FRAP and FFM studies revealed a two-component system for EYFP diffusion in non-infected NLFK cells, with the diffusion coefficient for faster-component of D = 50±5 µm2/s and D = 57±8 µm2/s.
The recent study on free diffusion of EGFP monomers – tetramers in the living mammalian cell nuclei revealed a biphasic system for EGFP diffusion [33].
The results are extended to similar kinetics of a fixed-bed system for solid diffusion into spherical particles.
Kd selection was carried out differently for the engineered barrier system (by diffusion studies) and for the geosphere (from the batch SDB).
The correct quantification of mixing in groundwater systems must account for diffusion, local-scale dispersion and the flow variability in heterogeneous flow fields (e.g., flow-focusing in high-conductivity and de-focusing in low-conductivity zones).
We prove the existence of a positive monotonous solution to the shadow system for each small diffusion rate (epsilon >0).
We develop a system for measuring rotational diffusion coefficients by using a fluorescent probe to label a particle.
Therefore, each time step involves the solution of two different problems: a nonlinear system of ODEs and a coupled linear system for the discrete diffusion equation: (7) (8 where α = (βC m A i d)/Δ t p, n is the current step, ∗ is an intermediate step, and n + 1 is the next time step.
Consider a generalised reaction-diffusion system for two reacting and diffusive species U and V of the form: (1) ∂ U ∂ t = f U (U, V ) + D U ∇ 2 U ∂ V ∂ t = f V (U, V ) + D V ∇ 2 V The diffusion constant D U, V [with units (length)/time] is an important parameter indicative of the diffusion mobility.
We begin by developing a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies.
We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics.
