The present paper is devoted to the possibility of efficient implementation for sorting algorithms using the Q-determinant concept. We have investigated four algorithms: shaker sort, Shell sort, quicksort and Batcher’s odd-even mergesort. To sort small arrays, we obtained representations in the form of Q-determinants for these algorithms. Then the structures of the obtained representations were analyzed and, as a result, for the general case we have described the representations of the sorting algorithms in the form of a Q-determinant. Also, for sorting algorithms there was considered the application of the method of designing effective programs using the concept of Q-determinant. This application has been tested on shared and distributed memory of parallel computing systems by developing eff ective programs for the Shell sort.

This paper discusses the integration of tight quantifiers into a system for checking the consistency of expert evaluations, significantly enhancing the system’s ability to analyze various scenarios. Existential and universal quantifiers enable the processing of more complex logical expressions, increasing both accuracy and flexibility. This approach allows for consideration of both individual probabilities of factors and their combinations, which is particularly important for solving complex problems related to risk assessment and strategic management. The paper provides a detailed description of consistency-checking algorithms, including graph-based visualization of relationships between expert evaluations and methods for resolving inconsistencies. Additionally, a modular approach to developing a software system for automating expert evaluation analysis is discussed. The proposed system demonstrates high versatility and applicability in risk management, forecasting, and decision-making tasks, making it a valuable tool for improving prediction accuracy and developing more effective management strategies.

Traditionally, fault-tolerant DBMS clusters using PostgreSQL or derivatives are built on replication machinery, operated via write-ahead log shipping. Default checks are aimed only at preserving the integrity of received records. In certain conditions replication start can lead to standby cluster node having data different from other nodes, or being unable to finish startup procedures. Existing high availability systems are forced to cope with the problem through recreating such nodes from backups, which is usually costly in terms of recovery time.
To address this issue, we propose an algorithm to prevent replication start when it is guaranteed to lead to data differences or node startup failure. For detection of such cases node collects information about write-ahead logs in the cluster and performs additional checks. If replication was blocked, automatic node synchronization for consequent replication start is available.
We have tested the algorithm on various real-world cluster confi gurations with simulated failures, and the experimental results indicate that algorithm substantially reduces the chance of nodes being non-eligible to restart.

The article proposes a method for managing and personalized script recommendations for Adobe After Effects, implemented using the CSXS extension and the web platform. The analysis of existing methods of searching, installing and running scripts is carried out, as well as their limitations, including the need for manual file movement, the lack of personalized selection and intelligent search. The architecture of the proposed solution is investigated, including the use of ExtendScript to execute scripts without restarting After Effects, as well as the implementation of a recommendation system based on vector text analysis using SentenceTransformers and vector data storage in Qdrant. The expediency of using the k-nearest neighbors method for selecting relevant scripts is substantiated, and a similarity threshold is set for filtering irrelevant results. The algorithm of personalized recommendations based on the analysis of selected scripts and interests of similar users is considered. Synchronization mechanisms between the web platform and the extension are described, providing access to saved scripts from any device.

Article is a study of methods for evaluating the effectiveness of feature selection algorithms and proposes a new methodology for their evaluation. It is noted that existing methods and approaches to assessment do not always adequately reflect the actual efficiency of algorithms, especially when applied to real problems. The article comprehensively discusses various opinions of researchers on the issues of internal and external validity of existing evaluation methods, assesses the impact of different parameters, including data volume, differences in algorithm implementations, and other factors. The authors propose a new integrated approach to evaluating the effectiveness of algorithms, which includesa set of indicators such as resource costs, stability, and task solution quality. One of the peculiarities of the proposed approach is the use of artificially generated data, which allows considering specific characteristics of real data and evaluating algorithms under controlled conditions. This makes it possible to more accurately determine their efficiency and reliability. The article also contains the results of preliminary testing of the proposed methodology using artifi cial data. The analysis of these results demonstrates the advantages of the new approach over traditional evaluation methods. In particular, it was found that the new methodology allows more accurately evaluating the stability and quality of algorithm performance, which is crucial for making decisions about choosing an appropriate algorithm for specific tasks. In conclusion, the authors emphasize the need for further development and expansion of the proposed methodology, as well as its adaptation for solving various types of tasks. They also point out the prospects for integrating this methodology into specialized software packages, which will make it accessible to a wide range of users and accelerate the implementation of innovative algorithms in practice.