The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Mobility edges and localization characteristics in one-dimensional quasiperiodic quantum walk

We construct a one-dimensional quasiperiodic quantum walk to investigate the localization–delocalization transition.The inverse participation ratio and Lyapunov exponent are employed as two indexes to determine the mobility edge, a critical energy to distinguish the energy regions of extended and localized states. The analytical solution of mobility edge is obtained by the Lyapunov exponents in global theory, and the consistency of the two indexes is confirmed. We further study the dynamic characteristics of the quantum walk and show that the probabilities are localized to some specific lattice sites with time evolution. This phenomenon is explained by the effective potential of the Hamiltonian which corresponds to the phase in the coin operator of the quantum walk.

Mobility edges in one-dimensional finite-sized models with large quasi-periodic disorders

We study the one-dimensional tight-binding model with quasi-periodic disorders,where the quasi-period is tuned to be large compared to the system size.It is found that this type of model with large quasi-periodic disorders can also support the mobility edges,which is very similar to the models with slowly varying quasi-periodic disorders.The energy-matching method is employed to determine the locations of mobility edges in both types of models.These results of mobility edges are verified by numerical calculations in various examples.We also provide qualitative arguments to support the fact that large quasi-periodic disorders will lead to the existence of mobility edges.

Sensitivity of forest phenology in China varies with proximity to forest edges

Background:Shifts in forest phenological events serve as strong indicators of climate change.However,the sensitivity of phenology events to climate change in relation to forest origins has received limited attention.Moreover,it is unknown whether forest phenology changes with the proximity to forest edge.Methods:This study examined the green-up dates,dormancy dates,time-integrated NDVI(LiNDVI,a measure of vegetation productivity in growing season),and their sensitivities to climatic factors along the gradients of distance(i.e.proximity)to forest edge(0–2 km)in China's natural forests(NF)and planted forests(PF).For the analysis,field-surveyed data were integrated with Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI from 2000 to 2022.Results:Our results reveal that PF had earlier green-up dates,later dormancy dates,and higher LiNDVI than NF.However,green-up sensitivities to temperature were higher at the edges of NF,whereas no such pattern was observed in PF.Conversely,the sensitivity of dormancy dates remains relatively stable from the inner to the edge of both NF and PF,except for a quadratic change in dormancy date sensitivity to precipitation found in NF.Additionally,we found that the green-up sensitivity to temperature increased with decreasing proximity to edge in NF evergreen forests,while it showed the opposite trend in PF evergreen forests.Furthermore,we observed that the precipitation impact on green-up dates shifts from postponing to advancing from the inner to the edge of NF,whereas precipitation dominantly postpones PF's green-up dates regardless of the proximity to edge.The LiNDVI exhibits higher sensitivity to precipitation at the edge areas,a phenomenon observed in NF but not in PF.Conclusions:These results suggest that the responses of forests to climate change vary with the distance to the edge.With increasing edge forests,which results from fragmentation caused by global changes,we anticipate that desynchronized phenological events along the distance to the edge could alter biogeochemical cycles and reshape ecosystem services such as energy flows,pollination duration,and the tourism industry.Therefore,we advocate for further investigations of edge effects to improve ecosystem modelling,enhance forest stability,and promote sustainable tourism.

Mobility edges generated by the non-Hermitian flatband lattice

We study the cross-stitch flatband lattice subject to the quasiperiodic complex potential exp(ix). We firstly identify the exact expression of quadratic mobility edges through analytical calculation, then verify the theoretical predictions by numerically calculating the inverse participation ratio. Further more, we study the relationship between the real–complex spectrum transition and the localization–delocalization transition, and demonstrate that mobility edges in this non-Hermitian model not only separate localized from extended states but also indicate the coexistence of complex and real spectrum.

General mapping of one-dimensional non-Hermitian mosaic models to non-mosaic counterparts:Mobility edges and Lyapunov exponents

We establish a general mapping from one-dimensional non-Hermitian mosaic models to their non-mosaic counterparts.This mapping can give rise to mobility edges and even Lyapunov exponents in the mosaic models if critical points of localization or Lyapunov exponents of localized states in the corresponding non-mosaic models have already been analytically solved.To demonstrate the validity of this mapping,we apply it to two non-Hermitian localization models:an Aubry-Andre-like model with nonreciprocal hopping and complex quasiperiodic potentials,and the Ganeshan-Pixley-Das Sarma model with nonreciprocal hopping.We successfully obtain the mobility edges and Lyapunov exponents in their mosaic models.This general mapping may catalyze further studies on mobility edges,Lyapunov exponents,and other significant quantities pertaining to localization in non-Hermitian mosaic models.

基于高空平台的边缘计算卸载:网络、算法和展望

高空平台(high altitude platform,HAP)技术与多接入边缘计算(multi-access edge computing,MEC)技术的结合将MEC服务器部署区域由地面扩展到空中,打破传统地面MEC网络的局限性,为用户提供无处不在的计算卸载服务。针对基于HAP的MEC卸载研究进行综述,首先,从HAP计算节点的优势、网络组成部分、网络结构、主要挑战及其应对技术4个方面分析基于HAP的MEC网络;其次,分别从图论、博弈论、机器学习、联邦学习等理论的角度对基于HAP的MEC卸载算法进行横向分析和纵向对比;最后,指出基于HAP的MEC卸载技术目前存在的问题,并对该技术的未来研究方向进行展望。

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

240 nm AlGaN-based deep ultraviolet micro-LEDs:size effect versus edge effect

240 nm AlGaN-based micro-LEDs with different sizes are designed and fabricated.Then,the external quantum efficiency(EQE)and light extraction efficiency(LEE)are systematically investigated by comparing size and edge effects.Here,it is revealed that the peak optical output power increases by 81.83%with the size shrinking from 50.0 to 25.0μm.Thereinto,the LEE increases by 26.21%and the LEE enhancement mainly comes from the sidewall light extraction.Most notably,transversemagnetic(TM)mode light intensifies faster as the size shrinks due to the tilted mesa side-wall and Al reflector design.However,when it turns to 12.5μm sized micro-LEDs,the output power is lower than 25.0μm sized ones.The underlying mechanism is that even though protected by SiO2 passivation,the edge effect which leads to current leakage and Shockley-Read-Hall(SRH)recombination deteriorates rapidly with the size further shrinking.Moreover,the ratio of the p-contact area to mesa area is much lower,which deteriorates the p-type current spreading at the mesa edge.These findings show a role of thumb for the design of high efficiency micro-LEDs with wavelength below 250 nm,which will pave the way for wide applications of deep ultraviolet(DUV)micro-LEDs.

Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges

In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio （SNR）, the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands （dominant, low, and high frequencies） separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

基于Solid Edge自动生成数控加工图的二次开发研究

基于Solid Edge软件平台,利用C#进行二次开发,设计出一款利用三维设计模型和组件图生成数控加工工艺图纸的批量设计软件,并详细展示了软件的工作流程、原理和关键技术。经应用验证,该软件具有出图快、信息匹配度高、适用模型范围广、操作灵活、界面友好等优点,以此实现了车间的无纸化高效数据传输和敏捷生产。

Satellite Communications with 5G, B5G, and 6G: Challenges and Prospects

Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.

基于ADAMS的多关节机械手手抓部分的运动学仿真研究

利用虚拟样机技术验证多关节机械手的机构设计是否正确,能否实现每分钟的抓取以及其载荷。运用Solid Edge ST5完成虚拟样机三维模型的建立,导入ADAMS中进行机械手机械系统的运动学分析。运用ADAMS对其中一个工作周期内的运动过程进行运动学的仿真,得出其各个关节在一个工作周期的位移特性曲线以及速度加速度曲线。本文介绍了多关节机械手具体的虚拟样机几何模型的建立,以及利用ADAMA/View提供的Parasolid模型数据交换接口导入模型,在添加约束时需要关键注意的细节也有介绍。在计算机广泛普及的今天,利用三维设计以及ADAMS软件进行机械手手抓部分的运动学分析,可以大大缩短设计周期,提高设计精度,降低产品开发成本。

A review on edge analytics:Issues,challenges,opportunities,promises,future directions,and applications

Edge technology aims to bring cloud resources(specifically,the computation,storage,and network)to the closed proximity of the edge devices,i.e.,smart devices where the data are produced and consumed.Embedding computing and application in edge devices lead to emerging of two new concepts in edge technology:edge computing and edge analytics.Edge analytics uses some techniques or algorithms to analyse the data generated by the edge devices.With the emerging of edge analytics,the edge devices have become a complete set.Currently,edge analytics is unable to provide full support to the analytic techniques.The edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply,small memory size,limited resources,etc.This article aims to provide a detailed discussion on edge analytics.The key contributions of the paper are as follows-a clear explanation to distinguish between the three concepts of edge technology:edge devices,edge computing,and edge analytics,along with their issues.In addition,the article discusses the implementation of edge analytics to solve many problems and applications in various areas such as retail,agriculture,industry,and healthcare.Moreover,the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues,emerging challenges,research opportunities and their directions,and applications.

Task Offloading in Edge Computing Using GNNs and DQN

In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices.

Edge modes in finite-size systems with different edge terminals

We investigate the behavior of edge modes in the presence of different edge terminations and long-range(LR)hopping.Here,we mainly focus on such model crystals with two different types of structures(type I:“…-P-Q-P-Q-…”and type II:“…=P-Q=P-Q=…”),where P and Q represent crystal lines(CLs),while the symbols“-”and“=”denote the distance between the nearest neighbor(NN)CLs.Based on the lattice model Hamiltonian with LR hopping,the existence of edge modes is determined analytically by using the transfer matrix method(TMM)when different edge terminals are taken into consideration.Our findings are consistent with the numerical results obtained by the exact diagonalization method.We also notice that edge modes can exhibit different behaviors under different edge terminals.Our result is helpful in solving novel edge modes in honeycomb crystalline graphene and transition metal dichalcogenides with different edge terminals.

Digital Twin-Assisted Semi-Federated Learning Framework for Industrial Edge Intelligence

The rapid development of emerging technologies,such as edge intelligence and digital twins,have added momentum towards the development of the Industrial Internet of Things(IIo T).However,the massive amount of data generated by the IIo T,coupled with heterogeneous computation capacity across IIo T devices,and users’data privacy concerns,have posed challenges towards achieving industrial edge intelligence(IEI).To achieve IEI,in this paper,we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server.In addition,we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIo T devices through the mapping of physical entities.We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded nonprivate data.As the joint problem is NP-hard and combinatorial and taking into account the reality of largescale device training,we develop a multi-agent hybrid action deep reinforcement learning(DRL)algorithm to find the optimal solution.Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.

用于抵抗扭转采取边缘弹性约束下的单轴压缩下板材极限强度:一种新的综合方法

The ultimate strength of platings under compression is one of the most important factors to be addressed in the ship design.Current Rules for ship structural design generally provide explicit strength check criteria against buckling for simply supported and clamped platings.Nevertheless,ship platings generally exhibit an intermediate behaviour between the simple support and the clamped conditions,which implies that the torsional stiffness of supporting members should be duly considered.Hence,the main aim of this study is the development of new design formulas for the ultimate strength of platings under uniaxial compression,with short and/or long edges elastically restrained against torsion.In this respect,two benchmark studies are performed.The former is devoted to the development of new equations for the elastic buckling coefficients of platings with edges elastically restrained against torsion,based on the results of the eigenvalue buckling analysis,performed by Ansys Mechanical APDL.The latter investigates the ultimate strength of platings with elastically restrained edges,by systematically varying the plate slenderness ratio and the torsional stiffness of supporting members.Finally,the effectiveness of the new formulation is checked against a wide number of finite element(FE)simulations,to cover the entire design space of ship platings.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.