METHODOLOGY for ASSESSING the FINANCIAL SECURITY of ENTERPRISES in the POST-PANDEMIC PERIOD of DIGITAL ECONOMY

The article considers the approaches to assessing the financial security of enterprises presented in the literature, determines the rsistance of the textile industry of Uzbekistan to the negative impact of the coronavirus pandemic on the basis of statistical data, and reveals a significant differentiation of textile industry enterprises in terms of financial stability. Based on data on small enterprises in the textile industry of Uzbekistan, a method for assessing the financial security of an enterprise in the post-pandemic period is proposed and tested, taking into account the complex influence of non-financial parameters of economic security and assessing the deviations of the economic situation at a given enterprise from the patterns emerging in the relevant segment of the economy. In this research an econometric model was developed to determine the factors affecting the chemical industry and express their interrelationship, based on the conducted econometric analysis, the directions of development in our country were determined. According to the authors, it is necessary to continue these directions in order to ensure the economic security of industry enterprises in the country. © 2022 ACM.

Association between measured air pollution levels in the troposphere and surface air with COVID-19 hospitalization in Tomsk, Russia, 2022

The results of a preliminary analysis of the relationship between the short-term impact of air pollution exposure on hospitalizations associated with COVID-19 in Tomsk, Russia are presented. The statistical data on air pollution and COVID-19 associated hospitalization were collected and analyzed for the period from March 16, 2022 to April 14, 2022. This period corresponds to a flat plateau of confirmed COVID-19 cases after the main pandemic wave in 2022 in Tomsk and the Tomsk region which were associated with omicron strain of SARS-CoV-2. It was found that all representative peaks in a graph of daily hospitalizations coincide with the peaks in graphs of measured levels of air pollution. The increase in hospitalizations occurred on the same days when air pollution levels increased, or with a slight lag of 1-2 days. This allows us to tentatively conclude that air pollution has a quick effect on infected persons and may provoke an increase in symptoms and severity of the disease. Further detailed research is required. © 2022 SPIE.

Self-Directed versus Guided Learning - Searching for the Sweet Spot with Lesson Activities in Moodle

In times of a pandemic many university courses have to be taught in an online learning format to respect the requirements of social distancing. Online learning takes place between the poles of self-direction by the student and guidance by the lecturer. In this paper a first-semester course in industrial economics for engineers, which was taught in the self-directed learning format of flipped classroom, was enhanced with lesson activities in the Learning Management System (LMS) Moodle as a means of guided learning with student-content interaction. The paper describes the design and the evaluation of the lesson activities. For the evaluation a student survey to gather the opinions and self-assessments of the students was conducted, and statistical data from Moodle on the performance of the students were collected. The results of the student survey show an overall positive evaluation of the lesson activities by the students who participated in the lesson activities. Furthermore, the results of the statistical data about the students' performance show a relation between participation in the lesson activities and exam success. As a caveat, however, the results also show that the majority of the students chose not to participate in the lesson activities. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.

A Detailed Mathematical Analysis of the Vaccination Model for COVID-19

This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average ARIMA (1, 1, 2), and hence, a forecast about the evolution of the COVID-19 vaccination in 60 days is presented. The theoretical aspect provides equilibrium points, reproduction number R0, and biologically feasible region of the proposed model. Also, we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform. On the numerical aspect, we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model. Moreover, numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model. Finally, the concerned numerical simulations are compared with the exact real available date given in the statistical aspect. © 2023 Authors. All rights reserved.

Accuracy of COVID-19 evolution models for different forecast horizons

Currently, the statistics on COVID-19 for many regions are accumulated for the time span of over than two years, which facilitates the use of data-driven algorithms, such as neural networks, for prediction of the disease's further development. This article provides a comparative analysis of various forecasting models of COVID-19 dynamics. The forecasting is performed for the period from 07/20/2020 to 05/05/2022 using statistical data from the regions of the Russian Federation and the USA. The forecast target is defined as the sum of confirmed cases over the forecast horizon. Models based on the Exponential Smoothing (ES) method and deep learning methods based on Long Short-Term Memory (LSTM) units were considered. The training data set included the data from all regions available in the full data set. The MAPE metric was used for model comparison, the evaluation of the effectiveness of LSTM in the learning process was carried out using cross-validation on the mean squared error (MSE) metric. The comparisons were made with the models from various literature sources, as well as with the baseline model "tomorrow as today" (for which the sum of cases over the forecast horizon is supposed to be equal to the current case number multiplied by the forecast horizon length). It was shown that on small horizons (up to 28 days) the "tomorrow as today” model and ES algorithms show better accuracy than LSTM. In turn, on longer horizons (28 days or more), the preference should be given to the more complex LSTM-based model. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Confirmed statistical data of Covid-19 cases that have accumulated sourced from (https://corona.riau.go.id/data-statistik/) in Riau Province on June 7, 2021, there were 63441 cases, on June 14, 2021, it increased to 65883 cases, on June 21, 2021, it increased to 67910, and on June 28, 2021, it increased to 69830 cases. Since the beginning of this pandemic outbreak, it has been observed that the case data continues to increase every week until this July. This study predicts cases of Covid-19 time series data in Riau Province using the LSTM algorithm, with a dataset of 64 lines. Long-Short Term Memory has the ability to store memory information for patterns in the data for a long time at the same time. Tests predicting historical data for Covid-19 cases in Riau Province resulted in the lowest RMSE value in the training data, which was 8.87, and the test data, which was 13.00, in the death column. The evaluation of the best MAPE value in the training data, which is 0.23%, is in the recovered column, and the evaluation of the best MAPE value in the test data, which is 0.27%, in the positive_number column. In the test to predict the next 30 days using the LSTM model that has been trained, it was found that the performance evaluation of the prediction results for the positive_number column and the death column was very good, the recovery column was categorized as good, the independent_isolation column and the care_rs column were categorized as poor. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Differential Privacy for Statistical Data of Educational Institutions

Electronic methods of managing the educational process are gaining popularity. Recently, a large number of user programs have appeared for such accounting. Based on this, the issue of personal data protection requires increased attention. The coronavirus pandemic has led to a significant increase in the amount of data distributed remotely, which requires information security for a wider range of workers on a continuous basis. In this article, we will consider such a relatively new mechanism designed to help protect personal data as differential privacy. Differential privacy is a way of strictly mathematical definition of possible risks in public access to sensitive data. Based on estimating the probabilities of possible data losses, you can build the right policy to “noise” publicly available statistics. This approach will make it possible to find a compromise between the preservation of general patterns in the data and the security of the personal data of the participants in the educational process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart Culture in Russia Pre-COVID: Development and Online Presence of Cultural Organizations

The pandemic-related restrictions on visiting theaters, museums, libraries, etc., as well as on organization of cultural events, had provided a boost to online forms of culture. The paper considers digitalization of culture and analyzes and assesses the development of “smart culture” within the framework of smart cities. First we aggregate the statistical data on attendance and digitalization of libraries and museums per the 8 Federal Okrugs of Russia in the Pre-COVID period of 2014–2019. We forecast that the growth of offline attendance during the timeframe of the National Project “Culture” will amount to 29%, which is considerably higher than the 15% required by the respective goal of the Project. Our results demonstrate uneven pace of digitalization for different types of cultural organizations and different regions of Russia. For instance, 50% of theaters in Moscow had websites by 2006, whereas the same threshold for the rest of Russia was only achieved around 2012. We expect that aiding people’s involvement with the culture-related ICT can provide social, economic and technical effects. At the same time, our findings might be useful for policy-makers engaged in e-culture and e-government development. © 2022, Springer Nature Switzerland AG.

Forecasting Time Series COVID-19 Statistical Data with Auto-Regressive Integrated Moving Average and Box-Jenkins' Models

The current epidemic situation due to COVID-19 is a public health disaster worldwide. Forecasting play's, a crucial role in determining the pandemic's hypothetical situation and economic situation. It provides the base for authorities, public health officials, management teams, and other stakeholders to plan for future preventive actions in their companies, citizens, and governments. This paper proposes Auto-Regressive Integrated Moving Average mathematical modeling in integration with Box-Jenkins' model-building approach examining the variation in pandemic severity through the Loess smoothed curves to forecast the COVID-19 pandemic situation. The time-plot and forecasting results show Chinese resilience to pact with pandemic situation effectively whereas India was severely affected by the pandemic. The future forecast for India shows the worst situation by the end of 2021. Pakistan and Bangladesh are the least affected among the specified countries while decline in weekly death cases has been observed in Iran till the end of 2021. We observed the Case Fatality Ratio (CFR) of 2.08% globally. © 2021 IEEE.

Problems of Building A Mathematical Model of Epidemic Taking into Account Vaccination

The paper considers the description and analysis of mathematical models of the spread of infectious diseases, the construction of a mathematical model of the spread of the COVID-19 epidemic, based on the models under consideration, numerical modeling of mathematical models of the spread of the disease with real statistical data and comparison of the mathematical models considered in this paper. © 2021 IEEE.

A Review of Simulation and Application of Agent-Based Model Approaches

In the past, various traditional methods used experiments and statistical data to examine and solve the occurred problem and social-environmental issue. However, the traditional method is not suitable for expressing or solving the complex dynamics of human environmental crisis (such as the spread of diseases, natural disaster management, social problems, etc.). Therefore, the implementation of computational modelling methods such as Agent-Based Models (ABM) has become an effective technology for solving complex problems arising from the interpretation of human behaviour such as human society, environment, and biological systems. Overall, this article will outline the ABM model properties and its applications in the criminology, flood management, and the COVID-19 pandemic fields. In addition, this article will review the limitations that occurred to be overcome in the further development of the ABM model. © 2021 Institute of Physics Publishing. All rights reserved.