Predicting COVID-19 cases in various scenarios using RNN-LSTM models aided by adaptive linear regression to identify data anomalies.

The evolution of the Sars-CoV-2 (COVID-19) virus pandemic has revealed that the problems of social inequality, poverty, public and private health systems guided by controversial public policies are much more complex than was conceived before the pandemic. Therefore, understanding how COVID-19 evolves in society and looking at the infection spread is a critical task to support efficient epidemiological actions capable of suppressing the rates of infections and deaths. In this article, we analyze daily COVID-19 infection data with two objectives: (i) to test the predictive power of a Recurrent Neural Network - Long Short Term Memory (RNN-LSTM) on the daily stochastic fluctuation in different scenarios, and (ii) analyze, through adaptive linear regression, possible anomalies in the reported data to provide a more realistic and reliable scenario to support epidemic control actions. Our results show that the approach is even more suitable for countries, states or cities where the rate of testing, diagnosis and prevention were low during the virus dissemination. In this sense, we focused on investigating countries and regions where the disease evolved in a severe and poorly controlled way, as in Brazil, highlighting the favelas in Rio de Janeiro as a regional scenario.

Intelligent Supernovae Classification Systems in the KDUST context.

With the advent of large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type Ia supernovae have been addressed by different machine learning techniques. In this article we present three solutions aimed at the future spectrometer of the KDUST project, within a scope of benchmark, considering three different methodologies. The systems presented here are the following: CINTIA (based on hierarchical neural network architecture), SUZAN (which incorporates the solution known as fuzzy systems) and DANI (based on Deep Learning with Convolutional Neural Networks). The characteristics of the systems are presented and the benchmark is performed considering a data set containing 15.134 spectra. The best performance is obtained by the DANI architecture which provides 96% accuracy in the classification of Type Ia supernovae in relation to other spectral types.

Data Science Strategies for Multimessenger Astronomy.

This article aims to identify and suggest data science strategies to strengthen scientific research in astronomy. The improvements in data workflow performance that can be provided by these strategies can be crucial to the multimessenger astronomy (MMA). A special focus is given to the treatment of raw data in the context of big data networks for BRICS astronomy initiatives. A preliminary design of a prototype that incorporates an MMA data cube into a data lake system is presented.

Analysis of chaotic saddles in high-dimensional dynamical systems: the Kuramoto-Sivashinsky equation.

This paper presents a methodology to study the role played by nonattracting chaotic sets called chaotic saddles in chaotic transitions of high-dimensional dynamical systems. Our methodology is applied to the Kuramoto-Sivashinsky equation, a reaction-diffusion partial differential equation. The paper describes a novel technique that uses the stable manifold of a chaotic saddle to characterize the homoclinic tangency responsible for an interior crisis, a chaotic transition that results in the enlargement of a chaotic attractor. The numerical techniques explained here are important to improve the understanding of the connection between low-dimensional chaotic systems and spatiotemporal systems which exhibit temporal chaos and spatial coherence.