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
The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response syst...The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.展开更多
The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approa...The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.展开更多
Virtualization of network/service functions means time sharing network/service（and affiliated）resources in a hyper speed manner.The concept of time sharing was popularized in the 1970s with mainframe computing.The s...Virtualization of network/service functions means time sharing network/service（and affiliated）resources in a hyper speed manner.The concept of time sharing was popularized in the 1970s with mainframe computing.The same concept has recently resurfaced under the guise of cloud computing and virtualized computing.Although cloud computing was originally used in IT for server virtualization,the ICT industry is taking a new look at virtualization.This paradigm shift is shaking up the computing,storage,networking,and ser vice industries.The hope is that virtualizing and automating configuration and service management/orchestration will save both capes and opex for network transformation.A complimentary trend is the separation（over an open interface）of control and transmission.This is commonly referred to as software defined networking（SDN）.This paper reviews trends in network/service functions,efforts to standardize these functions,and required management and orchestration.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
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
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
This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challen...This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.展开更多
To investigate changes of functional activation areas of the cerebral cortex and the connectivity of motor cortex networks （MCNs） in stroke patients during the recovery, five patients with the infarct in their left ...To investigate changes of functional activation areas of the cerebral cortex and the connectivity of motor cortex networks （MCNs） in stroke patients during the recovery, five patients with the infarct in their left hemispheres are recruited. Functional magnetic resonance imaging （fMRI） is performed in the second, fourth, eighth, and sixteenth weeks after the stroke. Images are analyzed using the professional software SPM5 to obtain the bilateral activation of the motor cortex in left and right handgrip tests. MCN data are extracted from the active areas, and the structural and functional characteristic parameters are computed to indicate the connectivity of the network. Results show that the ipsilesional hemisphere recruits more areas with less active extent during the handgrip test, compared with the contralesional hemisphere. MCN shows a higher overall degree of statistical independence and more statistical dependence among motor areas with the gradual recovery. It can help physicians understand the recovery mechanism.展开更多
This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking...This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking capacity for systems with uncertainties and disturbances. First, SMC discrete equivalent control law is designed on the basis of the nominal model of the system and the adaptive exponential reaching law, and subsequently, stability of the algorithm is analyzed. Second, RBF network is used to f...展开更多
A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predicti...A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.展开更多
Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership fu...A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.展开更多
Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor sig...Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.展开更多
A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the c...A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.展开更多
Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging ...Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput.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
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This project was supported in part by the Science Foundation of Shanxi Province (2003F028)China Postdoctoral Science Foundation (20060390318).
文摘The Radial Basis Functions Neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
文摘The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.
文摘Virtualization of network/service functions means time sharing network/service（and affiliated）resources in a hyper speed manner.The concept of time sharing was popularized in the 1970s with mainframe computing.The same concept has recently resurfaced under the guise of cloud computing and virtualized computing.Although cloud computing was originally used in IT for server virtualization,the ICT industry is taking a new look at virtualization.This paradigm shift is shaking up the computing,storage,networking,and ser vice industries.The hope is that virtualizing and automating configuration and service management/orchestration will save both capes and opex for network transformation.A complimentary trend is the separation（over an open interface）of control and transmission.This is commonly referred to as software defined networking（SDN）.This paper reviews trends in network/service functions,efforts to standardize these functions,and required management and orchestration.
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 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 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(62033008,62188101,62173343,62073339)the Natural Science Foundation of Shandong Province of China(ZR2024MF072,ZR2022ZD34)the Research Fund for the Taishan Scholar Project of Shandong Province of China.
文摘This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.
基金Supported by the National Natural Science Foundation of China (30670543)~~
文摘To investigate changes of functional activation areas of the cerebral cortex and the connectivity of motor cortex networks （MCNs） in stroke patients during the recovery, five patients with the infarct in their left hemispheres are recruited. Functional magnetic resonance imaging （fMRI） is performed in the second, fourth, eighth, and sixteenth weeks after the stroke. Images are analyzed using the professional software SPM5 to obtain the bilateral activation of the motor cortex in left and right handgrip tests. MCN data are extracted from the active areas, and the structural and functional characteristic parameters are computed to indicate the connectivity of the network. Results show that the ipsilesional hemisphere recruits more areas with less active extent during the handgrip test, compared with the contralesional hemisphere. MCN shows a higher overall degree of statistical independence and more statistical dependence among motor areas with the gradual recovery. It can help physicians understand the recovery mechanism.
文摘This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking capacity for systems with uncertainties and disturbances. First, SMC discrete equivalent control law is designed on the basis of the nominal model of the system and the adaptive exponential reaching law, and subsequently, stability of the algorithm is analyzed. Second, RBF network is used to f...
基金Supported by the National Nature Science Foundation of China (90716028)~~
文摘A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
基金The National Natural Science Foundation of China(No.51106025,51106027,51036002)Specialized Research Fund for the Doctoral Program of Higher Education(No.20130092110061)the Youth Foundation of Nanjing Institute of Technology(No.QKJA201303)
文摘A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.
文摘Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.
基金The National Natural Science Foundation of China(No.60875035)the Natural Science Foundation of Jiangsu Province(No.BK2008294)the National High Technology Research and Development Program of China(863 Program)(No.2006AA05A107)
文摘A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.
基金This work was funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.5199990914048)this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2020R1I1A3066543)In addition,this work was supported by the Soonchunhyang University Research Fund.
文摘Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput.
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.
