In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new gener...In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new generation. In addition to this model, we define remote server objects as session or entity objects according to their roles in a distributed application server, which separate information details from business operations for software reuse. A web store system is implement by using this multi-tier distributed object enterprise computing model.展开更多
Object oriented techniques make applications substantially easier to build by providing a high-level platform for appli-cation development. There have been a large number of projects based on the Distributed Object Or...Object oriented techniques make applications substantially easier to build by providing a high-level platform for appli-cation development. There have been a large number of projects based on the Distributed Object Oriented approach for solving complex problems in various scientific fields. One important aspect of Distributed Object Oriented systems is the efficient distribution of software classes among different processors. The initial design of the Distributed Object Oriented application does not necessarily have the best class distribution and may require to be restructured. In this paper, we propose a methodology for efficiently restructuring the Distributed Object Oriented software systems to get better performance. We use Distributed Object-Oriented performance (DOOP) model as guidance for our restructuring methodology. The proposed methodology consists of two phases. The first phase introduces a recursive graph clustering technique to partition the OO system into subsystems with low coupling. The second phase is concerned with mapping the generated partitions to the set of available machines in the target distributed architecture.展开更多
The low bandwidth hinders the development of mobile computing.Besides providing relatively higher bandwidth on communication layer, constructing adaptable upper application is important. In this paper, a framework of ...The low bandwidth hinders the development of mobile computing.Besides providing relatively higher bandwidth on communication layer, constructing adaptable upper application is important. In this paper, a framework of autoadapting distributed object is proposed, and evaluating methods of object performance are given as well. Distributed objects can adjust their behaviors automaticallyin the framework and keep in relatively good performance to serve requests of remoteapplications. It is an efficient way to implement the performance transparency formobile clients.展开更多
Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving o...Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.展开更多
Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging techn...Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging technologies,Vehicular Edge Computing(VEC)can provide essential assurance for the robustness of Artificial Intelligence(AI)algorithms to be used in the 6G systems.Therefore,in this paper,a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed,taking the object detection task as an example.This strategy includes two stages:model stabilization and model adaptation.In the former,the state-of-the-art methods are appended to the model to improve its robustness.In the latter,two targeted compression methods are implemented,namely model parameter pruning and knowledge distillation,which result in a trade-off between model performance and runtime resources.Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals,where the introduced trade-off outperforms the other strategies available.展开更多
Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simulta...Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.展开更多
Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems.New energy sources are continuously being connected to dist...Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems.New energy sources are continuously being connected to distribution grids;this,however,increases the complexity of the information structure of marketing and distribution businesses.The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks.As a solution,this paper presents a data model of"one graph of marketing and distribution"and a framework for graph computing,by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory.Specifically,this work aims to determine the correlation between distribution transformers and marketing users,which is crucial for elucidating the connection between marketing and distribution.In this manner,a novel identification algorithm is proposed based on the collected data for marketing and distribution.Lastly,a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads.Furthermore,an operation and maintenance(O&M)knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.展开更多
A computer model has been developed to simulate the distribution behaviors of Ni, Co. Sn. Ph,Zn, As, Sb, Bi, An and Ag in copper smelting process. The model assumes that the copper smelting furnaceis in thermodynamic ...A computer model has been developed to simulate the distribution behaviors of Ni, Co. Sn. Ph,Zn, As, Sb, Bi, An and Ag in copper smelting process. The model assumes that the copper smelting furnaceis in thermodynamic equilibrium. As many as 21 elements (Cu. S, Fe. Ni, Co. Sn, As, Sb. Bi, Ph. Zn.An. Ag. O, N, C, H, Ca, Mg, Al, and St) and 73 compounds are considered. This model accounts forphysical entrainment in the melts. The predictions by the present computer model are compared with theknown commercial data from Guixi Smelter in China, Home Smelter in Canada and Naoshima Smelter inJapan. The agreements between the computer predictions and the commercial data are excellent, so that thepresent computer model can be used to monitor and optimize the actual industrial operations of copper smelting. It is applicable to simulation of almost all copper pyrometallurgical processes.展开更多
Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization...Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization for DHM calibration.Latin Hypercube-once at a time （LH-OAT） was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO（SA-MO）.WetSpa model with non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis （MOSCEM-UA）algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case Ⅰ showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case Ⅱ indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.展开更多
Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for tradition...Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.展开更多
Nutrients supply especially like nutrients and oxygen play vital role in tissue engineering process.It is found that tissue could not grow very well in the middle of the scaffold because few nutrients could transport ...Nutrients supply especially like nutrients and oxygen play vital role in tissue engineering process.It is found that tissue could not grow very well in the middle of the scaffold because few nutrients could transport to the middle.Nutrient limitations would reduce cell proliferation and differentiation.In that case,there is urgent need to understand the nutrient distribution for both in vitro and in vivo study,as no technology is able for researchers to observe the nutrients transport during those process.In this paper,a numerical model coupling with VOF(volume of fluid)model and species transport model together for predicting the distribution of oxygen and glucose in the scaffold after implantation in to the site is developed.Comparing with our previous in vivo tests,the regenerated tissue distribution has a similar trend as oxygen distribution rather than glucose.The reported scaffold manufactured by additive manufacturing provided a good interconnected structure which facilitated the nutrient transportation in the scaffold.Considering nutrient transportation,this numerical model could be used in better understanding the nutrients transportation in the scaffold,and leading to a better understanding of tissue formation in the scaffold.展开更多
Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficie...Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.展开更多
文摘In this paper, we adopt Java platform to achieve a multi-tier distributed object enterprise computing model which provides an open, flexible, robust and cross-platform standard for enterprise applications of new generation. In addition to this model, we define remote server objects as session or entity objects according to their roles in a distributed application server, which separate information details from business operations for software reuse. A web store system is implement by using this multi-tier distributed object enterprise computing model.
文摘Object oriented techniques make applications substantially easier to build by providing a high-level platform for appli-cation development. There have been a large number of projects based on the Distributed Object Oriented approach for solving complex problems in various scientific fields. One important aspect of Distributed Object Oriented systems is the efficient distribution of software classes among different processors. The initial design of the Distributed Object Oriented application does not necessarily have the best class distribution and may require to be restructured. In this paper, we propose a methodology for efficiently restructuring the Distributed Object Oriented software systems to get better performance. We use Distributed Object-Oriented performance (DOOP) model as guidance for our restructuring methodology. The proposed methodology consists of two phases. The first phase introduces a recursive graph clustering technique to partition the OO system into subsystems with low coupling. The second phase is concerned with mapping the generated partitions to the set of available machines in the target distributed architecture.
文摘The low bandwidth hinders the development of mobile computing.Besides providing relatively higher bandwidth on communication layer, constructing adaptable upper application is important. In this paper, a framework of autoadapting distributed object is proposed, and evaluating methods of object performance are given as well. Distributed objects can adjust their behaviors automaticallyin the framework and keep in relatively good performance to serve requests of remoteapplications. It is an efficient way to implement the performance transparency formobile clients.
基金supported by Soongsil University Research Fund and BK 21 of Korea
文摘Foreground moving object detection is an important process in various computer vision applications such as intelligent visual surveillance, HCI, object-based video compression, etc. One of the most successful moving object detection algorithms is based on Adaptive Gaussian Mixture Model （AGMM）. Although ACMM-hased object detection shows very good performance with respect to object detection accuracy, AGMM is very complex model requiring lots of floatingpoint arithmetic so that it should pay for expensive computational cost. Thus, direct implementation of the AGMM-based object detection for embedded DSPs without floating-point arithmetic HW support cannot satisfy the real-time processing requirement. This paper presents a novel rcal-time implementation of adaptive Gaussian mixture model-based moving object detection algorithm for fixed-point DSPs. In the proposed implementation, in addition to changes of data types into fixed-point ones, magnification of the Gaussian distribution technique is introduced so that the integer and fixed-point arithmetic can be easily and consistently utilized instead of real nmnher and floatingpoint arithmetic in processing of AGMM algorithm. Experimental results shows that the proposed implementation have a high potential in real-time applications.
基金supported by the National Key Research and Development Program of China(2020YFB1807500)the National Natural Science Foundation of China(62072360,62001357,62172438,61901367)+4 种基金the key research and development plan of Shaanxi province(2021ZDLGY02-09,2020JQ-844)the Natural Science Foundation of Guangdong Province of China(2022A1515010988)Key Project on Artificial Intelligence of Xi'an Science and Technology Plan(2022JH-RGZN-0003)Xi'an Science and Technology Plan(20RGZN0005)the Xi'an Key Laboratory of Mobile Edge Computing and Security(201805052-ZD3CG36).
文摘Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging technologies,Vehicular Edge Computing(VEC)can provide essential assurance for the robustness of Artificial Intelligence(AI)algorithms to be used in the 6G systems.Therefore,in this paper,a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed,taking the object detection task as an example.This strategy includes two stages:model stabilization and model adaptation.In the former,the state-of-the-art methods are appended to the model to improve its robustness.In the latter,two targeted compression methods are implemented,namely model parameter pruning and knowledge distillation,which result in a trade-off between model performance and runtime resources.Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals,where the introduced trade-off outperforms the other strategies available.
基金Supported by Natural Science Foundation of China ( No. 60373061).
文摘Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.
基金This work was supported by the National Key R&D Program of China(2020YFB0905900).
文摘Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems.New energy sources are continuously being connected to distribution grids;this,however,increases the complexity of the information structure of marketing and distribution businesses.The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks.As a solution,this paper presents a data model of"one graph of marketing and distribution"and a framework for graph computing,by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory.Specifically,this work aims to determine the correlation between distribution transformers and marketing users,which is crucial for elucidating the connection between marketing and distribution.In this manner,a novel identification algorithm is proposed based on the collected data for marketing and distribution.Lastly,a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads.Furthermore,an operation and maintenance(O&M)knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.
文摘A computer model has been developed to simulate the distribution behaviors of Ni, Co. Sn. Ph,Zn, As, Sb, Bi, An and Ag in copper smelting process. The model assumes that the copper smelting furnaceis in thermodynamic equilibrium. As many as 21 elements (Cu. S, Fe. Ni, Co. Sn, As, Sb. Bi, Ph. Zn.An. Ag. O, N, C, H, Ca, Mg, Al, and St) and 73 compounds are considered. This model accounts forphysical entrainment in the melts. The predictions by the present computer model are compared with theknown commercial data from Guixi Smelter in China, Home Smelter in Canada and Naoshima Smelter inJapan. The agreements between the computer predictions and the commercial data are excellent, so that thepresent computer model can be used to monitor and optimize the actual industrial operations of copper smelting. It is applicable to simulation of almost all copper pyrometallurgical processes.
基金National Basic Research Program(973)of China(No.2010CB951102)Innovative Research Groups of the National Natural Science Foundation,China(No.51021006)National Natural Science Foundation of China(No.51079028)
文摘Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization for DHM calibration.Latin Hypercube-once at a time （LH-OAT） was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO（SA-MO）.WetSpa model with non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis （MOSCEM-UA）algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case Ⅰ showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case Ⅱ indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.
基金This research was supported by the Chung-Ang University Graduate Research Scholarship in 2021.This study was carried out with the support of‘R&D Program for Forest Science Technology(Project No.2021338C10-2223-CD02)’provided by Korea Forest Service(Korea Forestry Promotion Institute).
文摘Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase.Physical expansion limits exist for traditional standalone storage systems,such as I/O and file system capacity.However,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location.And this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network latency.This problem hinders deploying distributed storage systems to multiple data centers over WAN.It lowers the scalability of distributed storage systems to accommodate data storage needs.This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance.The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and cold.With assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance degradation.This paper also conducts simulations to evaluate the proposed and existing distributed storage methods.The result shows that our proposed method reduced latency by 38%compared to the existing method.Therefore,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.
基金supported by Versus Arthritis UK(Grant no:21977)European Commission via a H2020-MSCA-RISE programme(BAMOS,Grant no:734156)+1 种基金Innovative UK via Newton Fund(Grant no:102872)Engineering and Physical Science Research Council(EPSRC)via DTP CASE programme(Grant no:EP/T517793/1).
文摘Nutrients supply especially like nutrients and oxygen play vital role in tissue engineering process.It is found that tissue could not grow very well in the middle of the scaffold because few nutrients could transport to the middle.Nutrient limitations would reduce cell proliferation and differentiation.In that case,there is urgent need to understand the nutrient distribution for both in vitro and in vivo study,as no technology is able for researchers to observe the nutrients transport during those process.In this paper,a numerical model coupling with VOF(volume of fluid)model and species transport model together for predicting the distribution of oxygen and glucose in the scaffold after implantation in to the site is developed.Comparing with our previous in vivo tests,the regenerated tissue distribution has a similar trend as oxygen distribution rather than glucose.The reported scaffold manufactured by additive manufacturing provided a good interconnected structure which facilitated the nutrient transportation in the scaffold.Considering nutrient transportation,this numerical model could be used in better understanding the nutrients transportation in the scaffold,and leading to a better understanding of tissue formation in the scaffold.
基金supported by 2020 Foshan Science and Technology Project(Numbering:2020001005356),Baoling Qin received the grant.
文摘Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.
