As supply chains become increasingly digitized and decentralized, ensuring security, traceability, and data integrity has emerged as a critical concern. Blockchain technology has shown significant potential to address these challenges by providing immutable records, transparent data flows, and tamper-resistant transaction logs. However, the effective application of blockchain in real-world supply chains requires the careful evaluation of both architectural design and technical limitations, including scalability, interoperability, and privacy. This review systematically examines existing blockchain-based supply chain solutions, classifying them based on their structural models, cryptographic foundations, and storage strategies. Special attention is also given to underexplored humanitarian logistics scenarios. It introduces a three-dimensional evaluation framework to assess security, traceability, and integrity across different architectural approaches. In doing so, it explores key technological enablers, including advanced mechanisms such as zero-knowledge proofs (ZKPs) and cross-chain architectures, to meet evolving privacy and interoperability demands. Furthermore, this study outlines a conceptual cross-chain interaction scenario involving permissioned and permissionless blockchain networks, connected through a bridge mechanism and supported by representative smart contract logic. The model illustrates how decentralized stakeholders can interact securely across heterogeneous blockchain platforms. By integrating quantitative metrics, architectural simulations, and qualitative analyses, this paper contributes to a deeper understanding of blockchain’s role in next-generation supply chains, offering guidance for researchers and practitioners aiming to design resilient and trustworthy supply chain management (SCM) systems.

Blockchain-based data storage methods offer strong data integrity, decentralized security, and transparent access control but also face scalability challenges, high computational costs, and complex data management. This study provides a comprehensive review of on-chain, off-chain, and hybrid storage architectures, analyzing their security vulnerabilities, performance trade-offs, and industry-specific applications. On-chain data storage ensures immutability, data integrity, and high security by storing data directly on the blockchain; however, it is associated with high transaction costs and scalability limitations. In contrast, off-chain solutions reduce costs and enhance performance by storing data outside the blockchain but introduce risks related to data integrity and access security in decentralized environments. Hybrid approaches aim to balance security, cost, and scalability by integrating the strengths of both on-chain and off-chain solutions. This study examines the fundamental components of blockchain-based data storage systems, their sector-specific applications, and the technical challenges they present. Additionally, it explores the trade-offs between security, cost, and decentralization, offering insights into blockchain storage optimization strategies. As a result, this study evaluates the optimization of security protocols, the efficiency of hybrid systems, and the sustainability of distributed storage solutions, contributing to future research in this field.

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security.

Randomness plays a crucial role in numerous applications, with cryptography being one of the most significant areas where its importance is evident. A major challenge in cryptographic applications is designing a reliable key generator that meets stringent security requirements. Existing methods often suffer from predictability and fail to provide robust randomness, necessitating novel mathematical approaches. In this study, we propose an innovative mathematical framework that integrates quantum wave functions with chaotic systems to enhance the unpredictability and security of random number generation. The proposed approach leverages the inherent uncertainty of quantum mechanics and the dynamic behavior of chaos to generate statistically strong random sequences. The analysis results confirm that the proposed generator successfully passes all standard statistical randomness tests, demonstrating its effectiveness in cryptographic applications. Additionally, we present a practical implementation of the proposed method as an image encryption algorithm, showcasing its potential for real-world information security solutions. The findings suggest that this approach can contribute significantly to secure communication systems, financial transactions, and other domains requiring high-level cryptographic security.

Photovoltaic systems are among the most popular renewable energy sources due to their ease of installation and low operating costs. However, they are characterized by low efficiency, non-linear electrical properties, and sensitivity to radiant intensity on the panels. To address these limitations, researchers have focused on improving the efficiency of these systems. The most effective method for enhancing the maximum power point of a PV array is reconfiguration, which involves rearranging the connection structures of the panels. This study presents a method for determining the reconfiguration of panels based on their radiant intensity using a genetic algorithm (GA). The method matches the rows of the PV array to achieve similar radiant intensities, thereby increasing power efficiency. An algorithm was developed to enable the adaptive panels to connect to any row of the fixed section in a PV array divided into dual-adaptive and fixed sections, controlling this connection structure. This GA-based algorithm utilizes short-circuit currents obtained from specific points of the PV array to identify the most suitable connection structure within the solution space and generates control signals for reconfiguration. Simulation results with various array structures and shading scenarios demonstrate that the proposed method increases array efficiency and achieves results within a practically applicable cycle time.

Modern permissioned blockchain systems increasingly adopt hybrid data architectures in which critical metadata are anchored on-chain, while large or sensitive payloads are stored off-chain using infrastructures such as IPFS and cloud services. Although this paradigm improves scalability and cost efficiency, it introduces a coupled design challenge where latency, operational cost, and security must be balanced simultaneously. Existing Layer-2 and data-availability approaches primarily focus on throughput and verification, leaving data placement decisions in enterprise permissioned environments insufficiently explored. This paper formulates hybrid on-chain, IPFS, and cloud data placement as a multi-objective optimization problem that jointly encodes storage location, transaction execution mode, and key blockchain parameters, aiming to minimize latency and cost while maximizing integrity and resilience. To explore this high-dimensional design space without costly physical deployment, a digital-twin-based evaluation framework is proposed to approximate the performance, cost, and security behavior of a Fabric-class permissioned blockchain integrated with IPFS and cloud storage. The optimization problem is solved using NSGA-II, yielding a Pareto front that reveals fundamental trade-offs among hybrid configurations. The results demonstrate that hash-anchored off-chain storage consistently outperforms purely on-chain and purely off-chain strategies by reducing latency and cost while preserving strong integrity and replication guarantees. The proposed framework provides practical decision support for data-availability-aware permissioned blockchains in domains such as supply chains, healthcare, and disaster response.