We propose a multiple-tree overlay structure for resource discovery in unstructured P2P systems. Peers that have similar interests or hold similar type of resources will be grouped into a tree-like cluster. We exploit...We propose a multiple-tree overlay structure for resource discovery in unstructured P2P systems. Peers that have similar interests or hold similar type of resources will be grouped into a tree-like cluster. We exploit the heterogeneity of peers in each cluster by connecting peers with more capacities closer to the root of the tree. The capacity of a peer can be defined in different ways （e.g. higher network bandwidth, larger disk space, more data items of a certain type etc.） according to different needs of users or applications.展开更多
Flooding is the most famous technique for locating contents in unstructured P2P networks. Recently traditional flooding has been replaced by more efficient dynamic query （DQ） and different variants of such algorithm...Flooding is the most famous technique for locating contents in unstructured P2P networks. Recently traditional flooding has been replaced by more efficient dynamic query （DQ） and different variants of such algorithms. Dynamic query is a new flooding technique which could estimate a proper time-to-live （TTL） value for a query flooding by estimating the popularity of the searched files, and retrieve sufficient results under controlled flooding range for reducing network traffic. However, all DQ-like search algorithms are ＂blind＂ so that a large amount of redundant messages are caused. In this paper, we proposed a new search scheme, called Immune Search Scheme （ISS）, to cope with this problem. In ISS, an immune systems inspired concept of similarity-governed clone proliferation and mutation for query message movement is applied. Some assistant strategies, that is, shortcuts creation and peer traveling are incorporated into ISS to develop ＂immune memory＂ for improving search performance, which can make ISS not be blind but heuristic.展开更多
Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly ...Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.展开更多
Although anonymizing Peer-to-Peer （P2P） networks often means extra cost in terms of transfer efficiency, many systems try to mask the identities of their users for privacy consideration. By comparison and analysis o...Although anonymizing Peer-to-Peer （P2P） networks often means extra cost in terms of transfer efficiency, many systems try to mask the identities of their users for privacy consideration. By comparison and analysis of existing approaches, we investigate the properties of unstructured P2P anonymity, and summarize current attack models on these designs. Most of these approaches are path-based, which require peers to pre-construct anonymous paths before transmission, thus suffering significant overhead and poor reliability. We also discuss the open problems in this field and propose several future research directions.展开更多
It is universally acknowledged by network security experts that proactive peer-to-peer (P2P) worms may soon en-gender serious threats to the Internet infrastructures. These latent threats stimulate activities of model...It is universally acknowledged by network security experts that proactive peer-to-peer (P2P) worms may soon en-gender serious threats to the Internet infrastructures. These latent threats stimulate activities of modeling and analysis of the proactive P2P worm propagation. Based on the classical two-factor model,in this paper,we propose a novel proactive worm propagation model in unstructured P2P networks (called the four-factor model) by considering four factors:(1) network topology,(2) countermeasures taken by Internet service providers (ISPs) and users,(3) configuration diversity of nodes in the P2P network,and (4) attack and defense strategies. Simulations and experiments show that proactive P2P worms can be slowed down by two ways:improvement of the configuration diversity of the P2P network and using powerful rules to reinforce the most connected nodes from being compromised. The four-factor model provides a better description and prediction of the proactive P2P worm propagation.展开更多
Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidl...Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidly adopting LLMs for the classification of biomedical textual data in medical research.The LLM can derive insights from intricate,extensive,unstructured training data.Variants need to be accurately identified and classified to advance genetic research,provide individualized treatment,and assist physicians in making better choices.However,the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize.Such an approach may result in incorrect diagnoses,which could affect a patient’s prognosis and course of therapy.This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data.We have cleaned the clinical textual data using various text preprocessing methods,including stemming,tokenization,and stop word removal.The important features are extracted using Bag of Words(BoW)and Term Frequency-Inverse Document Frequency(TFIDF)feature engineering methods.The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error.According to the experimental results,the random forest model achieved 61%accuracy with 67%precision for class 9 using TFIDF features and 63%accuracy and a 73%F1 score for class 9 using Bag of Words features.The accuracy of the proposed BERT(Bidirectional Encoder Representations from Transformers)model was 70%with 5-fold cross-validation and 71%with 10-fold cross-validation.The research results provide a comprehensive overview of current LLM methods in healthcare,benefiting academics as well as professionals in the discipline.展开更多
With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based app...With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based approaches have achieved autonomous driving on well-structured urban roads,but these efforts primarily focus on urban road environments.In contrast,there are fewer deep learningmethods specifically designed for off-road traversable area detection,and their effectiveness is not yet satisfactory.This is because detecting traversable areas in complex outdoor environments poses significant challenges,and current methods often rely on single-image inputs,which do not align with contemporary multimodal approaches.Therefore,in this study,we propose a CFH-Net model for off-road traversable area detection.This model employs a Transformer architecture to enhance its capability of capturing global information.For multimodal feature extraction and fusion,we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion,thereby improving the perception capability of autonomous vehicles on unstructured roads.To address upsampling,we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features.Finally,we conducted experiments on the ORFD off-road dataset and achieved outstanding results.展开更多
The surge of distributed renewable energy resources has given rise to the emergence of prosumers,facilitating the low-carbon transition of distribution networks.However,flexible prosumers introduce bidirectional power...The surge of distributed renewable energy resources has given rise to the emergence of prosumers,facilitating the low-carbon transition of distribution networks.However,flexible prosumers introduce bidirectional power and carbon interaction,increasing the complexity of practical decision-making in distribution networks.To address these challenges,this paper presents a carbon-coupled network charge-guided bi-level interactive optimization method between the distribution system operator and prosumers.In the upper level,a carbon-emission responsibility settlement method that incorporates the impact of peer-to-peer(P2P)trading is proposed,based on a carbon-emission flow model and optimal power flow model,leading to the formulation of carbon-coupled network charges.In the lower level,a decentralized P2P trading mechanism is developed to achieve the clearing of energy and carbon-emission rights.Furthermore,an alternating direction method of multipliers with an adaptive penalty factor is introduced to address the equilibrium of the P2P electricity–carbon coupled market,and an improved bisection method is employed to ensure the convergence of the bi-level interaction.A case study on the modified IEEE 33-bus system demonstrates the effectiveness of the proposed model and methodology.展开更多
Tensegrity structures,embodying the principles of continuous tensioning and discrete compression,have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments,...Tensegrity structures,embodying the principles of continuous tensioning and discrete compression,have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments,owing to their flexible and resilient traits.By means of a straightforward and cost-effective method to achieve structure-driven,vibration-driven tensegrity shows great potential,particularly in tasks demanding random exploration.However,the design guidance for vibration-driven tensegrity and their performance evaluation in unstructured terrain remain unrevealed due to the complex dynamics of the structure.This paper presents a small six-bar tensegrity robot,driven by wireless vibration motors,designed for deployment in disaster rescue and search scenarios.Finite element simulation is used to investigate how structural characteristics,excitation parameters,and the arrangement of motors affect the kinematic performance of this tensegrity system.A prototype of the six-bar tensegrity robot with three motors located on the lower ends of the three lower struts is designed and manufactured after the numerical simulations.A simple control policy which adjusts the motion of the tensegrity robot by turning on or off the motors on different locations is proposed.The prototype with and without the control policy is tested in man-made environments of various complexity.It shows that the ability and efficiency of the tensegrity robot in exploring unstructured environments is significantly enhanced by the proposed control policy.It is believed that the potential of the vibration-driven tensegrity robot could be further exploited by integrating multi-source sensors and more intelligent control policies.展开更多
Traditional Computational Fluid Dynamics(CFD)simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing ...Traditional Computational Fluid Dynamics(CFD)simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations(VIV)of floating structures.To overcome these limitations,this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction.First,an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors.These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network(SE-CNN),which models the nonlinear coupling between flow disturbances and structural responses.The SE-CNN architecture incorporates an attention-based weighting mechanism through an embedded Squeeze-and-Excitation module,dynamically optimizing channel feature importance and improving sensitivity to critical flow characteristics.During training,multidimensional inputs,including pressure,velocity gradient,and displacement sequences,are used to capture the full complexity of fluid–structure interactions.Results demonstrate that the proposed method achieves a maximum amplitude prediction error of only 2.9%and a main frequency deviation below 0.03 Hz,outperforming conventional CNN models by reducing amplitude prediction error from 3.2%to 1.9%.The approach is validated using a representative semi-submersible platform,confirming its robustness across varying damping conditions and flow velocities.展开更多
Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的...Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的3个周期性尖峰群、不同主机发送或接收流量的重尾分布、p2p流量的长相关特性以及提出了ADTE的估计方法来区分信令和数据流量。本文的研究也显示出Napster在p2p流中占大部分,这暗示着超级节点和阶层式拓扑较纯p2p结构潜在的优势。同时,观察到在我国p2p的流量仅占Internet总流量的1%弱,这个值跟国外的数据有很大区别。我们分析了其中的原因并希望该结论为我国p2p软件的发展提供参考。展开更多
An upwind scheme based on the unstructured mesh is developed to solve ideal 2-D magnetohydrodynamics （MHD） equations. The inviscid fluxes are approximated by using the modified advection upstream splitting method （...An upwind scheme based on the unstructured mesh is developed to solve ideal 2-D magnetohydrodynamics （MHD） equations. The inviscid fluxes are approximated by using the modified advection upstream splitting method （AUSM） scheme, and a 5-stage explicit Runge-Kutta scheme is adopted in the time integration. To avoid the influence of the magnetic field divergence created during the simulation, the hyperbolic divergence cleaning method is introduced. The shock-capturing properties of the method are verified by solving the MHD shock-tube problem. Then the 2-D nozzle flow with the magnetic field is numerically simulated on the unstructured mesh. Computational results demonstrate the effects of the magnetic field and agree well with those from references.展开更多
基金Supported by the National High Technology Research and Development Program of China (2006AA10Z1E6)
文摘We propose a multiple-tree overlay structure for resource discovery in unstructured P2P systems. Peers that have similar interests or hold similar type of resources will be grouped into a tree-like cluster. We exploit the heterogeneity of peers in each cluster by connecting peers with more capacities closer to the root of the tree. The capacity of a peer can be defined in different ways （e.g. higher network bandwidth, larger disk space, more data items of a certain type etc.） according to different needs of users or applications.
基金Supported by the National Natural Science Foundation of China (90604012)
文摘Flooding is the most famous technique for locating contents in unstructured P2P networks. Recently traditional flooding has been replaced by more efficient dynamic query （DQ） and different variants of such algorithms. Dynamic query is a new flooding technique which could estimate a proper time-to-live （TTL） value for a query flooding by estimating the popularity of the searched files, and retrieve sufficient results under controlled flooding range for reducing network traffic. However, all DQ-like search algorithms are ＂blind＂ so that a large amount of redundant messages are caused. In this paper, we proposed a new search scheme, called Immune Search Scheme （ISS）, to cope with this problem. In ISS, an immune systems inspired concept of similarity-governed clone proliferation and mutation for query message movement is applied. Some assistant strategies, that is, shortcuts creation and peer traveling are incorporated into ISS to develop ＂immune memory＂ for improving search performance, which can make ISS not be blind but heuristic.
基金National Natural Science Foundation of China (No.60633020 and No. 90204012)Natural Science Foundation of Hebei Province (No. F2006000177)
文摘Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.
文摘Although anonymizing Peer-to-Peer （P2P） networks often means extra cost in terms of transfer efficiency, many systems try to mask the identities of their users for privacy consideration. By comparison and analysis of existing approaches, we investigate the properties of unstructured P2P anonymity, and summarize current attack models on these designs. Most of these approaches are path-based, which require peers to pre-construct anonymous paths before transmission, thus suffering significant overhead and poor reliability. We also discuss the open problems in this field and propose several future research directions.
基金Project (No. 09511501600) partially supported by the Science and Technology Commission of Shanghai Municipality, China
文摘It is universally acknowledged by network security experts that proactive peer-to-peer (P2P) worms may soon en-gender serious threats to the Internet infrastructures. These latent threats stimulate activities of modeling and analysis of the proactive P2P worm propagation. Based on the classical two-factor model,in this paper,we propose a novel proactive worm propagation model in unstructured P2P networks (called the four-factor model) by considering four factors:(1) network topology,(2) countermeasures taken by Internet service providers (ISPs) and users,(3) configuration diversity of nodes in the P2P network,and (4) attack and defense strategies. Simulations and experiments show that proactive P2P worms can be slowed down by two ways:improvement of the configuration diversity of the P2P network and using powerful rules to reinforce the most connected nodes from being compromised. The four-factor model provides a better description and prediction of the proactive P2P worm propagation.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Large language models(LLMs)and natural language processing(NLP)have significant promise to improve efficiency and refine healthcare decision-making and clinical results.Numerous domains,including healthcare,are rapidly adopting LLMs for the classification of biomedical textual data in medical research.The LLM can derive insights from intricate,extensive,unstructured training data.Variants need to be accurately identified and classified to advance genetic research,provide individualized treatment,and assist physicians in making better choices.However,the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize.Such an approach may result in incorrect diagnoses,which could affect a patient’s prognosis and course of therapy.This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data.We have cleaned the clinical textual data using various text preprocessing methods,including stemming,tokenization,and stop word removal.The important features are extracted using Bag of Words(BoW)and Term Frequency-Inverse Document Frequency(TFIDF)feature engineering methods.The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error.According to the experimental results,the random forest model achieved 61%accuracy with 67%precision for class 9 using TFIDF features and 63%accuracy and a 73%F1 score for class 9 using Bag of Words features.The accuracy of the proposed BERT(Bidirectional Encoder Representations from Transformers)model was 70%with 5-fold cross-validation and 71%with 10-fold cross-validation.The research results provide a comprehensive overview of current LLM methods in healthcare,benefiting academics as well as professionals in the discipline.
文摘With the advancement of deep learning in the automotive domain,more and more researchers are focusing on autonomous driving.Among these tasks,free space detection is particularly crucial.Currently,many model-based approaches have achieved autonomous driving on well-structured urban roads,but these efforts primarily focus on urban road environments.In contrast,there are fewer deep learningmethods specifically designed for off-road traversable area detection,and their effectiveness is not yet satisfactory.This is because detecting traversable areas in complex outdoor environments poses significant challenges,and current methods often rely on single-image inputs,which do not align with contemporary multimodal approaches.Therefore,in this study,we propose a CFH-Net model for off-road traversable area detection.This model employs a Transformer architecture to enhance its capability of capturing global information.For multimodal feature extraction and fusion,we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion,thereby improving the perception capability of autonomous vehicles on unstructured roads.To address upsampling,we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features.Finally,we conducted experiments on the ORFD off-road dataset and achieved outstanding results.
基金supported by Institutional Research Fund from Sichuan University(0-1 Innovation Research Project,2023SCUH0002)the Sichuan Science and Technology Program(2024YFHZ0312)+1 种基金the Chengdu Science and Technology Program(2024YF0600012HZ)the National Natural Science Foundation of China(U2166211 and 52177103).
文摘The surge of distributed renewable energy resources has given rise to the emergence of prosumers,facilitating the low-carbon transition of distribution networks.However,flexible prosumers introduce bidirectional power and carbon interaction,increasing the complexity of practical decision-making in distribution networks.To address these challenges,this paper presents a carbon-coupled network charge-guided bi-level interactive optimization method between the distribution system operator and prosumers.In the upper level,a carbon-emission responsibility settlement method that incorporates the impact of peer-to-peer(P2P)trading is proposed,based on a carbon-emission flow model and optimal power flow model,leading to the formulation of carbon-coupled network charges.In the lower level,a decentralized P2P trading mechanism is developed to achieve the clearing of energy and carbon-emission rights.Furthermore,an alternating direction method of multipliers with an adaptive penalty factor is introduced to address the equilibrium of the P2P electricity–carbon coupled market,and an improved bisection method is employed to ensure the convergence of the bi-level interaction.A case study on the modified IEEE 33-bus system demonstrates the effectiveness of the proposed model and methodology.
基金supported by the National Natural Science Foundation of China(Grant Nos.52178175 and 52108182)the National Natural Science Foundation of Zhejiang Province(Grant No.LZ23E080003).
文摘Tensegrity structures,embodying the principles of continuous tensioning and discrete compression,have emerged as fundamental frameworks in locomotive soft robotics for navigating uneven and unpredictable environments,owing to their flexible and resilient traits.By means of a straightforward and cost-effective method to achieve structure-driven,vibration-driven tensegrity shows great potential,particularly in tasks demanding random exploration.However,the design guidance for vibration-driven tensegrity and their performance evaluation in unstructured terrain remain unrevealed due to the complex dynamics of the structure.This paper presents a small six-bar tensegrity robot,driven by wireless vibration motors,designed for deployment in disaster rescue and search scenarios.Finite element simulation is used to investigate how structural characteristics,excitation parameters,and the arrangement of motors affect the kinematic performance of this tensegrity system.A prototype of the six-bar tensegrity robot with three motors located on the lower ends of the three lower struts is designed and manufactured after the numerical simulations.A simple control policy which adjusts the motion of the tensegrity robot by turning on or off the motors on different locations is proposed.The prototype with and without the control policy is tested in man-made environments of various complexity.It shows that the ability and efficiency of the tensegrity robot in exploring unstructured environments is significantly enhanced by the proposed control policy.It is believed that the potential of the vibration-driven tensegrity robot could be further exploited by integrating multi-source sensors and more intelligent control policies.
基金sponsored by the National Natural Science Foundation of China(Grant No.52301320)the Natural Science Founds of Fujian Province(No.2023J01790).
文摘Traditional Computational Fluid Dynamics(CFD)simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations(VIV)of floating structures.To overcome these limitations,this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction.First,an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors.These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network(SE-CNN),which models the nonlinear coupling between flow disturbances and structural responses.The SE-CNN architecture incorporates an attention-based weighting mechanism through an embedded Squeeze-and-Excitation module,dynamically optimizing channel feature importance and improving sensitivity to critical flow characteristics.During training,multidimensional inputs,including pressure,velocity gradient,and displacement sequences,are used to capture the full complexity of fluid–structure interactions.Results demonstrate that the proposed method achieves a maximum amplitude prediction error of only 2.9%and a main frequency deviation below 0.03 Hz,outperforming conventional CNN models by reducing amplitude prediction error from 3.2%to 1.9%.The approach is validated using a representative semi-submersible platform,confirming its robustness across varying damping conditions and flow velocities.
文摘Peer to Peer网络(简称p2p)作为一种新型的覆盖网络引起了越来越多研究者的兴趣。本文介绍了在我国进行的骨干互联网上p2p网络流量测量。与现有国外研究不同,本文的数据来源于核心路由器,因此克服了它们的缺陷。其研究集中在汇聚流中的3个周期性尖峰群、不同主机发送或接收流量的重尾分布、p2p流量的长相关特性以及提出了ADTE的估计方法来区分信令和数据流量。本文的研究也显示出Napster在p2p流中占大部分,这暗示着超级节点和阶层式拓扑较纯p2p结构潜在的优势。同时,观察到在我国p2p的流量仅占Internet总流量的1%弱,这个值跟国外的数据有很大区别。我们分析了其中的原因并希望该结论为我国p2p软件的发展提供参考。
文摘An upwind scheme based on the unstructured mesh is developed to solve ideal 2-D magnetohydrodynamics （MHD） equations. The inviscid fluxes are approximated by using the modified advection upstream splitting method （AUSM） scheme, and a 5-stage explicit Runge-Kutta scheme is adopted in the time integration. To avoid the influence of the magnetic field divergence created during the simulation, the hyperbolic divergence cleaning method is introduced. The shock-capturing properties of the method are verified by solving the MHD shock-tube problem. Then the 2-D nozzle flow with the magnetic field is numerically simulated on the unstructured mesh. Computational results demonstrate the effects of the magnetic field and agree well with those from references.
