Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computer-time and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the high-accuracy matching of the forecast model with real data.

In this study Non‐Linear forecasting models have been implemented to forecast the seven major cryptocurrencies. To the best of the authors knowledge, this is the first study to forecast the cryptocurrencies chaotic co‐movement forecasting using non‐linear models like Neural networks. The study finds that LSTM yields better result for lags 0 and 0‐3 and for large lags 0‐7, the ANN is the best. Further study confirms that predictions using variables like volume is not suitable for forecasting in any case. The findings of the study will impact Policy makers and investors.

The present study analyses BRICS (Brazil, Russia, India, China, South Africa) capital markets in both time and frequency domain using wavelets. We used artificial neural network techniques to forecast the co-movement among BRICS capital markets. Wavelet coherence and clustering estimates uncover the interesting dynamics among the BRICS capital markets co-movement. A wavelet coherence diagram shows a clear contagion effect among BRICS nations, and it favors short period investments over longer period investments. Overall study estimates indicate that co-movement among BRICS nations significantly differs statistically at different levels. Except for China during the great financial crisis period, significant levels of co-movement were observed between other BRICS nations and that lasted for a longer period of time. A wavelet clustering diagram demonstrates that investors would not get any substantial benefits of diversification by investing only in the ‘Russia and China’ or ‘India and South Africa’ capital markets. Lastly, the study attempts to forecast the BRICS capital market co-movement using two different types of neural networks. Further, RMSE (Root Mean Square Error) values confirm the correctness of the forecasting model. The present study answers the key question, “What kind of integration and globalization framework do we need for sustainable development?”.

Forest fires that occurred in Portugal on June 18, 2017, caused several tens of human casualties. The cause of their emergence, as well as many others that occurred in western Europe at the same time remained unknown. Taking into account consequences, including loss of human lives and endangerment of ecosystem sustainability, discovering of the forest fires causes is the very significant question. The heliocentric hypothesis has indirectly been tested, according to which charged particles are a possible cause of forest fires. We must point out that it was not possible to verify whether in this specific case the particles by reaching the ground and burning the plant mass create the initial phase of the formation of the flame. Therefore, we have tried to determine whether during the critical period, i. e. from June 15-19 there is a certain statistical connection between certain parameters of the solar wind and meteorological elements. Based on the hourly values of the charged particles flow, a correlation analysis was performed with hourly values of individual meteorological elements including time lag at Monte Real station. The application of the Adaptive Neuro Fuzzy Inference System models has shown that there is a high degree of connection between the flow of protons and the analyzed meteorological elements in Portugal. However, further verification of this hypothesis requires further laboratory testing.

In this research we search for a functional dependence between the occurrence of forest fires in the USA and the factors which characterize the solar activity. For this purpose we used several methods (R/S analysis, Hurst index) to establish potential links between the influx of some parameters from the sun and the occurrence of forest fires with lag of several days. We found evidence for a connection and developed a prognostic scenario based on the Adaptive neuro-fuzzy interference system (ANFIS) technique. This scenario allows the prediction between 79-93% of forest fires.