CLASSI: A classification model for the study of sequential processes and individual differences therein

In psychological research, one often aims at explaining individual differences in S-R profiles, that is, individual differences in the responses (R) with which people react to specific stimuli (S). To this end, researchers often postulate an underlying sequential process, which boils down to the specification of a set of mediating variables (M) and the processes that link these mediating variables to the stimuli and responses under study. Obviously, a crucial task is to chart how the individual differences in the S-R profiles are caused by individual differences in the S-M link and/or by individual differences in the M-R link. In this paper we propose a new model, called CLASSI, which was explicitly designed for this task. In particular, the key principle of CLASSI consists of reducing the S, M, and R nodes of a sequential process to a few mutually exclusive types and inducing an S-M and an M-R person typology from the data, with the S-M person types being characterized in terms of if S type then M type rules and the M-R person types in terms of if M type then R type rules. As such, the S-M and M-R person types and their associated if–then rules represent the important individual differences in the S-M and M-R links of the sequential process under study. An algorithm to fit the CLASSI model is described and evaluated in a simulation study. An application of CLASSI to data from the behavioral domain of anger and sadness is discussed. Finally, we relate CLASSI to other methods and discuss possible extensions. The first author is a post-doctoral fellow of the Fund for Scientific Research—Flanders (Belgium). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/05/04).     

有关具身认知的三种理论模型

具身认知强调了身体在认知中的关键作用,主张身体的构造和状态、身体的物理属性及其大脑与身体的特殊感觉—运动通道对认知的形成产生了根本影响。具身认知的相关理论虽然直接或者间接承认身体对心智的塑造关系,但是存在一些观点的不一致,导致具身认知理论难以统一到相同的框架上来,具身认知大致可以归结为三种模型:概念隐喻理论,知觉符号理论和感知运动模拟隐喻理论。     

Changes of hardness and electronic work function of Zr41.2Ti13.8Cu12.5Ni10Be22.5 bulk metallic glass on annealing

The hardness and electronic work function (EWF) of a bulk metallic glass, namely Zr_41.2Ti_13.8Cu_12.5Ni₁₀Be_22.5, have been studied experimentally, with an emphasis on the effect of heat treatments. The glass was annealed at different time and temperatures, and its hardness and EWF measured using the Rockwell indentation technique and a scanning Kelvin probe system, respectively. It is found that the EWF decreases with annealing time and temperature, whereas the hardness increases. This study shows a close relationship between hardness and EWF, indicating that the EWF could be a sensitive parameter for characterising and investigating the mechanical behaviour of BMG at the electronic level.     

Effects of annealing temperature on structural,optical, and electrical properties of antimony-doped tin oxide thin films

Antimony-doped tin oxide (ATO) films, approximately 320 nm in thickness, have been prepared by electron beam evaporation onto glass substrates. The films were annealed at temperatures between 400°C and 550°C in air and their structure and surface morphologies were observed by X-ray diffraction (XRD) and atomic force microscopy (AFM) after the different annealing treatments. XRD patterns of the ATO thin films as-deposited and annealed at 400°C showed that they were amorphous, but annealing beyond 400°C caused the films to become polycrystalline with tetragonal structure and orientated in the (1 1 0) direction. The grain size in the annealed films, obtained from the XRD analysis, was in the range 146–256 Å and this increased with the annealing temperature. The dislocation density, cell volume and strain were found to decrease gradually with increasing annealing temperature. Photoluminescence spectra revealed an intensive blue/violet peak at 420 nm, which increased gradually in height with annealing. It is suggested that an increase in the population of Sb⁺⁵ ions might be the reason for the enhancement of the blue/violet emission. The optical properties of the films were also investigated in the UV-visible-NIR region (300–1000 nm). The optical constants, namely the refractive index n and the extinction coefficient k in the visible region were calculated. The optical energy band gap, as determined by the dependence of the absorption coefficient on the photon energy at short wavelengths, was found to increase from 3.59 to 3.76 eV with annealing temperature.     

Influence of annealing on the photoluminescence of nanocrystalline ZnO synthesized by microwave processing

Zinc oxide (ZnO) nanoparticles have been synthesized by a microwave heating method. The resulting powders were annealed at temperatures from 400°C to 800°C for 4?h. The annealed powders were characterized in terms of their structural, morphological, and optical properties by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and room-temperature photoluminescence (PL) spectroscopy, respectively. The XRD patterns of the samples revealed the formation of phase-pure ZnO, apparently without any impurities. However, FT-IR spectra revealed the presence of carboxylate impurities, the signals of which disappear as the annealing temperatures are increased from 400°C to 800°C. SEM pictures showed increasing size ZnO nanoparticles with increase in annealing temperature. The powder annealed at 800°C is identified as hexagonal wurzite ZnO. The improved PL spectral performance of the ZnO powder correlates well with the improved crystalline quality of the nanoparticles, as revealed by the XRD and PL. Sharp ultraviolet (UV) band edge luminescence has been observed at 375?nm, which is suitable for application in UV light emitting devices. The results also showed that the excitation wavelength dependence of the luminescence arises from quantum confinement effects.     

基于多Agent的运动图像匹配任务分配策略研究

本文针对基于多Agent的运动图像匹配系统的任务分配策略进行了讨论,并结合遗传算法和模拟退火算法给出了一种新的基于多Agent的运动图像模式匹配系统的任务分配策略。     

The value of self-report measures as indicators of driving behaviors among young drivers

Although much of the knowledge in transportation psychology has been gained by means of self-report measures, there is still a dispute regarding the usefulness and validity of such instruments. This series of two studies employed multivariate statistical models to examine associations between self-report and objective measures in two samples of young drivers. Study 1 (n = 151) compared scores on the Multidimensional Driving Style Inventory (MDSI), a self-report questionnaire tapping four broad driving styles, with the naturalistic driving recorded by an in-vehicle data recorder (IVDR). Study 2 (n = 80) compared responses to the Reckless Driving Habits Scale, assessing the frequency with which drivers commit a set of risky behaviors, with driving measures collected by a simulator. This study also examined the personality trait of sensation seeking, as well as gender and driving experience. In Study 1, the analysis revealed positive associations between high scores on the risky and hostile driving styles measured by the MDSI and risky behaviors measured by the IVDR, as well as inverse correlations between the latter and high MDSI scores on the anxious and careful driving styles. Similarly, in Study 2 associations were found between the self-reported frequency of reckless driving habits and several risky behaviors measured by the driving simulator. In addition, risky behaviors correlated with the sociodemographic variables and sensation seeking. The two studies therefore show that self-report measures are reliable tools for assessing driving behaviors for purposes of research, evaluation, and intervention.     

MOSA method: a tool for solving multiobjective combinatorial optimization problems

The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, …) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods. In fact, for large‐scale problems, the simultaneous difficulties of 𝒩𝒫‐hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods. This paper develops the so‐called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi‐objective knapsack problems. Copyright © 1999 John Wiley & Sons, Ltd.     

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite well in recovering the underlying truth but frequently end in a local minimum. In this paper we evaluate whether or not this local minimum problem can be mitigated by means of two common strategies for avoiding local minima in combinatorial data analysis: simulated annealing (SA) and use of a multistart procedure. In particular, we propose a generic SA algorithm for hierarchical classes analysis and three different types of random starts. The effectiveness of the SA algorithm and the random starts is evaluated by reanalyzing data sets of previous simulation studies. The reported results support the use of the proposed SA algorithm in combination with a random multistart procedure, regardless of the properties of the data set under study. Eva Ceulemans is a post-doctoral fellow of the Fund for Scientific Research Flanders (Belgium). Iwin Leenen is a post-doctoral researcher of the Spanish Ministerio de Educación y Ciencia (programa Ramón y Cajal). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/05/04).     

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of dissimilarities in a maximum likelihood framework. In each iteration, the probability that a dissimilarity belongs to each of the blocks conforming to a partition of the original dissimilarity matrix, and the rest of parameters, are estimated in a simulated annealing based algorithm. A model selection strategy is used to test the number of latent classes and the dimensionality of the problem. Both simulated and classical dissimilarity data are analyzed to illustrate the model.