Publications by authors named "Antonio J Silva Neto"

Climate change has diversified negative implications on environmental sustainability and water availability. Assessing the impacts of climate change is crucial to enhance resilience and future preparedness particularly at a watershed scale. Therefore, the goal of this study is to evaluate the impact of climate change on the water balance components and extreme events in Piabanha watershed in the Brazilian Atlantic Forest.

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We present a methodology to identify multiple pollutant sources in the atmosphere that combines a data-driven dispersion model with Bayesian inference and uncertainty quantification. The dispersion model accounts for a realistic wind field based on the output of a multivariate dynamic linear model (DLM), estimated from measured wind components time series. The forward problem solution, described by an adjoint transient advection-diffusion partial differential equation, is then obtained using an appropriately stabilized finite element formulation.

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The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modeled as an inverse heat conduction problem. Identifying contact failures in the bonding process of different materials is crucial in many engineering applications, ranging from manufacturing, preventive inspection and even failure diagnosis. This can be modeled as an inverse heat conduction problem in multilayered media, where thermography temperature measurements from an exposed surface of the media are available.

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Nowadays, how to select the kernel function and their parameters for ensuring high-performance indicators in fault diagnosis applications remains as two open research issues. This paper provides a comprehensive literature survey of kernel-preprocessing methods in condition monitoring tasks, with emphasis on the procedures for selecting their parameters. Accordingly, twenty kernel optimization criteria and sixteen kernel functions are analyzed.

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We address the source characterization of atmospheric releases using adaptive strategies in Bayesian inference in combination with the numerical solution of the dispersion problem by a stabilized finite element method and uncertainty quantification in the measurements. The adaptive techniques accelerate the convergence of Monte Carlo Markov Chain (MCMC) algorithms, leading to accurate reconstructions of the source parameters. Such accuracy is illustrated by the comparison with results from previous works.

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We propose a methodology to estimate single and multiple emission sources of atmospheric contaminants. It combines hybrid metaheuristic/gradient-descent optimization techniques and Tikhonov-type regularization. The dispersion problem is solved by the Galerkin/Least-squares finite element formulation, which allows more realistic modeling.

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Background And Objective: For decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China.

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In this study, we describe the cDNA cloning, sequencing, and 3-D structure of the allergen hyaluronidase from Polybia paulista venom (Pp-Hyal). Using a proteomic approach, the native form of Pp-Hyal was purified to homogeneity and used to produce a Pp-specific polyclonal antibody. The results revealed that Pp-Hyal can be classified as a glycosyl hydrolase and that the full-length Pp-Hyal cDNA (1315 bp; GI: 302201582) is similar (80-90%) to hyaluronidase from the venoms of endemic Northern wasp species.

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Among bioluminescent beetles of Elateroidea superfamily, railroad-worms (Phengodidae) produce the widest range of colors, from green to red, using the same luciferin-luciferase system. Members of the Mastinocerini tribe display additional unique cephalic organs that emit red-shifted light, with Phrixothrix railroad-worms being the most dramatic cases with head lanterns emitting red light. Although the luciferases from the head lanterns of Phrixothrix hirtus and from the lateral lanterns of P.

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Beetle luciferases emit a wide range of bioluminescence colors, ranging from green to red. Firefly luciferases can shift the spectrum to red in response to pH and temperature changes, whereas click beetle and railroadworm luciferases do not. Despite many studies on firefly luciferases, the origin of pH-sensitivity is far from being understood.

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This work presents different applications in progress with the aid of the atomic force microscopy (AFM) technique for biomedical and biotechnological applications, comprising both the acquisition of three-dimensional images and spectroscopic force measurements, in the following systems: first, low-density lipoprotein (LDL)-glycosaminoglycans; second, lectins-polysaccharides; third, mycobacterium leprae cellular wall and Vesicular Stomatites Virus (VSV) with fibronectin laminin, and lipidic membranes; fourth, DNA-complex; and fifth, actin, as well as the development of surface functionalizing protocols and image restoration by means of mathematical techniques.

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