The article analyses the development of Italian and Spanish Renaissance theatre, as well as their common features and differences. It also highlights how the two theatrical schools made a significant contribution to the history of European theatre by artistically expressing Renaissance ideas, humanism, and popular themes. After the fall of the Roman Empire in the 5th century AD, under the influence of Christian ideology, classical art experienced a decline; however, by the 14th–16th centuries, the emergence of new social relations led to the revival of theatre. At the same time, the article scientifically examines the process by which Italian and Spanish theatre emerged from medieval conservatism to reach a stage of popular, humanistic, and artistically mature development.

Household food expenditure remains a critical determinant of nutritional outcomes and economic stability in developing economies. In Nigeria, rising food prices, income variability, and structural inefficiencies have intensified the need for optimized food spending strategies. This study develops a framework-based analytical model for optimizing household food expenditure in Lafia, Nasarawa State, using principles of linear programming and behavioral economics. Drawing on empirical insights and theoretical foundations from existing literature, the study integrates cost-minimization techniques with nutritional adequacy constraints to propose a sustainable expenditure optimization system. The methodology combines socio-economic data patterns with optimization models to identify efficient consumption bundles. Findings indicate that structured optimization frameworks can significantly reduce household food costs while maintaining nutritional balance. However, behavioral, cultural, and infrastructural constraints limit the full adoption of such models. The study contributes to the literature by offering a context-specific, scalable framework for food expenditure optimization and highlights policy implications for improving food security and economic resilience in Nigeria.

The increasing complexity of critical infrastructure systems in financial and healthcare ecosystems has intensified the demand for intelligent automation mechanisms capable of ensuring regulatory compliance, operational efficiency, and data integrity. Traditional compliance systems are largely rule-based, static, and unable to adapt to rapidly evolving regulatory frameworks and distributed cloud-native architectures. This research proposes a multidisciplinary analytical model for AI-powered regulatory automation and performance optimization in cloud-integrated environments. The study synthesizes concepts from artificial intelligence, cloud computing, regulatory analytics, and distributed systems engineering to design a unified compliance optimization framework.

The proposed model integrates machine learning-driven compliance monitoring, cloud-native orchestration mechanisms, and data lineage tracking systems to ensure transparency and auditability across heterogeneous infrastructures. Foundational cloud computing principles highlight the transformation of IT resources into scalable utility-based services (Buyya et al., 2009), which forms the basis for regulatory automation at scale. The study further examines how metadata management, secure access control, and AI-driven anomaly detection contribute to improving decision-making accuracy and reducing operational overhead.

A structured analytical framework is developed through systematic synthesis of existing literature in healthcare informatics, financial risk governance, and cloud systems engineering. The findings indicate that AI-enhanced compliance systems significantly improve regulatory responsiveness, reduce latency in audit processes, and enhance predictive risk detection capabilities. However, challenges such as model drift, interoperability constraints, and regulatory ambiguity persist.

This research contributes a comprehensive theoretical and applied model that bridges gaps between regulatory science and cloud-based AI systems, offering a scalable architecture for next-generation critical infrastructure governance.

Green chemistry is considered one of the most important scientific approaches aimed at reducing environmental pollution, increasing resource efficiency, and ensuring sustainable industrial development. The concept of green chemistry focuses on designing chemical products and processes that minimize or eliminate the use and generation of hazardous substances. This article analyzes the scientific foundations of green chemistry principles and evaluates their role in developing environmentally safe chemical processes. The study discusses the twelve principles of green chemistry proposed by Paul Anastas and John Warner and examines their practical implementation in modern industries such as pharmaceuticals, petrochemicals, polymer production, agriculture, and energy systems. Scientific data related to catalytic technologies, renewable raw materials, solvent-free synthesis, atom economy, waste minimization, and energy-efficient processes are analyzed based on published literature. The article also highlights the ecological and economic advantages of green chemistry technologies and identifies major challenges associated with industrial implementation. The findings demonstrate that green chemistry significantly contributes to reducing greenhouse gas emissions, minimizing toxic waste formation, conserving natural resources, and supporting sustainable development goals.

The growing accumulation of industrial wastes and the depletion of natural mineral resources have intensified interest in the development of alternative raw materials for ceramic technologies. In the present study, spent alumina-containing catalyst waste generated during natural gas purification at the Shurtan Gas Chemical Complex was investigated as a prospective raw material for ceramic material synthesis. The chemical composition, phase transformations, and thermal behavior of the waste material before and after thermal treatment were studied by chemical analysis, X-ray diffraction (XRD), differential thermal analysis (DTA), and thermogravimetric analysis (TG). The initial waste mainly consisted of gibbsite and γ-alumina phases, while calcination at 900 °C promoted complete dehydration and the formation of single-phase γ-Al₂O₃ with an alumina content exceeding 96 wt.%. Thermal analysis revealed that the principal weight loss occurred within the temperature range of 150–615 °C and was associated with dehydration and phase transformation processes. The obtained results demonstrate the possibility of utilizing alumina-containing industrial waste as a promising secondary raw material for the production of advanced ceramic materials.

The rapid expansion of digital transaction ecosystems has significantly increased the vulnerability of financial systems to sophisticated fraudulent activities. Traditional rule-based fraud detection systems are increasingly inadequate in addressing dynamic, large-scale, and adaptive fraud patterns. This research proposes a conceptual and analytical framework for a scalable intelligent fraud detection system leveraging advanced machine learning models to enhance detection accuracy, adaptability, and computational efficiency. The study synthesizes established anomaly detection techniques, outlier analysis methodologies, and modern machine learning approaches to construct a unified perspective on fraud identification in digital environments. Emphasis is placed on scalability across high-volume transaction streams, feature engineering strategies, and model optimization for real-time decision-making. Findings indicate that hybrid and ensemble-based learning systems outperform conventional approaches in detecting evolving fraud patterns while maintaining operational efficiency. However, challenges such as data imbalance, concept drift, and computational constraints remain critical barriers to full-scale deployment. The study contributes to the theoretical and practical understanding of scalable fraud detection architectures and outlines future directions for adaptive and explainable AI-driven financial security systems.

The article describes the activities of pharmacies and the system of pharmaceutical services to the population in the Andizhan region in the 1960s-1970s using statistical data based on archival documents.

This study analyzes the distribution areas and main climatic factors — annual average temperature and annual precipitation — of selected species belonging to the genus Helichrysum (H. arenarium, H. maracandicum, H. mussae, H. nuratavicum, H. thianschanicum) in Uzbekistan. Climatic indicators obtained from the WorldClim database were compared with plant coordinates using ArcGIS software. The results showed that each species is adapted to specific ecological and climatic conditions. Differences in temperature and moisture requirements among the species influence their natural distribution and sensitivity to climate change. These findings provide important insights into the ecological requirements of Helichrysum species and serve as a foundation for their conservation and preparation for future environmental changes.

Tourism is one of the fastest-growing sectors of the global economy and plays a significant role in the social and economic development of many countries. However, the rapid growth of tourism has also created serious environmental challenges in tourist regions. Excessive waste generation, air and water pollution, destruction of natural landscapes, overconsumption of resources, and biodiversity loss are among the major ecological issues observed in tourist destinations. This article examines the main environmental problems in tourist areas and proposes sustainable solutions for minimizing their negative impacts. The study emphasizes the importance of eco-tourism, environmental education, waste management systems, renewable energy use, and government regulations in achieving sustainable tourism development. The findings indicate that balancing tourism growth with environmental protection is essential for preserving natural and cultural resources for future generations.

The article analyzes the activity of protecting the socio-economic and legal interests of employees on the basis of trade unions based on sources and information from the periodical press and the Internet.

The exponential rise in healthcare-related data, which is fueled by electronic health records, wearable technologies, and even advanced diagnostic systems, has contributed to an increase in the need to have scalable health informatics data analytics infrastructures. Nevertheless, the sensitivity of healthcare information requires regulatory frameworks, especially the Health Insurance Portability and Accountability Act (HIPAA), to be strictly adhered to, making it a very complicated issue to organizations that are interested in balancing performance, scalability, and compliance. This work fills in the critical gap between the design of high-performance data analytics pipelines at scale, and regulatory compliance, by proposing a comprehensive framework to develop HIPAA-compliant data analytics pipelines at scale. A mixed-method research design is adopted, which involves using architectural modeling and empirical benchmarking based on synthetic healthcare data and standardized datasets such as MIMIC-III. The proposed framework uses compliance mechanisms that are directly applied to each stage of the data pipeline, such as ingestion, transformation, storage, and access layers, with embedded controls, including encryption, role-based access management, and automated audit logging. Quantitative assessment targets key performance indicators, such as the data processing latency, throughput, and compliance risk exposure measures. The results show that compliance-conscious design principles in data pipelines can decrease regulatory risk exposure by more than 35 percent and yet remain able to provide scalable performance over acceptable thresholds. Though compliance mechanisms add quantifiable computational overhead, architectural strategies can alleviate these effects with optimized strategies. This research study helps in filling the gap that exists between regulatory governance and data engineering, and provides a new, scalable, and compliance-oriented model of pipeline design. The framework provides actionable insights for healthcare organizations, data engineers, and policymakers aiming to implement secure, efficient, and regulation-compliant data analytics systems.

This article examines the phonetic level of literary text as a crucial element of its aesthetic expressiveness. Key components—rhythm, rhyme, stress, sound symbolism—are examined, as well as two sublevels: primary (onomatopoeia) and secondary (the individual use of sounds, including alliteration). Particular attention is paid to paranomasia—the clash of paronyms for expressiveness. Using Konstantin Voloshin's poem as an example, it demonstrates how the organization of sound creates the emotional and aesthetic impact of the text.

This article analyzes the technological process of de-oiling soybean seeds and the quality indicators of the meal obtained as a result of this process. The study explored methods for extracting oil from soybean seeds, the efficiency of the de-oiling process, and its impact on the composition of the meal. Moisture content, protein share, residual oil, ash content, and other important physicochemical indicators of the resulting meal were determined, and its nutritional value was evaluated. The results indicate that soybean meal is of significant importance as a high-protein feed for livestock and poultry.

The increasing integration of artificial intelligence (AI) into organizational decision-making has redefined how enterprises interpret uncertainty, manage change, and mitigate risk. This paper proposes a scalable AI-oriented architecture designed to enhance algorithmic sensemaking within humanocratic enterprises, emphasizing longitudinal adaptability in change management and risk control. Drawing upon theories of organizational behavior, humanocracy, and AI-enabled risk governance, the study synthesizes multidisciplinary perspectives to construct a layered architectural model integrating data-driven analytics, human-centered decision loops, and adaptive feedback mechanisms. The research examines how AI systems interact with organizational culture, particularly in environments transitioning from hierarchical to humanocratic structures, where autonomy, transparency, and distributed intelligence are central.

The methodology is conceptual-analytical, grounded in structured synthesis of existing literature on AI-driven risk management, organizational transformation, and algorithmic decision-making. The findings suggest that scalable AI architectures improve organizational responsiveness, enhance predictive risk control, and reduce cognitive bias in strategic decision-making. However, limitations emerge in the form of algorithmic opacity, cultural resistance, and contextual misalignment between AI models and human-centric organizational values.

The study contributes a novel framework that bridges algorithmic sensemaking and humanocracy, offering insights into sustainable digital transformation and long-term organizational resilience.

This study undertakes a comprehensive examination of the scope, objectives, and broader implications of the irrigation reforms introduced in Uzbekistan in 2003. Drawing extensively on legislative acts, regulatory frameworks, and previously unexplored archival records, the research critically evaluates how these reforms reshaped water management structures, influenced institutional arrangements, and affected agricultural practices at both regional and national levels. Through this evidence-based approach, the study seeks to clarify the historical significance of the 2003 reform package and to illuminate its role in the transformation of Uzbekistan’s irrigation governance system.

The increasing convergence of cyber-physical systems (CPS), digital twins, blockchain infrastructures, fog computing paradigms, and generative artificial intelligence has transformed the architecture of intelligent industrial ecosystems. However, the integration of these technologies introduces substantial challenges associated with scalability, latency, trust management, interoperability, and cyber resilience. Existing digital twin frameworks frequently lack decentralized trust mechanisms and adaptive intelligence necessary for real-time cyber-physical synchronization. This research proposes a hybrid architectural model integrating blockchain security, fog computing, and generative intelligence to improve the resilience, scalability, and security of cyber-physical digital twin systems. The study synthesizes theoretical and architectural insights from recent literature on digital twins, blockchain-enabled IoT systems, distributed computing, and AI-driven analytics. The proposed model establishes a multilayer architecture composed of physical sensing layers, fog intelligence layers, blockchain trust layers, generative intelligence modules, and digital twin orchestration mechanisms. The framework addresses critical vulnerabilities including data tampering, sybil attacks, double-spending threats, latency bottlenecks, and interoperability limitations. The research further evaluates architectural performance through analytical assessment of security, scalability, adaptability, and computational efficiency. Results indicate that the hybrid model significantly enhances operational reliability, decentralized trust validation, low-latency analytics, and adaptive decision-making in CPS environments. The study contributes a resilient and standards-aligned architecture suitable for Industry 4.0, healthcare systems, smart manufacturing, and intelligent infrastructure applications.

This study analyzes the composition and structural characteristics of sodium silicate solutions (water glass). The influence of the SiO₂/Na₂O ratio (silicate modulus) on polymerization degree, structural organization, and stability is examined. It is shown that these solutions have a colloidal-polymeric nature, forming structures from simple units to complex networks.

The effects of pH, concentration, and temperature on structural changes and gelation processes are also considered. The results indicate that increasing the silicate modulus enhances polymerization, viscosity, and stability, which are important for industrial applications such as construction materials and adhesives.

In this study, a nanocellulose and ZIF-8 composite aerogel was successfully synthesized via an in-situ growth method. The structural and elemental properties of the obtained hybrid aerogel were investigated using FTIR and EDX/XRF analyses. FTIR results confirmed the presence of characteristic functional groups belonging to both nanocellulose and ZIF-8, indicating successful integration of the metal–organic framework into the cellulose matrix. EDX/XRF analysis revealed distinct zinc-related peaks corresponding to Zn Kα and Zn Kβ emissions, confirming the incorporation of ZIF-8 particles within the porous aerogel structure. The obtained results demonstrated the successful formation of a stable hybrid porous material.

This article discusses the role of functional food products enriched with vitamins in the modern nutritional system, their physiological effects on the human body and their importance in health care. Also, the use of vitamins A, B and C in the food industry, their biological role and the features of their use in functional products are analyzed. The role of products enriched with vitamins in regulating metabolic processes, strengthening the immune system and meeting the needs of the body is substantiated.

The article describes the material and technical base of pharmacies in the Andizhan region in the 1960s-1970s and the mechanism for providing the population with medicines through statistical data based on archival documents.

The modernization of medical education has become one of the most important priorities in higher education systems worldwide. Rapid scientific and technological progress, the digitalization of healthcare, and increasing demands for qualified medical specialists require universities to improve the methods used for developing professional competence among students. This article examines innovative educational approaches aimed at enhancing professional competence in students of medical universities. Special attention is paid to competency-based education, simulation technologies, problem-based learning, digital learning platforms, interdisciplinary integration, and clinical practice. The study highlights the importance of combining theoretical knowledge with practical skills to prepare future healthcare professionals for real clinical situations. Modern teaching methods contribute not only to academic achievement but also to the development of communication skills, critical thinking, decision-making abilities, and professional ethics. The article also discusses the role of teachers in creating an interactive educational environment and supporting student-centered learning. The findings indicate that the integration of innovative pedagogical technologies significantly improves the quality of medical education and strengthens students’ readiness for professional activities in healthcare institutions.

The increasing integration of electronically coordinated engine control systems in modern passenger vehicles has transformed conventional ignition and engine-starting mechanisms into multi-module mechatronic processes involving synchronized interaction between power distribution units, control modules, relays, and sensor networks [1]. Understanding the operational structure of these systems is essential for both diagnostic applications and the development of auxiliary electronic solutions compatible with original vehicle architectures [2].

This study presents a comparative investigation of the engine starting systems implemented in Chevrolet Tracker and Chevrolet Onix vehicles equipped with the common 1.2 Turbo powertrain platform. The analysis was performed through practical electrical investigation and functional tracing of signal propagation during the engine start sequence. Particular attention was given to the interaction between the Body Control Module (BCM K9), Engine Control Module (ECM K20), ignition control logic, starter relay activation, and battery power routing [3].

The results demonstrate a substantial degree of architectural similarity between both vehicles, confirming the use of a unified General Motors electrical platform. At the same time, several differences were identified in the organization of low-voltage energy storage management, battery monitoring integration, and auxiliary logic modules. These distinctions reflect model-specific adaptations associated with vehicle packaging and electrical energy optimization strategies [4].

The findings provide a clearer understanding of ignition-system behavior in GM compact vehicles and establish an engineering foundation for future development of non-invasive remote engine-start modules and other BCM-integrated automotive electronic solutions [5].

The contemporary evolution of extradition law reflects a deeper structural transformation of international criminal justice under conditions of globalization, digitalization, and fragmentation of sovereign legal authority. Classical extradition doctrine historically developed within a territorially bounded legal order premised upon reciprocal interstate cooperation, procedural autonomy of sovereign states, and geographically localized criminal conduct. However, the expansion of cyber-enabled criminality, supranational human rights jurisprudence, and transnational judicial governance has increasingly destabilized the conceptual foundations of traditional extradition mechanisms. This article examines the extradition framework of the Republic of Uzbekistan as a representative example of a transitional post-Soviet legal system undergoing partial judicialization under the influence of international human rights standards and technologically deterritorialized criminality. The study argues that the principal contemporary crisis of extradition law lies not merely in procedural inefficiency, but in the growing incompatibility between territorially structured sovereignty and transnational forms of criminal conduct operating across fragmented digital jurisdictions. Particular attention is devoted to the persistence of prosecutorial centralization within extradition proceedings, the incomplete redistribution of coercive authority toward judicial institutions, and the gradual constitutionalization of extradition through supranational human rights constraints. Using comparative, doctrinal, and systemic methods, the article analyzes the interaction between national criminal procedure, international treaty obligations, judicial review mechanisms, and digital criminality. The study concludes that modernization of extradition in Uzbekistan requires not simply procedural reform, but conceptual restructuring of extradition itself as a human-rights-constrained mechanism of transnational criminal governance operating beyond the classical territorial paradigm of criminal jurisdiction.

The swift increase in world healthcare spending has increased the pressure on effective, evidence-based measures to reduce the costs, without reducing the quality of care. This paper will advance and justify an all-encompassing predictive modeling framework that will minimize healthcare expenditures by proactively identifying high-risk patients, allocating resources efficiently, and improving clinical decision making. The study uses more sophisticated machine learning methods - logistic regression, random forests, and gradient boosting models - to forecast costly events, such as hospital readmissions and long lengths of stay, using secondary datasets, such as electronic health records (EHRs) and insurance claims data. The suggested structure will merge administrative and clinical data streams to produce actionable insights and allow healthcare providers to intervene earlier and allocate resources more cost-effectively. The standard metrics used to assess model performance are accuracy, area under the receiver operating characteristic curve (AUC-ROC) and cost-saving estimates based on the predictive results. The results indicate that predictive modeling can considerably decrease unnecessary healthcare spending through better risk stratification, reduced avoidable hospitalizations and increased efficiency. The originality of this research is that it has developed a unified, scalable framework that has interconnected predictive analytics with real health care cost management practices. As opposed to the previous researches which concentrate on individual predictive models, this study provides a comprehensive approach which balances predictive knowledge and strategic/operational decision making. The framework presents both theoretical and practical additions to the field, giving policymakers and healthcare administrators an effective instrument in the process of attaining sustainable cost-cutting in the ever-resource-limited healthcare systems.