Adsorption of benzene on graphitized thermal carbon black: Reduction of the quadrupole moment in the adsorbed phase

The performance of intermolecular potential models on the adsorption of benzene on graphitized thermal carbon black at various temperatures is investigated. Two models contain only dispersive sites, whereas the other two models account explicitly for the dispersive and electrostatic sites. Using numerous data in the literature on benzene adsorption on graphitized thermal carbon black at various temperatures, we have found that the effect of surface mediation on interaction between adsorbed benzene molecules must be accounted for to describe correctly the adsorption isotherm as well as the isosteric heat. Among the two models with partial charges tested, the WSKS model of Wick et at. I that has only six dispersive sites and three discrete partial charges is better than the very expensive all-atom model of Jorgensen and Severance.(2) Adsorbed benzene molecules on graphitized thermal carbon black have a complex orientation with respect to distance from the surface and also with respect to loading. At low loadings, they adopt the parallel configuration relative to the graphene surface, whereas at higher loadings (still less than monolayer coverage) some molecules adopt a slant orientation to maximize the fluid-fluid interaction. For loadings in the multilayer region, the orientation of molecules in the first layer is influenced by the presence of molecules in the second layer. The data that are used in this article come from the work of Isirikyan and Kiselev,(3) Pierotti and Smallwood,(4) Pierce and Ewing,(5) Belyakova, Kiselev, and Kovaleva,(6) and Carrott et al.(7)

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.

Transpiration efficiency in a segregating population of sunflower: Inheritance, correlation with other traits and association with hybrid grain yield

Plants incorporate isotopes of carbon into their tissue at different rates because of discrimination against 13C relative to 12C during photosynthesis. This difference in discrimination has been negatively correlated with transpiration efficiency (TE) in many C3 species and so, carbon isotope discrimination (Δ) of leaf tissues has been proposed as a potential tool for selecting genotypes with improved performance under water limited conditions. The relationship between Δ and TE in sunflower has been described previously using diverse genotypes, but this relationship has not been investigated with material selected from a segregating population. In this study, the TE of twenty recombinant inbred lines from a population (HAR4 x SA52) segregating for Δ was evaluated in a rainout shelter experiment. A strong negative genetic correlation between TE and Δ was observed (rg = -0.58), confirming previous studies of sunflower with unrelated lines. In addition, TE was strongly correlated to plant height at the final harvest (rg = 0.64) and TDW (rg = 0.58), and moderately correlated to SLW (rg = 0.46) and SPAD (rg = 0.21) but not leaf number (rg = 0.02). Estimates of narrow sense heritability of TE and Δ were very high (0.82 and 0.77, respectively) suggesting that selection for these traits could occur in early generations of segregating populations. Grain yield evaluations under field conditions of hybrids contrasting for Δ showed that low Δ (high TE) hybrids had a yield advantage between 22-35% in dry environments where the yield was less than 2t/ha. While this level of yield advantage may not be realized in commercial breeding programs, computer simulations suggest that 10-15% yield improvements may be possible. Low Δ material selected from the population HAR4 x SA52 has been distributed to private seed companies for further evaluation.

Effect of line of sight propagation on capacity of an indoor MIMO system

In this paper the performance of a multiple input multiple output (MIMO) wireless communication system operating in an indoor environment, featuring both line of sight (LOS) and non-line of sight (NLOS) signal propagation, is assessed. In the model the scattering objects are assumed to be uniformly distributed in an area surrounding the transmitting and receiving array antennas. Mutual coupling effects in the arrays are treated in an exact manner. However interactions with scattering objects are taken into account via a single bounce approach. Computer simulations are carried out for the system capacity for varying inter-element spacing in the receiving array for assumed values of LOS/NLOS power fraction and signal to noise ratio (SNR).

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

Assessing an FPGA implemented MIMO testbed with the use of channel emulator

In this paper, a channel emulator for assessing the performance of MIMO testbed implemented in a field programmable gate array technology is described. The FPGA based MIMO system includes a signal generator, modulation/demodulation and space time coding/decoding modules. The emulator uses information about a wireless channel from computer simulations or actual measurements. In simulations, a single bounce scattering model for an indoor environment is applied. The generated data is stored in the FPGA board. The tests are performed for a 2times2 MIMO system that uses Alamouti scheme for space coding/decoding. The performed tests show proper operation of the FPGA implemented MIMO testbed. Good agreement between the results using measured and simulated channel data is obtained.

XSophe-Sophe-XeprView (R). A computer simulation software suite (v. 1.1.3) for the analysis of continuous wave EPR spectra

The XSophe-Sophe-XeprView((R)) computer simulation software suite enables scientists to easily determine spin Hamiltonian parameters from isotropic, randomly oriented and single crystal continuous wave electron paramagnetic resonance (CW EPR) spectra from radicals and isolated paramagnetic metal ion centers or clusters found in metalloproteins, chemical systems and materials science. XSophe provides an X-windows graphical user interface to the Sophe programme and allows: creation of multiple input files, local and remote execution of Sophe, the display of sophelog (output from Sophe) and input parameters/files. Sophe is a sophisticated computer simulation software programme employing a number of innovative technologies including; the Sydney OPera HousE (SOPHE) partition and interpolation schemes, a field segmentation algorithm, the mosaic misorientation linewidth model, parallelization and spectral optimisation. In conjunction with the SOPHE partition scheme and the field segmentation algorithm, the SOPHE interpolation scheme and the mosaic misorientation linewidth model greatly increase the speed of simulations for most spin systems. Employing brute force matrix diagonalization in the simulation of an EPR spectrum from a high spin Cr(III) complex with the spin Hamiltonian parameters g(e) = 2.00, D = 0.10 cm(-1), E/D = 0.25, A(x) = 120.0, A(y) = 120.0, A(z) = 240.0 x 10(-4) cm(-1) requires a SOPHE grid size of N = 400 (to produce a good signal to noise ratio) and takes 229.47 s. In contrast the use of either the SOPHE interpolation scheme or the mosaic misorientation linewidth model requires a SOPHE grid size of only N = 18 and takes 44.08 and 0.79 s, respectively. Results from Sophe are transferred via the Common Object Request Broker Architecture (CORBA) to XSophe and subsequently to XeprView((R)) where the simulated CW EPR spectra (1D and 2D) can be compared to the experimental spectra. Energy level diagrams, transition roadmaps and transition surfaces aid the interpretation of complicated randomly oriented CW EPR spectra and can be viewed with a web browser and an OpenInventor scene graph viewer.

RWMODEL II: Computer simulation of the Rescorla-Wagner model of Pavlovian conditioning

RWMODEL II simulates the Rescorla-Wagner model of Pavlovian conditioning. It is written in Delphi and runs under Windows 3.1 and Windows 95. The program was designed for novice and expert users and can be employed in teaching, as well as in research. It is user friendly and requires a minimal level of computer literacy but is sufficiently flexible to permit a wide range of simulations. It allows the display of empirical data, against which predictions from the model can be validated.

Stochastic gauges in quantum dynamics for many-body simulations

Quantum dynamics simulations can be improved using novel quasiprobability distributions based on non-orthogonal Hermitian kernel operators. This introduces arbitrary functions (gauges) into the stochastic equations. which can be used to tailor them for improved calculations. A possible application to full quantum dynamic simulations of BEC's is presented. (C) 2001 Elsevier Science B.V. All rights reserved.

Application of X-ray microtomography to numerical simulations of agglomerate breakage by distinct element method

Computer-aided tomography has been used for many years to provide significant information about the internal properties of an object, particularly in the medical fraternity. By reconstructing one-dimensional (ID) X-ray images, 2D cross-sections and 3D renders can provide a wealth of information about an object's internal structure. An extension of the methodology is reported here to enable the characterization of a model agglomerate structure. It is demonstrated that methods based on X-ray microtomography offer considerable potential in the validation and utilization of distinct element method simulations also examined.

Quantum phase-space simulations of fermions and bosons

We introduce a unified Gaussian quantum operator representation for fermions and bosons. The representation extends existing phase-space methods to Fermi systems as well as the important case of Fermi-Bose mixtures. It enables simulations of the dynamics and thermal equilibrium states of many-body quantum systems from first principles. As an example, we numerically calculate finite-temperature correlation functions for the Fermi Hubbard model, with no evidence of the Fermi sign problem. (c) 2005 Elsevier B.V. All rights reserved.

Adsorption of Lennard-Jones fluids in carbon slit pores of a finite length. A computer simulation study

The adsorption of simple Lennard-Jones fluids in a carbon slit pore of finite length was studied with Canonical Ensemble (NVT) and Gibbs Ensemble Monte Carlo Simulations (GEMC). The Canonical Ensemble was a collection of cubic simulation boxes in which a finite pore resides, while the Gibbs Ensemble was that of the pore space of the finite pore. Argon was used as a model for Lennard-Jones fluids, while the adsorbent was modelled as a finite carbon slit pore whose two walls were composed of three graphene layers with carbon atoms arranged in a hexagonal pattern. The Lennard-Jones (LJ) 12-6 potential model was used to compute the interaction energy between two fluid particles, and also between a fluid particle and a carbon atom. Argon adsorption isotherms were obtained at 87.3 K for pore widths of 1.0, 1.5 and 2.0 nm using both Canonical and Gibbs Ensembles. These results were compared with isotherms obtained with corresponding infinite pores using Grand Canonical Ensembles. The effects of the number of cycles necessary to reach equilibrium, the initial allocation of particles, the displacement step and the simulation box size were particularly investigated in the Monte Carlo simulation with Canonical Ensembles. Of these parameters, the displacement step had the most significant effect on the performance of the Monte Carlo simulation. The simulation box size was also important, especially at low pressures at which the size must be sufficiently large to have a statistically acceptable number of particles in the bulk phase. Finally, it was found that the Canonical Ensemble and the Gibbs Ensemble both yielded the same isotherm (within statistical error); however, the computation time for GEMC was shorter than that for canonical ensemble simulation. However, the latter method described the proper interface between the reservoir and the adsorbed phase (and hence the meniscus).

Towards realistic simulations of lava dome growth using the level set method

The level set method has been implemented in a computational volcanology context. New techniques are presented to solve the advection equation and the reinitialisation equation. These techniques are based upon an algorithm developed in the finite difference context, but are modified to take advantage of the robustness of the finite element method. The resulting algorithm is tested on a well documented Rayleigh–Taylor instability benchmark [19], and on an axisymmetric problem where the analytical solution is known. Finally, the algorithm is applied to a basic study of lava dome growth.