The integrity, transparency, and security of voting systems are crucial to maintaining the democratic process. Traditional electronic voting systems have faced several challenges, including vulnerabilities to hacking, fraud, and tampering. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to address these issues. This paper explores the application of blockchain-based solutions in creating secure and transparent voting systems. By leveraging the distributed ledger technology of blockchain, the proposed systems ensure data integrity, confidentiality, and voter authentication while enabling real-time auditing. Blockchain-based voting systems offer several advantages, including resistance to vote tampering, the prevention of double voting, and enhanced accessibility for remote and disabled voters. Moreover, the use of cryptographic techniques and smart contracts further enhances security and transparency, allowing for verifiable, auditable, and tamper-proof elections. This review highlights existing research and prototypes, discusses the challenges of implementing such systems, and provides future directions for the development of blockchain-enabled electoral solutions.
Blockchain-based smart contracts have garnered significant attention due to their potential to automate and enforce agreements in a decentralized and transparent manner. This abstract provides an overview of the implementation and security considerations associated with blockchain-based smart contracts. Smart contracts are self-executing contracts with predefined rules encoded on a blockchain, enabling automated and tamper-proof execution of contractual agreements. The implementation of smart contracts involves writing code in programming languages such as Solidity and deploying them on blockchain platforms such as Ethereum. However, the adoption of smart contracts introduces various security challenges, including vulnerabilities in the code, malicious actors, and regulatory compliance issues. This abstract discusses key security considerations for smart contracts, such as code auditing, formal verification, secure coding practices, and regulatory compliance. Additionally, it explores emerging trends and techniques for enhancing the security and resilience of blockchain-based smart contracts. By addressing these security considerations, blockchain-based smart contracts can realize their potential to revolutionize industries by enabling trustless and efficient execution of agreements while maintaining the integrity and confidentiality of transactions.
Sharareh Shahidi Hamedani, Patrick Brian Francis, Sarfraz Aslam
Cryptocurrency is a digital asset designed to work as a medium of exchange that utilizes cryptography for security, unlike traditional currencies. Generally, the acceptance of Cryptocurrency in Malaysia is still in its early stages of adoption. The study examined the key factors influencing Millennials' willingness in Malaysia and how regulation moderates the relationship between these factors and Millennials' willingness to adopt cryptocurrency. Data were collected using a questionnaire developed from the literature. A total of 385 respondents from the millennial age group participated in this study. SMART PLS software was used for analysis involving measurement and structural models. The findings indicated interesting results. Millennials were very aware of cryptocurrency. However, when applying the regulation moderator, the results showed a high understanding of the importance mainly derived from Social Influences. The adoption of the knowledge in practice was not significant. With the inclusion of the major banking institutions, the market scope of cryptocurrency will inevitably increase soon.
The increasing interaction between the equity market and cryptocurrencies has raised concerns about volatility spillovers; however, empirical evidence about sectoral-specific spillover effects in emerging markets is scarce and hard to find. Existing research mainly concentrates on developed markets and aggregate equity indices, leaving a research gap in comprehending how sectoral indices variations impact market interactions in developing financial markets like Thailand. This article investigates the mean and volatility spillover effects between the Thai stock market and leading cryptocurrencies from April 2019 to April 2024. Applying bivariate VAR (1)-BEKK-GARCH (1,1) with an asymmetry model, this study examines the aggregate and sectoral-specific mean and volatility spillovers across major Thai stock market sectors. The findings reveal the significant mean spillover effect from cryptocurrencies to the Thai stock market with sectoral variation, while sectors such as industrials and financials exerted significant linkages, and the agricultural and food sector remains unaffected. Additionally, volatility spillovers were predominantly transmitted from the Thai equity market to cryptocurrency. Moreover, asymmetry effects were observed, with the asymmetry effects mainly transmitted from the Thai equity market to cryptocurrency. These findings provide critical insights for both individual and institutional investors on risk management and portfolio diversification while also helping policymakers with guidance on regulatory measures to mitigate systemic risks in emerging financial markets.
This article presents a scoping review of literature on leadership in international schools from 2012 to 2022 identifying key themes and areas for further research. The review reveals a focus on the challenges of leadership practice within international schooling and outlines how these themes are understood within the reviewed literature body. The article responds to recurring calls for more theorisation around educational leadership within the context of international schooling and research focused on practices of leading embedded within intersectionalities, contextual factors, and place-based ontologies. Drawing on the themes identified within the scoping review, we recommend the examination of leadership development pathways, including current approaches that have not been captured by peer-reviewed literature. Importantly, we argue for complexity-cognisant research that is framed to encompass multiplicity and multi-perspectival investigation across the various iterations of international schools and educational leadership.
Blockchain technology has transformed secure data management by employing a decentralized framework that fundamentally depends on cryptographic methods. This paper investigates how hash functions (e.g., SHA-256), digital signatures (e.g., ECDSA), and Merkle trees enable blockchain’s core attributes—immutability, security, and transparency. A Python-based proof-of-concept demonstrates hashing’s pivotal role in linking blocks, ensuring resistance to tampering. The study assesses cryptography’s contributions, such as enhanced security, alongside limitations like quantum vulnerabilities and scalability constraints. It proposes future directions, including post-quantum cryptography and zero-knowledge proofs, to mitigate these challenges. Real-world applications in finance and supply chains highlight practical relevance. Findings confirm cryptography as the bedrock of blockchain, offering insights to bolster its resilience amid evolving technological demands.
Muhammad Farid Romadhoni Alqodr, A. Fajar Awaluddin, Annas Fajar Rohmani, Muhammad Salman Al Farisi
The rapid development of digital finance has led to widespread adoption of cryptocurrency and Non-Fungible Tokens (NFTs), including among Indonesian Muslims. However, a gap remains between state policy and Islamic legal perspectives on the legitimacy of these digital assets. While the Indonesian government recognizes cryptocurrency as a tradable commodity, Islamic authorities such as the Indonesian Ulema Council (MUI) consider it haram due to elements of gharar (uncertainty) and maysir (gambling). This normative divergence, coupled with the absence of integrated sharia-based digital education, creates confusion and increases the risk of speculative or unethical practices. This study adopts a descriptive qualitative approach through library research, drawing on academic literature, state regulations, fatwas, and digital media content. The findings reveal that community-based Islamic educationârooted in platforms such as pesantren, majelis taklim, and digital Muslim communitiesâplays a strategic role in enhancing sharia literacy. Digital educational media like YouTube, podcasts, and e-learning serve as powerful tools for contextualizing Islamic teachings in the digital economy. Collaborative educational initiatives involving scholars, educators, and influencers have shown positive outcomes in building awareness and ethical understanding of digital financial instruments. This research suggests that integrating fintech content into Islamic educational curricula and empowering da'i and PAI teachers with digital literacy skills are essential to developing an ethically grounded and technologically literate Muslim society.
AI-powered microloans are transforming financial inclusion by enabling microenterprises in financially excluded geographies to access critical capital through innovative technologies. This article examines how artificial intelligence addresses traditional microfinance challenges through alternative credit scoring systems that analyze diverse data sources beyond conventional credit histories. By leveraging mobile usage patterns, transaction histories, psychometric assessments, and other digital footprints, AI algorithms create comprehensive risk profiles that extend financial services to previously excluded entrepreneurs. The technology not only improves initial credit assessments but also enhances ongoing risk management through behavioral analytics that predict repayment issues before they materialize. Despite significant technical implementation challenges in connectivity-limited regions, the article explores promising solutions, including edge computing, explainable AI frameworks, adaptive learning systems, and federated learning approaches. Ethical considerations regarding data privacy, algorithmic bias, and interest rate transparency require careful attention to ensure these innovations promote genuine inclusion. The evolution of this field points toward embedded financial services, decentralized finance integration, and collaborative AI models that could further democratize access to capital for marginalized entrepreneurs worldwide.
Financial services enterprise systems are at a critical inflection point as traditional monolithic architectures struggle to meet evolving market demands, customer expectations, and regulatory requirements. This article explores the transformative potential at the intersection of artificial intelligence, cloud-native microservices, and intelligent data processing for building next-generation financial systems. It examines how these technological paradigms can be leveraged to overcome legacy challenges and regulatory pressures while creating more resilient, compliant, and innovative enterprise architectures. It provides a comprehensive roadmap for transformation, including assessment strategies, incremental modernization patterns, and DevSecOps implementations tailored to financial services. Through case studies of successful implementations and analysis of common challenges, the article offers practical insights for financial institutions navigating this complex evolution. Looking ahead, It identifies quantum-ready architecture, decentralized finance integration, and ambient computing as key developments that will shape future financial enterprise systems, emphasizing the importance of strategic preparation in an increasingly digital financial landscape.
Organ donation plays a critical role in saving lives, yet traditional systems often struggle with issues such as lack of transparency, data tampering risks, and inefficient donor-recipient matching. This paper presents a blockchain-based decentralized application (DApp) designed to enhance the security, transparency, and efficiency of organ donation and transplantation processes. By leveraging smart contracts on a private Ethereum blockchain, the system automates key operations including donor registration, organ availability tracking, and recipient prioritization, all while maintaining the integrity of sensitive medical records. The use of a decentralized architecture ensures that data is tamper-proof and accessible only to authorized entities such as hospitals and regulatory bodies. A queue-based organ allocation algorithm is implemented to match recipients based on medical urgency and compatibility factors like blood type and organ requirements. The system is built using React.js for the frontend and Solidity for smart contract development, with Truffle and Ganache supporting local blockchain deployment. This approach not only secures sensitive health data but also improves the fairness and responsiveness of organ distribution. Keywords: Blockchain, Organ Donation, Smart Contracts, Decentralized Application, Healthcare Data Security, Organ Matching Algorithm, Private Ethereum Network
This paper introduces the Cartesian Merkle Tree, a deterministic data structure that combines the properties of a Binary Search Tree, a Heap, and a Merkle tree. The Cartesian Merkle Tree supports insertions, updates, and removals of elements in $O(\log n)$ time, requires $n$ space, and enables membership and non-membership proofs via Merkle-based authentication paths. This structure is particularly suitable for zero-knowledge applications, blockchain systems, and other protocols that require efficient and verifiable data structures.
Robotic swarm intelligence is a rapidly evolving field that leverages principles of decentralized control, self-organization, and emergent behavior to enable effective coordination and collaboration in multi-robot systems. Inspired by biological swarms, such as ant colonies and bird flocks, swarm robotics focuses on the collective performance of simple agents interacting locally to achieve complex tasks. This approach enhances scalability, robustness, and adaptability in dynamic and unpredictable environments. Key applications include search and rescue, environmental monitoring, industrial automation, and military operations. Recent advancements in artificial intelligence, machine learning, and communication technologies have further improved swarm decision-making, task allocation, and formation control. This paper explores the fundamental principles, coordination strategies, and challenges in robotic swarm intelligence, highlighting future directions for optimizing collaboration in autonomous multi-robot systems.
The blockchain revolution, ignited by the cryptocurrency phenomenon, is rapidly transcending its financial origins, poised to redefine industries across the spectrum. This review paper plunges into the transformative power of blockchain technology, dissecting its expanding universe of applications beyond digital currencies. We unravel the core principles of decentralization, immutability, and transparency that fuel blockchain's potential to revolutionize data management and operational efficiency. A spotlight is cast on its burgeoning impact across diverse sectors, including supply chain fortification, the evolution of digital identity, healthcare innovation, intellectual property safeguarding, the reinvention of voting systems, and the rise of Decentralized Autonomous Organizations (DAOs). We navigate the challenges that lie ahead and chart the exciting future directions of this groundbreaking technology. This paper delivers a comprehensive exploration of "Blockchain Beyond Cryptocurrency," illuminating its capacity to forge a future defined by enhanced security, trust, and streamlined processes in countless real-world applications. Key Words: Blockchain, Distributed Ledger Technology (DLT), Decentralization, Immutability, Transparency, Smart Contracts, Supply Chain, Digital Identity, Healthcare, Voting, Intellectual Property, Decentralized Autonomous Organizations (DAOs), Non-Fungible Tokens (NFTs), Review.
This paper provides the first empirical evidence of whether the introduction of US spot Bitcoin ETFs affected the returns and volatility of major cryptocurrencies. Using data from December 18, 2017 to March 15, 2024, we apply an event-study methodology within a GARCH-based framework. Our results reveal a significant effect of the introduction of spot Bitcoin ETFs on cryptocurrency returns and volatility. The analysis shows a positive impact for Bitcoin, Ethereum, and Litecoin spot price returns around the event date. The volatility of Bitcoin and Ripple spot markets decreased following the introduction of spot Bitcoin ETFs, which supports the stabilization hypothesis for these two cases. We also examine the volatility spillovers using a wavelet coherence approach, and reveal significant volatility spillovers from Grayscale Bitcoin ETF to Bitcoin futures and to a lesser extend to the Bitcoin spot market. Our findings enhance the limited understanding of the price discovery and functioning of the cryptocurrency markets, which could be useful for investors, regulators, and policymakers. ⢠Study the impact of introduction of Spot Bitcoin ETFs on the cryptocurrency market. ⢠Apply event study methodology within a GARCH framework. ⢠Find a positive impact for Bitcoin, Ethereum, and Litecoin spot price returns. ⢠Volatility of Bitcoin and Ripple decreased, supporting the stabilization hypothesis. ⢠Wavelet coherence analysis reveals volatility spillovers from Bitcoin ETF to Bitcoin futures.
Decentralization is a foundational principle of permissionless blockchains, with consensus mechanisms serving a critical role in its realization. This study quantifies the decentralization of consensus mechanisms in proof-of-stake (PoS) blockchains using a comprehensive set of metrics, including Nakamoto coefficients, Gini, Herfindahl-Hirschman Index (HHI), Shapley values, and Zipfâs coefficient. Our empirical analysis across ten prominent blockchains reveals significant concentration of stake among a few validators, posing challenges to fair consensus. To address this, we introduce two alternative weighting models for PoS consensus: Square Root Stake Weight (SRSW) and Logarithmic Stake Weight (LSW), which adjust validator influence through non-linear transformations. Results demonstrate that SRSW and LSW models improve decentralization metrics by an average of 51% and 132%, respectively, supporting more equitable and resilient blockchain systems.
The rapid advancement of digital services and online interactions has highlighted the need for secure, user-centric identity management systems. Traditional identity solutions, often centralized and dependent on trusted third parties, pose challenges related to privacy, security, and control over personal data. Distributed Ledger Technology (DLT), particularly blockchain, offers a promising solution for decentralized identity management by enabling self-sovereign identities (SSI). Through the use of decentralized identifiers (DIDs) and verifiable credentials (VCs), DLT allows individuals to maintain full control over their personal information, eliminating the need for intermediaries while ensuring data integrity and privacy. This paper explores the key principles of DLT-based decentralized identity management, discussing its potential to enhance privacy, security, and interoperability in digital ecosystems. We examine the various technical frameworks, challenges, and standards in the field, with a focus on the integration of DLT with emerging technologies such as zero-knowledge proofs (ZKPs) and secure multiparty computation (SMPC). Additionally, we evaluate real-world use cases, from financial services to healthcare, and the role of regulatory frameworks in shaping the future of decentralized identity systems. Ultimately, DLT presents a paradigm shift in identity management, offering scalable, transparent, and trusted solutions for the digital age.
Decentralized Finance (DeFi) represents a paradigm shift in the financial ecosystem, leveraging blockchain technology to offer innovative, transparent, and permissionless financial services. By eliminating intermediaries, DeFi applications enable direct peer-to-peer transactions and smart contract-driven solutions for lending, borrowing, trading, and asset management. This paper explores the architecture and functionalities of blockchain-based DeFi applications, highlighting their potential to enhance financial inclusivity, reduce transaction costs, and improve system efficiency. Key technical components such as decentralized exchanges (DEXs), liquidity pools, and yield farming are examined, along with the role of governance tokens in community-driven ecosystems. The paper also addresses critical challenges, including scalability, security vulnerabilities, regulatory compliance, and market volatility, which can impact DeFi's adoption and sustainability. Through case studies and performance analyses of leading DeFi platforms, this study provides insights into the transformative potential of blockchain-based DeFi applications in reshaping traditional financial paradigms.
On June 2, 2024, Mexico held its federal elections. The majority of Mexican citizens voted in person at the polls in this historic election. For the first time though, Mexican citizens living outside their country were able to vote online via a web app, either on a personal device or using an electronic voting kiosk at one of 23 embassies and consulates in the U.S., Canada, and Europe. In total, 144,734 people voted outside of Mexico: 122,496 on a personal device and 22,238 in-person at a kiosk. Voting was open for remote voting from 8PM, May 18, 2024 to 6PM, June 2, 2024 and was open for in-person voting from 8AM-6PM on June 2, 2024. This article describes the technical and cryptographic tools applied to secure the ex-patriate component of the election and to enable INE (Mexico's National Electoral Institute) to generate provable election results within minutes of the close of the election. This article will also describe how the solutions we present scale to elections on a national level.
Muhammad Umar Farooq, Tanguy Cizain, Daniel Kaiser
The libp2p GossipSub protocol leverages a full-message mesh with a lower node degree and a more densely connected metadata-only (gossip) mesh. This combination allows an efficient dissemination of messages in unstructured peer-to-peer (P2P) networks. However, GossipSub needs to consider message size, which is crucial for the efficient operation of many applications, such as handling large Ethereum blocks. This paper proposes modifications to improve GossipSub's performance when transmitting large messages. We evaluate the proposed improvements using the shadow simulator. Our results show that the proposed improvements significantly enhance GossipSub's performance for large message transmissions in sizeable networks.
We revisit the longstanding open problem of implementing Nakamoto's proof-of-work (PoW) consensus based on a real-world computational task $T(x)$ (as opposed to artificial random hashing), in a truly permissionless setting where the miner itself chooses the input $x$. The challenge in designing such a Proof-of-Useful-Work (PoUW) protocol, is using the native computation of $T(x)$ to produce a PoW certificate with prescribed hardness and with negligible computational overhead over the worst-case complexity of $T(\cdot)$ -- This ensures malicious miners cannot ``game the system" by fooling the verifier to accept with higher probability compared to honest miners (while using similar computational resources). Indeed, obtaining a PoUW with $O(1)$-factor overhead is trivial for any task $T$, but also useless. Our main result is a PoUW for the task of Matrix Multiplication $MatMul(A,B)$ of arbitrary matrices with $1+o(1)$ multiplicative overhead compared to naive $MatMul$ (even in the presence of Fast Matrix Multiplication-style algorithms, which are currently impractical). We conjecture that our protocol has optimal security in the sense that a malicious prover cannot obtain any significant advantage over an honest prover. This conjecture is based on reducing hardness of our protocol to the task of solving a batch of low-rank random linear equations which is of independent interest. Since $MatMul$s are the bottleneck of AI compute as well as countless industry-scale applications, this primitive suggests a concrete design of a new L1 base-layer protocol, which nearly eliminates the energy-waste of Bitcoin mining -- allowing GPU consumers to reduce their AI training and inference costs by ``re-using" it for blockchain consensus, in exchange for block rewards (2-for-1). This blockchain is currently under construction.
The increasing application and deployment of blockchain in various services necessitates the assurance of the effectiveness of PBFT (Practical Byzantine Fault Tolerance) consensus service. However, the performance of PBFT consensus service is challenged in dynamic scenarios. The paper explores how to reduce the consensus processing time and maintenance cost of PBFT consensus service under software aging in dynamic scenarios. We first propose a PBFT system, consisting of three subsystems, one active-node subsystem, one standby-node subsystem and a repair subsystem. All the active nodes participate in the consensus and all standby nodes aim for fault-tolerance. Each aging/crashed nodes become standby nodes after completing its repairing in the repair subsystem. The nodes migrate between the active-node and standby-node subsystems in order to support the continuity of the PBFT consensus service while reducing maintenance cost. Then, we develop a Markov-chain-based analytical model for capturing the behaviors of the system and also derive the formulas for calculating the metrics, including consensus processing time, PBFT service availability, the mean number of nodes in each subsystem. Finally, we design a Multi-Objective Evolutionary Algorithm-based method for minimizing both the PBFT service response time and the PBFT system maintenance cost. We also conduct experiments for evaluation.
Bama Raja Segaran, Siti Nurulain Mohd Rum, Mohd Izuan Hafez Ninggal, Teh Noranis Mohd Aris
Abstract The rapid growth of carbon credit markets, driven by global efforts to mitigate climate change, highlights the critical need for transparency and accountabilityâparticularly in forest-based carbon offset projects. Forest ecosystems play a vital role in carbon sequestration; however, these projects are increasingly vulnerable to greenwashing, where organizations exaggerate or misrepresent their environmental impact to appear more sustainable than they are. This literature review explores the integration of blockchain technology and machine learning (ML) to enhance verification processes and reduce fraudulent practices in forest carbon credits. Blockchainâs decentralized, immutable ledger offers a transparent and tamper-proof system for recording carbon credit transactions, ensuring traceability and reducing the risk of manipulation. Smart contracts embedded within blockchain networks can automate verification and compliance processes, enhancing efficiency while minimizing the need for human oversight. However, while blockchain ensures transparency, it lacks real-time anomaly detection capabilities. ML algorithms, particularly supervised models such as Random Forest, XGBoost, and Neural Networks, are well-suited for detecting fraudulent patterns and verifying the authenticity of forest carbon credit transactions. These algorithms can process large datasets, including satellite imagery and corporate disclosures, to identify discrepancies and improve the accuracy of carbon sequestration claims. This review also examines key performance metrics such as accuracy, precision, recall, and processing time to evaluate the efficiency of various ML algorithms for real-time fraud detection. The findings suggest that integrating ML and blockchain technologies, combined with satellite data, can significantly strengthen transparency and verification in forest carbon credit markets. By enhancing verification mechanisms, this interdisciplinary approach helps mitigate greenwashing and fosters a more credible and transparent carbon credit market. It supports global sustainability efforts by ensuring that carbon sequestration claims from forest-based projects are both accurate and verifiable.
Anton Krivonogov, K. Starodubov, Alexander Prokofyev, Yuri Gromov
The article is devoted to the issue of sustainability of blockchain systems and their impact on the functioning of smart contracts that automate complex processes. An approach to determining the initial stability of a blockchain system is proposed, which includes the assessment of operational and technical parameters of the blockchain system using the method of direct expert evaluation. The proposed approach is tested on the example of the blockchain Ethereum.
Open access
Economic and Technological Systems Analysis
Digitalization and Economic Development in Agriculture
Traditional portfolio optimization techniques predominantly rely on the classical meanâvariance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional meanâvariance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)âwith market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in returnârisk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.