecting and classifying intrusions. We optimize the hyperparameters of the GRU-LSTM model by using a Bayesian optimization algorithm. The proposed method demonstrates excellent performance, outperforming conventional state-of-the-art techniques with an accuracy rate of 98.96%, which is a remarkable improvement. Additionally, it effectively detects zero-day attacks, contributing to digital twin-based ICPS cybersecurity resilience. [ABSTRACT FROM AUTHOR]

Creating digital twins of scenes has been widely studied in smart city applications. The development of 3D virtual games attracts many researchers. However, most products have high costs, neglect 3D recovery of the moving people, and lack structured analysis. In this paper, we present a mirror world based on PTZ cameras, which creates digital twins of the space and persons from video streamings, including real-time video registration for PTZ cameras and 3D recovery of the human pose and shape. Our goal is to quickly build a digital space to represent and visualize the physical world, leveraging the two tasks to improve the performance of structured scene understanding. We use the scene images from PTZ cameras to create the 3D scene model and propose an image edge alignment method to optimize the texture mismatching during real-time video registration. Then we propose a human analysis network for 3D recovery of the human pose and shape and add refinement to improve performance on two datasets. We place the 3D human bodies in the right position in the mirror world and utilize joint data from specific scenarios to drive the optimization of the system. Consequently, the mirror world provides 3D visualization and structured analysis of the scene with low cost, thereby enhancing users' spatial understanding. [ABSTRACT FROM AUTHOR]

With the development of simulation technology, more and more manufacturers have begun to use the digital twin to design workshops and factories. For these design scenarios under real-time interaction requirements with an excessive amount of model data, if the rendering is stuck, it will reduce the work efficiency. It is a key enabling technology to simplify and switch the geometry models with different resolutions, according to the distance of the viewpoint or the motion state to reduce the computational complexity. Existing model simplification methods emphasize the universality and efficiency under various scenarios, while the simplification performance in the 3D geometry models of industrial mechanisms is poor. This paper proposes a rapid simplification approach to the 3D geometry model of mechanisms in the digital twins-driven manufacturing system design context. A novel Vertex Saliency-oriented Classified Edge Collapse (VS-CEC) algorithm is proposed to simplify the shape feature of the 3D geometry model of mechanisms. It especially emphasizes solving the sharp shape preservation issues in the mechanical design scenario rather than a universal things design scenario. A vertex saliency factor is defined and integrated with the region boundary information obtained from the processing of detailed features to ensure visual fidelity as well as shape preservation such as sharp edges. Experiments show that this approach reduces the data model complexity more reasonably to speed up the rendering. It ensures that the digital twin model interacts quickly with the physical manufacturing system, and thus realizes the low-latency visual effect of cyber-physical synchronization. [ABSTRACT FROM AUTHOR]

Autoclave is the most important equipment in the composite curing process, and its real-time condition has a direct impact on the quality of composite materials. Therefore, rapid and precise fault diagnosis reasoning is of great significance for the autoclave. To address the shortage of signed directed graph (SDG)-based fault diagnosis method, this paper proposes a fault diagnosis method based on digital twin (DT) enhanced SDG for autoclave. Firstly, the SDG model of autoclave temperature control system is constructed, and the model is improved and enhanced by pre-fault transition state identification, fuzzy confirmation of node states, and simplification of potential branch circuits by using DT. The effectiveness of the method in this paper is verified by fault diagnosis based on SDG and DT-SDG methods respectively. The experimental results show that the method proposed in this paper can improve the speed and resolution of fault diagnosis by reducing the number of potential fault propagation paths and the number of inferences. [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]

Quantifying fault severity is a critical part of rolling bearing health management. There are numerous methods for evaluating the severity of rolling bearing faults that utilize various signals to predict the remaining useful life (RUL). However, the fault severity assessment methods based on digital twins still cannot accurately track the fault evolution. To address this issue, this paper proposes a data/physics-driven digital twin model for predicting the RUL of rolling bearings. Firstly, using theoretical damage mechanics, the evolution of fatigue damage of rolling bearings throughout their entire life cycle is described, for which a spalling fatigue evolution model is constructed. Secondly, fatigue defects predicted by the spalling fatigue evolution model are introduced into a dynamic model to construct a bearing high-fidelity model. Thirdly, a local-to-global dynamic updating framework is designed for the interactive coupling of real-time monitoring data of rolling bearings and corresponding simulated data, as well as updating the high-fidelity model parameters. Finally, a neural network is used to predict the RUL of rolling bearings. The approach is verified using test data of rolling bearings. The results demonstrate that the proposed digital twin model can more accurately address the problem of RUL prediction of rolling bearings. The remaining useful life prediction process of rolling bearing is based on digital twin. [ABSTRACT FROM AUTHOR]

In a medical emergency situation, real-time patient data sharing may improve the survivability of a patient. In this paper, we explore how Digital Twin (DT) technology can be used for real-time data storage and processing in emergency healthcare. We investigated various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we found Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations. We propose a cloud-based DT storage solution, in which real-time medical data are transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings. [ABSTRACT FROM AUTHOR]

Metaverses refer to immersive virtual worlds in which people, places, and things of the physical world are represented by their digital representations. The wide adoption of metaverses is expected to widely disrupt the way we interact in the virtual world by elevating our online interactive experiences and bringing a plethora of implications for businesses. Following a structured literature review of related research published in the last decade, we shed light on our current understanding of metaverses and reflect on the potentially transformative value of metaverses for businesses in the near future. We draw on an established research framework to organize the insights of existing literature across different levels of analysis and activities' purpose. Through this analysis, we reveal eight propositions on the changes brought by the use of metaverses and identify a number of open questions which could serve as future research avenues. [ABSTRACT FROM AUTHOR]

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open-source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on Github. [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]

Increasing numbers of lithium-ion batteries for new energy vehicles that have been retired pose a threat to the ecological environment, making their disassembly and recycling methods a research priority. Due to the variation in models and service procedures, numerous lithium-ion battery brands, models, and retirement states exist. This uncertainty contributes to the complexity of the disassembly procedure, which calls for a great deal of adaptability. Human–Robot Collaboration Disassembly (HRCD) mode maximizes the advantages of both humans and robots, progressively replacing single-person disassembly and single-machine disassembly to become the standard method for disassembling end-of-life lithium-ion batteries (LIBs). However, the HRCD process has more dimensions and uncertainties. In light of the obstacles above, this paper developed an HRCD environment with virtual and real interaction functions, which recommended real-time cooperation strategies in the dynamic production process and significantly enhanced the flexibility of disassembly operations. Based on the genetic algorithm (GA), the Disassembly Sequence Planning (DSP) is developed for waste LIBs in the source domain and imported into the knowledge base. Then, the rapid adaptive generation of HRCD task strategy for LIBs is generated, utilizing the transfer learning approach in the target domain. Two types of end-of-life automobile LIBs are analyzed as case study products. The results demonstrated that the proposed method could plan an effective action sequence, effectively reduce the design time of the target domain disassembly strategy, and enhance the flexibility of HRCD. [ABSTRACT FROM AUTHOR]

The paper collects prior studies about the cavities in the northern portion of Pompeii's fortification. The exceptional circumstances that characterize the unearthed city allow to ascribe those cavities to the blows of the artillery employed by the Romans in the first century BC during the siege. The survey of the cavities was carried out via traditional methods and enabled to derive ballistic data to quantify the power of the arms used. In conclusion, the paper suggests that a photogrammetric survey would be opportune. The precise and accurate geometric and radiometric characterization of the cavities will be prodromal to the virtual construction of 3D digital twins (negative–positive) and will support the mechanical analyses. The results will be employed to reconstruct Roman artillery (ballistae and catapults) suitable for a correct/reliable reconstruction of the siege followed by and innovative dissemination of the image of ancient Pompeii. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments. [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 new generation of intelligent manufacturing systems requires a deep integration of human-cyber-physical spaces. Visual analytics plays a critical role in effectively navigating humans through twin data, enabling them to make better decisions and discover new knowledge, contributing to the evolution of digital twins. However, due to the lack of study of visual analytics components and their inter-relations in the context of the digital twin, the visual analytic process of digital twin systems lacks guidance to fully integrate human domain experience and cognitive abilities into the intelligent decision-making process of digital twin systems. Therefore, this paper proposes a conceptual visual analytics framework for digital twins (DTVA). Moreover, from the perspective of data and models in digital twins, the works concerning key factors of visual analytics, including digital-twin visual representation (DTV), analytical reasoning (DTA), and human–machine interface (DTI), are analyzed from data flow, task flow, and visual flow. At last, a case study of DTVA-based anomaly detection, which focuses on the abnormality detection of crane operation status, is presented. This work can support digital twin visual analytics, put forward ideas for the direction of visual analytics, and provide a promising direction for digital twin visualization services. [ABSTRACT FROM AUTHOR]

Digital twin is a technology that integrates multi-physical, multi-scale, and multi-disciplinary attributes. At the same time, it has the characteristics of real-time synchronization, faithful mapping, and high fidelity. It can realize the interaction and integration of physical world and information world. In recent years, digital twin technology has attracted the attention of academic and business circles, especially its application. Based on this background, this paper summarizes the concept and evolution of digital twin, studies the application tools and platforms of digital twin, and discusses the relationship between digital twin and related technologies. Then we focus on the application scenarios of digital twin, and summarize the new trends and requirements of digital twin application. The purpose of this paper is to provide reference for the practice of digital twin concept and technology in related fields in the future. [ABSTRACT FROM AUTHOR]

Compared with traditional CNC machines, robot milling has the advantages of low cost, high flexibility, and strong adaptability, providing a new solution for complex surface machining. However, robot machining trajectory planning in the real world is time-consuming and has safety risks. At the same time, how to achieve 3D visualization monitoring in the milling process effectively is also a challenging problem. Digital twin technology, with its characteristics of multi-dimension, high-fidelity, virtual-real fusion, and real-time interaction, provides an effective way to solve these problems. For this purpose, the paper designs and implements a robot milling motion simulation and visualization monitoring system based on the digital twin system framework. The system uses the Unity3D platform to construct the robot's digital twin body, designs a material removal algorithm based on mesh deformation, and establishes a milling motion simulation model. Through virtual-real mapping technology, the system establishes a bidirectional communication between virtual and physical entities and achieves the result mapping of the robot milling motion simulation and the visualization monitoring of the milling process. Finally, the motion simulation and real-time visualization monitoring of the milling process are tested, verifying the effectiveness and timeliness of the system. [ABSTRACT FROM AUTHOR]

The development of modern manufacturing requires key solutions to enhance the intelligence of manufacturing such as digitalization, real-time monitoring, or simulation techniques. For smart robotic manufacturing, the modern approach regarding robot programming and process planning aims for both high efficiency and energy-awareness. During the design and manufacturing stages, optimization becomes crucial and can be fulfilled by means of appropriate digital manufacturing tools. This paper presents the development of a Digital Twin for a robotic deburring workcell along with the process planning and robot programming. Considering a large size workpiece, a new robot programming solution was implemented, based on image processing to safely re-machine only areas where burrs could not be completely removed in the main deburring routine. The work also covers the development of a new web platform to remotely monitor the robotic workcell, to trigger alerts for unexpected events and to allow the control to authorized personnel enabled by the employment of robot web services following an architectural RESTful style which establishes a communication link to the robot virtual controller. The aim of this research is to integrate the Digital Twin with the innovative proposals of Industry 4.0, offering a project-based model of smart robotic manufacturing and experience concepts such as Cyber-Physical System, digitalization, data acquisition, continuous monitoring, and intelligent solutions in a novel approach. Furthermore, the work covers energy consumption strategies for energy-aware robotic manufacturing. Finally, the results of an energy-efficient motion planning along with signal-based scheduling optimization of the robotic deburring cell are discussed. [ABSTRACT FROM AUTHOR]

The growing relevance of artificial intelligence (AI) for technical systems offers significant potential for the realization and operation of autonomous systems in complex and potentially unknown environments. However, unlike classical solution approaches, the functionality of an AI system cannot be verified analytically, which is why data-driven approaches such as scenario-based testing are used. With the increasing complexity of the required functionality of the AI-based system, the quantity, and quality of the data needed for development and validation also increase. To meet this demand, data generated synthetically using simulation is increasingly being used. Compared to the acquisition of real-world reference data, simulation offers the major advantage that it can be configured to test specific scenarios of interest. This paper presents an architecture for the systematic generation of virtual test scenarios to establish synthetically generated test data as an integral part of the development and validation process for AI systems. Key aspects of this architecture are the consistent use of digital twins as virtual 1-to-1 replicas and a simulation infrastructure that enables the generation of training and validation data for AI-based systems in appropriate quantity, quality, and time. In particular, this paper focuses on the application of the architecture in the context of two use cases from different application domains. [ABSTRACT FROM AUTHOR]

Time geography was conceptualized in the 1960s when the technology was very different from what we have today. Conventional time-geographic concepts therefore were developed with a focus on human activities and interactions in physical space. We now live in a smart, connected, and dynamic world with human activities and interactions increasingly taking place in virtual space enabled by modern information and communications technology. Coupled with recent advances in sensing and mobile technologies, it is now feasible to collect human dynamics data in both physical and virtual spaces with unprecedented spatial and temporal details in the so-called Big Data era. The Big Data era brings both opportunities and challenges to time geography. While the unprecedented data collected in the Big Data era can serve as useful data sources to time-geographic research, we also notice that some classical concepts in time geography are insufficient to properly handle human dynamics in today's hybrid physical–virtual world in many cases. This paper first discusses the evolving human dynamics enabled by technological advances to illustrate different types of hybrid physical–virtual space performed through internet applications, digital twins, and augmented reality/virtual reality/metaverse. We then review the classical time-geographic concepts of constraints, space–time path, space–time prism, bundle, project/situation, and diorama in a hybrid physical–virtual world to discuss potential extensions of some classical time-geographic concepts to bolster human dynamics research in today's hybrid physical–virtual world. [ABSTRACT FROM AUTHOR]

Industry 4.0 has been provided for the last 10 years to benefit the industry and the shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the business productivity; therefore, industry 4.0 has limitations. In this paper, there is a discussion of the industry 5.0 opportunities as well as limitations and the future research prospects. Industry 5.0 is changing paradigm and brings the resolution since it will decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines. The industrial revolution is improving customer satisfaction by utilizing personalized products. In modern business with the paid technological developments, industry 5.0 is required for gaining competitive advantages as well as economic growth for the factory. The paper is aimed to analyze the potential applications of industry 5.0. At first, there is a discussion of the definitions of industry 5.0 and advanced technologies required in this industry revolution. There is also discussion of the applications enabled in industry 5.0 like healthcare, supply chain, production in manufacturing, cloud manufacturing, etc. The technologies discussed in this paper are big data analytics, Internet of Things, collaborative robots, Blockchain, digital twins and future 6G systems. The study also included difficulties and issues examined in this paper head to comprehend the issues caused by organizations among the robots and people in the assembly line. [ABSTRACT FROM AUTHOR]

Compared with the application of Digital Twin (DT) in the industrial field, the application of DT in the field of education is still in its infancy. In this paper, a Digital Twin Campus (DTC) for teaching and learning is proposed. It is argued that DTC possesses two characteristics. First, DTC has a wide variety of employment orientations for students or teachers. Second, teaching-learning resources in DTC system is huge but divisible. These characteristics result in the optional difficulty for teaching-learning objects, i.e., curriculum resources, book information resources, and electronic-article resources. To solve this problem, this paper proposes a Double-layer Collaborative Filtering Algorithm Framework (DCFAF) to recommend teaching-learning objects for digital twin teachers or students in DTC. The recommended objects will be further optimized by simulation and prediction in the virtual space of DTC. DCFAF is designed based on the principle that similar teachers (or students) may prefer similar teaching-learning resources, and for the first time, it is given double-layer property by applying the divisible characteristics of teaching-learning resources. The double-layer property can effectively solve the problem of data sparsity in collaborative filtering algorithms. Finally, the superiority of DCFAF is verified on benchmark data sets including MovieLens100k and MovieLens1M which possess the above two characteristics of DTC. DTC constructed in this way can be expected to carry out simulation, prediction, optimization and feedback continuously with the help of DCFAF, so as to realize deep integration of the teaching-learning process between real campus and virtual campus to some extent. [ABSTRACT FROM AUTHOR]