Blockchain Papers

Dallas Courchene

AuraOS Second Prior Art Disclosure (N9–N13): Holographic Headers, Gas‑Free Fractal Ledger, Swarm Mesh, Decoupled VR Rendering, and Interactive Narrative FST. This paper extends the AuraOS sovereign cognitive substrate with five new claims. N9 embeds a 1.2 KB hyperdimensional snapshot of the entire codebase into every file header, enabling O(1) integrity verification. N10 replaces blockchain gas fees with RAM‑staking and Proof‑of‑Presence derived from device entropy. N11 describes a swarm mesh for collective learning, elastic distributed compute, and zero‑trust routing. N12 introduces VSA‑addressed decoupled rendering, where a smartphone controls photorealistic VR/AR worlds by sending only hyperdimensional addresses (not assets). N13 presents an FST‑constrained interactive movie/game engine where NPCs use generative dialogue within narrative bounds, and player actions (including free speech) change the story. All claims are published under AGPLv3 §13 to prevent corporate capture.

Orestis Alpos, Lioba Heimbach, Kartik Nayak, Sarisht Wadhwa

Auctions are now central to blockchain markets, settling NFT sales, token launches, DeFi liquidations, and arbitrage opportunities. Each on-chain bid is a public transaction whose inclusion is decided by a single consensus proposer per block. The proposer can observe pending bids, exclude competitors, and submit bids of their own, breaking the fairness guarantees of classical sealed-bid auctions. To enable latency-sensitive sealed-bid auctions in blockchain settings, we formalize four properties -- each necessary to prevent a concrete attack -- and design a protocol achieving all four: hiding bid contents, existence, and bidder identity until reveal (Hiding); counting all timely honest bids and rejecting late adversarial bids (Simultaneous Release); preventing silent withdrawal of committed bids (No Free Bid Withdrawal); and charging on-chain fees only to winners (Auction Participation Efficiency). Our protocol uses a timestamping oracle (instantiated with a committee of 2f_ts+1 timestampers) and a censorship-resistant inclusion predicate (instantiated using a FOCIL-based inclusion list), with only the winning bid settled on-chain. Our construction relies on two zero-knowledge proofs: an eligibility proof that anonymously proves deposit membership to the timestamping committee, and an auction proof that binds a bid to a specific auction for the inclusion list committee. We implement both using Groth16 over BN254 with Poseidon hashing in arkworks/Rust: the auction proof generates in 13 ms and verifies in under 1 ms; eligibility proofs for Merkle trees up to 2^32 bidders generate in 47-159 ms and verify in about 1 ms. Together, this yields a sealed-bid auction primitive practical for high-value, time-sensitive blockchain settings.

Muhammad Husnul Hamdala, Erna Kumalasari Nurnawati, Yuliana Rachmawati Kusumaningsih, Suparyanto

The threat to blockchain security has become increasingly critical in the era of quantum computing. This study analyzes the potential risks of quantum computers against crypto by simulating five attack scenarios using Shor’s Algorithm and Grover’s Algorithm. Shor is employed to exploit weaknesses in the elliptic curve digital signature algorithm (ECDSA) by factoring large integers to obtain private keys, while Grover accelerates the search for valid hashes or inputs, reducing complexity from O(2ⁿ) to O(√2ⁿ). The testing environment was built on a local Ethereum network using Ganache, with attack scripts implemented through Node.js, Python (Qiskit), and Hardhat. The results demonstrate that both quantum algorithms can compromise smart contracts, proof-of-stake consensus mechanisms, user wallets, and public key cryptography. Attacks were successfully carried out without original private keys, showing potential for asset theft, consensus manipulation, replay attacks, and increased computational load that could disrupt network availability. Although the simulations were conducted on classical hardware, the findings provide a realistic perspective that large-scale quantum computers will significantly increase the risk of blockchain security breaches. These findings emphasize the urgency of transitioning to post-quantum cryptography (PQC) through approaches such as lattice-based cryptography, hash-based signatures, and layered authentication. Implementation strategies can be divided into three phases: short-term (0–1 year) contract audits, wallet security reinforcement, and developer education; medium-term (1–2 years) PQC testing on crypto testnets and adoption of quantum-resistant validator nodes and long-term (1–3 years) full migration from ECDSA to PQC through key rotation and infrastructure updates.

Iosif M. Gershteyn, Jacob A. Alber

Quantum computing poses a real, broad-based, but bounded and substantially mitigable threat to Bitcoin and Ethereum. We separate the two quantum algorithms that public discussion routinely conflates: Shor's algorithm breaks the elliptic-curve signatures (ECDSA over secp256k1, BLS over BLS12-381) that authorize spending, whereas Grover's algorithm does not meaningfully threaten proof-of-work mining, which is protected by a merely quadratic speedup, fault-tolerant per-operation costs, a square-root parallelization wall, and difficulty adjustment. Folding hardware scaling, the falling resource requirement, a fault-tolerance readiness lag, and expert surveys into a single Monte-Carlo forecast yields a wide, bimodal arrival distribution for a cryptographically relevant quantum computer: about a one-in-six chance by 2035, near 30% by 2040, and about 60% by 2050. Exposure is concentrated and mostly migratable: of Bitcoin's roughly six million quantum-exposed coins only about 2.3 million are irreducibly at risk, while 50 to 65% of Ether sits at key-revealed accounts that can adopt post-quantum signatures. A timely migration beats even an optimistic 2035 machine, so the binding constraint is governance, not technology. A survey of the top twenty cryptocurrencies finds none fully post-quantum. Reproducible models accompany every quantitative claim.

Chuanjia Yao, Zhihui Jiang, Xufeng Su, Xinchun Ma · 10 authors

The management of medical insurance funds is pivotal to the development of medical consortia. These funds serve as the operational lifeline of medical consortia and constitute critical public resources essential for public welfare. Medical expense settlement involves multiple stakeholders, including patients, tiered healthcare institutions, and insurance administrative agencies. However, disputes frequently arise between medical insurance authorities and hospitals regarding expense legitimacy due to information asymmetry and interpretative discrepancies. Such conflicts impede smoothness of payment mechanism, thereby undermining consortium operations and inter-institutional collaboration. To address these challenges, this study proposes a blockchain-based framework integrating medical expense investigation with insurance settlement. The system employs two core components: Anomaly detection via the Isolation Forest (IF) algorithm to identify potentially irregular expenses. Consensus-driven adjudication using Fleiss Kappa-based smart contracts facilitated by an anonymous panel of medical experts. This design enhances coordination between expense oversight and settlement processes, thus streamlining dispute resolution for medical expense anomalies and improving the scientific governance of insurance funds. Experimentally, the statistical validity demonstrated for unsupervised anomaly detection of IF and potential engineering applicability indicated in the analysis of healthcare cost. The healthcare consortium blockchain based on a Delegated Proof-of-Stake (DPoS) consensus mechanism and smart contract batch processing achieved a peak throughput of 229.64 transactions per second (TPS) and reduced transaction costs by up to 3,095 gwei. This demonstrates scalability for real-world medical insurance collaborative settlement systems.

Antun Fagarazzi

The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearman’s rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.

Veeraragavan. K, E.Venugopal, T. C. Karthikheyan

No abstract is available for this record.

Oluwafemi Oloruntoba, Princewill Odum, Khadijah Audu, Sheriff Adepoju · 6 authors

<title>Abstract</title> This study presents a comparative analysis of cloud-native and Distributed Ledger Technology (DLT)-based synchronization models for resilient geospatial data management in multi-cloud environments. With the rising demand for real-time geospatial data in applications such as smart cities, disaster response, and environmental monitoring, ensuring data consistency, availability, and integrity across distributed cloud infrastructures has become increasingly critical. Cloud-native models offer high throughput and scalability through managed replication and consistency protocols but may be limited by eventual consistency and reliance on provider-managed security. In contrast, DLT-based models, particularly those using blockchain, enhance data integrity and auditability through decentralized, tamper-proof synchronization, albeit at the cost of increased latency and operational complexity. To evaluate these trade-offs, we propose a composite performance framework encompassing resilience, synchronization efficiency, and operational cost. Using simulation-based analysis, we assess both models under various failure scenarios and performance conditions. Results highlight the strengths and limitations of each approach and underscore the value of a hybrid model—combining the speed of cloud-native systems with the trust guarantees of DLT—for mission-critical geospatial applications. This research offers practical recommendations for system designers and contributes to the evolving integration of blockchain, cloud, and AI technologies in secure, multi-cloud geospatial infrastructures.

Salem Almehdhar

Objectives: The study aimed to examine the role of blockchain technology and its dimensions (transparency, security, transaction speed and efficiency, and operational cost reduction) in enhancing decentralized finance (DeFi) within the Saudi National Bank (SNB). Methodology: The study adopted a descriptive analytical approach. A structured questionnaire was used as the primary data collection instrument, distributed to a stratified random sample of 280 employees working in relevant departments within the bank. The collected data were statistically analyzed using SPSS. Results: The findings indicated that employees’ perceptions of blockchain technology were high, with an overall mean score of 4.08. The security dimension ranked first with a mean of 4.19, while the dependent variable recorded a high mean of 4.00. The results also confirmed the acceptance of the main research hypothesis at a significance level of 0.05. Collectively, the four dimensions explained 71.1% of the variance in enhancing decentralized finance. Security emerged as the most influential factor, followed by transparency, transaction speed and efficiency, and finally operational cost reduction. Conclusion: The study concludes that the application of blockchain technology across its various dimensions significantly contributes to strengthening decentralized finance within SNB. Security was identified as the most critical determinant, highlighting the need to reinforce cybersecurity infrastructure, develop regulatory and governance frameworks, and adopt a gradual approach toward transitioning to decentralized financial systems.

Shirish Tripathi, Ishmriti Acharya, Lalit Pant, Kanchan Rai · 5 authors

This paper presents a blockchain-based electronic voting system designed to address the persistent challenges of transparency, security, and integrity in democratic electoral processes. Traditional voting systems in countries like Nepal suffer from vote manipulation, ballot rigging, logistical inefficiencies, and limited public trust. To overcome these limitations, this work proposes a decentralized e-voting application built on the Ethereum blockchain, leveraging smart contracts for tamper-proof vote recording and enforcement of voting rules. The system incorporates multi-factor authentication, combining facial recognition via OpenCV with Voter ID and Date of Birth verification to ensure only eligible voters participate. MetaMask wallet integration enables secure blockchain transactions, while Web3.js facilitates real-time interaction between the frontend and the deployed smart contracts on Ganache. The methodology encompasses data collection, voter authentication, smart contract deployment, and result retrieval. This paper offers a scalable and cost-effective alternative to conventional voting methods, with future scope for public Ethereum deployment and expanded biometric authentication.

Jungsuk Han, Jongsub Lee, Tao Li

Decentralized autonomous organizations (DAOs) are entities without central leadership and operate based on a set of decision-making rules encoded into smart contracts using blockchain technology. In this study, we develop a theoretical model of DAO governance featuring strategic token trading under token-based voting to investigate potential conflicts of interest between a large participant (a “whale”) and many small participants. Our results show that ownership concentration has a negative effect on platform growth, but platform size, token illiquidity, and long-term incentives can mitigate this negative effect. We confirm these predictions using novel voting data on major DAOs from 2020 and 2024. This paper has been accepted by Lin William Cong for the Virtual Special Issue on Digital Finance. Funding: J. Han and J. Lee received financial support from the Institute of Management Research at Seoul National University. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.07033 .

Chenhuizi Wang, Chunjing Yu, Li Yang

This study examines PUPS, a representative Bitcoin ecosystem project, to elucidate the success mechanisms of Web3 meme projects. We test three hypotheses: (H1) community sentiment and social media virality constitute the fundamental drivers of meme asset valuation; (H2) core participants accumulate positions at low prices and distribute at peak valuations; (H3) meme diffusion is predominantly driven by internal imitation, significantly outweighing external marketing effects. Applying event study methodology, social network analysis, and the Bass diffusion model to social media and on-chain data, our findings support all hypotheses, revealing a ”propagation–sentiment–trading” pathway. We identify a distinctive ”community fingerprint” comprising 348 original holders and 5,036 6-core addresses, characterizing them as both community stabilizers and hype catalysts. This pattern illustrates the paradox of ”economic recentralization” within technically decentralized systems. Paradoxically, the founder's public assertion that ”everything will eventually go to zero” evolved into a cultural ritual that reinforced community consensus. This study concludes by proposing a ”meme financialization” framework, offering novel perspectives for understanding ”Attention as Capital”, ”Consensus as Value”, and ”Narrative as Asset” in Web3 ecosystems.

Sarah Allen, Pranay Anchuri, James Austgen, Maryam Bahrani · 25 authors

The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportunities and challenges are, and what open questions deserve attention. This survey paper asks what AI can do for blockchain-based technologies (broadly construed as "crypto") (crypto x AI), and vice versa (AI x crypto). We systematize existing work, summarize key takeaways, highlight open research questions, and offer a perspective on pervasive industry misconceptions, concluding that AI and crypto are still in the very early stages of meaningful integration.

Mahdi Manavi, Yunpeng Zhang, Guoning Chen

Securing access control in large-scale Internet of Things (IoT) deployments requires mechanisms that adapt to risk while preserving low latency for benign traffic. Permissioned blockchains such as Hyperledger Fabric offer auditability through smart contracts, but static endorsement policies impose the same validation depth on all requests, regardless of security posture. We propose a risk-adaptive enforcement layer for Hyperledger Fabric that couples an off-chain LSTM-based risk oracle with deterministic on-chain checks. The oracle assigns each request to a tier (Low, Moderate, High) and issues a signed attestation bound to the client identity and target key/version. Endorsing peers verify the attestation in chaincode and enforce tier-conditioned SBE policies without modifying the ordering service or consensus. Experiments on a Fabric testbed show that tier-conditioned endorsement strengthens validation for higher-risk requests while retaining low confirmation latency for benign workloads.

Akhil Sharma, Preethi Sharma

Bhutan has deployed a blockchain-based national identity system on Ethereum and issued a gold-backed sovereign token, while simultaneously developing a National AI Strategy 2025 that requires AI governance to align with Gross National Happiness constitutional principles. Ethereum protocol governance is conducted by the Ethereum Foundation and developer community without Bhutanese constitutional participation. This paper documents the constitutional command gap between Bhutan's GNH governance philosophy and the technical infrastructure on which its digital sovereignty depends, and presents the Fijishi Sovereign Identity Framework, Sovereign Algorithmic Immunity Doctrine, and Institutional Failover Charter as the constitutional command layer that ensures GNH principles govern AI and digital systems regardless of the underlying protocol architecture.

Matthias Gruber

This paper proposes a cosmological model — the Singularity-Bounded Holographic Class 4 Automaton (SB-HC4A) — derived from the convergence of four independently motivated frameworks: a five-class computational taxonomy that refines Wolfram's (2002) classification by separating fractal from random dynamics, a theoretical framework for self-referential computation in self-modeling systems (Gruber, 2015, 2026a, 2026b) which identifies self-referential simulation at criticality as a universal computational pattern, and 't Hooft's (1993, 2016) holographic automaton interpretation of quantum mechanics. The model proceeds by elimination: Classes 1–3 cannot sustain the universal computation the universe demonstrably supports; Class 5 (genuine randomness) makes physics fundamentally impossible; therefore the universe operates at Class 4 — the edge of chaos. Combined with the information-theoretic observation that singularities at every physical scale (Planck regime, particle interiors, event horizons, cosmological horizons, temporal endpoints) share the property of information impermeability and Bekenstein saturation, the model proposes that these singularities are structurally identical — scale-invariant instances of the same information boundary. The resulting architecture is a self-referential holographic Class 4 automaton bounded at every scale by singularity surfaces, where the observable interior is the "simulation" and the singularity boundary is the "substrate." All singularities — including temporal endpoints — are shown to be asymptotically unreachable from within the computational domain, strengthening the unification claim. Because singularities transform rather than destroy information, heat death constitutes a singularity transition that triggers cyclic renewal, with potential CPT signature alternation across cycles — connecting to Penrose's Conformal Cyclic Cosmology and Boyle and Turok's CPT-symmetric universe. All three cosmological endgames — heat death, Big Crunch, and Big Rip (Caldwell, 2002) — drive the computational domain to Bekenstein saturation, with the Big Rip uniquely producing a branching tree of daughter universes rather than a linear successor. This architecture is structurally identical to self-referential computational systems that operate at criticality, where implicit knowledge (substrate) is separated from explicit representation (simulation) by an information-opaque boundary. Self-modeling cognitive systems are thus local, scale-reduced instances of the same computational pattern the universe implements globally. Six weak points are identified, including the fundamental epistemological objection that Class 4 observers may be constitutionally incapable of determining whether this model describes the universe or merely the ceiling of their own computational capacity. Changelog v3 Major soundness-and-rigor revision in two passes (Fable 5-assisted), plus an author-driven reframe of the unreachability and observer material. Round 1 — soundness corrections (C1–C6, NEW-1–4): Taxonomy (§2.3/§3.2): the cellular-automaton classification now rests on computational reducibility (Rule 90 = reducible fractal, Class 3; Rule 30 and Rule 110 = computationally irreducible, Class 4); the undecidability of CA classification (Culík & Yu, 1988) is acknowledged. Necessity (§3, §10): "must/unique" claims softened to best-candidate/necessity-of-axioms; substrate determinism is now an explicit, stated-once assumption ('t Hooft, 2016), and the Class-4 elimination is conditional on it. Singularity unification (§5.2): the Identity-of-Indiscernibles argument is replaced by a single-surface ontology (one encoding surface; each singularity a local reflection), with an operational indiscernibility razor retained as a scoped secondary line. Kerr–Newman (§5.7): the naked-singularity / Compton-vs-Planck scale tension is named explicitly and addressed (conjecturally) via Einstein–Cartan torsion; "structural identity" is demoted to "striking correspondence carrying an unresolved tension." Entanglement and Bell (§6.5): an explicit Bell/CHSH treatment via holographic non-separability (entangled pair = one boundary locus; interior locality denied; ER=EPR and Van Raamsdonk wired in; Bohmian existence proof; entanglement-monogamy answer to superdeterminism; no-signalling). The James–Stein argument is demoted to a heuristic pending a discrete/CAT(0) formalization, and the genuine open obligation is reframed as deriving the Tsirelson bound (information causality flagged as a candidate route, not a proof). Reversibility and time (§8.4, new): a reversible/unitary substrate with an emergent thermodynamic arrow (coarse-graining + the Past Hypothesis); playback-reversal and the forward/boundary-ward asymmetry; the CPT theorem and block-universe as confirmation. Genericity (§9.6): Class-4 genericity rises with dimension, softening the fine-tuning worry; a seventh weak point (§9.7) on the saturation trigger. Round 2 — unreachability, the observer, and the saturation mechanism (author-driven): §5.3 reframed as "Unreachability Along Three Axes" — recession (horizons), scale-shielding (the Planck floor; interactions never resolve zero separation), and termination-without-arrival (the temporal termini are boundaries, not events) — with the BKL/Mixmaster observation that finite proper time need not bound computational depth, the realistic-Crunch causal fragmentation (asymptotic silence; the merged endpoint in no observer's past light cone), and the past's informational shrouding (Borde–Guth–Vilenkin). Black-hole complementarity (§8.2): added as the established local instance of the substrate/simulation duality, with the firewall problem flagged and no side taken. Saturation trigger (§5.4/§9.7): the honest status expanded — the ingredients the saturate-and-decompress mechanism needs (complexity sustained at high density, exact on/off symmetry, reversibility) each exist in known cellular automata (e.g. Day & Night), though no single rule yet combines all of them. Approximately 25 new references added and verified; the abstract and introduction were reconciled to all of the above.

Andrea Stazi

In an era where ethical innovation and sustainability are pivotal, traceability, traditionally aimed at ensuring food safety and market protection, has been driving policies of major world powers in the food industry and beyond, such as the European Union, the United States, and China. This evolution aligns with the dual pressures of consumer demand for transparency and the need for sustainable practices. In such a framework, the integration of IP rights, through trademarks and geographical indications, into traceability systems can be instrumental in building consumer trust and protecting the unique value of local and sustainably produced foods. Along with, or in addition to IP rights, “Regulatory Technology,” or RegTech, for example through distributed ledger technologies like blockchain, can provide an effective tool to ensure traceability, enhance the efficiency and transparency of the food supply chain, and promote the right to adequate food and sustainability.

Madhusudan Narayan, Ashutosh Sharma, Ashok Srivastava

This research investigates the use of financial technology (FinTech), decentralized financing (DeFi), and digital marketing (DM) to improve financial inclusion in India. It fills crucial research gaps by investigating the impact of behavioral intent, trust, usability, and social influence on mobile banking and DeFi uptake, as well as the role of DM in advancing these initiatives. A thorough literature analysis was undertaken, including databases such as Emerald Insight, ScienceDirect, and JSTOR, to identify gaps and analyze trends in FinTech and DM. DeFi’s blockchain-based strategy eliminates traditional intermediaries, increasing transparency, cost efficiency, and accessibility. DM is critical in fostering financial literacy and adoption by providing customized, culturally appropriate material. Adoption is influenced by key characteristics such as behavioral intent, trust, and usefulness. Partnerships between FinTech, DeFi platforms, and conventional banks are critical to increasing inclusiveness while resolving regulatory and ethical concerns. The research underlines the need of rules that encourage innovation while protecting consumers. 188 Financial institutions are urged to employ FinTech, DeFi, and DM to provide user-friendly, inclusive products, and increase their reach. Researchers should conduct qualitative and longitudinal research to capture cultural and societal influences while addressing regulatory and ethical issues in DeFi. This study offers practical insights into how FinTech, DeFi, and DM may increase financial inclusion, empower underprivileged populations, and promote economic growth and support economy.

Nadir Subaşı, Özen Özer

This chapter explores the critical role of data polishing and anomaly detection in enabling decentralized finance (DeFi)-driven digital transformation within the energy and utilities industry, with broader implications for dataintensive environments such as cryptocurrency markets and metaverse ecosystems. In an ideal digital infrastructure, decision-making systems operate on transparent, consistent, and high-quality data that support reliable automation, decentralized governance, and predictive analytics. Such an ecosystem presumes seamless data integrity, adaptive risk monitoring, and trustworthy financial and operational exchanges. In practice, however, industrial and financial platforms remain vulnerable to noisy datasets, measurement errors, systemic inconsistencies, and undetected anomalies, which undermine analytical accuracy and institutional confidence. Prior studies on machine learning, data cleaning, and blockchain-based energy systems emphasize preprocessing, normalization, and outlier detection as 78 prerequisites for intelligent operations. Parallel research on crypto-market anomalies and metaverse security highlights the relevance of statistical and learning-based surveillance models. Yet, these strands often remain methodologically fragmented, rarely examining their integrated function within DeFi-enabled infrastructures. This chapter addresses this gap by advancing a unified analytical framework grounded in data reliability theory and decentralized analytics. Focusing on the comparative evaluation of IQR, MAD, and LOF models applied to XRP/USD datasets since 2018, the paper assesses robustness, sensitivity, and computational efficiency. The findings demonstrate how systematic data polishing strengthens trustless financial architectures, enhances operational resilience, and supports sustainable digital transformation in energy and utility ecosystems.

V.I. Petrenko, M. Kh. Najajra, F. B. Tebueva, V. I. Pronin

The article presents an innovative method for distributed access control of robotic agents in a decentralized cyber-physical system (CPS), which combines an advanced architecture of graph attention neural networks (CAT-GNN) with blockchain technologies. The proposed approach aims to enhance the security, reliability, and fault tolerance of interactions between agents through dynamic behavioral anomaly analysis using CAT-GNN, capable of detecting complex spatio-temporal dependencies in agent behavior. The calculated anomaly score is used for adaptive adjustment of the trust level in agents, directly influencing access decisions to critical resources within the distributed system. Simulation experiments have demonstrated that the CAT-GNN detector outperforms the baseline STAD-GNN model in key metrics such as Accuracy, Fl-score, and ROC-AUC, showing high stability and precision in detecting malicious behavior while varying the number of agents and the proportion of malicious participants. The introduction of a dynamic trust mechanism significantly increased the proportion of successfully completed tasks from 63 to 82 %, while simultaneously reducing errors from over 18 to 8 %. The method relies on the integration of machine learning and distributed ledger protocols, ensuring transparency, immutability, and flexibility in access management. This comprehensive mechanism effectively counters internal and external threats, meeting modern security requirements of industrial and IoT systems. The proposed method is capable of effective scalability and adaptation to changing conditions of cyber-physical systems, confirming its high practical value and promising potential for broad application in critical infrastructures, industry, and transportation networks.

Grégoire Mommaerts

Abstract This article formulates an updated conditional existence theorem within the HoloGenesis framework. The theorem concerns the persistence of a finite, resonant, prestressed cosmic lattice whose coherence is maintained despite nonzero dissipation, leakage, and entropy production. The article corrects an important feature of earlier HoloGenesis formulations. The realized microwave structure of the lattice should no longer be described through a single undifferentiated “cloud frequency” near 160\,\mathrm{GHz}. The corrected architecture distinguishes the primitive subitron floor near 56.8\,\mathrm{GHz}, the base diagonal stride near 98.4\,\mathrm{GHz}, the CMB frequency-space spectral peak near 160.3\,\mathrm{GHz}, and the diagonal signal trace near 277.5\,\mathrm{GHz}. The CMB peak remains observationally central, but it is not the primitive floor of the lattice (54, 63, 77). Within the corrected framework, the cosmos is understood as a finite Dwelling: not an empty infinite container, but a voided spacetime lattice tessellated by subitron standing phase conditions and sustained through Kymium activity. The lattice is finite (infinite in expansion but finite within its span), structured, and coherent; it is also active, lossy, and subject to entropy-producing processes. If such a lattice remains statistically stationary, then its persistence creates a ledger problem. A finite coherent system with nonzero dissipation cannot maintain stationary coherence without compensating ordered input. The theorem is not presented as an independent empirical proof of an Emitter. It is stated as a conditional implication internal to the HoloGenesis postulates: if the lattice is finite, lossy, and statistically stationary, then its coherence cannot persist without a compensating inflow of ordered power. Energy balance requires positive input whenever dissipation and leakage are nonzero. Entropy balance further constrains the nature of that input: it cannot be merely thermal or disordered. It must be ordered, work-like, coherence-preserving, and compatible with large-scale isotropy. HoloGenesis identifies the admissible form of this input as coherent vibrational boundary work distributed isotropically or arrayed across the cosmic boundary condition. This structured sustaining input is termed the Beam Array. The deeper source of that input is termed the Emitter. The Beam Array names the mode of delivery; the Emitter names the non-dissipative source-term required by the persistence of the lattice. The resulting theorem may be stated as follows: under the assumptions of finite lattice structure, nonzero dissipation, nonnegative leakage, entropy production, and observed stationarity of the realized lattice mode, a sustaining source not reducible to the lattice’s own internal dissipative dynamics is required. The theorem remains conditional on the HoloGenesis ontology, but this does not weaken its internal force. If the HoloGenesis lattice is accepted as finite, lossy, and stationary, then the sustaining-input conclusion follows as a conservation-ledger consequence.

Satyapriya Satapathy, Ambarish G. Mohapatra, Jaideep Talukdar, Narayan Nayak · 6 authors

This chapter investigates the role of decentralized finance (DeFi)-enabled digital transformation in advancing sustainable and intelligent practices within the energy and utilities ecosystem, with specific emphasis on Internet of Things (IoT)-driven green steel production. In an ideal industrial landscape, energy-intensive manufacturing systems operate through transparent financing mechanisms, real-time data exchange, and decentralized governance structures that jointly promote efficiency, resilience, and environmental responsibility. Such an ecosystem is expected to harmonize renewable energy integration, adaptive production management, and inclusive investment models. However, contemporary steel manufacturing remains constrained by centralized financial control, limited data monetization, fragmented energy markets, and insufficient incentives for large-scale green transition. Existing studies on Industry 4.0, IoT-enabled 30 manufacturing, and smart energy management highlight the operational benefits of sensor networks, predictive maintenance, and machine learning–based optimization. Parallel research on blockchain and DeFi emphasizes peer-to-peer transactions and decentralized governance in energy markets. Yet, these bodies of work largely evolve in isolation, offering limited insight into their systemic convergence within green industrial production. This study addresses this gap by proposing an integrated conceptual framework that links IoT intelligence, DeFi-based financing, and decentralized energy coordination. Through critical synthesis and analytical evaluation, the paper demonstrates how trustless financial architectures can enhance data-driven decision-making, support renewable energy utilization, and enable scalable green steel ecosystems. By bridging technological and financial decentralization, this research advances a coherent pathway for sustainable industrial transformation.

Sumanta Bhattacharya

This chapter investigates the emerging integration of decentralized finance (DeFi) ecosystems and the Internet of Things (IoT) as a transformative pathway for developing inclusive and sustainable renewable energy markets. In an ideal digital energy economy, real-time data intelligence, transparent financial mechanisms, and decentralized governance structures function cohesively to ensure equitable access, operational efficiency, and long-term sustainability. Such a system envisions seamless peer-to-peer energy trading, automated financing, and adaptive grid management. In practice, however, existing energy infrastructures remain constrained by centralized financing, limited data interoperability, and fragmented technological adoption, which restrict scalability and social inclusion. Prior research on blockchain-based energy trading, smart grids, and IoT-enabled monitoring emphasizes efficiency gains and transparency in distributed energy systems. Parallel studies on DeFi highlight its potential to democratize capital access and automate financial processes 234through smart contracts. Yet, these streams largely operate independently, offering limited insight into their integrated sociotechnical and financial impacts. Moreover, ethical governance, data security, and cross-platform interoperability remain underexplored. Addressing these gaps, this chapter proposes an integrative conceptual framework grounded in digital ecosystem theory and decentralized governance principles. Through analytical synthesis, it demonstrates how IoT-driven energy data, blockchain-enabled smart contracts, and decentralized lending models can jointly enhance grid stability, financing inclusivity, and system trust. The findings provide strategic guidance for advancing resilient, ethical, and scalable smart energy economies.

Sirojev Muhriddin Ramazonov Iftixor

Ushbu maqolada cloud computing muhitida ta'lim muassasalarining maxfiy ma'lumotlarini himoya qilishda encryption (shifrlash) texnologiyasini qo'llash masalalari ko'rib chiqilgan. Zamonaviy ta'lim tizimlarida raqamlashtirishning jadal rivojlanishi axborot xavfsizligiga yangi talablar qo'ymoqda. Tadqiqotda AES, RSA, ECC kabi simmetrik va asimmetrik shifrlash algoritmlari tahlil qilingan, ularning ta'lim platformalarida qo'llanilishi, samaradorligi va cheklovlari o'rganilgan. Shuningdek, end-to-end encryption, zero-knowledge proof va post-kvant kriptografiya kabi ilg'or yondashuvlar ko'rib chiqilgan. Tadqiqot natijalari shuni ko'rsatadiki, to'g'ri tanlangan va tatbiq etilgan shifrlash tizimi ta'lim muassasalarining ma'lumotlar xavfsizligini 97% gacha ta'minlashi mumkin. Maqola dasturchilar, ta'lim texnologiyalari mutaxassislari va axborot xavfsizligi sohasidagi tadqiqotchilar uchun amaliy ahamiyat kasb etadi.