6 resultados para Field Dependence.

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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Magnetic insulators have proven to be usable as quantum simulators for itinerant interacting quantum systems. In particular the compound (C5H12N)2CuBr4 (for short: (Hpip)2CuBr4) was shown to be a remarkable realization of a Tomonaga–Luttinger liquid (TLL) and allowed us to quantitatively test the TLL theory. Substitution weakly disorders this class of compounds and thus allows us to use them to tackle questions pertaining to the effect of disorder in TLL as well, such as that of the formation of the Bose glass. In this paper we present, as a first step in this direction, a study of the properties of the related (Hpip)2CuCl4 compound. We determine the exchange couplings and compute the temperature and magnetic field dependence of the specific heat, using a finite temperature density matrix renormalization group procedure. Comparison with the measured specific heat at zero magnetic field confirms the exchange parameters and Hamiltonian for the (Hpip)2CuCl4 compound, giving the basis needed to begin studying the disorder effects.

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Self-efficacy has been identified as one of the most consistent variables that predict the outcome of alcohol treatment. However, many previous studies in this field failed to control for other important predictors (e.g., dependences severity, psychiatric symptoms, and treatment goal). Our study's first goal was to evaluate the predictive value of self-efficacy when most other relevant variables were statistically controlled. The second goal was to compare the predictive values of self-efficacy assessed with the Situational Confidence Questionnaire (SCQ), and general self-efficacy assessed with a single question.

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The quark-gluon plasma formed in heavy ion collisions contains charged chiral fermions evolving in an external magnetic field. At finite density of electric charge or baryon number (resulting either from nuclear stopping or from fluctuations), the triangle anomaly induces in the plasma the Chiral Magnetic Wave (CMW). The CMW first induces a separation of the right and left chiral charges along the magnetic field; the resulting dipolar axial charge density in turn induces the oppositely directed vector charge currents leading to an electric quadrupole moment of the quark-gluon plasma. Boosted by the strong collective flow, the electric quadrupole moment translates into the charge dependence of the elliptic flow coefficients, so that $v_2(\pi^+) < v_2(\pi^-)$ (at positive net charge). Using the latest quantitative simulations of the produced magnetic field and solving the CMW equation, we make further quantitative estimates of the produced $v_2$ splitting and its centrality dependence. We compare the results with the available experimental data.

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PURPOSE: To prospectively quantify in vitro the influence of gadopentetate dimeglumine and ioversol on the magnetic resonance (MR) imaging signal observed with a variety of musculoskeletal pulse sequences to predict optimum gadolinium concentrations for direct MR arthrography at 1.5 and 3.0 T. MATERIALS AND METHODS: In an in vitro study, T1 and T2 relaxation times of three dilution series of gadopentetate dimeglumine (concentration, 0-20.0 mmol gadolinium per liter) at ioversol concentrations with iodine concentration of 0, 236.4, and 1182 mmol iodine per liter (corresponding to 0, 30, and 150 mg of iodine per milliliter) were measured at 1.5 and 3.0 T. The relaxation rate dependence on concentrations of gadolinium and iodine was analytically modeled, and continuous profiles of signal versus gadolinium concentration were calculated for 10 pulse sequences used in current musculoskeletal imaging. After fitting to experimental discrete profiles, maximum signal-to-noise ratio (SNR), gadolinium concentration with maximum SNR, and range of gadolinium concentration with 90% of maximum SNR were derived. The overall influence of field strength and iodine concentration on these parameters was assessed by using t tests. The deviation of simulated from experimental signal-response profiles was assessed with the autocorrelation of the residuals. RESULTS: The model reproduced relaxation rates of 0.37-38.24 sec(-1), with a mean error of 4.5%. Calculated SNR profiles matched the discrete experimental profiles, with autocorrelation of the residuals divided by the mean of less than 5.0. Admixture of ioversol consistently reduced T1 and T2, narrowed optimum gadolinium concentration ranges (P = .004-.006), and reduced maximum SNR (P < .001 to not significant). Optimum gadolinium concentration was 0.7-3.4 mmol/L at both field strengths. At 3.0 T, maximum SNR was up to 75% higher than at 1.5 T. CONCLUSION: Admixture of ioversol to gadopentetate dimeglumine solutions results in a consistent additional relaxation enhancement, which can be analytically modeled to allow a near-quantitative a priori optimized match of contrast media concentrations and imaging protocol for a broad variety of pulse sequences.

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The integrated elliptic flow of charged particles produced in Pb+Pb collisions at √sNN = 2.76 TeV has been measured with the ATLAS detector using data collected at the Large Hadron Collider. The anisotropy parameter, v2, was measured in the pseudorapidity range |η| ≤ 2.5 with the event-plane method. In order to include tracks with very low transverse momentum pT, thus reducing the uncertainty in v2 integrated over pT, a 1 μb−1 data sample recorded without a magnetic field in the tracking detectors is used. The centrality dependence of the integrated v2 is compared to other measurements obtained with higher pT thresholds. The integrated elliptic flow is weakly decreasing with |η|. The integrated v2 transformed to the rest frame of one of the colliding nuclei is compared to the lower-energy RHIC data.

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The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.