Artificial neural system modeling of Monte Carlo simulations of polymers

In this work, a neural network was used to learn features in potential energy surfaces and relate those features to conformational properties of a series of polymers. Specifically, we modeled Monte Carlo simulations of 20 polymers in which we calculated the characteristic ratio and the temperature coefficient of the characteristic ratio for each polymer. We first created 20 rotational potential energy surfaces using MNDO procedures and then used these energy surfaces to produce 10000 chains, each chain 100 bonds long. From these results we calculated the mean-square end-to-end distance, the characteristic ratio and its corresponding temperature coefficient. A neural network was then used to model the results of these Monte Carlo calculations. We found that artificial neural network simulations were highly accurate in predicting the outcome of the Monte Carlo calculations for polymers for which it was not trained. The overall average error for prediction of the characteristic ratio was 4,82%, and the overall average error for prediction of the temperature coefficient was 0,89%.     

Intraspecific competition of predator for prey with variable rates in protected areas

Nonlinear Dynamics - In this present study, we systematically explore the periodicity (almost periodic nature) of a dynamical system in time-varying environment, which portrays a special case of...     

On the occurrence of polytypism in single crystal CdTe thin films

Cadmium telluride thin films have been found to exhibit polytypism. The polytypes are formed when the as grown amorphous CdTe thin films undergo amorphous to crystalline transformation. The transformed single crystal regions correspond to different polytypes. Besides the well known zinc blende type 3 C cubic phase and less often found wurtzite type 2 H phase, four new polytypes (5 H, 6 H, 6 R and 15 R) the only ones known to-date have been found in the present investigation. In addition to the new polytypes, a new structural variant has also been found. This has the same ‘c’ parameter as that of the 2 H phase but has its ‘a’ lattice parameter as ‘a₀ \documentclass{article}\pagestyle{empty}\begin{document}$ a_{\rm o} \sqrt {3} $\end{document}’ (a₀ being the common lattice parameter of the polytypes). A feasible mechanism making the formation of polytypes intelligible has been suggested.     

A ghost fluid Lattice Boltzmann method for complex geometries

A ghost fluid Lattice Boltzmann method (GF‐LBM) is developed in this study to represent complex boundaries in Lattice Boltzmann simulations of fluid flows. Velocity and density values at the ghost points are extrapolated from the fluid interior and domain boundary via obtaining image points along the boundary normal inside the fluid domain. A general bilinear interpolation algorithm is used to obtain values at image points which are then extrapolated to ghost nodes thus satisfying hydrodynamic boundary conditions. The method ensures no‐penetration and no‐slip conditions at the boundaries. Equilibrium distribution functions at the ghost points are computed using the extrapolated values of the hydrodynamic variables, while non‐equilibrium distribution functions are extrapolated from the interior nodes. The method developed is general, and is capable of prescribing Dirichlet as well as Neumann boundary conditions for pressure and velocity. Consistency and second‐order accuracy of the method are established by running three test problems including cylindrical Couette flow, flow between eccentric rotating cylinders and flow over a cylinder in a confined channel. Copyright © 2011 John Wiley & Sons, Ltd.     

Majumdar-Papapetrou class of nonstatic cylindrically symmetric Brans-Dicke-Maxwell fields

We have obtained relations between certain components of the metric and the electromagnetic potentials for source-free Brans-Dicke-Maxwell fields described by a nonstatic cylindrically symmetric Einstein-Rosen metric. These are important, in the sense that they generate a class of solutions that in a way can be said to belong to the class generated by similar relations obtained by Majumdar [1] and Papapetrou [2] for generalized static Einstein-Maxwell fields. The relations have further been used to reduce the B-D Maxwell equations to B-D vacuum equations and vice versa.     

Solid-fluid transition in a granular shear flow

The rheology of a granular shear flow is studied in a quasi-2D rotating cylinder. Measurements are carried out near the midpoint along the length of the surface flowing layer where the flow is steady and nonaccelerating. Streakline photography and image analysis are used to obtain particle velocities and positions. Different particle sizes and rotational speeds are considered. We find a sharp transition in the apparent viscosity (eta) variation with rms velocity (u). Below the transition depth we find that the rms velocity decreases with depth and eta proportional to u(-1.5) for all the different cases studied. The material approaches an amorphous solidlike state deep in the layer. The velocity distribution is Maxwellian above the transition point and a Poisson velocity distribution is obtained deep in the layer. The results indicate a sharp transition from a fluid to a fluid + solid state with decreasing rms velocity.     

An efficient evolutionary multi-objective framework for MEMS design optimisation: validation, comparison and analysis

The application of multi objective evolutionary algorithms (MOEA) in the design optimisation of microelectromechanical systems (MEMS) is of particular interest in this research. MOEA is a class of soft computing techniques of biologically inspired stochastic algorithms, which have proved to outperform their conventional counterparts in many design optimisation tasks. MEMS designers can utilise a variety of multi-disciplinary design tools that explore a complex design search space, however, still follow the traditional trial and error approaches. The paper proposes a novel framework, which couples both modelling and analysis tools to the most referenced MOEAs (NSGA-II and MOGA-II). The framework is validated and evaluated through a number of case studies of increasing complexity. The research presented in this paper unprecedentedly attempts to compare the performances of the mentioned algorithms in the application domain. The comparative study shows significant insights into the behaviour of both of the algorithms in the design optimisation of MEMS. The paper provides extended discussions and analysis of the results showing, overall, that MOGA-II outperforms NSGA-II, for the selected case studies.     

Synthesis of symmetrical 1,5-disubstituted granatanines

A general entry into symmetrical 1,5-disubstituted granatanines that involves an alkylative ring-closure on a 2,6-bis enolate piperidine intermediate is described.