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Management of metabolic disorders involves continuous monitoring of physiology. The growing use of glucose sensors, insulin pumps, and other wearable devices, as well as smartphone-based decision support apps, generates large amounts of real-time data, making diabetes a data-rich domain conducive to digital twin implementation. Mechanistic models of glucose–insulin dynamics have been developed in diabetes research, providing a solid foundation for developing digital twin representations of individual patients' metabolic processes. Artificial intelligence (AI) will play a role in improving the accuracy of digital twin technologies and enable their adaptation and personalization. Case studies have demonstrated that personalized approaches that leverage digital twin technology hold promise for optimizing the management of diabetes by tailoring interventions to individual patients' needs and characteristics. Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation. [Display omitted] • Cardiac imaging plays a crucial role in the evaluation and management of patients with congenital heart disease. • Advancements in cardiac imaging techniques will likely find rapid clinical application in the assessment of patients with congenital heart disease. • Strategies to assess clinical value and resources for training are needed before widespread implementation. [ABSTRACT FROM AUTHOR]

Gas leaks from subsea oil and gas facilities could cause significant ocean environment damage. Such leaks can cause fire and explosion, for example, a fire on the ocean surface west of Mexico's Yucatan peninsula. Detecting a gas leak is critical in managing fire and explosion risks. This study proposes using autonomous underwater vehicles -robotic fish- for gas leak plume detection. The robotic fish is equipped with advance two well-known deep learning models, Faster RCNN and YOLOV4. A physical experiment system of various sizes of underwater gas leaks is used to generate the benchmark dataset. The results demonstrated the YOLOV4 model has a stronger online real-time capability. It is 43 times faster than the Faster RCNN model with the same level of accuracy. This study verifies the feasibility of integrating deep learning models with the mobile vehicle for real-time autonomous gas leak detection. This contribution will enable the development of a safe and reliable digital twin of subsea emergency management. [ABSTRACT FROM AUTHOR]

Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies. Multiscale brain modeling, along with the availability of computational technologies and biomedical data, is growing rapidly, generating an intricate set of strategies, tools, and applications. Data-driven models use biological details at multiple scales to simulate brain activity, while task-driven models usually anticipate the functions needed to simulate behavior. Data-driven models, in turn, can proceed either bottom–up (starting from neuronal and synaptic mechanisms) or top–down (taking the moves from connectomics and brain signal recordings). We show here how the different modeling strategies can be integrated to generate hybrid simulators allowing to fill gaps in knowledge though principled rules, to uncover hidden parameter values, to generate testable hypotheses guiding future research, and to open new perspectives for artificial intelligence toward personalized medicine and brain digital twins. [ABSTRACT FROM AUTHOR]

The rapid development in water-energy systems calls for advanced modeling and optimization tools to improve the technologies throughputs and minimize their energy consumption. While having a wide footprint in multiple research areas, Artificial Intelligence applications in water-energy systems are still in early stages. There is a need for generalized models to fully characterize parametric relationships, optimize technologies' performance, and have the flexibility to apply to different complex and non-linear systems. This article describes a digital twin framework that can be used for that purpose. This framework provides fundamental understanding of the process as in process-driven models alongside with the accuracy and usability of data-driven methods. The framework's structure focused on modeling and optimization aspects of digital twin models. As a part of developing a novel humidification-dehumidification desalination technology, this framework was used to model energy consumption based on 16 different operational parameters and was accurate to within 1 % of the data. Pairwise parameters sensitivities were identified by the framework for two case studies involving the whole cycle operation and a critical subsystem of the technology. Moreover, the framework was used to optimize the technology performance through data-efficient Bayesian search methods. Different search realms were used in this process. The framework was able to identify minimum and maximum energy consumption regions by sampling only 25 data points out of possible 5157 cases. [Display omitted] • Digital twin and optimization of water-energy technologies • Stochastic models analysis and uncertainty distribution • Bayesian search methods to guide testing efforts • Correlation models to capture parametric sensitivities • Reduction in experimental test matrix by 206 times [ABSTRACT FROM AUTHOR]

The meticulous reconstruction of three-dimensional (3D) braided composite materials serves as a crucial foundation for achieving high-fidelity simulations. Nonetheless, the transition from tomographic images to a 3D mesh entails a laborious and time-intensive process. To address this, an integrated procedure based on artificial intelligence is proposed for reconstructing meshes from tomograms. The initial stage of the process involves employing artificial intelligence techniques to segment complex contours and optimize high-dimensional contours. This facilitates the input of high-quality images needed to reconstruct accurate digital twins with strong convergence. The subsequent reconstruction phase integrates various calculations, including shape interpolation, contour extraction, 3D surface reconstruction, 3D mesh reconstruction, and element data interpolation. During this process, optimization objectives are set to minimize the deviation between the digital twin's surface and the actual surface, as well as to optimize the aspect ratio of the element mesh. Upon completion of the aforementioned steps, high-quality input files suitable for finite element calculations are directly generated. Ultimately, the proposed method utilizes the reconstructed finite element model for mechanical analysis, and the results are found to be in good agreement with experimental tests. This method offers an efficient and rapid way to achieve high-quality reconstruction of complex digital twins. [Display omitted] • The proposed procedure to reconstruct meshes from tomograms is integrated, enabling batch processing. • The deep learning models are employed to segment the intricate yarns contours of 3D5D braided composites. • The Ramer–Douglas–Peucker algorithm is used to capture high-dimensional contour features and improve convergence. • A decimating filter employing the quadric-based edge-collapsed strategy improves volumetric meshing. [ABSTRACT FROM AUTHOR]

Simulation methods for composites manufacturing processes have existed for many years and more recently, with the acceleration of digital technologies, there is increased interest in them. However, industrial uptake and the routine use of these models in the design and manufacture of composite parts has not yet occurred at the same scale as simulation of mechanical performance. This Viewpoint/Perspective article presents and discusses the current state-of-the-art in composites manufacturing simulation and the barriers to their wider adoption, such as large material and process variabilities and the lack of fast numerical models. We also share some personal thoughts on future research directions and how recent academic developments in the field, aiming at increasing model efficiency, leveraging new more efficient hardware (e.g. GPUs) and recent developments in the field of Artificial Intelligence could allow process simulation to become one of the cornerstones of a Composites 4.0 strategy. At the end, the reader should be more familiar with the current state of the art in composites process simulations and have gained an understanding of avenues for progress, in particular their use within a composites 4.0 framework. [ABSTRACT FROM AUTHOR]

• Integrated digital twins Framework for revolutionizes defect diagnosis for pumps. • Domain adaptation boosts diagnostic model accuracy. • Promising potential for scalable industrial applications. Bearing defects in centrifugal pumps represent a prevalent source of equipment failure, often resulting in significant downtime and maintenance expenses. Deep learning algorithms can be used to detect defects. However, in the real world, there is always a scarcity of labeled data. To address this challenge, we propose a framework integrating digital twin technology with domain adaptation for accurate diagnosis of bearing defects. The proposed framework leverages the concept of digital twins to create a virtual representation of the pump bearing, enabling real-time monitoring and simulation of operating conditions. Domain adaptation techniques are then applied to transfer knowledge from synthetic data generated by the digital twin to the actual operating environment, overcoming the domain gap between synthetic and real-world data. The results highlight the potential of digital twin-assisted combined with domain adaptation techniques for enhancing predictive maintenance strategies in industrial applications. [ABSTRACT FROM AUTHOR]

The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types – mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector. • Industry 4.0 and 5.0 technologies are reshaping bioprocessing and enabling the paradigm shift from QbT to QbD. • Digital Twin technology enables predictive modelling and real-time control to optimize processes. • Hybrid modelling combines the strengths of different modelling approaches, increasing accuracy for diverse data sets. • Soft sensors have potential in assessing CQAs and optimising bioprocessing operations in real-time. [ABSTRACT FROM AUTHOR]

Alongside advancements in Artificial Intelligence (AI), significant progress has been made in big data processing, edge/cloud computing, and ubiquitous computing in the past two decades. These advancements catalyzed the development and adoption of Digital Twins (DT) across various domains, serving as virtual replicas of Physical Objects (POs). DTs provide advanced visualization and simulation capabilities, enabling effective estimation, optimization, and forecasting of PO's behaviors. However, the widespread adoption of DTs has introduced various security threats, vulnerabilities, and attacks. Despite ongoing research in DT applications and security, there is a lack of systematic review of the DT security literature across domains and architectural layers. This study fills this gap by systematically reviewing DT research, focusing on three interrelated aspects: DT applications, architectural layers, and security. We explore DT's architectural layers, functional requirements, application, and creation software to identify potential threats, attacks, and vulnerabilities specific to DT layers and application domains. We then systematize our findings under a unified security framework and pinpoint countermeasures against identified security challenges. Furthermore, our study explores DT's role in mitigating existing cyber threats, and we conclude our work by identifying open challenges and potential research directions. [ABSTRACT FROM AUTHOR]

This review article explores the potential and significance of digital twins (DTs) in advancing battery technology, specifically in electric vehicles (EVs). Due to their ability to accurately predict, diagnose, and enhance energy systems, DTs offer a transformative solution for addressing environmental concerns and improving energy storage capabilities. Moreover, DTs hold promise in facilitating vehicle-to-grid (V2G) integration and testing autonomous driving systems, while robust cybersecurity measures will be essential to protect sensitive data. The applications of DTs in estimating state of charge (SOC) and state of health (SOH) parameters demonstrate their effectiveness in optimizing battery performance. In addition to exploring the application of artificial intelligence (AI) and machine learning models for improving battery performance while highlighting real-world case examples, the study also highlights how digital twin (DT) technology can enhance battery design, increase longevity, and ensure safety. Furthermore, this study discusses the challenges of implementing DTs for battery packs, and the potential applications of this technology in future directions are discussed. In conclusion, DTs are set to revolutionize the future of electric transportation, promoting sustainability and efficiency in the EV ecosystem. • Explores how digital twin technology can advance battery technology in electric vehicles (EVs). • The paper also highlights applications in V2G integration and autonomous driving systems, while emphasizing cybersecurity. • Overall, digital twins offer promising solutions for sustainable and efficient EVs. [ABSTRACT FROM AUTHOR]

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions. [Display omitted] • Designing a hybrid bottom-up, top-down approach for automatic building digital model creation. • Align the capabilities of AI in scene understanding with domain knowledge to overcome challenges in indoor 3D reconstruction. • Developing a knowledge-based method for 3D space parsing in largescale indoor point cloud. • Reconstruction of the parametric building's floor plan using point cloud with low sensitivity to poor data quality. • Creation of dynamic parametric building digital models (LOD200) for flexible facility management and space analysis. [ABSTRACT FROM AUTHOR]

The increasing popularity of digital twins, alongside the rapid evolution of connectivity driven by the Internet of Things, highlights their potential to greatly aid in the development of smart cities. Digital twins are employed more commonly as smart cities grow and societies become more interconnected. With the growing need for this technology, there is a pressing demand for the automatic captioning of security events from the videos collected from these models. This is needed as Dtwin models generate a lot of data that makes it difficult to caption them manually. This is required for extracting rich and meaningful higher-level interpretations from images and videos. Current models often lack in-depth insights into these complex urban systems. Additionally, there is a need for a model that can interpret and explain images and videos effectively, leveraging a combination of machine learning and knowledge graph approaches. Therefore, in this paper, we developed the Digital Twin for the buildings and road network of the TEC (Tecnologico De Monterrey) district region and additionally developed the Knowledge Graph models for emulating security events with dense video captioning. This is done by designing an AI-based TEC District Digital Twin for emulating security events by leveraging knowledge graph. The proposed approach provides data and insights about the district's operations and security. This initiative will help district planners and managers to make better decisions by analyzing the real-time data. This is supposed to contribute to increased effectiveness of district services, transparency, and an efficient infrastructure. [ABSTRACT FROM AUTHOR]

Background: Surgery is a major component of health-care provision. Operative intervention often employs minimally invasive approaches incorporating digital cameras creating a 'digital twin' of both intracorporeal appearances and operative performance. Video recordings provide richer detail than the traditional operative note and can couple with advanced computer technology to unlock new analytic capabilities capable of driving surgical advancement via quality improvement initiatives and new technology design. Surgical video is however an under-utilized technology resource, in part, because ownership along with broader issues including purpose, privacy, confidentiality, copyright and inclusion in outputs have been poorly considered using outdated categorisation.Method: A first principles perspective on operative video classification as a useful public interest resource enshrining fundamental stakeholder (patients, physicians, institutions, industry and society) rights, roles and responsibilities.Result: A facility of noble purpose, understandable to all, for fair, accountable, safe and transparent access to large volumes of anonymised surgical videos of intracorporeal operations that enables advances through cross-disciplinary research is proposed. Technology can be exploited to protect all relevant parties respecting both citizen data-rights and the special status doctor-patient relationship. Through general consensus, the capability can be understood, established and iterated to perfection.Conclusion: Overall we argue that new and specific classification of surgical video enables responsible curation and serves the public good better than the current model. Rather than being thought of as a bicycle where discrete ownership is ascribed, such data are better viewed as being more like a park, a regulated amenity we should preserve for better human life. [ABSTRACT FROM AUTHOR]

The leading-edge technology of Digital Twin (DT) presents potential solutions for challenges associated with renewable energy resources (RER), particularly solar energy, such as optimal management, random nature and unpredictability, maintenance, security, and energy efficiency. These issues are more elevated today due to the widespread adoption of solar energy in the power system. A DT leverages advanced technologies including the Internet of Things (IoT), artificial intelligence, and computing techniques, to observe and confirm the state of physical entities, analyze data and derive valid information to monitor and optimize the entity's operation. This Perspective strives to trace the growing body of advances in the field and proposes opportunity areas for DT of RER (RERDT) and DT of solar energy (DTSE) in various application domains including forecasting, reliability analysis, security, and resiliency. Barriers that hinder the adoption of RERDT and DTSE technologies to meet its continuous operation are discussed, and also possible views as future trends to deal with the challenges are presented. RERDT and DTSE technologies promise to be transformative in enabling flexible and sustainable energy sources as well as in active real-time management since it provides a full comprehension of the resources. [ABSTRACT FROM AUTHOR]

Artificial intelligence and digital twins have become more popular in recent years and have seen usage across different application domains for various scenarios. This study reviews the literature at the intersection of the two fields, where digital twins integrate an artificial intelligence component. We follow a systematic literature review approach, analyzing a total of 149 related studies. In the assessed literature, a variety of problems are approached with an artificial intelligence-integrated digital twin, demonstrating its applicability across different fields. Our findings indicate that there is a lack of in-depth modeling approaches regarding the digital twin, while many articles focus on the implementation and testing of the artificial intelligence component. The majority of publications do not demonstrate a virtual-to-physical connection between the digital twin and the real-world system. Further, only a small portion of studies base their digital twin on real-time data from a physical system, implementing a physical-to-virtual connection. [ABSTRACT FROM AUTHOR]

• Examine the use of metaverse for transportation and logistics operations. • Establish an AI supported digital technological framework. • Propose and discuss future research areas. In the digital age, supported by disruptive technologies such as artificial intelligence (AI), blockchain (BC), digital twins (DT), and extended reality (ER), the use of metaverse for real-world operations has emerged. In this paper, we examine the use of metaverse for transportation and logistics operations. To be specific, we first examine the related literature by a selective critical review. Then, we discuss how the use of metaverse can facilitate transportation operations with some relevant cases such as the metaverse project of MTR (from Hong Kong). We establish an AI supported digital technological framework, called the ABCDE framework, for transportation and logistics companies to adopt with their use of metaverse. Finally, we propose and discuss several important future research areas. To the best of our knowledge, this is the first study which comprehensively examines the use of metaverse for transportation and logistics operations. The theoretical framework also lays the foundation for future studies. The findings and insights provide constructive and helpful guidance to both practitioners and academics on the future development of the topic. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and Machine learning (ML) are increasingly used for digital twin development in energy and engineering systems, but these models must be fair, unbiased, interpretable, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful in making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using an explainable, interpretable, and trustworthy AI, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. This paper aims to explain the ideas of XAI and IML and justify the important role of AI/ML for the digital twin components, which requires XAI to understand the prediction better. This paper explains the importance and fundamentals of XAI and IML in both local and global aspects in terms of feature selection, model interpretability, and model diagnosis and validation to ensure the reliable use of trustworthy AI/ML applications for RUL prediction. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to systematically review research related to the value of new technologies in the context of performance measurement and management. The review synthesizes findings from previous studies to provide a comprehensive understanding of the current state-of-the-art of various novel technologies and how they assist performance measurement and management across various industries. Scopus was selected as the main electronic database for this study. As a principal result, we found that research related to the value of novel technologies in the context of performance measurement and management is formed around six research streams: automation techniques, predictive analytics, real-time data collection, visualization, collaboration tools, and blockchain technology. As a conclusion, the research presents that novel technologies can be used to track and monitor performance, automate processes, and streamline operations. The findings of this review will bring more coherent value to the current state of the literature and contribute to a diverse body of knowledge. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) promise innovation for the marine renewable energy sector using modern technological advances and the existing maritime knowledge frameworks. The DT is a digital equivalent of a real object that reflects and predicts its behaviours and states in a virtual space over its lifetime. DTs collect data from multiple sources in pilots and leverage newly introduced low-cost sensor systems. They synchronize, homogenize, and transmit the data to a central hub and integrate it with predictive and learning models to optimize plant performance and operations. This research presents critical aspects of DT implementation challenges in marine energy digitalization DT approaches that use and combine data systems. Firstly, the DT and the existing framework for marine knowledge provided by systems are presented, and the DT's main development steps are discussed. Secondly, the DT implementing main stages, measurement systems, data harmonization and preprocessing, modelling, comprehensive data analysis, and learning and optimization tools, are identified. Finally, the ILIAD (Integrated Digital Framework for Comprehensive Maritime Data and Information Services) project has been reviewed as a best EU funding practice to understand better how marine energy digitalization DT's approaches are being used, designed, developed, and launched. • Digital Twin (DT) implementation risks and challenges in the marine digitalization. • Digital Twin (DT) existing marine-knowledge framework provided in marine observation and modelling systems. • The ILIAD (Integrated Digital Framework for Comprehensive Maritime Data and Information Services) project. • The DT of Ocean (DTO) to support the design, development and operation of innovative services related to oceans. [ABSTRACT FROM AUTHOR]

The European Commission (EC) has launched the European Green Deal communication, setting out the path for a fundamental transformation of Europe. Key element in this policy is a fully sustainable food system outlined in the farm-to-fork strategy. Such strategy requires a systems approach in which all aspects related to the production and consumption of sufficient and healthy food are considered, including economic, environmental (climate, ecosystems) and social aspects. Here, we present the systems approach concept for food production, following the farm-to-fork principle as embraced by the EC, and elaborate on how digitalisation and Artificial Intelligence (AI) can solve the challenges that a sustainable food system imposes. We present a number of research and innovation challenges and illustrate these by some specific examples. It is concluded that AI and digitalisation show great potential to support the transition towards a sustainable food system. This development will impact the roles and interactions of the actors in the entire value chain from farmers to consumers. Policy recommendations are made for a successful future implementation of AI in sustainable food production. • System approach is needed to achieve sustainable food systems. • AI and digitalisation are key to the transition towards sustainable food systems. • Data availability and use is important for actors in all stages from farm to fork. • Policy recommendations for a successful AI implementation in food systems. [ABSTRACT FROM AUTHOR]

• The manuscript investigates a digital twin of a robot arm typical of manufacturing processes. • This explores the virtual construction of the training and evaluation environments as well as the physical equivalency of this digital twin. • We train, using reinforcement learning, a robot arm to perform a task in virtual space and then map this learnt behaviour to a physical counterpart whilst illustrating linkages between the halves of the digital twin. [Display omitted] For many industry contexts, the implementation of Artificial Intelligence (AI) has contributed to what has become known as the fourth industrial revolution or "Industry 4.0" and creates an opportunity to deliver significant benefit to both businesses and their stakeholders. Robot arms are one of the most common devices utilised in manufacturing and industrial processes, used for a wide variety of automation tasks on, for example, a factory floor but the effective use of these devices requires AI to be appropriately trained. One approach to support AI training of these devices is the use of a "Digital Twin". There are, however, a number of challenges that exist within this domain, in particular, success depends upon the ability to collect data of what are considered as observations within the environment and the application of one or many trained AI policies to the task that is to be completed. This project presents a case-study of creating and training a Robot Arm Digital Twin as an approach for AI training in a virtual space and applying this simulation learning within physical space. A virtual space, created using Unity (a contemporary Game Engine), incorporating a virtual robot arm was linked to a physical space, being a 3D printed replica of the virtual space and robot arm. These linked environments were applied to solve a task and provide training for an AI model. The contribution of this work is to provide guidance on training protocols for a digital twin together with details of the necessary architecture to support effective simulation in a virtual space through the use of Tensorflow and hyperparameter tuning. It provides an approach to addressing the mapping of learning in the virtual domain to the physical robot twin. [ABSTRACT FROM AUTHOR]

A wide variety of computational models covering statistical, mechanistic, and machine learning (locked and adaptive) methods are explored for use in biopharmaceutical manufacturing. Limited discussion exists on how to establish the credibility of a computational model for application in biopharmaceutical manufacturing. In this work, we tried to use the American Society of Mechanical Engineers (ASME) Verification and Validation 40 (V&V 40) standard and FDA proposed AI/ML model life cycle management framework for Software as a Medical Device (SaMD) in biopharmaceutical manufacturing use cases, by applying to a set of curated hypothetical examples. We discussed the need for standardized frameworks to facilitate consistent decision making to enable efficient adoption of computational models in biopharmaceutical manufacturing and alignment of existing good practices with existing frameworks. In the study of our examples, we anticipate existing frameworks like V&V 40 can be adopted. [ABSTRACT FROM AUTHOR]

In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool in various domains, including materials design and discovery. This paper provides a comprehensive review of the AI methodologies integrated within this context, encompassing materials informatics, density functional theory, molecular dynamics, and finite elements analysis. We further delve into the transformative role of AI within process engineering, manufacturing, and industry 4.0, with a focus on manufacturing process optimization techniques. Highlighting the importance of active learning, self-correcting processing, and digital twins in the era of smart manufacturing, this review underscores the impact of big data and data quality. The paper provides an insight into the challenges and future prospects, pointing towards the tremendous potential AI holds for revolutionizing the field of materials science. • Comprehensive exploration of AI methods in Materials Design and Discovery. • Review of the transformative role of AI within process engineering. • Discussion on the importance of data quality and quantity in the era of Big Data. • Insight into challenges and potential of AI in the field of Materials Science. [ABSTRACT FROM AUTHOR]

The soaring potential of edge computing leads to the emergence of cloud–edge collaboration. This hierarchy enables the deployment of artificial intelligence models in the cyber–physical venue. This paper presents Dual Digital Twin, the next level of digital twin, in the presence of two levels of communication availability, for battery system real-time monitoring and control in electric vehicles. To implement the dual digital twin concept, an online adaptive model reduction problem is formulated with time scale differences induced by the time sensitivity property of industrial applications and limitations of infrastructure. To minimize the model reduction error and battery system control penalty, the online adaptive battery reduced order model framework is proposed, consisting of the gated recurrent unit neural network to construct battery internal states given Internet of things sensor measurements, and incremental learning techniques to facilitate the update of the reduced-order model given data stream. Moreover, we design the physics-informed update of the neural network using the Lyapunov stability theorem to enhance the synchronization with the physical battery behavior. A Li-ion battery and single particle digital twin model with electrolyte and thermal dynamics are utilized in the simulation to justify the effectiveness of the proposed framework. Numerical results demonstrate 1.70% average reduced-order model prediction error and 43.3% accuracy improvement with the novel physics-informed online adaptive framework. The method is also robust concerning varying environmental factors and noise. • Introduce the dual digital twin concept and its realization in EV batteries. • Online adaptive model reduction problem with intrinsic time scale difference in AIoT system. • Cloud–edge collaborated AIoT online adaptive battery ROM framework. • Physics-informed ROM incremental learning algorithm to track the real system dynamics. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to digitizing and modeling pressure swing adsorption (PSA) processes using an uncertainty-aware digital twin. PSA modeling presents unique challenges due to its complex and cyclic behavior, which lacks a steady state. By contributing to the literature on periodic systems, we provide valuable insights into the potential applications of artificial intelligence and digital twins beyond the field of cyclic processes. Our proposed methodology can enhance the understanding and optimization of complex systems across various industries and applications. The proposed digital twin is uncertainty-aware and reliable, continuously updating itself through online learning and utilizing a novel feedback tracker to accurately represent the PSA system. This robust and adaptable methodology supports optimal PSA system operation and facilitates informed decision-making for enhanced process operation. The results demonstrate that the proposed approach yields a reliable digital twin for the PSA unit, capable of tracking the process's complex dynamics and adapting to changes, including adsorbent degradation, which is a significant challenge in PSA operations. Overall, this work highlights the potential of advanced technologies, such as digital twins and artificial intelligence, to improve performance and efficiency in the field of process engineering. This work contributes to the ongoing efforts to optimize industrial processes and support sustainable development by providing a reliable and adaptable methodology for digitizing PSA processes. • Digital twin for a PSA system in syngas purification. • Online learning and uncertainty assessment to address uncertainty identification and computational feasibility. • Robust and adaptable digital twin methodology. [ABSTRACT FROM AUTHOR]

• Improved performance and longer service life would help the adoption of PEM electrolyzers for large-scale hydrogen production. • The Digital Twin could use real-time information from the system's sensors to optimise control and system life. • The data-driven approach improves knowledge of the plant's operating conditions by explicating latent information. Operating electrolyzers for producing green hydrogen is a critical emerging issue because of either the broader use of hydrogen for several scopes or the short life span and efficiency of these components. Digital Twin offers a new opportunity to effectively face these problems by improving online control and providing fault detection, diagnosis, and prediction services. Since the Digital Twin is, in fact, a virtual mirror of a real system continuously updated by information received from the field, it allows it to swiftly react to small signals of departure from standard or optimal conditions. Although Digital Twins are widely applied in different fields, comprehensive guidance on developing and designing a Digital Twin in the literature is still lacking. This manuscript aims to provide a comprehensive guide on how to build the Digital Twin of a PEM-Electrolyzer. In particular, the architecture of the Digital Twin is initially presented, then all its components are analyzed, showing the steps to be performed to build a Digital Twin for operating PEM-Electrolyser system. [ABSTRACT FROM AUTHOR]

The transition from automated processes to mechanisms that manifest intelligence through cognitive abilities such as memorisation, adaptability and decision-making in uncertain contexts, has marked a turning point in the field of industrial systems, particularly in the development of cyber–physical systems and digital twins. This evolution, supported by advances in cognitive science and artificial intelligence, has opened the way to a new era in which systems are able to adapt and evolve autonomously, while offering more intuitive interaction with human users. This article proposes a systematic literature review to gather and analyse current research on Cognitive Cyber–Physical Systems (CCPS), Cognitive Digital Twins (CDT), and cognitive interoperability, which are pivotal in a contemporary Cyber–Physical Enterprise (CPE). From this review, we first seek to understand how cognitive capabilities that are traditionally considered as human traits have been defined and modelled in cyber–physical systems and digital twins in the context of Industry 4.0/5.0, and what cognitive functions they implement. We explore their theoretical foundations, in particular in relation to cognitive psychology and humanities definitions and theories. Then we analyse how interoperability between cognitive systems has been considered, leading to cognitive interoperability, and we highlight the role of knowledge representation and reasoning. [ABSTRACT FROM AUTHOR]

Modeling and simulation of telecommunication networks have captivated significant inter-disciplinary attention, notably since the release of the fifth-generation (5G) technology and the heavy public interest associated with its Non-Standalone (NSA) and eventual Standalone (SA) architecture deployment. However, 5G and beyond (5G+) network simulation is rather challenging as the complexity of networks has massively multiplied, given the manifold network intricacies demanded by stringent 5G requirements like mobile mmWave, Massive Multiple-Input Multiple-Output (MIMO), Advanced Channel Coding, and more. Furthermore, the arrival of 5G marks a turning point in the evolution of mobile networks as it progresses towards the softwarization of networks and paves the path for future Artificial Intelligence (AI) native 6G and beyond networks. Consequently, classical simulation techniques have a high risk of becoming insufficient to model the demands of such drastically changing network dynamics, prompting researchers to view 5G+ networks from the perspective of Complex System Science (CSS). We move in that direction as we shed light on Agent-based modeling and simulation as a pragmatic and powerful tool to model 5G+ communication networks. Our approach involves a comprehensive survey of Agent-Based Simulation (ABS) and 5G+ networks, utilizing its insights to dissect the advantages, potential, and challenges of ABS in the context of 5G+ networks. Through proposing a prospective architecture for a simulator and its envisioned evolution into a Digital Twin (DT), we not only offer insights into the lessons learned but also chart out future research pathways in the flourishing domain of 5G+ modeling and simulation. To the best of our knowledge, this is the first study in the literature that tackles the complexities of modeling and simulating 5G+ networks, by adopting an ABS approach. [ABSTRACT FROM AUTHOR]

The implementation of emerging technologies generates new possibility for the Architectural Engineering and Construction sector. Specifically, the study focuses on the applications of Digital Twins in the heritage construction sector, an area characterized by significant potentials and challenges in various research fields (e.g., energy, digital technology, citizen engagement, cultural aspects, management, etc.). Bibliographic and scientometric analyses enable the systematic categorization of the literature, analyzing a total of 133 scientific publication realized in the years 2001–2023. The bibliometric analysis outlines time evolution, geographical distribution, type, and disciplinary fields of the publications. Meanwhile, the scientometric research identifies co-occurrence networks of authors, keyworks, and research topics. Thank to these techniques, the following research topics on Digital Twins in the Built Heritage have been identified and clustered: (i) Digital Twins and 3D scanner techniques; (ii) Digital Twins and virtual reality; (iii) Digital Twins and Heritage Building Information Model (HBIM); and (iv) Digital Twins and Internet of Things (IoT). Each technique is critically discussed, highlighting applications, methods, limitations, and gaps in the literature. This analysis also paves the way for future research directions and potential solutions to enhance the automation in the heritage sector. Additionally, a glossary of essential disciplinary terms is provided to guide the understanding of individual definitions as they apply to heritage constructions. The primary challenges relate to accuracy, security, interoperability, and synchronization of data and technologies from various sources, requiring appropriate verification and validation methodologies, as well as tailored standards, protocols, interfaces, and regulations. Moreover, ethical considerations should underlie the utilization of digitally driven predictions that incorporate human-computer techniques, and human–robot interactions. This extends to the formulation of specific policies, and legislation. Only through this approach, the immense potential of digital technologies can emerge to create cross-cutting professions, interactive engagement techniques, and compatible preservation and management activities. • Bibliometric and scientometric mapping of scientific articles published between 2001 and 2023. • Digital Twins in heritage constructions booming in publications, geographic distributions, types, disciplinary fields, and researchers. • Digital Twins in heritage constructions cover 3D techniques, virtual reality, HBIM, IoT, building management, and structural analysis. • Study reveals insights on interoperability, metaheuristic algorithms, intelligent matrixes, and decision support systems. • Six levels of application from pre-DT to Adaptive Intelligent DT with the interaction of 3D scanners, HBIM, IoT, Metaverse, and Artificial Intelligence. [ABSTRACT FROM AUTHOR]

Based on the rapid development of intelligent technologies in recent years, the digital transformation of the whole industry and society has become increasingly important. Among them, digital twins and artificial intelligence have great potentials in improving industry processes and further enhancing productivity. This paper proposes an Intelligent Digital Twin System (IDTS) based on artificial intelligence and digital twins for the paper industry. The system includes the prediction models for the stirring speed of the dump chest, the water consumption of the deflaker, the supply air pressure of the dryer, and the exhaust air temperature of the dryer. The sensors, 5G network slices, and other equipment collect related data during the papermaking process for generating twin data, and we use the prediction models to analyze the data and monitor important indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) for the manufacturing processes, which are used to improve the energy utilization and production efficiency of the paper industry and thus facilitate cost saving. We apply this intelligent digital twin system and its associated prediction models to an actual paper manufacturing factory and show their effectiveness by improving the operational efficiency and saving labor and maintenance costs. [ABSTRACT FROM AUTHOR]

Despite advances in instrumentation and measurement techniques, it is still necessary to update numerical models to simulate or predict some structural responses, for example. Thus, this work proposes a metaheuristic framework based on hybrid agents, an approach within the Artificial Intelligence (AI) topics for updating Finite Element (FE) numerical models. This framework aims to provide flexible non-deterministic strategies to guide the updating process, ranging from simple local search procedures to complex learning processes. Two case studies are presented: (i) a free–free aluminium beam tested under laboratory conditions and; (ii) a catamaran tested during a sea trial under real operating conditions. The updating process aimed to optimize the stiffness matrix while maintaining the mass matrix unchanged. The objective function seeks to minimize the differences between numerical and experimental modal parameters, namely, natural frequencies and vibration modes. Results from the digital twin framework showed that the difference in natural frequencies significantly decreased, for example, 9% to 1% for the free–free aluminium beam and 15% to 4% for the catamaran's main deck, when comparing the experimental with the updated FE model. As for the updated FE vibration modes, the Modal Assurance Criteria (MAC) values decreased slightly in both cases but within the acceptable MAC values (above 0.9), thus showing good consistency with the experimental vibration modes. In the end, the proposed framework was able to update the FE model directly using its respective reduced model, circumventing the"black box" of commercial packages. [ABSTRACT FROM AUTHOR]

The development of fuel cells highly depends on the reliable operation of fuel cells on test benches for testing purposes. Even though the test bench's control software contains an alarm module, it is only able to detect the most extreme failures due to the widespread operating parameter range of a fuel cell. This paper presents a novel machine learning based approach to monitor the operation of fuel cell stacks on a test bench and thereby ensuring the proper conduction of the tests. Methods for monitoring the operating conditions set by the test bench using clustering as well as methods to monitor the fuel cell's performance using digital twins are proposed. The developed methods are applied on real testing data to demonstrate their ability to detect even slight deviations that remained undiscovered to the state of the art monitoring system of the test bench. After automating, the proposed methods allow a more sensitive monitoring of the fuel cell operation on test benches leading to more usable data and clearer test results and thereby speeding up the development of fuel cells. • Lifelong performance monitoring of fuel cells. • Detecting anomalies using machine learning. • Using recurrent neural networks to predict fuel cell performance. • Neural network as digital twin. [ABSTRACT FROM AUTHOR]

Early failure detection is required for Fused Deposition Modelling (FDM) 3D printers to reduce material waste. Typically, such systems are created based on images captured during printing or sensor data for tracking the extruder's movement. This work presents a novel approach to sensor data-driven fault diagnosis, utilizing Artificial Intelligence (AI) technology to investigate the temperature imbalance in the extruder and printing surface. First, a Lightweight Convolutional Neural Network (LCNN) is proposed to detect faults from sensory data. The model's architecture concatenates the CNN layer to extract additional features, improving the model's performance while maintaining a lightweight configuration suitable for real-time monitoring systems. Second, the concept of Digital Twin (DT) technology for FDM 3D printer fault detection is introduced. The DT creates a virtual representation of a physical object, and its functionality is validated by examining the network's latency and System Overhead (SO) as the number of clients increases. The simulation results show that the proposed LCNN with a DT environment can effectively monitor, detect, and control the physical workplace with an F1-Score of 0.9981 and an average latency of 995. 4253 ms. Additionally, this research contributes to the development of future technologies for virtual condition monitoring of 3D printer abnormalities, which will be essential for intelligent and autonomous factories. [ABSTRACT FROM AUTHOR]

Digital twins are undergoing growth that enables highly informative and scaleable interaction between physical objects and virtual twins, which is of great significance to the life cycle analysis of composites. Real-time fine evolution is challenging due to vast combinations of input features and high-resolution calculated variables. Here, we systematically demonstrate an AI-based methodology for digital twinning of complex composite structures. First, three types of deep neural networks are created with optionally used autoencoders as surrogate models, with architectures and data processing inspired by the rule-of-mixture of composites. Second, the prediction accuracy and efficiency are evaluated quantitatively and qualitatively, demonstrating the feasibility of predicting 3D displacement and stress fields directly from sensing data of temperature, pressure and loading displacement, and the optimal architecture is selected to be the evolving digital twin. Finally, the real-time interactive experiments are relayed to the digital twin and demonstrated that it can interact with physical objects and evolve online with high accuracy. These results indicate that the computational time can be reduced by 3∼6 orders of magnitude with high information intensity and scalability compared with conventional numerical and experimental methods, which opens up the avenues for the cost-effective and efficient development of digital twin services for composites. [Display omitted] [ABSTRACT FROM AUTHOR]

Unmanned Air Vehicles (UAVs), i.e. drones, have become a key enabler technology of many reconnaissance applications in different fields, such as military, maritime, and transportation. UAVs offer several benefits, such as affordability and flexibility in deployment. However, their limited flight time due to energy consumption is one of the key limitations. Therefore, it is crucial to ensure that UAVs can complete the mission while consuming the least energy possible. In this paper, we propose a novel framework for UAV smart navigation to minimize the time and energy of planning mobile targets visitation. We develop a Deep Reinforcement Learning (DRL) approach to allow the drone to learn the targets' mobility pattern and build its least energy scanning strategy accordingly. We conduct an initial evaluation of the system and our proposed DRL model policy using simulation. Then, to overcome the time-consuming exploration phase of DRL, we develop a Digital Twin (DT) environment of 3D physics-based simulator, which can be used to train the DRL agent efficiently. We also developed a testbed based on hardware integration with the parrot ANAFI drone to verify the feasibility of the proposed methodology. Our findings confirm that the DRL-based agent can achieve performance close to that of a benchmark policy. Moreover, the testbed experiment validates the practicality of utilizing the DT environment for DRL exploration. [ABSTRACT FROM AUTHOR]

• A novel PINN architecture replicates electromagnetic responses of electric motors. • PDEs with rotational coordinate transformation well supervise a neural network. • Separated networks for a rotor and stator with interface loss improve the accuracy. • Learning rate annealing improves accuracy and robustness of the PINN. • The PINN is the faster yet similar accuracy than the finite element analysis. This study presents a novel physics-informed neural network (PINN) architecture designed to address the challenges of replicating an electric motor. The proposed architecture has three key features. First, it uses three partial differential equations with rotational coordinate transformation to supervise the neural network during training with limited data, which improves the accuracy of the solution. One of the differential equations is expressed in variational form to effectively compute the numerical integration. Second, separate networks are proposed for the rotor and stator domains due to their distinct characteristics during operation, namely, that the rotor rotates while the stator remains fixed. An interface loss is included in the entire loss function to compensate for the significant discontinuity and incompatibility between the separate networks when estimating the results of both domains. Third, a learning rate annealing method is introduced to update the adaptive weights of each loss term, thus improving the accuracy and robustness during the training of the neural network. The performance of the proposed PINN was validated using electromagnetic response datasets obtained from both measurements and finite element analyses. Systematic analysis demonstrated that the three features significantly improved the accuracy and robustness of the neural network when estimating the electromagnetic responses of an electric motor. Furthermore, the inference time of the PINN is ten times faster than that of a finite element analysis with a similar level of accuracy, making it suitable for control and design purposes in various real-world applications. Consequently, the versatility of the proposed PINN can accelerate the development of digital twins for intelligent systems by deploying an electric motor, and it could also be used for prognostics and health management because it can estimate electromagnetic responses under both normal and failure conditions. [ABSTRACT FROM AUTHOR]

We introduce a Convolutional Neural Network (CNN) to reduce grains' manual inspection time after image processing on raw 3D x-ray computed tomography (3DXRCT) images from a sample of granular material to obtain level-set function-based digital twins of individual grains. The CNN automatically distinguishes properly segmented digital grains with up to 90% of accuracy. This algorithm is trained using, ground-truth, level set-based digital grain representations from a natural soil sampled at Jaramijó (Ecuador). The implemented convolutional neural network provides groundbreaking processing power, reducing the, otherwise, manual inspection time expended for a small sample, e.g., 200 000 grains, from approximately a couple of weeks to only a few hours. Furthermore, transfer learning and training from scratch are compared for artificially graded granular materials such as Øysand (Norway) and Hostun sand (France). The CNN's learning process is interpreted by means of grain morphological parameters, i.e., sphericity, roundness, grain diameter, and volume-surface ratio. Hence, being able to automatically segment a greater amount of grains from 3DXRCT images of natural and artificial soils in a short period of time, enables us, for first time, to perform actual 3DLS-DEM-based virtual laboratory testing (a plug-and-play one-stop shop). Providing unprecedented and unique data for engineering applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discusses the concept of building performance simulation versus new digital trends. • Reviews Artificial Intelligence, Machine Learning, Digital Twins, Cyber-Physical Systems, Internet of Things, Data Mining. • Explores common notions about differences, similarities, strengths and limitations of each concept. • Reviews emerging work on the interface between these concepts. • New concepts enjoy traction and energize the building performance research area. In an increasingly digital world, there are fast-paced developments in fields such as Artificial Intelligence, Machine Learning, Data Mining, Digital Twins, Cyber-Physical Systems and the Internet of Things. This paper reviews and discusses how these new emerging areas relate to the traditional domain of building performance simulation. It explores the boundaries between building simulation and these other fields in order to identify conceptual differences and similarities, strengths and limitations of each of these areas. The paper critiques common notions about these new domains and how they relate to building simulation, reviewing how the field of building performance may evolve and benefit from the new developments. [ABSTRACT FROM AUTHOR]

• Predictive computational models are useful to complement clinical trials. • Disease models that infer causality in pathophysiology have been produced. • Quantitative System Pharmacology models are being enhanced with artificial intelligence (AI) and machine learning. • Virtual patients and digital twins provide representations of individual patients. • Regulations are being established to support the use of these predictive models. Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices. [ABSTRACT FROM AUTHOR]

Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent developments in imaging devices and artificial intelligence, such as computer vision, provide the necessary tools for this, though they are not yet integrated into infrastructure applications. In this paper, we propose an end-to-end pipeline that generates damage-augmented digital twins for buildings at LOD3, including geometrical information as well as data pertaining to damage condition and its characterization. Our framework incorporates multiple-view images to (1) create a level of detail model, (2) segment damage information, and (3) characterize damage. The core of the method is the structure from motion, which is used to reconstruct the building scene, and machine-learning models that segment and characterize damage. In contrast to current practices, our method does not require manual intervention, generates lightweight models, and can be applied to a wide range of assets. The results generated with our pipeline represent a significant step towards an automated infrastructure damage assessment. We intend to expand our work in the future to include real-time applications and applications to other types of infrastructure. Codes and data sets are publicly available (https://github.com/eesd-epfl/DADT%5fbuildings and https://doi.org/10.5281/zenodo.7767478). • A novel framework to generate Damage Augmented Digital Twins (DADT) of buildings. • State-of-the-art solutions for 3D models, crack segmentation and characterization. • Bench-marking data set for evaluating the automated generation of DADT of buildings. [ABSTRACT FROM AUTHOR]

Pre-clinical molecular imaging, particularly with mice, is an essential part of drug and radiopharmaceutical development. There remain ethical challenges to reduce, refine and replace animal imaging where possible. A number of approaches have been adopted to reduce the use of mice including using algorithmic approaches to animal modelling. Digital twins have been used to create a virtual model of mice, however, exploring the potential of deep learning approaches to digital twin development may enhance capabilities and application in research. Generative adversarial networks produce generated images that sufficiently resemble reality that they could be adapted to create digital twins. Specific genetic mouse models have greater homogeneity making them more receptive to modelling and suitable specifically for digital twin simulation. There are numerous benefits of digital twins in pre-clinical imaging including improved outcomes, fewer animal studies, shorter development timelines and lower costs. [Display omitted] [ABSTRACT FROM AUTHOR]

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port. [ABSTRACT FROM AUTHOR]

Metaverse is a virtual world that maps and interacts with the real world. It is a digital living space with a new social system created through Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Artificial Intelligence (AI), Cloud Computing (CC) and other technologies. With the rapid development of block chain technology, interactive technology, network and computing technology, communication network technology, digital twin technology, artificial intelligence technology, Internet of Things technology and electronic game technology, the metaverse has also ushered in a stage of rapid development. However, enormous difficulties and challenges remain in how to create a perfect metaverse system that can truly break the barriers between reality and virtual. The quality of content, device and interaction in the metaverse all have an important impact on the Quality of Experience (QoE). The purpose of this paper is to provide the latest research progress of the current metaverse QoE, which will be helpful to the future research of the metaverse QoE and provide the direction for further research. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technology is far from being comprehensive and mature, resulting in their piecemeal implementation in practice where some functions are automated by DTs, and others are still performed by humans. This piecemeal implementation of DTs often leaves practitioners wondering what roles (or functions) to allocate to DTs in a work system, and how might it impact humans. A lack of knowledge about the roles that humans and DTs play in a work system can result in significant costs, misallocation of resources, unrealistic expectations from DTs, and strategic misalignments. To alleviate this challenge, this paper answers the research question: When humans work with DTs, what types of roles can a DT play, and to what extent can those roles be automated? Specifically, we propose a two-dimensional conceptual framework, Levels of Digital Twin (LoDT). The framework is an integration of the types of roles a DT can play, broadly categorized under (1) Observer, (2) Analyst, (3) Decision Maker, and (4) Action Executor, and the extent of automation for each of these roles, divided into five different levels ranging from completely manual to fully automated. A particular DT can play any number of roles at varying levels. The framework can help practitioners systematically plan DT deployments, clearly communicate goals and deliverables, and lay out a strategic vision. A case study illustrates the usefulness of the framework. • Digital Twin human interaction • Digital Twin can play multiple roles in a work system. • Levels of Digital Twin framework provides a common language for understanding. • Digital Twin must be evaluated for each case – a "one size fits all" does not work. [ABSTRACT FROM AUTHOR]

We describe the integration of Bayesian decision theory in a digital twin framework to provide a process optimization tool for a biomass boiler. Our application is the Atikokan Generating Station, a 200 MW, biomass-fired tower boiler operated by Ontario Power Generation in Ontario, Canada. For this analysis, we use all available prior information as well as data from the biomass plant and from science-based models. Our objective is to determine a single-valued operational setpoint for the boiler that satisfies a set of objectives/constraints while accounting for uncertainty in boiler operations and in boiler measurements. This setpoint is then continuously updated at the frequency required by plant operations to provide dynamic control. This process of decision-making under uncertainty is a form of artificial intelligence and provides a formal methodology for making optimized decisions in complex systems. Our methodology consists of defining the decision space where all possible solutions reside, identifying the probability of outcomes given that a specific decision was made, creating a decision/cost model that relates the quantities of interest (QOIs) in the physical system (e.g. gross power output) to the decision QOIs (e.g. dollars), identifying the utility (the value to the user) of each outcome, and maximizing the expected utility (i.e. the decision). Once the decision (operational setpoint) is computed, we can predict all of the QOIs (boiler efficiency, O 2 concentration at the outlet, etc.) at the decision point from the science-based model using the parameter distributions computed as part of the Atikokan Digital Twin. • Digital twin of biomass boiler incorporates formal Bayesian decision theory to provide optimized operating-parameter set points which account for uncertainty. • Cost model translates the physics quantity to the decision quantity (often cost). • Utility maps the cost model to the value of the decision to the decision maker. • Optimal decision results from maximizing the expected utility. • Digital twin computes 790 outputs and their distributions at the optimal decision. [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]

The recognition of human actions based on artificial intelligence methods to enable Human–Robot Collaboration (HRC) inside working environments remains a challenge, especially because of the necessary huge training datasets needed. Meanwhile, Digital Twins (DTs) of human centered productions are increasingly developed and used in the design and operation phases. As instance, DTs are already helping industries to design, visualize, monitor, manage, and maintain their assets more effectively. However, few works are dealing with using DTs as a dataset generator tool. Therefore, this paper explores the use of a DT of a real industrial workstation involving assembly tasks with a robotic arm interfaced with Virtual Reality (VR) to extract a digital human model. The DT simulates assembly operations performed by humans aiming to generate self-labeled data. Thereby, a Human Action Recognition dataset named InHARD-DT was created to validate a real use case in which we use the acquired auto-labeled DT data of the virtual representation of the InHARD dataset to train a Spatial–Temporal Graph Convolutional Neural Network with skeletal data on one hand. On the other hand, the Physical Twin (PT) data of the InHARD dataset was used for testing. Obtained results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]