In this study, a three dimensional(3D) numerical model of six-degrees-of-freedom(6DOF) is applied to simulate the water entries of twin spheres side-by-side at different lateral distances and time intervals.The turbul...In this study, a three dimensional(3D) numerical model of six-degrees-of-freedom(6DOF) is applied to simulate the water entries of twin spheres side-by-side at different lateral distances and time intervals.The turbulence structure is described using the shear-stress transport k-ω(SST k-ω) model, and the volume of fluid(VOF) method is used to track the complex air-liquid interface. The motion of spheres during water entry is simulated using an independent overset grid. The numerical model is verified by comparing the cavity evolution results from simulations and experiments. Numerical results reveal that the time interval between the twin water entries evidently affects cavity expansion and contraction behaviors in the radial direction. However, this influence is significantly weakened by increasing the lateral distance between the two spheres. In synchronous water entries, pressure is reduced on the midline of two cavities during surface closure, which is directly related to the cavity volume. The evolution of vortexes inside the two cavities is analyzed using a velocity vector field, which is affected by the lateral distance and time interval of water entries.展开更多
Temperature effect on atomic deformation of nanotwinned Ni (nt-Ni) under localized nanoindentation is investigated in comparison with nanocrystalline Ni (nc-Ni) through molecular simulation.The nt-Ni exhibits enhanced...Temperature effect on atomic deformation of nanotwinned Ni (nt-Ni) under localized nanoindentation is investigated in comparison with nanocrystalline Ni (nc-Ni) through molecular simulation.The nt-Ni exhibits enhanced critical load and hardness compared to nc-Ni,where perfect,stair-rod and Shockley dislocations are activated at (111),(111) and (111) slip planes in nt-Ni compared to only SSockley dislocation nucleation at (111) and (111) slip planes of nc-Ni.The nt-Ni exhibits a less significant indentation size effect in comparison with nc-Ni due to the dislocation slips hindrance of the twin boundary.The atomic deformation associated with the indentation size effect is investigated during dislocation transmission.Different from the decreasing partial slips parallel to the indenter surface in nc-Ni with increasing temperature,the temperaturedependent atomic deformation of nt-Ni is closely related to the twin boundary:from the partial slips parallel to the twin boundary (~10 K),to increased confined layer slips and decreased twin migration(300 K–600 K),to decreased confined layer slips and increased dislocation interaction of dislocation pinning and dissociation (900 K–1200 K).Dislocation density and atomic structure types through quantitative analysis are implemented to further reveal the above-mentioned dislocation motion and atomic structure alteration.Our study is helpful for understanding the temperature-dependent plasticity of twin boundary in nanotwinned materials.展开更多
The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will resu...The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.展开更多
Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine ...Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.展开更多
The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are dist...The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.展开更多
The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data gen...The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.展开更多
This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the con...This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.展开更多
Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,...Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.展开更多
An emerging railway technology called smart railway promises to deliver higher transportation efficiency,enhanced comfort in services,and greater eco-friendliness.The smart railway is expected to integrate fifth-gener...An emerging railway technology called smart railway promises to deliver higher transportation efficiency,enhanced comfort in services,and greater eco-friendliness.The smart railway is expected to integrate fifth-generation mobile communication(5G),Artificial Intelligence(AI),and other technologies,which poses new problems in the construction,operation and maintenance of railway wireless networks.Wireless Digital Twins(DTs),which have recently emerged as a new paradigm for the design of wireless networks,can address these problems and enable the whole lifecycle management of railway wireless networks.However,there are still many scientific issues and challenges for railway-oriented wireless DT.Relevant key technologies to solve these problems are introduced and described,including characterization of materials'physical-EM properties,autonomous reconstruction of Three-dimensional(3D)environment model,AI-empowered environmental cognition,Ray-Tracing(RT),model-based and AIbased RT acceleration,and generation of multi-spectra sensing data.Moreover,this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on highperformance RT developed by our laboratory.This paper outlines the framework for realizing the wireless DT of smart railways,providing the direction for future research.展开更多
目的:比较传统双合垫(Twin block)矫治器与无托槽隐形矫治器矫治安氏二类下颌后缩的效果。方法:收集2020年1月至2023年7月本院93例安氏二类下颌后缩患者临床资料,按矫治器的不同分为传统TB(Twin block)组(n=48)和无托槽隐形组(n=45)。传...目的:比较传统双合垫(Twin block)矫治器与无托槽隐形矫治器矫治安氏二类下颌后缩的效果。方法:收集2020年1月至2023年7月本院93例安氏二类下颌后缩患者临床资料,按矫治器的不同分为传统TB(Twin block)组(n=48)和无托槽隐形组(n=45)。传统TB采用传统Twin block矫治器矫治,无托槽隐形组采用无托槽隐形矫治器矫治,矫治前、矫治6 m后采用数字化口腔全景X射线机测定两组蝶鞍中心、鼻根点、下牙槽座点形成的交角(Sella nasion B point angle,SNB)、蝶鞍中心、鼻根点、上牙槽座点形成的交角(Sella nasion A point angle,SNA)、上牙槽座点、鼻根点、下牙槽座点形成的交角(ANB angle,ANB)、下中切牙长轴与鼻根点-下牙槽座点连线的交角(L1-NB角)、上中切牙长轴与鼻根点和上齿槽座点连线的上交角(U1-NA角)、上中切牙切缘到鼻根点和上齿槽座点连线的垂直距离(U1-NA距)、下中切牙切缘到鼻根点-下牙槽座点连线的距离(L1-NB距)、下颌角点至下颌颏顶点的距离(Go-Gn距)水平;采用卧式口腔锥形束CT机测定上气道的总体积(V总)、最小横截面积(Smin)、最小矢状径(Lmin矢)、最小冠状径(Lmin冠)水平;同时采用中文版儿童口腔健康影响程度量表(Child perception questionnaire,CPQ11-14)评估口腔健康情况,并于矫治期间统计不良反应。结果:矫治后,两组SNB角、SNA角、ANB角、L1-NB角、U1-NA角、U1-NA距、L1-NB距、Go-Gn距均优于矫治前,组间无显著差异(P>0.05);矫治后,两组V总、Smin、Lmin矢、Lmin冠、CPQ11-14评分均高于矫治前,且传统TB组均高于无托槽隐形组(P<0.05);两组不良反应发生率比较,差异无统计学意义(P>0.05)。结论:传统Twin block与无托槽隐形矫治器矫治安氏二类下颌后缩均有良好效果,但前者改善气道效果更佳,而后者更能维持口腔健康。展开更多
基金China Academy of Launch Vehicle Technology(Grant No.CALT-2022-03)Science and Technology on Underwater Information and Control Laboratory(Grant No.2021-JCJQ-LB-030-05).
文摘In this study, a three dimensional(3D) numerical model of six-degrees-of-freedom(6DOF) is applied to simulate the water entries of twin spheres side-by-side at different lateral distances and time intervals.The turbulence structure is described using the shear-stress transport k-ω(SST k-ω) model, and the volume of fluid(VOF) method is used to track the complex air-liquid interface. The motion of spheres during water entry is simulated using an independent overset grid. The numerical model is verified by comparing the cavity evolution results from simulations and experiments. Numerical results reveal that the time interval between the twin water entries evidently affects cavity expansion and contraction behaviors in the radial direction. However, this influence is significantly weakened by increasing the lateral distance between the two spheres. In synchronous water entries, pressure is reduced on the midline of two cavities during surface closure, which is directly related to the cavity volume. The evolution of vortexes inside the two cavities is analyzed using a velocity vector field, which is affected by the lateral distance and time interval of water entries.
基金supported by the National Natural Science Foundation of China (Grant No.11572090)。
文摘Temperature effect on atomic deformation of nanotwinned Ni (nt-Ni) under localized nanoindentation is investigated in comparison with nanocrystalline Ni (nc-Ni) through molecular simulation.The nt-Ni exhibits enhanced critical load and hardness compared to nc-Ni,where perfect,stair-rod and Shockley dislocations are activated at (111),(111) and (111) slip planes in nt-Ni compared to only SSockley dislocation nucleation at (111) and (111) slip planes of nc-Ni.The nt-Ni exhibits a less significant indentation size effect in comparison with nc-Ni due to the dislocation slips hindrance of the twin boundary.The atomic deformation associated with the indentation size effect is investigated during dislocation transmission.Different from the decreasing partial slips parallel to the indenter surface in nc-Ni with increasing temperature,the temperaturedependent atomic deformation of nt-Ni is closely related to the twin boundary:from the partial slips parallel to the twin boundary (~10 K),to increased confined layer slips and decreased twin migration(300 K–600 K),to decreased confined layer slips and increased dislocation interaction of dislocation pinning and dissociation (900 K–1200 K).Dislocation density and atomic structure types through quantitative analysis are implemented to further reveal the above-mentioned dislocation motion and atomic structure alteration.Our study is helpful for understanding the temperature-dependent plasticity of twin boundary in nanotwinned materials.
基金Hebei Province Key Research and Development Project(No.20313701D)Hebei Province Key Research and Development Project(No.19210404D)+13 种基金Mobile computing and universal equipment for the Beijing Key Laboratory Open Project,The National Social Science Fund of China(17AJL014)Beijing University of Posts and Telecommunications Construction of World-Class Disciplines and Characteristic Development Guidance Special Fund “Cultural Inheritance and Innovation”Project(No.505019221)National Natural Science Foundation of China(No.U1536112)National Natural Science Foundation of China(No.81673697)National Natural Science Foundation of China(61872046)The National Social Science Fund Key Project of China(No.17AJL014)“Blue Fire Project”(Huizhou)University of Technology Joint Innovation Project(CXZJHZ201729)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201902218004)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201902024006)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201901197007)Industry-University Cooperation Collaborative Education Project of the Ministry of Education(No.201901199005)The Ministry of Education Industry-University Cooperation Collaborative Education Project(No.201901197001)Shijiazhuang science and technology plan project(236240267A)Hebei Province key research and development plan project(20312701D)。
文摘The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.
基金supported by the State Grid Jilin Province Electric Power Co,Ltd-Research and Application of Power Grid Resilience Assessment and Coordinated Emergency Technology of Supply and Network for the Development of New Power System in Alpine Region(Project Number is B32342210001).
文摘Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.
基金partially supported by the Strategic Priority Research Program of Chinese Academy of Science(No.XDB 34030000)the National Natural Science Foundation of China(Nos.11975293 and 12205348)。
文摘The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.
基金supported in part by the National Nature Science Foundation of China under Grant 62001168in part by the Foundation and Application Research Grant of Guangzhou under Grant 202102020515。
文摘The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.
基金supported by the Tunisian Ministry of Higher Education and Scientific Research under Grant LSE-ENIT-LR 11ES15funded in part by the PAQ-Collabora(PAR&I-Tk)program。
文摘This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.
基金supported by the Natural Science Foundation of Shanghai(No.23ZR1429300)Innovation Funds of CNNC(Lingchuang Fund,Contract No.CNNC-LCKY-202234)the Project of the Nuclear Power Technology Innovation Center of Science Technology and Industry(No.HDLCXZX-2023-HD-039-02)。
文摘Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.
基金supported by Beijing Natural Science Foundation(L212029,L221009)the National Natural Science Foundation of China(62271043,62371033)the Ministry of Education of China(8091B032123).
文摘An emerging railway technology called smart railway promises to deliver higher transportation efficiency,enhanced comfort in services,and greater eco-friendliness.The smart railway is expected to integrate fifth-generation mobile communication(5G),Artificial Intelligence(AI),and other technologies,which poses new problems in the construction,operation and maintenance of railway wireless networks.Wireless Digital Twins(DTs),which have recently emerged as a new paradigm for the design of wireless networks,can address these problems and enable the whole lifecycle management of railway wireless networks.However,there are still many scientific issues and challenges for railway-oriented wireless DT.Relevant key technologies to solve these problems are introduced and described,including characterization of materials'physical-EM properties,autonomous reconstruction of Three-dimensional(3D)environment model,AI-empowered environmental cognition,Ray-Tracing(RT),model-based and AIbased RT acceleration,and generation of multi-spectra sensing data.Moreover,this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on highperformance RT developed by our laboratory.This paper outlines the framework for realizing the wireless DT of smart railways,providing the direction for future research.
文摘目的:比较传统双合垫(Twin block)矫治器与无托槽隐形矫治器矫治安氏二类下颌后缩的效果。方法:收集2020年1月至2023年7月本院93例安氏二类下颌后缩患者临床资料,按矫治器的不同分为传统TB(Twin block)组(n=48)和无托槽隐形组(n=45)。传统TB采用传统Twin block矫治器矫治,无托槽隐形组采用无托槽隐形矫治器矫治,矫治前、矫治6 m后采用数字化口腔全景X射线机测定两组蝶鞍中心、鼻根点、下牙槽座点形成的交角(Sella nasion B point angle,SNB)、蝶鞍中心、鼻根点、上牙槽座点形成的交角(Sella nasion A point angle,SNA)、上牙槽座点、鼻根点、下牙槽座点形成的交角(ANB angle,ANB)、下中切牙长轴与鼻根点-下牙槽座点连线的交角(L1-NB角)、上中切牙长轴与鼻根点和上齿槽座点连线的上交角(U1-NA角)、上中切牙切缘到鼻根点和上齿槽座点连线的垂直距离(U1-NA距)、下中切牙切缘到鼻根点-下牙槽座点连线的距离(L1-NB距)、下颌角点至下颌颏顶点的距离(Go-Gn距)水平;采用卧式口腔锥形束CT机测定上气道的总体积(V总)、最小横截面积(Smin)、最小矢状径(Lmin矢)、最小冠状径(Lmin冠)水平;同时采用中文版儿童口腔健康影响程度量表(Child perception questionnaire,CPQ11-14)评估口腔健康情况,并于矫治期间统计不良反应。结果:矫治后,两组SNB角、SNA角、ANB角、L1-NB角、U1-NA角、U1-NA距、L1-NB距、Go-Gn距均优于矫治前,组间无显著差异(P>0.05);矫治后,两组V总、Smin、Lmin矢、Lmin冠、CPQ11-14评分均高于矫治前,且传统TB组均高于无托槽隐形组(P<0.05);两组不良反应发生率比较,差异无统计学意义(P>0.05)。结论:传统Twin block与无托槽隐形矫治器矫治安氏二类下颌后缩均有良好效果,但前者改善气道效果更佳,而后者更能维持口腔健康。
