Liquid nitrogen for the treatment of actinic keratosis: A longitudinal assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Synthesis and characterization of cross-linked molecularly imprinted polyacrylamide for the extraction/preconcentration of glyphosate and aminomethylphosphonic acid from water samples

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A contribuição de modelos utilizando uma abordagem modelo - código

In a world that is increasingly working with software, the need arises for effective approaches that encourage software reuse. The reuse practice must be aligned to a set of practices, procedures and methodologies that create a stable and high quality product. These questions produce new styles and approaches in the software engineering. In this way, this thesis aims to address concepts related to development and model-driven architecture. The model-driven approach provides significant aspects of the automated development, which helps it with produced models built in the specification phase. The definition of terms such as model, architecture and platform makes the focus becomes clearer, because for MDA and MDD is important to split between technical and business issues. Important processes are covered, so you can highlight the artifacts that are built into each stage of model-driven development. The stages of development: CSM, PIM, PSM and ISM, detailing the purpose of each phase in oriented models, making the end of each stage are gradually produced artifacts that may be specialized. The models are handled by different prospects for modeling, abstracting the concepts and building a set of details that portrays a specific scenario. This retraction can be a graphical or textual representation, however, in most cases is chosen a language modeling, for example, UML. In order to provide a practical view, this dissertation shows some tools that improve the construction of models and the code generate that assists in the development, keeping the documentation systemic. Finally, the paper presents a case study that refers to the theoretical aspects discussed throughout the dissertation, therefore it is expected that the architecture and the model-driven development may be able to explain important features to consider in software engineering

Análise de pedigree e estimativas de parâmetros genéticos para características reprodutivas em bovinos da raça Brahman

Pós-graduação em Ciência e Tecnologia Animal - FEIS

Metodologia de atribuição dos escores visuais e estudo de associação genômica para os escores de conformação, precocidade e musculosidade em bovinos Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

O modelo de Schrödinger não linear com um defeito integrável

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic relationship among reproductive traits in Nellore cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Parâmetros genéticos para características indicadoras de eficiência reprodutiva e produtiva de ovinos da raça Santa Inês

Pós-graduação em Genética e Melhoramento Animal - FCAV

Increasing the lateral line length of drip irrigation systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

REDUCTION OF TODA LATTICE HIERARCHY TO GENERALIZED KDV HIERARCHIES AND THE 2-MATRIX MODEL

Toda lattice hierarchy and the associated matrix formulation of the 2M-boson KP hierarchies provide a framework for the Drinfeld-Sokolov reduction scheme realized through Hamiltonian action within the second KP Poisson bracket. By working with free currents, which Abelianize the second KP Hamiltonian structure, we are able to obtain a unified formalism for the reduced SL(M + 1, M - k) KdV hierarchies interpolating between the ordinary KP and KdV hierarchies. The corresponding Lax operators are given as superdeterminants of graded SL(M + 1, M - k) matrices in the diagonal gauge and we describe their bracket structure and field content. In particular, we provide explicit free field representations of the associated W(M, M - k) Poisson bracket algebras generalising the familiar nonlinear W-M+1 algebra. Discrete Backlund transformations for SL(M + 1, M - k) KdV are generated naturally from lattice translations in the underlying Toda-like hierarchy. As an application we demonstrate the equivalence of the two-matrix string model to the SL(M + 1, 1) KdV hierarchy.

The Conway-Maxwell-Poisson-generalized gamma regression model with long-term survivors

In this paper, we proposed a flexible cure rate survival model by assuming the number of competing causes of the event of interest following the Conway-Maxwell distribution and the time for the event to follow the generalized gamma distribution. This distribution can be used to model survival data when the hazard rate function is increasing, decreasing, bathtub and unimodal-shaped including some distributions commonly used in lifetime analysis as particular cases. Some appropriate matrices are derived in order to evaluate local influence on the estimates of the parameters by considering different perturbations, and some global influence measurements are also investigated. Finally, data set from the medical area is analysed.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.

Implementation of two-stage Hopfield model and its application in nonlinear systems

This paper presents an efficient neural network for solving constrained nonlinear optimization problems. More specifically, a two-stage neural network architecture is developed and its internal parameters are computed using the valid-subspace technique. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty or weighting parameters for its initialization.

Two field BPS solutions for generalized Lorentz breaking models

In this work we present nonlinear models in two-dimensional space-time of two interacting scalar fields in the Lorentz and CPT violating scenarios. We discuss the soliton solutions for these models as well as the question of stability for them. This is done by generalizing a model recently published by Barreto and collaborators and also by getting new solutions for the model introduced by them.