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How to use the pay contract to encourage executives to implement innovation activities has become an important issue to be solved by the theoretical and practical circles. Based on the data of Shanghai and Shenzhen A-share listed companies from 2006 to 2015, this paper examines the impact of external compensation gap on corporate innovation, and further discusses the contextual effects of executive talent and property rights. The study finds that the external compensation gap of executives has a positive effect on enterprise innovation. Moreover, the higher talent executives possess, the more obvious the external compensation gap will promote the innovation of enterprises. In addition, compared with state-owned enterprises, the external compensation gap of non-state-owned enterprise executives has a more significant effect on corporate innovation. This paper not only expands the economic consequences of the external compensation gap, but also provides inspiration for companies to adjust the compensation structure and encourage executives to promote enterprise innovation.

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The consolidation properties of soil-bentonite (SB) backfills containing 20% of the weight of sodium-hexametaphosphate-modified calcium bentonite (SHMP-20CaB) and prepared with 0 mM to 1000 mM calcium chloride (CaCl2) solution were evaluated. The results indicated that both consolidation stress and CaCl2 concentration had significant effects on the consolidation behaviors of the SHMP-20CaB backfill. In general, an increase in consolidation stress resulted in a decrease in the void ratio (e) and in the coefficient of volume change (mv), alongside an increase in the coefficient of consolidation (cv). The increased CaCl2 concentration yielded a dropped void ratio, and a nonlinear decreased compression index (Cc) and rebound index (Cs), respectively, from 0.18 to 0.13 and from 0.022 to 0.010, and a nonlinear increase cv. A threshold CaCl2 concentration of 100 mM was observed at the inflection points of the Cc, Cs, and cv. In contrast, the mv was insensitive to the CaCl2 solutions. The deterioration in e, Cc, and Cs and the increase in cv were the result of a compressed diffuse double layer of the bentonite by the CaCl2 solution, which thus exerted certain negative effects on the consolidation behaviors of the SHMP-20CaB backfill.

Network intrusion detection system (NIDS) is a key component to identify abnormal behavior of network systems and plays an important role in preventing the occurrence of network attacks. Although a considerable number of machine learning methods have been applied in the field of intrusion detection, it is still a challenge for existing solutions to achieve a good classification performance. The existing traffic datasets generally have redundant and irrelevant features, which hinder classifiers from making more accurate predictions. Furthermore, a single classifier has limited classification performance and may not be able to achieve a better detection performance overall in the face of unbalanced multi-category traffic data. Therefore, in order to improve the classification performance of intrusion detection models, this paper proposes an adaptive ensemble model by combining feature selection techniques and effective ensemble methods. Firstly, a heuristic feature selection algorithm (NRS-SSA) is proposed by introducing the neighborhood dependency degree of the neighborhood rough set (NRS) into the salp swarm algorithm (SSA). Then, an improved adaptive weighted voting algorithm is designed. The SSA is introduced to optimize the weight matrix when setting the voting weight. Finally, we use the designed voting algorithm to combine the classification advantages of homogeneous classifiers and heterogeneous classifiers, respectively, and propose an M-Tree algorithm and an adaptive ensemble model. The experimental results on multiple intrusion detection datasets show that the proposed adaptive ensemble model achieves an advanced detection level.