The substantial increase in the number of daily new cases infected with coronavirus around the world is alarming, and several researchers are currently using various mathematical and machine learning-based prediction models to estimate the future trend of this pandemic. In this work, we employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the expected daily number of COVID-19 cases in Saudi Arabia in the next four weeks. We first performed four different prediction models; Autoregressive Model, Moving Average, a combination of both (ARMA), and integrated ARMA (ARIMA), to determine the best model fit, and we found out that the ARIMA model outperformed the other models. The forecasting results showed that the trend in Saudi Arabia will continue growing and may reach up to 7668 new cases per day and over 127,129 cumulative daily cases in a matter of four weeks if stringent precautionary and control measures are not implemented to limit the spread of COVID-19. This indicates that the Umrah and Hajj Pilgrimages to the two holy cities of Mecca and Medina in Saudi Arabia that are supposedly scheduled to be performed by nearly 2 million Muslims in mid-July may be suspended. A set of extreme preventive and control measures are proposed in an effort to avoid such a situation.
During obturation, air voids are undesirable as they may provide shelter for microorganisms or passage for fluids. This study aimed to compare the occurrence of voids between three calcium silicate-based sealers (CSBSs) (MTA-Fillapex, BioRoot-RCS, Bio-C) and the change in their volume after aging. In addition, we aimed to compare voids when using two sealer application methods: lentulo-spiral (LS) and gutta-percha (GP) cone. Thirty extracted mandibular premolars (n = 30) were endodontically prepared and obturated using single GP cone (SGPC) technique. Each sealer was applied to 10 teeth (n = 10) using LS or GP. Micro-computed tomography (micro-CT) was used to quantify the volume of root filling and voids before and after 8-week storage in a phosphate-rich medium. The percentage of root filling and voids were compared between the groups using a Mann–Whitney U test and Kruskal–Wallis test with a Bonferroni correction. Before aging, the percentages of root filling volume after obturation were comparable with no significant differences between sealers (p = 0.325) or application methods (p = 0.950). After aging, the voids’ volume increased significantly in all sealers (p ≤ 0.05). However, no significant differences were found between sealers (p = 0.302). In conclusion, voids in CSBSs may not reduce in size with aging; hence, SGPC should be carefully selected for suitable cases.
A common process control application is the cascaded two-tank system, where the level is controlled in the second tank. A nonlinear system identification approach is presented in this work to predict the model structure parameters that minimize the difference between the estimated and measured data, using benchmark datasets. The general suggested structure consists of a static nonlinearity in cascade with a linear dynamic filter in addition to colored noise element. A one-step ahead prediction error-based technique is proposed to estimate the model. The model is identified using a separable least squares optimization, where only the parameters that appear nonlinearly in the output of the predictor are solved using a modified Levenberg–Marquardt iterative optimization approach, while the rest are fitted using simple least squares after each iteration. Finally, MATLAB simulation examples using benchmark data are included.
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