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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]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research. [ABSTRACT FROM AUTHOR]

The rapid advancement of Industry 4.0 technologies has revolutionized industrial processes and production systems, creating unprecedented optimization opportunities. The Internet of Production (IoP) concept has emerged as a transformative approach that capitalizes on integrating cyber-physical systems, big data analytics, artificial intelligence, and the Industrial Internet of Things (IIoT) to drive efficient, flexible, and intelligent manufacturing. This study explores the current state of expert discourse on the symbiotic relationship between IoP and Industry 4.0, focusing on production technology, automation, and digitalization. For this purpose, we performed a content-centric qualitative review in a systematic manner. The findings show that connectivity, data-driven decision-making, flexibility, intelligent optimization, digital twins, and collaborative networks promotes IoP's potential within Industry 4.0. Overall, this study explains how harnessing the potential of IoP by enhancing efficiency, productivity, and innovation in industrial processes contributes to Industry 4.0, and how understanding the strengths, weaknesses, opportunities, and threats of IoP, leveraging advanced technologies, and data-driven approaches can pave the way for the future of efficient, flexible, and intelligent manufacturing systems. [ABSTRACT FROM AUTHOR]

The human-centered view emphasizes various aspects including usability, acceptance, and understandability, as they emerge in the interaction between humans and intelligent systems. For intelligent assistance systems to provide long-term added value in working environments, it is of major importance to develop a positive user experience. However, a positive user experience does not appear by itself but must be systematically investigated and designed. In this paper, we focus on the role of operator 5.0 in manufacturing and provide an overview of the main challenges and potentials related to working with intelligent systems. Drawing upon state-of-the-art literature and findings from industry (context and expert interviews and user experience workshops), this paper investigates the main discontinuous production processes (i.e., process monitoring, process optimization, process maintenance) that will undergo a series of changes due to integration of cutting-edge technologies (such as Digital Twins, Extended Reality, and Artificial Intelligence). It then identifies requirements and discusses key challenges for the realization of human-centered assistance systems. By highlighting underexplored research questions and potential improvements in industrial settings, this paper provides researchers and practitioners to establish a holistic understanding of the user experience along the industry 5.0 journey [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]

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]

Digital Twins (DT) are the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the untapped potential of DT in disaster risk management, and accordingly, proposes an improved Digital twin-based post-disaster risk management framework. This paper employs a systematic literature review approach focusing on digital post-disaster risk management twinning (DPRMT) and smart early warning systems derived from two databases: Scopus and Web of Science. After a screening process with exclusion criteria, the final analysis synthesizes findings from a selected set of 96 papers. The results revealed that previous studies are not beyond only providing general statements about DT. There is a need for diverse data collection methods, considering demographic and financial aspects, understanding social dynamics, employing dynamic models, recognizing interconnected systems, and giving due attention to the often-neglected recovery phase. This study proposes a comprehensive DPRMT concept framework leveraging decision-makers with a holistic and efficient approach that offers real-time, detailed, and data-driven modeling solutions to achieve insights into disaster-affected areas and communities. It is also helpful to optimize response planning, resource allocation, and scenario testing by capturing the dynamic and complex human behaviors and understanding interconnected systems and entities that are often overlooked in previous disaster risk management studies. [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]

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]

The digital twinning process is essential for transferring real-world objects to the Metaverse by creating accurate digital versions, known as digital twins. However, complex systems pose challenges in this process. With the increasing utilization of microwave components and circuits in telecommunication systems such as IoT, 5 G, and 6 G, the need for digital twins of these components arises. Nevertheless, high-frequency components exhibit intricate behavior, requiring advanced modeling techniques. Artificial intelligence (AI) provides a powerful tool for enhancing the reliability and accuracy of estimated models in such cases. In this study, a microstrip lowpass filter (LPF) is designed, fabricated, and measured as the physical twin. An intelligent digital twinning approach is employed using a machine learning method based on an adaptive neuro-fuzzy inference system (ANFIS), trained by a subtractive clustering algorithm. The resulting digital twin of the LPF proves valuable for communication networks and IoT applications. Moreover, this research showcases the applicability and accessibility of machine learning in creating digital twins of electromagnetic components for communication cyber-physical systems (CPSs) and the Metaverse. Furthermore, the proposed method exhibits adaptability to various passive and active electrical or electronic circuits. By harnessing the potential of AI and digital twinning, this study presents a robust and accurate approach for modeling and analyzing complex circuits, specifically in the context of communication systems and their integration into the Metaverse. The findings highlight the advantages of an intelligent digital twinning approach and its potential for advancing various domains involving complex circuitry. • Machine learning for accurate digital twins in telecom & Metaverse. • Microstrip filter case study shows IoT & network benefits. • Machine learning enhances electromagnetic twins for CPS & Metaverse. • AI-driven circuit modeling & analysis advances in telecom. • Robust digital twin solutions for IoT & future 5G/6G tech. [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]

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]

• Concept for digital twins of sorting plants using data-driven process models shown. • Data-driven process model of industrial-scale sensor-based sorting (SBS) developed. • SBS unit automatically assessed by near-infrared (NIR)-based process monitoring. • Trained artificial neural network predicts SBS behavior within ±3.0% error. • Process model successfully applied to simulate an entire SBS cascade. Sorting plants are crucial for effective recycling, but their optimization can be challenging due to the heterogeneity of waste streams. We introduce a novel approach to holistically optimize sorting plants using digital twins containing data-driven process models. To demonstrate their technical feasibility, we developed a data-driven process model for industrial-scale sensor-based sorting (SBS) units by combining near-infrared process monitoring with machine learning. Our results indicate a sorting performance change (F 1 -score) in the SBS unit by 0.22 a% for +1% occupation density and +0.19 a% for +1 wt% target material share. An artificial neural network predicted the SBS behavior with a 3.0% mean absolute error. Our case study demonstrates the potential of data-driven process models for digital twins by clarifying the influence of throughput fluctuations on SBS performance and simulating different SBS cascade designs, thus paving the way towards improved design and operation of sorting plants and a more circular future. [Display omitted] [ABSTRACT FROM AUTHOR]

Agriculture industry faces the challenge of increasing productivity by 50% from 2012 to 2050 while reducing water usage, given that it currently consumes 69% of the world's freshwater. To achieve this goal, smart technologies such as Artificial Intelligence (AI), Digital Twins (DT), and Internet of Things (IoT) are being increasingly utilized. However, the use of DT in agriculture is still in its early stages. This study proposes a smart irrigation framework inspired by digital twins in an application domain. The irrigation framework's sensors and actuators are linked to their virtual counterparts to create a digital twin. The IoT platform collects, aggregates, and processes data to determine daily irrigation requirements, and the behavior of the irrigation system is simulated. The proposed framework has two main advantages: evaluating the behavior of the digital twin and IoT platform in the context of agriculture before integrating them into the field and comparing various irrigation methods with current farming methods. By providing farmers with information about soil, weather, and crops, the system has the potential to improve farm operations and reduce water consumption. • Development of IoT platform for field tests collecting meteorological and soil data. • Establishing a virtual space through Digital Twin to simulate acquired factors. • Developing an intelligent control system to make decisions about irrigation needs. • Systematical assessment of the proposed approach through real-time experiments. [ABSTRACT FROM AUTHOR]