In this article, we propose the design of sensory motor level as part of a three-layered agent architecture inspired from the Multilevel Process Theory of Emotion (Leventhal 1979, 1980; Leventhal and Scherer, 1987). Our project aims at modeling emotions on an autonomous embodied agent, Petra, a more robust robot than our previous prototype Cherry. Our robot has been equipped with sonar and visi...

The goal of this paper is to show that the Insertion Modeling System[1] developed by A.A. Letichevsky of the department 100/105 of the Glushkov Institute of Cybernetics, National Academy of Science of Ukraine, Kyiv, Ukraine, can be used as an instrument for the modeling and analysis of complex distributed systems, such as a client-server architectures. The Insertion Modeling [1] is based on the...

An important problem in multilevel modeling is what constitutes a sufficient sample size for accurate estimation. In multilevel analysis, the major restriction is often the higher-level sample size. In this paper, a simulation study is used to determine the influence of different sample sizes at the group level on the accuracy of the estimates (regression coefficients and variances) and their s...

The understanding of transcription regulation is a major goal of today's biology. The challenge is to utilize diverse high-throughput data in order to infer mechanistic models of transcription control. We propose a new model which integrates transcription factor-gene affinity, protein abundance, and gene expression profiles. The model provides a detailed, yet computationally tractable descripti...

In property and casualty insurance, claims management is featured with modeling of semicontinuous insurance cost associated with individual risk transfer. This practice is further complicated by the multilevel structure of the insurance claims data, where a contract often contains a group of policyholders, each policyholder is insured under multiple types of coverage, and the contract is repeat...

We describe a multivariate, multilevel, pseudo maximum likelihood estimation method for multistage stratified cluster sampling designs, including finite population and unequal probability sampling. Multilevel models can be estimated with this method while incorporating the sampling design in the standard error computation. Design based adjustment of the likelihood ratio test (LRT) statistic is ...

Most data involving organizations are hierarchical in nature and often contain variables measured at multiple levels of analysis. Hierarchical linear modeling (HLM) is a relatively new and innovative statistical method that organizational scientists have used to alleviate some common problems associated with multilevel data, thus advancing our understanding of organizations. This article presen...

Cancer growth models may be divided into macroscopic models, which describe the tumor as a single entity, and microscopic ones, which consider the tumor as a complex system whose behavior emerges from the local dynamics of its basic components, the neoplastic cells. Mesoscopic models (e.g. as based on the Local Interaction Simulation Approach [Delsanto, P.P., Mignogna, R., Scalerandi, M., Schec...

In educational settings, researchers are likely to encounter multilevel data with cross-classified structure. However, due to the lack of familiarity and limitations of statistical software for cross-classified modeling, most researchers adopt less optimal approaches to analyze cross-classified multilevel data in testing measurement invariance. We conducted two Monte Carlo studies to investigat...