Henan Normal University

About: Henan Normal University is a education organization based out in Xinxiang, China. It is known for research contribution in the topics: Catalysis & Ionic liquid. The organization has 10863 authors who have published 11077 publications receiving 166773 citations.

Electrochemistry-Enabled Ir-Catalyzed Vinylic C-H Functionalization.

Abstract: Synergistic use of electrochemistry and organometallic catalysis has emerged as a powerful tool for site-selective C–H functionalization, yet this type of transformation has thus far mainly been limited to arene C–H functionalization. Herein, we report the development of electrochemical vinylic C–H functionalization of acrylic acids with alkynes. In this reaction an iridium catalyst enables C–H/O–H functionalization for alkyne annulation, affording α-pyrones with good to excellent yields in an undivided cell. Preliminary mechanistic studies show that anodic oxidation is crucial for releasing the product and regeneration of an Ir(III) intermediate from a diene-Ir(I) complex, which is a coordinatively saturated, 18-electron complex. Importantly, common chemical oxidants such as Ag(I) or Cu(II) did not give significant amounts of the desired product in the absence of electrical current under otherwise identical conditions.

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Abstract: By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties. First, many existing methods for building a single ad hoc surrogate are suitable for some special problems but may not work well on some other problems. Second, the optimization accuracy of DDEAs deteriorates if available data are not enough for building accurate surrogates, which is common in expensive optimization problems. To this end, this article proposes a novel DDEA with two efficient components. First, a boosting strategy (BS) is proposed for self-aware model managements, which can iteratively build and combine surrogates to obtain suitable surrogate models for different problems. Second, a localized data generation (LDG) method is proposed to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions. By integrating the BS and the LDG, the BDDEA-LDG algorithm is able to improve model accuracy and data quantity at the same time automatically according to the problems at hand. Besides, a tradeoff is empirically considered to strike a better balance between the effectiveness of surrogates and the time cost for building them. The experimental results show that the proposed BDDEA-LDG algorithm can generally outperform both traditional methods without surrogates and other state-of-the-art DDEA son widely used benchmarks and an arterial traffic signal timing real-world optimization problem. Furthermore, the proposed BDDEA-LDG algorithm can use only about 2% computational budgets of traditional methods for producing competitive results.