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13,620 results Β· page 28 of 568
Najla Alharbi, Tarek Moulahi
No abstract is available for this record.
Rashi Chauhan, Shivangi Saxena
Purpose : This study examined year-over-year (YoY) structural growth dynamics across four major cryptocurrency classesβBitcoin (BTC), Ethereum (ETH), stablecoins, and altcoins, for the period of 2020β2024. Research Methodology : A quantitative approach was employed to analyze YoY market capitalization trends across BTC, ETH, stablecoins, and altcoins, from 2020 to 2024, by using data from CoinMarketCap, by analyzing growth patterns through percentage and absolute market capitalization changes, supported by trend visualizations. A multiple linear regression model assessed the effect of time and asset type, with BTC as the reference category. The analyses were conducted using SPSS version 27. Findings : The findings revealed that 2023 was the period in which none of the cryptocurrency variants performed well due to factors such as regulatory pressure and a global economic slowdown. In contrast, 2024 marked a period of market correction, during which BTC and altcoins experienced a strong resurgence, followed by stablecoins. ETH remained robust throughout the period, supported by decentralized finance (DeFi) applications. Practical Implications : The results indicated that the cryptocurrency market functioned as a network of fragmented yet interconnected components, and continued to develop under a highly volatile and competitive environment. These findings provided important implications for investors, regulators, and scholars interested in the cryptocurrency market structure, risk behavior, and long-run predictability of cryptocurrencies. Originality/Value : This study presented a new application for a venue-specific market cap analysis in cryptocurrency spanning over five years. By leveraging YoY analysis, it provided insights into growth variances, market recovery, resilience, and increasing maturity of the crypto market in response to evolving rules and regulations.
Amro Saleem Alamaren, Korhan K. GΓΆkmenoΔlu, Nigar TaΕpΔ±nar
Abstract This study investigates the volatility spillover and connectedness networks among renewable energy sources (Biofuel, Fuel cell, Geothermal, Solar), green bonds, and cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin) in the U.S. market. To accomplish this objective, we analyzed data from November 15, 2017, to May 31, 2024, via the methods introduced by Diebold and Yilmaz (Int J Forecast 28:57β66, 2012) and BarunΓk and KΕehlΓk (J Financ Econometr 16:271 296, 2018). Our findings reveal that major global disruptionsβincluding the COVID-19 pandemic, the RussiaβUkraine war, the collapse of Silicon Valley Bank, and the Credit Suisse crisisβhave intensified volatility spillovers and financial contagion across markets, exacerbating their outcomes. The findings suggest that the effectiveness of green finance depends on its allocation across these sectors, highlighting the importance of examining each sector to understand the success of these financial initiatives. The influence of COVID-19 on the U.S. economy has increased transmission risk across markets. Renewable energy is less volatile than green bonds and cryptocurrencies are, with these indices reacting more quickly to short-term shocks. Investors should focus on short-term impacts to manage market risk effectively. By providing insights into how financial shocks propagate across sectors, emphasizing the need for a sector-specific approach to assessing financial sustainability, and underscoring the importance of short-term risk management strategies, this research offers valuable contributions to decision-makers and investors.
Yixiao Gao, Fei Li, Ruizhe Shi, Ruizhi Cheng Β· 7 authors
The past decade has witnessed the burgeoning and continuous development of blockchain and its applications. Besides various cryptocurrencies, an industry that has quickly embraced this trend is gaming. Thanks to the support of blockchain, games have started to incorporate non-fungible tokens (NFTs) that can enable a new gaming model, play-to-earn (P2E), which incentivizes users to participate and play. While recent studies looked at several NFT games qualitatively and individually, an in-depth understanding is still missing, particularly on how the P2E model has transformed traditional games. In this work, we set to conduct a measurement study of NFT games, aiming to gain a comprehensive understanding of the effectiveness of P2E in practice. For this purpose, we collect and analyze relevant NFT transaction data from the underlying blockchain (e.g., Ethereum) of 12 games, supplemented with various data scraped from their websites. Our study shows that (1) a few top wallets control unproportionally high percentage of NFTs, and the majority of wallets own only one or two NFTs and do not actively trade; (2) promotion events do boost the trade amount and the NFT price for some games, but their effect does not sustain; and (3) few players actually earned a profit, and players in 9 out of 12 games who traded NFTs have a negative profit on average. Motivated by these findings, we further investigate effective incentive mechanisms based on game theory to improve the trading profits that players can earn from these NFT games. Both modeling and simulation results confirm the effectiveness of the proposed incentive mechanism.
Al Mothana Al Shareef, Serap Ulusam Seçkiner
The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AIβBlockchain smart charging platform that combines load forecasting, dynamic pricing, and cryptocurrency-based incentives to enhance decentralized EV energy management in Gaziantep Province. An ensemble of forecasting models (SARIMA, LightGBM, N-BEATS, and TFT) predicts 2026 hourly electricity demand, while an adaptive inverse-sigmoid pricing mechanism generates real-time incentives and disincentives for EV charging behavior. A fuzzy logic-based behavioral model simulates both unmanaged and managed charging across three scenarios. Results show that managed charging reduces peak load by 22.43%, shifts 67.45% of energy demand to off-peak periods, and achieves 94.86% charging fulfillment under constrained grid conditions. The blockchain layerβimplemented through a custom ERC-20 token (SMARGE) on the Ethereum Sepolia testnetβenables secure, transparent, and low-cost microtransactions with an average confirmation time of 0.63 s. These findings demonstrate that tightly coupling AI forecasting with tokenized blockchain incentives can improve grid stability, lower operational costs, and enhance user autonomy in a scalable and decentralized manner. While promising, the study is limited by assumptions of synthetic user behavior and ideal communication conditions; future work will validate the platform in real-world pilot deployments and across different urban regions.
Kiran Bharadwaj Vedula, Rajesh Arunachalam
No abstract is available for this record.
Krill2026
KRILL β Bio-Inspired Architecture for IoT Consensus Decentralized IoT consensus without blockchain β inspired by ant colonies, immune systems & chemical diffusion. What is KRILL? The problem: Blockchain doesn't work for IoT. It's too heavy, too slow, and too expensive for devices running on batteries with 32KB of RAM. IoT needs to answer "What is the physical state of the world?" β not "Who has how much money?" The solution: KRILL replaces blockchain with 9 mechanisms borrowed from biology: Mechanism Biological inspiration What it does Stigmergic Consensus Ant pheromone trails Nodes "deposit" readings like ants deposit pheromones. Truth emerges from convergence, not voting. Pentastratic Immune System Human immune layers 5-layer anomaly detection: skin (format check) β innate (statistical) β adaptive (learned) β NK audit β autoimmune suppression. Metabolic State Cell metabolism Data has a "half-life" β old readings decay and die automatically. No infinite ledger. Entropic Data Valuation Thermodynamic entropy Network autonomously decides which data is worth storing based on information theory. Quorum Sensing Bacterial quorum sensing Nodes detect local density and switch modes (solo β quorum β swarm) without any coordinator. Horizontal Gene Transfer Bacterial gene sharing Firmware updates spread node-to-node like genes between bacteria. No update server needed. Morphogenetic Topology Embryonic development Network self-organizes its topology using reaction-diffusion (Turing patterns). Thymic Tolerance T-cell training in thymus System learns what "normal" looks like to avoid false alarms. Immunological Memory Vaccine/antibody memory Once the network detects an attack pattern, it "vaccinates" all nodes. The result: 1000x less energy than blockchain consensus Runs on a $2 ESP32 microcontroller (240KB RAM) Works with intermittent connectivity (mesh, BLE, LoRa, WiFi) No miners, no staking, no tokens β consensus is grounded in physical reality Scales to millions of nodes without coordinator Status: Research paper + engineering specification. No working implementation yet. Documents Document Description Research Paper (HTML) Full academic paper β mathematical formalizations, energy analysis, novelty assessment, risk analysis. 20 sections. Open in browser β Print β Save as PDF. Engineering Specification (HTML) Implementation reference β byte-level wire formats, state machines, pseudocode, test vectors, transport layers. Ready to code from. Source files (Markdown): krill-bioinspired-architecture.md β Research paper krill-bia-engineering-spec.md β Engineering spec Architecture at a Glance βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β KRILL Node (ESP32) β ββββββββββββ¬βββββββββββ¬βββββββββββ¬βββββββββββ¬ββββββββββββββ€ β Stigmer- β Immune β Metabolicβ Quorum β Morpho- β β gic β System β State β Sensing β genetic β β Consensusβ (5-layer)β (decay) β (modes) β Topology β ββββββββββββ΄βββββββββββ΄βββββββββββ΄βββββββββββ΄ββββββββββββββ€ β Transport: BLE mesh / WiFi / LoRa β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β PUF Identity + Ed25519 Enrollment β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ MVP β Where to Start If you want to implement KRILL, start with these 4 subsystems (the rest can be added later): ES-13 β Cryptographic enrollment (PUF + Ed25519 identity) ES-12 β Transport layer (BLE mesh for local, WiFi for bridging) ES-1 β Core data types and wire formats ES-3 β Stigmergic Consensus (the core algorithm) ES-10 β Main event loop and message dispatch Target hardware: ESP32 (Nano node) + nRF52840 (Dust node, optional) Why Not Blockchain? Blockchain (e.g. Ethereum) KRILL-BIA Consensus energy ~50 Wh/tx (PoW) or ~0.01 Wh/tx (PoS) ~0.00001 Wh/tx Minimum RAM 512MB+ 32KB (Dust), 240KB (Nano) State growth Infinite (append-only) Bounded (data decays) Offline tolerance Minutes before fork Days (pheromone half-life) Hardware cost $50+ SBC $2 ESP32 Finality Probabilistic (blocks) Convergent (pheromone field) Key Innovation: Physical-World Consensus Grounding Unlike blockchain where consensus is purely computational, KRILL grounds consensus in physical reality: Sensor readings must be physically plausible (a thermometer can't jump 50C in 1 second) Nodes that are physically closer have more weight (radio signal strength = distance proxy) The laws of physics constrain what values are possible β this is a defense layer that doesn't exist in financial systems This means an attacker must not only compromise the software but also defeat physics β a fundamentally harder problem. Contributing See CONTRIBUTING.md for how to get involved. Areas where help is most needed: Rust/C firmware for ESP32 (core protocol implementation) Simulation β model pheromone convergence with 100-10,000 virtual nodes Hardware testing β BLE mesh range, LoRa timing, PUF enrollment on real chips Security review β formal verification of immune system thresholds Documentation β diagrams, tutorials, translations License This project is licensed under the MIT License. Supporting This Work If KRILL is useful to your research or organization, consider supporting further development: ETH / ERC-20 / Base / Arbitrum / Polygon: 0x0BC290355c0B16B5B247701B7BC9AB2E1e61ffa7 Funds go toward: Reference firmware for ESP32 + nRF52840 Hardware test beds (100-node BLE mesh) Independent security audits Bug bounty program for protocol vulnerabilities Code contributions are equally welcome β see CONTRIBUTING.md.
Shrutika Khobragade, Pradnya Patil
Blockchain technology, characterized by its immutable, distributed ledger, has evolved significantly beyond its cryptocurrency origins, finding application in healthcare and organ donation systems. Specifically, Hyperledger Fabric emerges as a secure, enterprise grade solution for healthcare data management, with a primary focus on patient medical records. Traditional centralized storage of medical records poses challenges for patients, prompting the development of a Hyperledger Fabric-based system driven by smart contracts to enhance accessibility and security. In the realm of organ donation systems, blockchain is proposed as a remedy for the shortcomings of centralized models, offering heightened transparency and security. Notably, while previous solutions often leaned on Ethereum-based blockchains, this research pioneers the use of Hyperledger Fabric. Beyond organ donation, blockchain's attributes, including decentralization, transparency, and privacy, offer transformative potential in healthcare.
Meriem Youssef, Salma Gallas, Christian Urom
No abstract is available for this record.
Cevi Herdian
A Long Short-Term Memory (LSTM) neural network trained on hourly ETH/USDT market data from the Binance exchange is used in this study to examine short-term Ethereum price behavior. The proposed model emphasizes learning temporal dependencies and momentum-driven structures rather than relying on conventional linear forecasting assumptions, acknowledging the highly nonlinear and noise-dominated nature of cryptocurrency markets. The daily high price of Ethereum is selected as the target variable in the forecasting task, which is defined as a univariate regression problem. To ensure realistic predictive assessment, model performance is evaluated using a strictly out-of-sample testing methodology. Empirical findings demonstrate that the LSTM model achieves a strong statistical fit despite significant market volatility. The obtained resultsβRMSE of 127.33, MAE of 98.76, MSE of 16,213.76, MAPE of 2.73%, and an RΒ² of 0.96βindicate that a substantial portion of short-term price volatility is effectively captured by the nonlinear architecture. Even in a noise-dominated market, the low MAPE and high coefficient of determination suggest robust predictive alignment. Forecasts over the next five days reveal a recurring short-term directional pattern accompanied by widening prediction intervals, which reflect increasing uncertainty as the forecast horizon extends. This pattern underscores the intrinsic difficulty of achieving accurate price-level forecasts in highly volatile cryptocurrency markets. Overall, when applied to short-term cryptocurrency price dynamics, the results indicate that LSTM models are well-suited for capturing trend persistence and regime-related signals, affirming their usefulness as risk-aware decision-support tools rather than deterministic forecasting systems.
Swapna Mandu, P Thirumurugan, Sugur Balaji, Amboth Sirisha Β· 6 authors
The gig economy faces significant challenges with centralized platforms like Upwork and Fiverr, including high service fees (10β20%), opaque algorithms, unreliable reviews, and frequent payment disputes. To address these issues, this work proposes Work Bounty, a decentralized freelancing marketplace powered by Web3 and blockchain technologies. Built on the Ethereum blockchain, the platform utilizes smart contracts to automate critical processes such as job creation, bidding, work delivery, and escrow-based payments, thereby eliminating intermediaries and enhancing trust. Authentication is streamlined using MetaMask wallets, enabling secure, passwordless access tied to unique cryptographic addresses. Job details and deliverables are stored on the InterPlanetary File System (IPFS) to ensure immutable and tamper-resistant data storage, while a blockchain-based reputation system provides transparent, unalterable user ratings. Experimental evaluation on the Ethereum test network demonstrates that the system achieves a 100% success rate in smart contract executions and reduces overall transaction costs to 2β3% equivalent gas fees, compared to the 10β20% fees on centralized platforms. MetaMask authentication achieved a 98.7% success rate, and beta testing with freelancers and clients revealed 92% user satisfaction with the platform&s;s ease of use and trustworthiness. These results highlight the system&s;s potential to provide a secure, transparent, and cost-effective alternative to traditional freelancing platforms, fostering a more equitable and globally accessible gig economy aligned with Web3 principles.
Nourhaine Nefzi, A. Melki, Sahar Loukil, Ahmed Jeribi
Abstract This study investigates the dynamic connectedness within the cryptocurrency market by analyzing four distinct cryptomarket blocks: Bitcoin and Ethereum (conventional cryptocurrencies); PAXG, DGX, and GLC (gold-backed cryptocurrencies); LINK and MNK (decentralized finance); and THETA and MANA (nonfungible tokens). Using the time-varying parameter quantile vector autoregressive (TVP-Quantile VAR) model for the period 2019β2023, our analysis reveals significant insights into the risk transmission dynamics among cryptocurrencies. Both conventional cryptocurrencies exhibit a consistent net transmitter effect in extreme periods, whereas decentralized finance (DeFi) and nonfungible tokens (NFTs) shift between a net shock transmitter and a net shock receiver over time and quantiles. Moreover, our results shed light on the hedging and safe haven properties of these assets. By linking the dynamic connectedness findings with established literature on hedging and safe haven functions, we elucidate how these cryptocurrencies perform under varying market conditions. Specifically, we report that the role of LINK, MNK, THETA, and MANA as reliable safe-haven assets is contingent upon the observed period. We also observe the hedge and safe haven properties of selected gold-backed cryptocurrencies within the network. Overall, our findings suggest that, despite the dynamic connectedness of the cryptocurrency market, investors have the flexibility to diversify across these digital assets.
Necati Altemur, Δ°brahim Halil EkΕiΜ, Rizky Yudaruddin
Purpose This study aims to provide a comprehensive examination of the nonlinear and asymmetric relationships between global uncertainty indicators, namely, gold (GOLD), the US Dollar Index (DXY) and the Volatility Index (VIX), and major cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Cardano (ADA) and Binance Coin (BNB). It particularly focuses on how these dynamics evolve across different market conditions and the extent to which certain cryptocurrencies function as alternative safe-haven assets. Design/methodology/approach The analysis uses weekly data from January 2018 to June 2025, covering five major cryptocurrencies (BTC, ETH, XRP, ADA and BNB). To capture the dynamic and nonlinear relationships between global uncertainty indicators and cryptocurrency markets, the Quantile-on-Quantile Regression (QQR) approach is applied. Furthermore, the Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) technique is used as a robustness check to validate the findings. Findings The results demonstrate that the relationship between global uncertainty indicators and cryptocurrencies is neither linear, stationary nor unidirectional. Instead, it exhibits complex and asymmetric interactions that vary across quantiles and market conditions. Significant and predominantly inverse relationships are identified between the DXY, the VIX and cryptocurrencies, particularly at lower (0.05β0.30) and higher (0.70+) quantile levels. These findings suggest that investor behavior is influenced not only by economic fundamentals but also by uncertainty, market dynamics and risk perceptions. Originality/value This study is the first to apply QQR and QQKRLS methods to analyze the nonlinear and asymmetric linkages between global uncertainty indicators and major cryptocurrencies. It provides novel evidence on how these relationships shift across market conditions, offering fresh insights into the potential safe-haven role of cryptocurrencies.
Giulio Caldarelli
Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new oracle designs have emerged for Bitcoin Layer 1 since the 2015 transition to the Ethereum smart contracts era and whether subsequent Bitcoin improvement proposals have expanded oracles' implementability. Using Scopus and Web of Science searches, complemented by Google Scholar to capture protocol proposals, we observe that the indexed academic coverage remains limited, and many contributions circulate outside journal venues. Within the retrieved corpus, the main post-2015 shift is from multisig-style, which envisioned oracles as co-signers, toward attestation-based designs, mainly represented by Discreet Log Contracts (DLCs), which show stronger Bitcoin community compliance, tool support, and evidence of practical implementations in real-world scenarios such as betting and prediction-market mechanisms.
Nicolai Maisch, Shengjian Chen, Alexander Robertus, Samed AjdinoviΔ Β· 7 authors
This work presents a concept and implementation for the secure storage and transfer of quality-relevant data of milled workpieces from online-quality assurance processes enabled by real-time simulation models. It utilises Non-Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time-consuming and costly repetitive manual quality checks.
N. Priya, A. Rajaman, M.S. Ranjithkumar, R. Suganya Β· 5 authors
The leather export sector in India is confronted with repeated issues of providing financial transparency, traceability, and trust of the stakeholders because of a fragmented payment system and manual records. To resolve these, a blockchain-based financial transparency model is elaborated based on a distributed ledger, which operates under smart contracts, ensuring immutable records of transactions and automated verification. The model uses a secure financial exchange by using hash-based encryption and a consensusbased validation in order to synchronize export payments between decentralized nodes. The Ethereum-based Hyperledger Fabric was simulated to check the accuracy, latency, and scalability of the model. Experimental results indicate that there is a 31.7 % increase in financial traceability, 24.5 % decrease in processing delay, 18.9 % increase in cost efficiency, and 27.6 % high trust score among existing methods. The proposed framework will provide real-time, non-tampered, and verifiable financial transactions, which will provide a long-term solution to the Indian leather export industry with a pathway to transparent and responsible export management.
Vatsh Chheda, Shresth Gupta, Koustubh Angre, Steven Sawant Β· 5 authors
Traditional credential verification depends on centralized authorities and manual validation, which are often slow, expensive, and vulnerable to manipulation. This paper presents AnonHire, a decentralized system that enables secure, privacy-preserving verification of academic and employment credentials. The framework combines Self-Sovereign Identity (SSI), blockchain anchoring, InterPlanetary File System (IPFS) storage, and a mock Zero-Knowledge Proof (ZKP) layer for selective disclosure. Using Ethereum Sepolia smart contracts and an Express-Next.js stack, AnonHire provides credential issuance, verification, and revocation with minimal on-chain data and sub-second verification. Evaluations show low latency, low gas usage, and a practical path toward scalable, privacy-aware hiring ecosystems.
Frederik Salzmann
This working paper introduces the Blockchain First-Principles Analysis (BFPA) framework, a novel methodology for evaluating distributed ledger systems by constructing explicit derivation chains from physical laws and cryptographic assumptions through a praxeological action axiom to concrete protocol design decisions. The framework features a four-level axiom hierarchy (physics, cryptography, praxeology, social consensus), a Nash equilibrium gate for social layer stability, a four-stage stability profile, a lock-in typology distinguishing design-emergent, ecosystem-emergent, corporate-imposed, and regulatory-granted lock-in, and a network effect genesis model identifying five necessary conditions for spontaneous adoption. Systematic application to eight major blockchain systems (Bitcoin, Ethereum, Solana, Monero, XRP, Polkadot, Tezos, BNB Chain) reveals that epistemic design quality correlates weakly with market outcomes, while lock-in type and network effect genesis conditions are substantially stronger predictors. The analysis provides principled explanations for the Tezos Paradox and the Monero Paradox. Comments welcome.
Otabek Sattarov, Jaeyoung Choi
No abstract is available for this record.
Masato Kamba, Akiyoshi Sannai
Multi-implementation systems are increasingly audited against natural-language specifications. Differential testing scales well when implementations disagree, but it provides little signal when all implementations converge on the same incorrect interpretation of an ambiguous requirement. We present SPECA, a Specification-to-Checklist Auditing framework that turns normative requirements into checklists, maps them to implementation locations, and supports cross-implementation reuse. We instantiate SPECA in an in-the-wild security audit contest for the Ethereum Fusaka upgrade, covering 11 production clients. Across 54 submissions, 17 were judged valid by the contest organizers. Cross-implementation checks account for 76.5 percent (13 of 17) of valid findings, suggesting that checklist-derived one-to-many reuse is a practical scaling mechanism in multi-implementation audits. To understand false positives, we manually coded the 37 invalid submissions and find that threat model misalignment explains 56.8 percent (21 of 37): reports that rely on assumptions about trust boundaries or scope that contradict the audit's rules. We detected no High or Medium findings in the V1 deployment; misses concentrated in specification details and implicit assumptions (57.1 percent), timing and concurrency issues (28.6 percent), and external library dependencies (14.3 percent). Our improved agent, evaluated against the ground truth of a competitive audit, achieved a strict recall of 27.3 percent on high-impact vulnerabilities, placing it in the top 4 percent of human auditors and outperforming 49 of 51 contestants on critical issues. These results, though from a single deployment, suggest that early, explicit threat modeling is essential for reducing false positives and focusing agentic auditing effort. The agent-driven process enables expert validation and submission in about 40 minutes on average.
Athanasios Kranias
This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear modelsβRandom Forest, XGBoost, and a Multilayer Perceptronβare trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability.
Tuna Can Güleç, Elif Erer, Selim Duramaz
Abstract This study explores the higher-order moments of connectedness among cryptocurrency, commodity, bond, and stock markets from April 19, 2017, to December 29, 2023, on the basis of the GARCH-SK and TVP-VAR models. The findings reveal that Bitcoin and Ethereum act as significant net shock transmitters, especially during major events such as the COVID-19 pandemic and the RussiaβUkraine conflict. After mid-2021, these cryptocurrencies transitioned from net receivers to net transmitters of volatility owing to rising economic and geopolitical risks. These insights assist in portfolio diversification strategies. By combining shock transmitters with shock-resilient cryptocurrencies, investors can enhance their risk profiles. Diversification opportunities shift during financial crises, making it crucial to focus on shock transmitters, which are less influenced by various risk factors. Additionally, the study highlights cryptocurrencies as potential safe havens compared with traditional assets such as gold, bonds, and stocks, which often maintain or appreciate value during market stress. TVP-VAR-informed dynamic portfolio reallocation can improve risk-adjusted returns and lower volatility, aiding in capital preservation during high TCI periods. Overall, our findings suggest that portfolios that include cryptocurrencies generally outperform those that do not, emphasizing their role as effective diversifiers in portfolio optimization and financial stability.
Zeta Avarikioti, Ray Neiheiser, Krzysztof Pietrzak, Michelle Yeo
Over the last years, Ethereum has evolved into a public platform that safeguards the savings of hundreds of millions of people and secures more than $650 billion in assets, placing it among the top 25 stock exchanges worldwide in market capitalization, ahead of Singapore, Mexico, and Thailand. As such, the performance and security of the Ethereum blockchain are not only of theoretical interest, but also carry significant global economic implications. At the time of writing, the Ethereum platform is collectively secured by almost one million validators highlighting its decentralized nature and underlining its economic security guarantees. However, due to this large validator set, the protocol takes around 15 minutes to finalize a block which is prohibitively slow for many real world applications. This delay is largely driven by the cost of aggregating and disseminating signatures across a validator set of this scale. Furthermore, as we show in this paper, the existing protocol that is used to aggregate and disseminate the signatures has several shortcomings that can be exploited by adversaries to shift stake proportion from honest to adversarial nodes. In this paper, we introduce Wonderboom, the first million scale aggregation protocol that can efficiently aggregate the signatures of millions of validators in a single Ethereum slot (x32 faster) while offering higher security guarantees than the state of the art protocol used in Ethereum. Furthermore, to evaluate Wonderboom, we implement the first simulation tool that can simulate such a protocol on the million scale and show that even in the worst case Wonderboom can aggregate and verify more than 2 million signatures within a single Ethereum slot.
Yu-Heng Hsieh, Ching-Hsi Tseng, Bang-Yi Luo, Shyan-Ming Yuan
Modern passport systems face significant challenges in secure data sharing, real-time verification, and user-controlled authorization, particularly in cross-border scenarios. Existing digital passport solutions, often built on permissioned blockchains, suffer from limited transparency, scalability, and high operational costs. This paper proposes a decentralized passport management system based on an Ethereum Layer 2 architecture that combines global governance with high-throughput and cost-efficient passport operations. The system adopts a hybrid design in which a Global Passport Registry smart contract is deployed on the Ethereum mainnet for cross-country coordination, while passport issuance, access control, and identity management are handled on Layer 2 networks through country-operated Passport Managers and user-specific Personal Passport smart contracts. Extensive performance evaluations show that Ethereum Layer 1 throughput saturates at approximately 40β50 transactions per second (TPS), whereas the proposed Layer 2 deployment consistently exceeds 150 TPS and reaches up to 300 TPS under higher-performance environments, significantly surpassing the estimated system requirement of 70 TPS. These improvements result in faster response times, reduced congestion, and substantially lower transaction costs, demonstrating that public Ethereum Layer 2 infrastructures can effectively support a scalable, self-sovereign, privacy-preserving, and globally verifiable digital passport system suitable for real-world deployment.