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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.

Annotation: The field of all types of innovation is typically saturated with a wide range of conventionally applied technical solutions and materials. Therefore, the implementation of innovative changes must be adapted to and integrated with traditional technologies and materials. In general, innovation is not a goal in itself. The central objective of the entire innovation process is to achieve a final result that leads to a qualitative improvement of all parameters of the therapeutic process, while ensuring the continuity of technologies, engineering solutions, and materials — including consumables. Developers have presented their first quantum computer and joined the global race for “quantum supremacy.” The first 20-qubit computer employs modern superconducting technology.

Annotation: The article explores the integration of digital technologies into the assessment of eyebrow morphology and the planning of their correction as a new stage in the development of aesthetic medicine. The aim of the study is to examine the impact of advanced technologies on the eyebrow correction process, taking into account facial morphology, which is understood as the analysis of facial shape, proportions, and symmetry in order to create a personalized image. The study employed general scientific methods of cognition: analysis, synthesis, comparison, modeling, systematization, and generalization. The findings indicate that the modern approach to eyebrow shaping is based on a combination of anatomical knowledge, age-related morphological features, innovative digital modeling, and standardized aesthetic parameters. The interdisciplinary nature of eyebrow morphology is emphasized, as it integrates anatomical, aesthetic, and anthropometric knowledge to enable precise analysis and correction of eyebrow shape while considering individual patient characteristics such as age, gender, ethnicity, and facial structure. It is noted that effective eyebrow shaping cannot be achieved without taking into account the facial morphotype and deep anatomical changes, especially in older patients. The study shows that digital technologies are actively being implemented in the field of eyebrow modeling and correction, with the most significant being: artificial intelligence (AI); augmented and virtual reality (AR/VR); 3D printing; 3D photogrammetry; CAD/CAM technologies. AI provides a personalized approach to analysis and modeling, while AR/VR allows real-time testing and visualization of different shape options. 3D printing is used for the creation of customized stencils or models, and 3D photogrammetry enables the acquisition of highly accurate facial parameters. CAD/CAM technologies ensure symmetry in both aesthetic and surgical procedures. The prospects for further development in this field are outlined: implementation of AI-based mobile applications for instant visualization of results; development of biometric models that consider age-related changes; AR technologies for dynamic eyebrow shape fitting; application of 3D scanners to monitor intervention effectiveness; and the use of virtual simulations as a communication tool between specialists and patients. The practical value of the study lies in the implementation of a personalized digital approach to eyebrow correction planning based on scientifically grounded morphological parameters.