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
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
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
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
Recently, integrating Softwaredefined networking(SDN) and network functions virtualization(NFV) are proposed to address the issue that difficulty and cost of hardwarebased and proprietary middleboxes management. Howev...Recently, integrating Softwaredefined networking(SDN) and network functions virtualization(NFV) are proposed to address the issue that difficulty and cost of hardwarebased and proprietary middleboxes management. However, it lacks of a framework that orchestrates network functions to service chain in the network cooperatively. In this paper, we propose a function combination framework that can dynamically adapt the network based on the integration NFV and SDN. There are two main contributions in this paper. First, the function combination framework based on the integration of SDN and NFV is proposed to address the function combination issue, including the architecture of Service Deliver Network, the port types representing traffic directions and the explanation of terms. Second, we formulate the issue of load balance of function combination as the model minimizing the standard deviations of all servers' loads and satisfying the demand of performance and limit of resource. The least busy placement algorithm is introduced to approach optimal solution of the problem. Finally, experimental results demonstrate that the proposed method can combine functions in an efficient and scalable way and ensure the load balance of the network.展开更多
Real-time multimedia sharing in Consumer-centric Multimedia Network(CMN) requires usability anywhere, anytime and from any device. However, CMNs are usually located or implemented on application layer, which makes CMN...Real-time multimedia sharing in Consumer-centric Multimedia Network(CMN) requires usability anywhere, anytime and from any device. However, CMNs are usually located or implemented on application layer, which makes CMNs subjected to their fixed substrate security framework. A fundamental diversifying attribute for the customized security experiences of CMNs is pressing. This paper proposes a programmable network structure which is named Service Processing Chain(SPC) based on network function combination. The SPC is established by the ordinal combination of network functions in substrate switches dynamically, and therefore constructs a special channel for each CMN with required security. The construction and reconfiguration algorithms of SPC are also discussed in this paper. Evaluations and implementation show that above approaches are effective in providing multilevel security with flexibility and expansibility. It is believed that the SPC could provide customized security service and drive participative real-time multimedia sharing for CMNs.展开更多
To address the issues that middleboxes as a fundamental part of today's networks are facing, Network Function Virtualization(NFV)has been recently proposed, which in essence asserts to migrate hardware-based middl...To address the issues that middleboxes as a fundamental part of today's networks are facing, Network Function Virtualization(NFV)has been recently proposed, which in essence asserts to migrate hardware-based middleboxes into software-based virtualized function entities.Due to the demands of virtual services placement in NFV network environment, this paper models the service amount placement problem involving with the resources allocation as a cooperative game and proposes the placement policy by Nash Bargaining Solution(NBS). Specifically,we first introduce the system overview and apply the rigorous cooperative game-theoretic guide to build the mathematical model, which can give consideration to both the responding efficiency of service requirements and the allocation fairness.Then a distributed algorithm corresponding to NBS is designed to achieve predictable network performance for virtual instances placement.Finally, with simulations under various scenarios,the results show that our placement approach can achieve high utilization of network through the analysis of evaluation metrics namely the satisfaction degree and fairness index. With the suitable demand amount of services, the average values of two metrics can reach above 90%. And by tuning the base placement, our solution can enable operators to flexibly balance the tradeoff between satisfaction and fairness of resourcessharing in service platforms.展开更多
Objective:To investigate if manual acupuncture can improve the primary insomnia-induced impairments of attention network function and its safety.Methods:Totally 64 eligible participants were enrolled in a randomized c...Objective:To investigate if manual acupuncture can improve the primary insomnia-induced impairments of attention network function and its safety.Methods:Totally 64 eligible participants were enrolled in a randomized controlled trial,with 32 cases allocated to the treatment group and 32 cases allocated to the control group,respectively.The participants in the treatment group received real-acupuncture therapy[acupuncture at 'Five Spirits Acupoints' including Shéntíng(神庭 GV 24),Běnshén(本神 GB 13),Sìshéncōng(四神聪 EX-HN 1),Shéndào(神道 GV11)and Shénmén(神门 HT 7)]whereas participants in control group received sham-acupuncture therapy with Streitberger placebo-needle and same acupoints.Interventions were offered every two days and three times a week for total 8 weeks.Both Pittsburgh sleep quality index(PSQI)and Attention Network Task(ANT)were employed to assess the changes of sleep quality and attention network function at pretreatment and post-treatment,respectively.Meanwhile,adverse effects were monitored and recorded.Results:(1)After 8-week treatment,the total score of PSQI in the treatment group decreased from14.22±3.46 to 8.19±3.34(P<0.001),and the total score of PSQI in the control group decreased from12.84±3.90 to 11.41±3.90(P<0.05).The decrease in the treatment group was more significant than that in the control group(P<0.001).(2)After treatment,the alerting efficiency of both groups increased,the treatment group increased from 40.44±5.62 to 56.44±5.37(P<0.001),and the control group increased from 39.50±6.39 to 40.06±6.14(P<0.05).The increase in the treatment group was more significant than that in the control group(P<0.001).The total reaction time of both groups decreased,the treatment group decreased from 574.94±10.1 to 548.34±15.05(P<0.001),and the control group decreased from578.25±13.26 to 576.78±12.15(P<0.05).The decrease in the treatment group was more significant than that in the control group(P<0.001).(3)No obvious change in orienting efficiency was observed in both groups after treatment(P>0.05).(4)No serious adverse events were reported in this trial,except 2 patients from treatment group had slight hematoma after receiving acupuncture therapy.Conclusion:Acupuncture represents a safe and useful non-pharmacologic intervention option for primary insomniacs with impairments of attention network function(alertness and conflict processing/executive control).展开更多
In this paper, symbolic code matrix ,constant matrix and count matrix are defined .The first twomatrices are used to describe the elemental expression of augmented matrix and the nede admittance equa-tion is thus obta...In this paper, symbolic code matrix ,constant matrix and count matrix are defined .The first twomatrices are used to describe the elemental expression of augmented matrix and the nede admittance equa-tion is thus obtained. The third matrix is used to obtain the incoming degree matrix, and according to thematrix all the 1- factors of the Coates graph are given. By using the data code, the determinant is expandedand the same items in the expansion are merged. Thus the symbolic network function in which no term can-cellation occurs is generated.展开更多
The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the mo...The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the modified double-graph method, a new decomposition analysis-modified double-graph decomposition analysis is presented for finding symbolic network functions. Its advantages are that the resultant symbolic expressions are compact and contain no cancellation terms, and its sign evaluation is very simple.展开更多
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
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
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 https://github.com/Debbie-85/eeg-connectivity.展开更多
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
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
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
This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development ...This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development complexity.Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack.The proposed system uses technologies like Open vSwitch(OVS)and traffic control(TC)to emulate the space network's highly dynamic topology and time-varying link characteristics.The emulation results demonstrate the system's high reliability,and the user-space network stack reduces development complexity and debugging difficulty,providing convenience for the development of space network protocols and network functions.展开更多
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
Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determini...Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.展开更多
Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture t...Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.展开更多
基金supported by the China Postdoctoral Fund Project (No.44603)the National Natural Science Foundation of China (No.61309020)+1 种基金the National key Research and Development Program of China (No.2016YFB0800100, 2016YFB0800101)the National Natural Science Fund for Creative Research Groups Project(No.61521003)
文摘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.
基金supported by the R&D Program of Beijing Municipal Education Commission(Nos.KM202110858003 and2022X003-KXD)。
文摘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.
文摘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.
文摘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.
基金supported by the Foundation for Innovative Research Groups of the National Science Foundation of China (Grant No.61521003)The National Basic Research Program of China(973)(Grant No.2012CB315901,2013CB329104)+1 种基金The National Natural Science Foundation of China(Grant No.61372121,61309019,61309020)The National High Technology Research and Development Program of China(863)(Grant No.2015AA016102,2013AA013505)
文摘Recently, integrating Softwaredefined networking(SDN) and network functions virtualization(NFV) are proposed to address the issue that difficulty and cost of hardwarebased and proprietary middleboxes management. However, it lacks of a framework that orchestrates network functions to service chain in the network cooperatively. In this paper, we propose a function combination framework that can dynamically adapt the network based on the integration NFV and SDN. There are two main contributions in this paper. First, the function combination framework based on the integration of SDN and NFV is proposed to address the function combination issue, including the architecture of Service Deliver Network, the port types representing traffic directions and the explanation of terms. Second, we formulate the issue of load balance of function combination as the model minimizing the standard deviations of all servers' loads and satisfying the demand of performance and limit of resource. The least busy placement algorithm is introduced to approach optimal solution of the problem. Finally, experimental results demonstrate that the proposed method can combine functions in an efficient and scalable way and ensure the load balance of the network.
基金supported by The National Basic Research Program of China (973) (Grant No. 2012CB315901, 2013CB329104)The National Natural Science Foundation of China (Grant No. 61521003, 61372121, 61309019, 61572519, 61502530)The National High Technology Research and Development Program of China (863) (Grant No. 2015AA016102)
文摘Real-time multimedia sharing in Consumer-centric Multimedia Network(CMN) requires usability anywhere, anytime and from any device. However, CMNs are usually located or implemented on application layer, which makes CMNs subjected to their fixed substrate security framework. A fundamental diversifying attribute for the customized security experiences of CMNs is pressing. This paper proposes a programmable network structure which is named Service Processing Chain(SPC) based on network function combination. The SPC is established by the ordinal combination of network functions in substrate switches dynamically, and therefore constructs a special channel for each CMN with required security. The construction and reconfiguration algorithms of SPC are also discussed in this paper. Evaluations and implementation show that above approaches are effective in providing multilevel security with flexibility and expansibility. It is believed that the SPC could provide customized security service and drive participative real-time multimedia sharing for CMNs.
基金supported by The National Basic Research Program of China (973) (Grant No. 2012CB315901, 2013CB329104)The National Natural Science Foundation of China (Grant No. 61521003, 61372121, 61309019, 61572519, 61502530)The National High Technology Research and Development Program of China (863) (Grant No. 2015AA016102)
文摘To address the issues that middleboxes as a fundamental part of today's networks are facing, Network Function Virtualization(NFV)has been recently proposed, which in essence asserts to migrate hardware-based middleboxes into software-based virtualized function entities.Due to the demands of virtual services placement in NFV network environment, this paper models the service amount placement problem involving with the resources allocation as a cooperative game and proposes the placement policy by Nash Bargaining Solution(NBS). Specifically,we first introduce the system overview and apply the rigorous cooperative game-theoretic guide to build the mathematical model, which can give consideration to both the responding efficiency of service requirements and the allocation fairness.Then a distributed algorithm corresponding to NBS is designed to achieve predictable network performance for virtual instances placement.Finally, with simulations under various scenarios,the results show that our placement approach can achieve high utilization of network through the analysis of evaluation metrics namely the satisfaction degree and fairness index. With the suitable demand amount of services, the average values of two metrics can reach above 90%. And by tuning the base placement, our solution can enable operators to flexibly balance the tradeoff between satisfaction and fairness of resourcessharing in service platforms.
基金Supported by TCM Science and Technology Innovation Project of Shanghai Health and Family Planning Commission-Mobile internet-based guidance platform of ‘Preventive Treatment of Insomnia’(chronic disease management):ZYKC20161016
文摘Objective:To investigate if manual acupuncture can improve the primary insomnia-induced impairments of attention network function and its safety.Methods:Totally 64 eligible participants were enrolled in a randomized controlled trial,with 32 cases allocated to the treatment group and 32 cases allocated to the control group,respectively.The participants in the treatment group received real-acupuncture therapy[acupuncture at 'Five Spirits Acupoints' including Shéntíng(神庭 GV 24),Běnshén(本神 GB 13),Sìshéncōng(四神聪 EX-HN 1),Shéndào(神道 GV11)and Shénmén(神门 HT 7)]whereas participants in control group received sham-acupuncture therapy with Streitberger placebo-needle and same acupoints.Interventions were offered every two days and three times a week for total 8 weeks.Both Pittsburgh sleep quality index(PSQI)and Attention Network Task(ANT)were employed to assess the changes of sleep quality and attention network function at pretreatment and post-treatment,respectively.Meanwhile,adverse effects were monitored and recorded.Results:(1)After 8-week treatment,the total score of PSQI in the treatment group decreased from14.22±3.46 to 8.19±3.34(P<0.001),and the total score of PSQI in the control group decreased from12.84±3.90 to 11.41±3.90(P<0.05).The decrease in the treatment group was more significant than that in the control group(P<0.001).(2)After treatment,the alerting efficiency of both groups increased,the treatment group increased from 40.44±5.62 to 56.44±5.37(P<0.001),and the control group increased from 39.50±6.39 to 40.06±6.14(P<0.05).The increase in the treatment group was more significant than that in the control group(P<0.001).The total reaction time of both groups decreased,the treatment group decreased from 574.94±10.1 to 548.34±15.05(P<0.001),and the control group decreased from578.25±13.26 to 576.78±12.15(P<0.05).The decrease in the treatment group was more significant than that in the control group(P<0.001).(3)No obvious change in orienting efficiency was observed in both groups after treatment(P>0.05).(4)No serious adverse events were reported in this trial,except 2 patients from treatment group had slight hematoma after receiving acupuncture therapy.Conclusion:Acupuncture represents a safe and useful non-pharmacologic intervention option for primary insomniacs with impairments of attention network function(alertness and conflict processing/executive control).
基金The Project Supported by National Natural Science Foundation of China
文摘In this paper, symbolic code matrix ,constant matrix and count matrix are defined .The first twomatrices are used to describe the elemental expression of augmented matrix and the nede admittance equa-tion is thus obtained. The third matrix is used to obtain the incoming degree matrix, and according to thematrix all the 1- factors of the Coates graph are given. By using the data code, the determinant is expandedand the same items in the expansion are merged. Thus the symbolic network function in which no term can-cellation occurs is generated.
文摘The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the modified double-graph method, a new decomposition analysis-modified double-graph decomposition analysis is presented for finding symbolic network functions. Its advantages are that the resultant symbolic expressions are compact and contain no cancellation terms, and its sign evaluation is very simple.
基金supported by the National Natural Science Foundation of China,Nos.81871836(to MZ),82172554(to XH),and 81802249(to XH),81902301(to JW)the National Key R&D Program of China,Nos.2018YFC2001600(to JX)and 2018YFC2001604(to JX)+3 种基金Shanghai Rising Star Program,No.19QA1409000(to MZ)Shanghai Municipal Commission of Health and Family Planning,No.2018YQ02(to MZ)Shanghai Youth Top Talent Development PlanShanghai“Rising Stars of Medical Talent”Youth Development Program,No.RY411.19.01.10(to XH)。
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071451,62331025,and U21A20447)the National Key Research and Development Project(Grant No.2021YFC3002204)the CAMS Innovation Fund for Medical Sciences(Grant No.2019-I2M-5-019).
文摘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 https://github.com/Debbie-85/eeg-connectivity.
基金funded by the King Salman Center For Disability Research,through Research Group No.KSRG-2024-468。
文摘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.
基金supported in part by the National Natural Science Foundation of China(NSFC)under grant numbers U22A2007 and 62171010the Open project of Satellite Internet Key Laboratory in 2022(Project 3:Research on Spaceborne Lightweight Core Network and Intelligent Collaboration)the Beijing Natural Science Foundation under grant number L212003.
文摘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.
基金supported by the Researchers Supporting Project of King Saud University,Riyadh,Saudi Arabia,under Project RSPD2025R681。
文摘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.
基金supported by the National Natural Science Foundation of China under Grant No.62131012ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230712005。
文摘This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development complexity.Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack.The proposed system uses technologies like Open vSwitch(OVS)and traffic control(TC)to emulate the space network's highly dynamic topology and time-varying link characteristics.The emulation results demonstrate the system's high reliability,and the user-space network stack reduces development complexity and debugging difficulty,providing convenience for the development of space network protocols and network functions.
基金supported by Institute of Information&Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for the Smart City)in part by the National Research Foundation of Korea(NRF),Ministry of Education,through the Basic Science Research Program under Grant NRF-2020R1I1A3066543+1 种基金in part by BK21 FOUR(Fostering Outstanding Universities for Research)under Grant 5199990914048in part by the Soonchunhyang University Research Fund.
文摘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.
基金supported by the National Natural Science Foundation of China(62076157 and 61703253)the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20210033)the National Institutes of Health(R01MH123610 and R01EB006841).
文摘Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.
文摘Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.
