Vol 31, No 9 (2025)

One of the main challenges in organizing the efficient development of modern aircraft during the conceptual design phase is the provision of methodological support for a complex, iterative process of synthesizing and selecting design solutions under uncertainty. Uncertainty is the primary cause of unforeseen violations of critical constraints and errors in the determination of objective function values. At the same time, during the conceptual design phase, the main parameters and characteristics that define the overall appearance of the aircraft are selected. Failure to adequately consider uncertainty in computational and optimization tasks often results in uncompetitive and impractical design solutions. In recent years, there has been a growing number of studies aimed at addressing this issue by adapting traditional, deterministic models and algorithms to accommodate inaccurate input data. The article discusses the methods that are used to synthesize and select design solutions for aircraft design under uncertainty. At the same time, the main focus is on the latest achievements in statistical and intelligent computing, which reduce the time and increase the information content of the conceptual design stage. Approaches to dealing with parameter uncertainty, solving the problem of target uncertainty, and surrogate modeling to reduce computational costs are presented. The main provisions and conclusions presented in the framework of the review study are illustrated by examples from the world scientific literature.

An approach to modeling a multidimensional time series of atmospheric temperature data based on fuzzy Markov chains is considered. This approach allows us to solve the general scientific problem of constructing time series models with structural discontinuities, as well as solving the problem of parametric identification in the case when the model parameters depend on time. The paper compares the constructed models based on fuzzy Markov chains with other models proposed by the authors earlier. A numerical experiment using these data as an example showed the best quality of the proposed models, as well as a strictly justified approach to identifying areas of homogeneity of the time series.

We consider the architecture of an intelligent information system (hereinafter — IIS), which contains the necessary components for solving typical tasks of analysis, processing and synthesis of text data, as well as means for configuring such components and monitoring their operation. In this case, the main task was to create a modular structure of the system, allowing horizontal scaling by flexible customization of the components and the sequence of their work as part of a single automated data processing pipeline. To describe the system structure we used the "C4" model of three levels: Context, Containers, Components. The 4th level "Code" is not used in this article, as it represents, designing a solution for module level abstractions. As an example for the design we have chosen an information system for collecting data on literary works, processing unstructured texts of such documents, loading the information into the analytical storage and analytical constructions based on the available data. To test the design solutions, a service catalog for data extraction, transformation and loading pipelines, a catalog of interaction contracts between services and a project structure for a containerization and multi-container management tool — Docker Compose — were developed. Apache Airflow and its REST API for integration with the IIS configuration component was used as an orchestration solution. To configure and optimize the system settings, a diagram of components of the module of own development in Python using the Django framework is given, as well as a description of the logic of its components. In conclusion, the advantage of the used approach over the implementation of ready-made software products with a set of necessary functions is described.

The article outlines the results of developing a system designed to identify plagiarism in files containing program code. The automated system integrates multiple methods of program code analysis. It serves as a decision-support tool for university instructors by enabling the detection of assignments with plagiarism levels exceeding a predefined threshold. The system is specifically designed to evaluate student submissions within the university, allowing for the comparison of works among students in the same cohort to identify groups with similar or copied content. The system supports the analysis of assignments stored both locally and in cloud storage.