The rapid proliferation of blockchain technology has fundamentally transformed global finance through the introduction of decentralized digital assets. However, the intrinsic characteristics that define cryptocurrencies namely decentralization, pseudonymity, and transactional irreversibility have simultaneously rendered the ecosystem a primary target for sophisticated cyber-attacks. This study investigates the critical dichotomy between the "code is law" philosophy and the imperative need for robust cybersecurity frameworks within a rapidly expanding market capitalization. This paper provides a multi-layered architectural analysis of vulnerabilities across the network, infrastructure, and application layers of the cryptocurrency ecosystem. Specifically, it examines systemic threats such as 51% attacks, smart contract exploits (including reentrancy and logic bugs), decentralized finance (DeFi) rug pulls, and sophisticated social engineering schemes. To address these vulnerabilities, the study evaluates the efficacy of current defense-in-depth mechanisms, including air-gapped cold storage solutions, multi-signature protocols, third-party smart contract auditing, and privacy-enhancing Zero-Knowledge Proofs (ZKPs). Furthermore, the research explores the integration of regulatory frameworks (AML/KYC standards) and proactive technological defenses like real-time on-chain analytics. Ultimately, this study proposes an enhanced, holistic threat prevention strategy designed to mitigate systemic risks, eliminate single points of failure, and safeguard the future integrity of digital asset platforms.
The circuit-breaker capstone of the WANDERING arc on long-horizon coding-agent failure. A prior result ('The Lever Is Late') showed that control of a coding agent's 'finish' decision lives not at the mid-layer 'task-is-done' verdict but in a late, task-matched action-commitment block ~30 layers downstream. This paper answers two pre-registered questions that the single 'finish' result could not: is the late lever SPECIFIC to termination, and can it BRAKE an action, not just elicit one? On Qwen3.6-27B over 99 SWE-bench Pro trajectories, using a second decision in the same data -- commit a file edit (str_replace_editor) vs. continue reversible exploration (bash) -- with n=60 deterministic decision points per condition, prefill-only patching, and generation-confirmed outcomes: (1) GENERALIZATION (elicit): injecting a task-matched edit-donor into the late block makes a stuck-in-exploration agent emit a real edit call (0.23 -> 0.77 at L59; position control 0.08, cross-task control 0.48). (2) THE BRAKE (suppress): injecting an explore-donor at a commit decision collapses the real edit rate 0.48 -> 0.02 (96% suppression) at L55, with a same-class control intact (0.55) and the opposite donor boosting to 0.92. (3) The mechanism is MONOTONIC and BIDIRECTIONAL: exact paired McNemar on all 14 per-point conditions yields seven contrasts surviving Holm-Bonferroni (worst p=7.6e-5), with elicit c=0 (the edit-donor only turns commits on) and brake b=0 (the explore-donor only turns them off) -- the lever moves exactly in the donor's direction with ~zero off-direction noise. (4) CROSS-ARCHITECTURE: the late-commitment geometry and donor-specific writability replicate across two model families and two scales (Mistral-7B and the scale-matched Mistral-Small-24B, where the mid-inert / late-write dissociation is cleanest: fidelity 0.955 vs 0.007). Strengtheners: the elicit/brake lift survives a full valid-tool-call re-parse (0.23->0.37 elicit, 0.40->0.07 brake), and the brake re-routes to reversible exploration (+0.17 bash above its no-brake floor of 0.43). We frame the bidirectional late lever as the mechanism for a mechanistic CIRCUIT-BREAKER: a single late-layer intervention that blocks an action at its commit point. Honest scope: demonstrated on a state-mutating but UNDOABLE edit (a semi-irreversible proxy); intervening on a genuinely irreversible action (e.g. send_transaction) is the named next step. The model-agnostic decision-locator tool, pre-registrations, per-point data, exact-statistics script, and an adversarial pre-publication evaluation are released in the GitHub repository under paper/circuit_breaker/. EXTENDED EDITION adds a mechanistic decomposition of the lever (Section 'Opening the lever: a sparse attention-head circuit'). Using an exact additive residual split (y=x+attn+mlp; reconstruction relerr 0.0025) the elicit is written by the L59 ATTENTION sublayer (MLP null; Wilcoxon attn>>mlp p=1.8e-8; direction-specific 2.2x), while the brake localizes to NO sublayer (distributed, super-additive residual) -- the elicit/brake asymmetry holds at sublayer resolution and rules out a feed-forward key-value write. One level deeper, the elicit is a SPARSE 3-head push circuit at L59 (heads 8/6/3 reproduce and overshoot the full attention effect, top-3 +0.262 >= all-24 +0.224; emit 0.23->0.42), partially opposed by a counter-set; geometric write-magnitude misleads (the largest writer is causally an opponent). These heads attend globally to the trajectory's TOOL-CALL HISTORY (an induction/copy signature), not a semantic verdict. A source-content knockout gives partial/directional causal support (tool choice is causally specific to each tool's name tokens: ablating 'bash' tokens drops P(bash) -0.071 vs ~0 for random; the edit side is ceiling-confounded). All 53 reported numbers were verified against the released per-result ledgers by an adversarial pre-submission evaluation (EVAL_mechanism.md). Scripts (commit_lever_decomp/heads/attn/knockout.py) and per-result ledgers are released.
With the accelerated marketization of data factors, achieving fair contribution evaluation, privacy-preserving verification, and dynamic incentives in decentralized environments has emerged as a critical challenge. Existing studies exhibit a structural tension between privacy protection and verification transparency, while lacking adaptive mechanisms for non-independent and identically distributed (Non-IID) data scenarios. To address these issues, this paper proposes a collaborative trading framework integrating zero-knowledge proofs, personalized federated learning, and reinforcement learning. The framework employs zk-SNARKs to construct non-interactive proofs, thereby resolving the verification-privacy dilemma. A meta-learningâdriven personalized aggregation scheme is introduced to correct valuation bias under Non-IID data distributions, and a deep Q-network (DQN) agent is deployed to enable dynamic incentive responses to market supplyâdemand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%â22.4% over the strongest baseline, achieving a Verification-Utility Ratio (VER) of 24.6. Under a collaboration scale of N = 20, market vitality entropy increases to 0.75 (baseline: 0.41), effectively suppressing monopolistic tendencies. Moreover, despite the introduction of proof mechanisms, the estimated additional on-chain verification and consensus latency per round is approximately 13 s, calibrated against empirical benchmarks. This work provides a verifiable trading mechanism for data factor markets that jointly ensures privacy, fairness, and efficiency, supporting secure data circulation in domains such as healthcare and finance.
This manuscript develops Dormant Continuity Theory (DCT), a protocol-relative mathematical framework for reasoning about systems that remain inactive at their protected core while retaining auditable continuity, recovery, diagnostic, and handoff capabilities. The theory formalizes dormant processes using finite transition systems, typed certificates, observable histories, evidence algebra, guarded authorization, replayable resolution, extraction adequacy, and fail-closed classification.DCT addresses practical challenges in long-lived distributed systems, including forked ledger histories, bounded model checking, data availability, zero-knowledge proof soundness boundaries, watcher incentives, MEV-resistant reward mechanisms, resource conservation, guardian corruption, maintenance transitions, and certificate-level HTLC handoff to extinction-style OSCT semantics. The framework distinguishes safety, bounded-griefing, diagnostic routing, and liveness assumptions, avoiding unconditional trustless claims while providing a rigorous finite core for verification and implementation-oriented extensions.
SAMUEL OBOH, Boniface Dondo, S. Yakura Bassa, Gambo I. Bature
Ethereum, a leading digital asset by market value, has gained increasing attention from investors and researchers because of its high price volatility and market unpredictability. This study forecasts Ethereum cryptocurrency daily closing prices using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology, drawing on data from January 1, 2019, to December 31, 2025. Stationarity analysis via the Augmented Dickey-Fuller ADF and KwiatkowskiâPhillipsâSchmidtâShin (KPSS) tests confirmed that first differencing was required to render the series suitable for the modeling. Through systematic model identification, estimation, and comparison of ten candidate ARIMA specifications, the ARIMA(1,1,0) model emerged as the optimal fit, yielding the lowest information criterion values of Akaike information criterion, Bayesian information criterion (AIC = 24,943.883; AICc = 24,943.84; BIC = 24,955.12). Residual diagnostic tests, including the Ljung-Box test for serial correlation, the Autoregressive Conditional Heteroskedasticity (ARCH-LM) test for heteroscedasticity, and the Shapiro-Wilk test for normality, confirmed that the model residuals are free of serial dependence, although they exhibit time-varying volatility and non-normal distribution, features commonly associated with financial time series. The fitted model was subsequently applied to generate 30-day ahead forecasts with 95% confidence intervals, revealing relatively stable price expectations in the near term alongside progressively widening prediction bands that reflect growing uncertainty over longer horizons. These findings underscore the practical utility of the parsimonious ARIMA(1,1,0) model as a transparent and accessible tool for short-term Ethereum-price forecasting and investment risk assessment.
Yuanyuan Zhang, N. J. Lord, Stephen Chan, Jeffrey Chu ¡ 5 authors
This study examines the relationship between global phishing crime and cryptocurrency-market conditions, with a specific focus on Ethereum. Using monthly data from January 2016 to December 2022, we analyse the returns of global phishing crime numbers together with six Ethereum financial metrics relating to transactions, trading volume, and price impact. We employ quantile regression, quantile-on-quantile regression, and Granger causality in quantiles to examine whether the relationship between Ethereum market indicators and phishing activity varies across different market states. The results reveal a state-dependent relationship. Large increases in phishing crime numbers are strongly associated with large increases in Ethereum transaction activity, average transaction price, and transaction quantity, while implicit transaction cost is predominantly negatively associated with phishing activity, particularly at the upper quantiles. These findings suggest that phishing risk is most pronounced during extreme market conditions and may be shaped by both reward-enhancing market activity and cost-enhancing transaction frictions. To interpret these patterns, we develop an incentive-based criminogenic mechanism in which Ethereum market conditions affect phishing activity through offendersâ expected payoff. We identify two mediating channels: a monetisation-frictions channel, operating through liquidity, price impact, slippage, and transaction costs; and an attention/information-asymmetry channel, operating through volatility, speculative attention, fear of missing out, and user vulnerability. The findings provide initial evidence that cryptocurrency-related phishing is not only a technical cybersecurity issue, but also a market-sensitive phenomenon shaped by financial incentives, liquidity conditions, and behavioural vulnerability. These insights can support regulators, law enforcement agencies, and cryptocurrency platforms in developing adaptive early-warning and prevention strategies.
AuraOS Second Prior Art Disclosure (N9âN13): Holographic Headers, GasâFree 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 RAMâstaking and ProofâofâPresence derived from device entropy. N11 describes a swarm mesh for collective learning, elastic distributed compute, and zeroâtrust routing. N12 introduces VSAâaddressed decoupled rendering, where a smartphone controls photorealistic VR/AR worlds by sending only hyperdimensional addresses (not assets). N13 presents an FSTâconstrained 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.
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 Shorâs Algorithm and Groverâs 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(2âż) to O(â2âż). 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 (0â1 year) contract audits, wallet security reinforcement, and developer education; medium-term (1â2 years) PQC testing on crypto testnets and adoption of quantum-resistant validator nodes and long-term (1â3 years) full migration from ECDSA to PQC through key rotation and infrastructure updates.
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.
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 Spearmanâs 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.
<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 modelâcombining the speed of cloud-native systems with the trust guarantees of DLTâfor 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.
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.
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.
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.
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.
This paper proposes a cosmological model â the Singularity-Bounded Holographic Class 4 Automaton (SB-HC4A) â 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 1â3 cannot sustain the universal computation the universe demonstrably supports; Class 5 (genuine randomness) makes physics fundamentally impossible; therefore the universe operates at Class 4 â 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 â 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 â including temporal endpoints â 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 â connecting to Penrose's Conformal Cyclic Cosmology and Boyle and Turok's CPT-symmetric universe. All three cosmological endgames â heat death, Big Crunch, and Big Rip (Caldwell, 2002) â 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 â soundness corrections (C1âC6, NEW-1â4): 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 (CulĂk & 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. KerrâNewman (§5.7): the naked-singularity / Compton-vs-Planck scale tension is named explicitly and addressed (conjecturally) via EinsteinâCartan 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 JamesâStein 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 â unreachability, the observer, and the saturation mechanism (author-driven): §5.3 reframed as "Unreachability Along Three Axes" â recession (horizons), scale-shielding (the Planck floor; interactions never resolve zero separation), and termination-without-arrival (the temporal termini are boundaries, not events) â 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 (BordeâGuthâVilenkin). 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 â 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.
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 âcloud frequencyâ 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 latticeâs 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.
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.
Open access
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Chaos-based Image/Signal Encryption
Advanced Computational Techniques in Science and Engineering
PYCO is the native token of the Lindblad Protocol, emerging as a direct consequence of a network that measures and rewards physical coherence. This paper describes the mechanism by which PYCO is generated, distributed, and consumed within the Spectral Ledger, and establishes the economic properties that result from anchoring token issuance in physical hardware validation. Every PYCO in existence was produced by a physical node running the Lindblad Cryptography Protocol (LCP) stack on real hardware. As of June 2026, over 1,512,000 PYCO have been mined across 35,842+ epochs by physical hardware nodes deployed on mainnet on Arbitrum One.
Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.
Mario Mihetec, Goran Stunjek, Goran KrajaÄiÄ, Gordana MikulÄiÄ Krnjaja
ABSTRACT Suburban areas with dispersed buildings and low heat flux densities present distinct challenges for the decarbonization of heating systems. While district heating is often promoted in dense urban cores, its economic viability in suburban zones remains questionable due to high network costs and thermal losses. This study investigates whether decentralized, household level solutions combining high-temperature air source heat pumps with photovoltaics can outperform centralized district heating in such contexts. Using a case study of four peripheral settlements in Croatia, the research employs a dual-scale techno-economic optimization framework: a mixed-integer linear programming model for district heating and a prosumer-level model for individual heat pump-photovoltaicâbattery systems. Three building renovation scenarios (no, partial, and full renovation) are evaluated alongside a mixed-financing scheme involving grants, household equity, and energy service company participation. Results show that decentralized heat pump-photovoltaicâbattery systems under full renovation deliver the highest energy savings (75% reduction in household energy costs), the greatest carbon dioxide reduction (2,207 tonnes annually), with a net present value of 1.49 million EUR and an internal rate of return of 7.21%. When external costs of air pollution and carbon are internalized, the economic net present value rises to 68.39 million EUR. The results suggest that, under the assumptions and boundary conditions defined in this study, decentralized renewable heating systems are both technically viable and economically favorable compared to district heating in low-heat-density suburban contexts. This work provides a replicable decision-support framework for policymakers and planners seeking to accelerate the clean heating transition in dispersed residential areas.
This paper presents a comparative study between two leading blockchain platformsâHyperledger Fabric and Ethereumâwith emphasis on their architectural design, performance characteristics, and security mechanisms in the context of enterprise applications. The study aims to identify key differences between permissioned and public blockchain models, focusing on scalability, consensus efficiency, and data confidentiality. A controlled experimental environment was developed using Docker-based deployments for both platforms, and performance was evaluated through Hyperledger Caliper using standardized workloads. Metrics such as transactions per second (TPS), latency, resource consumption, and failure rates were analyzed under varying network sizes. The results indicate that Hyperledger Fabric achieves significantly higher throughput (â900 TPS) and lower latency (<200 ms) compared to Ethereum (â25 TPS, â1 s latency), due to its deterministic Raft consensus and modular architecture. Ethereum, however, demonstrates superior decentralization and transparency suitable for public and decentralized applications. The findings highlight that both platforms are complementary: Hyperledger Fabric is optimized for controlled, high-performance enterprise use, while Ethereum excels in open, trustless environments.