Background: The increasing competition for land between agriculture and energy production has created a need for innovative solutions that can optimize land use. Agrivoltaic systems, which co-locate solar panels and crops, have emerged as a promising approach to address this challenge.

Objective: This comprehensive review synthesizes the experimental findings and field applications of agrivoltaic systems to provide a holistic understanding of their potential benefits and challenges.

Methods: A systematic literature search was conducted to identify relevant studies published in the last decade. The selected studies were analyzed to extract data on the impact of agrivoltaic systems on crop production, energy generation, and economic viability.

Results: The review of 108 studies reveals that agrivoltaic systems can have a positive impact on both crop production and energy generation. The shading provided by the solar panels can improve crop yields and water use efficiency, while the vegetation can cool the solar panels and increase their efficiency. However, the performance of agrivoltaic systems is highly dependent on the specific design, crop type, and climate.

Conclusion: Agrivoltaic systems have the potential to be a key technology in the transition to a more sustainable food and energy system. However, further research is needed to optimize their design and management for different contexts.

Purpose: As data volumes expand exponentially, traditional data warehousing methodologies often struggle to meet the agility and scalability demands of modern enterprises. This study investigates the intersection of Cloud Computing, advanced data modeling techniques (specifically Data Vault), and Business Intelligence (BI) to understand how organizations can secure a sustainable competitive advantage.

Design/methodology/approach: The research employs a comprehensive architectural analysis and literature synthesis, examining the transition from legacy on-premise systems to cloud-native ecosystems. It evaluates the efficacy of Inmon, Kimball, and Data Vault methodologies when applied within modern Cloud ETL frameworks.

Findings: The analysis reveals that while traditional dimensional modeling remains relevant for the presentation layer, the Data Vault methodology offers superior adaptability for cloud-based data warehouses due to its decoupling of business keys and relationships. Furthermore, the adoption of cloud services is not merely an IT upgrade but a critical innovation driver that democratizes access to high-end BI and AI capabilities for SMEs.

Originality/value: This paper bridges the gap between technical data engineering concepts—such as hash-based integration and ELT pipelines—and strategic business outcomes, providing a roadmap for organizations seeking to leverage Big Data for innovation and market agility.

The rapid proliferation of data, the ubiquity of distributed computing, and the maturation of artificial intelligence (AI) have created both unprecedented opportunities and acute challenges for financial risk management. This article synthesizes theoretical foundations, engineering architectures, regulatory considerations, and applied techniques for building resilient real-time risk management systems that integrate event-sourced data architectures with advanced AI and continuous monitoring frameworks. Drawing on foundational texts in quantitative risk management and risk modeling, as well as contemporary case studies, practitioner reports, and domain-specific research on event sourcing, real-time monitoring, and backtesting protocols, the paper constructs a comprehensive conceptual and methodological blueprint. It introduces a layered methodology that maps business objectives to data governance, stream processing, model lifecycle management, and regulatory compliance, and it explicates how event sourcing and stream processing (e.g., Kafka-style architectures) provide the immutability, auditability, and temporal granularity necessary for robust backtesting, counterfactual analysis, and stress scenarios. The analysis situates real-time AI risk systems within the broader landscape of disaster-risk modeling, supply-chain risk, ESG integration, and adverse outcome pathways to show cross-disciplinary applicability. The results section offers a descriptive synthesis of expected system behaviors, failure modes, and governance levers rather than empirical numerical outputs, reflecting the textual and prescriptive nature of the study. The discussion provides detailed interpretations of trade-offs between latency, accuracy, interpretability, and regulatory traceability, and it outlines a research agenda and practical roadmap for implementation in financial institutions, fintechs, and regulatory sandboxes. The conclusion summarizes actionable principles and stresses the imperative for multidisciplinary governance combining technical, organizational, and regulatory controls.

This article presents a comprehensive, theoretically grounded framework for secure, cost-optimal, and integrity-preserving migration of enterprise data from proprietary relational database management systems (RDBMS) such as Oracle to open-source cloud-native platforms such as PostgreSQL. We synthesize insights from contemporary research in cloud data security, cost-aware migration strategies, data integrity verification, multi-cloud placement optimization, master data management, and cryptographic key exchange to construct an end-to-end methodology suitable for large-scale enterprise modernization projects. The framework addresses the three core tensions enterprises face during migration: (1) maintaining confidentiality, integrity, and availability while data moves across trust boundaries (security tension); (2) minimizing total cost of ownership and operational expenditure without sacrificing performance or regulatory compliance (cost tension); and (3) assuring semantic correctness—schema, stored-procedure, and business-logic fidelity—when translating proprietary constructs into open-source equivalents (semantic tension). The article provides a rigorous description of preparatory assessment, hybrid migration staging, verification and auditing techniques, workload-aware cost modeling, and long-term operational governance. We further analyze potential failure modes, adversarial threats, and practical limitations, and propose mitigation strategies including cryptographic auditing, incremental migration scheduling, schema refactoring heuristics, and master data harmonization controls. This contribution integrates prior empirical studies and theoretical models into a unified narrative and offers a prescriptive roadmap for researchers, system architects, and enterprise engineers seeking to modernize database infrastructure while preserving security, cost-effectiveness, and trustworthiness.

The transition from linear to circular business paradigms represents a critical evolution in sustainable economic practices, offering pathways to enhance resource efficiency, reduce environmental impact, and generate multi-stakeholder value. This paper critically examines the design, implementation, and performance of circular business models (CBMs) across multiple sectors, integrating insights from product design, service innovation, stakeholder engagement, and systemic sustainability frameworks. Drawing on empirical and theoretical studies, the research investigates the mechanisms through which CBMs capture intended environmental and economic value, explores the role of product life cycle extension strategies, and evaluates consumer acceptance as a determinant of successful circularity adoption. Particular emphasis is placed on operationalizing CBMs in contexts such as the fashion industry, construction, consumer goods, and industrial symbiosis networks, highlighting enablers and barriers to implementation. Methodologically, the study synthesizes qualitative case analyses, simulation-driven design methods, and life cycle assessment approaches to provide a holistic understanding of circular systems. Findings indicate that the integration of design-for-circularity principles, closed-loop service models, and systemic value stream thinking significantly enhances both environmental outcomes and commercial viability. The research underscores the importance of strategic stakeholder orchestration, technological enablement, and policy alignment in facilitating the transition toward a circular economy. Limitations include contextual variability in sector-specific applications and the evolving nature of circular measurement frameworks. Future research directions emphasize the need for longitudinal analyses, cross-industry comparative studies, and advanced modeling tools to optimize circular value creation. Overall, this paper contributes to the theoretical and practical understanding of circular business models, offering a comprehensive framework for academics, practitioners, and policymakers committed to sustainable innovation.

Background: Cloud computing has enabled unprecedented scalability and flexibility but also introduced persistent challenges in preserving data confidentiality, fine-grained access control, privacy compliance, and trust among cloud consumers and providers (Pearson, 2009; Yu et al., 2010). Cryptographic techniques such as attribute-based encryption and encrypted query processing have offered compelling technical controls for protecting data at rest and during computation (Goyal et al., 2006; Popa et al., 2011; Kamara & Lauter, 2010). Complementary governance and operational frameworks—embodied in standards and guidance such as ISO/IEC 27018 and NIST SP 800-210—address policy and accountability but leave gaps when deployed in multi-tenant, co-located, and dynamic cloud environments (ISO/IEC 27018:2019; NIST, 800-210).

Objective: This article develops an integrated theoretical framework for resilient, privacy-preserving, and accountable data control in multi-tenant cloud environments. The framework synthesizes cryptographic access control, encrypted query processing, zero-trust architectural principles, and trust/accountability mechanisms to address both technical and socio-procedural threats identified in the literature (Yu et al., 2010; Popa et al., 2011; Hariharan, 2025; Ko et al., 2011).

 Methods: We conduct an exhaustive conceptual analysis of existing techniques—attribute-based encryption, searchable and homomorphic encryption primitives, encrypted query processing, secure overlay storage, assured deletion, and co-residency and colocation resistance approaches—mapping each to threat scenarios in multi-tenant settings. The methodology articulates explicit design patterns and control compositions and evaluates them qualitatively against functional, performance, privacy, and trust dimensions reported in prior work (Goyal et al., 2006; Tang et al., 2012; Azar et al., 2014). We also construct normative guidance that aligns cryptographic controls with ISO/IEC 27018 and NIST access control recommendations (ISO/IEC 27018:2019; NIST SP 800-210).

 Results: The integrated framework identifies five interoperable layers: (1) cryptographic data encapsulation for strong confidentiality and fine-grained access control (Yu et al., 2010; Goyal et al., 2006); (2) encrypted query processing to enable useful computation without wholesale plaintext exposure (Popa et al., 2011); (3) runtime zero-trust enforcement for identity, segmentation, and micro-policy mediation (Hariharan, 2025; NIST, 800-210); (4) co-residency and placement-aware defenses to reduce physical and side-channel risk (Bates et al., 2014; Azar et al., 2014); and (5) accountability and trust services to support auditability, verifiable deletion, and governance (Ko et al., 2011; Tang et al., 2012). For each layer we describe operational design choices, threat mappings, and tradeoffs in performance and complexity. The framework also prescribes patterns for composing controls (e.g., ABE for policy expression combined with CryptDB-style adjustable encryption for queryability) and guidelines for aligning these compositions with ISO and NIST controls.

Conclusions: No single technology solves the combined challenges of scale, expressivity, privacy, and trust in multi-tenant clouds. Instead, multi-layered control compositions—grounded in modern cryptography and complemented by zero-trust runtime enforcement and accountability mechanisms—provide the best path toward resilient, policy-aligned cloud operations (Kamara & Lauter, 2010; Hariharan, 2025; Ko et al., 2011). Our framework exposes clear tradeoffs and research directions, notably in usability of cryptographic policies, efficiency of encrypted query processing, and standardization of verifiable deletion and placement guarantees. The theoretical synthesis offers concrete design patterns for practitioners and a research agenda for the academic community.

This article examines the intersection of blockchain technologies, Internet of Things (IoT) architectures, and artificial intelligence (AI)-enabled cybersecurity and governance frameworks across three high-stakes domains: industrial IoT (IIoT), healthcare data systems, and supply chain traceability. The need for novel, integrative approaches arises because traditional centralized architectures and conventional security controls struggle to provide provenance, tamper-resistance, and auditable trust in increasingly distributed cyber-physical environments. Drawing on theoretical and empirical work on blockchain platforms for industrial IoT, smart contracts, access control frameworks, and AI-based security analytics, this paper synthesizes a unified conceptual framework and proposes methodological building blocks for practical deployment and evaluation. The article first situates the problem in an historical and technical context, highlighting the nature of threats, regulatory constraints (such as HIPAA and GDPR), and the specific vulnerabilities introduced by scale, heterogeneity, and resource constraints in IoT environments. It then outlines a layered methodology combining permissioned blockchain ledgers, lightweight on-device agents, smart-contract-mediated policy enforcement, and AI-driven anomaly detection and identity & access management (IAM) analytics to provide transactional integrity, provenance, and adaptive response. The results section presents descriptive analyses of how each component contributes to overall resilience—transactional integrity, traceability, access control fidelity, and regulatory auditability—drawing on case exemplars from textile supply chains, electric power materials testing, clinical record protection, and pharma manufacturing governance. The discussion interprets trade-offs—performance, privacy, governance complexity—and addresses limitations, including scalability, consensus cost, and the challenge of aligning AI model explainability with legal accountability. The article concludes with concrete research directions and an agenda for experimental evaluation in production-like contexts, emphasizing interdisciplinary governance, standards alignment, and hybrid on-chain/off-chain architectures to reconcile security, compliance, and operational efficiency.

This article presents an integrative framework for designing resilient, automated monitoring and fault-tolerant control systems for critical building infrastructure, with an emphasis on heating, ventilation, and air-conditioning (HVAC) systems. The proposed framework synthesizes advances in GPU-accelerated anomaly detection, infrastructure-as-code (IaC) for reproducible deployment, self-correcting control strategies, and organizational preparedness in emergency scenarios. The study draws on interdisciplinary evidence spanning disaster and emergency planning, GPU concurrency and diagnostic automation, advanced anomaly detection in large sensor networks, automated deployment methods, and domain-specific best practices for HVAC control and fault detection. The framework is organized into four interlocking pillars: (1) high-throughput real-time anomaly detection using graph and spatio-temporal models accelerated on commodity GPUs, (2) deterministic, auditable deployment and lifecycle management of monitoring pipelines through IaC, (3) model-based self-correcting controls and fault-tolerant supervisory strategies aligned to ASHRAE standards, and (4) organizational preparedness and recovery planning to close the loop between technical detection, operational response, and disaster resilience. Methodological exposition details the system architecture, data handling, algorithmic choices, and software lifecycle practices; a descriptive results section synthesizes expected outcomes and system behavior under a range of fault modes; and an extended discussion elaborates theoretical implications, limitations, and a pathway for future research and deployment. The design emphasizes safety, reproducibility, and operational viability: detection precision and recall are framed alongside latencies achievable by GPU acceleration and the governance benefits afforded by IaC. The conclusions highlight how coordinated technical and organizational practices can materially improve the readiness, response, and recovery of building systems facing sensor faults, actuator failures, or anomalous dynamics during emergencies. This research contributes a pragmatic, integrative roadmap for researchers, facility engineers, and system integrators seeking to move beyond isolated algorithms toward production-ready resilience for critical built-environment infrastructure.

This article synthesizes theoretical perspectives and applied insights drawn from a curated set of contemporary and foundational works to present an integrative, publication-ready examination of graphics processing units (GPUs) as central engines of modern high-performance computing (HPC), machine learning, and real-time multimedia systems. We develop a coherent narrative that traces GPU evolution and architectural principles, explores GPU programming models and their implications for large-scale data mining and accelerated computing, interrogates energy, power, and thermal management across device-to-application layers, and details validation and manufacturing strategies for acoustic and thermal integrity. Methodologically, the work adopts a cross-disciplinary descriptive synthesis grounded in primary references, combining architectural analysis, systems-level power and thermal modeling concepts, and process- and design-oriented validation approaches. Results are presented as a rich descriptive analysis that elucidates (1) how architectural choices have shaped parallel programming paradigms and application performance, (2) the complex trade-offs between performance, energy consumption, and thermal constraints in GPU-centric systems, (3) mechanisms for integrated CPU–GPU power management in constrained environments such as mobile gaming, and (4) scalable acoustic and thermal validation strategies needed in modern GPU manufacturing. We interpret these findings to argue for a layered, co-designed approach that couples architectural innovations (including 3-D integration and GPU-in-memory concepts) with machine-learning-aided power/thermal management and scalable manufacturing validation. The discussion highlights limitations of current approaches—particularly the challenges in generalizing thermal models across heterogeneous stacks and the nascent state of AI-driven thermal control for GPUs—and proposes a future research agenda that emphasizes co-design, domain-specific cooling techniques, hardware/software power coordination, and standardized validation pipelines. This integrative treatment aims to inform researchers, system designers, and manufacturing engineers seeking to align GPU architecture, system-level energy efficiency, and robust validation practices in the era of AI-scale computing.