Purpose – we plan to conduct an empirical study and natural psycholinguistic experiment, to assess the language policy of the Republic of Kazakhstan and its impact on the possibilities of professional and communicative competencies in the use of linguistic aspects of the state language in civil aviation, to develop a system of measures to activate language transformation in the country's civil aviation. Methods. The study is based on a triangulation approach, which involves the simultaneous use of several research methods: identification in academic databases, Scopus and Google Scholar data regarding scientific publications; qualitative and content analysis, marked by scientific publications; method for cause-and-effect relationship of language transformation; comparative analysis of language policy; statistical method of assessing the language situation in the country; critical analysis of document strategies; the "problem tree" method; the method natural psycholinguistic experiment; methodological consistency in presenting research results; generalization method in drawing conclusions. Results. Based on the natural psycholinguistic experiment results, studying professional communication and discourse of radiotelephony in civil aviation, reviews of aviation experts on sources of misunderstanding and research into the context of key turns, it is shown that the softness of the language policy of Kazakhstan corresponds to the sluggish methods of professional and communicative competencies of using linguistic aspects of the state language in civil aviation, which requires taking radical measures for language transformation in the field of air transport. Conclusions. Based on the results of the study, the softness of Kazakhstan’s language policy and its “weak” influence on the formation of professional-communicative competencies in the use of linguistic aspects of the state language were proven, as revealed during a psycholinguistic experiment in the field of civil aviation. A “problem tree” model of language transformation was constructed, which became the fundamental basis for developing a roadmap for the formation of professional-communicative competencies in the use of the linguistic aspects of the state language in civil aviation.

The research focuses on developing a novel method for the automatic recognition of human psychoemotional states (PES) using deep learning technology. This method is centered on analyzing speech signals to classify distinct emotional states. The primary challenge addressed by this research is to accurately perform multiclass classification of seven human psychoemotional states, namely joy, fear, anger, sadness, disgust, surprise, and a neutral state. Traditional methods have struggled to accurately distinguish these complex emotional nuances in speech. The study successfully developed a model capable of extracting informative features from audio recordings, specifically mel spectrograms and mel-frequency cepstral coefficients. These features were then used to train two deep convolutional neural networks, resulting in a classifier model. The uniqueness of this research lies in its use of a dual-feature approach and the employment of deep convolutional neural networks for classification. This approach has demonstrated high accuracy in emotion recognition, with an accuracy rate of 0.93 in the validation subset. The high accuracy and effectiveness of the model can be attributed to the comprehensive and synergistic use of mel spectrograms and mel-frequency cepstral coefficients, which provide a more nuanced analysis of emotional expressions in speech. The method presented in this research has broad applicability in various domains, including enhancing human-machine interface interactions, implementation in the aviation industry, healthcare, marketing, and other fields where understanding human emotions through speech is crucial

Based on the results of empirical research and a natural psycholinguistic experiment, the study aims to justify the feasibility of an innovative prognostic tool for improving flight safety in low-altitude civil aviation in the Republic of Kazakhstan. This tool is based on a Markov-entropic model for assessing the risk of information-related errors of psycholinguistic nature arising in multilingual communication. Methods. This research uses a triangulation approach, which allows several research methods to be used simultaneously. These methods include: the causality method; multifactor analysis; Markov-entropic mathematical modelling; the entropy method; a natural psycholinguistic experiment; experimental scenario prediction; the graphic method; the method of logical sequence; and the method of generalisation. Results. Based on the results of the natural psycholinguistic experiment, a Markov-entropy model for assessing the risk of communicative errors of psycholinguistic nature arising in multilingual aviation communication was developed and verified. The application of this model allowed not only to quantify the probability of such errors, but also to identify the key factors influencing their occurrence in real operational scenarios. The psycholinguistic approach to risk modelling used has demonstrated its effectiveness in identifying hidden dynamic patterns inaccessible to traditional methods of analysis. It was found that entropic characteristics of the communication flow serve as a reliable indicator of its complexity and potential vulnerability, while Markov chains effectively describe the sequential states of the communicative act and the probabilities of transitions between them, which is critical for the predictive detection of potential failures. Conclusions. The results obtained confirm that the integration of psycholinguistic parameters into the risk assessment model significantly improves the accuracy of communicative error prediction and forms the basis for the development of effective preventive measures aimed at minimising the human factor and improving overall flight safety. The developed model is an innovative predictive tool with significant potential for practical application in the aviation industry.

In multilingual countries, a crucial issue is the preparation of future teachers for instructing in one or several additional languages. Proficiency in at least three languages is considered one of the fundamental educational competencies. This could include the second language of the country, the mastery of which contributes to preserving its cultural and linguistic diversity, as well as one or several foreign languages, such as English, which is widely used in international communication amidst the conditions of globalization. Kazakhstan has implemented a trilingual education policy known as the ‘Trinity of Languages’ initiative. It mandates the use of Kazakh, Russian, and English in schools with the aim of fostering national unity, preserving heritage languages, and enhancing global communication skills among students. The present study is dedicated to exploring the issue of preparing future teachers of natural sciences for instruction in the context of multilinguism. The demand for such teachers is explained by the gradual transition of general education schools in the Republic of Kazakhstan to teaching in three languages Kazakh, Russian and English and studying natural sciences in the primary grades in English. A future educator teaching a subject in a second (or foreign) language should possess well-developed communication skills, be able to independently plan lessons considering both language and subject-specific objectives, and teach while fostering the development of students’ knowledge in the subject, academic English language, and higher-order thinking. The necessity for preparing such professionals underscores the relevance of the conducted research.

The object of the study is the process of decarbonization of civil aviation by switching to environmentally friendly fuels, which is relevant for the majority of countries in the world seeking to reduce the carbon footprint of air transport. The study uses methods that comply with international standards, which allowed to draw conclusions applicable in a global context. Due to their features and characteristics, the results of the study made it possible to form a clear idea of the global industry program to reduce and offset carbon emissions, as well as to establish the level of readiness of the civil aviation of the Republic of Kazakhstan and related industries to switch to alternative types of aviation fuel. The constructed mathematical models of the dependence of the efficiency indicator Y (carbon footprint level) on a set of factor features Xi and made it possible to prove the presence of multicollinearity in the array of mutually independent factors affecting the efficiency indicator Y, as well as to prioritize them based on the strength of their influence on the size of the carbon footprint of air transportation. It is emphasized that the process of switching aviation to bio- and synthetic aviation fuel is complicated by the lack of a Strategy and related documents on the implementation of activities related to the transition of aviation to new generation fuels. The key points of constructing the algorithm for the transition of civil aviation to environmentally friendly fuel have been identified, which ensured the construction of its simulation model. The practical application of the proposed algorithm for the transition of civil aviation to new-generation fuel should become the fundamental basis for the formation of the country's Roadmap for the transition of aviation to SAF and LCAF

The aim of the research is guided by the results of empirical research and naturalpsycholinguistic experiment, to assess the impact of the linguistic aspects use of the state language in civil aviation of the Republic of Kazakhstan on the challenges of future aeromobility and the organization of safe air traffic of low-altitude aviation. Methods. The study is based on a triangulation approach, which involves the simultaneous use of several research methods: method of cause-and-effect relationship and comparative analysis; coding and standardization of variables; multivariate analysis; mathematical modeling; natural psycholinguistic experiment; maximum likelihood; experimental forecasting; threat gradation; rating; logical consistency and generalization. Results. Based on the results natural psycholinguistic experiment, study of professional communication and discourse of radiotelephony in civil aviation, feedback from aviation experts on sources of misunderstanding and research into phonetic features of key turns, a model for predicting flight safety threats has been developed, the use of which will ensure the safety of low-altitude aviation air traffic and timely adoption of measures to avoiding air accidents. Conclusions. Based on the results of the study, a model for predicting air traffic safety threats in low-altitude aviation has been developed using an integrated approach. The integration of the innovative dialect factor together with traditional variables makes the proposed model promising for practical application, allowing for the establishment of the level of flight safety threats, especially in multinational crews and regions with significant dialect differences, and also prevent the occurrence of incidents and air accidents.