Our study analyzes the combined impact of geopolitical risks and investor sentiment on the major cryptocurrencies, Bitcoin and Ethereum, using monthly data from December 1, 2020, to the end of April 2025. Through a rigorous econometric approach-including unit root tests (Dickey-Fuller (1979-1981) and Perron (1998)), cointegration techniques (Engle and Granger (1987) and Johansen (1990)), and error correction models (ECM and VECM)-we examined the long- and short-term dynamics between cryptocurrencies and three indices: investor sentiment, crypto market sentiment, and the composite geopolitical risk index. Our results confirm the existence of cointegration relationships between these crypto-assets and the indices, indicating structural interdependence during periods of global uncertainty. In the short term, fluctuations in investor sentiment and geopolitical risks significantly affect the returns of Bitcoin and Ethereum, with a rapid adjustment toward long-term equilibrium. Moreover, Ethereum appears to be slightly more sensitive to emotional and geopolitical shocks than Bitcoin. However, our study has certain limitations, notably the use of composite indices that may not capture all the qualitative nuances of the phenomena studied and the assumption of linearity in the modeled relationships. For future research, we suggest integrating nonlinear models and leveraging real-time sentiment data derived from artificial intelligence, as well as expanding the analysis to other segments of the crypto-asset market. Ultimately, our study enhances the understanding of exogenous factors influencing cryptocurrencies in an unstable global environment.
We analyze Trump’s memecoin launch, showing heterogeneous volatility spillovers driven by sentiment and fundamentals. Political signals amplified speculative dynamics, underscoring how politics increasingly shapes cryptocurrency markets and investor behavior.
Distributed ledger technologies (DLT) have been piloted in enterprises to improve transparency and trust relationships among multiple partners but have not managed to mature as planned. Even though most implementation projects demonstrate various opportunities for the involved partners, enterprises experience difficulties in assessing the technology’s impact on profitability in order to make valid investment decisions and improve productivity. This paper employs the dynamic capabilities theory to explore how enterprises can adapt and leverage DLT for improved profitability. It presents an applicable profitability assessment model and a collection of quantitative profitability factors of DLT in enterprise networks. The framework and its associated models are developed through an inductive Grounded Theory-based approach, composed of literature reviews and qualitative empirical studies from 40 participating mixed-industry blockchain, tangle, and hashgraph experts. To retrieve profitability factors in a structured manner, the framework features an integration model covering necessary assessment steps; a taxonomy and heat map characterizing the maturity and assessment situation of the DLT; as well as an assessment model to identify and monetize profitability factors.
Artificial intelligence (AI) has demonstrated remarkable success across various applications. In light of this trend, the field of automated trading has developed a keen interest in leveraging AI techniques to forecast the future prices of financial assets. This interest stems from the need to address trading challenges posed by the inherent volatility and dynamic nature of asset prices. However, crafting a flawless strategy becomes a formidable task when dealing with assets characterized by intricate and ever-changing price dynamics. To surmount these formidable challenges, this research employs an innovative rule-based strategy approach to train Deep Reinforcement Learning (DRL). This application is carried out specifically in the context of trading Bitcoin (BTC) and Ripple (XRP). Our proposed approach hinges on the integration of Deep Q-Network, Double Deep Q-Network, Dueling Deep Q-learning networks, alongside the Advantage Actor-Critic algorithms. Each of them aims to yield an optimal policy for our application. To evaluate the effectiveness of our Deep Reinforcement Learning (DRL) approach, we rely on portfolio wealth and the trade signal as performance metrics. The experimental outcomes highlight that Duelling and Double Deep Q-Network outperformed when using XRP with the increasing of the portfolio wealth. All codes are available in this \href{https://github.com/VerlonRoelMBINGUI/RL_Final_Projects_AMMI2023}{\color{blue}Github link}.
Eddie de Paula, Niel Bunda, Hezerul Abdul Karim, Nouar AlDahoul · 5 authors
This paper examines how decentralized energy systems can be enhanced using collaborative Edge Artificial Intelligence. Decentralized grids use local renewable sources to reduce transmission losses and improve energy security. Edge AI enables real-time, privacy-preserving data processing at the network edge. Techniques such as federated learning and distributed control improve demand response, equipment maintenance, and energy optimization. The paper discusses key challenges including data privacy, scalability, and interoperability, and suggests solutions such as blockchain integration and adaptive architectures. Examples from virtual power plants and smart grids highlight the potential of these technologies. The paper calls for increased investment, policy support, and collaboration to advance sustainable energy systems.
Flaviene Scheidt de Cristo, Jorge Augusto Meira, Jean-Philippe Eisenbarth, Radu State
Several distributed systems based on unstructured p2p networks, such as blockchains, rely on underlying protocols to disseminate messages in a fast and reliable way. As the state-of-the-art for message dissemination in blockchains, GossipSub guarantees delivery and resilience against attacks and byzantine faults by scaling pubsub dissemination without exceeding bandwidth or overloading peers. Although GossipSub relies heavily on the way its mesh is constructed, there is little insight into how different configuration parameters impact the overall performance of the system. This study analyzes the relationships between the configuration and the performance of GossipSub from a causal point of view using the concrete case of the XRPL. By employing graphical causal methods to investigate the strength of those connections, this study goes towards the direction of finding the best configuration for GossipSub for different domains, without the need for excessive empirical tests.
This article presents a comprehensive overview of smart contract implementation for automating compensation processes within Workday systems. It explores how blockchain-based smart contracts can transform human resources management by codifying compensation rules and policies into self-executing agreements. The integration enables organizations to automate performance-based bonuses, stock option vesting, and salary adjustments while ensuring transparency, accuracy, and compliance. Through detailed examination of technical requirements, integration architectures, and governance frameworks, the article demonstrates how these implementations deliver substantial benefits across operational efficiency, error reduction, and employee satisfaction. Both quantitative returns on investment and qualitative advantages like increased trust and fairness perception are addressed. The material offers practical insights for organizations considering smart contract adoption for modernizing compensation management.
Team lead at Upland.me Poland, Warsaw, Poltavskyi Dmytro
This article examines the role cryptographic methods play in protecting digital assets through blockchain systems, with a particular focus on their adjustment to contemporary challenges and technological trends. An endeavor is undertaken to systematize major cryptographic algorithms, their effective appraisal in data protection, and development prospects under quantum computing threats. The study is relevant because centralized systems increasingly depend on cryptography due to greater regulatory pressures and, above all, a need for security through secrecy. The scientific novelty lies in the detailed comparative analysis of the said methodology (hashing, digital signatures, zero-knowledge proofs) for cases relating to major blockchain platforms (Bitcoin, Ethereum, Zcash), which hence demonstrate varied approaches towards security provision. The study's methodological foundation consists of analyzing 13 sources, merging a qualitative examination of algorithms and ECDSA with zk-SNARKs with a quantitative assessment of their effectiveness. Hash functions and Merkle trees ensure data integrity while reducing the computational costs of verification; asymmetric cryptography and Zero-Knowledge Proofs guarantee authenticity and confidentiality for the function of the transaction. Main findings support that cryptography is the cornerstone technology for blockchain security, but it has to be tailored to meet new challenges. Development in post-quantum algorithms and the infusion of homomorphic encryption will soon become imperative for quantum threats. This paper strongly advocates hybrid solutions that would bring traditional ways merged with novelties, which will provide sustainability over time for digital assets. Thus, this article will be useful for Developers of Blockchain Systems, Cryptographers, Cybersecurity Experts, & Regulators willing to know how protection methods for digital assets evolve.
Samet Günay, Emrah İsmail Çevik, Mehmet Fatih Buğan, Sel Dibooğlu · 5 authors
Abstract Utilizing blockchain technology is transforming traditional business practices into a new paradigm, giving rise to what we refer to as blockchained models. This paper uses wavelet coherence analysis to identify the connectedness of blockchained sectoral indices with Bitcoin and the Fear and Greed Index that represents investor sentiment in the cryptocurrency market. Results show persistent and positive correlations between sector returns and investor sentiment and sectoral return series lead investor sentiment. The relationship between Bitcoin and sectoral indices is consistent for return series and suggests an in-phase (positive) relationship between these variables at all frequencies. We usually have found negative correlations for the co-movements of investor sentiment and sectoral volatility, where investor sentiment leads to sector return volatilities. The application of blockchain technology across various sectors, coupled with the proliferation of altcoins, appears to drive distinct price developments in these cryptocurrency sectors. These developments are predominantly influenced by sentimental factors, often diverging from the trends of Bitcoin.
Maninder Singh, William Bjorndahl, Gagangeet Singh Aujla, Joseph Camp
In the era of continuously increasing demand for bandwidth and revolutionary wireless technologies, efficient spectrum management is essential. This paper proposes a novel multi-tier tokenization approach for dynamic spectrum management. Leveraging the concept of heterogeneous tokenization of spectrum bands, we develop a decentralized framework based on blockchain technology that enables the sharing of spectrum among users. The spectrum space is represented by multi-planes, the first plane consists of unique spectrum bands converted into NFTs for long-term allocations, while the second plane involves subdividing these NFT spectrum bands for short-term usage by retail users through fungible tokens. The fungible tokens are dynamically traded and mapped using particle swarm optimization (PSO) to manage demand and supply. The paper presents formal models of the involved entities and algorithms for creating multi-tier tokens, dynamic token trading and demand-supply mapping using PSO. To enhance privacy, a zero-knowledge proof (ZKP) based approach is employed for user authentication. The proposed framework offers a secure, transparent, and scalable solution for spectrum management, addressing the limitations of traditional centralized approaches. Simulation results demonstrate the effectiveness of the framework in dynamic spectrum access, while providing privacy-aware and scalable solutions suitable for future wireless networks, including 6G.
David Umoru, Malachy Ashywel Ugbaka, Anake Fidelis Atseye, Samuel Manyo Takon · 18 authors
The financial market is a decentralized market made up of global network of businesses, forex, stock investment, and digital markets. The paper evaluated the patterns and interrelationships of volatilities in return amongst foreign exchange, stock, and bitcoin markets returns in oil importing nations. The Markov-Switching and quantile regression estimation methods were executed. Results indicate stock markets of Kenya and Uganda had the most frequent depreciating returns. Bitcoin returns were negatively and significantly influenced by changes in currency values, whereas change in bitcoin trading value causes a higher change in exchange rate returns. A percentage increase in stock market returns stimulates exchange rate returns to rise also but at a higher rate. Returns on exchange rates and Bitcoin markets are significant predictors of stock market returns. Exchange rate volatility dynamics occur in the opposite direction as those in stock markets and in the floor of Bitcoin market. Volatility was significantly observed when currency devalued confirming the erratic behaviors of investors to dwindling local currency values compared to the U.S. dollar. Financial markets authorities can use the research findings to support their choice to regulate the financial markets and shield investors from information asymmetry that could result from cross-market volatility interrelationships.
Sheik Mohamed, Nirmala.M, Tulasi Uma Rani N, Emuoyinbofarhe O.J.
The integration of Artificial Intelligence (AI) and Blockchain technology is revolutionizing cybersecurity by providing innovative, data-driven, and decentralized solutions. AI, through machine learning and deep learning, enables rapid and accurate detection of cyber threats such as malware, phishing, and zero-day attacks. Meanwhile, Blockchain ensures data integrity through its decentralized and tamper-resistant architecture, making it effective in securing sensitive information in sectors like healthcare, finance, and defense. This study explores how the convergence of AI and Blockchain can enhance cybersecurity and proposes sustainable strategies for a secure digital future. Real-world applications, such as the collaboration between the UK's National Health Service (NHS) and Google DeepMind, demonstrate the practical benefits of this integration. However, challenges remain, including data privacy concerns, infrastructure limitations, a shortage of skilled professionals, and regulatory uncertainties. The study recommends interdisciplinary research, the development of hybrid AI-Blockchain models, implementation in critical infrastructure, and strong public–private partnerships to build a resilient and scalable digital ecosystem.
Juan D. Saldarriaga-Loaiza, Johnatan M. Rodríguez‐Serna, Jesús M. López‐Lezama, Nicolás Muñóz-Galeano · 5 authors
The integration of non-conventional renewable energy sources (NCRES) plays a critical role in achieving sustainable and decentralized power systems. However, accurately assessing the economic feasibility of NCRES projects requires methodologies that account for policy-driven incentives and financing mechanisms. To support the shift towards NCRES, evaluating their financial viability while considering public policies and funding options is important. This study presents an improved version of the Levelized Cost of Electricity (LCOE) that includes government incentives such as tax credits, accelerated depreciation, and green bonds. We apply a flexible investment model that helps to find the most cost-effective financing strategies for different renewable technologies. To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. The model is tested in a case study in Colombia covering battery storage, large- and small-scale solar power, and wind energy. Results show that combining smart financing with policy support can significantly lower electricity costs, especially for technologies with high upfront investments. We also explore how changes in interest rates affect the results. This framework can help policymakers and investors design more affordable and financially sound renewable energy projects.
In 2023, the xSublimatio project showcased a fusion of art and science, presenting an interactive platform where molecules were transformed into digital artworks within the blockchain. This innovative concept leveraged advanced artificial intelligence predictions to bridge empirical precision with creative expression, offering a unique exploration of scientific data through artistic interpretation. The creation of xSublimatio involved meticulous selection and representation of molecules, blending scientific accuracy with aesthetic appeal. Through AlphaFold-inspired insights, the project reimagined molecular design, transcending traditional boundaries. During its presentation at the GDR ChemBio conference in Strasbourg, xSublimatio sparked insightful discussions within the French chemistry community. This article explores its technical implementation, its potential for introducing blockchain and non-fungible token concepts to diverse communities, and its broader implications for interdisciplinary collaboration and decentralized science.
Ruslan Shevchuk, Ihor Lishchynskyy, Marcin Ciura, Mariia Lyzun · 6 authors
Blockchain technology has emerged as a transformative solution to address specific aspects of emergency management systems by providing a decentralized and distributed ledger infrastructure that enhances data immutability, transparency, and traceability. This study presents a comprehensive bibliometric analysis of blockchain applications in emergency management covering the period from 2017 to 2024 and based on 248 research articles indexed in the Web of Science Core Collection. The analysis examines collaboration networks, co-citation patterns, citation bursts, and keyword trends to uncover key research clusters and emerging themes. Seven major clusters were identified, with their intellectual core built around influential publications that highlight blockchain’s role in improving transparency, efficiency, and trust in emergency response systems. The findings emphasize the growing impact of blockchain technology in enhancing preparedness and resilience during crises while identifying gaps in global collaboration and interdisciplinary innovation.
In this paper, we propose an analytical method to compute the collateral liquidation probability in decentralized finance (DeFi) stablecoin single-collateral lending. Our approach models the collateral exchange rate as a zero-drift geometric Brownian motion, and derives the probability of it crossing the liquidation threshold. Unlike most existing methods that rely on computationally intensive simulations such as Monte Carlo, our formula provides a lightweight, exact solution. This advancement offers a more efficient alternative for risk assessment in DeFi platforms.
Blockchain technology uses a consensus mechanism to create and finalize blocks. The consensus mechanism affects the total performance parameters of the blockchain network, such as throughput. In this paper, we present “Nazfast”, a simplified proof of stake—Byzantine fault tolerance based consensus mechanism to create and finalize blocks. The presented consensus is completed in multiple folds. For block producer and validation committee selection, we used a secure and speeded-up election mechanism, S&Sem, in Nazfast. The consensus is designed for fast block finalization in a malicious environment. The simulation result shows that we approximately achieved three block finalizations in 1 s with almost similar latency. We reduced and fixed the number of validators in the consensus to improve the throughput. We achieved a higher throughput among other consensus of the same family. Because we reduced the number of validators, the safety parameters of the consensus are at risk, so we used Sea Shield to improve the overall consensus safety. This is another blockchain to save nodes’ details when they join/unjoin the network as validators. By using all three parts together, our system is protected from 28-plus different attacks, and we maintain a high decentralization by using S&Sem. Finally, we also enhance the incentive mechanism of consensus to improve the liveness of the network.
With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the model parameters, which compromises the confidentiality of the models. In this paper, we propose a solution using zero-knowledge proofs, which allows the model owner to convince the public that a machine learning model is fair while preserving the secrecy of the model. To circumvent the efficiency barrier of naively proving machine learning inferences in zero-knowledge, our key innovation is a new approach to measure fairness only with model parameters and some aggregated information of the input, but not on any specific dataset. To achieve this goal, we derive new bounds for the fairness of logistic regression and deep neural network models that are tighter and better reflecting the fairness compared to prior work. Moreover, we develop efficient zero-knowledge proof protocols for common computations involved in measuring fairness, including the spectral norm of matrices, maximum, absolute value, and fixed-point arithmetic. We have fully implemented our system, FairZK, that proves machine learning fairness in zero-knowledge. Experimental results show that FairZK is significantly faster than the naive approach and an existing scheme that use zero-knowledge inferences as a subroutine. The prover time is improved by 3.1x--1789x depending on the size of the model and the dataset. FairZK can scale to a large model with 47 million parameters for the first time, and generates a proof for its fairness in 343 seconds. This is estimated to be 4 orders of magnitude faster than existing schemes, which only scale to small models with hundreds to thousands of parameters.
With the promise of greater decentralization and sustainability, Ethereum transitioned from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism. The new consensus protocol introduces novel vulnerabilities that warrant further investigation. The goal of this paper is to investigate the security of Ethereum's PoS system from an Internet routing perspective. To this end, this paper makes two contributions: First, we devise a novel framework for inferring the distribution of validators on the Internet without disturbing the real network. Second, we introduce a class of network-level attacks on Ethereum's PoS system that jointly exploit Internet routing vulnerabilities with the protocol's reward and penalty mechanisms. We describe two representative attacks: StakeBleed, where the attacker triggers an inactivity leak, halting block finality and causing financial losses for all validators; and KnockBlock, where the attacker increases her expected MEV gains by preventing targeted blocks from being included in the chain. We find that both attacks are practical and effective. An attacker executing StakeBleed can inflict losses of almost 300 ETH in just 2 hours by hijacking as few as 30 IP prefixes. An attacker implementing KnockBlock could increase their MEV expected gains by 44.5% while hijacking a single prefix for less than 2 minutes. Our paper serves as a call to action for validators to reinforce their Internet routing infrastructure and for the Ethereum P2P protocol to implement stronger mechanisms to conceal validator locations.
Flavio Corradini, Alessandro Marcelletti, Andrea Morichetta, Barbara Re
Blockchain technology has been widely adopted to enhance the security and the decentralisation of smart applications in large-scale pervasive systems. In such a context, data extraction is crucial as it provides a better understanding of the system’s behaviours. However, several challenges arise in automatically extracting data, due to the variety of data sources, such as transactions, events, contract storage, and the complexity of the blockchain structure. In particular, retrieving smart contract state changes remains unexplored despite its potential usage for discovering unexpected behaviour. For such reasons, in this work, we propose a novel methodology and a supporting application for extracting smart contract state changes and other execution-related data. The obtained data is then decoded and offered in a standard format to be easily reused. The methodology provides additional functionalities such as transaction filtering and capabilities for querying over extracted data. The effectiveness and the performance of the methodology were evaluated on three real-world projects from different EVM-based blockchains.
This paper presents an implementation of a Self-Sovereign Identity (SSI) framework using Ethereum-based standards to meet the technical requirements of the European Digital Identity (EUDI) Architecture Reference Framework (ARF). By leveraging ERC-734/ERC-735 standards, the proposed eSSI system enables decentralized key management, verifiable claims, and onchain auditability. A case study on the Sepolia testnet demonstrates functional alignment with EUDI goals, while highlighting the need for enhanced privacy mechanisms such as zero-knowledge proofs for full compliance.
What happens to innovation when the founder never shows up? This study reframes one of the most romanticized elements of entrepreneurship—the visionary founder—as a potential bottleneck to resilience, accountability, and governance in digital-native organizations. Focusing on Decentralized Autonomous Organizations (DAOs), we explore how startups can operate without a central leader, and what that means for the future of organizational design. Using a structured literature synthesis (n = 127 articles, PRISMA protocol) and multi-case comparative analysis of prominent DAO failures, we identify systemic governance vulnerabilities—ranging from role confusion and voting fatigue to social trust collapses. In response, we propose the Conflict-Proof DAO Model, a three-phase circular governance framework built around pre-crisis safeguards, active crisis protocols, and post-crisis institutionalization. Unlike prior models, this framework does not assume stability as the default—it assumes conflict. And it treats governance not as a bureaucratic add-on, but as a core infrastructure layer in founderless systems. Our findings suggest that resilience in decentralized startups is not a byproduct of automation or community goodwill, but a direct outcome of intentional design. This paper contributes to the emerging discourse on post-founder entrepreneurship, governance-as-code, and antifragile organizational models. For researchers, it offers a replicable framework for analyzing DAO failure patterns. For builders and policymakers, it presents a roadmap for surviving disruption—without waiting for a hero to fix things.
Existing blockchain sharding protocols have focused on eliminating imbalanced workload distributions. However, even with workload balance, disparities in processing capabilities can lead to differential stress among shards, resulting in transaction backlogs in certain shards. Therefore, achieving stress balance among shards in the dynamic and heterogeneous environment presents a significant challenge of blockchain sharding. In this paper, we propose ContribChain, a blockchain sharding protocol that can automatically be aware of node contributions to achieve stress balance. We calculate node contribution values based on the historical behavior to evaluate the performance and security of nodes. Furthermore, we propose node allocation algorithm NACV and account allocation algorithm P-Louvain, which both match shard performance with workload to achieve stress balance. Finally, we conduct extensive experiments to compare our work with state-of-the-art baselines based on real Ethereum transactions. The evaluation results show that P-Louvain reduces allocation execution time by 86% and the cross-shard transaction ratio by 7.5%. Meanwhile, ContribChain improves throughput by 35.8% and reduces the cross-shard transaction ratio by 16%.
Introduction: The ongoing war in Ukraine has triggered a large-scale humanitarian crisis, significantly affecting the mental health and psychosocial well-being of the population. In this context, Mental Health and Psychosocial Support (MHPSS) has become a vital component of humanitarian response, requiring coordinated and integrated systems aligned with global standards, such as the Inter-Agency Standing Committee (IASC) framework. Purpose: This research explores how MHPSS coordination mechanisms operate in wartime Ukraine, identifies key actors and systemic barriers, and evaluates the impact of coordinated approaches on access, resilience, and psychosocial well-being. Approach: The study employs a desk-based narrative synthesis methodology, drawing on peer-reviewed literature, humanitarian reports, and policy documents, including the Ukrainian government’s Concept for the Development of Mental Health Care until 2030. Analytical lenses include the IASC MHPSS intervention pyramid and localization theory. The study also proposes visual tools to analyze coordination structures and service delivery pathways. Results: The study finds that Ukraine’s MHPSS coordination system demonstrates notable adaptability and well-developed structures. Core activities such as 4W mapping, non-specialist training programs, and policy alignment initiatives have expanded access, particularly to community-based care. However, challenges including stigma, provider burnout, insufficient funding, and limited rural access continue to constrain effectiveness. Innovative strategies like telehealth platforms and mobile clinics reflect adaptive resilience. Overlapping mandates and data fragmentation further complicate service alignment. Nonetheless, coordinated efforts have reached over 1.2 million individuals in 2023, with early evidence suggesting reductions in psychological distress among internally displaced populations. These findings underscore the critical role of context-sensitive, decentralized approaches in building sustainable MHPSS systems in conflict-affected settings. Conclusions: Ukraine’s MHPSS coordination system demonstrates adaptability and effectiveness in crisis settings, but enduring structural challenges remain. Future priorities should include strengthening local leadership, ensuring long-term financing, and integrating services to ensure sustainable support in crisis contexts.