An investigation of the social learning and symbolic interaction models for the development of self-concepts and self-esteem

Two studies were conducted to investigate empirical support for two models relating to the development of self-concepts and self-esteem in upper-primary school children. The first study investigated the social learning model by examining the relationship between mothers' and fathers' self-reported self-concepts and self-esteem and the self-reported self-concepts and self-esteem of their children. The second study investigated the symbolic interaction model by examining the relationship between children's perception of the frequency of positive and negative statements made by parents and their self-reported self-concepts and self-esteem. The results of these studies suggested that what parents say to their children and how they interact with them is more closely related to their children's self-perceptions than the role of modelling parental attitudes and behaviours. The findings highlight the benefits of parents talking positively to their children.

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Flavor changing interaction models and strictly massless neutrinos

It is well known that experimental data, coming from solar and atmospheric neutrino detectors and also from experiments which look for neutrino oscillations. strongly suggest that neutrinos must have a mass different from zero. However at least the solar and/or the atmospheric neutrino data can be related to new flavor changing interactions beyond the standard model instead to the finite mass of neutrinos. This new physics may induce i) extra effects in neutrino-matter interactions, ii) CP violation in pion and lepton decays and, iii) muonium to antimuonium transition. We give two examples of models in which all those effects arise even with strictly massless neutrinos: the 331 model and multi-Higgs doublet extension of the standard model (mHDM) with flavor changing neutral currents in the charged lepton sector. It means that in this kind of models if neutrino masses were eventually needed, they will be independent of the parameters of the new interactions.

Generalized neighbor-interaction models induced by nonlinear lattices

It is shown that the tight-binding approximation of the nonlinear Schrodinger equation with a periodic linear potential and periodic in space nonlinearity coefficient gives rise to a number of nonlinear lattices with complex, both linear and nonlinear, neighbor interactions. The obtained lattices present nonstandard possibilities, among which we mention a quasilinear regime, where the pulse dynamics obeys essentially the linear Schrodinger equation. We analyze the properties of such models both in connection to their modulational stability, as well as in regard to the existence and stability of their localized solitary wave solutions.

Specification and Verification of Declarative Open Interaction Models - A Logic-based framework

The advent of distributed and heterogeneous systems has laid the foundation for the birth of new architectural paradigms, in which many separated and autonomous entities collaborate and interact to the aim of achieving complex strategic goals, impossible to be accomplished on their own. A non exhaustive list of systems targeted by such paradigms includes Business Process Management, Clinical Guidelines and Careflow Protocols, Service-Oriented and Multi-Agent Systems. It is largely recognized that engineering these systems requires novel modeling techniques. In particular, many authors are claiming that an open, declarative perspective is needed to complement the closed, procedural nature of the state of the art specification languages. For example, the ConDec language has been recently proposed to target the declarative and open specification of Business Processes, overcoming the over-specification and over-constraining issues of classical procedural approaches. On the one hand, the success of such novel modeling languages strongly depends on their usability by non-IT savvy: they must provide an appealing, intuitive graphical front-end. On the other hand, they must be prone to verification, in order to guarantee the trustworthiness and reliability of the developed model, as well as to ensure that the actual executions of the system effectively comply with it. In this dissertation, we claim that Computational Logic is a suitable framework for dealing with the specification, verification, execution, monitoring and analysis of these systems. We propose to adopt an extended version of the ConDec language for specifying interaction models with a declarative, open flavor. We show how all the (extended) ConDec constructs can be automatically translated to the CLIMB Computational Logic-based language, and illustrate how its corresponding reasoning techniques can be successfully exploited to provide support and verification capabilities along the whole life cycle of the targeted systems.

Learning Two-Person Interaction Models for Responsive Synthetic Humanoids

Imitation learning is a promising approach for generating life-like behaviors of virtual humans and humanoid robots. So far, however, imitation learning has been mostly restricted to single agent settings where observed motions are adapted to new environment conditions but not to the dynamic behavior of interaction partners. In this paper, we introduce a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners. From the observed interactions, low-dimensional motion models are extracted and a mapping between these motion models is learned. This interaction model allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner. The interaction model can be applied both to the animation of virtual characters as well as to the behavior generation for humanoid robots.

Universal tight binding model for chemical reactions in solution and at surfaces. II. Water

A revised water model intended for use in condensed phase simulations in the framework of the self consistent polarizable ion tight binding theory is constructed. The model is applied to water monomer, dimer, hexamers, ice, and liquid, where it demonstrates good agreement with theoretical results obtained by more accurate methods, such as DFT and CCSD(T), and with experiment. In particular, the temperature dependence of the self diffusion coefficient in liquid water predicted by the model, closely reproduces experimental curves in the temperature interval between 230 K and 350 K. In addition, and in contrast to standard DFT, the model properly orders the relative densities of liquid water and ice. A notable, but inevitable, shortcoming of the model is underestimation of the static dielectric constant by a factor of two. We demonstrate that the description of inter and intramolecular forces embodied in the tight binding approximation in quantum mechanics leads to a number of valuable insights which can be missing from ab initio quantum chemistry and classical force fields. These include a discussion of the origin of the enhanced molecular electric dipole moment in the condensed phases, and a detailed explanation for the increase of coordination number in liquid water as a function of temperature and compared with ice-leading to insights into the anomalous expansion on freezing. The theory holds out the prospect of an understanding of the currently unexplained density maximum of water near the freezing point. 

Interaction modeling using BPMN

Process choreographies describe interactions between different business partners and the dependencies between these interactions. While different proposals were made for capturing choreographies at an implementation level, it remains unclear how choreographies should be described on a conceptual level.While the Business Process Modeling Notation (BPMN) is already in use for describing choreographies in terms of interconnected interface behavior models, this paper will introduce interaction modeling using BPMN. Such interaction models do not suffer from incompatibility issues and are better suited for human modelers. BPMN extensions are proposed and a mapping from interaction models to interface behavior models is presented.

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

A review on slip models for gas microflows

Accurate modeling of gas microflow is crucial for the microfluidic devices in MEMS. Gas microflows through these devices are often in the slip and transition flow regimes, characterized by the Knudsen number of the order of 10-2∼100. An increasing number of researchers now dedicate great attention to the developments in the modeling of non-equilibrium boundary conditions in the gas microflows, concentrating on the slip model. In this review, we present various slip models obtained from different theoretical, computational and experimental studies for gas microflows. Correct descriptions of the Knudsen layer effect are of critical importance in modeling and designing of gas microflow systems and in predicting their performances. Theoretical descriptions of the gas-surface interaction and gas-surface molecular interaction models are introduced to describe the boundary conditions. Various methods and techniques for determination of the slip coefficients are reviewed. The review presents the considerable success in the implementation of various slip boundary conditions to extend the Navier-Stokes (N-S) equations into the slip and transition flow regimes. Comparisons of different values and formulations of the first- and second-order slip coefficients and models reveal the discrepancies arising from different definitions in the first-order slip coefficient and various approaches to determine the second-order slip coefficient. In addition, no consensus has been reached on the correct and generalized form of higher-order slip expression. The influences of specific effects, such as effective mean free path of the gas molecules and viscosity, surface roughness, gas composition and tangential momentum accommodation coefficient, on the hybrid slip models for gas microflows are analyzed and discussed. It shows that although the various hybrid slip models are proposed from different viewpoints, they can contribute to N-S equations for capturing the high Knudsen number effects in the slip and transition flow regimes. Future studies are also discussed for improving the understanding of gas microflows and enabling us to exactly predict and actively control gas slip. © Springer-Verlag 2012.

LUTI models, freight transport and freight facility location

The subject of this book is the new scientific research in the field of modelling the interaction between land use and transport (LUTI modelling). Transport and the location of activities in space have been important themes of study in engineering, social sciences and urban and regional planning

DN interaction from meson exchange

A model of the DN interaction is presented which is developed in close analogy to the meson-exchange KN potential of the Jülich group utilizing SU(4) symmetry constraints. The main ingredients of the interaction are provided by vector meson (ρ, ω) exchange and higher-order box diagrams involving D *N, DΔ, and D *Δ intermediate states. The coupling of DN to the π Λ c and π Σ c channels is taken into account. The interaction model generates the Λ c(2595)-resonance dynamically as a DN quasi-bound state. Results for DN total and differential cross sections are presented and compared with predictions of two interaction models that are based on the leading-order Weinberg-Tomozawa term. Some features of the Λ c(2595)-resonance are discussed and the role of the near-by π Σ c threshold is emphasized. Selected predictions of the orginal KN model are reported too. Specifically, it is pointed out that the model generates two poles in the partial wave corresponding to the Λ(1405)-resonance. © 2011 SIF, Springer-Verlag Berlin Heidelberg.

Traumatization and chronic pain: a further model of interaction.

Up to 80% of patients with severe posttraumatic stress disorder are suffering from "unexplained" chronic pain. Theories about the links between traumatization and chronic pain have become the subject of increased interest over the last several years. We will give a short summary about the existing interaction models that emphasize particularly psychological and behavioral aspects of this interaction. After a synopsis of the most important psychoneurobiological mechanisms of pain in the context of traumatization, we introduce the hypermnesia-hyperarousal model, which focuses on two psychoneurobiological aspects of the physiology of learning. This hypothesis provides an answer to the hitherto open question about the origin of pain persistence and pain sensitization following a traumatic event and also provides a straightforward explanatory model for educational purposes.

Lateral Dynamic Models for High-Speed Railway Bridges and Vehicles

The analysis of the running safety of railway vehicles on viaducts subject to strong lateral actions such as cross winds requires coupled nonlinear vehicle-bridge interaction models, capable to study extreme events. In this paper original models developed by the authors are described, based on finite elements for the structure, multibody and finite element models for the vehicle, and specially developed interaction elements for the interface between wheel and rail. The models have been implemented within ABAQUS and have full nonlinear capabilities for the structure, the vehicle and the contact interface. An application is developed for the Ulla Viaduct, a 105 m tall arch in the Spanish high-speed railway network. The dynamic analyses allow obtaining critical wind curves, which define the running safety conditions for a given train in terms of speed of circulation and wind speed