Game theory in network security for digital twins in industry

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.

A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider(AUG)is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications,in which path planning is an essential problem.Due to the complexity and variability of the ocean,accurate environment modeling and flexible path planning algorithms are pivotal challenges.The traditional models mainly utilize mathematical functions,which are not complete and reliable.Most existing path planning algorithms depend on the environment and lack flexibility.To overcome these challenges,we propose a path planning system for underwater intelligent internet vehicles.It applies digital twins and sensor data to map the real ocean environment to a virtual digital space,which provides a comprehensive and reliable environment for path simulation.We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters.The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing.The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility.The task-related reward function promotes the rapid convergence of the training.The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering,the HDT meaning hybridization of physical and digital domains,including deformation and energy efficiency can be built,where the essential parameters can be perceptually predicted in advance,by virtue of the fusion of physical sensors and digital information.The long short term memory(LSTM)can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks,thereby laying a foundation for the HDT.The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation.The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy.The manufacturing efficiency and comprehensive costs are accounted as consideration factors,which are perceptually predicted via LSTM.The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation.The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling(FDM).The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes.The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine(CMM)and 3D optical scanner.The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption,which have been verified with a three-way solenoid valve element,in which the maximum deformation was reduced by 39.78%and the mean absolute percentage error for perceptual prediction was 3.76%.

Digital Twins for Wastewater Treatment:A Technical Review

The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback.This review elucidates the foundational elements of digital twins,covering their concept,entities,domains,and key technologies.More specifically,we investigate the transformative potential of digital twins for the wastewater treatment engineering sector.Our discussion highlights the application of digital twins to wastewater treatment plants(WWTPs)and sewage networks,hardware(i.e.,facilities and pipes,sensors for water quality and activated sludge,hydrodynamics,and power consumption),and software(i.e.,knowledge-based and data-driven models,mechanistic models,hybrid twins,control methods,and the Internet of Things).Furthermore,two cases are provided,followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs.This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.

DNA Methylation Variation Is Identified in Monozygotic Twins Discordant for Congenital Heart Diseases

Aims:Multiple genes and environmental factors are known to be involved in congenital heart disease(CHD),but epigenetic variation has received little attention.Monozygotic(MZ)twins with CHD provide a unique model for exploring this phenomenon.In order to investigate the potential role of Deoxyribonucleic Acid(DNA)methyla-tion in CHD pathogenesis,the present study examined DNA methylation variation in MZ twins discordant for CHD,especially ventricular septal defect(VSD).Methods and Results:Using genome-wide DNA methylation profiles,we identified 4004 differentially methylated regions(DMRs)in 18 MZ twin pairs discordant for CHD,and 2826 genes were identified.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis revealed a list of CHD-associated pathways.To further investigate the role of DNA methylation in VSD,data from 7 pairs of MZ twins with VSD were analyzed.We identified 1614 DMRs corresponding to 1443 genes associated with arrhythmogenic right ventricular cardiomyopathy,cyclic guanosine monopho-sphate-protein kinase G(cGMP-PKG)signaling pathway by KEGG analysis,and cell-cell adhesion,calcium ion transmembrane transport by GO analysis.A proportion of DMR-associated genes were involved in calcium signaling pathways.The methylation changes of calcium signaling genes might be related to VSD pathogenesis.Conclusion:CHD is associated with differential DNA methylation in MZ twins.CHD may be etiologically linked to DNA methylation,and methylation of calcium signaling genes may be involved in the development of VSD.

Design of modified model of intelligent assembly digital twins based on optical fiber sensor network

Intelligent assembly of large-scale,complex structures using an intelligent manufacturing platform represents the future development direction for industrial manufacturing.During large-scale structural assembly processes,several bottleneck problems occur in the existing auxiliary assembly technology.First,the traditional LiDARbased assembly technology is often limited by the openness of the manufacturing environment,in which there are blind spots,and continuous online assembly adjustment thus cannot be realized.Second,for assembly of large structures,a single-station LiDAR system cannot achieve complete coverage,which means that a multi-station combination method must be used to acquire the complete three-dimensional data;many more data errors are caused by the transfer between stations than by the measurement accuracy of a single station,which means that the overall system's measurement and adjustment errors are increased greatly.Third,because of the large numbers of structural components contained in a large assembly,the accumulated errors may lead to assembly interference,but the LiDAR-assisted assembly process does not have a feedback perception capability,and thus assembly component loss can easily be caused when assembly interference occurs.Therefore,this paper proposes to combine an optical fiber sensor network with digital twin technology,which will allow the test data from the assembly entity state in the real world to be applied to the"twin"model in the virtual world and thus solve the problems with test openness and data transfer.The problem of station and perception feedback is also addressed and represents the main innovation of this work.The system uses an optical fiber sensor network as a flexible sensing medium to monitor the strain field distribution within a complex area in real time,and then completes real-time parameter adjustment of the virtual assembly based on the distributed data.Complex areas include areas that are laser-unreachable,areas with complex contact surfaces,and areas with large-scale bending deformations.An assembly condition monitoring system is designed based on the optical fiber sensor network,and an assembly condition monitoring algorithm based on multiple physical quantities is proposed.The feasibility of use of the optical fiber sensor network as the real-state parameter acquisition module for the digital twin intelligent assembly system is discussed.The offset of any position in the test area is calculated using the convolutional neural network of a residual module to provide the compensation parameters required for the virtual model of the assembly structure.In the model optimization parameter module,a correction data table is obtained through iterative learning of the algorithm to realize state prediction from the test data.The experiment simulates a largescale structure assembly process,and performs virtual and real mapping for a variety of situations with different assembly errors to enable correction of the digital twin data stream for the assembly process through the optical fiber sensor network.In the plane strain field calibration experiment,the maximum error among the test values for this system is 0.032 mm,and the average error is 0.014 mm.The results show that use of visual calibration can correct the test error to within a very small range.This result is equally applicable to gradient curvature surfaces and freeform surfaces.Statistics show that the average measurement accuracy error for regular surfaces is better than 11.2%,and the average measurement accuracy error for irregular surfaces is better than 14.8%.During simulation of large-scale structure assembly experiments,the average position deviation accuracy is 0.043 mm,which is in line with the designed accuracy.

Inaugural editorial-Digital Twins and Applications

Academician of the CAE member Youxian Sun from Zhejiang University initiated Digital Twins and Applications(ISSN 2995-2182).It is published by Zhejiang University Press and the Institution of Engineering and Technology and sponsored by Zhejiang Univer-sity.Digital Twins and Applications aim to provide a specialised platform for researchers,practitioners,and industry experts to publish high-quality,state-of-the-art research on digital twin technologies and their applications.

On future power system digital twins:A vision towards a standard architecture

The energy sector's digital transformation brings mutually dependent communication and energy infrastructure,tightening the relationship between the physical and the digital world.Digital twins(DT)are the key concept for this.This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain.By reviewing different definitions and applications,the authors present a new definition of DTs specifically tailored to power systems.Based on the proposed definition and extensive deliberations and consultations with distribution system operators,energy traders,and municipalities,the authors introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators'processes while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view.The authors also discuss their vision related to the integration of power system DTs into various phases of the system's life cycle,such as long-term planning,emphasising challenges that remain to be addressed,such as managing measurement and model errors,and uncertainty propagation.Finally,the authors present their vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture.

Rendering Frankenstein's monsters?The emergence and impacts of human digital twins

Human digital twins(HDTs)are set to revolutionise human-system integration and improve human capacities,productivity,and well-being by including human character-istics in system design.Reflecting real-life behaviours and emotional reactions,HDTs render virtual data-driven models of people.Their presence in many fields-including office work,education,and healthcare-showcases their ability to advance diagnoses,workforce training,and individualised learning.However,the general HDTs acceptance hypothesis calls for a critical analysis of their ethical ramifications and social influence.Emphasising the need of social adaptations to maintain stability,structural functionalism provides a suited framework to examine the roles of HDTs inside social systems.Despite their obvious advantages,HDTs raise ethical questions.While addressing concerns about user acceptance and ethical norms,engineers,designers and the legal regulators,but also the users-by means of‘owners’-must consider the substantial consequences of HDTs and their customisation.HDTs'duality,like a double-edged sword,calls for deliberate usage and considerable thought to negotiate the changing terrain of human-machine interaction.

Origin of nucleation and growth of extension twins in grains unsuitably oriented for twinning during deformation of Mg-1%Al

A large number of anomalous extension twins,with low or even negative twinning Schmid factors,were found to nucleate and grow in a strongly textured Mg-1Al alloy during tensile deformation along the extruded direction.The deformation mechanisms responsible for this behaviour were investigated through in-situ electron back-scattered diffraction,grain reference orientation deviation,and slip trace-modified lattice rotation.It was found that anomalous extension twins nucleated mainly at the onset of plastic deformation at or near grain boundary triple junctions.They were associated with the severe strain incompatibility between neighbour grains as a result from the differentbasal slip-induced lattice rotations.Moreover,the anomalous twins were able to grow with the applied strain due to the continuous activation ofbasal slip in different neighbour grains,which enhanced the strain incompatibility.These results reveal the complexity of the deformation mechanisms in Mg alloys at the local level when deformed along hard orientations.

Digital Twins for Engineering Asset Management:Synthesis,Analytical Framework,and Future Directions

Effective engineering asset management(EAM)is critical to economic development and improving livability in society,but its complexity often impedes optimal asset functionalities.Digital twins(DTs)could revolutionize the EAM paradigm by bidirectionally linking the physical and digital worlds in real time.There is great industrial and academic interest in DTs for EAM.However,previous review studies have predominately focused on technical aspects using limited life-cycle perspectives,failing to holistically synthesize DTs for EAM from the managerial point of view.Based on a systematic literature review,we introduce an analytical framework for describing DTs for EAM,which encompasses three levels:DT 1.0 for technical EAM,DT 2.0 for technical-human EAM,and DT 3.0 for technical-environmental EAM.Using this framework,we identify what is known,what is unknown,and future directions at each level.DT 1.0 addresses issues of asset quality,progress,and cost management,generating technical value.It lacks multi-objective self-adaptive EAM,however,and suffers from high application cost.It is imperative to enable closed-loop EAM in order to provide various functional services with affordable DT 1.0.DT 2.0 accommodates issues of human-machine symbiosis,safety,and flexibility management,generating managerial value beyond the technical performance improvement of engineering assets.However,DT 2.0 currently lacks the automation and security of human-machine interactions and the managerial value related to humans is not prominent enough.Future research needs to align technical and managerial value with highly automated and secure DT 2.0.DT 3.0 covers issues of participatory governance,organization management,sustainable development,and resilience enhancement,generating macro social value.Yet it suffers from organizational fragmentation and can only address limited social governance issues.Numerous research opportunities exist to coordinate different stakeholders.Similarly,future research opportunities exist to develop DT 3.0 in a more open and complex system.

Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly prevalent.There have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive capabilities.However,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic.In addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

Robust by design:Designing ICT infrastructures using Twins

Robust-by-design(RbD)is a design strategy that uses adversary emulation to strengthen the security of an information and communication infrastructure.It relies on two key components:the security twin and the threat actor twins.The security twin is a detailed database that outlines the different parts of the infrastructure,how they are connected,and their vulnerabilities.It also highlights the types of attacks each part could enable.On the other hand,the twin of a threat actor describes its potential attack surface,the attacks it can carry out,its strategy,and its ultimate goal,if any.This information comes from threat intelligence.RbD conducts independent simulations of various threat actors against the security twin to identify all possible attack paths they could exploit.Three types of analysis use this information to improve the robustness and resilience of the infrastructure.The first analysis fills in the gaps in threat intelligence by extending in-formation on threat actors and vulnerabilities.The second analysis focuses on selecting countermeasures aimed at eliminating attack paths or at least reducing their chances of success.Possible countermeasures include patching vulnerabilities,adjusting firewall rules,and implementing network segmentation.Information on attack paths guides the choice and configuration of these countermeasures.Once the infrastructure twin is updated to reflect countermeasure deployment,RbD performs further simulations to uncover any new attack paths that could be exploited and to identify additional coun-termeasures.The final analysis seeks to address the risks associated with any remaining unaddressed attack paths.

Drone-based digital twins for water quality monitoring:A systematic review

The rapid advancement of drone technology and digital twin systems has significantly transformed environmental monitoring,particularly in the field of water quality assess-ment.This paper systematically reviews the current state of research on the application of drones,digital twins,and their integration for water quality monitoring and management.It highlights key themes,insights,research trends,commonly used methodologies,and future directions from existing studies,aiming to provide a foundational reference for further research to harness the promising potential of these technologies for effective,scalable solutions in water resource management,addressing both immediate and long-term environmental challenges.The systematic review followed PRISMA guidelines,rigorously analysing hundreds of relevant papers.Key findings emphasise the effective-ness of drones in capturing real-time,high-resolution spatial and temporal data,as well as the value of digital twins for predictive and simulation-based analysis.Most importantly,the review demonstrates the potential of integrating these technologies to enhance sus-tainable water management practices.However,it also identifies a significant research gap in fully integrating drones with digital twins for comprehensive water quality manage-ment.In response,the review outlines future research directions,including improvements in data integration techniques,predictive models,and interdisciplinary collaboration.

Exploring Alternatives to Create Digital Twins from and for Process Simulation

In this work,Digital Twins based on Neural Networks for the steady state production of styrene were generated.Thus,both the Aspen Technology AI Model Builder(alternative 1)and a homemade MS Excel VBA code connected to Aspen HYSYS and Aspen Plus(alternative 2)were used with this same aim.The raw data used for generating the Digital Twins were obtained from process simulations using Aspen HYSYS and/or Aspen Plus,which were connected through a recycle-like stream via automation for solving the entire simulation flowsheet.Aspen HYSYS was used for solving the pre-heating,reaction,and stabilization sections of the process whereas Aspen Plus ensured the computing of the separation and purification columns.Both alternatives led to an excellent prediction showing the capability of creating Digital Twins from and for process simulation.

Numerical simulation on size effect of copper with nano-scale twins

To understand the tensile deformation of electro-deposited Cu with nano-scale twins, a numerical study was carried out based on a conventional theory of mechanism-based strain gradient plasticity （CMSG）. The concept of twin lamella strengthening zone was used in terms of the cohesive interface model to simulate grain-boundary sliding and separation. The model included a number of material parameters, such as grain size, elastic modulus, plastic strain hardening exponent, initial yield stress, as well as twin lamellar distribution, which may contribute to size effects of twin layers in Cu polycrystalline. The results provide information to understand the mechanical behaviors of Cu with nano-scale growth twins.

一次大磁暴主相期间ENA氧与氢的分离及分布特征——TWINS卫星观测

本文采用基于ENA(Energetic Neutral Atoms)次生电子起始脉冲高度分布,统计拟合分离中能段ENA两种主要成分氢和氧的方法,研发了实现ENA氢与氧分离的TWINS卫星原始数据处理软件;其中所需要的脉高分布模型,参照已有理论公式,利用TWINS(Two Wide-angle Imaging Neutral-atom Spectrometers)卫星标定数据进行拟合确定未知参数,再加以计算得到.将上述方法用于TWINS卫星实测数据,分离得到一次大磁暴主相期间ENA-H和ENA-O微分通量随观测视线的分布及其随主相增长的变化.分析发现:(1)ENA-H与ENA-O微分通量的强度和随观测视线的分布特征都有明显差别,从某种角度反映出ENA之源的O^+与H^+离子强度和分布之间的差异;(2)接近主相极大时,ENA-H有很强的低高度发射(LAE,Low Altitude Emission),出现在磁地方时午夜前极光和亚极光纬度区,意味着该区域较强的等离子片和环电流质子沉降,进入到外层基底以下较低高度大气层;而ENA-O则未有明显LAE产生;ENA-O强通量观测视线主要穿过广大环电流区,磁地方时主要在午夜之后以及黄昏前和黎明前后;(3)在磁暴主相快速增长期,ENA-O平均总通量持续增大,而ENA-H同步减小,ENA-O与ENA-H平均总通量的比率随环电流指数Dst绝对值的增大而大致成正比增长.

Evolution of twins and texture and its effects on mechanical properties of AZ31 magnesium alloy sheets under different rolling process parameters

In order to investigate the dependence of microstructure and mechanical properties on the rolling process parameters, AZ31 magnesium alloy sheets with different grain sizes, basal texture intensities and twinning types were obtained using hot rolling at various temperatures and reductions. The volume fractions of the extension, contraction and secondary twins in the as-rolled sheets depend on the grain size. The highest volume fractions of three types of twins are obtained at 523 K under the reduction of 10% when the average grain size value is the maximum. The critical reductions for complete dynamic recrystallization are 30% at 523 K and 40% at 473 K. The increase of yield strength is ascribed to both grain-refinement strengthening and basal texture strengthening at the first stage. When the grain size does not decrease with increasing the reduction, the yield strength is mainly influenced by the texture weakening.

Effects of deformation twins on microstructure evolution,mechanical properties and corrosion behaviors in magnesium alloys-A review

Due to lattice reorientation,grain segmentation,induced recrystallization,twins play a very important role in regulating texture,refining grains,improving mechanical properties and corrosion resistance,and has received more extensive attention.Numerous studies have shown that{10-12}<10-11>tensile twins(TTWs)are easily activated in large quantities due to the lower critical resolve shear stresses(CRSS).Introduction of TTWs under uniaxial compression improved the strength,ductility,and formability of magnesium(Mg)alloys.Moreover,TTWs produced by multi-directional impact forging(MDIF)can optimize the microstructure by dividing grains and promoting recrystallization,resulting in significant improvement of mechanical properties.Although{10-11}<10-12>compressive twins(CTWs)and{10-11}-{10-12}double twins(DTWs)can promote dynamic recrystallization(DRX),they are also favorable nucleation sites for cracks.In addition,the type and volume fraction of twins can affect the corrosion resistance,and they also play different roles in the corrosion process of different Mg alloys.Twins have shown great potential for improving structure and properties,but a comprehensive and critical discussion of twins in Mg alloys is still lacking.Therefore,based on previous studies,this article reviews the common types and variants of twins in Mg alloys,influencing factors,and their effects on the microstructure,mechanical properties and corrosion resistance.In addition,some interesting ideas are being proposed for further research.

Tensile Properties of Cu with Deformation Twins Induced by SMAT

High density nano-scale deformation twins were introduced in the surface layer of Cu sample by means of surface mechanical attrition treatment （SMAT） at room temperature. The Cu sample with deformation twins shows a yield strength of about 470 MPa in tension tests. The significant strengthening may be attributed to the effective inhibition of slip dislocations by abundant twin boundaries.