Novel load balancing DHT scheme for the heterogeneous system

The capacities of the nodes in the peer-to-peer system are strongly heterogeneous, hence one can benefit from distributing the load, based on the capacity of the nodes. At first a model is discussed to evaluate the load balancing of the heterogeneous system, and then a novel load balancing scheme is proposed based on the concept of logical servers and the randomized binary tree, and theoretical guarantees are given. Finally, the feasibility of the scheme using extensive simulations is proven.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.

Intelligent Dynamic Heterogeneous Redundancy Architecture for IoT Systems

The conventional dynamic heterogeneous redundancy(DHR)architecture suffers from the security threats caused by the stability differences and similar vulnerabilities among the executors.To overcome these challenges,we propose an intelligent DHR architecture,which is more feasible by intelligently combining the random distribution based dynamic scheduling algorithm(RD-DS)and information weight and heterogeneity based arbitrament(IWHA)algorithm.In the proposed architecture,the random distribution function and information weight are employed to achieve the optimal selection of executors in the process of RD-DS,which avoids the case that some executors fail to be selected due to their stability difference in the conventional DHR architecture.Then,through introducing the heterogeneity to restrict the information weights in the procedure of the IWHA,the proposed architecture solves the common mode escape issue caused by the existence of multiple identical error output results of similar vulnerabilities.The experimental results characterize that the proposed architecture outperforms in heterogeneity,scheduling times,security,and stability over the conventional DHR architecture under the same conditions.

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed topology.The MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,respectively.Firstly,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected formation.Secondly,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,respectively.Subsequently,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the group.Next,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior.Finally,a numerical simulation is intro-duced to confirm the validity of the proposed results.

Heterogeneous system synthesis of high quality PbS quantum dots for efficient infrared solar cells

As promising optoelectronic materials,lead sulfide quantum dots(PbS QDs)have attracted great attention.However,their applications are substantially limited by the QD quality and/or complicated synthesis.Herein,a facile new synthesis is developed for highly monodisperse and halide passivated PbS QDs.The new synthesis is based on a heterogeneous system containing a PbCl_(2)-Pb(OA)_(2)solid-liquid precursor solution.The solid PbCl_(2)inhibits the diffusion of monomers and maintains a high oversaturation condition for the growth of PbS QDs,resulting in high monodispersities.In addition,the PbCl_(2)gives rise to halide passivation on the PbS QDs,showing excellent stability in air.The high monodispersity and good passivation endow these PbS QDs with outstanding optoelectronic properties,demonstrated by a 9.43%power conversion efficiency of PbS QD solar cells with a bandgap of~0.95 eV(1,300 nm).We believe that this heterogeneous strategy opens up a new avenue optimizing for the synthesis and applications of QDs.

Distributed Adaptive Output Consensus of Unknown Heterogeneous Non-Minimum Phase Multi-Agent Systems

This article addresses the leader-following output consensus problem of heterogeneous linear multi-agent systems with unknown agent parameters under directed graphs.The dynamics of followers are allowed to be non-minimum phase with unknown arbitrary individual relative degrees.This is contrary to many existing works on distributed adaptive control schemes where agent dynamics are required to be minimum phase and often of the same relative degree.A distributed adaptive pole placement control scheme is developed,which consists of a distributed observer and an adaptive pole placement control law.It is shown that under the proposed distributed adaptive control scheme,all signals in the closed-loop system are bounded and the outputs of all the followers track the output of the leader asymptotically.The effectiveness of the proposed scheme is demonstrated by one practical example and one numerical example.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Enabling heterogeneous catalysis to achieve carbon neutrality: Directional catalytic conversion of CO_(2) into carboxylic acids

The increase in anthropogenic carbon dioxide(CO_(2))emissions has exacerbated the deterioration of the global environment,which should be controlled to achieve carbon neutrality.Central to the core goal of achieving carbon neutrality is the utilization of CO_(2) under economic and sustainable conditions.Recently,the strong need for carbon neutrality has led to a proliferation of studies on the direct conversion of CO_(2) into carboxylic acids,which can effectively alleviate CO_(2) emissions and create high-value chemicals.The purpose of this review is to present the application prospects of carboxylic acids and the basic principles of CO_(2) conversion into carboxylic acids through photo-,electric-,and thermal catalysis.Special attention is focused on the regulation strategy of the activity of abundant catalysts at the molecular level,inspiring the preparation of high-performance catalysts.In addition,theoretical calculations,advanced technologies,and numerous typical examples are introduced to elaborate on the corresponding process and influencing factors of catalytic activity.Finally,challenges and prospects are provided for the future development of this field.It is hoped that this review will contribute to a deeper understanding of the conversion of CO_(2) into carboxylic acids and inspire more innovative breakthroughs.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

A perceptual and predictive batch-processing memory scheduling strategy for a CPU-GPU heterogeneous system

When multiple central processing unit(CPU)cores and integrated graphics processing units(GPUs)share off-chip main memory,CPU and GPU applications compete for the critical memory resource.This causes serious resource competition and has a negative impact on the overall performance of the system.We describe the competition for shared-memory resources in a CPU-GPU heterogeneous multi-core architecture,and a sharedmemory request scheduling strategy based on perceptual and predictive batch-processing is proposed.By sensing the CPU and GPU memory request conditions in the request buffer,the proposed scheduling strategy estimates the GPU latency tolerance and reduces mutual interference between CPU and GPU by processing CPU or GPU memory requests in batches.According to the simulation results,the scheduling strategy improves CPU performance by8.53%and reduces mutual interference by 10.38%with low hardware complexity.

Lyapunov-Based Output Containment Control of Heterogeneous Multi-Agent Systems With Markovian Switching Topologies and Distributed Delays

This paper considers the mean square output containment control problem for heterogeneous multi-agent systems(MASs)with randomly switching topologies and nonuniform distributed delays.By modeling the switching topologies as a continuous-time Markov process and taking the distributed delays into consideration,a novel distributed containment observer is proposed to estimate the convex hull spanned by the leaders'states.A novel distributed output feedback containment controller is then designed without using the prior knowledge of distributed delays.By constructing a novel switching Lyapunov functional,the output containment control problem is then solved in the sense of mean square under an easily-verifiable sufficient condition.Finally,two numerical examples are given to show the effectiveness of the proposed controller.

Development of a convolutional neural network based geomechanical upscaling technique for heterogeneous geological reservoir

Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.

Silicon-based optoelectronic heterogeneous integration for optical interconnection

The performance of optical interconnection has improved dramatically in recent years.Silicon-based optoelectronic heterogeneous integration is the key enabler to achieve high performance optical interconnection,which not only provides the optical gain which is absent from native Si substrates and enables complete photonic functionalities on chip,but also improves the system performance through advanced heterogeneous integrated packaging.This paper reviews recent progress of silicon-based optoelectronic heterogeneous integration in high performance optical interconnection.The research status,development trend and application of ultra-low loss optical waveguides,high-speed detectors,high-speed modulators,lasers and 2D,2.5D,3D and monolithic integration are focused on.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

Synergy of heterogeneous Co/Ni dual atoms enabling selective C-O bond scission of lignin coupling with in-situ N-functionalization

Selective cleavage of Csp^(2)-OCH_(3)bond in lignin without breaking other types of C-O bonds followed by N-functionalization is fascinating for on-purpose valorization of biomass.Here,a Co/Ni-based dual-atom catalyst CoNiDA@NC prepared by in-situ evaporation and acid-etching of metal species from tailor-made metal–organic frameworks was efficient for reductive upgrading of various lignin-derived phenols to cyclohexanols(88.5%–99.9%yields),which had ca.4 times higher reaction rate than the single-atom catalyst and was superior to state-of-the-art heterogeneous catalysts.The synergistic catalysis of Co/Ni dual atoms facilitated both hydrogen dissociation and hydrogenolysis steps,and could optimize adsorption configuration of lignin-derived methoxylated phenols to further favor the Csp^(2)-OCH_(3)cleavage,as elaborated by theoretical calculations.Notably,the CoNi_(DA)@NC catalyst was highly recyclable,and exhibited excellent demethoxylation performance(77.1%yield)in real lignin monomer mixtures.Via in-situ cascade conversion processes assisted by dual-atom catalysis,various high-value N-containing chemicals,including caprolactams and cyclohexylamines,could be produced from lignin.

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

Mitigating Lattice Distortion of High‑Voltage LiCoO_(2)via Core‑Shell Structure Induced by Cationic Heterogeneous Co‑Doping for Lithium‑Ion Batteries

Inactive elemental doping is commonly used to improve the structural stability of high-voltage layered transition-metal oxide cathodes.However,the one-step co-doping strategy usually results in small grain size since the low diffusivity ions such as Ti^(4+)will be concentrated on grain boundaries,which hinders the grain growth.In order to synthesize large single-crystal layered oxide cathodes,considering the different diffusivities of different dopant ions,we propose a simple two-step multi-element co-doping strategy to fabricate core–shell structured LiCoO_(2)(CS-LCO).In the current work,the high-diffusivity Al^(3+)/Mg^(2+)ions occupy the core of single-crystal grain while the low diffusivity Ti^(4+)ions enrich the shell layer.The Ti^(4+)-enriched shell layer(~12 nm)with Co/Ti substitution and stronger Ti–O bond gives rise to less oxygen ligand holes.In-situ XRD demonstrates the constrained contraction of c-axis lattice parameter and mitigated structural distortion.Under a high upper cut-off voltage of 4.6 V,the single-crystal CS-LCO maintains a reversible capacity of 159.8 mAh g^(−1)with a good retention of~89%after 300 cycles,and reaches a high specific capacity of 163.8 mAh g^(−1)at 5C.The proposed strategy can be extended to other pairs of low-(Zr^(4+),Ta^(5+),and W6+,etc.)and high-diffusivity cations(Zn^(2+),Ni^(2+),and Fe^(3+),etc.)for rational design of advanced layered oxide core–shell structured cathodes for lithium-ion batteries.

Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain management.Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges.However,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes.There is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous networks.Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems.To address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain.The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network.This paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity systems.In addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among organizations.Overall,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.

Catalytic effect in lithium metal batteries: From heterogeneous catalyst to homogenous catalyst

Lithium metal batteries are regarded as prominent contenders to address the pressing needs owing to the high theoretical capacity.Toward the broader implementation,the primary obstacle lies in the intricate multi-electron,multi-step redox reaction associated with sluggish conversion kinetics,subsequently giving rise to a cascade of parasitic issues.In order to smooth reaction kinetics,catalysts are widely introduced to accelerate reaction rate via modulating the energy barrier.Over past decades,a large amount of research has been devoted to the catalyst design and catalytic mechanism exploration,and thus the great progress in electrochemical performance has been realized.Therefore,it is necessary to make a comprehensive review toward key progress in catalyst design and future development pathway.In this review,the basic mechanism of lithium metal batteries is provided along with corresponding advantages and existing challenges detailly described.The main catalysts employed to accelerate cathode reaction with emphasis on their catalytic mechanism are summarized as well.Finally,the rational design and innovative direction toward efficient catalysts are suggested for future application in metal-sulfur/gas battery and beyond.This review is expected to drive and benefit future research on rational catalyst design with multi-parameter synergistic impacts on the activity and stability of next-generation metal battery,thus opening new avenue for sustainable solution to climate change,energy and environmental issues,and the potential industrial economy.