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Mathematically Modelling The Dissolution Of Solid Dispersions

Meere, Martin, McGinty, Sean, Pontrelli, Giuseppe (2017)

Proceedings of Equadiff 14

A solid dispersion is a dosage form in which an active ingredient (a drug) is mixed with at least one inert solid component. The purpose of the inert component is usually to improve the bioavailability of the drug. In particular, the inert component is frequently chosen to improve the dissolution rate of a drug that is poorly soluble in water. The construction of reliable mathematical models that accurately describe the dissolution of solid dispersions would clearly assist with their rational design....

Maximizing multi–information

Nihat Ay, Andreas Knauf (2006)

Kybernetika

Stochastic interdependence of a probability distribution on a product space is measured by its Kullback–Leibler distance from the exponential family of product distributions (called multi-information). Here we investigate low-dimensional exponential families that contain the maximizers of stochastic interdependence in their closure. Based on a detailed description of the structure of probability distributions with globally maximal multi-information we obtain our main result: The exponential family...

Mean-field evolution of fermionic systems

Marcello Porta (2014/2015)

Séminaire Laurent Schwartz — EDP et applications

We study the dynamics of interacting fermionic systems, in the mean-field regime. We consider initial states which are close to quasi-free states and prove that, under suitable assumptions on the inital data and on the many-body interaction, the quantum evolution of the system is approximated by a time-dependent quasi-free state. In particular we prove that the evolution of the reduced one-particle density matrix converges, as the number of particles goes to infinity, to the solution of the time-dependent...

Metastability in reversible diffusion processes I: Sharp asymptotics for capacities and exit times

Anton Bovier, Michael Eckhoff, Véronique Gayrard, Markus Klein (2004)

Journal of the European Mathematical Society

We develop a potential theoretic approach to the problem of metastability for reversible diffusion processes with generators of the form − ϵ Δ + ∇ F ( · ) ∇ on ℝ d or subsets of ℝ d , where F is a smooth function with finitely many local minima. In analogy to previous work on discrete Markov chains, we show that metastable exit times from the attractive domains of the minima of F can be related, up to multiplicative errors that tend to one as ϵ ↓ 0 , to the capacities of suitably constructed sets. We show that these capacities...

Metastability in reversible diffusion processes II: precise asymptotics for small eigenvalues

Anton Bovier, Véronique Gayrard, Markus Klein (2005)

Journal of the European Mathematical Society

We continue the analysis of the problem of metastability for reversible diffusion processes, initiated in [BEGK3], with a precise analysis of the low-lying spectrum of the generator. Recall that we are considering processes with generators of the form − ϵ Δ + ∇ F ( · ) ∇ on ℝ d or subsets of ℝ d , where F is a smooth function with finitely many local minima. Here we consider only the generic situation where the depths of all local minima are different. We show that in general the exponentially small part of the spectrum...

Microscopic concavity and fluctuation bounds in a class of deposition processes

Márton Balázs, Júlia Komjáthy, Timo Seppäläinen (2012)

Annales de l'I.H.P. Probabilités et statistiques

We prove fluctuation bounds for the particle current in totally asymmetric zero range processes in one dimension with nondecreasing, concave jump rates whose slope decays exponentially. Fluctuations in the characteristic directions have order of magnitude t1/3. This is in agreement with the expectation that these systems lie in the same KPZ universality class as the asymmetric simple exclusion process. The result is via a robust argument formulated for a broad class of deposition-type processes....

Molecular motors and stochastic networks

Reinhard Lipowsky, Steffen Liepelt (2008)

Banach Center Publications

Molecular motors are nano- or colloidal machines that keep the living cell in a highly ordered, stationary state far from equilibrium. This self-organized order is sustained by the energy transduction of the motors, which couple exergonic or 'downhill' processes to endergonic or 'uphill' processes. A particularly interesting case is provided by the chemomechanical coupling of cytoskeletal motors which use the chemical energy released during ATP hydrolysis in order to generate mechanical forces and...

Molecular Simulation in the Canonical Ensemble and Beyond

Zhidong Jia, Ben Leimkuhler (2007)

ESAIM: Mathematical Modelling and Numerical Analysis

In this paper, we discuss advanced thermostatting techniques for sampling molecular systems in the canonical ensemble. We first survey work on dynamical thermostatting methods, including the Nosé-Poincaré method, and generalized bath methods which introduce a more complicated extended model to obtain better ergodicity. We describe a general controlled temperature model, projective thermostatting molecular dynamics (PTMD) and demonstrate that it flexibly accommodates existing alternative thermostatting...

Motor-Mediated Microtubule Self-Organization in Dilute and Semi-Dilute Filament Solutions

S. Swaminathan, F. Ziebert, I. S. Aranson, D. Karpeev (2010)

Mathematical Modelling of Natural Phenomena

We study molecular motor-induced microtubule self-organization in dilute and semi-dilute filament solutions. In the dilute case, we use a probabilistic model of microtubule interaction via molecular motors to investigate microtubule bundle dynamics. Microtubules are modeled as polar rods interacting through fully inelastic, binary collisions. Our model indicates that initially disordered systems of interacting rods exhibit an orientational instability...

Neuro-fuzzy modelling based on a deterministic annealing approach

Robert Czabański (2005)

International Journal of Applied Mathematics and Computer Science

This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty of the learning...

No production of entropy in the Euler fluid

R. F. Streater (2004)

Banach Center Publications

We derive the Euler equations as the hydrodynamic limit of a stochastic model of a hard-sphere gas. We show that the system does not produce entropy.

Non-equilibrium phase transitions, coherence and chaos

Geoffrey Sewell (1998)

Banach Center Publications

We present a scheme for the theory of phase transitions in open dissipative systems, and show that its demands are fulfilled by quantum stochastic models of open systems, such as the laser.

Nonisothermal systems of self-attracting Fermi-Dirac particles

Piotr Biler, Tadeusz Nadzieja, Robert Stańczy (2004)

Banach Center Publications

The existence of stationary solutions and blow up of solutions for a system describing the interaction of gravitationally attracting particles that obey the Fermi-Dirac statistics are studied.

Nonlinear evolution inclusions arising from phase change models

Pierluigi Colli, Pavel Krejčí, Elisabetta Rocca, Jürgen Sprekels (2007)

Czechoslovak Mathematical Journal

The paper is devoted to the analysis of an abstract evolution inclusion with a non-invertible operator, motivated by problems arising in nonlocal phase separation modeling. Existence, uniqueness, and long-time behaviour of the solution to the related Cauchy problem are discussed in detail.

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