Phenomenological approach to nonlinear Langevin equations

In this paper we address the problem of consistently constructing Langevin equations to describe fluctuations in nonlinear systems. Detailed balance severely restricts the choice of the random force, but we prove that this property, together with the macroscopic knowledge of the system, is not enough to determine all the properties of the random force. If the cause of the fluctuations is weakly coupled to the fluctuating variable, then the statistical properties of the random force can be completely specified. For variables odd under time reversal, microscopic reversibility and weak coupling impose symmetry relations on the variable-dependent Onsager coefficients. We then analyze the fluctuations in two cases: Brownian motion in position space and an asymmetric diode, for which the analysis based in the master equation approach is known. We find that, to the order of validity of the Langevin equation proposed here, the phenomenological theory is in agreement with the results predicted by more microscopic models

Stochastic generation of homogeneous isotropic turbulence with well-defined-spectra

A precise and simple computational model to generate well-behaved two-dimensional turbulent flows is presented. The whole approach rests on the use of stochastic differential equations and is general enough to reproduce a variety of energy spectra and spatiotemporal correlation functions. Analytical expressions for both the continuous and the discrete versions, together with simulation algorithms, are derived. Results for two relevant spectra, covering distinct ranges of wave numbers, are given.

Diffusion of passive scalars under stochastic convection

The diffusion of passive scalars convected by turbulent flows is addressed here. A practical procedure to obtain stochastic velocity fields with well¿defined energy spectrum functions is also presented. Analytical results are derived, based on the use of stochastic differential equations, where the basic hypothesis involved refers to a rapidly decaying turbulence. These predictions are favorable compared with direct computer simulations of stochastic differential equations containing multiplicative space¿time correlated noise.

Asymmetric stochastic switching driven by intrinsic molecular noise

Low-copy-number molecules are involved in many functions in cells. The intrinsic fluctuations of these numbers can enable stochastic switching between multiple steady states, inducing phenotypic variability. Herein we present a theoretical and computational study based on Master Equations and Fokker-Planck and Langevin descriptions of stochastic switching for a genetic circuit of autoactivation. We show that in this circuit the intrinsic fluctuations arising from low-copy numbers, which are inherently state-dependent, drive asymmetric switching. These theoretical results are consistent with experimental data that have been reported for the bistable system of the gallactose signaling network in yeast. Our study unravels that intrinsic fluctuations, while not required to describe bistability, are fundamental to understand stochastic switching and the dynamical relative stability of multiple states.

Langevin equations with multiplicative noise: Application to domain growth

Langevin Equations of Ginzburg-Landau form, with multiplicative noise, are proposed to study the effects of fluctuations in domain growth. These equations are derived from a coarse-grained methodology. The Cahn-Hiliard-Cook linear stability analysis predicts some effects in the transitory regime. We also derive numerical algorithms for the computer simulation of these equations. The numerical results corroborate the analytical predictions of the linear analysis. We also present simulation results for spinodal decomposition at large times.

Effective Electrodiffusion equation for non uniform nanochannels

We derive a one dimensional formulation of the Planck-Nernst-Poisson equation to describe the dynamics of of a symmetric binary electrolyte in channels whose section is of nanometric section and varies along the axial direction. The approach is in the spirit of the Fick-Jacobs di fusion equation and leads to a system of coupled equations for the partial densities which depends on the charge sitting at the walls in a non trivial fashion. We consider two kinds of non uniformities, those due to the spatial variation of charge distribution and those due to the shape variation of the pore and report one and three-dimensional solutions of the electrokinetic equations.

Asymmetric stochastic switching driven by intrinsic molecular noise

Low-copy-number molecules are involved in many functions in cells. The intrinsic fluctuations of these numbers can enable stochastic switching between multiple steady states, inducing phenotypic variability. Herein we present a theoretical and computational study based on Master Equations and Fokker-Planck and Langevin descriptions of stochastic switching for a genetic circuit of autoactivation. We show that in this circuit the intrinsic fluctuations arising from low-copy numbers, which are inherently state-dependent, drive asymmetric switching. These theoretical results are consistent with experimental data that have been reported for the bistable system of the gallactose signaling network in yeast. Our study unravels that intrinsic fluctuations, while not required to describe bistability, are fundamental to understand stochastic switching and the dynamical relative stability of multiple states.

Asymptotic analysis of stock price densities and implied volatilities in mixed stochastic models

In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin con- volution of functions de ned on (0;1), and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-di usion models and stochastic volatility models with jumps. We apply our general results to the Heston model with double exponential jumps, and make a detailed analysis of the asymptotic behavior of the stock price density, the call option pricing function, and the implied volatility in this model. We also obtain similar results for the Heston model with jumps distributed according to the NIG law.

The knapsack problem approach in solving partial hedging problems of options

En option är ett finansiellt kontrakt som ger dess innehavare en rättighet (men medför ingen skyldighet) att sälja eller köpa någonting (till exempel en aktie) till eller från säljaren av optionen till ett visst pris vid en bestämd tidpunkt i framtiden. Den som säljer optionen binder sig till att gå med på denna framtida transaktion ifall optionsinnehavaren längre fram bestämmer sig för att inlösa optionen. Säljaren av optionen åtar sig alltså en risk av att den framtida transaktion som optionsinnehavaren kan tvinga honom att göra visar sig vara ofördelaktig för honom. Frågan om hur säljaren kan skydda sig mot denna risk leder till intressanta optimeringsproblem, där målet är att hitta en optimal skyddsstrategi under vissa givna villkor. Sådana optimeringsproblem har studerats mycket inom finansiell matematik. Avhandlingen "The knapsack problem approach in solving partial hedging problems of options" inför en ytterligare synpunkt till denna diskussion: I en relativt enkel (ändlig och komplett) marknadsmodell kan nämligen vissa partiella skyddsproblem beskrivas som så kallade kappsäcksproblem. De sistnämnda är välkända inom en gren av matematik som heter operationsanalys. I avhandlingen visas hur skyddsproblem som tidigare lösts på andra sätt kan alternativt lösas med hjälp av metoder som utvecklats för kappsäcksproblem. Förfarandet tillämpas även på helt nya skyddsproblem i samband med så kallade amerikanska optioner.

Combinatorics on Words. New Aspects on Avoidability, Defect Effect, Equations and Palindromes

In this thesis we examine four well-known and traditional concepts of combinatorics on words. However the contexts in which these topics are treated are not the traditional ones. More precisely, the question of avoidability is asked, for example, in terms of k-abelian squares. Two words are said to be k-abelian equivalent if they have the same number of occurrences of each factor up to length k. Consequently, k-abelian equivalence can be seen as a sharpening of abelian equivalence. This fairly new concept is discussed broader than the other topics of this thesis. The second main subject concerns the defect property. The defect theorem is a well-known result for words. We will analyze the property, for example, among the sets of 2-dimensional words, i.e., polyominoes composed of labelled unit squares. From the defect effect we move to equations. We will use a special way to define a product operation for words and then solve a few basic equations over constructed partial semigroup. We will also consider the satisfiability question and the compactness property with respect to this kind of equations. The final topic of the thesis deals with palindromes. Some finite words, including all binary words, are uniquely determined up to word isomorphism by the position and length of some of its palindromic factors. The famous Thue-Morse word has the property that for each positive integer n, there exists a factor which cannot be generated by fewer than n palindromes. We prove that in general, every non ultimately periodic word contains a factor which cannot be generated by fewer than 3 palindromes, and we obtain a classification of those binary words each of whose factors are generated by at most 3 palindromes. Surprisingly these words are related to another much studied set of words, Sturmian words.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Production and partial characterization of lipase from Penicillium verrucosum obtained by submerged fermentation of conventional and industrial media

The growing interest in lipase production is related to the potential biotechnological applications that these enzymes present. Current studies on lipase production by submerged fermentation involve the use of agro-industrial residues aiming at increasing economic attractiveness. Based on these aspects, the objective of this work was to investigate lipase production by Penicillium verrucosum in submerged fermentation using a conventional medium based on peptone, yeast extract, NaCl and olive oil, and an industrial medium based on corn steep liquor, Prodex Lac (yeast hydrolysate), NaCl and olive oil, as well as to characterize the crude enzymatic extracts obtained. Kinetics of lipase production was evaluated and the highest enzymatic activities, of 3.15 and 2.22 U.mL-1, were observed when conventional and industrial media were used, respectively. The enzymatic extract showed optimal activity in the range from 30 to 40 °C and at pH 7.0. Although the industrial medium presents economical advantages over the conventional medium, the presence of agro-industrial residues rich in nitrogen and other important nutrients seemed to contribute to a reduction in lipase activity.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.

Investigations on the Structural, Spectral and Magnetic Interactions of Transition Metal Complexes of Multidentate Ligands from Di-2-pyridyl ketone and N(4)-substituted Thiosemicarbazides

The present work deals with the investigations on sthe structural spectral and magnetic interactions of transition metal complexes of multidentate ligands from D1-2-pyridyl ketone and N(4)-Substituted thiosemicarbazides.Thiosemicarbazones are thiourea derivatives with the general formula R2N— C(S)—NH—N=CR2. In the solution state, the thiosemicarbazones exhibit the thionethiol tautomerism similar to the keto-enol tautomerism, and in solution state the thiol form predominates and a deprotonation at the thiolate group in alcoholic medium enhances the coordination abilities ofthe thiosemicarbazones.The magnetochemistry of metal complexes of di-2-pyridyl ketone is a current hot subject of research, which mainly owes to the excellent structural diversity of the complexes ranging from cubanes to clusters, with promising ferromagnetic outputs.Only few efforts were aimed at the magnetochemistry of metal complexes of thiosemicarbazones, and that too were concerned with the complexes of bisttltioscinicarbazones). However, as far as the monothiosemicarbazones are concerned, the magnetochemistry of transition metal complexes of di-2-pyridyl ketone thiosemicarbazones turned up quite unexplored. Consequently, an investigation into it appeared novel and promising to us and that prompted this study, which can be regarded as the initial step towards exploring the magnetochemistry of thiosemicarbazone complexes, especially of di-2-pyridyl ketone derivatives.We could successfully isolate single crystals suitable for X-ray diffraction for the first three ligands. To conclude, we have synthesized some new thiosemicarbazones and their transition metal complexes and studied their structural, spectral and magnetic attributes. Some ofthe complexes revealed interesting stereochemistries and possible bridging characteristics with spectroscopic evidences. Unfortunately, single crystal Xray diffraction studies could not be carried out for many of these interesting compounds due to the lack of availability of suitable quality single crystals. However, the magnetic studies provided support for the proposed stereochemistry giving evidences for their magnetically concentrated nature. The magnetic susceptibilities measured at six different temperatures in the 80-298 K range are fitted into different magnetic equations, which provided an idea about the magnetic behavior of the compounds under study. Some of the copper, oxovanadium, nickel and cobalt complexes are found to possess anomalous magnetic moments, i.e., they revealed no regular gradation with temperature. However, some other copper complexes are observed to be antiferromagnetic, due to super-exchange pathways. The manganese complexes and one of the cobalt complexes are also observed to be antiferromagnetic in nature. However, some nickel complexes have turned up to be ferromagnetic. Accordingly, the versatile stereoehemistry and magnetic behavior of the complexes studied, prompt us to conclude that the transition metal complexes of di-2-pyridyl ketone thiosemicarbazones are promising systems for potential magnetic applications.