A spatial stochastic model for rumor transmission

We consider an interacting particle system representing the spread of a rumor by agents on the d-dimensional integer lattice. Each agent may be in any of the three states belonging to the set {0,1,2}. Here 0 stands for ignorants, 1 for spreaders and 2 for stiflers. A spreader tells the rumor to any of its (nearest) ignorant neighbors at rate lambda. At rate alpha a spreader becomes a stifler due to the action of other (nearest neighbor) spreaders. Finally, spreaders and stiflers forget the rumor at rate one. We study sufficient conditions under which the rumor either becomes extinct or survives with positive probability.

Influence of the initial condition in equilibrium last-passage percolation models

In this paper we consider an equilibrium last-passage percolation model on an environment given by a compound two-dimensional Poisson process. We prove an L-2-formula relating the initial measure with the last-passage percolation time. This formula turns out to be a useful tool to analyze the fluctuations of the last-passage times along non-characteristic directions.

Systematic Improvement of Density Functionals through Parameter-Free Hybridization Schemes

It is suggested here that the ultimate accuracy of DFT methods arises from the type of hybridization scheme followed. This idea can be cast into a mathematical formulation utilizing an integrand connecting the noninteracting and the interacting particle system. We consider two previously developed models for it, dubbed as HYB0 and QIDH, and assess a large number of exchange-correlation functionals against the AE6, G2/148, and S22 reference data sets. An interesting consequence of these hybridization schemes is that the error bars, including the standard deviation, are found to markedly decrease with respect to the density-based (nonhybrid) case. This improvement is substantially better than variations due to the underlying density functional used. We thus finally hypothesize about the universal character of the HYB0 and QIDH models.

Dissipative particle dynamics simulation of wettability alternation phenomena in the chemical flooding process

Wettability alternation phenomena is considered one of the most important enhanced oil recovery (EOR) mechanisms in the chemical flooding process and induced by the adsorption of surfactant on the rock surface. These phenomena are studied by a mesoscopic method named as dissipative particle dynamics (DPD). Both the alteration phenomena of water-wet to oil-wet and that of oil-wet to water-wet are simulated based on reasonable definition of interaction parameters between beads. The wetting hysteresis phenomenon and the process of oil-drops detachment from rock surfaces with different wettability are simulated by adding long-range external forces on the fluid particles. The simulation results show that, the oil drop is liable to spread on the oil-wetting surface and move in the form of liquid film flow, whereas it is likely to move as a whole on the water-wetting surface. There are the same phenomena occuring in wettability-alternated cases. The results also show that DPD method provides a feasible approach to the problems of seepage flow with physicochemical phenomena and can be used to study the mechanism of EOR of chemical flooding.

Testing Isotropy and a related Random Walk problem

One comes across directions as the observations in a number of situations. The first inferential question that one should answer when dealing with such data is, “Are they isotropic or uniformly distributed?” The answer to this question goes back in history which we shall retrace a bit and provide an exact and approximate solution to this so-called “Pearson’s Random Walk” problem.

A water quality information system in support of the state of Illinois standards review process /

Cover title.

The roles of exercise-induced immune system disturbances in the pathology of heat stroke - The dual pathway model of heat stroke

Heat stroke is a life-threatening condition that can be fatal if not appropriately managed. Although heat stroke has been recognised as a medical condition for centuries, a universally accepted definition of heat stroke is lacking and the pathology of heat stroke is not fully understood. Information derived from autopsy reports and the clinical presentation of patients with heat stroke indicates that hyperthermia, septicaemia, central nervous system impairment and cardiovascular failure play important roles in the pathology of heat stroke. The current models of heat stroke advocate that heat stroke is triggered by hyperthermia but is driven by endotoxaemia. Endotoxaemia triggers the systemic inflammatory response, which can lead to systemic coagulation and haemorrhage, necrosis, cell death and multi-organ failure. However, the current heat stroke models cannot fully explain the discrepancies in high core temperature (Tc) as a trigger of heat stroke within and between individuals. Research on the concept of critical Tc: as a limitation to endurance exercise implies that a high Tc may function as a signal to trigger the protective mechanisms against heat stroke. Athletes undergoing a period of intense training are subjected to a variety of immune and gastrointestinal (GI) disturbances. The immune disturbances include the suppression of immune cells and their functions, suppression of cell-mediated immunity, translocation of lipopolysaccharide (LPS), suppression of anti-LPS antibodies, increased macrophage activity due to muscle tissue damage, and increased concentration of circulating inflammatory and pyrogenic cytokines. Common symptoms of exercise-induced GI disturbances include diarrhoea, vomiting, gastrointestinal bleeding, and cramps, which may increase gut-related LPS translocation. This article discusses the current evidence that supports the argument that these exercise-induced immune and GI disturbances may contribute to the development of endotoxaemia and heat stroke. When endotoxaemia can be tolerated or prevented, continuing exercise and heat exposure will elevate Tc to a higher level (> 42 degrees C), where heat stroke may occur through the direct thermal effects of heat on organ tissues and cells. We also discuss the evidence suggesting that heat stroke may occur through endotoxaemia (heat sepsis), the primary pathway of heat stroke, or hyperthermia, the secondary pathway of heat stroke. The existence of these two pathways of heat stroke and the contribution of exercise-induced immune and GI disturbances in the primary pathway of heat stroke are illustrated in the dual pathway model of heat stroke. This model of heat stroke suggests that prolonged intense exercise suppresses anti-LPS mechanisms, and promotes inflammatory and pyrogenic activities in the pathway of heat stroke.

Parametric Estimation in Branching Processes with an Increasing Random Number of Ancestors

2000 Mathematics Subject Classification: 60J80, 62M05

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

Catalytic liquid- and gas-phase oxidations for the synthesis of intermediates and specialty chemicals: some examples of industrial relevance

Nowadays, it is clear that the target of creating a sustainable future for the next generations requires to re-think the industrial application of chemistry. It is also evident that more sustainable chemical processes may be economically convenient, in comparison with the conventional ones, because fewer by-products means lower costs for raw materials, for separation and for disposal treatments; but also it implies an increase of productivity and, as a consequence, smaller reactors can be used. In addition, an indirect gain could derive from the better public image of the company, marketing sustainable products or processes. In this context, oxidation reactions play a major role, being the tool for the production of huge quantities of chemical intermediates and specialties. Potentially, the impact of these productions on the environment could have been much worse than it is, if a continuous efforts hadn’t been spent to improve the technologies employed. Substantial technological innovations have driven the development of new catalytic systems, the improvement of reactions and process technologies, contributing to move the chemical industry in the direction of a more sustainable and ecological approach. The roadmap for the application of these concepts includes new synthetic strategies, alternative reactants, catalysts heterogenisation and innovative reactor configurations and process design. Actually, in order to implement all these ideas into real projects, the development of more efficient reactions is one primary target. Yield, selectivity and space-time yield are the right metrics for evaluating the reaction efficiency. In the case of catalytic selective oxidation, the control of selectivity has always been the principal issue, because the formation of total oxidation products (carbon oxides) is thermodynamically more favoured than the formation of the desired, partially oxidized compound. As a matter of fact, only in few oxidation reactions a total, or close to total, conversion is achieved, and usually the selectivity is limited by the formation of by-products or co-products, that often implies unfavourable process economics; moreover, sometimes the cost of the oxidant further penalizes the process. During my PhD work, I have investigated four reactions that are emblematic of the new approaches used in the chemical industry. In the Part A of my thesis, a new process aimed at a more sustainable production of menadione (vitamin K3) is described. The “greener” approach includes the use of hydrogen peroxide in place of chromate (from a stoichiometric oxidation to a catalytic oxidation), also avoiding the production of dangerous waste. Moreover, I have studied the possibility of using an heterogeneous catalytic system, able to efficiently activate hydrogen peroxide. Indeed, the overall process would be carried out in two different steps: the first is the methylation of 1-naphthol with methanol to yield 2-methyl-1-naphthol, the second one is the oxidation of the latter compound to menadione. The catalyst for this latter step, the reaction object of my investigation, consists of Nb2O5-SiO2 prepared with the sol-gel technique. The catalytic tests were first carried out under conditions that simulate the in-situ generation of hydrogen peroxide, that means using a low concentration of the oxidant. Then, experiments were carried out using higher hydrogen peroxide concentration. The study of the reaction mechanism was fundamental to get indications about the best operative conditions, and improve the selectivity to menadione. In the Part B, I explored the direct oxidation of benzene to phenol with hydrogen peroxide. The industrial process for phenol is the oxidation of cumene with oxygen, that also co-produces acetone. This can be considered a case of how economics could drive the sustainability issue; in fact, the new process allowing to obtain directly phenol, besides avoiding the co-production of acetone (a burden for phenol, because the market requirements for the two products are quite different), might be economically convenient with respect to the conventional process, if a high selectivity to phenol were obtained. Titanium silicalite-1 (TS-1) is the catalyst chosen for this reaction. Comparing the reactivity results obtained with some TS-1 samples having different chemical-physical properties, and analyzing in detail the effect of the more important reaction parameters, we could formulate some hypothesis concerning the reaction network and mechanism. Part C of my thesis deals with the hydroxylation of phenol to hydroquinone and catechol. This reaction is already industrially applied but, for economical reason, an improvement of the selectivity to the para di-hydroxilated compound and a decrease of the selectivity to the ortho isomer would be desirable. Also in this case, the catalyst used was the TS-1. The aim of my research was to find out a method to control the selectivity ratio between the two isomers, and finally to make the industrial process more flexible, in order to adapt the process performance in function of fluctuations of the market requirements. The reaction was carried out in both a batch stirred reactor and in a re-circulating fixed-bed reactor. In the first system, the effect of various reaction parameters on catalytic behaviour was investigated: type of solvent or co-solvent, and particle size. With the second reactor type, I investigated the possibility to use a continuous system, and the catalyst shaped in extrudates (instead of powder), in order to avoid the catalyst filtration step. Finally, part D deals with the study of a new process for the valorisation of glycerol, by means of transformation into valuable chemicals. This molecule is nowadays produced in big amount, being a co-product in biodiesel synthesis; therefore, it is considered a raw material from renewable resources (a bio-platform molecule). Initially, we tested the oxidation of glycerol in the liquid-phase, with hydrogen peroxide and TS-1. However, results achieved were not satisfactory. Then we investigated the gas-phase transformation of glycerol into acrylic acid, with the intermediate formation of acrolein; the latter can be obtained by dehydration of glycerol, and then can be oxidized into acrylic acid. Actually, the oxidation step from acrolein to acrylic acid is already optimized at an industrial level; therefore, we decided to investigate in depth the first step of the process. I studied the reactivity of heterogeneous acid catalysts based on sulphated zirconia. Tests were carried out both in aerobic and anaerobic conditions, in order to investigate the effect of oxygen on the catalyst deactivation rate (one main problem usually met in glycerol dehydration). Finally, I studied the reactivity of bifunctional systems, made of Keggin-type polyoxometalates, either alone or supported over sulphated zirconia, in this way combining the acid functionality (necessary for the dehydrative step) with the redox one (necessary for the oxidative step). In conclusion, during my PhD work I investigated reactions that apply the “green chemistry” rules and strategies; in particular, I studied new greener approaches for the synthesis of chemicals (Part A and Part B), the optimisation of reaction parameters to make the oxidation process more flexible (Part C), and the use of a bioplatform molecule for the synthesis of a chemical intermediate (Part D).

Thermal relaxation for particle systems in interaction with several bosonic heat reservoirs

The present thesis is concerned with the study of a quantum physical system composed of a small particle system (such as a spin chain) and several quantized massless boson fields (as photon gasses or phonon fields) at positive temperature. The setup serves as a simplified model for matter in interaction with thermal "radiation" from different sources. Hereby, questions concerning the dynamical and thermodynamic properties of particle-boson configurations far from thermal equilibrium are in the center of interest. We study a specific situation where the particle system is brought in contact with the boson systems (occasionally referred to as heat reservoirs) where the reservoirs are prepared close to thermal equilibrium states, each at a different temperature. We analyze the interacting time evolution of such an initial configuration and we show thermal relaxation of the system into a stationary state, i.e., we prove the existence of a time invariant state which is the unique limit state of the considered initial configurations evolving in time. As long as the reservoirs have been prepared at different temperatures, this stationary state features thermodynamic characteristics as stationary energy fluxes and a positive entropy production rate which distinguishes it from being a thermal equilibrium at any temperature. Therefore, we refer to it as non-equilibrium stationary state or simply NESS. The physical setup is phrased mathematically in the language of C*-algebras. The thesis gives an extended review of the application of operator algebraic theories to quantum statistical mechanics and introduces in detail the mathematical objects to describe matter in interaction with radiation. The C*-theory is adapted to the concrete setup. The algebraic description of the system is lifted into a Hilbert space framework. The appropriate Hilbert space representation is given by a bosonic Fock space over a suitable L2-space. The first part of the present work is concluded by the derivation of a spectral theory which connects the dynamical and thermodynamic features with spectral properties of a suitable generator, say K, of the time evolution in this Hilbert space setting. That way, the question about thermal relaxation becomes a spectral problem. The operator K is of Pauli-Fierz type. The spectral analysis of the generator K follows. This task is the core part of the work and it employs various kinds of functional analytic techniques. The operator K results from a perturbation of an operator L0 which describes the non-interacting particle-boson system. All spectral considerations are done in a perturbative regime, i.e., we assume that the strength of the coupling is sufficiently small. The extraction of dynamical features of the system from properties of K requires, in particular, the knowledge about the spectrum of K in the nearest vicinity of eigenvalues of the unperturbed operator L0. Since convergent Neumann series expansions only qualify to study the perturbed spectrum in the neighborhood of the unperturbed one on a scale of order of the coupling strength we need to apply a more refined tool, the Feshbach map. This technique allows the analysis of the spectrum on a smaller scale by transferring the analysis to a spectral subspace. The need of spectral information on arbitrary scales requires an iteration of the Feshbach map. This procedure leads to an operator-theoretic renormalization group. The reader is introduced to the Feshbach technique and the renormalization procedure based on it is discussed in full detail. Further, it is explained how the spectral information is extracted from the renormalization group flow. The present dissertation is an extension of two kinds of a recent research contribution by Jakšić and Pillet to a similar physical setup. Firstly, we consider the more delicate situation of bosonic heat reservoirs instead of fermionic ones, and secondly, the system can be studied uniformly for small reservoir temperatures. The adaption of the Feshbach map-based renormalization procedure by Bach, Chen, Fröhlich, and Sigal to concrete spectral problems in quantum statistical mechanics is a further novelty of this work.

Ergodicity and regularity of invariant measure for branching Markov processes with immigration

In this thesis we consider systems of finitely many particles moving on paths given by a strong Markov process and undergoing branching and reproduction at random times. The branching rate of a particle, its number of offspring and their spatial distribution are allowed to depend on the particle's position and possibly on the configuration of coexisting particles. In addition there is immigration of new particles, with the rate of immigration and the distribution of immigrants possibly depending on the configuration of pre-existing particles as well. In the first two chapters of this work, we concentrate on the case that the joint motion of particles is governed by a diffusion with interacting components. The resulting process of particle configurations was studied by E. Löcherbach (2002, 2004) and is known as a branching diffusion with immigration (BDI). Chapter 1 contains a detailed introduction of the basic model assumptions, in particular an assumption of ergodicity which guarantees that the BDI process is positive Harris recurrent with finite invariant measure on the configuration space. This object and a closely related quantity, namely the invariant occupation measure on the single-particle space, are investigated in Chapter 2 where we study the problem of the existence of Lebesgue-densities with nice regularity properties. For example, it turns out that the existence of a continuous density for the invariant measure depends on the mechanism by which newborn particles are distributed in space, namely whether branching particles reproduce at their death position or their offspring are distributed according to an absolutely continuous transition kernel. In Chapter 3, we assume that the quantities defining the model depend only on the spatial position but not on the configuration of coexisting particles. In this framework (which was considered by Höpfner and Löcherbach (2005) in the special case that branching particles reproduce at their death position), the particle motions are independent, and we can allow for more general Markov processes instead of diffusions. The resulting configuration process is a branching Markov process in the sense introduced by Ikeda, Nagasawa and Watanabe (1968), complemented by an immigration mechanism. Generalizing results obtained by Höpfner and Löcherbach (2005), we give sufficient conditions for ergodicity in the sense of positive recurrence of the configuration process and finiteness of the invariant occupation measure in the case of general particle motions and offspring distributions.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Branching random motions, nonlinear hyperbolic systems and traveling waves

A branching random motion on a line, with abrupt changes of direction, is studied. The branching mechanism, being independient of random motion, and intensities of reverses are defined by a particle's current direction. A soluton of a certain hyperbolic system of coupled non-linear equations (Kolmogorov type backward equation) have a so-called McKean representation via such processes. Commonly this system possesses traveling-wave solutions. The convergence of solutions with Heaviside terminal data to the travelling waves is discussed.This Paper realizes the McKean programme for the Kolmogorov-Petrovskii-Piskunov equation in this case. The Feynman-Kac formula plays a key role.