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Kalita V.M.,National Technical University of Ukraine | Kalita V.M.,Ukrainian Academy of Sciences | Snarskii A.A.,National Technical University of Ukraine | Snarskii A.A.,Institute for Information Recording NAS of Ukraine | And 2 more authors.
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2016

Magnetoactive elastomers (MAEs) are composite materials comprised of micrometer-sized ferromagnetic particles in a nonmagnetic elastomer matrix. A single-particle mechanism of magnetostriction in MAEs, assuming the rotation of a soft magnetic, mechanically rigid particle with uniaxial magnetic anisotropy in magnetic fields is identified and considered theoretically within the framework of an alternative model. In this mechanism, the total magnetic anisotropy energy of the filling particles in the matrix is the sum over single particles. Matrix displacements in the vicinity of the particle and the resulting direction of the magnetization vector are calculated. The effect of matrix deformation is pronounced well if the magnetic anisotropy coefficient K is much larger than the shear modulus μ of the elastic matrix. The feasibility of the proposed magnetostriction mechanism in soft magnetoactive elastomers and gels is elucidated. The magnetic-field-induced internal stresses in the matrix lead to effects of magnetodeformation and may increase the elastic moduli of these composite materials. © 2016 American Physical Society. Source

Snarskii A.A.,National Technical University of Ukraine | Snarskii A.A.,Institute for Information Recording NAS of Ukraine | Zorinets D.I.,National Technical University of Ukraine | Lande D.V.,National Technical University of Ukraine | Lande D.V.,Institute for Information Recording NAS of Ukraine
Physica A: Statistical Mechanics and its Applications | Year: 2016

This paper introduces the concept of Conjectural Link for Complex Networks, in particular, social networks. Conjectural Link we understand as an implicit link, not available in the network, but supposed to be present, based on the characteristics of its topology. It is possible, for example, when in the formal description of the network some connections are skipped due to errors, deliberately hidden or withdrawn (e.g. in the case of partial destruction of the network). Introduced a parameter that allows ranking the Conjectural Link. The more this parameter — the more likely that this connection should be present in the network. This paper presents a method of recovery of partially destroyed Complex Networks using Conjectural Links finding. Presented two methods of finding the node pairs that are not linked directly to one another, but have a great possibility of Conjectural Link communication among themselves: a method based on the determination of the resistance between two nodes, and method based on the computation of the lengths of routes between two nodes. Several examples of real networks are reviewed and performed a comparison to know network links prediction methods, not intended to find the missing links in already formed networks. © 2016 Elsevier B.V. Source

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