Aldi Bastiatul Fawait, Muh Jamil, Sitti Rahmah, Sugiarto Sugiarto
Perkembangan teknologi blockchain dalam beberapa tahun terakhir memberikan dampak besar terhadap sistem keuangan global, salah satunya melalui Ethereum (ETH) yang berfungsi sebagai aset kripto sekaligus fondasi ekosistem smart contract. Namun, volatilitas tinggi harga ETH membuat metode prediksi tradisional sulit menangkap pola nonlinier yang kompleks. Penelitian ini menerapkan metode Long Short-Term Memory (LSTM) untuk memprediksi harga ETH menggunakan data time-series dari investing.com periode 1 Januari 2021 hingga 21 Agustus 2025. Model LSTM dengan tiga lapisan menghasilkan performa baik dengan MAE 0,0387 dan R² 0,9741 pada data latih, serta MAE 22,59% dan R² 80,55% pada data uji. Hasil ini membuktikan bahwa LSTM efektif dalam mempelajari pola fluktuasi harga ETH meskipun akurasi pada data baru masih dapat ditingkatkan. Kontribusi penelitian ini adalah memperkuat literatur terkait prediksi kripto berbasis data jangka panjang sekaligus memberikan manfaat praktis bagi investor dan regulator dalam memahami dinamika volatilitas ETH.
Cryptocurrency is a highly volatile digital asset, necessitating accurate and adaptive forecasting methods. This study implements a Long Short-Term Memory (LSTM) model to predict the daily closing prices of two leading cryptocurrencies Bitcoin (BTC) and Ethereum (ETH) using historical data from Yahoo Finance and Binance. To enhance data richness and model robustness, datasets from both sources were vertically merged. The methodological framework included data preprocessing, Min–Max normalization, formation of 24-day sliding input windows, and training across three data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using the Root Mean Squared Error (RMSE). Results indicate that the LSTM model achieved high prediction accuracy, with the lowest RMSE values of 0.0137 for BTC and 0.0152 for ETH using the combined dataset with a 90:10 split. Beyond modeling, a web-based application was developed using Streamlit, enabling users to perform real-time predictions and export results. This study contributes to the field of cryptocurrency forecasting by demonstrating that multi-source data integration significantly improves predictive accuracy and model generalization. The proposed framework offers both theoretical insights and practical tools for researchers and investors in financial technology.
We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.
Several recent proposals implicitly or explicitly suggest making use of randomized transaction ordering within a block to mitigate centralization effects and to improve fairness in the Ethereum ecosystem. However, transactions and blocks are subject to gas limits and protocol rules. In a randomized transaction order, the behavior of transactions may change depending on other transactions in the same block, leading to invalid blocks and varying gas consumptions. In this paper, we quantify and characterize protocol violations, execution errors and deviations in gas consumption of blocks and transactions to examine technical deployability. For that, we permute and execute the transactions of over 335,000 Ethereum Mainnet blocks multiple times. About 22% of block permutations are invalid due to protocol violations caused by privately mined transactions or blocks close to their gas limit. Also, almost all transactions which show execution errors under permutation but not in the original order are privately mined transactions. Only 6% of transactions show deviations in gas consumption and 98% of block permutations deviate at most 10% from their original gas consumption. From a technical perspective, these results suggest that randomized transaction ordering may be feasible if transaction selection is handled carefully.
Bruno Mazorra, Burak Öz, Christoph Schlegel, Fei Wu
Ethereum's upcoming Glamsterdam upgrade introduces EIP-7732 enshrined Proposer--Builder Separation (ePBS), which improves the block production pipeline by addressing trust and scalability challenges. Yet it also creates a new liveness risk: builders gain a short-dated ``free'' option to prevent the execution payload they committed to from becoming canonical, without incurring an additional penalty. Exercising this option renders an empty block for the slot in question, thereby degrading network liveness. We present the first systematic study of the free option problem. Our theoretical results predict that option value and exercise probability grow with market volatility, the length of the option window, and the share of block value derived from external signals such as external market prices. The availability of a free option will lead to mispricing and LP losses. The problem would be exacerbated if Ethereum further scales and attracts more liquidity. Empirical estimates of values and exercise probabilities on historical blocks largely confirm our theoretical predictions. While the option is rarely profitable to exercise on average (0.82\% of blocks assuming an 8-second option time window), it becomes significant in volatile periods, reaching up to 6\% of blocks on high-volatility days -- precisely when users most require timely execution. Moreover, builders whose block value relies heavily on CEX-DEX arbitrage are more likely to exercise the option. We demonstrate that mitigation strategies -- shortening the option window or penalizing exercised options -- effectively reduce liveness risk.
Adam Zahir, Milan Groshev, Carlos J. Bernardos, Antonio de la Oliva
Edge computingbrings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept ofedge federationhas recently emerged as a key mechanism for dynamic resources and services sharing across edge systems managed by different administrative domains. However, existing federation solutions often rely on pre-established agreements and face significant limitations, including operational complexity, delays caused by manual operations, high overhead costs, and dependence on trusted third parties. In this context, Distributed Ledger Technologies (DLTs) such asblockchaincan create dynamic federation agreements that enable service providers to securely interact and share services without prior trust. This article first describes the problem of edge federation, using the standardized ETSImulti-access edge computing (MEC)framework as a reference architecture, and how it is being addressed. Then, it proposes a novel solution usingblockchainandsmart contractsto enable distributed MEC systems to dynamically negotiate and execute federation in a secure, automated, and scalable manner. We validate our framework’s feasibility through a performance evaluation using a private Ethereum blockchain, built on the open-source Hyperledger Besu platform. The testbed includes a large number of MEC systems and compares two blockchain consensus algorithms. Experimental results demonstrate that our solution automates the entire federation lifecycle-from negotiation to deployment–with a quantifiable overhead, achieving federation in approximately 18 seconds in a baseline scenario. The framework scales efficiently in concurrent request scenarios, where multiple MEC systems initiate federation requests simultaneously. This approach provides a promising direction for addressing the complexities of dynamic, multi-domain federations across the edge-to-cloud continuum.
The article examines the legal mechanism for regulating the circulation of virtual assets in Ukraine and the regulatory and legal support for countering illegal activities with various types of cryptocurrencies. The provisions of the Law of Ukraine “On Virtual Assets”, amendments and additions to civil legislation in terms of introducing the concept of “digital thing” are analyzed. It is proven that the provisions of the European Regulation “Markets in Crypto-Assets” (“MiCA”) are essential for the legal regulation of the circulation of virtual assets and countering illegal activities with them. The classification of virtual assets contained in the European Regulation “MiCA” is disclosed in order to understand the essence of various types of cryptocurrencies. The peculiarities of the circulation of such crypto-assets as Bitcoin, Ethereum are disclosed and noted; the concepts of “blockchain”, “validator”, “service token”, “crypto-asset issuer”, etc. are investigated. The role of a number of state bodies in countering the illegal circulation of virtual assets in Ukraine is highlighted. It is argued that the coordination of analytical work and the detection of risky transactions is provided by the State Financial Monitoring Service of Ukraine. It is substantiated that the detection of criminal schemes and ensuring the prosecution of those guilty of offenses with virtual assets is entrusted to the National Police, the Security Service of Ukraine, the State Bureau of Investigation, the Bureau of Economic Security, and the Prosecutor’s Office. Such bodies as the National Bank of Ukraine, the National Securities and Stock Market Commission, and the Ministry of Digital Transformation of Ukraine form a regulatory framework that should prevent the use of crypto-assets for illegal purposes. It is established that countering the illegal circulation of virtual assets in Ukraine is carried out both through preventive measures, analytical work and improvement of the regulatory and legal framework, and through operational-search and criminal-law jurisdiction. This comprehensive model allows responding to the latest challenges, in particular, the use of decentralized finance, anonymous technologies, and cross-border schemes for the illegal circulation of virtual assets.
The article examines the legal mechanism for regulating the circulation of virtual assets in Ukraine and the regulatory and legal support for countering illegal activities with various types of cryptocurrencies. The provisions of the Law of Ukraine “On Virtual Assets”, amendments and additions to civil legislation in terms of introducing the concept of “digital thing” are analyzed. It is proven that the provisions of the European Regulation “Markets in Crypto-Assets” (“MiCA”) are essential for the legal regulation of the circulation of virtual assets and countering illegal activities with them. The classification of virtual assets contained in the European Regulation “MiCA” is disclosed in order to understand the essence of various types of cryptocurrencies. The peculiarities of the circulation of such crypto-assets as Bitcoin, Ethereum are disclosed and noted; the concepts of “blockchain”, “validator”, “service token”, “crypto-asset issuer”, etc. are investigated. The role of a number of state bodies in countering the illegal circulation of virtual assets in Ukraine is highlighted. It is argued that the coordination of analytical work and the detection of risky transactions is provided by the State Financial Monitoring Service of Ukraine. It is substantiated that the detection of criminal schemes and ensuring the prosecution of those guilty of offenses with virtual assets is entrusted to the National Police, the Security Service of Ukraine, the State Bureau of Investigation, the Bureau of Economic Security, and the Prosecutor’s Office. Such bodies as the National Bank of Ukraine, the National Securities and Stock Market Commission, and the Ministry of Digital Transformation of Ukraine form a regulatory framework that should prevent the use of crypto-assets for illegal purposes. It is established that countering the illegal circulation of virtual assets in Ukraine is carried out both through preventive measures, analytical work and improvement of the regulatory and legal framework, and through operational-search and criminal-law jurisdiction. This comprehensive model allows responding to the latest challenges, in particular, the use of decentralized finance, anonymous technologies, and cross-border schemes for the illegal circulation of virtual assets.
Pixels and market cycles both move NFT prices. Non-fungible tokens (NFTs) are unique digital assets, often used to represent ownership of digital art, collectibles, and other media, secured on blockchain networks like Ethereum. The rise of NFTs has led to the creation of a multi-billion-dollar market for digital art and collectibles, making it a key area of interest for researchers, artists, and investors. Using 94,039 transactions from 26 major generative Ethereum collections, this study extracts 196 machine-quantified image descriptors - color, composition, palette structure, geometry, texture, and deep-learning embeddings - and applies a three-stage filter to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, tighter compositional concentration, and greater curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value once explicit traits are included. To assess state dependence, a Bayesian dynamic mixed-effects panel with cycle effects is estimated, allowing Composition Focus - Saturation - the ratio of saturation in the central region to the whole image, capturing vividness and concentration at the focal area - to vary across market regimes. Collection-level heterogeneity (brand premia) is absorbed by random effects. The time-varying coefficients exhibit clear regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while the grand-mean effect is small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.
Proposer anonymity in Proof-of-Stake (PoS) blockchains is a critical concern due to the risk of targeted attacks such as malicious denial-of-service (DoS) and censorship attacks. While several Secret Single Leader Election (SSLE) mechanisms have been proposed to address these threats, their practical impact and trade-offs remain insufficiently explored. In this work, we present a unified experimental framework for evaluating SSLE mechanisms under adversarial conditions, grounded in a simplified yet representative model of Ethereum's PoS consensus layer. The framework includes configurable adversaries capable of launching targeted DoS and censorship attacks, including coordinated strategies that simultaneously compromise groups of validators. We simulate and compare key protection mechanisms - Whisk, and homomorphic sortition. To the best of our knowledge, this is the first comparative study to examine adversarial DoS scenarios involving multiple attackers under diverse protection mechanisms. Our results show that while both designs offer strong protection against targeted DoS attacks on the leader, neither defends effectively against coordinated attacks on validator groups. Moreover, Whisk simplifies a DoS attack by narrowing the target set from all validators to a smaller list of known candidates. Homomorphic sortition, despite its theoretical strength, remains impractical due to the complexity of cryptographic operations over large validator sets.
Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption.
The Generalized Tempered Stable (GTS) distribution extends classical stable laws through exponential tempering, preserving the power-law behavior while ensuring finite moments. This makes it especially suitable for modeling heavy-tailed financial data. However, the lack of closed-form densities poses significant challenges for simulation. This study provides a comprehensive and systematic comparison of GTS simulation methods, including rejection-based algorithms, series representations, and an enhanced Fast Fractional Fourier Transform (FRFT)-based inversion method. Through extensive numerical experiments on major financial assets (Bitcoin, Ethereum, the S&P 500, and the SPY ETF), this study demonstrates that the FRFT method outperforms others in terms of accuracy and ability to capture tail behavior, as validated by goodness-of-fit tests. Our results provide practitioners with robust and efficient simulation tools for applications in risk management, derivative pricing, and statistical modeling.
Noha E. El-Attar, Marwa Salama, Mohamed Abdelfattah, Sanaa Taha
Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories.
Rowdy Chotkan, Bulat Nasrulin, Jérémie Decouchant, Johan Pouwelse
Spam poses a growing threat to blockchain networks. Adversaries can easily create multiple accounts to flood transaction pools, inflating fees and degrading service quality. Existing defenses against spam, such as fee markets and staking requirements, primarily rely on economic deterrence, which fails to distinguish between malicious and legitimate users and often exclude low-value but honest activity. To address these shortcomings, we present StarveSpam, a decentralized reputation-based protocol that mitigates spam by operating at the transaction relay layer. StarveSpam combines local behavior tracking, peer scoring, and adaptive rate-limiting to suppress abusive actors, without requiring global consensus, protocol changes, or trusted infrastructure. We evaluate StarveSpam using real Ethereum data from a major NFT spam event and show that it outperforms existing fee-based and rule-based defenses, allowing each node to block over 95% of spam while dropping just 3% of honest traffic, and reducing the fraction of the network exposed to spam by 85% compared to existing rule-based methods. StarveSpam offers a scalable and deployable alternative to traditional spam defenses, paving the way toward more resilient and equitable blockchain infrastructure.
This paper introduces Lyquor, a decentralized platform that reimagines blockchain infrastructure through a service-centric model where nodes selectively host smart contracts (called Lyquids) while preserving global composability. We present three key innovations: (1) Fate-Constrained Ordering (FCO), which decouples consensus from execution to enable selective hosting without sacrificing Layer-1 grade composability; (2) Direct Memory Architecture (DMA), which eliminates state access bottlenecks by providing each contract with persistent, byte-addressable virtual memory; and (3) Universal Procedure Call (UPC), which enables fault-tolerant, programmable coordination across distributed off-chain computation. Together, these components are powered by a Rust-macroed unified programming model where on-chain and off-chain logic coexist seamlessly, supporting both traditional smart contract patterns and novel distributed applications. Lyquor addresses critical limitations in existing systems while maintaining compatibility with Ethereum APIs, offering a path toward truly scalable decentralized computation.
Ethereum’s introduction of smart contracts has significantly expanded blockchain use cases, enabling decentralized applications. Since all transactions are publicly available, the system can be modeled as a complex network, allowing us to uncover emergent user behavior and explore the underlying dynamics of the ecosystem. In this study, we focus on analyzing the structural differences within the Ethereum system across three distinct market regimes: bull, bear, and sideways. To achieve this, we apply a Hidden Markov Model to the log-return time series to uncover the underlying states, revealing three differentiated states, each corresponding to a specific market regime. Next, we investigate the network structural differences across these regimes, finding meaningful variations. During the bear regime, the out-degree distribution is more heterogeneous, with the largest hub exhibiting more extreme out-degree values. Additionally, during the bull and sideways regimes, we observe higher levels of reciprocity, clustering, and modularity compared to the bear regime. These findings suggest that during bull and sideways markets, the interaction patterns are more complex, and the community structure is more cohesive. Overall, our work underscores how market conditions shape trading patterns and the structural properties of the Ethereum transaction network, providing new insights into the interplay between market regimes, network topology, and user behavior in decentralized ecosystems.
Moritz Grundei, Vipindev Adat Vasudevan, Kishori Konwar, Muriel Medard
The data availability problem is a central challenge in blockchain systems and lies at the core of the accessibility and scalability issues faced by platforms such as Ethereum. Modern solutions employ several approaches, with data availability sampling (DAS) being the most self-sufficient and minimalistic in its security assumptions. Existing DAS methods typically form cryptographic commitments on codewords of fixed-rate erasure codes, which restrict light nodes to sampling from a predetermined set of coded symbols. In this paper, we introduce a new approach to DAS that modularizes the coding and commitment process by committing to the uncoded data while performing sampling through on-the-fly coding. The resulting samples are significantly more expressive, enabling light nodes to obtain, in concrete implementations, up to multiple orders of magnitude stronger assurances of data availability than from sampling pre-committed symbols from a fixed-rate redundancy code as done in established DAS schemes using Reed Solomon or low density parity check codes. We present a concrete protocol that realizes this paradigm using random linear network coding (RLNC).
Decentralization has an important geographic dimension that conventional metrics, such as stake distribution, often overlook. Where validators operate affects resilience to regional shocks (e.g., outages, natural disasters, or government intervention) as well as fairness in reward access. Yet major blockchain protocols do not encode geographical location in their rules; instead, validator locations emerge from a combination of economic incentives, regulatory constraints, infrastructure availability, and validator deployment choices. When certain locations offer systematic advantages, validators may strategically co-locate to maximize expected rewards, as observed in Ethereum, where validators cluster along the Atlantic corridor, which exhibits favorable latency. In this paper, we propose a formal model of validators' geographical positioning incentives under Ethereum's protocol design, capturing the interaction between its two block-building paradigms, local and external block building, and the geographical distribution of validators and information sources. We analytically characterize the model under a mean-field approximation and complement this analysis with an agent-based simulation calibrated with real-world latency data to quantify how these incentives translate into geographical concentration under heterogeneous geographic and infrastructural conditions. Our results show that Ethereum's block-building architecture is not geographically neutral. Both paradigms generate location-dependent payoffs and incentives to relocate closer to payoff-relevant parties in order to reduce propagation delays, although through different underlying mechanisms. Asymmetric access to information sources further amplifies geographical centralization. We also demonstrate that consensus parameters, such as attestation thresholds and slot times, modulate latency sensitivity and can amplify these effects, acting as protocol-level levers. Finally, we discuss the implications of our findings for protocol design and outline potential mitigation directions informed by our analysis.
Investors may tend to turn to safe-haven assets to avoid high volatility in financial markets and protect themselves from risks during times of uncertainty created by crisis periods. In the study, haven, hedge and diversification characteristics of traditional and digital assets such as gold, silver, US dollar, euro, US 10-year bond, Brent oil, Bitcoin and Ethereum against BIST100, S&P500 and DAX indices were examined using EGARCH (1,1) and DCC-GARCH (1,1) models in seven different crisis periods that have a high impact on global markets, including geopolitical, financial and health crises, using 20-year daily data covering the period 01.01.2005–01.01.2025. The findings indicate that the US dollar has consistently demonstrated strong safe-haven characteristics against the BIST100, S&P 500, and DAX in various crises. The assets that exhibited significant haven characteristics only in one crisis period were gold against the S&P 500, Brent oil, and the Euro against the DAX. The 10-year US bond exhibited safe-haven properties relative to the S&P 500 and DAX during various crisis periods. While no significant results were achieved regarding silver’s haven or hedge characteristics, it was observed that the assets generally played a diversifying role in periods other than those yielding these results. According to EGARCH (1,1) results, Ethereum was identified as a haven candidate against DAX; however, DCC-GARCH (1,1) estimates did not confirm this result.
Ruba Islayem, Haya R. Hasan, Ahmad Musamih, Khaled Salah · 5 authors
The leather supply chain comprises numerous organizations and stakeholders, particularly when sustainability aspects are taken into account, making it a complex system. The complexity inherent in such systems can lead to inaccurate information, lack of transparency, and limited data provenance. Moreover, there has been a surge in the call for sustainable practices within leather production, propelled by growing environmental consciousness and ethical considerations. In this paper, we address these challenges by proposing a blockchain-based solution designed to ensure trusted and secure traceability and sustainability throughout the entire life cycle of leather products. By harnessing the inherent capabilities of Ethereum smart contracts and blockchain technology, such as decentralization, immutability, data integrity, and transparency, we guarantee the secure and reliable tracing of materials from the farm to the final consumer. Moreover, we provide proof of sustainability by which certification agencies monitor, audit, and approve the sustainable processes and practices carried out by the different stakeholders at all stages of production to ensure compliance with industry standards and regulations. The paper presents the blockchain-based system architecture, implementation, and validation of algorithms and smart contracts. It also evaluates the security measures and cost-effectiveness of the system to offer valuable insights into its robustness and efficiency. We have made the developed smart contracts code publicly available on GitHub.
The transition to post-quantum cryptography poses an unprecedented challenge for Bitcoin and Ethereum, as it involves implementing a defensive downgrade that imposes immediate, severe costs with no tangible benefits. While quantum computers capable of breaking secp256k1 require between 523–2,500 logical qubits, with the author deriving 523 logical qubits as an algorithmic lower bound (not inclusive of arithmetic and ancilla qubits) for a canonical Shor/phase-estimation circuit using the formula QL = 2⌈log2(n)⌉ + 2 + ⌈log2(2 + 1/(2ε))⌉ for ε = 0.001, and conservative estimates ranging up to 2,500 logical qubits based on comprehensive resource models—significantly less than the 2,100–2,400 logical qubits es- timated for general elliptic curves—current systems achieve only ∼100 logical qubits. IBM’s quantum roadmap projects 500–1,000 logical qubits by 2029, placing the critical threshold within 4–10 years depending on which estimate proves accurate. This timeline collides with the reality that convincing decentralized communities to accept 50% capacity loss and 2– 3× fee increases may take 10–15 years in themselves, based on historical governance patterns where even beneficial upgrades required 2–5+ years. Current testnet implementations on per- missioned systems show measurable performance degradation. Critically, this data comes from fundamentally different architectures than permissionless networks, which will likely experience 30–50% additional performance degradation due to global verification requirements, heterogeneous hardware, and compounding propagation delays. This methodological limitation—extrapolating from permissioned to permissionless systems—represents a critical infrastructure failure that introduces massive uncertainty into migration planning. Com- pounding this challenge, secp256k1 is not officially approved by NIST under FIPS 186-5 or SP 800-186, creating additional regulatory vulnerabilities. Beyond transient impacts, PQC creates permanent state bloat, with quantum-resistant accounts requiring 59 times more storage (1,952 bytes / 33 bytes = 59.2× for ML-DSA-65), thereby accelerating centralization- tion. This paper presents a comprehensive framework acknowledging these harsh realities. While we propose specific BIP/EIP implementations and optimization strategies that might achieve 50–60% capacity retention, we recognize that the quantum threat timeline may now be shorter than even the minimum viable migration period. Unlike beneficial upgrades like SegWit (which took 20 months for activation and 5+ years for 50% adoption despite offering improvements), PQC migration is a purely defensive measure imposing only costs. The stark reality: blockchain communities must choose between accepting immediate emergency action or facing quantum vulnerability by 2029.
With the rapid advancement of blockchain technology and the expanding influence of decentralized finance (DeFi), centralized trading systems (CEX) increasingly confront critical challenges including single points of failure, insufficient data transparency, and elevated transaction costs. Therefore, it is imperative to develop and explore decentralized trading systems that are compatible with and adaptive to quantitative trading strategies. This study focuses on constructing a framework for decentralized trading systems suitable for quantitative strategies, aiming to reduce transaction costs, enhance strategy transparency, and improve risk resistance capabilities. By analyzing the technical architecture (including public chain adaptations: Solana for high-frequency strategies and Ethereum Layer 2 for medium-frequency strategies), innovations in decentralized exchanges (DEX) (such as order-book based dYdX and AMM based Uniswap V3), and the application of on-chain quantitative tools (like Nansen for whale behavior tracking and automated execution tools based on Aave smart contracts), the study confirms the feasibility and advantages of decentralized design in quantitative scenarios.
This study explores the application of Self-Sovereign Digital Identity (SSDI) and blockchain technology in forest supply chain management to improve traceability, sustainability and regulatory compliance. It addresses how these technologies can overcome the limitations of traditional identity management and improve forestry operations’ transparency, efficiency, and environmental accountability. An Ethereum-based blockchain framework was used for this study, focusing on metrics such as transaction throughput and latency. Experimental tests were conducted to analyze the performance of SSDI in forest supply chains, focusing on real-time data management and secure identity control. A framework aligned with the Forest 4.0 initiative was proposed to evaluate the efficacy of SSDI. The results show that the integration of SSDI with blockchain significantly improves traceability and sustainability within forest supply chains, with high transaction rates and reduced latency. The decentralized system improves transparency and trust, promotes efficient identity management among stakeholders, and improves compliance with environmental regulations. Our study is among the first to apply SSDI in forestry, advancing digital transformation in this sector. Demonstrating SSDI’s capacity to streamline data handling and boost traceability, it offers practical recommendations for stakeholders seeking sustainable and digitally secure supply chain management practices. • Improve traceability using blockchain-enabled identity systems in forest supply chains. • Enhance transparency with decentralized records and digital identity verification. • Analyze performance through transaction rates and latency metrics in real-time. • Streamline data handling with efficient credential issuance and verification. • Promote sustainability through intelligent integration of digital and monitoring tools.
Ch. Rupa, Sai Varshitha. G, D Divya, Thippa Reddy Gadekallu · 6 authors
The world faces a severe blood shortage, with a gap of 1.95 million units, highlighting the need for efficient blood allocation and management systems. Traditional cloud and blockchain approaches have been explored for blood bank management but faced implementation challenges. This study proposes designing and developing a decentralized Binance blockchain-based application framework to ensure transparency and security. It uses the AdaBoost algorithm to predict the availability of the nearest blood bank and blood donor. Supply chain management provides transparency without the intervention of third parties thereby preventing blood crimes. Metamask is incorporated for crypto transactions in the Binance Smart Chain test network (BSC). BSC stands out for its low transaction fees and high scalability, enabling swift transaction processing at a fraction of the cost compared to Ethereum. The smart contracts are deployed using hardhat configuration enabling BscScan as an Application Programming Interface (API) gateway to record transactions within the decentralized application (dApp). The proposed system achieved an accuracy of 99.5%, demonstrating the robustness of the AdaBoost model in predicting blood availability. The integration of blockchain technology ensures transparency, immutability, and secure traceability of blood transactions across the network.