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This paper examines the ramification of the platform's dual role on its featuring strategy, sellers' pricing strategies, and consumer welfare. A digital platform operates the marketplace that connects third-party merchants to consumers and simultaneously participates in its marketplace by selling its own product directly to consumers. This dual role raises the concern that the platform may be self-preferencing, leading to unfair competition and harming both merchants and consumers. We develop a game-theoretical model to investigate the implication of dual role on the platform's product featuring strategy, the seller's pricing strategy, and the consumer's welfare. We show that the platform presents an "intermediary bias" such that it may not feature the best possible product to consumers, regardless of whether the platform is under dual role or sole marketplace mode. Surprisingly, dual role, in some scenarios, may mitigate the intermediary bias and intensify price competition in its marketplace. Consequently, prohibiting the platform from selling directly to consumers may not always be welfare-improving to consumers. Furthermore, banning the platform's perfect product imitation can harm consumers when dual role is still allowed. History: Anthony Dukes served as the senior editor for this article. Funding: This work was supported by the Natural Science Foundation of China [Grants 72202160, 72342026, and 71902142] and the Pujiang Scholar [Grant 22PJC115]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2022.0338. [ABSTRACT FROM AUTHOR]

Various indoles and carbazoles were synthesized from o‐alkenyl anilines and o‐aryl anilines by the β‐C(sp2)−H bond amination cyclization without any oxidants or hydrogen acceptors. Likewise, employing o‐alkyl anilines as substrates, indole products are also obtained by the β‐C(sp3)−H bond amination cyclization, and dehydrogenation. In this cross‐dehydrogenative coupling, many different functional groups are tolerated, and hydrogen gas is the only by‐product, thereby providing an intrinsically waste‐free approach for the synthesis of indoles and carbazoles. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a metaheuristic algorithm based on the survival strategy of rabbits proposed in 2022. ARO has favorable optimization performance, but it still has some shortcomings, such as weak exploitation capacity, easy to fall into local optima, and serious decline of population diversity at the later stage. In order to solve these problems, we propose an improved multi-strategy artificial rabbits optimization, called IMARO, based on ARO algorithm. In this paper, a roulette fitness distance balanced hiding strategy is proposed so that rabbits can find better locations to hide more reasonably. Meanwhile, in order to improve the deficiency of ARO which is easy to fall into local optimum, an improved non-monopoly search strategy based on Gaussian and Cauchy operators is designed to improve the ability of the algorithm to obtain the global optimal solution. Finally, a covariance restart strategy is designed to improve population diversity when the exploitation is stagnant and to improve the convergence accuracy and convergence speed of ARO. The performance of IMARO is verified by comparing original ARO algorithm with six basic algorithms and seven improved algorithms. The results of CEC2014, CEC2017, CEC2022 show that IMARO has a good exploitation and exploration ability and can effectively get rid of local optimum. Moreover, IMARO produces optimal results on six real-world engineering problems, further demonstrating its efficiency in solving real-world optimization challenges. [ABSTRACT FROM AUTHOR]

Rapid progresses concerning wearable electronics call for stretchable batteries. Owing to the merits of high energy density and wide operating voltage range, organic electrolyte‐based lithium‐ion batteries (LIBs) take a large proportion in the powering markets. Due to the high sensitivity to moisture and oxygen, it is difficult to obtain long‐term reliable stretchable organic electrolyte‐based LIBs when they are directly exposed to the air. Benefiting from the outstanding gas impermeability of liquid metal (LM) and relatively good barrier properties for reactive acid gas molecules by polyisobutylene (PIB), an LM‐PIB seal is demonstrated for organic electrolyte‐based LIBs. In the seal, LM can prevent the inward permeation of oxygen and water vapor, and PIB can block the outward permeation of hydrolysis acid generated from the side reactions during the operation of the organic electrolyte‐based LIBs, which helps prevent the detrimental interaction between the hydrolysis acid and LM. A specific capacity of over 120 mAh g−1 is achieved and the battery shows consistent electrochemical performances under multiple deformations. This study offers a different design approach for long‐term operation of stretchable organic electrolyte‐based LIBs. [ABSTRACT FROM AUTHOR]

The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish's summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm's ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems. [ABSTRACT FROM AUTHOR]