The problems of managing modern complex organizational and manufacturing systems, such as international production corporations, regional economies, sectoral ministries, etc., in conditions of fierce competition are primarily related to the need to consider the activity of organizational and manufacturing objects that make up a multi-level manufacturing system, that is, the ability to efficiently solve the problem of coordinating interests. This problem cannot be solved efficiently without the use of modern scientific achievements and appropriate software. As an example, we can cite the active systems theory pioneered by Prof. V. M. Burkov and his students, which successfully claims to be a constructive implementation of the idea of coordinated planning. This paper proposes new models and methods of coordinated planning of two-level organizational and manufacturing systems. Our models and methods use original compromise criteria and the corresponding constructive algorithms. The original aggregated volume-time models are used as models of organizational and manufacturing objects. We present a well-founded software structure for the proposed methods of coordinated planning. It contains an intelligent interface for using the presented results in solving applied problems.

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) across military, commercial, and civilian domains creates unprecedented security challenges while simultaneously offering significant operational advantages. Current detection and tracking systems face mounting pressure to balance effectiveness with deployment complexity and cost constraints. This paper presents a geospatial detection and movement analysis system for Unmanned Aerial Vehicles that addresses critical security challenges through innovative mathematical and software solutions. The research introduces a methodology for UAV monitoring that minimizes sensor requirements, utilizing a single optical sensor equipped with distance measurement capabilities. The core of this work focuses on developing and evaluating an algorithm for three-dimensional (3D) coordinate determination and trajectory prediction without requiring direct altitude measurement. The proposed approach integrates computer vision detection results with a mathematical model that defines spatial relationships between camera parameters and detected objects. Specifically, the algorithm estimates altitude parameters and calculates probable flight trajectories by analyzing the correlation between apparent size variation and measured distance changes across continuous detections. The system implements a complete analytical pipeline, including continuous detection processing, geospatial coordinate transformation, trajectory vector calculation, and visualization on geographic interfaces. Its modular architecture supports real-time analysis of video streams, representing detected trajectories as vector projections with associated uncertainty metrics. The algorithm's capability to provide reliable trajectory predictions is demonstrated through validation in synthetically generated environments. It offers a cost-effective monitoring solution for small aerial objects across diverse environmental conditions. This research contributes to the development of minimally-instrumented UAV tracking systems applicable in both civilian and defense scenarios.

The article is devoted to special methods for distributed databases that allow to accelerate data reconciliation in information systems, such as IoT, heterogeneous multi-computer systems, analytical administrative management systems, financial systems, scientific management systems, etc. A method for ensuring data consistency using a transaction clock is proposed and the results of experimental research for the developed prototype of a financial system are demonstrated. The transaction clock receives transactions from client applications and stores them in appropriate queues. The queues are processed based on the transaction priority. The highest priority queue is processed before the lowest priority queue. This allows you to determine which important data (such as financial transactions) should be processed first. The article justifies the replacement of the Merkle tree with a hashing algorithm and the use of the Bloom spectral filter to improve the Active Anti-Entropy method to accelerate eventual consistency. For its effective use, the filter generation algorithm is modified, which allowed to increase the speed of its generation and maintain a sufficient level of collision resistance.

As software systems continue to grow more complex, evaluating software quality becomes increasingly critical. This study analyzes existing software quality models, including McCall, Boehm, FURPS, and ISO Systems and Software Engineering – Systems and Software Quality Requirements and Evaluation (SQuaRE), with a specific focus on the ISO/IEC 25010:2023 standard. The research aims to assess the completeness of these models and explore interdependencies among key quality attributes relevant to software requirements engineering. The paper identifies key characteristics and associated metrics based on ISO/IEC standards using comparative analysis and a literature review. Findings show that ISO/IEC 25010:2023 provides the most comprehensive structure, with Functional Suitability and Compatibility identified as essential due to their universally recommended metrics. Survey data from 328 practicing analysts in Ukraine and internationally demonstrate a gap between theoretical models and real-world requirements documentation practices, particularly for non-functional requirements. The identified dependencies between quality attributes enable a more integrated and structured approach to identifying and analyzing non-functional requirements in IT projects. The study emphasizes that software quality models must be tailored to project-specific goals and constraints, with attention to trade-offs and stakeholder needs during the requirements specification, prioritization, and validation processes. The findings support the adaptation of quality models to specific project constraints and emphasize the business analyst’s role in tailoring quality criteria for practical use in software development.

The rapid growth of the video game industry and its reliance on digital distribution have created new opportunities for data-driven sales forecasting. Social media platforms serve as influential environments where consumer sentiment, trends, and discussions impact purchasing behaviors. This study examines the potential of using sentiment analysis of social media data to predict video game sales. While traditional sales forecasting models mainly depend on historical sales data and statistical techniques, sentiment analysis offers real-time insights into consumer interest and market demand. This paper reviews existing research on video game sales prediction, the application of sentiment analysis in the gaming industry, and sentiment-based forecasting models in other domains. The findings highlight a significant research gap in applying sentiment analysis to video game sales forecasting, despite its demonstrated efficacy in related fields. The study emphasizes the advantages and challenges of integrating sentiment analysis with traditional forecasting methods and proposes future research directions to enhance predictive accuracy.

Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient Task Scheduling (TS) in Cloud-Fog Computing (CFC) environments is crucial for meeting the diverse resource demands of modern applications. This paper introduces the Sewing Training-Based Optimization (STBO) algorithm, a novel approach to resource-aware task scheduling that effectively balances workloads across cloud and fog resources. STBO categorizes Virtual Machines (VMs) into low, medium, and high resource utilization queues based on their computational power and availability. By dynamically allocating tasks to these queues, STBO minimizes delays and ensures that tasks with stringent deadlines are executed in optimal environments, enhancing overall system performance. The algorithm leverages processing delays, task deadlines, and VM capabilities to assign tasks intelligently, reducing response times and improving resource utilization. Experimental results demonstrate that STBO outperforms existing scheduling algorithms in reducing makespan by 21.6%, improved energy usage by 31%, and maximizing throughput by 27.8%, making it well-suited for real-time, resource-intensive applications in CFC systems.

The article presents a modern approach to analysing public opinion based on Ukrainian-language content from Telegram channels. This study presents a hybrid clustering approach that combines DBSCAN and K-means algorithms to analyse vectorised Ukrainian-language social media posts in order to detect public opinion trends. The methodology relies on a multilingual neural network–based text vectorisation model, which enables effective representation of the semantic content of posts. Experiments conducted on a corpus of 90 Ukrainian-language messages (collected between March and May 2025) allowed for the identification of six principal thematic clusters reflecting key areas of public discourse. Despite the small volume of the corpus (90 messages), the sample is structured and balanced by topic (news, vacancies, gaming), which allows you to test the effectiveness of the proposed methodology in conditions of limited data. This approach is appropriate in the case of the analysis of short texts in low-resource languages, where large-scale corpora are not available. A special advantage of this approach is the use of semantic vector representation and the construction of graphs of lexical co-occurrence networks (term co-occurrence networks), which demonstrate a stable topological structure even with small amounts of data. It allows you to identify latent topic patterns and coherent clusters that have the potential to scale to broader corpora. The authors acknowledge the limitations associated with sample size, but emphasise the role of this study as a pilot stage for the development of a universal, linguistically adaptive method for analysing public discourse. In the future, it is planned to expand the body to increase the representativeness and accuracy of the conclusions. The paper proposes a hybrid method of automatic thematic cluster analysis of short texts in social media, in particular Telegram. Vectorisation of Ukrainian-language messages is implemented using the transformer model multilingual-e5-large-instruct. A combination of HDBSCAN and K-means algorithms was used to detect clusters. More than 36,000 messages from three Telegram channels (news, games, vacancies) were analysed, and six main thematic clusters were identified. To identify thematic trends, a hybrid clustering approach was used, in which the HDBSCAN algorithm was used at the first stage to identify dense clusters and identify "noise" points, after which K-means were used to reclassify residual ("noise") embeddings to the nearest cluster centres.
Such a two-tier strategy allows you to combine the advantages of flexible allocation of free-form clusters from HDBSCAN and stable classification of less pronounced groups through K-means. It is especially effective when working with fragmented short texts of social networks. To validate the quality of clustering, both visualisation tools (PCA, t-SNE, word clouds) and quantitative metrics were used: Silhouette Score (0.41) and Davis-Boldin index (0.78), which indicate moderate coherence and resolution of clusters. Separately, the high level of "noise" in HDBSCAN (34.2%) was analysed, which may be due to the specifics of short texts, model parameters, or stylistic fragmentation of Telegram messages. The results obtained show the effectiveness of combining modern vectorisation models with flexible clustering methods to identify structured topics in fragmented Ukrainian-language content of social networks. The proposed approach has the potential to further expand to other sources, types of discourse, and tasks of digital sociology. As a result of processing 90 messages received from three different channels (news, gaming content, and vacancies), six main thematic clusters were identified. The largest share is occupied by clusters related to employment (28.2%) and security-patriotic topics (24.7%). The average level of "noise" after the initial HDBSCAN clustering was 34.2%. Additional analysis revealed that post lengths varied significantly, ranging from short announcements (average of 10 words) to analytical texts (over 140 words). Visualisations (timelines, PCA, t-SNE, word clouds, term co-occurrence graphs) confirm the thematic coherence of clusters and reveal changes in thematic priorities over time. The proposed system is an effective tool for detecting information trends in the environment of short, fragmented texts and can be used to monitor public sentiment in low-resource languages.