Search Results (547)

Progressively revealed road disruptions can invalidate truck–unmanned aerial vehicle (UAV) relief plans while deliveries and recovery operations are already under way. We develop ER-HLNS-PR, an event-responsive framework that reconciles realized task, vehicle, and material states, preserves active service and recovery commitments, and repairs affected unfinished routes through bounded deterministic search. The evaluation combines five scenario families, controlled comparisons, sensitivity analyses, and execution-invariant audits. In the principal comparison of 20 independent task–road instances with three repetitions per method, ER-HLNS-PR increased time-critical completion by 16.5 percentage points (descriptive 95% paired bootstrap interval: 8.2–25.0) and reduced priority-weighted censored completion time (PWCT) by 2444.2 s relative to a mission-priority-matched adaptive large-neighborhood search baseline; routine completion decreased by 11.5 percentage points. In a separate repair-scope control, suffix-local repair reduced ownership changes, execution rebindings, and repeated reassignment relative to global re-auction, while the completion and PWCT differences spanned zero. Relative to a full-route rolling-horizon comparator, weighted-service, PWCT, and runtime differences were small and their intervals included zero. Overall, the results distinguish two effects: stronger urgent-service performance relative to the mission-priority-matched baseline and lower plan disruption under local repair, alongside an explicit urgent-versus-routine service trade-off. Full article

This article examines how the commercial circulation of Indigenous Australian artefacts produces a structured encounter between sacred Indigenous value systems and secular economic frameworks. Drawing on case studies from Papunya Tula, tourist markets, Bali boomerangs, and auction practices, it analyses how works grounded in Dreaming narratives, collective custodianship, and relational responsibilities to Country are translated into commodities assessed through price, provenance, and aesthetic authenticity. Methods include historical analysis of shifting research paradigms, material culture analysis of certificates and intellectual property cases, and review of contemporary museum and tourism practices. Rather than focusing solely on commodification or cultural appropriation, this article examines the mechanisms through which Indigenous forms of value are translated into categories that become legible within museums, markets, and legal systems. The findings suggest that market institutions reward narrow expectations of ‘tradition,’ dislocate works from their custodial contexts, and reconfigure sacred potency as a sellable experience or object. This does not suggest that religious value is wholly displaced; rather, a major difficulty emerges in how it is rendered legible within market systems that operate according to different logics of value. By tracing these processes of translation, the article illuminates how religious value becomes visible, transformed, or obscured within neoliberal marketplaces, highlighting the ongoing ethical and epistemic tensions that arise when sacred creative practices encounter commercial environments. Full article

Sealed-bid auctions require bidders to submit concealed bids within a prescribed bidding period, after which the bids are opened and compared to determine the highest bidder. However, existing auction systems still face challenges in protecting bidder privacy and bid data, verifying auction results, and reducing dependence on centralized auctioneers. This paper proposes LAV-Auction, a linkable anonymous and verifiable sealed-bid auction scheme for consortium blockchain. A registration authority publishes a ring of eligible public keys. Each bidder submits a commitment, a bid ciphertext containing the bid value and commitment randomness, and an auction-scoped linkable ring signature. The signature hides which registered key was used, while its key image enables the contract to reject repeated bids from the same credential in the same auction. After the contract fixes the accepted record order, the auction committee decrypts the bid ciphertexts. A verifier recomputes the digest of the frozen ledger records. It then verifies a Groth16 proof showing that the announced winner corresponds to a valid frozen bid with the highest value. LAV-Auction provides bidder anonymity, bid confidentiality before committee decryption, duplicate bid prevention, and verifiable winner correctness over the frozen valid bid set. Experiments on a Hyperledger Fabric implementation demonstrate practical computation, communication, and scalability trade-offs. The results show that LAV-Auction achieves millisecond-level result verification through succinct Groth16 proofs while maintaining compact result certification payloads under increasing bidder scales. Full article

Carbon emissions trading (CET) requires not only efficient allowance allocation but also stable transactions and protection of sensitive trading information. This study proposes a trustworthy CET framework that integrates stable matching with privacy-preserving computing. Carbon trading is modeled as a dynamic many-to-many matching problem in which reservation prices, trading quantities, and time priority are used to construct bilateral preferences, while blocking-pair elimination ensures pairwise stability. Paillier homomorphic encryption, zk-SNARKs, and blockchain are further incorporated to support secure declaration, matching, and settlement. Real-market-calibrated simulations based on China’s national carbon market show that the proposed SMCETS mechanism maintains zero blocking pairs under excess-supply, excess-demand, and equilibrium conditions, including under reservation-price perturbations and trading-quantity variations. It also achieves higher Benefit Index and total surplus values than the Vickrey and discriminatory-price auction benchmarks in the tested settings. Prototype evaluation further demonstrates practical runtime feasibility. These results suggest that stable matching and privacy-preserving technologies can be jointly incorporated into digital carbon-trading mechanisms to support more reliable and trustworthy market operation. Full article

Reciprocal waiting can stop decentralized robots at symmetric intersections even when local commands maintain separation. RingBreak uses a local conflict core estimate, queueing, clockwise orbiting, release point recovery, and an empirical velocity projection to restore goal-directed motion. Its transition conditions and contact metric are specified for reproducibility. On six fixed layouts repeated with three controller seeds, RingBreak completed 18 of 18 runs without contact; an MPC-CBF deadlock auction implementation completed 6 of 18 without contact. In 20 paired head-on and four-way conflict layouts, RingBreak and a tuned goal-directed artificial potential field (APF) controller both completed every run without contact, but RingBreak had a shorter mean run time (19.24 versus 35.19 s). A separate evaluation independently perturbed starts and goals in seven patterns. Both methods completed 60 of 70 such trials without contact; APF had a shorter mean run time (34.38 versus 38.97 s), and both usually timed out at the walled intersection. Within RingBreak, removing release reduced contact-free completion to 19 of 70, and removing separation projection reduced it to 3 of 50. These results support the faster clearance of the tested reciprocal conflicts and do not provide a general advantage or a formal safety or deadlock-freedom guarantee. Full article

This paper uses linear mean–variance preferences within the Markowitz mean–variance framework to characterize bidders’ trade-off between return and risk and extends first- and second-price sealed-bid auction models to risky environments. Compared with conventional models that maximize expected payoff or represent risk aversion through expected utility, this framework does not rely on a specific utility function, characterizes bidders’ risk attitudes more directly, and facilitates quantitative analysis of how those attitudes affect equilibrium bidding, optimal reserve prices, seller revenue, and social welfare under the adopted mean–variance criterion. We characterize the equilibrium bidding strategies, optimal reserve prices, and seller’s expected revenue in the two auction formats, together with their rankings; analyze the effects of bidders’ variance aversion and the number of bidders; and trace the new findings to bidders’ variance aversion. For social welfare, we quantify the welfare loss caused by mean–variance preferences, rank social welfare across auction formats and preference types, and show that the welfare-maximizing reserve price lies below the seller-optimal reserve price in each format. These results inform the choice of an appropriate reserve price in practice to reduce the welfare loss caused by bidders’ variance aversion. Finally, we briefly consider three extensions: an asymmetric model, exogenous shocks to bidders’ payoffs, and nonlinear mean–variance preferences. Full article

Existing contract-net-based UAV scheduling methods commonly use static or event-switched objective weights and provide limited evidence on how state-responsive scoring behaves under failures and communication impairment. This paper proposes a Dynamic Weight Multi-Objective Algorithm (DW-MOA) within a manager-mediated contract-net framework. Five candidate-level objectives are converted into normalized benefit utilities and combined using non-negative state-dependent weights. An independent urgency-gated spatial incentive supports cross-region dispatch without introducing negative objective weights. The method is evaluated against Traditional-CNP, Static-MOA, and HCNP-2022-adapted through matched dynamic scenarios, together with ablation, sensitivity, normalization-stability, communication-impairment, and scalability analyses. DW-MOA improves temporal performance and completed-load balance relative to Traditional-CNP, while comparisons with the stronger baselines reveal trade-offs among response time, normalized energy expenditure, flight distance, and load balance rather than uniform superiority. The mechanism remains computationally tractable at the largest tested scale and shows measurable degradation under modeled auction delay and candidate-bid loss. These findings support DW-MOA as an interpretable state-responsive scheduling method for normalized multi-UAV simulations, while physical energy calibration, continuous wireless-channel modeling, and hardware validation remain subjects for future work. Full article

The rapid decarbonization of European power grids has elevated solar photovoltaic (PV) generation to a central pillar of the energy mix, yet high penetration levels introduce volumetric and ramp risk. On 12 August 2026, a total solar eclipse over Spain caused a forecastable contraction in PV generation, offering a natural stress test for both system operations and electricity markets. The novelty of this study lies in its explicit three-layer decomposition of the event into the physical generation shortfall, the operational ramp requirement, and the day-ahead price co-movement, together with an identification-aware treatment that separates anticipatory bidding from a causal price effect. Using 15 min resolution data from Red Eléctrica de España (REE) and the Iberian Energy Market Operator (OMIE) for 11–13 August, the analysis documents a peak PV shortfall of 78.9% (3.00 GW) at 20:45 CET and a cumulative deficit of 4.48 GWh during the 19:30–22:00 window, with ramp rates 57% steeper than the clear-sky baseline. Day-ahead prices exhibited a peak premium of +45.43 €/MWh (+24.6%) at 20:30 relative to a structural counterfactual; however, because day-ahead auctions clear before delivery, this price movement is interpreted as anticipatory bidding rather than a purely causal shock, an identification challenge that is explicitly discussed and left to future work using intraday and balancing market data. An illustrative economic decomposition is also provided, together with a discussion of the conditions under which co-located battery energy storage (BESS) could mitigate asset exposure to similar events, offering insights ahead of the 2 August 2027 eclipse in southern Spain. Full article

The growing penetration of variable renewable energy (VRE) is increasing the frequency of very low and negative prices, although these events also depend on demand, transmission capacity, price-regime persistence and flexibility resources. This study examines which pre-auction and diagnostic variables are associated with negative day-ahead prices across European bidding zones, and whether these relationships remain stable over time and transferable across markets. More than 736,000 observations from 12 bidding zones in 2019–2025 were analysed, with sample coverage varying by data completeness. A gate-closure-audited Extreme Gradient Boosting (XGBoost) model achieved moderate risk-ranking performance and positive probabilistic skill in 2024–2025. The strongest pre-auction signals were completed-auction price history, calendar features and structural load relationships. Leave-one-market-out validation showed partial and heterogeneous transferability, supporting local calibration. A separate diagnostic layer revealed market-specific, non-linear associations involving VRE forecasts, residual load and cross-border exchange. Negative prices are therefore interpreted as screening signals for market configurations potentially associated with limited surplus absorption, rather than direct evidence of a flexibility shortfall. The framework separates operational pre-auction prediction from later market diagnosis and provides a reproducible basis for local early-warning applications. All SHAP, ALE and scenario results are interpreted as predictive associations and model diagnostics, not as causal effects or direct measures of physical flexibility. Full article

This paper presents a hybrid ARX-LSTM forecasting framework for the Spanish Continuous Intraday Market (MIC), targeting the hourly volume-weighted average price two hours ahead. A correlation analysis over 2024 data identifies Day-Ahead Market and Intraday Auction prices as the dominant predictors, while renewable generation forecasts provide complementary information. The proposed architecture combines a linear autoregressive model with exogenous inputs to capture market-driven dependencies and a Long Short-Term Memory network to model residual nonlinear dynamics. Evaluated using a rolling-origin procedure, the hybrid model achieves an RMSE of 9.02 €/MWh and R² of 0.961, outperforming standalone benchmark models. Full article

This study addresses dynamic multi-UAV task allocation under online task release, static obstacles, deadlines, limited energy, and vehicle failures. We propose an event-triggered path-based bid assignment method (ET-PBBA). At each mission event, an A-star module estimates the static-obstacle travel cost of feasible UAV–task pairs, while a normalized score combines reward, path length, predicted energy, and secondary load–energy pressure. A one-pass sorted scan then produces an event-local one-to-one assignment. Paired-seed mechanism tests show that event-time activation raises completion rate by 0.046 in the main scenario and 0.065 under failure stress relative to 10 s periodic allocation. The path-dependent physical-cost block provides the dominant improvement over reward-only ranking, whereas the balance term is not significant in the ordinary scenarios. For 10 UAVs and 50 tasks, ET-PBBA reduces average completion time by 5.2% and 4.1% and total energy by 4.3% and 3.0% relative to auction and CBBA, respectively, although Hungarian remains the stronger centralized reference. Under failure stress, completion reaches 0.790 and deadline violations decrease to 9.73. Single-UAV replay completes all 50 nominal waypoints with 9.16 cm mean error, providing auxiliary tracking evidence rather than validation of nonlinear or online multi-UAV execution. Full article

Public procurement tenders in Bulgaria are in the focus of public attention, despite the transparency of the process, which is conducted online through the Public Procurement Agency platform, participants require greater trust in the conduct of the procedures themselves, as they are not yet fully automated. Blockchain solutions are used in a wide range of businesses, as they offer transparency of transactions, trust between parties and data immutability and also seem suitable for conducting e-tenders for public procurement. This publication offers a solution based on a private blockchain HyperLedger Fabric, which in terms of structure and functionality builds on the existing solution. It ensures bidder identities are confidential from the unauthorized participants and transparency of the work of administrators in the platform and their actions when changing the status of a public procurement. The proposed blockchain based solution improves the evaluation process, reducing certain technical and human-error risks. The results show that it provides greater transparency and efficiency in the spending of public funds, achieving the set quantitative indicators with automated evaluation and qualitative factors defined in the smart contract. Full article

Smallholder and family farms underpin Mediterranean horticulture, yet their incomes are exposed to price risk because they sell as price-takers in opaque channels. In Ierapetra, Crete, greenhouse vegetables are traded at several independent physical auctions. This study quantifies within-venue price risk using 6281 product-day observations (daily minimum, maximum and mean prices) for six greenhouse products, November 2019–May 2026. Four indicators are computed per product-day: within-day relative spread, daily price change, coefficient of variation and a downside income-at-risk gap. Dispersion is compared across products against a reference band built from the within-day spreads of the three higher-priced products (p25–p75), a common descriptive yardstick drawn from the data themselves. On the same day, the highest price exceeds the lowest by 23% on average, and by above 100% on extreme days. A low-end seller earns 16–19% below the daily mean for commodities. The contrasts survive restriction to actively traded days and comparison at equal price levels. All series come from a single operator: the analysis therefore documents dispersion within one venue, and the contribution of fragmentation across venues remains open. Price risk is treated as a determinant of farm resilience rather than a measure of it; a unified, transparent digital auction is assessed as a design-conditional adaptation, its effect hinging on buyer participation, product representation and grading. Full article

In engineering R&D organizations, resource contention among autonomous projects creates complex scheduling challenges in distributed multi-project environments. Traditional coordination mechanisms often prioritize global efficiency while overlooking inter-project fairness, which can lead to stakeholder resistance and execution delays. This study proposes a transparent decision support approach for the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). We develop a two-stage scheduling mechanism that integrates Multi-Criteria Decision-Making (MCDM) into the global coordination process. First, an enhanced Chaotic Genetic Algorithm (CGA) with elitism generates local schedules. Second, global resource conflicts are resolved using MCDM methods. Inter-project fairness is implemented by limiting each project’s relative objective deterioration and by evaluating the dispersion of the resulting project-level burdens. Validation through an Unmanned Aerial Vehicle R&D case study and extensive experiments shows that the TOPSIS-based mechanism achieves a significantly lower standard deviation than the auction-based mechanism under high resource contention. The approach supports transparent and fairness-aware conflict resolution by making trade-offs among project-level outcomes explicit to managers. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article