Search

A (Ti,W)C cermet with graded properties was prepared using high‐frequency spark plasma sintering (HF‐SPS). The effects of the sintering process on the microstructure and mechanical properties of the prepared cermet material were studied, and the densification behavior of the material was analyzed using scanning electron microscopy and energy‐dispersive X‐ray spectroscopy. Owing to the skin effect and pulse current discharge in HF‐SPS, the experimental results showed different sintering effects, as indicated by the current density difference between the surface layer and bulk. On one hand, the surface‐layer grains were larger, the metal phase was concentrated, and the hardness was high; however, the bulk grains were smaller, the metal phase was uniformly distributed, and the strength and toughness were high, causing the material to account for the higher hardness of the surface layer and the bulk strength and toughness. The skin effect of pulse direct current was key for the performance gradient distribution, whereas increases in the current density and sintering temperature decreased the skin effect. In general, the Joule heat effect was more significant, leading to abnormal grain growth. [ABSTRACT FROM AUTHOR]

The input of the multi-document summarization task is usually long and has high redundancy. Encoding multiple documents is a challenge for the Seq2Seq architecture. The way of concatenating multiple documents into a sequence ignores the relation between documents. Attention-based Seq2Seq architectures have slightly improved the cross-document relation modeling for multi-document summarization. However, these methods ignore the relation between sentences, and there is little improvement that can be achieved through the attention mechanism alone. This paper proposes a hierarchical approach to leveraging the relation between words, sentences, and documents for abstractive multi-document summarization. Our model employs the Graph Convolutional Networks (GCN) for capturing the cross-document and cross-sentence relations. The GCN module can enrich semantic representations by generating high-level hidden features. Our model achieves significant improvement over the attention-based baseline, beating the Hierarchical Transformer by 3.4/1.64, 1.92/1.44 ROUGE-1/2 F1 points on the Multi-News and WikiSum datasets, respectively. Experimental results demonstrate that our delivered method brings substantial improvements over several strong baselines. [ABSTRACT FROM AUTHOR]

Large-scale optimizations (LSOPs) with high dimensional decision variables have become one of the most challenging problems in engineering optimization. High dimensional information causes serious interference to the algorithm optimization performance. The optimization performance of the algorithms will be seriously degraded. Competitive swarm optimizer (CSO) is a robust algorithm to tackle LSOPs. However, CSO randomly selects two particles to compare, then generates the winner and the loser. Although this search mechanism can enhance the diversity of the swarm, a single comparison is difficult to guarantee the quality of winners and losers. Therefore, there exists a risk of producing unqualified solutions. In order to enhance the quality of solution, a novel CSO with dynamic multi-competitions and convergence accelerator, namely DMCACSO, is designed in this paper. In the DMCACSO, a dynamic multi-competitions based evolutionary information is designed to pick out the losers more efficiently and improve the quality of winners. In addition, a convergence accelerator with hybrid evolutionary strategy is developed to speed up the particle search when the algorithm is a state of stagnation. The experiment results in solving large-scale benchmark functions from CEC2010 and CEC2013 indicate that the DMCACSO has competitive optimization performance by comparing with some state-of-the-art algorithms. Finally, the DMCACSO is effective in terms of quality in solving an actual feature selection problem. • A CSO with dynamic multi-competitions and convergence accelerator is designed. • A dynamic multiple competition strategy is proposed to enhance population diversity. • A convergence accelerator with hybrid evolutionary strategy is developed to speed convergence. [ABSTRACT FROM AUTHOR]

• Companies hired executives with green experience have better ESG performance. • Executives' green education and practice experience promote corporate ESG performance. The former has a stronger effect. • Government subsidies play a positive moderating role between executives' green experience and corporate ESG performance. • The incentive effect of executives' green experience is more powerful in non-high-tech industries and eastern region. This paper innovatively explores the impact of executives' green experience on corporate environmental, social, and governance (ESG) performance, simultaneously highlights the moderating role of government subsidies. Using data on Chinese listed companies from 2009 to 2022, we find that companies hired executives with green experience perform better in ESG, and the positive effect of executives' green experience is more pronounced in non-high-tech industries and eastern region. Specifically, education and practice experience both have contribution, and the education experience effects greater. In addition, we reveal the positive moderating role of government subsidies. [ABSTRACT FROM AUTHOR]