Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks

In the paper, a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples.

Application of Auto-regressive Linear Model in Understanding the Effect of Climate on Malaria Vectors Dynamics in the Three Gorges Reservoir

It is important to understand the dynamics of malaria vectors in implementing malaria control strategies. Six villages were selected from different sections in the Three Gorges Reservoir fc,r exploring the relationship between the climatic ｜：actors and its malaria vector density from 1997 to 2007 using the auto-regressive linear model regressi^n method. The result indicated that both temperature and precipitation were better modeled as quadratic rather than linearly related to the density of Anopheles sinensis.

Analysis of Quality of Life in Cancer Patients by Structural Equation Model

Many people have been dead of cancer. The life quality of patients with cancer has aroused great concern from the public and specialists. In this paper, an index system of life quality is proposed to evaluate the quality of life, which includes 6 first-level indexes and 34 second-level indexes. Then, a structural equation model (SEM) based on these in-dexes and relationships among them is constructed for the analysis of quality of life in cancer patients. Furthermore, we offer a definite linear algorithm for the calculation of SEM. This method is more objective and scientific compared with traditional methods, such as descriptive analysis, some simple test methods and so on.

Finite element model updating for large span spatial steel structure considering uncertainties

In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.

Seismic fragility analysis of bridges by relevance vector machine based demand prediction model

A relevance vector machine(RVM)based demand prediction model is explored for efficient seismic fragility analysis(SFA)of a bridge structure.The proposed RVM model integrates both record-to-record variations of ground motions and uncertainties of parameters characterizing the bridge model.For efficient fragility computation,ground motion intensity is included as an added dimension to the demand prediction model.To incorporate different sources of uncertainty,random realizations of different structural parameters are generated using Latin hypercube sampling technique.Mean fragility,along with its dispersions,is estimated based on the log-normal fragility model for different critical components of a bridge.The effectiveness of the proposed RVM model-based SFA of a bridge structure is elucidated numerically by comparing it with fragility results obtained by the commonly used SFA approaches,while considering the most accurate direct Monte Carlo simulation-based fragility estimates as the benchmark.The proposed RVM model provides a more accurate estimate of fragility than conventional approaches,with significantly less computational effort.In addition,the proposed model provides a measure of uncertainty in fragility estimates by constructing confidence intervals for the fragility curves.

Stage-structured models for interacting wild and sterile mosquitoes

Using the sterile insect technique,in which sterile mosquitoes are released to reduce or eradicate the wild mosquito population,is an effective weapon to prevent transmission of mosquito-borne diseases. To study the impact of the sterile insect technique on the disease transmissions,we formulate stage-structured discrete-time mathematical models,based on difference equations,for the interactive dynamics of the wild and sterile mosquitoes. We incorporate different strategies for releasing sterile mosquitoes,investigate the model dynamics,and compare the impact of the different release strategies.Numerical examples are also provided to demonstrate dynamical features of the models.

Investigation of Neutrino-Nucleon Interaction through Intermediate Vector Boson (IVB)

This work deals with the interaction of neutrino with the nucleon considering data taken from different experiments. It is assumed that the interaction of neutrino with nucleons go through the intermediate vector boson (IVB) which may be the W or Z with effective mass of the order of 80 GeV. The neutrino wave function is obtained via perturbation technique to calculate the weak leptonic current. On the other hand, the quark current is estimated using the measured experimental data of deep inelastic scattering of neutrino-nucleon interaction. Eventually the total interaction transition matrix is calculated as a function of momentum transfer square, q2 and qualitatively compared with the available experimental data. Besides, a comparative study is also done to explore the influence of the target composition during the neutrino weak interactions. In this context an investigation of neutrino-proton and neutrino-neutron interactions are carried out to calculate the deep inelastic cross section in both cases.

Mathematical Apparatus for Selection of Optimal Parameters of Technical, Technological Systems and Materials Based on Vector Optimization

We presented Mathematical apparatus of the choice of optimum parameters of technical, technological systems and materials on the basis of vector optimization. We have considered the formulation and solution of three types of tasks presented below. First, the problem of selecting the optimal parameters of technical systems depending on the functional characteristics of the system. Secondly, the problem of selecting the optimal parameters of the process depending on the technological characteristics of the process. Third, the problem of choosing the optimal structure of the material depending on the functional characteristics of this material. The statement of all problems is made in the form of vector problems of mathematical (nonlinear) programming. The theory and the principle of optimality of the solution of vector tasks it is explained in work of https://rdcu.be/bhZ8i. The implementation of the methodology is shown on a numerical example of the choice of optimum parameters of the technical, technological systems and materials. On the basis of mathematical methods of solution of vector problems we developed the software in the MATLAB system. The numerical example includes: input data (requirement specification) for modeling;transformation of mathematical models with uncertainty to the model under certainty;acceptance of an optimal solution with equivalent criteria (the solution of numerical model);acceptance of an optimal solution with the given priority of criterion.

基于因果正则化极限学习机的风电功率短期预测方法

随着风电并网比例的逐年提高,电力系统对风电功率预测的准确性和稳定性提出了更高要求。对于同一风电场而言,为了避免不同特征选择方法所选择的风电场特征子集不同,从因果关系的角度出发,提出了一种基于因果正则化极限学习机(causal regularized extreme learning machine, CRELM)的风电功率短期预测方法。首先将极限学习机(extreme learning machine, ELM)建模为结构因果模型(structural causal model, SCM),在此基础上计算隐藏层神经元与输出层神经元之间的平均因果效应向量。然后将该平均因果效应向量与输出层权重相结合构成因果正则化项,在最小化训练误差的同时最大化网络的因果关系,以进一步提升模型的预测准确性和预测稳定性。最后,以国内蒙西某风电场数据为例,与采用特征选择或不采用特征选择的预测模型相对比,验证了所提方法的有效性和适用性。

Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.

磁矢量势的全球电磁感应时间域三维有限元正演计算方法

全球电磁感应测深方法能获得地球深部电导率结构分布,目前被广泛应用于地球内部结构与热状态研究.地磁台站与地磁卫星观测的电磁感应数据为时间序列信号,在时间域分析全球电磁感应数据,特别是分析频谱极宽的磁暴脉冲响应,具有天然的优势.然而,当前的全球电磁感应数据解释技术一般在频率域进行,缺少时间域中的研究成果.为了弥补缺少时间域全球电磁感应数据解释方法的问题,本文开发了一种基于磁场矢量势的全球电磁感应时间域三维有限元并行正演求解方法,具备高精度快速计算源于地球外部时变电流源的地球感应电磁场时间序列的能力,特别适合于计算与分析频谱极宽的磁暴脉冲电磁感应时变响应.首先,建立基于磁场矢量势的全球电磁感应时间域微分控制方程,结合磁层外部电流源的物理属性建立边界条件和初始条件,从而构建出全球电磁感应时间域初始边界值问题.然后,利用四面体矢量有限元技术和无条件稳定的隐式后退欧拉公式,分别实现磁场矢量势的空间域和时间域离散,进而获得不同时刻的实系数大型有限元线性方程组,借助于高性能并行直接求解器,快速高精度地求解不同时刻的磁场矢量势与感应磁场.最后,利用理论模型验证本文算法的正确性.利用Dst磁暴环电流指数建立的时间域电流源与真实地球三维电性模型,研究"澳科一号"后续卫星200 km轨道对中国和澳大利亚下方地幔转换带高导体的探测能力,结果表明这些地幔转换带异常体在200 km卫星轨道高度能产生明显异常.综上所述,本文开发的时间域全球电磁感应方法不仅具备精确快速计算全球感应电磁场时间序列响应的能力,还能为反演与解释"澳科一号"等我国地磁卫星观测数据提供技术支持.

A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues

A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R 2, cross-validation R 2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R cu 2 ?0.82, Q cu 2 ?0.77, E rmse?0.44 (GPLS+SVM); ESCK, R cu 2 ?0.84, Q cu 2 ?0.82, E rmse?0.20 (GPLS+INN), respectively.

高精度矢量数据的公路快速逆向建模方法

数字交通的快速发展对道路基础设施三维数字化提出了新的要求。针对人工建立公路三维模型费时费力等问题,研究了一种基于高精度矢量数据的快速逆向建模方法。以三维激光扫描点云为基础数据源,首先提取高速公路中各目标高精度矢量数据,并存储为空间矢量地图。然后针对道路场景各目标模型结构,提出自适应节点重建、参照线放样重建及实例化重建等多种关键建模方法;最后基于MAXscript三维建模语言进行开发,建立精细化的高速公路三维模型。以某段高速公路实际数据为例进行实验,验证该方法的可行性。

基于多线性材料硬化的向量式有限元C型薄壁型钢屈曲分析

C型薄壁型钢在实际工程中较常见,在力的作用下容易发生屈曲失效。传统有限元法预测结构屈曲变形已经有一套较为成熟的方案,但程序较烦琐。基于向量式有限元(VFIFE)方法编制了三角形壳单元的Fortran计算程序。通过Matlab软件编写前处理与后处理程序,将计算结果与经典的双曲壳屈曲计算结果对比,考察其正确性。考虑几何非线性问题与材料非线性问题,引入多线性材料硬化模型,计算了不同尺寸的C型薄壁型钢在轴压荷载下的屈曲特性。根据位移-轴力曲线判断屈曲荷载,通过后处理程序绘制出屈曲发生后的变形,并与现有的试验结果对比。结果表明:引入多线性材料硬化模型的向量式有限元方法能够较好地预测结构屈曲荷载以及屈曲变形,为预测结构的屈曲变形提供一种新的、可靠参考。

人口老龄化对产业结构升级的影响——基于重庆市的实证研究

随着人口老龄化程度的日益加剧,经济社会尤其是产业结构的发展必将受到影响。基于重庆市人口老龄化和产业结构现状,选取1997-2022年的时间序列数据分析人口老龄化对产业结构升级的影响及作用机制,测度人口老龄化在产业结构升级过程中的贡献度。结果表明,人口老龄化有利于产业结构高级化的演进且存在长期持续拉动效应,尤其对第三产业的结构影响最为显著。因此,重庆市要顺应当前人口老龄化发展趋势,高度重视人口老龄化对产业结构调整带来的冲击,制定出契合自身实际的发展之策。

基于结构化特征重构的高光谱图像分类

特征提取是高光谱图像分类的关键.现有分类方法在特征提取时,往往忽略特征的信息保有量和空间分布等因素,导致输出的特征可能面临低信息保有量与无序分布等问题,预测结果不佳.为此,本文提出一种基于结构化特征重构的高光谱图像分类方法,能够有效地减少特征提取过程中信息丢失,提高信息保有量,并充分考虑特征的空间分布,增强特征的判别性.借鉴重构思想以及自表达理论,建立结构特征重构的特征表示模型,可提升图像信息的利用率,并描述反映有序分布的结构信息.针对建立的多变量模型,设计一种基于交替更新的优化策略来求解模型.利用支持向量机来对特征进行分类计算和标签预测.利用Salinas、Pavia Center、Botswana以及Houston数据进行实验验证,结果表明,本文算法优于现有的分类模型,在OA(Overall Accuracy)、AA(Average Accuracy)以及Kappa系数等指标上平均提升了2.6%、3.9%、3.3%.

基于原子类型电性拓扑状态指数的最小点火能预测模型研究

针对危险化学品最小点火能的试验测量存在一定的困难且具有很大不确定性的问题,收集了61种化学品的最小点火能试验值,并仅根据其分子结构计算了原子类型电性拓扑状态指数(ETSI),最终选择了23种ETSI作为分子描述符进行建模。首先,尝试基于61种危险化学品的最小点火能值,通过多元线性回归和支持向量机进行QSPR建模,但所建模型的内部稳定性很差,不满足基本的要求。经分析可能是因为描述符太多而样本相对较少造成的。通过从数据集中删除13个最小点火能值,ETSI的种类减少到14个,并采用相同的方法进行QSPR建模。经验证,新建模型在拟合能力、内部稳定性和外部预测能力3个方面均具有优异的性能,可以在不需要购买任何昂贵的软件和硬件的前提下,方便、快捷地预测化学品的最小点火能。

A novel molecular distance edge vector as applied to chemical modeling of quantitative structure-retention relationships:Various gas chromatographic retention behaviors of polychlorinated dibenzofurans on different polarity-varying stationary phases

Based on the identical group as a pseudo atom instead of a typical atom, a novel modified molecular dis-tance-edge (MDE) vector μ was developed in our laboratory to characterize chemical structure of polychlorinated diben-zofurans (PCDFs) congeners and/or isomers. Quantitative structure-retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention behavior of PCDFs were then generated by multiple linear regression (MLR) method for non-polar, moderately polar, and polar stationary phases. Four excellent models with high correlation coefficients, R=0.984-0.995, were proposed for non-polar columns (DB-5, SE-54, OV-101). For the moder-ately polar columns (OV-1701), the correlation coefficient of the developed good model is only 0.958. For the polar col-umns (SP-2300), the QSRR model is poor with R=0.884. Then cross validation with leave-one out of procedure (CV) is performed in high correlation with the non-polar (Rcv=992-0.974) and weakly polar (Rcv=921) columns and in little cor-relation (Rcv=0.834) with the polar columns. These results show that the new μ vector is suitable for describing the re-tention behaviors of PCDFs on non-polar and moderately polar stationary phases and not for the various gas chroma-tographic retention behaviors of PCDFs on the different po-larity-varying stationary phases.

中国金融结构变迁对技术创新的影响研究

金融结构能否推动技术创新,是我国经济能否成功转型的关键。本文通过融资结构、银行业结构、资本市场结构三大维度来表征金融结构,使用VAR模型探究其对技术创新的影响。结果表明:我国金融机构体系发展不均衡和资本市场结构的比重不平衡导致金融资源分布不均衡。融资结构(力度最强)和资本市场结构变动将对技术创新产生正向冲击,银行业结构变动(力度最弱)将对技术创新产生负向冲击。最后在此基础上提出针对性的政策建议。

畜牧产业结构变动对农业经济高质量增长的影响

为探究畜牧产业结构变动对农业经济高质量增长的影响,研究利用2021年3月至2022年9月时间序列数据,以向量自回归模型实证检验畜牧产业结构变动对农业经济高质量增长的影响。结果表明,畜牧产业结构变动能够赋能农业经济高质量增长;畜牧产业结构变动对农业经济高质量增长的冲击响应周期约为18个月,且直至第18个月仍有7.795%贡献率。研究表明,研究结果有利于建立畜牧产业良性循环机制,打造高端畜牧养殖模式,优化农畜产品产业链,促进农业经济高质量增长。