Othman A.M. Omar

Bio: Othman A.M. Omar is an academic researcher from Ain Shams University. The author has contributed to research in topics: Wind power & Stochastic modelling. The author has an hindex of 3, co-authored 9 publications receiving 24 citations.

Models for covid‐19 daily confirmed cases in different countries

Abstract: 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.

Enhancement of On-grid PV System under Irradiance and Temperature Variations Using New Optimized Adaptive Controller

Abstract: Solar Energy is one of the key solutions to future electrical power generation. Photovoltaic Plants (PV) are fast growing to satisfy electrical power demand. Different maximum power point tracking techniques (MPPT) are used to maximize PV systems generated power. In this paper, on grid PV system model in MATLAB SIMULINK is tested under sudden irradiance and cell temperature variations. Incremental Conductance MPPT is used to maximize generated power from the PV system with the help of new adaptive controller to withstand these heavy disturbances. The new adaptive controller is tuned for optimal operation using two different optimization techniques (Invasive weed and Harmony search).Optimization results for the two techniques are compared. .A robustness test is made to check system stability to withstand different random irradiance and cell temperature patterns without failure to track the maximum power point.Finally, a brief comparison is made with a previous literature and the new adaptive controller gives better results.

Fractional stochastic modelling of COVID-19 under wide spread of vaccinations: Egyptian case study

Abstract: 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.

Fractional Stochastic Models for COVID-19: Case Study of Egypt.

Abstract: In this paper, COVID-19 dynamics are modelled with three mathematical dynamic models, fractional order modified SEIRF model, stochastic modified SEIRF model, and fractional stochastic modified SEIRF model, to characterize and predict virus behavior. By using Euler method and Euler-Murayama method, the numerical solutions for the considered models are obtained. The considered models are applied to the case study of Egypt to forecast COVID-19 behavior for the second virus wave which is assumed to be started on 15 November 2020. Finally, comparisons between actual and predicted daily infections are presented.

COVID-19 Deterministic and Stochastic Modelling with Optimized Daily Vaccinations in Saudi Arabia.

Abstract: In this paper, we investigate the stochastic nature of the COVID-19 temporal dynamics by generating a fractional-order dynamic model and a fractional-order-stochastic model. Initially, we considered the first and second vaccination doses as multiple vaccinations were initiated worldwide. The concerned models are then tested for the Saudi Arabia second virus wave, which is assumed to start on 1st March 2021. Four daily vaccination scenarios for the first and second dose are assumed for 100 days from the wave beginning. One of these scenarios is based on function optimization using the invasive weed optimization algorithm (IWO). After that, we numerically solve the established models using the fractional Euler method and the Euler-Murayama method. Finally, the obtained virus dynamics using the assumed scenarios and the real one started by the government are compared. The optimized scenario using the IWO effectively minimizes the predicted cumulative wave infections with a 4.4 % lower number of used vaccination doses.

Numerical methods for engineers

Abstract: The fifth edition of "Numerical Methods for Engineers" continues its tradition of excellence. Instructors love this text because it is a comprehensive text that is easy to teach from. Students love it because it is written for them--with great pedagogy and clear explanations and examples throughout. The text features a broad array of applications, including all engineering disciplines. The revision retains the successful pedagogy of the prior editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation, preparing the student for what is to come in a motivating and engaging manner. Each part closes with an Epilogue containing sections called Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Approximately 80% of the end-of-chapter problems are revised or new to this edition. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Users will find use of software packages, specifically MATLAB and Excel with VBA. This includes material on developing MATLAB m-files and VBA macros.

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Abstract: This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

Enhancing the Performance of Photovoltaic Systems under Partial Shading Conditions Using Cuttlefish Algorithm

Abstract: Improving the control of Photovoltaic (PV) power plants is an increasing interest worldwide. This improvement would hopefully help in reaching the maximum benefit of PV performance all the time. A lot of challenges are facing the control of PV systems such as Maximum Power Point Tracking (MPPT) of PV. Partial shading has become recently one of the most important challenges facing MPPT. This calls for improving the control strategy of PV power plants to cope with it. The work in this paper utilizes a relatively new optimization method which is the Cuttlefish Algorithm (CFA) to tune a Second Order Amplifier (SOA) for enhancing the PV system performance. The CFA has proved to be a very effective and fast optimization technique that can reach an accurate optimum solution with minimum effort and time. In addition, the SOA also has been found capable of enhancing the PV system performance at any condition if well-tuned. The main objective of this paper is to enhance the performance of a PV system under partial shading conditions through the use of a well selected combination of both the CFA and SOA. The needed mathematical models along with the required computer simulations are developed and the obtained results are analyzed. The reached at conclusions prove that the proposed control system and strategy are successful in achieving the declared objectives of the paper.