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EN
We measured the absorption coefficient and the reduced scattering coefficient of Intralipid solution and human forearm tissues in vivo by measuring diffuse reflectance with a charge-coupled device, examining the techniques involved. The experimental results indicate that the error is less than or equal to 8% using the diffusion theory, under the condition that the reduced scattering coefficient is one order of magnitude greater than the absorption coefficient. The stability and precision of optical property measurements are significantly improved by using the multistep iterative fitting method and using the ring-zone-constraint method to determine the diffuse reflectance center. The efficiency of reverse algorithm is greatly enhanced by selecting a one-dimensional array on the straight line crossing both the entry point and the diffusion center for fitting.
2
Content available remote Computational methods for Volterra - Fredholm integral equations
EN
Integral equations in space-time play very important role in mechanics and technology. Particular cases of these equations called mixed integral equations or Volterra-Fredholm integral equations arise in the heat conduction theory [4, 6] and the diffusion theory. Moreover, a current density in electromagnetism is determined by the Volterra-Fredholm integral equations [4]. Nonlinear counterparts of the equations studied in [1] are mathematical models of the spatio-temporal development of an epidemic (the spread of the disease in the given population). Some initial-boundary problems for a number of partial differential equations in physics are reducible to the considered integral equations [2- 3, 6], In this paper the general theory of these equations is used in the projection methods. Presented methods lead to a system of algebraic equations or to a system of Volterra integral equations. The convergence of studied algorithm is proved, the error estimate is established. The presented theory is illustrated by numerical examples.
EN
The conceptual development of diffusion models within marketing has focused on marketing mix variables and on a simple process representing the influence of others in the social system. In most models only a limited number of variables is considered due to the difficulty of modeling interactions when multiple variables are incorporated. The final conclusion of the paper that can be derived from joining the two fields of research (diffusion theory and models) is that although they have evolved in different directions, each would benefit from a unification. This paper suggests possible synergy and reciprocal benefits of integration.
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