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.
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.
Blockchain is the new age internet equivalent evolution along with the rise of artificial intelligence they both are revolutionizing a new way of safer, faster, and government free interaction for real time financial verification. This study primarily investigates the role of these new age technologies in providing better security and transparent transactions in accounting systems related to crypto notably in decentralized finance (DeFi) ecosystems currently prevailing on solana, Ethereum and Base networks. These blockchain networks currently power billions of transactions in value using great techniques to ensure immutable, verifiable audit trails while removing any 3rd party interference In 2025 major accounting firms and compliance institutions will use AI-driven analytics, anomaly detection and predictive modelling to provide much better scope and structure of audit; this will help to decrease human errors and compliance delays by an estimate of 30%. AI along with blockchain technologies will provide real time decentralized monitoring and forensic analysis via smart contracts with include the major one such as ERC-20The combination of blockchain’s immutable ledger and AIs supremely adaptive intelligence creates a new wave of real time auditing right from retrospective verification to predictive ongoing assurance. However due to delays and legislative fragmentation issues and government ethical remain major obstacles for successful development. Finally our study demonstrates that the combination of Al with blockchain marks a fundamental change toward transparent, automated, and resilient accounting ecosystems capable of maintaining confidence in an increasingly digital and decentralized global economy.
Open access
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Blockchain Technology Applications and Security
Internet of Things and AI
Innovations and Analysis in Business and Education
This study does a bibliometric analysis of financial inclusion research within the framework of a sustainable economy, utilizing papers indexed in a prominent scientific database from 2000 to 2025. The study utilizes performance analysis and scientific mapping methodologies through VOSviewer and Bibliometrix to investigate publication patterns, prominent authors, institutions, countries, and networks of keyword co-occurrence. The findings indicate that financial inclusion and sustainable development form the primary conceptual core, intricately linked to economic growth, financial development, and sustainability. Contemporary research is mostly focused on digital issues, including fintech, digital financial inclusion, and decentralized finance, which progressively associate inclusive finance with environmental performance, green innovation, and the reduction of carbon emissions. Networks of international collaboration indicate that emerging economies, notably China, India, Pakistan, and South Africa, assume a prominent role, but such collaboration is predominantly localized rather than entirely global. The study elucidates the structure and history of this interdisciplinary domain, identifies significant research clusters and deficiencies, and delineates avenues for further exploration of inclusive and sustainable financial systems.
The fast growth of Internet of Things (IoT) technologies has turned smart cities into big data ecosystems for intelligent mobility, energetic efficiency and public services. But this increasing reliance on IoT data raises significant privacy issues because of the perpetually gathered sensor readings, inter-organisational sharing and algorithmic analyses. In this paper, we focus on the state-of-the-art IoT data-sharing methods that preserve privacy and protect recent progress in preserving privacy while sharing data in the IoT personal record by preserving statistical value. It combines traditional approaches, including anonymisation, differential privacy, federated learning, secure multiparty computation and homomorphic encryption with new technologies (e.g., blockchain-enabled governance, edge intelligence or zero-knowledge proofs) (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The paper analyses the impact of hybrid architectures combining edge-cloud cooperation and decentralised access control for improving data protection, in terms of not losing performance or interoperability. Conclusions: Summary of the main findings, Barriers to Implementation. This paper identifies several ongoing barriers, including computational expense, related to past research. Personal data while preserving its analytical worth. It combines cutting-edge technologies like blockchain-enabled governance, edge intelligence, and zero-knowledge proofs with traditional strategies like anonymisation, differential privacy, federated learning, secure multiparty computation, and homomorphic encryption (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The study investigates how hybrid architectures that incorporate decentralised access control and edge-cloud collaboration can improve data security without compromising interoperability or performance. The results point to enduring obstacles, such as interoperability, computational overhead, and regulatory compliance, especially in urban settings with limited resources. In order to integrate privacy-by-design principles into IoT analytics for smart city governance, the study suggests a multi-layered conceptual framework. To maintain public confidence in urban digital transformation, this framework places a strong emphasis on open data policies, citizen consent procedures, and the incorporation of cutting-edge cryptographic techniques. The information adds to the current discussion on how to balance privacy and innovation in smart cities and provides guidance for system architects, legislators, and municipal IT leaders who want to adopt IoT responsibly.
Hassan Raza, Tsendayush Erdenetsogt, Muhammad Mohsin Kabeer, Muhammad Arsalan Aslam · 5 authors
The block chain technology has become a potential solution to improving security, privacy, and trust on contemporary data management systems. Conventional centralized systems are easily breached, tampered with and unauthorized access makes it necessary to have decentralized systems that cannot easily be tampered with. Block chain offers immutability, transparency, and cryptographic security and smart contracts offer automated access control and auditing. Sensitive information is safeguarded using privacy-saving methods, such as encryption, a zero-knowledge proof, and decentralized identity schemes. Scalability and collaboration are further increased with integration with cloud and big data systems. This review identifies the uses of Block chain, challenges and future research direction, which shows that Block chain is capable of changing the way secure and privacy-conscious data management is achieved.
Ra’ed Fawzi Aburoub, Nabeel Mahdi Althabhawi, Mohamad Rizal Abd Rahman, Ammar Abbas Kadhim
This paper explores the lifecycle of a smart contract, from the stages of coding and deployment to execution and verification, in order to show that a smart contract can indeed be self-executing, transparent, and immutable. While such functionalities introduce efficiency, trust, and reliability within industries such as financial, supply chain management, and health sectors, smart contracts at the same time have a host of technical and legal challenges arising. This paper identifies key issues: critical vulnerabilities in coding, deployment on immutable blockchains, address assignment complexities, triggering mechanisms, and aspects of privacy. This study has adopted a critical analytical approach to evaluate the technical and legal aspects of smart contract formation, complemented by inductive reasoning to derive general insights and recommendations from specific cases and patterns. The study states that the apt legal framework must be provided for liability, regulatory compliance, and solutions that would be unlooked-for. It further supports hybrid models that blend automation with human oversight, superior communication protocols regarding updating an address, and the use of technologies that allow transparency with the preservation of confidentiality in a balance. The concrete ideas it offers are attempts at technology design aligned with legal frameworks by bringing developers, regulators, and stakeholders together in implementing certain solutions. It emphasizes that continuous research will hence be important to assure reliability, security, and equitability in the adoption of smart contracts, expanding possibilities for their application in an increasingly changing digital environment.
International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Open access
Advanced Data Storage Technologies
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
The DeepFake tech has had a theatrical impact on the visual arts, not only the provision of creative technology, but also the question of authenticity, copyright and misinformation. The deep learning and generative adversarial networks (GANs) produce deepfakes artificial images, which are extremely harmful to art. The article discusses the DeepFake detection and management within visual art work with emphasis on the practical application of analysis through multiple-layered approaches that would assist in ensuring the presence of the digital authenticity. DeepFake was managed through three core approaches, namely AI-Based Detection Frameworks, Blockchain-Based Authentication System, and Human-AI Collaborative Review Models. The decentralized strategy was based on blockchain technology, which was the Non-Fungible Token (NFT) registration by the cryptographic hashing to authenticate the provenance and ownership of the artworks. The human-AI composite system has integrated the inspection of the specialists on the visual level with the automatic monitoring of the anomalies to increase the readability and reduce the number of false alarms. The experiment revealed that the AI-based systems, blockchain approaches, and the collusion between human beings and AI detected 92.3, 87.6 and 94.1 % of people respectively. These findings suggest that the incorporation of algorithmic intelligence, a safe check, and human knowledge can help in quite a powerful DeepFake verification and management in the field of visual arts.
Open access
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Aesthetic Perception and Analysis
Digital Media and Visual Art
Generative Adversarial Networks and Image Synthesis
Healthcare collaborative processes still encounter major challenges, particularly regarding the interoperability of heterogeneous information systems, the traceability of medical interventions, and the secure sharing of patient data under strict privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper presents a patient-centric, blockchain-based framework designed to overcome these limitations. The proposed solution integrates smart contracts and non-fungible tokens (NFTs) within the Ethereum blockchain to ensure data integrity, traceability, and privacy preservation. Furthermore, a compliance-by-design mechanism is embedded into the smart contracts to enable self-supervision of collaborative workflows without third-party intervention. A Proof-of-Authority (PoA) consensus protocol is also adopted to optimize validation efficiency and significantly reduce computational and energy costs.
1. Assistant Professor., Dr. Avinash Gudimetla, Katta Kameswara Rao, Pudi Eswar Prasanth · 7 authors
Swarm Intelligence and the Internet of Things (IoT)are rapidly evolving fields that intersect to create innovative solutions. This abstract explores how swarm intelligence, inspired by collective behavior in natural systems, can be applied to enhance the efficiency, scalability, and adaptability of IoT networks.It discusses key concepts such as decentralized decision making,self organization, and emergent intelligence withinIoT environments. The abstract also highlights practical applications, benefits, and challenges of integrating swarm intelligence algorithms with IoT technologies, paving the way for advanced autonomous systems and intelligent networks in diverse domains.
Perkembangan teknologi komputer dan telekomunikasi meningkatkan efisiensi pengolahan data, namun menimbulkan tantangan keamanan, khususnya pada data rekam medis elektronik (RME) yang bersifat sensitif. Penelitian ini mengimplementasikan metode Zero-Knowledge Proof (ZKP) dan Revest Shamir Adleman (RSA) untuk meningkatkan keamanan dan privasi RME. ZKP memungkinkan pembuktian tanpa mengungkapkan informasi rahasia, sedangkan RSA menjaga kerahasiaan dan integritas data melalui enkripsi-dekripsi. Hasilnya, entropi data meningkat 24,53% (4,8314 menjadi 6,0165 bits/byte) setelah enkripsi RSA 2048-bit dengan padding OAEP berbasis SHA-256. Protokol ZKP metode Schnorr berhasil diimplementasikan tanpa membocorkan rahasia pengguna. Pengujian pada 100 pengguna simultan menunjukkan waktu respons rata-rata 1,8 detik dengan keberhasilan permintaan di atas 94%. Tantangan utama adalah beban komputasi autentikasi ZKP dan efisiensi saat jumlah pengguna bertambah. Integrasi RSA dan ZKP terbukti efektif meningkatkan keamanan, menjaga privasi, dan mempertahankan kinerja sistem RME.
Zero-knowledge proofs (ZKPs) have evolved from foundational interactive proof systems to highly efficient, scalable, and trusted-setup-free constructions powering today’s privacy-preserving and blockchain applications. The field began with the seminal works of Goldwasser, Micali, and Rackoff (GMR) and Goldreich, Micali, Wigderson (GMW) in the 1980s, which introduced interactive proofs, knowledge complexity, and showed that all NP languages admit zero-knowledge proofs. The 1990s brought non-interactive ZK (NIZK) via the CRS model (Blum–Feldman–Micali) and practical sigma-protocols like Schnorr proofs, establishing foundational tools still used today. From the 2000s through early 2010s, research integrated pairings, PCPs, and cryptographic soundness, culminating in pairing-based NIZKs and early succinct argument systems. The SNARK revolution accelerated with QAP-based zk-SNARKs (Gennaro–Gentry–Parno), practical implementations like Pinocchio and libsnark, and the highly efficient Groth16 proving system that became a blockchain standard. Since 2018, the field has shifted toward transparent, universal, and post-quantum-secure systems. Innovations include Bulletproofs (short proofs without trusted setup), zk-STARKs (scalable and PQ-secure), PLONK (universal/updatable setup), and Halo/Halo2 enabling recursive proofs without trusted setup. These advances underpin modern Zcash deployments, zk-rollups, and privacy-preserving scaling systems across Web3. Overall, the ZKP landscape has progressed from theoretical constructs to practical, scalable, and secure systems central to modern cryptography and decentralized computation.
Stablecoin ENTISQ (ENUR TAGA ISTIQARAR) Technical Whitepaper v1.1 1. Executive Summary ENTISQ (Energy + Nur + Taqa + Istiqarar) is an innovative digital asset backed by the economic fundamentals of the GCC energy sector and synthetically pegged to AED and SAR. ENTISQ creates a new class of stable assets by combining currency stability with the region’s energy foundation. Objective: Provide a reliable stablecoin for cross-border payments, B2B transactions, energy contract settlements, and Web3 integrations within the GCC. 2. Mission & Vision Mission: Deliver a stable, transparent, and predictable digital asset for the GCC linking currency and energy markets. Vision: ENTISQ aims to become the benchmark digital currency of the region, serving as a foundation for a sustainable economy and energy sector.
ASES — Arabian Sustainable Energy StablecoinTechnical Whitepaper v1.1 1. Executive Summary ASES (Arabian Sustainable Energy Stablecoin) is an innovative digital asset backed by the economic fundamentals of the GCC energy sector and synthetically pegged to AED and SAR. ASES creates a new class of stable assets by combining currency stability with the region’s energy foundation. Objective: Provide a reliable stablecoin for cross-border payments, B2B transactions, energy contract settlements, and Web3 integrations within the GCC. 2. Mission & Vision Mission: Deliver a stable, transparent, and predictable digital asset for the GCC linking currency and energy markets. Vision: ASES aims to become the benchmark digital currency of the region, serving as a foundation for a sustainable economy and energy sector.
Mekanisme konsensus memainkan peran krusial dalam menentukan efisiensi dan skalabilitas sistem blockchain. Dua algoritma yang paling umum digunakan adalah Proof of Work dan Proof of Stake, masing-masing dengan karakteristik performa yang berbeda dalam menghadapi beban transaksi tinggi. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja kedua mekanisme konsensus tersebut melalui pendekatan eksperimental berbasis simulasi. Pengujian dilakukan menggunakan Framework Hardhat dalam lingkungan lokal dengan dua skenario utama: transaction scaling dan burst transaction. Empat metrik evaluasi digunakan, yaitu throughput, transaction latency, finality time, dan mempool congestion. hasil pengujian menunjukkan bahwa Proof of Stake secara konsisten unggul dalam keempat metrik, dengan throughput tinggi, latency dan finality time yang stabil, serta mempool congestion yang terkendali. Sebaliknya, Proof of Work mengalami penurunan performa signifikan pada beban tinggi akibat proses mining yang tetap dan tidak adaptif. Uji statistik Mann-Whitney U menunjukkan bahwa perbedaan performa tersebut signifikan secara statistik di hampir semua metrik. Penelitian ini memberikan wawasan lebih dalam mengenai kelebihan dan keterbatasan masing-masing mekanisme konsensus dalam kondisi beban tinggi dengan menggunakan hardhat, serta kontribusi nya terhadap pemahaman skalabilitas blockchain dalam dunia nyata. Temuan ini mengindikasikan bahwa Proof of Stake lebih sesuai untuk implementasi blockchain berskala besar yang memerlukan efisiensi dan kecepatan tinggi.
Abstract This study aims to sociologically understand the ideas about organizations, mechanisms to realize those contained in Decentralized Autonomous Organization (DAO), and Web3. It specifically focuses on the unique relationship between people and objects in this context. In this study, DAO refers to an organization on the Internet that uses blockchain technology and that has no specific administrator (that is decentralized and flat), whereas Web3 refers to the way that the Web is organized premised on such an organization. A DAO attempts to reduce uncertain elements and create a firm, flat organization by introducing the physical technology of blockchain into an organization composed of people. However, its operation is more intricate. For example, blockchain is not purely an object but humans are embedded within it and their desires are harnessed as its driving force. This study attempts to describe such an intricate construct and specifically focuses on the effort needed to create a “flat” organization. It is not simply realized mechanically through the blockchain but people are also substantially involved, including in the pre-discussion process (off-chain). For example, whether a DAO becomes a flat organization may depend on the original relationships between its members. The workings of the blockchain are more dependent on the people participating in it and their relationships than they appear. Recently, some DAOs have placed less emphasis on protocols, such as on the automatic execution of the content of contracts, and more emphasis on community. However, irrespective of where they place the emphasis, no essential difference exists between them on the point that people are involved in them to a greater or lesser extent.
Digital forensics faces unprecedented challenges with the emergence of digital twins and metaverse technologies. This paper presents the first comparative analysis between blockchain-based and traditional database systems for managing digital twin evidence in forensic investigations. We conducted controlled experiments comparing the Ethereum blockchain with IPFS storage against traditional SQL databases for digital twin evidence management. Our findings reveal that while blockchain provides superior data integrity and immutability, crucial for forensic applications, traditional databases offer better performance consistency. The blockchain implementation showed faster average storage times but higher variability in retrieval operations. Both systems maintained forensic integrity through hash verification, though blockchain's immutable nature provides additional security guarantees essential for legal proceedings. This research contributes to the development of robust digital forensic methodologies for emerging technologies in the metaverse era.
As data is an essential asset for any DeFi application, selecting an oracle is a critical decision for its success. To date, academic research has mainly focused on improving oracle technology and internal economics, while the drivers of oracle choice on the client side remain largely unexplored. This study addresses this gap by gathering insights from leading DeFi protocols, uncovering their rationale for oracle selection and their preferences regarding whether to outsource or internalize data-request mechanisms. Data are collected from founders, C-level executives, and oracle engineers of 32 DeFi protocols, whose combined total value locked (TVL) exceeds 55% of the oracle-using DeFi segment. The study leverages a one-time mixed-method survey, using tailored question paths for in-house versus third-party oracle users. Quantitative answers are summarized, compared across groups, and examined through Spearman rank-order correlations to explore pairwise associations among evaluation dimensions, while open-ended responses are inductively coded into keywords and broader themes to triangulate common selection motives and switching challenges. Insights support the view that protocol choices are tied to technological dependencies, in which the immutability of smart contracts amplifies lock-in, hindering agile switching among data providers. Furthermore, when viable third-party solutions exist, protocols generally prefer to outsource rather than build and maintain internal oracle mechanisms.
Myriam Nonaka, F. Javier Marín-Rodríguez, Alexander Jiricny, Miguel Romance · 7 authors
We construct the Bitcoin User Network (BUN) directly from raw blockchain data up to late 2025, which allows us to explore its mesoscopic properties and trace its temporal evolution. In particular, we analyze the structure of connected components and directed assortativity through the four variants of Newman's coefficient, implemented via custom algorithms and a dedicated database. Building on this, to characterize the distribution of structural influence, we introduce direction-sensitive centrality measures based on PageRank and HITS, which provide a complementary global analysis of the BUN and reveal a persistently unequal and increasingly core-periphery structure. In addition, we complement the structural analysis with a study of Bitcoin's price volatility using high-frequency market data. Overall, our results reveal a clear pattern of concentration within distribution: although the protocol is decentralized by design, the emergent user network evolves toward an asymmetric mesoscopic structure that indicates the existence of a few large-scale connected components that function as the critical backbone of the system.
Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.