Spin dynamics of strongly-doped La_{1-x}Sr_xMnO_3

Cold neutron triple-axis measurements have been used to investigate the nature of the long-wavelength spin dynamics in strongly-doped La$_{1-x}$Sr$_{x}$MnO$_3$ single crystals with $x$=0.2 and 0.3. Both systems behave like isotropic ferromagnets at low T, with a gapless ($E_0 < 0.02$ meV) quadratic dispersion relation $E = E_0 + Dq^2$. The values of the spin-wave stiffness constant $D$ are large ($D_{T=0}$ = 166.77 meV$\AA^2$ for $x$=0.2 and D$_{T=0}$ = 175.87 meV$\AA^2$ for $x$=0.3), which directly shows that the electron transfer energy for the $d$ band is large. $D$ exhibits a power law behavior as a function of temperature, and appears to collapse as T -> T_C. Nevertheless, an anomalously strong quasielastic central component develops and dominates the fluctuation spectrum as T -> T_C. Bragg scattering indicates that the magnetization near $T_C$ exhibits power law behavior, with $\beta \simeq 0.30$ for both systems, as expected for a three-dimensional ferromagnet.Comment: 4 pages (RevTex), 3 figures (encapsulated postscript

Структура и функции ароматазы и ее нестероидные ингибиторы

The analysis of the structure and function of aromatase (SYP19) - enzyme from the family of cytochrome P-450 that catalyzes the aromatization of six-membered ring A of the steroidal skeleton, namely transformation of androgens into estrogens peripheral and tumor tissues in the body, has been performed, and data in its non-steroidal inhibitors have been summarized.Проведен анализ структуры и функции ароматазы (СYP19) - фермента семейства цитохромов Р-450, кодируемого расположенным на коротком плече 15-й хромосомы (локус 15q21) геном CYP19A1, катализирующего ароматизацию шестичленного цикла А стероидного скелета и обуславливающего метаболизм стероидных гормонов, а именно трансформацию андрогенов в эстрогены периферическими и опухолевыми тканями организма. Обобщены данные по ее нестероидным ингибиторам. Обсуждены особенности первичной и пространственной структуры СYP19, обуславливающие взаимодействие с мембраной клетки и субстратом, обеспечивающие транспорт последнего между липидным бислоем мембраны и активным центром фермента. Показано, что наиболее эффективными ингибиторами СYP19 в настоящее время являются соединения, содержащие в составе молекулы азольные и азиновые гетероциклические фрагменты

Drought Impact Is Alleviated in Sugar Beets (Beta vulgaris L.) by Foliar Application of Fullerenol Nanoparticles

Over the past few years, significant efforts have been made to decrease the effects of drought stress on plant productivity and quality. We propose that fullerenol nanoparticles (FNPs, molecular formula C-60(OH)(24)) may help alleviate drought stress by serving as an additional intercellular water supply. Specifically, FNPs are able to penetrate plant leaf and root tissues, where they bind water in various cell compartments. This hydroscopic activity suggests that FNPs could be beneficial in plants. The aim of the present study was to analyse the influence of FNPs on sugar beet plants exposed to drought stress. Our results indicate that intracellular water metabolism can be modified by foliar application of FNPs in drought exposed plants. Drought stress induced a significant increase in the compatible osmolyte proline in both the leaves and roots of control plants, but not in FNP treated plants. These results indicate that FNPs could act as intracellular binders of water, creating an additional water reserve, and enabling adaptation to drought stress. Moreover, analysis of plant antioxidant enzyme activities (CAT, APx and GPx), MDA and GSH content indicate that fullerenol foliar application could have some beneficial effect on alleviating oxidative effects of drought stress, depending on the concentration of nanoparticles applied. Although further studies are necessary to elucidate the biochemical impact of FNPs on plants; the present results could directly impact agricultural practice, where available water supplies are often a limiting factor in plant bioproductivity

Identification of Material Properties of Elastic Plate Using Guided Waves Based on the Matrix Pencil Method and Laser Doppler Vibrometry

Ultrasonic based inspection of thin-walled structures often requires prior knowledge of their mechanical properties. Their accurate estimation could be achieved in a non-destructive manner employing, e.g., elastic guided waves. Such procedures require efficient approaches for experimental data extraction and processing, which is still a challenging task. An advanced automated technique for material properties identification of an elastic waveguide is proposed in this investigation. It relies on the information on dispersion characteristics of guided waves, which are extracted by applying the matrix pencil method to the measurements obtained via laser Doppler vibrometry. Two objective functions have been successfully tested, and the advantages of both approaches are discussed (accuracy vs. computational costs). The numerical analysis employing the synthetic data generated via the mathematical model as well as experimental data shows that both approaches are stable and accurate. The influence of the presence of various modes in the extracted data is investigated. One can conclude that the influence of the corruptions related to the extraction of dispersion curves is not critical if the majority of guided waves propagating in the considered frequency range are presented. Possible extensions of the proposed technique for damaged and multi-layered structures are also discussed

Identification of Material Properties of Elastic Plate Using Guided Waves Based on the Matrix Pencil Method and Laser Doppler Vibrometry

Ultrasonic based inspection of thin-walled structures often requires prior knowledge of their mechanical properties. Their accurate estimation could be achieved in a non-destructive manner employing, e.g., elastic guided waves. Such procedures require efficient approaches for experimental data extraction and processing, which is still a challenging task. An advanced automated technique for material properties identification of an elastic waveguide is proposed in this investigation. It relies on the information on dispersion characteristics of guided waves, which are extracted by applying the matrix pencil method to the measurements obtained via laser Doppler vibrometry. Two objective functions have been successfully tested, and the advantages of both approaches are discussed (accuracy vs. computational costs). The numerical analysis employing the synthetic data generated via the mathematical model as well as experimental data shows that both approaches are stable and accurate. The influence of the presence of various modes in the extracted data is investigated. One can conclude that the influence of the corruptions related to the extraction of dispersion curves is not critical if the majority of guided waves propagating in the considered frequency range are presented. Possible extensions of the proposed technique for damaged and multi-layered structures are also discussed

Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

Numerical methods, including machine-learning methods, are now actively used in applications related to elastic guided wave propagation phenomena. The method proposed in this study for material-properties characterization is based on an algorithm of the clustering of multivariate data series obtained as a result of the application of the matrix pencil method to the experimental data. In the developed technique, multi-objective optimization is employed to improve the accuracy of the identification of particular parameters. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green’s matrix is employed iteratively and the obtained solution is used for filter construction with decreasing bandwidths providing nearly noise-free classified data (with mode separation). The filter provides data separation between all guided waves in a natural way, which is needed at the second stage, where a more laborious method based on the minimization of the slowness residuals is applied to the data. The method might be further employed for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates, etc

Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

Numerical methods, including machine-learning methods, are now actively used in applications related to elastic guided wave propagation phenomena. The method proposed in this study for material-properties characterization is based on an algorithm of the clustering of multivariate data series obtained as a result of the application of the matrix pencil method to the experimental data. In the developed technique, multi-objective optimization is employed to improve the accuracy of the identification of particular parameters. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green&rsquo;s matrix is employed iteratively and the obtained solution is used for filter construction with decreasing bandwidths providing nearly noise-free classified data (with mode separation). The filter provides data separation between all guided waves in a natural way, which is needed at the second stage, where a more laborious method based on the minimization of the slowness residuals is applied to the data. The method might be further employed for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates, etc