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Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. Although XLORE is an English-Chinese bilingual knowledge graph, there are only 423,974 cross-lingual links between English instances and Chinese instances. We present XLORE2, an extension of the XLORE that is built automatically from Wikipedia, Baidu Baike and Hudong Baike. We add more facts by making cross-lingual knowledge linking, cross-lingual property matching and fine-grained type inference. We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.

Developing the new energy vehicle (NEV) industry significantly reduces pollutant emissions in the transportation sector, promotes high-quality carbon peaks, and reduces dependence on oil imports. Industrial policies also support the NEV industry, constantly enhancing its international competitiveness. The Dual Credit Policy, implemented in 2017, has pressured automotive manufacturers to transform their production models, reduce the output of traditional fuel vehicles, and increase the production of NEV. This study analyzes the effects of the Dual Credit Policy on corporate competitiveness before and after implementing it, using listed companies in China's NEV industry as the research subjects. The results indicate that the Dual Credit Policy significantly enhanced corporate competitiveness through substantial innovation. Additionally, the difference-in-differences (DID) model results reveal that the policy's promotional effect is more pronounced in traditional vehicle companies due to higher pressure. Heterogeneity tests show that the policy has a more significant effect on state-owned enterprises and that regional marketization differences lead to a greater promotional impact on enterprises in the central and eastern regions. [ABSTRACT FROM AUTHOR]

This paper explores the paradoxical impact of environmental regulations on green innovation in the manufacturing of new energy vehicles (NEVs) from a stakeholder perspective. We address the dual challenge of accelerating green innovation across various diffusion stages and refining environmental regulations for effective stakeholder engagement, including the central government, upstream suppliers, and internal operations teams. First, we utilize Stackelberg game theory to analyze the strategic interaction and behavioral rationale between local governments and NEV manufacturers at different stages of innovation diffusion, represented by specific parameter sets. Second, we examine the roles of four key stakeholders, exploring their unique impact mechanisms and potential to influence the game's Nash equilibrium. Finally, the game models' validity and primary conclusions are corroborated with real-world case studies, prominently including the ongoing shift of Chinese automakers towards NEVs. Results demonstrate that: (1) environmental regulations should be calibrated according to the diffusion stages of green innovation, and (2) misaligned stakeholder interests can lead to environmental regulations that inadvertently hinder, rather than promote, NEV manufacturers' green innovation efforts. [ABSTRACT FROM AUTHOR]

With the advancement of farmer professional cooperatives in China, the prevalence of the "empty shell" phenomenon has posed a significant challenge to the health and sustainability of these cooperatives. Utilizing data from 1570 such cooperatives in Guizhou Province, China, this study uses the multi-time differential model and the spatial Durbin model to study the impact of demonstration cooperatives on the "empty shell" of cooperatives and the spatial spillover effect of demonstration cooperatives. The study results show the following: (1) Agricultural professional cooperatives are rated as national or provincial demonstration cooperatives, which are conducive to their development, and the policy evaluation of demonstration cooperatives can effectively inhibit the "empty shell" phenomenon of cooperatives. (2) National and provincial demonstration cooperatives can effectively inhibit the "empty shell" of cooperatives in the region. (3) When there are provincial demonstration cooperatives in the surrounding areas, there will be a spatial spillover effect and the empty shell of regional cooperatives will be inhibited. However, to restrain the "empty shell" of cooperatives, it is essential to capitalize on the spatial policy influence of "demonstration cooperatives" while being vigilant against potential spatial siphoning effects. [ABSTRACT FROM AUTHOR]

The existing foreign object detection methods in coal mine belt transportation are low in detection precision and slow in detection speed, and YOLOv3 algorithm has faster detection speed and higher detection precision. However, when it is used in foreign object detection in coal mine belt transportation, there are problems such as poor detection effect on small targets, easy to appear missing detection and imbalance of positive and negative samples. In order to solve the above problems, Fast_YOLOv3 algorithm is designed. By improving the priori box and bounding box, the algorithm is adapted to the detection scenario of small target foreign object in coal mine belt transportation. By adding the deconvolution network, the algorithm is able to improve the detection capability of small target foreign object. By introducing the Focal Loss to improve the cross entropy of the negative sample confidence in the loss function, the algorithm is able to solve the problem of imbalance in the number of positive and negative samples so as to improve the detection precision. The StiPic data enhancement method is designed to preprocess the coal belt transportation image to improve the training efficiency of the Fast_YOLOv3 model and the detection precision of small target foreign objects. The experimental and field test results show that the Fast_YOLOv3 algorithm can detect foreign objects in the belt transportation with an average precision of 90.12% , an average detection time of 35 ms, and a detection rate of 93.50% for small target foreign objects, which meets the requirements of foreign objects detection precision and real-time detection in the belt transportation field.