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The purpose of this research is to explore the impact of capital market opening on inefficient labour investment of enterprises and its impact path. This paper takes 2010–2019 A-share non-financial listed companies in Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) as research objects and samples, and uses DID method to examine the impact of capital market opening on labour investment efficiency of listed companies.We collected 22567 pieces of data.The results show that the capital market opening system significantly reduces inefficient labour investment of enterprises, mainly through reducing the information asymmetry and the agency costs as the main paths. This research shows that the capital market opening is of positive significance to the sustainable development of enterprises, and it proposes targeted suggestions for the government, listed companies and market investors to effectively reduce the inefficient labour investment of enterprises. The research provides more feasible references for capital market opening and corporate governance, and also offers theoretical evidence for the implementation of 'Shanghai-Hong Kong Stock Connect' program. [ABSTRACT FROM AUTHOR]

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore. [ABSTRACT FROM AUTHOR]

This paper reports on a corpus-based study aimed at reexamining the typological status and diachronic change of motion expressions in Chinese, drawing on parallel texts consisting of autonomous motion expressions in Old Chinese (OC) and its Modern Chinese (MoC) translation. The results show that MoC significantly differs from OC both in the preference of lexicalization patterns (Talmyan typology) and semantic components distributed in discourse (Slobinian typology) when narrating similar motion scenes. However, these results fail to support the viewpoint that Chinese has undergone a change from a verb- to a satellite-frame (Li 1993; Talmy 2000; Peyraube 2006; Shi & Wu 2014). It is argued that (i) the Talmyan typology and the Slobinian typology should be treated separately. In Talmyan typology, the diachrony of Chinese demonstrates the change of a V- to a parallel-frame, in that satellite- and verb-framed constructions in MoC have equal frequency and show no bias for the encoding of subtypes of autonomous motion. In Slobinian typology, MoC remains as a Path-salient language, as it gives considerable weight to the expression of Path; (ii) the dominant lexicalization pattern in a language varies from one sub-domain of motion to another (see also Lamarre 2003), and thus the typology of motion expressions is sub-domain-specific; and (iii) motivating forces and blocking forces, furthermore, co-exist diachronically for the typological evolution of motion encoding due to the idiosyncrasy of the morphosyntactic system. [ABSTRACT FROM AUTHOR]

This paper re-examines from an evolutionary perspective the typological status of Chinese, with regard to the issue of how the information of motion events is encoded (Talmy 2000; Slobin 2004). We investigate, with emphasis on the roles of both language structure and language use, the four periods of Chinese (Old, Middle, Pre-Modern and Modern) in terms of parameters such as path, manner and ground, and compare with typologically different languages, namely, verb-framed languages like Spanish and satellite-framed languages like English. Our statistical study shows that (i) Chinese has been undergoing a typological shift from a verb-framed language to a satellite-framed language, and Pre-Modern Chinese is a stepped-up period with respect to the speed of evolution; (ii) Modern Chinese adopts diverse patterns to encode motion events, which are different from both typical verb-framed languages and typical satellite-framed languages. We thus conclude that (i) contrary to Peyraube's (2006) claim, the typological shift in Chinese has not yet been achieved; (ii) there is little justification for classifying Chinese as an equipollently framed language as in Slobin (2004) and Chen and Guo (2009). Therefore, there is no need to posit an equipollent type for Chinese; and (iii) Modern Chinese is in a transitional state from a V-type to an S-type due to the evolution of its motion expressions. It also favors the S-type in both its morphosyntactic properties and language use. [ABSTRACT FROM AUTHOR]

The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning's accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly. [ABSTRACT FROM AUTHOR]

Abstract With the development of society, plastics play a significant role in daily supplies owing to their advantages. Whereas, insufficient scratch resistance and vulnerable plastic surfaces result in the constraint of their range of application fields, for instance, electronic products. Hence, it is a highly desirable objective of researchers to investigate hardening coatings for protecting plastic surfaces, by selecting polyurethane acrylate (PUA) as film-forming materials attributing to their adjustable features. This article reveals components of PUA and principles for its hardening modification, and summaries various methods of hardening modification of PUA-based coatings, such as improving the crosslinking density, strengthening hydrogen bonding, incorporating rigid groups into molecular structure, introducing inorganic nanoparticles into resin matrix and transferring linear PUA into hyperbranched analogs. Moreover, optimal strategies for the preparation of PUA-based hardening films from above five tactics are discussed. [ABSTRACT FROM AUTHOR]