95 results about "Functional networks" patented technology

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

The invention relates to the technical field of medical data processing and nerve regulation and control, in particular to a method for synchronously activating post-stroke neuroplasticity through photoacoustic, which comprises the following steps of: 1, constructing a stroke specific brain region target map: fusing brain structure connection data and functional network data of a patient, generating a three-dimensional target map containing the high-metabolism semi-dark band coordinate set and the cross-hemisphere compensatory connection intersection; 2, calculating a time-space synchronization focusing parameter; 3, synchronous stimulation and dynamic regulation are executed, wherein the ultrasonic transducer and the laser source are controlled to emit time-space synchronous sound waves and light pulses; monitoring the blood perfusion variable quantity and the neurotransmitter concentration ratio of the target brain area in real time; when the blood perfusion variable quantity does not reach the expectation or the transmitter ratio is unbalanced, the stimulation intensity is dynamically adjusted, and the focusing coordinate is translated in the direction away from the infarction area. Through photoacoustic stimulation of time-space synchronization, the difference between the propagation speeds of sound waves and light waves is overcome, and accurate collaboration of neural restoration is achieved.

The present application relates to a zero trust-based service system access control method and apparatus, a device, and a medium. The method comprises: triggering, among core network functional network elements of a network, a session management function network element or an AAA server to send a user re-authentication request to a user terminal, so that the user terminal returns current user information to the AAA server; upon the AAA server verifying that current user identity information is consistent with user identity information corresponding to a target user, determining an identity authentication success result; on the basis of the identity authentication success result and the current user identity information, the AAA server initiating a biometric authentication request to the user terminal, so that the AAA server checks whether most recent biometric information of the target user returned by the user terminal matches pre-stored biometric information of the target user, and if so, returns a biometric authentication success result to the user terminal, so as to authorize the target user to continue accessing a target service system. The present application can prevent several security issues, such as unauthorized control over authorized devices.

The invention relates to a depression risk detection method based on self-adaptive multi-scale neighborhood perception fusion space-time diagram convolution, and aims to solve the problems that traditional depression diagnosis is subjective and existing electroencephalogram detection space-time feature modeling is insufficient. The method comprises the following steps: acquiring electroencephalogram signals of a testee, and extracting frequency spectrum features through a frequency domain feature extraction module; in combination with an adjacent matrix (independent of an electrode physical distance) which is adaptively learned and normalized in training, embedded features are generated through a graph convolutional neural network; and then capturing long-time-history dependence through a dynamic feature extraction module, realizing feature multi-level fusion by means of a multi-cascade multi-scale convolution module, finally integrating local and global features through an adaptive feature fusion module, and inputting a prediction layer to output a detection result. According to the method, electroencephalogram signal time-space domain joint modeling is achieved, brain region function connection dynamic and multi-scale neural features are effectively captured, information redundancy and key feature loss are avoided, classification accuracy and generalization ability are remarkably improved, depression-related key brain region and function network features can be revealed, and the method has high interpretability and potential clinical application value.

The invention provides an automatic test system and method for a third-party high-power repeater, and relates to the technical field of automatic test equipment, and the system comprises an integrated reconfigurable passive link box, a control host, a vector signal generator and a vector signal analyzer. The integrated reconfigurable passive link box comprises a box body, a radio frequency switch matrix network, an embedded function network, a control port, an input port, an output port, an uplink interface and a downlink interface. The radio frequency switch matrix network and the embedded function network are arranged in the box body. The input port, the output port, the uplink interface and the downlink interface are arranged on the surface of the box body. The vector signal generator is connected with the input port, the vector signal analyzer is connected with the output port, and a third-party high-power repeater to be tested is arranged between the uplink interface and the downlink interface. According to the invention, the radio frequency switch matrix network and the embedded function network are packaged in the integrated reconfigurable passive link box, and the control host is utilized to carry out automatic testing on the third-party high-power repeater according to the preset process, so that frequent operation processes are avoided, and damage to instruments is reduced.

The invention discloses a personalized MCI electrical stimulation intervention method based on a dynamic brain network, and belongs to the technical field of electrical stimulation target determination. Individual heterogeneity of a brain function network of a patient is considered, specific frequency band selection is carried out on electroencephalogram data sets of a normal group and a cognitive impairment group, an effective data set is constructed, and the effective data set is determined. The method comprises the following steps: respectively constructing average dynamic brain network connection of total test times of a normal group and a cognitive impairment group based on an adaptive method of dynamic time-varying weight optimization of reaction time, and then determining a main abnormal frequency band through the difference of the average dynamic brain network connection of the two groups; and then target points are determined for the core nodes connected with the average dynamic brain network of the main abnormal frequency band, and compared with traditional single-frequency-band analysis, the application proposes that the effective data set is constructed by the specific frequency band to determine the main abnormal frequency band; in addition, stimulation targets are positioned according to the core nodes connected with the average dynamic brain network of the abnormal frequency band, and the reliability of target selection is improved.

The invention provides a brain network construction and analysis method based on multivariable analysis. The method is mainly used for multi-view analysis of a brain structure and a functional network. The technical problem to be solved is that information loss is caused by neglecting necessary multivariate relationships among brain region nodes when univariate analysis is carried out on a brain function network and a brain structure network. According to the method, the canonical correlation analysis method based on data driving is used for constructing the single-mode brain network, the problem that a traditional univariate analysis method neglects necessary multivariate relations is avoided, the complex relation between the brain structure and functions can be reflected more accurately, and a more reliable basis is provided for deep research of a brain working mechanism.

The invention relates to the technical field of brain function network analysis, in particular to a post-stroke aphasia patient fMRI focal recognition method based on deep learning. The method comprises the following steps of: acquiring a region consistency value for geometric structures of matched local regions of two adjacent fMRI images in an fMRI image sequence, similarity of gray distribution and an image matching degree, and dividing a same time window by using the region consistency value; and obtaining a functional connection coefficient according to the difference of the region consistent values of the fMRI images in two adjacent time windows and the difference of the geometric structure features and the gray level distribution features of the local regions, determining the convolution kernel size of deep learning of the fMRI images in the time windows by using the functional connection coefficient, and performing focal recognition on the fMRI images in the time windows. According to the method, the function connection coefficient presenting the cooperation degree of the brain region activity modes in the adjacent time windows is analyzed, the convolution kernel size of the time windows is determined in a self-adaptive mode, and the recognition effect on the local focus of the patient is improved.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

The application relates to the technical field of wireless communication, in particular to a service providing method and device, a computer device and a storage medium. The method comprises the following steps: receiving an initial service request sent by a service demander; performing address inverse transformation on a target FQDN to obtain a service network address; replacing the target FQDN in the initial service request with the service network address to obtain a target service request; and sending the target service request to a service provider, so that the service provider provides services to the service demander according to a functional network element corresponding to the service network address. The application realizes the sending of the service network address under the condition that only the SEPP network element performs address inverse transformation on the target FQDN to obtain the service network address, without the functional adjustment of other network elements, so that the communication mechanism does not need to additionally construct a DNS or a database, the management and maintenance cost of maintaining the mapping relationship between the domain network FQDN and the service network address is reduced, and the risk of service transmission interruption is also reduced.

The present invention proposes a muscle dynamic synergy analysis method based on dynamic community detection. First, the surface electromyographic signals of the human body during movement are collected to construct a time-varying electromyographic functional network; then, the muscle community clustering of the single-layer electromyographic functional network is detected to obtain the muscle communities of each layer, and the muscle communities are arranged in the order of corresponding time snapshots to form a community set; a resolution parameter is set to adjust the sensitivity of each layer of the electromyographic functional network to the community, and a coupling parameter is set to adjust the coupling degree between layers; then, the modularity is optimized with maximizing the multi-layer modularity as an indicator; finally, the clusters of strongly interconnected nodes in the electromyographic functional network are divided into different modules, and the node clusters within the same module are regarded as muscle groups with strong synergy at this moment. As the muscle synergy changes dynamically over time, this change process is revealed through dynamic community detection, thereby realizing dynamic decoding of muscle synergy in movement changes.

The invention discloses a public network domain name shunting method and a communication system. Comprising the steps that an uplink classifier user plane function network element receives a DNS analysis request message initiated by user equipment, the message comprises a public network domain name to be analyzed, and a target IP address of the message is a first IP address; whether the public network domain name meets a DNS redirection rule is judged, if yes, the request message is sent to an intranet DNS server corresponding to the public network domain name, and a DNS analysis response message fed back by the intranet DNS server is received; and converting the analysis result from the second IP address to a virtual IP address according to a target IP address conversion rule, and sending a DNS analysis response message of which the analysis result is the virtual IP address to the user equipment. According to the method and the device, the technical problem that the common public network service cannot be normally accessed due to the fact that the same IP address is returned to two DNS analysis requests of the public network service shunted by the internal network and the common public network service by a content distribution network service provider is solved.

The invention relates to a preparation method and system of a functional cement-based material, relates to the technical field of civil construction materials, and solves the problem that the stability, economical efficiency and multifunctional cooperation of the material are difficult to consider due to lack of function-structure-process system regulation and control in the two methods at present. The method comprises the following steps: adding cement particles with a preset initial particle size after acquiring a starting signal of a granulator, starting the granulator to rotate, spraying water according to a preset water-cement ratio, monitoring the particle size and the water content of the particles in real time, and dynamically adjusting the rotating speed to reach the standard; after granulation is completed, a nozzle is switched to spray a functional material, the thickness and uniformity of a coating are monitored, and parameters are optimized to reach the standard; then cold pressing and quality evaluation are carried out, curing is carried out after the standard is reached, and finally the functional concrete is obtained. The cement-based material has the advantages that a continuous functional network is efficiently formed, various functions are given to the cement-based material, and manufacturability and performance are both considered.

The present invention discloses a smoothed group information-guided independent component analysis method for brain functional network analysis, which belongs to the technical field of independent component analysis of brain images. The present invention includes performing independent component analysis to obtain components at the group level as reference signals; calculating voxel features to construct a graph regularization term; using voxel features and reference signals as guidance, using multi-objective functions to perform iterative solutions to estimate the independent components of individual subjects; and calculating the time series corresponding to each component in the individual subject based on the extracted components. The present invention overcomes the limitation of the group information-guided independent component analysis method currently widely used in the field of brain functional network extraction that does not optimize the smoothness of the extracted components, and obtains a more accurate brain functional network. Voxel features are introduced as a guide in the process of constructing the objective function, which enhances the spatial smoothness and functional correlation of the results, and can help the new method learn a network that is more in line with the actual working mechanism of the brain.

A method for identifying brain-state dependent functional areas of unitary pooled activity (FAUPAs) using a statistical model that does not require a priori knowledge of the activity-induced ideal response signal time course is provided. A system for identifying a functional network in a brain of a living object includes a FAUPA identifier configured to identify FAUPAs by analyzing a plurality of images of the brain over a predetermined period. The plurality of images include a plurality of voxels, and the FAUPA identifier analyzes each voxel of the plurality of voxels in relation to one or more surrounding voxels of each voxel until each voxel of the plurality of voxels is evaluated. A brain network identification module configured to construct the functional network based on the identified FAUPAs that are functionally connected. A display module configured to display images of the brain depicting the FAUPAs included in the functional network.

The application relates to a method and device for predicting the risk of depression of a post-stroke patient, a medium and an electronic device. The method comprises: acquiring a brain image of the post-stroke patient and a standard graph of a depression functional network, the standard graph of the depression functional network being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the standard graph of the depression functional network; acquiring a standard graph of depression structural disconnection, the standard graph of the depression structural disconnection being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the standard graph of the depression structural disconnection; inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model to determine the risk of depression of the post-stroke patient after several months of onset through the prediction model. The application can predict the risk of depression according to the direct impact of a stroke lesion on a brain network responsible for emotion regulation in the brain.

The application belongs to the technical field of communication, and particularly relates to a network performance testing method, a network performance testing device, a computer readable medium, an electronic device and a computer program product. The method comprises the following steps: establishing a network performance testing channel for communication connection with a specified network element, wherein the specified network element is a functional network element specified in a core network of a communication system; and performing data communication with the specified network element based on the network performance testing channel, so as to obtain network performance parameters of communication traffic. The application can improve the accuracy of network performance optimization.

The present invention discloses a method for constructing a functional connectivity network, a system and an electronic device, including: using a variational autoencoder network to establish a multivariate Gaussian distribution probability model for each brain region of interest through unsupervised learning, using Jensen-Shannon divergence to calculate the degree of similarity between the probability distributions of any two brain regions, and obtaining an adjacency matrix of paired brain regions as a brain functional network; using a two-sample t-test to select a feature set with a higher discrimination, and inputting the selected feature set into a support vector machine for classification; inputting the feature sets of different dimensional models into a support vector machine classifier, and using adaboost enhancement method to combine multiple classifiers to obtain a stronger classifier. The present invention uses a generative learning method to improve the statistical power of group data and the generalization ability of observation data variability in the diagnosis of mild cognitive impairment, and further improves the diagnostic accuracy of mild cognitive impairment through the joint enhancement of multidimensional models.

A modular spacecraft includes a backbone support structure to provide structural support and integrate various functional networks. The backbone support structure is equipped with mechanical connectors for the secure attachment of multiple modules, and supports a plurality of network systems that includes power distribution, data transmission, and a thermal fluid loop. This modular design allows for the flexible and scalable assembly of the modular spacecraft, enabling different configurations and facilitating the integration of various modules with distinct functions or payloads. The backbone support structure's integrated networks ensure that all modules operate cohesively, enhancing the modular spacecraft's operational efficiency and adaptability.

The invention provides a user plane path management method, device and system, and is applied to the technical field of communication. The user plane path management method provided by the invention comprises the following steps: a control network element receives a first message; the first message is used for indicating establishment of a user plane path; the control network element determines a plurality of user plane path nodes according to the first message; the user plane path node is a user plane function network element or access network equipment; and controlling the network element to establish a user plane path among the plurality of user plane path nodes. Based on the scheme, an existing fixed user plane path is not limited any more, the control network element is introduced, the user plane path nodes are flexibly selected by the control network element, the user plane path between the user plane path nodes is flexibly established, and the method is suitable for the appeal of a future new service for the user plane path.

A data processing method and a communication apparatus. An event needing to be simulated can be injected into data generation function network elements of single domains (such as an RAN domain and a CN domain), respectively; the data generation function network element of each single domain simulates and generates simulation data for the corresponding domain on the basis of the injected event, and returns the simulation data to a management service consumer network element; and the management service consumer network element can determine final data (second data) on the basis of the simulation data corresponding to each single domain. Data is generated on the basis of single domain granularity. In addition, the simulation data simulated and generated by data generation function network elements of different single domains all targets data having the same transmission feature, realizing the collaboration of the data generated by different single domains. That is, by performing feature alignment of simulation inputs across the data generation functional network elements of different single domains, independent acquisition of the data by different single domains and the collaboration of the data acquired by the single domains are achieved, thereby providing more accurate data.

The embodiment of the invention provides an autism auxiliary diagnosis method. The autism auxiliary diagnosis method comprises the following steps: constructing an individual brain function connection diagram; based on a community detection algorithm of a graph theory, performing functional region division on the individual brain function link graph to generate an individual brain map; taking the surface area percentage of the functional network of the healthy control group in the cerebral cortex as a response variable, taking the individual age as a smooth nonlinear independent variable, introducing an average head movement parameter as a linear covariable, and constructing a normal population development reference model; and comparing the surface area percentage of the functional network of the individual brain map with the normal population development reference model of the same age, and quantifying the deviation degree to realize auxiliary diagnosis of the autism. According to the autism auxiliary diagnosis method provided by the embodiment of the invention, individualized autism auxiliary diagnosis considering individual differences can be realized. The embodiment of the invention further provides an autism auxiliary diagnosis device and electronic equipment.

This invention discloses a hypergraph representation method for brain functional networks. The steps include: preprocessing resting-state functional magnetic resonance imaging (fMRI) to obtain time series data for all brain regions; dividing the entire time series into multiple overlapping sub-sequence segments using a sliding window; constructing a dynamic brain functional network and transforming it into an optimization model; constructing a hypergraph of the dynamic brain functional network using the nearest neighbor algorithm; dynamically modifying the hypergraph structure through convolution operations and extracting features to obtain a new dynamic hypergraph; extracting the Laplacian matrix of the dynamic hypergraph; constructing the manifold regularization term of the Laplacian matrix and simultaneously introducing the manifold regularization term and the L1 norm regularization term into the optimization model to obtain the hypergraph representation of the brain functional network. This invention is used to represent functional interactions and higher-order relationships between multiple brain regions, determine discriminative brain functional network classification features, and effectively improve the classification performance of brain disease features.

Method for analyzing a mechatronic system, wherein the mechatronic system has one or more functions, the functions comprising one or more hardware and / or software functions, and in the method - a first network (20) in the form of a tree graph with several hierarchy levels and nodes (201, 202, 203, 204, 205, 206) arranged in the hierarchy levels is provided, wherein the nodes (201-206) are each representative of one of the functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the functions represented by the nodes (201-206), wherein the top hierarchy level has a single node (201) as the initial node, - a node of the tree graph of the first network (20) is specified as the first trigger node, - Software network data is provided that is representative of the software functions of the mechatronic system and their dependencies, - depending on the first trigger node and the software network data, a second network (30) in the form of a tree graph with several hierarchy levels and nodes (301, 302, 303, 304, 305) arranged in the hierarchy levels is determined, wherein the nodes (301-305) are each representative of one of the software functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the software functions represented by the nodes (301-305), wherein the top hierarchy level has a single node (301) as the initial node, and - depending on the first network of action (20) and the second network of action (30), the mechatronic system is analyzed, wherein an analysis of the mechatronic system includes that - the first network (20) is extended by the second network (30) such that the first trigger node of the first network (20) corresponds to the initial node of the second network (30), and - the mechatronic system is analyzed depending on the extended first network of action (40), wherein the mechatronic system has one or more diagnostic functions that are representative of one or more software functions for diagnosing the mechatronic system, in which - a node of the tree graph of the extended first network (40) is specified as the second trigger node, - one of the diagnostic functions is specified and assigned to the second trigger node, - Diagnostic network data is provided that is representative of the diagnostic functions of the mechatronic system and their dependencies, - depending on the second trigger node and the diagnostic network data, a third network (50) is determined in the form of a tree graph with several hierarchy levels and nodes (501, 502, 503, 504, 505) arranged in the hierarchy levels, wherein the nodes (501-505) are each representative of one of the diagnostic functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the diagnostic functions represented by the nodes (501-505), wherein the top hierarchy level has a single node (501) as the initial node, such that the second trigger node of the extended first network (40) corresponds to the initial node of the third network (50), and - depending on the extended first network (40) and the third network (50) the mechatronic system is analyzed.

The invention discloses a functional network structure design method and application of a high-thermal-conductivity wave-absorbing composite material. The method comprises the following steps: S1, adding wave-absorbing functional particles into a polymer matrix by a melt blending or solution mixing method to obtain a wave-absorbing composite material; and crushing or crushing the wave-absorbing composite material to obtain the wave-absorbing functional particles. S2, mixing the wave-absorbing functional particles with the heat-conducting functional particles, so that the wave-absorbing functional particles are coated with the heat-conducting functional particles to form composite particles; and then loading the composite particles into a mold, pre-pressing to discharge gas among the particles, then carrying out hot pressing, and cooling to obtain the composite material with the three-dimensional communicated heat-conducting and wave-absorbing dual-function network structure. According to the invention, the heat-conducting functional particles are selectively distributed at a polymer particle micro-area interface through a hot-pressing process to construct an isolation structure, a continuous heat-conducting network is formed, and the composite material with excellent heat-conducting property and wave-absorbing property is obtained and can be used as an electronic chip packaging material and used in the field of electromagnetic compatibility.

The application relates to a user identity authentication system, method, device, network equipment and storage medium. The system comprises at least one network, the network comprises a distributed network and a functional network, the distributed network comprises a visiting network, wherein the visiting network is used for receiving a service request sent by a user terminal, and forwarding the service request to a target network corresponding to a target service in the case that the visiting network cannot provide the target service corresponding to the service request; the target network is used for sending an authentication request to the user terminal according to the service request; the target network is also used for receiving a terminal digital certificate sent by the user terminal in response to the authentication request, checking the terminal digital certificate, and providing the target service for the user terminal in the case that the terminal digital certificate passes the check. The system can make other networks authenticate the identity of the user terminal when the user needs to use the network other than the visiting network.

Functional networks are mapped for individuals and group populations based on magnetic resonance imaging, and the resulting functional mapping data (e.g., probabilistic maps of functional networks and / or integration zones where multiple functional networks overlap and / or interact) are used to guide or otherwise monitor the delivery of neuromodulation therapies. Individual-specific functional network maps can be generated based on an overlapping template matching that is capable of assigning multiple networks to a given grayordinate.