Stochastic Quantization of Scalar Fields in Einstein and Rindler Spacetime

We consider the stochastic quantization method for scalar fields defined in a curved manifold and also in a flat space-time with event horizon. The two-point function associated to a massive self-interacting scalar field is evaluated, up to the first order level in the coupling constant, for the case of an Einstein and also a Rindler Euclidean metric, respectively. Its value for the asymptotic limit of the Markov parameter is exhibited. The divergences therein are taken care of by employing a covariant stochastic regularization

Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed

Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed.business cycle; continuous time econometrics; investment cycle; inventory cycle; maximum entropy spectral analysis; parametric resonance

A Qualitative Study on the Perceived Value of Emotional Intelligence Training on Foster Parents

The experiences of a child in the foster care system rely heavily on the preparedness ofthe foster parent. For decades, researchers and practitioners have written about the challenges that foster children face while in the foster care system and discussed ways to assist them. This research discusses another way to improve the preparedness of a foster child as they go through the foster care system—improving the parenting skills of the foster parent using emotional intelligence. The entirety of this study is the analysis and study of this specific research question, “What perceived impact can emotional intelligence training (IV) have on how licensed foster parents treat children in their homes (DV)?” The researcher’s hypothesis was “If states offered emotional intelligence training for foster parents, then their parenting skills would improve.” After using surveys to question foster parents in North and South Carolina, the research concluded that foster parents strongly believed they would greatly benefit from incorporating a comprehensive emotional intelligence training program into the training regimen. The researcher concludes the study by developing a shell emotional intelligence training program aimed to maximize the potential of each foster parent as they care for children in their home

Excited States in U(1)2+1 Lattice Gauge Theory and Level Spacing Statistics in Classical Chaos

Cette thèse est organisé en deux parties. Dans la première partie nous nous adressons à un problème vieux dans la théorie de jauge - le calcul du spectre et des fonctions d'onde. La stratégie que nous proposons est de construire une base d'états stochastiques de liens de Bargmann, construite à partir d'une distribution physique de densité de probabilité. Par la suite, nous calculons les amplitudes de transition entre ces états par une approche analytique, en utilisant des intégrales de chemin standards ainsi que la théorie des groupes. Également, nous calculons numériquement matrices symétrique et hermitienne des amplitudes de transition, via une méthode Monte Carlo avec échantillonnage pondéré. De chaque matrice, nous trouvons les valeurs propres et les vecteurs propres. En appliquant cette méthode â la théorie de jauge U(l) en deux dimensions spatiales, nous essayons d'extraire et de présenter le spectre et les fonctions d'onde de cette théorie pour des grilles de petite taille. En outre, nous essayons de faire quelques ajustement dynamique des fenêtres de spectres d'énergie et les fonctions d'onde. Ces fenêtres sont outiles de vérifier visuellement la validité de l'hamiltonien Monte Carlo, et de calculer observables physiques. Dans la deuxième partie nous étudions le comportement chaotique de deux systèmes de billard classiques, par la théorie des matrices aléatoires. Nous considérons un gaz périodique de Lorentz à deux dimensions dans des régimes de horizon fini et horizon infini. Nous construisons quelques matrices de longueurs de trajectoires de un particule mobile dans ce système, et réalisons des études des spectres de ces matrices par l'analyse numérique. Par le calcul numérique des distributions d'espacement de niveaux et rigidité spectral, nous constatons la statistique des espacements de niveaux suggère un comportement universel. Nous étudions également un tel comportement pour un système optique chaotique. En tant que quasi-système de potentiel, ses fluctuations dans l'espacement de ses niveaux suivent aussi un comportement GOE, ce qui est une signature d'universalité. Dans cette partie nous étudions également les propriétés de diffusion du gaz de Lorentz, par la longueur des trajectoires. En calculant la variance de ce quantité, nous montrons que dans le cas d'horizons finis, la variance de longueurs est linéaire par rapport au nombre de collisions de la particule dans le billard. Cette linéarité permet de définir un coefficient de diffusion pour le gaz de Lorentz, et dans un schéma général, elle est compatible avec les résultats obtenus par d'autres méthodes

Identification and molecular characterization of the Rdr1 resistance gene from roses

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Modeling of metal nanocluster growth on patterned substrates and surface pattern formation under ion bombardment

This thesis addresses the metal nanocluster growth process on prepatterned substrates, the development of atomistic simulation method with respect to an acceleration of the atomistic transition states, and the continuum model of the ion-beam inducing semiconductor surface pattern formation mechanism. Experimentally, highly ordered Ag nanocluster structures have been grown on pre-patterned amorphous SiO^2 surfaces by oblique angle physical vapor deposition at room temperature. Despite the small undulation of the rippled surface, the stripe-like Ag nanoclusters are very pronounced, reproducible and well-separated. The first topic is the investigation of this growth process with a continuum theoretical approach to the surface gas condensation as well as an atomistic cluster growth model. The atomistic simulation model is a lattice-based kinetic Monte-Carlo (KMC) method using a combination of a simplified inter-atomic potential and experimental transition barriers taken from the literature. An effective transition event classification method is introduced which allows a boost factor of several thousand compared to a traditional KMC approach, thus allowing experimental time scales to be modeled. The simulation predicts a low sticking probability for the arriving atoms, millisecond order lifetimes for single Ag monomers and ≈1 nm square surface migration ranges of Ag monomers. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns. The second topic specifies the acceleration scheme utilized in the metallic cluster growth model. Concerning the atomistic movements, a classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multi-dimensional Boolean valued functions in three dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. The third topic is the formation of ripple structures on ion bombarded semiconductor surfaces treated in the first topic as the prepatterned substrate of the metallic deposition. This intriguing phenomenon has been known since the 1960\'s and various theoretical approaches have been explored. These previous models are discussed and a new non-linear model is formulated, based on the local atomic flow and associated density change in the near surface region. Within this framework ripple structures are shown to form without the necessity to invoke surface diffusion or large sputtering as important mechanisms. The model can also be extended to the case where sputtering is important and it is shown that in this case, certain \\lq magic\' angles can occur at which the ripple patterns are most clearly defined. The results including some analytic solutions of the nonlinear equation of motions are in very good agreement with experimental observation

Modeling of metal nanocluster growth on patterned substrates and surface pattern formation under ion bombardment

This thesis addresses the metal nanocluster growth process on prepatterned substrates, the development of atomistic simulation method with respect to an acceleration of the atomistic transition states, and the continuum model of the ion-beam inducing semiconductor surface pattern formation mechanism. Experimentally, highly ordered Ag nanocluster structures have been grown on pre-patterned amorphous SiO2 surfaces by oblique angle physical vapor deposition at room temperature. Despite the small undulation of the rippled surface, the stripe-like Ag nanoclusters are very pronounced, reproducible and well-separated. The first topic is the investigation of this growth process with a continuum theoretical approach to the surface gas condensation as well as an atomistic cluster growth model. The atomistic simulation model is a lattice-based kinetic Monte-Carlo (KMC) method using a combination of a simplified inter-atomic potential and experimental transition barriers taken from the literature. An effective transition event classification method is introduced which allows a boost factor of several thousand compared to a traditional KMC approach, thus allowing experimental time scales to be modeled. The simulation predicts a low sticking probability for the arriving atoms, millisecond order lifetimes for single Ag monomers and about 1 nm square surface migration ranges of Ag monomers. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns. The second topic specifies the acceleration scheme utilized in the metallic cluster growth model. Concerning the atomistic movements, a classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multi-dimensional Boolean valued functions in three dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. The third topic is the formation of ripple structures on ion bombarded semiconductor surfaces treated in the first topic as the prepatterned substrate of the metallic deposition. This intriguing phenomenon has been known since the 1960s and various theoretical approaches have been explored. These previous models are discussed and a new non-linear model is formulated, based on the local atomic flow and associated density change in the near surface region. Within this framework ripple structures are shown to form without the necessity to invoke surface diffusion or large sputtering as important mechanisms. The model can also be extended to the case where sputtering is important and it is shown that in this case, certain "magic" angles can occur at which the ripple patterns are most clearly defined. The results including some analytic solutions of the nonlinear equation of motions are in very good agreement with experimental observation.:1 Introduction: Atomistic Models 1 1.1 Density Functional Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Schroedinger equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Density functional theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Molecular Dynamics Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Lagrangian mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.2 MD algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Lattice Monte Carlo simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3.1 Thermodynamic variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.3.2 Metropolis Algorithm and limit theorem . . . . . . . . . . . . . . . . . . . . . 15 1.3.3 Kinetic Monte Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.3.4 Imaginary time reaction diffusion . . . . . . . . . . . . . . . . . . . . . . . . . 24 2 Cluster Growth on Pre-patterned Surfaces 29 2.1 Nanocluster growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 Classical nucleation theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.2 Cluster growth on substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.1.3 Experimental motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Local flux and surface ad-monomer diffusion . . . . . . . . . . . . . . . . . . . . . . 35 2.2.1 Surface topography and local flux . . . . . . . . . . . . . . . . . . . . . . . . 35 2.2.2 Surface gas diffusion under inhomogeneous flux . . . . . . . . . . . . . . . . . 37 2.2.3 Surface migration of ad-monomers . . . . . . . . . . . . . . . . . . . . . . . . 40 2.2.4 Simulation vs. experimental gauge . . . . . . . . . . . . . . . . . . . . . . . . 45 2.3 Nucleation models: Surface gas condensation . . . . . . . . . . . . . . . . . . . . . . 46 2.3.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.3.2 Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.3.3 Evolution of sticking probability . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.3.4 Evolution of Ag cluster growth . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.3.5 Simulation time and system evolution . . . . . . . . . . . . . . . . . . . . . . 57 2.4 Extended cluster growth model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.4.1 Modified setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.4.2 Simulation result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.4.3 Comparison with experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3 A Markov chain model of transition states 63 3.1 Acceleration of thin film growth simulation . . . . . . . . . . . . . . . . . . . . . . . 63 3.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.3 Transition states of Markov chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.3.1 Local transition events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.3.2 The Monte-Carlo method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.4 Effective transitions of objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.4.1 Convergence of the local fluctuation . . . . . . . . . . . . . . . . . . . . . . . 67 3.4.2 The importance of individual local transitions . . . . . . . . . . . . . . . . . . 68 3.4.3 The modified algorithm for effective transition states . . . . . . . . . . . . . . 69 3.5 Cluster growth simulation models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.5.1 The configuration energy and migration barriers . . . . . . . . . . . . . . . . 72 3.5.2 Transition events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.5.3 Comparison with Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.5.4 Cluster growth stability evaluation . . . . . . . . . . . . . . . . . . . . . . . . 78 3.6 Stability of modified convergence limit . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.6.1 Acceleration of convergence to Gibbs field . . . . . . . . . . . . . . . . . . . . 80 3.6.2 Relative convergence speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.6.3 1D Ag models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.6.4 Stability theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4 Ion beam inducing surface pattern formation 89 4.1 Ion-inducing pattern formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.1.1 Bradley-Harper equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.1.2 Nonlinear continuum models . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.1.3 Other approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.2 Simulation of surface defects induced by ion beams . . . . . . . . . . . . . . . . . . . 94 4.2.1 MD simulation of single ion impact . . . . . . . . . . . . . . . . . . . . . . . . 94 4.2.2 Monte-Carlo simulations of surface modification . . . . . . . . . . . . . . . . 96 4.2.3 Curvature dependent surface diffusion . . . . . . . . . . . . . . . . . . . . . . 102 4.3 Continuum model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.3.1 Equation of motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 4.3.2 A travelling wave solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.3.3 Lyapunov stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.3.4 Comparison with experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 4.3.5 Approximate solutions for other angles . . . . . . . . . . . . . . . . . . . . . . 110 4.4 Contribution of other effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.4.1 Surface diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.4.2 Surface Sputtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 5 Summary 119 Appendix 123 A The discrete reaction diffusion equation . . . . . . . . . . . . . . . . . . . . . . . . . 123 B The derivation of the solution (2.20) . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 C Contribution of overlapping migration area . . . . . . . . . . . . . . . . . . . . . . . 125 D The RGL potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 E Stability of the traveling wave solution . . . . . . . . . . . . . . . . . . . . . . . . . . 12