Ontology of Designing

The aim of this study is to structure the aspects of control and safety in technical systems using the concepts of entropy, negentropy, and information, in order to identify additional factors for assessing controllability and loss of control in the process of decision-making or the loss of emergence. The article examines specific properties of ergatic systems that contribute to forming the informational basis for describing the structure, control, and safety levels of technical systems. A two-level ontological framework is proposed for evaluating the controllability of a technical system through the analysis of its informational characteristics. Negentropy is interpreted as an indicator of the system’s condition, reflecting the combined effect of internal properties and external influences on its functionality. The configuration and number of safety subsystems define the upper limits of controllability and safety within a technical system, including in scenarios involving emergence. The dualism of safety lies in capturing both the negentropy (describing the state of order in the system, as well as its actual — or worst-case — levels and structure of control and safety) and the information about the state of control and safety (expressed through the required number and configuration of safety subsystems needed to achieve optimal performance). The article compares several concepts and regularities from information theory and thermodynamics for their applicability within the ontological framework of safety dualism. The analysis of safety dualism enables the comparison of structural and control architectures in technical system designs, the identification of factors contributing to successful or failed control, the definition of conditions under which emergence is lost, expert assessment of communication and control systems, and the development of optimal control and safety regimes—taking into account both resource depletion and the need for resource replenishment.

The article explores an effective approach to detecting anomalies in cryptocurrency transactions using neural network models, including convolutional, deep, and gated recurrent units (GRUs), and compares their performance with other existing methods for identifying illicit transactions in cryptocurrency networks. A research of relevant studies is conducted in the fields of transaction data analysis in the cryptocurrency network, data visualization for transaction analysis, and the use of computer vision techniques for detecting anomalous behavior. The subject area of the study is defined. The problem of detecting anomalies in cryptocurrency transactions is based on the fact that these transactions are pseudonymous, i.e. there are no direct indications of the identity of the sender and recipient. The relevance and contribution of this work lie in the development of a method capable of identifying anomalous transactions with high accuracy in near real-time. Experimental studies were conducted using a dataset of cryptocurrency transactions, applying both neural and non-neural classifiers. The results are compared against existing approaches in the field. The experiments demonstrated that gated recurrent units outperformed other neural models in this task, achieving an accuracy of 0.94, precision of 0.95, recall of 0.93, and F1-score of 0.94, indicating the high effectiveness of the proposed model. Nonetheless, this approach showed slightly lower performance compared to traditional machine learning algorithms, such as optimized distributed gradient boosting. The novelty of the proposed approach lies in its use of statistical characteristics derived from the transaction graph, combined with deep learning and gradient boosting techniques. The approach can be applied in the development of software tools for detecting illicit cryptocurrency transactions within information security systems and digital forensics.

The article examines the development of the information support system of the territorial administrative body. To date, there is a notable lack of published research focused on the specific features of information support for administrative bodies, especially for the level of city districts. The concepts of a multi-level information and control complex and a service-oriented architecture have been adapted to the context of information support for administrative management in a city district. To substantiate the selection of innovative technologies and services in the development of such systems, the article proposes methods and models grounded in a system-targeted approach. The goals and objectives of territorial administration have been systematically structured and analyzed. The article presents models for organizing expert evaluations based on assessing the impact of innovative technologies and software products on the achievement of administrative goals and objectives, while also considering their interaction with the existing information infrastructure. The proposed methods and models enable the evaluation of the relevance and effectiveness of innovative technologies in supporting the key functions of the information and control complex for administrative management at the city district level.

This article advances the author’s approach to solving data mining problems by integrating methods from explainable artificial intelligence and constraint programming theory. It proposes a method for mining frequent closed patterns that accounts for feature hierarchies. The approach is based on the construction a binary search tree and eliminates the need for a preliminary candidate generation stage. Feature hierarchy constraints are handled through specialized procedures that reduce the search space, thereby mitigating the effects of combinatorial explosion. In contrast to commonly used algorithms, the proposed method employs a depth-first rather than a breadth-first search tree traversal strategy. Its core component is a logical inference procedure that computes the closure of a given feature set. The method also supports the incorporation of additional constraints to further reduce the search space. Compared to existing approaches based on logical inference, it avoids redundant computations when determining closures across feature sets.

The growing dimensionality of data, driven by the multitude of heterogeneous time series, requires the development of efficient methods for their processing and compression. This article presents an approach to data compression where the data are represented as time series, using granulation with fuzzy logic methods. The study analyzes average daily temperature data in the Republic of Tatarstan collected from 1881 to 2024. Data granulation enabled a significant compression of the data volume. Fuzzy summarization was applied to transform the original numerical data into information granules, facilitating the automatic generation of granular descriptions of time series behavioral patterns. The summarization of time series states was carried out using fuzzy logic methods, including a rule set, membership functions for each season, interval-based linguistic variables, and a defuzzification software module. The implementation of the proposed approach demonstrated a reduction in data volume from 52,534 to 7,504 points, achieving a compression ratio of approximately 85%. The developed methods are applicable for analyzing large datasets across various domains.

The design of urban development projects relies on approaches that involve the creation of new methods for knowledge extraction and representation. These approaches enable the adaptation of digital models to changing conditions and facilitate the coordination of interdisciplinary requirements. This article addresses the formalization of knowledge related to building structure types, regulatory standards, operational conditions, and other data that must be considered in urban development processes. Based on an analysis of design workflows and regulatory and reference documentation, an ontology has been developed to support the evaluation of urban development projects. The article proposes a method for analyzing such projects that integrates data from informational, geometric, and ontological models to verify the compliance of digital models and design documentation with relevant norms and standards. The method is applicable across a wide range of urban development projects; its use depends on the completeness of the databases and rule sets involved. The software implementation of the proposed method reduces the labor intensity of project review and contributes to improving the overall quality of urban development outcomes.