Tese Acadêmica Inédita submetida para promoção para professor titular

ABSTRACT We propose a new time series model for continuous data supported on the open unit interval \$\$ łeft(0,1\right) \$\$, motivated by applications in environmental and energy systems. The Matsuoka autoregressive moving average (MARMA) model combines the Matsuoka distribution-a uniparametric member of the canonical exponential family-as the conditional distribution with a flexible ARMA-type structure for the conditional mean. Parameters are estimated via partial maximum likelihood, allowing for random, time-dependent covariates and enabling standard asymptotic inference. To construct out-of-sample prediction intervals, we explore a bootstrap-based procedure that captures the uncertainty in the dynamic structure. A simulation study evaluates the finite-sample performance of the method. The model is applied to the monthly proportion of electricity generated in the United States from all sources, except conventional hydropower. This application highlights the model's utility in capturing serial dependence, ensuring predictions remain within bounds, and providing reliable forecast intervals-key features for robust energy system planning and environmental policy analysis.

The transport equation for a scattering medium is an integro-differential equation, which is commonly solved by the use of numerical schemes. A related issue, which is rarely addressed in these kind of approaches is the pertinent question of convergence. Many mesh structured schemes, like finite differences and finite volumes typically make use of a line-by-line iteration. In this direction, the present work focuses on the question whether it is in principal possible to determine, if a numerical scheme is convergent for a specific computational resource, where the iteration scheme is to be executed. To this end an iterative scheme using the finite difference and discrete ordinates method was implemented, where the solution of this scheme is given by a linear algebra system, which is also the case for other numerical approaches. Thus generally speaking, the scheme converges if and only if the spectral radius of the coefficient matrix is less than one. Since the spectral radius is the norm of the eigenvalues, i.e. the roots of the characteristic polynomial, we used proper collocation points and the inverse discrete Fourier transform matrix to obtain the polynomial's coefficients. Then, upon applying the Budan-Fourier theorem to estimate how many roots exist in the two regions (−∞,−1){\$}{\$}(-{\backslash}infty ,-1){\$}{\$}and (1,∞){\$}{\$}(1, {\backslash}infty ){\$}{\$}, respectively, one can infer on the spectral radius and thus the convergence of the numerical scheme. In summary, if the two root test results in: (a) (0, 0), then the scheme is convergent; (b) (odd, any), then the scheme is divergent; (c) (even, even > 0), then the test is inconclusive. This test is performed without explicitly allocating the coefficient matrix. As results of this analysis we show graphs for some cases with regions of convergence for combinations of two parameters. Although this result is conclusive with respect to convergence, one observes that computational complexity leads to an exponential growth, while most of the iteration schemes are of polynomial growth, so that such a test so far takes too much time even for coarse meshes. Hence, we discuss some perspectives to circumvent this shortcoming in order to open pathways for the use of the presented convergence criterion in applications.

We consider the radiative transfer equation in Cartesian geometry in a one-dimensional heterogeneous participant medium with two layers. The numerical solutions to the linear problem is discussed and obtained by a combination of the discrete ordinates and the finite difference methods. The objective of this work is to show a simple parametric formula for the thickness of one of the layers for a specified transmissivity. Further, we present error estimates for the considered steady-state transport equations with azimuthal symmetry and isotropic scattering.

Anais Graphica 2024: XV International Conference on Graphics Engineering for Arts and Design (978-65-272-1059-7) - Hypercal3d: Um Aplicativo De Geometria Descritiva

This paper discusses dynamic ARMA-type regression models for positive time series, which can handle bounded non-Gaussian time series without requiring data transformations. Our proposed model includes a conditional mean modeled by a dynamic structure containing autoregressive and moving average terms, time-varying covariates, unknown parameters, and link functions. Additionally, we present the PTSR package and discuss partial maximum likelihood estimation, asymptotic theory, hypothesis testing inference, diagnostic analysis, and forecasting for a variety of regression-based dynamic models for positive time series. A Monte Carlo simulation and a real data application are provided.

Oil has become a prevalent global pollutant, stimulating the research to improve the techniques to separate oil from water. Materials with special wetting properties—primarily those that repel water while attracting oil—have been proposed as suitable candidates for this task. However, one limitation in developing efficient substrates is the limited available volume for oil absorption. In this study, we investigate the efficacy of disordered fractal materials in addressing this challenge, leveraging their unique wetting properties. Using a combination of a continuous model and Monte Carlo simulations, we characterize the hydrophobicity and oleophilicity of substrates created through ballistic deposition (BD). Our results demonstrate that these materials exhibit high contact angles for water, confirming their hydrophobic nature while allowing significant oil penetration, indicative of oleophilic behavior. The available free volume within the substrates varies from 60% to 90% of the total volume of the substrate depending on some parameters of the BD. By combining their water and oil wetting properties with a high availability of volume, the fractal substrates analyzed in this work achieve an efficiency in separating oil from water of nearly 98%, which is significantly higher compared to micro-pillared surfaces made from the same material but lacking a fractal design.