Blockchain Papers

Jingrui Li, Divykumar Patel

No abstract is available for this record.

Seyedeh Fatemeh Mottaghi, Bertram I. Steininger

No abstract is available for this record.

Diana Olamma Okpala

This study conducts a performance evaluation of a blockchain-based Human Resource Management System (HRMS) utilizing smart contracts to enhance organizational efficiency and scalability. Despite blockchain’s transformative potential through decentralization, transparency, and immutability, empirical research on its scalability for large-scale HRMS applications remains limited. This research addresses this gap by designing, implementing, and testing a blockchain-based HRMS prototype with a simulated dataset of 5000 users across five core HR modules: recruitment, employee management, payroll, leave, and exit/retirement. Leveraging the Ethereum development network, Solidity for smart contract development, and Hyperledger Caliper for performance benchmarking, the study evaluates transaction latency and throughput under escalating transaction loads (5 to 5000 transactions). Results demonstrate exceptional scalability, with consistently low average latency (0.07 - 2.11 seconds) and high throughput (2.4 - 78.1 TPS), affirming the system’s robustness for high-volume HR operations. The findings provide evidence-based insights and recommendations for designing scalable blockchain solutions, contributing to advanced HR practices and organizational performance optimization.

Kaung Myat Kyaw

No abstract is available for this record.

Fatemeh Erfan, Mohammad Yahyatabar, Martine Bellaïche, Talal Halabi

No abstract is available for this record.

Melissa Mazura

Smart contracts frequently fail due to transaction reverts, yet diagnosing the causes of these failures remains challenging. We present an analysis pipeline that automatically extracts and clusters invariants from on-chain reverted transactions, uncovering the underlying conditions that trigger failures. At the core of our approach is ReBERT, a custom embedding model fine-tuned on invariant data, which outperforms existing semantic similarity models in capturing subtle predicate relationships. Our analysis reveals meaningful clusters of failure causes—such as Access Control, Data Flow, and Status Checks—that highlight recurring vulnerabilities in smart contract execution. These findings advance understanding of failure patterns for Ethereum Smart Contracts.

Mantri Christ Elison, Martin Victor K, Gifton Paul Immanuel

The objective of this research is to develop an R&D (Research and Development) for the hardiness relay alert system, including applying the machine learning, and the fuzzy logic networks for the real time Ethereum transaction 'match failure' detection and the improved Ethereum blockchain security.As an example, the system is computing on the transactions due to the fact the system for transaction analysis corresponds with concrete intrinsic characteristics and thus it mainly takes out suspicious or malicious transactions.The logistic regression, support vector machines (SVM) decision tree and random forests are used in this research and optimized by grid search.Finally, on the other hand, uncertainty problems and false alarms are solved where fuzzy membership functions are used to put transaction attributes into linguistic hobbled variables (such as 'low', 'medium' and 'high').The conclusion of this descriptive research is that fuzzy logic integration with machine learning can improve the approach of anomaly mediation compared to the rules based approach and it is superior to rules based approach.Finally, the effectiveness of the models is detailed and replicated in various graphical representations of the decision making process and membership functions to show that the system can be deployed in real time to secure blockchain networks.

Prashant Joshi

In this study, we examined the regime-dependent dynamics and interrelationships among major cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Monero (XMR), using high-frequency one-minute data from January 2020 to April 2025. To capture the presence of latent structural shifts without assuming Markovian transitions, we employed a Gaussian Mixture Model (GMM), which flexibly clustered distributions into two, empirically distinct regimes. Regime-specific Vector Autoregressive (VAR) models were then estimated to analyze interdependencies, spillovers, and shock transmission mechanisms across these digital assets. In the calm regime, the return dynamics were primarily self-driven, with limited cross-asset responses. Conversely, the volatile regime exhibited stronger and more persistent interlinkages, with BTC consistently acting as the principal transmitter of shocks to ETH and XMR, while ETH acts as a secondary transmitter, whereas XMR remains largely a risk recipient, absorbing external shocks with limited feedback into the system. These findings were corroborated through impulse response functions and forecast error variance decompositions, which consistently revealed asymmetric interdependence structures across the regimes. The Granger causality indicated more stable and statistically significant causal relationships in the calm regime than in the volatile regime. Furthermore, the Bai-Perron structural break tests confirmed the absence of significant deterministic breaks in the return series, reinforcing the validity of the GMM-based regime identification. These findings have practical implications for investors, regulators, and risk managers when modeling contagion and developing risk management strategies in cryptocurrency markets, especially during periods of heightened volatility.

Aubain Nzokem

The paper presents two series representations of a L{\'e}vy process for the Generalized Tempered Stable (GTS) distribution: a series representation generated by the inverse tail integral and a short noise representation. Both series representations are used to simulate the daily returns of Bitcoin and Ethereum. The Q-Q plot analysis shows smooth linear patterns, indicating strong agreement between the empirical and theoretical GTS distributions.

Soumyajit Sarkar

No abstract is available for this record.

Nishank P. Kuppa, Vijay K. Madisetti

Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.

Antonios Giatzis, Stamatis Papangelou, Christos K. Georgiadis

The emergence of blockchain technology has resulted in the creation of various blockchain networks and their accompanying programming languages for creating smart contracts. Each smart contract language, with its unique characteristics, advantages, and drawbacks, leverages blockchain technology to create a secure, transparent, and efficient way to execute agreements without the need for intermediaries. In this study, we compared the Solidity language used in the Ethereum blockchain network and Sui Move, which is used for the Sui blockchain network, highlighting contrasting approaches to smart contract development. In this paper, we explore the aspects of both languages, such as their structure, operational models, and security components. By evaluating these factors, the study uncovers the strengths and limitations of each language, highlighting their security and performance features that developers must take into account when choosing a smart contract language to build robust, efficient, and scalable decentralized applications that are addressed at a wide audience. Additionally, the work entails a framework that helps developers transition existing applications built on Solidity to Sui Move and take advantage of Sui Move’s unique characteristics without sacrificing functionality. It is a step-by-step approach to modify data forms, refactor logic processes, and deploy modules as needed while ensuring that migration is smooth and the maximum benefits of the Sui-based blockchain network are achieved. The knowledge and resources offered are designed to enable developers to create more secure, scalable, and efficient decentralized systems.

William C. Johnson

No abstract is available for this record.

Lee, Kristy Ho Yan

No abstract is available for this record.

Joshika Vallabheneni, S. Sarvanan, M. Geetha, Karri Reddy · 6 authors

No abstract is available for this record.

Aysajan Eziz

Ethereum’s rollup-centric roadmap assumes that adoption of layer-2 (L2) rollups will relieve congestion on the layer-1 (L1) mainnet, but there is little causal evidence on how large that relief is across major protocol changes. We construct a daily panel for Ethereum from August 2021 to December 2024 and estimate the total effect of L2 adoption on L1 congestion using a regime-aware interrupted time-series design that spans the London, Merge, and Dencun upgrades and adjusts for macro demand. In the pre-Dencun regime, a 10 percentage point increase in L2 adoption reduces median L1 base fees by about 11%—roughly 4–5 Gwei per 21k-gas transfer—and yields similar declines in a harmonized congestion index, with only modest changes in block utilization. Combining these elasticities with a Merge-era counterfactual path for L2 adoption, we estimate that observed adoption avoided about $80–$90 million in base and priority fees over 137 days, or roughly $0.6–$0.7 million per day. These effects are precise while L2 adoption is still ramping up but become statistically local once adoption exceeds 85% after Dencun, implying that congestion relief is economically meaningful yet regime-specific. Our findings support continued investment in L2 infrastructure alongside L1 mechanism design and illustrate how to benchmark congestion relief in other multi-layer digital platforms.

Grar Hicham, CHAIBI HASNA, Faqihi Moulay Ahmed

Blockchain technology is increasingly recognized as a transformative tool in public sector governance. This paper examines how Morocco’s public procurement process might incorporate blockchain, especially smart contracts. Using blockchain’s primary properties—transparency, traceability, and immutability—the research shows how smart contracts can automate procurement processes, lower fraud, and increase budgetary efficiency. Highlighting its technical feasibility, a simulation of blockchain-based procurement is created using a local Ethereum network. Legal-institutional issues as well as comparative global case studies (Estonia, Chile, UAE) are addressed. Provided that legal, technical, and institutional changes go hand in hand with the deployment of blockchain, the results point to its great potential to enhance governance and service delivery. This study focuses primarily on one of the cornerstones of this technological advance, namely smart contracts, which are true catalysts for automation, securing contractual commitments in public procurement, guaranteeing compliance with contractual stipulations and optimizing budget allocations. These self-executing protocols eliminate intermediaries, streamline bureaucratic processes and establish an immutable audit trail that promotes accountability and public confidence. The study also provides a simulation of the integration of blockchain into a public procurement system, illustrating in concrete terms its potential impact on the efficiency, transparency and accountability of administrative processes. The paper also stresses the importance of interoperability between blockchain platforms and existing administrative infrastructures, which is essential to ensure a smooth and scalable transition. The results highlight considerable potential: the adoption of blockchain in public administration is redefining governance paradigms by fostering trust between stakeholders, including the State, citizens and economic players. This technology not only enhances transparency and accountability, but also paves the way for a fairer, more responsive and resilient administration, capable of meeting the complex challenges of contemporary governance. As such, blockchain can reduce corruption, improve the delivery of public services and optimize the allocation of resources.

Daniel Maldonado-Ruiz, Chan Hwang, Elina Jankovskaja, Karima Sadykova · 7 authors

No abstract is available for this record.

Yahaya Saidu, Shuhaida Mohamed Shuhidan, Dahiru Adamu Aliyu, Izzatdin Abdul Aziz · 5 authors

The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a comprehensive bibliometric and systematic literature review to explore emerging trends, research patterns, and methodologies in integrating BC, IoT, and AI into traceability systems. In the study, 530 documents from the SCOPUS database for bibliometric analysis were examined, alongside a detailed review of 43 selected articles from multiple databases. The findings highlighted a significant increase in research output in recent years, with a dominant focus on agricultural supply chains and SCM. Notably, India and China lead the field in publications and citations. Furthermore, key authors and influential journals significantly contributed to advancing the research. The analysis also revealed a predominance of experimental and hybrid research methodologies, with Ethereum and Hyperledger Fabric emerging as the most widely adopted platforms for system implementations. The study identified critical research trends, including the growing role of AI-driven analytics, the importance of real-time IoT data collection, and the critical need for secure, tamper-proof data provided by BC. However, challenges, such as interoperability, scalability, and standardization, remain hindering the widespread adoption of these technologies. The paper proposes a four-layer conceptual framework for integrating BC, IoT, and AI into future traceability systems, emphasizing their potential to enhance transparency, security, and efficiency across various application areas. The paper concludes by offering directions for future research, highlighting the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.

Mridul Kumar Gupta, Mradul Singh Tawar, Mrityunjay Kumar

No abstract is available for this record.

Sayed Ahmad Seyar Sawayz, Athota Kavitha

This review paper presents an examination of Blockchain and Smart Contract platforms focusing on their uses in various industries and addressing their implementation challenges. A notable gap exists in current literature: there is no comprehensive review that encompasses blockchain applications, implementation challenges, smart contract development platforms, and publication trends. Our quantitative analysis of publication trends, examining 9,288 papers from IEEE Xplore and Springer databases between 2019 and 2024, provides insights into the trajectory of blockchain research across sectors. It examines platforms like Ethereum, which enables decentralized application development but faces scalability constraints, and Hyperledger Fabric, which offers robust enterprise solutions. The research explores how blockchain revolutionizes industries including healthcare through secure patient data management, supply chain through enhanced traceability, and education through verifiable credentials. This review synthesizes current trends in blockchain platforms and implementation challenges across diverse industries.

David Krause

No abstract is available for this record.

Asif Mahmud, Kareem Kamal, Ahmed Wasif Reza

Cryptocurrency mining data centers consume 100-200 times more energy than conventional office areas annually. Regulating power consumption, cooling mechanisms, and thermal control performance is crucial to creating a greener and more energy-efficient crypto-mining data center. This paper presents a new cryptocurrency mining data center design that is both environmentally friendly and energy-efficient. The design considers popular green and energy-saving data center cooling and temperature management approaches, as well as cost-effective operations. The total monthly cost of the proposed data center is 358025 USD, with renewable energy generating 68520 kW of electricity. The monthly profit from Bitcoin mining is 3200806.969 USD, while Ethereum mining is 2317353.503 USD. The PUE number is 1.04, and the DCiE is 96.15 percent. These statistics help determine the model’s conclusion.

Luís Costa, Elisabete Vieira, Mara Madaleno

Purpose: This work analyzes whether cryptocurrencies significantly influence Euronext stock returns. Design/methodology/approach: To this end, this quantitative research analyzes companies from 4 Euronext financial markets between 2017 and 2022 using the panel data methodology. The Generalized Method of Moments (GMM) methodology was also used to make the analysis more robust. Findings: This study concluded that Bitcoin and Ethereum positively and statistically significantly influence Euronext stock returns. Their notoriety caused them to lose the safe haven characteristics they displayed in a more embryonic phase and led them to be influenced by the same systemic factors that affect the stock market. Originality/value: The results of this study are immensely important for private and institutional investors investing in Euronext stocks and looking to diversify their portfolios. Keywords: Stock Returns; Euronext; Bitcoin; Ethereum; Cryptocurrencies DOI: https://doi.org/10.58869/EJABM11(1)/06