The use of Information Technologies for the implementation of the statistical analysis of Polymer product prices

A wide introduction of computer technology in all spheres of activity of organizations and enterprises creates the prerequisites for an active use of information technologies to implement the statistical analysis.This article is devoted to the use of information technologies in statistical methods on the example of price analysis for polymer products. The statistical analysis was performed using the "Statistica" software on the basis of available data.In this work they used the method of exponential smoothing and neural network methods of system analysis. On the basis of monthly data, during the period from January 2012 to December 2016, the production volume prediction was developed until December 2017.Comparing the results of two methods application - neural networks and exponential smoothing, it follows that both methods predict the trend of production volume growth, and their greatest volume will be in December 2017. However, in the course of prediction by the method of exponential smoothing, the model showed that in December 2017 the production volume will make just over 125 thousand tons, which is 5 thousand tons more than the selected neural network model showed. At the same time, during the exponential smoothing method, a larger error (5.49%) was observed on the cross-check, than within the model obtained during the application of neural networks (2.47%)

Optimization of auto insurance management system as an important element of the automotive industry

All over the world, car owners know that car insurance is an integral part of owning a vehicle. It will protect a vehicle from any risks.The third party liability insurance for the damage to life, health and the property of third parties is called OSAGO.According to analysts, in order to stabilize the situation in OSAGO market, it is necessary to raise the base tariff by 40-60% and change the territorial coefficients in the most unprofitable regions.Research relevance: prediction is an important control function for all systems. It is important for the state and for insurance companies. 65% of Russia insurance fees are charged from auto insurance, so prediction is necessary for the state and for the insurance market as a whole.In order to predict a price, we use ARIMA, the autoregression model and an integrated moving average model, because it is the most popular parametric time series model. The peculiarity of STATISTICA model implementation is that within the framework of one dialogue it is possible to conduct all stages of the classic Box-Jenkins scheme: model identification by the means of autocorrelation and private autocorrelation functions, parameter estimation, adequacy estimation, the prediction of future values.As a time series, the data of the estimated average price for OSAGO and average payment for OSAGO were chosen to consider the method for the period from January 2010 to April 2017 and the values were determined for the next 11 months. After that, the graphs and the table were developed for an average price prediction for OSAGO and the average repayment for it:After the forecast that the cost of insurance premiums in the secondary auto insurance market in the Russian Federation (at least for the near future) has a growth trend of 5-5.5%, which may be explained by the forecasted rate of inflation for the next year.After the forecast it is possible to conclude that an average price for OSAGO will vary from 7400 to 9400 rubles, and average insurance payments will take the positions from 66000 to 71000 rubles

Prophylactic rivaroxaban in the early post-discharge period reduces the rates of hospitalization for atrial fibrillation and incidence of sudden cardiac death during long-term follow-up in hospitalized COVID-19 survivors

Introduction: While acute Coronavirus disease 2019 (COVID-19) affects the cardiovascular (CV) system according to recent data, an increased CV risk has been reported also during long-term follow-up (FU). In addition to other CV pathologies in COVID-19 survivors, an enhanced risk for arrhythmic events and sudden cardiac death (SCD) has been observed. While recommendations on post-discharge thromboprophylaxis are conflicting in this population, prophylactic short-term rivaroxaban therapy after hospital discharge showed promising results. However, the impact of this regimen on the incidence of cardiac arrhythmias has not been evaluated to date.Methods: To investigate the efficacy of this therapy, we conducted a single center, retrospective analysis of 1804 consecutive, hospitalized COVID-19 survivors between April and December 2020. Patients received either a 30-day post-discharge thromboprophylaxis treatment regimen using rivaroxaban 10 mg every day (QD) (Rivaroxaban group (Riva); n = 996) or no thromboprophylaxis (Control group (Ctrl); n = 808). Hospitalization for new atrial fibrillation (AF), new higher-degree Atrioventricular-block (AVB) as well as incidence of SCD were investigated in 12-month FU [FU: 347 (310/449) days].Results: No differences in baseline characteristics (Ctrl vs Riva: age: 59.0 (48.9/66.8) vs 57 (46.5/64.9) years, p = n.s.; male: 41.5% vs 43.7%, p = n.s.) and in the history of relevant CV-disease were observed between the two groups. While hospitalizations for AVB were not reported in either group, relevant rates of hospitalizations for new AF (0.99%, n = 8/808) as well as a high rate of SCD events (2.35%, n = 19/808) were seen in the Ctrl. These cardiac events were attenuated by early post-discharge prophylactic rivaroxaban therapy (AF: n = 2/996, 0.20%, p = 0.026 and SCD: n = 3/996, 0.30%, p < 0.001) which was also observed after applying a logistic regression model for propensity score matching (AF: χ2-statistics = 6.45, p = 0.013 and SCD: χ2-statistics = 9.33, p = 0.002). Of note, no major bleeding complications were observed in either group.Conclusion: Atrial arrhythmic and SCD events are present during the first 12 months after hospitalization for COVID-19. Extended prophylactic Rivaroxaban therapy after hospital discharge could reduce new onset of AF and SCD in hospitalized COVID-19 survivors

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

River runoff variability at watercourses of the Ural river basin

The article is devoted to spatial and temporal variabilities of the river runoff on the example of the river Ural within the territory of the Russian Federation. The author points out the necessity of investigating fluctuations in the water resources of a transboundary river for the purposes of long-term water management planning. Using the coefficient of variation and asymmetry, the annual runoff variability is analyzed

СТАТИСТИЧЕСКИЕ МЕТОДЫ АНАЛИЗА И ПРОГНОЗИРОВАНИЯ НАДЕЖНОСТИ КОММЕРЧЕСКОГО БАНКА

In article the banking system as one of the most important structures of market economy is considered. Forecasting of reliability of commercial bank is carried out, using statistical methods of the analysis. The analysis of reliability of commercial bank consists in calculation of various indicators of a financial condition of banks on the basis of data of its financial statements which allow to gain an impression about the main results of activity of bank by means of regression, correlation, cluster, factorial analyses. Various methods and approaches which were offered by domestic authors for the analysis and an assessment of reliability of commercial banks, testify to importance of this problem. Authors in work mean probability of by reliability of bank that work of bank during some period will satisfy to certain criteria, i.e. probability of that the bank will prove as reliable. In work general scientific methods were used: analysis and synthesis, comparisons, generalizations, system approach. In process research of the actual material methods of the economical and statistical analysis were used. The main tendencies are as a result allocated, problems of ensuring reliability of the banking sector are revealed.В статье рассматривается банковская система, как одна из самых важных структур рыночной экономики. Проводится прогнозирование надежности коммерческого банка, используя статистические методы анализа. Анализ надежности коммерческого банка заключается в расчете различных показателей финансового состояния банков на основе данных его финансовой отчетности, которые позволяют получить представление об основных результатах деятельности банка с помощью регрессионного, корреляционного, кластерного, факторного анализов. Различные методы и подходы, которые были предложены отечественными авторами для анализа и оценки надежности коммерческих банков, свидетельствуют о важности данной проблемы. Авторы в работе под надежностью банка подразумевают вероятность того, что работа банка в течение некоторого промежутка времени будет удовлетворять определенным критериям, т.е. вероятность того, что банк проявит себя как надежный. В работе использовались общенаучные методы: анализа и синтеза, сравнения, обобщения, системного подхода. В процессе исследовании фактического материала использовались методы экономико-статистического анализа. В результате выделены основные тенденции, выявлены проблемы обеспечения надежности банковского сектора

Long-Term Outcomes of COVID-19 in Hospitalized Type 2 Diabetes Mellitus Patients

With the onset of the coronavirus pandemic, it has become clear that patients with diabetes are at risk for more severe and fatal COVID-19. Type 2 diabetes mellitus (T2D) is a major risk factor for adverse COVID-19 outcomes. The goal of study was to assess the characteristics and outcomes of hospitalized patients with COVID-19 with or without T2D in the hospital and at 10-month follow-up (FU). Methods: A total of 2486 hospitalized patients in the first wave of COVID-19 were analyzed according to the absence/presence of T2D, with 2082 (84.1%) patients in the control COVID-19 group and 381 (15.5%) in the T2D group. Twenty-three patients had other types of diabetes and were therefore excluded from the study. In-hospital mortality and cardiovascular endpoints (myocardial infarction, stroke, cardiovascular deaths and hospitalizations and composite endpoints) at the 10-month follow-up were analyzed. To remove bias in patients’ characteristics disproportion, Propensity Score Matching (PSM) was used for hospital and follow-up endpoints. Results. Hospital mortality was considerably greater in T2D than in the control COVID-19 group (13.89% vs. 4.89%, p p p = 0.018). The most significant predictors of hospital death in T2D patients were a high CRP, glucose, neutrophils count, and Charlson Comorbidity Index. The follow-up of patients over 10 months showed a non-significant increase for all endpoints in the T2D group (p > 0.05), and significant increase in stroke (p p = 0.090), but became significant in cardiovascular hospitalizations (p = 0.023). Conclusion. In T2D patients with COVID-19, an increase in hospital mortality, stroke and cardiovascular hospitalizations rates in the follow-up was observed

AUTOMATION OF THE PROCESS OF CONVERTING CURRENCIES INTO ROUBLES ON THE BASIS OF TELEGRAM

This article describes the creation of a Telegram bot that will help us quickly convert any currency into rubles. The main thing for the user will be the simplicity and speed of using this bot. And to a large extent it simplifies the work for many employees who often have to convert foreign currency into rubles. The goal is to create a convenient environment in which users can quickly and without much difficulty convert currencies into rubles. Method or methodology of work: the article considers a method in which any user, specifying the letter code of the currency, can find out the exchange rate for today in rubles. For realization the programming language Python and programming environment PyCharm are used. Result: a tool is developed by which the user can find out the ruble exchange rate in the selected currency. Scope of the results: the data will be used by bank employees or any other users who are interested in the ruble exchange rate

Investigation of hs-TnI and sST-2 as Potential Predictors of Long-Term Cardiovascular Risk in Patients with Survived Hospitalization for COVID-19 Pneumonia

Introduction: COVID-19 survivors reveal an increased long-term risk for cardiovascular disease. Biomarkers like troponins and sST-2 improve stratification of cardiovascular risk. Nevertheless, their prognostic value for identifying long-term cardiovascular risk after having survived COVID-19 has yet to be evaluated. Methods: In this single-center study, admission serum biomarkers of sST-2 and hs-TnI in a single cohort of 251 hospitalized COVID-19 survivors were evaluated. Concentrations were correlated with major cardiovascular events (MACE) defined as cardiovascular death and/or need for cardiovascular hospitalization during follow-up after hospital discharge [FU: 415 days (403; 422)]. Results: MACE was a frequent finding during FU with an incidence of 8.4% (cardiovascular death: 2.8% and/or need for cardiovascular hospitalization: 7.2%). Both biomarkers were reliable indicators of MACE (hs-TnI: sensitivity = 66.7% & specificity = 65.7%; sST-2: sensitivity = 33.3% & specificity = 97.4%). This was confirmed in a multivariate proportional-hazards analysis: besides age (HR = 1.047, 95% CI = 1.012–1.084, p = 0.009), hs-TnI (HR = 4.940, 95% CI = 1.904–12.816, p = 0.001) and sST-2 (HR = 10.901, 95% CI = 4.509–29.271, p < 0.001) were strong predictors of MACE. The predictive value of the model was further improved by combining both biomarkers with the factor age (concordance index hs-TnI + sST2 + age = 0.812). Conclusion: During long-term FU, hospitalized COVID-19 survivors, hs-TnI and sST-2 at admission, were strong predictors of MACE, indicating both proteins to be involved in post-acute sequelae of COVID-19

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.11Nsciescopu