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 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.

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.

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).

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 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.

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 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 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.