35,735 research outputs found
TWO FORMULAS FOR SMARANDACHE LCM RATIO SEQUENCES
In this paper, a reduction formula for Smarandache LCM ratio sequences SLR(6)and
SLR(7) are given
Neural Mechanism of Language
This paper is based on our previous work on neural coding. It is a
self-organized model supported by existing evidences. Firstly, we briefly
introduce this model in this paper, and then we explain the neural mechanism of
language and reasoning with it. Moreover, we find that the position of an area
determines its importance. Specifically, language relevant areas are in the
capital position of the cortical kingdom. Therefore they are closely related
with autonomous consciousness and working memories. In essence, language is a
miniature of the real world. Briefly, this paper would like to bridge the gap
between molecule mechanism of neurons and advanced functions such as language
and reasoning.Comment: 6 pages, 3 figure
Thinning and thickening in active microrheology
When pulling a probe particle in a many-particle system with fixed velocity,
the probe's effective friction, defined as average pulling force over its
velocity, , first keeps constant (linear
response), then decreases (thinning) and finally increases (thickening). We
propose a three-time-scales picture (TTSP) to unify thinning and thickening
behaviour. The points of the TTSP are that there are three distinct time scales
of bath particles: diffusion, damping, and single probe-bath (P-B) collision;
the dominating time scales, which are controlled by the pulling velocity,
determine the behaviour of the probe's friction. We confirm the TTSP by
Langevin dynamics simulation. Microscopically, we find that for computing the
effective friction, Maxwellian distribution of bath particles' velocities works
in low Reynolds number (Re) but fails in high Re. It can be understood based on
the microscopic mechanism of thickening obtained in the limit. Based on
the TTSP, we explain different thinning and thickening observations in some
earlier literature
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