Bayesian inference and wind field statistical modeling applied to multiple source estimation.

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. The Bayesian inference tool accounts for uncertainty in the concentration data and automatically states the balance between the prior and the likelihood. The source parameters are estimated by a Metropolis in Gibbs Monte Carlo Markov chain (MCMC) algorithm with adaptive steps. The MCMC algorithm is initialized with a maximum a posteriori estimator obtained with particle swarm optimization to accelerate convergence. Finally, the proposed methodology seems to outperform inversion techniques from previous works.

An approach to robust condition monitoring in industrial processes using pythagorean membership grades.

In this paper, a robust approach to improve the performance of a condition monitoring process in industrial plants by using Pythagorean membership grades is presented. The FCM algorithm is modified by using Pythagorean fuzzy sets, to obtain a new variant of it called Pythagorean Fuzzy C-Means (PyFCM). In addition, a kernel version of PyFCM (KPyFCM) is obtained in order to achieve greater separability among classes, and reduce classification errors. The approach proposed is validated using experimental datasets and the Tennessee Eastman (TE) process benchmark. The results are compared with the results obtained with other algorithms that use standard and non-standard membership grades. The highest performance obtained by the approach proposed indicate its feasibility.

Criteria for optimizing kernel methods in fault monitoring process: A survey.

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. A kernel evaluation framework is further provided for helping in the selection and adjustment of kernel functions. The proposal is validated via a KPCA-based monitoring scheme and two well-known benchmark processes.

Uncertainty quantification and atmospheric source estimation with a discrepancy-based and a state-dependent adaptative MCMC.

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. Moreover, the technique used to simulate the corresponding dispersion problem allowed us to introduce relevant meteorological information. The uncertainty quantification also improves the quality of reconstructions. Numerical examples using data from the Copenhagen experimental campaign illustrate the effectiveness of the proposed methodology. We found errors in reconstructions ranging from 0.11% to 8.67% of the size of the search region, which is similar to results found in previous works using deterministic techniques, with comparable computational time.

Source characterization of airborne pollutant emissions by hybrid metaheuristic/gradient-based optimization techniques.

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. The accuracy of the proposed inversion model is tested under different contexts with experimental data. To identify single and multiple emissions, we use experimental field data. We consider different configurations for both the Tikhonov-type functional and optimization techniques. Several single and composite data misfit functions are tested. We also use a discrepancy-based choice rule for the regularization parameter. The resulting inversion tool is highly versatile and presents accurate results under different contexts with a competitive computational cost.

Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment.

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. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment. METHODS: An inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms. RESULTS: A comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined. CONCLUSIONS: The solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres.

Interdisciplinaridade em ciência, tecnologia e inovação / Interdisciplinarity in science, technology and innovation

A interdisciplinaridade é considerada de grande relevância para o desenvolvimento da ciência, da tecnologia e da inovação. Partindo dessa premissa, esta obra foi elaborada com o objetivo de promover a reflexão sobre teorias e práticas interdisciplinares e de contribuir para o diálogo institucional e para a adoção de novas modalidades de geração de conhecimento. Seus capítulos foram produzidos por cientistas e pesquisadores de reconhecida expressão nacional e internacional das mais diversas formações, e trazem à discussão resultados de pesquisas, oferecendo elementos para analises e tomadas de decisão pelos seus gestores.Dividido em três partes, este livro destina-se à comunidade acadêmica, constituindo um referencial importante para todos aqueles que têm estudado, implementado e desenvolvido projetos de pesquisa e ensino de pós-graduação multi e interdisciplinaridade em ciência, tecnologia e inovação.

The influence of the loop between residues 223-235 in beetle luciferase bioluminescence spectra: a solvent gate for the active site of pH-sensitive luciferases.

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. Through comparative site-directed mutagenesis and modeling studies, using the pH-sensitive luciferases (Macrolampis and Cratomorphus distinctus fireflies) and the pH-insensitive luciferases (Pyrearinus termitilluminans, Phrixotrix viviani and Phrixotrix hirtus) cloned by our group, here we show that substitutions dramatically affecting bioluminescence colors in both groups of luciferases are clustered in the loop between residues 223-235 (Photinus pyralis sequence). The substitutions at positions 227, 228 and 229 (P. pyralis sequence) cause dramatic redshift and temporal shift in both groups of luciferases, indicating their involvement in labile interactions. Modeling studies showed that the residues Y227 and N229 are buried in the protein core, fixing the loop to other structural elements participating at the bottom of the luciferin binding site. Changes in pH and temperature (in firefly luciferases), as well as point mutations in this loop, may disrupt the interactions of these structural elements exposing the active site and modulating bioluminescence colors.