baldness的委婉说法

英语国家的禁忌范围很广泛,主要有死亡、两性关系、某些生理功能(上厕所、例假)、年老等等。这些社会禁忌(social taboo)反映在语言上便是禁忌语。委婉语的使用是避开禁忌的有效途径。还有一种禁忌是个人禁忌(per-sonal taboo),如某些个人缺陷——“秃顶”(baldness)。

Mercury concentrations in blood and feathers of nestling Bald Eagles in coastal and inland Virginia

Background:Mercury(Hg) and methylmercury are widely considered significant issues for wildlife,and in particular,piscivorous birds due to their widespread availability and neurotoxic properties.Whereas a substantial number of studies of Hg contamination of Bald Eagles(Haliaeetus leucocephalus) have been conducted throughout the east coast of the United States,little has been done that directly addresses Hg contamination in Bald Eagles in Virginia,particularly the inland population.Methods:We collected blood and feather samples from nestling Bald Eagles in the coastal plain,piedmont,and western regions of Virginia in an effort to determine which areas of the state were more likely to contain populations showing evidence of Hg toxicity.We analyzed the samples for total Hg using a Milestone DMA-80.Results:Samples collected from individuals located in the coastal region exhibited low concentrations of Hg compared to those further inland located on freshwater rivers and reservoirs.Samples collected from the inland population exhibited levels in some areas that are approaching what may be considered to be sub-lethal to avian health(blood:mean 0.324 mg/kg,SE = 0.13,range = 0.06-0.97 mg/kg;feather:mean = 8.433 mg/kg,SE = 0.3,range = 3.811-21.14 mg/kg).Conclusions:Even after accounting for known point-sources of Hg,the inland eagle population in Virginia is susceptible to concentrations of Hg that are significantly higher than their coastal counterparts.Moreover,several locations besides those currently known to be impacted by point-sources are exhibiting concentrations that are approaching a sub-lethal level.

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

Optimal FOPID Controllers for LFC Including Renewables by Bald Eagle Optimizer

In this study,a bald eagle optimizer(BEO)is used to get optimal parameters of the fractional-order proportional-integral-derivative(FOPID)controller for load frequency control(LFC).SinceBEOtakes only a very short time in finding the optimal solution,it is selected for designing the FOPID controller that improves the system stability and maintains the frequency within a satisfactory range at different loads.Simulations and demonstrations are carried out using MATLAB-R2020b.The performance of the BEOFOPID controller is evaluated using a two-zone interlinked power system at different loads and under uncertainty of wind and solar energies.The robustness of the BEO-FOPID controller is examined by testing its performance under varying system time constants.The results obtained by the BEOFOPID controller are compared with those obtained by BEO-PID and PID controllers based on recent metaheuristics optimization algorithms,namely the sine-cosine approach,Jaya approach,grey wolf optimizer,genetic algorithm,bacteria foraging optimizer,and equilibrium optimization algorithm.The results confirm that the BEO-FOPID controller obtains the finest result,with the lowest frequency deviation.The results also confirm that the BEOFOPID controller is stable and robust at different loads,under varying system time constants,and under uncertainty of wind and solar energies.

Bald Eagle Search Optimization Algorithm Combined with Spherical Random Shrinkage Mechanism and Its Application

Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best.To avoid BES collapse at local optima,inspired by the fact that the volume of the sphere is the largest when the surface area is certain,an improved bald eagle search optimization algorithm(INMBES)integrating the random shrinkage mechanism of the sphere is proposed.Firstly,the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population.Secondly,the population splits into elite and non-elite groups and the Bernoulli chaos is applied to elite group to tap around potential solutions of the INMBES.The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES.The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10,30,50,and 100 dimensions numerical optimization confirm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third.The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES’s performance ranks first and has achieved satisfactory accuracy in solving practical problems.

Prediction of network public opinion based on bald eagle algorithm optimized radial basis function neural network

Purpose-In the new era of highly developed Internet information,the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approach-Aiming at the complex and nonlinear characteristics of the network public opinion,considering the accuracy and stability of the applicable model,a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network(BES-RBF)is proposed.Empirical research is conducted with Baidu indexes such as“COVID-19”,“Winter Olympic Games”,“The 100th Anniversary of the Founding of the Party”and“Aerospace”as samples of network public opinion.Findings-The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information,has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/value-A method for optimizing the central value,weight,width and other parameters of the radial basis function neural network with the bald eagle algorithm is given,and it is applied to network public opinion trend prediction.The example verifies that the prediction algorithm has higher accuracy and better stability.

THE BALD TRUTH——ADD HAIR LOSS TO THE STRESSES FACING MILLENNIALS

Several weeks after starting his job with tech company Tencent,247-year-old Mr.Guo identified a problem:He was losing his hair.He now fears becoming as bald as colleagues who are decades his senior.

”Das Wunder ist bald zu Ende“

Der Vizeminister der staatlichen Umweltbehrde, Pan Yue, überdie Risiken des Wirtschaftswachstums und die gravierenden1kologischen Schden im

基于IBES-XGBoost的高速铁路沿线风速预测模型

为保证高速铁路沿线风灾预警信息具有较高时效性,需要进行高速铁路沿线超短期风速的提前多步预测。针对众多预测模型在预测中可能存在较大误差的问题,采用Tent混沌映射和BFGS拟牛顿法对秃鹰搜索算法进行改进,并用改进的秃鹰搜索算法(IBES)对XGBoost的初始参数进行优化。在构建IBES-XGBoost模型时,加入风速以外的其他气象特征,以提高预测精度。实验结果表明:(1)改进的秃鹰算法相比其他智能优化算法有更好的寻优能力,与其他模型相比IBES-XGBoost在超短期风速的提前多步预测上有着较高的精度和较好的拟合效果。(2)Tent混沌映射和BFGS拟牛顿法对秃鹰算法有着较好的改进效果。(3)IBES-XGBoost能为高速铁路规范下的大风预警提供可靠的提前多步预测结果。

基于DBN和BES-LSSVM的矿用压风机异常状态识别方法

针对矿用压风机这类分布式系统的异常类别复杂、识别精度低等问题,提出了一种基于深度置信网络(DBN)和最小二乘支持向量机(LSSVM)的异常状态识别方法。首先,分析压风机组成系统及其运行机理,确定常见的异常状态类型;其次,采用DBN无监督学习方式充分挖掘监测数据中异常特征并快速提取;然后,利用秃鹰搜索算法(BES)优化LSSVM的超参数,构建最优的BES-LSSVM分类模型;最后,将DBN提取的异常特征作为BES-LSSVM模型的输入,对矿用压风机异常状态进行识别。试验验证与对比分析结果表明,相较于GA,PSO,GWO算法,BES算法的求解精度和收敛速度均有所提高,同时DBN-BES-LSSVM模型在测试集上平均识别精度达到94.65%,较PCA-LSSVM模型、DBN模型和DBN-LSSVM模型的识别精度分别提高了10.53%,5.84%和3.76%,验证了DBN-BES-LSSVM模型在矿用压风机异常特征提取以及特征识别方面的优越性。

t分布与螺旋黏菌搜索的混沌自适应秃鹰搜索算法

针对秃鹰搜索算法搜索精度低、收敛速度慢及易于陷入局部最优的不足,提出t分布与螺旋黏菌搜索的混沌自适应秃鹰搜索算法.首先引入混沌Bernoulli映射进行种群初始化,丰富种群多样性;然后在搜索空间猎物阶段利用螺旋黏菌搜索策略依搜索进程动态修正位置更新方式,提高算法全局搜索能力和收敛精度;在俯冲捕获猎物阶段引入自适应惯性权重策略平衡算法全局搜索与局部开发,提高算法求解精度;最后利用t分布随机扰动策略依概率对种群个体变异,增加算法跳离局部最优、找到全局最优的概率.利用基准函数对算法寻优性能进行实验评估,并引入Wilcoxon秩和检验评估算法搜索性能.结果表明:改进秃鹰搜索算法在寻优精度和收敛速度上都得到了更大提升.

海洋环境下深水区立管腐蚀速率预测

为提升海洋环境下深水区立管腐蚀速率的预测精度,建立基于改进秃鹰搜索算法(Improved Bald Eagle Search, IBES)的EDGM(1, 1,ρ)腐蚀速率预测模型。通过Sine混沌映射初始化种群、莱维飞行策略、折射反向学习策略和柯西高斯变异策略提高秃鹰搜索算法的寻优能力和收敛速度;利用IBES算法优化EDGM(1, 1,ρ)中的参数ρ,建立IBES-EDGM(1, 1,ρ)模型以提高立管腐蚀速率的预测精度。以南海某海洋深水区立管数据为基础进行腐蚀速率预测,分析对比3种模型的预测结果。结果表明,优化后的模型与原模型相比误差更小,且预测精度得到了提高,能够更准确地预测深海立管的腐蚀速率,为后续管道系统的维修和更换提供理论参考。

试论丰特努瓦之战

丰特努瓦之战是9世纪中叶加洛林王朝内战期间一次具有重要意义的大规模会战。从制度传统和先皇虔诚者路易生前的权力安排以及其后嗣诸位君王基本诉求等角度进行研究,可以明晰对此役爆发深层缘由的认知,进一步揭橥其本质属性,从而更深入地理解这一战役的基本态势和特征。关于此役的后果和历史意义,9世纪史家和18世纪前后的学者有诸种评说,但皆失之武断、片面。因而对其作战样态、交战过程和伤亡人数以及直接、间接后果等问题逐一加以重新审视,不仅有助于更为真实地展现此役的状貌,也有助于从战争这一特定维度来认识当时加洛林帝国政治、社会变革的基本走势。

基于改进秃鹰优化算法的海上混合光伏波浪能转换器阵列优化

近年来,海上能源发电技术备受瞩目,一种新兴趋势是将波浪能转换器(wave energy converter,WEC)与海上光伏(offshore floating photovoltaic,OFPV)相结合,形成混合光伏-波浪能转换器系统(hybrid PV-wave energy converter,HPV-WEC)。HPV-WEC具有提高海上空间利用率,降低成本以及实现功率稳定输出等优势。为了充分利用HPV-WEC系统之间的协同效应,在不增加新设备的情况下提高能源产量,提出了一种基于改进秃鹰优化算法(improved bald eagle search algorithm,IBES)的HPV-WEC阵列布局优化策略。IBES结合了莱维飞行策略和模拟退火(simulated annealing,SA)机制,以平衡局部开发和全局探索之间的关系。为了评估IBES在优化HPV-WEC阵列方面的有效性,进行了5个浮标和8个浮标规模的阵列优化,并将IBES与其他5种算法进行了比较。实验结果表明,IBES表现出实现最大总功率输出并具有显著的收敛特性。

基于改进FCM的冲压件缺陷图像分割算法

在工业质检过程中,冲压件缺陷图像分割作为缺陷检测的重要环节,直接影响缺陷检测效果。而传统的模糊C均值(FCM)聚类算法未考虑到空间邻域信息,对于噪声干扰较为敏感,导致分割精度较差,且其整体易受初始值的影响,造成收敛速度变慢。针对上述问题,提出一种改进的FCM算法。采用内核诱导距离中的简单两项代替传统的欧氏距离,将原有的空间像素映射到高维特征空间,提高线性可分概率和计算速度;利用图像像素之间的空间相关性,通过引入改进的马尔可夫随机场对FCM目标函数进行修正,提高算法的抗噪能力以及分割精度;采用秃鹰搜索(BES)算法确定FCM的初始聚类中心,提高算法的收敛速度,同时避免算法陷入局部极值的情况。为验证改进FCM算法的性能,选取划分熵、划分系数、Xie_Beni系数以及迭代次数作为评价指标,并与近年来先进的图像分割算法进行对比。实验结果表明,改进FCM算法具有更好的抗噪能力,能得到更好的缺陷分割效果,对工业生产中的冲压件缺陷检测有一定的应用价值。

基于改进秃鹰算法的微电网群经济优化调度研究

传统优化算法相较于智能优化算法在求解微电网群优化调度问题中较难寻出可行解或最优解,对此提出一种基于融合反向学习和柯西变异改进的秃鹰算法(IBES),在秃鹰搜索空间猎物阶段采用融合反向学习和柯西变异策略,使得秃鹰算法有效跳出局部最优,解决算法求解精度低等问题。通过与粒子群算法(PSO)、麻雀算法(SSA)、鲸鱼算法(WOA)进行对比,仿真结果表明IBES寻优精度更高,可有效减少微电网群系统的经济成本。

基于秃鹰搜索算法优化的三维多无人机低空突防

三维空间环境复杂,多无人机的低空突防航迹规划计算量大,现有多目标秃鹰搜索算法存在求解易趋于中心点及精度低的缺陷,为此提出基于改进多目标秃鹰搜索算法(IMBES)的三维多无人机低空突防方法.构建三维环境模型、威胁源模型、无人机物理约束模型、多无人机协同约束模型以及路径平滑度约束模型,确定多目标代价函数.设计耦合混沌映射初始化,有效提高初始化种群质量;设计基于“侦察鹰”的自适应高斯游走策略,平衡开发与搜索能力;引入快速非支配排序,进一步提高算法效率.利用秃鹰位置与无人机速度、转弯角度、爬升角度的对应关系,采用IMBES高效搜索无人机构型空间,找到最优的帕累托前沿.实验结果表明,IMBES的成功率为70.5%.与现有路径规划方法相比,所提方法的优化能力强、能耗低,适用于多无人机协同低空突防.

基于LDA-IBES-RELM的光伏阵列故障诊断方法

针对光伏阵列故障诊断准确率偏低的问题,提出了一种基于改进秃鹰搜索算法(IBES)优化正则化极限学习机(RELM)的故障诊断方法。首先在Simulink建立光伏阵列仿真模型,模拟典型故障并提取故障特征数据,同时利用线性判别分析(LDA)对特征量降维作为故障诊断模型的输入;其次利用Logistic混沌映射、Levy飞行策略和柯西高斯变异扰动策略对秃鹰算法进行改进;最后将IBES用于对RELM的隐层参数寻优。实验结果表明:LDA-IBES-RELM模型与BES-RELM、IBES-RELM模型对比,得到的故障诊断准确率为97.71%,优于其他两种模型,验证了LDA-IBESRELM模型用于光伏阵列故障诊断的有效性和实用性。

基于PWLCM和秃鹰俯冲机制改进的野狗优化算法

针对野狗优化算法(Dingo Optimization Algorithm,DOA)收敛速度偏慢和寻优精度较低等问题,提出一种基于PWLCM和秃鹰机制改进的野狗优化算法(Improved Dingo Optimization Algorithm,IDOA)。首先,使用具有遍历性的分段线性混沌映射(Piecewise Linear Chaotic Map,PWLCM)初始化野狗种群,有效增加野狗种群多样性。其次,在迫害策略中引入秃鹰俯冲机制,加快野狗捕获猎物的速度,加强算法探索局部的能力。最后,在食腐策略引入螺旋搜索因子,增强算法的局部寻优能力,提升算法的寻优速度和求解精度。仿真实验数据、消融实验以及Wilcoxon秩和检验均表明,与其他对比算法相比,提出的IDOA在所有测试函数上有着更佳的寻优速度以及寻优精度;与其他改进的野狗优化算法相比,所提出的IDOA展现出更好的整体性能。

外施椰糠及泥炭土对高寒草甸鼠害型秃斑地的改良效果

高寒草甸鼠害型秃斑地是黑土滩型退化草地的起始阶段。为探究外施椰糠及泥炭土对秃斑地改良效果,本研究选取青藏高原东部玛曲县典型鼠害型秃斑地,以未采取治理措施的秃斑地为对照(CK),采取浅翻(SP)、浅翻(SP)+施椰糠(CF)、浅翻(SP)+施椰糠(CF)+施泥炭(PS)3种措施,调查3年内不同措施下秃斑地植物群落特征和土壤理化性状,并采用冗余分析法探讨环境因子对植物群落特征的影响。结果表明,3种处理措施下秃斑地恢复到第3年时:1)SP+CF处理下植物盖度、高度和生物量显著大于CK(P<0.05)。治理措施和年份间的交互作用,对植物盖度、高度、物种数和生物量具有显著影响。2)SP+CF处理与CK相比显著提高了土壤最大持水量、0-10 cm表层土壤N、P、有机质和C/P(P<0.05)。3)偏冗余分析表明,土壤有机质(OM)、土壤毛管持水量(Cc)和总孔隙度(Tp)对植物群落特征的方差解释净效应达显著水平(P<0.05)。综上,3种措施均可恢复秃斑地植被和土壤,其中浅翻与椰糠相结合的处理措施效果最好。