Abstract The COVID-19 death predictions are helpful for the formulation of public policies, allowing the use of more effective social isolation strategies with less economic and social impact. This article evaluates a wide range of forecasting methods to identify the best models for predicting cumulative and daily deaths caused by COVID-19 in Brazil, considering a forecast for a seven-day horizon. With the seven-day horizon, the predictions have more accuracy. The dataset is from Oxford Covid-19 Government Response Tracker. The jackknife resampling technique was implemented, thus providing an accurate estimate for evaluating the predictive capacity of the models. Each model was fitted with 266 jackknife samples considering 30-day training bases. The comparison between predictions was made using the average results, considering R2, MAPE, RMSE, and MAE. Models from different classes were adopted: 1 ETS, 4 ARIMA, 18 regression models, and 7 machine learning algorithms. The cumulative death models produce better results than daily deaths, as the cumulative death models are less influenced by time series components: cycle and seasonality. The best results for predicting daily deaths were attained by the Ridge regression method. The best results for predicting cumulative deaths were obtained by the Cubist regression method.

In the present work, cysteine and allyl alcohol were added to citrate baths as additives to Cu-Zn coatings on steel substrates. In order to verify the effects of the deposition parameters (current density, mechanical stirring speed, and additives) on the coating composition, electrochemical behavior, morphology, and microstructure properties of Cu-Zn coatings, the electrodeposition of the alloy was carried out using an experimental composite design 2³, in which these parameters were considered the entry variables and the measured properties were the response variables. The confidence level was 95% and the results were shown as response surface diagrams. It was possible to verify that the current density affected the zinc content in the coating, while the coating produced from cysteine-contained bath presented the worse anticorrosive performance. In a general way, it was possible to observe that the studied parameters affected the morphology, grain size, and the electrochemical behavior of these coatings, although only a few response variables were statistically influenced by them.

In the present work, stainless steel and Ni-Ti commercial orthodontic wires were immersed in artificial saliva solutions, containing or not F- ions, in different pH values, during 30 days. The weight loss and the Ni2+ content in the solutions at 15 and 30 days of exposition were evaluated using a composite design 2³ and response surface methodology. The open circuit potential (OCP) was measured over the same period of time. Polarization curves and morpHological analysis of the wires before and after the corrosion experiments were also carried out. The results showed that corrosion of the studied metal alloys depended on a combination among saliva pH, the exposition time, and the concentration of F- ions in the solution. The critical condition was observed for Ni-Ti wires at pH = 3.0, and high concentration of F- ions, causing a decrease in the OCP values and an increase in Ni dissolution and corrosion current density.

This paper presents experimental data of the biosorption of manganese onto Sargassum filipendula in both batch and fixed-bed column systems. Batch equilibrium data were used for nonlinear fittings of the Langmuir and Freundlich isotherms. A mathematical model based on mass balances in the fluid and in the sorbent was applied to represent the experimental fixed-bed column data. The utilization of isotherm parameters from the batch experiments in the breakthrough model implied a significant mismatch in relation to the laboratory data. Alternatively, additional fixed-bed column data provided new parameters for the isotherm evaluation, and the corresponding simulated profile of the breakthrough curve reached better agreement to the experimental results.

Cathodic polarization curves of Cu-Co alloys were galvanostatically obtained on a platinum net, using electrolytes containing copper and cobalt sulfates, sodium citrate and boric acid (pH values ranging from 4.88 to 6.00), with different mechanical stirring conditions. In order to evaluate quantitatively the influence of the applied current density and the mechanical stirring on the cathodic efficiency, the alloy composition for the Cu-Co alloy deposition process, and the average deposition potential, an experimental central composite design 2² was employed, and three current density intervals (0.11 to 0.60, 0.50 to 1.98 and 2.44 to 9.94 mA.cm-2) were chosen from the polarization curves for this purpose. The results indicated that the current density (mainly in the range between 0.11 and 0.60 mA.cm-2) affected significantly all the studied variables. In the intermediate range (0.50 to 1.98 mA.cm-2), only the average potential was influenced by the current density. On the other hand, the mechanical stirring had a significant effect only on the copper content, for both the lowest (0.11 to 0.60 mA.cm-2) and the highest current density range (2.44 to 9.94 mA.cm-2). Indeed, in the last range, none of the studied deposition parameters presented significant influence on the studied variables, except for the copper content. This could probably be explained by the direct incorporation of Cu-Citrate complexes in the coating, which was enhanced at high current values.