Dynamics of a Nonautonomous Plant Disease Model with General Nonlinear Incidence Rate and Time-Varying Impulse

In this paper, for controlling the spread of plant diseases, a nonautonomous SEIS (Susceptible → Exposed → Infectious → Susceptible) epidemic model with a general nonlinear incidence rate and time-varying impulsive control strategy is proposed and investigated. This novel model could result in an objective criterion on how to control plant disease transmission by replanting of healthy plants and removal of infected plants. Using the method of small amplitude perturbation, the sufficient conditions under which guarantee the globally attractive of the disease-free periodic solution and the permanence of the disease are obtained, that is, the disease dies out if R12>1.

Translational - torsional coupled model based nonlinear dynamic analysis of an NGW planetary gear train

This paper aims to investigate the nonlinear dynamic behaviors of an NGW planetary gear train with multi-clearances and manufacturing/assembling errors. For this purpose, an analytical translational- torsional coupled dynamic model is developed considering the effects of time-varying stiffness, gear backlashes and component errors. Based on the proposed model, the nonlinear differential equations of motion are derived and solved iteratively by the Runge-Kutta method. An NGW planetary gear reducer with three planets is taken as an example to analyze the effects of nonlinear factors. The results indicate that the backlashes induce complicated nonlinear dynamic behaviors in the gear train. With the increment of the backlashes, the gear system has experienced periodic responses, quasi-periodic response and chaos responses in sequence. When the planetary gear system is in a chaotic motion state, the vibration amplitude increases sharply, causing severe vibration and noise. The present study provides a fundamental basis for design and parameter optimization of NGW planetary gear trains.

Robust Stability of Nonlinear Plants with a Non-symmetric Prandtl-Ishlinskii Hysteresis Model

In this paper, robust stability of nonlinear plants represented by non-symmetric Prandtl-Ishlinskii （PI） hysteresis model is studied. In general, PI hysteresis model is the weighted superposition of play or stop hysteresis operators, and the slopes of the operators are considered to be the same. In order to make a hysteresis model, a modified form of non-symmetric play hysteresis operator with unknown slopes is given. The hysteresis model is described by a generalized Lipschitz operator term and a bounded parasitic term. Since the generalized Lipschitz operator is unknown, a new condition using robust right coprime factorization is proposed to guarantee robust stability of the controlled plant with the hysteresis nonlinearity. As a result, based on the proposed robust condition, a stabilized plant is obtained. A numerical example is presented to validate the effectiveness of the proposed method.

Nonlinear System Identification with Unknown Piecewise Time-Varying Delay

Identification of nonlinear systems with unknown piecewise time-varying delay is concerned in this paper.Multiple auto regressive exogenous(ARX) models are identified at different process operating points,and the complete dynamics of the nonlinear system is represented by using a combination of a normalized exponential function as the probability density function with each of the local models.The parameters of the local ARX models and the exponential functions as well as the unknown piecewise time-varying delays are estimated simultaneously under the framework of the expectation maximization(EM) algorithm.A simulation example is applied to demonstrating the proposed identification method.

The effects of cold region meteorology and specific environment on the number of hospital admissions for chronic kidney disease:An investigate with a distributed lag nonlinear model

Objective:To explore the effects of daily mean temperature(°C),average daily air pressure(hPa),humidity(%),wind speed(m/s),particulate matter(PM)2.5(μg/m3)and PM10(μg/m3)on the admission rate of chronic kidney disease(CKD)patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence.Methods:The R language Distributed Lag Nonlinear Model(DLNM),Excel,and SPSS were used to analyze the disease and meteorological data of Harbin from 01 January 2010 to 31 December 2019 according to the inclusion and exclusion criteria.Results:Meteorological factors and air pollution influence the number of hospitalizations of CKD to vary degrees in cold regions,and differ in persistence or delay.Non-optimal temperature increases the risk of admission of CKD,high temperature increases the risk of obstructive kidney disease,and low temperature increases the risk of other major types of chronic kidney disease.The greater the temperature difference is,the higher its contribution is to the risk.The non-optimal wind speed and non-optimal atmospheric pressure are associated with increased hospital admissions.PM2.5 concentrations above 40μg/m3 have a negative impact on the results.Conclusion:Cold region meteorology and specific environment do have an impact on the number of hospital admissions for chronic kidney disease,and we can apply DLMN to describe the analysis.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

绿色金融对低碳全要素生产率的非线性异质调节研究

本文基于2009~2022年我国省际面板数据和门槛模型,实证探析绿色金融与低碳全要素生产率的非线性关系及异质调节机制。实证证明:绿色金融对低碳全要素生产率具有显著“U”型非线性影响。空间异质性分析显示,东部地区绿色金融始终具有显著“赋能效应”,满足“梅特卡夫法则”;中部地区绿色金融对低碳全要素生产率具有显著线性促进效果,负向效果不显著;西部地区绿色金融对低碳全要素生产率的影响表现为显著“倒梯型”特征,且具有“中等绿色金融发展水平陷阱”。进一步研究发现,经济发展水平、产业结构、外商直接投资、城市化水平、金融监管、低碳技术异质调节着绿色金融与低碳全要素生产率的非线性关系。

多因素耦合下沥青混合料拉压差异力学特性

针对现行沥青路面设计时材料力学参数取值的可靠性不足,基于道路材料的拉压差异特性,开展了多因素耦合影响下沥青混合料拉压力学参数非线性特性及量化关系研究。结果表明,沥青混合料拉压应力应变特性符合双模量理论的双线性特征;拉压力学参数随加载速度增大先急剧增加后趋于平缓,且呈幂函数关系;与温度呈负指数函数关系,且温度增幅越高力学参数减小幅度越大;与沥青用量呈指数衰减模型函数关系,高温时(>30℃)沥青用量的变化对力学参数影响相对较小,最大力学参数所对应的沥青用量与马歇尔试验得到的最佳沥青用量基本一致;就影响程度而言,温度对力学参数的影响最大,各因素对强度的影响最为显著,抗拉力学参数较抗压力学参数更为敏感;据此建立了多因素耦合作用下沥青混合料的拉压力学参数量化取值模型,并推荐规范常用温度15℃(20℃)下的压拉强度比及压拉模量比分别为6.5(7.5)、1.6(1.7),压缩模强比及拉伸模强比分别为400(1700)、450(2000)。

球型钢丝绳隔振器非对称迟滞模型

针对球型钢丝绳隔振器的非线性刚度和阻尼特性导致迟滞模型表征困难的问题,提出了非线性弹性因子和修正因子改进的归一化Bouc-Wen迟滞模型,结合Simulink仿真完善改进的迟滞模型,采用最小二乘法和遗传算法相结合的方法对改进模型进行参数识别。通过数值仿真绘制GGQ25-62L型球型钢丝绳隔振器在不同工况的迟滞环,并和实验数据进行对比。结果表明,参数识别方法准确可靠,实验数据和理论计算曲线具有较强的一致性,该模型能够有效地描述隔振器的动态特性。

含竞争指标的广义可加混合效应树高-胸径模型

广义可加混合效应模型(GAMM)兼具参数模型与非参数模型的优点,同时适于处理多层次分组数据。通过运用广义可加混合效应模型模拟胸径及树高之间关系,加入竞争因子作为辅助变量,并与传统非线性混合效应模型进行比较,能够为建立树高曲线及提高模型精度提供新方法。根据吉林省汪清林业局金沟岭林场2块100 m×100 m次生混交林样地中的实测单木数据,按照7∶3比例随机划分建模与验证数据。随机效应设定为林木分级,辅助变量选择大于对象木胸高断面积之和(B_(AL))或简单竞争指数(Hegyi指数,H_(EG)),根据随机效应的设定位置共构建15个广义可加混合效应模型,对照模型以Logistic及Richard方程为基础模型,共构建6个非线性混合效应树高-胸径模型。结果表明:所有广义可加混合效应模型均能较好地描述自变量与树高之间的关系,决定系数(R^(2))为0.8897~0.8998,相对均方根误差(R_(RMSE))为17.87%~18.74%,平均绝对误差(M_(AE))为1.7881~1.8745 m,赤池信息量(A_(IC))为4120.42~4162.23,均优于相同自变量下的非线性混合模型,R^(2)平均提高0.005,相对均方根误差、平均绝对误差、赤池信息量分别平均降低0.46%、0.0587 m、41.49。对于验证数据的预测可以看出,模型5具有最小的预测相对均方根误差,为20.28%,同时具有最小的预测平均绝对误差,为2.1038 m。但部分广义可加混合效应模型的预测表现略差于非线性混合模型。综合考虑参数与非参数估计显著性、模型估计精度及预测能力,所有模型中的最优模型为模型5,即以B_(AL)为辅助变量,考虑唯一全局平滑函数并以具有相同扭曲程度的分组水平平滑函数为基础添加随机效应。竞争因子选择B AL作为辅助变量能够提升树高模型的精度,而选择Hegyi指数为辅助变量的促进效果不明显。研究建立的广义可加混合效应树高胸径模型相较于传统非线性混合效应模型具有更高的估计精度及预测效果,B AL适宜作为树高模型的辅助变量来反映林木竞争状况的影响。

Development of distributed time-variant gain model for nonlinear hydrological systems

In this paper, a rainfall-runoff modeling system is developed based on a nonlinear Volterra functional series and a hydrological conceptual modeling approach. Two models, i.e. the time-variant gain model (TVGM) and the distributed time-variant gain model (DTVGM) that are built on the platform of Digital Elevation Model (DEM), Remote Sensing (RS) and Unit Hydro-logical Process were proposed. The developed DTVGM model was applied to two cases in the Heihe River Basin that is located in the arid and semiarid region of northwestern China and the Chaobai River basin located in the semihumid region of northern China. The results indicate that, in addition to the classic dynamic differential approach to describe nonlinear processes in hy-drological systems, it is possible to study such complex processes through the proposed sys-tematic approach to identify prominent hydrological relations. The DTVGM, coupling the advan-tages of both nonlinear and distributed hydrological models, can simulate variant hydrological processes under different environment conditions. Satisfactory results were obtained in fore-casting the time-space variations of hydrological processes and the relationships between land use/cover change and surface runoff variation.

基于分布滞后非线性模型分析体感温度对乌鲁木齐市高血压住院数的影响

目的探讨体感温度与高血压患者住院数的关联性和滞后效应,并分析不同等级高血压与体感温度之间的影响差异。方法使用2019—2021年乌鲁木齐市10家医院高血压住院患者信息,从乌鲁木齐市气象局和环境监测站获取得到日均气压、环境温度、相对湿度、风速、日照时长、NO2、SO2、CO、O3、PM10、PM2.5数据。采用分布滞后非线性模型探讨体感温度和高血压患者住院数之间的暴露-滞后-效应关系。结果在日均体感温度与每日不同高血压住院数总暴露反应关系中,总高血压住院数、2级高血压住院数、3级高血压住院数与日均体感温度的暴露-反应曲线均呈非线性。极端日均体感低温(P5:-17.2℃)对每日总高血压住院数、3级高血压住院数的累积滞后效应会随着滞后天数的增加逐渐增强,并在累积滞后第14天时累积相对危险度(cumulative relative risk,CRR)达到最高,分别为2.025(95%CI:1.191~3.442)、2.171(95%CI:1.268~3.716)。在极端体感高温(P_(95):25.0℃)下,日均体感温度对每日总高血压住院数、2级高血压住院数和3级高血压住院数有影响,累积滞后影响分别在第2 d、第9 d和第2 d达到最强,CRR分别为0.72(95%CI:0.55~0.94)、0.57(95%CI:0.33~0.99)、0.69(95%CI:0.53~0.91)。结论体感温度与高血压患者住院数存在负效应关系,公共卫生部门可以根据预测的体感温度制定高血压预警,提示高血压易感人群在体感温度变化时注意防范,为降低高血压发病率提供新的防治思路。

降雨量与脑卒中入院的关联性:基于分布滞后非线性模型

背景脑卒中是严重危害人类健康的主要慢性病,降雨量与脑卒中发病的关系尚未明确。目的分析南昌市降雨量与脑卒中入院的关联性,为制定脑卒中综合防治策略和措施提供科学依据。方法收集江西省卫生健康委员会信息中心DRGs管理系统中南昌市2015—2019年脑卒中入院数据及全国城市空气质量实时发布平台大气污染物和南昌市气象基站数据,分析脑卒中入院患者、大气污染物及气象因素的基本特征。采用Spearman秩相关分析探究脑卒中入院例数与大气污染物和气象因素的相关性,利用分布滞后非线性模型分析降雨量与脑卒中入院的关系,并按照性别、年龄(<65岁和≥65岁)进行分层分析,lag代表滞后天数。结果2015—2019年南昌市脑卒中入院患者79523例,其中男性(49072例,61.71%)、年龄≥65岁(48092例,60.48%)的患者所占比例较大,冬(12月~次年2月)、春(3~5月)季脑卒中入院例数分别为20065例(25.23%)、20358例(25.60%)。降雨量与脑卒中入院呈非线性关系,并存在一定的滞后效应。降雨量对脑卒中入院的效应在lag1、lag2的RR值均为1.009,95%CI分别为1.000~1.019、1.001~1.016。分层分析显示:较高降雨量对男性脑卒中入院例数的主要效应为lag6,RR值为1.003;对女性脑卒中入院例数的主要效应为lag1和lag2,RR值分别为1.018(95%CI=1.004~1.031)、1.020(95%CI=1.009~1.031);对65岁以下缺血性脑卒中入院例数的主要效应为lag1(RR=1.016,95%CI=1.003~1.030)、lag2(RR=1.018,95%CI=1.007~1.029)。结论短期暴露于较高降雨量可增加脑卒中入院风险,女性、65岁以下人群对降雨暴露更为敏感,应加强对该人群的防护。

超深层碳酸盐岩气藏产能预测模型及影响因素研究

超深层碳酸盐岩气藏具有储集介质多样、非均质性强和高温高压等典型特征,且普遍采用斜井开发,导致气井产能评价难度大。考虑该类气藏的多重储集介质、应力敏感性、非线性渗流、阈压效应和井斜等因素,基于Forchheimer气相微分方程、应力敏感和气相渗流物理模拟实验数据建立三项式产能预测模型,对不同区域、不同类型碳酸盐岩储层的气井产能进行预测,并分析上述因素对产能的影响规律。结果表明:孔隙型和低渗透孔洞型储层受阈压效应的影响,缝洞型和高渗透孔洞型储层受非线性渗流的影响,因此三项式产能预测模型更适合储层类型多样的超深层碳酸盐岩气藏;不同类型储层表现出不同程度的应力敏感性和非线性渗流特征,需进行各类储层岩心的物理模拟实验确定产能方程所需的各项参数;井斜会产生负表皮因子影响气井产能,当井斜角大于55°时,各类储层的气井产能开始快速提升,且对缝洞型储层的改善效果最明显;阈压效应的产能抑制作用在低压差阶段很强,而应力敏感性和非线性渗流的影响主要体现在高压差阶段;启动压力梯度和非线性渗流系数分别为0.01~0.048MPa/m和10^(9)~10^(12)m^(-1)时显著抑制气井产能,而应力敏感造成的产能损失较稳定;影响气井产能的主控因素是表皮系数和地层系数,而开发阶段的非线性渗流效应对高渗透储层以及阈压效应对低渗透储层的影响也较为显著。

我国金融发展收敛性测度与收敛机制检验

本文基于非线性时变因子模型对我国30个省级地区2005—2018年的综合金融发展水平指数进行检验,通过收敛内生识别算法对我国现存的金融收敛集团进行识别,并进一步对收敛集团的分布以及收敛路径变化进行分析。研究结果发现,我国金融发展整体不存在收敛的趋势,东、中、西、东北四大经济区域也均表现出发散特征。本文进一步内生识别出4个金融收敛集团,其分布呈现“高水平集团成员少,低水平集团成员多”的金字塔式形态,且低水平收敛集团与高水平收敛集团间差距明显,存在“东部发达,中、西以及东北欠发达”的金融极化现象。基于此,本文认为收敛集团间应“因地制宜”地采取差异化发展策略,集团内应构建竞争与合作并存的发展机制,从而加快金融收敛速度,促进各地金融水平协同发展。

Short-term effects of low-level PM_(2.5),PM_(10),O_(3),and tropical meteorological conditions on emergency department visits for respiratory diseases in Haikou,China

Objective:To assess the correlation between atmospheric pollutants,meteorological factors,and emergency department visits for respiratory diseases in Haikou City.Methods:Daily data on atmospheric pollutants,meteorological factors,and emergency department visits for respiratory diseases in Haikou City from 2018 to 2021 were collected.The Spearman rank correlation test was used to analyze the correlation,and a distributed lag non-linear model was employed to analyze the health effects and lag impacts of environmental factors.Subgroup analyses were conducted based on sex and age.Results:According to the criteria of International Classification of Diseases(ICD-10:J00-J99),a total of 221913 cases were included,accounting for 21.3%of the total emergency department visits in Haikou City.For every 1℃increase in temperature,the risk of emergency department visits increased by 1.029%(95%CI 1.016%-1.042%).Relative humidity greater than 80%reduced the risk of visits,while higher atmospheric pressure(>1010 hpa)also decreased the likelihood of daily emergency department visits.Higher concentrations of PM_(2.5)(30-50μg/m^(3)),PM10(>60μg/m^(3)),and O_(3)(75-125μg/m^(3))were associated with increased visits.Higher temperatures(>25℃)have a greater impact on females and children aged 0-14 years,while males are more sensitive to low atmospheric pressure.Individuals aged 65 and above exhibited increased sensitivity to O_(3)concentration,and the effects of PM2.5,PM10,and O_(3)are more pronounced in individuals over 14 years old.Conclusions:Short-term exposure to high temperatures,particulate matter pollutants(PM_(2.5)and PM_(10)),and ozone(O_(3))is associated with increased emergency department visits for respiratory diseases.

Adaptive control of a class of nonlinear time-varying systems with multiple models

The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.

基于折减因子的奥氏体不锈钢螺栓高温应力-应变模型

不锈钢螺栓具有显著的耐久性、延性和耐火性,因此它们被广泛地应用于栓接的半刚性节点中。不锈钢螺栓如同其基材(不锈钢棒材)一样,在常温和高温下都表现出平滑的非线性特征,并且它们的应力-应变曲线中没有明确定义的屈服点。这种材料行为可以用不同的材料模型来表征,其中最常用的是Ramberg-Osgood公式或其修订版。然而,在现存不锈钢的材料模型中,相关力学参数的预测公式并不适用于经受冷作硬化和冷铸工艺的奥氏体和双相体不锈钢螺栓。因此,本工作基于先前研究中高温下不锈钢螺栓的折减因子建立它们的非线性高温材料模型。倘若在五个常温力学参数(杨氏模量、比例极限、屈服强度、极限强度和极限应变)已知的情况下,基于高温折减公式计算某一温度下的五个力学参数,进而评估试验应力-应变曲线与预测曲线之间的相关性。这种预测方法表明预测曲线与试验曲线具有明显的一致性,说明基于折减因子所建立的高温材料模型具有准确的预测精度。因此,联合不锈钢螺栓的高温折减公式,基于常温下的五个力学参数就可以准确预测其在某一温度下的应力-应变曲线。

多并网逆变器与电网的谐波交互建模与分析

当越来越多的并网逆变器连接到电网的同一公共连接点时,逆变器与电网之间的谐波交互作用对系统的稳定可靠运行是一个潜在威胁。根据电路理论,首先对阻抗网络的谐波谐振产生机理进行分析。为建立更加逼近实际并网系统的逆变器输出阻抗模型,提出将死区效应和开关器件非理想特性考虑到模型中来增加其精确性,并以LCL型滤波器双闭环控制系统为例做详细的模型推导。利用提出的输出阻抗模型,对多个并网逆变器与电网组成的分布式阻抗网络进行建模及谐波交互分析。仿真结果验证了所提出建模方法和谐波交互分析方法的正确性和有效性。

基于声学特征空间非线性流形结构的语音识别声学模型

从语音信号声学特征空间的非线性流形结构特点出发,利用流形上的压缩感知原理,构建新的语音识别声学模型.将特征空间划分为多个局部区域,对每个局部区域用一个低维的因子分析模型进行近似,从而得到混合因子分析模型.将上下文相关状态的观测矢量限定在该非线性低维流形结构上,推导得到其观测概率模型.最终,每个状态由一个服从稀疏约束的权重矢量和若干个服从标准正态分布的低维局部因子矢量所决定.文中给出了局部区域潜在维数的确定准则及模型参数的迭代估计算法.基于RM语料库的连续语音识别实验表明,相比于传统的高斯混合模型(Gaussian mixture model,GMM)和子空间高斯混合模型(Subspace Gaussian mixture model,SGMM),新声学模型在测试集上的平均词错误率(Word error rate,WER)分别相对下降了33.1%和9.2%.