Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising and estimating future COVID-19 cases to control the spread and help countries prepare their healthcare systems. In this study, time-series models- Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) are used to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%–80% of global cumulative cases are located. Initial combinations of the model parameters were selected using the auto-ARIMA model followed by finding the optimized model parameters based on the best fit between the predictions and test data. Analytical tools Auto-Correlation function (ACF), Partial Auto-Correlation Function (PACF), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. Evaluation metrics Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) were used as criteria for selecting the best model. A case study was presented where the statistical methodology was discussed in detail for model selection and the procedure for forecasting the COVID-19 cases of the USA. Best model parameters of ARIMA and SARIMA for each country are selected manually and the optimized parameters are then used to forecast the COVID-19 cases. Forecasted trends for confirmed and recovered cases showed an exponential rise for countries such as the United States, Brazil, South Africa, Colombia, Bangladesh, India, Mexico and Pakistan. Similarly, trends for cumulative deaths showed an exponential rise for countries Brazil, South Africa, Chile, Colombia, Bangladesh, India, Mexico, Iran, Peru, and Russia. SARIMA model predictions are more realistic than that of the ARIMA model predictions confirming the existence of seasonality in COVID-19 data. The results of this study not only shed light on the future trends of the COVID-19 outbreak in top-16 countries but also guide these countries to prepare their health care policies for the ongoing pandemic. The data used in this work is obtained from publicly available John Hopkins University’s COVID-19 database.

We report the use of a single layer of two-dimensional hexagonal boron nitride (SL-hBN) as the thinnest insulating barrier to microbial corrosion induced by the sulfate-reducing bacteria Desulfovibrio alaskensis G20. We used electrochemical methods to assess the corrosion resistance of SL-hBN on copper against the effects of both the planktonic and sessile forms of the sulfate-reducing bacteria. Cyclic voltammetry results show that SL-hBN-Cu is effective in suppressing corrosion effects of the planktonic cells at potentials as high as 0.2 V ( vs Ag/AgCl). The peak anodic current for the SL-hBN coatings is ∼36 times lower than that of bare Cu. Linear polarization resistance tests confirm that the SL-hBN coatings serve as a barrier against corrosive effects of the G20 biofilm when compared to bare Cu. The SL-hBN serves as an impermeable barrier to aggressive metabolites and offers ∼91% corrosion inhibition efficiency, which is comparable to much thicker commercial coatings such as polyaniline. In addition to impermeability, the insulating nature of SL-hBN suppresses galvanic effects and improves its ability to combat microbial corrosion.

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Modern drilling techniques, notably horizontal drilling and hydraulic fracturing, have enabled unconventional oil production (UOP) from the previously inaccessible Bakken Shale Formation located throughout Montana, North Dakota (ND) and the Canadian province of Saskatchewan. The majority of UOP from the Bakken shale occurs in ND, strengthening its oil industry and businesses, job market, and its gross domestic product. However, similar to UOP from other low-permeability shales, UOP from the Bakken shale can result in environmental and human health effects. For example, UOP from the ND Bakken shale generates a voluminous amount of saline wastewater including produced and flowback water that are characterized by unusual levels of total dissolved solids (350 g/L) and elevated levels of toxic and radioactive substances. Currently, 95% of the saline wastewater is piped or trucked onsite prior to disposal into Class II injection wells. Oil and gas wastewater (OGW) spills that occur during transport to injection sites can potentially result in drinking water resource contamination. This study presents a critical review of potential water resource impacts due to deterministic (freshwater withdrawals and produced water management) and probabilistic events (spills due to leaking pipelines and truck accidents) related to UOP from the Bakken shale in ND.

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