Fraudulent activities on blockchain networks threaten the integrity and reliability of decentralized finance ecosystems. Accurately identifying malicious nodes such as phishing or ransomware addresses, within large-scale blockchain transaction graphs remains a critical challenge due to their dynamic, sparse, and continuously evolving topologies. Transfer learning offers a powerful paradigm for fraud detection because many fraudulent schemes, including ransomware and phishing, are often orchestrated by overlapping actor groups that share behavioral and structural patterns across networks. Leveraging these shared representations enables knowledge transfer from previously observed fraud types to emerging ones. However, the complex and multi-modal nature of digital financial systems introduces substantial challenges for graph-based transfer learning. Fraudulent activities are shaped by diverse modalities including graph structure, transaction sequences, temporal price dynamics, and textual metadata, while distributional shifts frequently occur across time and platforms. Existing graph transfer learning methods struggle to model such multi-modal dependencies and to align divergent feature distributions. To tackle these challenges, we develop a Multi-mOdal Enhanced Graph Transfer Learning (MOE-GTL) framework which incorporates graph, temporal, and textual modalities for fraudulent node detection. We further introduce Temporal-aware Maximum Mean Discrepancy (TMMD), a regularization mechanism that explicitly aligns multi-modal feature distributions between source and target graphs over time. Extensive experiments reveal that our MOE-GTL model notably improves the accuracy of fraudulent node classifications on Ethereum and Solana transaction graphs.
Against the complex characteristics of the Ethereum transaction network and the limitations of existing graph embedding methods based on random walks, which fail to effectively capture transaction temporal dynamics and the flow of funds, we propose a fraud detection algorithm for Ethereum, ETX2Vec (Ethereum Transactions (TX) to Vector), which improves upon transaction subgraph construction and random walk strategies. First, in terms of transaction subgraph construction, we extract the first-order predecessor and successor neighboring nodes of the target node to reconstruct the transaction subgraph, enabling the random walk to effectively capture the complete flow of funds. Second, in the design of the random walk strategy, we introduce two key improvements: (1) the next node is selected based on the non-decreasing principle of transaction timestamps, effectively capturing the temporal dynamics of transactions within the network, and (2) a biased random walk strategy is designed based on both transaction timestamps and amounts, with a parameter α introduced to control the weighting of these factors when calculating transition probabilities. Experimental results show that ETX2Vec achieves an average performance of 96.04% in downstream node classification tasks, outperforming the best model in similar studies by 3.74%, and even surpassing neural network models such as GAT and GCN. This demonstrates that ETX2Vec is more effective at understanding and processing the Ethereum transaction network, leading to the learning of high-quality node embedding vectors.
ABSTRACT This paper explores how financial innovation and environmental sustainability intersect by analyzing spillovers between FinTech, blockchain energy use, and green finance. Using a Quantile Vector Autoregression (QVAR) framework, we examine weekly data from 2018 to 2024 across 11 digital, environmental, and macro‐financial indices. Our findings reveal a striking asymmetry: FinTech and equity markets consistently act as systemic shock transmitters, especially during crises and booms, while blockchain energy consumption behaves as a passive shock absorber. Notably, Ethereum's energy profile remains sensitive to market exuberance even after its transition to proof‐of‐stake. Connectedness weakens markedly in tranquil regimes but resurges sharply at market extremes, underscoring the fragility of digital–green linkages. These results advance the literature on climate‐FinTech integration by showing how digital finance volatility propagates to sustainability assets. We call for targeted policy interventions that align blockchain development with climate goals and promote transparency and resilience in digital financial markets.
Money laundering enables malicious actors to integrate illegal profits into the legitimate economy and has long been a central concern in financial regulation. Blockchain systems introduce new channels for laundering through decentralized, pseudonymous, and cross-border asset transfers. In this context, blockchain exploiters often rely on laundering to conceal fund origins and enable cash-out.
In decentralized finance (DeFi), accidental cryptocurrency transfers to incorrect wallet addresses are a large usability and security issue, frequently causing permanent loss of funds. We present CryptoSafeSend, a smart contract-based safety protocol for transactions featuring a cryptographically secure passcode verification scheme supporting conditional transfers. This work addresses higher-level security issues by introducing a PBKDF2-based key derivation function, which generates strong encryption keys based on Firebase's Firebase Unique Identifier. Secret passcodes are encrypted in Advanced Encryption Standard operating in Galois/Counter Mode functions deployed underneath the Web Crypto API, and the initialization vector and ciphertext are Base64 encoded for off-chain reliable storage and Firebase Firestore-based messaging. The protocol prevents unauthorized exploitation by safely binding off-chain passcode transmission to a matching on-chain verification, reinforcing user trust without undermining decentralization. Also, to ensure recoverability, CryptoSafeSend integrates a 7-day on-chain escrow lock, after which unclaimed funds become permissionlessly refundable to the sender, preserving decentralization while eliminating reliance on trusted intermediaries. Testing on an Ethereum testnet confirms negligible gas overhead, immunity against double claims, and strong security guarantees, qualifying CryptoSafeSend as a valuable constituent in next-generation secure digital asset protocols.
Luigi Crisci, Lorenz Schuler, Herbert Jordan, Bernhard Scholz
The Ethereum state database uses Merkle Patricia Trie (MPT), which suffers from large witness proof sizes and high storage overhead. Verkle Tries have been proposed as a replacement, offering witness proofs below 150 bytes through vector commitments and Inner Product Argument aggregation. However, deploying a Verkle Trie in a high-throughput, short block-time blockchain such as Sonic, which produces a block every 300 milliseconds, introduces substantial engineering challenges related to storage efficiency, commitment computation costs, and the need to serve both live and historical state queries in real time. We present SonicDB S6, a production-grade Rust Verkle Trie database for the Sonic blockchain, which leverages its non-forking property to enable aggressive storage optimizations. Occupancy-aware node specializations, selected via an $\mathcal{O}(k n^2)$ dynamic program, reduce live storage by 97.8\%. Delta nodes that record only changed slots reduce archive storage by 95\%. Batched updates, multi-threaded commitment computation, and homomorphic Pedersen caching yield $3.2\times$ higher throughput than a persistent Geth Verkle baseline while sustaining production block-rate performance.
Recent advancements, specifically the 2026 whitepaper by Google Quantum AI, Stanford University, and the Ethereum Foundation (arXiv:2603.28846), have demonstrated the resource feasibility of breaking secp256k1 elliptic curve cryptography using fault-tolerant quantum computation (≤ 1200 logical qubits and ≤ 90 million Toffoli gates). While their work validates this capability via zero-knowledge STARK proofs without disclosing explicit circuits, we provide the continuous operator-theoretic framework that explains the exact physical collapse mechanism underlying their discrete resource results. By modeling cryptographic hardness as a stable, invariant computational manifold, we show that quantum vulnerability is a manifestation of a Birman-Schwinger instability. We prove that, within this model, the introduction of a transverse quantum operator (e.g., Shor's algorithm implemented via Quantum Phase Estimation) forces a resolvent singularity in the classical generator when the resource perturbation parameter crosses a critical threshold (μ_c). We establish a strict Hardness Phase Transition, demonstrating that cryptographic security is equivalent to the point 1 remaining outside the spectrum of the Birman-Schwinger kernel. Furthermore, we formalize zero-knowledge proofs (such as the Groth16-wrapped STARK artifacts published by Babbush et al.) as highly constrained Boolean projectors. We show that these proofs trigger an epistemic spectral collapse via Zeno stabilization, certifying the non-invertible regime without decohering the raw computational state into the public domain. The manuscript includes an exact analytic toy model demonstrating bound-state collapse into the continuum, explicitly mapping the destruction of exponential cryptographic isolation to a polynomial scattering state. This formalization transitions cryptographic failure from a domain of discrete computational estimates to a continuous framework of operator-theoretic necessity.
This paper analyses MicroMix, a noncustodial Ethereum mixer that unlinks deposits from withdrawals using browser-side zkSNARKs, a centralised relayer, and on-chain enforcement via Semaphore and Mixer contracts. The study formalises core acceptance conditions—value conservation, nullifier uniqueness, external‑nullifier scoping, and signal binding—and evaluates risks that persist despite sound cryptography, including timing correlation in small anonymity sets, Sybil pool distortion, single‑relayer censorship, ETH payout liveness under gas‑stipend limits, ERC‑20 heterogeneity, circuit–verifier input/order mismatches, and cross‑chain replay. The work proposes concrete mitigations: randomised scheduling and probabilistic batching, multi‑denomination support, decentralised relayer participation with user-paid fallbacks, guarded call patterns with reentrancy protection, SafeERC20 enforcement and token whitelisting, strict public‑input ordering and signal‑to‑field mapping, a fixed mixer-scoped external nullifier, and chain-bound proofs. With these measures, MicroMix can preserve unlinkability while improving liveness and correctness in adversarial environments, advancing practical, privacy‑preserving withdrawals on Ethereum.
Este artigo discute como padrões ERC e mecanismos de comunicação cross‑contract sustentam a interoperabilidade entre contratos inteligentes em Ethereum e EVM‑chains. Padrões de tokens como ERC‑20, ERC‑721, ERC‑777 e ERC‑1155 definem interfaces mínimas para transferência, consulta de saldo e eventos, permitindo que carteiras, DEXs, marketplaces e outras aplicações interajam de forma uniforme com ativos fungíveis e não fungíveis, enquanto extensões multi‑token como o ERC‑1155 combinam características de ERC‑20 e ERC‑721 e suportam transferências em lote. A interoperabilidade é reforçada pelo padrão ERC‑165, que introduz um mecanismo padronizado de detecção de interfaces via supportsInterface(bytes4), permitindo que contratos verifiquem, on‑chain, se outros contratos implementam interfaces específicas antes de interagir com eles. Na prática, a comunicação cross‑contract em Ethereum é implementada via opcodes de chamada (CALL, DELEGATECALL, STATICCALL), que permitem compor funcionalidades entre contratos, mas introduzem riscos de segurança como reentrância cross‑contract, em que contratos mal projetados são reentrados por outros antes de atualizar seu estado, abrindo espaço para exploração. Estudos e guias de segurança catalogam padrões de ataques de reentrância, incluindo reentrância entre funções e entre contratos, e recomendam padrões como checks‑effects‑interactions, uso de mutexes e desenho criterioso de callbacks em tokens com hooks (como ERC‑777) para mitigar esses riscos. Conclui‑se que a interoperabilidade de contratos na Web3 depende tanto de padrões de interface bem definidos (ERCs, ERC‑165) quanto de práticas seguras de comunicação cross‑contract, com impacto direto na liquidez, composabilidade DeFi e governança de protocolos.<br>
Este artigo apresenta um esboço estruturado sobre “Análise de Custo de Deploy em Diferentes EVMs.”. O objetivo é analisar os fundamentos técnicos e econômicos do custo de implantação de contratos inteligentes em Ethereum L1, diversas Layer‑2 (rollups) e outras chains EVM‑compatíveis, discutindo implicações para o ecossistema Web3 e tendências de mercado. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na decomposição do custo de deploy em componentes de gas (execução, armazenamento de código, dados de transação) e em como upgrades recentes – como Cancun/Deneb e a introdução de blobs de dados – alteram a estrutura de custos, especialmente para rollups que publicam dados em L1. Estudos mostram que, enquanto o gas é uma unidade abstrata consistente, o custo econômico por byte de código e por transação varia significativamente entre L1 (onde picos históricos chegaram a dezenas de dólares por transação) e L2s, onde taxas médias frequentemente ficam abaixo de centavos, especialmente após a redução em até 94% do custo por byte de dados com blobs. Ao mesmo tempo, análises de mercado indicam que L2 fees são estruturalmente compostas por uma parcela L1 (custo de dados e liquidação) mais uma parcela L2 (execução local), de modo que mudanças na economia de gas da L1 impactam indiretamente o custo de deploy e operação nas L2s. Conclui‑se que decisões de arquitetura e de escolha de EVM para deploy devem considerar não apenas o custo imediato de gas, mas também a herança de segurança, a volatilidade das taxas e a dependência em upgrades de protocolo que alteram a economia de dados e execução.<br>
Este artigo analisa o design de contratos inteligentes orientados a eventos como estratégia para maximizar a eficiência de indexação off‑chain em protocolos Web3. Em plataformas como Ethereum, eventos são registrados como logs com topics indexados, permitindo que indexadores filtrem rapidamente transações relevantes sem precisar reexecutar o estado completo do contrato, o que torna a indexação por eventos uma abordagem muito mais eficiente do que consultas frequentes ao estado on‑chain via eth_call. Frameworks de indexação como The Graph organizam esses eventos em subgraphs, definidos por manifests que especificam quais contratos e eventos acompanhar, e por esquemas que mapeiam logs para entidades armazenadas em bancos de dados otimizados para consulta via GraphQL. Boas práticas de desenvolvimento orientado a eventos recomendam projetar contratos de modo que toda transição de estado relevante para a aplicação gere um evento completo (com dados suficientes para reconstruir o contexto off‑chain), reduzindo a necessidade de chamadas adicionais à cadeia, o que melhora desempenho de indexing e diminui custos de infraestrutura. Técnicas como uso de entidades imutáveis em subgraphs, cache inteligente de eth_call e cálculo de derivadas off‑chain a partir de eventos (em vez de leitura repetida de storage) podem acelerar indexação e consultas em ordens de grandeza, especialmente em cenários de histórico extenso e baixa densidade de eventos relevantes. Conclui‑se que contratos orientados a eventos, aliados a arquiteturas de indexação especializadas, são fundamentais para tornar dados on‑chain consultáveis em escala, suportando experiências de usuário ricas e composabilidade DeFi com menor custo operacional.<br>
Bobba Pavan Santosh, Bommeneni Pavan Madhav, Dr. J. R. Jayavelu, Dr. P. Dhivya
Cryptocurrencies have found their way into contemporary financial systems as a significant component of modern-day financial systems because of their decentralized nature, their ease of adoption and uptake. Ether is considered to be one of the most actively traded currencies and its value tends to be highly volatile. It is not easy to forecast the market price trend of Ethereum due to the influence that technical trends, investor behavior, and external factors have over the market. In this project, the researcher will use machine learning to assess the future price direction of Ethereum the following day through the use of Python. The past trends of prices are analyzed and augmented with various technical indicators in order to reflect the market trends and momentum. The best potential machine learning model was selected after training and evaluating many models using Logistic Regression. The results demonstrate that machine learning may be used to provide rational insights into the price movement of Ethereum and to aid in decision-making using these insights.
The contemporary education landscape is often marred by escalating costs and centralized pedagogical structures, which collectively create significant barriers to entry for millions of potential learners worldwide. This paper presents \textbf{Skill Link}, a sophisticated decentralized platform designed to democratize skill acquisition through a specialized credit-based barter system. Unlike conventional e-learning platforms that rely on traditional currency transactions, Skill Link enables a frictionless exchange of knowledge by utilizing a virtual credit economy where participants earn and spend "learning credits." To address the critical issue of credential fraud in decentralized environments, the platform integrates Ethereum-based blockchain technology to ensure the absolute immutability and verifiable authenticity of all earned certificates. Key innovations include a multi-tiered course classification system, an automated mock assessment framework with negative marking capabilities, an intelligent context-aware AI assistant powered by advanced language models, and a rigorous verification mechanism for professional social links (LinkedIn, GitHub, Indeed). Developed using the robust Django framework, Python-based Web3 utilities, and a secure PostgreSQL/SQLite back-end, Skill Link provides a highly secure, transparent, and scalable ecosystem for peer-to-peer knowledge sharing, ultimately fostering a global community of experts and lifelong learners. The system's architecture emphasizes data integrity through atomic transactions and cryptographic verification, ensuring a trustless environment for global skill exchange.
Automated matching engines execute millions of orders per session, yet systematic asymmetries in latency, order size, and market access compound into persistent execution disparities that erode participant trust. We formulate provably fair order matching as a Constrained Markov Decision Process and propose CPO-FOAM (Constrained Policy Optimization with Feedback-Optimized Adaptive Margins). An inner loop computes an analytic trust-region step on the Fisher information manifold; a PID-controlled outer loop dynamically tightens safety margins, suppressing the sawtooth oscillations endemic to Lagrangian methods under non-stationary dynamics. Group fairness (demographic parity, equalized odds) enters the CMDP cost vector while individual Lipschitz fairness is enforced deterministically via spectral normalization. We prove BIBO stability and that the integral term drives steady-state violations to zero. On LOBSTER NASDAQ data across six market regimes, CPO-FOAM recovers 95.9% of unconstrained throughput at 2.5% constraint violation frequency; on crypto-asset LOB data under MEV injection it captures 98.4% of the reward envelope at 3.2% CVF. The method scales sub-linearly to M=8 constraints, settles on-chain within one Ethereum block, and yields a 2.1X reward improvement on Safety-Gymnasium, confirming domain-agnostic generalization.
The agricultural sector is essential for global food security but continues to face challenges in supply chain management, including lack of transparency, traceability, and data integrity. This study proposes AgroChain, a blockchain-based framework designed to enhance governance and trust in the Agricultural Supply Chain (ASC). The system is built on the Quorum blockchain platform, an enterprise version of Ethereum, which integrates Zero-Knowledge Proof (ZKP) protocols to ensure data privacy while maintaining secure and transparent transactions. AgroChain introduces a scalable process model that separates the registry of agricultural records from the actual data, enabling efficient data handling. Smart contracts are used to automate key supply chain operations such as record creation, validation, transfer, and deletion, allowing end-to-end traceability from farm to consumer. The framework also incorporates rolebased access control for stakeholders including farmers, distributors, retailers, and consumers. Experimental results indicate that AgroChain improves transparency, accountability, and interoperability, demonstrating the potential of blockchain technology to transform agricultural supply chain governance.
We present DSKAG-IT-SIG, a family of information-theoretic transaction signature schemes that achieve unconditional existential unforgeability under adaptive chosen-message attack by computationally unbounded adversaries. The construction derives per-transaction MAC keys through DSKAG, a deterministic symmetric key agreement protocol requiring no key transmission, no handshake, and no public key infrastructure. We prove (Theorem 1) that the forgery advantage of any unbounded adversary making q queries is at most q * 2^{-128} in standard mode, reducing to the statistical uniformity of DSKAG-derived keys and the pairwise independence of HMAC-SHA256 under a uniform key. We prove (Theorem 2) that cross-domain forgery advantage is at most 2^{-128} + negl(lambda), reducing to the statistical key isolation of DSKAG across policy domains. Both bounds are unconditional and independent of any computational hardness assumption. Standard-mode signatures are 30 bytes, a 97.8% reduction versus Falcon-512 (666 bytes) and compatible with ISO 20022 SWIFT message fields without re-engineering. The NexusKey composite policy digest binds asset class, jurisdiction, KYC level, and chain identity into the key derivation path; policy bypass is cryptographically equivalent to key forgery. A four-layer UltraHonk zero-knowledge proof system (143,802 gates, no trusted setup, 16 KB proof) enables permissionless on-chain compliance verification, deployed on Ethereum Sepolia and Arbitrum Sepolia. Version 2.2. 17 pages, 7 tables. Three independent academic institutions validated the construction: no structural attacks found.
Este artigo analisa estratégias de otimização de gas em Ethereum a partir de duas dimensões principais: o custo dos opcodes da EVM e as escolhas de estruturas de dados em Solidity. A tabela de opcodes da EVM e a evolução do gas schedule mostram que operações de armazenamento e acesso externo, como SSTORE, SLOAD, CALL, BALANCE e EXT*, estão entre as mais caras, especialmente após EIPs como a 2929, que aumentaram o custo de acessos “frios” a contas e slots de storage para refletir melhor seu impacto na execução e na camada de armazenamento. Estudos recentes sobre custos de armazenamento evidenciam que uma escrita em SSTORE pode custar cerca de 22.100 gas para 32 bytes (aprox. 690 gas/byte), enquanto leituras via SLOAD também são significativamente caras, motivando pesquisas sobre técnicas como SSTORE2 e mecanismos para corrigir “overcharge” em leitura/escrita de storage, com ganhos médios de até 30–32% em fees para certos padrões de uso. Boas práticas de otimização de gas em Solidity incluem reduzir o número de acessos a storage movendo valores frequentemente lidos para variáveis em memória, empacotar variáveis em slots de 32 bytes (storage packing), preferir tipos fixos a dinâmicos quando possível, evitar cópias desnecessárias de arrays de storage para memória e desenhar estruturas de dados que minimizem gravações em storage. A literatura e guias de otimização indicam que a escolha entre arrays, mappings, structs e padrões de layout impacta diretamente o custo de execução, especialmente em loops que interagem com storage ou estruturas dinâmicas. Conclui‑se que a otimização de gas em Ethereum é um problema tanto de engenharia de baixo nível, ligado ao custo de opcodes e ao modelo warm/cold de acessos, quanto de design de dados e algoritmos, com implicações econômicas diretas para usuários, protocolos DeFi e estratégias de design de L2s.<br>
Efficient pricing mechanisms are critical for optimizing water consumption, managing demand, and promoting equitable resource distribution. This paper presents a Time-of-Use (TOU) dynamic pricing model integrated with Blockchain Technology (BT) to address these challenges. The proposed model categorizes time into Peak, Off-Peak, and Shoulder hours, applying distinct pricing tiers responsive to demand fluctuations, further refined through seasonal adjustments and demand constraints to support sustainability and regulatory compliance. Implemented on the Polygon blockchain - chosen for its low transaction cost, high scalability, and ethereum compatibility - the framework leverages Smart Contracts (SC) for secure, transparent and automated real-time pricing adjustments. Full on-chain data storage eliminates off-chain dependencies, ensuring data integrity and traceability throughout the water supply chain. Pseudonymous blockchain identities maintain user privacy while providing full auditability via immutable on-chain records. Security vulnerabilities such as input manipulation, arithmetic overflows, and unauthorized access receive proactive mitigation through secure coding practices, role-based access control, and logic constraints. Performance evaluation using the k6 load testing tool under simulated real-world conditions shows robust system behavior: response times ranged from 2.14s to 5.64s, with the 90th and 95th percentiles at 3.5s and 4.26s, respectively. Latency ranged from 5.96ms (median) to 49.54ms (95th percentile), validating the system’s responsiveness, scalability, and reliability under concurrent user requests. The test results further highlight the system’s ability to handle concurrent user requests efficiently, demonstrating reliable performance under simulated varying workloads.
N. V. Ravindhar, A. Manju, S. Murugesan, T. K. S. Rathish Babu
Electric Vehicle-to-Grid (V2G) arrangements stand at the center of bidirectional energy exchange in modern smart grids and are, however, challenged by real-time decision-making, load balancing, and the security of transaction validation. This paper has proposed an energy-efficient optimization framework based on a Bio-Inspired Deep Learning Controller using a Monarch Butterfly Optimization (MBO) algorithm with Gated Recurrent Unit (GRU) network for optimizing charging and discharging schedules across EV fleets. GRU networks forecast short-term grid demand and EV battery availability while MBO tunes the controller weights dynamically to adapt to scheduling under varying conditions. Furthermore, in order to maintain the trust over the transaction in a tamper-resistant fashion, a blockchain layer is embedded with the use of smart contracts to keep a track of authentication, pricing, and energy transfer log records for V2G. The proposed system shows charging cost reduction of 19.6%, peak load shaving efficiency of 23.2%, and forecast accuracy of 96.4%, in all mobility scenarios evaluated. The architecture also contributes to improving grid regulation response time by 28% and reducing EV queuing delay by 31%. Simulated by using MATLAB/Simulink, TensorFlow, and Ethereum-based blockchain, the architecture renders a scalable and secure framework for V2G coordination. It is noted that the findings are based on simulation, and co-simulation experiments, and the actual conditions of deployment like latency in communications, non-idealities of the hardware and regulatory factors are not factored into the analysis. Furthermore, the model facilitates real-time adaptation, strengthens grid resilience, and guides EV operation according to concurrent market conditions for energy.
Sreekanth Vp, Ronny Sebastian Roy, Surya Gopalakrishnan, Thanmaya S Krishna
The proliferation of cyber threats and the increasing digital vulnerability of students necessitate innovative educational approaches to cybersecurity awareness. Traditional methods of teaching cybersecurity often fail to engage learners effectively, resulting in poor knowledge retention and limited practical skills. This paper introduces Zyberix, an interactive educational game that leverages Web3 technologies to teach cybersecurity concepts through immersive gameplay and blockchain-based incentives. By integrating Unity game engine with Ethereum smart contracts, IPFS decentralized storage, and NFT rewards, Zyberix creates an engaging learning environment that simulates realistic cyber threats including phishing, malware, and social engineering attacks. The system rewards successful challenge completion with verifiable Non-Fungible Tokens (NFTs), providing both motivation and an introduction to blockchain technology. This research demonstrates how gamified experiential learning combined with Web3 technologies can significantly enhance cybersecurity education, addressing the critical gap between theoretical knowledge and practical application while fostering digital literacy among students.
Drissia Ennagoura, Kamal El Kehal, Abdelhamid Berdai, Safae Merzouk · 8 authors
Cryptocurrency price prediction is challenging due to strong nonlinearity and high volatility. This paper comparatively evaluates three forecasting models for Ethereum (ETH): SARIMAX with exogenous technical indicators, Long Short-Term Memory (LSTM) networks, and Facebook Prophet. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Exponential Moving Average (EMA) are incorporated to enhance signal quality. Empirical results reveal clear trade-offs between predictive accuracy, profitability, and risk. SARIMAX achieves the highest directional accuracy (75.00%) with limited profitability, while LSTM yields the highest cumulative profit (23.84%) at the cost of higher drawdown. Prophet provides a balanced compromise between accuracy and risk. The study contributes by jointly evaluating statistical forecasting accuracy and trading-oriented performance metrics, offering practical insights into model suitability for different investor risk profiles.
Traditional consensus mechanisms, such as Proof of Stake (PoS), increasingly reveal an excessive dependency on large liquidity providers. Although the Proof of Liquidity (PoL) mechanism serves as a critical paradigm for incentivizing sustained liquidity provision and ensuring market stability, its transition from asset staking to active liquidity management significantly increases the complexity of underlying smart contract economic models and interaction logic. This renders hidden liquidity logic flaws difficult to detect via traditional methods, seriously threatening the system stability and user asset security of mainstream DeFi and emerging PoL ecosystems. To address this, we propose the LiquiLM framework, which integrates Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN). By establishing a dynamic interaction between liquidity-critical contracts and flaw descriptions, the framework effectively bridges the semantic gap between underlying code implementations and high-level liquidity intents. We evaluate the performance of LiquiLM on 1,490 validation contracts (covering precision, recall, specificity, and F1-score). The results show that it achieves significant effectiveness in auditing and explaining liquidity flaws: in experiments using Gemini 3 Pro and GPT-4o as backbone models, respectively, the F1-scores both exceed 90%. Furthermore, through an in-depth audit of 1,380 real-world PoL and Ethereum economic contracts, LiquiLM successfully identifies 238 high-risk contracts and assists in discovering 10 vulnerabilities that have received CVE certification.
O presente artigo analisa a gestão de estado em blockchains públicas, com foco em técnicas de pruning e estratégias de arquivamento eficiente, diante do crescimento contínuo de dados e da necessidade de equilibrar segurança, disponibilidade histórica e custos de infraestrutura. Em redes baseadas em máquinas de estado, como aquelas compatíveis com a Ethereum Virtual Machine, o estado global – composto por contas, contratos e dados de armazenamento – é mantido em estruturas de dados do tipo árvore de Merkle-Patricia (MPT), que crescem monotonicamente à medida que novas transações modificam o estado, levando nós completos e de arquivo a consumirem dezenas de terabytes após alguns anos de operação em produção. Essa dinâmica impõe desafios estruturais a operadores de nós, que precisam escolher entre manter histórico completo, realizar pruning de blocos e estados antigos ou recorrer a nós especializados para consultas históricas, afetando diretamente o grau de descentralização e o custo de participação na rede. A metodologia adotada combina revisão bibliográfica de pesquisas em bancos de dados para blockchains, documentação técnica de clientes de nós – entre os quais Geth, Erigon e equivalentes em outras redes – e estudos recentes sobre statelessness, expiração de estado (state expiry), árvores de Verkle e bancos de dados forkless, discutindo as implicações dessas abordagens para a gestão de estado de longo prazo. Os resultados indicam que estratégias de pruning ao nível de blocos e de estado, combinadas a modelos de nós diferenciados (full, pruned, archive, light) e a técnicas de instantâneos (snapshotting) e arquivamento externo, permitem reduzir significativamente o volume de armazenamento exigido de nós validadores sem sacrificar a capacidade de validação e a segurança da cadeia. Por outro lado, a dependência crescente de archive nodes e de infraestruturas especializadas para consultas históricas levanta questões relevantes acerca de centralização e do custo de reconstrução do estado em cenários adversos, estimulando pesquisa em modelos de clientes sem estado (stateless clients), provas compactas de estado e bancos de dados desenhados especificamente para as cargas de trabalho de blockchains. Conclui-se que a gestão eficiente de estado constitui componente crítico da sustentabilidade de longo prazo da Web3, demandando abordagens integradas que combinem técnicas de pruning, desenho criterioso de estruturas de dados, políticas explícitas de retenção histórica e modelos econômicos que incentivem a operação de nós com diferentes perfis de armazenamento.
O presente artigo examina técnicas de compressão de dados em blocos de blockchain e sua relação com a redução de custos de armazenamento on-chain, considerando tanto o consumo de espaço em disco pelos nós da rede quanto o custo econômico de inclusão de dados em transações. Em plataformas como o Ethereum, parcela significativa do custo de transações – especialmente para rollups e aplicações que utilizam calldata de maneira extensiva – está associada ao armazenamento e à propagação de bytes de dados na camada base, o que tem motivado a adoção de estratégias de compressão de calldata, ajuste de parâmetros de gas e a introdução de novas formas de armazenamento temporário, a exemplo de blobs de dados. A metodologia empregada baseia-se em revisão bibliográfica de trabalhos sobre estratégias de armazenamento on-chain, análise de propostas de melhoria do ecossistema (Ethereum Improvement Proposals – EIPs) voltadas à redução de custos de calldata, exame de relatórios técnicos sobre compressão de dados em soluções de camada 2 e análise recente do impacto de mecanismos como o EIP-4844 (proto-danksharding) sobre a estrutura de custos de rollups. As evidências levantadas indicam que técnicas de compressão aplicadas ao payload de transações podem reduzir da ordem de cinco vezes o tamanho efetivo dos dados enviados, traduzindo-se em economias de aproximadamente 50% no gasto de gas para determinadas operações em redes de segunda camada, sem exigir mudanças disruptivas no protocolo subjacente. Paralelamente, abordagens estruturais – entre as quais a separação de dados de disponibilidade em blobs não permanentes e o emprego de rollups que publicam apenas provas e raízes de estado, em vez de dados completos – contribuem para reduzir a pressão de armazenamento permanente sobre os nós da camada 1. Conclui-se que a compressão de dados em blocos, combinada a ajustes de modelo de dados (blobs, rollups, off-chain storage) e a técnicas de compressão clássicas (run-length, delta, dicionário, entropia), constitui peça central na estratégia de escalabilidade e sustentabilidade econômica da Web3, não obstante levante desafios relevantes quanto à complexidade de implementação, compatibilidade entre clientes e preservação da verificabilidade de longo prazo.