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.

Nonparametric mixtures of factor analyzers

The mixtures of factor analyzers (MFA) model allows data to be modeled as a mixture of Gaussians with a reduced parametrization. We present the formulation of a nonparametric form of the MFA model, the Dirichlet process MFA (DPMFA). The proposed model can be used for density estimation or clustering of high dimensiona data. We utilize the DPMFA for clustering the action potentials of different neurons from extracellular recordings, a problem known as spike sorting. DPMFA model is compared to Dirichlet process mixtures of Gaussians model (DPGMM) which has a higher computational complexity. We show that DPMFA has similar modeling performance in lower dimensions when compared to DPGMM, and is able to work in higher dimensions. ©2009 IEEE.

Perylene-based fluorescent liquid crystal dye guest-host mixtures

We present a study on a series of dye guest-host mixtures using fluorescent perylene-based molecules as the guest dye in an organosiloxane host. These hosts have temperature-independent switching, at room temperature, through 90° for fields of the order of 10 Vrms/μm. Perylene molecules have been grafted onto the organosiloxane moiety via an alkyl spacer producing novel and rugged fluorescent dyes that are readily miscible in the host. Micro-separation of the low molar mass siloxane groups in the mesophases tend to form smectic phases. These planes produce an effective two-dimensional polymer backbonethat engenders the rugged mechanical properties of polymeric liquid crystals onto these low molar mass ferroelectric liquid crystals. In this study we show how the introduction of the dye molecules affects the electro-optic properties of the organosiloxane host. © 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,.

Filtration in some tropical intertidal bivalves exposed to mercury and cadmium mixtures

Three species of intertidal filter feeding bivalves (Modiolus carvalhoi, Modiolus sp. and Donax spiculum) exposed to mercury and cadmium filtered significantly less volume of water under individual metal and metal mixture stress. Mercury and cadmium in mixtures interacted additively and more than additively (Synergism) in depressing the filtration rate of the bivalves.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.