Demographic bottlenecks in tropical plant regeneration: A comparative analysis of causal influences

Mortality factors that act sequentially through the demographic transitions from seed to sapling may have critical effects on recruitment success. Understanding how habitat heterogeneity influences the causal factors that limit propagule establishment in natural populations is central to assess these demographic bottlenecks and their consequences. Bamboos often influence forest structure and dynamics and are a major factor in generating landscape complexity and habitat heterogeneity in tropical forests. To understand how patch heterogeneity influences plant recruitment we studied critical establishment stages during early recruitment of Euterpe edulis, Sloanea guianensis and Virola bicuhyba in bamboo and non-bamboo stands in the Brazilian Atlantic forest. We combined observational studies of seed rain and seedling emergence with seed addition experiments to evaluate the transition probabilities among regeneration stages within bamboo and non-bamboo stands. The relative importance of each mortality factor was evaluated by determining how the loss of propagules affected stage-specific recruitment success. Our results revealed that the seed addition treatment significantly increased seedling survivorship for all three species. E. edulis seedling survival probability increased in the addition treatment in the two stand types. However, for S. guianensis and V. bicuhyba this effect depended strongly on artificially protecting the seeds, as both species experienced increased seed and seedling losses due to post-dispersal seed predators and herbivores. Propagules of all three species had a greater probability of reaching subsequent recruitment stages when protected. The recruitment of large-seeded V. bicuhyba and E. edulis appears to be much more limited by post-dispersal factors than by dispersal limitation, whereas the small-seeded S. guianensis showed an even stronger effect of post-dispersal factors causing recruitment collapse in some situations. We demonstrated that E. edulis, S. guianensis and V. bicuhyba are especially susceptible to predation during early compared with later establishment stages and this early stage mortality can be more crucial than stand differences as determinants of successful regeneration. Among-species differences in the relative importance of dispersal vs. establishment limitation are mediated by variability in species responses to patch heterogeneity. Thus, bamboo effects on the early recruitment of non-bamboo species are patchy and species-specific, with successional bamboo patches exerting a far-reaching influence on the heterogeneity of plant species composition and abundance. © 2012 Perspectives in Plant Ecology, Evolution and Systematics.

Estudo do método Monte Carlo e sua aplicação no modelo SIR

In this work we present a discussion and the results of the simulation of disease spread using the Monte Carlo method. The dissemination model is the SIR model and presents as main characteristic the disease evolution among individuals of the population subdivided into three groups: susceptible (S), infected (I) and recovered (R). The technique used is based on the introduction of transition probabilities S-> I and I->R to do the spread of the disease, they are governed by a Poisson distribution. The simulation of the spread of disease was based on the randomness introduced, taking into account two basic parameters of the model, the power of infection and average time of the disease. Considering appropriate values of these parameters, the results are presented graphically and analysis of these results gives information on a group of individuals react to the changes of these parameters and what are the chances of a disease becoming a pandemic

Evaluation of effective nucleon-nucleon potential and description of α-cluster type nuclei in restricted dynamics approach

The application of the Restricted Dynamics Approach in nuclear theory, based on the approximate solution of many-particle Schrödinger equation, which accounts for all conservation laws in many-nucleon system, is discussed. The Strictly Restricted Dynamics Model is used for the evaluation of binding energies, level schemes, E2 and Ml transition probabilities as well as the electric quadrupole and magnetic dipole momenta of light a-cluster type nuclei in the region 4 ≤ A ≤ 40. The parameters of effective nucleonnucleon interaction potential are evaluated from the ground state binding energies of doubly magic nuclei 4He, 16O and 40Ca.

Coordenadas vestidas: formulação segundo a interpretação de Dirac-Feynman para a mecânica quântica e aplicações em CQED

Pós-graduação em Física - IFT

The search for a potentially new molecular species, SiAs: A theoretical contribution

Electronic states of a new molecular species, SiAs, correlating with the three lowest dissociation channels are characterized at a high-level of theory using the CASSCF/MRCI approach along with quintuple-xi quality basis sets. This characterization includes potential energy curves, vibrational energy levels, spectroscopic parameters, dipole and transition dipole moment functions, transition probabilities, and radiative lifetimes. For the ground state (X-2 Pi), an assessment of spin-orbit effects and the interaction with the close-lying A(2)Sigma(+) state is also reported. Similarities and differences with other isovalent species such as SiP and CAs are also discussed.

A theoretical contribution characterizing a potentially new molecular species: MgAs

A new molecular species, MgAs, is investigated theoretically for the first time at the CASSCF/MRCI level using quintuple-zeta quality basis sets. Potential energy curves for the lowest-lying electronic states are presented as well as the associated spectroscopic constants. Dipole and transition moment functions for selected states complement this characterization. Estimates of transition probabilities and radiative life-times for the most important transitions are also reported. The effect of spin-orbit interactions is clearly reflected on the potential energy curves. Comparisons with BeAs, BeN, and BeP are made where pertinent. (C) 2011 Elsevier B.V. All rights reserved.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Autoionizing states and their relevance in electron-ion recombination

Atomic physics plays an important role in determining the evolution stages in a wide range of laboratory and cosmic plasmas. Therefore, the main contribution to our ability to model, infer and control plasma sources is the knowledge of underlying atomic processes. Of particular importance are reliable low temperature dielectronic recombination (DR) rate coefficients. This thesis provides systematically calculated DR rate coefficients of lithium-like beryllium and sodium ions via ∆n = 0 doubly excited resonant states. The calculations are based on complex-scaled relativistic many-body perturbation theory in an all-order formulation within the single- and double-excitation coupled-cluster scheme, including radiative corrections. Comparison of DR resonance parameters (energy levels, autoionization widths, radiative transition probabilities and strengths) between our theoretical predictions and the heavy-ion storage rings experiments (CRYRING-Stockholm and TSRHeidelberg) shows good agreement. The intruder state problem is a principal obstacle for general application of the coupled-cluster formalism on doubly excited states. Thus, we have developed a technique designed to avoid the intruder state problem. It is based on a convenient partitioning of the Hilbert space and reformulation of the conventional set of pairequations. The general aspects of this development are discussed, and the effectiveness of its numerical implementation (within the non-relativistic framework) is selectively illustrated on autoionizing doubly excited states of helium.

Charakterisierung und Konvergenz mehrdimensionaler kontinuierlicher Verzweigungsprozesse

In dieser Arbeit wird eine Klasse von stochastischen Prozessen untersucht, die eine abstrakte Verzweigungseigenschaft besitzen. Die betrachteten Prozesse sind homogene Markov-Prozesse in stetiger Zeit mit Zuständen im mehrdimensionalen reellen Raum und dessen Ein-Punkt-Kompaktifizierung. Ausgehend von Minimalforderungen an die zugehörige Übergangsfunktion wird eine vollständige Charakterisierung der endlichdimensionalen Verteilungen mehrdimensionaler kontinuierlicher Verzweigungsprozesse vorgenommen. Mit Hilfe eines erweiterten Laplace-Kalküls wird gezeigt, dass jeder solche Prozess durch eine bestimmte spektral positive unendlich teilbare Verteilung eindeutig bestimmt ist. Umgekehrt wird nachgewiesen, dass zu jeder solchen unendlich teilbaren Verteilung ein zugehöriger Verzweigungsprozess konstruiert werden kann. Mit Hilfe der allgemeinen Theorie Markovscher Operatorhalbgruppen wird sichergestellt, dass jeder mehrdimensionale kontinuierliche Verzweigungsprozess eine Version mit Pfaden im Raum der cadlag-Funktionen besitzt. Ferner kann die (funktionale) schwache Konvergenz der Prozesse auf die vage Konvergenz der zugehörigen Charakterisierungen zurückgeführt werden. Hieraus folgen allgemeine Approximations- und Konvergenzsätze für die betrachtete Klasse von Prozessen. Diese allgemeinen Resultate werden auf die Unterklasse der sich verzweigenden Diffusionen angewendet. Es wird gezeigt, dass für diese Prozesse stets eine Version mit stetigen Pfaden existiert. Schließlich wird die allgemeinste Form der Fellerschen Diffusionsapproximation für mehrtypige Galton-Watson-Prozesse bewiesen.

On the recurrence of random walks in Lévy random environments

This thesis investigates one-dimensional random walks in random environment whose transition probabilities might have an infinite variance. The ergodicity of the dynamical system ''from the point of view of the particle'' is proved under the assumptions of transitivity and existence of an absolutely continuous steady state on the space of the environments. We show that, if the average of the local drift over the environments is summable and null, then the RWRE is recurrent. We provide an example satisfying all the hypotheses.

EVALUATING INFRASTRUCTURE PERFORMANCE TO ASSIST FACILITY MANAGEMENT TOWARD SUSTAINABLE SYSTEMS

Civil infrastructure provides essential services for the development of both society and economy. It is very important to manage systems efficiently to ensure sound performance. However, there are challenges in information extraction from available data, which also necessitates the establishment of methodologies and frameworks to assist stakeholders in the decision making process. This research proposes methodologies to evaluate systems performance by maximizing the use of available information, in an effort to build and maintain sustainable systems. Under the guidance of problem formulation from a holistic view proposed by Mukherjee and Muga, this research specifically investigates problem solving methods that measure and analyze metrics to support decision making. Failures are inevitable in system management. A methodology is developed to describe arrival pattern of failures in order to assist engineers in failure rescues and budget prioritization especially when funding is limited. It reveals that blockage arrivals are not totally random. Smaller meaningful subsets show good random behavior. Additional overtime failure rate is analyzed by applying existing reliability models and non-parametric approaches. A scheme is further proposed to depict rates over the lifetime of a given facility system. Further analysis of sub-data sets is also performed with the discussion of context reduction. Infrastructure condition is another important indicator of systems performance. The challenges in predicting facility condition are the transition probability estimates and model sensitivity analysis. Methods are proposed to estimate transition probabilities by investigating long term behavior of the model and the relationship between transition rates and probabilities. To integrate heterogeneities, model sensitivity is performed for the application of non-homogeneous Markov chains model. Scenarios are investigated by assuming transition probabilities follow a Weibull regressed function and fall within an interval estimate. For each scenario, multiple cases are simulated using a Monte Carlo simulation. Results show that variations on the outputs are sensitive to the probability regression. While for the interval estimate, outputs have similar variations to the inputs. Life cycle cost analysis and life cycle assessment of a sewer system are performed comparing three different pipe types, which are reinforced concrete pipe (RCP) and non-reinforced concrete pipe (NRCP), and vitrified clay pipe (VCP). Life cycle cost analysis is performed for material extraction, construction and rehabilitation phases. In the rehabilitation phase, Markov chains model is applied in the support of rehabilitation strategy. In the life cycle assessment, the Economic Input-Output Life Cycle Assessment (EIO-LCA) tools are used in estimating environmental emissions for all three phases. Emissions are then compared quantitatively among alternatives to support decision making.

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^

Distribution of quality adjusted life expectancy: A systems approach

Statistical methods are developed which assess survival data for two attributes; (1) prolongation of life, (2) quality of life. Health state transition probabilities correspond to prolongation of life and are modeled as a discrete-time semi-Markov process. Imbedded within the sojourn time of a particular health state are the quality of life transitions. They reflect events which differentiate perceptions of pain and suffering over a fixed time period. Quality of life transition probabilities are derived from the assumptions of a simple Markov process. These probabilities depend on the health state currently occupied and the next health state to which a transition is made. Utilizing the two forms of attributes the model has the capability to estimate the distribution of expected quality adjusted life years (in addition to the distribution of expected survival times). The expected quality of life can also be estimated within the health state sojourn time making more flexible the assessment of utility preferences. The methods are demonstrated on a subset of follow-up data from the Beta Blocker Heart Attack Trial (BHAT). This model contains the structure necessary to make inferences when assessing a general survival problem with a two dimensional outcome. ^

AN ILLNESS-DEATH PROCESS WITH TIME-DEPENDENT COVARIATES

A general model for the illness-death stochastic process with covariates has been developed for the analysis of survival data. This model incorporates important baseline and time-dependent covariates to make proper adjustment for the transition probabilities and survival probabilities. The follow-up period is subdivided into small intervals and a constant hazard is assumed for each interval. An approximation formula is derived to estimate the transition parameters when the exact transition time is unknown.^ The method developed is illustrated by using data from a study on the prevention of the recurrence of a myocardial infarction and subsequent mortality, the Beta-Blocker Heart Attack Trial (BHAT). This method provides an analytical approach which simultaneously includes provision for both fatal and nonfatal events in the model. According to this analysis, the effectiveness of the treatment can be compared between the Placebo and Propranolol treatment groups with respect to fatal and nonfatal events. ^

Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^