The article presents the results of a study of surface waters and fish (perch (Perca fluviatilis L.), carp (Cyprinus carpio L.), bream (Abramis brama L.), pike (Esox lucius L.) of the Irtysh river. The content of Cu and Pb in the organs and tissues of fish living in the Irtysh River within the city of Semey was determined by the ditizon photometric method. The ecological assessment of the results indicates that the concentrations of Cu and Pb in the studied fish specimens do not exceed the MPC. A significant difference in the specific features of the accumulation of heavy metals has been established, depending on the type of fish nutrition and the time of year. The Pb content in various organs of perch, carp, bream and pike ranges from 0.062 mg/kg to 0.083 mg/kg. The highest concentration of Cu is observed in predatory fish (3.27 mg/kg), the lowest – in planktonivores (2.82 mg/kg). Pb also mainly accumulates in pike (0.073 mg/kg). In spring, the content of Pb in the gills of perch and pike increases, Cu – in the gills of bream and pike. The maximum concentrations of Cu in fish were observed in summer, which is associated with an increase in the food supply. The Pb content in summer, on the contrary, decreases due to its adsorption by suspended solids and deposition with them into bottom sediments. 

Excessive greenhouse gas (GHG) emissions are an environmental problem. Studies to determine cost-effective ways to reduce GHG emissions have revealed the need to model the dynamics of emissions of carbon dioxide, nitrous oxide, methane, and other gases. In this study, the calculation of CO2 equivalent emissions from industrial processes and production in the territory of the Republic of Kazakhstan was carried out. When forecasting, the data provided by the UN Framework Convention on Climate Change were used. To predict CO2 emissions from industrial production, tools for analysis and forecasting of time series were used: Prophet method, Cluster analysis of k-means time series, modern versions of ARIMA algorithms, exponential smoothing methods, and linear regression. This study presents comparative simulation results based on a baseline scenario with no action until 2045.This study compares four models to suggest an effective one for future CO2 emission forecasting. The accuracy comparison is conducted using various error measures, with the mean absolute percentage error (MAPE) chosen as the metric for comparison. 

Despite the fact that more and more attention is being paid to the development of nontraditional and renewable energy all over the world, the coal industry continues to be one of the most important industries for the Republic of Kazakhstan. All the main branches of the coal industry are represented in Kazakhstan: mining and processing 3,3 percent of the world's coal reserves are concentrated in the Republic. This paper presents a study of the combustion processes of Karazhyrin coal grade D (lower calorific value is in the range from 18855 to 21788 kJ/kg), which is non-design fuel. The studies were carried out on the operating boiler E-90-3,9/440 at various steam outputs in order to compile a regime map of the boiler. This coal is used not only in the region, but also outside it. In the course of the study, the dependences of the coefficient of performance (COP) of the gross boiler unit on the heat output and steam output were established.

As a result of the mathematical processing of the obtained experimental data (for three heat loads of 50 t/h, 75 t/h, 90 t/h), analytical dependencies were obtained that describe the change in gross efficiency and coal consumption depending on the heat and steam output of the boiler, at the same time, the coefficient of determination is within acceptable limits. 

Currently, every second inhabitant of the planet is registered in Vkontakte, Whatsapp, Instagram, Skype, Periscope and other social networks. Worst of all, social media can also endanger human life. In many cases, illicit trafficking in drugs and psychotropic substances can spread through these social networks, so special attention should be paid to studying the problems of trust in information in the virtual space.

Since social network analysis is a field that is practiced all over the world, it is not surprising that there are many programs, as well as the creation of a system using the Python programming language, which analyzes texts in various subject areas that our society needs and tries to monitor the entire virtual world. We can achieve this goal by introducing the necessary algorithms into a special program.

In order to detect and prevent illegal actions, it was considered to organize the monitoring of social networks and the prevention of crimes organized via the Internet by creating software for monitoring social networks.

The article discusses the types of cyber threats and methods of protection against them, as well as a program for monitoring social networks in the Python environment and shows its results. Based on the results obtained, he considered the possibility of prevention by identifying types of threats. 

This article is devoted to the issues of determining the needs and preferences of the population in fermented dairy products, in particular yogurt, by means of a questionnaire as one of the methods of obtaining information. As part of the research, a survey was developed consisting of 20 structured questions covering a wide range of issues related to demand, and https://docs.google.com/forms a link was posted on the platform and sent to the survey respondents (the answer to the question is mandatory), the definition of consumer preferences by type, volume and producers of yoghurts, as well as the frequency of purchases and the attitude of consumers was established to the range of manufactured yogurts. 100 people from all regions of the Republic of Kazakhstan took part in the survey. The sequence of questions corresponds to logic, the order of questions contributes to an active survey of the respondent. As part of the survey, a descriptive research method was used, data from respondents' questionnaires were processed, systematized and presented in the form of diagrams and tables. The survey results show that there is a demand for yogurt, and mostly preference is given to yogurt with fillers. The main criteria for choosing yogurt were the composition, which indicates that people are interested in consuming the most useful products for the body, in general, people prefer yogurts with functional properties, and therefore the yogurt production market is gradually expanding, increasing consumer interest in food in general. 

The article discusses measures to provide the population with food and the principles of their distribution during an emergency, in order to meet the needs of the population. Two types of food aid are characterized – short-term and long-term, lasting from several days or weeks (shortterm) to several months or years (long-term).

The basic requirements for food products are described, the main of which are: the speed of preparation, the absence of complex preparation, compliance with calorie requirements and traditional consumption, transportation and storage conditions, as well as price-quality ratio.

Special attention is paid to the balance of the food basket for the affected population, the need for compliance in macro- and microelement composition. Special attention is paid to water quality control, which is of paramount importance for the life support of the population during emergencies. The safety requirements for the process of transportation, storage, distribution and distribution organization are described. Possible methods of water treatment and purification, such as boiling, filtration, chemical disinfection, as well as methods of its reservoir storage, are presented.

The requirements of the norms for the use of water during normal life and in an emergency situation are given. 

With the rapid development of big data and Internet technologies, companies engaged in big data financial platforms collect and systematize massive data through their own platforms, improve credit scoring parameters and use machine learning methods to conduct complex and scientific credit scoring assessments. Thus, banks face big problems when building credit scoring. Based on the limitations of the existing system and methods of personal credit rating, it is necessary to study personal credit rating based on machine learning methods, improve the parameters and scoring system of personal credit rating, clarify data collection channels, use dynamic desensitization technology. To reduce the sensitivity of the data, the LOF test method is used to verify the emission data and the random forest method is used to fill in missing data values. Then you use the gradient-boosting decision tree method to view important indicators, process proven indicators using a metric system model based on logistic regression, and get a personal credit score. Finally, the model is tested using a BP neural network, and the model is used to predict the level of personal credit. The study shows that machine learning can further improve the accuracy of individuals' credit ratings and provide a scientific basis and background information for commercial banks' credit ratings. 

The analysis of sentiment in user comments finds application in many areas, such as evaluating the quality of goods and services, analyzing emotions in messages, and detecting phishing advertisements. There are numerous methods for analyzing the sentiment of textual data in the Russian language, but automatic sentiment analysis of Russian-language texts is much less developed than for other major world languages. This article is part of a broader study on the creation of an information system for detecting dangerous content in the cyberspace of Kazakhstan. The purpose of this article is to provide an analytical review of the different approaches to sentiment analysis of Russian-language texts and to compare modern methods for solving the problem of text classification. Additionally, the article seeks to identify development trends in this area and select the best algorithms for use in further research. The review covers different methods for text data preprocessing, vectorization, and machine classification for sentiment analysis of texts, and it concludes with an analysis of existing databases on this topic. The article identifies some of the main unresolved problems in sentiment analysis of Russianlanguage texts and discusses planned further research.