Abstract This article examines the problem of ensuring Byzantine fault tolerance in the distributed ledger systems of a smart city. To improve the security of distributed ledger systems, it is proposed to use the Hashgraph distributed consensus protocol, in which events are organized as a directed acyclic graph and nodes exchange “gossip about gossip.” The traditional Hashgraph protocol is supplemented with a developed trust model that reduces the influence of malicious devices when reaching consensus in the digital infrastructures of a smart city.
Advanced Research in Systems and Signal Processing
Blockchain and Distributed Ledger Technology (DLT) represent a paradigm shift in digital record-keeping and transaction processing, offering unprecedented levels of transparency, security, and immutability. These technologies, which underpin cryptocurrencies like Bitcoin and innovative applications across various sectors, are rapidly evolving. However, their widespread adoption and integration into mainstream economic and social systems are contingent upon the establishment of robust standardization and regulatory frameworks. This paper provides a comprehensive survey of the current landscape of blockchain and DLT standardization and regulation. We examine the critical role of key standardization bodies, including ISO, ITU-T, IEEE, and W3C, analyzing their contributions, published standards, and ongoing initiatives. We also examine the existing regulatory approaches across various regions and evaluate international harmonization efforts. Furthermore, this survey identifies and discusses the overlaps, gaps, and conflicts in current standards development, as well as the technical, legal, and governance challenges inherent in this field. Finally, we highlight opportunities for enhancing interoperability, coordinating global efforts, and promoting inclusivity in standardization, offering recommendations for future directions to establish a harmonized and secure global blockchain ecosystem.
Abstract Blockchain technology is emerging as one of the most profound and cutting-edge innovations of the twenty-first century, providing a decentralized, immutable system for recording transactions. It has enabled the tokenization of distinctive digital assets, including art, music and real estate, through non-fungible tokens (NFTs). NFTs enable asset transfers by operating on pseudonymous blockchain networks, thereby preventing the disclosure of the owner’s real-world identity. While it enhances user privacy and innovation, it also creates significant anti-money laundering and counter-terrorism financing challenges. Fraudsters and other bad-faith actors can use these assets to obfuscate dirty money and illicit financial transactions, given lax or non-existent regulations on NFTs and extremely lax Know-Your-Customer compliance. In light of the above, the authors explore the nexus between NFTs and financial crime (with a particular focus on the legal frameworks of the Sultanate of Oman, the United Arab Emirates and the United Kingdom) in this article. The paper aims to evaluate how each jurisdiction’s response to NFT-related abuse has evolved and been effective in practice. This will be done through a review of existing laws, enforcement, regulations and regulatory gaps. The article ends with specific policy recommendations to enhance regulatory certainty, enforcement effectiveness and international cooperation, supporting an innovation-first approach to the NFT space tempered by necessary measures to prevent criminal abuse.
Jianlong Xu, C. F. Xu, Rongtao Zhang, Feixiang Diao · 5 authors
With the wide application of blockchain technology in finance, IoT, healthcare, and other fields, phishing scams have emerged as a growing security threat. Existing detection methods often lack in-depth modeling of the directional properties of transaction flows and struggle to effectively capture diverse transaction behaviors, directional relationships, and key neighbor dependencies. To address these limitations, we propose TGAT-MPGCN, a direction-aware phishing detection model that constructs three complementary first-order subgraphs, a sending graph, a receiving graph, and a bidirectional graph to explicitly capture transaction directionality. By integrating a graph-attention mechanism with weighted neighbor aggregation, the model enhances feature learning. Experimental evaluations on an Ethereum transaction dataset demonstrate the superior performance of our approach, achieving an accuracy of 97.21%, an AUC of 0.9721, an F1-score of 0.9719, a recall of 0.9629, and a precision rate of 98.11%, significantly outperforming traditional detection methods. This study offers a practical and scalable solution for accurate phishing detection in blockchain transaction networks.
The development of blockchain technology has given rise to new innovations in the form of smart contracts, which are widely used in digital asset transactions, including Non-Fungible Tokens (NFTs). One case that highlights this phenomenon is Ghozali Everyday, where smart contracts play a crucial role in regulating the buying and selling of NFTs. However, the implementation of smart contracts in Indonesia faces legal challenges, particularly regarding the use of cryptocurrency as a payment instrument, which is still prohibited by Bank Indonesia regulations, even though electronic contracts are recognized as valid by the ITE Law. This study uses a normative juridical method with a legislative and conceptual approach. The analysis is conducted using Hans Kelsen's Hierarchy of Norms theory and Gustav Radbruch's Legal Validity theory to assess legal certainty, fairness, and utility in regulating smart contracts and cryptocurrencies in Indonesia. The results show a conflict of norms that creates legal uncertainty and limited legal protection for digital asset transaction actors. Therefore, regulatory updates are needed that are adaptive, consistent with the hierarchy of laws and regulations, and provide more comprehensive protection for consumers and businesses.
Decentralized Autonomous Organizations (DAOs) represent a transformative shift in organizational structures, leveraging blockchain technology to enable decentralized governance, transparency, and automation through smart contracts
R.S Gokulnath, N Shri Bharani Priya, A Vijay, G Balaji · 5 authors
Web3 technologies are an exciting new frontier in digital asset ownership and decentralized systems, but certain factors, namely the complexity of blockchain interfaces, may preclude mass adoption. This research introduces Hey SOL, a conversational interface that facilitates interaction with the Solana blockchain by allowing its users to conduct Web3 capabilities using simple prompts and chat commands. Hey SOL integrates directly with the Solana blockchain with the Model Context Protocol (MCP), providing a relatively secure and reliable interaction while eliminating much of the technical burden on users. Overall, extensive testing shows that conversational interfaces like Hey SOL can improve usability and facilitate a better user experience than the traditional blockchain applications.
Web3, referring to the next-generation decentralized web, has gained extensive attention from industry and academia. Its popularity drives considerable demand for continuous development and delivery of web3 applications and services. Blockchain is an essential technology that enables web3 but comes with considerable operational cost. Blockchain-as-a-service (BaaS) is considered as a promising solution for supporting web3 applications; however, existing BaaS platforms are conceptual, built for specific applications, or not optimized for emerging web3 applications. This work analyzes the primary features and fundamental requirements of decentralized web3 applications and articulates the critical role of blockchain for web3. To fill the gap, we introduce DAPPaaS, the first customizable BaaS platform specializing in web3 applications, with carefully designed goals and principles to meet the goals. Our key technical contributions lie on the approaches enabling component modularization, communication efficiency, automated scaling and performance monitoring, distributed deployment, and resource optimization. We implement a real-world application based on DAPPaaS and conduct extensive performance evaluations to validate its utility and efficiency.
This deposit formalizes a decentralized framework for certifying and archiving AI models, scientific content, and intellectual creations using NFTs and blockchain metadata. It includes a ready-to-use NFT smart contract (ERC-721), structured metadata, digital certificate with SHA256 hash, and a minting interface. The DOI reference is embedded for scientific traceability. All files are timestamped, signed (Anne Povie), and packed for archival or minting purposes. This submission is intended to support researchers, developers, and creators in producing immutable, verifiable scientific artifacts using Web3 infrastructur e.
Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between network friction and base-layer monetary velocity. Using a verified Transaction Cost Index (TCI) derived from Blockchain.com on-chain data and Hansen's (2000) threshold regression, we identify a definitive structural break at the 90th percentile of friction (TCI ~ 1.63). The analysis reveals a bifurcation in network utility: while the network exhibits robust velocity growth of +15.44% during normal regimes, this collapses to +6.06% during shock regimes, yielding a statistically significant Net Utility Contraction of -9.39% (p = 0.012). Crucially, Instrumental Variable (IV) tests utilizing Hashrate Variation as a supply-side instrument fail to detect a significant relationship in a linear specification (p=0.196), confirming that the velocity constraint is strictly a regime-switching phenomenon rather than a continuous linear function. Furthermore, we document a "Crypto Multiplier" inversion: high friction correlates with a +8.03% increase in capital concentration per entity, suggesting that congestion forces a substitution from active velocity to speculative hoarding.
Medical imaging is essential for clinical diagnosis, yet real-world data frequently suffers from corruption, noise, and potential tampering, challenging the reliability of AI-assisted interpretation. Conventional reconstruction techniques prioritize pixel-level recovery and may produce visually plausible outputs while compromising anatomical fidelity, an issue that can directly impact clinical outcomes. We propose a semantic-aware medical image reconstruction framework that integrates high-level latent embeddings with a hybrid U-Net architecture to preserve clinically relevant structures during restoration. To ensure trust and accountability, we incorporate a lightweight blockchain-based provenance layer using scale-free graph design, enabling verifiable recording of each reconstruction event without imposing significant overhead. Extensive evaluation across multiple datasets and corruption types demonstrates improved structural consistency, restoration accuracy, and provenance integrity compared with existing approaches. By uniting semantic-guided reconstruction with secure traceability, our solution advances dependable AI for medical imaging, enhancing both diagnostic confidence and regulatory compliance in healthcare environments.
Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman · 7 authors
We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Request for Comment 20 (ERC-20) transactions shifted on November 3, two days before the election, while exchange trading volumes reacted only on Election Day. Automated smart-contract activity adjusted much later, with structural breaks appearing in January 2025. We validate these shifts using surrogate-based robustness tests. Complementary energy-spectrum analysis of Bitcoin and Ethereum identifies pronounced post-election turbulence, and a structural vector autoregression confirms a regime shift in stablecoin dynamics. Overall, human-driven stablecoin flows act as early-warning indicators of political stress, preceding both exchange behavior and algorithmic responses.
This study proposes a gamification-based educational model that integrates blockchain concepts and reinforcement learning (RL) principles for elementary students.While blockchain education is often abstract and unsuitable for younger learners, the proposed card game-based approach allows students to experience hash functions, consensus algorithms, and distributed ledgers through interactive activities.Instructional design followed the Dick and Carey model, and the MDA framework was applied to align game mechanics with cognitive, emotional, and social objectives.RL mechanisms such as exploration-exploitation balance and reward shaping were embedded to sustain engagement and motivation.The model's effectiveness was evaluated through expert review involving four technology specialists and nine elementary school teachers.Results showed consistently positive ratings across five metrics (Innovation, Effectiveness, Applicability, Motivation, Efficiency), with averages above 3.8 on a 5-point scale.Particularly high scores were recorded for Innovation (M=4.14) and Efficiency (M=4.09).These findings indicate that the model is both novel and practical, offering a promising approach to making abstract technical concepts accessible at the elementary level.
This study examines the emerging convergence of triple-entry accounting, blockchain technology, and machine learning as a transformative framework for enhancing financial transparency. Using a bibliometric analysis of Scopus-indexed publications from 2000 to 2025, the research identifies key intellectual structures, thematic clusters, and temporal trends that shape this field. The results show that blockchain serves as the foundational infrastructure enabling immutable, verifiable accounting records, while machine learning functions as an analytical layer that strengthens anomaly detection, continuous auditing, and fraud prevention. Triple-entry accounting is found to be evolving from a conceptual innovation into a practical accounting architecture supported by cryptographic verification and distributed ledger systems. The study highlights significant implications for auditors, regulators, and organizations seeking to modernize financial reporting through automation and secure digital ecosystems. Although promising, the research also notes limitations related to data scope, conceptual depth, and the need for empirical validation. Overall, the findings underscore the potential of technologically integrated accounting systems to redefine trust, accountability, and transparency in modern financial environments.
Abstract Taxation justice and fiscal federalism are foundational pillars for building an inclusive, democratic, and sustainable nation. As countries diversify economically and socially, the role of equitable taxation becomes central to financing public goods, reducing inequality, and strengthening socio-political cohesion. Fiscal federalism, which concerns the distribution of financial powers and resources across central, state, and local governments, further reinforces the principles of subsidiarity, autonomy, and accountability required in a modern democratic state. This research paper analyses how taxation justice and fiscal federalism contribute to nation-building, examines structural gaps in existing fiscal arrangements, and highlights the need for transparent resource allocation, participatory governance, and decentralized fiscal empowerment. Using qualitative secondary data and descriptive analysis, the study demonstrates that taxation systems that are equitable, efficient, and progressive combined with a well-designed fiscal federalism framework help strengthen democratic participation, reduce regional disparities, support sustainable development, and stabilize public finance. The paper concludes by offering policy recommendations to enhance the equity and efficiency of taxation systems, thereby improving fiscal governance and promoting inclusive nation-building.
Margaret Simangunsong, Zamzami Zamzami, Parmadi Parmadi
This study aims to: 1) identify and analyze the degree of fiscal decentralization and the level of financial independence of regencies and cities in North Sumatra Province during the period 2015–2024; and 2) analyze the variation in regional revenue realization based on its components, as well as the variation in regional financial capacity across regencies/cities during the same period. The methods used in this research include the Fiscal Decentralization Degree Ratio (DDF), the Regional Financial Independence Ratio (RKKD), and a Two-Way ANOVA test, supported by SPSS 20 software. The findings show that the degree of fiscal decentralization remains relatively low from year to year, indicating a strong dependence on central government transfers. Similarly, the regional financial independence ratio is also categorized as low, with an instructive pattern of relationship, meaning that regional governments still have limited ability to finance development needs independently. The Two-Way ANOVA test results reveal significant differences in regional revenue realization both across regencies/cities and across years within the study period. The largest variations are attributed to the differing characteristics of each regency/city, including economic potential, effectiveness in managing locally generated revenue, and variations in regional fiscal structures. These findings highlight the importance of enhancing fiscal capacity and optimizing local revenue sources throughout North Sumatra.
A Modular DSP Architecture for Extreme-Precision Computation of π Author: José Ignacio Peinador SalaContact: joseignacio.peinador@gmail.comORCID: 0009-0008-1822-3452 🎯 TL;DR: What's This About? Problem: Calculating π at extreme precision hits a "Memory Wall" — parallel algorithms choke on shared memory access. Breakthrough: We discovered that π's calculation can be decomposed using modular arithmetic (mod 6), creating 6 independent computation channels with zero inter-thread communication. Key Insight: This decomposition is grounded in a formal isomorphism with polyphase filter banks in Digital Signal Processing (DSP), a bridge between number theory and engineering established in our companion work. Result: ✅ 100 million digits of π computed with just 6.8 GB RAM (95% parallelisation efficiency) ✅ Shared-Nothing architecture with strictly isolated memory per channel ✅ Stride-6 transition leaf with exact phase correction, compressing recursion depth by 2.6× ✅ Open-source implementation in Python/gmpy2, executable on Google Colab's free tier Why it matters: This architecture transforms an intrinsically memory-bound problem into a CPU-bound one, enabling near-linear scaling on commodity hardware without specialised HPC infrastructure. 📖 Executive Summary This repository hosts the reference implementation and experimental validation of the Hybrid Stride-6 architecture for extreme-precision computation of π. The architecture exploits the arithmetic structure of the Chudnovsky series by decomposing it into six independent modular channels, each processed by a dedicated worker with its own memory space. The decomposition is not an ad hoc optimisation but rests on a rigorous mathematical foundation: the polyphase isomorphism between modular arithmetic on ℤ/6ℤ and multirate signal processing. This isomorphism guarantees perfect reconstruction (no information loss across channels) and orthogonality (no inter-channel interference). The architecture is validated through the 100M Barrier Run: computing 10⁸ digits of π on a resource-constrained Google Colab instance (2 vCPUs, 12 GB RAM) in under 20 minutes, with 95% parallelisation efficiency and a sustained throughput of over 83,000 digits per second. 🏆 Key Contributions 🔬 Theoretical Foundations (Summarised from Companion Work) Polyphase Isomorphism: Formal proof that modular decomposition of integer-indexed series is equivalent to polyphase decimation in DSP Hexagonal Lattice Connection: Geometric motivation via the A₂ lattice (densest circle packing in the plane) Perfect Reconstruction Guarantee: Mathematical proof that the six channels recombine without aliasing or leakage ⚡ Computational Architecture Shared-Nothing Design: Six independent Python processes with strictly isolated memory spaces Stride-6 Transition Leaf: Processes blocks of 6 consecutive terms in a single operation, reducing recursion tree depth by log₂6 ≈ 2.585 Critical Phase Correction: Direct accumulation of the linear term B(k) prevents off-by-one-stride phase errors 📊 Experimental Validation 100M Barrier Run: 100 million digits computed on 12 GB RAM with 95% parallel efficiency Orthogonality Verification: ℓ² norm of channel terms matches norm of original series to machine precision Reference Comparison: All 10⁸ digits match y-cruncher reference values exactly 📈 Performance Highlights 🚀 "The 100M Barrier Run" — Extreme Validation Metric Result Significance Digits Calculated 100,000,000 Exascale-capable architecture Total Time 1,194.32 s (19.90 min) Sustained performance on cloud hardware Parallel Efficiency 95% (1.90× speedup) Near-linear scaling on 2 cores Peak RAM Usage ~6.8 GB Runs within 12 GB Colab limit Throughput 83,729 digits/second Competitive with optimised implementations Numerical Integrity Bit-exact match with y-cruncher Zero cumulative error 🏗️ Architectural Comparison Aspect Monolithic Binary Splitting Hybrid Stride-6 (This Work) y-cruncher (State-of-Art) Memory Pattern Contiguous, saturates bus Local per core, optimises cache Sequential disk I/O Parallel Model Fine-grained synchronisation Embarrassingly parallel (6 processes) Optimised with locks Scalability Memory-bound CPU-bound, linear to 6 cores Disk-speed limited RAM Requirement Entire dataset in memory Working set reduced 6× Uses disk as RAM Design Philosophy Maximise single-thread speed Maximise resource efficiency Maximise absolute speed 🚀 Quick Start & Reproduction 1. Instant Online Experiment (Recommended) Click above to run the complete experimental validation in Google Colab — no installation required! 2. Key Experiments to Reproduce The companion notebook provides step-by-step reproduction of all manuscript claims: Theoretical Foundation: Verify the polyphase decomposition and energy conservation Stride-6 Algorithm: Test parallel computation with arbitrary precision (100k digits) 100M Barrier Run: Reproduce the full-scale benchmark (requires ~7 GB RAM) Performance Analysis: Measure speedup and parallel efficiency ⚙️ Technical Implementation Details The "Stride-6" Computational Engine Unlike conventional Binary Splitting (processes terms individually), our engine implements a compressed transition leaf that calculates the aggregate effect of 6 consecutive terms: def stride6_leaf(k_start): """Calculate compressed transition for block [k, k+5]""" P, Q, B_acc = 1, 1, 0 for m in range(6): n = k_start + m P_n, Q_n, B_n = compute_chudnovsky_term(n) P *= P_n Q *= Q_n B_acc += B_n # Critical phase accumulation T_leaf = Q * B_acc # Correct phase synthesis return P, Q, T_leaf Key Innovation: Direct accumulation of the linear term B(n) prevents phase drift, preserving arithmetic integrity at any scale. Shared-Nothing Architecture Each of the 6 workers operates in complete memory isolation: Independent address spaces (no shared memory locks) Local garbage collection (prevents heap fragmentation) Cache-optimised access patterns (maximises L1/L2 utilisation) Numerical Stability Guarantees Orthogonal decomposition — zero information loss (verified experimentally) Arbitrary precision backend (gmpy2) with proven numerical stability Exact phase correction in the Stride-6 leaf 📚 Citation & Academic Use If this work contributes to your research, please cite: @article{peinador2026modularDSP, title={A Modular DSP Architecture for Extreme-Precision Computation of π}, author={Peinador Sala, José Ignacio}, journal={Zenodo}, year={2026}, doi = {10.5281/zenodo.17768718}, url = {https://github.com/NachoPeinador/Arquitectura-de-Hibridacion-Algoritmica-en-Z-6Z} } The companion theoretical work establishing the polyphase isomorphism is: @article{peinador2026polyphase, title={Polyphase Isomorphism between Modular Arithmetic and Multirate Signal Processing}, author={Peinador Sala, José Ignacio}, year={2026}, publisher={Zenodo}, doi = {10.5281/zenodo.17680023} } 🌐 The Broader Research Programme This architecture is one component of a larger investigation into the computational and physical consequences of the ℤ/6ℤ modular symmetry. Related projects include: Polyphase Isomorphism: Formal mathematical proof of the isomorphism between modular arithmetic and DSP. Modular Substrate Theory: Unified framework for cosmology and hadronic physics. Topological State Preparation: Quantum register initialisation and dissipative protection via ℤ/6ℤ superselection. Common Thread: All projects leverage modular arithmetic (ℤ/6ℤ) as a fundamental organising principle across mathematics, physics, and computation. ⚖️ Licensing & Usage ✅ Academic & Research Use (Free) Available under PolyForm Noncommercial License 1.0.0: Permitted: Academic research, teaching, personal projects, non-commercial forks Requirements: Attribution, license preservation, non-commercial use ⛔ Commercial Use (License Required) Commercial applications require explicit permission, including: Integration into proprietary software products Commercial hardware benchmarking services SaaS platforms and cloud computing services 💼 For Commercial Licensing Inquiries:Contact: joseignacio.peinador@gmail.comSubject: "Commercial License Inquiry — Modular π Architecture" 🌟 Acknowledgments This independent research was enabled by: Infrastructure & Tools Google Colab for democratised computational resources Python ecosystem (gmpy2, NumPy, SciPy, Jupyter) for scientific computing GitHub for open collaboration infrastructure Data & References y-cruncher for validation benchmarks Digital Signal Processing community for foundational theory Community & Inspiration The open-source scientific community for collective knowledge advancement Independent researchers worldwide pushing boundaries outside traditional institutions Last updated: June 2026 | Version: 3.0 | Status: Actively Maintained
The maritime sector is undergoing a profound digital transformation (e.g., e-Navigation) but currently operates in a complex environment without a defined trust model, creating a strong need for secure communication. Current technical efforts, such as the Maritime Connectivity Platform (MCP), rely on traditional, centralized PKIs. This approach introduces single points of trust and failure and utilizes revocation mechanisms (like CRLs and OCSP) that are inadequate, especially in offline maritime scenarios. This thesis proposes a "privacy-aware" Distributed PKI (DPKI) architecture built on a Permissioned Distributed Ledger (PDL) to overcome these limitations. The solution employs a "Dual-Chain" model to logically separate information: an Identity channel stores PII (Personally Identifiable Information) with access restricted to Ports and Maritime Authorities, while a Certificate channel stores anonymous (pseudonymous) X.509 certificates, accessible to all members. In this decentralized model, actors (Ocean Carriers, Ports, Authorities) maintain independence by managing their own nodes; carriers can even deploy nodes on ships. This eliminates the single point of trust and failure. A Proof of Concept using Hyperledger Fabric was developed to validate the architecture. The primary innovation is the ability to enable offline certificate verification (e.g., Ship-to-Ship scenarios) by leveraging the local copy of the ledger. The "Dual-Chain" model provides selective privacy, balancing operational anonymity with controlled "linkability" by authorities. The use of standard X.509 certificates ensures native interoperability with existing protocols like TLS and SECOM while the PDL guarantees data integrity, non-repudiation, and auditability.
This study introduces a functional EEG-based Multi-Factor Authentication (EEG-MFA) system engineered for accessibility and security utilizing affordable consumer hardware. Our version uses the BioAmp EXG Pill ( |3,000) with Arduino UNO, which is far cheaper than standard biometric systems that need expensive medical-grade equipment (|50,000–|500,000). It gets 86.7% authentication accuracy when the signal is good.The system uses three authentication factors: a password (knowledge), a pattern (behavior), and an EEG biometric (inherence). This makes it more secure. We utilize One-Class SVM with RBF kernel (nu=0.1) for user modeling, which means we don’t have to collect fake data, which is a big problem when using biometrics. The system learns brain patterns unique to each user using just 3–5 enrollment recordings (12 seconds each) and a simple electrode setup (3 electrodes: forehead + ears).Recent improvements in open-source EEG gear have made it much cheaper. With devices like the BioAmp EXG Pill (around 3,000 rupees), OpenBCI boards (100–500 dollars), and NeuroSky MindWave (100 dollars), students can do projects and small-scale research that weren’t possible before with medical-grade equipment. This lower price makes it possible to look into EEG authentication outside of established labs, utilizing real-world consumer technology that has its own problems. Some of the most important new features are: (1) an adaptive learning mechanism that lowers the False Rejection Rate from 20% to 0% over five sessions while keeping the False Acceptances at zero; (2) a tolerance margin system (10%) that makes up for differences in electrode placement; and (3) a complete end-to-end implementation with FastAPI backend, PostgreSQL database, and Next.js frontend.When we tested with real consumer hardware, we found that the most important performance aspect was signal quality (electrode preparation). With the right setup, we got an 80% genuine acceptance rate and a 0% imposter acceptance rate. The 10% Equal Error Rate (EER) is higher than medical-grade systems (¡5%), but it shows that it is possible to use it for specialized security applications, educational research, and proof-of-concept deployments where cost is more important than accuracy.
This paper explores how Bitcoin and Ethereum differ from traditional financial assets such as gold, Brent crude oil, the S&P 500 and Apple Inc. in terms of risk, return and integration with the traditional financial market over the period of 2018-2025. The thesis evaluates whether these digital assets can serve as viable components of a diversified investment portfolio. The motivation stems from the recent institutionalization of cryptocurrencies, including the recent approval of spot Bitcoin and Ethereum ETFs and wide public interest. 2858 observations of log returns were used to analyse correlation, multivariate regression, volatility, CAPM regression and Sharpe ratio. The results show that Bitcoin and Ethereum exhibit very low correlations with traditional assets, which supports their ability to act as diversifiers. The regression models revealed that gold and the S&P 500 have small but statistically significant explanatory power for cryptocurrency returns, while Apple Inc. and Brent crude oil do not. Volatility analysis confirms that Bitcoin and especially Ethereum are much more volatile than all traditional assets in the sample. CAPM results show that both digital assets respond positively to market movements, implying slow financial integration. Returns of cryptocurrencies were extremely high, but when the Sharpe ratios were computed, cryptocurrencies showed weak risk-adjusted performance, compared to Apple Inc. and gold. Overall, the findings show that cryptocurrencies are assets with high risk and are driven more by crypto-specific factors, but are increasingly integrating into the broader traditional financial market. They provide diversification benefits but only in small allocations.
Abstract- This survey paper presents a comprehensive examination of blockchain technology and its diverse applications across multiple domains. It begins by outlining the historical evolution and fundamental structure of blockchain systems, followed by an in-depth discussion of the core technologies that enable decentralized and secure operations. The study further explores the potential applications of blockchain, highlighting its transformative impact on various industries. Additionally, the paper analyzes the key challenges associated with blockchain adoption, offering insights into current limitations and areas requiring further research. Keyword- Blockchain technology, Consensus mechanisms, Smart contracts, Cryptography, Distributed Ledger, Supply Chain Management, Healthcare Data Security, Identity Management, Asset Tokenization, Blockchain Applications
The rapid growth of financial technology (fintech) has transformed the global economic landscape, including the Islamic finance sector, which seeks to align innovation with Shariah principles. This study aims to analyze the opportunities and challenges of applying blockchain technology and smart contracts in the Islamic fintech ecosystem, particularly in the context of strengthening Islamic financial principles in the digital era. It employs a Systematic Literature Review (SLR) approach combined with qualitative descriptive analysis of fifteen scientific articles indexed in Scopus, ScienceDirect, Garuda, and Sinta, covering the period from 2020 to 2025. The data was analyzed thematically to identify patterns of findings, research gaps, and academic and practical implications. The results indicate that blockchain technology and smart contracts have the potential to enhance transparency, efficiency, and accountability in Islamic financial transactions. Their implementation also opens opportunities for product innovation, such as smart sukuk and Islamic crowdfunding, which foster Shariah-based financial inclusion. However, challenges remain, including unclear Shariah digital regulations, technological complexity, low digital literacy, and issues of ethics and data security. The synthesis of findings highlights the need for collaboration among regulators, technology experts, and scholars to develop adaptive and Shariah-compliant fintech standards.