Digital Twin Platform Research Articles

Digital twin water conservancy early warning system based on physical level and numerical simulation in intelligent water conservancy construction

Abstract. In 2023, the Central Committee of the Communist Party of China and The State Council issued the Overall Layout Plan for the Construction of Digital China, emphasizing the construction of a smart water conservancy system with digital twin basins as the core. The Guangxi Zhuang Autonomous Region government has clearly proposed to actively build a "digital twin Pinglu Canal", and attaches great importance to the construction of digital twin platform application services during the construction of the Western land-Sea New Passage (Pinglu) canal. Aiming at the problems such as unsatisfactory early warning effect, inaccurate data and imprecise model of existing smart water conservancy technology, this paper proposes a digital twin system for disaster early warning based on physical level and numerical simulation technology, establishes a visual digital water conservancy model and digital twin smart water conservancy cloud platform, and combines multi-physics field and multi-dimensional coupling algorithm to respond to technical pain points. To achieve scientific and accurate monitoring and intelligent management, and contribute to the construction of digitally empowered Pinglu Canal.

UAV Geo-Localization Dataset and Method Based on Cross-View Matching.

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual-real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric.

Enhancing internal supply chain management in manufacturing through a simulation-based digital twin platform

Digital Twin (DT) technology is profoundly changing the manufacturing landscape and supply chain management with its ability to create real-time digital replicas of physical processes, allowing for enhanced monitoring and optimized decision-making. However, the analysis of scientific literature reveals that further efforts are needed to spread the use of Industry 4.0 technologies, in the specific context of Internal Supply Chains (ISCs). The main aim of this study is to design, develop test and validate a multi-plant Simulation-Based DT production planning platform for ISCs management. A modular architecture is adopted, and the focus is on a Simulation-Based Digital Twin module, which uses an object-oriented structure and enables what-if analyses, involving several scheduling rules and ISC configurations. The proposed solution ensures flexibility and scalability, two crucial features in a constantly evolving market environment. The platform is tested and validated through a case study, involving a corporate group in the Oil & Gas manufacturing sector, which needs to improve the ISC performance, under a Make-To-Order production strategy. The comparison with a baseline scenario, where the platform is not adopted, shows that the proposed approach can significantly reduce the average flow time, the average tardiness, the number of late orders. This study has important practical implications because enables proactive and smart decision-making, aimed at resource optimization and continuous improvement, through predictive analytics and scenario analysis.

Leveraging Digital Twins and Intrusion Detection Systems for Enhanced Security in IoT-Based Smart City Infrastructures

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems.

Optimization method of cable structure demolition driven by digital twin evolution model

The large-span cable structure has the problems of complex component connection, cumbersome demolition process and difficult process coordination, which lead to high construction safety risk and low efficiency. Moreover, the optimization of the construction process scheme is difficult, and the convergence and visualization of the finite element analysis are insufficient, which hinders the construction efficiency. This study proposes a cable structure demolition optimization method driven by the digital twin (DT) evolution model. From the perspective of virtual-real mapping, the DT evolution process of cable structure demolition is clarified. The virtualization method of physical demolition process is formed by integrating visual modeling, simulation modeling and data modeling. Taking full account of the temporal and spatial evolution of the demolition process, a DT evolution model of the demolition process is established. Starting from the information flow, component flow, control flow and their relationship, the problems existing in the process of demolition execution are analyzed. Driven by the twin evolution process, a data mapping method for the demolition process is proposed. The safety risk is characterized by the mechanical properties (MPs) of the structure, and the expression language of the data association relationship is established. The particle swarm optimization back propagation neural network (PSO-BPNN) is improved, and an intelligent analysis method of MPs in the whole process of demolition is proposed. The best construction scheme is obtained by integrating the changes in MPs between the various demolition steps. The information of the demolition evolution process is divided into functional modules, and a DT platform is developed to efficiently guide the actual construction. Taking the demolition process of a cable truss experimental model as an example, the effectiveness of the proposed method is verified. The research results show that the DT evolution model can accurately map the actual demolition process. The deep learning method analyzes the mechanical properties of the structure and obtains the best construction scheme quickly and accurately. The development and application of the twin platform improves the degree of visualization and realizes the efficient guidance of the virtual model to the actual construction.

12524 Predictive Factors For Type 2 Diabetes Remission: Insights From A Digital Twin Intervention Program At 18 Months

Abstract Disclosure: P. Shamanna: None. M. Dharmalingam: None. A. Vadavi: None. A. Keshavamurthy: None. S. Bhonsley: None. M. Thajudeen: None. A. Balasubramanian: None. S.R. Joshi: None. Background and aims: Digital twin (DT) platform uses Artificial Intelligence (AI) and Internet of Things, to integrate multi-dimensional data for precision nutrition and health recommendations via the mobile app. Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Evidence suggests that remission is more likely in individuals with early type 2 diabetes (T2D) who are not using insulin therapy and who can engage in significant weight loss. We evaluated the predictive value of baseline parameters on T2D remission, including duration of diabetes, HbA1c levels, diabetic medications as drug count, and CGM metrics, with the objective to improvise the patient selection strategies for remission Methods We analyzed T2D patients (n=209) who completed 18 months of digital twin intervention. Potential predictors were examined by logistic regression analyses, with model performance evaluated by accuracy, precision, recall, and Area under the Receiver Operating Characteristic Curve (AUC-ROC) metrics. Results The model achieved an accuracy of 78.57%, precision of 79.41%, recall of 93.10%, and AUC-ROC of 0.745. Significant inverse associations with remission were observed for the duration of diabetes (OR: 0.74), baseline HbA1c (OR: 0.76), and the number of anti-diabetic medications as oral drugs (OR: 0.77), indicating decreased odds of remission with increasing values. Conversely, positive associations were found for Time Below Range 2 at baseline (OR: 1.22) and HOMA2IR at baseline (OR: 1.24), suggesting increased odds of remission with higher values in these metrics. We describe the Receiver Operating Characteristic (ROC) curve for the diabetes remission prediction model, with the area under the curve (AUC) indicating the model's discriminatory ability. The duration of diabetes emerged as the most significant negative predictor, followed by baseline HbA1c and the number of diabetic drugs, while counterintuitive findings like the positive association of baseline HOMA2IR with remission suggest areas for further investigation. Conclusion We identified baseline parameters significantly influencing the likelihood of T2D remission in people undergoing digital twin intervention. The odds ratios indicate the effect sizes of these predictors, highlighting the importance of personalized, data-driven treatment approaches that potentially enhance remission outcomes. Further validation of these findings in larger external cohorts would be useful. Presentation: 6/1/2024

12600 Predictors Of Diabetes Remission: The Impact Of Early Intervention Through A Digital Twin Platform

Abstract Disclosure: S.R. Joshi: None. M. Dharmalingam: None. A. Vadavi: None. A. Keshavamurthy: None. S. Bhonsley: None. M. Thajudeen: None. A. Balasubramanian: None. M. Panandikar: None. P. Shamanna: None. Introduction and Objective: Digital twin (DT) platform uses Artificial Intelligence (AI) and Internet of Things, to integrate multi-dimensional data for precision nutrition and health recommendations via the mobile app. We aimed to identify the pre-intervention anthropometric and biochemical parameters that predict diabetes remission (HbA1c < 6.5% without medications for over 3 months). Methods: 209 patients with T2D completed 540 days (18 months) of DT intervention. Results: 129 (61.7%) achieved remission of T2DM (R group). At baseline, patients in R group, compared with patients in the Non-remission Group (group NR) (n=80) had shorter duration of diabetes (yrs) (R: 2.9±2.6, 95% CI 2.5 to 3.4) vs. (NR: 4.7±2.2, 95% CI 4.2 to 5.2), p= <0.0001; lower baseline HbA1c % (R: 8.6±1.9, 95% CI 8.3 to 9) vs. (NR: 9.5±1.7, 95% CI 9.1 to 9.9), p=0.0008; lower estimated HbA1c (eA1C) (R: 7.6±2.2, 95% CI 7.2 to 8) vs. (NR: 8.9±2.3, 95% CI 8.4 to 9.4), p= <0.0001; lower glucose management indicator (GMI) % (R: 7.5±1.6, 95% CI 7.2 to 7.8) vs. (NR: 8.4±1.8, 95% CI 7.9 to 8.8), p=0.0006; ; lower Time Above Range level 1 (TAR1 %; target 180-250 mg/dl < 25% time) (R:21.3±17.6, 95% CI 18.2 to 24.4) vs. (NR: 31.2±17.2, 95% CI 27.3 to 35), p<0.0001; lower Time Above Range level 2 (TAR2 %; target >250 mg/dl < 5% time) (R: 15±24.5, 95% CI 10.8 to 19.3) vs. (NR: 24.2±26.3, 95% CI 18.2 to 29.9), p=0.01; higher Time In Range (TIR; target 70-180 mg/dl >70% time) (R: 59.5±31.5, 95% CI 54 to 65) vs. (NR: 43.3±30.9, 95% CI 36.4 to 50.2), p=0.0003; lower fasting plasma glucose (mg/dl) (R: 161±53.4, 95% CI 151.7 to 170.3) vs. (NR: 186.4±69.3, 95% CI 171 to 201.8), p=0.003; higher HOMA2B (%) (R: 56.6±31.7, 95% CI 51.1 to 62.1) Vs (NR: 43.6±27.5, 95% CI 37.4 to 49.6), p=0.002; and comparable fasting insulin (mIU/L) (R: 13.9±10.1, 95% CI 12.2 to 15.7) vs. (NR: 11.8±7.2, 95% CI 10.2 to 13.4), p=0.09 (ns), HOMA2IR (%) (R:1.9±0.93, 95% CI 1.8 to 2.1) vs (NR:1.8±0.84, 95% CI 1.6 to 2), p=0.33 (ns); weight (kg) (R:78.9±14.4, 95% CI 76.2 to 81.6) vs (NR:78.1±12.8, 95% CI 75.3 to 81), p=0.71 (ns). The mean baseline anti-diabetic drug count was lower in R (1.4) Vs NR (2.1). At 540 days the change was as follows: comparable Time Below Range1 - TBR1 %(< 70-54 mg/dl) < 4% (R: 3.5±14.9, 95% CI 0.92 to 6.1) Vs (NR: 1.9±8.3, 95% CI 0.07 to 3.8), p=0.38 (ns); lower HbA1c (R: 5.9±0.35, 95% CI 5.8 to 6) Vs (NR: 7.2±1.1, 95% CI 7 to 7.5), p<0.0001. Conclusion: Shorter diabetes duration, lower baseline HbA1c, eA1C, GMI, and TAR, higher TIR, lower fasting plasma glucose, and higher beta-cell function are key predictors of diabetes remission. Presentation: 6/3/2024

Digital twin technology (DTT) in a developing construction industry: evaluation of the awareness and usage among professionals in Nigeria

Technological innovations play a crucial role in project delivery across all sectors of the economy. This research seeks to assess the awareness of digital twin technology (DTT) among construction industry professionals to promote the sustainable completion of construction projects. A quantitative approach was adopted by using a survey questionnaire with convenience sampling. Questionnaires were self-administered to gather information about the industry professionals’ awareness and utilization of DTT in construction projects. Mean Item Score (MIS), Standard Deviation (SD), Spearman, and Kruskal-Wallis tests were used to analyze the collected data. Professionals within the construction industry have demonstrated a high level of familiarity with various technological advancements with computer systems, Wi-Fi, 2D Graphics, Vertex Digital twin platform, and GPS identified as the most commonly utilized tools in the field. Spearman correlation analysis revealed a significant relationship between DTT awareness and usage levels. The study provided valuable information on the present awareness and DTT utilization within the construction sector. It proposed methods through which professionals can familiarize themselves with empowering tools to enhance productivity and performance within the industry.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 1. Calibration workflow

In this work, a high-fidelity digital twin was developed to support the design and testing of control strategies for drug product manufacturing via direct compression. The high-fidelity digital twin platform was based on typical pharmaceutical equipment, materials, and direct compression continuous processes. The paper describes in detail the material characterization, the Discrete Element Method (DEM) model and the DEM model parameter calibration approach and provides a comparison of the system’s response to the experimental results for stepwise changes in the API concentration at the mixer inlet. A calibration method for a cohesive DEM contact model parameter estimation was introduced. To assure a correct prediction for a wide range of processes, the calibration approach contained four characterization experiments using different stress states and different measurement principles, namely the bulk density test, compression with elastic recovery, the shear cell, and the rotating drum. To demonstrate the sensitivity of the DEM contact parameters to the process response, two powder characterization data sets with different powder flowability were applied. The results showed that the calibration method could differentiate between the different material batches of the same blend and that small-scale material characterization tests could be used to predict the residence time distribution in a continuous manufacturing process.

Construction of data base containing flow field information under different blade placement of pumping station in digital twin

Abstract The data base in digital twin technology includes various data such as the structure, performance, and operating status of the physical object. And the data base is the key point for the construction of the digital twin system. However, the data bases of different digital twin systems usually contain different information. In this paper, the flow state of the pumping station under different angles of four blades is selected as the research object. The research methods in this paper mainly include Latin hypercube sampling, numerical simulation and neural network prediction. The Latin hypercube sampling is used to select the typical Angle difference model. The numerical simulation is used to obtain flow field information. The neural network prediction method is used to construct a complete data base. The study incorporates an actual large-scale mixed-flow pump station to head, efficiency, and shaft power as the prediction targets. It utilizes the different angle difference data of ten sets of four blades as the training set, forty sets of remaining samples as the experiment set, and employs the backpropagation neural network method for constructing the prediction network. By changing the experiment set and test set, the method can efficiently complete the construction of data base including actual test and numerical simulation, which provides a solid foundation for the construction of digital twin platform.

Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

A digital twin model of urban utility tunnels and its application

Background Multiple pipelines in utility tunnels may lead to various accidents and serious social impact. In the era of digitalization, how to better model the operation of a utility tunnel, dynamically predict the accident evolutions, and support corresponding decision-makings are essential issues. Methods In this study, a CFD-based digital twin framework for accidents in utility tunnels is proposed. First, Kalman filtering is applied to correct the parameter drift of sensors used for long-term monitoring. A data interaction system is then developed based on Internet of Things (IOT) and OPC Unified Architecture (OPC UA) to comprehensively manage data transmission within the utility tunnel. Subsequently, a natural gas leakage prediction model is developed to enable the efficient prediction of the spatial and temporal distribution in the case of leakage. Finally, these components are integrated for visualization in a digital twin platform for natural gas leakage in utility tunnels. Additionally, numerical simulations are employed to validate of the proposed method. Results The utility tunnel data transmission system based on IoT and OPC UA proposed in this paper is case-validated. By comparing the simulation results at 10 s, 20 s, 30 s, and 40 s, the model accurately predicts the methane concentration at the leak position after 10 seconds and maintains acceptable accuracy thereafter. The simulation results of different cases are introduced to verify the reliability of the risk indicator proposed in this paper, which increases with the leakage rate. Finally, A process for visualizing numerical simulation is proposed into a digital twin. Conclusions The proposed predictive digital twin technology facilitates the rapid risk assessment of and emergency management of natural gas accidents in utility tunnels. Based on the results of predictive model, a risk indicator is introduced to evaluate the natural gas accidents.

Building Digital Twins to Overcome Digitalization Barriers for Automating Construction Site Management

Construction sites are highly unpredictable environments involving a wide variety of stakeholders with complex information exchanges, which lead to the well-known inefficiencies and unproductivity of the construction sector. The adoption of Building Digital Twins (BDT) in the construction site is a promising solution to this issue, by automating data acquisition and knowledge extraction processes and providing what-if scenario simulation capabilities. Furthermore, the current research sets the principles to define, replicate, and scale-up the architecture of a Building Digital Twin Platform (BDTP), conceived as a scalar ecosystem, which allows to seamlessly manage on-site construction processes, integrating cross-cutting domains for the construction site optimization (Progress monitoring, Quality control, Operational Health and Safety, Equipment control, and Production planning). The starting point of the research is a comprehensive diagnosis of on-site process inefficiencies and the barriers to its digitalization leading to the user requirements, which have been underpinned by questionnaires and interviews addressed within an open innovation user-centered approach around Living Labs. The research has been conceived following the Design Science Research (DSR) methodology and based on the Plan-Do-Check-Act (PDCA) analysis for the continuous improvement of the construction process. By means of the adoption of the standard Business Process Model and Notation (BPMN), based on the BDTP architecture, the research has resulted in BPMN workflows stemmed from the Digital Twin (DT) where the DT itself is an actor in a service-oriented data-exchange workflow. Moreover, the use of a BDTP can pave the way for the transition from user-driven construction management to hybrid management, coexisting with both human and digital actors and merging expert knowledge with artificial intelligence techniques.

Study on Rapid Simulation of the Pre-Cooling Process of a Large LNG Storage Tank with the Consideration of Digital Twin Requirements

The pre-cooling of a large LNG storage tank involves complex phenomena such as heat transfer, low-temperature flow, gas displacement, and vaporization. The whole pre-cooling process could take up to 50 h. For large-scale, full-capacity storage tanks, it is particularly important to accurately control the pre-cooling temperature. Digital twin technology can characterize and predict the full life cycle parameters from the beginning of pre-cooling development to the end and even the appearance of damage in real time. The construction of a digital twin platform requires a large number of data samples in order to predict the operating state of the device. Therefore, a simulation method with high computational efficiency for the pre-cooling process of LNG tanks is of great importance. In this paper, the mixture model and discrete phase model (DPM) are applied to simulate the pre-cooling process of a large LNG full-capacity tank. Following Euler–Lagrange, the DPM greatly simplifies the solution process. Compared with the experimental results, the maximum error of the DPM simulation results is less than 11%. Such a highly efficient simulation method for the large LNG full-capacity storage tank can make it possible to build the digital twin platform that needs hundreds of data model samples.

Research on key technology of smart grid based on holographic data

Abstract With the increasing scale and complexity of power projects, designers face more and more challenges in terms of design quality and efficiency. In order to improve design quality and reduce human-caused errors, utilizing technologies such as big data and strong artificial intelligence, this manuscript proposes a key technology for smart grid based on holographic data. Firstly, the concept and key technologies of holographic intelligent design were proposed. Then, the overall plan of holographic smart grid was constructed, and the implementation methods of the main functions of holographic smart grid were elaborated. Finally, the construction process of the holographic digital twin platform was explored.

Mitigating Security Threats for Digital Twin Platform: A Systematic Review with Future Scope and Research Challenges

Mitigating Security Threats for Digital Twin Platform: A Systematic Review with Future Scope and Research Challenges

Design of Digital Twin Platform for Hypertube Core Facilities

Design of Digital Twin Platform for Hypertube Core Facilities

Construction of a digital twin of chronic graft vs. host disease patients with standard of care

There is an unmet medical need for new clinical trials to evaluate novel therapies in chronic graft-versus-host disease (cGvHD). Disease rarity, ethical issues regarding placebo arms, time, and cost impede clinical trial conduct. Digital twin (DT) technology enables virtual clinical trial arm construction using historical data, circumventing these obstacles. We evaluated the feasibility of constructing a DT trial arm using a large database of real-world clinical trial data and performed an efficacy assessment of a standard-of-care (SOC) drug to examine agreement with literature data. We constructed a flGvHD DT cohort (cGvHD patients at first-line treatment) (2042 patients; 32 cohorts) using the Trial Accelerator™ Digital Twin platform and derived an SOC arm from this cohort (flGvHD DT SOC cohort) (438 patients; eight cohorts); we analyzed the efficacy of SOC (prednisone) (overall response rate (ORR)) at six months. Our analysis results are in agreement with literature: flGvHD DT: disease onset time: 7.58 months post-allogeneic hematopoietic cell transplantation; most used graft source: peripheral blood stem cells; flGvHD DT SOC: ORR at six months for prednisone: 52.7%. It is feasible to construct a DT cohort using existing clinical trial data; a DT SOC arm can potentially replace a control arm in clinical trials.

The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.

Volumetric Muscle Loss (VML) injuries are characterized by significant loss of muscle mass, usually due to trauma or surgical resection, often with a residual open wound in clinical settings and subsequent loss of limb function due to the replacement of the lost muscle mass with non-functional scar. Being able to regrow functional muscle in VML injuries is a complex control problem that needs to override robust, evolutionarily conserved healing processes aimed at rapidly closing the defect in lieu of restoration of function. We propose that discovering and implementing this complex control can be accomplished by the development of a Medical Digital Twin of VML. Digital Twins (DTs) are the subject of a recent report from the National Academies of Science, Engineering and Medicine (NASEM), which provides guidance as to the definition, capabilities and research challenges associated with the development and implementation of DTs. Specifically, DTs are defined as dynamic computational models that can be personalized to an individual real world "twin" and are connected to that twin via an ongoing data link. DTs can be used to provide control on the real-world twin that is, by the ongoing data connection, adaptive. We have developed an anatomic scale cell-level agent-based model of VML termed the Wound Environment Agent Based Model (WEABM) that can serve as the computational specification for a DT of VML. Simulations of the WEABM provided fundamental insights into the biology of VML, and we used the WEABM in our previously developed pipeline for simulation-based Deep Reinforcement Learning (DRL) to train an artificial intelligence (AI) to implement a robust generalizable control policy aimed at increasing the healing of VML with functional muscle. The insights into VML obtained include: 1) a competition between fibrosis and myogenesis due to spatial constraints on available edges of intact myofibrils to initiate the myoblast differentiation process, 2) the need to biologically "close" the wound from atmospheric/environmental exposure, which represents an ongoing inflammatory stimulus that promotes fibrosis and 3) that selective, multimodal and adaptive local mediator-level control can shift the trajectory of healing away from a highly evolutionarily beneficial imperative to close the wound via fibrosis. Control discovery with the WEABM identified the following design principles: 1) multimodal adaptive tissue-level mediator control to mitigate pro-inflammation as well as the pro-fibrotic aspects of compensatory anti-inflammation, 2) tissue-level mediator manipulation to promote myogenesis, 3) the use of an engineered extracellular matrix (ECM) to functionally close the wound and 4) the administration of an anti-fibrotic agent focused on the collagen-producing function of fibroblasts and myofibroblasts. The WEABM-trained DRL AI integrates these control modalities and provides design specifications for a potential device that can implement the required wound sensing and intervention delivery capabilities needed. The proposed cyber-physical system integrates the control AI with a physical sense-and-actuate device that meets the tenets of DTs put forth in the NASEM report and can serve as an example schema for the future development of Medical DTs.

Vulnerability analysis of super high-rise building security system based on Bayesian network and digital twin technology

Recent years have seen the increasing need to prevent illegal intrusions in super high-rise buildings. To improve the security system of super high-rise buildings by adding dynamic vulnerability assessments for illegal intrusion risks and rectification plan optimization capabilities, while also to increase its visualization level, a method utilizing digital twin technology and integrating Bayesian networks is proposed. To validate this method, a case study within a prominent super high-rise building in the Central Business District (CBD) of Beijing is conducted, in which the vulnerability analysis results have shown that among all the tertiary parent nodes, the coverage extent of the video surveillance system most significantly influences the vulnerability. By using the comprehensive blind spot analysis function of the established digital twin platform, thoughtful additions and angle adjustments of surveillance cameras have successfully increased the score of the security level from 84 to 90. This has proved that the method proposed in this study can provide decision support for the vulnerability assessment and improvement for the security system of super high-rise buildings.