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Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating avatars (herein defined as an extension of digital twins) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

Background: Integration of a patient's non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019–10–07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013–11–12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( ICC ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error ( χ 2 ) of LV myocardial strain, strain rate, and cavity volume. Results: A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients ( χ 2 < 1.6), but minimum parameter reproducibility was poor ( ICC min = 0.01). Iterative reduction yielded a reproducible ( ICC min = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs ( χ 2 < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05). Conclusions: By applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient's underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making. [ABSTRACT FROM AUTHOR]

The aim of this research is to investigate tourist marketing within the embryonic context of the metaverse in order to comprehend the building blocks and the primary technologies employed in the sector. A systematic literature review (SLR) was conducted on 386 articles, with an overall qualitative approach that included 86 references, all of which dealt with the topic of the metaverse and had direct or potential implications for the tourism sector (hotels, restaurants, means of transport, leisure activities and destination itself). The articles are taken from: Science Direct, Taylor & Francis, Emerald, Springer and Google Scholar. The SLR was carried out according to the PRISMA search protocol. The results indicate the technologies that have been most thoroughly studied at the confluence of marketing, tourism, and the metaverse (AI, virtual reality, augmented reality, mixed reality, blockchain, tokens (NFTs) and digital twins). Moreover, they establish the foundational components of tourism marketing in the metaverse for the first time (tourism products, the metaverse as a distribution and branding channel for tourism and, tourist customer as protagonist). Finally, the study exposes research gaps and recommends future directions for exploration (monetization of products in the metaverse, promotion and marketing strategies in the metaverse, new profiles for marketing professionals, policy development that regulates commercial activity in the metaverse). [ABSTRACT FROM AUTHOR]

Accurate CO2 tracking in electric substation construction is vital for climate efforts, using monocular SLAM for monitoring despite challenges like sunlight and complex terrain. Additionally, these methods typically yield only aggregate carbon emission data, thereby lacking the granularity necessary for precise monitoring throughout the construction process. These limitations compromise mapping accuracy and impede the integration of digital twins and IoT technologies. Addressing these issues, this paper proposed a methodology combining red, green, and blue (RGB) cameras and multi-camera collaboration with digital design systems, enhancing SLAM capabilities. The advanced technique integrated methods including overlap estimation, depth reasoning, noise reduction, and surface reconstruction to create accurate 3D models, enhancing scene reconstruction and real-time CO2 tracking during construction and operation. Leveraging continuous on-site camera monitoring as a substitute for manual inspections, it significantly contributes to the compilation of a comprehensive carbon emission database within a digital twin framework. Experimental results confirmed the proposed method's superiority over previous works in real-time CO2 estimation, enhancing decision-making, resource management, and sustainable energy development. Overall, besides its application in substation construction for CO2 monitoring, this methodology can also be applied to carbon tracking in various other construction projects. [ABSTRACT FROM AUTHOR]

To improve the dynamic adaptability and flexibility of the process route during manufacturing, a dynamic optimization method of the multi-process route based on an improved ant colony algorithm driven by digital twin is proposed. Firstly, based on the analysis of the features of the manufacturing part, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by the digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example for verification analysis, which shows that this method can obtain a process route that gets closer to practical production. [ABSTRACT FROM AUTHOR]

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. [ABSTRACT FROM AUTHOR]

With neutron diffraction, the local stress and texture of metallic components can be analyzed non-destructively. For both, highly accurate positioning of the sample is essential, requiring the measurement at the same sample location from different directions. Current sample-positioning systems in neutron diffraction instruments combine XYZ tables and Eulerian cradles to enable the accurate six-degree-of-freedom (6DoF) handling of samples. However, these systems are not flexible enough. The choice of the rotation center and their range of motion are limited. Industrial six-axis robots have the necessary flexibility, but they lack the required absolute accuracy. This paper proposes a visual servoing system consisting of an industrial six-axis robot enhanced with a high-precision multi-camera tracking system. Its goal is to achieve an absolute positioning accuracy of better than 50 μ m. A digital twin integrates various data sources from the instrument and the sample in order to enable a fully automatic measurement procedure. This system is also highly relevant for other kinds of processes that require the accurate and flexible handling of objects and tools, e.g., robotic surgery or industrial printing on 3D surfaces. [ABSTRACT FROM AUTHOR]

Existing interturn fault detecting methods rely on winding impedance, winding current, and dissolved gases. They are effective only when the insulation is severely damaged. This paper proposes a novel detection method based on fusion analysis of electrothermal characteristics including winding currents, temperatures of four areas on the tank wall, top oil and ambient temperatures, which can identify the interturn fault at an early stage. When an incipient interturn fault occurs, the heat generated by the faulty turns is transferred to the oil and tank wall, leading to an increase in top oil and tank wall temperatures. Thus, the incipient fault can be detected by analysing these electrothermal characteristic parameters. Borrowing the idea of digital twin (DT), this method establishes a high‐fidelity simulation model to simulate the transformer electrothermal characteristics under different operating conditions. Afterward, an intelligent neural network is adopted to extract the quantitative relationship between the eight feature attributions and fault conditions. Finally, this neural network is utilized to detect the incipient interturn fault for the transformer entity. Case studies are conducted on a 100 kVA transformer with oil natural air natural (ONAN) cooling mode. The detection accuracy is improved by 68.5% compared to the winding current‐based method. [ABSTRACT FROM AUTHOR]

Due to the requirement for highly precise synchronous sampling and the substantial reliance on time difference calculations, the current partial discharge (PD) localization based on the time difference of arrival is only applicable in certain situations. As digital twin technology has advanced, it is possible to employ virtual models to support gas‐insulated switchgear (GIS) PD localization. To do this, we propose a meta‐learning (ML) network with the aid of digital twin for actual GIS PD localization. Firstly, a GIS digital twin model was established to acquire an auxiliary simulated sample library. Then, a temporal convolutional network is established to extract the discriminable features, effectively obtain the time dependence between features, and improve the accuracy of localization. Next, ML is adopted to quickly learn meta‐knowledge that can be applied across tasks, and the model's sensitivity to task changes is improved. Finally, the model is fine‐tuned through a limited number of samples from the target task, and high precise PD localization is achieved. The experimental results demonstrate that the ML has an average localization error of only 9.25 cm and a probability density rose to 93% within 20 cm, which is clearly superior to previous methods. [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]

In the context of modern agricultural production mode and domestic energy consumption, profound changes have taken place in agricultural and rural energy consumption, resulting in the demand for new technology development in various sectors of source, network, and load in rural energy systems. Agricultural energy internet (AEI) has promoted the development of renewable energy and agricultural electrification in villages. The construction of the AEI is crucial for achieving the synergistic development of agriculture, energy, and environment. The basic theory and key technologies of AEI are investigated and the prospects for the direction of agricultural energy technology are conducted. The research investigation shows that the AEI framework proposed by China Agricultural University is of great significance for realizing agricultural electrification and reducing agricultural carbon emissions. The article describes in detail the key data and characteristics of agricultural electrification loads. Digital twin technology and virtual power plant technology will be crucial to the construction of advanced AEI. [ABSTRACT FROM AUTHOR]

The process of identifying the optimal unknown variables for the creation of a precision fuel-cell performance forecasting model using optimization techniques is known as parameter identification of the proton exchange membrane fuel cell (PEMFC). Recognizing these factors is crucial for accurately forecasting and assessing the fuel cell's performance, as they may not always be included in the manufacturer's datasheet. Six optimization algorithms--the Walrus Optimizer (WO), the Tunicate Swarm Algorithm (TSA), the Harris Hawks Optimizer (HHO), the Heap Based Optimizer (HBO), the Chimp Optimization Algorithm (ChOA), and the Osprey Optimization Algorithm (OOA) were used to compute six unknown variables of a PEMFC. Also, the proposed WO method was compared with other published works' methods such as the Equilibrium Optimizer (EO), Manta Rays Foraging Optimizer (MRFO), Neural Network Algorithm (NNA), Artificial Ecosystem Optimizer (AEO), Slap Swarm Optimizer (SSO), and Vortex Search Approach with Differential Evolution (VSDE). Minimizing the sum squares error (SSE) between the estimated and measured cell voltages requires treating these six parameters as choice variables during optimization. The WO algorithm yielded an SSE of 1.945415603, followed by HBO, HHO, TSA, ChOA, and OOA. Given that WO accurately forecasted the fuel cell's performance, it is appropriate for the development of digital twins for fuel cell applications and control systems for the automobile industry. Furthermore, it was shown that the WO convergence speed was faster than the other approaches studied. [ABSTRACT FROM AUTHOR]

Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity. However, the digital twin model has the characteristics of high fidelity and scalability, and the digital twin surface can be obtained by a device with a scanning accuracy of 0.02mm to achieve the representation of the real surface of the workpiece. The surface defect detection system for digital twin models is proposed based on the improved YOLOv5 model in this paper. Firstly, the digital twin model of the workpiece is reconstructed by the point cloud data obtained by the scanning device, and the surface features with defects are captured. Subsequently, the training dataset is calibrated based on the defect surface, where the defect types include Inclusion, Perforation, pitting surface and Rolled-in scale. Finally, the improved YOLOv5 model with CBAM mechanism and BiFPN module was used to identify the surface defects of the digital twin model and compare it with the original YOLOv5 model and other common models. The results show that the improved YOLOv5 model can realize the identification and classification of surface defects. Compared with the original YOLOv5 model, the mAP value of the improved YOLOv5 model has increased by 0.2%, and the model has high precision. On the basis of the same data set, the improved YOLOv5 model has higher recognition accuracy than other models, improving 11.7%, 3.4%, 6.2%, 33.5%, respectively. As a result, this study provides a practical and systematic detection method for digital twin model surface during the intelligent production process, and realizes the rapid screening of the workpiece with defects. [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]

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the Bald Eagle Search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization --ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers. [ABSTRACT FROM AUTHOR]

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention. [ABSTRACT FROM AUTHOR]

This research paper explores the integration of novel technologies in hospital emergency evacuations, particularly in Operating Rooms (ORs) and Emergency Departments (EDs). It examines the application of advanced tools like simulation modeling, Building Information Modeling (BIM), Digital Twin technology, sensor data, and Artificial Intelligence (AI) to improve evacuation strategies in the building. The study extends to in-depth case studies for assessing the practicality of existing protocols, while also highlighting the critical importance of staff training and preparedness. Additionally, it addresses the ethical and psychological impacts of emergencies on patients and healthcare staff, underscoring the need for technology to be complemented with human-centered care. The paper concludes by emphasizing the ongoing necessity for innovative research in enhancing safety and operational resilience in healthcare emergency management. [ABSTRACT FROM AUTHOR]

The aerospace industry prioritises safety protocols to prevent accidents that can result in injuries, fatalities, or aircraft damage. One of the potential hazards that can occur while manoeuvring aircraft in and out of a hangar is collisions with other aircraft or buildings, which can lead to operational disruption and costly repairs. To tackle this issue, we have developed the Smart Hangar project, which aims to alert personnel of increased risks and prevent incidents from happening. The Smart Hangar project uses computer vision, LiDAR, and ultra-wideband sensors to track all objects and individuals within the hangar space. These data inputs are combined to form a real-time 3D Digital Twin (DT) of the hangar environment. The Active Safety system then uses the DT to perform real-time path planning, collision prediction, and safety alerts for tow truck drivers and hangar personnel. This paper provides a detailed overview of the system architecture, including the technologies used, and highlights the system's performance. By implementing this system, we aim to reduce the risk of accidents in the aerospace industry and increase safety for all personnel involved. Additionally, we identify future research directions for the Smart Hangar project. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation. [ABSTRACT FROM AUTHOR]

The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine's geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model's accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process. [ABSTRACT FROM AUTHOR]

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China's coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China's and even the world's coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified. [ABSTRACT FROM AUTHOR]

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations. [ABSTRACT FROM AUTHOR]

In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased. [ABSTRACT FROM AUTHOR]

A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data are integrated into the Unity3D platform to realize online diagnosis and updated with real-time working status data. Finally, an industrial test of the digital twin model is conducted, allowing for its comparison with other advanced methods in order to verify its accuracy and application feasibility. It was found that the accuracy of the proposed method for the entire reducer was 99.5%, higher than that of other methods based on individual components (e.g., 93.5% for bearings, 96.3% for gear shafts, and 92.6% for shells). [ABSTRACT FROM AUTHOR]

This paper considers what we know about the potential for disinformation in hydrological data when used for the evaluation of hydrological models. This will generally arise from epistemic uncertainties associated with hydrological observations, particularly from nonstationary or extrapolated rating curves for discharges, and poor rainfall and snowmelt information when interpolated over basin areas. Approaches based on information theory are not well suited to consideration of such epistemic uncertainties in model evaluation and an alternative approach based on setting limits of acceptability independent of any model runs is suggested. This allows for both the rejection of all models tried, and for acceptability of models across different model structures and parameter sets. The paper concludes with some suggestions for future research on defining disinformative data for both point and spatial observables, studying model failures, and defining new observations with a view to having the greatest impact on reducing model uncertainties. [ABSTRACT FROM AUTHOR]

Despite the recognized gut-brain axis link, natural variations in microbial profiles between patients hinder definition of normal abundance ranges, confounding the impact of dysbiosis on infant neurodevelopment. We infer a digital twin of the infant microbiome, forecasting ecosystem trajectories from a few initial observations. Using 16S ribosomal RNA profiles from 88 preterm infants (398 fecal samples and 32,942 abundance estimates for 91 microbial classes), the model (Q-net) predicts abundance dynamics with R2 = 0.69. Contrasting the fit to Q-nets of typical versus suboptimal development, we can reliably estimate individual deficit risk (Md) and identify infants achieving poor future head circumference growth with -76% area under the receiver operator characteristic curve, 95% ± 1.8% positive predictive value at 98% specificity at 30 weeks postmenstrual age. We find that early transplantation might mitigate risk for -45.2% of the cohort, with potentially negative effects from incorrect supplementation. Q-nets are generative artificial intelligence models for ecosystem dynamics, with broad potential applications. [ABSTRACT FROM AUTHOR]

Urban areas, with their dense populations and complex infrastructures, are increasingly susceptible to various risks, including environmental challenges and infrastructural strain. This paper delves into the transformative potential of digital twins—virtual replicas of physical entities—for mitigating these risks. It specifically explores the role of digital twins in reducing disaster risks, such as those posed by earthquakes and floods, through a comprehensive bibliometric-based literature review. Digital twins could contribute to risk reduction by combining data analytics, simulation, and predictive modeling by creating virtual replicas of physical entities and integrating real-time data streams to better address and manage risks in urban environments. In detail, they can help city planners and decision-makers analyze complex urban systems, simulate potential scenarios, and predict potential outcomes. This proactive approach allows both the identification of vulnerabilities and better implementation of targeted mitigation strategies to enhance urban resilience and sustainability. More informed decisions can be made relying on simulations, and it can also be possible to optimize resource allocation and better respond to emerging challenges. This work reviews the key publications in this domain, with the aim of finding relevant papers that can be useful to urban planners and policy-makers. The paper concludes by discussing the broader implications of these findings and identifying challenges in the widespread adoption of digital twin technology, including data privacy concerns and the need for interdisciplinary collaboration. It also outlines prospective avenues for future research in this emerging field. [ABSTRACT FROM AUTHOR]

To facilitate the integration of autonomous unmanned air vehicles (UAVs) in day-to-day life, it is imperative that safe navigation can be demonstrated in all relevant scenarios. For UAVs using a navigational protocol driven by artificial neural networks, training and testing data from multiple environmental contexts are needed to ensure that bias is minimised. The reduction in predictive capacity when faced with unfamiliar data is a common weak point in trained networks, which worsens the further the input data deviates from the training data. However, training for multiple environmental variables dramatically increases the man-hours required for data collection and validation. In this work, a potential solution to this data availability issue is presented through the generation and evaluation of photo-realistic image datasets from a simulation of 3D-scanned physical spaces which are theoretically linked in a digital twin (DT) configuration. This simulation is then used to generate environmentally varied iterations of the target object in that physical space by two contextual variables (weather and daylight). This results in an expanded dataset of bicycles that contains weather and time-varied components of the same images which are then evaluated using a generic build of the YoloV3 object detection network; the response is then compared to two real image (night and day) datasets as a baseline. The results reveal that the network response remained consistent across the temporal axis, maintaining a measured domain shift of approximately 23% between the two baselines. [ABSTRACT FROM AUTHOR]

The use of a digital twin as an enabling technology for industry 4.0 provides control systems engineers with novel tools for modelling, designing, and controlling complex systems, providing a deep understanding of the physical asset based not only on its physics but also the real system's response. It is particularly critical for uniformity temperature control applications, where providing a reasonable model of the system's diffusion is always affected by the physical behavior of the system's components required for heating, cooling, or power distribution. In this paper, a digital twin is used to represent a multivariable thermoelectric system employed for temperature uniformity distribution control with potential applications in semiconductor manufacturing. The modelling employs a five-step methodological framework consisting of the stages: target system definition, system description, multiphysics and data-driven simulation, behavioral matching, and implementation to represent the system's temperature distribution accurately. The temperature distribution is measured using an infrared thermal camera to perform model behavioral matching on heating and cooling temperature uniformity applications. The obtained results indicated that using digital twins not only increases the accuracy of the system's representation but can also provide the system with novel information that can be leveraged for the design and implementation of smart control systems. [ABSTRACT FROM AUTHOR]

Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system. [ABSTRACT FROM AUTHOR]

Digital twins based on real-world scenarios are heavily reliant on extensive on-site data, representing a significant investment in information technology. This study aims to maximize the capabilities of visual sensors, like cameras in controlled-environment agriculture, by acquiring more target-specific information at minimal additional cost. This approach not only reduces investment but also increases the utilization rate of existing equipment. Utilizing YOLOv7, this paper introduces a system with rotatable pan-tilt cameras for the comprehensive monitoring of large-scale greenhouse ventilation systems. To mitigate the computational load on edge servers at greenhouse sites caused by an abundance of video-processing tasks, a Region of Interest (ROI) extraction method based on tracking is adopted. This method avoids unnecessary calculations in non-essential areas. Additionally, we integrate a self-encoding approach into the training phase, combining object detection and embedding to eliminate redundant feature extraction processes. Experimental results indicate that ROI extraction significantly reduces the overall inference time by more than 50%, and by employing LSTM to classify the state of the fan embedding sequences, a 100% accuracy rate was achieved. [ABSTRACT FROM AUTHOR]

Concert halls have led to increasingly complex spaces that cannot be thought of as static 'containers' anymore. This complexity makes them viable to be launched towards industry 4.0 and to be considered a function of the activities that they can provide during their life cycle. They are characterized by dynamic objects that contain sophisticated sub-systems and add to the capability to influence both environmental variables and user behavior. This article explains an adaptive concert hall at an early stage, in which a network of sensors that gather real-time data on environmental factors such as temperature, air humidity and air velocity are considered, focusing on their direct and indirect intercorrelations with the acoustic quantities to optimize the room acoustic response. The proposed methodology is controlled by a digital twin (DT) based on building information modeling (BIM), integrated with sensors, actuators, and acoustic measurements and algorithms. By analyzing the data, algorithms identify patterns, and an autonomous fine-tune setting is achieved, including the novelty for which a natural variable acoustic field becomes possible during a musical execution without the use of any electroacoustic system support. The hall becomes a natural active instrument to be included in the composer's score. A case study is presented. [ABSTRACT FROM AUTHOR]

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System. [ABSTRACT FROM AUTHOR]

In the era of intelligence and automation, robots play a significant role in the field of automated welding, enhancing efficiency and precision. However, challenges persist in scenarios demanding complexity and higher precision, such as low welding planning efficiency and inaccurate weld seam defect detection. Therefore, based on digital twin technology and kernel correlation filtering algorithm, a welding tracking model is proposed. Firstly, the kernel correlation filtering algorithm is used to train the filter on the first frame of the collected image, determine the position of image features in the region, extract histogram features of image blocks, and then train the filter using ridge regression to achieve welding trajectory tracking. Additionally, an intelligent weld seam detection model is introduced, employing a backbone feature network for feature extraction, feature fusion through a feature pyramid, and quality detection of weld seams through head classification. During testing of the tracking model, the maximum tracking error is -0.232 mm, with an average absolute tracking error of 0.08 mm, outperforming other models. Comparatively, in tracking accuracy, the proposed model exhibits the fastest convergence with a precision rate of 0.845, surpassing other models. In weld seam detection, the proposed model excels with a detection accuracy of 97.35% and minimal performance loss at 0.023. In weld seam quality and melt depth error detection, the proposed model achieves errors within the range of -0.06 mm, outperforming the other two models. These results highlight the outstanding detection capabilities of the proposed model. The research findings will serve as technical references for the development of automated welding robots and welding quality inspection. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments. [ABSTRACT FROM AUTHOR]

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

Automated disassembly is increasingly in focus for Recycling, Re-use, and Remanufacturing (Re-X) activities. Trends in digitalization, in particular digital twin (DT) technologies and the digital product passport, as well as recently proposed European legislation such as the Net Zero and the Critical materials Acts will accelerate digitalization of product documentation and factory processes. In this contribution we look beyond these activities by discussing digital information for stakeholders at the Re-X segment of the value-chain. Furthermore, we present an approach to automated product disassembly based on different levels of available product information. The challenges for automated disassembly and the subsequent requirements on modeling of disassembly processes and product states for electronic waste are examined. The authors use a top-down (e.g., review of existing standards and process definitions) methodology to define an initial data model for disassembly processes. An additional bottom-up approach, whereby 5 exemplary electronics products were manually disassembled, was employed to analyze the efficacy of the initial data model and to offer improvements. This paper reports on our suggested informal data models for automatic electronics disassembly and the associated robotic skills. [ABSTRACT FROM AUTHOR]

With the widespread application of computer numerical control (CNC) machine tools in high-precision manufacturing, their machining accuracy has garnered significant attention. Thermal errors generated during machining processes are one of the primary factors affecting accuracy. Although thermal error compensation technologies have been extensively researched and implemented in practice to improve machine accuracy, existing methods still face limitations in the dynamic thermal behavior analysis and adaptability in practical applications. This paper delves into the thermal error compensation technologies for CNC machine tools, exploring measurement, prediction, and compensation methods. Firstly, it enhances the accuracy and efficiency of measurements by optimizing the layout of temperature measurement points through a detailed analysis of the mechanisms of thermal error generation. Secondly, it introduces a prediction framework based on digital twin technology to accurately simulate and predict the thermal behavior of machine tools. Lastly, it employs an optimized back propagation neural network (BPNN) for intelligent modeling of thermal errors, thereby improving the prediction accuracy and response speed. These studies not only aid in improving the design and operation of machine tools but also provide theoretical and technical support for high-precision machining. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to calibrating the digital twin of a laboratory mill used for copper ore milling. By integrating computer vision techniques for real-time data extraction and employing DualSPHysics simulations for various milling scenarios, including balls only, balls with ore, and balls with slurry, we achieve a high degree of accuracy in matching the digital twin's behavior with actual mill operations. The calibration process is detailed for mills with three different diameters, highlighting the adjustments in simulation parameters necessary to account for the absence of ore. Understanding the dynamics between the suspension within the mill and the operation of the grinders is crucial for the future improvement of the grinding process. This knowledge paves the way for optimizing the process, not only in terms of the quality of the end product but primarily in terms of energy efficiency. A profound understanding of these interactions will enable engineers and technologists to design mills and grinding processes in a way that maximizes efficiency while minimizing energy consumption. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) is increasingly employed in various domains, notably enhancing learning and experiences in cultural heritage (CH). This study examines the effects of gamified and non-gamified VR experiences within virtual museum environments, highlighting the concept of a digital twin and its focus on cultural heritage. It explores how these VR modalities affect visitor motivation, engagement, and learning outcomes. For this purpose, two versions were developed: a gamified virtual reality version incorporating interactive gaming elements like achievements, profiles, leaderboards, and quizzes and a non-gamified virtual reality version devoid of these elements. This study, using an experimental design with 76 participants (38 in each group for the gamified and non-gamified experiences), leverages the Wieng Yong House Museum's digital twin and its fabric collection to assess the educational and experiential quality of virtual museum visits. The findings indicate that while gamification significantly boosts the reward dimension of visitor engagement, its influence is most pronounced in the effort dimension of motivation; however, its impact on learning outcomes is less marked. These insights are instrumental for integrating VR and gamification into museum environments. [ABSTRACT FROM AUTHOR]

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and personal health knowledge graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange while ensuring high levels of accuracy in data representation and health insights. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it, enhancing the precision and accuracy of the care provided. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As a proof of concept, we have demonstrated the versatility of our digital twins by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes. [ABSTRACT FROM AUTHOR]

In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised workflow tailored for the creation of Level of Detail 3 (LOD3) models. Our research methodology capitalizes on the integration of Apple LiDAR technology alongside photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAVs). The proposed process unfolds with the transformation of point cloud data into Industry Foundation Classes (IFC) models, which are subsequently refined into LOD3 Geographic Information System (GIS) models leveraging the Feature Manipulation Engine (FME) workbench 2022.1.2. This orchestrated synergy among Apple LiDAR, UAV-derived photogrammetric point clouds, and the transformative capabilities of the FME culminates in the development of precise LOD3 GIS models. Our proposed workflow revolutionizes this landscape by integrating multi-source point clouds, imbuing them with accurate semantics derived from IFC models, and culminating in the creation of valid CityGML LOD3 buildings through sophisticated 3D geometric operations. The implications of this technical innovation are profound. Firstly, it elevates the capacity to produce intricate infrastructure models, unlocking new vistas for modeling digital twins. Secondly, it extends the horizons of GIS applications by seamlessly integrating enriched Building Information Modeling (BIM) components, thereby enhancing decision-making processes and facilitating more comprehensive spatial analyses. [ABSTRACT FROM AUTHOR]

This article introduces a new approach for validating directional overcurrent protection schemes in ring-topology electrical distribution systems with distributed energy resources (DERs). The proposed protection scheme incorporates overcurrent and directional functions and addresses DER-induced challenges such as variable short circuit levels. This study employs real-time and offline simulations to evaluate the performance of the protection coordination scheme using a digital twin under DER-supplied loads. The utilization of digital twins offers the possibility to simulate different scenarios, providing real-time responses to dynamic changes and allowing for informed decision-making in response to disturbances or faults. This study aims to present a new approach to validate the performance of the proposed protection scheme when the load is entirely supplied by DERs, highlighting issues such as false trips and protection system blindness resulting from changes in short circuit currents. The results show a breakdown in the coordination of the protection scheme during the fault conditions, demonstrating the effectiveness of digital twins in validating the protection scheme's performance. Performing an analysis in the electromagnetic transient (EMT) domain improves the validation and refines the results. [ABSTRACT FROM AUTHOR]

This paper describes a novel architecture that aims to create a template for the implementation of an IT platform, supporting the deployment and integration of the different digital twin subsystems that compose a complex urban intelligence system. In more detail, the proposed Smart City IT architecture has the following main purposes: (i) facilitating the deployment of the subsystems in a cloud environment; (ii) effectively storing, integrating, managing, and sharing the huge amount of heterogeneous data acquired and produced by each subsystem, using a data lake; (iii) supporting data exchange and sharing; (iv) managing and executing workflows, to automatically coordinate and run processes; and (v) to provide and visualize the required information. A prototype of the proposed IT solution was implemented leveraging open-source frameworks and technologies, to test its functionalities and performance. The results of the tests performed in real-world settings confirmed that the proposed architecture could efficiently and easily support the deployment and integration of heterogeneous subsystems, allowing them to share and integrate their data and to select, extract, and visualize the information required by a user, as well as promoting the integration with other external systems, and defining and executing workflows to orchestrate the various subsystems involved in complex analyses and processes. [ABSTRACT FROM AUTHOR]