Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

A Phase Grating Approach to Modeling Surface Diffusion in FDTD Room Acoustics Simulations

In this paper, a method for modeling diffusive boundaries in finite difference time domain (FDTD) room acoustics simulations with the use of impedance filters is presented. The proposed technique is based on the concept of phase grating diffusers, and realized by designing boundary impedance filters from normal-incidence reflection filters with added delay. These added delays, that correspond to the diffuser well depths, are varied across the boundary surface, and implemented using Thiran allpass filters. The proposed method for simulating sound scattering is suitable for modeling high frequency diffusion caused by small variations in surface roughness and, more generally, diffusers characterized by narrow wells with infinitely thin separators. This concept is also applicable to other wave-based modeling techniques. The approach is validated by comparing numerical results for Schroeder diffusers to measured data. In addition, it is proposed that irregular surfaces are modeled by shaping them with Brownian noise, giving good control over the sound scattering properties of the simulated boundary through two parameters, namely the spectral density exponent and the maximum well depth.

Molecular Modeling on Inhibitor Complexes and Active-Site Dynamics of Cytochrome P450 C17, a Target for Prostate Cancer Therapy

A molecular model for the P450 enzyme cytochrome P450 C17 (CYP17) is presented based on sequence alignments of multiple template structures and homology modeling. This enzyme plays a central role in the biosynthesis of testosterone and is emerging as a major target in prostate cancer, with the recently developed inhibitor abiraterone currently in advanced clinical trials. The model is described in detail, together with its validation, by providing structural explanations to available site-directed mutagenesis data. The CYP17 molecule in this model is in the form of a triangular prism, with an edge of similar to 55 angstrom and a thickness of similar to 37 angstrom. It is predominantly helical, comprising 13 alpha helices interspersed by six 3(10) helices and 11 beta-sheets. Multinanosecond molecular dynamics simulations in explicit solvent have been carried out, and principal components analysis has been used to reveal the details of dynamics around the active site. Coarse-grained methods have also been used to verify low-frequency motions, which have been correlated with active-site gating. The work also describes the results of docking synthetic inhibitors, including the drug abiraterone and the natural substrate pregnenolone, in the CYP17 active site together with molecular dynamics simulations on the complexes. (C) 2010 Elsevier Ltd. All rights reserved.

The triazatruxene derivative azatrux binds to the parallel form of the human telomeric G-quadruplex under molecular crowding conditions: Biophysical and molecular modeling studies

The present study has employed a combination of spectroscopic, calorimetric and computational methods to explore the binding of the three side-chained triazatruxene derivative, termed azatrux, to a human telomeric G-quadruplex sequence, under conditions of molecular crowding. The binding of azatrux to the tetramolecular parallel [d(TGGGGT)](4) quadruplex in the presence and absence of crowding conditions, was also characterized. The data indicate that azatrux binds in an end-stacking mode to the parallel G-quadruplex scaffold and highlights the key structural elements involved in the binding. The selectivity of azatrux for the human telomeric G-quadruplex relative to another biologically relevant G-quadruplex (c-Kit87up) and to duplex DNA was also investigated under molecular crowding conditions, showing that azatrux has good selectivity for the human telomeric G-quadruplex over the other investigated DNA structures. (C) 2011 Elsevier Masson SAS. All rights reserved.

Molecular modeling of calixarenes with group I metal ions

Molecular mechanics calculations have been used to model the geometries of the complexes of Group I metal ions with calix[n]arenes (n = 4,5). A simple procedure in which the calixarene atoms are assigned partial charges on the basis of AM1 calculations and the metal ions are allowed to bind electrostatically to the calixarenes produces surprising good results when the resulting structures are compared to known crystallographic data on the complexes. Encapsulated solvent molecules and/or counterions can be included in the calculations and, indeed, are necessary to reproduce the X-ray data.

Larval Transport Modeling of Deep-Sea Invertebrates Can Aid the Search for Undiscovered Populations

Background: Many deep-sea benthic animals occur in patchy distributions separated by thousands of kilometres, yet because deep-sea habitats are remote, little is known about their larval dispersal. Our novel method simulates dispersal by combining data from the Argo array of autonomous oceanographic probes, deep-sea ecological surveys, and comparative invertebrate physiology. The predicted particle tracks allow quantitative, testable predictions about the dispersal of benthic invertebrate larvae in the south-west Pacific. Principal Findings: In a test case presented here, using non-feeding, non-swimming (lecithotrophic trochophore) larvae of polyplacophoran molluscs (chitons), we show that the likely dispersal pathways in a single generation are significantly shorter than the distances between the three known population centres in our study region. The large-scale density of chiton populations throughout our study region is potentially much greater than present survey data suggest, with intermediate ‘stepping stone’ populations yet to be discovered. Conclusions/Significance: We present a new method that is broadly applicable to studies of the dispersal of deep-sea organisms. This test case demonstrates the power and potential applications of our new method, in generating quantitative, testable hypotheses at multiple levels to solve the mismatch between observed and expected distributions: probabilistic predictions of locations of intermediate populations, potential alternative dispersal mechanisms, and expected population genetic structure. The global Argo data have never previously been used to address benthic biology, and our method can be applied to any non-swimming larvae of the deep-sea, giving information upon dispersal corridors and population densities in habitats that remain intrinsically difficult to assess.

Modeling of subsurface pulsed radar for nondestructive testing of structures

The use of pulsed radar for investigating the integrity of structural elements is gaining popularity and becoming firmly established as a nondestructive test method in civil engineering. Difficulties can often arise in the interpretation of results obtained, particularly where internal details are relatively complex. One approach that can be used to understand and evaluate radar results is through numerical modeling of signal propagation and reflection. By comparing the results of a numerical modeling with those from field measurements, engineers can gain valuable insight into the probable features embedded beneath the surface of a structural element. This paper discusses a series of numerical techniques for modeling subsurface radar and compares the precision of the results with those taken from real field data. It is found that more complex problems require more sophisticated analysis techniques to obtain realistic results, with a consequential increase in the computational resources to carry out the modeling.

A micromechanics approach for damage modeling of polymer matrix composites

A new model for damage evolution in polymer matrix composites is presented. The model is based on a combination of two constituent-level models and an interphase model. This approach reduces the number of empirical parameters since the two constituent- level models are formulated for isotropic materials, namely fiber and matrix. Decomposition of the state variables down to the micro-scale is accomplished by micromechanics. Phenomenological damage evolution models are then postulated for each constituent. Determination of material parameters is made from available experimental data. The required experimental data can be obtained with standard tests. Comparison between model predictions and additional experimental data is presented.

Molecular modeling of N-terminal domains of NMDA-receptor. Study of ligand binding with N-terminal domains

Using the molecular-graphic complex Sybyl6.7.2, computational construction of spatial models for N-terminal domains (of NR1- and NR2B-subunits) of NMDA-receptor was conducted. On the basis of the constructed models and also CoMFA method the conclusion is made about presence of the binding site for the compounds similar to iphenprodyl in two N-terminal domains of NR1- and NR2B-subunits. The obtained data can be used for constructing new ligands.

MODELING TRENDS IN SOCIAL FLUIDITY - THE CORE MODEL AND A MEASURED-VARIABLE APPROACH COMPARED

In this paper we employ Erikson and Goldthorpe's core model of social fluidity and a 'measured variable' approach to analyse trends in social mobility among men in the Republic of Ireland. Our analyses provide no evidence that the changes associated with industrialization have led to the increases in social fluidity predicted by the liberal theory of industrialism. The measured-variable approach we employ consistently provides a better fit to the Irish data than the core model. The application of the former model points to a degree of importance of the hierarchy dimension which is not captured adequately by the core model. It also suggests that the well-known distinctiveness of the Irish social mobility regime is open to explanation in terms of general dimensions rather than the peculiarities of the Irish case.

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

Development and assessment of a robust optimization metric for WAMPAC modeling

A PMU based WAMS is to be placed on a weakly coupled section of distribution grid, with high levels of distributed generation. In anticipation of PMU data a Siemens PSS/E model of the electrical environment has been used to return similar data to that expected from the WAMS. This data is then used to create a metric that reflects optimization, control and protection in the region. System states are iterated through with the most desirable one returning the lowest optimization metric, this state is assessed against the one returned by PSS/E under normal circumstances. This paper investigates the circumstances that trigger SPS in the region, through varying generation between 0 and 110% and compromising the network through line loss under summer minimum and winter maximum conditions. It is found that the optimized state can generally tolerate an additional 2 MW of generation (3% of total) before encroaching the same thresholds and in one instance moves the triggering to 100% of generation output.

Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions

This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.

Human body shadowing in cellular device-to-device communications: channel modeling using the shadowed κ−μ fading model

Using device-to-device communications as an underlay for cellular communications will provide an exciting opportunity to increase network capacity as well as improving spectral efficiency. The unique geometry of device-to-device links, where user equipment is often held or carried at low elevation and in close proximity to the human body, will mean that they are particularly susceptible to shadowing events caused not only by the local environment but also by the user's body. In this paper, the shadowed κ - μ fading model is proposed, which is capable of characterizing shadowed fading in wireless communication channels. In this model, the statistics of the received signal are manifested by the clustering of multipath components. Within each of these clusters, a dominant signal component with arbitrary power may exist. The resultant dominant signal component, which is formed by the phasor addition of these leading contributions, is assumed to follow a Nakagami- m distribution. The probability density function, moments, and the moment-generating function are also derived. The new model is then applied to device-to-device links operating at 868 MHz in an outdoor urban environment. It was found that shadowing of the resultant dominant component can vary significantly depending upon the position of the user equipment relative to the body and the link geometry. Overall, the shadowed κ - μ fading model is shown to provide a good fit to the field data as well as providing a useful insight into the characteristics of the received signal.