Since the outbreak in December 2019, in Wuhan (China) of COVID-19, approved drugs are still lacking and the world is seeking effective treatment. The purpose of this article is to review the medicinal plants with potential to be used as complementary therapies against COVID-19. Bibliographic information was searched in several databases (Google Scholar, PubMed, Scopus, ScienceDirect, PROTA, ResearchGate and GLOBEinMED), to retrieve relevant papers on (1) plants used to manage common symptoms of COVID-19, (2) plant secondary metabolites with confirmed inhibitory effects on COVID-19 and (3) plants exhibiting pharmacological activities of relevance for COVID-19 management. A total of 230 species was recorded as potential source of ingredients for the fight against the 2019 novel corona virus. Of these species, 30 contain confirmed antiCOVID-19 secondary metabolites, 90 are used traditionally to manage at least 3 common symptoms of COVID-19, 10 have immunostimulant activity, 52 have anti-inflamatory activity, 14 have antiviral properties and 78 species are documented as used to treat malaria. A PCA analysis showing cluster formatting among the recorded species indicates 4 groups of species and an array of possibility of using individual species or a combination of species for their complementary effects. The authors argue that Cameroonian medicinal plants can be of potential contribution to the fight against COVID-19. Further applied research is needed to provide more scientific evidence for their efficacy, to establish standard formulations and clinical studies as part of efforts to develop therapies for COVID-19.

This paper presents a reconfiguration of electric power distribution network based on the symbiotic organism search algorithm (SOS). The goal here is to come out with an optimal reconfiguration of a power distribution network that minimises the active power losses for a good power flow. This method is applied to IEEE 33 bus and the results show a significant reduction of active power losses. The execution time for this algorithm is found to be smaller compared to other metaheuristic algorithms.

Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.

This paper presents the sliding mode control of a three-phase parallel active filter based on a twolevel voltage converter to compensate for the harmonic currents of the pollutant loads. In order to calculate the reference harmonic currents, the p-q algorithm is used and the PWM is used to generate the control pulses of the inverter. Simulations in the Matlab-Simulink environment are provided to validate the theoretical study. The results obtained seem satisfactory in the harmonic compensation quality and the correction of the power factor. The selected comparison criteria are the transient regime and the Harmonic Distortion Rate in the line current. ARTICLE HISTORY

This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical energy consumption. This accuracy will be characterized by coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The PSO will allow to optimally design the Neuro-fuzzy forecasting. This method is implemented on Cameroon consumption data over the 24-years period in order to forecast energy consumption for the next years. Using this model, we were able to estimate that electricity consumption will be 1867 GWH in 2028 with 0.20158 RMSE and 0.62917% MAPE. The simulation results obtained show that implementation of this new optimized Neuro-fuzzy model on consumption data for a long period presents better results on prediction of electrical energy consumption compared to single artificial intelligence models of literature such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).