Ferroelectric liquid crystal mixtures containing chiral ether and ester compounds with the 2-arylidene-p-menthan-3-one skeleton

Some 1R,4R-2-(4-phenylbenzylidene)-p-menthane-3-one derivatives containing the ether or ester linking group between benzene rings of the arylidene fragment have been studied as chiral dopants in ferroelectric liquid crystal systems based on the eutectic mixture (1:1) of two phenylbenzoate derivatives CmH2m+1OC6H4COOC6 H4OCnH2n+1 (n = 6; m = 8, 10). The ferroelectric properties of these compositions (spontaneous polarization, rotation viscosity, smectic tilt angle as well as quantitative characteristics of their concentration dependences) were compared with those for systems including chiral dopants containing no linking group. Ferroelectric parameters of the induced ferroelectric compositions studied have been shown to depend essentially on the presence of the linking group between benzene rings and its nature as well as on the number of the benzene rings in the rigid molecular core of the chiral dopants used. For all ferroelectric liquid crystal systems studied, the influence of the chiral dopants on the thermal stability of N*, SmA and SmC* mesophases has been quantified. The influence of the linking group nature in the dopant molecules on the characteristics of the systems studied is discussed taking into account results of the conformational analysis carried out by the semi-empirical AM1 and PM3 methods.

New chiral ether derivatives of 2-arylidene-p-menthane-3-ones as components of induced ferroelectric systems

New 2-arylidene-p-menthane-3-ones containing the ether bridging group in the arylidene fragment have been synthesized and studied as chiral dopants in ferroelectric liquid crystal mixtures. The ferroelectric properties of these compositions were compared with those for compositions including chiral dopants that do not contain any bridging group. The influence of bridging group and terminal alkyl substituent length in the dopant molecule on the ferroelectric parameters of systems studied is discussed. © 2001 OPA (Overseas Publishers Association) N.V. Published by license under the Gordon and Breach Science Publishers imprint, a member of the Taylor & Francis Group,.

Finite element solution of Navier Stokes equations adapted to a priori error estimates

The paper is based on qualitative properties of the solution of the Navier-Stokes equations for incompressible fluid, and on properties of their finite element solution. In problems with corner-like singularities (e.g. on the well-known L-shaped domain) usually some adaptive strategy is used. In this paper we present an alternative approach. For flow problems on domains with corner singularities we use the a priori error estimates and asymptotic expansion of the solution to derive an algorithm for refining the mesh near the corner. It gives very precise solution in a cheap way. We present some numerical results.

Monte Carlo-Based Bayesian group object tracking and causal reasoning

We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts. © 2013 Springer-Verlag Berlin Heidelberg.

Approximate Bayesian Computation for Smoothing

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. © Taylor & Francis Group, LLC.