Moment-Based Calculation of Parameters for the Storage Zone Model for River Dispersion

Theoretical methods have been developed to calculate values of parameters of the storage zone model for river mixing. Analytical solutions of the Laplace-transformed equations of the storage zone model are related to the observed concentration distribution in order to determine model parameters in both the moment matching method and the maximum likelihood method, which were developed in this study. The results obtained by comparison with experimental data show that the parameters calculated by the moment matching method are in good agreement with the observed values of storage zone model parameters, whereas results from the maximum likelihood method and several existing methods are not in good agreement with the experimentally observed values. Dispersion data from natural streams show that the calculated concentration curves from the numerical solutions of the storage zone model with the parameters calculated by the moment matching method fit the observed concentration curves very well. It can be concluded that parameters of the storage zone model calculated using the moment matching method can properly explain the natural dispersion processes in real streams.     

Quantification of two-dimensional NOE spectra via a combined linear and nonlinear least-squares fit

Determining the volumes of peaks in 2D NMR spectra can be prohibitively difficult in cases of overlapping, broad lines. Deconvolution and parameter estimation can be attempted on either the time-domain or the frequency-domain data. We present a method of estimating spectral parameters from frequency-domain data, using a combination of Lorentzian and Gaussian lineshapes for reference lines. This approach combines a previously published method of projecting the data on a linear space spanned by reference lines with a nonlinear least-squares fitting algorithm. Comparison of this method with other published methods of frequency-domain deconvolution shows that it is both more precise and more accurate when estimating 2D volumes.     

Statistical models for PET and SPECT data

This article outlines the statistical developments that have taken place in emission tomography during the past decade or so. We discuss the statistical aspects of the modelling of the projection data and define the additive Poisson regression model. This leads to the use of the method of maximum likelihood as a means of estimating the underlying isotope concentration within a given region of a patient's body, and to the use of the EM algorithm to compute the reconstruction. The need for the regulation of the maximum likelihood solution is tackled using Bayesian techniques. A number of algorithms for the computation of regularized solutions are outlined. The issue of parameter estimation is discussed and some open issues are mentioned.     

Evaluation of two methods for estimating item response theory parameters when assessing differential item functioning.

Recent legal developments appear to sanction the use of psychometrically unsound procedures for examining differential item functioning (DIF) on standardized tests. More appropriate approaches involve the use of item response theory (IRT). However, many IRT-based DIF studies have used F. M. Lord's (see record 1987-17535-001) joint maximum likelihood procedure, which can lead to incorrect and misleading results. A Monte Carlo simulation was conducted to evaluate the effectiveness of two other methods of parameter estimation: marginal maximum likelihood estimation and Bayes modal estimation. Sample size and data dimensionality were manipulated in the simulation. Results indicated that both estimation methods (a) provided more accurate parameter estimates and less inflated Type I error rates than joint maximum likelihood, (b) were robust to multidimensionality, and (c) produced more accurate parameter estimates and higher rates of identifying DIF with larger samples. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

H4 acetylation, XIST RNA and replication timing are coincident and define x;autosome boundaries in two abnormal X chromosomes

A novel method of estimating enzyme kinetic parameters is presented using the Lambert omega function coupled with nonlinear regression. Explicit expressions for the substrate and product concentrations in the integrated Michaelis-Menten equation were obtained using the omega function which simplified kinetic parameter estimation as root-solving and numerical integration of the Michaelis-Menten equation were avoided. The omega function was highly accurate in describing the substrate and product concentrations in the integrated Michaelis-Menten equation with an accuracy of the order of 10(-16) when double precision arithmetic was used. Progress curve data from five different experimental systems were used to demonstrate the suitability of the omega function for kinetic parameter estimation. In all cases, the kinetic parameters obtained using the omega function were almost identical to those obtained using the conventional root-solving technique. The availability of highly efficient algorithms makes the computation of omega simpler than root-solving or numerical integration. The accuracy and simplicity of the omega function approach make it an attractive alternative for parameter estimation in enzyme kinetics.     

Simple Method for Quick Estimation of Leaky-Aquifer Parameters

Simple method and explicit equations are proposed for estimating the parameters of leaky aquifers from drawdown at an observation well, which avoid the curve matching or initial estimate of the parameter. The proposed method is computationally simple and the calculations can be performed even on a handheld calculator. The application of the methods is illustrated, using published data sets. The new method yields quick and accurate estimates of the leaky-aquifer parameters, if observed drawdowns do not contain large errors. The proposed method can also analyze the early drawdowns for accurate characteristics/parameters of a confined aquifer, if the conductance of the aquitard is assigned a zero value. It is hoped that the proposed method would be of help to field engineers and practitioners.     

基于粒子群最大似然估计的焊缝早期隐性损伤磁记忆精确定位模型

针对由于焊接残余应力、磁场噪声等干扰,造成磁记忆检测在焊缝早期隐性损伤位置定量评价上的困难,提出基于粒子群算法优化的最大似然估计磁记忆梯度定量模型.通过对预制未焊透缺陷的Q235焊接试件进行焊缝疲劳拉伸实验,同步对比扫描电镜和X射线检测结果,发现磁记忆信号梯度对早期隐性损伤位置反应比较敏感,并获得了梯度随着与隐性损伤的距离增大而减小的衰减变化规律,构建隐性损伤位置参数与磁记忆梯度的非线性函数,考虑磁场噪声对隐性损伤定位结果的影响,引入最大似然估计建立目标函数,进一步考虑目标函数的非线性容易陷入局部极值而非全局极值的问题,采用具有全局搜索能力的粒子群算法对目标函数进行优化,建立基于粒子群最大似然估计的焊缝隐性损伤位置磁记忆定量模型,验证结果表明定位误差仅为3.48%,为实际工程中利用磁记忆技术及时发现早期隐性损伤并精确定位提供了新的思路.      

Mixed Integer Nonlinear Least-Squares Problem for Damage Detection in Truss Structures

We present a mixed integer nonlinear least-squares problem for identifying damage in truss structures from their measured response. In detecting damage based on parameter estimation, the number of unknown parameters is often less than that of measurements, which gives rise to nonunique solutions. To overcome the difficulty, we formulate damage detection as a mixed integer nonlinear least-squares problem, where the subset of unknown parameters is sought that best represents damaged sites. To solve the problem, we present four heuristic algorithms based on the greedy algorithm. One is its direct application. The other three select the near-optimal subsets more efficiently by linearizing the error function, by applying the line search, and by grouping unknown parameters. We assess the performance of these algorithms along with conventional regularization methods through numerical experiments, where many synthetic damage cases are tested. The effect of modeling and measurement errors on the estimate is also studied. We found from the numerical experiments that the linearization-based approach was more efficient than the direct application while the two methods gave reasonably accurate estimates.     

Frequency Domain Identification of Multi-Input, Multi-Output Systems Considering Physical Relationships between Measured Variables

This paper presents a new frequency domain identification method for multi-input, multi-output (MIMO) systems. Based on experimentally determined frequency response function data, rational polynomial transfer function models of structural systems are identified. Known physical relationships between the measured variables are incorporated in this MIMO frequency domain identification method. The method has three stages: (1) an initial estimation model is generated using a linear least-squares method, (2) the Steiglitz–McBride method is applied to improve the initial estimation model, and (3) a maximum likelihood estimator is optimized using the Levenberg–Marquardt method. For verification of the method, two experimental studies are conducted using shaking table tests; one is the system identification of a smart base-isolated structure employing a magnetorheological (MR) damper, and the other is for an actively controlled two-story, bench-scale building employing an active mass driver. Using the developed method, system models of the experimental structures are estimated, and simulated time histories for the models are compared with measured responses. These comparisons demonstrate that the proposed method is quite effective and robust for system identification of MIMO systems. A graphic user interface program, named MFDID, has been developed to realize the suggested method.     

Data Preprocessing–Based Parametric Cost Model for Building Projects: Case Studies of Korean Construction Projects

For construction to progress smoothly, effective cost estimation is vital, particularly in the conceptual and schematic design stages. In these early phases, despite the fact that initial estimates are highly sensitive to changes in project scope, owners require accurate forecasts which reflect their supplying information. Thus, cost estimators need reliable estimation strategies. In practice, parametric cost estimation, which utilizes historical cost data, is the most commonly used method in these initial phases. Therefore, compilation of historical data pertaining to appropriate cost variance governing parameters is a prime requirement. However, data mining (data preprocessing) for denoising internal errors or abnormal values must be performed before this compilation. To address this issue, this research proposes a statistical methodology for data preprocessing. Moreover, a statistically preprocessed data–based parametric (SPBP) cost model is developed based on multiple regression equations. Case studies of Korean construction projects verify that the model enhances cost estimate accuracy and reliability than conventional cost models.     

基于3参数Weibull分布钢筋混凝土盐腐蚀环境中可靠性寿命分析

为研究钢筋混凝土在耦合盐环境中的腐蚀劣化规律及寿命分布,将钢筋混凝土试件置于0.32 mol·L?1 NaCl和0.4 mol·L?1 MgSO₄盐溶液中,利用电化学工作站定期无损检测,以极化曲线、交流阻抗图谱及电化学参数指标进行耐久性分析,选择3参数Weibull进行可靠性建模,通过Anderson-Darling法（A-D）进行先验假设检验,采用相关系数优化法(CCOM)、极大似然法(MLM)及矩估计法(MEM)进行参数估计,综合可靠度曲线、密度曲线、失效率曲线对钢筋混凝土在氯盐、硫酸盐、镁盐腐蚀环境中的寿命进行分析,研究结果表明:腐蚀离子综合作用下,极化曲线逐渐向腐蚀电流密度增大和负电位方向移动,交流阻抗图谱不断左移并向阻抗实部收缩,钢筋锈蚀发生的阻力逐渐减小,概率逐渐增大。可靠性寿命曲线初期保持不变,后期加速下降,密度曲线呈单峰对称状,失效率曲线初始基本保持不变,后期线性增加。3种参数估计方法中,CCOM和MLM参数估计值相近且稳定准确,所得可靠性寿命曲线相似,MEM参数估计值误差较大,对于钢筋混凝土加速试验得到的小样本失效数据建议用CCOM和MLM进行参数估计,并进行可靠性寿命分析。C35钢筋混凝土在硫酸镁及氯化钠腐蚀环境中的可靠性寿命周期约为760 d。      

Estimation of Unsaturated Hydraulic Parameters from Infiltration and Internal Drainage Experiments

Inverse problem of determining unsaturated soil hydraulic properties from transient infiltration and internal drainage events are analyzed. Hydraulic properties are assumed to be described by van Genuchten’s relationships. The inverse problem is solved using Levenberg-Marquardt method while the forward problem is solved using a mass conservative finite difference numerical scheme. The bias induced by different objective functions on the parameter estimates with error free and noisy data are analyzed. Field experiments are conducted at two sites to compare the parameter estimates obtained from the infiltration and internal drainage tests. The results indicate that some objective functions induce undue bias in the estimated parameters in the presence of noise in the data and as such selecting a suitable objective function should be given due importance in the parameter estimation. The comparison of the parameter estimates from infiltration and internal drainage experiments at two sites indicates that the parameter estimates are close to each other. It is concluded that infiltration experiment, which is simpler and of short duration can be an alternative to internal drainage experiment for estimating the unsaturated soil parameters.     

Span-Dependent Distributions of the Bending Strength of Spruce Timber

Tests data of bending strengths of a large number of timber beams of different spans obtained at the Swedish Institute for Wood Technology Research reveal a statistical structure that can be represented in a simple probabilistic model of series system type. A particular feature of the data from one of the large test series is that unintentionally the data became randomly censored upwards. This censoring of the data rules out both the moment estimation method and the maximum likelihood method. Instead valid parameter estimates can be obtained by maximizing the posterior density defined as the likelihood function multiplied by a suitably chosen noninformative prior density (MP method). Subsequently using bias factors assessed by simulation ensures that the corrected MP estimates are unbiased. A closed form analytical expression for the distribution function of the bending strength of a beam with any given number of defect clusters follows from the obtained distribution model for the bending strength of the random single defect cluster. The empirical distribution function of bending test results for a sample of beams with two defect clusters is well predicted, and for long beams with several defect clusters the same is the case in the lower tail up to at least about the 50% probability level.     

Missing Data Techniques for Structural Equation Modeling.

As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Numerical techniques and mathematical modelling for CI857-controlled gene expression and cell growth in recombinant E. coli

Recombinant gene expression, monitored by beta-galactosidase activity, is studied in a pL, pR-CI857 plasmid expression system in temperature-induced E. coli batch cultures. The experimental procedure has been mathematically modelled, and the corresponding parameters are estimated from specific statistical and numerical methods, basically by using a global least-squares procedure under some constraints induced by the model. The numerical techniques proposed in this work act by accumulation of data coming from several runs of the modelled experiment, so that more accuracy is obtained in the parameter estimation. In particular, for the production process, an extra-model parameter depending on an indicator vector is introduced for each run of the experiment in order to globalize the data. The analysis of the data obtained leads to an integrated model for both cell growth and gene expression, which describes an asymmetric dynamics between culture growth and recombinant protein yield, and can serve to predict the maximal value of accumulated gene expression and the time required for it to be achieved at any age of the preinducing cell growth.     

Estimation of IRT graded response models: Limited versus full information methods.

The performance of parameter estimates and standard errors in estimating F. Samejima’s graded response model was examined across 324 conditions. Full information maximum likelihood (FIML) was compared with a 3-stage estimator for categorical item factor analysis (CIFA) when the unweighted least squares method was used in CIFA’s third stage. CIFA is much faster in estimating multidimensional models, particularly with correlated dimensions. Overall, CIFA yields slightly more accurate parameter estimates, and FIML yields slightly more accurate standard errors. Yet, across most conditions, differences between methods are negligible. FIML is the best election in small sample sizes (200 observations). CIFA is the best election in larger samples (on computational grounds). Both methods failed in a number of conditions, most of which involved 200 observations, few indicators per dimension, highly skewed items, or low factor loadings. These conditions are to be avoided in applications. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A model-based fMRI analysis with hierarchical Bayesian parameter estimation.

A recent trend in decision neuroscience is the use of model-based functional magnetic resonance imaging (fMRI) using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences because of the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual differences. Here we demonstrate the application of hierarchical Bayesian parameter estimation to model-based fMRI using the example of decision making in the Iowa Gambling Task. First, we used a simulation study to demonstrate that hierarchical Bayesian analysis outperforms conventional (individual- or group-level) maximum likelihood estimation in recovering true parameters. Then we performed model-based fMRI analyses on experimental data to examine how the fMRI results depend on the estimation method. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

卷取机转动惯量测量计算的新方法

在铝带（ 箔） 冷轧机等设备的卷取机张力控制系统中,动态补偿环节需要准确的转动惯量,本文介绍一种采用最小二乘方法计算卷取或开卷机转动惯量的新方法。这种方法能在很大程度上消除因手工绘制曲线和电机空载电流偏小等因素引起的转动惯量测量计算误差。     

Improving Cost Estimates of Construction Projects Using Phased Cost Factors

Central to cost-based competition is the capability to accurately predict the cost of delivering a project. Most literature on cost estimation focuses on specific estimation methods as generic techniques and little attention has been paid to the unique requirements at each project stage. This note attempts to identify the critical factors for effective estimation at various stages of typical construction projects. Drawing from organization control theory and cost estimating literature, this note develops a theoretical framework that identifies the critical factors for effective cost estimation during each project phase of a conventional construction project. The underlying logic is that as a cost estimating effort progresses, both task programmability and output measurability improve. As a result, control effort will shift from input-oriented control to a combination of output and behavior control.     

Filtering properties of an adaptive trend operator and the application for determining the arterial blood velocity in retinal vessels

This study deals with methods focused on estimating blood velocity. The estimation of the linear trend function of a non-stationary signal based on the adaptive recursive estimation of the mean value function is used for the determination of the time delay of two indicator dilution curves. The filter property of this trend operator depends on the choice of a constant parameter c, the so-called adaptation factor. The functional connection between the filter property and the adaptation factor is considered in such a way that an objective calculation of arterial blood velocity in retinal vessels is possible.