Phonon anomalies due to strong-electronic correlations in layered organic metals

We show how the coupling between the phonons and electrons in a strongly correlated metal can result in phonon frequencies that have a nonmonotonic temperature dependence. Dynamical mean-field theory is used to study the Hubbard-Holstein model that describes the kappa-(BEDT-TTF)(2)X [where BEDT-TTF is bis-(ethylenedithia-tetrathiafulvalene)] family of superconducting molecular crystals. The crossover with increasing temperature from a Fermi liquid to a bad metal produces phonon anomalies that are relevant to recent Raman scattering and acoustic experiments.

Ferromagnetism, paramagnetism, and a Curie-Weiss metal in an electron-doped Hubbard model on a triangular lattice

Motivated by the unconventional properties and rich phase diagram of NaxCoO2 we consider the electronic and magnetic properties of a two-dimensional Hubbard model on an isotropic triangular lattice doped with electrons away from half-filling. Dynamical mean-field theory (DMFT) calculations predict that for negative intersite hopping amplitudes (t < 0) and an on-site Coulomb repulsion, U, comparable to the bandwidth, the system displays properties typical of a weakly correlated metal. In contrast, for t > 0 a large enhancement of the effective mass, itinerant ferromagnetism, and a metallic phase with a Curie-Weiss magnetic susceptibility are found in a broad electron doping range. The different behavior encountered is a consequence of the larger noninteracting density of states (DOS) at the Fermi level for t > 0 than for t < 0, which effectively enhances the mass and the scattering amplitude of the quasiparticles. The shape of the DOS is crucial for the occurrence of ferromagnetism as for t > 0 the energy cost of polarizing the system is much smaller than for t < 0. Our observation of Nagaoka ferromagnetism is consistent with the A-type antiferromagnetism (i.e., ferromagnetic layers stacked antiferromagnetically) observed in neutron scattering experiments on NaxCoO2. The transport and magnetic properties measured in NaxCoO2 are consistent with DMFT predictions of a metal close to the Mott insulator and we discuss the role of Na ordering in driving the system towards the Mott transition. We propose that the Curie-Weiss metal phase observed in NaxCoO2 is a consequence of the crossover from a bad metal with incoherent quasiparticles at temperatures T > T-* and Fermi liquid behavior with enhanced parameters below T-*, where T-* is a low energy coherence scale induced by strong local Coulomb electron correlations. Our analysis also shows that the one band Hubbard model on a triangular lattice is not enough to describe the unusual properties of NaxCoO2 and is used to identify the simplest relevant model that captures the essential physics in NaxCoO2. We propose a model which allows for the Na ordering phenomena observed in the system which, we propose, drives the system close to the Mott insulating phase even at large dopings.

Algebraic Bethe ansatz for the anisotropic supersymmetric U model

We present an algebraic Bethe ansatz for the anisotropic supersymmetric U model for correlated electrons on the unrestricted 4(L)-dimensional electronic Hilbert space x(n=l)(L)C(4)(where L is the lattice length). The supersymmetry algebra of the local Hamiltonian is the quantum superalgebra U-q[gl(2\1)] and the model contains two symmetry-preserving free real parameters; the quantization parameter q and the Hubbard interaction parameter U. The parameter U arises from the one-parameter family of inequivalent typical four-dimensional irreps of U-q[gl(2\1)]. Eigenstates of the model are determined by the algebraic Bethe ansatz on a one-dimensional periodic lattice.

Spin-orbit mixing and nephelauxetic effects in the electronic spectra of nickel(II)-encapsulating complexes involving nitrogen and sulfur donors

A study of spin-orbit mixing and nephelauxetic effects in the electronic spectra of nickel(II)-encapsulating complexes involving mixed nitrogen and sulfur donors is reported. As the number of sulfur donors is systematically varied through the series [Ni(N6-xSx)](2+) (x = 0-6), the spin-forbidden (3)A(2)g --> E-1(g) and (3)A(2g) --> (1)A(1g) transitions undergo a considerable reduction in energy whereas the spin-allowed transitions are relatively unchanged. The [Ni(diAMN(6)sar)](2+) and [Ni(AMN(5)Ssar)](2+) complexes exhibit an unusual band shape for the (3)A(2g) --> T-3(2g) transition which is shown to arise from spin-orbit mixing of the E spin-orbit levels associated with the E-1(g) and T-3(2g) states. A significant differential nephelauxetic effect also arises from the covalency differences between the t(2g) and e(g) orbitals with the result that no single set of Racah B and C interelectron repulsion parameters adequately fit the observed spectra. Using a differential covalency ligand-field model, the spectral transitions are successfully reproduced with three independent variables corresponding to 10Dq and the covalency parameters f(t) and f(e), associated with the t(2g) and e(g) orbitals, respectively. The small decrease in f(t) from unity is largely attributed to central-field covalency effects whereas the dramatic reduction in f(e) with increasing number of sulfur donors is a direct consequence of the increased metal-ligand covalency associated with the sulfur donors. Covalency differences between the t(2g) and e(g) orbitals also result in larger 10Dq values than those obtained simply from the energy of the (3)A(2g) --> T-3(2g) spin-allowed transition.

Geometric and electronic information from the spectroscopy of six-coordinate copper(II) compounds

The ground and excited state geometry of the six-coordinate copper(II) ion is examined in detail using the CuF64- and Cu(H2O)(6)(2+) complexes as examples. A variety of spectroscopic techniques are used to illustrate the relations between the geometric and electronic properties of these complexes through the characterization of their potential energy surfaces.

Role of cytokines in mediating mechanical hypernociception in a model of delayed-type hypersensitivity in mice

In the present study, we used the electronic version of the von Frey test to investigate the role of cytokines (TNF-alpha and IL-1 beta) and chemokines (KC/CXCL-1) in the genesis of mechanical hypernociception during antigen-induced inflammation in mice. The nociceptive test consisted of evoking a hindpaw flexion reflex with a hand-held force transducer (electronic anesthesiometer) adapted with a 0.5 mm(2) polypropylene tip. The intraplantar administration of methylated bovine serum albumin (mBSA) in previously immunized (IM), but not in sham-immunized (SI) mice, induced mechanical hypernociception in a dose-dependant manner. Hypernociception induced by antigen was reduced in animals pretreated with IL-lra and reparixin (a non-competitive allosteric inhibitor of CXCR2), and in TNF receptor type 1 deficient (TNFR1-/-) mice. Consistently, antigen challenge induced a time-dependent release of TNF-alpha, IL-1 beta and KC/CXCL-1 in IM, but not in SI, mice. Consistently, antigen challenge induced a time-dependent release of TNF-alpha, IL-1 beta and KC/CXCL-1 in IM, but not in SI, mice. The increase in TNF-alpha levels preceded the increase in IL-1 beta and KC/CXCL1. Antigen-induced release of IL-1 beta and KC/CXCL1 was reduced in TNFR1-/- mice, and TNF-alpha induced hypernociception was inhibited by IL-lra and reparixin. Hypernociception induced by IL-1 beta in immunized mice was inhibited by indomethacin, whereas KC/CXCL1-induced hypernociception was inhibited by indomethacin and guanethidine, Antigen-induced hypernociception was reduced by indomethacin and guanethidine and abolished by the two drugs combined. Together, these results suggest that inflammation associated with an adaptive immune response induces hypernociception that is mediated by an initial release of TNF-alpha, which triggers that subsequent release of IL-1 beta and KC/CXCL1. The latter cytokines in turn stimulate the release of the direct-acting final mediator, prostanoids and sympathetic amines. (C) 2008 European Federation of Chapters of the International Association for the Study of Pain. Published by Elsevier Ltd. All rights reserved.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

A unit cell waveguide model of a reflectarray formed by microstrip patches and slots

An equivalent unit cell waveguide approach (WGA) is described to obtain reflection coefficient phase curves for designing a microstrip patch reflectarray supported by a ground plane with periodic apertures or slots. Based on the presented theory, a computer algorithm for determining the reflection coefficient of a plane wave normally incident on a multi-layer structure of patches and apertures is developed. The validity of the developed algorithm is verified by comparing the obtained results with those published in the literature and the ones generated by Agilent High Frequency Structure Simulator (HFSS). A good agreement in all the presented examples is obtained, proving that the developed theory and computer algorithm can be an effective tool for designing multi-layer microstrip reflectarrays with a periodically perforated ground plane. (C) 2003 Wiley Periodicals, Inc.

The Sticky Information Macro Model: Beyond Perfect Foresight

Sticky information monetary models have been used in the macroeconomic literature to explain some of the observed features regarding inflation dynamics. In this paper, we explore the consequences of relaxing the rational expectations assumption usually taken in this type of model; in particular, by considering expectations formed through adaptive learning, it is possible to arrive to results other than the trivial convergence to a fixed point long-term equilibrium. The results involve the possibility of endogenous cyclical motion (periodic and a-periodic), which emerges essentially in scenarios of hyperinflation. In low inflation settings, the introduction of learning implies a less severe impact of monetary shocks that, nevertheless, tend to last for additional time periods relative to the pure perfect foresight setup.

Synechocystis ferredoxin/ferredoxin-NADP+-reductase/NADP+ complex: Structural model obtained by NMR-restrained docking

FEBS Letters 579 (2005) 4585–4590

An integer programming model for the minimum interval graph completion problem

The minimum interval graph completion problem consists of, given a graph G = ( V, E ), finding a supergraph H = ( V, E ∪ F ) that is an interval graph, while adding the least number of edges |F| . We present an integer programming formulation for solving the minimum interval graph completion problem recurring to a characteri- zation of interval graphs that produces a linear ordering of the maximal cliques of the solution graph.

Electronic tongue: a versatile tool for mineral and fruit-flavored waters recognition

Natural mineral waters (still), effervescent natural mineral waters (sparkling) and aromatized waters with fruit-flavors (still or sparkling) are an emerging market. In this work, the capability of a potentiometric electronic tongue, comprised with lipid polymeric membranes, to quantitatively estimate routinely quality physicochemical parameters (pH and conductivity) as well as to qualitatively classify water samples according to the type of water was evaluated. The study showed that a linear discriminant model, based on 21 sensors selected by the simulated annealing algorithm, could correctly classify 100 % of the water samples (leave-one out cross-validation). This potential was further demonstrated by applying a repeated K-fold cross-validation (guaranteeing that at least 15 % of independent samples were only used for internal-validation) for which 96 % of correct classifications were attained. The satisfactory recognition performance of the E-tongue could be attributed to the pH, conductivity, sugars and organic acids contents of the studied waters, which turned out in significant differences of sweetness perception indexes and total acid flavor. Moreover, the E-tongue combined with multivariate linear regression models, based on sub-sets of sensors selected by the simulated annealing algorithm, could accurately estimate waters pH (25 sensors: R 2 equal to 0.99 and 0.97 for leave-one-out or repeated K-folds cross-validation) and conductivity (23 sensors: R 2 equal to 0.997 and 0.99 for leave-one-out or repeated K-folds cross-validation). So, the overall satisfactory results achieved, allow envisaging a potential future application of electronic tongue devices for bottled water analysis and classification.