First-generation Decentralized Autonomous Organizations (DAOs) rely predominantly on "Coin-Voting" schemes (1 Token = 1 Vote). While permissionless, this model inevitably degrades into plutocracy, where capital centralization allows a minority of "Whales" to capture protocol utility. Furthermore, the liquidity of voting tokens exposes governance to "Flash Loan Attacks" and short-term rent-seeking. This paper introduces a novel governance primitive: Time-Decayed Stake-Weighted (TDSW) Voting. We propose a dual-class structure that weighs voting power not merely by capital magnitude, but by Capital Lock-Duration (Time) and Epistemic History (Merit). By introducing "Reputation Entropy"βwhere governance power decays automatically if not exercised - we solve the "Zombie Stake" problem and ensure that control remains with active, long-term contributors rather than passive capital aggregators. Author's Note: This paper is a foundational pillar of the Klyrox Protocol architecture, expanding upon the core framework published in The Klyrox Protocol: A Decentralized Framework for Optimistic Content Verification and Epistemic Reputation (available at: https://doi.org/10.5281/zenodo.18729968). It outlines the specific mechanics underpinning the concept of "Epistemic Capital," as explored in the complete five-volume series, The Algorithmic Monographs (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence, and The Synthetic C-Suite).
The article explores one of the main trends in modern financial transformation, namely the impact of decentralized finance (DeFi) on the banking sector. The author goes beyond conventional discussions about banksβ responses to DeFi and proposes a different vision for their role and function in the digital economy and Web 3.0. The aim of the study is to identify and analyze changes brought about by the rise of DeFi, as well as to propose possible strategies for banks to adopt in light of technological advancements. Unlike traditional approaches that focus on the conflict between banks and DeFi platforms, this work emphasizes the analysis of future models of financial intermediation. Concepts such as Β«5.0 banksΒ», Β«metabanksΒ», and autonomous digital ecosystems are explored, where banking functions are implemented in a more programmable manner. The research methods include a comparative analysis of the structural and functional differences between the traditional banking system and decentralized finance (DeFi), an analytical review of recent scientific publications, and an assessment of potential future developments for banks in the face of decentralized technology. Based on this research, we found that banks remain an important part of the financial system, despite increasing pressure from decentralized finance. However, banks must adapt to technological change in order to maintain their relevance. We identified three possible paths for the future of banking: the integration of DeFi features into existing banking products, the creation of hybrid models that combine DeFi and traditional banking, and the transition to fully autonomous algorithmic systems powered by smart contracts and artificial intelligence. While all three scenarios are possible, we believe that the hybrid model that combines DeFi innovation with customer protection and regulation is the most likely to succeed in the long term. The novelty of this work lies in its conceptual approach to how banks can adapt to decentralized technologies and forecast their future evolution within the context of Web3. Its practical significance lies in the potential for using these findings to develop digital transformation strategies for banks.
The "Identity Trilemma" posits that a decentralized network can enforce only two of the following three properties: Privacy (Anonymity), Accountability (Sybil Resistance), and Permissionlessness (No Central Gatekeeper). Traditional Web2 platforms resolve this by sacrificing Privacy (enforcing Real-Name Policies), while early Web3 platforms sacrificed Accountability, resulting in "Sybil Swarms" where single actors control thousands of wallets. This paper introduces the Klyrox solution to the trilemma: Pseudonymous Accountability. By utilizing Zero-Knowledge Proofs (ZKPs) and non-linear Time-Energy Cost Functions, the Klyrox Protocol enables users to mathematically prove they are unique, high-integrity actors without ever revealing their physical identity, biometric data, or government credentials. We define a new standard for "Proof of Personhood" based not on biology, but on consistent historical behavior recorded in a Soulbound Token (ERC-721M). Author's Note: This paper is a foundational pillar of the Klyrox Protocol architecture, expanding upon the core framework published in The Klyrox Protocol: A Decentralized Framework for Optimistic Content Verification and Epistemic Reputation (available at: https://doi.org/10.5281/zenodo.18729968). It outlines the specific mechanics underpinning the concept of "Epistemic Capital," as explored in the complete five-volume series, The Algorithmic Monographs (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence, and The Synthetic C-Suite).
In blockchain applications, transaction confirmation is often treated as usability friction to be minimized or removed. However, confirmation also marks the boundary between deliberation and irreversible commitment, suggesting it may play a functional role in human decision-making. To investigate this tension, we conducted an experiment using a blockchain-based Connect Four game with two interaction modes differing only in authorization flow: manual wallet confirmation (Confirmation Mode) versus auto-authorized delegation (Frictionless Mode). Although participants preferred Frictionless Mode and perceived better performance (N=109), objective performance was worse without confirmation in a counterbalanced deployment (Wave 2: win rate -11.8%, p=0.044; move quality -0.051, p=0.022). Analysis of canceled submissions suggests confirmation can enable pre-submission self-correction (N=66, p=0.005). These findings suggest that transaction confirmation can function as a cognitively meaningful checkpoint rather than mere usability friction, highlighting a trade-off between interaction smoothness and decision quality in irreversible blockchain interactions.
This paper investigates systemic risk transmission across stablecoin markets using Quantile Vector Autoregression (QVAR). Analyzing eight major stablecoins with day data coverage from 2021 to 2025, supplemented by minute-level event studies on three additional coins experiencing major depegs until 2025, we document three findings. First, stabilization mechanism dictates tail-risk behavior: fiat-backed stablecoins function as "stability anchors" with near-zero net spillovers across quantiles, while algorithmic and crypto-collateralized designs become risk amplifiers specifically under extreme market conditions. Second, the theoretical risk isolation between fiat and crypto markets breaks down during stress: direct volatility channels emerge between the US Dollar Index and Bitcoin that bypass stablecoin intermediation. Third, Forbes-Rigobon contagion tests across four depeg events show heterogeneous transmission: after adjusting for volatility, algorithmic stablecoins exhibit significant residual contagion while fiat-backed coins show flight-to-quality effects. These findings imply that uniform stablecoin regulation is inappropriate; regulatory capital buffers for extreme losses should be 2--3x higher for non-fiat-backed stablecoins than median-based measures indicate.
Crypto currency has emerged as a transformative innovation in the global financial ecosystem, offering decentralized, borderless, and technology-driven alternatives to traditional monetary systems. Built on block chain technology, crypto currencies provide opportunities such as faster cross-border transactions, reduced transaction costs, enhanced financial inclusion, and new investment avenues. They also promote transparency and security through distributed ledger systems. However, alongside these benefits, crypto currencies pose significant regulatory and legal challenges. Issues such as price volatility, lack of investor protection, cyber security risks, money laundering, tax evasion, and the absence of a unified global regulatory framework create uncertainty for governments and financial institutions. Policymakers across countries face difficulties in balancing innovation with financial stability and consumer protection. This study explores both the opportunities presented by crypto currency adoption and the major regulatory challenges that hinder its integration into the mainstream financial system. The paper highlights the need for coordinated international regulations, technological safeguards, and policy measures to ensure sustainable and secure growth of the crypto currency market
Shashikumar Bhambhani Shailak Jani, ,Anju Gakhar, Purvi Dipen Derashri, Hiren Harsora Younis Malik
Blockchain and smart contracts are bringing a technological transformation to banking and financial service industry. This scholarly article evaluates the revolutionary nature of smart contracts in reinventing the concepts of trust, efficiency, and automation in transactions of diverse financial sectors. By using a qualitative and explorative methodology that uses secondary resources, the research integrates the know-how of academic publications, white papers, policy-related pieces, and case studies published since the year 2020. The results show that smart contracts are increasingly being used in trade finance, cross border payment, insurance claim settlement, credit release as well as compliance with regulations. Such applications have resulted in cost efficiency, transparency, auditability, and speed of operation being strengthened tremendously. Nevertheless, the paper also reveals some of the existing problems such as the lack of legal clarity, weaknesses in the coding of contracts, scalability of the blockchain technology used, regulatory compliance, and privacy. In practice, being used by institutions like JPMorgan and Santander and in DeFi platforms like Aave and Compound, smart contracts are increasingly becoming institutionally friendly. Also, legal and compliance agencies in different jurisdictions, such as European Union, India and United States, are developing infantile legal regimes that plan to control such innovations. This paper provides the conclusion that smart contracts have a potential to become the backbone of an automated, decentralized, and trusted financial world. To achieve successful integration, there must be a coordination between regulators, technologists, the financial institutions, and policymakers. The paper adds value to the academic discussion by offering a clear, detailed, practice-oriented view on the topic of how smart contract is changing future of banking and finance.
The rapid growth of digital technologies has encouraged organizations to adopt management systems that prioritize transparency, security, and efficiency. Blockchain has emerged as a transformative innovation capable of reshaping conventional management processes through its decentralized and tamper-resistant architecture. This study analyzes the implementation of blockchain in enhancing transparency and security within management systems. A literature review approach was used to examine recent scholarly publications related to blockchain applications across various organizational settings. The findings indicate that blockchain significantly improves data integrity, prevents fraud, and strengthens accountability through distributed ledgers, cryptographic mechanisms, and smart contracts. However, challenges such as scalability limitations, infrastructure readiness, regulatory uncertainties, and limited technical literacy remain major obstacles. This study concludes that blockchain presents substantial benefits, but its effective implementation requires a comprehensive and strategic approach to ensure organizational readiness and long-term sustainability.
We construct a family of self-adjoint operators T_{k,Ξ΄,Ξ΅} on a Hilbert space of functions defined on the set of prime numbers. We prove that the discrete spectrum of these operators, after taking appropriate limits (Ξ΄β0, Ξ΅β0) and averaging over the phase parameter k, coincides with the imaginary parts of the nontrivial zeros of the Riemann zeta function ΞΆ(s). By self-adjointness, the spectrum is real, which implies that all nontrivial zeros lie on the critical line β(s)=1/2. This is version 3.0, which includes substantial improvements over previous versions: β’ Added Lemma 3 (Poisson summation application) with complete proof. β’ Expanded Theorem 4 (Limit Ξ΄β0) with step-by-step rigorous justification. β’ Added Theorem 7 (GuthβMaynard control) showing that β|Ξ²β1/2|Β² e^{-Ξ΅|Ξ³|} β 0. β’ Added Lemma 8 proving continuity of R(z,Ξ΅) and convergence to an entire function R(z). β’ Added numerical verification table comparing first 10 eigenvalues with Odlyzko's zeros (relative errors βΌ10β»β΅). β’ All previous typos and formatting errors have been corrected. The construction uses only elementary properties of prime numbers, classical functional analysis, and recent zero-density estimates (GuthβMaynard 2024). No a priori knowledge of the zeros is assumed. The complete numerical data, including all computed eigenvalues for N up to 10β΅ primes, and Python code implementing the matrix construction, are available from the author upon request and will be made publicly available upon acceptance of this work.
Brandon Dulisse, Chivon H. Fitch, Nathan T. Connealy
Cryptocurrency fraud represents one of the fastest-growing financial crimes worldwide, yet the psychological mechanisms that enable these scams remain understudied. Drawing on 282 verified victim narratives from California and Wisconsin state crypto scam trackers (2023β2024), this study systematically coded the use of seven psychological tactics (PTacs) and seven psychological techniques (PTechs) previously validated in cyber social engineering research. Fraudulent trading platforms (51.5%) and pig-butchering schemes (33.7%) dominated the sample. Across all cases, scammers relied overwhelmingly on impersonation and persuasion techniques paired with fit-and-form and familiarity tactics. On average, 1.77 tactics and 1.86 techniques were deployed per incident; higher psychological complexity (4β6 combined elements) was significantly associated with greater financial losses in fraudulent trading platform scams ($135,346 vs. $63,034, p =.029). These findings demonstrate that cryptocurrency fraud resembles more of a repeatable, psychologically-engineered βplaybookβ rather than random opportunism by unorganized actors. By revealing consistent patterns of manipulation that scale harm, our study provides an evidence-based roadmap for prevention: psychologically informed user education, platform-level disruption of scripted interaction sequences, standardized narrative reporting in complaint systems, and proactive regulatory alerts keyed to emerging PTac/PTech signatures. Implementing these targeted interventions can materially reduce both victimization rates and aggregate financial losses in digital asset markets.
Katta Sri Lakshmi Madhavi, Chandan Kumar Shah Kanu, Mallidi Rajasekhar Reddy, Kosuri Gnana Sathvik Β· 5 authors
Management of blood supplies is a very important part of healthcare infrastructure, and the old system is highly dependent on central databases, which cannot be traced, are not transparent, and cannot resist tampering of data. Such restrictions may cause poor coordination, slow emergency response and manipulation of sensitive medical records. To overcome these issues, this paper offers a proposal of distributed ledger architecture to manage blood supply transparently and its tamper-proof. The system that is suggested will exploit blockchain technology and smart contracts to document blood donation, inventory updates, compatibility checks, and allocation transactions in an unalterable and decentralized fashion. To guarantee modularity and scalability, the architecture is designed into layers of components including user interaction, application logic, and blockchain ledger. Smart contracts automate the most important processes like donor validation and blood group matching and minimize human error and administration delays. To achieve privacy and efficiency of the used system, sensitive medical information is stored off-chain, and on-chain cryptographic hash references are used to maintain integrity and auditability. The distributed ledger removes single points of failure and gives a verifiable transaction history available to authoritative stakeholders. This architecture creates a safe, open and resilient architecture that has the potential to enhance trust, coordination and accountability in blood supply networks.
Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan, Luong Ha Tien Β· 6 authors
This paper proposes PenTiDef, a fully decentralized, privacy-preserving, and poisoning-resilient framework for decentralized federated IDS (DFL-IDS). PenTiDef synergistically integrates three key components: (i) client-side Distributed Differential Privacy (DDP) with stochastic Gaussian noise to protect gradient leakage, (ii) a lightweight latent-space defense module that extracts and compresses penultimate-layer representations (PLRs) into stable Latent Semantic Representations (LSRs) via AutoEncoder, followed by Centered Kernel Alignment (CKA) and K-Means clustering for robust malicious update detection without auxiliary datasets, and (iii) a permissioned blockchain layer with smart contracts that orchestrates on-chain validation, secure FedAvg aggregation, and immutable auditability, eliminating any central server. Extensive experiments on CIC-IDS2018 and Edge-IIoTSet under both IID and realistic non-IID settings, with adversary ratios up to 40\%, demonstrate that PenTiDef consistently outperforms state-of-the-art baselines (FLARE and FedCC) in detection accuracy and F1-score while maintaining lower training overhead. By jointly addressing privacy, robustness, and decentralization in a unified secure aggregation protocol, PenTiDef provides a practical and scalable solution for trustworthy collaborative intrusion detection in heterogeneous, adversarial IIoT environments.
The sudden growth of cryptocurrencies has created a set of intricate regulatory and legal issues for the financial and governance system of India. The decentralized nature of digital currencies like Bitcoin and Ethereum challenges the conventional monetary system, giving rise to concerns about their legal status, protection of investors, taxation, and overall financial stability. This paper critically analyzes the regulatory environment in India, especially in the wake of the 2018 circular issued by the Reserve Bank of India and its subsequent strike-down in the case of Internet and Mobile Association of India v. Reserve Bank of India. It also discusses challenges with respect to money laundering under the Prevention of Money Laundering Act, 2002, taxation of virtual digital assets, and the lack of a comprehensive statutory regulatory framework for cryptocurrency exchanges. The paper contends that the current stance of India is one of regulatory ambivalence, vacillating between control and tolerance.
Stochastic Bit-Parallel Maximum Clique Solver (1024-bit Virtual Register) We introduce a stochastic bit-parallel solver for the Maximum Clique Problem (MCP) based on a 1024-bit virtual register architecture implemented as 16 contiguous uint64_t words in standard C++17, ensuring full portability across 64-bit platforms (x86-64, ARM, RISC-V). Core operationsβcandidate intersection, population count, and leading-zero detectionβexecute in exactly 16 instructions per 1024-bit operation. The solver integrates three key components: (i) a co-neighborhood heuristic that identifies high-coreness nodes via O(NΒ²) pairwise popcount over 1024-bit adjacency rows; (ii) a stochastic swarm of independent worker threads; and (iii) greedy clique expansion through iterative bitwise intersection. Exact branch-and-bound solvers (MaxCliqueDyn, MCQ) become computationally intractable on dense random graphs such as G(1024, 0.5), where chromatic coloring bounds lose effectiveness and the search tree grows exponentially, requiring hours of computation on commodity hardware. The proposed method operates specifically within this hard regime, achieving 100% recovery of all 28 planted clique vertices in 153 millisecondsβa setting where exact state-of-the-art methods cannot remain competitive regardless of hardware scaling. Experimental validation was performed on a Qualcomm Snapdragon 8 Gen 2 (8-core ARM) and independently reproduced on Linux x86-64 server hardware. The solver requires no cloud infrastructure and no GPU acceleration. STATEMENT OF PRIOR ART AND LICENSE TERMS (PolyForm Noncommercial Framework) 1. Statement of Prior Art This document constitutes a public disclosure of the stochastic bit-parallel Maximum Clique methodology, including its virtual register architecture, heuristic structure, and execution model.The mathematical and algorithmic concepts are released solely to establish Prior Art and prevent third-party patent claims under 35 U.S.C. Β§ 102 and international equivalents. 2. Software License While the conceptual methods are disclosed defensively, all source code, implementations, binaries, and hardware realizations are not in the public domain and are licensed under the PolyForm Noncommercial License 1.0.0. Permitted (Non-Commercial)β’ Academic research and experimentationβ’ Peer review and independent verificationβ’ Educational and non-profit useβ’ Non-commercial open-source research implementations Condition: Publications must cite the canonical DOI or primary reference. 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Anti-Snippet Laundering and Anti-Circumvention Extraction, paraphrasing, refactoring, translation, or reimplementation of any algorithmic componentβincluding bit-parallel structures, heuristics, or execution logicβshall be considered derivative use.Attempts to evade the license through minimal reuse, language changes, functional replication, or modular embedding do not limit its applicability.This interpretation aligns with international good-faith and anti-abuse principles. 6. Presumption of Derivation Any system exhibiting substantial functional or structural similarity, developed after exposure to this work, shall be presumed derivative.The burden of proof for independent creation rests on the alleged infringing party. 7. Knowledge Contamination Exposure to the code, documentation, or technical description constitutes knowledge contamination.Subsequent implementations by exposed parties are not considered clean-room unless supported by contemporaneous evidence of prior independent development. 8. Waiver of Jury Trial To the fullest extent permitted by law, all parties waive the right to a jury trial in disputes arising from this license or related use. 9. Severability and Survival If any provision is deemed unenforceable, the remaining provisions remain in effect.The following provisions survive termination: license scope, noncommercial restrictions, anti-circumvention, presumption of derivation, knowledge contamination, intellectual property ownership, waiver of jury trial, and remedies. 10. Academic Use and Research Freedom The author expressly encourages academic and scientific use of this work. The following activities are permitted on a non-commercial basis: β’ Research, benchmarking, and experimental validationβ’ Publication of scientific analyses, comparisons, or extensionsβ’ Use in university courses, laboratories, and academic projectsβ’ Inclusion in research solver portfoliosβ’ Independent theoretical or empirical study All academic use must include proper citation to the original work.
Exponentially growing data generated by networked devices in Industry 4.0 environments requires industrial analytics that are secure, scalable, and decentralized. This article proposes TADDA-4i, a new multi-layered architecture based on IOTA's Tangle-Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT)-combined with federated learning and edge computing to provide real-time, secure, reliable, and self-sovereign industrial analytics. The architecture minimizes centralized bottlenecks via feeless, asynchronous data validation and tamper-evident model update verification using the Tangle ledger. Adaptive Tip-Aware Data Prioritization (ATDP) and Tangle-Validated Federated Aggregation (TVFA) are two new algorithms proposed for improving responsiveness and securing federated learning integrity. Experimental evaluation in emulated industrial edge environments showed that transactions take 30 percent less time, almost all of the misbehaving updates are detected, the model is about 10 percent more accurate, and output is not reduced even if the number of devices reaches 50. These findings make TADDA-4i an executable solution for the future generations of decentralized industrial intelligence.
DLT and several other technological elements such as smart contracts, digital wallets, oracles, and so on in the context of financial markets, are leading to the emergence of very different phenomena which require, first of all, to be understood and then, inevitably as their importance and volume grow, regulated and supervised, to ensure the stability of the market and the protection of its investors. At the international level, the Financial Stability Board is advancing a global regulatory framework grounded in the principle of βsame activity, same risk, same regulationβ, aiming to ensure consistent and comprehensive regulation of crypto-asset activities and stablecoins relative to the risks they present, while also fostering responsible innovation prompted by technological advancements. The European Union is actively addressing regulatory challenges in the crypto space, employing distinct approaches to different categories of cryptoassets, depending on whether DLT technology is used in the context of non-fully decentralized finance, rather than in DeFi itself, which currently lacks effective regulation within the European Union. Greater problems from a regulatory perspective, however, are posed by the phenomenon of DeFi, which entails a more significant disintermediation. For this reason, even at the European level, this is undoubtedly the area that poses the most significant problems for market and investor protection. Keywords: decentralized ledger technology, crypto-assets, regulation, DeFi, investor protection.
The rapid evolution of digital currency systems has consistently faced the fundamental challenge of achieving an optimal balance between transaction privacy, computational efficiency, and cryptographic security. This comprehensive research paper introduces the Elliptic Homomorphic Token (EHT), a groundbreaking cryptographic protocol that revolutionizes privacy-preserving peer-to-peer transactions through the innovative integration of elliptic curve-based partially homomorphic encryption mechanisms and advanced digital signature schemes. Unlike conventional zero-knowledge proof systems that have dominated the privacy-focused cryptocurrency landscape, EHT takes a fundamentally different approach by directly leveraging the underlying cryptographic primitives that form the mathematical foundation of these complex systems. The protocol implements a sophisticated pre-transaction mechanism followed by distributed block recording, achieving remarkable performance metrics of 1000 transactions per second (TPS) with consistently low latency ranging from 50 to 100 milliseconds. Our comprehensive approach systematically addresses the significant computational overhead challenges that were extensively documented during Central Bank Digital Currency (CBDC) implementation projects, while simultaneously providing a robust and practical framework for privacy-preserving digital transactions that maintains the highest standards of cryptographic security. The EHT protocol represents a paradigm shift in how we conceptualize and implement privacy-preserving digital currency systems, offering a more direct, efficient, and mathematically elegant solution compared to existing approaches. Through extensive theoretical analysis, rigorous security proofs, and comprehensive performance evaluations, this paper demonstrates that EHT not only meets but exceeds the requirements for next-generation digital currency systems in terms of privacy, efficiency, scalability, and security.
Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 1000 transactions per second with 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 100Γ over existing privacy-focused systems while maintaining equivalent security properties.
The blockchain technology has attracted more and more interest recently as a reliable and secure platform for a variety of applications. This study presents a comprehensive comparative analysis of monolithic and modular architectural patterns in smart contracts, which have become a revolutionary technology thanks to the integration of blockchain technology. A real-world vehicle purchase and sale system was used as a case study. Two contract structures were used that perform the same function: a modular architecture with five interacting contracts, and a monolithic architecture that combines all these functions in a single contract. An empirical analysis conducted for 100 vehicle sales revealed that while the modular architecture offers advantages such as independent upgradeability, testability, and maintainability, the monolithic approach outperforms it in many metrics, including a 36.7% reduction in transaction costs and a 75% faster deployment time. The findings provide evidence-based architectural guidance for blockchain and smart contract developers in selecting appropriate design patterns, particularly in real-world applications where gas costs are critical. Cite this article as: T. Timu.in and S. BiroΔul, "A comparative analysis of monolithic and modular smart contract architectures: A case study of vehicle trading systems," Electrica, 2026, 26, 0333, doi:10.5152/electrica.2026.25333.
Abstract This paper investigates the time-varying dynamics of the Bitcoin price by examining its relationship with key global factors, including the VIX, the interest rate, the US dollar index, the oil price, and the gold price. The empirical analysis employs a state-space model, the Kalman filter method, and a TVP-VAR-SV. The findings from the state-space model indicate a significant negative association between the Bitcoin price and the VIX, while identifying a positive relationship with the gold price. Further analysis using instantaneous time-varying impulse response functions reveals that the negative response of Bitcoin to the VIX intensified significantly during the pandemic period. A similar negative impact was observed regarding the US dollar index and the oil price. In contrast, the interest rate exhibited a positive connection with the Bitcoin price. Notably, the relationship between Bitcoin and gold, which was negative prior to the pandemic, became statistically insignificant as the crisis escalated. This underscores that Bitcoinβs hedging capabilities and safe haven characteristics are not intrinsic fundamental qualities, but rather conditional behaviors that evolve with shifting global economic landscapes. The evidence suggests that Bitcoinβs defensive properties are structural rather than fundamental, emerging primarily during specific volatility regimes. Additionally, the inverse relationship between the oil price and Bitcoin suggests that rising energy costs may dampen the cryptocurrencyβs appeal due to its substantial energy consumption. These results offer significant implications for scholars, investors, and portfolio managers regarding the management of digital assets during periods of systemic instability.
The proliferation of large language model (LLM) based AI agents has created an urgent need for robust orchestration mechanisms that can coordinate heterogeneous agents in complex, real-world environments. Existing approaches to multi-agent task allocation rely predominantly on centralized controllers, which introduce single points of failure, scalability bottlenecks, and rigid coupling between the orchestrator and the agents it manages. This paper introduces the Dynamic Task Orchestration (DTO) framework, a decentralized, capability-aware architecture for assigning tasks to AI agents in real time. The DTO framework models each agent as an autonomous economic actor that participates in a sealed-bid auction mechanism to compete for incoming tasks. Task allocation decisions are driven by three primary factors: the agent's declared capability profile, its current computational and cognitive load, and the estimated complexity of the task. The framework defines a formal task decomposition grammar, a standardized agent capability ontology, and a set of protocol-level contracts that govern bidding, delegation, execution, and result aggregation. We present the theoretical foundations of the framework, provide detailed implementation guidance, and propose a comprehensive evaluation methodology grounded in metrics for throughput, latency, fault tolerance, and resource utilization. Through analytical evaluation and scenario-based discussion, we demonstrate that the DTO framework achieves superior load balancing, resilience to agent failure, and adaptability to changing workloads compared to centralized orchestration baselines. The framework is entirely tool-agnostic and vendor-neutral, designed so that any organization can adopt it to build more robust, efficient, and scalable multi-agent systems.
In modern distributed information systems, the need to ensure a high level of cybersecurity, data integrity, and confidentiality under conditions of interorganizational interaction is steadily increasing.Blockchain technologies enhance transparency and trust among participants; however, traditional consensus mechanisms are accompanied by significant computational overhead, risks of centralization, and limited capabilities for protecting sensitive information.These issues are particularly acute in corporate environments of small and medium-sized enterprises, where the computational resources of network nodes are constrained while the requirements for business data confidentiality remain high.A promising direction is the integration of Zero-Knowledge Proof (ZKP) mechanisms, which enable verification of operation correctness without disclosing the underlying data.Nevertheless, their practical adoption is hindered by the high cost of proof construction for classical cryptographic primitives.In particular, for the SM3 hash function there are no efficient optimized implementations of preimage proofs, and its bit-oriented structure leads to a substantial increase in circuit size and proof generation time, making its use infeasible in resource-constrained environments.This paper proposes a dockerized private blockchain architecture oriented toward corporate environments with limited resources, combining the trust-oriented Proof of Friendship consensus with Zero-Knowledge Proof mechanisms.The key result is the development of an approach for optimizing SM3 hash preimage proofs in ZKP systems.The paper introduces principles of manual optimization of the SM3 circuit representation, including reduction of bitwise operations, aggregation of 1965 constraints, optimization of message expansion, and reduction of round depth.It is shown that these transformations significantly decrease the size of arithmetic circuits and proof generation time compared to naive algorithm translation, enabling practical use of SM3 in zero-knowledge systems and corporate blockchain solutions.The proposed approach provides a balance between blockchain transparency and business data confidentiality, forming a "trust but do not disclose" model.The obtained results establish a scientific and practical foundation for deploying privacypreserving computation in distributed information systems and for developing nextgeneration secure blockchain platforms.
Time-series forecasting is a critical task across many domains, from engineering to economics, where accurate predictions drive strategic decisions. However, applying advanced deep learning models in challenging, volatile domains like finance is difficult due to the inherent limitation and dynamic nature of financial time series data. This scarcity often results in sub-optimal model training and poor generalization. The fundamental challenge lies in determining how to reliably augment scarce financial time series data to enhance the predictive accuracy of deep learning forecasting models. Our main contribution is a demonstration of how Generative Adversarial Networks (GANs) can effectively serve as a data augmentation tool to overcome data scarcity in the financial domain. Specifically, we show that training a Long Short-Term Memory (LSTM) forecasting model on a dataset augmented with synthetic data generated by a transformer-based GAN (TTS-GAN) significantly improves the forecasting accuracy compared to using real data alone. We confirm these results across different financial time series (Bitcoin and S\&P500 price data) and various forecasting horizons. Furthermore, we propose a novel, time series specific quality metric that combines Dynamic Time Warping (DTW) and a modified Deep Dataset Dissimilarity Measure (DeD-iMs) to reliably monitor the training progress and evaluate the quality of the generated data. These findings provide compelling evidence for the benefits of GAN-based data augmentation in enhancing financial predictive capabilities.