Blockchain Papers

MR MEET CHANDRAKANT JOSHI

No abstract is available for this record.

Ihor Zubenko, Andrii Shpak, Anastasiia Sydorchuk

The article examines the role of cryptocurrencies in developing electronic commerce and transforming modern payment infrastructure within the digital economy. Particular attention is paid to the economic nature of cryptocurrencies as innovative financial instruments and their increasing use in online commercial transactions. The study analyzes the key features of applying digital currencies in e-commerce, including decentralization, transaction transparency, the high speed of cross-border payments, and reduced dependence on traditional financial intermediaries. The advantages of cryptocurrency payments over conventional systems are identified, such as lower transaction costs, enhanced security through blockchain technology, and expanded international market access for businesses and consumers. Conversely, the article outlines the main challenges and risks limiting the widespread adoption of cryptocurrencies in electronic commerce. These encompass significant price volatility, technical and infrastructural barriers, cybersecurity threats, and the absence of unified legal regulation across many jurisdictions. Special attention is devoted to analyzing the practical experience of leading international companies–including Amazon, Shopify, PayPal, Microsoft, Expedia, and Rakuten–that have implemented or tested cryptocurrency payment solutions. Results demonstrate that these practices improve payment efficiency, accelerate international settlements, reduce commission fees, and increase overall transaction security. The study concludes that integrating cryptocurrencies into e-commerce represents a natural evolutionary stage of the digital economy. Ultimately, cryptocurrencies possess substantial potential to strengthen electronic commerce and support its ongoing development.

Olivier Atangana

The strong interest in central bank digital currencies (CBDCs) arises in a context of increased digitization of payments and a growing search for more resilient and inclusive solutions. Among the desired features of CBDCs, offline payment constitutes a central challenge. It ensures the resilience of payment systems, promotes financial inclusion, and guarantees transaction continuity in the absence of network connectivity. However, unlikeonline payments, offline payments for CBDCs impose specific constraints and sometimes conflicting requirements in terms of security, privacy, fraud prevention, auditability, and integration with existing infrastructures. Consequently, this thesis focuses on the anal ysis and formalization of these offline payment requirements, as well as on the study of technical solutions capable of addressing them in a coherent manner. Accordingly, basedon this analysis leading to a structured taxonomy, the thesis introduces several original frameworks illustrating different strategies for satisfying these requirements. The first framework, PrivTEE-Pay, relies on a single-ledger architecture and exploits trusted execution environments combined with cryptographic primitives such as blind signatures and zero-knowledge proofs (zk-SNARKs). The second framework extends a conventionalpayment architecture through the integration of a secure smart card, the DigiVault card, coupled with a smartphone. This combined approach also relies on privacy-enhancing technologies and on the fraud detection model MarkoPayChain, based on Markov chains. A third framework, Block-PAD, proposes a hybrid architecture combining a central ledger for monetary issuance and a blockchain for delayed synchronization of offline transactions.Finally, the thesis complements these contributions with an advanced offline fraud detection framework, based on a combination of expert rules, explainable machine learning models, and hidden Markov chains. Moreover, these different frameworks are experimentally evaluated using simulators and synthetic datasets dedicated to offline CBDC payments. The results show that the proposed solutions make it possible to address the requirements identified in the taxonomy, each through explicit trade-offs. This thesis thus provides concrete contributions to the design of resilient, secure, performant, auditable, and privacy-preserving offline CBDC payment systems that can integrate into existing payment infrastructures.

Kathari Santosh, Neha Jain, Annapurna Mishra, Sajiv G · 6 authors

Smart contracts are self-executing digital agreements deployed on blockchain platforms that automate business processes with transparency and security. While they eliminate the need for intermediaries, their major limitation lies in their static logic, which lacks adaptability to dynamic conditions such as supply chain disruptions, market fluctuations, or contract breaches. This rigidity often leads to inefficiencies, delays, and financial losses in real-world applications. To address this challenge, we propose a hybrid framework called SmartGPO, which integrates Graph Neural Networks with Proximal Policy Optimization. The research aims to enhance the adaptability and intelligence of smart contracts by combining structural awareness and decision-making capabilities. The proposed system models the contract environment as a graph, where nodes represent entities and edges denote their interactions. GNNs generate relational embeddings, which are then used by the PPO agent to learn optimal contract execution policies through reward-driven training. SmartGPO achieves superior performance in dynamic contract workflows, with an execution success rate of 98.4 % and a decision-making accuracy of 98.1 %, outperforming traditional and standalone models. The framework also demonstrates improved gas cost reduction and faster processing time. Future enhancements include integrating multi-agent learning, real-time oracle connectivity, and legal compliance layers to further improve security, scalability, and trust. This research marks a step forward in developing intelligent, adaptive smart contracts for real-world blockchain applications.

Ezinne Victory Kanu, Charles Chibuisi Ehiemere, Ishaku Adamu Akyala, Eric Terkuma Chia · 5 authors

Despite global commitments under SDG-3, maternal mortality rates remain disproportionately high in Sub-Saharan Africa. This review examines how health policies have shaped outcomes between 2014 and 2024 in Nigeria, Rwanda, South Africa, and Gabon. A comparative narrative review was conducted using WHO, World Bank, UNFPA, DHS, and national policy documents. Guided by the Walt & Gilson Policy Triangle and the WHO Health System Building Blocks, policies were assessed for context, content, actors, process, and health system capacity. Data were synthesized thematically to compare implementation and outcomes. Rwanda achieved substantial declines through decentralized financing, performance-based funding, and community health worker integration. South Africa reduced deaths via integration of HIV and maternal services but still faces equity gaps. Gabon improved financial access but rural infrastructure and workforce limitations constrain outcomes. Nigeria’s fragmented governance and weak PHC financing explain stagnation despite multiple reforms. Implementation quality, not policy presence, drives progress. Strengthening governance, financing transparency, workforce readiness, and community engagement remains crucial for achieving SDG-3. This study highlights cross-country lessons transferable to similar contexts.

V. Thamilarasi, S. Biruntha, Biswo Ranjan Mishra, S. Tamizharasu · 6 authors

No abstract is available for this record.

B. Srinivasarao, Shaik Reddi Khasim, M Lavanya, K. Antony Sudha · 6 authors

No abstract is available for this record.

Naga Sekhar Madala, Ali Elrashidi, S. Saranya, A. Mathankumar · 6 authors

As the Internet of Things grows rapidly, more and more companies are using dispersed sensor networks. Companies in this sector focus on smart cities, intelligent transportation, healthcare, and industrial automation. The security and reliability of the Internet of Things are challenged by factors such as device heterogeneity, limited processing resources, and decentralised data generation, even as real-time data collecting and automation are taking place. There is a risk that data could be compromised due to threats. Systematic fraud or anomalous activity detection fails when it relies on centralised security. By incorporating AI and blockchain technology, this design enhances the reliability and security of distributed sensor networks. By analysing sensor-collected data, machine learning algorithms can detect fraudulent activity, unusual operational patterns, and real-time intrusions. Integrity of data, authentication of devices, and auditability of networks are all enhanced by blockchain technology, which generates an immutable distributed ledger. The immutability of ledger data makes this feasible. Securely enabling IoT nodes to work together without centralised authorities reduces the likelihood of failure points. This study demonstrates the use of decentralised trust systems and predictive intelligence to detect anomalies and secure data. Compared with conventional Internet of Things security measures, experimental results demonstrate higher detection accuracy, fewer false positives, and greater system resilience. To ensure the integrity of missioncritical data and the reliability of operations, the platform employs scalable, secure, and intelligent Iot.

Shivam Bahuguna, Abhishek Danu

Artificial General Intelligence (AGI) and blockchain technology combined together can create a revolutionary change towards decentralized intelligent systems. The type of intelligence known as AGI which strives to duplicate human-level cognitive functions is expected to bring revolutionary changes to decision-making processes and automation systems. Blockchain provides digital interactions with transparency, security and trust through its decentralized system where data entries cannot be altered. AGI and blockchain together can create secure, autonomous and self-learning networks. By implementing decentralized systems, biases and centralization along with data manipulation risks are significantly reduced. This chapter covers the fundamental principles of AGI and blockchain systems to examine their distinct advantages and the new opportunities that emerge when they work together. The paper investigates how blockchain technology secures AGI operations while simultaneously improving transparency within those systems and discusses AGI&s;s potential to enhance blockchain protocols. The chapter further explores application opportunities in decentralized finance, cybersecurity, healthcare and governance sectors through the fusion of AGI and blockchain. Furthermore, the chapter discusses ethical and regulatory challenges we face regarding bias, privacy, accountability and governance within AGI-blockchain systems. The chapter finally concludes with future directions, research priorities and policy recommendations to promote the responsible development as well as deployment of AGI-blockchain systems.

Rana Hassam Ahmed, Muhammad Sarfraz Khan, Amirmohammad Delshadi, Naseer Ahmad · 5 authors

Internet of Medical Things (IoMT) provides the possibility to conduct continuous monitoring of health, perform intelligent diagnostics, and make a clinical decision based on data. Nonetheless, there are security, privacy, scalability, latency, and energy issues with large-scale deployment. Although Federated learning (FL) provides less exposure to data, and blockchain provides trust, current solutions that combine both blockchain and FL have high consensus overhead, fixed privacy, and adversarial resilience. To handle them, we present an Edge-Intelligent Hierarchical Blockchain-IoMT framework that integrates Hierarchical FL (HFL), Adaptive Differential Privacy (ADP), Lightweight Homomorphic Encryption (LHE), Zero-Knowledge Proof (ZKP) authentication, and an Energy-Aware PoS with Edge Learning (PoS-EL) consensus. Hierarchical aggregation minimizes bottlenecks in communication. ADP minimizes security vs utility. ZKP achieves authentication and PoS-EL minimizes energy consumption. Experiments on real-world data demonstrate 99.21% accuracy of detecting anomalies, 34% decreased latency, 41% decreased energy usage, 52 percent lower blockchain overhead and 97 percent resistance to adversarial attacks, which justifies the framework in real-time, mission-critical IoMT systems.

Sandeep Kumar, Siddharth Thapliyal, J. Singh, Swati Rani · 6 authors

Due to a combination of both rigid binary structures within formal identification systems and ambiguous laws; along with discriminatory practices, third gender individuals continue to be excluded from formal identity systems. Most existing centralized identity verification systems have failed to provide non-binary identity solutions, resulting in limited access to banking services, education services, legal protection and access to health care. This paper examines the ability of decentralized identity systems using blockchain technologies to provide users with secure, private and self-managed identity options for third gender individuals. It evaluates the core technology of decentralized identity systems, specifically decentralized identifiers (DIDs); verifiable credentials (VCs); smart contracts; and zero knowledge proof; through an examination of real-world examples. Additionally, the paper outlines the technical and legal constraints associated with decentralized identity systems, specifically literacy requirements; lack of consistency in national frameworks for recognition; and risk of symbolic inclusion (i.e., "being included" rather than having the rights of recognition) rather than actual structural reforms.

Wenhao Zhang, Zhenpeng Tang, Xiaowen Zhuang, Yi Cai · 5 authors

The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods.

P. Saranya, A. W. Ali, Meesala Shobha Rani, H. Shaheen

The current land transfer system in India is beset with inefficiencies, delays and increased costs due to its reliance on manual processes, extensive paperwork and involvement of multiple intermediaries such as real estate agents and government officials. Discrepancies and verification challenges arise from land records maintained in physical ledgers or decentralized digital formats across various government departments, often leading to fraudulent transactions, disputed ownership claims and unauthorized land sales. The limited transparency and access to land records, further exacerbate corruption and undermine trust in the system. This chapter explores how blockchain technology and smart contracts can revolutionize the land transfer system in India by addressing these inherent challenges. The emergence and growing popularity of blockchain technology is mainly due to the success and influence of cryptocurrencies like Bitcoin and Ethereum. Ethereum has become the backbone of the decentralized finance sector, further driving its adoption and market perception. Blockchain&s;s decentralized and immutable ledger ensures the authenticity and security of ownership data, while smart contracts automate the entire land transfer process, reducing the need for intermediaries and minimizing human error. The integration of these technologies fosters transparency by providing real-time access to unified land records for all stakeholders, significantly reducing the risk of fraud. Additionally, the streamlined process can greatly reduce the cost and time associated with land transactions. By providing a clear, tamper-proof chain of ownership, blockchain technology also aids in dispute resolution, offering a transformative solution for modernizing land management in India. The proposed work aims to enhance property ownership by creating smart contracts with the terms of the land sale, including the agreed price, property details and conditions for transfer. It verifies the ownership of the seller and checks for any encumbrances or legal issues with the property. This can be done automatically by querying the blockchain ledger. Once the verification is done, the payment can be transferred and the ownership records would be updated on the blockchain ledger,transferring the ownership rights to the buyer and providing a tamper proof and transparent record of the transaction.

Susan Zehra, Stephan Olariu

As vehicles evolve into mobile computers, future transportation systems will depend on secure data sharing and collaborative computation between cars and roadside infrastructure. Ensuring security, privacy, and accountability in such dynamic networks is challenging because vehicles continuously join, leave, and relay data under intermittent connectivity. This paper introduces VERA-VANET (Verifiable Encrypted Routing and Attestation), a cryptographic protocol that makes vehicular communication both secure and mathematically verifiable. When an Access Point (AP) disseminates encrypted job chunks, each vehicle processes only the data it is authorized to handle. Every packet is signed and acknowledged, producing compact, aggregatable proofs of delivery verifiable by the AP or cloud. For correctness, vehicles attach lightweight zero-knowledge proofs that confirm computations without revealing data. Under disconnections, vehicles safely store, carry, forward, and trace encrypted chunks through cryptographic receipts, making forgery and undetected tampering infeasible. VERA-VANET combines asymmetric encryption, aggregate signatures, and zero-knowledge attestations, and integrates with IEEE 802.11p / C-V2X using trusted hardware for secure key storage.

Punya Shree J, Surabhi Saxena, Neha Singhal

Information security is built on authentication, and foundational passwords and PINs are no longer sufficient to change cyber threats. The given paper uses the model by Bonneau et al. (that is, The Quest to Replace Passwords) to qualitatively compare the traditional knowledge factors with the newly emerged solutions such as biometrics, behavioral analysis, FIDO2/passkeys, multi-factor schemes, and Zero-Knowledge Proofs according to their security, usability, deployability, and privacy. Our analysis summarizes the strengths, weaknesses and threat models of each of the categories and then summarizes the trade offs in a comparison table. We observe that more modern approaches have a tendency to enhance security at the cost of usually introducing usability, cost, and scalability problems. Behavioral biometrics are vulnerable to privacy and spoofing threats; FIDO2/passkeys are simple to operate but they rely on synchronization infrastructure; and Zero-Knowledge Proofs are secure at the cost of computation. Hybrid and multi-factor designs provide the optimal tradeoff between these factors nowadays, and research in the future should enhance the possibilities of new methods of practical large scale identity systems.

Fatemeh Erfan, Martine Bellaïche, Talal Halabi

Integrating blockchain into the Industrial Internet of Things (IIoT) has emerged as a promising solution for preserving data privacy and ensuring IoT security. Among various blockchain platforms, Ethereum stands out due to its support for smart contracts and its interoperability with lightweight communication protocols. Despite these advantages, particularly within Ethereum-based networks, IIoT systems remain vulnerable to large-scale threats such as Sybil attacks. These attacks pose a critical security risk because an adversary generates numerous fake entities to infiltrate and compromise the network, ultimately undermining its integrity and availability. Existing approaches utilize Ethereum smart contracts and lightweight protocols such as MQTT to secure IIoT communications, but often overlook sophisticated threats such as Sybil attacks, which introduce fraudulent nodes into the network. Conventional detection methods typically depend on centralized monitoring, undermining scalability and privacy, and there remains a lack of publicly available datasets representing adversarial behaviors in IIoT environments. In this paper, an Ethereum-based IIoT network is first developed, and a publicly available dataset is released through the GitHub repository. An advanced method is then proposed to detect and prevent Sybil attacks in a PoA-based IIoT network using decentralized federated learning. During the detection phase, a convolutional neural network (CNN) is employed within the decentralized federated learning framework, achieving an average detection accuracy and recall of 91.13% and 91.37% among clients, respectively. In the prevention phase, a secure smart contract is designed to manage a dynamic reputation system, effectively preventing Sybil nodes from remaining active on the network.

Tahsin Galip TEKİN, Sait Patır

In this study, it is aimed to compare quantitative forecasting methods (traditional and learning based) in cryptocurrency market. For his purpose the daily prices between 16 September 2017 – 15 September 2022 of Bitcoin, Ethereum, Binance Coin and Monero were analyzed with five different methods: ARIMA, exponential smoothing, artificial neural networks, RNN and LSTM.In the results it is indicated that exponential smoothing method is the most successful method at forecasting daily prices. The method has high performance in forecasting BTC, ETH and BNB daily prices. But at forecasting daily XMR prices, artificial neural networks method was the most successful one.The other point which was detected in this study is deep learning based methods made some unsuccessful forecasts. This is thought to be due to the fact that deep learning methods require more data. In future studies, using other quantitative methods (e.g. GRU, XGBoost, transformer models) on other cryptocurrencies will contribute to the literature.

S. Sridevi, RIYAZULLA RAHMAN J, Jobin Thomas, Komalavalli C · 6 authors

The rapidly evolving NFT (Non-Fungible Tokens) ecosystem has brought about a wealth of opportunities for investors and creators alike. However, the allure of this burgeoning market has also attracted a host of malicious actors, who have exploited vulnerabilities to perpetrate rug pull incidents, a form of exit scam where project developers abruptly abandon their project, absconding with investors' funds. To safeguard oneself against these pernicious schemes, it is crucial to understand the mechanisms underlying rug pulls, as well as the strategies for detecting and avoiding them. In this paper, we discuss the anatomy of an NFT rug-pull and a detailed investigation and study on how these rug-pulls are executed. In this study, we have made an attempt to investigate the common signs along with the cautionary steps to identify and avoid them. Finally, we have also designed and proposed an effective mitigation strategy for a rug pull with deterministic mathematical modelling.

Venkatesh Babu R, Vignesh D, Sibaath Ahmed S, M P Ramkumar · 5 authors

The conventional messaging platform such as WhatsApp or Telegram is based on a centralized server, which fundamentally creates a gateway to censorship, surveillance, and points of failure. In essence, that is damaging to the user privacy and information security. In this paper, therefore, we develop and implement a Decentralized Chat Application (DCA) using Ethereum blockchain. Based on the fundamental capabilities of distributed ledger technologies, namely, immutability, transparency, and trustlessness, we are building a reliable, censorship-resistant chat service. The application operates under Ethereum Smart Contracts to handle decentralized user identities and to store public encryption keys safely as well as establishing chat channels. Our practical message content and media flows are stashed into an effective Peer-to-Peer (P2P) network, potentially stashed in off-chain storage such as Interplanetary File System(IPFS), but all messages are End-to-End Encrypted.The DCA model provides a good framework of the next-gen secure, private, and autonomous social interaction, as it leaves the end-users with complete ownership and control over their digital communication by abandoning the central authority.

Pierre-Luc Dallaire-Demers, BTQ Technologies Team

Bitcoin already faces a quantum threat through Shor attacks on elliptic-curve signatures. This paper isolates the other component that public discussion often conflates with it: mining. Grover's algorithm halves the exponent of brute-force search, promising a quadratic edge to any quantum miner of Bitcoin. Exactly how large that edge grows depends on fault-tolerant hardware. No prior study has costed that hardware end to end. We build an open-source estimator that sweeps the full attack surface: reversible oracles for double-SHA-256 mining and RIPEMD-based address preimages, surface-code factory sizing, fleet logistics under Nakamoto-consensus timing, and Kardashev-scale energy accounting. A parametric sweep over difficulty bits b, runtime caps, and target success probabilities reveals a sharp transition. At the most favourable partial-preimage setting (b = 32, 2^224 marked states), a superconducting surface-code fleet still requires about 10^8 physical qubits and about 10^4 MW. That load is comparable to a large national grid. Tightening to Bitcoin's January 2025 mainnet difficulty (b about 79) explodes the bill to about 10^23 qubits and about 10^25 W, approaching the Kardashev Type II threshold. These numbers settle a narrower question than "Is Bitcoin quantum-secure?" Once Grover mining is lifted from asymptotic query counts to fault-tolerant physical cost, practical quantum mining collapses under oracle, distillation, and fleet overhead. To push mining into non-trivial consensus effects, one must invoke astronomical quantum fleets operating at energy scales that lie far above present-day civilization.

A. Mahadeer, R. Arulprakasam, R. Gurusamy, Yilun Shang

Pseudorandom number generators (PRNGs) are foundational in cryptography, providing the unpredictability required for key generation and data protection. Petri nets provide a structured mathematical framework for modeling systems with concurrency, asynchrony, distribution, and nondeterminism. This paper proposes a Petri net token-flow PRNG for grayscale image encryption and instantiates it in a permutation-diffusion cipher. The Petri net is initialized from a SHA-256 digest, and the induced token flow yields two keystreams for pixel permutation and XOR-based diffusion. On standard grayscale benchmarks, the cipher produces near-uniform ciphertext histograms, high entropy, low adjacent-pixel correlation, high NPCR, and lossless decryption quality. These results suggest that Petri net-driven keystreams are a viable alternative to chaos-based generators for image protection, combining the modeling strengths of Petri nets with established permutation-diffusion techniques.

Michiru Tokino

Version: v1.6.4 (June 2026) Major additions in this version: phased migration protocol with cryptographic quarantine (Section 6.4.4), sensitivity boundaries delineating the statistical decoupling threshold up to mu = 1.9% (Section 6.7), and integration of recent empirical MEV findings (Mancino & Rezzoli, 2025). Abstract Contemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem encompassing decentralization, security, scalability, and thermodynamic sustainability. Legacy Proof-of-Work networks confront diminishing security budgets due to the exhaustion of block subsidies, while Proof-of-Stake systems inherently risk oligarchic centralization. This paper establishes a Unified Monetary-Supply Framework that resolves these structural conflicts by synthesizing the deterministic Customized Halving schedule with the probabilistic regeneration logic of the Proof of Rinne (PoR). We demonstrate that by enforcing a "Thermodynamic Statute of Limitations" on dormant assets, the protocol functions as a Non-Equilibrium Thermodynamic Engine. This architecture transforms entropic asset attrition—traditionally viewed as systemic loss—into a regenerative security budget. The remainder of the abstract, covering the SDE and Fokker-Planck validation, the ZKP owner recovery model, and the resulting equilibrium, is in the manuscript. Data & Code AvailabilityThe mathematical models and high-precision stochastic simulations (e.g., Monte Carlo paths, SDE convergence, and Fokker-Planck distributions) presented in this manuscript are fully reproducible. The corresponding Python simulation suite and open-source models are made available at the author's GitHub repository (rincoin-regenerative-simulations) to ensure scientific transparency. Integrity & Provenance This document is anchored to the Bitcoin blockchain via OpenTimestamps. The proof file verification_data_v1.6.4.ots, included in the files below, covers the SHA-256 digest of Tokino_Rincoin_v1.6.4.pdf: 5269207ea7e363e8df312ed50c00afc119b43e6fa5d3c717e6a7d8fc9863147b The archived proof is in its as-submitted form: it commits the digest to the public OpenTimestamps calendars and does not itself embed the Bitcoin attestations. Completing it against those calendars — which both verification paths below do automatically — yields three Bitcoin attestations, the earliest in block 952366. An OpenTimestamps proof carries no wall-clock time of its own — any date reported for it is read from a Bitcoin block header. To verify, upload the PDF and the .ots file to opentimestamps.org, or with a Bitcoin node: ots verify -f Tokino_Rincoin_v1.6.4.pdf verification_data_v1.6.4.ots — the -f flag is required because the proof's filename differs from the document's. The provenance of this document is recorded in a separate signed artifact, the Rincoin Provenance Certificate (10.5281/zenodo.21415730), which binds this whitepaper to the digest above and is the reference for the full anchoring detail. That certificate carries its own OpenPGP signature, Bitcoin anchor, and PAdES signature; this whitepaper itself carries the OpenTimestamps proof only. Zenodo archival gives this record a persistent identifier and an independent retrieval path; it is not itself a cryptographic control. Validation_Scientific_Provenance_v1.6.4.pdf in the files below is an earlier certificate edition, retained as evidence. It is superseded by the record cited above. Correspondence & AffiliationPrimary Author: Tokino, Michiru (時乃 満)Affiliation: Rincoin Core Research Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram) Keywords: Rincoin, Proof of Rinne (PoR), regenerative crypto-economics, non-equilibrium thermodynamics, non-equilibrium steady state (NESS), stochastic differential equations (SDE), Fokker-Planck equation, recirculation incentive mechanism, macroeconomic homeostasis, Nash equilibrium, cryptographic vault, zero-knowledge proofs (ZKP), modular blockchain architecture, account abstraction, blockchain tetra-lemma, MEV mitigation, sandwich attack resistance, sensitivity analysis, statistical decoupling threshold, phased migration protocol

Renangi Sandeep, Thupakula Leena Sri, K Ananda Rutvik Reddy, Nimmakayala Kethana · 6 authors

This growth of digital learning platforms has presented a twin need: to deliver a learner a highly personalized educational journey and to deliver academic credentials that are verifiable, safe, and unchangeable. The current systems tend to address these goals separately and as a result, there are disjointed ecosystems with complex recommendation engines without trusted credentialing systems and sound certification systems that do not provide any course selection guidance. To fill this gap, this paper presents the Integrated Adaptive Learning and Certification Framework (IALCF), a new architecture that integrates into a LightGBM-based recommendation system a blockchain-based digital certification protocol in a synergistic manner. The recommendation module is an active learner profile analyzer that uses past performance, real-time interaction metrics and dynamically recommenders, predicting course selection with an accuracy of 98.7 and mean absolute error (MAE) of 1.18. The certification module is based on a more advanced X.509 standard with a delegated Proof-of-Stake (dPoS) blockchain, which forms a tamper-evident credential storage and an efficient verification algorithm, which has a verification success rate of over 95 percent in high-load conditions. The experimental findings reveal that the IALCF is a scalable, efficient and safe end-to-end solution to contemporary e-learning settings and is effective in integrating personalized learning with credible management of credentials.

Vasanthan Athiththan, Pavithira Sivasothy

Blockchain technology has emerged as a secure and decentralized solution for data management across various domains. However, existing consensus mechanisms face challenges related to security, scalability, and energy efficiency, while blockchains remain vulnerable to sophisticated attacks such as double spending, selfish mining, and Sybil attacks. This paper proposes a novel hybrid blockchain security framework that integrates a Hybrid Consensus Algorithm (HCA) combining Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) with Machine Learning based attack detection. The hybrid consensus improves transaction finality and reduces energy consumption, while the ML module detects anomalous behaviors in real time. Experimental evaluation using a private Ethereum based blockchain demonstrates that the proposed approach improves attack detection accuracy up to 96.8 %, reduces consensus latency by 34 %, and enhances throughput by 27 % compared to traditional PoW based systems. The results confirm that integrating hybrid consensus with intelligent security mechanisms significantly strengthens blockchain resilience.