一类有被动免疫的流行病模型研究

对一类具有被动免疫的流行病模型进行了定性分析和讨论.得到了这些模型的基本再生数R0,对有些模型得到了地方病平衡点的全局稳定性.并且在MSIR模型中还考虑了接种.     

一类具有垂直传染和预防接种的SEIR传染病模型

建立并分析了一类具有垂直传染和预防接种的SEIR传染病模型,得到了该模型的基本再生数.通过对基本再生数的讨论和分析,得到了该模型的平衡点的稳定性和持续性.     

一类带有一般出生率的SIS传染病模型的全局分析

将一般出生率系数引入S IS传染病模型,得到了种群灭绝和疾病灭绝的阈值条件.分别借助S tokes定理和D u lac函数对染病者的数量模型和染病者在种群中所占比例的模型进行了讨论,得到了相应模型的全局动力学行为.     

基于半参数回归分析的Markov机制转换模型

对AR-Markov模型做了研究和扩展,构建基于半参数模型的Markov机制转换模型,对新息服从正态分布的假设条件进行了改进,并用得到的研究结果对沪深300股指做了实证研究,得到了稳定的窗宽和状态转移概率,并给出了不同状态之间的转移轨迹.     

股价波动的指数O-U过程模型

本文针对股价波动的几何布朗运动模型对收益率假设的缺陷,对该模型进行了改进,并建立了股价波动的指数OkU过程模型,得到了比传统模型更好的结果.     

面向下一代光通信的VCSEL激光器仿真模型

根据L-I经验公式得到参数待定的L-I模型,并利用遗传算法搜索最优参数得到参数模型,对结果进行拟合优度检验,求出误差.在该模型基础上对环境温度进行修正并且考虑电流瞬时变化的影响,建立微分方程模型,采用Ode23求解,并利用遗传算法搜索最优参数得到参数,对结果进行拟合优度检验,得出了该激光器不同温度下的L-I特性曲线和激光器小信号幅频响应.同时根据速率方程推导出了带宽响应的数学模型,对参数归一化后,利用遗传算法搜索最优参数得到参数.     

随机环境下NLAR模型的非遍历性

本文对随机环境下的NLAR模型进行了分析，并得到该模型非遍历的一个判别准则。     

兼具生产与消费功能的政府开支模型

本文研究了兼具生产与消费功能的政府开支模型 ,对这个模型进行了均衡分析与动态分析 ,由此得到了四个相关的例题 .证明了本模型是内生模型 ,经济增长与折现率和相对风险厌恶系数负相关 ,以及均衡的不稳定性 .并与只考虑一种开支的AK模型做了比较 ,得到一些特定性的结论 .     

具有线性趋势的回归信度模型中的估计和检验

研究具有线性趋势回归信度模型的参数估计和检验. 对该模型的回归系数和随机效应的方差,利用正交变换法得到了它们的极大似然估计, 并得到了参数的无偏估计. 对随机效应和是否有线性趋势采用似然比检验, 得到了似然统计量较好的近似$P$值, 并对检验的功效进行了模拟研究.     

应用多刚体离散化方法对梁的动力屈曲和后屈曲分析

基于梁的多刚体离散化模型（有限段模型）,建立了梁的链式多刚体-铰链-弹簧系统模型,利用坐标变换方法建立了相应的非线性多自由度系统的参数振动方程,并利用约束参数法对所得到的多度系统的Mathieu-Hill方程进行了梁的动力屈曲分析,得到系统的参数共振域．因为所用的离散化模型与动力方程对梁的变形并无限制,所以可以用所得到的数学模型在其失稳域对梁的动力后屈曲进行数值仿真分析．通过实例的数值仿真,证明了这种梁的参数振动模型与分析方法的正确性．     

具有相关误差的多元线性模型的更新方程

已知的线性模型的更新方程是在对模型加了不相关误差结构的约束, 或只对带有固定参数的一元线性模型考虑的. 本文考虑具有相关误差的多元线性模型下的更新方程, 给出了在补充参数, 数据或指标时, 未知参数阵的最佳线性无偏估计及残积阵的更新方程. 公式适用于固定参数与随机参数两种情形.     

Recent advances of variational model in medical imaging and applications to computer aided surgery

In this paper, we review some mathematical models in medical image processing. Due to the superiority in modeling and computation, variational methods have been proven to be powerful techniques, which have been extremely popular and dramatically improved in the past two decades. On one hand, many models have been proposed for nearly all kinds of applications. On the other hand, a lot of models can be globally optimized and also many computation tools have been introduced. Under the variational framework, we focus on two basic problems in medical imaging: image restoration and segmentation, which are core components for kinds of specific tasks. For image restoration, we discuss some models on both additive and multiplicative noises. For image segmentation, we review some models on both whole image segmentation and specific target delineation, with the later being a key step in computer aided surgery. Additionally, we present some models on liver delineation and give their applications to living donor liver transplantation.     

Modelling and numerical simulation of high strength fibre reinforced concrete corbels

In the present study, new constitutive models for high strength steel fibre reinforced concrete (HSSFRC) have been formulated by means of a regression analysis of many experimental data (from literature) by using SPSS-statistical program. This proposed constitutive models have been employed for formulating the material finite element models to study the behaviour of HSSFRC corbels.     

Experimental tour-based travel demand models

In this paper some experimental sequential models for the simulation of trip-chains are presented; the models have been calibrated on the basis of a survey made in a medium-sized town.This work is part of a research, effected by the Department of Regional Planning of the University of Calabria, to forecast travel demand and to analyze travel behavior of the transport system users (Festa et al., 2000).The travel choices of individuals have been reproduced simulating the decisional process in sequential steps, by models based on the random utility theory.The tour generation models, proposed by Festa et al. (2001), have been once again calibrated introducing a new set of variables in the systematic utility function of the alternatives of choice.Some models for the travel type choice simulation are also presented; the models have a Binomial Logit functional structure, with trip-tour and trip-chain as choice alternatives.     

Recent developments in dynamic advertising research

A variety of continuous-time differential functions have been developed to investigate dynamic advertising problems in business and economics fields. Since major dynamic models appearing before 1995 have been reviewed by a few survey papers, we provide a comprehensive review of the dynamic advertising models published after 1995, which are classified into six categories: (i) Nerlove–Arrow model and its extensions, (ii) Vidale–Wolfe model and its extensions, (iii) Lanchester model and its extensions, (iv) the diffusion models, (v) dynamic advertising-competition models with other attributes, and (vi) empirical studies for dynamic advertising problems. For each category, we first briefly summarize major relevant before-1995 models, and then discuss major after-1995 models in details. We find that the dynamic models reviewed in this paper have been extensively used to analyze various advertising problems in the monopoly, duopoly, oligopoly, and supply chain systems. Our review reveals that the diffusion models have not been used to analyze advertising problems in supply chain operations, which may be a research direction in the future. Moreover, we learn from our review that very few publications regarding dynamic advertising problems have considered the supply chain competition. We also find that very few researchers have used the diffusion model to investigate the dynamic advertising problems with product quality as a decision variable; and, the pricing decision has not been incorporated into any extant Lanchester model. The paper ends with a summary of our review and suggestions on possible research directions in the future.     

Factor models for multivariate count data

We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few approaches have been proposed for their analysis. Our model class allows for a variety of distributions of the factors in the exponential family. The proposed framework includes a large number of previously proposed factor and random effect models as special cases and leads to many new models that have not been considered so far. Whereas previously these models were proposed separately as different cases, our framework unifies these models and enables one to study them simultaneously. We estimate the Poisson factor models with the method of simulated maximum likelihood. A Monte-Carlo study investigates the performance of this approach in terms of estimation bias and precision. We illustrate the approach in an analysis of TV channels data.     

Application of fuzzy time series in prediction of time between failures &; faults in software reliability assessment

Since last seventies, various software reliability growth models (SRGMs) have been developed to estimate different measures related to quality of software like: number of remaining faults, software failure rate, reliability, cost, release time, etc. Most of the exiting SRGMs are probabilistic. These models have been developed based on various assumptions. The entire software development process is performed by human being. Also, a software can be executed in different environments. As human behavior is fuzzy and the environment is changing, the concept of fuzzy set theory is applicable in developing software reliability models. In this paper, two fuzzy time series based software reliability models have been proposed. The first one predicts the time between failures (TBFs) of software and the second one predicts the number of errors present in software. Both the models have been developed considering the software failure data as linguistic variable. Usefulness of the models has been demonstrated using real failure data.     

An Empirical Evaluation of Investigating Transportation Mode Attributes for Commodities

A number of transportation demand models have been formulated using the abstract mode concept. Unfortunately, most of these models use a priori economic theory to derive the variables used in the analysis. This study describes an empirical approach for the specification of mode attributes used by purchasers of transportation modes for the movement of commodities. Results reported from a study conducted on the Montreal-Toronto corridor in Canada suggest that a number of important variables have been neglected in models of carrier selection for freight.     

Reviews on the Research of Dynamic Mortality Models

Mortality forecasting is the basis of population forecasting. In recent years, new progress has been made in mortality models. From the earliest static mortality models, mortality models have been developed into dynamic forecasting models including time terms, such as Lee-Carter model family, CBD model family and so on. This paper reviews and sorts out relevant literature on mortality forecasting models. With the development of dynamic models, some scholars have developed a series of mortality improvement models based on the level of mortality improvement. In addition, with the progress of mortality research, multi-population mortality modeling attracted the attention of researchers, and the multi-population forecasting models have been constantly developed and improved, which play an important role in the mortality forecasting. With the continuous enrichment and innovation of mortality model research methods, new statistical methods (such as machine learning) have been applied in mortality modeling, and the accuracy of fitting and prediction has been improved. In addition to the extension of classical modeling methods, issues such as small-area population or missing data of the population, the elderly population, the related population mortality modeling are still worth studying.     

A Survey of Manpower Planning Models and Their Application

Since the late 1950s, much work has been done on developing models of manpower systems which may be used for the purposes of planning. Many organizations have made successful use of such models, but in spite of these successes, manpower planning models are only gradually coming into widespread use. The aim of this paper is to review the models which have been developed, concentrating on their assumptions and applications rather than on mathematical or statistical details. A common theme of successful applications is that good presentation of results and ease of use are more important to users than theoretical sophistication.