Improving Measurement Precision of Test Batteries Using Multidimensional Item Response Models.

A conventional way to analyze item responses in multiple tests is to apply unidimensional item response models separately, one test at a time. This unidimensional approach, which ignores the correlations between latent traits, yields imprecise measures when tests are short. To resolve this problem, one can use multidimensional item response models that use correlations between latent traits to improve measurement precision of individual latent traits. The improvements are demonstrated using 2 empirical examples. It appears that the multidimensional approach improves measurement precision substantially, especially when tests are short and the number of tests is large. To achieve the same measurement precision, the multidimensional approach needs less than half of the comparable items required for the unidimensional approach. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Geometry-Dependent R-Curve for Quasi-Brittle Materials

Fracture of quasi-brittle materials such as ceramics and cement-based materials can be described by an R -curve, defined as fracture resistance. The R -curve can be constructed as the envelope of the strain energy release rates ( G ). In this study, R -curve is defined as an envelope of G -curves obtained by varying the dimensions of identical specimens, while keeping the geometry and the initial flaw size constant. By approximating the G -curve with a second-order function of the crack length, a simple formulation of the R -curve is derived by solving a differential equation. Three parameters are needed for the proposed R -curve. These parameters can be obtained by testing a notched-beam specimen and using linear elastic fracture mechanics. The proposed R -curve simulates well the geometry dependency as well as other characteristics of the fracture response of quasi-brittle materials.     

Design of statistical databases: A methodology for the conceptual step

The aim of the conceptual step in database design is to describe data involving in the application in a formal and abstract way, without any concern to the specific model and language chosen for the implementation. In statistical applications, data are described at different levels of aggregation, from elementary facts of the reality to complex aggregations such as classifications, time series, indexes. The paper describes a methodology for conceptual design of statistical databases that provides the designer suitable strategies for defining such different levels of aggregation starting from user requirements, and checking the completeness, coherence and minimality of the conceptual schema at the different levels. The methodology makes use of two data models for the representation of data: for elementary data the Entity-Relationship model, widely used in database applications, and for summary data a new model is proposed, designed to be an effective trade-off between expressive power and simplicity of use.     

The stability robustness determination of state space models with real unstructured perturbations

This paper considers the robust stability of a linear time-invariant state space model subject to real parameter perturbations. The problem is to find the distance of a given stable matrix from the set of unstable matrices. A new method, based on the properties of the Kronecker sum and two other composite matrices, is developed to study this problem; this new method makes it possible to distinguish real perturbations from complex ones. Although a procedure to find the exact value of the distance is still not available, some explicit lower bounds on the distance are obtained. The bounds are applicable only for the case of real plant perturbations, and are easy to compute numerically; if the matrix is large in size, an iterative procedure is given to compute the bounds. Various examples including a 46th-order spacecraft system are given to illustrate the results obtained. The examples show that the new bounds obtained can have an arbitrary degree of improvement over previously reported ones. This work has been supported by the Natural Sciences and Engineering Research Council of Canada under Grant No. A4396.     

A time series approach to fatigue crack propagation

The inherently nonlinear phenomenon of fatigue crack propagation is modeled as a linear random process. To a first approximation, simple, nonstationary time series models are introduced and standard techniques for determining the parameters of autoregressive integrated moving-average processes are applied. Multiplicative time series models are next utilised for the representation of a group of crack history curves. Implementation of the models on the Virkler experimental data set yields satisfactory results. Reliable Gaussian approximations to the distribution of the time required by a crack to reach a specified critical length are obtained, and the usefulness of the approach is demonstrated when updating lifetime predictions after periodic inspections.     

一类可由基本模型嵌套组成的工作流系统的性能分析方法

工作流是近年来网络领域里研究的一个热点,对工作流模型的性能分析是工作流研究的一个重要内容.实际的模型往往规模大而复杂,这为分析其性能带来了很大的困难.但有一类模型可由工作流的四种基本模型(顺序、并行、选择、循环)嵌套组成,对于这类模型,本文给出了计算其性能乘积解的数学公式.文中给出了如何由这四种基本模型嵌套组成实际模型的方法,推导出了计算这四种基本模型的性能等价公式,并按建立实际模型相反的顺序,用由里到外逐层的分析方法,给出了计算实际模型的性能乘积解.     

Validation of a Three-Dimensional Computational Fluid Dynamics Model of a Contact Tank

A three-dimensional (3D) computational fluid dynamics (CFD) model of a contact tank is presented in this paper. The model results are compared against 3D velocities and flow through curve (FTC) data, representing a tracer concentration profile, from a 1:8 scale physical model. The objective is to demonstrate that CFD models can simulate both the FTC and the 3D velocity field quite well. Simultaneous validation of velocities and FTC is important in ascertaining the predictive capabilities of CFD models, as physical model studies indicate that different baffle arrangements can lead to similar FTCs. Therefore, a good prediction of only FTC, as presented in previous 3D CFD model studies, does not necessarily imply a correct simulation of the flow field.     

Ecophysiology of ochratoxigenic Aspergillus ochraceus and Penicillium verrucosum isolates. Predictive models for fungal spoilage prevention - a review

Ochratoxin A (OTA) is a secondary metabolite produced by several species of Aspergillus and Penicillium; among them Aspergillus ochraceus and Penicillium verrucosum are two ochratoxigenic species capable of growing in different climates and thus contamination of food crops with OTA can occur worldwide. OTA can be found in a wide range of foods such as cereals, coffee, cocoa, spices, beer, wine, dried vine fruit, grapes and meat products. OTA is toxic to animals, it presents neurotoxic, immunotoxic and nephrotoxic effects. It has been implicated in a human kidney disorder known as Balkan Endemic Nephropathy. This review focuses on the ecophysiology of ochratoxin-producing Aspergillus ochraceus and Penicillium verrucosum, the effect of environmental factors on their germination, mycelial growth, and OTA production. Knowledge of environmental conditions required for sucessive stages of fungal development represent the first step towards preventing mycotoxin formation. Predictive models for different stages of fungal development are presented, which allow prediction of the time before spoilage as a function of the abiotic factors. Finally, the implications of these studies in management of barley, coffee and grapes are described. This can help to identify the critical control points in their production, storage and distribution processes.