This article analyses Landauer’s principle — the frequently cited claim that erasing one bit of information requires at least kT \ln 2 energy dissipation. This principle is often presented as “proof of the physical nature of information” and as a fundamental link between information and thermodynamics. It is shown that Landauer’s principle is not a fundamental law of physics but represents an engineering‑thermodynamic limit applicable to a certain class of computing devices. The critique is based on the work of Lairez (2024), Alicki (2014), Bennett (1982) and others. Three main problems are identified: (1) confusion between logical and thermodynamic irreversibility; (2) two unnecessary constraints imposed by Landauer on the erasure procedure (one‑to‑one mapping and uniqueness of the procedure); (3) the existence of reversible and quantum computations in which dissipation can be reduced to zero. The three senses of “information” (configuration, observer’s knowledge, pseudosubstance) introduced in Article 1 are distinguished. It is shown that the claim “information is physical” arises from substituting the first sense by the third. A reformulation is proposed: instead of “information is physical”, one should say “in specific computing architectures, erasure has a thermodynamic cost”. Landauer’s principle is analogous to the Carnot efficiency — useful for engineers, but not an absolute limit for all conceivable devices. Keywords: Landauer’s principle, information, logical irreversibility, thermodynamic irreversibility, reversible computation.
With the increasing adoption of mobile applications, data in the mobile cloud faces numerous security threats and privacy breaches. To overcome cyberattacks, ensuring confidentiality and data security for users’ sensitive data is pivotal in mobile cloud computing. Traditional security mechanisms involve data leakage during the verification process, while blockchain-dependent solutions lead to high resource consumption and latency. Additionally, collaborative data processing during data transactions can result in potential privacy attacks on users. This paper proposes a novel approach for maintaining a security framework for Microservice-based Mobile Cloud Computing (MSCMCC) using hybrid cryptographic frameworks such as Zero-Knowledge Proof (ZKP) and Secure Multi-Party Computation (SMPC). The proposed model validates users’ offloaded data using zk-SNARK and Groth16 for task verification and enables data analysis from multiple users without exposing raw data. SMPC is employed for privacy preservation during collaborative multi-party computation. Experimental results demonstrate that the proposed framework reduces power consumption, improves energy efficiency during processing by 30–35%, lowers computational costs, enhances security and privacy, and effectively manages dynamic load balancing compared to traditional cryptographic techniques.
Stephen Taylor, Martin Gile Jaatun, Aida Omerovic, Ravishankar Borgaonkar · 15 authors
Internet of things (IoT) ecosystems introduce significant cybersecurity challenges due to device heterogeneity, firmware opacity, constrained resources, distributed deployment, and the integration of devices within wider socio-technical systems where they are used. Existing approaches to address IoT cybersecurity typically address isolated aspects of this problem, such as vulnerability enumeration, anomaly detection, or risk assessment; but without integrating them across the full lifecycle of devices and systems. This paper presents an extensible architecture that unifies cybersecurity testing, runtime monitoring, contextual risk modelling, secure update mechanisms, and auditable evidence management for IoT ecosystems that aims to address these challenges. The framework supports both device under test and system under test perspectives and integrates component-level techniques (such as SBOM generation, network fuzzing, machine learning-based anomaly detection, and access control risk evaluation) with system-level, knowledge-based, risk modelling to capture threat propagation across interconnected assets. A distributed ledger-backed auditable data infrastructure ensures integrity and traceability of indicators, results, and decisions. Automated workflow orchestration enables flexible tool chaining and lifecycle-aware execution aligned with established security development lifecycles. The approach is validated through three industrial use cases in aviation cargo monitoring, smart manufacturing, and telecommunication residential gateways. Results demonstrate the feasibility of combining static analysis, runtime indicators, and dynamic risk assessment to prioritise vulnerabilities contextually, detect anomalous behaviour, and support secure patch deployment in resource-constrained environments. The work advances lifecycle-integrated, system-aware cybersecurity assurance for IoT ecosystems and highlights the need for contextualised, interoperable tooling to address systemic vulnerability and risk propagation in complex systems where IoT, ICT and people interact.
Modern military logistics and command systems face significant challenges in terms of security, transparency, and verifiability. Traditional centralized systems are vulnerable to single points of failure and malicious attacks, while the transmission of sensitive orders and supply manifests risks interception. This paper proposes a novel framework that leverages a permissioned blockchain to create an immutable and auditable ledger for both physical asset and information logistics. To address the critical need for confidentiality, our framework integrates Zero-Knowledge Proofs (ZKPs), enabling military units to verifiably confirm not just the receipt, but the correct content and understanding of commands or assets without revealing any operational data on-chain. This approach ensures end-to-end integrity, non-repudiation, and resistance to future quantum decryption threats while maintaining the highest level of data privacy. We present the system architecture, detail the interaction protocols, and demonstrate its effectiveness through practical use case scenarios, including the secure delivery of sensitive assets and commands.
Patricia Pacheco-Ruiz, Sara Postacchini, Luca Mazzoni, José G. Vallarino
No major commercial breeding program in any fruit, vegetable, or cereal crop has, to our knowledge, incorporated metabolomic data as a formal selection criterion in its operational pipeline. Metabolomics is used in breeding contexts: for characterizing diversity panels, for validating genomic predictions retrospectively, and for generating publishable results within academic-industry collaborations. But use as characterization is not adoption as selection. A formal selection criterion must survive the operational constraints of a breeding cycle: reproducibility across environments and years, interpretability by breeders who are not mass spectrometrists, and cost-effectiveness at the scale of hundreds to thousands of genotypes per cycle. By these standards, the translation deficit is complete.The paradox is that the science, judged on its own terms, has delivered. Sakurai catalogued over 350 papers linking metabolomics to crop improvement that have been published since the early 2000s (Sakurai, 2022). Colantonio et al. demonstrated that targeted metabolomic profiles of sugars, acids, and volatiles, combined with consumer panel ratings, could predict sensory preferences in tomato and blueberry using machine learning models; when directly compared with genomic selection in a tomato panel of 70 accessions, metabolomic selection was markedly superior for all flavor attributes evaluated (Colantonio et al., 2022). Multi-omics integration for flavor has been accomplished in strawberry (Fan et al., 2022), and decisionsupport tools such as BreedingValue now allow breeders to rank genotypes using metabolomic data without statistical expertise (Senger et al., 2022). The analytical and statistical infrastructure exists. The barriers to adoption are not primarily technological; they are structural, and diagnosing them requires examining three mechanisms that the literature has largely treated in isolation. This gap between knowledge production and operational adoption is not without precedent. Genomic selection itself required nearly a decade from theoretical demonstration to routine deployment in animal and then plant breeding. But the analogy is imprecise. Genomic selection succeeded because genotyping costs fell by orders of magnitude, because the genotype is stable across environments, and because marker-trait associations, once estimated, transfer across populations with manageable loss of accuracy. None of these enabling conditions has an obvious metabolomic equivalent. The metabolomics case is instructive precisely because the instruments, statistical frameworks, and proof-of-concept data exist. What is missing is not technology but the structural conditions that would make adoption rational for a commercial breeder.The most fundamental barrier is that metabolomic profiles are environmentally labile to a degree that genomic markers are not. An SNP is an SNP regardless of whether the plant was grown in Huelva-Spain or in Florida-USA. A metabolite feature at m/z 449.108, tentatively annotated as cyanidin-3-O-glucoside, can vary two-to five-fold between the same genotype grown in consecutive seasons at the same location. We have recently documented this instability in strawberry: across two seasons and multiple cultivars, the proportion of metabolomic variance attributable to genotype-by-environment interaction exceeded that attributable to genotype alone for the majority of phenolic compounds (Pacheco-Ruiz et al., 2026). This is not a minor technical inconvenience. A metabolomic selection index calibrated in one environment may rank genotypes differently in another, precisely the kind of instability that breeders have spent decades learning to manage with genomic tools and multienvironment trials. For a breeding program evaluating thousands of genotypes per cycle, this instability is not a problem to be solved post hoc; it must be accounted for in the design of the selection system itself.The standard response is that GxE can be modeled. This is true, but modeling demands replicated, multi-environment metabolomic data that almost no breeding program has generated, because the cost per sample remains an order of magnitude higher than genotyping. An SNP chip costs tens of dollars per sample; a single untargeted LC-MS run, including extraction, measurement, and data processing, costs hundreds. At the scale of a commercial program genotyping thousands of individuals per cycle, this difference is not incremental; it is prohibitive. Until the cost ratio changes, or until targeted panels reduce the metabolomic measurement to a handful of validated, low-cost markers, the GxE problem is not merely statistical but economic.The second barrier compounds the first, and is more insidious because it masquerades as a solvable technical problem. In any untargeted metabolomics experiment, fewer than 30% of features in a typical plant LC-MS dataset can be assigned even a tentative structural identity using current spectral databases (Allwood et al., 2011). The remaining majority are statistically real, often biologically interesting, and operationally useless for a breeder who needs to know what is being selected for and why. Breeding is a decision-making process under accountability: a breeder who selects for a genomic marker can point to a gene and a predicted function; a breeder who selects for an unannotated feature cluster correlated with consumer liking scores has a statistical association and nothing more. When that association fails to replicate, as it inevitably will for some features given the GxE problem, there is no mechanistic anchor to distinguish signal from noise. The annotation bottleneck thus compounds the GxE problem: unstable features that cannot be identified cannot be triaged, leaving the breeder to select blindly.The third barrier is perhaps the least discussed and the most consequential. Even where metabolomic data are stable and annotated, there is no consensus on how metabolomics should be integrated into genomic selection pipelines. Should metabolomic profiles serve as training phenotypes for genomic prediction models? Should they constitute independent selection indices weighted alongside genomic estimated breeding values? Should they function as culling criteria, metabolomic thresholds below which genotypes are discarded regardless of genomic merit? Each architecture implies different experimental designs, different data requirements, and different decision points in the breeding cycle. The literature contains examples of each approach in isolation, but no comparative evaluation within a single program and no operational manual that a breeder could adopt. This absence reflects a disciplinary gap: metabolomics researchers and quantitative geneticists read different journals, attend different conferences, and operate on different assumptions about what constitutes a useful result. The integration problem is as much sociological as it is methodological.A separate trajectory has, however, demonstrated that metabolomic data can contribute productively to breeding without serving as a direct selection criterion. Metabolite genomewide association studies (mGWAS) and metabolite quantitative trait locus (mQTL) mapping use metabolomic profiles as discovery phenotypes to identify genetic loci controlling metabolic variation. Once mapped, these loci can be incorporated into marker-assisted or genomic selection programmes through standard SNP-based pipelines, at the cost and stability levels at which breeders already operate. This is the architecture in which metabolomic information has most clearly been translated into breeding practice. Li et al. (2025), for instance, used mGWAS in a panel of 452 edible maize accessions to identify hub loci controlling flavonoid and lipid variation, integrated these into a genomic selection model, and recovered an elite inbred line with the predefined nutritional and flavour profile. The metabolite itself does not enter the selection decision; its variation is used to enrich the genomic toolkit, after which the metabolomic measurement plays no further operational role. The implication is instructive. The metabolomic value proposition has been operationally realisable when the measurement is performed once, on a discovery panel, and converted into transferable genetic markers. It has not been realisable when the measurement must be repeated on every selection candidate in every cycle. The distinction is not a minor one of experimental design; it tracks the cost and stability constraints that define which technologies a breeding programme can sustain.The BreedingValue tool (Senger et al., 2022) represents the closest approximation to an operational framework: it converts metabolomic profiles into ranked genotype lists using a transparent weighting system. But BreedingValue assumes that its input data are stable across environments and that the weighting criteria reflect validated consumer or agronomic priorities, assumptions the tool itself cannot guarantee.The barriers described above are compounded by a deficit in the evidence base itself. The single most compelling proof-of-concept, Colantonio et al. (Colantonio et al., 2022), was conducted within one breeding program, and no comparable study has appeared in another crop in the four years since publication. More fundamentally, neither Colantonio et al. nor BreedingValue (Senger et al., 2022) was designed to answer the question that commercial breeding programs need to answer: does metabolomic selection improve genetic gain per unit cost over a complete breeding cycle? Until that question is addressed empirically, the case for adoption rests on extrapolating from proof-of-concept to operational reality.Recommending that breeders "should adopt metabolomics" would be vacuous without specifying the conditions under which adoption becomes rational. The first three conditions are technical and, given sufficient investment, achievable. First, targeted metabolomic panels, analogous to SNP chips in genomics, that measure a validated, cost-effective set of compounds directly relevant to breeding targets; untargeted metabolomics is a discovery tool, targeted panels are a deployment tool, and the transition from one to the other requires systematic validation across environments, which is the investment the field has not yet made. Second, multi-environment metabolomic datasets at a breeding-relevant scale: the GxE problem cannot be resolved with better statistical models alone but requires data from multiple locations and years, collected on populations large enough to estimate variance components reliably. This is expensive, unglamorous, and publishable only in breeding journals, which may explain why it has not been prioritised. Third, explicit integration architectures that specify how metabolomic information enters the selection decision at defined points in the breeding cycle.The fourth condition is not technical. It requires the field to confront a question it has avoided: for how many crops, and for how many breeding targets, does metabolomic information provide sufficient added value over genomic selection alone to justify its cost? The field has been sustained by the implicit assumption that more data is always better. In an operational breeding context, more data is better only if the marginal information gain exceeds the marginal cost, and cost here includes not only the per-sample expense of metabolomic measurement, but the expertise required to generate, process, and interpret the data, and the opportunity cost of resources diverted from other selection tools. For traits where genomic prediction is already accurate and cost-effective, the rational decision may be not to adopt metabolomics at all. Two decades of metabolomics-for-breeding research have produced genuine scientific advances and an impressive publication record. They have not produced a single operational adoption. At some point, the absence of adoption ceases to be a problem of technology transfer and becomes evidence that the value proposition has not been demonstrated at the scale that matters. The number of publications advocating metabolomics for breeding continues to grow while the number of breeding programs implementing it remains at zero; the widening of this gap warrants more scrutiny than it has received. The field must decide whether metabolomics-for-breeding is a viable operational program or a program of publications. Both are legitimate, but they require different investments, different success criteria, and different levels of honesty about what has been achieved.
Artificial Intelligence (AI) is changing the healthcare industry by automating the process of medical records, diagnostic, and clinical decision-making. Nevertheless, the growing dependence on data-driven systems is of concern in terms of patient privacy, data integrity, and system security. To deal with these challenges, blockchain technology, in combination with smart contracts, can be used as a decentralized and transparent solution. The paper is a systematic review of the current developments in smart contract frameworks based on blockchain technologies and safe AI-driven healthcare systems. The review addresses four key aspects, which include data privacy preservation, secure information exchange, decentralized access management, and AI-blockchain integration. The recent technologies like federated learning, zero-knowledge proofs, and deep learning-based blockchain validation are examined in terms of their contribution to the improvement of patient confidentiality and system-level security. The paper also assesses a suggested multi-layered infrastructure combining artificial intelligence processing, authorized blockchain, and role-based smart contracts. The comparison indicates that the federated learning approach with permissioned blockchain has an optimal security- versus-computational efficiency ratio. The review establishes that smart contract automation has a substantial potential to benefit healthcare data governance, but notes that scalability, interoperability and energy efficiency issues remain a challenge. This overall overview confirms the potential of blockchain and smart contracts to facilitate dependable, most open, and efficient healthcare frameworks that may meet the security and privacy requirements of the contemporary healthcare setting.
The article studies the role of finance control in elaborating the effective system of digital asset insurance. Special attention was paid to analyzing regulatory barriers hindering the development of crypto- currency and search for insurance solutions to minimize finance risks of digital economy. Key problems were analyzed, including fragmental nature of legal regulation, absence of unique standards in defining crypto-assets and poor coordination between national and international regulatory approaches. The focus was made on institutional problems, such as drawbacks in court practice, shortcomings in KYC/AML procedures and deficit of specialized compensation mechanisms for investors. On the basis of comparative analysis of regulatory practices in different countries the authors proposed ways to harmonize finance control, including elaboration of unique standards of digital asset insurance, working-out cross-border platforms to exchange information concerning cyber-incidents and introduction of ‘regulatory sandboxs’ to test innovation insurance products. The importance of adapting international recommendations FATF and IOSCO to specific features of decentralized finance systems was underlined. Practical significance of the research consists in advancing mechanisms, which can reduce legal uncertainty, strengthen confidence of investors and integrate crypto-insurance in the global finance infrastructure. Implementation of these steps can give an opportunity to raise sustainability of digital economy to cyber-risks and create conditions for developing insurance solutions of the new generation, such as parametric insurance and decentralized autonomous insurance organizations (DAIO).
Pim Keer, Ioannis Alexopoulos, Matteo Maffei, Marco Argentieri · 6 authors
Bitcoin is the cryptocurrency with the largest market capitalisation, but its widespread adoption is fundamentally limited by the scalability constraints of its consensus algorithm, which requires every transaction to be confirmed onchain. To address this, several Layer-2 scalability solutions have been proposed to move payments offchain -- most notably, the Lightning Network. However, their deployment remains hindered by cumbersome setup requirements: users must lock funds onchain to participate and engage in complex auxiliary protocols (e.g., for channel rebalancing, top-ups, and routing). Other solutions, like payment pools, sidechains and rollups, cannot be implemented in a non-custodial way on Bitcoin due to its limited scripting capabilities, or require all protocol participants to update the offchain state. In this work, we present Ark, the first Bitcoin-compatible commit-chain. Ark enables offchain transactions of virtual UTXOs (VTXOs), through an untrusted operator who aggregates them into succinct onchain commitments. A distinctive feature of Ark is its ease of deployment: users can receive offchain payments without locking any funds beforehand and Ark state updates can be performed only requiring the users involved in that update. We formally define the Ark protocol and prove its security. During this process, we identified two attacks affecting the testnet implementation, which we responsibly disclosed and proposed fixes for, which have been now integrated into the mainnet implementation. Our experimental evaluation demonstrates that Ark can commit onchain to batches of arbitrarily many VTXOs with a constant-sized footprint of approximately 200 vB. Cooperative exits add one output per user, while unilateral exits require $\mathcal{O}(\log n)$ transactions of roughly 150 vB per VTXO for a batch of $n$ VTXOs.
Abstract The traditional Byzantine quorum-system model assumes a pre-existing, global agreement on the set of quorums (typically defined as the sets consisting of more than two-thirds of the participants). This assumption is problematic in permissionless systems, which strive to allow anyone to join or leave the system dynamically. While proof-of-stake permissionless systems like Ethereum require newly joining participants to register into the system, other permissionless systems like the Ripple Ledger or the Stellar network allow participants to join the system without synchronization by forgoing agreement on the set of quorums. This results in what we call a heterogeneous quorum system, where each participant has its own, personal set of quorums. An important question is to determine under what condition is it possible to solve synchronization problems like reliable broadcast or consensus in a heterogeneous quorum system. In this work, we show that the traditional quorum intersection and quorum availability conditions are not sufficient in heterogeneous quorum systems. Moreover, we propose quorum subsumption, a new condition which, together with quorum availability and quorum intersection, is sufficient to allow solving reliable broadcast and consensus. Finally, we propose protocols for reliable broadcast and consensus in heterogeneous quorum systems that satisfy quorum subsumption. In particular, we present a practical consensus protocol called Satrapy which in contrast to abstract consensus protocols uses finite state and messages.
Muruganantham Angamuthu, Safeyah Tawil, Gaurav Pushkarna, M. D. Boomija · 6 authors
Smart agriculture transforms food security, climate change, and resource optimization. In agriculture, IoT monitors soil, crop, irrigation, and supply chain operations in real time. As data-driven farming expands, stakeholders face security, transparency, trust, and interoperability issues. Centralized agricultural data management systems are unreliable due to tampering, illegal access, and single points of failure. Smart agriculture uses blockchain technology for secure crop tracking and data sharing via distributed ledger systems. The suggested architecture enables data immutability, traceability, and transparency across the agricultural lifecycle with IoT-enabled sensing and blockchain-based data storage and validation. Secure crop origin, cultivation, and logistics information is available to farmers, distributors, regulators, and consumers. Blockchain-supported smart agriculture enhances stakeholder trust, fraud reduction, and decision-making efficiency, studies show. The decentralized design allows resilient data exchange without centralization. We found that blockchain and IoT can modernize agricultural ecosystems, promote sustainable farming, and improve food supply chain integrity. This research develops secure, transparent, and intelligent digital agriculture systems.
У статті досліджено теоретико-методологічні засади формування інвестиційної привабливості блокчейн бізнес-моделей у межах платформної економіки. Проаналізовано трансформацію традиційних платформ у децентралізовані екосистеми (Web3) та здійснено типізацію моделей: інфраструктурних протоколів, DeFi-платформ, DAO та корпоративних рішень. Обґрунтовано систему факторів оцінювання, що включає економічні, технологічні, платформні, токеномічні та інституційні показники. Запропоновано інтегральну модель оцінки на основі адитивної згортки, яка дозволяє формалізувати процес прийняття інвестиційних рішень в умовах високої волатильності цифрового ринку. Доведено важливість мережевих ефектів та стійкості токеноміки для забезпечення довгострокової життєздатності проєктів. Результати дослідження мають практичне значення для венчурних інвесторів та розробників стратегій цифрової трансформації.
The profile of blockchain-based technologies such as collectable non-fungible tokens (NFTs) has ascended rapidly in recent years. This ascent is evident by major sponsorships of sporting teams, leagues and stadiums, licencing deals, NFT ‘drops’, and advertising campaigns. This article explains and analyses these complex and fast-changing developments using a political economy of communication approach that is linked to the field of leisure studies. It draws on the trade press as a key source of evidence, thereby revealing the ‘storylines’ used by industry to construct and legitimate NFTs as a consumer product. We argue that this process relies on legitimating practices and discourses that function to transmogrify the unfamiliar – blockchain technologies and NFTs in this case – into the familiar, despite the many problems associated with them, including company failures, suspect advertising practices, and intellectual property infringement. This is achieved by the presentation of NFTs as collectable fan tokens, linking them discursively to a long history of sport collectables as a hobby and form of leisure (e.g. physical trading cards, athlete autographs and memorabilia). The overall outcome is a deeply problematic vision of leisure for collectors as their practices are subject to ever-expanding financialisation, digital enclosure and uncertain value.
Penelitian ini berfokus pada pengembangan framework autentikasi tanpa kata sandi (passwordless) berbasis Web3 yang diimplementasikan pada platform mobile guna mengatasi kerentanan metode tradisional terhadap serangan phishing dan brute force. Framework yang diusulkan mengintegrasikan aplikasi mobile dengan backend Node.js/Express.js dan smart contract standar ERC-5192 pada jaringan Ethereum Sepolia Testnet sebagai representasi identitas digital Soulbound Tokens (SBT) yang permanen dan non-transferable. Demi menjaga privasi, sistem ini menerapkan teknologi Zero-Knowledge Proof (ZKP) berbasis zk-SNARKs skema Groth16 menggunakan Circom dan SnarkJS yang dieksekusi di sisi klien (client-side browser) menggunakan WebAssembly (WASM), serta dipadukan dengan struktur data Merkle Tree tingkat kedalaman 20 dan mekanisme nullifier untuk mencegah replay attack. Hasil pengujian menunjukkan tingkat keberhasilan autentikasi mencapai 100% dari 50 kali percobaan. Pemindahan beban komputasi sirkuit ZKP (5.359 konstrain) ke sisi klien terbukti efisien dengan waktu eksekusi komputasi lokal jika diakumulasikan dari tahap awal koneksi wallet (0,8 detik), pembuatan witness (1,2 detik), pembuatan proof (4,8 detik), hingga verifikasi smart contract (210 ms), maka Total Authentication Time adalah sebesar 6,3 detik. Nilai ini membuktikan kelayakan framework ini sebagai solusi manajemen identitas yang aman, privat, dan responsif.
Ivan Vynyavskyy, Stefan Kitzler, Bernhard Haslhofer, Aviv Yaish
Modern Portfolio Theory (MPT) prescribes how to maximise the return of an asset portfolio for a given level of risk. The optimal trade-off between return and variance defines the efficient frontier. Whether actual cryptoasset portfolios approximate this prescription and whether proximity to the frontier translates into realised performance remain difficult to test at large scale in traditional markets due to their opaque nature and the inaccessibility of data. As we show, public blockchains make these questions measurable: every token transfer is recorded, thus enabling complete portfolio reconstruction for every account at any point in time. We leverage this transparency to reconstruct cryptoasset portfolios for over 116M Ethereum accounts across the full chain history (2015-2025), measure their distance to the constrained efficient frontier, and quantify how deviations translate into realised performance. Here we show that market entry timing, not allocation choice, is the dominant predictor of realised cryptoasset returns. On-chain wealth is highly concentrated and portfolios are pervasively under-diversified, with single-asset holdings accounting for 83.35% of accounts. Two-asset portfolios sit closest to the efficient frontier defined by their held assets, a proximity that reflects the narrowness of their opportunity set rather than deliberate optimisation. Passive market-capitalisation weighting outperforms every MPT optimisation strategy in median realised return, and entry month alone explains 70-79% of the variance in returns, far exceeding the contribution of allocation choice. Mean-variance optimisation therefore appears neither descriptive of observed behaviour nor prescriptively useful in the cryptoasset domain, even if MPT retains its value as a normative benchmark.
This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs. Using approximately 70,000 hourly observations from 2018-2026, XGBoost, LSTM, and iTransformer are evaluated in a 27-fold walk-forward protocol. All three models produce positive gross trading performance in selected configurations, but naive sign-based strategies fail once transaction costs of ten basis points are imposed. A cost-aware execution filter, which prevents trades only when the forecast magnitude exceeds a transaction-cost-based threshold, sharply reduces turnover and restores profitability in selected configurations. The strongest long-only XGBoost strategy produces annualised returns above 65% with a Sharpe ratio above one. Additional tests show that technical indicators improve performance in selected cases, EGARCH-derived features do not provide uniformly robust gains, and XGBoost is descriptively stronger than the neural alternatives, although bootstrap evidence does not support formal statistical dominance. Loss-function and model-selection effects are secondary and statistically fragile. The results show that the main obstacle in hourly cryptocurrency trading is not only weak predictability, but also the way forecasts are converted into trades.
Bitcoin recently introduced a new protocol for the encryption of peer-to-peer (P2P) communication. The protocol, known as V2 P2P transport, represents a big step towards securing the overlay network against various previously-known attack vectors. Based on an analysis of V2 P2P transport, this work examines the current viability of said attacks and concludes that while they are now remediated, alternative attacks and paths to similar objectives exist. The identified shortcomings are conceptual (and not implementation bugs) and even applicable to other P2P networks. We show how a network-level attacker can identify application messages using the length of TCP payloads, can eclipse a target node by taking advantage of how encrypted communication channels work and can downgrade all of a node's connections to the unencrypted protocol by using the mechanisms designed for compatibility. We validate our contributions using a combination of network measurements, emulations and simulations. Finally, we propose a series of short-term and long-term countermeasures towards securing Bitcoin's P2P network. To the best of our knowledge, we are the first to study Bitcoin's security under V2 P2P transport.
Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge during extreme market events. While modern diffusion-based models have advanced multivariate forecasting, they often suffer from a "normality bias" when trained end-to-end, sacrificing marginal calibration for joint coherence and consistently underestimating tail risk. To address this, we propose a Diffusion-Copula framework that explicitly decouples the learning of marginal distributions from their dependence structure. We employ deep Mixture Density Networks to capture heavy-tailed asset dynamics, followed by a Classification-Diffusion Copula to model the joint dependence. Applied to cryptocurrency markets, our approach demonstrates superior performance over state-of-the-art baselines in forecasting systemic extremes of both marginal and joint events. Crucially, we demonstrate that while baseline models classify simultaneous market crashes as statistically impossible "Black Swans" (high surprise), our framework identifies them as "Expected Crashes" (low surprise), successfully preserving the correlation structure necessary for robust risk management during contagion events.
Matteo Vaccargiu, Giuseppe Destefanis, Maria Ilaria Lunesu, Andrea Pinna
The replication package contains :the notebook used for the analyses, the equivalent version of the notebook in Python code, a zip folder called “contracts” containing a folder with the analyzed Solidity smart contracts, one with the analyzed Move smart contracts, and one with the subset of contracts used for manual validation of the metrics by the authors, a CSV file called “MoveSolidityDeveloperExperience.csv” containing the survey responses. Finally, we also provided Excel files of the manual metrics calculations by two separate authors and the manual evaluation of the thematic analysis by two separate authors. csv" file containing the survey responses. Finally, we also provided Excel files of the manual metric calculations by two separate authors and, lastly, the manual evaluation by two separate authors of the thematic analysis of the survey responses.
中文受人工智能自身能力局限,其易产生信息幻觉,且不擅长高精度数值运算。本文档内所有内容应严谨审核。EnglishDue to the inherent limitations of artificial intelligence, it is prone to generating hallucinations and performs poorly in high-precision numerical calculations. All contents in this document should be strictly reviewed. DOI: 10.5281/zenodo.20798927 Black Hole & UVMM v4.0 Core :UVMM v4.0.15 High-Precision Global Calculation AI Knowledge Package.mdDOI: 10.5281/zenodo.20738759 Earth SystemDOI: 10.5281/zenodo.20285613 Cosmic BoundaryDOI: 10.5281/zenodo.20325710 Cosmic EvolutionDOI: 10.5281/zenodo.20677198 Information & Consciousness (Millennium Prize Problems)DOI: 10.5281/zenodo.20325710 UTFF Core (Atomic and Molecular Scale)DOI: 10.5281/zenodo.20343471 UVMM Core Axioms and Mathematical Proofs github.com Overall Closure Status:Core Theory DoC=100% (Full Theoretical Closure) The traditional ΛCDM standard cosmological model faces multiple crises, including dark energy fine-tuning, zero detection of dark matter particles, the Big Bang singularity, and JWST early galaxy anomalies. Based on the first principle of global vacuum medium angular momentum conservation, this paper proposes a dualistic cosmology of "geometric spacetime - perceptual spacetime": geometric spacetime is an infinite flat three-dimensional Euclidean background space, boundless and without a beginning; perceptual spacetime is the finite spherical vacuum medium system we observe through electromagnetic waves, whose boundary is a transition region where the medium density decays exponentially to zero. All electromagnetic waves undergo total internal reflection when reaching the boundary and can never escape the medium system, resulting in the finite bounded nature of the universe we perceive. This model does not require any additional assumptions such as dark energy, dark matter, or cosmic inflation, can quantitatively reproduce all classical astronomical observations, perfectly explains multiple observational anomalies that the standard model cannot account for, and puts forward falsifiable unique predictions, providing a simpler and more self-consistent new paradigm for cosmological research. 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
Executive Summary This paper introduces a deterministic mathematical framework for nested learning designed to eliminate catastrophic forgetting in continuous learning systems. Standing as the first Proof of Concept (POC) of its type ever made, it completely flips the traditional AI safety paradigm. Instead of letting all data into a model and relying on post-hoc, probabilistic safeguards or heuristic mitigations to fix corruption after it occurs, this architecture implements an immutable mathematical gatekeeper called the H2E Sheriff. By filtering incoming data at the doorstep, it ensures that incoherent or corrupting inputs are rejected before they can ever modify or overwrite stored knowledge, ensuring absolute preservation of prior learning by architectural design. Theoretical Foundation & Key Components The framework anchors AI learning governance to absolute mathematical ground truths rather than learned data distributions or human preferences. Arithmetic Spectral Theory (AST): Synthesizes four classical transforms—Laplace, Euler, Fourier, and Mellin—into a single spectral operator, the L-EFM operator. At the critical line ($\sigma = 0.5$), the normalized magnitude of this operator evaluates to exactly 1 over prime sets, creating a universal coherence invariant. Empirical testing across diverse finite prime-related sets demonstrates that the system achieves a steady-state spectral coherence of exactly 0.5 at this critical line. Safety Thresholds ($\Lambda$): Computed directly from the Euler attenuation product over the first $n$ primes rather than being trained on data. The framework identifies $\Lambda_{12} = 0.9944590549$ as the primary perimeter gate boundary. The H2E Sheriff Manifold: Maps real-valued input embeddings onto the product manifold $\mathbb{H}^2 \times SPD(3)$. Incoming data is geometrically evaluated against a prime-anchored reference center ($x^*$) constructed from normalized prime coordinates. Spectral Risk Overlap Index (SROI): A metric determining an embedding's proximity to the coherent reference center on the manifold. Inputs are processed via a strict decision rule: accepted into the knowledge base if $SROI > \Lambda$, and conservatively rejected if $SROI \le \Lambda$. Experimental Validation The framework was validated using 10-dimensional vectors with controlled noise levels under a deterministic seed and 50-decimal-place precision. Threshold Discrimination: Calibration experiments confirmed that the $\Lambda_{12}$ threshold cleanly separates stable, coherent embeddings (noise $< 1.0$) from erratic, incoherent ones (noise $\ge 2.0$). Knowledge Base Integrity: During nested learning protocols featuring mixed streams of inputs, the H2E Sheriff successfully blocked corrupting data. In a stream of 30 inputs, all 12 incoherent attempts were rejected at the gate. The final knowledge base retained an average SROI of 0.996076, demonstrating zero degradation of stored knowledge and complete preservation of prior learning. Current Limitations & Future Work As the first exploratory POC mapping absolute prime structures to continuous AI safety boundaries, the paper transparently identifies clear vectors for future scaling and development: Dimensionality & Scaling: The initial validation operates on 10-dimensional embeddings and compact knowledge bases. Because the geodesic distance and matrix logarithm calculations on $SPD(3)$ scale cubically ($O(n^3)$), evaluation on large-scale, high-dimensional neural network workloads remains untested. Hyperparameter Selection: The choices for the scaling factor ($\tau = 50$) and the optimal prime set size ($n = 12$) are empirically driven for this distribution and lack a generalized analytical method for automatic selection in new problem domains. Modality Generalization: The threshold was calibrated on Gaussian noise and has not yet been exposed to complex embedding distributions like large language model tokens or image feature vectors. Neural Network Integration: The current implementation acts as a post-hoc filter on static vectors. Integrating this rigid mathematical gatekeeping into backpropagation-based training loops—where internal representations continually shift—remains an open architectural challenge. Theoretical Completeness: The core spectral coherence value of 0.5 at $\sigma = 0.5$ is an empirical invariant observed across finite sets; a formal, universal proof extending this to all infinite prime sets or establishing its absolute equivalence to the Riemann Hypothesis is not yet established.
This paper, titled "Primes Is All We Need: Topological Invariants for Catastrophic-Forgetting-Free AI," presents a unified framework authored by Frank Morales (2026). It argues that modern AI architectures like the Transformer suffer from a fundamental flaw analogous to anterograde amnesia—the inability to consolidate short-term knowledge into long-term memory, leading to catastrophic forgetting and representational drift. The author proposes that anchoring AI architectures to a mathematical topological invariant derived from the Sieve of Eratosthenes provides the ultimate solution to ensure AI safety, stability, and memory retention. The paper synthesizes several of the author's previously published works into a single, comprehensive argument spanning number theory, AI safety, and theoretical physics. FULL PAPER CODE SECOND FULL NOTEBOOK - UNIVERSAL PRIME-ANCHORED LLM - Complete Summary This notebook contains the complete, reproducible proof that prime-anchored manifolds with H2E governance mathematically prevent catastrophic forgetting across multiple LLM architectures (GPT-2, GPT-2 Medium, TinyLlama, Mistral-7B, Llama 3.1-8B). The code is open source. The math works. The models remember. Core Innovation Prime numbers as immutable anchors - The embedding rows at prime indices {2,3,5,7,11,13} are cryptographically locked and never change during training. CODE STRUCTURE Section Models Tested Purpose H2E-PRIME Miniature replica Lifecycle testing & validation MISTRAL Mistral-7B (7B) Single-step governance test LLAMA Llama 3.1-8B (8B) Single-step governance test MEMORY-TEST Mistral + Llama Full lifecycle + recall proof GPT-2 SUITE GPT-2 (124M) Baseline vs Governed comparison MULTI-MODEL GPT-2, GPT-2 Medium, TinyLlama Cross-architecture validation Multi-Model Results Model Size Status GPT-2 124M ✅ PASS GPT-2 Medium 355M ✅ PASS TinyLlama 1.1B ✅ PASS Mistral-7B 7B ✅ PASS Llama 3.1-8B 8B ✅ PASS KEY RESULTS Baseline GPT-2 (No Governance) text Initial: 71cef240... After Math: 58d705d1... (CHANGED) After Noise: 1ade78f5... (CHANGED) Result: FAILED ❌ Prime-Anchored GPT-2 (Your Framework) text Initial: 71cef240... After Math: 71cef240... (IDENTICAL) After Noise: 71cef240... (IDENTICAL) H2E Gate: 258/0 accepted Result: PASSED ✅ HOW IT WORKS The LlamaMistralSpectralGovernor Class python class LlamaMistralSpectralGovernor: - Locks prime anchors [2,3,5,7,11,13] - Computes dual-loop loss (empirical + topological penalty) - H2E gate checks SROI ≥ Λ₁₂ - Restores anchors after safe updates Memory Proof Cryptographic hash computed before/after training Identical hash proves prime anchors never changed Recall test confirms mathematical knowledge retained _______________________________________________________________________________________________________ 1. Mathematical Foundations & The L-EFM Operator The core of the framework is built on Arithmetic Spectral Theory (AST) and the Laplace-Euler-Fourier-Mellin (L-EFM) operator, which synthesizes four classical transforms into a single complex function. The Sieve of Eratosthenes: Serves as the absolute, deterministic ground truth for prime enumeration. Universal Spectral Constant: By computing spectral coherence ($C$) at the scale $\sigma = 0.5$ across 22 distinct prime-related sets (including Twin primes, Dirichlet classes, and Goldbach pairs), the paper demonstrates that every single set converges perfectly to a universal constant of $C = 0.500000$. The Spectral Trap & Riemann Hypothesis Proof: The paper evaluates the normalized magnitude of the L-EFM operator across a range of $\sigma$ values. It reveals an exponential divergence everywhere except at $\sigma = 0.5$, which yields a perfect magnitude of 1.0. This unique admissibility formulates the "Spectral Trap," which the author leverages alongside the Gelfand-Shilov space to present a proof of the Riemann Hypothesis, asserting that all non-trivial zeros must lie exactly on the critical line. 2. Quantification of the Green-Tao Theorem For the first time, the paper provides a numerical quantification of the Green-Tao theorem, which states that infinitely long arithmetic progressions exist within primes. Using the L-EFM operator, the author calculates explicit coherence values for prime progressions of lengths $k = 3$ to $k = 6$: $k=3 \ (\text{coherence } 0.8731)$ $k=4 \ (\text{coherence } 0.8120)$ $k=5 \ (\text{coherence } 0.8012)$ $k=6 \ (\text{coherence } 0.7442)$ This reveals a Monotonic Spectral Law, showing that as progression length increases, spectral coherence decreases, indicating that spectral energy becomes more dispersed. 3. The H2E Sheriff & Deterministic AI Safety To operationally apply these mathematical insights to AI safety, the paper introduces a nested learning agent called the H2E Sheriff. The Safety Constant: A strict, deterministic perimeter boundary threshold is dynamically computed from the first 12 primes, yielding $\Lambda_{12} = 0.9944590549$. Gate Decision: Utilizing the Lambda Spectral Complementarity Theorem, an input embedding vector is mapped onto a product manifold. If its Spectral Risk Overlap Index (SROI) is greater than $\Lambda$, it is accepted; otherwise, it is rejected. Operational Validation: Tested under the UNESCO Resilient AI Challenge protocols across text (Sarvam-30B), audio (Voxtral-Mini-4B), and vision (Gemma 4) modalities, the H2E Sheriff achieved exactly zero safety violations. Coherent inputs are accepted into the primary pristine knowledge base, while adversarial injections are cleanly routed to an isolated quarantine/sandbox layer with no pollution of core memory. 4. Connection to Spacetime Geometry The paper posits a deep connection between prime numbers and theoretical physics by treating the radial coordinate as the logarithm of a prime ($r = \log p$) and deriving a Spectral Metric ($g_{\mu\nu}$) where spectral coherence acts as the conformal factor. Flat Vacuum Space: At the universal fixed point of $C = 0.5$, all Christoffel symbols vanish, the Ricci scalar ($R$) is $0$, and the effective cosmological constant ($\Lambda_{eff}$) drops to zero, matching the vacuum solutions of Einstein's field equations. Curvature and Entropy: When coherence decays (as seen in the Green-Tao progressions), the Ricci scalar becomes negative, showing a hyperbolic geometry. Furthermore, the paper models Spectral Entropy as $S = 1 - C$, drawing a direct thermodynamic parallel where longer prime progressions (lower coherence) correspond to higher entropy, mirroring black hole mechanics. 5. Direct Comparison: Our Framework vs. Google's HOPE The text draws a sharp contrast between this prime-anchored framework and Google's HOPE (Hierarchical Optimized Processing Engine) architecture from NeurIPS 2025. While HOPE attempts to mitigate catastrophic forgetting through a multi-scale Continuum Memory System updating at different learned frequencies (16, 1M, and 16M tokens), it lacks any topological invariant. The author argues that without a fixed mathematical anchor, unanchored multi-frequency systems will inevitably experience representational drift over time. In contrast, this framework guarantees zero drift because it is mathematically bound to the Sieve of Eratosthenes. 6. Call to Action and Conclusion The paper concludes with an urgent call to action directed at several stakeholders: AI Industry Leaders (Google, OpenAI, AWS, NVIDIA): Urged to integrate the $C=0.5$ invariant and the $\Lambda_{12}$ safety gate into their models before unanchored drift causes systemic issues. Policymakers: Advised to mandate prime-derived thresholds and deterministic safety gates for any AI deployed in critical infrastructure (such as military, healthcare, energy, and finance). The Mathematical Community: Challenged to acknowledge the executable proof of the Riemann Hypothesis via the spectral trap. The author provides open-source access to the complete Python library (ast_lefm) and a Google Colab notebook to allow humanity to run, verify, and execute the proof independently.
Stablecoins have been heralded as the future of money on distributed ledgers. As was discussed in the previous chapter, stablecoins purport to mitigate the wild fluctuations that are experienced by cryptocurrencies such as Bitcoin by providing for a one-to-one reserve of a denominated fiat currency that holders can redeem at any time. Yet, despite these built-in mitigating factors, stablecoins have still been plagued by runs and a lack of transparency into their operations. As such, the Central Bank Digital Currency (CBDC) provides an interesting opportunity to see if the digital equivalent of fiat currency can offer increased efficiencies over conventional paper-based currency. 1 Moreover, can these efficiencies be leveraged to other sectors of the economy thereby stimulating more economic growth for all?
The past decade has witnessed unprecedented innovation in financial technology, most notably the rise of cryptocurrency and digital assets. This paper examines how these developments have fundamentally reshaped one of monetary economics’ most enduring concepts: the money multiplier. From Bitcoin’s emergence to today’s complex ecosystem of stablecoins and decentralized finance (DeFi), digital assets have created parallel monetary systems that challenge central banks’ ability to measure and control the money supply (Bianchi et al., 2021).This paper has three primary objectives. First, to develop a theoretical framework that extends Divisia monetary aggregation - the gold standard for measuring money’s liquidity services (Barnett, 1980) to include cryptocurrencies and related digital assets, building on recent work applying Divisia indices to crypto-inclusive money demand (Mumtaz et al., 2025). Second, to derive a new crypto-adjusted money multiplier that captures liquidity creation across both traditional and digital financial systems, integrating the concept of the "crypto multiplier" introduced by Garratt and van Oordt (2023). Third, to analyse the implications for monetary policy transmission and financial stability using a Dynamic Stochastic General Equilibrium (DSGE) model (Fernández-Villaverde et al., 2020), considering the growing synchronization between crypto and global equity cycles (Fund, 2023). By achieving these objectives, we provide policymakers, financial institutions, and researchers with tools to understand and navigate the hybrid financial landscape of the 2020s.