基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Grain Yield,Biomass Accumulation,and Leaf Photosynthetic Characteristics of Rice under Combined Salinity-Drought Stress

Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in saline lands,primarily due to insufficient water resources and inadequate irrigation facilities.Consequently,combined salinity-drought stress poses a major threat to rice production.In this study,two salinity levels(NS,non-salinity;HS,high salinity)along with three drought treatments(CC,control condition;DJ,drought stress imposed at jointing;DH,drought stress imposed at heading)were performed to investigate their combined influences on leaf photosynthetic characteristics,biomass accumulation,and rice yield formation.Salinity,drought,and their combination led to a shortened growth period from heading to maturity,resulting in a reduced overall growth duration.Grain yield was reduced under both salinity and drought stress,with a more substantial reduction under the combined salinity-drought stress.The combined stress imposed at heading caused greater yield losses in rice compared with the stress imposed at jointing.Additionally,the combined salinity-drought stress induced greater decreases in shoot biomass accumulation from heading to maturity,as well as in shoot biomass and nonstructural carbohydrate(NSC)content in the stem at heading and maturity.However,it increased the harvest index and NSC remobilization reserve.Salinity and drought reduced the leaf area index and SPAD value of flag leaves and weakened the leaf photosynthetic characteristics as indicated by lower photosynthetic rates,transpiration rates,and stomatal conductance.These reductions were more pronounced under the combined stress.Salinity,drought,and especially their combination,decreased the activities of ascorbate peroxidase,catalase,and superoxide dismutase,while increasing the contents of malondialdehyde,hydrogen peroxide,and superoxide radical.Our results indicated a more significant yield loss in rice when subjected to combined salinity-drought stress.The individual and combined stresses of salinity and drought diminished antioxidant enzyme activities,inhibited leaf photosynthetic functions,accelerated leaf senescence,and subsequently lowered assimilate accumulation and grain yield.

Investigation of hydroxyl-terminated polybutadiene propellant breaking characteristics and mechanism impacted by submerged cavitation water jet

A submerged cavitation water jet(SCWJ)is an effective method to recycle solid propellant from obsolete solid engines by the breaking method.Solid propellant's breaking modes and mechanical process under SCWJ impact are unclear.This study aims to understand those impact breaking mechanisms.The hydroxyl-terminated polybutadiene(HTPB)propellant was chosen as the research material,and a self-designed test system was used to conduct impact tests at four different working pressures.The high-speed camera characterized crack propagation,and the DIC method calculated strain change during the impact process.Besides,micro and macro fracture morphologies were characterized by scanning electron microscope(SEM)and computed tomography(CT)scanning.The results reveal that the compressive strain concentration region locates right below the nozzle,and the shear strain region distributes symmetrically with the jet axis,which increases to 4% at first 16th ms,the compressive strain rises to 2% and 6% in the axial and transverse direction,respectively.The two tensile cracks formed first at the compression strain concentrate region,and there generate many shear cracks around the tensile cracks,and those shear cracks that develop and aggregate cause the cracks to become wider and cut through the tensile cracks,forming the tensile-shear cracks and the impact parts eventually fail.The HTPB propellant forms a breaking hole shaped conical after impact 10 s.The mass loss increases by 17 times at maximum,with the working pressure increasing by three times.Meanwhile,the damage value of the breaking hole remaining on the surface increases by 7.8 times while 2.9 times in the depth of the breaking hole.The breaking efficiency is closely affected by working pressures.The failure modes of HTPB impacted by SCWJ are classified as tensile crack-dominated and tensile-shear crack-dominated damage mechanisms.

Morphological and biochemical characteristics associated with autophagy in gastrointestinal diseases

Autophagy is a cellular catabolic process characterized by the formation of double-membrane autophagosomes.Transmission electron microscopy is the most rigorous method to clearly visualize autophagic engulfment and degradation.A large number of studies have shown that autophagy is closely related to the digestion,secretion,and regeneration of gastrointestinal(GI)cells.However,the role of autophagy in GI diseases remains controversial.This article focuses on the morphological and biochemical characteristics of autophagy in GI diseases,in order to provide new ideas for their diagnosis and treatment.

Liquefaction susceptibility and deformation characteristics of saturated coral sandy soils subjected to cyclic loadings-a critical review

Coral sandy soils widely exist in coral island reefs and seashores in tropical and subtropical regions.Due to the unique marine depositional environment of coral sandy soils,the engineering characteristics and responses of these soils subjected to monotonic and cyclic loadings have been a subject of intense interest among the geotechnical and earthquake engineering communities.This paper critically reviews the progress of experimental investigations on the undrained behavior of coral sandy soils under monotonic and cyclic loadings over the last three decades.The focus of coverage includes the contractive-dilative behavior,the pattern of excess pore-water pressure(EPWP)generation and the liquefaction mechanism and liquefaction resistance,the small-strain shear modulus and strain-dependent shear modulus and damping,the cyclic softening feature,and the anisotropic characteristics of undrained responses of saturated coral sandy soils.In particular,the advances made in the past decades are reviewed from the following aspects:(1)the characterization of factors that impact the mechanism and patterns of EPWP build-up;(2)the identification of liquefaction triggering in terms of the apparent viscosity and the average flow coefficient;(3)the establishment of the invariable form of strain-based,stress-based,or energy-based EPWP ratio formulas and the unique relationship between the new proxy of liquefaction resistance and the number of cycles required to reach liquefaction;(4)the establishment of the invariable form of the predictive formulas of small strain modulus and strain-dependent shear modulus;and(5)the investigation on the effects of stress-induced anisotropy on liquefaction susceptibility and dynamic deformation characteristics.Insights gained through the critical review of these advances in the past decades offer a perspective for future research to further resolve the fundamental issues concerning the liquefaction mechanism and responses of coral sandy sites subjected to cyclic loadings associated with seismic events in marine environments.

Low-temperature characteristicsof rubbers and performance testsof type 120 emergencyvalve diaphragms

Purpose–The type 120 emergency valve is an essential braking component of railway freight trains,butcorresponding diaphragms consisting of natural rubber(NR)and chloroprene rubber(CR)exhibit insufficientaging resistance and low-temperature resistance,respectively.In order to develop type 120 emergency valverubber diaphragms with long-life and high-performance,low-temperatureresistant CR and NR were processed.Design/methodology/approach–The physical properties of the low-temperature-resistant CR and NRwere tested by low-temperature stretching,dynamic mechanical analysis,differential scanning calorimetryand thermogravimetric analysis.Single-valve and single-vehicle tests of type 120 emergency valves werecarried out for emergency diaphragms consisting of NR and CR.Findings–The low-temperature-resistant CR and NR exhibited excellent physical properties.The elasticityand low-temperature resistance of NR were superior to those of CR,whereas the mechanical properties of thetwo rubbers were similar in the temperature range of 0℃–150℃.The NR and CR emergency diaphragms metthe requirements of the single-valve test.In the low-temperature single-vehicle test,only the low-temperaturesensitivity test of the NR emergency diaphragm met the requirements.Originality/value–The innovation of this study is that it provides valuable data and experience for futuredevelopment of type 120 valve rubber diaphragms.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

A survey on multi-agent reinforcement learning and its application

Multi-agent reinforcement learning(MARL)has been a rapidly evolving field.This paper presents a comprehensive survey of MARL and its applications.We trace the historical evolution of MARL,highlight its progress,and discuss related survey works.Then,we review the existing works addressing inherent challenges and those focusing on diverse applications.Some representative stochastic games,MARL means,spatial forms of MARL,and task classification are revisited.We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications.We also address critical operational aspects,such as hyperparameter tuning and computational complexity,which are pivotal in practical implementations of MARL.Afterward,we make a thorough overview of the applications of MARL to intelligent machines and devices,chemical engineering,biotechnology,healthcare,and societal issues,which highlights the extensive potential and relevance of MARL within both current and future technological contexts.Our survey also encompasses a detailed examination of benchmark environments used in MARL research,which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios.In the end,we give our prospect for MARL and discuss their related techniques and potential future applications.

Gravel hardness effect on compaction characteristics of gravelly soil

The compaction characteristics of gravelly soil are affected by gravel hardness.To investigate the evolution and influencing mechanism of different gravel hardness on the compaction characteristics of gravelly soil,heavy compaction tests and crushing tests were conducted on gravelly soils with gravels originated from hard,soft and extremely soft rocks.According to orthogonal experiments and variance analysis,it was found that hardness has a significant impact on the maximum dry density of gravelly soil,followed by gravel content,and lastly,moisture content.For gravel compositions with an average saturated uniaxial compressive strength less than 60 MPa,the order of compacted maximum dry density is soft gravels>hard gravels>extremely soft gravels.Each type of gravelly soil has a threshold for gravel content,with 60%for hard and soft gravels and 50%for extremely soft gravels.Beyond these thresholds,the compacted dry density decreases significantly.There is a certain interaction between hardness,gravel content,and moisture content.Higher hardness increases the influence of gravel content,whereas lower hardness increases the influence of moisture content.Gravelly soils with the coarse aggregate(CA)between 0.7 and 0.8 typically achieve higher dry densities after compaction.In addition,the prediction equations for the particle breakage rate and CA ratio in the Bailey method were proposed to estimate the compaction performance of gravelly soil preliminarily.The results further revealed the compaction mechanism of different gravelly soils and can provide reference for subgrade filling construction.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.

Hyperbolic Tangent Function-Based Protocols for Global/Semi-Global Finite-Time Consensus of Multi-Agent Systems

This paper investigates the problem of global/semi-global finite-time consensus for integrator-type multi-agent sys-tems.New hyperbolic tangent function-based protocols are pro-posed to achieve global and semi-global finite-time consensus for both single-integrator and double-integrator multi-agent systems with leaderless undirected and leader-following directed commu-nication topologies.These new protocols not only provide an explicit upper-bound estimate for the settling time,but also have a user-prescribed bounded control level.In addition,compared to some existing results based on the saturation function,the pro-posed approach considerably simplifies the protocol design and the stability analysis.Illustrative examples and an application demonstrate the effectiveness of the proposed protocols.

Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

This paper is concerned with consensus of a secondorder linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are estimated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed protocol can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invariant multi-agent systems.

Molecular Epidemiology and Clinical Characteristics of Hand, Foot and Mouth Disease in North Sichuan Region, China, 2018-2023: A Descriptive Study

Background: Hand, foot and mouth disease (HFMD) remains an important public health problem in China. Many studies on the epidemiological characteristics of HFMD have been reported, but studies in North Sichuan region have been neglected. Methods: HFMD-related enterovirus infected cases were clinically confirmed and underwent real-time RT-PCR (rRT-PCR) from May 2018 to October 2023 in Guangyuan Central Hospital. Results: During 2018-2023, other EV (437 cases, 81.08%) was the most predominant serotype followed by CV-A16 (94 cases, 17.44%), EV-A71 (8 cases, 1.48%) was the least predominant serotype. Peak infections occurred in July and October. There were no significant differences in gender, age and serotypes. HFMD was concentrated in children under 47 months of age, with the highest incidence in children aged 12 - 23 months and the highest proportion of other EV infections in the whole age group. COVID-19 did not cause significant changes in gender, age and serotype. Overall, there was a significant increase in the proportion of children aged 12 - 23 months infected with CV-A16, and an increase in the proportion of children aged over 36 months infected with other EVs. Conclusions: The incidence of HFMD caused by EV-A71 has decreased significantly, and other EVs have become the main pathogens of HFMD in North Sichuan region in recent years. In the prevention and control of CV-A16, more attention should be paid to children aged 12 - 23 months and the dominant serotype should be closely monitored. Our study highlights the importance of developing of new diagnostic reagents and vaccines for the prevention and control of enterovirus infection. This study for the first time provides insights into district interventions to local conditions.

Sleep quality and characteristics of older adults with acute cardiovascular disease

Sleep plays a vital role in restoring the physical and mental health of people with cardiovascular disease.However,the hospital setting is not a conducive environment for sleep.Sleep interruptions by members of the care team,including vital sign checks,medication delivery,and blood draws for laboratory investigations,are routinely done in many hospitals.Frequent interruptions by staff and noise by other patients have been cited as barriers to restorative sleep in the hospital.

Some of the Chemical and Physical Characteristics of the Graff River in Kut City, Iraq

Some of the chemical and physical water qualities of the Graff River in the city of Kut were studied, and for two sites of the river, One was at the Crimea site, the other in the Jihad district, and for the period from October/2018 to March/2019, Seven variables of river water have been analyzed: temperature PH, electrical conductivity, TDS, turbidity, alkaline, and chloride. The results showed that most of the chemical and physical water properties of the river were in normal proportions and did not rise, except for the turbidity, which was at a very high level, and that the pH values were close to the basal side. The results of the statistical analysis revealed positive significant relationships between the pH and (chloride and TDS). On the other hand, between electrical conductivity and both previous variables as well. And a negative significant connection between temperature and alkaline.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

This paper examines the difficulties of managing distributed power systems,notably due to the increasing use of renewable energy sources,and focuses on voltage control challenges exacerbated by their variable nature in modern power grids.To tackle the unique challenges of voltage control in distributed renewable energy networks,researchers are increasingly turning towards multi-agent reinforcement learning(MARL).However,MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase.This unpredictability can lead to unsafe control measures.To mitigate these safety concerns in MARL-based voltage control,our study introduces a novel approach:Safety-ConstrainedMulti-Agent Reinforcement Learning(SC-MARL).This approach incorporates a specialized safety constraint module specifically designed for voltage control within the MARL framework.This module ensures that the MARL agents carry out voltage control actions safely.The experiments demonstrate that,in the 33-buses,141-buses,and 322-buses power systems,employing SC-MARL for voltage control resulted in a reduction of the Voltage Out of Control Rate(%V.out)from0.43,0.24,and 2.95 to 0,0.01,and 0.03,respectively.Additionally,the Reactive Power Loss(Q loss)decreased from 0.095,0.547,and 0.017 to 0.062,0.452,and 0.016 in the corresponding systems.

Targeted multi-agent communication algorithm based on state control

As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multiagent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of Star Craft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.