Objective: to determine the diagnostic performance of magnetic resonance hysterosalpingography (HSG-MRI), using laparoscopy as the reference method. Materials and methods: 22 patients were included. All underwent HSG-MRI with a 1.5 Tesla resonator and then laparoscopy with chromotubation. Two radiologists examined the MRIs, determining tubal patency by consensus. Descriptive and diagnostic performance analyses were performed. Results: HSG-MRI had a success rate of 91%. Study duration was 49 ± 15 minutes, volume injected 26 ± 16 cm³ and pain scale 30 ± 19 out of 100. Sensitivity and specificity of HSG-MRI were 100% for global and left Cotte test, and 25% and 93.3% for right Cotte test, respectively. There were 2 minor complications and no major complications. Discussion: our initial results demonstrated high sensitivity and specificity. Although other studies analyzed the ability of HSG-MRI to assess tubal patency with good results, the use of a flawed reference standard left room for reasonable doubt, preventing a recommendation based on solid evidence. However, when comparing our results with those published, we observed a high degree of concordance insofar as the positive effusion is correctly diagnosed with a specificity of 100% or with a percentage close to this figure.

Demographic policy is one of the key tasks of almost any state at the present time. It correlates with the solution of pressing problems in the economic and social spheres, directly depends on the state of healthcare, education, migration policy and other factors and directly affects the socio-economic development of both individual regions and the country as a whole. Many Russian and foreign researchers believe that demographic indicators very accurately reflect the socio-economic and political situation of the state. The relevance of the study is due to the fact that for the progressive socio-economic development of any country, positive demographic dynamics are necessary. The main sign of the negative demographic situation that has developed in modern Russia and a number of countries, primarily European, is the growing scale of depopulation (population extinction). The purpose of this work was to analyze the existing demographic policy of Russia and compare demographic trends in Russia and other countries. The work uses methods of statistical data analysis, comparison of statistical indicators of fertility, mortality, natural population decline, migration, marriage rates in Russia and the Republic of Srpska, methods of retrospective analysis, research of the institutional environment created by the action of state and national programs “Demography”, “Providing accessible and comfortable housing and public services for citizens of the Russian Federation”, “Strategy of socio-economic development for the period until 2024”, Presidential decrees, etc. Research has shown that despite measures taken to overcome the demographic crisis, Russia’s population continues to decline. According to the Federal State Statistics Service of the Russian Federation (Rosstat), as of 1 January 2023, 146.45 million people lived in Russia. By 1 January 2046, according to a Rosstat forecast published in October 2023 the country’s population will decrease to 138.77 million people. To solve demographic problems in the Russian Federation, a national project “Demography” was developed and approved. The government has allocated more than 3 trillion rubles for its implementation. However, it is not possible to completely overcome the negative trend. The authors proposed a number of economic and ideological measures within the framework of agglomeration, migration, and family support policies that can be used within the framework of socio-economic development strategies and national programs aimed at overcoming the demographic crisis.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

An exhaustive analysis and evaluation of fertility indicators in a society including many ethnic groups might provide valuable insights into any discrepancies. This study aims to systematically analyse the fertility rates over specific periods and investigate the differences in levels and patterns between local and expatriate women in Saudi Arabia using the existing data. This analysis used data from credible sources published by the General Authority for Statistics in the Saudi census 2022. The calculation of period fertility indicators started with the most straightforward rates and advanced to more complex ones, followed by a comprehensive description of the advantages and disadvantages of each. The aim was to ascertain fluctuations in fertility rates and analyse temporal patterns. Multiple studies consistently show that the fertility rate among expats in Saudi Arabia is lower than that among Saudi native women. However, the reason for this discrepancy still needs to be discovered since the definitive effect of contraceptive techniques has yet to be confirmed. Moreover, the reproductive trends that have occurred since the early 1980s will persist, although with additional precautions in place.

The article examines the current state of fertility processes in Kazakhstan, the diversity of reproductive scenarios, and the reasons for their formation. The authors proceed by analysing the sovereign demographic system formed in Kazakhstan in the first quarter of the 21st century based on the Kazakh ethnic group. Cluster analysis was performed for demographic zones, considering indicators such as the proportion of Kazakhs in the urban population and the total fertility rate in cities. We believe that case technology allows us to demonstrate the differences in the reproductive attitudes and behaviour of urban Kazakhs, ultimately determining the trends in reproductive processes in the country. The focus is given to the socio-cultural and socio-economic differences across the regions of Kazakhstan and their impact on fertility processes in the context of the accelerated urbanisation of Kazakhs. The main variants of adaptation of the reproductive behaviour of Kazakhs to new urban living conditions are described, and an assumption is made about further prospects for maintaining or changing birth rates in Kazakhstan.