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Vaccine manufacturing fosters the prevention, control, and eradication of infectious diseases. Recombinant DNA and in vitro (IVT) mRNA vaccine manufacturing technologies were enforced to combat the recent pandemic. Despite the impact of these technologies, there exists no scientific announcement that compares them. Digital Shadows are employed in this study to simulate each technology, investigating root cause deviations, technical merits, and liabilities, evaluating cost scenarios. Under this lens we provide an unbiased, advanced comparative technoeconomic study, one that determines which of these manufacturing platforms are suited for the two types of vaccines considered (monoclonal antibodies or antigens). We find recombinant DNA technology to exhibit higher Profitability Index due to lower capital and starting material requirements, pertaining to lower Minimum Selling Price per Dose values, delivering products of established quality. However, the potency of the mRNA, the streamlined and scalable synthetic processes involved and the raw material availability, facilitate faster market penetration and product flexibility, constituting these vaccines preferable whenever short product development cycles become a necessity. [ABSTRACT FROM AUTHOR]

In continuous powder handling processes, precise and consistent feeding is crucial for ensuring the quality of the final product. The intermixing effect caused by agitators, which alters the powder's bulk density, flow rate, and flow patterns, plays a significant role in this process, yet it is often overlooked. This study combines discrete element method (DEM) modeling and experiments using a commercial-scale feeder to propose a Digital Twin (DT) framework. The DEM model accurately captures key flow features, such as bypass trajectories, stagnant zones, and preferential flow patterns, while providing quantitative predictions for the feed factor and zones prone to material accumulation. Scenario analysis is performed to identify the most favorable operating ranges of the screw-agitator ratio and screw speed, considering the cohesive properties of the powder. The study demonstrates that powders with poor flow characteristics require tighter operational constraints, as the screw-agitator ratio is susceptible to variations in mass feed rate. This contribution highlights the importance of selecting an appropriate screw-agitator ratio instead of maintaining a fixed value. Properly choosing this ratio helps determine an optimal operation window, which aims to achieve a minimum agitation level needed to induce unhindered flow and reduce variability in the mass flow rate. [ABSTRACT FROM AUTHOR]

While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for regional sea-suface height emulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill compared to a state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate initial steps for physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models. [ABSTRACT FROM AUTHOR]

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. [ABSTRACT FROM AUTHOR]

Beyond 5G networks provide solutions for next-generation communications, especially digital twins networks (DTNs) have gained increasing popularity for bridging physical and digital space. However, current DTNs pose some challenges, especially when applied to scenarios that require efficient and multimodal data processing. Firstly, current DTNs are limited in communication and computational efficiency, since they require to transmit large amounts of raw data collected from physical sensors, as well as to ensure model synchronization through high-frequency computation. Second, current models of DTNs are domain-specific (e.g. E-health), making it difficult to handle DT scenarios with multimodal data processing requirements. Finally, current security schemes for DTNs introduce additional overheads that impair the efficiency. Against the above challenges, we propose a large language model (LLM) empowered DTNs framework, LLM-Twin. First, based on LLM, we propose digital twin semantic networks (DTSNs), which enable more efficient communication and computation. Second, we design a mini-giant model collaboration scheme, which enables efficient deployment of LLM in DTNs and is adapted to handle multimodal data. Then, we designed a native security policy for LLM-twin without compromising efficiency. Numerical experiments and case studies demonstrate the feasibility of LLM-Twin. To our knowledge, this is the first to propose an LLM-based semantic-level DTNs. [ABSTRACT FROM AUTHOR]

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks. [ABSTRACT FROM AUTHOR]

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

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

The infiltration of heterogenous fleets of autonomous Unmanned Aerial Vehicles (UAVs) in smart cities is leading to the consumerization of city air space which includes infrastructure creation of roads, traffic design, capacity estimation, and trajectory optimization. This study proposes a novel autonomous Advanced Aerial Mobility (AAM) logistical system for high density city centers. First, we propose a real-time 3D geospatial mining framework for LiDAR data to create a dynamically updated digital twin model. This enables the identification of viable airspace volumes in densely populated 3D environments based on the airspace policy/regulations. Second, we propose a robust city airspace dynamic 4D discretization method (Skyroutes) for autonomous UAVs to incorporate the underlying real-time constraints coupled with externalities, legal, and optimal UAV operation based on kinematics. An hourly trip generation model was applied to create 1138 trips in two scenarios comparing the cartesian discretization to our proposed algorithm. The results show that the AAM enables a precise airspace capacity/cost estimation, due to its detailed 3D generation capabilities. The AAM increased the airspace capacity by up to 10%, the generated UAV trajectories are 50% more energy efficient, and significantly safer. [ABSTRACT FROM AUTHOR]

With the current development of the 5G infrastructure, there presents a unique opportunity for the deployment of battery-less mmWave reflect-array-based sensors. These fully-passive devices benefit from having a larger detectability than alternative battery-less solutions to create self-monitoring megastructures. The presented 'smart' skin sensor uses a Van-Atta array design enabling ubiquitous local strain monitoring for the structural health monitoring of composite materials featuring wide interrogation angles. Proof-of-concept prototypes of these 'smart' skin millimeter-wave identification tags, that can be mounted on or embedded within common materials used in wind turbine blades, present a highly-detectable radar cross-section of − 33.75 dBsm and − 35.00 dBsm for mounted and embedded sensors respectively. Both sensors display a minimum resolution of 202 μ -strain even at 40 ∘ off-axis enabling interrogation of the fully-passive sensor at oblique angles of incidence. When interrogated from a proof-of-concept reader, the fully-passive, sticker-like mmID enables local strain monitoring of both carbon fiber and glass fiber composite materials. The sensors display a repeatable and recoverable response over 0–3000 μ -strain and a sensitivity of 7.55 kHz/ μ -strain and 7.92 kHz/ μ -strain for mounted and embedded sensors, respectively. Thus, the presented 5G-enabled battery-less sensor presents massive potential for the development of ubiquitous Digital Twinning of composite materials in future smart cities architectures. [ABSTRACT FROM AUTHOR]

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat. [ABSTRACT FROM AUTHOR]

The boom-type roadheader plays a crucial role in coal mining. However, conducting the real-time monitoring of the mechanical performance and comprehensive adaptive cutting in the dynamic cutting process are challenging. To address these issues, a digital twin system that integrates the elements of "shape, performance, and control" for roadheaders is presented in this paper. The system comprises three components: physical space, service space, and twin space. The service space forms the core of the entire system. Within this space, twin models and control models are created using numerical simulation, artificial intelligence and multi-source data fusion technology. These models serve the purpose of predicting the roadheader's mechanical performance and controlling the swing speed of the cutting arm. The physical space is built using technologies such as robot kinematics, electrical systems, hydraulic transmission, and other relevant techniques. This approach facilitates the transmission of multi-sensor data to twin models. The control model then manages the roadheader's function based on the output signals from the control model. The twin space is constructed utilizing physical rendering engines, databases, and 3D modelling tools. This space visualizes and stores the movement, performance, and control parameters of the roadheader. The results demonstrate that the average absolute error between the measured data from the test's three position strain gauges and the predicted data from the twin system is 10.38 MPa. Furthermore, the twin system achieves an average update interval of 0.34 s, allowing real-time stress monitoring of the structural components of the roadheader and preventing damage caused by overload. The proposed control model enables adaptive adjustment of the swing speed of the cutting arm in approximately 0.3 s. This improvement significantly enhances the adaptive cutting capabilities of roadheaders when dealing with complex coal and rock formations. [ABSTRACT FROM AUTHOR]

Performance in many racing sports depends on the ability of the athletes to produce and maintain the highest possible work i.e., the highest power for the duration of the race. To model this energy production in an individualized way, an adaptation and a reinterpretation (including a physiological meaning of parameters) of the three-component Margaria–Morton model were performed. The model is applied to the muscles involved in a given task. The introduction of physiological meanings was possible thanks to the measurement of physiological characteristics for a given athlete. A method for creating a digital twin was therefore proposed and applied for national-level cyclists. The twins thus created were validated by comparison with field performance, experimental observations, and literature data. Simulations of record times and 3-minute all-out tests were consistent with experimental data. Considering the literature, the model provided good estimates of the time course of muscle metabolite concentrations (e.g., lactate and phosphocreatine). It also simulated the behavior of oxygen kinetics at exercise onset and during recovery. This methodology has a wide range of applications, including prediction and optimization of the performance of individually modeled athletes. [ABSTRACT FROM AUTHOR]

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

This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aiming at the problems of backward calibration method of smoke alarm, low production efficiency and difficult real-time monitoring, a digital twin system modeling and monitoring method of smoke alarm calibration is proposed. First, through the analysis of smoke alarm calibration requirements, the overall framework design of the digital twin calibration system for smoke alarm is proposed, and then the twin model of smoke box is constructed by running the digital twin five-dimensional model. The physical smoke box, geometric model, physical model, rule model and behavior model are introduced in detail; Then the communication system architecture, data acquisition and data mapping are used to construct the twin data model; Finally, the feasibility was verified by the calibration system of an enterprise. Through the system, the qualified rate of smoke alarm calibration was increased from 98 to 99.6%, and the repair rate of defective products was reduced by 1.6%. [ABSTRACT FROM AUTHOR]

This work presents the first attempt to create a physics-based digital twin model for predictive analysis of damage evolution during the use of ground stone tools (GSTs) in transformative tasks, encompassing the processing of raw resources for nutritional and non-alimentary purposes. The proposed methodology introduces a digital twin of the GSTs developed from 3D models generated using a photogrammetric technique based on Structure-from-Motion and Multi-View Stereo reconstruction. These models serve as the foundation for the development of the finite element (FE)-based digital twin model of the GSTs that exploits a contact formulation and the phase-field approach to simulate tool damage during pounding and grinding tasks. Defining the initial relative positions of the stones, their mechanical behaviour, and controlling the movement of the active stone in a way as close as possible to the real one, the digital twin model has been devised to evaluate how the surface damage is affected by perturbations in the loading conditions. The simulated damage is compared with the surface traces observed from experiments. The developed digital twin model aims at demonstrating its potentials for the GSTs investigations, as a supporting tool for experiments and for simulated tests on the archaeological records. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5–6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy. [ABSTRACT FROM AUTHOR]

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

Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption. [ABSTRACT FROM AUTHOR]

Semiconductor device optimization using computer-based prototyping techniques like simulation or machine learning digital twins can be time and resource efficient compared to the conventional strategy of iterating over device design variations by fabricating the actual device. Ideally, simulation models require perfect calibration of material parameters for the model to represent a particular semiconductor device. This calibration process itself can require characterization information of the device and its precursors and extensive expert knowledge of non characterizable parameters and their tuning. We propose a hybrid method to calibrate multiple simulation models for a device using minimal characterization data and machine learning-based prediction models. A photovoltaic device is chosen as the example for this technique where optical and electrical simulation models of an industrially manufactured silicon solar cell are calibrated and the simulated device performance is compared with the measurement data from the physical device. [ABSTRACT FROM AUTHOR]

Even though the Covid-19 pandemic seems to be stagnating or decreasing across the world, a resurgence of the disease or the occurrence of other epidemics caused by the aerial dissemination of pathogenic biological agents cannot be ruled out. These agents, in particular the virions of the Covid-19 disease, are found in the particles originating from the sputum of infected symptomatic or asymptomatic people. In previous research, we made use of a three-dimensional Computational Fluid Dynamics (CFD) model to simulate particle transport and dispersion in ventilated semi-confined spaces. By way of illustration, we considered a commuter train coach in which an infected passenger emitted droplets (1 and 10 µm) and drops (100 and 1000 µm) while breathing and coughing. Using an Eulerian approach and a Lagrangian approach, we modelled the dispersion of the particles in the turbulent flow generated by the ventilation of the coach. The simulations returned similar results from both approaches and clearly demonstrated the very distinct aerodynamics of the aerosol of airborne droplets and, at the other end of the spectrum, of drops falling or behaving like projectiles depending on their initial velocity. That numerical study considered passengers without protective masks. In this new phase of research, we first used literature data to develop a model of a typical surgical mask for use on a digital manikin representing a human. Next, we resumed the twin experiment of the railway coach, but this time, the passengers (including the infected one) were provided with surgical masks. We compared the spatial and temporal distributions of the particles depending on whether the spreader passenger wore a mask at all, and whether the mask was perfectly fitted (without leaks) or worn loosely (with leaks). Beyond demonstrating the obvious value of wearing a mask in limiting the dissemination of particles, our model and our simulations allow a quantification of the ratio of particles suspended in the coach depending on whether the infected passenger wears a mask or not. Moreover, the calculations carried out constitute only one illustrative application among many others, not only in public transport, but in any other public or private ventilated space on the basis of the same physical models and digital twins of the places considered. CFD therefore makes it possible to estimate the criticality of the occupation of places by people with or without a mask and to recommend measures in order to limit aerial contamination by any kind of airborne pathogen, such as the virions of Covid-19. [ABSTRACT FROM AUTHOR]

The recruitment of patients for rare or complex cardiovascular diseases is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative. In this paper we present an unprecedented cardiovascular computer model which, relying on the latest GPU-acceleration technologies, replicates the full multi-physics dynamics of the human heart within a few hours per heartbeat. This opens the way to extensive simulation campaigns to study the response of synthetic cohorts of patients to cardiovascular disorders, novel prosthetic devices or surgical procedures. As a proof-of-concept we show the results obtained for left bundle branch block disorder and the subsequent cardiac resynchronization obtained by pacemaker implantation. The in-silico results closely match those obtained in clinical practice, confirming the reliability of the method. This innovative approach makes possible a systematic use of digital twins in cardiovascular research, thus reducing the need of real patients with their economical and ethical implications. This study is a major step towards in-silico clinical trials in the era of digital medicine. [ABSTRACT FROM AUTHOR]

April 15th, 2019: Notre-Dame Cathedral in Paris was burning, the spire collapsed on the nave, vaults crumbled and most of the timber roof was gone. In the post-disaster context, the authenticity and the monitoring of the archaeological remains are crucial for their potential reuse during reconstruction. This paper analyzes the collapsed transverse arch from the nave of Notre-Dame as a case study of reconstruction, using the digital twin framework. We propose four facets for the digital twin experiment—physical anastylosis, reverse engineering, spatio-temporal tracking of assets, and operational research—that are described in detail, while being assembled to support a hybrid reconstruction hypothesis. The digital twin can realize the parallel unfolding of physical-native and digital-native processes, while acquiring and storing heterogeneous information as semantically structured data. The results demonstrate that the proposed modeling method facilitates the formalization and validation of the reconstruction problem and increases solutions performances. As result, we present a digital twin framework application ranging from acquisition to data processing that informs a successful hybrid reconstruction hypothesis. [ABSTRACT FROM AUTHOR]

The advancement of digital twin technology has significantly impacted the utilization of virtual cities in the realm of smart cities and mobility. Digital twins provide a platform for the development and testing of various mobility systems, algorithms, and policies. In this research, we introduce DTUMOS, a digital twin framework for urban mobility operating systems. DTUMOS is a versatile, open-source framework that can be flexibly and adaptably integrated into various urban mobility systems. Its novel architecture, combining an AI-based estimated time of arrival model and vehicle routing algorithm, allows DTUMOS to achieve high-speed performance while maintaining accuracy in the implementation of large-scale mobility systems. DTUMOS exhibits distinct advantages in terms of scalability, simulation speed, and visualization compared to current state-of-the-art mobility digital twins and simulations. The performance and scalability of DTUMOS are validated through the use of real data in large metropolitan cities including Seoul, New York City, and Chicago. DTUMOS' lightweight and open-source environment present opportunities for the development of various simulation-based algorithms and the quantitative evaluation of policies for future mobility systems. [ABSTRACT FROM AUTHOR]

Currently, the methods used to represent loads do not differ between the characteristics that compose them or the nature of these. Therefore, the purpose of this research is to develop digital twins mirroring load models that can be used for more precise studies on power-flows and stability within the National Transmission Grid (NTG). Off-line sampling data of different electric measurements have been used in six substations of the Guayaquil (Ecuador) area. These values were organized by statistical methods and by time periods, to determine the parameters that make up the static load model. Dynamic models are also constructed for the same six substations using the analysis of current and voltage signals obtained from the substations. All data is organized to show a digital twin mirroring visual representation of the disturbances that may occur in the substation buses. A more accurate description of the static and dynamic responses can be obtained by replacing the general model that is currently used by engineers and planners with off-line sampling data. Digital twins help the electric utility businesses gather, visualise, and contextualise data from different sources, and enable to act on data, and to understand what-if modelling stability scenarios. [ABSTRACT FROM AUTHOR]

Railway maintenance is a complex and complicated task in the railway industry due to the number of its components and relationships. Ineffective railway maintenance results in excess cost, defective railway structure and components, longer possession time, poorer safety, and lower passenger comfort. Of the three main maintenance approaches, predictive maintenance is the trendy one, and is proven that it provides the highest efficiency. However, the implementation of predictive maintenance for the railway industry cannot be done without an efficient tool. Normally, railway maintenance is corrective when some things fail or preventive when maintenance is routine. A novel approach using an integration between deep reinforcement learning and digital twin is proposed in this study to improve the efficiency of railway maintenance which other techniques such as supervised and unsupervised learning cannot provide. In the study, Advantage Actor Critic (A2C) is used to develop a reinforcement learning model and agent to fulfill the need of the study. Real-world field data over four years and 30 km. is obtained and applied for developing the reinforcement learning model. Track geometry parameters, railway component defects, and maintenance activities are used as parameters to develop the reinforcement learning model. Rewards (or penalties) are calculated based on maintenance costs and occurring defects. The new breakthrough exhibits that using reinforcement learning integrated with digital twin can reduce maintenance activities by 21% and reduce the occurring defects by 68%. Novelties of the study are the use of A2C which is faster and provides better results than other traditional techniques such as Deep Q-learning (DQN), each track geometry parameter is considered without combining into a track quality index, filed data are used to develop the reinforcement learning model, and seven independent actions are included in the reinforcement learning model. This study is the world's first to contribute a new guideline for applying reinforcement learning and digital twins to improve the efficiency of railway maintenance, reduce the number of defects, reduce the maintenance cost, reduce the possession time for railway maintenance, improve the overall safety of the railway operation, and improve the passenger comfort which can be seen from its results. [ABSTRACT FROM AUTHOR]

Assembly is a critical step in the manufacturing process. Robotic assembly technology in automatic production lines has greatly improved the production efficiency. However, in assembly process, dynamic disturbances such as processing time change and advance delivery may occur, which cause the scheduling deviation. Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements in smart manufacturing. Digital twin (DT) has the characteristics of virtual-reality interaction and real-time mapping. In this paper, we propose a DT-based framework of task rescheduling for robotic assembly line (RAL) and its key methodologies, thus to realize the timely and dynamic adjustment of scheduling plan under uncertain interferences. First, a DT model of RAL task rescheduling composed of physical entity (PE), virtual entity (VE), and virtual-reality interaction mechanism is proposed. Then, a mathematical model is established. By analyzing the adaptive objective thresholds from the perspectives of event trigger and user demand trigger, a DT-driven multi-level (production unit level and line level) rescheduling strategy is proposed. Taking both the computing time and solution quality into consideration, the precedence graph is introduced to propose a rescheduling approach based on an improved discrete fireworks algorithm. Finally, the effectiveness of the proposed model and approach are verified by task scheduling experiments of RAL. [ABSTRACT FROM AUTHOR]

Discovering malicious packets amid a cloud of normal activity, whether you use an IDS or gather and analyze machine and device log files on company infrastructure, may be challenging and time consuming. The vulnerability landscape is rapidly evolving, and it will only become worse as more and more developing technologies, such as IoT, Industrial Automation, CPS, Digital Twins, etc are digitally connected. A honey trap aids in identifying malicious packets easily as, after a few rapid calibrations to eliminate false positives. Besides analyzing and reporting particular invasion patterns or toolkits exploited, it also assists in preventing access to actual devices by simulating the genuine systems and applications functioning in the network thus delaying as well as baffling the invader. In order to analyze and evaluate the hackers' behavior, an ensemble of research honeypot detectors has been deployed in our work. This paper delivers a robust outline of the deployment of containerized honeypot deployment, as a direct consequence, these are portable, durable, and simple to deploy and administer. The instrumented approach was monitored and generated countless data points on which significant judgments about the malevolent users' activities and purpose could be inferred. [ABSTRACT FROM AUTHOR]

Remanufacturing is widely recognised as a key contributor to the circular economy (CE) as it extends the in-use life of products, but its synergy with Industry 4.0 (I4.0) has received little attention when compared to manufacturing. An agglomeration of I4.0 technologies and methodologies is reflected in the emerging digital twin (DT) concept, which has been identified as a life-extending enabler. This article captures the design and demonstration of a DT model that optimises remanufacturing planning using data from different instances in a product's life cycle. The model uses a neural network for remaining useful life predictions and the Bees Algorithm for decision making within a DT. The model is validated using a real case study. The findings support the idea that intelligent tools within a DT can enhance decision-making if they have visibility and access to the product's current status and reliable remanufacturing process information. [ABSTRACT FROM AUTHOR]

Autonomous driving is gradually moving from single-vehicle intelligence to internet of vehicles, where traffic participants can share the traffic flow information perceived by each other. When the sensing technology is combined with the internet of vehicles, a sensor network all over the road can provide a large-scale of traffic flow data, thus providing a basis for building a traffic digital twin model. The digital twin can enable the traffic system not only to use past and present information, but also to predict traffic conditions, providing more effective optimization for autonomous driving and intelligent transportation, so as to make long-term rational planning of the overall traffic state and enhance the level of traffic intelligence. The current mainstream traffic sensors, namely radar and camera, have their own advantages, and the fusion of these two sensors can provide more accurate traffic flow data for the generation of digital twin model. In this paper, an end-to-end digital twin system implementation approach is proposed for highway scenarios. Starting from a paired radar-camera sensing system, a single-site radar-camera fusion framework is proposed, and then using the definition of a unified coordinate system, the traffic flow data between multiple sites is combined to form a dynamic real-time traffic flow digital twin model. The effectiveness of the digital twin building is verified based on the real-world traffic data. [ABSTRACT FROM AUTHOR]

A geometric digital twin (gDT) model capable of leveraging acquired 3D geometric data plays a vital role in digitizing the process of structural health monitoring. This study presents a framework for generating and updating digital twins of existing buildings by inferring semantic information from as-is point clouds (gDT's data) acquired regularly from laser scanners (gDT's connection). The information is stored in updatable Building Information Models (BIMs) as gDT's virtual model, and dimensional outputs are extracted for structural health monitoring (gDT's service) of different structural members and shapes (gDT's physical part). First, geometric information, including position and section shape, is obtained from the acquired point cloud using domain-specific contextual knowledge and supervised classification. Then, structural members' function and section family type is inferred from geometric information. Finally, a BIM is automatically generated or updated as the virtual model of an existing facility and incorporated within the gDT for structural health monitoring. Experiments on real-world construction data are performed to illustrate the efficiency and precision of the proposed model for creating as-is gDT of building structural members. [ABSTRACT FROM AUTHOR]

Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively. [ABSTRACT FROM AUTHOR]

Digital twin can be defined as a digital equivalent of an object of which it can mirror its behavior and status or virtual replicas of real physical entities in Cyberspace. To an extent, it also can simulate and predict the states of equipment or systems through smart algorithms and massive data. Hence, the digital twin is emerging used in intelligent manufacturing Systems in real-time and predicting system failure and also has introduced into a variety of traditional industries such as construction, Agriculture. Rare earth production is a typical process industry, and its Extraction Process enjoys the top priority in the industry. However, the extraction process is usually characterized by nonlinear behavior, large time delays, and strong coupling of various process variables. In case of failures happened in the process, the whole line would be shut down. Therefore, the digital twin is introduced into the design of process simulation to promote the efficiency and intelligent level of the Extraction Process. This paper proposes the techniques to build the rare earth digital twin such as soft measurement of component content, component content process simulation, control optimization strategy, and virtual workshop, etc. At the end, the validity of the model is verified, and a case study is conducted to verify the feasibility of the whole Digital twin framework. [ABSTRACT FROM AUTHOR]

Railway turnouts are essential in the train traffic route management for modern railways. Despite significant devotion to railway turnout research, one of their most common failures has not been thoroughly investigated, which is a fatigue over the turnout crossing nose. At the crossings, wheel-rail discontinuity imparts high-frequency high-magnitude forces, which are the source of fatigue failure over the crossing nose. In this study, a novel approach built on "Peridynamics" (PD) has been developed to obtain new insights into the fatigue cracks. A recent approach using "crack on mid-plane" has also been employed in this study to enhance the limited capability of Peridynamics. This paper is the world's first to investigate fatigue failures over a crossing nose from fracture mechanics perspective. This paper also introduces a novel adaptive time-mapping method as an alternative to earlier time-mapping methods for fatigue models proposed in the open literature. The new model has been verified against both Finite Element Method and experimental data. It reveals that our new approach can simulate fatigue damage, particularly in mode I crack propagation. The study has provided important insights on the fatigue crack development, which is not possible before by existing Peridynamics fatigue model. The new approach on the basis of "adaptive time-mapping" and "crack on mid-plane" is demonstrated to be effective and efficient in PD simulations. [ABSTRACT FROM AUTHOR]

A correct modal analysis of girder bridge decks requires a correct characterisation of the deformation of their cross-section, governed by the longitudinal bending of the girders and the transverse bending of the slab. This paper presents a novel method that allows the modal analysis of girder bridge decks by applying a matrix formulation that reduces the structural problem to one degree of freedom for each girder: the deflection at the centre of the beam span. A parametric study is presented that analyses the structural response of 64 girder bridge decks. The study compares the dynamic structural response obtained by the proposed method with that obtained by traditional grillage calculation methods. The method is experimentally contrasted by a dynamic load test of a full-scale girder bridge. As a result of the analysis, the proposed method reflects adequate convergence with the experimental dynamic structural response. The use of the proposed novel analysis method contributes to the intelligent modelling process for the analysis of the dynamic behaviour of bridges opening the way to easily feed a Digital Twin accelerating the demands of the Decision Support System in real time. [ABSTRACT FROM AUTHOR]

The concept of digital twin has been introduced for some time, yet one fundamental element of digital twin, digital material, has not been thoroughly studied. To interact with the physical product, the digital twin should always truthfully reflect the responses under various stimuli. In this paper, the deformation and fracture behavior of high impact polystyrene (HIPS) under the influencing factors of strain rate and stress triaxiality are studied to construct the material's digital model. A digital twin of air conditioner product is further built and tested under virtual drop test. Comparing to experimental results, the acceleration curve, crazing induced whitening and the fracture events can all be captured by the digital twin. Our work demonstrates the importance of material characterization as an essential step to construct an accurate digital twin and shows a promising future of digital twin in virtual testing to replace traditional "trial and error" experiments. [ABSTRACT FROM AUTHOR]

For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic material models, which might not be feasible from physical experiments alone. However, synthesizing realistic microstructures with realistic microstructural attributes is highly challenging. Thus, it is often oversimplified via rough approximations that may yield an inaccurate representation of the physical world. Here, we propose a novel deep learning method that can synthesize realistic three-dimensional microstructures with controlled structural properties using the combination of generative adversarial networks (GAN) and actor-critic (AC) reinforcement learning. The GAN-AC combination enables the generation of microstructures that not only resemble the appearances of real specimens but also yield user-defined physical quantities of interest (QoI). Our validation experiments confirm that the properties of synthetic microstructures generated by the GAN-AC framework are within a 5% error margin with respect to the target values. The scientific contribution of this paper resides in the novel design of the GAN-AC microstructure generator and the mathematical and algorithmic foundations therein. The proposed method will have a broad and substantive impact on the materials community by providing lenses for analyzing structure-property-performance linkages and for implementing the notion of 'materials-by-design'. [ABSTRACT FROM AUTHOR]

Nowadays everyone should be aware of the importance of reducing CO2 emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before undertaking a modification in any part of a building focused on improving the energy performance, it is generally better to carry out simulations to evaluate its effectiveness. Using Artificial Neural Networks (ANNs) allows a digital twin of the building to be obtained for specific characteristics without using very expensive software. This can simulate the effect of a single or combined intervention on a particular floor or an event on the remaining floors. In this paper, an example has been developed based on ANN. The results show a reasonable correlation between the real data of the Operative Temperature with the Energy Consumption and their estimates obtained through an ANN model, trained using an hourly basis, on each of the floors of an office building. This model confirms it is possible to obtain simulations in existing public buildings with an acceptable degree of precision and without laborious modelling, which would make it easier to achieve the nZEB target, especially in existing public office buildings. [ABSTRACT FROM AUTHOR]

This paper aims to promote a quantum framework that analyzes Industry 4.0 cyber-physical systems more efficiently than traditional simulations used to represent integrated systems. The paper proposes a novel configuration of distributed quantum circuits in multilayered complex networks that enable the evaluation of industrial value creation chains. In particular, two different mechanisms for the integration of information between circuits operating at different layers are proposed, where their behavior is analyzed and compared with the classical conditional probability tables linked to the Bayesian networks. With the proposed method, both linear and nonlinear behaviors become possible while the complexity remains bounded. Applications in the case of Industry 4.0 are discussed when a component's health is under consideration, where the effect of integration between different quantum cyber-physical digital twin models appears as a relevant implication. [ABSTRACT FROM AUTHOR]

To address the problems of poor welding completeness and inefficient configuration for defective automotive body-in-white panels, we propose a method for detecting and configuring the welding completeness of automotive body-in-white panels based on digital twin (DT) and mixed reality (MR). The method uses DT to build an MR-oriented DT framework for the detections and configuration of body-in-white panel welding completeness. We propose a method to build a DT knowledge base for panels, a Yolov4-based welding completeness detection method, and a MR-based configuration method for the welding completeness in panels. Our team develop a panel welding completeness detection and configuration system to fully validate the effectiveness of the method. [ABSTRACT FROM AUTHOR]