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Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks. [ABSTRACT FROM AUTHOR]

The rapid progression of Decentralized Finance (DeFi) has established Decentralized Exchanges (DEX) as critical elements in the financial landscape. Nevertheless, the open and transparent nature of DEX makes them susceptible to strategic manipulations, especially the sandwich attack. During such maneuvers, ill-intentioned actors exploit price slippage by positioning their transactions strategically around a target's order to reap unfair profits. This paper introduces a ground-breaking framework rooted in mechanism design game theory to lessen the impact of sandwich attacks. The framework delineates the precise strategy of the sandwich attack and its repercussions, shedding light on the tactical aspects and utility functions pertinent to both the attackers and the ordinary traders, subsequently referred to as workers. The discussion extends to defining utility functions for both the market and the workers, emphasizing the benefits of liquidity provision for the market and the potential profits and losses for the workers. The proposal encourages adopting a market-centric mechanism design grounded in game theory, wherein the market, operating as the designer, creates rules to maximize its utility while considering the workers' utility. Through a meticulous analysis of this game-theoretic approach, the study identifies optimum strategies for all the involved parties, demonstrating that these strategies can reach a balanced state. Further, this study presents a comparative view against existing research, highlighting the limitations of contemporary solutions and asserting the effectiveness of the proposed model in protecting the interests of both the market and the workers. Ultimately, this research furnishes stakeholders with new perspectives and instruments to thwart sandwich attacks and lays a foundation for creating resilient and fair decentralized trading infrastructures. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of reservation prices in all‐pay auctions based on the Bayesian Nash equilibrium of symmetric distributions with binary and correlated types. Our study finds that reservation prices affect players' behavior in two ways. Given the reservation price, some give up the original strategy of offering a bid lower than such minimum requirement. Therefore, the reservation prices may discourage the effort supply of weaker players. However, stronger innovators do not modify their strategies: They will start giving bids above the reservation price. Hence, setting a quality standard is effective in boosting effort supply. Our study contributes to the function of this particular pattern in the contests, thereby gaining more insight into designing and assessing such competition under different circumstances. [ABSTRACT FROM AUTHOR]

The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R2 score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R2 scores in the sea ice extent prediction. [ABSTRACT FROM AUTHOR]