TetraKlein is a unified computational, cryptographic, and extended-reality (XR) architecture developed by Baramay Station Research Inc., a Canadian non-profit research organization.This repository publishes the mathematical framework, AIR constraint system, XR physics formulations, digital-twin convergence rules, and verification pipeline defining the TetraKlein system. TetraKlein integrates: Post-quantum cryptography (Kyber, Dilithium, Module-LWE/SIS) Zero-knowledge proof systems (AIR, STARKs, IVC, folding, FRI) Verifiable compute engines (SP1, RISC Zero, Brevis, zkSync-derived provers) XR physics and rendering pipelines (TK-U, XR-TSU kernels, foveation models) Digital-Twin Convergence (DTC lineage, projection operators) Hypercube ledger topology (HBB, Recursive Tesseract Hashing) IPv6-native mesh identity and routing (Yggdrasil, PQC-authenticated overlays) Cross-layer AIR constraints enabling recursive multi-domain verification This repository is intended for researchers, engineers, cryptographers, XR developers, and academic institutions looking to analyze, extend, or experimentally validate a unified verifiable-compute architecture. Research-Stage Disclaimer The current version of the TetraKlein architecture is an early-stage, research-oriented framework. It is not a production system and makes no claims of operational readiness, security guarantees, clinical or industrial safety, or real-world deployment feasibility. All mathematical models, AIR constraints, XR physics bindings, digital-twin operators, and ledger constructs are subject to heavy scrutiny, independent validation, and long-term peer review. The material in this repository should be treated strictly as a research roadmap—a foundation for future work that will require extensive testing, reproducibility studies, formal verification, adversarial analysis, and multi-year refinement by the broader scientific and engineering community before any practical use is considered. Key Capabilities 1. Deterministic TK-VM Execution Layer The TetraKlein Virtual Machine provides a deterministic, low-degree constrained execution environment: XR frame physics evolution pose + camera kinematics TSU-compatible energy constraints DTC projection hypercube-ledger synchronization ZK-friendly opcode semantics verifiable state transitions All TK-VM semantics map directly into algebraic AIR constraints. 2. End-to-End Zero-Knowledge Verification TetraKlein supports: AIR-constrained STARK proving recursive IVC frame folding multi-epoch ledger commitment verifiable rendering pipeline deterministic cross-domain proofs (XR → DTC → Ledger) Every XR frame, physics update, and identity transition is provable. 3. Post-Quantum Identity & Routing Identity and routing combine: Kyber-1024 key-encapsulation Dilithium-V signatures MLWE/SIS identity kernels Yggdrasil IPv6 self-authenticating mesh PQC-bound routing and handshake protocol hypercube-coordinate ledger addressing 4. Hypercube Blockchain Base (HBB) The ledger uses a hypercube topology: adjacency enforced by AIR constraints spectral operators (E1–E4) Recursive Tesseract Hashing multi-epoch finality and spectral stability provable routing correctness deterministic fragment propagation 5. Digital Twin Convergence (DTC) The digital-twin framework provides: XR → DTC projection operator inverse-projection (Ledger → DTC → XR) multi-agent DTC coupling Lyapunov-style stability envelopes convergence-time bounding real-world sensor model coupling (non-invasive) Licensing TetraKlein adopts a dual-license structure: Scientific Content Creative Commons Attribution 4.0 (CC-BY-4.0)All mathematical material, papers, equations, AIR tables, and technical documentation. Software MIT License (simple, permissive)Apache License 2.0 (patent-safe, industry standard) This ensures maximum compatibility with: Ethereum / zkSync RISC Zero / SP1 StarkWare-style STARK ecosystems academic reproduction open-source research About the Original 2025 Manuscript This repository includes the full, unmodified original paper: “TetraKlein: A Post-Quantum, Zero-Knowledge, Multidimensional Cryptographic Network for Mid–21st Century Civilization Infrastructure”Michael Tass MacDonald — November 22, 2025 This archival version is included as-is for historical reference.It may contain: speculative or unverified early-stage ideas preliminary constructions non-peer-reviewed material conceptual frameworks later replaced or refined The unified monograph supersedes this original document. Roadmap Phase 1 — Public Monograph Release (Completed) Unified 1,600+ page architecture TK-VM execution model AIR tables (TK-U … TK-Z) PQC identity system XR physics + DTC Hypercube ledger + RTH Recursive folding pipeline (TK-Y / TK-Z) Phase 2 — Reference Implementation (In Progress) minimal TK-VM interpreter TK-W ledger sponge (Poseidon-style) hypercube router (TK-V) proving-fragment diffusion (TK-X) SP1 / RISC-Zero test harness Phase 3 — XR/DTC Prototype (2026) OpenXR prototyping XR-physics frame pipeline real-time DTC–ledger synchronization Phase 4 — Formal Verification (2026–2027) Coq/Isabelle/HOL formalization independent reproducibility testing security analysis academic peer review and conference submissions Citing This Work A full permanent Zenodo DOI will be provided Citation MacDonald, M. T. (2025). TetraKlein: A Unified Architecture. Baramay Station Research Inc. Public Edition. CC-BY-4.0 / MIT / Apache-2.0. Contact Baramay Station Research Inc.Canadian Non-Profit R&D (Saskatchewan)Director & Principal Investigator: Michael Tass MacDonald Contact michael@baramaystationresearchinc.ca Mission TetraKlein aims to advance: open, verifiable computation transparent scientific methodology reproducible XR and digital-twin research post-quantum cryptography zero-knowledge trust frameworks decentralized, identity-bound networks This project is released to help researchers, developers, and institutions build provable, reliable, and safe computational systems for the coming decades.
ABSTRACT The proliferation of phishing scam tokens on the Ethereum blockchain, including honeypot, rug pull, and impersonation schemes, poses a grave threat to financial security. Although earlier studies have documented detection accuracies that exceed 95%, they frequently depend on random train‐test partitions. These partitions frequently overestimate real‐world performance by disregarding the temporal progression of phishing behaviors. This study addresses the methodological gap by employing a temporally validated evaluation. A labeled dataset comprising 5408 Ethereum token contracts was constructed. This dataset was verified through a two‐stage process that integrated cyber threat intelligence and on‐chain evidence. A total of 16 discriminative features were extracted, reflecting transaction volume, network structure, and temporal behavior. In lieu of employing random partitioning, temporal validation (70% training, 15% validation, and 15% testing) was adopted to assess generalizability to emerging threats. Six machine learning models (LightGBM, XGBoost, Random Forest, Gradient Boosting, Decision Tree, and MLP) were tuned via GridSearchCV. LightGBM demonstrated optimal performance, attaining 85.59% accuracy, 81.63% F1‐score, and 92.02% AUC on temporally held‐out data. The feature ablation process yielded the identification of transaction volume as the most discriminative factor, with a corresponding increase in performance of 13.09 points on the performance scale. Conversely, temporal features exhibited a marginal decline in performance, with a decrease of 0.87 points. Temporal validation resulted in a 3.95‐point‐percentage decrease compared to random splitting, thereby exposing the optimistic bias present in prior studies. Despite the fact that the resulting F1‐score of 81.63% falls short of the 85% threshold stipulated in the literature, it is indicative of a realistic deployment expectation. This work underscores the importance of temporal validation for reliable fraud detection research.
Los NFTs (Non Fungible Tokens) son activos digitales que nacieron alrededor del año 2014, gracias a la tecnología de blockchain, y se popularizaron con la plataforma de código abierto Ethereum. Unos años más tarde, con la pandemia del año 2020, encontraron su pico más alto de ventas. En este artículo indagaremos sobre el contrato de lectura de un sitio web de arte digital exclusivo llamado SuperRare, nacido en el año 2018. Haremos un breve recorrido sobrelas regularidades y diferencias en la construcción del enunciador y del enunciatario en el período que abarca del año 2021 al 2024 inclusive. ¿Cuál es su enunciatario? ¿Quiénes pueden consumir arte digital, coleccionarlo o venderlo? ¿Hay, como plantean en los textos de presentación, una descentralización real de la gobernanza en esta “comunidad digital”? El análisis detallado de sus secciones y mutaciones nos permitió problematizar la idea de mercado colaborativo, identificando asimetrías no sólo en la forma de participación, sino también en una jerarquización paulatina del enunciador. Es decir, un sitio que se presenta como comunitario, pero que pareciera ser desmentido en su propio funcionamiento discursivo.
Hackers may create malicious solidity programs and deploy it in the Ethereum block chain. These malicious smart contracts try to attack legitimate programs by exploiting its vulnerabilities such as reentrancy, tx.origin attack, bad randomness, deligatecall and so on. This may lead to drain of the funds, denial of service and so on . Hence, it is necessary to identify and prevent the malicious smart contract before deploying it into the blockchain. In this paper, we propose an ML based malicious smart contract detection mechanism by analyzing the EVM opcodes. After balancing the opcode frequency dataset with SMOTE algorithm, we transformed opcode frequencies to the binary values (0,1) using an entropy based supervised binning method. Then, an explainable AI model is trained with the proposed binary opcode based features. From the implementations, we found that the proposed mechanism can detect 99% of malicious smart contracts with a false positive rate of only 0.01. Finally, we incorporated LIME algorithm in our classifier to justify its predictions. We found that, LIME algorithm can explain why a particular smart contract app is declared as malicious by our ML classifier based on the binary value of EVM opcodes.
Do Ethereum's Layer-2 (L2) rollups actually decongest the Layer-1 (L1) mainnet once protocol upgrades and demand are held constant? Using a 1245-day daily panel from August 5, 2021 to December 31, 2024 that spans the London, Merge, and Dencun upgrades, we link Ethereum fee and congestion metrics to L2 user activity, macro-demand proxies, and targeted event indicators. We estimate a regime-aware error-correction model that treats posting-clean L2 user share as a continuous treatment. Over the pre-Dencun (London+Merge) window, a 10 percentage point increase in L2 adoption lowers median base fees by about 13% -- roughly 5 Gwei at pre-Dencun levels -- and deviations from the long-run relation decay with an 11-day half-life. Block utilization and a scarcity index show similar congestion relief. After Dencun, L2 adoption is already high and treatment support narrows, so blob-era estimates are statistically imprecise and we treat them as exploratory. The pre-Dencun window therefore delivers the first cross-regime causal estimate of how aggregate L2 adoption decongests Ethereum, together with a reusable template for monitoring rollup-centric scaling strategies.
Maternal mortality in Sub-Saharan Africa remains critically high, accounting for 70% of global deaths despite representing only 17% of the world population. Current digital health interventions typically deploy artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies in isolation, missing synergistic opportunities for transformative healthcare delivery. This paper presents IyaCare, a proof-of-concept integrated platform that combines predictive risk assessment, continuous vital sign monitoring, and secure health records management specifically designed for resource-constrained settings. We developed a web-based system with Next.js frontend, Firebase backend, Ethereum blockchain architecture, and XGBoost AI models trained on maternal health datasets. Our feasibility study demonstrates 85.2% accuracy in high-risk pregnancy prediction and validates blockchain data integrity, with key innovations including offline-first functionality and SMS-based communication for community health workers. While limitations include reliance on synthetic validation data and simulated healthcare environments, results confirm the technical feasibility and potential impact of converged digital health solutions. This work contributes a replicable architectural model for integrated maternal health platforms in low-resource settings, advancing progress toward SDG 3.1 targets.
Blockchain systems, such as Bitcoin and Ethereum 2.0, face vulnerabilities under bandwidth-constrained partitions, where throughput collapses and latency increases. In addition, adversaries can exploit inconsistencies to launch double-spending attacks. This study presents a lightweight dual-layer countermeasure that integrates a robust freezing threshold ( ) with multi-signal disconnection proofs to enhance performance and security without altering consensus rules. Controlled simulation experiments on Bitcoin (PoW) and Ethereum 2.0 (PoS) show throughput gains exceeding 1000% in Ethereum and over 100% in Bitcoin, with inconsistency reduced by up to 64% and latency bounded within 5-6 blocks/s. These results confirm that attacker-aware thresholds and multi-signal validation substantially improve blockchain resilience under partitioned network conditions.
Romulo de Moraes, Arthur G. Bubolz, Denner Ayres, Vinícius Teixeira Pinto · 5 authors
Este artigo apresenta uma arquitetura para loterias descentralizadas na rede Ethereum, baseada em contratos inteligentes, aleatoriedade verificável e otimização de armazenamento por meio de Merkle Tree. A proposta visa reduzir o custo médio das transações (gas fees) e aprimorar a escalabilidade onchain, comparando três abordagens distintas de armazenamento: array, mapping e Merkle Tree. Os resultados mostram que o consumo de gas evidencia uma vantagem expressiva da Merkle Tree, reduzindo em até três ordens de magnitude o custo total, o que confirma sua eficiência e potencial para aplicações descentralizadas de alta demanda.
André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors
Cross-chain bridges are a blockchain interoperability middleware that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose XChainWatcher (Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using XChainWatcher, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. XChainWatcher successfully identified the transactions that led to losses of $611M and $190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (cctx) that should not have accepted, failed attempts to exploit Nomad, over $7.8M worth of tokens locked on one chain but never released on Ethereum, and $200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 cctxs across three blockchains, capturing more than $4.2B in token transfers.
The digital transformation of education necessitates secure, private, and learner-centric methods for verifying academic credentials. Conventional verification processes expose sensitive personally identifiable information, creating privacy risks that conflict with data protection regulations like GDPR. Existing blockchain solutions for educational credential verification face persistent challenges including prohibitive transaction costs, privacy vulnerabilities, and inflexible verification models. This paper presents VeriZKP, a proof-of-concept architecture demonstrating gas-free credential verification on Ethereum using zero-knowledge proofs. The core innovation lies in separating on-chain trust anchoring from off-chain cryptographic computation, enabling a novel cost-elimination mechanism. The system leverages Ethereum’s view functions through pre-compiled verifier contracts to achieve zero gas consumption for verification operations while preserving privacy through selective disclosure mechanisms. Our prototype, evaluated on Ethereum Sepolia testnet, validates the fundamental feasibility of this approach. Results demonstrate complete elimination of verification costs, practical client-side proof generation times of 1.02-1.63 seconds on standard hardware, and support for multi-attribute credential verification. The architecture proves both economically viable and performant for blockchain-based identity systems.
Reaching consensus in Proof-of-Stake (PoS) based consensus protocols, requires supermajority agreement among participating validator nodes. Such protocols need significant network resources due to the concurrent voting of a large number of consensus nodes. As a solution, these nodes are divided into committees, with each committee voting individually at a dedicated time slot. In this paper, we introduce CliqueSensus, a protocol that, given a distribution of consensus nodes into committees, lets them self-organize into small, ephemeral clusters structured in clique topologies, to accelerate the voting process, while using only a small fraction of the network resources required by conventional message dissemination methods. Our evaluation demonstrates that our protocol exhibits rapid convergence and operates with minimal network overhead. We focus on the PoS consensus algorithm adopted by Ethereum 2.0. In addition to our protocol, we also analyze and simulate the clustering approach that Ethereum has adopted, showcasing that our protocol can reduce validation message dissemination time by 23% to 70%, while requiring about 190 times fewer validation message forwards.
Matthieu Pigaglio, Onur Ascigil, Michał Król, Felix Lange · 9 authors
Layer-2 protocols such as rollups can help address Ethereum's throughput limits. An efficient data availability layer is key for layer-2 support in Ethereum, but broadcast methods do not scale. A promising approach is the selective distribution of layer-2 data and its verification by data availability sampling (DAS). Integrating DAS with Ethereum consensus is, however, a challenge, as data must be shared and sampled within 4 seconds of each consensus slot.
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Based on interviews, we propose TxSum, a new task and dataset of 100 complex Ethereum transactions annotated with natural-language summaries and step-wise semantic labels (intent, mechanism, etc.). We then introduce MATEX, a multi-agent system that emulates human experts' dual-process reasoning. MATEX achieves the highest faithfulness and intent clarity among strong baselines. It boosts user comprehension by 23.6% on complex transactions and doubles users' ability to find real attacks, significantly reducing blind signing.
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new user-centered NLP task for Ethereum transaction understanding, and construct a dataset of 187 complex Ethereum transactions annotated with transaction-level summaries and token flow-level semantic labels. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.
Rollups are central to blockchain scalability, but their governance is still evolving. Existing voting models risk capture by large stakeholders or inactive delegates. This paper introduces RepRoll, a governance model that uses delegated reputation scores backed by verifiable activity logs. Reputation grows through provable contributions: fraud-proof submissions, code audits, uptime guarantees, and community moderation. These contributions are recorded through a decentralized attestation layer similar to optimistic verification. Votes in protocol upgrades weigh both token stake and reputation, reducing plutocratic influence. A simulation of 10,000 participants demonstrates that RepRoll improves proposal quality and reduces governance attacks. We deploy a prototype on an Ethereum Layer-2 testnet, showing low on-chain overhead. The paper discusses vulnerabilities such as collusion, reputation laundering, and sybil amplification, and proposes cryptographic mitigations.
With the rapid evolution of the Decentralized Web (DWeb), decentralized technologies have paved new avenues for Web3 applications and the authentication of digital assets. Among them, Non-Fungible Tokens (NFTs) have gained significant popularity due to their immutability and uniqueness, reshaping the landscape of artistic creation, marketing, and intellectual property protection. However, current blockchain-based NFT implementations still face core challenges within decentralized architecture: how to maintain decentralization while ensuring the visual uniqueness of digital assets and reducing storage costs. The rampant issue of duplication undermines the scarcity of digital art and erodes market confidence in copyright authenticity. Moreover, high gas fees and energy consumption further hinder the widespread adoption of NFTs, while reliance on external storage solutions like InterPlanetary File System (IPFS) introduces risks of data instability and loss. To address these challenges, this article presents the UniqueNFT framework, a novel architecture that deeply integrates blockchain oracles with decentralized storage verification mechanisms. The framework achieves three key technological breakthroughs: Using image inversion and generation techniques based on Encoder for Editing (E4E) and StyleGAN3, it extracts compact and expressive semantic features from NFT images, enabling efficient data compression and significantly reducing on-chain storage volume; The Crypto-Mask algorithm, by utilizing the hash value of blockchain user information (user-controlled SHA-256 digest of Ethereum address, user nickname, and registration time), ensures the visual uniqueness of NFTs; A smart contract extension compatible with the ERC721 standard, demonstrating UniqueNFT’s seamless integration within the blockchain ecosystem. By leveraging the technologies of the Decentralized Web, our framework represents an important step forward in enhancing the security and uniqueness of digital assets. It not only innovatively resolves the issues of NFT duplication and homogenization but also injects new vitality and long-term momentum into the creation of a trusted, sustainable blockchain-based digital asset ecosystem.
The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (≈$85 at 20 gwei), document submission around 85 thousand gas (≈$6), and revocation about 50 thousand gas (≈$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10–30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Efficient energy sharing among solar-based microgrids was crucial for enhancing grid reliability, scalability, and sustainability in modern energy systems. This research presents a novel blockchain-powered decentralized energy trading framework that integrates Raspberry Pi 4, IoT-driven real-time monitoring, and Ethereum-based smart contracts to facilitate seamless and secure peer-to-peer (P2P) energy exchange. The proposed system enables real-time data acquisition and transmission of critical energy parameters, including current, voltage, and power generation, from five interconnected solar microgrids. Raspberry Pi 4 serves as the centralized edge computing node, aggregating and transmitting real-time energy data to the ThingSpeak IoT platform, where advanced AI-driven analytics optimize grid efficiency. Blockchain technology, specifically Ethereum with Ganache, was employed to create a tamper-proof, transparent, and trustless energy marketplace, eliminating reliance on centralized energy intermediaries. The incorporation of Solidity-based smart contracts automates transactions, ensuring secure, immutable, and fair energy trading while enabling dynamic pricing models based on real-time demand-supply conditions. Python, integrated with Web3.py, facilitates seamless interaction between Raspberry Pi 4 and the blockchain network, ensuring low-latency transaction execution and verifiable trade settlements. Through the integration of IoT-enabled smart grids, blockchain-based energy transactions, and AI-driven predictive analytics, the proposed system offers a scalable, autonomous, and energy-efficient solution for decentralized energy management. Experimental validation confirms the system's effectiveness, demonstrating its ability to achieve real-time energy balancing, seamless P2P trading, and enhanced security through blockchain immutability. This cutting-edge approach significantly advances the adoption of renewable energy sources, optimizes microgrid autonomy, and reinforces the resilience of next-generation smart power networks, paving the way for a sustainable and decentralized energy economy.
Jordan, Herbert, Jezek, Kamil, Subotic, Pavle, Scholz, Bernhard
The State Database of a blockchain stores account data and enables authentication. Modern blockchains use fast consensus protocols to avoid forking, improving throughput and finality. However, Ethereum's StateDB was designed for a forking chain that maintains multiple state versions. While newer blockchains adopt Ethereum's standard for DApp compatibility, they do not require multiple state versions, making legacy Ethereum databases inefficient for fast, non-forking blockchains. Moreover, existing StateDB implementations have been built on key-value stores (e.g., LevelDB), which make them less efficient. This paper introduces a novel state database that is a native database implementation and maintains Ethereum compatibility while being specialized for non-forking blockchains. Our database delivers ten times speedups and 99% space reductions for validators, and a threefold decrease in storage requirements for archive nodes.
Abdul Razaque, Saule Amanzholova, Galimkair Mutanov, Olga Ussatova · 8 authors
This article focuses on developing an anti-corruption system for certifying students’ academic achievements in Kazakhstani higher education institutions by utilizing blockchain and artificial intelligence AI technologies. We specifically propose the Academic Integrity Verification System (AIVS), a revolutionary system that combines blockchain’s tamper-proof storage with AI’s anomaly detection capabilities. The system reduces major risks in traditional academic record management while ensuring transparency, precision, and proactive fraud detection. The simulation was conducted at the International Information Technology University (IITU) using Ethereum-based blockchain and AI models. In simulated testnet experiments, AIVS achieved an 85% reduction in verification time compared to traditional processes and delivered a 95% overall model accuracy in record validation. These results demonstrate the potential of blockchain and AI integration for improving efficiency and integrity in academic verification workflows. These findings demonstrate that our proposed AIVS enhances academic transparency, reduces corruption, and provides a scalable framework for secure academic record management. The proposed strategy marks a significant step forward in the governance of digital education in Kazakhstan and abroad.
Stablecoin -Stable-Caucasus (STCAUX)Technical Whitepaper v1.0 Multi-Asset Commodity-Backed Stablecoin for the South Caucasus Region1. Executive Summary StableCaucasus is a multi-component stable digital asset based on the economic foundation of the South Caucasus and pegged to: ● regional currencies (AZN, GEL, AMD) oriented toward USD exchange rates; ● commodity indices: gold, agriculture, metals mining, oil and gas; ● the region’s energy and resource potential; ● Logistics ● uranium as a strategic component and future driver of global energy The goal of StableCaucasus is to create a regional, resilient digital asset that reflects the real economic potential of the Caucasus and provides users with stability, liquidity, and protection against currency volatility. 2. Mission & VisionMissionTo create the world’s first multi-commodity stable asset representing the South Caucasus as a center of: ● agricultural production, ● energy, ● gold mining, ● Logistics and strategic natural resources Vision To position StableCaucasus as: ● a settlement unit for cross-border trade between the Caucasus, GCC, EU, and Central Asia; ● a protective asset for investors; ● a digital instrument for governments, banks, logistics operators, and exporters. 3. Value Proposition StableCaucasus provides: ✔ Stability — the multi-index model reduces dependence on any single asset. ✔ Trust -each part of the formula is based on public financial and macroeconomic data. ✔ Regional relevance- partially reflects the real economy of the South Caucasus. ✔ Collateralization - the asset is backed by auditable and transparent baskets. ✔ Web3 infrastructure - BNB Chain and Ethereum compatible.
This research paper presents the design, architecture, and evaluation of a Hybrid Blockchain Database System, a decentralized–distributed model that integrates blockchain’s immutability with the high-performance data processing capabilities of traditional database systems. The proposed hybrid architecture addresses key limitations of fully on-chain storage—such as scalability constraints, high transaction costs, and limited query efficiency—by combining on-chain verification layers with off-chain distributed storage engines. The system introduces a multi-layered architectural model consisting of: (1) a blockchain consensus and validation layer for ensuring trust and tamper-resistant auditability; (2) a distributed database layer for high-speed read/write operations; (3) an interoperability layer that synchronizes state between on-chain and off-chain data; and (4) an IPFS/Filecoin-based decentralized storage framework for storing large datasets and metadata. This design enables secure state anchoring, cryptographic data integrity, and scalable data indexing across heterogeneous storage environments. A detailed performance evaluation is conducted using a combination of Ethereum-compatible smart contracts, IPFS storage, and distributed NoSQL/SQL databases. Key metrics such as transaction throughput, read/write latency, storage overhead, and consistency verification time are analyzed under varying workloads. Experimental results demonstrate that the hybrid system improves data scalability by 70–85%, reduces storage costs by over 90%, and maintains strong auditability through periodic blockchain state commitments. Security analysis is performed using static and dynamic testing tools to identify common vulnerabilities such as broken access control, inconsistent state updates, or missing verification boundaries, ensuring robustness of the hybrid synchronization logic. Additionally, the paper discusses design trade-offs involving decentralization vs. performance, on-chain transparency vs. gas efficiency, and consistency vs. latency across distributed components. This work provides a comprehensive blueprint for the development of real-world hybrid blockchain database systems suitable for supply chain management, enterprise data systems, government registries, financial applications, and Web3 infrastructure. The results highlight how hybrid architectures can bridge the gap between decentralized trust and scalable data management in next-generation digital ecosystems.
The purpose of this study is to address the persistent security and privacy challenges in cloud-based Electronic Health Record (EHR) sharing by proposing a blockchain-enabled architecture that integrates decentralized storage and smart contracts. Traditional mobile cloud solutions improve data accessibility but rely on centralized control, making them vulnerable to unauthorized access, single points of failure, and limited patient transparency. To overcome these limitations, this research designs a user-centric access control framework that leverages the Ethereum blockchain, smart contracts, and the InterPlanetary File System (IPFS) within a mobile cloud environment. The methodology involves developing and deploying a prototype on Amazon Web Services, supported by an Android-based mobile application that enables healthcare providers and patients to interact with the blockchain network. Experimental evaluation was conducted using wearable sensor data to test the performance, scalability, and resilience of the proposed system. The findings indicate that the framework ensures secure EHR exchange, enforces fine-grained access policies, and achieves reduced latency compared to conventional centralized approaches. Unauthorized requests were reliably detected and blocked through the smart contract mechanism, while authorized users accessed records with minimal delay. The results also confirm the lightweight overhead of the system, making it practical for mobile healthcare environments. The practical implications of this work lie in offering a tamper-resistant, transparent, and patient-centric solution for medical data sharing, thereby improving trust, reducing administrative overhead, and supporting real-time healthcare services in distributed and resource-constrained settings.
The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.