About the journal

Annotation: The study presents a comprehensive analysis of factors determining the seasonal fluctuations in demand for services of fishing tourist centers. The relevance of the work is defined by the growing role of domestic tourism and the necessity of strengthening the economic sustainability of enterprises in the recreational fishing sector, which face uneven workload throughout the calendar year. The objective of the study is to conduct a comprehensive analysis of factors defining seasonal attendance fluctuations at fishing tourist centers and to identify key determinants of demand in different periods of the year. The methodological framework includes a systematic review of specialized scientific publications, statistical analysis of consumer behavior data and content analysis of applied marketing strategies. According to the results, biological cycles (spawning events, migration processes) and climatic conditions are the primary drivers of peak attendance seasons. At the same time, the growing importance of service diversification and digital marketing contributes to attracting tourists in the off-season. As a result, an integrated model for managing seasonal demand is described, based on a combination of operational, pricing and communication tools. The obtained conclusions have practical significance for managers of fishing tourist centers, tourism development specialists and regional authorities responsible for planning and developing tourist infrastructure.

Annotation: This article presents a theoretical analysis of the parameters of friction discs used in high-performance automotive clutches, with a focus on the interrelation between chemical composition, geometric characteristics, and thermal load. The study is based on an interdisciplinary approach integrating tribology, materials science, and thermal mechanics. Particular attention is given to the interpretation and comparison of published empirical data reflecting the influence of key elements in friction composites on the coefficient of friction and thermal stability. The geometric and operational parameters of clutches under racing conditions are examined, including high-frequency engagement cycles, limited cooling capacity, and localized thermal gradients in multilayer stacks. A comparative analysis of the thermal behavior of carbon-ceramic and cast-steel discs is conducted, justifying the applicability of each type under specific loading and thermal saturation modes. The study establishes computational relationships between material composition, heat resistance, clutch configuration, and critical slip conditions, enabling the formulation of rational criteria for selecting materials and geometries suitable for clutches operating under extreme conditions. The proposed approach can be integrated into engineering procedures for component selection and evaluation using digital computer-aided design tools. This paper will be of interest to design engineers, powertrain specialists, tribology and thermal analysis researchers, and developers of CAD/CAE systems for frictional components.

Annotation: This article analyzes architectural patterns that enable autonomous recovery in cloud-native systems, which are essential for maintaining high availability and performance. Three primary patterns are examined: Redundancy & Replication, Proactive Recovery, and Auto-Scaling. The study evaluates their effectiveness using real-world data, providing a comparative assessment based on metrics like cost reduction and performance improvement. The analysis underscores the necessity of these patterns for managing the operational complexity of modern distributed systems. Recommendations are provided for implementing these strategies to enhance the reliability and cost-efficiency of cloud applications.

Annotation: The relevance of the study is determined by the need to create tools for objective identification of digital asset market phases based on the behavioral characteristics of network participants, which are recorded not by price, but by on-chain metrics. In the current environment of growing cryptocurrency market volatility and the insufficient effectiveness of classic technical analysis indicators, there is an increasing demand for algorithms capable of detecting latent market states, in particular the accumulation phase, even before price changes occur. The purpose of this article is to create an algorithmic model for detecting the accumulation phase in the cryptocurrency market by applying cluster analysis to a set of on-chain indicators, which allows for more accurate and timely assessment of market dynamics for use in investment decision support systems. The research methodology is based on the use of blockchain network behavioral metrics (Realized Cap HODL Waves, Dormancy, SOPR, Exchange Outflow Volume, Address Balance Distribution) and unsupervised cluster learning algorithms. HDBSCAN was used as the main clustering method, allowing for the adaptive identification of market phases without fixing their number. A procedural diagram was constructed covering the stages of data collection, processing, normalization, cluster distribution, and interpretation of results. The research results reflect the construction of an effective algorithm capable of grouping time intervals by similarity of on-chain behavior and identifying accumulation phases taking into account the time dynamics of metrics. A classification architecture has been implemented that does not depend on price data and can function in real time as part of cryptanalysis systems. The conclusions prove the effectiveness of the proposed model for detecting accumulation phases based on the dynamics of on-chain data structures. It has been established that the algorithm is capable of ensuring early detection of changes in market behavior and reducing dependence on traditional speculative indicators. Prospects for further research are related to the integration of time dependencies into the cluster model, the extension of the system to other digital assets, the introduction of aggregated accumulation indices and self-learning components, taking into account macroeconomic and off-network influences.