A Finite-Time Convergent Analysis of Continuous Action Iterated Dilemma

Dear Editor,In this letter, a finite-time convergent analysis of continuous action iterated dilemma(CAID) is proposed. In traditional evolutionary game theory, the strategy of the player is binary(cooperation or defection), which limits the number of strategies a player can choose from.

Optimization of magnetic field design for Hall thrusters based on a genetic algorithm

Magnetic field design is essential for the operation of Hall thrusters.This study focuses on utilizing a genetic algorithm to optimize the magnetic field configuration of SPT70.A 2D hybrid PIC-DSMC and channel-wall erosion model are employed to analyze the plume divergence angle and wall erosion rate,while a Farady probe measurement and laser profilometry system are set up to verify the simulation results.The results demonstrate that the genetic algorithm contributes to reducing the divergence angle of the thruster plumes and alleviating the impact of high-energy particles on the discharge channel wall,reducing the erosion by 5.5%and 2.7%,respectively.Further analysis indicates that the change from a divergent magnetic field to a convergent magnetic field,combined with the upstream shift of the ionization region,contributes to the improving the operation of the Hall thruster.

Dynamic convergent shock compression initiated by return current in high-intensity laser-solid interactions

We investigate the dynamics of convergent shock compression in solid cylindrical targets irradiated by an ultrafast relativistic laser pulse.Our particle-in-cell simulations and coupled hydrodynamic simulations reveal that the compression process is initiated by both magnetic pressure and surface ablation associated with a strong transient surface return current with density of the order of 10^(17) A/m^(2) and lifetime of 100 fs.The results show that the dominant compression mechanism is governed by the plasma β,i.e.,the ratio of thermal pressure to magnetic pressure.For targets with small radius and low atomic number Z,the magnetic pressure is the dominant shock compression mechanism.According to a scaling law,as the target radius and Z increase,the surface ablation pressure becomes the main mechanism generating convergent shocks.Furthermore,an indirect experimental indication of shocked hydrogen compression is provided by optical shadowgraphy measurements of the evolution of the plasma expansion diameter.The results presented here provide a novel basis for the generation of extremely high pressures exceeding Gbar(100 TPa)to enable the investigation of high-pressure physics using femtosecond J-level laser pulses,offering an alternative to nanosecond kJ-laser pulse-driven and pulsed power Z-pinch compression methods.

Distributed Application Addressing in 6G Network

To ensure the extreme performances of the new 6G services,applications will be deployed at deep edge,resulting in a serious challenge of distributed application addressing.This paper traces back the latest development of mobile network application addressing,analyzes two novel addressing methods in carrier network,and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture,combining the new 6G capabilities of computing&network convergence,endogenous intelligence,and communication-sensing integration.This paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.

Reference model of future ubiquitous convergent network and context-aware telecommunication service platform

A reference model for future ubiquitous convergent network is analyzed. To provide user-centric, intelligent, personalized service, this article presents a context-aware telecommunication service platform （CaTSP） to adapt to dynamically changing context. This article focuses on the new design method of context-aware telecommunication service platform and its architecture. Through the use of model-driven architecture （MDA） and semantic web technologies, CaTSP can enable context reasoning and service personalization adaption. This article explores a new approach for service intelligence, personalization, and adaptability in the semantic web service computing era.

The Richtmyer-Meshkov instability of a double-layer interface in convergent geometry with magnetohydrodynamics

The interaction between a converging cylindrical shock and double density interfaces in the presence of a saddle magnetic field is numerically investigated within the framework of ideal magnetohydrodynamics.Three fluids of differing densities are initially separated by the two perturbed cylindrical interfaces.The initial incident converging shock is generated from a Riemann problem upstream of the first interface.The effect of the magnetic field on the instabilities is studied through varying the field strength.It shows that the Richtmyer-Meshkov and Rayleigh-Taylor instabilities are mitigated by the field,however,the extent of the suppression varies on the interface which leads to non-axisymmetric growth of the perturbations.The degree of asymmetry of the interfacial growth rate is increased when the seed field strength is increased.

Investigation of convergent Richtmyer–Meshkov instability at tin/xenon interface with pulsed magnetic driven imploding

The Richtmyer–Meshkov instability at the interface of solid state tin material and xenon gases under cylinder geometry is studied in this paper. The experiments were conducted at FP-1 facility in Institute of Fluid Physics, China Academy of Engineering Physics(CAEP). The FP-1 facility is a pulsed power driver which could generate high amplitude magnetic field to drive metal liner imploding. Convergent shock wave was generated by impacting a magnetic-driven aluminium liner onto a inner mounted tin liner. The convergent evolution of the disturbance pre-machined onto the tin liner's inner surface was diagnosed by x-radiography. The spike amplitudes were derived from x-ray frames and were compared with linear theory.An analytical model containing material strength effect was derived and matched well to the experimental results. This sensibility of the disturbance evolution to material strength property shines light to the application of Richtmyer–Meshkov instability to infer material strength.

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

Guest Editorial:Special issue on recurrent dynamic neural networks:Theory and applications

Recurrent dynamic neural network has been proven to be a powerful tool in the online solving of problems with consid-erable complexity and has been applied to various fields.In recent years,various recurrent dynamic neural networks have been developed to solve complex time‐varying problems,such as time‐varying matrix inversion,time‐varying nonlinear opti-misation,motion control of manipulators and so on.However,some thorny issues remain,including,but not limited to,sensitivity to noises,slow convergent speed,and high computational complexity.

Fully asynchronous distributed optimization with linear convergence over directed networks

We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it for synchronized or randomly activated implementation,which may create deadlocks in practice.In sharp contrast,we propose a fully asynchronous push-pull gradient(APPG) algorithm,where each node updates without waiting for any other node by using possibly delayed information from neighbors.Then,we construct two novel augmented networks to analyze asynchrony and delays,and quantify its convergence rate from the worst-case point of view.Particularly,all nodes of APPG converge to the same optimal solution at a linear rate of O(λ^(k)) if local functions have Lipschitz-continuous gradients and their sum satisfies the Polyak-?ojasiewicz condition(convexity is not required),where λ ∈(0,1) is explicitly given and the virtual counter k increases by one when any node updates.Finally,the advantage of APPG over the synchronous counterpart and its linear speedup efficiency are numerically validated via a logistic regression problem.

Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

Deep learning is the process of determining parameters that reduce the cost function derived from the dataset.The optimization in neural networks at the time is known as the optimal parameters.To solve optimization,it initialize the parameters during the optimization process.There should be no variation in the cost function parameters at the global minimum.The momentum technique is a parameters optimization approach;however,it has difficulties stopping the parameter when the cost function value fulfills the global minimum(non-stop problem).Moreover,existing approaches use techniques;the learning rate is reduced during the iteration period.These techniques are monotonically reducing at a steady rate over time;our goal is to make the learning rate parameters.We present a method for determining the best parameters that adjust the learning rate in response to the cost function value.As a result,after the cost function has been optimized,the process of the rate Schedule is complete.This approach is shown to ensure convergence to the optimal parameters.This indicates that our strategy minimizes the cost function(or effective learning).The momentum approach is used in the proposed method.To solve the Momentum approach non-stop problem,we use the cost function of the parameter in our proposed method.As a result,this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter.To verify that the learning works to test the strategy,we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.

Meshfree-based physics-informed neural networks for the unsteady Oseen equations

We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen equations.Firstly,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatiotemporal points to train the neural network instead of forming a mesh.Specifically,we optimize the neural network by minimizing the loss function to satisfy the differential operators,initial condition and boundary condition.Then,we prove the convergence of the loss function and the convergence of the neural network.In addition,the feasibility and effectiveness of the method are verified by the results of numerical experiments,and the theoretical derivation is verified by the relative error between the neural network solution and the analytical solution.

A SUPERLINEARLY CONVERGENT SPLITTING FEASIBLE SEQUENTIAL QUADRATIC OPTIMIZATION METHOD FOR TWO-BLOCK LARGE-SCALE SMOOTH OPTIMIZATION

This paper discusses the two-block large-scale nonconvex optimization problem with general linear constraints.Based on the ideas of splitting and sequential quadratic optimization(SQO),a new feasible descent method for the discussed problem is proposed.First,we consider the problem of quadratic optimal(QO)approximation associated with the current feasible iteration point,and we split the QO into two small-scale QOs which can be solved in parallel.Second,a feasible descent direction for the problem is obtained and a new SQO-type method is proposed,namely,splitting feasible SQO(SF-SQO)method.Moreover,under suitable conditions,we analyse the global convergence,strong convergence and rate of superlinear convergence of the SF-SQO method.Finally,preliminary numerical experiments regarding the economic dispatch of a power system are carried out,and these show that the SF-SQO method is promising.

Computational Analysis for Computer Network Model with Fuzziness

A susceptible,exposed,infectious,quarantined and recovered(SEIQR)model with fuzzy parameters is studied in this work.Fuzziness in the model arises due to the different degrees of susceptibility,exposure,infectivity,quarantine and recovery among the computers under consideration due to the different sizes,models,spare parts,the surrounding environments of these PCs and many other factors like the resistance capacity of the individual PC against the virus,etc.Each individual PC has a different degree of infectivity and resis-tance against infection.In this scenario,the fuzzy model has richer dynamics than its classical counterpart in epidemiology.The reproduction number of the developed model is studied and the equilibrium analysis is performed.Two different techniques are employed to solve the model numerically.Numerical simulations are performed and the obtained results are compared.Positivity and convergence are maintained by the suggested technique which are the main features of the epidemic models.

Hierarchical Federated Learning: Architecture, Challenges, and Its Implementation in Vehicular Networks

Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.

废气入射管道参数对缸内EGR分层的影响

为了在某款摩托车汽油机缸内实现废气再循环(exhaust gas recirculation,EGR)分层以减少泵气损失,降低NOx排放,将原有的进气旁通系统改造为EGR系统,使用GT-POWER模型求解出3000 r/min、60 mg进气量工况下废气入射管道以及进排气道的边界条件和初始条件,并将这些条件导入发动机的CONVERGE模型中进行计算,通过对比不同废气入射管径、不同安装角度、不同安装距离条件下的缸内流动特性、缸内速度场以及缸内废气质量分数分布,确定了最佳废气入射管道参数。结果表明:在3000 r/min、60 mg进气量工况下,当废气入射管径为5 mm,入射角度为17.5°,安装距离为22 mm时,气缸内能实现EGR分层。

基于PISO算法的汽车喷油器优化仿真研究

通过三维数值模拟软件Converge结合PISO算法,针对重型直喷柴油机,研究喷油器的不同孔径、孔深和喷油倾角对发动机燃烧和排放性能的影响,得到柴油发动机最佳热效率的喷油器参数设置方案。结果表明:相较于喷孔深度和喷孔倾角,喷孔直径的改变对发动机热效率的影响更大,且可有效改善发动机的NO_(x)和碳烟排放。当喷孔直径为0.179 mm、0.219 mm及0.259 mm时,分别能实现1.4%、3.4%、和2.8%的增幅,热效率提升较为显著。

Comparative transcriptomic evidence of physiological changes and potential relationships in vertebrates under different dormancy states

Dormancy represents a fascinating adaptive strategy for organisms to survive in unforgiving environments.After a period of dormancy,organisms often exhibit exceptional resilience.This period is typically divided into hibernation and aestivation based on seasonal patterns.However,the mechanisms by which organisms adapt to their environments during dormancy,as well as the potential relationships between different states of dormancy,deserve further exploration.Here,we selected Perccottus glenii and Protopterus annectens as the primary subjects to study hibernation and aestivation,respectively.Based on histological and transcriptomic analysis of multiple organs,we discovered that dormancy involved a coordinated functional response across organs.Enrichment analyses revealed noteworthy disparities between the two dormant species in their responses to extreme temperatures.Notably,similarities in gene expression patterns pertaining to energy metabolism,neural activity,and biosynthesis were noted during hibernation,suggesting a potential correlation between hibernation and aestivation.To further explore the relationship between these two phenomena,we analyzed other dormancy-capable species using data from publicly available databases.This comparative analysis revealed that most orthologous genes involved in metabolism,cell proliferation,and neural function exhibited consistent expression patterns during dormancy,indicating that the observed similarity between hibernation and aestivation may be attributable to convergent evolution.In conclusion,this study enhances our comprehension of the dormancy phenomenon and offers new insights into the molecular mechanisms underpinning vertebrate dormancy.

Valence Bands Convergence in p-Type CoSb_(3) through Electronegative Fluorine Filling

Band convergence is considered to be a strategy with clear benefits for thermoelectric performance,generally favoring the co-optimization of conductivity and Seebeck coefficients,and the conventional means include elemental filling to regulate the band.However,the influence of the most electronegative fluorine on the CoSb_(3) band remains unclear.We carry out density-functional-theory calculations and show that the valence band maximum gradually shifts downward with the increase of fluorine filling,lastly the valence band maximum converges to the highly degenerated secondary valence bands in fluorine-filled skutterudites.

Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order dynamics.In order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global information.Then,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of PEG.Eventually,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and pursuers.The pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade capture.PEG is a classic noncooperative game.It has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].