Enhanced Redox Electrocatalysis in High‑Entropy Perovskite Fluorides by Tailoring d–p Hybridization

High-entropy catalysts featuring exceptional properties are,in no doubt,playing an increasingly significant role in aprotic lithium-oxygen batteries.Despite extensive effort devoted to tracing the origin of their unparalleled performance,the relationships between multiple active sites and reaction intermediates are still obscure.Here,enlightened by theoretical screening,we tailor a high-entropy perovskite fluoride(KCoMnNiMgZnF_(3)-HEC)with various active sites to overcome the limitations of conventional catalysts in redox process.The entropy effect modulates the d-band center and d orbital occupancy of active centers,which optimizes the d–p hybridization between catalytic sites and key intermediates,enabling a moderate adsorption of LiO_(2)and thus reinforcing the reaction kinetics.As a result,the Li–O2 battery with KCoMnNiMgZnF_(3)-HEC catalyst delivers a minimal discharge/charge polarization and long-term cycle stability,preceding majority of traditional catalysts reported.These encouraging results provide inspiring insights into the electron manipulation and d orbital structure optimization for advanced electrocatalyst.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Hybrid multiple attribute decision making model based on entropy

From the viewpoint of entropy, this paper investigates a hybrid multiple attribute decision making problem with precision number, interval number and fuzzy number. It defines a new concept： project entropy and the decision is taken according to the values. The validity and scientific nature of the given is proven.

Security analysis of access control model in hybrid cloud based on security entropy

To resolve the problem of quantitative analysis in hybrid cloud,a quantitative analysis method,which is based on the security entropy,is proposed.Firstly,according to the information theory,the security entropy is put forward to calculate the uncertainty of the system' s determinations on the irregular access behaviors.Secondly,based on the security entropy,security theorems of hybrid cloud are defined.Finally,typical access control models are analyzed by the method,the method's practicability is validated,and security and applicability of these models are compared.Simulation results prove that the proposed method is suitable for the security quantitative analysis of the access control model and evaluation to access control capability in hybrid cloud.

Magnetohydrodynamics hemodynamics hybrid nanofluid flow through inclined stenotic artery

The present study aims to perform computational simulations of twodimensional(2D)hemodynamics of unsteady blood flow via an inclined overlapping stenosed artery employing the Casson fluid model to discuss the hemorheological properties in the arterial region.A uniform magnetic field is applied to the blood flow in the radial direction as the magneto-hemodynamics effect is considered.The entropy generation is discussed using the second law of thermodynamics.The influence of different shape parameters is explored,which are assumed to have varied shapes(spherical,brick,cylindrical,platelet,and blade).The Crank-Nicolson scheme solves the equations and boundary conditions governing the flow.For a given critical height of the stenosis,the key hemodynamic variables such as velocity,wall shear stress(WSS),temperature,flow rate,and heat transfer coefficient are computed.

A novel procedure for identifying a hybrid QTL-allele system for hybrid-vigor improvement, with a case study in soybean(Glycine max)yield

“Breeding by design” for pure lines may be achieved by construction of an additive QTL-allele matrix in a germplasm panel or breeding population, but this option is not available for hybrids, where both additive and dominance QTL-allele matrices must be constructed. In this study, a hybrid-QTL identification approach, designated PLSRGA, using partial least squares regression(PLSR) for model fitting integrated with a genetic algorithm(GA) for variable selection based on a multi-locus, multi-allele model is described for additive and dominance QTL-allele detection in a diallel hybrid population(DHP). The PLSRGA was shown by simulation experiments to be superior to single-marker analysis and was then used for QTL-allele identification in a soybean DPH yield experiment with eight parents. Twenty-eight main-effect QTL with 138 alleles and nine QTL × environment QTL with 46 alleles were identified, with respective contributions of 61.8% and 23.5% of phenotypic variation. Main-effect additive and dominance QTL-allele matrices were established as a compact form of the DHP genetic structure. The mechanism of heterosis superior-to-parents(or superior-to-parents heterosis, SPH) was explored and might be explained by a complementary locus-set composed of OD+(showing positive over-dominance, most often), PD+(showing positive partial-to-complete dominance, less often) and HA+(showing positive homozygous additivity, occasionally) loci, depending on the parental materials. Any locus-type, whether OD+, PD + and HA+, could be the best genotype of a locus. All hybrids showed various numbers of better or best genotypes at many but not necessarily all loci, indicating further SPH improvement. Based on the additive/dominance QTL-allele matrices, the best hybrid genotype was predicted, and a hybrid improvement approach is suggested. PLSRGA is powerful for hybrid QTL-allele detection and cross-SPH improvement.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

基于ICEEMDAN多尺度熵与NGO-HKELM的转子故障诊断

针对电机转子故障信号非平稳、敏感的故障特征不能有效提取,传统分类器参数智能优化算法存在优化速度慢、调整参数多、易陷入局部最优等问题提出基于ICEEMDAN-MSE-KPCA与NGO-HKELM优化的转子故障诊断方法。首先,采用改进的自适应噪声完全集合经验模态分解(improved complete empirical mode decomposition with adaptive noise,ICEEMDAN)方法对转子振动信号进行分解和重构;计算重构信号的多尺度样本熵(multiscale sample entropy,MSE),形成特征向量,通过核主成分分析(kernel principal component analysis,KPCA)方法对高维的特征向量进行降维;最后,将降维后的特征向量输入北方苍鹰算法(northern goshawk optimization,NGO)优化的混合核极限学习机(hybrid extreme learning machine,HKELM)进行转子故障分类。研究结果表明,基于ICEEMDAN-MSE-KPCA与NGO-HKELM优化的转子故障诊断模型,平均识别准确率可达97.7273%,平均寻优时间为1.0681 s,收敛速度快、准确率高以及分类效果好。

Second Law Analysis of Magneto Radiative GO-MoS_(2)/H_(2)O–(CH_(2)OH)_(2) Hybrid Nanofluid

Entropy Generation Optimization(EGO)attained huge interest of scientists and researchers due to its numerous applications comprised in mechanical engineering,air conditioners,heat engines,thermal machines,heat exchange,refrigerators,heat pumps and substance mixing etc.Therefore,the study of radiative hybrid nanofluid(GO-MoS_(2)/C_(2)H_(6)O_(2)–H_(2)O)and the conventional nanofluid(MoS_(2)/C_(2)H_(6)O_(2)–H_(2)O)is conducted in the presence of Lorentz forces.The flow configuration is modeled between the parallel rotating plates in which the lower plate is permeable.The models which govern the flow in rotating system are solved numerically over the domain of interest and furnished the results for the temperature,entropy generation and thermophysical characteristics of the hybrid as well as conventional nanofluids,respectively.It is examined that the thermal profile intensifies against stronger thermal radiations and magnetic field.The surface of the plate is heated due to the imposed thermal radiations and magnetic field which cause the increment in the temperature.It is also observed that the temperature declines against more rotating plates.Further,the entropy production increases for more dissipative effects and declines against more magnetized fluid.Thermal conductivities of the hybrid nanofluid enhances promptly in comparison with regular liquid therefore,under consideration hybrid nanofluid is reliable for the heat transfer.Moreover,dominating thermal transport is perceived for the hybrid nanofluid which showed that hybrid suspension GO-MoS_(2)/C_(2)H_(6)O_(2)–H_(2)O is better for industrial,engineering and technological uses.

Adaptive Region Boosting method with biased entropy for path planning in changing environment

Path planning in changing environments with difficult regions, such as narrow passages and obstacle boundaries, creates significant chal- lenges. As the obstacles in W-space move frequently, the crowd degree of C-space changes accordingly. Therefore, in order to dynamically improve the sampling quality, it is appreciated for a planner to rapidly approximate the crowd degree of different parts of the C-space, and boost sample densities with them based on their difficulty levels. In this paper, a novel approach called Adaptive Region Boosting （ARB） is proposed to increase the sampling density for difficult areas with different strategies. What＇s more, a new criterion, called biased entropy, is proposed to evaluate the difficult degree of a region. The new criterion takes into account both temporal and spatial information of a specific C-space region, in order to make a thorough assessment to a local area. Three groups of experiments are conducted based on a dual-manipulator system with 12 DoFs. Experimental results indicate that ARB effectively improves the success rate and outperforms all the other related methods in various dynamical scenarios.

基于CNN-LSTM网络的交直流电网故障线路识别方法

针对交直流混联电网故障特征复杂、故障线路识别率低的局限性,提出一种基于模糊熵(FuzzyEn)结合改进的卷积神经-长短期记忆网络(CNN-LSTM)的交直流电网故障线路识别方法。首先使用改进的小波阈值滤波算法对数据进行降噪处理,再利用模糊熵提取信号的故障特征。接下来在卷积神经网络(CNN)的全连接层上,添加一层长短期记忆网络(LSTM),对模糊熵提取的故障特征进行分类,最终实现故障线路的准确识别。实验结果表明,该算法识别准确率达到了99.5%,能够有效地诊断出交直流混联电网故障线路,同时在10 dB噪声干扰下仍达到96.0%的识别准确率,具有较好的抗噪声能力。

An overlapping grid spectral collocation analysis on a newly developed hybrid nanofluid flow model

The present study investigates the axisymmetric stagnation point radiativeflow of a Cu-Al2O3/water hybrid nanofluid over a radially stretched/shrunk disk.In this paper,a new mathematical model has been developed by taking into consideration the concept of different nanoparticles concentration in a hybrid nanofluid,which are Brownian motion and thermophor-esis of nanoparticles.A new model for entropy generation has also been provided in the present study.The non-dimensional governing equations of the developed mathematical model are solved using newly developed and efficient overlapping grid spectral collocation method.Numerical stability and residual error test are provided here to show the accuracy of the numer-ical method in this mathematical model.The outcomes offluidflow,temperature,and two different types of concentration profiles are depicted,and described in graphical and tabular forms.For the limiting instances,comparison shows excellent agreement among current and results established in the literature.Increasing the strength of magneticfield is seen to increase the radial component offluid velocity as well as the entropy generated within the system.Two different nanofluid concentration profiles are increasing and decreasing with rising thermophor-esis and Brownian motion parameters,respectively,from a particular height above the disk because of the revised nanofluid boundary condition.Temperature profile increases here with increasing Biot number,and increasing Brinkman number causes higher entropy generation number for both stretching and shrinking disks.The enhanced thermal characteristics of the hybrid nanofluid over the single particle nanofluid has been observed.

Influences of double diffusion upon radiative flow of thin film Maxwell fluid through a stretching channel

This work explores the influence of double diffusion over thermally radiative flow of thin film hybrid nanofluid and irreversibility generation through a stretching channel.The nanoparticles of silver and alumina have mixed in the Maxwell fluid(base fluid).Magnetic field influence has been employed to channel in normal direction.Equations that are going to administer the fluid flow have been converted to dimension-free notations by using appropriate variables.Homotopy analysis method is used for the solution of the resultant equations.In this investigation it has pointed out that motion of fluid has declined with growth in magnetic effects,thin film thickness,and unsteadiness factor.Temperature of fluid has grown up with upsurge in Brownian motion,radiation factor,and thermophoresis effects,while it has declined with greater values of thermal Maxwell factor and thickness factor of the thin film.Concentration distribution has grown up with higher values of thermophoresis effects and has declined for augmentation in Brownian motion.

基于改进变分模态分解和优化堆叠降噪自编码器的轴承故障诊断

针对滚动轴承在噪声干扰下故障特征难以提取的问题,提出一种改进变分模态分解(VMD)和复合缩放排列熵(CZPE)的特征提取新方法,并利用优化堆叠降噪自编码器(SDAE)进行故障分类。首先,提出由“余弦相似度—峭度—包络熵”新综合评价指标自适应优化分解参数的改进VMD方法,并通过该指标筛选分解后的本征模态函数(IMF)分量;然后,为提取更全面的故障特征,引入新的复合缩放排列熵对各有效IMF的故障特征进行量化;最后,提出一种基于鼠群优化算法(RSO)与麻雀搜索算法(SSA)的混合算法优化SDAE网络超参数,将故障特征输入优化后SDAE网络中得到分类结果。采用美国CWRU轴承数据集进行验证,实验结果表明该方法能全面稳定地提取背景噪声下的故障特征,且与其他方法相比具有更好的抗噪性能和更高的故障诊断准确率。

熵最优与改进SCA的图像分割及其图像识别应用

针对传统图像分割效率低、精度差的不足,提出一种混合变异正余弦算法的多阈值图像分割方法。为提高SCA算法的寻优性能,设计拉丁超立方种群初始化改进种群多样性;以非线性转换因子动态调节算法搜索能力;融入惯性权重机制提升算法全局寻优;结合高斯和拉普拉斯分布混合变异对个体扰动,使算法跳离局部最优。将Cross熵作为适应度函数,利用HMSCA求解分割阈值。实验结果表明,该算法可以提升图像分割精度和效率。将其应用于火灾图像识别,能够实现火焰源与背景分离,得到更好的分割效果。

基于延迟线组合调相的高能效自适应混合预编码方案

针对毫米波大规模多输入多输出系统采用混合预编码技术所带来的高功耗影响,基于延迟线组合调相器件设计了一种间接全连接型混合预编码系统结构,并提出一种自适应连接的混合预编码方案,可大幅减少与天线直连的调相器数量从而降低硬件造成的功耗损失。方案将编码矩阵的求解分为数字编码矩阵、模拟编码矩阵和开关组成的连接交换矩阵3个部分进行交替优化;提出一种基于互补正交投影矩阵的算法来解决模拟编码矩阵的优化;利用功率约束条件和最小二乘算法简化数字编码矩阵的求解;针对连接交换矩阵的离散组合优化问题,利用低复杂度的交叉熵算法进行优化。仿真结果表明,所提方案可以保证系统性能在接近纯数字预编码方案的同时有效提高系统的能量效率。

基于HMFDE和t-SNE的旋转机械故障诊断方法

针对旋转机械的故障特征提取较难,以及单一类型的特征无法全面反映故障特性的问题,提出了一种基于混合多尺度波动散布熵(HMFDE)、t分布-随机邻域嵌入(t-SNE)和郊狼优化算法(COA)优化极限学习机(ELM)的旋转机械故障诊断方法。首先,采用特征加权提出了混合多尺度波动散布熵方法,并将其用于提取旋转机械振动信号的故障特征;随后,采用t-SNE方法对混合故障特征进行了特征降维,挑选出了最能够反映故障特性的特征子集,构建了敏感特征样本;最后,采用郊狼优化算法对极限学习机的输入权重和隐含层阈值进行了优化,完成了旋转机械的故障识别和分类;以齿轮箱和滚动轴承故障数据集为对象,对基于HMFDE、t-SNE和COA-ELM的故障诊断方法进行了实验,验证了方法的有效性。研究结果表明:采用HMFDE-t-SNE-CAO-ELM故障诊断方法可以取得100%的故障识别准确率,该方法能够有效地诊断旋转机械的不同故障类型和损伤;相较于基于单一类型特征的故障诊断方法,其准确率分别可以提高0.68%、22.42%、29.18%(齿轮箱)和1.43%、8.23%、23.67%(滚动轴承),虽然牺牲了一定的计算效率,但准确率得到了明显的提高;相较于其他常规故障分类器,COA-ELM的故障识别准确率具有明显的优势。

Entropy generation for mixed convection flow in vertical annulus with two regions hydromagnetic viscous and Cu-Ag water hybrid nanofluid through porous zone: a comparative numerical study

The primary determination of this study is a numerical investigation of the entropygeneration (EG) in the steady two-region flow of viscous fluid and hybrid nanofluid (NF) in along-infinite vertical annulus having a clear region as well as porous media. Stoke’s and single-phase NF models are used to study the viscous fluid and hybrid nanofluid (HNF) heat transferdevelopments, respectively. Two types of nanoparticles are taken, such as copper (Cu) and sil-ver (Ag) within base fluid water to make it a HNF. Darcy-Brinkman law is also used to examinethe flow through the porous zone in the annulus. Necessary quantities have been used in thesystem of equations to transfer them into non-dimensional forms. For momentum and energytransport, the numerical results are evaluated for various model parameters and are examinedvia the shooting method in MATHEMATICA. It is noted that the momentum and energy trans-port are more significant when two immiscible fluids in a clear vertical annulus are taken. Thefindings also indicate that two-phase momentum and heat flow are greater when a NF is used in Region-II and lower when a HNF is used. The temperature (in Region-II) falls with a high na-nomaterials volume fraction (see Figure 4) while it is increased when the Hartman number isincreased. Moreover, velocity declines with increment in nanomaterials volume fraction. Thus,higher thermal conductivity can be accomplished by using a magnetic field.

DeLuca－Termini混合熵的合理性分析及其推广

对ＤｅＬｕｃａ和Ｔｅｒｍｉｎｉ提出的混合熵的合理性进行了分析并加以推广，对其性质作了详细证明。并指出，本文混合熵的表达式具有更明确的数学及物理含义。

基于近似熵变化量判据的混合直流输电系统纵联保护方案

高压直流输电线路行波保护对信号采样频率要求高,保护性能受过渡电阻及噪声干扰影响较大,需配合纵联保护识别故障位置。而现有纵联差动保护易受分布电容影响,传输数据量较大,动作速度有待提升。为了解决上述问题,提出一种基于近似熵变化量判据的混合直流线路纵联保护方案。近似熵需要的计算数据量较小,且兼具高灵敏性、高可靠性优势。故障发生后,近似熵变化量呈现明显的峰值特征,基于1模电压电流近似熵变化量构建的保护判据能快速准确地识别高阻接地故障,同时能减小噪声干扰和分布电容的影响。基于乌东德±800 kV混合直流输电系统仿真模型对所提保护方案进行验证。结果表明,所提保护方案仅需5 kHz采样频率,无需严格的时间同步,且在经800Ω过渡电阻接地及10 dB信噪比场景下能够正确、快速地动作。