No quadro da chegada da Covid-19 no Rio Grande do Sul (RS), em março de 2020, iniciamos o monitoramento da pandemia utilizando um Sistema de Informação Geográfica (SIG) online. Ele foi desenvolvido dentro do Projeto SIG Litoral, da Universidade Federal do Rio Grande do Sul, com a estratégia de disponibilizar à sociedade análises espaciais e dados organizados sobre a doença, reunindo pesquisadoras/es, bolsistas e voluntárias/os. O portal, voltado à comunicação em saúde, possibilita a visualização de dados nas escalas global, nacional e regional, além do monitoramento das populações indígenas da Amazônia e o mapeamento das redes de solidariedade no Litoral Norte do RS. Nesse artigo, enfocamos principalmente o monitoramento da Covid-19 no RS, abordando as ferramentas utilizadas, os desafios para obtenção de dados e algumas análises espaciais realizadas. Mostramos divergências entre as fontes de dados, indicando o problema da subnotificação e as diferentes metodologias empregadas pelos sistemas de vigilância sanitária. Por fim, mostramos alguns indicadores sobre números de acessos ao SIG e alcance geográfico do monitoramento da Covid-19 pela UFRGS, repercutindo positivamente na sociedade ao dar transparência aos dados oficiais, contribuindo para a pluralidade do debate sobre a pandemia, a interação de equipes multidisciplinares e a formação acadêmica de estudantes.

Charge trapping is studied in the context of random telegraph noise (RTN) and low-frequency noise (1/f noise), aiming unified statistical modeling. Analytical formulations for 1/f noise (frequency domain) and RTN (time domain) have been derived, using a single modeling framework, where model parameters are the same in frequency and time domain. The modeling addresses the time dependent variability in the electrical behavior of MOSFETs, discussing the variability due to a single trap and the ensemble of traps. In the work here presented we detail the role of the observation window, in both time and frequency domain. We discuss how it impacts the variance of drain current (or threshold voltage) measured over time, and the number of observable traps in a given time window or frequency window. Besides analytical modeling, experimental results are presented and discussed.

In this work, we develop the asymptotic theory of the Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA) for trend-stationary stochastic processes without any assumption on the specific form of the underlying distribution. All results are presented and derived under the general framework of potentially overlapping boxes for the polynomial fit. We prove the stationarity of the DFA and DCCA, viewed as stochastic processes, obtain closed forms for moments up to second order, including the covariance structure for DFA and DCCA and a miscellany of law of large number related results. Our results generalize and improve several results presented in the literature. To verify the behavior of our theoretical results in small samples, we present a Monte Carlo simulation study and an empirical application to econometric time series.

The feeding behavior of growing-finishing pigs was analyzed to study prandial correlations and the probability of starting a new feeding event. The data were collected in real-time based on 157,632 visits by a group of 70 growing-finishing pigs (from 30.4 to 115.5 kg body weight, BW) to automatic feeders. The data were collected over 84 days, during which period the pigs were kept in conventional (by phase and by group) or precision (with daily and individual adjustments) feeding programs. A criterion to delimit each meal was then defined based on the probability of an animal starting a new feeding event within the next minute since the last visit. Prandial correlations were established between meal size and interval before meal (pre-prandial) or interval after meal (post-prandial) using Pearson correlation analysis. Post-prandial correlations (which can be interpreted as hunger-regulating mechanisms) were slightly stronger than pre-prandial correlations (which can be interpreted as satiety regulation mechanisms). Both correlations decreased as the animals’ age increased but were little influenced by the feeding programs. The information generated in this study allows a better understanding of pigs’ feeding behavior regulation mechanisms and could be used in the future to improve precision feeding programs.