In this paper, daily confirmed cases of COVID-19 in different countries are modelled using different mathematical regression models. The curve fitting is used as a prediction tool for modeling both past and upcoming coronavirus waves. According to virus spreading and average annual temperatures, countries under study are classified into three main categories. First category, the first wave of the coronavirus takes about two-year seasons (about 180 days) to complete a viral cycle. Second category, the first wave of the coronavirus takes about one-year season (about 90 days) to complete the first viral cycle with higher virus spreading rate. These countries take stopping periods with low virus spreading rate. Third category, countries that take the highest virus spreading rate and the viral cycle complete without stopping periods. Finally, predictions of different upcoming scenarios are made and compared with actual current smoothed daily confirmed cases in these countries.

Voltage regulation is a crucial task for electrical grids in the presence of high penetration levels of renewable energies. The regulation of generator excitation improves the stability of the power system. An essential tool for controlling the excitation of generators is the automatic voltage regulator (AVR). It is advised to use a controller to increase the reliability of an AVR. A survey about different types of controllers is proposed in this paper. Then, a novel optimized PID-Based model reference fractional adaptive controller is proposed, with detailed mathematical modeling. The novel controller was compared to the controllers in the survey. The novel proposed controller proved its superiority over the other controllers through its fast response and low rising and settling times. Moreover, the proposed controller smoothly and instantaneously tracked dynamic reference changes.

This work predicts the dynamics of the COVID-19 under widespread vaccination to anticipate the virus's current and future waves. We focused on establishing two population-based models for predictions: the fractional-order model and the fractional-order stochastic model. Based on dose efficacy, which is one of the main imposed assumptions in our study, some vaccinated people will probably be exposed to infection by the same viral wave. We validated the generated models by applying them to the current viral wave in Egypt. We assumed that the Egyptian current wave began on 10^th September 2021. Using current actual data and varying our models’ fractional orders, we generate different predicted wave scenarios. The numerical solution of our models is obtained using the fractional Euler method and the fractional Euler Maruyama method. At the end, we compared the current predicted wave under a high vaccination rate with the previous viral wave. Through this comparison, the vaccination control effect is quantified.

Simulation of Schottky barrier carbon nanotube field effect transistors (SB-CNTFETs) is presented to yield the electrostatic potential, carrier concentration and current within the device. The simulator is based on a self-consistent solution of Poisson's equation and the carrier transport equation. The finite element method is used for solving Poisson's equation while the non-equilibrium Green's function formalism is used to model the carrier transport. The developed simulator is used to investigate the effects of device parameters on device performance.