941 resultados para Linear transformations


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An experimental study of the acoustic emission generated during a martensitic transformation is presented. A statistical analysis of the amplitude and lifetime of a large number of signals has revealed power-law behavior for both magnitudes. The exponents of these distributions have been evaluated and, through independent measurements of the statistical lifetime to amplitude dependence, we have checked the scaling relation between the exponents. Our results are discussed in terms of current ideas on avalanche dynamics.

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The Technologies setting at Agricultural production system have the main characteristics the vertical productivity, reduced costs, soil physical, chemical and biological improvement to promote production sustainable growth. Thus, the study aimed to determine the variability and the linear and special correlations between the plant and soil attributes in order to select and indicate good representation of soil physical quality for forage productivity. In the growing season of 2006, on the Fazenda Bonança in Pereira Barreto (SP), the productivity of autumn corn forage (FDM) in an irrigated no-tillage system and the soil physical properties were analyzed. The purpose was to study the variability and the linear and spatial correlations between the plant and soil properties, to select an indicator of soil physical quality related to corn forage yield. A geostatistical grid was installed to collect soil and plant data, with 125 sampling points in an area of 2,500 m². The results show that the studied properties did not vary randomly and that data variability was low to very high, with well-defined spatial patterns, ranging from 7.8 to 38.0 m. On the other hand, the linear correlation between the plant and the soil properties was low and highly significant. The pairs forage dry matter versus microporosity and stem diameter versus bulk density were best correlated in the 0-0.10 m layer, while the other pairs - forage dry matter versus macro - and total porosity - were inversely correlated in the same layer. However, from the spatial point of view, there was a high inverse correlation between forage dry matter with microporosity, so that microporosity in the 0-0.10 m layer can be considered a good indicator of soil physical quality, with a view to corn forage yield.

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We study the effect of a magnetic field on the martensitic transition of a Cu-Al-Mn shape-memory alloy. The martensitic transition has been studied through resistance measurements under applied magnetic fields ranging from 0 to 50 kOe. Negative magnetoresistance showing an almost linear dependence with the square of the magnetization has been observed. This magnetoresistive effect is associated with the existence of small ferromagnetic Mn-clusters. Its strength and thermal dependence is different in both phases. The martensitic transition temperature is slightly increased and its spread in temperature significantly reduced upon increasing the field. These results show the existence of magnetoelastic coupling, which favors the nucleation of those martensitic variants with the easy magnetization axis aligned with the field.

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We discuss intrinsic noise effects in stochastic multiplicative-noise partial differential equations, which are qualitatively independent of the noise interpretation (Itô vs Stratonovich), in particular in the context of noise-induced ordering phase transitions. We study a model which, contrary to all cases known so far, exhibits such ordering transitions when the noise is interpreted not only according to Stratonovich, but also to Itô. The main feature of this model is the absence of a linear instability at the transition point. The dynamical properties of the resulting noise-induced growth processes are studied and compared in the two interpretations and with a reference Ginzburg-Landau-type model. A detailed discussion of a different numerical algorithm valid for both interpretations is also presented.

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We discuss the relation between spacetime diffeomorphisms and gauge transformations in theories of the YangMills type coupled with Einsteins general relativity. We show that local symmetries of the Hamiltonian and Lagrangian formalisms of these generally covariant gauge systems are equivalent when gauge transformations are required to induce transformations which are projectable under the Legendre map. Although pure YangMills gauge transformations are projectable by themselves, diffeomorphisms are not. Instead, the projectable symmetry group arises from infinitesimal diffeomorphism-inducing transformations which must depend on the lapse function and shift vector of the spacetime metric plus associated gauge transformations. Our results are generalizations of earlier results by ourselves and by Salisbury and Sundermeyer. 2000 American Institute of Physics.

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New results on the theory of constrained systems are applied to characterize the generators of Noethers symmetry transformations. As a byproduct, an algorithm to construct gauge transformations in Hamiltonian formalism is derived. This is illustrated with two relevant examples.

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The relationship between the Poincar and Galilei groups allows us to write the Poincar wave equations for arbitrary spin as a Fourier transform of the Galilean ones. The relation between the Lagrangian formulation for both cases is also studied.

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We generalize the analogous of Lee Hwa Chungs theorem to the case of presymplectic manifolds. As an application, we study the canonical transformations of a canonical system (M, S, O). The role of Dirac brackets as a test of canonicity is clarified.

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We develop a theory of canonical transformations for presymplectic systems, reducing this concept to that of canonical transformations for regular coisotropic canonical systems. In this way we can also link these with the usual canonical transformations for the symplectic reduced phase space. Furthermore, the concept of a generating function arises in a natural way as well as that of gauge group.

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Polymorphous Si is a nanostructured form of hydrogenated amorphous Si that contains a small fraction of Si nanocrystals or clusters. Its thermally induced transformations such as relaxation, dehydrogenation, and crystallization have been studied by calorimetry and evolved gas analysis as a complementary technique. The observed behavior has been compared to that of conventional hydrogenated amorphous Si and amorphous Si nanoparticles. In the temperature range of our experiments (650700 C), crystallization takes place at almost the same temperature in polymorphous and in amorphous Si. In contrast, dehydrogenation processes reflect the presence of different hydrogen states. The calorimetry and evolved gas analysis thermograms clearly show that polymorphous Si shares hydrogen states of both amorphous Si and Si nanoparticles. Finally, the total energy of the main SiH group present in polymorphous Si has been quantified.

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We study the effect of a magnetic field on the martensitic transition of a Cu-Al-Mn shape-memory alloy. The martensitic transition has been studied through resistance measurements under applied magnetic fields ranging from 0 to 50 kOe. Negative magnetoresistance showing an almost linear dependence with the square of the magnetization has been observed. This magnetoresistive effect is associated with the existence of small ferromagnetic Mn-clusters. Its strength and thermal dependence is different in both phases. The martensitic transition temperature is slightly increased and its spread in temperature significantly reduced upon increasing the field. These results show the existence of magnetoelastic coupling, which favors the nucleation of those martensitic variants with the easy magnetization axis aligned with the field.

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The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.