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Foot and mouth disease (FMD) is a highly contagious, viral vesicular disease affecting cloven-hoofed livestock species. The disease has negative impacts on livestock health and wellbeing as well as international trade opportunities for affected countries. The United States (U. S.), while currently free from FMD, has developed preparedness and response resources, including biosecurity guidance, for many livestock industry sectors with the aim of balancing robust disease control with business continuity. However, guidance on effective biosecurity strategies for auction markets is lacking. Therefore, the objective of this scoping review is to identify peer-reviewed literature that describes biosecurity strategies which decrease the risk of FMD introduction into auction markets or mitigation strategies that prevent FMD transmission from auction markets. Eight reports were included in this review with six reports discussing FMD biosecurity risks from auction markets, and two reports discussing FMD outbreak mitigation strategies involving auction markets. No reports describing FMD biosecurity risks to or strategies for auction markets were identified for inclusion in this review. This review demonstrates the association between livestock movement through auction markets and FMD introduction risk to herds as well as the potential for auction markets to contribute to widespread dissemination of FMD in the absence of biosecurity strategies. Developing resources to address this current preparedness gap is a critical need illustrated by the evidence identified in this review. Effective biosecurity strategies and risk mitigation guidance for auction markets would substantially bolster the U. S. livestock industry's resiliency in the event of an FMD incursion.

This study aimed to investigate factors associated with the welfare (lameness, body condition, and human-animal interaction) and economic value (sale price) of cull dairy cows sold at auctions in Panama. In an observational study, 5,734 cull cows were evaluated using publicly available YouTube videos from individual sales at the country's main auction facilities. The incidence of cows with low BCS and lameness was determined by visual scoring. Negative human-animal interactions were quantified based on the frequency of aggressive behaviors received by cows in the sales ring. The sale price was recorded in US dollars per kilogram of live weight (USD/kg). A total of 20.9% of the cows presented low BCS, 2.06% exhibited lameness, and 92.61% received aggressive interactions during the marketing process. The frequency of aggressive interactions was associated with genetic group, BCS, presence of lameness and horns, animal behavior, and auction location. Cows with lameness had a sale price 0.02 USD/kg lower compared with cows without lameness. Likewise, animals with a BCS ≤2 had a price 0.14 USD/kg lower than that of cows with a score >2. Additionally, cows classified as calm had a 0.04 USD/kg lower price than alert cows. Cull dairy cows sold at Panama auction have conditions that compromise their welfare, as demonstrated by the incidence of low BCS and high frequency of negative interactions during handling. Factors such as genetic group, body condition, cows' behavior, presence of lameness and horns, and auction location influenced the occurrence of negative interactions. Furthermore, thin cows, those with lameness, and those with calm behavior were economically evaluated less favorably, indicating an association between compromised welfare and the reduced market value of these animals.

Simultaneous ascending auctions find extensive applications in spectrum licensing and advertising space allocation. However, existing quantum sealed-bid auction protocols suffer from dual limitations: they cannot support multi-item simultaneous bidding scenarios, and their reliance on complex quantum resources along with requiring full quantum operational capabilities from bidders fails to accommodate practical constraints of quantum resource-limited users. To address these challenges, this paper proposes a multi-party semi-quantum simultaneous ascending auction protocol based on single-particle states. The protocol employs a trusted honest third party (HTP) responsible for quantum state generation, distribution, and security verification. Bidders determine their groups through quantum measurements and privately encode their bid vectors. Upon successful HTP authentication, each bidder obtains a unique identity code. During the bidding phase, HTP dynamically updates quantum sequences, allowing bidders to submit bids for multiple items by performing only simple unitary operations. HTP announces the highest bid for each item in real time and iteratively generates auction sequences until no new highest bid emerges, thereby achieving simultaneous ascending auctions for multiple items. It acts as a quantum-secured signaling layer, ensuring unconditional security for bid transmission and identity verification while maintaining classical auction logic. Quantum circuit simulations validate the protocol's feasibility with current technology while satisfying critical security requirements, including anonymity, verifiability, non-repudiation, and privacy preservation. It provides a scalable semi-quantum auction solution for resource-constrained scenarios.

Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of future participants is uncertain. In this article, we introduce the Conformal Online Auction Design (COAD), a novel mechanism that maximizes revenue by quantifying uncertainty in bidder values without relying on known distributions. COAD incorporates both bidder and item features, using historical data to design an incentive-compatible mechanism for online auctions. Unlike traditional methods, COAD leverages distribution-free uncertainty quantification techniques and integrates machine learning methods, such as random forests, kernel methods, and deep neural networks, to predict bidder values while ensuring revenue guarantees. Moreover, COAD introduces bidder-specific reserve prices, based on the lower confidence bounds of bidder valuations, contrasting with the single reserve price commonly used in the literature. We demonstrate the practical effectiveness of COAD through an application to real-world eBay auction data. Theoretical results and extensive simulation studies further validate the properties of our approach. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Achieving deep decarbonization of the power sector is essential for China's carbon neutrality goal and global climate mitigation. However, the coordination among emission reduction effectiveness, carbon market stability, and energy security remains unclear. This study develops a bottom-up multi-agent simulation model, Electricity and Carbon Coupling Multi-Agent System (ECMAS), integrating the power market with primary and secondary carbon markets to capture the adaptive behaviors of 2,241 heterogeneous power enterprises under alternative carbon market designs. Four policy scenarios are simulated to evaluate different pathways of quota tightening and auction introduction. Results show that rapidly synchronizing quota reductions with high auction shares imposes excessive carbon pressure, leading to carbon price collapse, premature fossil capacity retirement, and supply risks. In contrast, gradually introducing auctions alongside smooth quota tightening stabilizes carbon prices, supports phased low-carbon investment, and achieves sustained emission reductions. These findings provide evidence-based guidance for improving China's carbon market and offer transferable insights for global carbon market design under deep decarbonization.

The rise of distributed energy resources (DERs) and sophisticated digital communication systems has led to the emergence of Peer-to-Peer (P2P) energy trading as a viable method for facilitating flexible, autonomous, and efficient energy exchange within Local Energy Communities (LECs). This paper proposes a novel market model for P2P energy trading by integrating a multi-k double auction framework with the Gale-Shapley stable matching algorithm and metaheuristic optimization. The multi-k double auction offers greater pricing flexibility compared to traditional mechanisms such as uniform, discriminatory, and pay-as-bid auctions. The trading price is computed using a weighted parameter k balancing buyer and seller preferences, where k is evaluated for static values (0, 0.5, 1) and optimized dynamically using Ant Colony Optimization (ACO). Buyer-seller matching is performed using a constraint-aware Gale-Shapley algorithm that respects buyers' budget limits and sellers' capacity constraints. The model is validated using realistic data derived from photovoltaic generation, load forecasts, and prosumer preferences based on the IEEE 13 and IEEE 37-node residential feeders. Simulations were conducted over hourly trading periods divided into bidding and power exchange intervals. In the present work, the proposed hybrid architecture achieves improved social welfare, fairness in price allocation, higher market liquidity, and enhanced satisfaction for participants. This framework establishes a scalable and adaptive decision-making process that supports stable, efficient, and equitable operations in decentralized energy markets.

In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem. To address this problem, we propose a novel diffusion strategy for machine learning (ML) models (FedDif) to maximize the performance of the global model with non-IID data. FedDif enables the local model to learn different distributions before parameter aggregation by passing the local models to users via device-to-device communication. Furthermore, we theoretically demonstrate that FedDif can circumvent the weight-divergence problem. Based on this theory, we propose a communication-efficient diffusion strategy for ML models that can determine the trade-off between learning performance and communication cost using auction theory. The experimental results show that FedDif improves the top-1 test accuracy by up to 20.07 %p and reduces communication costs by up to 45.27 % compared to FedAvg.

This study examines the associations between linearly scored phenotypic traits and auction sales prices of young event horses in Ireland, aiming to identify key traits influencing market value. Data from 307 horses sold at public auctions (2022-2023) were analysed using regression analysis, binary optimisation, and Principal Component Analysis (PCA). Regression identified Head-neck Connection, Quality of Legs, Walk length of Stride, and Scope as highly significant predictors of sales price (p < 0.001), with Length of Croup, Trot Elasticity, Trot Balance, and Take-off Direction also significant (p < 0.05). Optimised regression reduced the number of relevant traits from 37 to 8, streamlining evaluation. PCA highlighted eight principal traits, including Scope, Elasticity, and Canter Impulsion, explaining 61.19% of variance in the first four components. These results demonstrate that specific conformation, movement, and athleticism traits significantly affect auction outcomes. The findings provide actionable insights for breeders and stakeholders, suggesting that targeted selection for high-impact traits could accelerate genetic progress and improve market returns. Furthermore, these traits could underpin the development of economic or buyer indices to enhance valuation accuracy and transparency, with potential application across equestrian disciplines to align breeding objectives with market demands.

In this brief, two fast discrete-time Wang kWTA (Fast Wang kWTA) algorithms are presented with an application in sealed-bid uniform price auctions. These algorithms can either be implemented in centralized or distributed manner. The structure of the Fast Wang kWTA is essentially the same as the original Wang k-winner-take-all (kWTA), except that our state update method is based on bisection method instead of gradient descent. By that, the number of iterations for getting correct output is largely reduced. Besides, the number is just a factor depended on the guess of the maximum input value. It is independent of the number of inputs, the number of winners, and the learning step size. The number of iterations is far smaller than the number required in the original Wang kWTA. In sequel, this Fast Wang kWTA is particularly suitable to be applied in solving the winner (resp. price) determination in real time and in distributed manner for a sealed-bid auction. In addition, the Fast Wang kWTA can ensure bidding price protection even if the communicated data are not encrypted and leaked.

Freeform lens design for producing a high-contrast irradiance distribution from a zero-&#xe9;tendue source can be related to an optimal transport problem. This problem can be discretized into a linear assignment problem, but existing solutions are often computationally expensive and time-consuming. To accelerate the process, we introduce rejection sampling for faster discretization and a heuristic auction algorithm with an optimization step size adapted to optical features enabling the rapid solution of the linear assignment problem. Furthermore, freeform surface construction is automatically calculated to ensure surface continuity. The effectiveness of the proposed method is demonstrated through four examples designed to generate near-field and far-field distributions. These examples involve generating distributions in the shape of zero-background letters "BIT" and an Einstein portrait, from either parallel beams or point sources. In these examples, our method exhibits improved optical performance while significantly increasing the overall computational efficiency.

I formally establish the existence of a mapping between a class of information design games with multiple senders and a class of all-pay auctions. I fully characterize this mapping and show how to use it to find equilibria in the information design game. The mapping allows for a straightforward comparative statics analysis of equilibria in the latter class of games. I use it to study the effect of the tie-breaking rule on the distributions of posteriors and the receiver's payoff.

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