Volume 8.
Issue 2.

Wheat (Triticum aestivum L.) is a strategic crop in Uzbekistan, yet its technological and baking quality is increasingly compromised by infestation from Eurygaster integriceps (Sunn pest). This study evaluates the impact of pest-induced enzymatic activity on protein content, gluten quality, and dough rheology across four local wheat varieties (Tanya, Yaksart, Durdona, and Javohir). Physicochemical analyses, gluten index measurements, farinograph tests, and laboratory baking trials were conducted to quantify quality deterioration associated with varying degrees of pest infestation. The findings show a significant reduction in baking performance, particularly in gluten structure and loaf volume, due to proteolytic degradation. However, technological interventions such as blending with high-gluten flour and optimizing fermentation parameters partially restored end-product quality. The study provides region-specific evidence and practical recommendations for mitigating the effects of Eurygaster infestation, contributing to sustainable grain utilization and food processing resilience in pest-prone areas.

The prolonged crisis in North-Eastern Nigeria, shaped by the Boko Haram insurgency and the state’s multifaceted response, has become one of the most complex humanitarian and security emergencies in contemporary Africa. This article offers a theoretically grounded and empirically anchored analysis of how media systems, intelligence institutions, humanitarian governance, and cyber-infrastructure interact to produce, transform, and sometimes undermine political stability in this conflict environment. Drawing strictly on the literature provided, including Kegley’s global political transformation framework, Wolfsfeld’s media–conflict model, Lowenthal’s intelligence–policy nexus, McQuail’s public interest theory of media, and extensive humanitarian and Nigerian security literature, the study develops a multi-layered conceptual architecture linking information flows, strategic communication, intelligence gathering, and humanitarian action.

The article argues that North-Eastern Nigeria represents a paradigmatic case of twenty-first-century hybrid conflict, where insurgency is not only fought with guns and bombs but also through narratives, digital infrastructures, humanitarian governance, and intelligence credibility. The Boko Haram conflict illustrates how state fragility, media convergence, and intelligence failures converge to produce cycles of violence, displacement, and mistrust between civilian populations and governing institutions. The media environment, transformed by digital convergence and pervasive computing, has radically altered how conflict is perceived, reported, and politicized, often creating what Wolfsfeld describes as a “political contest over meaning” in which insurgents, governments, and humanitarian actors struggle for narrative dominance.

Using a qualitative analytical methodology rooted in political communication theory, intelligence studies, and humanitarian governance literature, the article examines how Nigerian and international media representations, intelligence failures and reforms, and humanitarian operational dilemmas shape both local realities and global perceptions of the conflict. It finds that weak intelligence coordination, politicized media narratives, and inconsistent humanitarian strategies have collectively contributed to an environment in which civilian trust is eroded, accountability is weakened, and conflict resolution is delayed.

The study further integrates cyber and intelligence scholarship to demonstrate how digital infrastructures, cyber vulnerabilities, and information warfare now play a constitutive role in both insurgent operations and state responses. Boko Haram’s use of digital communication, combined with Nigeria’s limited cyber-intelligence capacity, has introduced a new layer of insecurity that traditional military strategies cannot adequately address.

Ultimately, the article concludes that sustainable peace and reconstruction in North-Eastern Nigeria depend not only on military success but on the rebuilding of information ecosystems, intelligence professionalism, media accountability, and humanitarian legitimacy. By situating the Nigerian case within broader trends in world politics and digital transformation, the article contributes to both theoretical debates on contemporary conflict and practical discussions on how fragile states can better manage the intersection of security, media, and humanitarian governance.

The rapid evolution of financial markets and the proliferation of digital financial services have significantly increased the volume and complexity of data generated by investors, institutions, and customers. This transformation has created a critical need for intelligent analytical systems capable of interpreting complex financial signals and customer behavior patterns. Artificial intelligence and machine learning have emerged as powerful tools for addressing this challenge, particularly in the areas of stock market forecasting, risk management, and customer propensity prediction. This research develops a comprehensive conceptual framework for integrating machine learning-based propensity prediction models with decision engine architectures to improve forecasting accuracy and financial decision-making. The study synthesizes insights from financial time-series forecasting, machine learning algorithms, big data analytics, and behavioral finance to construct an advanced predictive decision support system suitable for modern financial institutions.

The research examines multiple predictive techniques including artificial neural networks, support vector machines, hybrid ARIMA models, and rule-based analytical systems that have historically been applied to financial forecasting. Additionally, the study explores the integration of customer-level data analytics with market-level financial indicators to develop decision engines capable of predicting both market movement and investor behavior. Particular attention is given to the role of feature engineering, data preparation, and hybrid modeling strategies in improving predictive accuracy. The study further discusses the importance of fairness, transparency, and ethical considerations in the deployment of machine learning models within financial decision-making processes.

The proposed framework combines predictive modeling techniques with automated decision engines to enable financial institutions to derive actionable insights from complex datasets. The results indicate that hybrid machine learning approaches combined with intelligent decision engines can significantly enhance forecasting reliability and enable proactive financial strategies. The research contributes to the growing body of literature on financial artificial intelligence by providing a comprehensive theoretical model that integrates financial time-series forecasting with customer propensity analytics.

The accelerating pace of digital transformation has compelled organizations to re-examine the sustainability, scalability, and economic viability of legacy software ecosystems, particularly those anchored in traditional quality assurance and testing paradigms. While cloud migration and legacy system modernization have long been treated as primarily infrastructural or architectural challenges, contemporary scholarship increasingly recognizes the pivotal role of automation and artificial intelligence in reshaping the epistemic and operational foundations of software quality assurance. This study advances a comprehensive theoretical and methodological framework that integrates automation-driven digital transformation with cloud-based legacy modernization, placing special emphasis on the reconfiguration of quality assurance pipelines into AI-augmented, self-optimizing systems. Drawing on an extensive body of cloud migration, software modernization, and testing literature, and grounded in the automation blueprint articulated by Tiwari (2025), the article articulates how the convergence of artificial intelligence, cloud-native architectures, and automated testing practices constitutes a paradigm shift rather than an incremental improvement.

The research is designed as a qualitative meta-analytic and conceptual synthesis of authoritative scholarly sources, enabling a deep examination of historical trajectories, contemporary challenges, and future trajectories of legacy modernization. The methodology employs systematic comparative interpretation across multiple streams of literature, including cloud economics, risk management, performance optimization, enterprise IT strategy, and software engineering modernization frameworks. By embedding Tiwari’s AI-augmented quality assurance blueprint within the broader discourse on legacy system migration, the study demonstrates how automation is no longer an auxiliary tool but a central organizing principle of modern digital infrastructure.

The results reveal that organizations which treat automation and AI as integral components of migration planning achieve not merely technical uplift but structural transformation of governance, cost management, and innovation capacity. Rather than replicating legacy inefficiencies in cloud environments, AI-driven testing pipelines reconstitute quality assurance as a continuous, adaptive, and predictive discipline. The discussion situates these findings within ongoing scholarly debates about risk, cost, organizational resistance, and technological lock-in, arguing that AI-augmented quality assurance is the decisive differentiator between superficial cloud adoption and genuine digital transformation. Ultimately, the study contributes a theoretically grounded and practically relevant model for integrating automation, artificial intelligence, and cloud migration into a unified modernization strategy capable of sustaining enterprise competitiveness in the digital era.

High-quality master data is fundamental to operational efficiency, regulatory compliance, customer experience, and strategic decision-making within financial institutions. However, organizations frequently encounter challenges related to duplicate records, inconsistent data standards, incomplete customer information, and fragmented enterprise data repositories. Conventional data quality management approaches often depend on manually defined validation rules that struggle to adapt to evolving business environments and increasing data complexity. This paper presents an Artificial Intelligence-based Enterprise Data Quality Framework designed to strengthen master data governance through intelligent automation and predictive data quality assessment. The proposed framework combines machine learning-based anomaly detection, duplicate record identification, entity resolution, intelligent data profiling, and automated quality scoring to improve the accuracy and consistency of enterprise master data. In addition, explainable artificial intelligence techniques are incorporated to provide transparency into automated quality recommendations and facilitate governance decisions. The framework is evaluated using representative financial institution master data scenarios involving customer, vendor, and account information. The results demonstrate improvements in duplicate detection, data completeness, consistency assessment, and governance efficiency while reducing manual validation effort. The proposed framework offers financial organizations a scalable approach for modernizing enterprise data governance and supporting reliable data-driven decision-making.

The article analyzes the impact of the information environment and globalization processes on youth health from a scientific and pedagogical perspective. In the context of the rapid intensification of digital communication flows, social networks, mass culture, and transnational information influence, the article highlights risk factors affecting young people’s psychological, social, and behavioral well-being. It also demonstrates how information overload, misinformation, cyberbullying, destructive content, and the transformation of values influence school and university students’ emotional stability, stress resilience, and attitudes toward a healthy lifestyle.

The study is devoted to the analysis and conceptual integration of two key classes of privacy-enhancing technologies (PETs), federated learning (FL) and differential privacy (DP), with the aim of developing a holistic framework for solving marketing analytics tasks. The methodological basis of the work relies on a systematic review of current specialized literature and authoritative industry analytical materials, followed by a synthesis of the identified approaches. The obtained results demonstrate that the combination of the decentralized FL architecture with the formal mathematical guarantees of DP creates the conditions for building high-accuracy predictive models applicable to such key tasks as conversion rate estimation, target audience segmentation, and personalization of interactions, while eliminating the need for centralization and direct disclosure of sensitive user data. In conclusion, it is substantiated that the proposed FL-DP framework can be regarded as a technologically robust and ethically sound solution that forms the basis for the transition to a new generation of marketing analytics, despite the persisting significant challenges associated with its practical implementation. The article is intended for data specialists, researchers in the field of machine learning, and professionals involved in the development and implementation of marketing strategies focused on building analytics systems with privacy as a priority.

Earth system variability, driven by fluctuations in radiative balance, atmospheric dynamics, and climate system instability, has emerged as a central determinant of global health outcomes and macroeconomic performance. This study investigates the interconnected relationship between large-scale environmental variability, population health conditions, and international economic trajectories. It conceptualizes Earth system variability as a multi-component phenomenon encompassing radiometric instability, climate-induced physiological stress, and systemic disruptions in healthcare and economic productivity.

The research integrates environmental monitoring systems, health outcome frameworks, and macroeconomic growth theory to construct a multi-layer analytical model. Satellite-based Earth observation systems, including CERES and MODIS, provide foundational understanding of radiation budget stability and long-term climate monitoring (Priestley et al., 2011; Loeb et al., 2008). These systems highlight that even minor gaps in radiometric data can significantly influence climate interpretation and predictive modeling accuracy.

At the health system level, chronic disease epidemiology—particularly chronic kidney disease—illustrates the sensitivity of human populations to environmental stressors and systemic vulnerabilities (Kovesdy, 2022). Health literacy and adaptive healthcare interventions further influence population resilience, shaping outcomes under environmental uncertainty (Nutbeam, 2008; Shah et al., 2021). Systematic evidence suggests that reduced health literacy exacerbates disease burden and increases healthcare costs, thereby influencing macroeconomic productivity.

At the macroeconomic scale, climate variability is strongly associated with reduced global economic growth through productivity losses, increased healthcare expenditure, and infrastructure inefficiencies (Dwivedi et al., 2025). These effects are amplified by systemic interactions between environmental instability and institutional capacity.

The findings indicate that Earth system variability impacts economic expansion through three primary channels: (i) radiative and atmospheric instability affecting climate predictability, (ii) increased disease burden reducing labor productivity, and (iii) systemic healthcare and infrastructure costs constraining economic growth.

The study concludes that global economic resilience increasingly depends on integrated Earth observation systems, health system adaptability, and climate-responsive policy frameworks capable of mitigating cascading environmental and socioeconomic risks.

The article examines the role of digital transformation in the strategic management system of construction production based on an analysis of contemporary empirical and theoretical research in the fields of construction management, building information modeling, digital twins, and logistical production systems. The study integrates data concerning the technological, organizational, and analytical factors of construction process controllability, emphasizing the multi-level and mediated nature of digital solutions' impact on the stability and predictability of production parameters. It is shown that digital technologies do not act as independent determinants of management efficiency but exert their influence through mechanisms of data integration, synchronization of project life cycle stages, and the formation of centralized analytical frameworks. Particular attention is given to the role of BIM environments, ERP systems, BI dashboards, and cloud platforms as key transformation mediators that ensure information comparability and the reproducibility of managerial procedures. The analysis demonstrates that full-cycle data processing mediates the connection between digital tools and the stability of schedule-network models, the allocation of resource flows, and the speed of managerial responses, whereas fragmented digitalization creates a "digital illusion" effect and increases the likelihood of uncoordinated changes. The informational density of the production environment can serve both as a factor in enhancing process transparency and as a source of managerial risks in the absence of centralized analytics and cross-platform compatibility. This article can be useful for researchers and practitioners in the fields of strategic construction management, the digital transformation of production systems, and the integration of information technologies into organizational management frameworks.

The accelerating convergence of artificial intelligence, data-centric engineering, and automated software operations has fundamentally reconfigured how contemporary organizations design, deploy, and maintain complex technological systems. Within this evolving landscape, AI-driven DevOps has emerged not merely as a set of operational tools but as a new epistemological and organizational paradigm that integrates machine learning, predictive analytics, and intelligent automation into the entire software and systems life cycle. This research article develops a comprehensive and theoretically grounded investigation of AI-driven DevOps in relation to predictive maintenance, condition-based monitoring, and algorithmic governance of operational risk, drawing on interdisciplinary literature from production and operations management, prognostics and health management, and artificial intelligence research. Building on the foundational synthesis provided by Varanasi (2025), which situates machine learning–based intelligent automation as the backbone of modern deployment and maintenance strategies, this study extends the scope of analysis toward organizational, economic, and epistemic consequences of embedding predictive intelligence into software and industrial ecosystems.

The article advances three central contributions. First, it conceptualizes AI-driven DevOps as a socio-technical system in which algorithmic learning mechanisms, human expertise, and organizational routines co-evolve, rather than as a purely technical automation layer, thereby aligning with contemporary debates in data-intensive operations management (Feng and Shanthikumar, 2018). Second, it integrates insights from predictive maintenance and prognostics research to demonstrate how AI-driven DevOps acts as a unifying governance architecture that synchronizes software reliability, asset health, and service continuity across digital and physical domains (Jardine et al., 2005; Fink et al., 2020). Third, it critically examines barriers, risks, and institutional frictions that accompany large-scale adoption of AI-enabled operations, including issues of explainability, data trust, and economic justification, which remain persistent across both industrial maintenance and software engineering contexts (Giada and Rossella, 2021; Boppiniti, 2020).

Methodologically, the study adopts a qualitative integrative research design that synthesizes peer-reviewed scholarship, industry-oriented conceptual frameworks, and reflective analyses from multiple AI application domains. Rather than treating DevOps, predictive maintenance, and AI governance as isolated research streams, the article reconstructs them as a single, interconnected field of inquiry centered on the problem of uncertainty management in complex systems. The results demonstrate that AI-driven DevOps produces measurable epistemic and organizational benefits not because it eliminates uncertainty, but because it redistributes and reframes uncertainty through predictive models, continuous learning pipelines, and feedback-driven automation, as argued in Varanasi (2025) and Hoffmann and Lasch (2023).

The discussion situates these findings within broader theoretical debates about digitalization, platformization, and data-intensive decision-making, emphasizing that AI-driven DevOps represents a transition from reactive operational control to anticipatory and adaptive governance. At the same time, the article acknowledges enduring challenges related to model opacity, skill mismatches, and uneven economic returns, which complicate the promise of intelligent automation despite its technical maturity (Grooss, 2024; Gugaliya and Naikan, 2020). By synthesizing these diverse perspectives into a unified analytical framework, this research offers both scholars and practitioners a deeper understanding of how AI-driven DevOps is reshaping the future of software engineering, industrial maintenance, and organizational risk management in the era of intelligent systems (Varanasi, 2025).

The global automotive industry is undergoing a multidimensional transformation driven by technological innovation, environmental sustainability concerns, digitalization, changing consumer lifestyles, and evolving mobility policies. These transformations are reshaping vehicle purchasing behavior, market dynamics, and competitive strategies across both conventional and electric vehicle segments. This paper investigates the relationship between contemporary automotive market trends and their impact on vehicle purchasing decisions through a comprehensive analytical review of the provided literature. The study examines major determinants influencing consumer vehicle preferences, including environmental awareness, economic incentives, technological advancements, repair cost predictability, forecasting systems, sustainability valuation, and electric vehicle adoption patterns.

The research adopts a conceptual and analytical review methodology grounded exclusively in the supplied references. The paper integrates theories related to consumer attitudes, transportation economics, sustainable mobility, machine learning applications, and predictive market analytics. Particular emphasis is placed on the growing influence of electric vehicles, data-driven automotive systems, environmental policy frameworks, and digital forecasting technologies on modern vehicle purchasing decisions. The study also analyzes how psychological factors such as range anxiety, lifestyle preferences, and environmental consciousness shape consumer behavior in increasingly technology-intensive automotive markets.

The findings indicate that vehicle purchasing decisions are no longer determined solely by price and performance considerations. Instead, consumers evaluate vehicles through multidimensional frameworks involving sustainability concerns, long-term operational costs, technological compatibility, government incentives, and digital information accessibility. Electric vehicle adoption has emerged as one of the most influential trends affecting market structures, although barriers including charging infrastructure limitations, range anxiety, and high initial purchase costs continue to constrain widespread adoption. Additionally, machine learning and predictive analytics increasingly support vehicle valuation, repair estimation, price forecasting, and market trend analysis, thereby transforming automotive retail and consumer decision-making processes.

The paper concludes that automotive market trends reflect broader economic, environmental, and technological transitions that fundamentally reshape consumer purchasing logic. The research contributes to the understanding of how sustainability transitions, intelligent technologies, and policy-driven mobility changes influence automotive consumption patterns. The study further identifies strategic implications for manufacturers, policymakers, dealerships, and mobility service providers seeking to adapt to rapidly evolving automotive ecosystems.

The article examines engineering approaches for strengthening trust in AI systems through data-layer controls that make decisions explainable and verifiable in audits. The widening regulatory and organizational demands for the traceability of training data, the reproducibility of pipelines, and the defensible documentation of model behavior in production drive practical relevance. Scientific novelty lies in integrating provenance capture, lineage graphs, feature-store governance, and standardized documentation artifacts into one coherent data-layer framework that produces machine-checkable evidence. The work describes lifecycle evidence generation from ingestion to inference, studies how provenance models and lineage datasets support inspection, and analyzes how documentation instruments complement technical traces. Special attention is given to preventing evidence gaps caused by opaque preprocessing, weak versioning, and incomplete logging. The study aims to systematize a data-layer architecture that supports explainability and auditability without relying on new model classes. A comparative analysis of recent research, a synthesis of published frameworks, and a structured review of sources are employed in this study. The conclusion summarizes actionable controls and their expected audit outputs. The article targets engineers, MLOps teams, risk functions, and internal/external auditors.

This scholarly study is aimed at developing an integrated prioritization model for social and administrative infrastructure facilities, applicable to urban planning for territorial communities of Ukraine under the exceptionally stringent budgetary constraints of 2024–2025. The methodological framework of the research is grounded in multi-criteria decision-making analysis (MCDM), supplemented by a systematized examination of the regulatory domain of DBN and an analysis of urban planning implementation practices using case studies from the Kyiv Region (the Myronivka and Bohuslav communities). This approach ensures the comparability of heterogeneous factors, ranging from technical feasibility and life-cycle cost to social significance and administrative expediency, and makes it possible to establish a transparent rationale for ranking facilities within recovery and development programs. The research identifies stable relationships between engineering optimization of design solutions, the adoption of energy-efficient technologies, and the parameters of budgetary and time performance: a reduction in costs of up to 25% and a decrease in project implementation duration of up to 40% were documented. The practical applicability of the developed design methodology is demonstrated, including its effectiveness in completing state expert review procedures with a stated success rate of 100%. Taken together, the results substantiate the proposition that a sustainable development trajectory is attainable provided that high-technology design solutions are incorporated into urban planning documentation and aligned with regulatory requirements, financial limits, and territorial recovery priorities.

The transition from perimeter-based security to Zero Trust Architecture (ZTA) represents a fundamental shift in the defensive posture of modern digital ecosystems. This research provides an exhaustive investigation into the integration of ZTA across heterogeneous environments, including Java microservices, the Internet of Things (IoT), defense networks, and automotive systems. By synthesizing contemporary advancements in blockchain technology, radio frequency fingerprinting, and artificial intelligence, the study evaluates how "never trust, always verify" principles can be operationalized to mitigate sophisticated cyber threats. Central to this analysis is the application of ZTA within microservices architectures, utilizing the Strangler Fig pattern for legacy modernization and ensuring granular security at the service-to-service level. Furthermore, the research explores the use of behavioral analysis and adaptive multifactor authentication to refine threat determination processes. The methodology employs a comprehensive systematic review and theoretical modeling to assess the efficacy of various ZTA implementations. Results indicate that while ZTA significantly reduces the attack surface, its success is contingent upon the seamless integration of DevSecOps, real-time identity verification, and the use of robust cryptographic hardware. The discussion addresses the complexities of implementing ZTA in resource-constrained IoT environments and the critical need for standardized protocols in automotive and defense sectors. This article concludes that ZTA is not merely a technical configuration but a holistic security philosophy essential for the resilience of next-generation infrastructure.

The article examines design methodologies for constructing AI-native growth platforms in niche financial institutions. The relevance of the study is determined by the rapid transition of financial services toward unified, real-time, and model-centric architectures that overcome the limitations of legacy multi-engine systems. The novelty lies in presenting an integrated analytical synthesis of architectural, operational, and governance principles that jointly define AI-native scalability. The work describes the structural transformation of data pipelines, analyzes constraints of SQL, NoSQL, and NewSQL systems, and studies decisioning, fraud detection, customer intelligence, and regulatory automation workflows. Special attention is given to the role of embedded MLOps and distributed intelligence in sustaining continuous learning. The study aims to identify methodological foundations enabling small institutions to achieve enterprise-grade analytical performance. Comparative analysis, source evaluation, and conceptual generalization are applied. The conclusion outlines an integrated model of AI-native growth and defines practical implications for banks, regulators, and technology designers.

Digital twin technology has emerged as a transformative paradigm in cyber-physical systems, enabling the creation of dynamic virtual representations of physical assets, industrial infrastructures, and even human biological processes. While digital twins offer significant benefits for predictive maintenance, operational optimization, and real-time system monitoring, their increasing integration with critical infrastructures introduces complex cybersecurity challenges. Industrial control systems, smart manufacturing environments, and healthcare digital twins rely heavily on interconnected networks, cloud platforms, and distributed data architectures that are vulnerable to sophisticated cyber threats. Recent cyber incidents targeting industrial environments demonstrate that adversaries increasingly exploit the operational technology layer to disrupt physical processes. Consequently, the convergence of digital twin technology with cybersecurity frameworks has become a critical research priority. This study investigates the design of security-aware digital twin ecosystems capable of supporting resilient cyber-physical infrastructures. Drawing upon interdisciplinary literature from cybersecurity, industrial automation, digital twin architectures, and artificial intelligence research, the article develops a comprehensive conceptual framework for secure digital twin environments. Particular emphasis is placed on integrating cyber threat intelligence, blockchain-based data integrity mechanisms, intrusion detection systems, and generative artificial intelligence for proactive cyber defense. The research examines how digital twins can be used not only as operational monitoring tools but also as cybersecurity instruments that simulate attack scenarios, detect anomalies in industrial processes, and support security operations centers in real-time threat analysis. The study further explores the role of distributed ledger technologies in enabling secure data sharing among digital twin networks while maintaining transparency and trust in collaborative industrial ecosystems. Additionally, the article investigates emerging applications of digital twin security architectures in healthcare systems, where human digital twins and medical digital representations require strong privacy and security protections. Through extensive theoretical analysis, the research identifies critical architectural components required for secure digital twin ecosystems, including data governance frameworks, simulation-based threat detection mechanisms, and AI-driven sensor fusion capabilities. The findings suggest that combining digital twins with blockchain infrastructures, threat intelligence analytics, and machine learning-based anomaly detection can significantly enhance the resilience of cyber-physical systems. However, the increasing complexity of these systems introduces challenges related to privacy protection, computational scalability, and regulatory governance. The article concludes by proposing a conceptual roadmap for the development of next-generation security-aware digital twin infrastructures capable of supporting both industrial and biomedical cyber-physical environments.

The study is oriented toward an integrated comprehension of the physiological, biomechanical, and psychoemotional determinants of a dancer’s professional transformation during the transition from the domain of competitive ballroom dancing to the entertainment-show format on ocean cruise liners. The transition is analyzed on the basis of the author’s clinical and training case material and is interpreted through the figurative continuum from sprint to marathon, in which competitive ballroom dancing, grounded predominantly in submaximal anaerobic power and short-term interval loads, is contrasted with the functional profile of onboard work that presupposes the predominance of prolonged aerobic endurance, stable tolerance of monotonous repetitive actions, and specific sensorimotor adaptation to continuously changing support characteristics. Within the study, the neurophysiological mechanisms of neuroplasticity underlying the formation of the sea legs phenomenon are examined in detail; the structure and frequency of injuries are analyzed with an emphasis on the redistribution of mechanical loads from the distal segments of the lower limb (the ankle joint) to proximal links, namely the knee joint and the lumbar spine; and the phenomenon of a nutritional paradox is described under conditions of practically unlimited access to buffet-style nutrition onboard. On the basis of integrating data from sports medicine, maritime physiology, and dietetics, personalized protocols for the periodization of the training process and for nutritional support strategies have been developed, aimed at optimizing the dancer’s functional performance capacity and reducing the probability of developing relative energy deficiency in sport syndrome (RED-S) under the conditions of a prolonged contract cycle of 6–9 months.

The rapid digital transformation of banking and financial systems has fundamentally altered the operational, transactional, and security architecture of modern financial institutions. The expansion of digital banking platforms, cloud-integrated infrastructures, Internet of Things ecosystems, decentralized financial services, and artificial intelligence-enabled financial technologies has simultaneously increased operational efficiency and amplified cybersecurity vulnerabilities. Financial institutions increasingly face sophisticated cyberattacks including ransomware, phishing, identity theft, insider threats, distributed denial-of-service attacks, digital payment fraud, and data privacy breaches. Consequently, contemporary research has focused on integrating artificial intelligence and blockchain technologies as complementary mechanisms for strengthening cybersecurity resilience, fraud detection, privacy protection, and decentralized trust management within the banking sector. This study critically examines the evolving relationship between artificial intelligence, blockchain technology, and cybersecurity in digital banking and financial risk management.

The article adopts a qualitative and interpretive research methodology based on extensive theoretical synthesis and systematic evaluation of contemporary scholarly literature. The study explores blockchain-enabled decentralization, smart contract security, AI-driven anomaly detection, predictive cyber defense, digital identity management, fraud analytics, and cloud-integrated financial infrastructures. Particular emphasis is placed on the convergence of blockchain and artificial intelligence for real-time threat intelligence and fraud prevention in financial ecosystems. The study further investigates the implications of cybersecurity threats on digital banking adoption, institutional trust, regulatory compliance, and organizational resilience.

The findings indicate that artificial intelligence significantly enhances predictive threat analysis, behavioral anomaly detection, and automated incident response, while blockchain contributes immutable recordkeeping, decentralized authentication, transparency, and data integrity. The integration of these technologies creates adaptive cybersecurity architectures capable of mitigating evolving cyber threats in highly digitized financial environments. However, challenges including scalability limitations, interoperability concerns, governance complexity, regulatory uncertainty, privacy dilemmas, and computational costs remain substantial barriers to implementation.

The study concludes that the future of financial cybersecurity will depend on multidimensional collaboration among regulators, financial institutions, technology developers, and cybersecurity specialists. Hybrid AI-blockchain frameworks represent a transformative direction for securing digital banking infrastructures and enhancing long-term financial resilience in an increasingly interconnected digital economy.

The rapid evolution of cloud computing has catalyzed a paradigm shift toward distributed intelligence, where the orchestration of computational resources is no longer a centralized function but a decentralized, autonomous process. This research article explores the intricate intersection of container orchestration, Software-Defined Networking (SDN), and Artificial Intelligence (AI) within the context of modern enterprise architecture and scientific workflows. By synthesizing recent advancements in Kubernetes automation, network virtualization, and accelerated testing for secure payment systems, this study provides a holistic view of how distributed machine learning and inference delivery networks are reshaping the digital landscape. We examine the role of AI in cloud-assisted smart factories and the burgeoning Metaverse, while addressing the critical need for cross-layer resource orchestration to ensure Quality of Service (QoS) and energy efficiency. The methodology focuses on the development of simulation frameworks for cloud orchestration testing and the integration of decentralized probabilistic management. Results indicate that the convergence of these technologies leads to significantly enhanced operational efficiency, though challenges remain in ensuring security and seamless resource delivery across heterogeneous networks. This article concludes with a discussion on the future perspectives of distributed intelligence and the ongoing transformation of business operations through integrated information systems.

The sustainable development of the education system in the context of digital transformation directly depends on the digital competence of future teachers. This article comprehensively studies the pedagogical capabilities, functional characteristics and impact of artificial intelligence (AI) modules on the formation and development of digital competence of future teachers. The study revealed the effectiveness of artificial intelligence-based adaptive learning, analysis of learning activities, automated assessment and the formation of individual educational trajectories. Experimental results showed that the use of artificial intelligence modules significantly develops the cognitive, operational and reflexive components of the digital competence of future teachers. At the end of the study, scientifically based conclusions and practical recommendations were developed for the higher pedagogical education system.

The growing global demand for sustainable energy sources and environmentally friendly chemical feedstocks has heightened interest in utilizing agricultural residues as alternative resources. Millet crop residues, including stalks, leaves, and husks, represent a largely untapped biomass resource with significant potential for biofuel production and chemical applications. This study explores the conversion of millet residuals into affordable biofuels such as bioethanol, biogas, and biodiesel, and evaluates their potential as precursors for value-added chemical products. The theoretical foundation integrates principles of biomass valorization, renewable energy engineering, and green chemistry, establishing a multi-disciplinary framework for assessing both technical feasibility and economic viability.

A comprehensive literature review synthesizes current research on biomass utilization, highlighting studies on bioenergy development in China and smart irrigation systems that impact feedstock availability (Zhou, 2009; Bai et al., 2009; Atzori et al., 2017). Comparative analysis identifies gaps in large-scale millet residue valorization, particularly concerning process optimization, energy efficiency, and integration with existing energy systems. The methodology employs a systematic evaluation of physicochemical characteristics of millet biomass, process modeling for thermochemical and biochemical conversion pathways, and life cycle assessment to determine environmental impacts. Pilot-scale case studies demonstrate practical implementation, including anaerobic digestion, pyrolysis, and enzymatic hydrolysis techniques, producing biofuels with energy yields comparable to conventional sources while reducing greenhouse gas emissions.

Findings indicate that millet crop residues can supply significant energy potential while serving as feedstock for chemicals such as organic acids, bio-based polymers, and fertilizers. Critical analysis reveals challenges in feedstock collection, preprocessing, and scalability, emphasizing the importance of integrating smart agricultural practices and energy planning (Deshwal & Singh, 2025). The study further highlights the socioeconomic benefits of decentralized biofuel production, including rural employment, energy security, and reduction of agricultural waste disposal issues. Limitations include regional variations in crop residue availability, technological maturity, and policy frameworks affecting bioenergy adoption.

This research contributes to sustainable energy and green chemistry domains by demonstrating that millet crop residues are a viable, low-cost resource for renewable energy and chemical production. The outcomes provide actionable insights for policymakers, engineers, and researchers aiming to promote circular bioeconomy strategies in agriculture-intensive regions.

The landscape of clinical trials is undergoing a fundamental paradigm shift driven by the convergence of Artificial Intelligence (AI), Machine Learning (ML), and decentralized operational models. This research article explores the transition from traditional, site-centric randomized clinical trials to digital, participant-centric models enabled by remote monitoring and predictive analytics. By synthesizing foundational work on model interpretability, such as SHAP (SHapley Additive exPlanations), with contemporary frameworks for decentralized clinical trials (DCTs), this study investigates how AI-driven strategies can enhance trial efficiency while maintaining scientific rigor. We examine the critical role of explainable AI (XAI) in gaining clinician trust and meeting regulatory requirements, particularly in the context of real-time monitoring and adaptive design. The paper further discusses the necessity of pragmatism in trial design, utilizing the PRECIS-2 tool and real-world data to bridge the gap between controlled experimental efficacy and real-world clinical effectiveness. Finally, we address the ethical imperatives of equity, diversity, and inclusion (EDI) within AI-enabled trials, arguing that algorithmic interventions must be intentionally designed to mitigate bias and broaden participant access.

The accelerating digitization of organizational systems, combined with the proliferation of artificial intelligence across industrial, environmental, and governance domains, has profoundly reshaped how contemporary enterprises conceptualize, manage, and mitigate risk. Change management, once grounded primarily in procedural controls and human judgment, has increasingly become a data driven, algorithmically mediated domain in which predictive systems continuously evaluate the potential consequences of organizational transformation. This article develops a comprehensive theoretical and empirical synthesis of predictive artificial intelligence as a core infrastructure for risk governed change management and organizational resilience. Drawing on a wide range of interdisciplinary scholarship, including disaster resilience theory, intelligent process automation, predictive maintenance, and digital supply chain surveillance, the study situates predictive risk scoring within broader socio technical systems of governance and control. Central to this analysis is the concept of Change Advisory Boards as epistemic and regulatory institutions that must now operate within environments of algorithmic foresight and automated decision support, as demonstrated by recent developments in predictive risk scoring for change management (Varanasi, 2025).

The article advances three interconnected arguments. First, predictive artificial intelligence fundamentally redefines organizational risk from a retrospective assessment of failure to a prospective calculus of probabilistic futures, thereby transforming how change initiatives are authorized, sequenced, and monitored. Second, the integration of predictive risk scoring into governance structures such as Change Advisory Boards generates new forms of institutional rationality that blend human expertise with machine based inference, producing both enhanced resilience and novel forms of opacity and ethical risk. Third, these systems must be understood within a larger ecology of digital infrastructures, including Internet of Things enabled environments, intelligent manufacturing, smart cities, and climate adaptive systems, all of which contribute streams of data that feed algorithmic risk engines.

Methodologically, the study employs a qualitative integrative research design grounded in interpretive synthesis of the provided scholarly corpus. Rather than treating predictive models as purely technical artifacts, the article analyzes them as socio technical constructs embedded in regulatory regimes, organizational cultures, and epistemological assumptions about risk and control. The results demonstrate that predictive artificial intelligence enhances the anticipatory capacity of organizations, allowing them to simulate the cascading effects of change across complex systems, but also introduces challenges related to transparency, accountability, and institutional trust.

The discussion situates these findings within broader debates on the governance of artificial intelligence, the limitations of existing risk management standards, and the future of human centric decision making in algorithmically mediated organizations. By articulating a theoretically grounded framework for predictive AI enabled change management, this article contributes to the emerging field of digital risk governance and provides a foundation for future empirical and normative research on resilient, ethically governed intelligent enterprises.

The increasing digitization of risk-coverage and indemnity service environments has intensified the need for robust, scalable, and secure identity verification mechanisms. Traditional authentication approaches, including password-based systems and static biometrics, exhibit inherent vulnerabilities such as spoofing susceptibility, limited adaptability, and inadequate resilience against evolving adversarial threats. This paper proposes a comprehensive technical framework for machine-driven physiological signal interpretation systems designed to enhance identity validation in risk-indemnity domains through high-integrity access control and policy-conformant architectures.

The proposed framework integrates deep learning-based feature extraction, multi-modal physiological signal processing, and iterative learning control (ILC) methodologies to enable dynamic, adaptive identity verification. Leveraging advances in convolutional neural networks and 3D facial reconstruction techniques, the system captures complex spatial-temporal biometric patterns, including facial geometry, behavioral traits, and physiological responses. These features are further refined through iterative learning mechanisms, enabling continuous system optimization and convergence under uncertain and evolving operational conditions (Bristow et al., 2006; Longman, 2000).

A key contribution of this research lies in the integration of robust control theory with biometric authentication systems, ensuring stability, reliability, and resilience in real-time environments. The framework incorporates anti-spoofing mechanisms, domain transfer learning, and identity-discriminative feature modeling to mitigate adversarial risks (Tu et al., 2019; Ding et al., 2014). Additionally, governance-aligned design principles are embedded within the system architecture, facilitating regulatory compliance, auditability, and ethical data management (Laheri, 2025).

The findings indicate that multi-modal physiological systems significantly outperform traditional authentication methods in terms of accuracy, adaptability, and resistance to spoofing attacks. However, challenges related to computational complexity, data privacy, and system scalability remain critical considerations. This research contributes to the advancement of intelligent identity verification infrastructures by presenting a unified, technically rigorous framework that bridges machine learning, control systems, and biometric security within indemnity service ecosystems.