Flooding is one of the most prevalent types of natural catastrophes, and it can cause extensive damage to infrastructure and the natural environment. The primary method of flood risk management is flood susceptibility mapping (FSM), which provides a quantitative assessment of a region’s vulnerability to flooding. The objective of this study is to develop new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), and harmony search (HS), with the decision table classifier (DTB). The proposed algorithms were applied in the province of Sulaymaniyah, Iraq. Sentinel-1 synthetic aperture radar (SAR) data satellite images were used for flood monitoring (on 27 July 2019), and 160 flood occurrence locations were prepared for modeling. For the training and validation datasets, flood occurrence data were coupled to 1 flood-influencing parameters (slope, altitude, aspect, plan curvature, distance from rivers, land cover, geology, topographic wetness index (TWI), stream power index (SPI), rainfall, and normalized difference vegetation index (NDVI)). The certainty factor (CF) approach was used to determine the spatial association between the effective parameters and the occurrence of floods, and the resulting weights were employed as modeling inputs. According to the pairwise consistency technique, the NDVI and altitude are the most significant factors in flood modeling. The area under the receiver operating characteristic (AUROC) curve was used to evaluate the accuracy and effectiveness of ensemble models. The DTB-GA model was found to be the most accurate (AUC = 0.889), followed by the DTB-PSO model (AUC = 0.844) and the DTB-HS model (AUC = 0.812). This research’s hybrid models provide a reliable estimate of flood risk, and the risk maps are reliable for flood early-warning and control systems.

The purpose of the work is to explore the potential of online learning in modern conditions and to assess the possibilities of its use at different stages of the educational process. The reason for the mass appeal to this form of education is that the entire education system from primary to higher level underwent a transformation during the period of isolation associated with the spread of COVID-19. Practice has shown that in the current conditions, the existing resources of educational institutions can effectively transform formal education into online education with the help of virtual classes and other key online tools. Today, taking into account the unstable epidemiological situation in the world, online training continues to be relevant for various educational organizations and is used today along with the traditional form of training. For this reason, the study of the potential of this form of education will not lose its relevance for a long time. The use of online learning technologies places high demands on teachers.

In recent years, distance learning has become widespread in both secondary and higher educational institutions around the world. However, the pace of the spread of remote technologies has significantly increased with the introduction of physical distancing and quarantine against the background of the development of the COVID-19 pandemic, since most educational institutions in various countries of the world were closed to hold the spread of the virus. Many educational institutions were not fully ready for the mass introduction of distance learning, and if in developed countries schools and universities had the opportunity to use developments in the educational process of various programs and technologies for conducting online learning, then in countries and regions where there were serious problems with the implementation of distance learning, attempts were made to use the possibilities of social networks and messengers for organizing the educational process. However, since these resources were not fully intended for the implementation of full-fledged pedagogical activities, their use in the educational process, along with the advantages, had several disadvantages.

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The outbreak of the deadly coronavirus, which is increasing the number of victims every day, has created many changes in today’s world. The use of various masks is the most important social tool against this virus. Given the importance of rapid and quality supply of masks in the current situation, it is necessary to study supply chain in particular. In this research, the design of a closed chain supply chain network for different types of masks is assessed. The studied supply chain includes suppliers, manufacturers, distributors, and retailers in the forward flow and collection centers, separate centers, recycling centers, and disposal centers in the backward flow. In this regard, a multi-objective mathematical model with the objectives of increasing the total profit and reducing the total environmental impact, and maximizing social responsibility is presented. The optimization of this mathematical model has been done using a fuzzy optimization approach in GAMS software. The results of this study show that maximizing the total profit and minimizing the environmental effects and maximizing social responsibility are in contrast to each other. In addition, the sensitivity analysis indicated that the customers’ demand can affect all aspects of the sustainable supply chain simultaneously.

This article is essentially a dataset necessary for analysing the taxation. The data analysis has allowed to determine the optimal taxation model, when the criteria of economic efficiency and equity are incompatible. The dataset has allowed the use of the method of successive concessions in tax optimization. The practical significance of the dataset lies in the ability to simultaneously improve the efficiency and equity in taxation. The dataset was obtained by using the method of expert estimates. A group of experts was asked to rank the taxes established by the Tax Code of the Russian Federation, in descending order of importance. Only strict rankings were allowed. The consistency of expert opinion was evaluated using the Kendall coefficient of concordance. The data set was supplemented with the expert ranking data of the basic principles of taxation, such as the principle of equity; the principle of certainty and accuracy of taxes; the principle of ease of tax collection for taxpayers; the principle of efficiency; the principle of commitment. The dataset can be used in the future to determine a rational amount of taxation depending on the established criteria.