Digital twin paradigm: A systematic literature review

A Comprehensive Review of Modeling of Solid Oxide Fuel Cells: From Large Systems to Fine Electrodes

Commercialization of solid oxide fuel cell (SOFC) systems requires improved SOFC performance and durability, which is highly dependent on the coupling of the SOFC stack with other auxiliary components, SOFC stack configuration, and electrode microstructure. Optimization of SOFC systems at the system/stack/cell/electrode scale via experimentation is expensive and challenging, whereas numerical modeling can be fast and cost-effective. Although many excellent reviews on SOFCs have been published, the previous articles lack practical problem-oriented literature classification and do not cover new emerging models, such as artificial intelligence (AI) assisted models, heterogeneous models, and so on. These models are important for accelerating the solution of large-scale multiphysics models and describing mesoscopic electrode behaviors. In this review, a top-down approach is adopted that can truly guide SOFC system/stack/cell/electrode design to meet targeted applications. Another distinct feature of this review is the inclusion of the latest developments in SOFC modeling. This review offers a thorough summary and in-depth analysis of an extensive collection of research on SOFC simulations, classifying the models into distinct categories based on their varying scales, and serves as a valuable tool to assist researchers in selecting the most suitable models for diverse research objects.

Could digital twins be the next revolution in healthcare?

A Digital Twin (DT) can be understood as a representation of a real asset, a virtual replica of a physical object, process, or even a system. They have been used in managing healthcare facilities, streamlining care processes, personalizing treatments, and enhancing patient recovery. The potential impact of this tool on our society and its well-being is quite significant. A quick review of the literature was carried out using the terms ('Digital Twins') and ('Digital Health'), and (Health Care) with a time interval of up to 5 years (2018-23). Using the PRISMA Method, the search was conducted in six academic databases: IEEE Xplore, Dimensions, Scopus, Web of Science, PubMed, and ACM. After applying the search strings and the exclusion criteria, a total of 13 publications were identified and listed to constitute and support the discussion of this article. The selected studies were categorized into 2 groups according to their application in healthcare: A group of clinical applications, subdivided into topics on personalized care and reproduction of biological structures and another group of operational applications, subdivided into topics such as optimization of operational processes, reproduction of physical structures, and development of devices and drugs. The use of DT in healthcare presents important challenges related to data integration, privacy, and interoperability. However, trends indicate exciting potential in personalizing treatment, prevention, remote monitoring, informed decision-making, and process management, which can result in significant improvements in quality and efficiency in healthcare.

A conceptual ethical framework to preserve natural human presence in the use of AI systems in education

In recent years, there has been a remarkable increase of interest in the ethical use of AI systems in education. On one hand, the potential for such systems is undeniable. Used responsibly, they can meaningfully support and enhance the interactive process of teaching and learning. On the other hand, there is a risk that natural human presence may be gradually replaced by arbitrarily created AI systems, particularly due to their rapidly increasing yet partially unguided capabilities. State-of-the-art ethical frameworks suggest high-level principles, requirements, and guidelines, but lack detailed low-level models of concrete processes and according properties of the involved actors in education. In response, this article introduces a detailed Unified Modeling Language (UML)-based ancillary framework that includes a novel set of low-level properties. Whilst not incorporated in related work, particularly the ethical behavior and visual representation of the actors are intended to improve transparency and reduce the potential for misinterpretation and misuse of AIS. The framework primarily focuses on school education, resulting in a more restrictive model, however, reflects on potentials and challenges in terms of improving flexibility toward different educational levels. The article concludes with a discussion of key findings and implications of the presented framework, its limitations, and potential future research directions to sustainably preserve natural human presence in the use of AI systems in education.

Predicting contrast sensitivity functions with digital twins

We developed and validated digital twins (DTs) for contrast sensitivity function (CSF) across 12 prediction tasks using a data-driven, generative model approach based on a hierarchical Bayesian model (HBM). For each prediction task, we utilized the HBM to compute the joint distribution of CSF hyperparameters and parameters at the population, subject, and test levels. This computation was based on a combination of historical data (N = 56), any new data from additional subjects (N = 56), and "missing data" from unmeasured conditions. The posterior distributions of the parameters in the unmeasured conditions were used as input for the CSF generative model to generate predicted CSFs. In addition to their accuracy and precision, these predictions were evaluated for their potential as informative priors that enable generation of synthetic quantitative contrast sensitivity function (qCSF) data or rescore existing qCSF data. The DTs demonstrated high accuracy in group level predictions across all tasks and maintained accuracy at the individual subject level when new data were available, with accuracy comparable to and precision lower than the observed data. DT predictions could reduce the data collection burden by more than 50% in qCSF testing when using 25 trials. Although further research is necessary, this study demonstrates the potential of DTs in vision assessment. Predictions from DTs could improve the accuracy, precision, and efficiency of vision assessment and enable personalized medicine, offering more efficient and effective patient care solutions.

Towards realizing nano-enabled precision delivery in plants

Nanocarriers (NCs) that can precisely deliver active agents, nutrients and genetic materials into plants will make crop agriculture more resilient to climate change and sustainable. As a research field, nano-agriculture is still developing, with significant scientific and societal barriers to overcome. In this Review, we argue that lessons can be learned from mammalian nanomedicine. In particular, it may be possible to enhance efficiency and efficacy by improving our understanding of how NC properties affect their interactions with plant surfaces and biomolecules, and their ability to carry and deliver cargo to specific locations. New tools are required to rapidly assess NC-plant interactions and to explore and verify the range of viable targeting approaches in plants. Elucidating these interactions can lead to the creation of computer-generated in silico models (digital twins) to predict the impact of different NC and plant properties, biological responses, and environmental conditions on the efficiency and efficacy of nanotechnology approaches. Finally, we highlight the need for nano-agriculture researchers and social scientists to converge in order to develop sustainable, safe and socially acceptable NCs.

A Review of Digital Twinning for Rotating Machinery

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

Can digital twin efforts shape microorganism-based alternative food?

With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.

Stakeholder perspectives on contributors to delayed and inaccurate diagnosis of cardiovascular disease and their implications for digital health technologies: a UK-based qualitative study

OBJECTIVE

The aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed to identify challenges in the process of diagnosing CVD, to identify discrepancies between patient and clinician experiences of CVD diagnosis, and to identify the requirements of future health technology solutions intended to improve CVD diagnosis.

DESIGN

Semistructured focus groups and one-to-one interviews to generate qualitative data that were subjected to thematic analysis.

PARTICIPANTS

UK-based individuals (N=32) with lived experience of diagnosis of CVD (n=23) and clinicians with experience in diagnosing CVD (n=9).

RESULTS

We identified four key themes related to delayed or inaccurate diagnosis of CVD: symptom interpretation, patient characteristics, patient-clinician interactions and systemic challenges. Subthemes from each are discussed in depth. Challenges related to time and communication were greatest for both stakeholder groups; however, there were differences in other areas, for example, patient experiences highlighted difficulties with the psychological aspects of diagnosis and interpreting ambiguous symptoms, while clinicians emphasised the role of individual patient differences and the lack of rapport in contributing to delays or inaccurate diagnosis.

CONCLUSIONS

Our findings highlight key considerations when developing digital technologies that seek to improve the efficiency and accuracy of diagnosis of CVD.

Digital Twins in Agriculture and Forestry: A Review

Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic.

Data-driven models and digital twins for sustainable combustion technologies

We highlight the critical role of data in developing sustainable combustion technologies for industries requiring high-density and localized energy sources. Combustion systems are complex and difficult to predict, and high-fidelity simulations are out of reach for practical systems because of computational cost. Data-driven approaches and artificial intelligence offer promising solutions, enabling renewable synthetic fuels to meet decarbonization goals. We discuss open challenges associated with the availability and fidelity of data, physics-based numerical simulations, and machine learning, focusing on developing digital twins capable of mirroring the behavior of industrial combustion systems and continuously updating based on newly available information.

Towards a Human-Centric Digital Twin for Human-Machine Collaboration: A Review on Enabling Technologies and Methods

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human-machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human-machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area.

Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems

The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are integrated into the dynamic models. These can then be used without ASEKFs to predict the actual energy use of the system under the desired operating conditions, including scenarios that differ from typical functioning. The approach is validated offline with reference to the electricity consumption of an automatic coffee machine, which represents a real test environment and a blueprint to design DTs for other industrial systems. The appliance is observed by measuring the supply voltage and the absorbed current. The accuracy of the results is analyzed and discussed. This method is developed in the context of energy consumption prediction and optimization in the manufacturing industry through refined energy management and planning.

An online monitoring method of milling cutter wear condition driven by digital twin

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

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Digital twins are considered the next step in IoT-based cyber-physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

Novel statistical time series data augmentation and machine learning based classification of unobtrusive respiration data for respiration Digital Twin model

Digital Twin (DT), a concept of Healthcare (4.0), represents the subject's biological properties and characteristics in a digital model. DT can help in monitoring respiratory failures, enabling timely interventions, personalized treatment plans to improve healthcare, and decision-support for healthcare professionals. Large-scale implementation of DT technology requires extensive patient data for accurate monitoring and decision-making with Machine Learning (ML) and Deep Learning (DL). Initial respiration data was collected unobtrusively with the ESP32 Wi-Fi Channel State Information (CSI) sensor. Due to limited respiration data availability, the paper proposes a novel statistical time series data augmentation method for generating larger synthetic respiration data. To ensure accuracy and validity in the augmentation method, correlation methods (Pearson, Spearman, and Kendall) are implemented to provide a comparative analysis of experimental and synthetic datasets. Data processing methodologies of denoising (smoothing and filtering) and dimensionality reduction with Principal Component Analysis (PCA) are implemented to estimate a patient's Breaths Per Minute (BPM) from raw respiration sensor data and the synthetic version. The methodology provided the BPM estimation accuracy of 92.3% from raw respiration data. It was observed that out of 27 supervised classifications with k-fold cross-validation, the Bagged Tree ensemble algorithm provided the best ML-supervised classification. In the case of binary-class and multi-class, the Bagged Tree ensemble showed accuracies of 89.2% and 83.7% respectively with combined real and synthetic respiration dataset with the larger synthetic dataset. Overall, this provides a blueprint of methodologies for the development of the respiration DT model.

Digital Twin of a Gear Root Crack Prognosis

Digital twins play a significant role in Industry 4.0, offering the potential to revolutionize machinery maintenance. In this paper, we introduce a new digital twin designed to address the open problem of predicting gear root crack propagation. This digital twin uses signal processing and model fitting to continuously monitor the condition of the root crack and successfully estimate the remaining time until immediate maintenance is required for the physical asset. The functionality of this new digital twin is demonstrated through the experimental data obtained from a planetary gear, where comparisons are made between the actual and estimated severity of the fault, as well as the remaining time until maintenance. It is shown that the digital twin addresses the open problem of predicting gear root crack propagation.

Sports venue digital twin technology from a spectator virtual visiting perspective

The purpose of this article is to adopt a customer-centric perspective the and introduce digital twin technology as a solution for mega-sport event management. This conceptual model article focuses on the potentially drastic role of digital twin technology in modern sports events, explaining in detail different aspects of its impact. The main research question is "How (and why) do sports venue digital twin emerging technologies prospectively impact the sports spectators" customer experiences?" It contributes to understanding how and why sports venue digital twins make events more customer-centred by enhancing fan experiences and engagement. Subsequently, it aims to position digital twin technology as an innovative solution for mega-sport event management across various customer experience touchpoints. By examining the intersection of digital twins and sports events from a customer-centric lens, this article will elucidate the intricacies involved in leveraging this emerging technology to transform stakeholder and fan experiences at major sporting events. Finally, we outline and explain the obstacles, challenges, opportunities, and perspectives of digital twin technology at an intersection with sports events from a customer-centric perspective. The use of digital twins potentially enables the creation of hyper-realistic virtual replicas of sports venues, providing immersive and personalized experiences for spectators. This technology allows event organizers to optimize resource allocation, streamline logistics, and improve operational efficiency.

Development of an Operational Digital Twin of a Locomotive Parking Brake for Fault Diagnosis

In recent years, a growing role in digital technologies has been filled by model-based digital twinning. A digital twin produces a one-to-one mapping of a physical structure, operating in the digital domain. Combined with sensor technology and analytics, a digital twin can provide enhanced monitoring, diagnostic, and optimization capabilities. This research harnesses the significant capabilities of digital twining for the unmitigated challenge of fault type classification of a locomotive parking brake. We develop a digital twin of the locomotive parking brake and suggest a method for fault type classification based on the digital twin. The diagnostic ability of the method is demonstrated on a large experimental dataset.

Digital twin for healthcare systems

Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.

Intelligent Digital Twins for Personalized Migraine Care

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.

FlyDetector-Automated Monitoring Platform for the Visual-Motor Coordination of Honeybees in a Dynamic Obstacle Scene Using Digital Paradigm

Vision plays a crucial role in the ability of compound-eyed insects to perceive the characteristics of their surroundings. Compound-eyed insects (such as the honeybee) can change the optical flow input of the visual system by autonomously controlling their behavior, and this is referred to as visual-motor coordination (VMC). To analyze an insect's VMC mechanism in dynamic scenes, we developed a platform for studying insects that actively shape the optic flow of visual stimuli by adapting their flight behavior. Image-processing technology was applied to detect the posture and direction of insects' movement, and automatic control technology provided dynamic scene stimulation and automatic acquisition of perceptual insect behavior. In addition, a virtual mapping technique was used to reconstruct the visual cues of insects for VMC analysis in a dynamic obstacle scene. A simulation experiment at different target speeds of 1-12 m/s was performed to verify the applicability and accuracy of the platform. Our findings showed that the maximum detection speed was 8 m/s, and triggers were 95% accurate. The outdoor experiments showed that flight speed in the longitudinal axis of honeybees was more stable when facing dynamic barriers than static barriers after analyzing the change in geometric optic flow. Finally, several experiments showed that the platform can automatically and efficiently monitor honeybees' perception behavior, and can be applied to study most insects and their VMC.

Advancements of Nanotechnology and Nanomaterials in Environmental and Human Protection for Combatting the COVID-19 During and Post-pandemic Era: A Comprehensive Scientific Review

In December 2019, an outbreak of unknown pneumonia emerged in Wuhan City, Hubei Province, China. It was later identified as the SARS-CoV-2 virus and has since infected over 9 million people in more than 213 countries worldwide. Massive papers on the topic of SARS-CoV-2 that have already been published are necessary to be analyzed and discussed. This paper used the combination of systematic literature network analysis and content analysis to develop a comprehensive discussion related to the use of nanotechnology and materials in environmental and human protection. Its is shown that various efforts have been made to control the transmission of this pandemic. Nanotechnology plays a crucial role in modern vaccine design, as nanomaterials are essential tools for antigen delivery, adjuvants, and mimics of viral structures. In addition, nanomaterials and nanotechnology also reported a crucial role in environmental protection for defence and treating the pandemic. To eradicate pandemics now and in the future, successful treatments must enable rapid discovery, scalable manufacturing, and global distribution. In this review, we discuss the current approaches to COVID-19 development and highlight the critical role of nanotechnology and nanomaterials in combating the virus in the human body and the environment.

Intelligent Health Monitoring of Cable Network Structures Based on Fusion of Twin Simulation and Sensory Data

The precise and effective prognosis of safety risks is vital to ensure structural safety. This study proposed an intelligent method for the health monitoring of cable network structures, based on the fusion of twin simulation and sensory data. Firstly, the authors have established a framework that integrate simulation data with sensory data. The authors have established a high-fidelity twin model using genetic algorithm. The mechanical parameters of the structures were obtained based on the twin model. The key components of the structure are captured by using Bayesian probability formula and multiple mechanical parameters. The fusion mechanism of twin simulation and random forest (RF) was established to capture the key influencing factors. The coupling relationship between structural safety state and key factors was obtained, and the safety maintenance mechanism was finally formed. In view of the risk prognosis of the structure, the establishment method for the database of influencing factors and maintenance measures was formed. The authors used the Speed Skating Gymnasium of 2022 Winter Olympic Games (symmetric structure) as the case study for validating the feasibility and effectiveness of the proposed method. The theoretical method formed in this study has been applied to the symmetric structure, which provides ideas for the safety maintenance of large symmetric structures. Meanwhile, this research method also provides a reference for the health monitoring of asymmetric structures.

Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview

This paper presents an overview of various types of virtual museums (ViM) as native artifacts or as digital twins (DT) of physical museums (PM). Depending on their mission and features, we discuss various enabling technologies and sensor equipment with their specific requirements and complexities, advantages and drawbacks in relation to each other at all stages of a DT's life cycle. A DT is a virtual construct and embodies innovative concepts based on emerging technologies (ET) using adequate sensor configurations for (meta-)data import and exchange. Our keyword-based search for articles, conference papers, (chapters from) books and reviews yielded 43 contributions and 43 further important references from Industry 4.0, Tourism and Heritage 4.0. After closer examination, a reference corpus of 40 contributions was evaluated in detail and classified along with their variants of DT-content-, communication-, and collaboration-centric and risk-informed ViMs. Their system features correlate with different application areas (AA), new or improved technologies-mostly still under development-and sensors used. Our proposal suggests a template-based, generative approach to DTs using standardized metadata formats, expert/curator software and customers'/visitors' engagement. It advocates for stakeholders' collaboration as part of a comprehensive validation and verification assessment (V&VA) throughout the DT's entire life cycle.

AI-Augmented Business Process Management Systems: A Research Manifesto

AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.

The conundrum in smart city governance: Interoperability and compatibility in an ever-growing ecosystem of digital twins

Today, technological developments are ever-growing yet fragmented. Alongside inconsistent digital approaches and attitudes across city administrations, such developments have made it difficult to reap the benefits of city digital twins. Bringing together experiences from five research projects, this paper discusses these digital twins based on two digital integration methodologies—systems and semantic integration. We revisit the nature of the underlying technologies, and their implications for interoperability and compatibility in the context of planning processes and smart urbanism. Semantic approaches present a new opportunity for bidirectional data flows that can inform both governance processes and technological systems to co-create, cross-pollinate, and support optimal outcomes. Building on this opportunity, we suggest that considering the technological dimension as a new addition to the trifecta of economic, environmental, and social sustainability goals that guide planning processes, can aid governments to address this conundrum of fragmentation, interoperability, and compatibility.

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

An Interoperable Digital Twin with the IEEE 1451 Standards

The shop floor or factory floor is the area inside a factory where manufacturing production is executed. The digitalisation of this area has been increasing in the last few years, introducing the Digital Twin (DT) and the Industry 4.0 concepts. A DT is the digital representation of a real object or an entire system. A DT includes a high diversity of components from different vendors that need to interact with each other efficiently. In most cases, the development of standards and protocols does not consider the need to operate with other standards and protocols, causing interoperability issues. Transducers (sensors and actuators) use the communication layer to exchange information with digital contra parts, and for this reason, the communication layer is one of the most relevant aspects of development. This paper covers DT development, going from the physical to the visualisation layer. The reference architecture models, standards, and protocols focus on interoperability to reach a syntactic level of communication between the IEEE 1451 and the IEC 61499 standards. A semantic communication layer connects transducer devices to the digital representation, achieving a semantic level of interoperability. This communication layer adds semantics to the communication process, allowing the development of an interoperable DT based on the IEEE 1451 standards. The DT presented reaches the syntactic and semantic levels of interoperability, allowing the monitoring and visualisation of a prototype system.

Technical Considerations for the Conformation of Specific Competences in Mechatronic Engineers in the Context of Industry 4.0 and 5.0

The incursion of disruptive technologies, such as the Internet of Things, information technologies, cloud computing, digitalization and artificial intelligence, into current production processes has led to a new global industrial revolution called Industry 4.0 or Manufacturing 4.0. This new revolution proposes digitization from one end of the value chain to the other by integrating physical assets into systems and networks linked to a series of technologies to create value. Industry 4.0 has far-reaching implications for production systems and engineering education, especially in the training of mechatronic engineers. In order to face the new challenges of the transition from manufacturing 3.0 to Industry 4.0 and 5.0, it is necessary to implement innovative educational models that allow the systematic training of engineers. The competency-based education model has ideal characteristics to help mechatronic engineers, especially in the development of specific competencies. This article proposes 15 technical considerations related to generic industrial needs and disruptive technologies that serve to determine those specific competencies required by mechatronic engineers to meet the challenges of Industry 4.0 and 5.0.

Design, Modeling and Implementation of Digital Twins

A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges.

Dynamic Reduction-Based Virtual Models for Digital Twins—A Comparative Study

Digital models are the foundation of digital twins, which form the basis of autonomous off-road vehicles. Developing virtual models of off-road vehicles using dynamic reduction techniques is one of several approaches. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results for digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built virtual modules of powertrain, electrical, and control systems in a problem-solving environment.

Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems

Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.

Digital Twin and Cloud BIM-XR Platform Development: From Scan-to-BIM-to-DT Process to a 4D Multi-User Live App to Improve Building Comfort, Efficiency and Costs

Digital twins (DTs) and building information modelling (BIM) are proving to be valuable tools for managing the entire life cycle of a building (LCB), from the early design stages to management and maintenance over time. On the other hand, BIM platforms cannot manage the geometric complexities of existing buildings and the large amount of information that sensors can collect. For this reason, this research proposes a scan-to-BIM process capable of managing high levels of detail (LODs) and information (LOIs) during the design, construction site management, and construction phases. Specific grades of generation (GOGs) were applied to create as-found, as-designed, and as-built models that interact with and support the rehabilitation project of a multi-level residential building. Furthermore, thanks to the sharing of specific APIs (Revit and Autodesk Forge APIs), it was possible to switch from static representations to novel levels of interoperability and interactivity for the user and more advanced forms of building management such as a DT, a BIM cloud, and an extended reality (XR) web platform. Finally, the development of a live app shows how different types of users (professionals and non-expert) can interact with the DT, in order to know the characteristics with which the environments have been designed, as well as the environmental parameters, increasing their degree of control, from the point of view of improving comfort, use, costs, behaviour, and good practices. Finally, the overall approach was verified through a real case study where the BIM-XR platform was built for energy improvements to existing buildings and façade renovations.

Ship’s Digital Twin—A Review of Modelling Challenges and Applications

The Ship’s Digital Twin (SDT) is a digital record of a ship’s behaviour or a software clone, which can be used to simulate scenarios that are expensive or hardly feasible to perform on a real object and especially in real time. The purpose of the SDT is to achieve cost reduction, obtain timely warnings of irregularities, and optimise individual ship system performances or the operation of the whole ship and to assist ship management. The aim of this paper is to describe the concept of the SDT and clarify some perplexities that may occur from initial introduction to concept. To that end, the paper identifies the steps in the SDT formulation process and methods used in each step of the process. Furthermore, a four-step iterative procedure for the SDT development is proposed. The applications of the concept are numerous, and some of them are presented in a review analysis in this paper. The presented analysis leads to a conclusion that should give some direction to future research in this area.

Applications of Digital Twin across Industries: A Review

One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.

Process intensification and digital twin – the potential for the energy transition in process industries

The work defines and discusses process intensification (PI) and digital twin (DT) as potential tools to accelerate the energy transition through their applications in the process industries. The PI technologies take advantage of innovative principles in equipment design and control to improve the physical process, while the DT offers the virtual model of the plant as an environment for production optimization. The effects of both tools on the energy transition are evaluated not only from the point of applications but also from the possibility of implementation and barriers in process industries. Although they are beneficial, the deployment of PI and DT requires not only infrastructure and capital investment but the knowledge and cooperation of different levels of plant personnel. Besides review of individual implementation, this work explores the concept of combining PI and DT which can make them the enabler of each other and bring a breakthrough in optimization of process design and control.

Digital Twin for Urban Planning in the Green Deal Era: A State of the Art and Future Perspectives

This paper provides a state of the art of contemporary Digital Twins (DTs) projects for urban planning at an international level. The contribution investigates the evolution of the DT concept and contextualises this tool within the scientific-cultural debate, highlighting the interconnection between global policies and local needs/wishes. Specifically, six case studies of DTs are compared, illustrating their application, content, technological infrastructure, and priority results. The projects presented provide an overview of the existing DT typologies, focusing on the evaluative/prefigurative use and the limits/potential of the tool in light of the socio-health, climate, and environmental crises. Reflections on DT reveal, on the one hand, its potential role in supporting decision-making and participatory processes and, on the other, the potential utopian trend of data-driven planning encouraged by public–private investments in the smart city/twin city sector. In conclusion, the study underlines the innovative role of DT as a cutting-edge scientific format in the disciplinary framework but highlights that the practical use of the tool is still in an experimental research-action phase. From this theoretical-critical review, it is possible to hypothesise new research paths to implement the realism and application potential of DTs for urban planning and urban governance.

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.