Observação e controlo da segurança estrutural de barragens de betão: utilização integrada de modelos numéricos e de modelos de separação de efeitos

Dissertação de Natureza Científica elabora da no âmbito do protocolo de cooperação entre o ISEL e o LNEC para obtenção do grau de Mestre em Engenharia Civil

Plant derived alkaloid (-)-cassine induces anti-inflammatory and anti-hyperalgesics effects in both acute and chronic inflammatory and neuropathic pain models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Rebound Effects from Increased Efficiency in the Use of Energy by UK Households

In this paper, we use CGE modelling techniques to identify the impact on energy use of an improvement in energy efficiency in the household sector. The main findings are that 1) when the price of energy is measured in natural units, the increase in efficiency yields only to a modification of tastes, changing as a result, the composition of household consumption; 2) when households internalize efficiency, the improvement in energy efficiency reduces the price of energy in efficiency units, providing a source of improved competitiveness as the nominal wage and the price level both fall; 3) the short-run rebound can be greater than the long run rebound if the household demand elasticity is the same for both time frames, however, the short run rebound is always lower than in the long-run if the demand for energy is relatively more elastic in the long-run; 4) the introduction of habit formation changes the composition of household consumption, modifying the magnitude of the household rebound only in the short-run. In this period, household and economy wide rebound are lowest for external habit formation and highest when consumers’ preferences are defined using a conventional utility function.

The Ins and Outs of Unemployment: A Conditional Analysis

We analyze how unemployment, job finding and job separation rates react to neutral and investment-specific technology shocks. Neutral shocks increase unemployment and explain a substantial portion of unemployment volatility; investment-specific shocks expand employment and hours worked and mostly contribute to hours worked volatility. Movements in the job separation rates are responsible for the impact response of unemployment while job finding rates for movements along its adjustment path. Our evidence qualifies the conclusions by Hall (2005) and Shimer (2007) and warns against using search models with exogenous separation rates to analyze the effects of technology shocks.

The effects of employment uncertainty and wealth shocks on the labor supply and claiming behavior of older American workers

Unemployment rates in developed countries have recently reached levels not seenin a generation, and workers of all ages are facing increasing probabilities of losingtheir jobs and considerable losses in accumulated assets. These events likely increasethe reliance that most older workers will have on public social insurance programs,exactly at a time that public finances are suffering from a large drop in contributions.Our paper explicitly accounts for employment uncertainty and unexpectedwealth shocks, something that has been relatively overlooked in the literature, butthat has grown in importance in recent years. Using administrative and householdlevel data we empirically characterize a life-cycle model of retirement and claimingdecisions in terms of the employment, wage, health, and mortality uncertainty facedby individuals. Our benchmark model explains with great accuracy the strikinglyhigh proportion of individuals who claim benefits exactly at the Early RetirementAge, while still explaining the increased claiming hazard at the Normal RetirementAge. We also discuss some policy experiments and their interplay with employmentuncertainty. Additionally, we analyze the effects of negative wealth shocks on thelabor supply and claiming decisions of older Americans. Our results can explainwhy early claiming has remained very high in the last years even as the early retirementpenalties have increased substantially compared with previous periods, andwhy labor force participation has remained quite high for older workers even in themidst of the worse employment crisis in decades.

Effects of pneumonectomy on nitric oxide synthase expression and perivascular edema in the remaining lung of rats

Pneumonectomy is associated with high mortality and high rates of complications. Postpneumonectomy pulmonary edema is one of the leading causes of mortality. Little is known about its etiologic factors and its association with the inflammatory process. The purpose of the present study was to evaluate the role of pneumonectomy as a cause of pulmonary edema and its association with gas exchange, inflammation, nitric oxide synthase (NOS) expression and vasoconstriction. Forty-two non-specific pathogen-free Wistar rats were included in the study. Eleven animals died during or after the procedure, 21 were submitted to left pneumonectomy and 10 to sham operation. These animals were sacrificed after 48 or 72 h. Perivascular pulmonary edema was more intense in pneumonectomized rats at 72 h (P = 0.0131). Neutrophil density was lower after pneumonectomy in both groups (P = 0.0168). There was higher immunohistochemical expression of eNOS in the pneumonectomy group (P = 0.0208), but no statistically significant difference in the expression of iNOS. The lumen-wall ratio and pO2/FiO2 ratio did not differ between the operated and sham groups after pneumonectomy. Left pneumonectomy caused perivascular pulmonary edema with no elevation of immunohistochemical expression of iNOS or neutrophil density, suggesting the absence of correlation with the inflammatory process or oxidative stress. The increased expression of eNOS may suggest an intrinsic production of NO without signs of vascular reactivity.

The acute effects of systemic cytokines on peripheral nerve function in humans

Cytokines have been shown to cause a reduction in nerve conduction when examined using animal models. Such effects, if shown in humans, could result in detrimental effects to physical function during periods heightened systemic cytokine concentrations. The study investigated the acute effects of cytokines on nerve conduction velocity (NCV) and functional measures. Measures were taken under both basal and elevated cytokine concentrations to determine any corresponding changes to NCV. A significant positive correlation was found between the cytokine IL-6 and NCV at 2 hours post-exercise (r=0.606, p=0.048). A significant negative correlation was found between IL-1ra and NCV at 24 hours post-exercise (r=-0.652, p=0.021). A significant positive correlation was also found between IL-1ra and endurance at 1 hour post-exercise (r=0.643, p=0.033). As such, it would seem that IL-6 may potentially act to enhance nerve function while other cytokines such as IL-1ra may have negative effects and reduce NCV.

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Additive Hazards Models with Latent Treatment Effectiveness Lag Time

In many clinical trials to evaluate treatment efficacy, it is believed that there may exist latent treatment effectiveness lag times after which medical procedure or chemical compound would be in full effect. In this article, semiparametric regression models are proposed and studied to estimate the treatment effect accounting for such latent lag times. The new models take advantage of the invariance property of the additive hazards model in marginalizing over random effects, so parameters in the models are easy to be estimated and interpreted, while the flexibility without specifying baseline hazard function is kept. Monte Carlo simulation studies demonstrate the appropriateness of the proposed semiparametric estimation procedure. Data collected in the actual randomized clinical trial, which evaluates the effectiveness of biodegradable carmustine polymers for treatment of recurrent brain tumors, are analyzed.

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

Environmental Effects and Aquatic Organisms: Investigations of Molecular Mechanisms of Carcinogenesis

Cancers of the reproductive system are among the leading causes of mortality in women in the United States. While both genetic and environmental factors have been implicated in their etiology, the extent of the contribution of environmental factors to human diseases remains controversial. To better address the role of environmental exposures in cancer etiology, there has been an increasing focus on the development of nontraditional, environmentally relevant models. Our research involves the development of one such model, Gonadal tumors have been described in the softshell clam (Mya arenaria) in Maine and the hardshell clam (Mercenaria spp.) from Florida. Prevalence of these tumors is as high as 40% in some populations in eastern Maine and 60% in Some areas along the Indian River in Florida. The average tumor prevalence in Maine and Florida is approximately 20 and 11%, respectively. An association has been suggested between the use of herbicides and the incidence of gonadal tumors in the softshell clam in Maine. The role of environmental exposures in the development of the tumors in Mercenaria in Florida is unknown, however, there is evidence that genetic factors may contribute to its etiology. Epidemiologic studies of human populations in these same areas show a higher than average mortality rate due to cancers of the reproductive system in women, including both ovarian and breast career. The relationship, if any, among these observations is unknown, Our studies on the molecular basis of this disease in clams may provide additional information on environmental exposures and their possible link to cancer in clams and other organisms, including humans.

In situ imaging and height reconstruction of phase separation processes in polymer blends during spin coating

Spin coating polymer blend thin films provides a method to produce multiphase functional layers of high uniformity covering large surface areas. Applications for such layers include photovoltaics and light-emitting diodes where performance relies upon the nanoscale phase separation morphology of the spun film. Furthermore, at micrometer scales, phase separation provides a route to produce self-organized structures for templating applications. Understanding the factors that determine the final phase-separated morphology in these systems is consequently an important goal. However, it has to date proved problematic to fully test theoretical models for phase separation during spin coating, due to the high spin speeds, which has limited the spatial resolution of experimental data obtained during the coating process. Without this fundamental understanding, production of optimized micro- and nanoscale structures is hampered. Here, we have employed synchronized stroboscopic illumination together with the high light gathering sensitivity of an electron-multiplying charge-coupled device camera to optically observe structure evolution in such blends during spin coating. Furthermore the use of monochromatic illumination has allowed interference reconstruction of three-dimensional topographies of the spin-coated film as it dries and phase separates with nanometer precision. We have used this new method to directly observe the phase separation process during spinning for a polymer blend (PS-PI) for the first time, providing new insights into the spin-coating process and opening up a route to understand and control phase separation structures. © 2011 American Chemical Society.

Effects of Wingwall Configurations on the Behavior of Integral Abutment Bridges

This research includes parametric studies performed with the use of three-dimensional nonlinear finite element models in order to investigate the effects of cantilever wingwall configurations on the behavior of integral abutment bridges located on straight alignment and zero skew. The parametric studies include all three types of cantilever wingwalls; inline, flared, and U-shaped wingwalls. Bridges analyzed vary in length from 100 to 1200 feet. Soil-structure and soil-pile interaction are included in the analysis. Loadings include dead load in combination with temperature loads in both rising and falling temperatures. Plasticity in the integral abutment piles is investigated by means of nonlinear plasticity models. Cracking in the abutments and stresses in the reinforcing steel are investigated by means of nonlinear concrete models. The effects of wingwall configurations are assessed in terms of stresses in the integral abutment piles, cracking in the abutment walls, stresses in the reinforcing steel of abutment walls, and axial forces induced in the steel girders. The models developed are analyzed for three types of soil behind the abutments and wingwalls; dense sand, medium dense sand, and loose sand. In addition, the models consider both the case of presence and absence of predrilled holes at the top nine feet of piles. The soil around the piles below the predrilled holes consists of very stiff clay. The results indicate that for the stresses in the piles, the critical load is temperature contraction and the most critical parameter is the use of predrilled holes. However, for both the stresses in the reinforcing steel and the axial forces induced in the girders, the critical load is temperature expansion and the critical parameter is the bridge length. In addition, the results indicate that the use of cantilever wingwalls in integral abutment bridges results in an increase in the magnitude of axial forces in the steel girders during temperature expansion and generation of pile plasticity at shorter bridge lengths compared to bridges built without cantilever wingwalls.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.