Annotation: Energy modernization of objects with historical and cultural value represents one of the most complex and multifaceted challenges in the fields of construction and restoration research. It lies at the intersection of two of modern society’s priority directions — preserving the authenticity of monuments and achieving ambitious goals for reducing carbon footprint and enhancing buildings’ energy efficiency. Within the framework of this study, the aim is to identify existing challenges and pathways for integrating energy-efficient solutions in the modernization of historical buildings. The methodological foundation was a comprehensive systems analysis of specialized publications addressing methods for evaluating and implementing energy-saving technologies in historical buildings, as well as a synthesis of contemporary approaches in non-destructive testing, the application of digital building information models (HBIM), and life-cycle cost assessment (LCCA). The findings obtained and the described decision-making framework are of interest to design engineers, restoration architects, heritage protection authorities, and government institutions involved in the preparation and implementation of historical renovation programs. They make it possible to devise a balanced approach whereby sustainable development requirements harmoniously coincide with the objectives of cultural heritage conservation.

Annotation: This paper analyzes the phenomenon of clean beauty and its impact on the consumer behavior of women over 35 years of age. The relevance of the study is justified by the rapid growth of the clean cosmetics segment in the market and the shift of mature consumers’ value orientations toward conscious consumption and holistic healthcare. The aim of the research is to identify and analyze the main catalysts shaping the purchasing patterns of women aged 35+, and to substantiate a synergistic model that combines external cosmetic practices with internal nutraceutical interventions. The methodology includes a systematic analysis of specialized scientific publications on consumer behavior, dermatocosmetology, and functional nutrition science, as well as the processing of empirical data obtained in the course of practical nutritional work. The results indicate that for the target group clean beauty is not merely a marketing category but responds to the demand for safety, proven efficacy, and a harmonious unity of external appearance and health status. A conceptual model is described, demonstrating that maximum fulfillment of the audience’s needs is achieved by integrating clean cosmetics with personalized nutraceutical programs aimed at correcting metabolic and hormonal imbalances. The scientific novelty of the study lies in the conceptualization of the clean beauty trend as an entry point into a comprehensive health management system for women of mature age. The practical significance of the results lies in their applicability for cosmetic brand marketers, preventive medicine specialists, nutritionists, and cosmetologists.

Annotation: A web developer's corporate identity is a multi-level strategic asset that integrates visual, functional and emotional components. It strengthens market position, increases trust, facilitates targeted communication and forms a long-term reputation in the rapidly changing digital world.

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.

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.

Annotation: Hallucinations in large language models (LLMs) are a systemic problem that manifests itself when models generate information that does not correspond to the ground truth or input data. This phenomenon significantly limits the application of LLMs in mission-critical domains such as medicine, law, research, and journalism, where the accuracy and reliability of information are of utmost importance. This paper provides a comprehensive analysis of three key factors that contribute to hallucinations: issues related to the quality and structure of training data; architectural features of transformer models that predispose them to error accumulation; and the lack of built-in fact-checking mechanisms, due to which models rely solely on statistical regularities. Each of these factors is discussed in detail using relevant research, and potential solutions are proposed. The paper includes three dedicated graphs that visualize the relationship between various model parameters and the occurrence of hallucinations. The results of the study indicate the need for a comprehensive approach to improving LLM, including both improving data preprocessing methods and modifying the model architecture and introducing additional verification mechanisms.