Analytic perturbation theory for bound states in the transfer matrix spectrum of weakly correlated lattice ferromagnetic spin systems

We consider general d-dimensional lattice ferromagnetic spin systems with nearest neighbor interactions in the high temperature region ('beta' << 1). Each model is characterized by a single site apriori spin distribution taken to be even. We also take the parameter 'alfa' = ('S POT.4') - 3 '(S POT.2') POT.2' > 0, i.e. in the region which we call Gaussian subjugation, where ('S POT.K') denotes the kth moment of the apriori distribution. Associated with the model is a lattice quantum field theory known to contain a particle of asymptotic mass -ln 'beta' and a bound state below the two-particle threshold. We develop a 'beta' analytic perturbation theory for the binding energy of this bound state. As a key ingredient in obtaining our result we show that the Fourier transform of the two-point function is a meromorphic function, with a simple pole, in a suitable complex spectral parameter and the coefficients of its Laurent expansion are analytic in 'beta'.

Experimental Investigation of the Hydrodynamic Coefficients of a Remotely Operated Vehicle Using a Planar Motion Mechanism

The determination of hydrodynamic coefficients of full scale underwater vehicles using system identification (SI) is an extremely powerful technique. The procedure is based on experimental runs and on the analysis of on-board sensors and thrusters signals. The technique is cost effective and it has high repeatability; however, for open-frame underwater vehicles, it lacks accuracy due to the sensors' noise and the poor modeling of thruster-hull and thruster-thruster interaction effects. In this work, forced oscillation tests were undertaken with a full scale open-frame underwater vehicle. These conducted tests are unique in the sense that there are not many examples in the literature taking advantage of a PMM installation for testing a prototype and; consequently, allowing the comparison between the experimental results and the ones estimated by parameter identification. The Morison's equation inertia and drag coefficients were estimated with two parameter identification methods, that is, the weighted and the ordinary least-squares procedures. It was verified that the in-line force estimated from Morison's equation agrees well with the measured one except in the region around the motion inversion points. On the other hand, the error analysis showed that the ordinary least-squares provided better accuracy and, therefore, was used to evaluate the ratio between inertia and drag forces for a range of Keulegan-Carpenter and Reynolds numbers. It was concluded that, although both experimental and estimation techniques proved to be powerful tools for evaluation of an open-frame underwater vehicle's hydrodynamic coefficients, the research provided a rich amount of reference data for comparison with reduced models as well as for dynamic motion simulation of ROVs. [DOI: 10.1115/1.4004952]

A note on the local power of the LR, Wald, score and gradient tests

This paper examines the local power of the likelihood ratio, Wald, score and gradient tests under the presence of a scalar parameter, phi say, that is orthogonal to the remaining parameters. We show that some of the coefficients that define the local powers remain unchanged regardless of whether phi is known or needs to be estimated, where as the others can be written as the sum of two terms, the first of which being the corresponding term obtained as if phi were known, and the second, an additional term yielded by the fact that phi is unknown. The contribution of each set of parameters on the local powers of the tests can then be examined. Various implications of our main result are stated and discussed. Several examples are presented for illustrative purposes

Local power properties of some asymptotic tests in symmetric linear regression models

In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the class of symmetric linear regression models. This is a wide class of models which encompasses the t model and several other symmetric distributions with longer-than normal tails. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, Monte Carlo simulations are presented. An empirical application to a real data set is considered for illustrative purposes. (C) 2011 Elsevier B.V. All rights reserved.

Experimental investigations of heat transfer coefficients of cutting fluids in metal cutting processes: analysis of workpiece phenomena in a given case study

This paper reports an experimental method to estimate the convective heat transfer of cutting fluids in a laminar flow regime applied on a thin steel plate. The heat source provided by the metal cutting was simulated by electrical heating of the plate. Three different cooling conditions were evaluated: a dry cooling system, a flooded cooling system and a minimum quantity of lubrication cooling system, as well as two different cutting fluids for the last two systems. The results showed considerable enhancement of convective heat transfer using the flooded system. For the dry and minimum quantity of lubrication systems, the heat conduction inside the body was much faster than the heat convection away from its surface. In addition, using the Biot number, the possible models were analyzed for conduction heat problems for each experimental condition tested.

SUPERLINEAR ELLIPTIC PROBLEMS WITH SIGN CHANGING COEFFICIENTS

Via variational methods, we study multiplicity of solutions for the problem {-Delta u = lambda b(x)vertical bar u vertical bar(q-2)u + au + g(x, u) in Omega, u - 0 on partial derivative Omega, where a simple example for g( x, u) is |u|(p-2)u; here a, lambda are real parameters, 1 < q < 2 < p <= 2* and b(x) is a function in a suitable space L-sigma. We obtain a class of sign changing coefficients b(x) for which two non-negative solutions exist for any lambda > 0, and a total of five nontrivial solutions are obtained when lambda is small and a >= lambda(1). Note that this type of results are valid even in the critical case.

Asymptotic integral kernel for ensembles of random normal matrices with radial potentials

The method of steepest descent is used to study the integral kernel of a family of normal random matrix ensembles with eigenvalue distribution P-N (z(1), ... , z(N)) = Z(N)(-1)e(-N)Sigma(N)(i=1) V-alpha(z(i)) Pi(1 <= i<j <= N) vertical bar z(i) - z(j)vertical bar(2), where V-alpha(z) = vertical bar z vertical bar(alpha), z epsilon C and alpha epsilon inverted left perpendicular0, infinity inverted right perpendicular. Asymptotic formulas with error estimate on sectors are obtained. A corollary of these expansions is a scaling limit for the n-point function in terms of the integral kernel for the classical Segal-Bargmann space. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3688293]

Liquid-Liquid Equilibrium Data for the System Lard plus Oleic Acid plus Ethanol + Water at 318.2 K: Cholesterol Distribution Coefficients

This research reports liquid liquid equilibrium data for the system lard (swine fat), cis-9-octadecenoic acid (oleic acid), ethanol, and water at 318.2 K, as well as their correlation with the nonrandom two-liquid (NRTL) and universal quasichemical activity coefficient (UNIQUAC) thermodynamic equations, which have provided global deviations of 0.41 % and 0.53 %, respectively. Additional equilibrium experiments were also performed to obtain cholesterol partition (or distribution) coefficients to verify the availability of the use of ethanol plus water to reduce the cholesterol content in lard. The partition experiments were performed with concentrations of free fatty acids (commercial oleic acid) that varied from (0 to 20) mass % and of water in the solvent that varied from (0 to 18) mass %. The percentage of free fatty acids initially present in lard had a slight effect on the distribution of cholesterol between the phases. Furthermore, the distribution coefficients decreased by adding water in the ethanol; specifically, it resulted in a diminution of the capability of the solvent to remove the cholesterol.

Validation of reference genes for RT-qPCR normalization in common bean during biotic and abiotic stresses

Selection of reference genes is an essential consideration to increase the precision and quality of relative expression analysis by the quantitative RT-PCR method. The stability of eight expressed sequence tags was evaluated to define potential reference genes to study the differential expression of common bean target genes under biotic (incompatible interaction between common bean and fungus Colletotrichum lindemuthianum) and abiotic (drought; salinity; cold temperature) stresses. The efficiency of amplification curves and quantification cycle (C (q)) were determined using LinRegPCR software. The stability of the candidate reference genes was obtained using geNorm and NormFinder software, whereas the normalization of differential expression of target genes [beta-1,3-glucanase 1 (BG1) gene for biotic stress and dehydration responsive element binding (DREB) gene for abiotic stress] was defined by REST software. High stability was obtained for insulin degrading enzyme (IDE), actin-11 (Act11), unknown 1 (Ukn1) and unknown 2 (Ukn2) genes during biotic stress, and for SKP1/ASK-interacting protein 16 (Skip16), Act11, Tubulin beta-8 (beta-Tub8) and Unk1 genes under abiotic stresses. However, IDE and Act11 were indicated as the best combination of reference genes for biotic stress analysis, whereas the Skip16 and Act11 genes were the best combination to study abiotic stress. These genes should be useful in the normalization of gene expression by RT-PCR analysis in common bean, the most important edible legume.

Evaluating different methods of microarray data normalization

Abstract Background With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. Results Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. Conclusion In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.