Blockchain Papers

Eyal Briman, Nimrod Talmon, Angela Kreitenweis, Muhammad Idrees

Abstract The Optimism Retroactive Project Funding (RetroPGF) is a key initiative within the blockchain ecosystem that retroactively rewards projects deemed valuable to the Ethereum and Optimism communities. Managed by the Optimism Collective, a decentralized autonomous organization (DAO), RetroPGF represents a large-scale experiment in decentralized governance. Funding rewards are distributed in OP tokens, the native digital currency of the ecosystem. As of this writing, four funding rounds have been completed, collectively allocating over $100M, with an additional $1.3B reserved for future rounds. However, we identify significant shortcomings in the current allocation system, underscoring the need for improved governance mechanisms given the scale of funds involved. Leveraging computational social choice techniques and insights from multiagent systems, we propose improvements to the voting process by recommending the adoption of a utilitarian moving phantoms mechanism [1]. This mechanism was originally introduced by Freeman et al. [1], is designed to enhance social welfare (using the $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm) while satisfying strategyproofness–two key properties aligned with the application’s governance requirements. Our analysis provides a formal framework for designing improved funding mechanisms for DAOs, contributing to the broader discourse on decentralized governance and public goods allocation.

Shu-Jie Cao, Dongning Guo

This paper studies proof-of-work Nakamoto consensus protocols under bounded network delays, settling two long-standing questions in blockchain security: What is the most effective attack on block safety under a given block confirmation latency? And what is the resulting probability of safety violation? A Markov decision process (MDP) framework is introduced to precisely characterize the system state (including the blocktree and timings of all blocks mined), the adversary's potential actions, and the state transitions due to the adversarial action and the random block arrival processes. An optimal attack, called bait-and-switch, is proposed and proved to maximize the adversary's chance of violating block safety by "beating Nakamoto in the race". The exact probability of this violation is calculated for any given confirmation depth using Markov chain analysis, offering fresh insights into the interplay of network delay, confirmation rules, and blockchain security.

Aparna Singh, Geetanjali Rathee, Chaker Abdelaziz Kerrache, Mohamed Chahine Ghanem

The very high growth of Intelligent Transportation Systems (ITS) has generated an urgent requirement for secure, effective, and context-aware data sharing mechanisms, especially over heterogeneous and geographically dispersed settings. This work suggests a new architecture that combines a relay chain-driven encryption system with a modified Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme to tackle the double impediment of dynamic access and low-latency communication. The model proposes a context-aware smart contract on a worldwide relay chain that checks against data properties, including event type, time, and geographical region, to specify the suitable level of encryption policy. From such relay-directed judgment, On-Board Units (OBUs) encrypt data end-to-end by utilising CP-ABE and store ciphertext inside localised regional blockchains, preventing dependence on symmetric encryption or off-chain storage. High-sensitivity events are secured with firm, multi-attribute access rules, whereas common updates use light policies to help reduce processing burdens. The crypto system also adds traceability and low-latency revocation, with global enforcement managed through the relay chain. This distributed, scalable model provides a proper balance between responsiveness in real time and security and is extremely apt for next-gen vehicular networks that function across multi-jurisdictional domains.

Sai Teja Reddy Adapala, Yashwanth Reddy Alugubelly

The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional cybersecurity paradigms are ill-equipped to address. This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems. The protocol integrates three technological pillars: (1) non-spoofable agent identity via W3C Decentralized Identifiers (DIDs); (2) communication integrity via NIST-standardized post-quantum cryptography (PQC); and (3) verifiable, privacy-preserving policy compliance using the Halo2 zero-knowledge proof (ZKP) system. We formalize an adversary model extending Dolev-Yao for agentic threats and validate the protocol against the STRIDE framework. Our quantitative evaluation used a discrete-event simulation, calibrated against cryptographic benchmarks, to model 1,000 agents. The simulation showed a 0 percent success rate across 20,000 attack trials. For policy verification, analysis of the simulation logs reported a median proof-generation latency of 2.79 seconds, establishing a performance baseline for this class of security. While the evaluation is simulation-based and early-stage, it offers a reproducible baseline for future empirical studies and positions Aegis as a foundation for safe, scalable autonomous AI.

Bhanu Sri Katta

Contemporary digital environments are supported by digital identities to enable secure access to, for example, governance, healthcare, and finance applications. Nevertheless, such centralized, lack of user control, noninteroperable identity systems are drawbacks of present identity systems. In this paper, we present a decentralized, privacy- preserving, and inter-operable identity model as part of a conceptual framework for the blockchain based approach on the above-mentioned problems. To enhance trust and compliance, the framework also utilises consent-based mechanisms, layered-architecture and self-sovereign principles. Real-world use cases are described to illustrate the utility of the model in real life applications such as cross-border identity verification, sharing of health data, and validation of educational credentials.

Paridhi Talwar, Aman Jain, Eugene Pinsky

Dramatic price swings and the possibility of extreme returns have made Bitcoin a hot topic of interest for investors and researchers alike. With the help of advanced neural network models including CNN, RCNN, and LSTM networks, this paper has delved deep into the intricacies of Bitcoin price behavior. We will study different time intervals—close-to-close, close-to-open, open-to-close, and day-to-day—to find a pattern that we can use to develop an investment strategy. The average volatility over a year, six months, and three months is compared with the predictive power of volatility versus a traditional buy-and-hold strategy. Our findings point out the strengths and weaknesses of each neural network model and provide useful insights into optimizing cryptocurrency portfolios. This study contributes to the literature on the price prediction and volatility analysis of cryptocurrencies, thus providing useful information to both researchers and investors to execute strategic steps within the volatile cryptocurrency market.

Émerson Rodrigues. ALVES

O ensino de Matemática desempenha um papel fundamental na formação do pensamento lógico, crítico e na capacidade de resolução de problemas. Este trabalho explora a integração entre conceitos matemáticos de sequências e progressões e a tecnologia Blockchain do Bitcoin. O objetivo principal é desenvolver uma abordagem didática inovadora que utilize a Blockchain como ferramenta para ensinar matemática de forma prática e relevante para os alunos do Ensino Médio. O trabalho aborda a estrutura da Blockchain, as sequências numéricas, progressões aritméticas (PA) e progressões geométricas (PG), a sequência de Fibonacci, relacionando-as com o funcionamento do Bitcoin e à visualização da cadeia de blocos por meio do site mempool. A proposta se concretiza por meio de uma sequência didática que explora os conceitos de sequência numérica, progressões aritméticas e geométricas, associando-os à estrutura da Blockchain e ao modelo de política monetária da rede Bitcoin. A metodologia envolveu aulas expositivas e atividades práticas, buscando desenvolver habilidades críticas, matemáticas e tecnológicas. Essa dissertação também analisa os dados obtidos a partir de um questionário aplicados aos alunos, avaliando a eficácia da metodologia proposta

Siswoyo Aris Munandar, Fahrurrozi Fahrurrozi

Purpose – This study aims to analyze the controversy surrounding cryptocurrency from the perspectives of Muhammadiyah and Nahdlatul Ulama (NU), focusing on their respective fatwas, the underlying juridical argumentation, and the social and economic implications of these religious rulings in Indonesia.Methodology – Employing a qualitative normative legal approach, the research conducts a comprehensive document analysis of official fatwas issued by Muhammadiyah and NU, complemented by secondary sources such as academic articles, news reports, and government regulations related to cryptocurrency in Indonesia.Findings – Both Muhammadiyah and NU consistently declare crypto-currencies haram (forbidden) primarily because of Islamic legal principles prohibiting gharar (excessive uncertainty), maisir (gambling), and the lack of state sanctions and consumer protection. While sharing this conclusion, the two organizations differ in their juridical methodologies, with NU exhibiting more contextual flexibility through internal debates and regional councils. Implications – The fatwas serves as authoritative guidance shaping Muslim consumer choices and government regulations, reinforcing Islamic ethical standards in financial transactions. However, they also create tension between technological innovation and religious compliance, posing challenges to fintech adoption and inclusive economic growth. The findings suggest the need for ongoing dialogue between scholars, regulators, and industry stakeholders to reconcile Sharia compliance with digital financial innovation.Originality – This study provides an original contribution by offering a comparative, in-depth analysis of the legal reasoning within the Muhammadiyah and NU fatwas on cryptocurrency, linking doctrinal argumentation to broader socioeconomic outcomes. It fills a gap in the existing literature that mostly catalogs fatwa content without examining their interpretive nuances and practical impacts in Indonesia’s unique socio-religious context.

Cătălin Gheorghe, Oana Panazan, Hind Alnafisah, Ahmed Jeribi

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence.

Meera Gladis Kurian, Yuhua Chen

Hash functions are fundamental components in both cryptographic and non-cryptographic systems, supporting secure authentication, data integrity, fingerprinting, and indexing. While the Ascon family, selected by the National Institute of Standards and Technology (NIST) in 2023 for lightweight cryptography, has been extensively evaluated in its authenticated encryption mode, its hashing and extendable-output variants, namely Ascon-Hash256, Ascon-XOF128, and Ascon-CXOF128, have not received the same level of empirical attention. This paper presents a structured benchmarking study of these hash variants using both the SMHasher framework and custom Python-based simulation environments. SMHasher is used to evaluate statistical and structural robustness under constrained, patterned, and low-entropy input conditions, while Python-based experiments assess application-specific performance in Bloom filter-based replay detection at the network edge, Merkle tree aggregation for blockchain transaction integrity, lightweight device fingerprinting for IoT identity management, and tamper-evident logging for distributed ledgers. We compare the performance of Ascon hashes with widely used cryptographic functions such as SHA3 and BLAKE2s, as well as high-speed non-cryptographic hashes including MurmurHash3 and xxHash. We assess avalanche behavior, diffusion consistency, output bias, and keyset sensitivity while also examining Ascon-XOF's variable-length output capabilities relative to SHAKE for applications such as domain-separated hashing and lightweight key derivation. Experimental results indicate that Ascon hash functions offer strong diffusion, low statistical bias, and competitive performance across both cryptographic and application-specific domains. These properties make them well suited for deployment in resource-constrained systems, including Internet of Things (IoT) devices, blockchain indexing frameworks, and probabilistic authentication architectures. This study provides the first comprehensive empirical evaluation of Ascon hashing modes and offers new insights into their potential as lightweight, structurally resilient alternatives to established hash functions.

Sonny Muhammad Ikhsan Mangkuwinata, Aswita Nova, Buchari, Fitrie Rahmawaty · 5 authors

Educational financing is a vital component of the education system that plays a significant role in ensuring equitable access, improving quality, and enhancing the efficiency of educational implementation. This study examines various education financing models implemented in Indonesia, ranging from government and community-based sources to philanthropic and social solidarity alternatives such as ta’awun funds. The purpose of this article is to explore and analyze the models of education financing. The method used is a literature review by collecting information from various sources relevant to the topic. The results indicate that Indonesia applies a mixed financing system, which is a modification of several international models, such as the Power Equalizing model, the Foundation Plan, and direct subsidy schemes like the School Operational Assistance (BOS). Previous studies emphasize the importance of careful planning, strong supervision, and accountability in managing education funds. Furthermore, the decentralization of education requires synergy between the central and local governments to ensure effective budget utilization. In conclusion, no single model is entirely ideal; therefore, a combination of models that are adaptive to the socio-economic conditions of each region, while considering the principles of equity, efficiency, and sustainability, is necessary. With effective financing, education in Indonesia is expected to become a key driver of human resource development and national progress.

Mohsen Rahmanikivi, Cristina Pérez‐Solà, Víctor Garcia-Font

The integration of blockchain technology with IoT architectures holds immense potential for advancing application design and enhancing security properties. However, the resource constraints typically present in IoT devices pose a challenge. This paper explores the feasibility of running a lightweight Bitcoin wallet on IoT devices and identifies the minimum requirements for their successful operation. A review of the literature is used to identify existing integration architectures and derive the wallet needs. The study evaluates performance metrics such as execution time, memory usage, network data transmission, and power consumption to determine the feasibility of deploying these architectures.

Zonggui Tian, Du Zhang

Humans have the ability to incrementally learn, accumulate, update, and apply knowledge from dynamic environments. This capability, known as continual learning or lifelong learning, is also a long-term goal in the development of artificial intelligence. However, neural network-based continual learning suffers from catastrophic forgetting: the acquisition of new knowledge typically disrupts previously learned knowledge, leading to partial forgetting and a decline in the model’s overall performance. Most current continual learning methods can only mitigate catastrophic forgetting and fail to incrementally improve the overall performance. In this work, we aim to incrementally improve performance within sample incremental context by utilizing inter-stage edges as a pathway for explicit knowledge transfer in continual graph learning. Building on this pathway, we propose a knowledge-augmented replay method by leveraging evolving subgraphs of important nodes. This method enhances the distinction between patterns associated with different node classes and consolidates previously learned knowledge. Experiments on phishing detection in Ethereum transaction networks validate the effectiveness of the proposed method, demonstrating effective knowledge retention and augmentation while overcoming catastrophic forgetting and incrementally improving performance. The results also reveal the relationship between average accuracy and average forgetting. Lastly, we identify the key factor to incremental performance improvement, which lays a foundation for convergence of continual graph learning.

Robert M. Campbell

Bitcoin and Ethereum’s current combined 71% market dominance creates an unprecedented systemic risk as quantum computing threatens their cryptographic foundations. A successful quantum attack would not merely compromise individual chains but trigger cascading failures across exchanges, stablecoins, DeFi protocols, and tokenized assets—potentially destroying trillions in value. This paper presents a comprehensive framework for transitioning Bitcoin and Ethereum to post-quantum cryptography. We analyze vulnerabilities in ECDSA and SHA-256, evaluate NIST-standardized algorithms (ML-DSA, SLH-DSA, ML-KEM) alongside emerging alternatives, and propose a phased migration strategy using hybrid cryptographic schemes. Our proof-of-concept demonstrates quantum-safe transactions with acceptable performance trade-offs, including detailed soft fork mechanisms, backward compatibility solutions, and incentive structures to achieve network-wide adoption before quantum threats materialize. While theoretical models suggest a 42-month migration timeline, our analysis of real-world complexity, workforce constraints, and historical precedents indicates a more realistic 6–8-year timeline. The framework addresses critical challenges, including smart contract verification, cross-chain compatibility, and miner coordination, to ensure seamless transition while maintaining network security and functionality.

Asst. Prof. Panchami M Hegde, Asst. Prof. Swetha M

Carpooling has emerged as one of the most practical strategies for reducing the growing challenges of traffic congestion, fuel consumption, and environmental pollution, yet conventional carpooling systems that are operated through centralized platforms continue to face numerous issues that restrict their effectiveness and adoption. Existing solutions largely depend on intermediaries to coordinate between drivers and passengers, creating a system that lacks transparency, suffers from high service costs, and exposes user data to privacy risks and security breaches. Moreover, traditional systems are often criticized for inefficient dispute resolution, a reliance on single points of failure such as central servers, and the absence of mechanisms that foster accountability and long-term trust among users. These weaknesses make centralized carpooling platforms vulnerable to manipulation, biased practices, and technical outages, thereby limiting their scope as sustainable mobility solutions. To address these persistent challenges, blockchain technology—specifically the Ethereum ecosystem—offers a transformative alternative. Ethereum supports the development of decentralized applications (dApps) driven by smart contracts, which are self-executing agreements coded directly onto the blockchain. By embedding business logic into these contracts, processes such as ride creation, ride booking, payment settlements, user verification, and rating are automated, ensuring that interactions remain tamper-proof, transparent, and immune to third-party manipulation.

Nasim Paykari, Mohamed Rahouti, Damian M. Lyons

Navigation in unstructured, GPS-denied environments, such as forests and agricultural fields, poses persistent challenges for heterogeneous robotic teams. While visual homing and Wide Area Visual Navigation (WAVN) enable lightweight, map-free operation, their effectiveness in large-scale, decentralized settings can be constrained by the absence of a coordination mechanism that accounts for varying reliability across robots. This article examines the innovative combination of blockchain techniques with WAVN to tackle visual navigation issues in diverse mobile robots used in unstructured sectors like agriculture and forestry. It addresses GPS reliance, adapts to environmental shifts, and reduces computational burdens by integrating RoboStake, a novel blockchain Proof-of-Stake (PoS) mechanism, into the WAVN system. This solution seeks to bolster cooperative navigation by assessing the reliability of each robot’s navigational input. With methods including a stake weight function, a PoS consensus score, and a navigability function, this strategy confronts the computational hurdles of coordinating robots and verifying data. Lastly, we showcase how the proposed approach upholds critical navigability features of the WAVN system and present results from scalable simulation experiments to highlight the improved efficiency achieved through enhanced cooperation.

Grzegorz Dudek, Witold Orzeszko, Piotr Fiszeder

Cryptocurrency markets are characterized by ex-treme volatility, making accurate forecasts essential for effective risk management and informed trading strategies. Traditional deterministic (point) forecasting methods are inadequate for capturing the full spectrum of potential volatility outcomes, underscoring the importance of probabilistic approaches. To address this limitation, this paper introduces probabilistic fore-casting methods that leverage point forecasts from a wide range of base models, including statistical (HAR, GARCH, ARFIMA) and machine learning (e.g. LASSO, SVR, MLP, Random Forest, LSTM) algorithms, to estimate conditional quantiles of cryp-tocurrency realized variance. To the best of our knowledge, this is the first study in the literature to propose and systematically evaluate probabilistic forecasts of variance in cryptocurrency markets based on predictions derived from multiple base models. Our empirical results for Bitcoin demonstrate that the Quantile Estimation through Residual Simulation (QRS) method, partic-ularly when applied to linear base models operating on log-transformed realized volatility data, consistently outperforms more sophisticated alternatives. Additionally, we highlight the robustness of the probabilistic stacking framework, providing comprehensive insights into uncertainty and risk inherent in cryptocurrency volatility forecasting. This research fills a sig-nificant gap in the literature, contributing practical probabilistic forecasting methodologies tailored specifically to cryptocurrency markets.

Alvaro Gómez Vieites, Christian Delgado-von-Eitzen, Diego Estévez Garcia

For years, combining the immutability associated with blockchain technology with the European Union’s General Data Protection Regulation (GDPR) has been considered a practically unsolvable conflict due to the very nature of blockchain and the GDPR. This article presents the GAVIN project (GDPR-Compliant Blockchain-Based Architecture for Universal Learning, Education and Training Information Management), a pioneering initiative that overcomes this challenge through an innovative technical and legal approach to trusted digital academic certification. Developed by atlanTTic (University of Vigo) and funded by the European Union, GAVIN proposes a scalable architecture that combines off-chain storage, encrypted Hash-Based Message Authentication Code (HMAC) anonymization, access notarization, and blockchain-based access control. The legal validation of the working prototype under development demonstrates that blockchain decentralization is compatible with GDPR compliance. The model is presented as a replicable reference for institutions wishing to leverage distributed ledger technologies without compromising personal data protection. This paper details the legal design, technical architecture, and compliance mechanisms, offering a practical framework for implementing decentralized systems with privacy by design.

Hesam Azadjou, Suraj Chakravarthi Raja, Ali Marjaninejad, Francisco J. Valero‐Cuevas

Like mammals, robots must rapidly learn to control their bodies and interact with their environment despite incomplete knowledge of their body structure and surroundings. They must also adapt to continuous changes in both. This work presents a bio-inspired learning algorithm, General-to-Particular (G2P), applied to a tendon-driven quadruped robotic system developed and fabricated in-house. Our quadruped robot undergoes an initial five-minute phase of generalized motor babbling, followed by 15 refinement trials (each lasting 20 seconds) to achieve specific cyclical movements. This process mirrors the exploration-exploitation paradigm observed in mammals. With each refinement, the robot progressively improves upon its initial "good enough" solution. Our results serve as a proof-of-concept, demonstrating the hardware-in-the-loop system's ability to learn the control of a tendon-driven quadruped with redundancies in just a few minutes to achieve functional and adaptive cyclical non-convex movements. By advancing autonomous control in robotic locomotion, our approach paves the way for robots capable of dynamically adjusting to new environments, ensuring sustained adaptability and performance.

Independent Algorithmic Trading Consultant and Quantitative Researcher serving international financial institutions Los Angeles, USA, Maksim Baradziuk

The study is devoted to identifying and analyzing the synergistic interaction between the theoretical principles of behavioral finance and applied methodologies for developing high-r eturn algorithmic strategies in the digital asset segment. In conditions where the efficient market hypothesis demonstrates limitations in its applicability, especially in environments with increased volatility and underdeveloped infrastructure—such as cryptocurrency markets and decentralized finance (DeFi) ecosystems—behavioral biases emerge as important determinants of market inefficiency. The paper presents a framework that combines the targeted exploitation of cognitive patterns, including the disposition effect and the phenomenon of herd behavior, with the application of advanced technological solutions. Based on four original case studies—ranging from the development of a proprietary backtesting mechanism incorporating elements of chaotic process modeling to the construction of a predictive risk management system for DeFi—the practical implementation of the proposed approach is demonstrated. The results obtained confirm the superiority of the hybrid architecture over traditional methods: from effectively reducing crash risk in DeFi carry trade strategies to maintaining portfolio resilience under market stress conditions and generating ultra-high returns (CAGR exceeding 200% with MDD of 30%). The study’s findings reinforce the validity of the adaptive markets hypothesis and confirm the applied value of the synthetic methodology for modern algorithmic trading. The information reflected in the study will be of interest to asset managers, quantitative fund specialists, and researchers focused on creating next-generation algorithms.

Yuki Sawai, Kyoichi Asano, Yohei Watanabe, Mitsugu Iwamoto

Range arguments are a type of zero-knowledge proofs that aim to prove that a prover's committed value falls within a specified range for a verifier. Previously, most range arguments were constructed based on the DLOG assumption, and hence, exponentiation operation is required for proof generation and verification. In addition, it is generally known that splitting a zero-knowledge proof protocol into a preprocessing phase and an online phase makes computation after fixing the input efficient. Still, such protocol has yet to be known for range arguments. This paper proposes an efficient range arguments protocol with a preprocessing phase. Our proposal takes a new approach by using arithmetic circuits to express the constraints that the prover must prove. The prover (resp. verifier) can generate (resp. verify) a part of proof based on multiplication and addition operations instead of exponentiation operations. Our range argument is a generic construction that does not rely on any particular mathematical assumptions, which enables us to construct a post-quantum range argument. The implementation evaluation shows that the total computation time for the prover and verifier in the online phase is efficient compared to Bulletproofs, one of the state-of-the-art range proofs. Especially, the prover computation is efficient.

Makoto Nakakita, Tomoki Toyabe, Teruo Nakatsuma

This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management.

Benjamin Kraner, Luca Pennella, Nicolò Vallarano, Claudio J. Tessone

We introduce a micro-velocity framework for analysing the on-chain circulation of Lidos liquid-staking tokens, stETH, and its wrapped ERC-20 form, wstETH. By reconstructing full transfer and share-based accounting histories, we compute address-level velocities and decompose them into behavioural components. Despite their growing importance, the micro-level monetary dynamics of LSTs remain largely unexplored. Our data reveal persistently high velocity for both tokens, reflecting intensive reuse within DeFi. Yet activity is highly concentrated: a small cohort of large addresses, likely institutional accounts, are responsible for most turnover, while the rest of the users remain largely passive. We also observe a gradual transition in user behavior, characterized by a shift toward wstETH, the non-rebasing variant of stETH. This shift appears to align with DeFi composability trends, as wstETH is more frequently deployed across protocols such as AAVE, Spark, Balancer, and SkyMoney. To make the study fully reproducible, we release (i) an open-source pipeline that indexes event logs and historical contract state, and (ii) two public datasets containing every Transfer and TransferShares record for stETH and wstETH through 2024-11-08. This is the first large-scale empirical characterisation of liquid-staking token circulation. Our approach offers a scalable template for monitoring staking asset flows and provides new, open-access resources to the research community.

Xiaolin Zhu, Jijun Li, Shiting Yang, Weiguang Zhang · 11 authors

The present study aims to investigate the orientation-dependent mechanical behaviors of ZnO single crystals under nanoindentation by molecular dynamics simulation. The load–indentation depth curves, atomic displacement, shear strain and dislocations for the c-plane, m-plane and a-plane ZnO single crystals were analyzed in detail. The simulation results showed that the elastic deformation stage of the loading curves for the three oriented ZnO single crystals can be described well by the Herz elastic contact model. The Young modulus values for the c-plane, m-plane and a-plane ZnO were calculated to be 122.5 GPa, 158.3 GPa and 170.5 GPa, respectively. The onset of plastic deformation occurred first in a-plane ZnO, then in m-plane ZnO, and lastly in c-planeZnO. The atomic displacement vectors in the three oriented ZnO single crystals were in good agreement with the primary activated slip systems predicted by the maximum Schmid factor. For the c-plane ZnO, the activated pyramidal {112¯2} slip system led to a complex dislocation pattern surrounding the indenter. A U-shaped prismatic half-loop was formed in the [211¯0] direction, confirming the activation of the prismatic {101¯0} slip system. For the m-plane ZnO, the activated prismatic {101¯0} slip system led to the preferential nucleation of dislocations along the 11¯20 and [2¯110] directions. A prismatic loop was formed and emitted along the [2¯110] direction, governed by a confined glide on {101¯0} planes. For the a-plane ZnO, the activated prismatic {101¯0} slip system led to dislocations concentrated in the [1¯1¯20] direction beneath the indentation pit, emitting a prismatic loop along this direction. Perfect dislocation (with a Burgers vector of 1/3 ) is the dominant dislocation in the three oriented ZnO single crystals. The findings are expected to deepen insights into the anisotropic mechanical properties of ZnO single crystals, offering guidance for the development and applications of ZnO-based devices.