A panchromatic study of stellar populations in Galactic Globular Clusters

The aim of this Thesis is to investigate (i) how common the bimodal Blue Straggler Stars (BSS) radial distribution is in stellar clusters and (ii) which are the physical processes that can produce this bimodality. We discuss possible relations between the properties of the BSS radial distribution and the dynamical state of the hosting clusters by making use of dynamical models and simulations. When relevant, we also discuss the possible links with some cluster "anomalies" and the effects of a massive object (like Imtermediate Mass Black Hole) in the cluster center. To this purpose we present the observational multiwavelength studies of the BSS populations and their radial distributions in 5 GGCs: M5, M55, M2, NGC 2419 and NGC 6388.

The dynamics of early-type galaxies as a tool to understand their hot coronae and their IMF

Dynamical models of galaxies are a powerful tool to study and understand several astrophysical problems related to galaxy formation and evolution. This thesis is focussed on a particular type of dynamical models, that are widely used in literature, and are based on the solution of the Jeans equations. By means of a numerical Jeans solver code, developed on purpose and able to build state-of-the-art advanced axisymmetric galaxy models, two of the main currently investigated issues in the field of research of early-type galaxies (ETGs) are addressed. The first topic concerns the hot and X-ray emitting gaseous coronae that surround ETGs. The main goal is to explain why flat and rotating galaxies generally exhibit haloes with lower gas temperatures and luminosities with respect to rounder and velocity dispersion supported systems. The second astrophysical problem addressed concerns instead the stellar initial mass function (IMF) of ETGs. Nowadays, this is a very controversial issue due to a growing number of works on ETGs, based on different and independent techniques, that show evidences of a systematic variation of the IMF normalization as a function of galaxy velocity dispersion or mass. These studies are changing the previous opinion that the IMF of ETGs was the same as that of spiral galaxies, and hence universal throughout the whole large family of galaxies.

On the Luminous and Dark Matter Distribution in Early-Type Galaxies

The way mass is distributed in galaxies plays a major role in shaping their evolution across cosmic time. The galaxy's total mass is usually determined by tracing the motion of stars in its potential, which can be probed observationally by measuring stellar spectra at different distances from the galactic centre, whose kinematics is used to constrain dynamical models. A class of such models, commonly used to accurately determine the distribution of luminous and dark matter in galaxies, is that of equilibrium models. In this Thesis, a novel approach to the design of equilibrium dynamical models, in which the distribution function is an analytic function of the action integrals, is presented. Axisymmetric and rotating models are used to explain observations of a sample of nearby early-type galaxies in the Calar Alto Legacy Integral Field Area survey. Photometric and spectroscopic data for round and flattened galaxies are well fitted by the models, which are then used to get the galaxies' total mass distribution and orbital anisotropy. The time evolution of massive early-type galaxies is also investigated with numerical models. Their structural properties (mass, size, velocity dispersion) are observed to evolve, on average, with redshift. In particular, they appear to be significantly more compact at higher redshift, at fixed stellar mass, so it is interesting to investigate what drives such evolution. This Thesis focuses on the role played by dark-matter haloes: their mass-size and mass-velocity dispersion correlations evolve similarly to the analogous correlations of ellipticals; at fixed halo mass, the haloes are more compact at higher redshift, similarly to massive galaxies; a simple model, in which all the galaxy's size and velocity-dispersion evolution is due to the cosmological evolution of the underlying halo population, reproduces the observed size and velocity-dispersion of massive compact early-type galaxies up to redshift of about 2.

Barry Saltzman and the Theory of Climate

Barry Saltzman was a giant in the fields of meteorology and climate science. A leading figure in the study of weather and climate for over 40 yr, he has frequently been referred to as the "father of modern climate theory." Ahead of his time in many ways, Saltzman made significant contributions to our understanding of the general circulation and spectral energetics budget of the atmosphere, as well as climate change across a wide spectrum of time scales. In his endeavor to develop a unified theory of how the climate system works, lie played a role in the development of energy balance models, statistical dynamical models, and paleoclimate dynamical models. He was a pioneer in developing meteorologically motivated dynamical systems, including the progenitor of Lorenz's famous chaos model. In applying his own dynamical-systems approach to long-term climate change, he recognized the potential for using atmospheric general circulation models in a complimentary way. In 1998, he was awarded the Carl-Gustaf Rossby medal, the highest honor of the American Meteorological Society "for his life-long contributions to the study of the global circulation and the evolution of the earth's climate." In this paper, the authors summarize and place into perspective some of the most significant contributions that Barry Saltzman made during his long and distinguished career. This short review also serves as an introduction to the papers in this special issue of the Journal of Climate dedicated to Barry's memory.

Petri Net Siphon Analysis and Network Centrality Measures for Identifying Combination Therapies in Signaling Pathways

Aberrant behavior of biological signaling pathways has been implicated in diseases such as cancers. Therapies have been developed to target proteins in these networks in the hope of curing the illness or bringing about remission. However, identifying targets for drug inhibition that exhibit good therapeutic index has proven to be challenging since signaling pathways have a large number of components and many interconnections such as feedback, crosstalk, and divergence. Unfortunately, some characteristics of these pathways such as redundancy, feedback, and drug resistance reduce the efficacy of single drug target therapy and necessitate the employment of more than one drug to target multiple nodes in the system. However, choosing multiple targets with high therapeutic index poses more challenges since the combinatorial search space could be huge. To cope with the complexity of these systems, computational tools such as ordinary differential equations have been used to successfully model some of these pathways. Regrettably, for building these models, experimentally-measured initial concentrations of the components and rates of reactions are needed which are difficult to obtain, and in very large networks, they may not be available at the moment. Fortunately, there exist other modeling tools, though not as powerful as ordinary differential equations, which do not need the rates and initial conditions to model signaling pathways. Petri net and graph theory are among these tools. In this thesis, we introduce a methodology based on Petri net siphon analysis and graph network centrality measures for identifying prospective targets for single and multiple drug therapies. In this methodology, first, potential targets are identified in the Petri net model of a signaling pathway using siphon analysis. Then, the graph-theoretic centrality measures are employed to prioritize the candidate targets. Also, an algorithm is developed to check whether the candidate targets are able to disable the intended outputs in the graph model of the system or not. We implement structural and dynamical models of ErbB1-Ras-MAPK pathways and use them to assess and evaluate this methodology. The identified drug-targets, single and multiple, correspond to clinically relevant drugs. Overall, the results suggest that this methodology, using siphons and centrality measures, shows promise in identifying and ranking drugs. Since this methodology only uses the structural information of the signaling pathways and does not need initial conditions and dynamical rates, it can be utilized in larger networks.

Evolutionary algorithms for the modeling of bioreactors

This work aims to study the application of Genetic Algorithms in anaerobic digestion modeling, in particular when using dynamical models. Along the work, different types of bioreactors are shown, such as batch, semi-batch and continuous, as well as their mathematical modeling. The work intendeds to estimate the parameter values of two biological reaction model. For that, simulated results, where only one output variable, the produced biogas, is known, are fitted to the model results. For this reason, the problems associated with reverse optimization are studied, using some graphics that provide clues to the sensitivity and identifiability associated with the problem. Particular solutions obtained by the identifiability analysis using GENSSI and DAISY softwares are also presented. Finally, the optimization is performed using genetic algorithms. During this optimization the need to improve the convergence of genetic algorithms was felt. This need has led to the development of an adaptation of the genetic algorithms, which we called Neighbored Genetic Algorithms (NGA1 and NGA2). In order to understand if this new approach overcomes the Basic Genetic Algorithms (BGA) and achieves the proposed goals, a study of 100 full optimization runs for each situation was further developed. Results show that NGA1 and NGA2 are statistically better than BGA. However, because it was not possible to obtain consistent results, the Nealder-Mead method was used, where the initial guesses were the estimated results from GA; Algoritmos Evolucionários para a Modelação de Bioreactores Resumo: Neste trabalho procura-se estudar os algoritmos genéticos com aplicação na modelação da digestão anaeróbia e, em particular, quando se utilizam modelos dinâmicos. Ao longo do mesmo, são apresentados diferentes tipos de bioreactores, como os batch, semi-batch e contínuos, bem como a modelação matemática dos mesmos. Neste trabalho procurou-se estimar o valor dos parâmetros que constam num modelo de digestão anaeróbia para o ajustar a uma situação simulada onde apenas se conhece uma variável de output, o biogas produzido. São ainda estudados os problemas associados à optimização inversa com recurso a alguns gráficos que fornecem pistas sobre a sensibilidade e identifiacabilidade associadas ao problema da modelação da digestão anaeróbia. São ainda apresentadas soluções particulares de idenficabilidade obtidas através dos softwares GENSSI e DAISY. Finalmente é realizada a optimização do modelo com recurso aos algoritmos genéticos. No decorrer dessa optimização sentiu-se a necessidade de melhorar a convergência e, portanto, desenvolveu-se ainda uma adaptação dos algoritmos genéticos a que se deu o nome de Neighboured Genetic Algorithms (NGA1 e NGA2). No sentido de se compreender se as adaptações permitiam superar os algoritmos genéticos básicos e atingir as metas propostas, foi ainda desenvolvido um estudo em que o processo de optimização foi realizado 100 vezes para cada um dos métodos, o que permitiu concluir, estatisticamente, que os BGA foram superados pelos NGA1 e NGA2. Ainda assim, porque não foi possivel obter consistência nos resultados, foi usado o método de Nealder-Mead utilizado como estimativa inicial os resultados obtidos pelos algoritmos genéticos.

An efficient Jeans modelling of axisymmetric galaxies with multiple stellar components

Dynamical models of stellar systems represent a powerful tool to study their internal structure and dynamics, to interpret the observed morphological and kinematical fields, and also to support numerical simulations of their evolution. We present a method especially designed to build axisymmetric Jeans models of galaxies, assumed as stationary and collisionless stellar systems. The aim is the development of a rigorous and flexible modelling procedure of multicomponent galaxies, composed of different stellar and dark matter distributions, and a central supermassive black hole. The stellar components, in particular, are intended to represent different galaxy structures, such as discs, bulges, halos, and can then have different structural (density profile, flattening, mass, scale-length), dynamical (rotation, velocity dispersion anisotropy), and population (age, metallicity, initial mass function, mass-to-light ratio) properties. The theoretical framework supporting the modelling procedure is presented, with the introduction of a suitable nomenclature, and its numerical implementation is discussed, with particular reference to the numerical code JASMINE2, developed for this purpose. We propose an approach for efficiently scaling the contributions in mass, luminosity, and rotational support, of the different matter components, allowing for fast and flexible explorations of the model parameter space. We also offer different methods of the computation of the gravitational potentials associated of the density components, especially convenient for their easier numerical tractability. A few galaxy models are studied, showing internal, and projected, structural and dynamical properties of multicomponent galaxies, with a focus on axisymmetric early-type galaxies with complex kinematical morphologies. The application of galaxy models to the study of initial conditions for hydro-dynamical and $N$-body simulations of galaxy evolution is also addressed, allowing in particular to investigate the large number of interesting combinations of the parameters which determine the structure and dynamics of complex multicomponent stellar systems.

Dynamical behaviour on the parameter space: new populational growth models proportional to beta densities

We present new populational growth models, generalized logistic models which are proportional to beta densities with shape parameters p and 2, where p > 1, with Malthusian parameter r. The complex dynamical behaviour of these models is investigated in the parameter space (r, p), in terms of topological entropy, using explicit methods, when the Malthusian parameter r increases. This parameter space is split into different regions, according to the chaotic behaviour of the models.

Dynamical analysis in growth models: Blumberg’s equation

We present a new dynamical approach to the Blumberg's equation, a family of unimodal maps. These maps are proportional to Beta(p, q) probability densities functions. Using the symmetry of the Beta(p, q) distribution and symbolic dynamics techniques, a new concept of mirror symmetry is defined for this family of maps. The kneading theory is used to analyze the effect of such symmetry in the presented models. The main result proves that two mirror symmetric unimodal maps have the same topological entropy. Different population dynamics regimes are identified, when the intrinsic growth rate is modified: extinctions, stabilities, bifurcations, chaos and Allee effect. To illustrate our results, we present a numerical analysis, where are demonstrated: monotonicity of the topological entropy with the variation of the intrinsic growth rate, existence of isentropic sets in the parameters space and mirror symmetry.

Generalized models from Beta(p,2) densities with strong allee effect: dynamical approach

A dynamical approach to study the behaviour of generalized populational growth models from Bets(p, 2) densities, with strong Allee effect, is presented. The dynamical analysis of the respective unimodal maps is performed using symbolic dynamics techniques. The complexity of the correspondent discrete dynamical systems is measured in terms of topological entropy. Different populational dynamics regimes are obtained when the intrinsic growth rates are modified: extinction, bistability, chaotic semistability and essential extinction.

Dynamical downscaling: Assessment of model system dependent retained and added variability for two different regional climate models

On the lack of stratospheric dynamical variability in low-top versions of the CMIP5 models

We describe the main differences in simulations of stratospheric climate and variability by models within the fifth Coupled Model Intercomparison Project (CMIP5) that have a model top above the stratopause and relatively fine stratospheric vertical resolution (high-top), and those that have a model top below the stratopause (low-top). Although the simulation of mean stratospheric climate by the two model ensembles is similar, the low-top model ensemble has very weak stratospheric variability on daily and interannual time scales. The frequency of major sudden stratospheric warming events is strongly underestimated by the low-top models with less than half the frequency of events observed in the reanalysis data and high-top models. The lack of stratospheric variability in the low-top models affects their stratosphere-troposphere coupling, resulting in short-lived anomalies in the Northern Annular Mode, which do not produce long-lasting tropospheric impacts, as seen in observations. The lack of stratospheric variability, however, does not appear to have any impact on the ability of the low-top models to reproduce past stratospheric temperature trends. We find little improvement in the simulation of decadal variability for the high-top models compared to the low-top, which is likely related to the fact that neither ensemble produces a realistic dynamical response to volcanic eruptions.

Energy criterion to select the behavior of dynamical masses in technicolor models

We propose a quite general ansatz for the dynamical mass in technicolor models. We impose on this ansatz the condition that it should lead to the deepest minimum of energy. This criterion selects a particular form of the technifermion self energy. © 2002 Elsevier Science B.V. All rights reserved.

Renormalization-group calculation of dynamical properties for impurity models

The numerical renormalization-group method was originally developed to calculate the thermodynamical properties of impurity Hamiltonians. A recently proposed generalization capable of computing dynamical properties is discussed. As illustrative applications, essentially exact results for the impurity specttral densities of the spin-degenerate Anderson model and of a model for electronic tunneling between two centers in a metal are presented. © 1991.

Integrable Models: from Dynamical Solutions to String Theory

We review the status of integrable models from the point of view of their dynamics and integrability conditions. A few integrable models are discussed in detail. We comment on the use it is made of them in string theory. We also discuss the SO(6) symmetric Hamiltonian with SO(6) boundary. This work is especially prepared for the 70th anniversaries of Andr, Swieca (in memoriam) and Roland Koberle.