Estimativa do expoente de Hurst, por meio da transformada Wavelet, de séries temporais de precipitação de chuvas das regiões climáticas do estado de São Paulo no período de 1978 a 1997

Pós-graduação em Física - IGCE

Modelo cinético estocástico para a transcrição considerando colisões entre as moléculas de RNA polimerase

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Cálculo fracionário aplicado ao movimento browniano

This work aims to study several diffusive regimes, especially Brownian motion. We deal with problems involving anomalous diffusion using the method of fractional derivatives and fractional integrals. We introduce concepts of fractional calculus and apply it to the generalized Langevin equation. Through the fractional Laplace transform we calculate the values of diffusion coefficients for two super diffusive cases, verifying the validity of the method

Superdiffusion in a non-Markovian random walk model with a Gaussian memory profile

Most superdiffusive Non-Markovian random walk models assume that correlations are maintained at all time scales, e. g., fractional Brownian motion, Levy walks, the Elephant walk and Alzheimer walk models. In the latter two models the random walker can always "remember" the initial times near t = 0. Assuming jump size distributions with finite variance, the question naturally arises: is superdiffusion possible if the walker is unable to recall the initial times? We give a conclusive answer to this general question, by studying a non-Markovian model in which the walker's memory of the past is weighted by a Gaussian centered at time t/2, at which time the walker had one half the present age, and with a standard deviation sigma t which grows linearly as the walker ages. For large widths we find that the model behaves similarly to the Elephant model, but for small widths this Gaussian memory profile model behaves like the Alzheimer walk model. We also report that the phenomenon of amnestically induced persistence, known to occur in the Alzheimer walk model, arises in the Gaussian memory profile model. We conclude that memory of the initial times is not a necessary condition for generating (log-periodic) superdiffusion. We show that the phenomenon of amnestically induced persistence extends to the case of a Gaussian memory profile.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

Analysis of eruptive and seismic sequences to improve the short-and long-term eruption forecasting

Forecasting the time, location, nature, and scale of volcanic eruptions is one of the most urgent aspects of modern applied volcanology. The reliability of probabilistic forecasting procedures is strongly related to the reliability of the input information provided, implying objective criteria for interpreting the historical and monitoring data. For this reason both, detailed analysis of past data and more basic research into the processes of volcanism, are fundamental tasks of a continuous information-gain process; in this way the precursor events of eruptions can be better interpreted in terms of their physical meanings with correlated uncertainties. This should lead to better predictions of the nature of eruptive events. In this work we have studied different problems associated with the long- and short-term eruption forecasting assessment. First, we discuss different approaches for the analysis of the eruptive history of a volcano, most of them generally applied for long-term eruption forecasting purposes; furthermore, we present a model based on the characteristics of a Brownian passage-time process to describe recurrent eruptive activity, and apply it for long-term, time-dependent, eruption forecasting (Chapter 1). Conversely, in an effort to define further monitoring parameters as input data for short-term eruption forecasting in probabilistic models (as for example, the Bayesian Event Tree for eruption forecasting -BET_EF-), we analyze some characteristics of typical seismic activity recorded in active volcanoes; in particular, we use some methodologies that may be applied to analyze long-period (LP) events (Chapter 2) and volcano-tectonic (VT) seismic swarms (Chapter 3); our analysis in general are oriented toward the tracking of phenomena that can provide information about magmatic processes. Finally, we discuss some possible ways to integrate the results presented in Chapters 1 (for long-term EF), 2 and 3 (for short-term EF) in the BET_EF model (Chapter 4).

Polyelectrolytes and their counterions studied by EPR spectroscopy

In this thesis methods of EPR spectroscopy were used to investigate polyion-counterion interactions in polyelectrolyte solutions. The fact that EPR techniques are local methods is exploited and by employing spin-carrying (i.e., EPR-active) probe ions it is possible to examine polyelectrolytes from the counterions’ point of view. It was possible to gain insight into i) the dynamics and local geometry of counterion attachment, ii) conformations and dynamics of local segments of the polyion in an indirect manner, and iii) the spatial distribution of spin probe ions that surround polyions in solution. Analysis of CW EPR spectra of dianion nitroxide spin probe Fremy’s salt (FS, potassium nitrosodisulfonate) in solutions of cationic PDADMAC polyelectrolyte revealed that FS ions and PDADMAC form transient ion pairs with a lifetime of less than 1 ns. This effect was termed as dynamic electrostatic attachment (DEA). By spectral simulation taking into account the rotational dynamics as a uniaxial Brownian reorientation, also the geometry of the attached state could be characterized. By variation of solvent, the effect of solvent viscosity and permittivity were investigated and indirect information of the polyelectrolyte chain motion was obtained. Furthermore, analysis of CW EPR data also indicates that in mixtures of organic solvent/water PDADMAC chains are preferentially solvated by the organic solvent molecules, while in purely aqueous mixtures the PDADMAC chain segments were found in different conformations depending on the concentration ratio R of FS counterions to PDADMAC repeat units.Broadenings in CW EPR spectra of FS ions were assigned to spin-exchange interaction and hence contain information on the local concentrations and distributions of the counterions. From analysis of these broadenings in terms of a modified cylindrical cell approach of polyelectrolyte theory, radial distribution functions for the FS ions in the different solvents were obtained. This approach breaks down in water above a threshold value of R, which again indicates that PDADMAC chain conformations are altered as a function of R. Double electron-electron resonance (DEER) measurements of FS ions were carried out to probe the distribution of attached counterions along polyelectrolyte chains. For a significant fraction of FS spin probes in solution with a rigid-rod model polyelectrolyte containing charged Ru2+-centers, a bimodal distance distribution was found that nicely reproduced the spacings of direct and next-neighbor Ru2+-centers along the polyelectrolyte: 2.35 and 4.7 nm. For the system of FS/PDADMAC, DEER data could be simulated by assuming a two-state distribution of spin probes, one state corresponding to a homogeneous (3-dimensional) distribution of spin probes in the polyelectrolyte bulk and the other to a linear (1-dimensional) distribution of spin probes that are electrostatically condensed along locally extended PDADMAC chain segments. From this analysis it is suggested that the PDADMAC chains form locally elongated structures of a size of at least ~5 nm.

Numerical simulation of ion transport through ion channels and solid-state nanopores

Ion channels are pore-forming proteins that regulate the flow of ions across biological cell membranes. Ion channels are fundamental in generating and regulating the electrical activity of cells in the nervous system and the contraction of muscolar cells. Solid-state nanopores are nanometer-scale pores located in electrically insulating membranes. They can be adopted as detectors of specific molecules in electrolytic solutions. Permeation of ions from one electrolytic solution to another, through a protein channel or a synthetic pore is a process of considerable importance and realistic analysis of the main dependencies of ion current on the geometrical and compositional characteristics of these structures are highly required. The project described by this thesis is an effort to improve the understanding of ion channels by devising methods for computer simulation that can predict channel conductance from channel structure. This project describes theory, algorithms and implementation techniques used to develop a novel 3-D numerical simulator of ion channels and synthetic nanopores based on the Brownian Dynamics technique. This numerical simulator could represent a valid tool for the study of protein ion channel and synthetic nanopores, allowing to investigate at the atomic-level the complex electrostatic interactions that determine channel conductance and ion selectivity. Moreover it will provide insights on how parameters like temperature, applied voltage, and pore shape could influence ion translocation dynamics. Furthermore it will help making predictions of conductance of given channel structures and it will add information like electrostatic potential or ionic concentrations throughout the simulation domain helping the understanding of ion flow through membrane pores.

Novel simulation methods for Coulomb and hydrodynamic interactions

This thesis presents new methods to simulate systems with hydrodynamic and electrostatic interactions. Part 1 is devoted to computer simulations of Brownian particles with hydrodynamic interactions. The main influence of the solvent on the dynamics of Brownian particles is that it mediates hydrodynamic interactions. In the method, this is simulated by numerical solution of the Navier--Stokes equation on a lattice. To this end, the Lattice--Boltzmann method is used, namely its D3Q19 version. This model is capable to simulate compressible flow. It gives us the advantage to treat dense systems, in particular away from thermal equilibrium. The Lattice--Boltzmann equation is coupled to the particles via a friction force. In addition to this force, acting on {it point} particles, we construct another coupling force, which comes from the pressure tensor. The coupling is purely local, i.~e. the algorithm scales linearly with the total number of particles. In order to be able to map the physical properties of the Lattice--Boltzmann fluid onto a Molecular Dynamics (MD) fluid, the case of an almost incompressible flow is considered. The Fluctuation--Dissipation theorem for the hybrid coupling is analyzed, and a geometric interpretation of the friction coefficient in terms of a Stokes radius is given. Part 2 is devoted to the simulation of charged particles. We present a novel method for obtaining Coulomb interactions as the potential of mean force between charges which are dynamically coupled to a local electromagnetic field. This algorithm scales linearly, too. We focus on the Molecular Dynamics version of the method and show that it is intimately related to the Car--Parrinello approach, while being equivalent to solving Maxwell's equations with freely adjustable speed of light. The Lagrangian formulation of the coupled particles--fields system is derived. The quasi--Hamiltonian dynamics of the system is studied in great detail. For implementation on the computer, the equations of motion are discretized with respect to both space and time. The discretization of the electromagnetic fields on a lattice, as well as the interpolation of the particle charges on the lattice is given. The algorithm is as local as possible: Only nearest neighbors sites of the lattice are interacting with a charged particle. Unphysical self--energies arise as a result of the lattice interpolation of charges, and are corrected by a subtraction scheme based on the exact lattice Green's function. The method allows easy parallelization using standard domain decomposition. Some benchmarking results of the algorithm are presented and discussed.

Theoretical investigation of the interaction of a polymer film with a nanoparticle

The fundamental aim in our investigation of the interaction of a polymer film with a nanoparticle is the extraction of information on the dynamics of the liquid using a single tracking particle. In this work two theoretical methods were used: one passive, where the motion of the particle measures the dynamics of the liquid, one active, where perturbations in the system are introduced through the particle. In the first part of this investigation a thin polymeric film on a substrate is studied using molecular dynamics simulations. The polymer is modeled via a 'bead spring' model. The particle is spheric and non structured and is able to interact with the monomers via a Lennard Jones potential. The system is micro-canonical and simulations were performed for average temperatures between the glass transition temperature of the film and its dewetting temperature. It is shown that the stability of the nanoparticle on the polymer film in the absence of gravity depends strongly on the form of the chosen interaction potential between nanoparticle and polymer. The relative position of the tracking particle to the liquid vapor interface of the polymer film shows the glass transition of the latter. The velocity correlation function and the mean square displacement of the particle has shown that it is caged when the temperature is close to the glass transition temperature. The analysis of the dynamics at long times shows the coupling of the nanoparticle to the center of mass of the polymer chains. The use of the Stokes-Einstein formula, which relates the diffusion coefficient to the viscosity, permits to use the nanoparticle as a probe for the determination of the bulk viscosity of the melt, the so called 'microrheology'. It is shown that for low frequencies the result obtained using microrheology coincides with the results of the Rouse model applied to the polymer dynamics. In the second part of this investigation the equations of Linear Hydrodynamics are solved for a nanoparticle oscillating above the film. It is shown that compressible liquids have mechanical response to external perturbations induced with the nanoparticle. These solutions show strong velocity and pressure profiles of the liquid near the interface, as well as a mechanical response of the liquid-vapor interface. The results obtained with this calculations can be employed for the interpretation of experimental results of non contact AFM microscopy

Stochastische Modelle für nichtlineare Antwortfunktionen in ungeordneten Systemen

In dieser Arbeit werden nichtlineare Experimente zur Untersuchung der Dynamik in amorphen Festkörpern im Rahmen von Modellrechnungen diskutiert. Die Experimente beschäftigen sich mit der Frage nach dynamischen Heterogenitäten, worunter man das Vorliegen dynamischer Prozesse auf unterschiedlichen Zeitskalen versteht. Ist es möglich, gezielt 'langsame' oder 'schnelle' Dynamik in der Probe nachzuweisen, so ist die Existenz von dynamischen Heterogenitäten gezeigt. Ziel der Experimente sind deshalb sogenannte frequenzselektive Anregungen des Systems. In den beiden diskutierten Experimenten, zum einen nichtresonantes Lochbrennen, zum anderen ein ähnliches Experiment, das auf dem dynamischen Kerreffekt beruht, werden nichtlineare Antwortfunktionen gemessen. Um eine Probe in frequenzselektiver Weise anzuregen, werden zunächst einer oder mehrere Zyklen eines oszillierenden elektrischen Feldes an die Probe angelegt. Die Experimente werden zunächst im Terahertz-Bereich untersucht. Auf dieser Zeitskala findet man phonon-ähnliche kollektive Schwingungen in Gläsern. Diese Schwingungen werden durch (anharmonische) Brownsche Oszillatoren beschrieben. Der zentrale Befund der Modellrechnungen ist, daß eine frequenzselektive Anregung im Terahertz-Bereich möglich ist. Ein Nachweis dynamischer Heterogenitäten im Terahertz-Bereich ist somit durch beide Experimente möglich. Anschliessend wird das vorgestellte Kerreffekt-Experiment im Bereich wesentlich kleinerer Frequenzen diskutiert. Die langsame Reorientierungsdynamik in unterkühlten Flüssigkeiten wird dabei durch ein Rotationsdiffusionsmodell beschrieben. Es werden zum einen ein heterogenes und zum anderen ein homogenes Szenario zugrundegelegt. Es stellt sich heraus, daß wie beim Lochbrennen eine Unterscheidung durch das Experiment möglich ist. Das Kerreffekt-Experiment wird somit als eine relativ einfache Alternative zur Technik des nichtresonanten Lochbrennens vorgeschlagen.

Mechanics and dynamics of liposomes and cells studied by QCM and ECIS

The present thesis introduces a novel sensitive technique based on TSM resonators that provides quantitative information about the dynamic properties of biological cells and artificial lipid systems. In order to support and complement results obtained by this method supplementary measurements based on ECIS technique were carried out. The first part (chapters 3 and 4) deals with artificial lipid systems. In chapter 3 ECIS measurements were used to monitor the adsorption of giant unilamellar vesicles as well as their thermal fluctuations. From dynamic Monte Carlo Simulations the rate constant of vesicle adsorption was determined. Furthermore, analysis of fluctuation measurements reveals Brownian motion reflecting membrane undulations of the adherent liposomes. In chapter 4 QCM-based fluctuation measurements were applied to quantify nanoscopically small deformations of giant unilamellar vesicles with an external electrical field applied simultaneously. The response of liposomes to an external voltage with shape changes was monitored as a function of cholesterol content and adhesion force. In the second part (chapters 5 - 8) attention was given to cell motility. It was shown for the first time, that QCM can be applied to monitor the dynamics of living adherent cells in real time. QCM turned out to be a highly sensitive tool to detect the vertical motility of adherent cells with a time resolution in the millisecond regime. The response of cells to environmental changes such as temperature or osmotic stress could be quantified. Furthermore, the impact of cytochalasin D (inhibits actin polymerization) and taxol (facilitate polymerization of microtubules) as well as nocodazole (depolymerizes microtubules) on the dynamic properties of cells was scrutinized. Each drug provoked a significant reduction of the monitored cell shape fluctuations as expected from their biochemical potential. However, not only the abolition of fluctuations was observed but also an increase of motility due to integrin-induced transmembrane signals. These signals were activated by peptides containing the RGD sequence, which is known to be an integrin recognition motif. Ultimately, two pancreatic carcinoma cell lines, derived from the same original tumor, but known to possess different metastatic potential were studied. Different dynamic behavior of the two cell lines was observed which was attributed to cell-cell as well as cell-substrate interactions rather than motility. Thus one may envision that it might be possible to characterize the motility of different cell types as a function of many variables by this new highly sensitive technique based on TSM resonators. Finally the origin of the broad cell resonance was investigated. Improvement of the time resolution reveals the "real" frequency of cell shape fluctuations. Several broad resonances around 3-5 Hz, 15-17 Hz and 25-29 Hz were observed and that could unequivocally be assigned to biological activity of living cells. However, the kind of biological process that provokes this synchronized collective and periodic behavior of the cells remains to be elucidated.

A moment symbolic representation of Lévy processes with applications

By using a symbolic method, known in the literature as the classical umbral calculus, a symbolic representation of Lévy processes is given and a new family of time-space harmonic polynomials with respect to such processes, which includes and generalizes the exponential complete Bell polynomials, is introduced. The usefulness of time-space harmonic polynomials with respect to Lévy processes is that it is a martingale the stochastic process obtained by replacing the indeterminate x of the polynomials with a Lévy process, whereas the Lévy process does not necessarily have this property. Therefore to find such polynomials could be particularly meaningful for applications. This new family includes Hermite polynomials, time-space harmonic with respect to Brownian motion, Poisson-Charlier polynomials with respect to Poisson processes, Laguerre and actuarial polynomials with respect to Gamma processes , Meixner polynomials of the first kind with respect to Pascal processes, Euler, Bernoulli, Krawtchuk, and pseudo-Narumi polynomials with respect to suitable random walks. The role played by cumulants is stressed and brought to the light, either in the symbolic representation of Lévy processes and their infinite divisibility property, either in the generalization, via umbral Kailath-Segall formula, of the well-known formulae giving elementary symmetric polynomials in terms of power sum symmetric polynomials. The expression of the family of time-space harmonic polynomials here introduced has some connections with the so-called moment representation of various families of multivariate polynomials. Such moment representation has been studied here for the first time in connection with the time-space harmonic property with respect to suitable symbolic multivariate Lévy processes. In particular, multivariate Hermite polynomials and their properties have been studied in connection with a symbolic version of the multivariate Brownian motion, while multivariate Bernoulli and Euler polynomials are represented as powers of multivariate polynomials which are time-space harmonic with respect to suitable multivariate Lévy processes.

Mutually catalytic branching at infinite rate

The purpose of this doctoral thesis is to prove existence for a mutually catalytic random walk with infinite branching rate on countably many sites. The process is defined as a weak limit of an approximating family of processes. An approximating process is constructed by adding jumps to a deterministic migration on an equidistant time grid. As law of jumps we need to choose the invariant probability measure of the mutually catalytic random walk with a finite branching rate in the recurrent regime. This model was introduced by Dawson and Perkins (1998) and this thesis relies heavily on their work. Due to the properties of this invariant distribution, which is in fact the exit distribution of planar Brownian motion from the first quadrant, it is possible to establish a martingale problem for the weak limit of any convergent sequence of approximating processes. We can prove a duality relation for the solution to the mentioned martingale problem, which goes back to Mytnik (1996) in the case of finite rate branching, and this duality gives rise to weak uniqueness for the solution to the martingale problem. Using standard arguments we can show that this solution is in fact a Feller process and it has the strong Markov property. For the case of only one site we prove that the model we have constructed is the limit of finite rate mutually catalytic branching processes as the branching rate approaches infinity. Therefore, it seems naturalto refer to the above model as an infinite rate branching process. However, a result for convergence on infinitely many sites remains open.

Thermal fluctuations and boundary conditions in the lattice Boltzmann method

The lattice Boltzmann method is a popular approach for simulating hydrodynamic interactions in soft matter and complex fluids. The solvent is represented on a discrete lattice whose nodes are populated by particle distributions that propagate on the discrete links between the nodes and undergo local collisions. On large length and time scales, the microdynamics leads to a hydrodynamic flow field that satisfies the Navier-Stokes equation. In this thesis, several extensions to the lattice Boltzmann method are developed. In complex fluids, for example suspensions, Brownian motion of the solutes is of paramount importance. However, it can not be simulated with the original lattice Boltzmann method because the dynamics is completely deterministic. It is possible, though, to introduce thermal fluctuations in order to reproduce the equations of fluctuating hydrodynamics. In this work, a generalized lattice gas model is used to systematically derive the fluctuating lattice Boltzmann equation from statistical mechanics principles. The stochastic part of the dynamics is interpreted as a Monte Carlo process, which is then required to satisfy the condition of detailed balance. This leads to an expression for the thermal fluctuations which implies that it is essential to thermalize all degrees of freedom of the system, including the kinetic modes. The new formalism guarantees that the fluctuating lattice Boltzmann equation is simultaneously consistent with both fluctuating hydrodynamics and statistical mechanics. This establishes a foundation for future extensions, such as the treatment of multi-phase and thermal flows. An important range of applications for the lattice Boltzmann method is formed by microfluidics. Fostered by the "lab-on-a-chip" paradigm, there is an increasing need for computer simulations which are able to complement the achievements of theory and experiment. Microfluidic systems are characterized by a large surface-to-volume ratio and, therefore, boundary conditions are of special relevance. On the microscale, the standard no-slip boundary condition used in hydrodynamics has to be replaced by a slip boundary condition. In this work, a boundary condition for lattice Boltzmann is constructed that allows the slip length to be tuned by a single model parameter. Furthermore, a conceptually new approach for constructing boundary conditions is explored, where the reduced symmetry at the boundary is explicitly incorporated into the lattice model. The lattice Boltzmann method is systematically extended to the reduced symmetry model. In the case of a Poiseuille flow in a plane channel, it is shown that a special choice of the collision operator is required to reproduce the correct flow profile. This systematic approach sheds light on the consequences of the reduced symmetry at the boundary and leads to a deeper understanding of boundary conditions in the lattice Boltzmann method. This can help to develop improved boundary conditions that lead to more accurate simulation results.