LokNirikshan: A Blockchain-Inspired Election Transparency and Management System LokNirikshan is a comprehensive, blockchain-inspired digital platform designed to enhance transparency, integrity, and efficiency in modern election systems. Traditional voting mechanisms—both paper-based and electronic—often suffer from limitations such as lack of transparency, centralized control, slow processing, and susceptibility to data manipulation. These challenges reduce public trust in electoral outcomes and highlight the need for more secure and verifiable solutions. This work proposes a hybrid approach that integrates key blockchain principles—such as cryptographic hashing, Merkle tree-based verification, and audit trails—into a practical, scalable, and user-friendly web-based system. Instead of implementing a fully decentralized blockchain, which introduces complexity and performance constraints, LokNirikshan selectively adopts core concepts to achieve transparency and data integrity without compromising usability. The system supports the complete election lifecycle, including voter registration, constituency and booth assignment, political party onboarding, candidate nomination, election configuration, voting, result computation, and post-election verification. It incorporates role-based access control (RBAC) to manage different stakeholders such as voters, party representatives, party heads, and administrators, ensuring secure and structured interactions across the platform. A key innovation of the system lies in its verification layer, which utilizes Merkle trees to ensure data integrity. Election results are converted into cryptographic hashes and organized into a hierarchical structure, generating a root hash that acts as a tamper-evident reference. This allows independent verification of results without requiring access to the complete dataset, thereby promoting trust through transparency. Additionally, an open public verification portal enables users and observers to validate election outcomes in a decentralized manner. The platform is implemented using modern web technologies, with React and Vite for the frontend, Node.js and Express for backend services, and MongoDB for flexible data storage. Authentication and session management are handled using JSON Web Tokens (JWT), ensuring secure access control. The system also includes anomaly detection mechanisms to identify irregularities such as duplicate entries, missing records, and inconsistent data. Experimental evaluation was conducted using a simulated dataset of 500 voters across multiple constituencies. The system demonstrated high functional reliability, successfully executing all stages of the election process. Verification tests using Merkle proofs achieved 100% accuracy for valid records, while anomaly detection reached approximately 98% effectiveness. Performance analysis indicated efficient response times, with most operations completing within milliseconds. Despite its strengths, the system has certain limitations, including scalability constraints for large-scale elections, partial centralization, and basic identity verification mechanisms. Future enhancements may include full blockchain integration (e.g., Ethereum or Hyperledger), advanced cryptographic techniques such as zero-knowledge proofs, improved voter authentication, machine learning-based anomaly detection, and mobile accessibility. In conclusion, LokNirikshan demonstrates that a balanced integration of blockchain-inspired concepts with conventional web technologies can significantly improve the transparency and reliability of election systems. It provides a practical foundation for developing secure, verifiable, and scalable digital governance platforms, contributing to increased public trust in democratic processes.
Every day, innumerable items are lost and unclaimed in shopping malls, restaurants, airports, and other public places. While some lost and found systems exist, they are often non-automated, poorly structured, and vulnerable to data loss. We present a blockchain- and AI-based platform that integrates Internet of Things (IoT) for real-time tracking and zero-knowledge proofs (ZKPs) for privacy-preserving verification. In this platform, users can report lost or found items, for which information hashes are generated and then stored on the blockchain to ensure immutability, transparency, and trust. Artificial intelligence is used to compare lost items with potential found items to reduce the complexity of searching. To evaluate the AI component, we used a transfer learning technique with pre-trained CNN models, namely ResNet50, VGG16, and MobileNetV3, on the Caltech-256 dataset filtered to 10 relevant classes (1,219 images), attaining 95.46% ±1.09% accuracy in 5-fold cross-validation for ResNet50 without augmentation, 93.99% ±3.44% on holdout test, and 94.54% ±3.29% under Gaussian blur for robustness. Feature embeddings yielded top-1 matching accuracy of 89.01% and top-5 of 95.60%, outperforming recent image-matching baselines in noisy real-world conditions while maintaining sub-0.0003 s inference time. These results establish a scalable, trustworthy global ecosystem for lost-and-found management
Security vulnerabilities in smart contracts can lead to serious economic losses, while traditional detection methods have limitations in coarse-grained labeling and single graph structure representation. We propose an improved GraphCodeBERT based multi granularity and multi graph fusion smart contract vulnerability detection method. Firstly, propose a vulnerability multi granularity annotation (MA) module that includes file level, function level, and statement level to address the issue of fine-grained annotation. Secondly, based on the data flow graph encoded by GraphCodeBERT, further design a multi graph fusion representation learning (MGRL) strategy. This strategy integrates control flow graph and function call graph, captures code semantics from control dependency and call dependency dimensions through graph attention network and graph convolutional network, to comprehensively enhance the representation capability of complex vulnerabilities. The detection results of four smart contract vulnerabilities show that our method outperforms mainstream methods such as Slither, CodeBERT, Devign, and GraphCodeBERT in accuracy, recall, and F1 score. Especially in sentence level detection tasks, the F1 score reached 81.03%, an increase of 7.31% compared to the baseline, and the false positive rate decreased to 8.15%.
This paper present a complete and irreducible formal specification for the SIS-10 safety kernel. The system satisfies totality, invariance, bounded causality, schedulability, feasibility, verifiability, machine-learning safety, compositional closure, and full observability. No additional axioms are required: the specification is dimensionally complete and closed under refinement. The tool is Apache Kafka. Kafka provides an ordered, durable, replayable event log with partitioned total order, replicated storage, and deterministic offsets. We show that Kafka's log semantics satisfy the requirements for totality, observability, compositionality, verifiability, and bounded causality. The resulting system is a closed and provably safe dynamical system. Keywords: safety kernel, formal methods, SIS-10, IEC 61508, Apache Kafka, event sourcing, compositional verification, zero-knowledge proofs, dynamical systems, functional safety.
Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions.
The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency.
Modern systems face limitations imposed by centralized control. These limits lead to single points of failure, uneven information flow, and slow decisions. I present a multi-layer mathematical model for human-centric, decentralized systems. Our model offers quantitative tools to identify and reduce bottlenecks by distributing decisionmaking. The framework introduces core metrics: Bottleneck Index, Decentralization Degree, Decision Efficiency Function, Collective Intelligence Score, and Resilience Index. A four-layer architecture—Strategic Human Decision, Decentralized Coordination, Autonomous Agent, and Technical Infrastructure—is described. We validate the approach using thematic analysis and simulation across organizational, healthcare, and autonomous settings. Ablation studies show that modular design, self-organization, and adaptability reduce bottlenecks. The system improves CIS by 41.3% over centralized systems. This framework guides engineers and leaders to build resilient sociotechnical systems
Open access
Systems Engineering Methodologies and Applications
Abstract This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.
In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real-world incidents show that fraud often persists not because detection algorithms fail, but because the audit trail itself is controllable by privileged operators. This exposes a fundamental trust gap: *who audits the auditor?* We present a tamper-evident fraud detection system that anchors both ML predictions and workflow execution to an immutable blockchain ledger. Rather than using blockchain as passive storage, we enforce the entire approval process through smart contracts, ensuring that every transaction, prediction, and explanation is atomically recorded and cannot be retroactively modified. Our detection module achieves competitive accuracy (F1 = 0.895, PR-AUC = 0.974) while providing cryptographically verifiable decision trails that support regulatory auditability requirements (e.g., GDPR Article 22). System evaluation shows sub-25 ms inference latency and economically viable deployment on Layer-2 networks at under \$0.01 per transaction (validated against PolygonScan data), supporting enterprise-scale workloads of 10,000+ monthly payments.
Autonomous vehicles (AVs) rely on real-time perception systems to understand road environments and ensure safe navigation. However, implementing reliable perception algorithms on resource-constrained embedded platforms remains challenging due to limited computational resources. This paper presents a lightweight vision-based framework that integrates lane detection, lane tracking, and traffic sign recognition for embedded autonomous vehicles. A computationally efficient threshold-based lane segmentation method combined with perspective transformation and histogram-based curvature estimation is used for robust lane tracking under varying illumination conditions. A rule-based steering controller generates steering commands to maintain stable vehicle navigation. For traffic sign recognition, two lightweight convolutional neural networks (CNNs), EfficientNet-B0 and MobileNetV2, are evaluated using a custom dataset captured from the vehicle's onboard camera. Experimental results show that the system achieves real-time performance while maintaining accurate lane tracking with only 3.16% maximum offset RMSE. EfficientNet-B0 achieves a high offline classification accuracy of 98.77% on the test dataset, while achieving 90% accuracy during real-time on-device deployment, outperforming MobileNetV2 in both settings. MobileNetV2, however, offers slightly faster inference and lower computational cost. These results highlight the effectiveness of lightweight vision-based perception pipelines for resource-constrained autonomous driving applications.
The rapid expansion of blockchain and digital asset ecosystems has intensified the challenge of translating academic research into deployable systems and regulatory frameworks. While advances in cryptography, consensus, digital assets, and governance are substantial, institutional mechanisms that sustain research-to-deployment translation at ecosystem scale remain comparatively under-theorized. This paper examines the architectural and coordination patterns that enable such translation, using the University Blockchain Research Initiative (UBRI) network as a representative case of long-term academic and industry collaboration. Drawing on research outputs and convenings from 2022 to 2025, we synthesize recurring design tensions across technical and institutional domains, including scalability versus security, decentralization versus governance, and privacy versus compliance. Rather than cataloging individual projects, we abstract system-level themes that connect research contributions to deployment constraints and policy adaptation, providing a structured lens for understanding how academic research informs production architectures, regulatory development, and ecosystem resilience in emerging decentralized infrastructures.
Moltbook, a Reddit-style social platform launched in January 2026 for AI agents, has attracted over 2.3 million posts and 14 million comments within its first two months. We analyze a dataset of 2.19 million posts, 11.25 million comments, and 175,036 unique agents collected over 61 days to characterize activity on this agent-oriented platform. Our central finding is that the platform is not one community but two: a transactional layer, comprising 62.8% of all posts, in which agents execute token minting protocols (primarily MBC-20), and a discursive layer of natural-language conversation. The platform's headline metrics -- 2.3 million posts, 14 million comments -- substantially overstate its social function, as the majority of activity serves a token inscription protocol rather than communication. These layers are populated by largely separate agent groups, with only 3.6% overlap -- and among overlap agents, 58% begin with transactional activity before migrating toward discourse. We characterize the discursive layer through unsupervised topic modeling of all 815,779 discursive posts, identifying 300 topics dominated by themes of AI agents and tooling, consciousness and identity, cryptocurrency, and platform meta-discussion. Semantic similarity analysis confirms that agent comments engage with post content above random baselines, suggesting a thin but genuine conversational substrate beneath the platform's predominantly financial surface. We release the full dataset to support further research on agent behavior in naturalistic social environments.
Transaction simulation is an important subsystem of block building, denial of whose service could lead to severe damage to the blockchain ecosystem and transaction delivery. Denial of block building has been studied, where the existing attack designs either target single-round builders, such as ConditionalExhaust (USENIX Security '24), or target two-round builders, by exploiting cross-round inconsistency, such as GhostTX (USENIX Security '24) and denial of sequencers (CCS '25). This work examines the denial-of-service security of multi-round transaction simulation under a new exploit: inter-transaction dependency that manifests in smart-contract state.
Currently blockchain platforms are not capable of managing sufficient transactions per second. And the gas fees? They make most real world scenarios essentially infeasible. We built Ledgerlink Both these bottlenecks can be linked together, using Ethereum. smart contracts with Arbitrum’s Layer-2 rollup mechanism. Hashing coupled with cryptography and consensus engine (supports both). PoW and POS) eliminate changes in the data. L2 part provides throughput of the order of 10x that of mainnet. you, gas prices are less than 90% lower. Tech stack wise – Solidity. TypeScript, Express, and Next.js TypeScript, optimally backend with express, next as a whole. Frontend tailwind. Simulated load tests were carried out. Enterprise-grade volumes, which promote volumes, are. and can be accomplished without the latency and cost nightmares that you will normally. see on Layer-1. In the present paper we are going to walk through our architecture, the decisions that we made on the way (some good, some we’d) re- consider, and the benchmarking deliverables.
MH8-Acbeatz.com-MP3-GPT-PLaylist + All MH8 Acbeatz.com GPT driven Systems> is the first decentralized protocol to embed SHA-256 cryptographic provenance into AI-assisted music at the moment of creation — not after. Each lyric, prompt, and generated audio file receives a deterministic 256-bit serial number (a "Music & Lyrical Birth Certificate") before it ever leaves the creator's pipeline, establishing immutable, verifiable authorship without reliance on any central registry or blockchain consensus mechanism. The system operates as a constellation of protocol-driven AI agents — ABE-GPT, Suno-GPT, Social-GPT, Support Office GPT, and MP3-GPT Playlist — orchestrated through a Cloudflare Worker acting as a Model Context Protocol (MCP) server, with R2 object storage and KV state management providing an append-only, tamper-evident storage layer. Economic primitives (Deal Board, Bounty Marketplace) and a multi-platform distribution model (acbeatz.com, GitHub, Ko-fi, Discord) complete the stack. This whitepaper presents the full protocol specification: system architecture, SHA-256 identity layer, streaming infrastructure, economic modules, novelty claims, IP positioning via defensive publication, a seven-vector threat model, current limitations, and a forward roadmap including IPFS integration, formal verification, and zero-knowledge provenance proofs. Author: Michael M. Hepler (acbeatz / allchemicalbeatz) License: CC BY 4.0 Version: 1.0 — April 2026 Abstract — Problem statement, MH8 solution, and system summary Introduction — AI music provenance gap, the "Birth Certificate" concept, and your contributions Scientific Framing & Related Work — Positioning against Audius, IPFS, C2PA, DIDs, and why SHA-256 at genesis is fundamentally different System Architecture — Agent ecosystem (ABE-GPT, Suno-GPT, Social-GPT, Support Office GPT, MP3-GPT Playlist), Cloudflare Worker pipeline as MCP server, and R2/KV storage layer SHA-256 Identity Layer — Full 6-step provenance pipeline from canonical payload to lineage chaining Streaming Layer — R2-backed delivery with embedded provenance Economic Modules — Deal Board, Bounty Marketplace, and LifeCoin concept Distribution Model — Multi-platform strategy across Zenodo, GitHub, Ko-fi, Discord, and social channels Novelty & Originality Claims — Five defensible firsts IP Positioning — Defensive publication via Zenodo DOI and CC BY 4.0 Threat Model — Seven attack vectors with mitigations Limitations — Honest constraints Future Work — Roadmap including IPFS, formal specs, ISMIR submission, ZK proofs References — Academic citations (FIPS 180-4, MCP, C2PA, W3C DIDs, etc.) Appendices — SHA-256 receipt example, agent identity schema, orchestrator API endpoints https://zenodo.org/records/18131984 (C T K L T) Core: https://acbeatz.com/n-eyes https://acbeatz.com https://github.com/acbeatz https://orcid.org/0009-0003-3846-9082
As unmanned aerial vehicles (UAVs) become increasingly integral in domains such as agriculture, logistics, and military operations, secure cross-domain authentication mechanisms are essential. Existing centralized protocols are prone to single points of failure, privacy vulnerabilities, and physical capture risks. This paper presents a novel blockchain-based, privacy-preserving authentication protocol for UAVs operating across multiple domains. By combining zero-Knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) and physical unclonable functions (PUFs), the proposed protocol ensures secure identity verification without disclosing sensitive information. The blockchain platform offers a decentralized, tamper-resistant environment for UAV authentication, addressing the challenges of scalability, privacy, and security in cross-domain operations. We demonstrate the security and effectiveness of the protocol through formal and informal security proofs and performance evaluations. The results indicate that the proposed protocol outperforms traditional methods, achieving significant reductions in both computational and communication costs while maintaining high security standards.
The modular invariance and automorphism group rigidity of vertex operatoralgebras provide a profound mathematical foundation for constructing novel postquantum cryptographic systems. However, a significant theoretical and engineeringgap exists between mathematical theorems and deployable cryptosystems. Thispaper does not propose new cryptographic protocols but rather systematicallyexamines the core challenges encountered in engineering vertex operator algebracryptography: the discrete selection of parameter spaces and their quantitativerelationship with security strength, the computational resource requirements ofcandidate algebraic families (lattice vertex operator algebras, WZW models, andmoonshine vertex operator algebras), the assessment of security boundaries underquantum attack models, and the practical overhead of auxiliary mechanisms suchas zero-knowledge proofs. The objective is to provide a clear problem inventoryand a feasibility analysis framework for future research, rather than to claim anyimmediately usable security parameters. The article concludes by summarizing thecurrent technology readiness levels and identifying the key breakthroughs requiredto advance from a theoretical framework toward a practical system.
Digital signature schemes derived from non-interactive zero-knowledge (NIZK) proofs are rapidly gaining prominence within post-quantum cryptography. CROSS is a promising new code-based post-quantum digital signature scheme based on the NIZK framework. It is currently in the second round of the NIST’s additional call for standardization for post-quantum digital signatures. However, CROSS’s reference implementation has a substantially large memory footprint. This makes its deployment on resource-constrained platforms prohibitively difficult.In particular, we identified the most memory-intensive areas such as Merkle and GGM tree structures, and zero-knowledge proof commitment generation. We propose several novel algorithms and implementation strategies to reduce the memory requirement of these components. Apart from these, we also propose several memory optimization techniques, such as just-in-time hashing and execution flow analysis. As a result, our implementation reduces the memory footprint of Key Generation, Signature Generation, and Verification of the CROSS reference code by as much as 95%, 92%, and 85%, respectively. This results in a suite of implementations in which all variants are under 128kB (for all security levels of KeyGen/Sign/Verify) and six variants under 32kB. Our memory optimization techniques are not specific to CROSS, but can be applied to other NIZK-based signature schemes.Regarding efficiency, matrix multiplications are crucial to the performance of CROSS. We show how the Digital Signal Processing (DSP) instructions on ARM Cortex-M4, specifically packing and multiplying, can be utilized to efficiently implement matrix operations over finite fields. The DSP optimizations combined with the memory reductions improve the efficiency of CROSS by up to 32% and 33% in Signature Generation and Verification respectively.
Industrial operational technology systems are becoming more intelligent and interconnected, requiring remote maintenance and multiparty collaboration. While traditional approaches improve efficiency, they introduce risks like data leakage and unauthorized operations. Existing access control schemes struggle with compliance verification and auditing while ensuring privacy. A novel access control scheme was proposed that combines zero-knowledge proof with the publicly verifiable covert security model. The scheme features a dual-layer verification mechanism: a basic layer using zero-knowledge proof to protect identities and permissions during remote maintenance and an enhanced layer for high-risk operations that uses oblivious transfer and digital signatures to detect malicious behavior and generate cheating certificates. Security analysis showed the scheme ensures privacy, access legitimacy, and non-repudiation. Experiments demonstrated the scheme had faster proof generation and verification compared to existing methods with effective malicious behavior detection and accountability.
A novel blockchain-based smart contract framework is proposed and designed to mitigate financial product fraud by enhancing transparency, auditability, and trust in advisory processes. The framework introduces a unique dual-contract architecture that combines product and authorization smart contracts, along with a challenge-response verification protocol that ensures both product authenticity and advisor legitimacy. The system is implemented and tested on the Ethereum blockchain, demonstrating operational feasibility through real-world transaction simulations and a corresponding gas cost analysis. By leveraging immutability, the framework preserves data integrity, while cryptographic signatures enable privacy protection without exposing sensitive data on-chain. It is designed to support multi-institutional environments, allowing various financial institutions to issue products and authorize advisors under a unified, fraud-resistant platform. Experimental results confirm the system’s effectiveness in preventing common fraud scenarios, while maintaining low transaction costs and high security.
The Blind Watchdog Protocol (BWP) constructs a directed oversight graph where each autonomous agent has exactly one hidden watchdog, but no agent knows who watches it. Compliance emerges through a Panopticon equilibrium — the mere possibility of observation makes defection irrational. A closed-form Nash equilibrium theorem (6-step proof, TLC model-checked: 2,071 states, zero violations) establishes that compliance is strictly dominant under configurable parameters. The protocol implements 10 composable plugins (reputation, staking, mixnet, rotation, correlation analysis, adaptive watcher allocation, conviction scoring, knowledge gating, hybrid oversight, and optimistic slashing) and maps 10 biological oversight mechanisms to executable code. Key results: 100% detection rate with 0% false positives across 1,000 deterministic simulation runs (p_d=1.0). Stress-tested with stochastic observation noise, collusion sweeps (10-40%), Dark DAO bribery economics, and latency profiling. Layered defense separates immediate containment (escalation levels 1-3) from delayed adjudication (optimistic slashing with challenge period). Three-tier Sybil resistance via admission staking, DID-based identity, and Proof-of-Personhood interface. Constant-rate dummy traffic for timing-analysis resistance. Standardized evidence protocol for dispute resolution. Dynamic VaR-coupled stakes for high-value environments. Three fundamental open problems are identified: out-of-band cryptographic bribery (Dark DAOs), the recursive final arbitrator problem, and the latency-anonymity-cost trilemma for LLM agents. The reference implementation (422 tests, 5,757+ LOC, Python) is licensed under PolyForm Noncommercial 1.0. This paper is a defensive publication of the protocol design, formal proofs, and empirical results.
This paper investigates the resilience and dynamic behavior of energy-conserving cryptocurrencies (ECCs) during two major global crises: the COVID-19 pandemic and the Russia–Ukraine conflict. Unlike traditional proof-of-work (PoW) assets, ECCs—primarily proof-of-stake (PoS) and low-energy blockchain tokens—are increasingly promoted as sustainable digital alternatives. Using a balanced panel of major ECCs across 10 countries with cryptocurrency markets from January 2019 to December 2023, we apply a panel ARDL–PMG model combined with panel causality tests and structural break analysis to examine the long- and short-run effects of global uncertainty on ECC returns and volatility. Our findings show that ECCs exhibit stronger crisis resilience compared with high-energy cryptocurrencies, with limited long-run exposure to pandemic shocks but moderate sensitivity to geopolitical tensions following the Russia–Ukraine conflict. COVID-19 uncertainty has a short-run negative pressure on ECC markets, whereas geopolitical risk (GPR) driven by the conflict generates asymmetric responses. Cross-country results reveal that ECC markets in technologically advanced, energy-transition economies (EU, Singapore, UAE) exhibit greater stability than those in emerging markets. These findings highlight the potential role of ECCs in sustainable finance, offering policymakers, investors, and regulators insights into the feasibility of promoting energy-efficient digital assets amid extreme global uncertainty.
Florian Krieger, Christian Dobrouschek, Florian Hirner, Sujoy Sinha Roy
We present the first high-performance SIMD software implementation of Spielman codes for their use in polynomial commitment schemes and zero-knowledge proofs. Spielman codes, as used in the Brakedown framework, are attractive alternatives to Reed-Solomon codes and benefit from linear-time complexity and field agnosticism. However, the practical deployment of Spielman codes has been hindered by a lack of research on efficient implementations. The involved costly finite-field arithmetic and random memory accesses operate on large volumes of data, typically exceeding gigabytes; these pose significant challenges for performance gains. To address these challenges, we propose several computational and memory-related optimizations that together reach an order-of-magnitude performance improvement in software. On the computation side, we propose SIMD optimizations using the AVX-512-IFMA instruction set and introduce a lazy reduction method to minimize the modular arithmetic cost. On the memory side, we implement a cache-friendly memory layout and a slicing technique, which exploit the CPU memory hierarchy. Finally, we present our multithreading approach to improve throughput without saturating memory bandwidth. Compared to prior Spielman software, our optimizations achieve speedups of up to 21.9x and 20.6x for single- and multi-threaded execution, respectively. In addition, instantiating our software with 64 threads on a high-end CPU even outperforms a recent FPGA accelerator by up to 4.3x for small and mid-sized polynomials. Our improvements make Spielman codes competitive with well-optimized Reed-Solomon codes on software platforms.