Ioannis Tzannetos, Danai Balla, Aris Pagourtzis, Vassilios Vescoukis
Non-fungible tokens (NFTs) have created vibrant digital marketplaces where unique assets are exchanged across domains such as art, gaming, and music. While current infrastructures are optimized for pairwise, currency-backed trades, they provide limited support for multi-party swaps of indivisible assets based on user preferences. In practice, liquidity is not always desirableâparticipants may wish to exchange directly for assets they deem equally valuable, bypassing auctions or currency markets. In this paper, we propose BarterSwap, a protocol to address this gap by leveraging the Top Trading Cycles (TTC) algorithm to enable efficient multi-party NFT exchanges on Ethereum. Our protocol identifies preference-based dependencies among users and executes swaps without requiring external liquidity. We implement and deploy our solution on the Ethereum blockchain, demonstrating that it remains practical for a reasonably large number of participants. Finally, we release our implementation publicly and provide a detailed cost analysis, offering a concrete path toward fair and efficient preference-based NFT exchanges.
The growth of decentralized data ecosystems has increased the need for transparent and traceable contract agreements between organizations. Although the Eclipse Dataspace Components offer a flexible, open-source framework for sovereign data exchange, they present limitations in terms of end-to-end transparency and traceability of these agreements. This thesis explores how blockchain technologies, specifically smart contracts and tokenized assets, can enhance the Eclipse Dataspace Components to address these limitations. We introduce a model in which contract agreements are represented as non-fungible tokens. These tokens represent uniquely identifiable off-chain contracts whose state changes are immutably recorded on the blockchain. This allows for contract life-cycle monitoring and tamper-proof traceability across dataspace participants. The implementation includes a custom ERC-721 smart contract deployed on the Sepolia Testnet, as well as a decentralized application that connects its functionality to the Eclipse Dataspace Components. The evaluation is conducted using a Minimum Viable Dataspace hosted on two separate servers, representing one data provider and one data consumer. The evaluation demonstrates that agreements based on smart contracts significantly improve transparency and traceability while maintaining data sovereignty. Overall, the results show that blockchain-based contract agreements build trust without modifying the existing workflows of the Eclipse Dataspace Components. This provides a viable path toward the management of trustworthy and sovereign contracts in future dataspaces.
This paper examines three paradigms of cooperative intelligence in computing: parallel processing, distributed computing, and multi-agent orchestration. Each paradigm has a distinct architectural logic, a distinct set of tradeoffs, and a distinct counterpart in the collective behavior of biological systems. The hive mind concept, understood not as a single model but as a spectrum of collective organization, provides the organizing framework for comparing all three. Parallel processing, characterized by its tightly coupled, shared-memory architecture, is the computational equivalent of a unified hive: a system that achieves emergent intelligence through massive, synchronized coordination, prioritizing raw speed and coherent state. Distributed computing, with its loosely coupled, distributed-memory model, reflects a decentralized swarm in which autonomous units operating under local rules produce scalable, fault-tolerant collective behavior without centralized control. Multi-agent orchestration corresponds to a third biological archetype, the coordinated superorganism: a system in which role-specialized agents communicate through explicit protocols to accomplish tasks beyond the reach of any individual unit or undifferentiated collective. These three paradigms are not sequential stages of development. They are distinct architectural choices, each optimized for a different class of problem, and each present in current production AI systems. The most capable systems in deployment today combine all three, using tightly coupled GPU infrastructure for model training, federated or distributed networks for privacy-preserving inference, and orchestrated agent teams for complex multi-step workflows. Understanding where each paradigm excels, where it fails, and how the biological analogy that illuminates its structure eventually reaches its limits is the central focus of this analysis. The final section addresses those limits directly, arguing that the hive mind framework is a productive lens for architectural design but must not be extended to prescribe how machine cognition operates at the execution layer.
The Hamiltonian cycle problem is a well-known NP-complete problem in graph theory. This problem relates to lots of practical problems such as designing very large scale integration (VLSI) and travel-ling salesman problem (TSP). Since it is NP-complete, there is no efficient algorithm to solve the Hamiltonian cycle problem, and hence, its solution is valuable. In this paper, we propose new physical zero-knowledge proof protocols for the Hamiltonian cycle problem, whereby an entity can prove its knowledge of a solution to another entity without leaking any information about the valuable solution. Our protocols are more efficient than the previous protocols. We also propose a physical zero-knowledge proof protocol for TSP, one of whose building blocks is a new representation of an integer commitment with a secure addition protocol.
Barbara Bigliardi, Virginia Dolci, Alberto Petroni, Benedetta Pini
How are digital technologies transforming public sector supply chains, and what factors condition their effectiveness? Despite the growing interest in this domain, the literature remains fragmented, with a lack of longitudinal studies, citizen-centered evaluations, and cross-country comparisons. This study addresses these gaps through a systematic review of 71 Scopus-indexed articles, combining descriptive mapping with a keyword-based bibliometric analysis. The approach identifies consolidated and emerging themes, particularly within the âBusiness, Management and Accountingâ subject area, where methodological heterogeneity and limited generalizability persist. Findings reveal increasing scholarly attention to technologies such as blockchain, AI, and e-procurement, highlighting both operational modernization and newer concerns such as sustainability, digital governance, and decentralized finance. The paper contributes by structuring dispersed knowledge into a coherent framework, offering a roadmap for research and practical guidance for public administrators seeking value-driven digital transformation.
This technical report presents the reference implementation of Ternary Moral Logic (TML) within the Ethereum Virtual Machine (EVM) ecosystem. It addresses the limitations of traditional "Code is Law" architectures by introducing a finite state machine that enforces a mandatory third stateâthe "Sacred Zero" or Epistemic Holdâallowing smart contracts to pause execution when pre-defined ethical conditions are unmet. The report moves beyond theoretical ethics to specify the Solidity design patterns, storage layouts, and cryptographic verification methods required to make TML enforcement non-bypassable and auditable. Key Technical Contributions: Finite State Machine (FSM): Implements a mandatory "Sacred Zero" state (State 0) that acts as an "Epistemic Hold," distinguishing between valid (1), invalid (-1), and uncertain (0) transaction states. Dual-Lane Latency Architecture: Defines a "Fast Lane" for synchronous, clear-cut transactions and a "Slow Lane" for ambiguous cases requiring governance or oracle resolution, preventing head-of-line blocking. Cryptographic Provenance: Utilizes EIP-712 typed data signing to bind off-chain AI/Oracle verdicts to on-chain execution, preventing replay attacks and ensuring distinct domain separation. Privacy Preservation: Integrates Zero-Knowledge Proofs (ZK-SNARKS) to verify the execution of moral logic models without revealing sensitive input data or proprietary model weights ("Glass Box" architecture). Immutable Core Pattern: Rejects standard upgradeable proxy patterns in favor of an "Immutable Core" architecture to eliminate administrative "God Mode" and ensure constitutional constraints cannot be bypassed by key holders. Formal Verification: Demonstrates safety and liveness properties (e.g., "No Silent Pause," "Eventual Resolution") using TLA+ (Temporal Logic of Actions) to mathematically prove the system's robustness.
This paper challenges the conventional divide between productive and non-productive assets by proposing that scarcity, rather than internal cash flow generation, is the fundamental source of value across all asset classes. Interim payments such as dividends, rents, or coupons, represent one modality of monetizing scarcity, but terminal resale and other mechanisms serve equivalent roles. We develop a valuation framework in which scarcity is modeled as a latent, time-varying state variable shaped by economic pressures on demand and supply. A class of monetization functions, characterized by monotonicity and curvature, maps scarcity states into observable or forecast cash flows. This formulation allows discounted cash flow (DCF) logic to be reinterpreted as a general pricing mechanism for intertemporal scarcity. The framework accommodates both terminal-value assets, such as Bitcoin or gold, and income-generating assets, such as equities or bonds. We formally demonstrate the equivalence between terminal and periodic payoff structures and introduce a classification of assets according to their scarcity mechanism, whether physical, contractual, algorithmic, or reputational. By embedding scarcity at the core of valuation, this approach dissolves artificial distinctions in asset classification and establishes a unified foundation for pricing financial claims across diverse contexts.
Fricson Vinicio George Tenorio, Dalys Roxana Castro Bustamante, Mario Alfredo FernĂĄndez SolĂs
Local government administrative management faces increasing pressures in fiscal crisis scenarios, particularly in territories where reduced national transfers limit operational capacity and public service delivery. This study analyzes the administrative management models and local governance practices of the Municipal Decentralized Autonomous Government (GAD) of Arenillas, Ecuador, during the 2023â2024 period. Its purpose is to identify the main constraints, assess citizen perceptions of service quality, and propose institutional optimization strategies. A mixed-methods, descriptive, and cross-sectional design was used, integrating citizen surveys, semi-structured interviews with municipal officials, and documentary analysis of regulations, budgets, and institutional processes. Results show intermediate satisfaction levels regarding transparency, citizen participation, and administrative efficiency, alongside persistent bureaucratic practices, weaknesses in institutional communication, and limitations derived from fiscal reductions. Qualitative findings highlight gaps in technological modernization, limited process systematization, and weak alignment between planning and execution. These outcomes are contrasted with contemporary frameworks of New Public Management, digital government, and collaborative governance. The study concludes that strengthening organizational culture, expanding participatory mechanisms, digitalizing procedures, and improving inter-institutional coordination are key to consolidating efficient municipal management. The research provides contextualized evidence to guide local policy actions in settings characterized by fiscal constraints.
Fausto Daniel Santos Tapia, Luis Felipe TrĂĄvez GarcĂa
This article presents a comprehensive methodological proposal aimed at brand design and strategic brand management, using as a case study the academic outreach project between the University and the agricultural associations registered under the Decentralized Autonomous Government of Pichincha, distributed across its eight cantons. The proposed methodological approach is based on the active participation of graphic designers in collaborative processes with local communities, generating spaces for knowledge exchange, co-creation, and capacity building. This process seeks to develop a graphic system composed of visual identity, packaging, and a distinctive visual style that enhances the positioning of agricultural products in local and regional markets, integrating productive, cultural, and territorial attributes. This interaction not only promotes the recognition of ancestral knowledge and community practices but also enables social innovation processes driven by design. Thanks to its adaptable structure, the proposal is replicable in rural contexts. Its methodology, based on participatory design, contextual diagnosis, and the integration of local cultural narratives, can be adjusted to diverse productive and sociocultural dynamics. This flexibility enables its implementation by local governments and non-governmental organizations, aligning brand management with sustainable development goals and fostering a design culture committed to social transformation and communicational equity.
Adah Patrick Eneojo, Olorunmaiye Theophilus, Dr Emmanuel Bola Jonah K, Adah William Arome · 7 authors
Uptake of the Basic Minimum Package of Health Services (BMPHS) in Kogi State has been limited by supplyâside constraints, demandâside barriers, and placeâbased vulnerabilities concentrated in riverine and rural LGAs. The IMPACT rollout (2022â2025) combined Decentralized Facility Financing (DFF) with bundled Continuous Quality Improvement (CQI) supports to strengthen facility responsiveness, stabilize commodities, and expand outreach. We used a quasiâexperimental, mixedâmethods design on a facilityâmonth DHIS2 panel (2019â2025; n = 96 PHCs). Quantitative inference triangulated three counterfactual generators: augmented twoâway fixedâeffects DifferenceâinâDifferences (DiD) for average effects, Interrupted Time Series (ITS) segmented regression to decompose immediate (level) and sustained (slope) impacts, and facilityâlevel counterfactuals via synthetic control and matrix completion for robustness. Multilevel mixedâeffects models estimated heterogeneity; causal mediation (bootstrap, 5,000 sims) quantified pathways (coldâchain uptime, outreach frequency, commodity availability). Qualitative interviews and supervision records explained fidelity and contextual moderators. Costing used activityâbased methods with probabilistic sensitivity analysis. DFF plus CQI produced both rapid operational gains and durable system strengthening. Primary policyârelevant estimates: DiD DPT3 +6.2 percentage points, ITS immediate level change αâ = +3.7pp, and ITS slope αâ = +0.12 pp/month. Mediation attributed ~41% of the DPT3 gain to improved coldâchain uptime; outreach and commodity availability explained large shares of ANC1 and IPTp3 gains. Results are robust across laggedâoutcome DiD, matrix completion, generalized synthetic control, eventâstudy checks, and autocorrelation corrections. Costâeffectiveness benchmarks show programâlevel ICERs consistent with high probability of value for money for composite BMPHS gains. To maximize equitable BMPHS gains, prioritize coldâchain resilience, predictable and timely disbursements, and earmarked outreach financing for highâenvironmentalârisk LGAs. Embed both the ITS level (αâ) and slope (αâ) as complementary KPIs in routine dashboards: αâ signals rapid operational fixes; αâ signals durable system strengthening. Scaleâup should pair DFF with CQI, protected commodity lines, and contextâsensitive outreach modalities to sustain and equitably distribute gains.
Muhammad Umar Farooq Qaisar, Weijie Yuan, Lin Zhang, Shehzad Ashraf Chaudhry · 6 authors
Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.
Aim: This study examines whether and how the disposition effect shapes Ethereum investorsâ selling decisions. It asks whether investors are more likely to realize gains than losses, whether this asymmetry strengthens during high-volatility periods, and whether it weakens around major protocol upgrades, including the Merge, Shapella, and Dencun. Methodology: The study builds a high-frequency address-day panel for 2020â2024 using public on-chain data and labeled centralized-exchange deposit clusters as conservative proxies for sell decisions. Rolling cost bases are reconstructed under FIFO and value-weighted rules, and unrealized gains and losses are linked to realized sales through discrete-time logit and Cox hazard models. The design also includes event windows and robustness checks. Findings: The framework is designed to identify three mechanisms: asymmetric realization of gains over losses, stronger gain realization under high volatility, and attenuation around major protocol-upgrade events. Implications: The study offers a transparent design for analyzing behavioral bias in crypto-asset markets with verifiable blockchain data. It is relevant to exchanges, regulators, and market designers concerned with investor behavior and risk management. Originality/value: The article extends behavioral finance to Ethereum by using public ledger data rather than brokerage records and by integrating behavioral bias, volatility regimes, and protocol events in one framework.