Dynamics and vibration reduction performance of asymmetric tristable nonlinear energy sink

With its complex nonlinear dynamic behavior,the tristable system has shown excellent performance in areas such as energy harvesting and vibration suppression,and has attracted a lot of attention.In this paper,an asymmetric tristable design is proposed to improve the vibration suppression efficiency of nonlinear energy sinks(NESs)for the first time.The proposed asymmetric tristable NES(ATNES)is composed of a pair of oblique springs and a vertical spring.Then,the three stable states,symmetric and asymmetric,can be achieved by the adjustment of the distance and stiffness asymmetry of the oblique springs.The governing equations of a linear oscillator(LO)coupled with the ATNES are derived.The approximate analytical solution to the coupled system is obtained by the harmonic balance method(HBM)and verified numerically.The vibration suppression efficiency of three types of ATNES is compared.The results show that the asymmetric design can improve the efficiency of vibration reduction through comparing the chaotic motion of the NES oscillator between asymmetric steady states.In addition,compared with the symmetrical tristable NES(TNES),the ATNES can effectively control smaller structural vibrations.In other words,the ATNES can effectively solve the threshold problem of TNES failure to weak excitation.Therefore,this paper reveals the vibration reduction mechanism of the ATNES,and provides a pathway to expand the effective excitation amplitude range of the NES.

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

12.6μm-Thick Asymmetric Composite Electrolyte with Superior Interfacial Stability for Solid-State Lithium-Metal Batteries

Solid-state lithium metal batteries(SSLMBs)show great promise in terms of high-energy-density and high-safety performance.However,there is an urgent need to address the compatibility of electrolytes with high-voltage cathodes/Li anodes,and to minimize the electrolyte thickness to achieve highenergy-density of SSLMBs.Herein,we develop an ultrathin(12.6μm)asymmetric composite solid-state electrolyte with ultralight areal density(1.69 mg cm^(−2))for SSLMBs.The electrolyte combining a garnet(LLZO)layer and a metal organic framework(MOF)layer,which are fabricated on both sides of the polyethylene(PE)separator separately by tape casting.The PE separator endows the electrolyte with flexibility and excellent mechanical properties.The LLZO layer on the cathode side ensures high chemical stability at high voltage.The MOF layer on the anode side achieves a stable electric field and uniform Li flux,thus promoting uniform Li^(+)deposition.Thanks to the well-designed structure,the Li symmetric battery exhibits an ultralong cycle life(5000 h),and high-voltage SSLMBs achieve stable cycle performance.The assembled pouch cells provided a gravimetric/volume energy density of 344.0 Wh kg^(−1)/773.1 Wh L^(−1).This simple operation allows for large-scale preparation,and the design concept of ultrathin asymmetric structure also reveals the future development direction of SSLMBs.

Asymmetric Drying and Wetting Trends in Eastern and Western China

As an important factor that directly affects agricultural production, the social economy, and policy implementation,observed changes in dry/wet conditions have become a matter of widespread concern. However, previous research has mainly focused on the long-term linear changes of dry/wet conditions, while the detection and evolution of the non-linear trends related to dry/wet changes have received less attention. The non-linear trends of the annual aridity index, obtained by the Ensemble Empirical Mode Decomposition(EEMD) method, reveal that changes in dry/wet conditions in China are asymmetric and can be characterized by contrasting features in both time and space in China. Spatially, most areas in western China have experienced transitions from drying to wetting, while opposite changes have occurred in most areas of eastern China. Temporally, the transitions occurred earlier in western China compared to eastern China. Research into the asymmetric spatial characteristics of dry/wet conditions compensates for the inadequacies of previous studies, which focused solely on temporal evolution;at the same time, it remedies the inadequacies of traditional research on linear trends over centennial timescales. Analyzing the non-linear trend also provides for a more comprehensive understanding of the drying/wetting changes in China.

Asymmetric Electrolytes Design for Aqueous Multivalent Metal Ion Batteries

With the rapid development of portable electronics and electric road vehicles,high-energy-density batteries have been becoming front-burner issues.Traditionally,homogeneous electrolyte cannot simultaneously meet diametrically opposed demands of high-potential cathode and low-potential anode,which are essential for high-voltage batteries.Meanwhile,homogeneous electrolyte is difficult to achieve bi-or multi-functions to meet different requirements of electrodes.In comparison,the asymmetric electrolyte with bi-or multi-layer disparate components can satisfy distinct requirements by playing different roles of each electrolyte layer and meanwhile compensates weakness of individual electrolyte.Consequently,the asymmetric electrolyte can not only suppress by-product sedimentation and continuous electrolyte decomposition at the anode while preserving active substances at the cathode for high-voltage batteries with long cyclic lifespan.In this review,we comprehensively divide asymmetric electrolytes into three categories:decoupled liquid-state electrolytes,bi-phase solid/liquid electrolytes and decoupled asymmetric solid-state electrolytes.The design principles,reaction mechanism and mutual compatibility are also studied,respectively.Finally,we provide a comprehensive vision for the simplification of structure to reduce costs and increase device energy density,and the optimization of solvation structure at anolyte/catholyte interface to realize fast ion transport kinetics.

融合Dead-ends和离线监督Actor-Critic的动态治疗策略生成模型

强化学习对数学模型依赖性低,利用经验便于架构和优化模型,非常适合用于动态治疗策略学习。但现有研究仍存在以下问题:1)学习策略最优性的同时未考虑风险,导致学到的策略存在一定的风险;2)忽略了分布偏移问题,导致学到的策略与医生策略完全不同;3)忽略患者的历史观测数据和治疗史,从而不能很好地得到患者状态,进而导致不能学到最优策略。基于此,提出了融合Dead-ends和离线监督Actor-Critic的动态治疗策略生成模型DOSAC-DTR。首先,考虑学到的策略所推荐的治疗行动的风险性,在Actor-Critic框架中融入Dead-ends概念;其次,为缓解分布偏移问题,在Actor-Critic框架中融入医生监督,在最大化预期回报的同时,最小化所学策略与医生策略之间的差距;最后,为了得到包含患者关键历史信息的状态表示,使用基于LSTM的编码器解码器模型对患者的历史观测数据和治疗史进行建模。实验结果表明,DOSAC-DTR相比基线方法有更好的性能,可以得到更低的估计死亡率以及更高的Jaccard系数。

Acoustic Bilayer Gradient Metasurfaces for Perfect and Asymmetric Beam Splitting

We experimentally and theoretically present a paradigm for the accurate bilayer design of gradient metasurfaces for wave beam manipulation,producing an extremely asymmetric splitting effect by simply tailoring the interlayer size.This concept arises from anomalous diffraction in phase gradient metasurfaces and the precise combination of the phase gradient in bilayer metasurfaces.Ensured by different diffraction routes in momentum space for incident beams from opposite directions,extremely asymmetric acoustic beam splitting can be generated in a robust way,as demonstrated in experiments through a designed bilayer system.Our work provides a novel approach and feasible platform for designing tunable devices to control wave propagation.

Effects of asymmetric coupling and boundary on the dynamic behaviors of a random nearest neighbor coupled system

This study investigates the dynamical behaviors of nearest neighbor asymmetric coupled systems in a confined space.First, the study derivative analytical stability and synchronization conditions for the asymmetrically coupled system in an unconfined space, which are then validated through numerical simulations. Simulation results show that asymmetric coupling has a significant impact on synchronization conditions. Moreover, it is observed that irrespective of whether the system is confined, an increase in coupling asymmetry leads to a hastened synchronization pace. Additionally, the study examines the effects of boundaries on the system's collective behaviors via numerical experiments. The presence of boundaries ensures the system's stability and synchronization, and reducing these boundaries can expedite the synchronization process and amplify its effects. Finally, the study reveals that the system's output amplitude exhibits stochastic resonance as the confined boundary size increases.

Nickel catalysts with asymmetric steric hindrance for ethylene polymerization

α-Diimide catalysts have attracted widespread attention due to their unique chain walking characteristics.A series ofα-diimide nickel/palladium catalysts with different electronic effects and steric hindrances were designed and synthesized for olefin polymerization.In this work,we synthesized a series of asymmetricα-diimide nickel complexes with different steric hindrances and used them for ethylene polymerization.These nickel catalysts have high ethylene polymerization activity,up to 6.51×10^(6)g·mol^(−1)·h^(−1),and the prepared polyethylene has a moderate melting point and high molecular weight(up to 38.2×10^(4)g·mol^(−1)),with a branching density distribution between 7 and 94 branches per 1000 carbons.More importantly,the polyethylene prepared by these catalysts exhibits excellent tensile properties,with strain and stress reaching 800%and 30 MPa,respectively.

Surface activity and cleaning performance of rosin-based quaternary ammonium salt type asymmetric Gemini surfactants

Rosin,a renewable and abundant resource,has been extensively processed and chemically modified to endow it with special properties,especially in the surfactant industry.In this study,four rosin-based quaternary ammonium asymmetric gemini surfactants(RGS-2-n)with different alkyl chain lengths(n=12,14,16,18)were synthesized using a simple two-step method based on dehydroabietylamine as the raw material.The feasibility of these surfactants for cleaning purposes was comprehensively evaluated,suggesting that the surfactants own high surface activity and good cleaning performance.Furthermore,by successfully introducing the amine group of dehydroabietylamine into the hydrophilic group of the surfactants,we avoided its potential harm to the environment and water pollution.Density functional theory proves rosin-based gemini surfactants with asymmetric structure can further improve cleaning efficiency.Overall,our findings suggests that RGS-2-n surfactants are promising and sustainable candidates for cleaning electric plates,and provide new opportunities for rosin application in the electric industry.

基于不确定性估计的离线确定型Actor-Critic

Actor-Critic是一种强化学习方法,通过与环境在线试错交互收集样本来学习策略,是求解序贯感知决策问题的有效手段.但是,这种在线交互的主动学习范式在一些复杂真实环境中收集样本时会带来成本和安全问题离线强化学习作为一种基于数据驱动的强化学习范式,强调从静态样本数据集中学习策略,与环境无探索交互,为机器人、自动驾驶、健康护理等真实世界部署应用提供了可行的解决方案,是近年来的研究热点.目前,离线强化学习方法存在学习策略和行为策略之间的分布偏移挑战,针对这个挑战,通常采用策略约束或值函数正则化来限制访问数据集分布之外(Out-Of-Distribution,OOD)的动作,从而导致学习性能过于保守,阻碍了值函数网络的泛化和学习策略的性能提升.为此,本文利用不确定性估计和OOD采样来平衡值函数学习的泛化性和保守性,提出一种基于不确定性估计的离线确定型Actor-Critic方法(Offline Deterministic Actor-Critic based on UncertaintyEstimation,ODACUE).首先,针对确定型策略,给出一种Q值函数的不确定性估计算子定义,理论证明了该算子学到的Q值函数是最优Q值函数的一种悲观估计.然后,将不确定性估计算子应用于确定型Actor-Critic框架中,通过对不确定性估计算子进行凸组合构造Critic学习的目标函数.最后,D4RL基准数据集任务上的实验结果表明:相较于对比算法,ODACUE在11个不同质量等级数据集任务中的总体性能提升最低达9.56%,最高达64.92%.此外,参数分析和消融实验进一步验证了ODACUE的稳定性和泛化能力.

无人机辅助物联网中基于Safe Actor-Critic的信息年龄最小化研究

无人机作为一种新的通信设备,有望在物联网数据采集、监控等业务中发挥关键作用。为保证所采集数据的时效性,利用信息年龄来衡量无人机从物联网设备接收到的数据新鲜度。通过联合优化无人机轨迹和无人机与物联网设备的关联策略以最小化信息年龄加权和,并保证无人机累积飞行能量消耗满足预算要求。由于上述问题同时受短期和长期约束条件的限制,将问题建模为受约束的马尔可夫决策过程(CMDP),并利用Safe Actor-Critic来求解。仿真结果表明,所提算法在最小化信息年龄的同时,能有效保证能量预算。

Enhanced and asymmetric signatures of hybridization at climatic margins: Evidence from closely related dioecious fig species

Hybridization plays a significant role in biological evolution. However, it is not clear whether ecological contingency differentially influences likelihood of hybridization, particularly at ecological margins where parental species may exhibit reduced fitnesses. Moreover, it is unknown whether future ecosystem change will increase the prevalence of hybridization. Ficus heterostyla and F. squamosa are closely related species co-distributed from southern Thailand to southwest China where hybridization, yielding viable seeds, has been documented. As a robust test of ecological factors driving hybridization, we investigated spatial hybridization signatures based on nuclear microsatellites from extensive population sampling across a widespread contact range. Both species showed high population differentiation and strong patterns of isolation by distance. Admixture estimates exposed asymmetric interspecific gene flow.Signatures of hybridization increase significantly towards higher latitude zones, peaking at the northern climatic margins. Geographic variation in reproductive phenology combined with ecologically challenging marginal habitats may promote this phenomenon. Our work is a first systematic evaluation of such patterns in a comprehensive, latitudinally-based clinal context, and indicates that tendency to hybridize appears strongly influenced by environmental conditions. Moreover, that future climate change scenarios will likely alter and possibly augment cases of hybridization at ecosystem scales.

Spatially engineering tri-layer nanofiber dressings featuring asymmetric wettability for wound healing

Inspired by the skin structure,an asymmetric wettability tri-layer nanofiber membrane(TNM)consisting of hydrophilic inner layer loaded with lidocaine hydrochloride(LID),hydrophobic middle layer with ciprofloxacin(CIP)and hydrophobic outer layer has been created.The hydrophobic outer layer endows the TNM with waterproof function and anti-adhesion from contaminants.The hydrophobic middle layer with CIP preserves long-term inhibition of bacteria growth and the hydrophilic inner layer with LID possesses optimal waterabsorbing capacity and air permeability.The TNM dramatically elevates the water contact angles from 10°(inner layer)to 120(outer layer),indicating an asymmetric wettability,which could directionally transport wound exudate within the materials and meanwhile maintain a comfortable and moist environment to promote wound healing.Furthermore,the sequential release of LID and CIP could relieve pain rapidly and achieve antibacterial effect in the long run,respectively.In addition,the TNM shows superior biocompatibility towards L929 cells.The in vivo results show the TNM could prevent infection,accelerate epithelial regeneration and significantly accelerate wound healing.This study indicates the developed TNM with asymmetrical wettability and synergetic drug release shows great potential as a wound dressing in clinical application.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A Hybrid Dung Beetle Optimization Algorithm with Simulated Annealing for the Numerical Modeling of Asymmetric Wave Equations

In the generalized continuum mechanics(GCM)theory framework,asymmetric wave equations encompass the characteristic scale parameters of the medium,accounting for microstructure interactions.This study integrates two theoretical branches of the GCM,the modified couple stress theory(M-CST)and the one-parameter second-strain-gradient theory,to form a novel asymmetric wave equation in a unified framework.Numerical modeling of the asymmetric wave equation in a unified framework accurately describes subsurface structures with vital implications for subsequent seismic wave inversion and imaging endeavors.However,employing finite-difference(FD)methods for numerical modeling may introduce numerical dispersion,adversely affecting the accuracy of numerical modeling.The design of an optimal FD operator is crucial for enhancing the accuracy of numerical modeling and emphasizing the scale effects.Therefore,this study devises a hybrid scheme called the dung beetle optimization(DBO)algorithm with a simulated annealing(SA)algorithm,denoted as the SA-based hybrid DBO(SDBO)algorithm.An FD operator optimization method under the SDBO algorithm was developed and applied to the numerical modeling of asymmetric wave equations in a unified framework.Integrating the DBO and SA algorithms mitigates the risk of convergence to a local extreme.The numerical dispersion outcomes underscore that the proposed SDBO algorithm yields FD operators with precision errors constrained to 0.5‱while encompassing a broader spectrum coverage.This result confirms the efficacy of the SDBO algorithm.Ultimately,the numerical modeling results demonstrate that the new FD method based on the SDBO algorithm effectively suppresses numerical dispersion and enhances the accuracy of elastic wave numerical modeling,thereby accentuating scale effects.This result is significant for extracting wavefield perturbations induced by complex microstructures in the medium and the analysis of scale effects.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

A Multifunctional Chiral Metasurface with Asymmetric Transmission and Linear-Polarization Conversion

In this paper,a multifunctional chiral metasurface is presented to achieve asymmetric transmission(AT)and linear-polarization conversion(LPC).The designed metasurface consists of a cross swords-like shape and two holes in the lower side of the unit cell.In the frequency band from 8.3 GHz to 10.4 GHz,AT is realized with more than 90%efficiency and the same chiral metasurface transforms linear polarized wave into its orthogonal counterpart with high efficiency.For LPC,the polarization conversion ratio(PCR)is greater than 95%.The proposed metasurface is stable against the incident angles of striking electromagnetic(EM)waves up to 60°for both operations of AT and LPC.

Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.