Simple Interaction Model Research Articles

Amorphization and C segregation based surface generation of Reaction-Bonded SiC/Si composites under micro-grinding

Micro-grinding of Reaction-Bonded SiC/Si composites (RB–SiC/Si) were conducted to investigate the surface generation mechanism. The results showed that amorphization occurred for both SiC and Si phases, and C segregation appeared on SiC surface and at the interface of SiC and Si. The surface generation mechanism changed from micro-breaking to smoother surface, but accompanied by formation of many micro-pits at phase boundaries with the decrease of feed rate. It was identified that the material removal mode was closely related to amorphization and C segregation (i.e. amorphization would promote ductile material removal while C segregation would aggravate surface fracture and increase the number of micro-pits). Furthermore, it was found that the distribution of micro-pits corresponded with the random and stochastic properties of diamond grits, and the formation of micro-pits during machining process determined the surface roughness. To explain the mechanism of micro-pits formation, a simple interaction model between diamond grit and workpiece was proposed.

Shear-enhanced adsorption of a homopolymeric globule mediated by surface catch bonds.

The adsorption of a single collapsed homopolymer onto a planar smooth surface in shear flow is investigated by means of Brownian hydrodynamics simulation. While cohesive intra-polymer forces are modeled by Lennard-Jones potentials, surface-monomer interactions are described by stochastic bonds whose two-state kinetics is characterized by three parameters: bond formation rate, bond dissociation rate and an effective catch bond parameter that describes how the force acting on a surface-monomer bond influences the dissociation rate. We construct adsorption state diagrams as a function of shear rate and all three surface-monomer bond parameters. We find shear-induced adsorption in a small range of parameters for low dissociation and association rates and only when the surface-monomer bond is near the transition between slip and catch bond behavior. By mapping on a simple surface-monomer interaction model with conservative pair potentials we try to estimate the conservative potential parameters necessary to observe shear-induced surface adsorption phenomena.

The Effects of Temperature on Vortex-pairing Noise in Axisymmetric Subsonic Jets

Thermal effects on the sound generation by vortex pairing are investigated in axisymmetric subsonic jets. Direct numerical simu- lation of compressible Navier-Stokes equations is performed for an accurate description of near-field flow dynamics and far-field sound. As the core temperature increasing, the positions of vortex roll-up and pairing move upstream, and the noise intensity is enhanced. In addition, sound predicted by Lilley-Goldstein acoustic analogy is consistent with the result of simulation in all cases. A further analysis of sound sources of the Lilley-Goldstein equation shows that the thermodynamic component has remarkable influence in non-isothermal jets, especially in the cold one. Moreover, sound generation in the isothermal jet is specifically studied by a simple nonlinear interaction model based on instability waves which are obtained by solving linear parabolized stability equa- tions. The major radiation pattern given by the model is found to be in good agreement with the directly computed data, suggesting the great importance of nonlinear interaction in subsonic jet noise.

Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures.

Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.

Interaction Effects in Latent Growth Models: Evaluation of Alternative Estimation Approaches

The purpose of this investigation is to compare and evaluate 2 approaches for estimating interaction effects in latent growth models (LGMs): the unconstrained approach and the latent moderated structural equations (LMS) approach. To reduce the complexity of modeling interactions in LGMs, we created difference-product indicators to replace the traditional product indicators used in the unconstrained approach because these differences in original indicators represent changes over time. Our focus was to verify the performance of this simplified interaction model of LGMs with difference-product indicators by using the unconstrained approach and comparing it with the LMS approach. Our simulation study showed that the LMS approach generally resulted in smaller biases in the estimated parameters and was consistently more precise than the unconstrained approach under normal conditions, particularly when the sample size was small. When normality assumptions were violated, however, the unconstrained approach was shown to be more robust than the LMS approach in terms of the bias of estimation. In summary, we generally recommend both the unconstrained approach and the LMS approach if the indicators are normally distributed and when the sample size is large enough (e.g., not less than 500). Under normal conditions, the LMS approach is preferred. If normality assumptions are violated, however, the unconstrained approach is recommended. Under the most stringent conditions when normality assumptions are severely violated and the sample size is small, the results from both the unconstrained approach and the LMS approach should be treated with caution, and alternative procedures might be considered.

Extended space method for parameter identifiability of DAE systems

Mathematical models of physical systems often have parameters that must be identified from physical data. This makes the analysis of the parameter identifiability of the given model system an essential prerequisite. Thus far, several methods have been proposed for analyzing the parameter identifiability of ordinary differential equation (ODE) systems. But, to the best of our knowledge, the parameter identifiability of differential algebraic equation (DAE) systems has scarcely been analyzed as a specific topic. Traditional differential algebraic (DA) methods developed for ODE systems are often applied directly on DAE systems. These methods, however, are not always applicable, e.g., when the prime ideal condition is not satisfied by a DAE system. In this paper, we propose a novel method to analyze the identifiability of DAE systems, based on the concept of space extension, through which the algebraic and differential variables can be decoupled. Furthermore, an inherent, low-dimensional, regular ODE system can be obtained, which is the external equivalent of the original DAE system. Subsequently, the differential algebraic (DA) method can then be used to analyze the identifiability of the low-dimension ODE system. Theoretical analysis is also presented for the proposed method. Two examples, including a simplified interaction model and an isothermal reactor system, are presented to illustrate the detailed steps and effectiveness of the proposed method.

Sphericall: A Human/Artificial Intelligence interaction experience

Multi-agent systems are now wide spread in scientific works and in industrial applications. Few applications deal with the Human/Multi-agent system interaction. Multi-agent systems are characterized by individual entities, called agents, in interaction with each other and with their environment. Multi- agent systems are generally classified into complex systems categories since the global emerging phenomenon cannot be predicted even if every component is well known. The systems developed in this paper are named reactive because they behave using simple interaction models. In the reactive approach, the issue of Human/system interaction is hard to cope with and is scarcely exposed in literature. This paper presents Sphericall, an application aimed at studying Human/Complex System interactions and based on two physics inspired multi-agent systems interacting together. The Sphericall device is composed of a tactile screen and a spherical world where agents evolve. This paper presents both the technical background of Sphericall project and a feedback taken from the demonstration performed during OFFF Festival in La Villette (Paris).

Detection of epistatic effects with logic regression and a classical linear regression model

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

The design of software-agent architecture for customer relationship management in utility service-based business

A challenge facing businesses establishing an effective customer relationship management (CRM) system is the need for integrating data from different sources. Many data integration approaches have been proposed and widely adopted to improve customer services. However, not all data integration approaches are suitable for utility service-based business due to its unique characteristics. The study explores the idea of using software agents to address the integration issue of building CRM systems for utility service-based businesses. The study proposes two simple agent interaction models, the request-response model and the subscription-publishing model as well as six categories of agents to meet the need. The utility and validity of the proposed approach are examined and verified in a real-world case. They are implemented with extensible markup language (XML) and applied to create an agent-based architecture for a CRM system for a telecommunication company in Taiwan.

Molecular Force Field Development for Aqueous Electrolytes: 1. Incorporating Appropriate Experimental Data and the Inadequacy of Simple Electrolyte Force Fields Based on Lennard-Jones and Point Charge Interactions with Lorentz-Berthelot Rules.

It is known that none of the available simple molecular interaction models of aqueous electrolytes based on SPC/E water and their associated force fields are able to reproduce the concentration dependence of important thermodynamic properties of even the simplest electrolyte, NaCl, at ambient conditions over the entire experimentally accessible concentration range [ Mouc̆ka , F. ; Nezbeda , I. ; Smith , W. R. J. Chem. Phys. 2013 , 138 , 154102 ]. This paper explores the possibility of improving their performance by incorporating concentration-dependent experimental data for the total ionic chemical potential and the density into the fitting procedure, in addition to experimental values of solubility and solid chemical potential. We describe a general parameter estimation methodology for a studied class of models that incorporates the aforementioned experimental data. When the entire concentration range is considered, although the resulting force field is a slight improvement over others currently available in the literature, overall quantitative agreement with the experimental data over this range remains unsatisfactory. This indicates an inherent limitation of such simple molecular interaction models and strongly suggests that more complex mathematical forms of such models are required to quantitatively predict the properties of aqueous electrolyte solutions when the entire concentration range is of interest. Our parameter estimation methodology is also applicable to such cases.

The structure of spatial networks and communities in bicycle sharing systems.

Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.

A Parametric Study of a Drive by Bridge Inspection System Based on the Morlet Wavelet

Many of the bridges currently in use worldwide are approaching the end of their design lives. However, rehabilitating and extending the lives of these structures raises important safety issues. There is also a need for increased monitoring which has considerable cost implications for bridge management systems. Existing structural health monitoring (SHM) techniques include vibration-based approaches which typically involve direct instrumentation of the bridge and are important as they can indicate the deterioration of the bridge condition. However, they can be labour intensive and expensive. In the past decade, alternative indirect vibration-based approaches which utilise the response of a vehicle passing over a bridge have been developed. This paper investigates such an approach; a low-cost approach for the monitoring of bridge structures which consists of the use of a vehicle fitted with accelerometers on its axles. The approach aims to detect damage in the bridge while obviating the need for direct instrumentation of the bridge. Here, the effectiveness of the approach in detecting damage in a bridge is investigated using a simplified vehicle-bridge interaction (VBI) model in theoretical simulations and a scaled VBI model in a laboratory experiment. In order to identify the existence and location of damage, the vehicle accelerations are recorded and processed using a continuous Morlet wavelet transform and a damage index is established. A parametric study is carried out to investigate the effect of parameters such as the bridge span length, vehicle speed, vehicle mass, damage level and road surface roughness on the accuracy of results.

A reduced computational and geometrical framework for inverse problems in hemodynamics

The solution of inverse problems in cardiovascular mathematics is computationally expensive. In this paper, we apply a domain parametrization technique to reduce both the geometrical and computational complexities of the forward problem and replace the finite element solution of the incompressible Navier-Stokes equations by a computationally less-expensive reduced-basis approximation. This greatly reduces the cost of simulating the forward problem. We then consider the solution of inverse problems both in the deterministic sense, by solving a least-squares problem, and in the statistical sense, by using a Bayesian framework for quantifying uncertainty. Two inverse problems arising in hemodynamics modeling are considered: (i) a simplified fluid-structure interaction model problem in a portion of a stenosed artery for quantifying the risk of atherosclerosis by identifying the material parameters of the arterial wall on the basis of pressure measurements; (ii) a simplified femoral bypass graft model for robust shape design under uncertain residual flow in the main arterial branch identified from pressure measurements.

Thermodynamics for proton binding of phytate in KNO3(aq) at different temperatures and ionic strengths

Potentiometric measurements were performed in KNO3(aq), to determine the apparent protonation constants of phytate at different temperatures (278.15≤T (K)≤323.15) and ionic strengths (0.25≤I (mol)dm−3≤3.0) values. In general, the protonation constants decrease with increasing both temperature and ionic strength. The data reported were critically compared with previous results obtained in KCl and the values are in a good agreement, considering the experimental errors and slight differences between the activity coefficients of the various species in KCl and KNO3. Experimental data were then modeled as a function of temperature and ionic strength using, with comparable results, two approaches: the extended Debye–Hückel equation and the specific ion interaction theory (SIT). The single specific ion interaction coefficients, ɛ, were also determined. The corresponding values are higher than those in Na+ media. The protonation constants were also analyzed considering a simplified weak interaction model using an empirical equation that contains an additional term which takes into account the formation of weak complexes. The results obtained for the modeling of the protonation constants are in agreement with the literature findings.Thermodynamic protonation parameters were also obtained at different temperatures and ionic strengths. The proton association process is slightly exothermic and the enthalpic contribution is less negative than that in NaCl solution. As observed in other cases for phytate anion, the major contribution for the proton association is entropic in nature. A rough value of Δcp was also determined. Finally, a complete set of suggested data is proposed at different temperatures and ionic strengths.

TU‐G‐103‐05: Affordable Supercomputer‐Based Monte Carlo CT Dose Calculations: A Hardware Comparison Between Nvidia M2090 GPU and Intel Xeon Phi 5110p Coprocessor

Purpose: To develop a fast Monte Carlo code ARCHER (a testbed for Accelerated Radiation‐transport Computation in Heterogeneous EnviRonments) to calculate CT imaging organ doses on heterogeneous computing systems. Methods: ARCHER simulates the transport of low‐energy (1∼140keV) photons in heterogeneous media using two selectable interaction models, simple and detailed. It includes an extensive library of voxelized human phantoms such as RPI‐Pregnant women with 3, 6, 9 month gestation, extended RPI‐Adult male and female phantoms representing patients of different obesity level. It contains a validated scanner model based on a GE LightSpeed third‐generation 16‐multi‐detector CT. A series of scan protocols are predefined, including a combination of scan mode (helical or axial), beam collimation (5, 10, or 20 mm) and kVp (80, 100, 120 or 140). ARCHER is targeted for three platforms, CPU (an Intel Xeon X5650 6‐core processor), GPU (Nvidia M2090 GPU), and coprocessor (Intel Xeon Phi 5110p coprocessor), and correspondingly has three code variants developed in MPI‐OpenMP, CUDA and offload OpenMP respectively. Results: Absorbed doses to 28 organs/tissues of dosimetric interest were calculated. MCNPX was used as the accuracy benchmark. The average difference of the organ doses calculated by MCNPX and ARCHER were found to be less than 5% and 0.5% for simple and detailed interaction models respectively, indicating an excellent agreement. While ARCHER's three variants were 63∼1207x faster than MCNPX, the GPU and coprocessor codes were 1.6∼3.4x faster than the CPU (6 core) code. Conclusion: The accelerator‐based new Monte Carlo code ARCHER is developed for accurate and fast CT imaging organ dose calculations. Its full capability to simulate patients of different genders and body sizes receiving different scan protocols exhibits high clinical value.

A Nonparametric Test to Detect Quantitative Trait Loci Where the Phenotypic Distribution Differs by Genotypes

Searching for genetic variants involved in gene-gene and gene-environment interactions in large-scale data raises multiple methodological issues. Many existing methods have focused on the problem of dimensionality, trying to explore the largest number of combinations between risk factors while considering simple interaction models. Despite evidence demonstrating the efficacy of these methods in simulated data, their application in real data has been unsuccessful so far. The classical test of a linear marginal genetic effect has been widely used for agnostic genome-wide association studies, with the underlying idea that most variants involved in interactions might display marginal effect on the phenotypic mean. Although this approach may allow for the identification of genetic variants involved in interactions in many scenarios, the linear marginal effects of some causal alleles on the phenotypic mean might not be always detectable at genome-wide significance level. We introduce in this study a general association test for quantitative trait loci that compare the distributions of phenotypic values by genotypic classes as opposed to most standard tests that compare phenotypic means by genotypic classes. Using simulations we show that in presence of interactions, this approach can be more powerful than the standard test of the linear marginal effect, with a gain of power increasing with increasing interaction effect and decreasing frequencies of the interacting exposures. We demonstrate the potential utility of our method on real data by analyzing mammographic density genome-wide data from the Nurses' Health Study.

Impact of Confinement-Induced Cooperative Molecular Orientation Change on the Ferroelectric Size Effect in Ultrathin P(VDF-TrFE) Films

We demonstrate a distinct confinement induced cooperative lattice orientation change in ultrathin P(VDF-TrFE) films by using various characterization techniques, including X-ray diffraction (XRD), two-dimensional X-ray diffraction (2D-XRD), grazing incidence X-ray diffraction (GIXD), and infrared reflection absorption spectra (IRRAS). Both the polymer chains and the molecular dipoles that are perpendicular to the polymer backbone showed a lying-down orientation change with decreasing thickness. The dipole orientation change showed a good correlation with the thickness dependence of remnant polarization, which were measured from highly reproducible ferroelectric loops in a wide range of thickness (30–250 nm) on inert electrode with suppressed “dead layer” effect. A simple microscopic molecular dipole interaction model revealed that the free energy of different orientation states was related to the thickness and lateral dimension of the polymer crystallites. The findings reveal a unique molecular orientation driven size effect in ferroelectric polymer films, providing new insights into the nature of ferroelectricity and orientation mechanisms in polymers relevant to the design of emerging flexible electronic devices.

地盤と構造物の相互作用を考慮した土留め挙動の解析手法

An estimation method of retaining structures used at deep excavation sites, such as an open-cut tunneling site, is investigated in this article. A frame analysis is widely used in Japanese practices. However, based on the field measurements, this frame analysis sometimes leads to conservative design, i.e., calculated deflections of retaining walls are greater than those measured in situ. To achieve more rational design, the authors have reviewed the frame analysis critically and proposed alternative method utilizing a simple soil-retaining wall interaction model using elastic-perfectly plastic spring to estimate the behavior of retaining walls. Some case studies of real open-cut tunneling works were carried out to check the validity of the proposed method. Calculated deflections and their modes by the proposed method can fit the measured deflections better than or similar to those by frame analysis, especially for a case of clayey soils.

Longitudinal displacement in viscoelastic arteries:A novel fluid-structure interaction computational model, and experimental validation

Recent in vivo studies, utilizing ultrasound contour and speckle tracking methods, have identified significant longitudinal displacements of the intima-media complex, and viscoelastic arterial wall properties over a cardiac cycle. Existing computational models that use thin structure approximations of arterial walls have so far been limited to models that capture only radial wall displacements. The purpose of this work is to present a simple fluid-struture interaction (FSI) model and a stable, partitioned numerical scheme, which capture both longitudinal and radial displacements, as well as viscoelastic arterial wall properties. To test the computational model, longitudinal displacement of the common carotid artery and of the stenosed coronary arteries were compared with experimental data found in literature, showing excellent agreement. We found that, unlike radial displacement, longitudinal displacement in stenotic lesions is highly dependent on the stenotic geometry. We also showed that longitudinal displacement in atherosclerotic arteries is smaller than in healthy arteries, which is in line with the recent in vivo measurements that associate plaque burden with reduced total longitudinal wall displacement. This work presents a first step in understanding the role of longitudinal displacement in physiology and pathophysiology of arterial wall mechanics using computer simulations.

Modeling complex equilibria in isothermal titration calorimetry experiments: Thermodynamic parameters estimation for a three-binding-site model

Isothermal titration calorimetry (ITC) is a powerful technique that can be used to estimate a complete set of thermodynamic parameters (e.g., Keq (or ΔG), ΔH, ΔS, and n) for a ligand-binding interaction described by a thermodynamic model. Thermodynamic models are constructed by combining equilibrium constant, mass balance, and charge balance equations for the system under study. Commercial ITC instruments are supplied with software that includes a number of simple interaction models, for example, one binding site, two binding sites, sequential sites, and n-independent binding sites. More complex models, for example, three or more binding sites, one site with multiple binding mechanisms, linked equilibria, or equilibria involving macromolecular conformational selection through ligand binding, need to be developed on a case-by-case basis by the ITC user. In this paper we provide an algorithm (and a link to our MATLAB program) for the nonlinear regression analysis of a multiple-binding-site model with up to four overlapping binding equilibria. Error analysis demonstrates that fitting ITC data for multiple parameters (e.g., up to nine parameters in the three-binding-site model) yields thermodynamic parameters with acceptable accuracy.