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The panel data of 50 new energy vehicle enterprises in Shanghai and Shenzhen A-shares from 2012 to 2021 are selected to empirically analyze the impact of government subsidies on the innovation of new energy vehicle enterprises and to further discuss the differences between such an impact in different forms and regions. The study finds that, first, government subsidies have a certain promotion effect on the innovation of new energy vehicle enterprises, and an inverted U-shaped relationship exists thereof. Second, at the enterprise level, government subsidies have a significant effect on the innovation of non-state enterprises, downstream vehicle enterprises, and enterprises with lower establishment years, and the inverted-U trend is evident. Third, at the regional level, government subsidies have a more significant effect on the innovation of enterprises in non-eastern regions and low-environmental regulation regions, and the inverted-U-shaped relationship trend is more apparent. The study establishes the nonlinear relationship between government subsidies and the innovation of new energy vehicle enterprises through empirical research, which expands the theory of enterprise innovation and has a certain guiding significance for improving the innovation capability of new energy vehicle enterprises in the future.

Nowadays, detecting and evaluating the internal structure of packages becomes a crucial task for logistics systems to guarantee reliability and security. However, prior solutions such as X-ray diffraction and WiFi-based detection are not suitable for this purpose. X-ray-based methods usually require manual analysis or image processing algorithms with high complexity, while WiFi-based solutions may fail to detect complex structures due to the significant error of the RF-signal features. In this article, we propose RF-Detector, a low-cost RFID solution for performing three-dimensional (3D) structure detection of items contained in the packages, including the item orientations and relative locations. We thoroughly investigate a brand-new sensing model for RFID-based 3D structure detection, i.e., revolving scanning. We propose not only the fundamental revolving model but also a novel calibration method for the undesired deployments. We have implemented a prototype system to evaluate the performance of RF-Detector. Extensive evaluations in real settings show the effectiveness of RF-Detector, achieving very high accuracy of the internal 3D structure detection.

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