Fermi acceleration and adiabatic invariants for non-autonomous billiards.

Recent results concerned with the energy growth of particles inside a container with slowly moving walls are summarized, augmented, and discussed. For breathing bounded domains with smooth boundaries, it is proved that for all initial conditions the acceleration is at most exponential. Anosov-Kasuga averaging theory is reviewed in the application to the non-autonomous billiards, and the results are corroborated by numerical simulations. A stochastic description is proposed which implies that for periodically perturbed ergodic and mixing billiards averaged particle energy grows quadratically in time (e.g., exponential acceleration has zero probability). Then, a proof that in non-integrable breathing billiards some trajectories do accelerate exponentially is reviewed. Finally, a unified view on the recently constructed families of non-ergodic billiards that robustly admit a large set of exponentially accelerating particles is presented.

Billiards: a singular perturbation limit of smooth Hamiltonian flows.

Nonlinear multi-dimensional Hamiltonian systems that are not near integrable typically have mixed phase space and a plethora of instabilities. Hence, it is difficult to analyze them, to visualize them, or even to interpret their numerical simulations. We survey an emerging methodology for analyzing a class of such systems: Hamiltonians with steep potentials that limit to billiards.

Chaotic scattering by steep repelling potentials.

Consider a classical two-dimensional scattering problem: a ray is scattered by a potential composed of several tall, repelling, steep mountains of arbitrary shape. We study when the traditional approximation of this nonlinear far-from-integrable problem by the corresponding simpler billiard problem, of scattering by hard-wall obstacles of similar shape, is justified. For one class of chaotic scatterers, named here regular Sinai scatterers, the scattering properties of the smooth system indeed limit to those of the billiards. For another class, the singular Sinai scatterers, these two scattering problems have essential differences: though the invariant set of such singular scatterers is hyperbolic (possibly with singularities), that of the smooth flow may have stable periodic orbits, even when the potential is arbitrarily steep. It follows that the fractal dimension of the scattering function of the smooth flow may be significantly altered by changing the ratio between the steepness parameter and a parameter which measures the billiards' deviation from a singular scatterer. Thus, even in this singular case, the billiard scattering problem is utilized as a skeleton for studying the properties of the smooth flow. Finally, we see that corners have nontrivial and significant impact on the scattering functions.

G-CSF control of neutrophils dynamics in the blood.

White blood cell neutrophil is a key component in the fast initial immune response against bacterial and fungal infections. Granulocyte colony stimulating factor (G-CSF) which is naturally produced in the body, is known to control the neutrophils production in the bone marrow and the neutrophils delivery into the blood. In oncological practice, G-CSF injections are widely used to treat neutropenia (dangerously low levels of neutrophils in the blood) and to prevent the infectious complications that often follow chemotherapy. However, the accurate dynamics of G-CSF neutrophil interaction has not been fully determined and no general scheme exists for an optimal G-CSF application in neutropenia. Here we develop a two-dimensional ordinary differential equation model for the G-CSF-neutrophil dynamics in the blood. The model is built axiomatically by first formally defining from the biology the expected properties of the model, and then deducing the dynamic behavior of the resulting system. The resulting model is structurally stable, and its dynamical features are independent of the precise form of the various rate functions. Choosing a specific form for these functions, three complementary parameter estimation procedures for one clinical (training) data set are utilized. The fully parameterized model (6 parameters) provides adequate predictions for several additional clinical data sets on time scales of several days. We briefly discuss the utility of this relatively simple and robust model in several clinical conditions.