Presence of ε-martensite as an intermediate phase during the strain-induced transformation of SUS304 stainless steel

We have investigated the formation process of α′-martensite from the γ-phase induced by external strain using in situ synchrotron diffraction experiments, combined with Lorentz transmission electron microscopy (TEM) and high-resolution TEM observations. It is clearly demonstrated that ε-martensite with hexagonal symmetry appears as an intermediate structure during the plastic deformation of SUS304 stainless steel. In addition to stacking faults and dislocations, interfaces between the twin structures presumably play a key role in the formation of ε-martensite.     

Angel or demon? Self-monitoring differences in the mental representations of current versus former romantic partners

We investigated whether high and low self-monitors had (a) segregated/integrated and (b) uniformly favorable (angelic)/uniformly unfavorable (demonic) mental representations of actual romantic partners. Self-monitoring was assessed using the 25-item Self-Monitoring Scale and the structure of mental representations was calculated using a modified segregation/integration measure. In Study 1, low self-monitors were more likely than high self-monitors to have (a) segregated and (b) disproportionally favorable mental representations of their current romantic partners. In Study 2, low self-monitors were more likely than high self-monitors to have (a) segregated and (b) generally unfavorable mental representations of their former romantic partners. Self-monitoring differences were not moderated in either study by relationship longevity or conflict.     

A bio-inspired model of behavior considering decision-making and planning,spatial attention and basic motor commands processes

Cognitive architectures (CA) are an IA approach to implement computer systems with human-like behavior. Fundamental exhibited human capabilities include planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or are restrained to too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. To try to alleviate the problem, we present a bio-inspired computational model to solve the autonomous navigation problem of a computational entity in a controlled context. This proposal is the result of the interaction between planning and decision-making, spatial attention and the motor cognitive functions. The proposed model is based on neuroscientific evidence concerning the involved cognitive functions and is part of a more general cognitive architecture. In the case study developed to validate our idea, we can see that the processes previously identified play an important role to accomplish spatial navigation. In the case study presented, an agent achieves the navigation over an unexplored maze from an initial to a final position successfully. The reunited results motivate us to continue improving our model considering attentional information to influence the agent’s motor behavior.     

Implicit processing of emotional words by children with Post-Traumatic Stress Disorder: An fMRI investigation

Background/ObjectiveIn the last decade, socio-political violence in Colombia (South America) has created an environment of extreme/chronic stress. In this study, brain imaging technology (fMRI) and behavioral task performance were used to measure potential deficits in executive functioning for emotional processing in Colombian children.MethodParticipants (22 Post-Traumatic Stress Disorder, PTSD and 22 neurotypical, NT) were asked to perform a word task with implicit emotional salience, which required them to report the color of the ink in which a positive, negative or neutral word was printed.ResultsMixed design analysis of variance showed no group differences in accuracy for determining ink color when presented as a positive or neutral word. However, PTSD children were significantly less accurate (negative words) and notably slower (both positive and negative words) at determining ink color when presented in the context of an emotional word. PTSD processing of positive and negative words was associated with hypoactivation in the superior and middle frontal gyri of the right hemisphere in comparison to NT children.ConclusionsThese results may reflect a deficit in executive functioning for emotionally laden stimuli, perhaps induced as a by-product of their traumatic experiences.     

Truth in Applicative Theories

We give a survey on truth theories for applicative theories. It comprises Frege structures, universes for Frege structures, and a theory of supervaluation. We present the proof-theoretic results for these theories and show their syntactical expressive power. In particular, we present as a novelty a syntactical interpretation of ID₁ in a applicative truth theory based on supervaluation.     

Standard errors of fit indices using residuals in structural equation modeling

The asymptotic standard errors of the correlation residuals and Bentler's standardized residuals in covariance structures are derived based on the asymptotic covariance matrix of raw covariance residuals. Using these results, approximations of the asymptotic standard errors of the root mean square residuals for unstandardized or standardized residuals are derived by the delta method. Further, in mean structures, approximations of the asymptotic standard errors of residuals, standardized residuals and their summary statistics are derived in a similar manner. Simulations are carried out, which show that the asymptotic standard errors of the various types of residuals and the root mean square residuals in covariance, correlation and mean structures are close to actual ones.The author is indebted to the reviewers for their comments and suggestions which have led to an improvement of this work.     

Chunk hierarchies and retrieval structures: Comments on Saariluoma and Laine

The empirical results of Saariluoma and Laine are discussed and their computer simulations are compared with CHREST, a computational model of perception, memory and learning in chess. Mathematical functions such as power functions and logarithmic functions account for Saariluoma and Laine's correlation heuristic and for CHREST very well. However, these functions fit human data well only with game positions, not with random positions. As CHREST, which learns using spatial proximity, accounts for the human data as well as Saariluoma and Laine's correlation heuristic, their conclusion that frequency-based heuristics match the data better than proximity-based heuristics is questioned. The idea of flat chunk organisation and its relation to retrieval structures is discussed. In the conclusion, emphasis is given to the need for detailed empirical data, including information about chunk structure and types of errors, for discriminating between various learning algorithms.     

实验性恶心与呕吐——大脑皮层与内脏相关研究的新途径

大脑皮层与内脏关系的脑机制是揭示心身疾病和心理疾病的重要依据。本文综述了经典条件学习与期待性恶心与呕吐的关系以及实验性恶心与呕吐的研究方法。作者认为,实验性恶心与呕吐结合脑成像技术是研究大脑皮层与内脏关系神经机制的重要途径。     

Sample size and bentler and Bonett's nonnormed fit index

Bentler and Bonett's nonnormed fit index is a widely used measure of goodness of fit for the analysis of covariance structures. This note shows that contrary to what has been claimed the nonnormed fit index is dependent on sample size. Specifically for a constant value of a fitting function, the nonnormed index is inversely related to sample size. A simple alternative fit measure is proposed that removes this dependency. In addition, it is shown that this new measure as well as the old nonnormed fit index can be applied to any fitting function that measures the deviation of the observed covariance matrix from the covariance matrix implied by the parameter estimates for a model.     

Latent variable modeling in heterogeneous populations

Common applications of latent variable analysis fail to recognize that data may be obtained from several populations with different sets of parameter values. This article describes the problem and gives an overview of methodology that can address heterogeneity. Artificial examples of mixtures are given, where if the mixture is not recognized, strongly distorted results occur. MIMIC structural modeling is shown to be a useful method for detecting and describing heterogeneity that cannot be handled in regular multiple-group analysis. Other useful methods instead take a random effects approach, describing heterogeneity in terms of random parameter variation across groups. These random effects models connect with emerging methodology for multilevel structural equation modeling of hierarchical data. Examples are drawn from educational achievement testing, psychopathology, and sociology of education. Estimation is carried out by the LISCOMP program.Presidential address delivered at the Psychometric Society meetings in Los Angeles, USA and Leuven, Belgium, July 1989. The research was supported by Grant No. SES-8821668 from the National Science Foundation and by Grant No. OERI-G-86-003 from the Office for Educational Research and Improvement, Department of Education. I thank Leigh Burstein, Mike Hollis, Linda Muthén, and Albert Satorra for helpful discussions and Tammy Tam, Jin-Wen Yang, Suk-Woo Kim, and Lynn Short for computational assistance. Designs were created by Arlette Collier, Rita Ling and Jennifer Edic-Bryant.