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The reliability and methodology of genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells have advanced. CHO-GEMs have aided in cell line and process development, thus impacting on biomanufacturing efficiency. An integrative model structure can incorporate multiple layers and capture condition-specific cell regulation. Integration of CHO-GEMs with artificial intelligence (AI) and advanced algorithms will enable autonomous bioreactor management for digital biomanufacturing. Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms. [ABSTRACT FROM AUTHOR]

In the realm of Digital Twins (DTs), industry experts have emphasised the pivotal concept of the Federation of Twins, envisioning seamless collaboration across sectors driven by shared semantics. In response to this challenge, the Cognitive Digital Twin (CDT) integrates the DT framework with formal semantics, specifically ontologies. This paper introduces a comprehensive five-step methodology for CDT development. Furthermore, it becomes possible to incorporate human expertise into the DT ecosystem by adopting an ontological approach. The CDT enhances DT services with advanced reasoning capabilities, leading to a profound semantic enrichment of the data. The presented methodology has been validated using a use case where the CDT is employed to detect malfunctions, significantly reducing manual intervention. This paper advocates for the adoption of CDTs, which represent a harmonious fusion of formal semantics and human expertise, enhancing system efficiency and operational performance. • A comprehensive 5-step methodology leveraging ontologies enriches Digital Twin (DT) development and semantics of the data. • Cognitive Digital Twins (CDTs) use formal semantics and facilitate the formalisation of human expertise. • Practical validation through real-world use cases demonstrates CDT's potential for reducing manual intervention. [ABSTRACT FROM AUTHOR]

The increasing demand for sustainable aviation solutions has intensified the development of hydrogen-fueled gas turbine combustors, necessitating advanced manufacturing techniques like Additive Manufacturing (AM). Traditional methods fall short in addressing the complex needs of these combustors, making AM essential due to its ability to create intricate, efficient, and lightweight components. This letter highlights AM's transformative impact on hydrogen gas turbine production, emphasizing enhanced design, assembly, and performance. AM provides significant design flexibility, enabling the fabrication of complex lattice and honeycomb structures that improve fuel efficiency and aircraft performance. It also streamlines assembly by integrating multiple parts into a single component, reducing complexity and waste, and thus optimizing cost and environmental impact. Additionally, AM's capabilities for rapid prototyping and on-demand production accelerate development cycles and foster customization. Furthermore, the letter discusses the integration of Design for Additive Manufacturing (DfAM), and Artificial Intelligence (AI) in optimizing combustion chamber designs. These technologies enhance predictive performance, and structural integrity, and address key challenges such as emissions and combustion stability. AI advances digital twin technology, significantly boosting efficiency and reducing the environmental footprint of hydrogen gas turbines. [ABSTRACT FROM AUTHOR]

[Display omitted] • Design a blockchain-based secure key establishment protocol for cross-domain IoT architecture using Physically Unclonable Functions (PUFs). • The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. • The proposed protocol employs a cross-domain trust-building method that helps the IoT devices derive keys from the multiple accumulator factors. • We integrated cross-domain device authentication into the on-chain accumulator to resourcefully authenticate the unlinkable identities of IoT devices from distinct domains. • We implemented the proof-of-concept prototype of the designed protocol. The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed. [ABSTRACT FROM AUTHOR]

• 3D shape analysis for refitting archeological artefacts. • Minoan art. • The origins of Zeus iconography. • Palaeokastro Kouros. • Digital twins for conservation and restoration of works of art and ancient statues. A unique anthropomorphic statue, representing a young man standing tall ca. 50 cm. and primarily made of ivory and with gold decorative elements, was discovered heavily fragmented and burnt during the extensive excavations at the Bronze Age site of Palaikastro in Crete, Greece in 1987, 1988 and 1990. Through a painstaking restoration process of nearly a decade long, the statue was brought back to its original shape from ca. 200 recovered fragments and it is now exhibited in the Siteia Archaeological Museum in East Crete. The statue and the remaining fragments that the conservators were not able to refit on the statue were recently 3D documented by means of a high-precision optical scanner, resulting in the virtual re-positioning of many remaining fragments, correction of the statue's right foot orientation and its overall posture, and a better understanding of its assembly technique. This article presents these results and the digital processes that lead to them. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the concept of machinability by considering the dynamic, tribological, and thermo-mechanical interactions encountered at the tool-chip-machined surface interfaces. The paper provides a demonstration of the capabilities and gaps of the physics-based models for the characterization of the machining performance and the prediction of machinability of difficult-to-cut materials, including additively manufactured (AM) materials, nanocrystalline (NC) materials, fibre reinforced polymers (FRP), metal matrix composites reinforced with ceramic hard particles (MMC), and ceramic matrix composites (CMC). The utilization of efficient computation methods for accurate prediction of force, torque, power consumption, cutting temperature, deflection errors, vibration amplitudes, chatter stability, and thermomechanical interactions in the tool-workpiece system is discussed. The development of thermally-activated dissolution-diffusion wear models to describe the chemical reactions at the tool-chip-workpiece contact interfaces is also presented. These predictions are critical for identifying multi-objectives optimal machining conditions. The integration of predictive machining models within the framework of digital twins in cyber-physical spaces, for in-process monitoring and adaptive control, is demonstrated. Future research for developing new models that can characterize the machinability of AM and NC materials, by considering the effects of varying material microstructure and anisotropy, is presented for conventional and micro-machining operations. [ABSTRACT FROM AUTHOR]

Reusing shopfloor data within Digital Twins offers the potential to optimize manufacturing-, production planning-, and engineering processes. Reusing data is only possible if the data is structured independently of the involved project teams. Current approaches focus on valuable, but complex standardization initiatives. This paper introduces device recognition assistants as a new, flexible data management method for Digital Twins. These assistants are based on time series classifiers to structure the communicated raw data. The assistants are experimentally evaluated. The results show the large potential for semantic annotations via time series classifications to assist the reuse of data within Digital Twins. [ABSTRACT FROM AUTHOR]

In the sintering of advanced ceramics, a digital twin consisted of a finite element model can predict the final shape and microstructure of a sintered ceramic. However, to minimise uncertainties, it is essential to continually update the mechanical properties in the digital twin using data collected from the manufacturing process. One promising approach for achieving this is through artificial neural networks (ANN). This study introduces a machine learning strategy to update the constitutive behaviour of advanced ceramics in finite element analysis of sintering deformation. A major challenge in implementing machine learning in material processing is the huge amount of data required by the training and validation of an ANN, which are often unavailable or incomplete in a real manufacturing process. This study demonstrates that the data requirement can be reduced by employing a two-step training technique. Firstly, the ANN is trained using a nonlinear constitutive law, which describes a general relationship between the strain rates and stresses. Subsequently, the weights and biases of the ANN are transferred for the retraining using limited experimental data for an actual ceramic. It is shown that such approach can successfully capture the nonlinear constitutive behaviour of fine-grained alumina without demanding for large amount of experimental data. A case study is provided, highlighting the feasibility of implementing the ANN in a commercial finite element package, to replace the constitutive law and predict the shrinkage and distortion of a sintering part. In particular, the sintered dumb-bell shape part, simulated using the retrained ANN, showed grain size and relative density that markedly different from those using the nonlinear constitutive law. It is important to note that the proposed methodology is generic and can be used to create ANNs to replace constitutive laws in finite element analysis in digital twins for a wide range of other engineering processes. [ABSTRACT FROM AUTHOR]

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

Thermal error significantly influences the accuracy of precision computer numerical control (CNC) machine tools. The key to compensating thermal error lies in selecting appropriate temperature measurement points and establishing an accurate error prediction model. Traditional methods separate measurement points selection and prediction modeling, that is, selecting temperature measurement points first and then establishing the prediction model using these points. These methods are difficult to achieve optimal matching between measurement points and the prediction model, resulting in shortcomings in modeling accuracy. To address these challenges, an integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools is proposed. A dual-stage attention-based long short-term memory combined with convolutional neural network (DA-CLSTM) error modeling method is proposed to accurately predict the thermal error for different numbers of temperature measurement points. Then, an integrated method of virtual-real temperature measurement points layout and error modeling is proposed, which ensures the temperature measurement points and the error prediction model are closely matched, offering rational sensors layout and high-accuracy prediction. Finally, experiments are conducted on the spindle system test bench to validate the method proposed in this research. Through integrated optimization of measurement points layout and the prediction model, the proposed method achieves high-accuracy thermal error prediction across various sensor counts and maintains reliable prediction ability when the number of sensors is reduced. This method is applied to a digital twin system of MKL7150 grinding machine, and effectively improves the accuracy of actual machining. • An integrated optimization method for measurement points layout and error modeling of CNC machine tools is proposed. • A dual-stage attention-based long short-term memory combined with convolutional neural network model is designed. • Genetic algorithm is used to optimize the layout of measurement points to achieve the optimal prediction effect. • The proposed method achieves high prediction accuracy and maintains accuracy well with fewer sensors. • The method is validated on the test bench and embedded into the digital twin system to achieve thermal error prediction. [ABSTRACT FROM AUTHOR]

Biorefineries offer a new solution for the sustainable production of chemicals, materials, fuels, heat, and power while reducing dependence on non-renewable resources and helping to mitigate climate change. To ensure a broad implementation of biorefineries, efficiency is vital in the planning phase and must be monitored during operation. However, optimizing biorefinery processes can be challenging due to the inherent complexity of biomass feedstock, its variability, and the several technology combinations available. Digital mapping or modeling through approaches such as Digital Twins can lead to a better understanding of the processes involved. A Digital Twin fed by continuous process data can contribute to optimizing biorefinery processes, improving efficiency and helping to minimize adverse impacts. However, acquiring all relevant process parameters can be challenging due to the need to implement sensors and the volume and quality of the generated data. Here, we present the concept of Lean Digital Twin for biorefinery optimization to achieve efficient operation and monitoring while keeping overall data acquisition, processing, and storage as simple as possible. We show that implementing data-driven optimization strategies can help ensure the sustainability of biorefineries. [ABSTRACT FROM AUTHOR]

The accidental leakage of gaseous hydrogen from hydrogen refueling station facilities has the potential to result in a large fire and explosion catastrophe. The ability to forecast the spatiotemporal evolution of hydrogen plumes in real-time is crucial for monitoring the distribution of plume concentrations and mitigating the risk of fire and explosion. The utilization of deep learning has been extensively employed in a diverse range of spatiotemporal forecasting tasks. However, these approaches encounter limitations in accurately quantifying uncertainty when predicting spatiotemporal concentrations and plume boundaries. This research paper introduces a novel model called DPSTNN_H 2 Evolution, which is a deep probabilistic spatial-temporal neural network designed for forecasting the spatiotemporal evolution of hydrogen plumes. The benchmark dataset is constructed by the implementation of numerical simulations pertaining to the inadvertent leak of hydrogen within a hydrogen refueling station. The findings of the study indicate that incorporating quantified uncertainty information might enhance the precision of plume boundaries and the resilience of plume concentrations when predicting the spatiotemporal development of hydrogen. By employing Monte Carlo sampling with a sample size of m = 100 and a dropout rate of p = 0.1 , the model demonstrates the ability to provide real-time inference for 10 instances of plumes, while maintaining a high level of accuracy with R2 = 0.9829. In comparison to the current state-of-the-art model, the proposed model demonstrates superior accuracy and robustness in forecasting the spatiotemporal evolution of hydrogen plumes. In general, our proposed model has the potential to serve as a dependable option for the development of a digital twin for emergency management of hydrogen refueling stations. • Probabilistic spatial-temporal neural network model for hydrogen plume evolutions is proposed. • Normalized uncertainty contours and uncertainty intervals to improve accuracy and robustness. • Numerical experiment of hydrogen leakage and dispersion in refueling station are conducted. • Optimal hyper-parameters of the proposed model are determined • Comparison between the proposed model with point-estimation based model is conducted [ABSTRACT FROM AUTHOR]

3D scanners have great potential to boost the efficiency of geometric inspection if their traceability is established by identifying measurement bias and uncertainty. This article introduces a digital twin for a structured-light 3D scanner that provides these two essential measures in a real-time manner. The error quantification starts from iterated scans taken from a CMM-calibrated ball-plate artifact at eight different configurations. The differences between the erroneous scan readings of the artifact's distance features and the corresponding calibrated values in the CMM set give a spatial distribution of the scanner's error. Then, three polynomial functions map the Cartesian coordinates of the scan set onto their counterparts from the CMM set. In this error model, a least-square approach determines the polynomial coefficients. Evaluating the error model for a given point cloud returns a vector field of volumetric errors, which is then followed by error corrections and then GD&T calculations. The uncertainty propagation is pursued through the three steps of model development, error map evaluation, and GD&T analysis. The Monte-Carlo estimations benchmark and validate the GUM framework for the uncertainty associated with the outputs of each step. Experimental verifications of the proposed methodology suggest that the error correction improves the mean and the standard deviation of measurement bias by 50% and 27%, respectively. The uncertainty map provides the coverage probabilities of 62%, 88%, and 96% for the coverage factors of 1, 2, and 3, while the expected theoretical levels of confidence are 68%, 95%, and 100%. • The digital twin provides error maps for scan data as a field of error vectors. • Corrections reduced the error average from −10 to −5 μm and error dispersion by 27%. • Ellipsoidal uncertainty regions grant coverage probabilities close to expected levels. • GUM framework reliably estimates the uncertainty of digital twin outputs. • Fast uncertainty analysis allows using digital twin in real-time in-process metrology. [ABSTRACT FROM AUTHOR]

The feed system is a typical mechatronics system, as well as motion control equipment, that serves as a fundamental component of CNC machine tools. Developing a precise mechatronic integrated model of the motion control of the feed system is a crucial step in achieving the digital twin and virtual machining. The extant literature extensively covers the dynamic modeling and identification of feed systems, the characterization and traceability of position deviations, and the study and optimization of motion control algorithms. Nevertheless, there is a deficiency in the analysis and optimization of the uncertainty pertaining to the motion control position of the feed system. This paper focuses on the positioning repeatability and considers the random position deviation that occurs during the movement of the feed system. The current research on positioning repeatability primarily concentrates on static factors such as geometric error and assembly error. Three primary causes of uncertainty in the motion control position of the feed system are identified: disturbance uncertainty, parameter uncertainty, and model uncertainty. Each component of uncertainty is thoroughly classified and studied based on the specific characteristics of the feed system. This study provides a comprehensive overview of the current data-driven control approaches used to address uncertainty in the feed system. It specifically focuses on the data-driven controller turning method, feedforward compensation method, and controller design method. The review concludes by discussing the latest research progress and limitations and suggests hopeful trends. Specifically, when conducting integrated modeling of the feed system, it is crucial to thoroughly account for the impact of stochastic processes. Additionally, the issue of switching between dynamic and static states in the dynamic model should be taken into consideration. Simultaneously, it is necessary to examine a more suitable approach that integrates the system model with the uncertainty characteristics to develop a more robust optimization strategy for controlling and efficiently managing the spread of position deviation in the feed system. • A comprehensive review of uncertainty for machine tool feed systems. • This work presents a new viewpoint on the uncertainty in the position control of feed system's motion. • The full description encompasses the multi-source uncertainty in feed system. • This study reviews data-driven control algorithms for feed systems uncertainty. [ABSTRACT FROM AUTHOR]

Astronaut training is complex due to its specific infrastructure and equipment requirements, which are often costly. Immersive technologies can simulate entire environments, allowing for interactive user experiences. Previous research has confirmed the effectiveness of immersive technologies in training and learning. However, simulations alone are insufficient; participants must also engage with vehicles, machinery, and spacecraft systems to understand their impact on the environment and adjacent objects. This study develops four scenarios within three modules, inspired by NASA and ESA astronaut training programs. The first module presents a theoretical scenario using mixed reality (MR), incorporating topics from ESA's training modules. The second module offers two practical scenarios: an ISS emergency simulation that includes Hohmann transfer and circular motion concepts, and a spacewalk for repair tasks. In these scenarios, digital twins of the ISS propulsion and navigation systems, as well as a spacesuit, were created. The third module simulates a spacecraft launch, utilizing 3D models from SpaceX's Falcon Heavy and digital twins of its propulsion and navigation systems. Participants interacted with the digital twins and scenarios, generating data that was stored and analyzed against the ISS dataset from the Jet Propulsion Laboratory (JPL) and telemetry from the SpaceX Falcon Heavy launch. In this work's initial phase, the main objective was to assess whether digital twins could be integrated with immersive technologies for effective training. A mean squared error metric was employed to compare the digital twins with the physical object data, confirming the alignment of the developed digital twins with the actual systems, thereby validating their utility for astronaut training. • Mixed reality and digital twins can be used to improve astronaut's training. • Mixed reality scenarios are a safe way to learn and train about a specific topic. • Mixed reality foster training experiences: Promoting creative problem-solving skills. • Digital Twins enhances immersive technologies: Leveraging digital twins capacities for immersive training. [ABSTRACT FROM AUTHOR]

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]

Modern cyber–physical production systems provide advanced solutions to enhance factory throughput and efficiency. However, monitoring its behaviour and performance becomes challenging as the complexity of a manufacturing system increases. Artificial Intelligence (AI) provides techniques to manage not only decision-making tasks but also to support monitoring. The integration of AI into a factory can be facilitated by a reliable Digital Twin (DT) that enables knowledge-based and data-driven approaches. While computer vision and convolutional neural networks (CNNs) are crucial for monitoring production systems, the need for extensive training data hinders their adoption in real factories. The proposed methodology leverages the Digital Twin of a factory to generate labelled synthetic data for training CNN-based object detection models. Regarding their position and state, the focus is on monitoring entities in manufacturing systems, such as parts, components, fixtures, and tools. This approach reduces the need for large training datasets and enables training when the actual system is unavailable. The trained CNN model is evaluated in various scenarios, with a real case study involving an industrial pilot plant for repairing and recycling Printed Circuit Boards (PCBs). [ABSTRACT FROM AUTHOR]

There currently exists a need for a platform that provides users with the ability to perform extensive, repeatable and meaningful simulation and testing for the hardware and software which compose vehicle/autonomous vehicle systems whilst being broadly accessible, widely supported and provides robust features and development tools. The contemporary implementations of similar systems are either financially exorbitant or highly contained. The system reflected in this paper aims to fill a gap in the industry of vehicle/autonomous vehicle development by extending on currently existing open-source software to provide a highly streamlined platform to support the production of general road vehicle and autonomous vehicle driving systems. The software tools and hardware components chosen for the system will be discussed, followed by the features constructed throughout the development process. The end result of the system is a platform that allows for quick, repeatable, accurate, and nearly endless testing of a digital twin of real life vehicles. This system will allow users to gain valuable simulation and testing data of hardware and software components in a manner which is not always feasible using the traditional methods of autonomous vehicle testing. [ABSTRACT FROM AUTHOR]

The benefits of having digital twins of urban buildings are numerous. However, a major difficulty encountered in their creation from airborne LiDAR point clouds is the effective means of accurately reconstructing significant occlusions amidst point density variations and noise. To bridge the noise/sparsity/occlusion gap and generate high fidelity 3D building models, we propose APC2Mesh which integrates point completion into a 3D reconstruction pipeline, enabling the learning of dense geometrically accurate representation of buildings. Specifically, we leveraged complete points generated from occluded ones as input to a linearized skip attention-based deformation network for 3D mesh reconstruction. In our experiments, conducted on 3 different scenes, we demonstrate that: (1) Compared to SoTA methods like NDF, Points2Poly, Point2Mesh, and a few others, APC2Mesh ranked second in positional RMSE and first in directional RSME, with error magnitudes of 0.0134 m and 0.1581, respectively. This indicates the efficacy of APC2Mesh in handling the challenges of airborne building points of diverse styles and complexities. (2) The combination of point completion with typical deep learning-based 3D point cloud reconstruction methods offers a direct and effective solution for reconstructing significantly occluded airborne building points. As such, this neural integration holds promise for advancing the creation of digital twins for urban buildings with greater accuracy and fidelity. Our source code is available at https://github.com/geospatial-lab/APC2Mesh. [ABSTRACT FROM AUTHOR]

Moving a world leading numerical weather prediction system that runs on a dedicated, bespoke, high performance computing cluster and supporting infrastructure, into the heart of a digital twin for climate change adaptation and extreme weather events has been a challenging and exciting journey. In this paper we describe this journey with a focus on those aspects required to leverage the pre-exascale EuroHPC systems that have been made available to Destination Earth (DestinE) to run its computational representation[1]. EuroHPC systems can be effectively used for DestinE and are in fact key assets to deliver the computational power required for Earth system digital twins at global km-scale resolution. At the same time, EuroHPC systems were newly installed and procedures to run them efficiently are evolving. We find that each of these systems is operated by a national hosting entity that implements its own procedures, e.g. for identity and access management, specific system configuration like schedulers, filesystems, software management systems, and specific, sometimes vendor associated, toolchains, tooling, and container runtimes. In particular, the different scheduling policies encountered, required us to adapt our workflows for each site. We found that having dedicated resources available, which was trialed in a period from 16th February to 14th April on LUMI, allowed to achieve high occupation rates, with 92% on the reserved GPU allocation and greater than 97% efficiency on the CPU reservation. Also a stronger focus on federation of these systems, with a focus not only on federation of identities and accounts, but also in the areas of data ownership/transfer, observability, services and service accounts, maintenance coordination and performance portability is required. In general, it should become much easier to transfer a workload, or a digital twin system, from one EuroHPC site to the next and run and maintain them across several sites concurrently. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents a turning point in the thinking of the production model since it is based on digitalized production systems with the aim of improving productivity, product quality, and delivery time to the customer. The digitalization and evolution of information technology allowed the emulation of production system virtual models, namely in the concept of Digital Twin (DT), with the ability to simulate different scenarios providing support for better decision making. This concept not only represents a virtual copy of the physical world that obtains information about the state of the value chain but also illustrates a system capable of changing the development of the production activity according to the fulfillment of the intended business goals. In literature, the concept of the Digital Twin is exhaustively treated as a stand-alone factory (one digital factory represents one physical factory) and underestimates the possibility of a DT oriented to a customized product (a project) that requires decentralized production systems. This paper brings to discussion the relevance of product customized applying DT to smart customization, and the inclusion of decentralized production systems supported by Cloud Manufacturing. [ABSTRACT FROM AUTHOR]

For a chemical looping hydrogen generation system, the set of input gas flow rate needed to be adjusted on the reactivity of oxygen carriers, in order to guarantee gas conversion and to obtain higher hydrogen purity. However, the oxygen carriers often decayed upon repeated cycles, leading to pressing need for dynamic alignment between oxygen carrier reactivity and process parameters. Herein, we propose a reaction optimization control method based on machine learning methods that can establish a real-time matching system between the flow rate of input gas and the reactivity of oxygen carriers. The results show that the prediction of hydrogen yield from oxygen carriers when using the optimized Gradient Boosting Decision Tree model is in general agreement with the experimental results (R2 = 0.87, MAE = 0.49). Based on high-precision prediction results, the method is capable of realizing the input gas flow based on the predicted hydrogen production in real time in less than 10s and realizing digital twin with the expansion of the dataset. We envision that the proposed control method can accelerate the industrial application of chemical looping hydrogen generation by predicting accurate operating parameters that can reduce fuel costs and guarantee reactivity of oxygen carriers. • Interpretable relationship between reaction variables and cyclic hydrogen production through machine learning modeling. • Hyperparameter optimization of machine learning models to solve overfitting problems. • The predicted hydrogen production for the new input oxygen carriers is in general agreement with the experimental results. • Digital twin system solves the problem of unstable reduction depth in chemical looping hydrogen production. [ABSTRACT FROM AUTHOR]

The catalytic hydrogenation of carbon dioxide (CO 2) to methanol presents a significant opportunity for both mitigating climate change and producing a valuable chemical feedstock. While existing reviews delve into diverse modeling strategies, the role and potential of machine learning (ML) approaches remain largely unexplored. This review addresses the gap by comprehensively exploring the mechanism, workflow, and application of ML models in the methanol production process. The review highlights the significance of ML application in catalytic CO 2 hydrogenation for methanol synthesis, emphasizing process optimization, predicting methanol performance indicators, thermodynamic modeling, reaction kinetics, and assessing catalyst activity. Furthermore, the review delves into cutting-edge approaches like hybrid models, gray-box models, and digital twins, showcasing their potential to revolutionize the methanol production process. This comprehensive review serves as a valuable resource for forthcoming research aimed at optimizing the CO 2 conversion process to efficiently and sustainably produce methanol. [Display omitted] • Machine learning models are extensively employed in catalytic hydrogenation to produce methanol. • This review is dedicated to the analysis and synthesis of these models as well as to their applications. • ML exhibits high accuracy in modeling CO 2 hydrogenation into methanol. • ML models predict key performance indicators of methanol and optimize reaction conditions. • Innovative ML, gray box, digital twin, and Hybrid algorithms leverage vast databases in this domain. [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 paper presents a digital twin-assisted online monitoring and control system for machining operations. Process information including Cutter-Workpiece Engagement (CWE) maps are extracted from the virtual model of the process and fused with the CNC internal data as well as the measured external sensor data. Several applications are illustrated to demonstrate the system. Adaptive control of cutting load based on directly measured forces or reconstructed forces from an accelerometer-integrated tool holder is demonstrated. The spindle current data integrated with CWE information from the virtual model is utilized for the estimation of tool wear progression and tool breakage detection. Digital twin-assisted chatter detection and avoidance are presented by avoiding false alarms at the cutter's entry into and exit from work material which triggers transient vibrations. It is shown that by utilization of the digital twin data and information fusion, robust process monitoring and control can be achieved while anomalies and false alarms are avoided. [ABSTRACT FROM AUTHOR]

The relevance of digital solutions to enable sustainable value creation, the monitoring and ultimately the reduction of the environmental footprint has increased. New information technologies and increased data availability along a product's lifecycle offer more tools to assess product sustainability than ever before. This paper presents how a Digital Twin needs to be designed to help monitor product-level energy efficiency and how such a Digital Twin can be implemented. State-of-the-art research on Digital Twins and their development, lifecycle assessment, and energy efficiency are introduced. There is a lack of evaluated implementations of methodical DT development in different scenarios, especially for the evaluation of product-level energy efficiency during production. Results of a research project with the focus on the development and implementation of Digital Twins are presented. The use case is set within the scenario of the sequential development of a product simultaneously with the development of the Digital Twin. The focus is put on the implementation of an energy consumption prediction and product carbon footprint analytics module as part of the Digital Twin. The development approach is presented and recommendations for the development of future Digital Twins are derived. [ABSTRACT FROM AUTHOR]

In this research work, a discrete event simulation model (DES) is developed as a digital twin of the supply chain of Antarctic campaigns in a navy ship. This simulation aims to describe the system with all its details and complexity and, through this, support decision-making that identifies a supply chain configuration that significantly improves efficiency based on costs and operating times. For this, the Anylogistix simulation tool generates scenarios and visualizes results through charts and key performance indicators. [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]

This case study shows how ÖBB-Infrastruktur AG, which provides the Austrian rail infrastructure on behalf of the Republic of Austria, uses a criticality analysis based on an accurate digital representation of the track and route network, including the intersection of different specialist views. This is done to derive objective decision-making bases for asset management to control the availability of routes and assets and the allocation of resources. The calculation of criticality and the classification of assets into modular classes for standardisation are illustrated using the example of the wear-driven 'switch'. [ABSTRACT FROM AUTHOR]

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins offer numerous benefits, such as improved efficiency, reduced downtime, and enhanced decision-making, they also come with unique challenges when they need to be developed for labor-intensive manufacturing systems. In this paper, we discuss the main challenges and their implications that arise from existing research. Considering these challenges, we propose a framework for developing data-driven Digital Twins of labor-intensive manufacturing systems as an initial step towards addressing these challenges. We illustrate the challenges associated with Digital Twins of labor-intensive manufacturing systems through a practical case study derived from our collaboration with two companies. In the case study, we make necessary preparations for developing Digital Twins for decision support in job scheduling in a hybrid machine-worker environment while considering the well-being of workers. [ABSTRACT FROM AUTHOR]

Data-driven Digital Twin approaches have gained popularity in research literature, particularly in relation to Industry 4.0 achievements. Data-driven Digital Twins process widely available real-time data in an automated manner to extract models from various systems of interest. However, these model extraction approaches commonly lack systematic integration of expert knowledge from human experts, such as engineers. Combining valuable expert knowledge with data-driven model extraction approaches is a highly complex task that can significantly contribute to more accurate models within shorter time periods. In this paper, we provide a comprehensive overview of the research done on fusion of data and expert knowledge for Digital Twins to identify and describe the current gap. Resulting from our findings, we propose an initial framework for integrating expert knowledge and data for Digital Twin model extraction. Subsequently, we provide an overview of the main challenges and opportunities in fusing data and expert knowledge in the context of Digital Twins. [ABSTRACT FROM AUTHOR]

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. [ABSTRACT FROM AUTHOR]

Digital Twins allow for designing, operating, and optimizing running systems by replicating the operations of the physical components in a digital environment. Interconnectivity between Digital Twin models and corresponding real-world counterparts facilitates continuous examining of the actual system, resulting in nearly real-time analysis and decision-making support. Validation is an integral part of Digital Twins as underlying models must accurately reflect the corresponding physical systems according to predefined objectives. The near real-time nature of Digital Twins demands a continuous validation process to ensure models' accuracy. Labor-intensive manufacturing, where humans are at the heart of manufacturing processes, is a sector that encompasses a wide range of industries, from toys and apparel to medical devices and automotive components. This sector continues to play a vital role in emerging economies, offering employment opportunities that mitigate poverty and enhance social stability. Enabling Digital Twins for labor-intensive manufacturing systems opens many opportunities towards humancentricity and improvement of well-being of human operators. In these systems, however, human data must also be considered for Digital Twin development and the corresponding validation processes. Handling human data further complicates the creation and validation of Digital Twins. To the best of our knowledge, there has not been a comprehensive study on the validation of Digital Twins in labor-intensive manufacturing. In this paper, we review Digital Twin validation in manufacturing, focusing on systems that feature data from human operations. As a result, we outline the current challenges of validation of Digital Twins in labor-intensive manufacturing environments and suggest future research directions. [ABSTRACT FROM AUTHOR]

In light of the global industrial sector's steadfast pursuit of sustainable solutions to mitigate carbon emissions and efficiently minimize energy inefficiencies, the significance of novel approaches to energy optimization becomes increasingly evident. This research presents a novel digital twin concept that is designed to enhance the efficiency and effectiveness of high-temperature heat pumps in industrial renewable energy systems. Through the utilization of real-time data and complex computer modeling techniques, our proposed digital twin model seamlessly presents a comprehensive perspective on energy flows. This approach identifies inefficiencies and delivers practical insights to mitigate waste. The incorporation of this approach into pre-existing eco-conscious renewable energy systems has the potential to greatly enhance the effectiveness, predictability, and long-term viability of industrial processes. The empirical findings, obtained from multiple case studies conducted in industrial settings, provide evidence of the potential benefits in terms of energy waste reduction, and maximize the durability of systems. The results of our initial studies provide a foundation for the utilization of digital twin technologies in the field of industrial renewable energy systems optimization, representing a significant advancement towards a more environmentally sustainable industrial landscape. [ABSTRACT FROM AUTHOR]

• Cultural heritage is a multi-disciplinary research domain; however data interoperability is really achieved. • Cultural heritage research relies on the integration of data deriving from social sciences and humanities and natural and exact sciences. • A semantic-aware digital twin is offering research opportunities towards the development of large-scale knowledge-bases in cultural heritage, essential for the implementation of the European Commission vision for a European Collaborative Cloud for Cultural Heritage (ECCCH). • Human and machines readable systems, based on a dedicated ontology provide the technological frame for the implementation of the ECCCH.2. The methodologies of Cultural Heritage research and in particular those related to conservation and the analytical study of heritage assets and works of art, recently grouped under the term Heritage Sciences, engage a broad range of disciplines, each with its own characteristic workflows for generating data. Consequently, an emerging challenge is the need to define a digital framework for multi-source data integration, associated with a single heritage asset, but generated by various tools and methods which are often pursued by different research groups and at different times. This digital framework is discussed in this essay as the digital twin of a heritage asset, comprising of the documentation data associated with a heritage asset and its virtual representation. To best describe and define a Heritage Digital Twin ontology and its associated knowledge graph, we use a specific example drawing from art historical and analytical investigation of a 13th century Italian painting masterpiece, the Crucifixion of Christ, by Giovanni Baronzio, one of the leaders of the so-called Rimini School of painting which was greatly influenced by the work of the famous Renaissance master Giotto di Bondone. [ABSTRACT FROM AUTHOR]

Pressure swing adsorption(PSA) based gas separation technology is widely applied in chemical industries, especially in hydrogen purification. Its complex mechanism and non-steady state feature restrict the separation performance adjustment and application on efficient gas utilization. Process simulation, machine learning and modelling combined innovating gas management methodology is proposed in this paper to perform accurate gas separation for natural gas based syngas mixture. VB script program is employed to drive Aspen adsorption simulation, and then classical and regression tree(CART), polynomial regression(PNR) and surrogate model are comprehensively used to perform machine learning. Consequently, the coefficient matrix is obtained to quantify the separation performance. This method holds the potential for broader application in refineries and SPCs hybrid hydrogen network, facilitating the comprehensive utilization of hydrogen containing gases. Case study results show this methodology can conserve natural gas and gas productivity by 21.3 %, as well as carbon emission by 8211.6 Nm3/h. This work highlights the promising application of digital twin technology within the chemical industry, showcasing how process simulation, machine learning, and modeling can collectively revolutionize gas management and resource conservation. [Display omitted] • Novel PSA based gas allocation instead of gas separation technology. • Simulation, modelling and machine learning combined gas accommodating methodology. • An insight technology for integration of refinery and SPC. • A novel gas allocation method for zero waste of hydrogen. • An insight application of digital twin technology for gas efficient utilization. [ABSTRACT FROM AUTHOR]

This study analyzes a hydrogen-methane thermal power plant installed in a high school. In this plant, photovoltaic energy is used to produce hydrogen through two alkaline electrolyzers. The hydrogen is stored under pressure and utilized as fuel, mixed with methane in a commercial boiler for heating purposes. A digital twin of the hybrid system was developed, and simulations were conducted using Matlab/Simulink software. The study compares two possible system configurations: in the first one, the energy produced by the photovoltaic array is sufficient to meet the required amount of hydrogen throughout the simulation year; in the second one, the electrolyzers come into operation only when green electricity is available. For each configuration, several simulations were performed, considering variable sizes of the photovoltaic array, hydrogen storage capacity, and hydrogen-to-methane ratio to determine the optimal plant configuration from an economic perspective. Results indicate that, with current operating expense and capital expenditure costs, the system can achieve a payback time of 16 years with 100 % hydrogen combustion, provided the photovoltaic array and hydrogen storage subsystems are sized appropriately. In terms of environmental benefits, the optimized system configuration enables an annual CO 2 savings of 155 tons. [Display omitted] • A green H 2 - NG mixture for a 35 kW th boiler was analyzed. • H 2 is produced with a 15 kW el electrolyzer supplied by a mix of PV and grid energy. • Matlab Simulink and Autodesk Revit softwares were used for system simulation. • Sub-systems size optimization was performed to increase system economic feasibility. • Economical and environmental analysis on the general system were done. [ABSTRACT FROM AUTHOR]

A clean solar hydrail, a locomotive operated by solar and hydrogen energy, has received significant attention as an alternative to a current fossil fuel-based locomotive. Nevertheless, the feasibility test of solar hydrail is still challenging since it has not yet been practically developed. Herein, we employed digital twin technology to design and operate solar hydrail and diesel locomotive. 30 years of operation simulation starting from 2021 was conducted based on the data of Gangwon-do, South Korea. It was confirmed that replacing the single diesel locomotive with solar hydrail can provide about 59 % lower net present cost and annual harmful emission reduction of approximately 300 tons. Sensitivity analyses were also conducted to mitigate future uncertainty, and the solar hydrail was a better option than the diesel locomotive in every scenario. This study demonstrates that solar hydrail is the successful substitute for the diesel locomotive currently operated in South Korea. • 30 years of operation was simulated for solar hydrail and diesel locomotive. • Solar hydrail can have $2,026,279 lower NPC than diesel locomotive. • Solar hydrail yearly saves about 300 tons of emissions compared to diesel locomotive. [ABSTRACT FROM AUTHOR]

Accident scenarios leading to full or partial fire engulfment represent a serious threat to tanks used for the transportation and storage of dangerous goods. The present study focuses on the impact of partial engulfing fire scenarios on an LPG tank. An experimentally validated CFD modeling approach was further developed and applied to perform a parametric study by varying the exposure mode and the filling degree. The results of the simulations show that both these factors have a strong influence on the pressurization rate, the energy accumulation, and the high-temperature mechanical weakening of the tank structure. Fire scenarios where the flame zone is at the top or at the end of the tank are particularly critical when the liquid filling degree is low or medium. Liquid thermal stratification is evident in all the case studies analyzed and, in most of them, it strongly affects the vessel pressurization curve. The comparison to the results obtained using a lumped model showed that this provides non-conservative pressurization results for high filling levels (80%). The results show that a validated CFD approach can be used as a virtual workbench to support tank design within a digital twinning approach. [ABSTRACT FROM AUTHOR]

The cyber-agricultural system (CAS) integrates cybersystems with the physical world of agriculture via sensing, modeling, and actuation, and leverages the three pillars of functional cyber-physical systems (CPSs): computation, control, and communication: Advances in computation (i.e., ubiquitous, multimodal sensing, modeling/reasoning enabled by complex computation capabilities, and off-the-shelf deep learning models) have opened up numerous opportunities in CAS. Progress in control/actuation is characterized by advanced agricultural machinery and the rise of agricultural robotics (e.g., dexterous manipulation and harvesting, interactive sensing, precision spraying, mechanical operations, and weed culling). Advanced communication is enabling sensors, actuators, and platforms to coordinate and collaborate using internet of things (IoT) principles/tools. The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS – sensing, modeling, and actuation – and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development. [ABSTRACT FROM AUTHOR]

Hydrogen release and dispersion from hydrogen refuelling stations have the potential to cause explosion disaster and bring significant causalities and economic losses to the surroundings. Real-time spatial hydrogen plume concentration prediction is essential for the quick emergency response planning to dissipate such flammable vapor cloud and prevent explosion disaster. Deep learning approaches have recently been applied to real-time gas release and dispersion modeling, however, are 'over-confident' for spatial plume concentration and boundary estimation, which could not support the robust decision-makings. This study proposes a hybrid deep probability learning-based spatial hydrogen plume concentration prediction model, namely DPL_H 2 Plume by integrating deep learning and Variational Bayesian Inference. Numerical model of hydrogen release and dispersion from hydrogen refuelling station is built to construct the benchmark dataset. By using such dataset, two pre-defined parameters, namely Monte Carlo sampling number m = 300 and dropout probability p = 0.1 are determined to ensure the model's tradeoff between inference accuracy and efficiency. Comparison between our proposed model and the state-of-the-art model is also conducted. The results demonstrate that our model exhibits a competitive accuracy of R 2 = 0.97 as well as an inference time 3.32 s. In addition, our model gives the comprehensive estimations including not only spatial hydrogen plume concentration but also its uncertainty. Also, our model provides the more accurate estimation at plume boundary compared to the state-of-the-art model. Overall, our proposed model could provide reliable alternative for constructing a digital twin for emergency management of hydrogen refuelling station. • Deep probabilistic learning-based hydrogen plume prediction model is proposed. • Optimal hyperparameters of variational Bayesian inference are determined. • Model accurately predicts real-time spatial hydrogen plume concentration given scenario-related parameters. • Model predicts additional uncertainty information for comprehensive emergency decision-makings. [ABSTRACT FROM AUTHOR]

Classic cars hold substantial value in the automotive industry, and the restoration process plays a pivotal role in increasing their worth. Ensuring the certification of these restoration processes is vital, as is keeping owners well-informed about the ongoing procedures taking place in their appreciated vehicles. By monitoring and controlling these restoration activities, the management of classic car workshops can effectively optimize their operations, empower owners with pertinent information, and preserve the authentic nature of these vintage automobiles. This study aims to develop a digital twin system for a classic car bodywork restoration shop workshop. The latter integrates multiple sensor technologies, including location tracking, humidity and temperature sensing, accelerometer monitoring, and smart plugs, to facilitate the identification of ongoing activities of classic car bodywork restoration process instances. By leveraging these sensors, the digital twin system may simulate and control workshop operations more effectively. We perform a systematic rapid review of related work and, based on state-of-the-art practices, we identify existing architectures and software applications used for creating digital twin systems. Then, we propose the architecture for our digital twin system, detailing its functionalities. We aim at contributing to advancing digital twin technology in classic car bodywork restoration, enhancing its authenticity, and fostering improved management practices, and overall experience for classic car owners. [ABSTRACT FROM AUTHOR]

Digital twins and virtual reality are pivotal technologies in the context of Industry 4.0, facilitating the design, simulation, optimization, and remote interaction with production systems. These technologies also present new prospects for developing immersive and hyper-realistic digital factories within the metaverse. This paper aims to enhance collaboration and communication in a 3D virtual world, focusing on shared reality and the metaverse's integration in digital factories. Our main contribution enables users to have full-body immersive interaction with virtual 3D assets and realistic locomotion in a 3D environment, fostering flexibility in collaboration and environment monitoring/control. To achieve this, we propose a systematic methodology for designing and implementing a digital twin-based factory with an IoT infrastructure. By replicating sensors and actuators in digital twins, real-time asset management synchronized with the physical factory is realized, empowering full-body interaction. To enable full-body interaction with virtual equipment, we employ a human motion tracking system. A proof-of-concept case study, developed using Unity3D game engine, Solace PubSub+ event streaming APIs, and Awinda Xsens wearable inertial sensors as the motion tracking system, validates our proposed methodology. The results of the case study demonstrate the successful integration of digital twins, virtual reality, and the metaverse, enabling real-time monitoring and control, full-body interaction, and immersive user experiences in the virtual environment. [ABSTRACT FROM AUTHOR]

Quality control is a critical aspect in today's fast-paced and competitive business landscape. The increasing digital transformation of manufacturing companies allows for the implementation of even proactive quality control strategies. This, however, requires the proper integration and analysis of shop floor data, regarding monitoring, diagnosis, and prognostics. This is to support defect recognition and recuperation, along with potential system reconfiguration based on knowledge extraction and human experience integration. Digital twins, being virtual replicas of physical assets, support real-time monitoring, analysis, and optimization. However, quality-related aspects may not be related to monitored parameters, thus solely data-driven models may not be accurate enough for proactive quality control. In this work, a hybrid digital twin is proposed, where data-driven models are used to finetune the behaviour of the digital twin based on its physics model. A use case concerning an industrial asset and the heat transfer to a steel bar is investigated with the results presented and commented on. [ABSTRACT FROM AUTHOR]

In today's VUCA-defined age, accelerated by the rapid digital transformation brought on by the COVID-19 pandemic, the business sector needs to be able to adapt to expected and unexpected changes. Consistently improving the product quality and managing the whole product life cycle, starting from the new product development (NPD) process, and the related data, are important aspects of keeping a company competitive in a global landscape. This paper highlights the growing potential of Industry 4.0, particularly the digital thread (DTH) and digital twin (DT), in addressing these challenges. The research revealed existing gaps in clarity and practical guidelines for digital twin prototypes (DTP) and DTH in the existing literature. Moreover, additional issues within product life cycle management (PLM) were stated, regarding frequent data loss and the complexity of product audits in the perspective of a continuous improvement. Therefore, a definition and novel framework for DTP and DTH implementation was proposed, after analyzing and comparing state of the art literature. This framework is additionally based on product audits carried out during the design stage of the product development process using requirements engineering and quality control information added within the suggested framework. Preliminary tests with DTP and DTH commercial off-the-shelves and prototype solutions were carried out with a consumer product. The tests enabled delivering a better understanding of DTPs, their potential, and their implications for decision-making and implementation. [ABSTRACT FROM AUTHOR]

The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders. [ABSTRACT FROM AUTHOR]

While digital technologies provide the backbone for digital twins of product, process, and performance, early adopters of digitalization recommend that technologies need to be supported by reengineered processes to unlock capacities and empower people. While the existing research literature provides insights into digital technologies, holistic consideration of product development processes and their responses to interventions is found lacking. This study focuses on the experiential inputs of manufacturing professionals to build a perspective of a customer-centric digitalized value chain enriched with digital twins of product, process, and performance. Authors interacted with SME stakeholders, through brainstorming workshops organized under the aegis of a premier Indian industry association. Based on the insights drawn from the expert interactions, monitoring, optimization, integration, and customer satisfaction have been established as the strategic steps of digitalization. This study adds to the digital twin-based engineering knowledge by introducing a versatile process-technology-performance matrix. SMEs can adopt this matrix to tailor solutions to their unique business needs, enabling flexible creation of digital twins. This aspect makes this study relevant and novel for manufacturing SMEs preparing for implementing transformative digital technologies. [ABSTRACT FROM AUTHOR]

Nowadays, the construction industry's digital transformation processes are challenging civil engineering education. In general, students and society underestimate the civil engineering field as an outdated and archaic knowledge area. Despite the defragmentation and heterogeneity of the sector, advances have been made on the path for Construction 4.0 and 5.0 scenarios. From this perspective, the CONSTRUCT-Gequaltec group of the Faculty of Engineering of the University of Porto have been sponsoring research and technological development using hardware and software targeting construction management solutions. This paper presents the work in progress mainly connected to the curricular units regarding project management in the Construction Section of the Civil Engineering Department. It also targets assumptions of some expected results across the research and innovation priorities from ECTP Innovative Built Environment and United Nations Sustainable Development Goals (SDGs). Two fundamental elements of the teaching practices are presented as the Construction Sites of the Future (Laboratory-Based Education initiative) and Digital products catalogue targeting Digital Twins in Construction (Project Based Learning initiative). The learning outcomes address increasing knowledge of innovative solutions, delivering skills to deploy 5.0 actions, and targeting human-centred and sustainable attitudes and values. [ABSTRACT FROM AUTHOR]

The operation of heavy equipment in the construction industry requires high-skilled workers: they must safely operate the equipment in an efficient manner to avoid project delays or damages, both to equipment and material as to their colleagues and themselves. Operator productivity and safety are therefore critical in the construction industry. The advancements in technologies such as digitization and automation are more and more applied in the industry, leading to more data and knowledge on the construction equipment and environment. This can be leveraged by a digital twin to provide value for the operator. In this work, we present a digital twin architecture applied to an off highway vehicle, which provides real-time feedback to the operator while operating the vehicle. Monitoring the interactions between an off highway vehicle and workers will result in a safer and more productive environment. This work focuses on detection of the environment and monitoring the state of the vehicle. Images of a low cost camera are used to extract information of the vehicle implement as well as the environment. The motion and state of the machine can be realistically visualized in real-time in Unreal, using a 3D CAD (Computer-Aided Design) geometry model, enriched with additional relevant information and color schemes. A modular pub-sub architecture enables the communication between the sensors and camera, the data processing and the visualization of the digital twin. [ABSTRACT FROM AUTHOR]

Software-Defined Radios are radio communications devices that have been growing and developing on a larger scale in recent years. Communications are intrinsically embedded in our day by day, thus presenting a higher motivation to use software-defined radios due to its attractive cost. However they present technical limitations. This paper addresses this problem, which is the non-linearity behaviour of gain and frequency in the LimeSDR-USB. That is, this equipment is used to produce a FM signal with an associated frequency and gain before being parameterised according to the internal parameters of each software-defined radio. Each software-defined radio presents a value of frequency and gain of its own, which correlates to the generated signals at the output level. To avoid this, machine learning networks were used, in which networks were trained to adapt to the non-linearity of these devices and correct it without the user noticing. This way, the user sets a desired frequency and gain in a signal, at the output of the software-defined radios, and a neural network calculates which values the software-defined radios should be parameterised, thus mitigating the non-linearity behaviour. This paper presents the evaluation of a laboratory prototype based on low-cost commercial software-defined radios equipment, to replace an expensive metrologically calibrated equipment used for radio frequency tests on a new concept of industrial test station, with description of the integration of Digital Twins, with their physical and virtual parts. [ABSTRACT FROM AUTHOR]