Virtual state scattering with cold electrons: para-xylene and para-difluorobenzene

The scattering of electrons with kinetic energies down to a few meV by para-xylene and para-difluorobenzene has been observed experimentally with an electron beam energy resolution of 0.95 to 1.5 meV (full width half maximum). At low electron energies the collisions can be considered as cold scattering events because the de Broglie wavelength of the electron is considerably larger than the target dimensions. The scattering cross sections measured rise rapidly at low energy due to virtual state scattering. The nature of this scattering process is discussed using s- and p-wave phase shifts derived from the experimental data. Scattering lengths are derived of, respectively, -9.5+/-0.5 and -8.0+/-0.5 a.u. for para-xylene and para-difluorobenzene. The virtual state effect is interpreted in terms of nuclear diabatic and partially adiabatic models, involving the electronic and vibronic symmetries of the unoccupied orbitals in the target species. The concept of direct and indirect virtual state scattering is introduced, through which the present species, in common with carbon dioxide and benzene, scatter through an indirect virtual state process, whereas other species, such as perfluorobenzene, scatter through a direct process. (C) 2005 American Institute of Physics.

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.

A Virtual Inspection Technique for Assessing the Dimensional Accuracy of Forged Compressor Blades Using FE Modeling and CMM Inspection

This paper presents research for developing a virtual inspection system that evaluates the dimensional tolerance of forged aerofoil blades formed using the finite element (FE) method. Conventional algorithms adopted by modern coordinate measurement processes have been incorporated with the latest free-form surface evaluation techniques to provide a robust framework for the dimensional inspection of FE aerofoil models. The accuracy of the approach had been verified with a strong correlation obtained between the virtual inspection data and coordinate measurement data from corresponding aerofoil components.

Analytical Elastic Stiffness Model for 3D Woven Orthogonal Interlock Composites

This research presents the development of an analytical model to predict the elastic stiffness performance of orthogonal interlock bound 3D woven composites as a consequence of altering the weaving parameters and constituent material types. The present approach formulates expressions at the micro level with the aim of calculating more representative volume fractions of a group of elements to the layer. The rationale in representing the volume fractions within the unit cell more accurately was to improve the elastic stiffness predictions compared to existing analytical modelling approaches. The models developed in this work show good agreement between experimental data and improvement on existing predicted values by models published in literature.

Discovery of novel Agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening

The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on two-dimensional (2D) similarity, three-dimensional (3D) pharmacophore searches, and docking studies by using the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compounds followed by extraction of structural neighbors of functionally confirmed hits resulted in identification of 15 compounds active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Parametric FIR Design of Propagation Loss Filters in Digital Waveguide String Models

One of the attractive features of sound synthesis by physical modeling is the potential to build acoustic-sounding digital instruments that offer more flexibility and different options in its design and control than their real-life counterparts. In order to develop such virtual-acoustic instruments, the models they are based on need to be fully parametric, i.e., all coefficients employed in the model are functions of physical parameters that are controlled either online or at the (offline) design stage. In this letter we show how propagation losses can be parametrically incorporated in digital waveguide string models with the use of zero-phase FIR filters. Starting from the simplest possible design in the form of a three-tap FIR filter, a higher-order FIR strategy is presented and discussed within the perspective of string sound synthesis with digital waveguide models.

Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads. 

The four I-model for scaffolding the professional development of experienced teachers in the use of virtual learning environments for classroom teaching.

Models of professional development for teachers have been criticized for not being embedded in the context in which teachers are familiar, namely their own classrooms. This paper discusses an adapted-Continuous Practice Improvement model, which qualitative findings indicate was effective in facilitating the transfer of creative and innovative teaching approaches from the expert or Resident Teacher’s school to the novice or Visiting Teachers’ classrooms over the duration of the project. The cultural shift needed to embed and extend the use of online teaching across the school was achieved through the positive support and commitment of the principals in the Visiting Teachers’ schools, combined with the success of the professional development activities offered by the Visiting Teachers to their school-based colleagues.

A spatio-temporal 2D-models framework for human pose recovery in monocular sequences

This paper addresses the pose recovery problem of a particular articulated object: the human body. In this model-based approach, the 2D-shape is associated to the corresponding stick figure allowing the joint segmentation and pose recovery of the subject observed in the scene. The main disadvantage of 2D-models is their restriction to the viewpoint. To cope with this limitation, local spatio-temporal 2D-models corresponding to many views of the same sequences are trained, concatenated and sorted in a global framework. Temporal and spatial constraints are then considered to build the probabilistic transition matrix (PTM) that gives a frame to frame estimation of the most probable local models to use during the fitting procedure, thus limiting the feature space. This approach takes advantage of 3D information avoiding the use of a complex 3D human model. The experiments carried out on both indoor and outdoor sequences have demonstrated the ability of this approach to adequately segment pedestrians and estimate their poses independently of the direction of motion during the sequence. (c) 2008 Elsevier Ltd. All rights reserved.

CoMFA and homology-based models of the glycine binding site of N-methyl-D-aspartate receptor

Homology modeling was used to build 3D models of the N-methyl-D-aspartate (NMDA) receptor glycine binding site on the basis of an X-ray structure of the water-soluble AMPA-sensitive receptor. The docking of agonists and antagonists to these models was used to reveal binding modes of ligands and to explain known structure-activity relationships. Two types of quantitative models, 3D-QSAR/CoMFA and a regression model based on docking energies, were built for antagonists (derivatives of 4-hydroxy-2-quinolone, quinoxaline-2,3-dione, and related compounds). The CoMFA steric and electrostatic maps were superimposed on the homology-based model, and a close correspondence was marked. The derived computational models have permitted the evaluation of the structural features crucial for high glycine binding site affinity and are important for the design of new ligands.

Modelling 3D camera movement for vibration characterisation and multiple object identification with application to lighting assessment

Utilising cameras as a means to survey the surrounding environment is becoming increasingly popular in a number of different research areas and applications. Central to using camera sensors as input to a vision system, is the need to be able to manipulate and process the information captured in these images. One such application, is the use of cameras to monitor the quality of airport landing lighting at aerodromes where a camera is placed inside an aircraft and used to record images of the lighting pattern during the landing phase of a flight. The images are processed to determine a performance metric. This requires the development of custom software for the localisation and identification of luminaires within the image data. However, because of the necessity to keep airport operations functioning as efficiently as possible, it is difficult to collect enough image data to develop, test and validate any developed software. In this paper, we present a technique to model a virtual landing lighting pattern. A mathematical model is postulated which represents the glide path of the aircraft including random deviations from the expected path. A morphological method has been developed to localise and track the luminaires under different operating conditions. © 2011 IEEE.

The phase diagram of water at negative pressures: Virtual ices

The phase diagram of water at negative pressures as obtained from computer simulations for two models of water, TIP4P/2005 and TIP5P is presented. Several solid structures with lower densities than ice Ih, so-called virtual ices, were considered as possible candidates to occupy the negative pressure region of the phase diagram of water. In particular the empty hydrate structures sI, sII, and sH and another, recently proposed, low-density ice structure. The relative stabilities of these structures at 0 K was determined using empirical water potentials and density functional theory calculations. By performing free energy calculations and Gibbs-Duhem integration the phase diagram of TIP4P/2005 was determined at negative pressures. The empty hydrates sII and sH appear to be the stable solid phases of water at negative pressures. The phase boundary between ice Ih and sII clathrate occurs at moderate negative pressures, while at large negative pressures sH becomes the most stable phase. This behavior is in reasonable agreement with what is observed in density functional theory calculations.

Assessment of rolling resistance models in discrete element simulations

Particulate systems are of interest in many disciplines. They are often investigated using the discrete element method because of its capability to investigate particulate systems at the individual particle scale. To model the interaction between two particles and between a particle and a boundary, conventional discrete element models use springs and dampers in both the normal and tangential directions. The significance of particle rotation has been highlighted in both numerical studies and physical experiments. Several researchers have attempted to incorporate a rotational torque to account for the rolling resistance or rolling friction by developing different models. This paper presents a review of the commonly used models for rolling resistance and proposes a more general model. These models are classified into four categories according to their key characteristics. The robustness of these models in reproducing rolling resistance effects arising from different physical situations was assessed by using several benchmarking test cases. The proposed model can be seen to be more general and suitable for modelling problems involving both dynamic and pseudo-static regimes. An example simulation of the formation of a 2D sandpile is also shown. For simplicity, all formulations and examples are presented in 2D form, though the general conclusions are also applicable to 3D systems.

A Theoretical and Experimental Study of Resonance in a High Performance Engine Intake System: Part 2

The unsteady gas dynamic phenomena in a racecar airbox have been examined, and resonant tuning effects have been considered. A coupled 1D/3D analysis, using the engine simulation package Virtual 4-Stroke and the CFD package FLUENT, was used to model the engine and airbox. The models were experimentally validated. An airbox was designed with a natural frequency in the region of 75 Hz. A coupled 1D/3D analysis of the airbox and a Yamaha R6 4 cylinder engine predicted resonance at the single-cylinder induction frequency; 75 Hz at an engine speed of 9000 rpm.