From 0D to 3D:Hierarchical structured high-performance free-standing silicon anodes based on binder-induced topological network architecture

Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal issues,a free-standing anode with a"corrugated paper"shape on micro-scale and a topological crosslinking network on the submicron and nano-scale is designed.Essentially,an integrated three-dimensional electrode structure is constructed based on robust carbon nanotubes network with firmly anchored SiNPs via forming interlocking junctions.In which,the hierarchical interlocking structure is achieved by directional induction of the binder,which ensures well integration during cycling so that significantly enhances mechanical stability as well as electronic and ionic conductivity of electrodes.Benefiting from it,this anode exhibits outsta nding performance under harsh service conditions including high Si loading,ultrahigh areal capacity(33.2 mA h cm^(-2)),and high/low temperatures(-15-60℃),which significantly extends its practical prospect.Furthermore,the optimization mechanism of this electrode is explored to verify the crack-healing and structure-integration maintaining along cycling via a unique self-stabilization process.Thus,from both the fundamental and engineering views,this strategy offers a promising path to produce high-performance free-standing electrodes for flexible device applications especially facing volume effect challenges.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.

Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

Topology and Semantic Information Fusion Classification Network Based on Hyperspectral Images of Chinese Herbs

Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is better at representing the structure of hyperspectral data.This paper proposes a deep learning model called Topology and semantic information fusion classification network(TSFnet)that incorporates a topology structure and semantic information transmis-sion network to accurately classify traditional Chinese medicine in hyperspectral images.TSFnet uses a convolutional neural network(CNN)to extract features and a graph convolution network(GCN)to capture potential topological relationships among different types of Chinese herbal medicines.The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets.Additionally,the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.

Subdivision surface modeling system based on arbitrary topological curves network and combined subdivision

Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated surface.This modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.

Study of Radio over Fiber Transmission with a Single Line in Ring Topology for Deployment of MIMO Antenna Access Points in a Cell-less Network

The massive growth of wireless traffic goes hand in hand with the deployment of advanced radio interfaces as well as network densification. This growth has a direct impact on the radio access architecture, which today is moving from centralized to distributed deployments through the use of a large number of access points (APs). This paper verifies the feasibility of deploying multiple APs in series on a single line in a ring topology in a cell-less network. On the one hand, this technique will further improve the communication capacity and flexibility of a Radio-over-Fiber (RoF) based mobile communication system and will reduce its construction cost. And on the other hand, this deployment topology is a solution to achieve a massive cell-free Multiple-Input Multiple-Output (MIMO) architecture and a cost-effective fronthaul solution. First, a passive optical add/drop multiplexer (OADM) is used to extract and add downlink and uplink signals from the remote access points of one kilometer. Then, a deployment model is developed with version 17 Optisystem software. The results obtained showed that the quadrature amplitude modulation (QAM) does not adapt to this multi-carrier transmission to deploy several AP in series on a single line. Thus, the performance degradation increases when the number of APs integrated on the line increases.

A Study of Cellular Neural Networks with Vertex-Edge Topological Descriptors

The CellularNeuralNetwork(CNN)has various parallel processing applications,image processing,non-linear processing,geometric maps,highspeed computations.It is an analog paradigm,consists of an array of cells that are interconnected locally.Cells can be arranged in different configurations.Each cell has an input,a state,and an output.The cellular neural network allows cells to communicate with the neighbor cells only.It can be represented graphically;cells will represent by vertices and their interconnections will represent by edges.In chemical graph theory,topological descriptors are used to study graph structure and their biological activities.It is a single value that characterizes the whole graph.In this article,the vertex-edge topological descriptors have been calculated for cellular neural network.Results can be used for cellular neural network of any size.This will enhance the applications of cellular neural network in image processing,solving partial differential equations,analyzing 3D surfaces,sensory-motor organs,and modeling biological vision.

Comparative Study of Valency-Based Topological Descriptor for Hexagon Star Network

A class of graph invariants referred to today as topological indices are inefficient progressively acknowledged by scientific experts and others to be integral assets in the depiction of structural phenomena.The structure of an interconnection network can be represented by a graph.In the network,vertices represent the processor nodes and edges represent the links between the processor nodes.Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks.A topological descriptor is a numerical total related to a structure that portray the topology of structure and is invariant under structure automorphism.There are various uses of graph theory in the field of basic science.The main notable utilization of a topological descriptor in science was by Wiener in the investigation of paraffin breaking points.In this paper we study the topological descriptor of a newly design hexagon star network.More preciously,we have computed variation of the Randic0 R0,fourth Zagreb M4,fifth Zagreb M5,geometric-arithmetic GA;atom-bond connectivity ABC;harmonic H;symmetric division degree SDD;first redefined Zagreb,second redefined Zagreb,third redefined Zagreb,augmented Zagreb AZI,Albertson A;Irregularity measures,Reformulated Zagreb,and forgotten topological descriptors for hexagon star network.In the analysis of the quantitative structure property relationships(QSPRs)and the quantitative structure activity relationships(QSARs),graph invariants are important tools to approximate and predicate the properties of the biological and chemical compounds.We also gave the numerical and graphical representations comparisons of our different results.

Graph theoretical analysis of limestone fracture network damage patterns based on uniaxial compression test

The topological attributes of fracture networks in limestone,subject to intense hydrodynamics and intricate geological discontinuities,substantially influence the mechanical and hydraulic characteristics of the rock mass.The dynamical evolution of fracture networks under stress is crucial for unveiling the interaction patterns among fractures.However,existing models are undirected graphs focused on stationary topology,which need optimization to depict fractures'dynamic development and rupture process.To compensate for the time and destruction terms,we propose the damage network model,which defines the physical interpretation of fractures through the ternary motif.We focus primarily on the evolution of node types,topological attributes,and motifs of the fracture network in limestone under uniaxial stress.Observations expose the varying behavior of the nodes'self-dynamics and neighbors'adjacent dynamics in the fracture network.This approach elucidates the impact of micro-crack behaviors on large brittle shear fractures from a topological perspective and further subdivides the progressive failure stage into four distinct phases(isolated crack growth phase,crack splay phase,damage coalescence phase,and mechanical failure phase)based on the significance profile of the motif.Regression analysis reveals a positive linear and negative power correlation between fracture network density and branch number to the rock damage resistance,respectively.The damage network model introduces a novel methodology for depicting the interaction of two-dimensional(2D)projected fractures,considering the dynamic spatiotemporal development characteristics and fracture geometric variation.It helps dynamically characterize properties such as connectivity,permeability,and damage factors while comprehensively assessing damage in rock mass fracture networks.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

Enhanced Mechanism for Link Failure Rerouting in Software-Defined Exchange Point Networks

Internet Exchange Point(IXP)is a system that increases network bandwidth performance.Internet exchange points facilitate interconnection among network providers,including Internet Service Providers(ISPs)andContent Delivery Providers(CDNs).To improve service management,Internet exchange point providers have adopted the Software Defined Network(SDN)paradigm.This implementation is known as a Software-Defined Exchange Point(SDX).It improves network providers’operations and management.However,performance issues still exist,particularly with multi-hop topologies.These issues include switch memory costs,packet processing latency,and link failure recovery delays.The paper proposes Enhanced Link Failure Rerouting(ELFR),an improved mechanism for rerouting link failures in software-defined exchange point networks.The proposed mechanism aims to minimize packet processing time for fast link failure recovery and enhance path calculation efficiency while reducing switch storage overhead by exploiting the Programming Protocol-independent Packet Processors(P4)features.The paper presents the proposed mechanisms’efficiency by utilizing advanced algorithms and demonstrating improved performance in packet processing speed,path calculation effectiveness,and switch storage management compared to current mechanisms.The proposed mechanism shows significant improvements,leading to a 37.5%decrease in Recovery Time(RT)and a 33.33%decrease in both Calculation Time(CT)and Computational Overhead(CO)when compared to current mechanisms.The study highlights the effectiveness and resource efficiency of the proposed mechanism in effectively resolving crucial issues inmulti-hop software-defined exchange point networks.

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

Topological probability and connection strength induced activity in complex neural networks

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world （SW） connections. Network elements are described by two-dimensional map neurons （2DMNs） with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied.

GIS and Networks: Business Anomalies and Topological Errors, Linear Elements Case

The quality control of geographic data, especially from a topological and semantic perspective, is a must for its good management and use. However, while updating spatial data, some sorts of anomalies are affecting it, due to negligence or non-respect of business and topological rules. Hence the necessity of a solution that enables detecting theses anomalies. Nowadays, Geographic Information Systems (GIS) have become essential for decision-making in any project that manages spatial data. GIS functionalities and tools give the possibility of defining the topology of vector data. Nevertheless, the topology alone does not respond to the needs in matter of defining specific rules for every facility network. This means, we could find topological errors in the spatial database, but taking into account business rules, they are correct and vice versa. The main objective of this article is firstly to define business rules for the linear elements of a network. Secondly to premeditate the algorithms that detect the violation of the defined rules in order to have a good quality control of geographic data.

On Harmonic and Ev-Degree Molecular Topological Properties of DOX,RTOX and DSL Networks

Topological indices enable to gather information for the underlying topology of chemical structures and networks.Novel harmonic indices have been defined recently.All degree based topological indices are defined by using the classical degree concept.Recently two novel degree concept have been defined in graph theory:ve-degree and evdegree.Ve-degree Zagreb indices have been defined by using ve-degree concept.The prediction power of the ve-degree Zagreb indices is stronger than the classical Zagreb indices.Dominating oxide,silicate and oxygen networks are important network models in view of chemistry,physics and information science.Physical and mathematical properties of dominating oxide,silicate and oxygen networks have been considerably studied in graph theory and network theory.Topological properties of the dominating oxide,silicate and oxygen networks have been intensively investigated for the last few years period.In this study we examined,the first,the fifth harmonic and ev-degree topological indices of dominating oxide(DOX),regular triangulene oxide network(RTOX)and dominating silicate network(DSL).

On Vertex-Edge-Degree Topological Descriptors for Certain Crystal Networks

Due to the combinatorial nature of graphs they are used easily in pure sciences and social sciences.The dynamical arrangement of vertices and their associated edges make them flexible(like liquid)to attain the shape of any physical structure or phenomenon easily.In the field of ICT they are used to reflect distributed component and communication among them.Mathematical chemistry is another interesting domain of applied mathematics that endeavors to display the structure of compounds that are formed in result of chemical reactions.This area attracts the researchers due to its applications in theoretical and organic chemistry.It also inspires the mathematicians due to involvement of mathematical structures.Regular or irregular bonding ability of molecules and their formation of chemical compounds can be analyzed using atomic valences(vertex degrees).Pictorial representation of these compounds helps in identifying their properties by computing different graph invariants that is really considered as an application of graph theory.This paper reflects the work on topological indices such as ev-degree Zagreb index,the first ve-degree Zagrebindex,the first ve-degree Zagrebindex,the second ve-degree Zagreb index,ve-degree Randic index,the ev-degree Randic index,the ve-degree atom-bond connectivity index,the ve-degree geometric-arithmetic index,the ve-degree harmonic index and the ve-degree sum-connectivity index for crystal structural networks namely,bismuth tri-iodide and lead chloride.In this article we have determine the exact values of ve-degree and ev-degree based topological descriptors for crystal networks.