Backtracking search integrated with sequential quadratic programming for nonlinear active noise control systems

A fractional-order model of COVID-19 considering the fear effect of the media and social networks on the community

Since December 2019, the world has experienced from a virus, known as Covid-19, that is highly transmittable and is now spread worldwide. Many mathematical models and studies have been implemented to work on the infection and transmission risks. Besides the virus's transmission effect, another discussion appears in the community: the fear effect. People who have never heard about coronavirus, face every day uncertain and different information regarding the effect of the virus and the daily death rates from sources like the media, the medical institutions or organizations. Thus, the fear of the virus in the community can possibly reach the point that people become scared and confused about information polluted from different networks with long-term trend discussions. In this work, we use the Routh-Hurwitz Criteria to analyze the local stability of two essential critical points: the disease-free and the co-existing critical point. Using the discretization process, our analysis have shown that one should distinguish between the spread of "awareness" or "fear" in the community through the media and others to control the virus's transmission. Finally, we conclude our theoretical findings with numerical simulations.

A computational tool for trend analysis and forecast of the COVID-19 pandemic

Purpose:

This paper proposes a methodology and a computational tool to study the COVID-19 pandemic throughout the world and to perform a trend analysis to assess its local dynamics.

Methods:

Mathematical functions are employed to describe the number of cases and demises in each region and to predict their final numbers, as well as the dates of maximum daily occurrences and the local stabilization date. The model parameters are calibrated using a computational methodology for numerical optimization. Trend analyses are run, allowing to assess the effects of public policies. Easy to interpret metrics over the quality of the fitted curves are provided. Country-wise data from the European Centre for Disease Prevention and Control (ECDC) concerning the daily number of cases and demises around the world are used, as well as detailed data from Johns Hopkins University and from the Brasil.io project describing individually the occurrences in United States counties and in Brazilian states and cities, respectively. U. S. and Brazil were chosen for a more detailed analysis because they are the current focus of the pandemic.

Results:

Illustrative results for different countries, U. S. counties and Brazilian states and cities are presented and discussed.

Conclusion:

The main contributions of this work lie in (i) a straightforward model of the curves to represent the data, which allows automation of the process without requiring interventions from experts; (ii) an innovative approach for trend analysis, whose results provide important information to support authorities in their decision-making process; and (iii) the developed computational tool, which is freely available and allows the user to quickly update the COVID-19 analyses and forecasts for any country, United States county or Brazilian state or city present in the periodic reports from the authorities.

A Stochastic Intelligent Computing with Neuro-Evolution Heuristics for Nonlinear SITR System of Novel COVID-19 Dynamics

The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into four classes; that is, susceptible (S), infected (I), treatment (T), and recovered (R). The comprehensive details of each class along with the explanation of every parameter are provided, and the dynamics of novel COVID-19 are represented by calculating the solution of the mathematical SITR system using feed-forward artificial neural networks (FF-ANNs) trained with global search genetic algorithms (GAs) and speedy fine tuning by sequential quadratic programming (SQP)—that is, an FF-ANN-GASQP scheme. In the proposed FF-ANN-GASQP method, the objective function is formulated in the mean squared error sense using the approximate differential mapping of FF-ANNs for the SITR model, and learning of the networks is proficiently conducted with the integrated capabilities of GA and SQP. The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials. The results of statistical evaluations further authenticate the convergence and prospective accuracy of the FF-ANN-GASQP method.

Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems involving Stochastic Wind

In this research work, bio-inspired computational heuristic algorithms (BCHAs) integrated with active-set algorithms (ASA) were designed to study integrated economics load dispatch problems with valve point effects involving stochastic wind power. These BCHAs are developed through variants of genetic algorithms based on a different set of routines for reproduction operators in order to make exploration and exploitation in the entire search space for finding the global optima, while the ASA is used for rapid local refinements of the results. The designed schemes are estimated on different load dispatch systems consisting of a combination of thermal generating units and wind power plants with and without valve point loading effects. The accuracy, convergence, robustness and complexity of the proposed schemes has been examined through comparative studies based on a sufficiently large number of independent trails and their statistical observations in terms of different performance indices.