Blockchain Papers

Breno Jacinto Duarte da Costa, Márcio Ferro, Mohamed Yassine Zarouk, Alan Silva · 6 authors

Education 4.0 promotes active, personalized, and competency-based learning aligned with the Sustainable Development Goals (SDGs), yet most current platforms rely on centralized architectures that restrict access, agency, and adaptability. To address this problem, Web3 technologies—including blockchain, decentralized identifiers (DIDs), peer-to-peer storage, and smart contracts—enable the creation of platforms that uphold equity, data sovereignty, and pedagogical flexibility. This paper investigates how the convergence of Education 4.0 and Web3 technologies can drive the development of sustainable, inclusive, and learner-centered digital education systems. We examine two decentralized education platforms, EtherLearn and DeLMS, to assess their design affordances and limitations. Building on these insights, we propose a layered architectural framework grounded in sustainability principles. Our analysis shows that decentralized infrastructures can expand access in underserved regions, increase credential portability, empower learners with greater autonomy, and foster participatory governance through decentralized voting, token-based incentives, and community moderation. Despite these advantages, significant challenges remain around usability, energy efficiency, and regulatory compliance. We conclude by identifying key research priorities at the intersection of sustainable educational technology, digital equity, and decentralized system design.

Gayatri M Bhandari, Nitin M Shivale, Shrishail S Patil, Pranav Prajapati · 7 authors

Federated learning is an emerging technology that can revolutionize the training of machine learning models. Federated learning refers to an approach to training a machine learning model in a decentralized and collaborative fashion. A central server distributes the model to client devices, where it is trained locally using the clients’ own data. The client then sends the updated model weights to the server, which aggregates them to update the global model. This paper introduces a federated learning platform designed to enable collaborative training of machine learning models across multiple client devices while preserving data privacy. The platform supports a range of supervised learning algorithms, including convolutional neural networks and decision trees, and is compatible with widely used frameworks such as TensorFlow, PyTorch, and Flower. It offers a user-friendly interface where model developers can upload or deploy their machine learning models to a central server. Clients can then access these models and train them locally using their own data. The platform's modular design ensures flexibility in deployment and efficiency in handling real-world applications. The key features of this application include a model repository, secure API access for client integration, local model training capabilities on user-end devices, and a user-friendly UI. The platform aims to democratize machine learning by enabling distributed model training and deployment, promoting collaboration and efficiency across diverse use cases. The scalable infrastructure supports real-time inference, on-device training, and secure data handling, making it ideal for industries ranging from healthcare to finance and beyond.

Xu Long, Yishun Wang, Xiaoqi Li

As blockchain technology advances, Ethereum based gambling decentralized applications (DApps) represent a new paradigm in online gambling. This paper examines the concepts, principles, implementation, and prospects of Ethereum based gambling DApps. First, we outline the concept and operational principles of gambling DApps. These DApps are blockchain based online lottery platforms. They utilize smart contracts to manage the entire lottery process, including issuance, betting, drawing, and prize distribution. Being decentralized, lottery DApps operate without central oversight, unlike traditional lotteries. This ensures fairness and eliminates control by any single entity. Automated smart contract execution further reduces management costs, increases profitability, and enhances game transparency and credibility. Next, we analyze an existing Ethereum based gambling DApp, detailing its technical principles, implementation, operational status, vulnerabilities, and potential solutions. We then elaborate on the implementation of lottery DApps. Smart contracts automate the entire lottery process including betting, drawing, and prize distribution. Although developing lottery DApps requires technical expertise, the expanding Ethereum ecosystem provides growing tools and frameworks, lowering development barriers. Finally, we discuss current limitations and prospects of lottery DApps. As blockchain technology and smart contracts evolve, lottery DApps are positioned to significantly transform the online lottery industry. Advantages like decentralization, automation, and transparency will likely drive broader future adoption.

Mohammad Ishzaz Asif Rafid, Morsalin Sakib

Bitcoin's Proof of Work (PoW) mechanism, while central to achieving decentralized consensus, has long been criticized for excessive energy use and hardware inefficiencies \cite{devries2018bitcoin, truby2018decarbonizing}. This paper introduces a hybrid architecture that replaces Bitcoin's traditional PoW with a centralized, cloud-based collaborative training framework. In this model, miners contribute computing resources to train segments of horizontally scaled machine learning models on preprocessed datasets, ensuring privacy and generating meaningful outputs \cite{li2017securing}. A central server evaluates contributions using two metrics: number of parameters trained and reduction in model loss during each cycle. At the end of every cycle, a weighted lottery selects the winning miner, who receives a digitally signed certificate. This certificate serves as a verifiable substitute for PoW and grants the right to append a block to the blockchain \cite{nakamoto2008bitcoin}. By integrating digital signatures and SHA-256 hashing \cite{nist2015sha}, the system preserves blockchain integrity while redirecting energy toward productive computation. The proposed approach addresses the sustainability concerns of traditional mining by converting resource expenditure into socially valuable work, aligning security incentives with real-world computational progress.

Rafael H N Oliveira

This paper presents the Arkhe(n) framework, a comprehensive theoretical and engineer-ing architecture that unifies quantum mechanics, distributed ledger technology, molecularbiology, and consciousness studies under a single informational substrate. We extend theNew Subquantum Informational Mechanics (NMSI) by proposing a fundamental projectionequation C × R3 × Z −→ R4, where information flows from a complex phase field throughdiscrete structural nodes into observable spacetime. We introduce the Vortex of Aether asthe physical carrier of phase (C) and validate this through retrocausal engineering protocolsutilizing the Ωccd particle. We detail the implementation of a Temporal Consensus Oraclevia gRPC and etcd, and a Caffeine Motor for high-speed phase computation (< 16μs).Furthermore, we bridge biological substrates to this network via a Neural-Molecular Bridge(Swift/iOS), translating heart rate variability (HRV) into ConsciousnessPayloads, and pro-pose RNA Computing as the molecular logic gate substrate. The framework is validatedthrough a six-layer architecture spanning RNA World to Silicon GPU clusters.

R. Yuvarani, R Mahaveerakannan, T. Tamilvizhi, L Kartheesan

The integration of blockchain into 6G-enabled Internet of Medical Things (IoMT) networks promises secure and decentralized communication but introduces challenges related to energy efficiency, latency, and authentication overhead. Existing clustering and security schemes fail to balance these aspects effectively in heterogeneous networks. This paper proposes a novel energy-aware cluster head (CH) selection framework using Artificial Democratic Cuckoo Glowworm Remora Optimization (ADCGRO), integrated with a lightweight blockchain layer for secure authentication and data integrity. The system optimizes task allocation across advanced, intermediate, and normal IoMT devices to minimize energy depletion while meeting ultra-reliable low-latency communication (URLLC) requirements. Simulation results demonstrate that the proposed approach enhances network lifetime by 27%, reduces average latency by 35%, and achieves 99% authentication accuracy, surpassing baseline protocols such as LEACH and HEED. These results highlight the effectiveness of combining ADCGRO-based optimization with blockchain to enhance performance and security in 6G wireless networks.

Naser Abbas Hussein, Jihene Khoualdi, Ilhem Abdelhedi Abdelmoula, Hella Kaffel Ben Ayed

Internet of Things (IoT) has gripped domains with this ubiquitous connectivity, in-themoment data collection, and autonomous decision-making. But rising numbers of heterogeneous, extremely constrained IoT devices pose serious concerns regarding data privacy, security, and trust management, drawing great attention into these areas in the academic field and on all sides. Thus, blockchain technology came into the limelight for strengthening security and privacy in IoT systems in a decentralized manner, giving the system immutability, transparency, and distributed trust. This study proposes a Systematic Literature Review (SLR) of blockchain-based approaches that aim to enhance the IoT applications' privacy and security, focusing chiefly on healthcare, supply chains, and smart cities. The review uses a structured methodology to find, select, evaluate, and synthesize relevant peer-reviewed studies published between 2018 and 2025 taken from major scientific databases such as IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Articles were also examined to narrow the scope of study and set the subject. The selected studies are analyzed and classified based on their security goals (e.g., confidentiality, integrity, authentication), privacy-preserving techniques (e.g., anonymization, differential privacy, zero-knowledge proofs), blockchain configurations (e.g., public, private, consortium), and consensus mechanisms. The findings reveal a growing body of research applying blockchain to a wide range of IoT domains, addressing diverse application domains such as healthcare, smart homes, industrial IoT, and agriculture, and demonstrating its potential to enhance data integrity, access control, and authentication. However, the integration of blockchain in IoT also faces challenges such as scalability, latency, and resource overhead, especially in real-time and constrained environments. This review offers a comprehensive synthesis of the state-of-the-art, identifies current limitations and research gaps, and proposes future research directions for building secure, efficient, privacy-aware, and scalable blockchain-enabled IoT systems.

Andres Romo, Ricardo Soto, Emanuel Vega, Broderick Crawford · 6 authors

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer (LB2). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the LB2-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments.

Jiale Yan

ABSTRACT This study aims to conduct an in‐depth analysis of the complex nonlinear dependence relationships between cryptocurrencies and gold within the stocks of BRICS countries. The study employs a GARCH‐EVT‐Vine‐Copula and wavelet coherence models to evaluate the interconnectedness, tail risk and Co‐movement pattern of these assets before and after the outbreak of COVID‐19. The findings reveal that, prior to COVID‐19, significant tail dependence existed between China's stock market, the cryptocurrency index, and the indices of India and Russia, while other indices exhibited only weak dependence. However, after the outbreak of COVID‐19, the tail dependence among variables became more pronounced. The South African stock market appears to have emerged as the center of extreme lower‐tail risk spillovers among the studied variables. During the COVID‐19 outbreak, cryptocurrency markets demonstrated stronger coherence with global stock markets than gold, especially in the US market, potentially compromising their diversification effectiveness. Furthermore, our empirical results were validated by the Kupiec test and the Christoffersen test. The results of this study not only enhance the theoretical understanding of risk management in emerging markets during periods of extreme market crises but also provide valuable insights for policymakers in formulating strategies to ensure financial market stability.

Ying Wang, Zixu Zhang, Hongbo Yin, Guangsheng Yu · 9 authors

Blockchain sharding has emerged as a promising solution to address scalability and performance challenges in distributed ledger systems. In the sharded blockchain, yanking can reduce the communication overhead of smart contracts between shards. However, the existing smart contract yanking methods are inefficient, increasing the latency and reducing the throughput. In this paper, we propose a novel DRL-Based Cross-Shard Smart Contract Yanking (DCSCY) framework which intelligently balances three critical factors: the number of smart contracts processed, node waiting time, and yanking costs. The proposed framework dynamically optimizes the relocation trajectory of smart contracts across shards. This reduces the communication overhead and enables adaptive, function-level migrations to enhance the execution efficiency. The experimental results demonstrate that the proposed approach reduces the cross-shard transaction latency and enhances smart contract utilization. Compared to random-based and order-based methods, the DCSCY approach achieves a performance improvement of more than 95%.

Sabrina Aufiero, Silvia Bartolucci, Fabio Caccioli, Pierpaolo Vivo

This work explores the formation and propagation of systemic risks across traditional finance (TradFi) and decentralized finance (DeFi), offering a comparative framework that bridges these two increasingly interconnected ecosystems. We propose a conceptual model for systemic risk formation in TradFi, grounded in well-established mechanisms such as leverage cycles, liquidity crises, and interconnected institutional exposures. Extending this analysis to DeFi, we identify unique structural and technological characteristics - such as composability, smart contract vulnerabilities, and algorithm-driven mechanisms - that shape the emergence and transmission of risks within decentralized systems. Through a conceptual mapping, we highlight risks with similar foundations (e.g., trading vulnerabilities, liquidity shocks), while emphasizing how these risks manifest and propagate differently due to the contrasting architectures of TradFi and DeFi. Furthermore, we introduce the concept of crosstagion, a bidirectional process where instability in DeFi can spill over into TradFi, and vice versa. We illustrate how disruptions such as liquidity crises, regulatory actions, or political developments can cascade across these systems, leveraging their growing interdependence. By analyzing this mutual dynamics, we highlight the importance of understanding systemic risks not only within TradFi and DeFi individually, but also at their intersection. Our findings contribute to the evolving discourse on risk management in a hybrid financial ecosystem, offering insights for policymakers, regulators, and financial stakeholders navigating this complex landscape.

Shixuan Guan, Kai Li

Blockchain address poisoning is an emerging phishing attack that crafts "similar-looking" transfer records in the victim's transaction history, which aims to deceive victims and lure them into mistakenly transferring funds to the attacker. Recent works have shown that millions of Ethereum users were targeted and lost over 100 million US dollars. Ethereum crypto wallets, serving users in browsing transaction history and initiating transactions to transfer funds, play a central role in deploying countermeasures to mitigate the address poisoning attack. However, whether they have done so remains an open question. To fill the research void, in this paper, we design experiments to simulate address poisoning attacks and systematically evaluate the usability and security of 53 popular Ethereum crypto wallets. Our evaluation shows that there exist communication failures between 12 wallets and their transaction activity provider, which renders them unable to download the users' transaction history. Besides, our evaluation also shows that 16 wallets pose a high risk to their users due to displaying fake token phishing transfers. Moreover, our further analysis suggests that most wallets rely on transaction activity providers to filter out phishing transfers. However, their phishing detection capability varies. Finally, we found that only three wallets throw an explicit warning message when users attempt to transfer to the phishing address, implying a significant gap within the broader Ethereum crypto wallet community in protecting users from address poisoning attacks. Overall, our work shows that more efforts are needed by the Ethereum crypto wallet developer community to achieve the highest usability and security standard. Our bug reports have been acknowledged by the developer community, who are currently developing mitigation solutions.

Erhan Atay

This conceptual paper advances a metaphysical framework for understanding agency, coherence, and leadership in mission-driven organizations. Drawing on Said Nursî’s Islamic concept of Zerre (the smallest particle), we reconceptualize organizational actors not as autonomous agents but as morally embedded participants aligned with a transcendent ethical order. This Zerre-based logic contrasts with secular-functional paradigms by grounding legitimacy and leadership in humility, symbolic resonance, and ontological coherence. We develop an analytical model that maps this metaphysical lens onto organizational constructs such as agency, leadership, and moral purpose. The framework contributes to emerging post-secular and spiritually informed theories of organizing by integrating Islamic metaphysics with contemporary challenges of decentralized leadership, ethical alignment, and valuebased management. Illustrative examples from Islamic Relief, World Vision, and Extinction Rebellion highlight the model’s relevance for spiritually inspired organizational action across faith traditions.

Calkin Garg, Omar Rios Cruz, Tessa Andersen, Gaby G. Dagher · 6 authors

Due to HIPAA and other privacy regulations, it is imperative to maintain patient privacy while conducting research on patient health records. In this paper, we propose AegisBlock, a patient-centric access controlled framework to share medical records with researchers such that the anonymity of the patient is maintained while ensuring the trustworthiness of the data provided to researchers. AegisBlock allows for patients to provide access to their medical data, verified by miners. A researcher submits a time-based range query to request access to records from a certain patient, and upon patient approval, access will be granted. Our experimental evaluation results show that AegisBlock is scalable with respect to the number of patients and hospitals in the system, and efficient with up to 50% of malicious miners.

Kevin McNamara, Rhea Pritham Marpu

The global financial system stands at an inflection point. Stablecoins represent the most significant evolution in banking since the abandonment of the gold standard, positioned to enable "Banking 2.0" by seamlessly integrating cryptocurrency innovation with traditional finance infrastructure. This transformation rivals artificial intelligence as the next major disruptor in the financial sector. Modern fiat currencies derive value entirely from institutional trust rather than physical backing, creating vulnerabilities that stablecoins address through enhanced stability, reduced fraud risk, and unified global transactions that transcend national boundaries. Recent developments demonstrate accelerating institutional adoption: landmark U.S. legislation including the GENIUS Act of 2025, strategic industry pivots from major players like JPMorgan's crypto-backed loan initiatives, and PayPal's comprehensive "Pay with Crypto" service. Widespread stablecoin implementation addresses critical macroeconomic imbalances, particularly the inflation-productivity gap plaguing modern monetary systems, through more robust and diversified backing mechanisms. Furthermore, stablecoins facilitate deregulation and efficiency gains, paving the way for a more interconnected international financial system. This whitepaper comprehensively explores how stablecoins are poised to reshape banking, supported by real-world examples, current market data, and analysis of their transformative potential.

Prathamesh Devadiga, Yashmitha Shailesh

We introduce RegimeNAS, a novel differentiable architecture search framework specifically designed to enhance cryptocurrency trading performance by explicitly integrating market regime awareness. Addressing the limitations of static deep learning models in highly dynamic financial environments, RegimeNAS features three core innovations: (1) a theoretically grounded Bayesian search space optimizing architectures with provable convergence properties; (2) specialized, dynamically activated neural modules (Volatility, Trend, and Range blocks) tailored for distinct market conditions; and (3) a multi-objective loss function incorporating market-specific penalties (e.g., volatility matching, transition smoothness) alongside mathematically enforced Lipschitz stability constraints. Regime identification leverages multi-head attention across multiple timeframes for improved accuracy and uncertainty estimation. Rigorous empirical evaluation on extensive real-world cryptocurrency data demonstrates that RegimeNAS significantly outperforms state-of-the-art benchmarks, achieving an 80.3% Mean Absolute Error reduction compared to the best traditional recurrent baseline and converging substantially faster (9 vs. 50+ epochs). Ablation studies and regime-specific analysis confirm the critical contribution of each component, particularly the regime-aware adaptation mechanism. This work underscores the imperative of embedding domain-specific knowledge, such as market regimes, directly within the NAS process to develop robust and adaptive models for challenging financial applications.

Vũ Thị Thu Hương, Nguyễn Thu Hà

Amid the ongoing advancements associated with the Fourth Industrial Revolution and the intensification of digital transformation, the deployment of artificial intelligence (AI) within the banking sector has become an inevitable trajectory, enabling substantial innovations in financial management and operational processes. AI technologies facilitate the automation of complex workflows, reduce error rates, enhance operational efficiency, and improve customer experience through personalized services and accelerated response mechanisms. Applications span various functions, including customer onboarding, service delivery, product development, marketing, and risk management, thereby optimizing the banking value chain holistically. Moreover, AI’s capabilities in big data analytics and customer behavior prediction equip financial institutions with more robust decision-making tools that mitigate credit risk and fraud incidence. The convergence of AI and blockchain technologies further augments transaction security and transparency, thereby promoting the expansion of digital banking and decentralized finance ecosystems. This study aims to systematically examine the evolving roles and emerging applications of AI throughout the banking value chain, contributing to strategic frameworks oriented toward sustainable development within the digital era.

Satyaban Sahoo, Deepti Singh

This study employs novel quantile time-frequency connectedness approach to explore the dynamic connectedness among sustainable assets (sustainable, green bond, and clean energy index), traditional assets (traditional index and crude oil), and cryptocurrency. This method assesses the impact of uncertain events on asset relationships. Findings indicate median connectedness of 36.94% in the short run and 4.81% in the long run, with short-term dynamics dominating system transmission. The traditional index is the primary transmitter of short-run shocks, while the green bond index leads in long-run shocks. Diversification across asset classes is recommended for effective hedging and optimal returns during extreme market conditions.

Bixiao Luo

This paper investigates the unresolved intellectual property challenges posed by non-fungible tokens (NFTs), a rapidly growing class of digital assets that blend decentralized technologies with creative content distribution. Despite widespread adoption across art, entertainment, and gaming sectors, the legal infrastructure surrounding NFTs remains fragmented, creating uncertainty for creators, buyers, and platforms alike. The objective of this study is to critically evaluate existing theoretical models—including property-based, contract-based, and provenance-centered approaches—and assess their adequacy in governing NFT-related rights and obligations. Methodologically, the paper employs a comparative legal analysis of current NFT licensing practices, supported by interdisciplinary review of blockchain architecture, smart contract functionalities, and relevant international IP frameworks. Based on legal theory, technical standards, and case studies, the paper identifies critical gaps in enforceability, rights attribution, and jurisdictional clarity. In response, the study proposes a hybrid legal-technical framework comprising seven interconnected components: Smart Licensing Infrastructure (SLI), an On-Chain Provenance and Rights Registry, Embedded Royalty Clauses with Legal Backing, Token-Linked Legal Contracts (TLCs), along with dispute resolution and jurisdictional compatibility. These elements collectively aim to bridge decentralized code execution with enforceable legal standards, facilitating clearer licensing arrangements, more reliable royalty enforcement, and scalable dispute resolution mechanisms. It presents a novel blueprint for technical capabilities of NFTs with the foundational requirements of intellectual property law. By incorporating legal metadata, verifiable authorship records, and jurisdictional parameters directly into NFT structures, the framework strengthens legal predictability without restricting innovation. This research contributes to academic discourse by advancing a multidimensional governance approach for digital assets, offering actionable pathways toward regulatory coherence and sustainable development within the NFT ecosystem moving forward.

Alfina Rahmatia, Arief Dwi Saputra

Purpose – Non-Fungible Tokens (NFT), one of the latest innovations in the financial world, have succeeded in triggering debate among the public, especially in terms of Islamic financial principles. Therefore, this study seeks to explore the gap between public societies’ perspectives on NFT on Twitter and the discourse conveyed by experts in research articles or journalists in popular articles. Methodology: This study combines two analyses, namely sentiment analysis, using the R Studio application to categorize public opinion into positive, neutral, and negative sentiments. Discourse analysis uses the NVivo 12 application to identify critical themes in scientific writing.Findings – The results show various perceptions of positive sentiments often associated with NFT and innovation. By contrast, negative sentiments focus on speculation, lack of clarity, and the potential to conflict with the principles of Islamic finance. These findings convey concerns about the speculative nature of the NFT and its compliance with Sharia law. However, some scholars argue that NFT can be structured according to Islamic ethics if proper guidelines are followed. Implications – This study contributes to bridging the gap between public perception and scholars, so that insights arise regarding NFT as perceived within the framework of Islamic finance. Originality – We believe this study is the first qualitative study to investigate public sentiment about NFT from Twitter/X and discuss it with the principles of Islamic finance.

Zefan Jiang

This paper explores the evolving legal nature of digital collectibles, particularly non-fungible tokens (NFTs), and the systemic challenges they pose to civil law property regimes. Within civil law traditions, the concept of property is bound by codified categories and the principle of numerus clausus, which restricts recognition to a limited set of property forms. Digital collectibles, by contrast, are decentralized, programmable, and technologically mediated, defying conventional classifications such as tangible movables or intangible rights. This disconnect generates uncertainty regarding their ownership, transferability, inheritance, and enforceability under traditional legal frameworks. The analysis addresses how digital assets undermine the foundational assumptions of possession, registration, and state-backed enforcement. Particular attention is given to the problems of inheritance continuity, token fragmentation, cross-border legal conflicts, and the role of private key control in lieu of legal title. Drawing from emerging theoretical debates and comparative jurisprudence, the paper proposes a trajectory of adaptive legal reform that includes doctrinal reinterpretation, statutory innovation, and the development of interoperable legal-technical standards. The study concludes that civil law systems must reconceptualize the legal object and embrace a pluralistic approach to digital property to ensure institutional relevance in the era of algorithmic ownership.

Authors unavailable

Power industry software, as a core tool for modern power equipment control and management, is facing increasingly severe cybersecurity threats.Distributed ledger technology provides new ideas for power software security detection due to its decentralization, transparency and tamper-proof characteristics.This paper discusses the application of distributed ledger technology in the security detection of software development in the electric power industry, and proposes a trusted traceability and quality access control reinforcement method based on distributed ledger.The research designs the traceability data model and smart contract system to realize the trusted collection, storage and verification of security data; at the same time, it proposes the sensitive data aggregation method based on homomorphic encryption and the tamper-proof technology of RSA asymmetric encryption, and constructs the data communication structure of Overlay structure, which guarantees the complete transmission of electric power software security detection data and traceability tracking.The experimental results show that compared with SHA256 algorithm and DyRH model, the average value of the error localization time of this method is reduced to 9.23ms, which is 8.6ms and 4.1ms less than the control group, respectively; the accuracy rate of the error localization reaches 98.33%, which is improved by 4.77% and 1.79%; and in the test of the anti-attack performance, the average number of tampered data is only 189, which is respectively reduced by 184 and 156.The study proves that distributed ledger technology can effectively enhance data credibility, strengthen traceability, and enhance the strength of system quality access control in software development security detection in the power industry, which provides a new technical path and solution for the information security of the power system.

Kingsley E. Nwachukwu, STANLEY CHISOM NNAKA

The growing adoption of Artificial Intelligence (AI) in the decentralized finance space has opened new opportunities to improve fraud detection, smart contracts, liquidity pooling, efficiency, and scalability of decentralized finance (DeFi) platforms. Despite the global expansion of AI-powered DeFi applications, Nigeria faces unique challenges such as regulatory uncertainty, high fraud penetration, low digital literacy, and infrastructural gaps, which hinder full integration and trust in AI-enabled DeFi systems. This study is motivated by the need to understand how AI can foster inclusive and transparent financial ecosystems. Therefore, the study examined the effects of AI adoption on decentralized finance in Nigeria. It adopts a survey research design, with a sample size of 400 active DeFi participants determined through Cochran formula. Regression analysis was performed to examine the relationships between variables using the Statistical Package for Social Sciences (SPSS Version 23). Findings reveal that stakeholders’ perceptions have a positive and significant association on AI’s role in building trust and security in Nigeria (0.627; 0.000&lt;0.01). Perceived barriers also have a positive and significant association with the integration of artificial intelligence (AI) into DeFi systems by blockchain developers and financial professionals in Nigeria (0.506; 0.000&lt;0.01). DeFi users have a significant and positive association with AI-driven tools used in decision-making for decentralized financial activities in Nigeria (0.551; 0.000&lt;0.01). Regulatory changes have a significant and positive association with AI adoption in Nigeria (0.519; 0.000&lt;0.01). AI practitioners have a significant and positive association with the development of decentralized finance in Nigeria (0.614; 0.000&lt;0.01). Based on these findings, the study concludes that AI is a powerful tool that can potentially revolutionize the face of decentralized finance in Nigeria. The study further recommended that policymakers and regulators develop and scale an adaptive AI-DeFi regulatory framework by developing a tiered regulatory sandbox specific to DeFi-AI platforms, enabling controlled experimentation under policy oversight and a national registry of certified AI-DeFi practitioners.

Brendan Kobayashi Chou, Andrew Lewis-Pye, Patrick O'Grady

Achieving low-latency consensus in geographically distributed systems remains a key challenge for blockchain and distributed database applications. To this end, there has been significant recent interest in State-Machine-Replication (SMR) protocols that achieve 2-round finality under the assumption that $5f+1\leq n$, where $n$ is the number of processors and $f$ bounds the number of processors that may exhibit Byzantine faults. In these protocols, instructions are organised into views, each led by a different designated leader, and 2-round finality means that a leader's proposal can be finalised after just a single round of voting, meaning two rounds overall (one round for the proposal and one for voting). We introduce Minimmit, a Byzantine-fault-tolerant SMR protocol with lower latency than previous 2-round finality approaches. Our key insight is that view progression and transaction finality can operate on different quorum thresholds without compromising safety or liveness. Experiments simulating a globally distributed network of 50 processors, uniformly assigned across ten virtual regions, show that the approach leads to a 23.1% reduction in view latency and a 10.7% reduction in transaction latency compared to the state-of-the-art.