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Orchestrating Network Functions in Software-Defined Networks 认领 引用 被引量：2
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作者 Hongchao Hu Lin Pang +1 位作者 Zhenpeng Wang Guozhen Cheng 《China Communications》 SCIE CSCD 2017年第2期104-117,共14页
Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some o... Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently. 展开更多
关键词 software-defined network network function function orchestrating
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Network Function Virtualization Technology:Progress and Standardization 认领 引用 被引量：1
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作者 Huiling Zhao Yunpeng Xie Fan Shi 《ZTE Communications》 2014年第2期3-7,共5页
Network innovation and business transformation are both necessary for telecom operators to adapt to new situations, but operators face challenges in terms of network bearer complexity, business centralization, and IT/... Network innovation and business transformation are both necessary for telecom operators to adapt to new situations, but operators face challenges in terms of network bearer complexity, business centralization, and IT/CT integration. Network function virtualization （NFV） may inspire new development ideas, but many doubts still exist within industry, especially about how to introduce NFV into an operator＇ s network. This article describes the latest progress in NFV standardization, NFV requirements and hot technology issues, and typical NFV applications in an operator networks. 展开更多
关键词 network functions virtualization （NFV） overlay network virtual extensible LAN （VXLAN） service chaining
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Optimize the Deployment and Integration for Multicast-Oriented Virtual Network Function Tree 认领 引用
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作者 Ying Chang Hongxue Yang Qinghua Zhu 《Journal of Beijing Institute of Technology》 EI CAS 2022年第5期513-523,共11页
Due to the development of network technology,the number of users is increasing rapidly,and the demand for emerging multicast services is becoming more and more abundant,traffic data is increasing day by day,network no... Due to the development of network technology,the number of users is increasing rapidly,and the demand for emerging multicast services is becoming more and more abundant,traffic data is increasing day by day,network nodes are becoming denser,network topology is becoming more complex,and operators’equipment operation and maintenance costs are increasing.Network functions virtualization multicast issues include building a traffic forwarding topology,deploying the required functions,and directing traffic.Combining the two is still a problem to be studied in depth at present,and this paper proposes a two-stage solution where the decisions of these two stages are interdependent.Specifically,this paper decouples multicast traffic forwarding and function delivery.The minimum spanning tree of traffic forwarding is constructed by Steiner tree,and the traffic forwarding is realized by Viterbi-algorithm.Use a general topology network to examine network cost and service performance.Simulation results show that this method can reduce overhead and delay and optimize user experience. 展开更多
关键词 multicast service function chain(SFC) network functions virtualization(NFV) software defined network(SDN) Viterbi-algorithm
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Enhanced Reliability in Network Function Virtualization by Hybrid Hexagon-Cost Efficient Algorithm 认领 引用
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作者 D.Jeyakumar C.Rajabhushanam 《Computer Systems Science & Engineering》 SCIE EI 2022年第12期1131-1144,共14页
In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communic... In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication services.Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding graph.To achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation. 展开更多
关键词 Network function virtualized(NFV) hybrid hexagon-cost efficient algorithm(HH-CE) cost-aware important measure(CIM) reliability network services
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SAVNFV:Towards a Scalable and Accurate Inter-Domain Source Address Validation Platform with Network Functions Virtualization 认领 引用
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作者 Shu Yang Zequn Zhang Laizhong Cui 《Big Data Mining and Analytics》 EI CSCD 2026年第2期536-553,共18页
Nowadays,Source Address Validation(SAV)is increasingly important for defending Distributed Denial of Service(DDoS)attacks and other malicious activities.Existing ingress/edge filtering-based solutions,such as SAVI,fil... Nowadays,Source Address Validation(SAV)is increasingly important for defending Distributed Denial of Service(DDoS)attacks and other malicious activities.Existing ingress/edge filtering-based solutions,such as SAVI,filter spoofed source addresses using Access Control Lists(ACL)or unicast Reverse Path Forwarding(uRPF),but they only provide coarse-grained filtering.Source Address Validation in intra-domain and inter-domain NETworks(SAVNET)has recently attracted much attention in both industry and the Internet Engineering Task Force(IETF),deploying SAV inside Internet Service Provider(ISP)networks and generating SAV tables by binding source address prefixes with incoming interfaces.However,SAVNET requires upgrading almost all routers across networks,which is impractical—especially in inter-domain scenarios.Moreover,due to policy routing and load balancing,determining the exact incoming interface for each source prefix is challenging.In this paper,we propose SAVNFV,a Network Functions Virtualization(NFV)based platform that provides SAV capabilities by building a“clean”virtual overlay network.SAVNFV randomly generates paths for each flow through a centralized controller,making the SAV table easy to obtain.The paths are periodically refreshed,and packets are transmitted through multiple routes,making it nearly impossible for attackers to identify the correct incoming interface.We formulate the multi-path transmission as an optimization problem and prove it to be NP-Complete,then design approximation algorithms with theoretical guarantees.Comprehensive simulations show that SAVNFV blocks 94.6%more malicious traffic than traditional solutions while maintaining acceptable path stretch.We also implement the system using open-source routing software and build a real-world experimental platform to further validate our design. 展开更多
关键词 Source Address Validation(SAV) Network Functions Virtualization(NFV) Distributed Denial of Service(DDoS)attacks multi-path routing
Network Structure-function Decoupling Underlying the Cognitive Impairment in Ischemic Leukoaraiosis 认领 引用
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作者 Mengxue Wang Wenjuan Huang +5 位作者 Yixin Zhu Tong Lu Zhengsheng Zhang Chunming Xie Zan Wang Qingguo Ren 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2026年第6期717-722,共6页
Cerebral small vessel disease(CSVD)encompasses a spectrum of pathological processes that affect the small arteries,capillaries,and venules of the brain.The neuroimaging features include white matter hyperintensities(W... Cerebral small vessel disease(CSVD)encompasses a spectrum of pathological processes that affect the small arteries,capillaries,and venules of the brain.The neuroimaging features include white matter hyperintensities(WMH),lacunar infarcts,cerebral microbleeds,and enlarged perivascular spaces. 展开更多
关键词 cognitive impairment network structure function decoupling white matter hyperintensities wmh lacunar infarctscerebral microbleedsand pathological processes neuroimaging features cerebral small vessel disease csvd encompasses enlarged perivascular spaces cerebral small vessel disease
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Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats 认领 引用 被引量：3
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作者 Yunting Xiang Xiangxin Xing +6 位作者 Xuyun Hua Yuwen Zhang Xin Xue Jiajia Wu Mouxiong Zheng He Wang Jianguang Xu 《Neural Regeneration Research》 SCIE CAS CSCD 2025年第5期1495-1504,共10页
Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network lev... Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery. 展开更多
关键词 brain functional networks end-to-end nerve transfer end-to-side nerve transfer independent component analysis nerve repair peripheral plexus injury resting-state functional connectivity
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rTMS Improves Cognitive Function and Brain Network Connectivity in Patients With Alzheimer’s Disease 认领 引用 被引量：1
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作者 XU Gui-Zhi LIU Lin +4 位作者 GUO Miao-Miao WANG Tian GAO Jiao-Jiao JI Yong WANG Pan 《生物化学与生物物理进展》 SCIE CAS CSCD 北大核心 2025年第8期2131-2145,共15页
Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,n... Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment. 展开更多
关键词 transcranial magnetic stimulation Alzheimer’s disease power spectral density electroencephalogram brain functional network
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Radial Basis Function Neural Network Adaptive Controller for Wearable Upper-Limb Exoskeleton with Disturbance Observer 认领 引用
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作者 Mohammad Soleimani Amiri Sahbi Boubaker +1 位作者 Rizauddin Ramli Souad Kamel 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期3113-3133,共21页
Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled pe... Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons. 展开更多
关键词 Adaptive neural network controller disturbance observer upper-limb exoskeleton rehabilitation robotics Lyapunov stability radial basis function network
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Workshop on“AI-Enabled Network Evolution:Cloud Native&AI Innovation for Telco Future” 认领 引用
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《China Standardization》 2026年第3期59-59,共1页
The ITU in joint collaboration with ETSI NFV is organizing a workshop on“AI-Enabled Network Evolution:Cloud Native&AI Innovation for Telco Future”,which will take place on June 24,2026.The workshop will focus on... The ITU in joint collaboration with ETSI NFV is organizing a workshop on“AI-Enabled Network Evolution:Cloud Native&AI Innovation for Telco Future”,which will take place on June 24,2026.The workshop will focus on:·the integration of AI and telecom cloud/NFV based on the latest technological trends on telecom cloud architecture,network function orchestration,cloud-native and AI-native evolution. 展开更多
关键词 ai innovation network function orchestration function orchestrationcloud native telco future ai enabled network evolution telecom cloud architecture cloud native ai native evolution
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A study of connectivity features analysis in brain function network for dementia recognition 认领 引用
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作者 Siying Li Peng Wang +6 位作者 Zhenfeng Li Lidong Du Xianxiang Chen Jie Sun Libin Jiang Gang Cheng Zhen Fang 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第1期79-93,共15页
Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuro... Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at http://gffzz188fe103f8f1460as666bu6cf5vqw6q9n.ffgz.tsg.suse.edu.cn/Debbie-85/eeg-connectivity. 展开更多
关键词 Electroencephalography Brain function network Machine learning Feature selection Dementia recognition
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Effective Edge-Cloud Interplay for NFV-Based Optical Metro-Access Networks Supporting IoT Services 认领 引用 被引量：2
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作者 Jin Feiming Zhang Yukun +2 位作者 Li Hanxue Amr Tolba Zhang Tiantian 《China Communications》 SCIE EI CSCD 2025年第4期117-128,共12页
In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the V... In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the Virtual Machines(VMs)cannot be successfully launched due to the server overload.In addition,transferring the data from the AP to the remote DC may cause an undesirable delivery delay.For this end,we propose a promising solution considering the interplay between the cloud DC and edge APs.More specifically,bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service(QoS).In this work,when the cloud DC resource becomes limited,especially for delay sensitive but not computing-dependent IoT applications,we degrade their VMs and migrate them to edge APs instead of the remote DC.To avoid excessive VM degradation and computing offloading,we derive appropriate VM degradation coefficients based on classic microeconomic theory.Simulation results demonstrate that our algorithms improve the service providers'utility with the ratio from 34%to 89%over traditional cloud-centric solutions. 展开更多
关键词 computing offloading edge-cloud interplay network function virtualization optical metroaccess network service degradability
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Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning 认领 引用
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作者 Seyha Ros Seungwoo Kang +3 位作者 Taikuong Iv Inseok Song Prohim Tam Seokhoon Kim 《Computers, Materials & Continua》 SCIE EI 2025年第5期1649-1665,共17页
Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G era.NFV decomposes the network resource paradigm,demonstrating the efficient utilization... Recently,Network Functions Virtualization(NFV)has become a critical resource for optimizing capability utilization in the 5G/B5G era.NFV decomposes the network resource paradigm,demonstrating the efficient utilization of Network Functions(NFs)to enable configurable service priorities and resource demands.Telecommunications Service Providers(TSPs)face challenges in network utilization,as the vast amounts of data generated by the Internet of Things(IoT)overwhelm existing infrastructures.IoT applications,which generate massive volumes of diverse data and require real-time communication,contribute to bottlenecks and congestion.In this context,Multiaccess Edge Computing(MEC)is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function(VNF)sequences within Service Function Chaining(SFC).This paper proposes the use of Deep Reinforcement Learning(DRL)combined with Graph Neural Networks(GNN)to enhance network processing,performance,and resource pooling capabilities.GNN facilitates feature extraction through Message-Passing Neural Network(MPNN)mechanisms.Together with DRL,Deep Q-Networks(DQN)are utilized to dynamically allocate resources based on IoT network priorities and demands.Our focus is on minimizing delay times for VNF instance execution,ensuring effective resource placement,and allocation in SFC deployments,offering flexibility to adapt to real-time changes in priority and workload.Simulation results demonstrate that our proposed scheme outperforms reference models in terms of reward,delay,delivery,service drop ratios,and average completion ratios,proving its potential for IoT applications. 展开更多
关键词 Deep reinforcement learning graph neural network multi-access edge computing network functions virtualization software-defined networking
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Service Function Chain Migration in LEO Satellite Networks 认领 引用 被引量：2
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作者 Geng Yuhui Wang Niwei +5 位作者 Chen Xi Xu Xiaofan Zhou Changsheng Yang Junyi Xiao Zhenyu Cao Xianbin 《China Communications》 SCIE CSCD 2024年第3期247-259,共13页
With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)sat... With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity. 展开更多
关键词 network function virtualization(NFV) resource allocation satellite networks service function chain(SFC) SFC migration SFC placement soft-ware defined network(SDN)
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Altered functional connectivity networks of hippocampal subregions in remitted late-onset depression:a longitudinal resting-state study 认领 引用 被引量：4
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作者 Zan Wang Yonggui Yuan +4 位作者 Feng Bai Hao Shu Jiayong You Lingjiang Li Zhijun Zhang 《Neuroscience Bulletin》 SCIE CAS CSCD 2015年第1期13-21,共9页
The regional specifi city of hippocampal abnormalities in late-life depression（LLD） has been demonstrated in previous studies. In this study,we sought to examine the functional connectivity（FC） patterns of hippoca... The regional specifi city of hippocampal abnormalities in late-life depression（LLD） has been demonstrated in previous studies. In this study,we sought to examine the functional connectivity（FC） patterns of hippocampal subregions in remitted late-onset depression（r LOD）,a special subtype of LLD. Fourteen r LOD patients and 18 healthy controls underwent clinical and cognitive evaluations as well as resting-state functional magnetic resonance imaging scans at baseline and at ~21 months of follow-up. Each hippocampus was divided into three parts,the cornu ammonis（CA）,the dentate gyrus,and the subicular complex,and then six seed-based hippocampal subregional networks were established.Longitudinal changes of the six networks over time were directly compared between the rL OD and control groups. From baseline to follow-up,the r LOD group showed a greater decline in connectivity of the left CA to the bilateral posterior cingulate cortex/precuneus（PCC/PCUN）,but showed increased connectivity of the right hippocampal subregional networks with the frontal cortex（bilateral medial prefrontal cortex/anterior cingulate cortex and supplementary motor area）. Further correlative analyses revealed thatthe longitudinal changes in FC between the left CA and PCC/PCUN were positively correlated with longitudinal changes in the Symbol Digit Modalities Test（r = 0.624,P = 0.017） and the Digit Span Test（r = 0.545,P = 0.044） scores in the r LOD group. These results may provide insights into the neurobiological mechanism underlying the cognitive dysfunction in r LOD patients. 展开更多
关键词 remitted late-onset depression hippocampal subregional network functional connectivity functional magnetic resonance imaging cognitive dysfunction
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Prediction of the residual strength of clay using functional networks 认领 引用 被引量：9
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作者 S.Z.Khan Shakti Suman +1 位作者 M.Pavani S.K.Das 《Geoscience Frontiers》 SCIE CAS CSCD 2016年第1期67-74,共8页
Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of s... Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks（FN） using data available in the literature. The performance of FN was compared with support vector machine（SVM） and artificial neural network（ANN） based on statistical parameters like correlation coefficient（R）, Nash–Sutcliff coefficient of efficiency（E）, absolute average error（AAE）, maximum average error（MAE） and root mean square error（RMSE）. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output. 展开更多
关键词 Landslides Residual strength Index properties Prediction model Functional networks
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Changes in brain functional network connectivity after stroke 认领 引用 被引量：5
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作者 Wei Li Yapeng Li +1 位作者 Wenzhen Zhu Xi Chen 《Neural Regeneration Research》 SCIE CAS CSCD 2014年第1期51-60,共10页
Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore func... Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke. 展开更多
关键词 nerve regeneration brain injury stroke motor areas functional magnetic resonanceimaging brain network independent component analysis functional network connectivity neuralplasticity NSFC grant neural regeneration
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Brain functional connectivity network studies of acupuncture: a systematic review on resting-state fMRI 认领 引用 被引量：70
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作者 Rong-lin Cai Guo-ming Shen +1 位作者 Hao Wang Yuan-yuan Guan 《Journal of Integrative Medicine》 SCIE CAS CSCD 2018年第1期26-33,共8页
Background： Functional magnetic resonance imaging （fMRI） is a novel method for studying the changes of brain networks due to acupuncture treatment. In recent years, more and more studies have focused on the brain f... Background： Functional magnetic resonance imaging （fMRI） is a novel method for studying the changes of brain networks due to acupuncture treatment. In recent years, more and more studies have focused on the brain functional connectivity network of acupuncture stimulation. Objective： To offer an overview of the different influences of acupuncture on the brain functional connec- tivity network from studies using resting-state fMRI. Search strategy： The authors performed a systematic search according to PRISMA guidelines, The database PubMed was searched from January 1, 2006 to December 31, 2016 with restriction to human studies in English language. Inclusion criteria： Electronic searches were conducted in PubMed using the keywords ＂acupuncture＂ and ＂neuroimaging＂ or ＂resting-state fMRI＂ or ＂functional connectivity＂, Data extraction and analysis： Selection of included articles, data extraction and methodological quality assessments were respectively conducted by two review authors. Results： Forty-four resting-state fMRI studies were included in this systematic review according to inclu- sion criteria. Thirteen studies applied manual acupuncture vs. sham, four studies applied electro- acupuncture vs. sham, two studies also compared transcutaneous electrical acupoint stimulation vs. sham, and nine applied sham acupoint as control. Nineteen studies with a total number of 574 healthy subjects selected to perform fMRI only considered healthy adult volunteers. The brain functional connec- tivity of the patients had varying degrees of change. Compared with sham acupuncture, verum acupunc- ture could increase default mode network and sensorimotor network connectivity with pain-, affective- and memory-related brain areas. It has significantly greater connectivity of genuine acupuncture between the periaqueductal gray, anterior cingulate cortex, left posterior cingulate cortex, right anterior insula, limbic/paralimbic and precuneus compared with sham acupuncture. Some research had also shown that acupuncture could adjust the limbic-paralimbic-neocortical network, brainstem, cerebellum, subcortical and hippocampus brain areas. Conclusion： It can be presumed that the functional connectivity network is closely related to the mech- anism of acupuncture, and central integration plays a critical role in the acupuncture mechanism. 展开更多
关键词 Resting-state functional magnetic resonance Acupuncture Functional connectivity Functional network Complementary medicine Alternative medicine
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Analysis of a phase synchronized functional network based on the rhythm of brain activities 认领 引用 被引量：11
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作者 李凌 金贞兰 李斌 《Chinese Physics B》 SCIE EI CAS CSCD 2011年第3期512-518,共7页
Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this ... Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram （EEG） data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta （4-7 Hz）, alpha （8-13 Hz） and beta （14-30 Hz） bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coetticient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm, 展开更多
关键词 electroencephalogram phase synchronization rhythm functional brain network
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Global Functional Network Connectivity Disturbances in Parkinson’s Disease with Mild Cognitive Impairment by Resting-State Functional MRI 认领 引用 被引量：5
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作者 Xin-xin SHUAI Xiang-chuang KONG +2 位作者 Yan ZOU Si-qi WANG Yu-hui WANG 《Current Medical Science》 SCIE CAS 2020年第6期1057-1066,共10页
Examining the spontaneous BOLD activity to understand the neural mechanism of Parkinson’s disease(PD)with mild cognitive impairment(MCI)is a focus in resting-state functional MRI(rs-fMRI)studies.This study aimed to i... Examining the spontaneous BOLD activity to understand the neural mechanism of Parkinson’s disease(PD)with mild cognitive impairment(MCI)is a focus in resting-state functional MRI(rs-fMRI)studies.This study aimed to investigate the alteration of brain functional connectivity in PD with MCI in a systematical way at two levels:functional connectivity analysis within resting state networks(RSNs)and functional network connectivity(FNC)analysis.Using group independent component analysis(ICA)on rs-fMRI data acquired from 30 participants(14 healthy controls and 16 PD patients with MCI),16 RSNs were identified,and functional connectivity analysis within the RSNs and FNC analysis were carried out between groups.Compared to controls,patients with PD showed decreased functional connectivity within putamen network,thalamus network,cerebellar network,attention network,and self-referential network,and increased functional connectivity within execution network.Globally disturbed,mostly increased functional connectivity of FNC was observed in PD group,and insular network and execution network were the dominant network with extensively increased functional connectivity with other RSNs.Cerebellar network showed decreased functional connectivity with caudate network,insular network,and self-referential network.In general,decreased functional connectivity within RSNs and globally disturbed,mostly increased functional connectivity of FNC may be characteristics of PD.Increased functional connectivity within execution network may be an early marker of PD.The multi-perspective study based on RSNs may be a valuable means to assess functional changes corresponding to specific RSN,contributing to the understanding of the neural mechanism of PD. 展开更多
关键词 Parkinson's disease resting-state functional MRI resting-state functional connectivity functional network connectivity
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