Under decompression, bubbles can form in the human body, and these can be found both within the body tissues and the bloodstream. Mathematical models for the growth of both types of bubbles have previously been presented, but they have not been coupled together. This work thus explores the interaction between the growth of tissue and blood-borne bubbles under decompression, specifically looking at the extent to which they compete for the common resource of inert gas held in solution in the tissues. The influence of tissue bubbles is found to be significant for densities as low as 10 ml(-1) for tissues which are poorly perfused. However, the effects of formation of bubbles in the blood are not found until the density of bubble production sites reaches 10(6) ml(-1). From comparison of the model predictions with experimental evidence for bubbles produced in animals and man under decompression, it is concluded that the density of tissue bubbles is likely to have a significant effect on the number of bubbles produced in the blood. However, the density of nucleation sites in the blood is unlikely to be sufficiently high in humans for the formation of bubbles in the blood to have a significant impact on the growth of the bubbles in the tissue.
Fourier analysis and cross-correlation function are successfully applied to
improving the conventional gravity model of interaction between cities by
introducing a time variable to the attraction measures (e.g., city sizes). The
traditional model assumes spatial interaction as instantaneous, while...
The attraction measure, scaling exponent, and impedance function of the
gravity model are redefined using the concepts from fractals and spatial
complexity. Firstly, the attraction measure of spatial interaction in human
systems is defined by the product of traffic inflow and outflow. Based on the...
We study the opinion formation in a voter-like model defined on
A square lattice of linear size $L$. The agents may be in three different
States, representing any public debate with three choices (yes, no, undecided).
We consider heterogeneous agents that have different convictions about their
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