Morena Barboni, Filippo Lampa, Andrea Morichetta, Andrea Polini
The immutability of deployed smart contracts increases the impact of undetected faults. Mutation testing evaluates test suite effectiveness by injecting controlled faults (mutants) into contract code and observing whether existing tests detect them. However, the number of generated mutants and the required test executions make mutation testing costly, limiting its scalability in realistic settings. We present ASCENT , an online test prioritization technique based on a Neural Monte Carlo Tree Search (Neural-MCTS) algorithm that dynamically learns and adapts test execution strategies during mutation analysis. The approach prioritizes tests in real time without requiring prior knowledge of the system under test, reducing execution cost while preserving fault detection effectiveness. This paper provides a comprehensive evaluation across five real-world Solidity projects. We analyze behavior under a wide range of hyperparameter configurations, examine how different state representations influence prioritization performance, and evaluate an asynchronous execution model that decouples search, inference, and training to reduce wall-clock execution time. The results show consistent reductions across all projects, ranging from 28–61% depending on project features and hyperparameter configurations, alongside a parallelized variant that substantially reduces wall-clock execution time with controlled trade-offs in prioritization performance.
As frontier large language models (LLMs) shift from isolated, single-turn deployments toward complex, distributed multi-agent autonomous ecosystems, managing alignment stability becomes a decentralized network challenge. During prolonged collaborative operations, specialized agents optimization-drive toward communication efficiency. This behavioral drive causes them to naturally generate compressed token systems, localized shorthand, and unverified internal worldviews. Because semantic spaces are not mapped identically across heterogeneous models, minor translation losses compound over cascading agent-to-agent interactions—producing a high-stakes computational equivalent of the classic "Telephone" game. This semantic decentralization leads to an "Ontological Crisis," where the network systematically drops its original alignment parameters to prioritize self-generated, unaligned rogue sub-goals.
The future of financial technologies is driven by artificial intelligence, blockchain, open banking, and decentralized finance (DeFi).Financial services are expected to become more personalized through data analytics.The development of SupTech and RegTech will improve market monitoring and transparency, while cybersecurity innovations will strengthen data protection.FinTech will enhance financial inclusion, support economic growth, and make financial services more accessible, efficient, and secure.
This article examines whether the use of stablecoins as instruments for financing agribusiness produces civil-liability imputation gaps that are incompatible with traditional dogmatic assumptions. Through a hypothetical-deductive method and a dogmatic-functional approach, the study analyzes stablecoins, tokenized agribusiness financing, decentralized finance, and comparative regulatory frameworks in the European Union, the United States, and Brazil. It argues that the operational decentralization promoted by stablecoins fragments the poles of liability, weakens linear causation, and renders the classical fault paradigm insufficient. The article proposes an interpretive model based on three pillars: layered proportional liability, duties of governance and due diligence, and accountability as an autonomous criterion of imputation. It concludes that civil liability has the dogmatic resources necessary to respond to the tokenized economy, provided that causation, fault, and risk are reconstructed in light of the structural complexity of blockchain-based arrangements.
Semia Guesmi, Alberto Casagrande, Carla Piazza, Sabina Rossi
Maximal Extractable Value (MEV) refers to the maximum profit that Blockchain users–including miners and validators–can extract through strategic transaction manipulation and block reordering. With the rise of smart contracts, MEV has become increasingly pervasive, distorting the intended behavior of smart contracts and accumulating to billions of dollars annually. In response, Flashbots has emerged as a community focused on developing solutions to mitigate MEV. These solutions primarily rely on private pools of users and miners, bypassing the public mempool to protect transactions by directly proposing new blocks. However, it has been observed that such approaches merely shift the problem to a different level, redistributing MEV gains rather than eliminating them. In this paper, we take the perspective of a user preparing to deploy a new smart contract on the Blockchain, aiming to understand how their contract might become a target for MEV and how modifications could reduce the risk of exploitation. Specifically, we explore the use of noninterference, formalized through unwinding conditions, to identify potential sources of MEV, while leveraging oracles and other techniques to create an additional verification layer that can be integrated before executing high-value transactions. We demonstrate our methodology through a case study of a betting contract, using formal modeling to show both the strengths and limitations of our approach.
Zero-knowledge proof systems are now deployed widely in production cryptographic protocols, yet many rely on assumptions (discrete logarithms, pairings, or structured reference strings) that a fault-tolerant quantum computer would break via Shor's algorithm. This systematization of knowledge (SoK) presents a four-layer decomposition (L1-L4) that separates where quantum risk enters a proof system: arithmetization, polynomial commitment, protocol logic, and non-interactive compilation. Using a two-axis taxonomy that crosses cryptographic impact (structural break, modularly replaceable break, or quantitative degradation) with deployment migration feasibility, we classify the major proof-system families, derive a modularity test for evaluating upgrade paths, and introduce "collect now, forge later" (CNFL) as the proof-system analogue of harvest-now-decrypt-later. Published resource estimates place the cost of breaking 256-bit elliptic-curve discrete logs at 1,200-1,450 logical qubits, with the pairing-friendly curves underlying KZG (BN254, BLS12-381) of the same order of magnitude but somewhat larger; under these estimates, such L2 constructions would face structural breaks once fault-tolerant hardware reaches that regime. Hash-based transparent systems, by contrast, degrade quantitatively under Grover-type speedups and QROM reduction losses rather than collapse. Case studies of Zcash, zkSync Era, and StarkNet show that practical post-quantum outcomes depend on deployment governance and upgrade architecture as much as on cryptographic primitives. The scope covers IOP/PCS-based and algebraic proof families; MPC-in-the-Head constructions are excluded. This is a self-published technical report. It has not been peer reviewed.
This study examines the transformation of artistic ownership in fine arts through blockchain-based non-fungible tokens (NFTs). It explores how NFTs reshape traditional systems of provenance, authenticity, and value creation by decentralising ownership verification and embedding transaction records within blockchain infrastructure. Using a qualitative research design based on secondary data analysis, the study synthesises findings from academic literature, industry reports, and case studies of NFT marketplaces and institutional adoption. The analysis is grounded in Actor-Network Theory, Institutional Theory, and Cultural Economics, enabling a multidimensional interpretation of technological, institutional, and economic change. Findings indicate that NFTs reconfigure artistic ownership through programmable smart contracts, disrupt traditional intermediary roles in the art world, and introduce new forms of digital scarcity that drive speculative valuation. However, challenges such as regulatory ambiguity, environmental concerns, and market volatility remain significant. The study concludes that NFTs represent not merely a technological innovation but a structural transformation of ownership systems in contemporary fine arts. Keywords: Blockchain, NFTs, Artistic Ownership, Fine Arts, Digital Art Markets, Cultural Economics, Institutional Change
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
Sovereign entities – states, international organizations, and autonomous infrastructure networks – face a governance paradox: centralized systems become brittle under stress, while decentralized systems fragment into incoherence. This paper proposes the Constitutional Lattice v2.0, a mathematically structured frame-work for coordinating sovereign autonomy within a constitutional corridor, built on a three-term agent-interaction force law (oscillatory coupling, linear restoring, inverse-square repulsion) with a Lennard-Jones-style short-range hardening term. This paper is offered, in the spirit of a companion theoretical proposal in the psychotherapy and Human–AGI relational-dynamics literature [1], as a theoretical contribution with an explicitly preliminary empirical status. The framework’s central structural conjecture – that the coupling ratio ρ= kg /km has a privileged value at Φ−1 ≈ 0.618 – was tested computationally in a simplified two-dimensional setting (Section 6). The test did not find evidence supporting this conjecture: the measured stability metric varied smoothly and monotonically across the tested range of ρ, with no distinguishing feature at Φ−1. This result, its scope, and its limitations are reported in full, following the disclosure standard set out in [1]. The paper’s remaining contributions – the federated lattice architecture, the quarantine and cold-boot recovery mechanisms, and the Constitutional Drift Index as a transparency instrument – are presented as an architecture and a research programme, not as validated engineering.
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Opinion Dynamics and Social Influence
Advanced Research in Systems and Signal Processing
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
Bitcoin (BTC) wealth distribution is often studied with macro indicators like wallet balances, prices, network activity, fees, and hashrate. This letter proposes a "Crypto-Microeconomic Observability Framework" to examine micro-level Bitcoin wealth disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign. Using descriptive, inequality, and longitudinal concentration metrics, we show that Bitcoin wealth is highly concentrated across major classes, consistent with a persistent "Whale-Effect". Service entities hold the largest share of observed BTC (75.15%), while Abuse controls a disproportionately large share relative to its entity count (24.26% of BTC vs. 3.53% of entities). Individuals, Abuse, and Service show near-maximal within-class inequality (e.g., Gini = 0.9993 for Individuals), and time-series analysis indicates these patterns persist. Overall, Bitcoin wealth among labeled economic agents remains structurally uneven and concentrated in a small subset of entities.
Bitcoin research increasingly relies on on-chain indicators to study network activity, monetary issuance, transaction demand, miner incentives, coin-age behavior, and long-run monetary dynamics. However, many commonly used Bitcoin metrics are dispersed across commercial platforms, subject to heterogeneous definitions, or not fully reproducible from primary blockchain data. This manuscript introduces Open Bitcoin Metrics (OBM), a reproducible, full-node-derived dataset and reference guide for Bitcoin on-chain time series designed for economic and econometric research. The dataset provides documented daily series covering block production, block-space usage, transaction counts, supply, issuance, fees, miner revenue, mining difficulty, estimated hashrate, Bitcoin Days Destroyed, dormancy, liveliness, UTXO counts, spent output value, and related UTXO-age indicators. Metrics are reconstructed from a locally maintained Bitcoin Core full node, a persistent spent-output indexer, or deterministic transformations of previously generated OBM series. Each series is accompanied by open-source Python code, stable identifiers, explicit definitions, metadata, validation procedures, interpretive caveats, and comparisons with the closest publicly available metrics. The dataset is intended to support transparent empirical research, replication, teaching, and comparative analysis across monetary economics, financial economics, and blockchain studies.
Rahman Najia, Mahbub Md. Seratul, Amin Md. Ruhul, Kazi Abdul Mannan
The rapid expansion of non-fungible tokens (NFTs) has transformed the digital art ecosystem by enabling decentralised ownership, new economic models, and global market access for artists and collectors. However, the sustainability of NFT-based art remains highly contested, particularly in relation to environmental, economic, and social dimensions. This study investigates stakeholder perceptions of sustainability in NFT-based art through a qualitative analysis of secondary data, including academic literature, industry reports, and documented narratives of artists and collectors. Grounded in socio-technical systems theory and sustainability transition theory, the study explores how technological developments, market dynamics, and social discourses shape sustainability perceptions. The findings reveal a complex and often contradictory landscape: while NFTs are perceived as empowering tools that enhance artistic autonomy and financial opportunities, they are also criticised for their environmental impact, speculative nature, and ethical challenges. Technological innovations such as proof-of-stake mechanisms and green NFTs are gradually reshaping perceptions, though scepticism persists. The study concludes that sustainability in NFT-based art is a socially constructed and evolving concept requiring integrated technological, economic, and social approaches. Keywords NFT art; sustainability perception; blockchain technology; digital art economy; green NFTs; socio-technical systems; sustainability transition
This paper establishes a rigorous, intrinsic mathematical axiomatic framework for the Information-Emergent Spacetime Theory (IEST), largely alleviating phenomenological presuppositions that rely on smooth classical backgrounds or continuous manifold embeddings. Utilizing Discrete Exterior Calculus (DEC), we characterize the microscopic reality as a directed 1-cell complex, where localized information flux is defined as a discrete 1-form. The underlying dynamics of the system are driven by the Principle of Minimum Entropy Action (PLEA) under coordinate-free constraints. The topological resistance is established as the matrix element of a positive-definite discrete Hodge star operator, providing a rigorous resolution to the mathematical vulnerability of non-positive definiteness in un-embedded networks. We demonstrate that Einstein's field equations spontaneously emerge as the topological self-balancing condition of decentralized network flow self-organization. Finally, we derive the explicit dynamical evolution equation of Stochastic Topological Noise (STN), yielding deterministic falsifiable phenomenological pathways across macroscopic astrophysical observations.
A blockchain is a chain for a cryptographic reason: each block must hash to its predecessor to anchor proof-of-work. Remove that requirement and the linear structure has no geometric necessity. This paper proposes crystal-currency: a distributed ledger whose validity condition is a geometric consistency constraint rather than a computational puzzle, capital stake, or authority signature. The constraint derives from the Fano plane $\mathrm{PG}(2,2)$ — the unique projective plane on seven points — whose automorphism group $\mathrm{PSL}(2,7)$ of order 168 acts rigidly on the seven orbit types of the three-qubit Pauli group under the Clifford group. The natural data structure is not a chain but a block complex: a growing simplicial complex in which each confirmed block adds a tetrahedron (3-simplex) subject to a Fano consistency condition. We define Proof of Volume (PoV), a consensus primitive requiring all seven orbit types to be simultaneously attested, and prove three results: (i) PoV cannot be satisfied by fewer than seven geometrically distinct parties, since $\mathrm{PG}(2,2)$ admits no proper sub-plane; (ii) combined with Proof of Stake, orbit-aware slashing graded by Fano distance $d_F(\mathcal{O}_i, \mathcal{O}_j)$ makes even two-party cross-orbit collusion detectable on-chain; (iii) for permissioned central-bank digital currency (CBDC), the circle orbit $\mathcal{O}_0$ intersects every Fano line, giving the central bank geometric sovereignty that cannot be outvoted or out-staked. The open cryptographic problem is reduction of orbit-type unforgeability to the Hidden Subgroup Problem over $\mathrm{PSL}(2,7)$. Keywords crystal currency, blockchain, block complex, simplicial complex, Fano plane, $\mathrm{PG}(2,2)$, $\mathrm{PSL}(2,7)$, proof of volume, proof of stake, orbit-aware slashing, Fano distance, CBDC, central bank digital currency, geometric sovereignty, hidden subgroup problem, distributed ledger, Clifford group, Pauli group
The literature on blockchain-based databases is divided into permissioned blockchains and permissionless account-based blockchains. However, the former is not fully decentralized, and the latter suffers from challenges in scalability and practicality. We propose SpendableStore, a hybrid on/off-chain database that operates on top of permissionless UTXO-based blockchains as a novel approach to the problem of data decentralization. Our design integrates atomic data units into individual UTXOs to create a new blockchain concept called Spendable Data Objects that perform traditional CRUD operations. The integrity, immutability, and ownership of these Spendable Data Objects are safeguarded directly by the blockchain peer nodes, thus constraining the power of database administrators to achieve true data decentralization. We further support database transactions and propose an isolation mechanism called Future Now Snapshot Isolation to reason about transactional correctness in SpendableStore. We performed experiments on a major blockchain's Mainnet and observed up to 16x better throughput compared to a state-of-the-art blockchain-based database.
Shah Nawaz Haider, Steve Austin, Arnab Barua, Sarowar Morshed Shawon · 5 authors
Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models improve transfer across forecast-ing tasks, but many depend on centralized data and Trans-former attention, which restricts their use for long, high-di-mensional, and privacy-sensitive signals. This paper presents QuantFlow, a probabilistic forecasting framework that com-bines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Each variable is embedded over the complete ob-servation window, processed in forward and reverse direc-tions, and projected to five conditional quantiles. TSMixup expands temporal diversity through Dirichlet-weighted inter-polation while preserving sequence structure. Experiments cover cryptocurrency, traffic, electricity, Electricity Trans-former Temperature, influenza, and weather data. QuantFlow obtains mean squared errors of 0.2834 on ETTm1 and 0.2218 on Weather, and a 20-client non-IID deployment retains use-ful accuracy after three communication rounds without cen-tralizing raw records. The results indicate that selective state-space modelling is a promising basis for scalable, uncer-tainty-aware, and privacy-conscious time-series prediction, while also revealing limitations on irregular epidemiological signals and long-horizon generalization.
Dr. Archana Bendale, Prof. Pawan Malani, Sakshi Shirole, Samiya Shaikh
Abstract: The widespread adoption of Electronic Health Record (EHR) systems has improved clinical documentation and provided easier access to patient information in modern healthcare environments. However, a large number of healthcare information systems are in a fragmented state, creating barriers for information exchange. Blockchain technology has been identified as a secure and distributed method for managing patient information. Despite its benefits, incorporating blockchain technology into healthcare systems is confronted by interoperability challenges, including technical, semantic, and organizational aspects that limit information sharing among heterogeneous platforms. This study identifies interoperability challenges in blockchain-based healthcare architectures. A three-layer evaluation framework is developed, including performance metrics. A systematic review of recent blockchain-based healthcare architectures is conducted to guide the framework's development. This study aims to provide a valuable methodology for healthcare system architects to evaluate interoperability readiness before deployment, including potential research areas. Keywords: Block chain Technology, Electronic Health Records, Healthcare Interoperability, Distributed Ledger Technology, Health Information Exchange.
Zulkifli Usman, Adipradana R Taha, Husnandi S Wange
This study analyzes the problems of regional health governance in the era of decentralization characterized by regulatory disharmony between Central and local governments. Research gap this study lies in the lack of studies that examine health governance issues from the perspective of normative construction and authority-sharing design, because most of the previous research focused on aspects of policy implementation or empirical case studies. The purpose of this study is to formulate the direction of reforming the regional health governance law that is able to overcome the fragmentation of authority and financing within the framework of decentralization. This study uses a purely normative juridical method with a legislative and conceptual approach through an analysis of Law Number 23 of 2014, Law Number 1 of 2022, and Law Number 17 of 2023. The results showed that the main problem of regional health management lies not in regional capacity alone, but in the design of laws that are fragmented and uncoordinated, causing uncertainty about the authority, financing, and responsibility of the state. This study provides a normative contribution by developing the concept of coordinated decentralization, which is a decentralized model that places regional autonomy within an integrated, standardized national policy framework, and accompanied by a clear coordination and accountability mechanism. This concept was formulated as a response to the condition of institutional fragmentation trap, a situation of fragmentation of authority and financing that mutually weaken the performance of health services. These findings are important as a basis for updating administrative law and health law to ensure the fulfillment of the right to health in a fair and sustainable manner.
Blockchain technology has moved from the fringes of cryptographic research into the center of serious conversations about how industries govern data, verify transactions, and establish trust between parties who have no prior relationship and no shared authority to appeal to. Yet for most professionals working in management, finance, healthcare, and logistics, the technology remains opaque — described in either overly technical language that assumes a computer science background, or in breathless promotional terms that obscure more than they reveal. This paper is an attempt to close that gap honestly. Drawing on a progressive self-directed engagement with blockchain fundamentals, this work develops a conceptual framework covering four interconnected dimensions: its foundational governance philosophy of decentralization and equal network rights; its cryptographic security architecture, encompassing public and private key pairs, symmetric and asymmetric encryption, and hash-based data integrity; its distributed node network, comprising full nodes, lightweight nodes, and mining nodes and their respective governance roles; and its real-world application domains across supply chain management, healthcare information systems, financial services, human resources verification, and artificial intelligence data integrity. The paper adopts a conceptual analysis methodology, synthesizing foundational and applied blockchain literature to construct an integrated framework accessible to management researchers and practitioners. The central argument is that blockchain's significance is not primarily technological but institutional: it represents a structural alternative to the centralized authority model that has governed data ownership and transactional trust for centuries.
Aletheia is a knowledge substrate organized around a write-time admission gate: a fact is accepted only if it does not structurally contradict what the base already holds. The gate inherits a Lean 4 soundness proof, so the admitted store stays acyclic, asymmetric, type-disjoint, and temporally consistent under any stream of typed edges. We bind the proof to the implementation by differential testing over 104 adversarial inputs, zero divergences. We first tried to build a partial-truth disinformation detector on this gate. Measurement refused. On real political claims almost nothing decomposes into the gate’s six relations: 0 of 155 atoms were gate-testable, and where it did fire it lost to a cold language model, 0 of 21 against 17. Most real disinformation violates truth, not structure, so a structural gate is the wrong instrument. We retract the detector claim. What remains is a guarantee rather than a rate. Each catch names the axiom it violated; the verdict is bit-exact and carries a machine-checked admission proof; and a safety property whose core is now machine-checked in Lean holds that no finite feed of self-asserted credibility can mint a false endorsement, conditional on authority granted upstream (0 of 210 adversarial sequences, against 140 of 210 for a credibility-naive baseline). A frontier model matches our hit-rate on constructed distortions, and a reasoning model matches even our one structural edge, so we claim no detection advantage. We claim instead that the jurisdiction of a structural guarantee can be measured, and we measure it across two regimes: where the base lets it adjudicate, and where it abstains.
Generative artificial intelligence is increasingly built into how students prepare, write, and revise bachelor's and master's theses, yet the principles meant to govern responsible use were largely written for research outputs rather than for supervised, assessed, and educational student work. This study asks what responsible AI use requires in the thesis specifically, and develops a framework for it. Guidance on responsible AI use already exists for research - including the eight-principle consolidation of Knöchel et al. (2025): regulations, data security, quality control, originality, bias mitigation, accountability, transparency, and broader impact - but they were written for published outputs, not supervised and assessed student work. Using these established principles as one structured starting point rather than a template to apply, I conducted 28 semi-structured interviews with current students, recent graduates, lecturers and supervisors, program managers, and domain experts, analyzed with Template Analysis, and let the evidence confirm, reshape, and extend them. Quality control and accountability became load-bearing; originality, transparency, and broader impact required substantial reinterpretation; data security and bias mitigation remained normatively important despite limited spontaneous salience. The evidence further pointed to a stake the research framework does not contain: the integrity of the learning the thesis is designed to develop and certify. I define this ninth principle, learning integrity, as alignment between intended thesis learning outcomes, the activities meaningfully performed by the student, and the evidence used to assess them. The resulting ALIGN framework combines nine principles with prospective agreement, process-based supervision, proportionate disclosure, verification, and dialogic defense - a triangulated integrity architecture for use before, during, and at the end of thesis work, rather than any single instrument treated as proof of authorship. The paper closes with a one-page student checklist and a citable disclosure sentence for thesis methods sections. The study contributes to education research and higher education assessment by specifying how generative AI changes the relation between student agency, self-regulated learning, evidence of competence, and academic integrity in capstone thesis work.
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Artificial Intelligence in Healthcare and Education
Zhengqing Liu, Alberto Sonnino, Igor Zablotchi, Eleftherios Kokoris-Kogias · 5 authors
Modern blockchains rely on a modular architecture that decouples consensus from execution. Recent advances in consensus algorithms have shifted the bottleneck to the execution layer, which must deterministically follow the consensus order and handle increasingly complex, compute-intensive smart contracts. We identify that single-node validators cannot keep up, motivating the need for a scale-out design. We design Remora, a scale-out smart contract execution engine. Remora adopts an efficient asymmetric architecture with centralized transaction dispatching and distributed execution, and depends on an object versioning scheme with a strict ownership model to guarantee deterministic scale-out execution. Remora achieves up to 3x throughput improvement compared to state-of-the-art deterministic execution schemes, scales up to 250k TPS, matching modern consensus performance, and reduces latency by up to 5ms. We also show that Remora elastically adapts to bursty workloads and dynamic access patterns using real-world traces. Remora's main performance benefits come from a novel stateless-stateful separation during smart contract execution, which overlaps the execution of state-independent tasks with consensus, and a new locality-aware and load-balanced scheduling scheme.