Enterprise Financing Mode and Technological Innovation Behavior Selection: An Empirical Analysis Based on the Data of the World Bank’s Survey of Chinese Private Enterprises

In China, private enterprises are becoming more and more important subjects of technological innovation, however(at the same time) financing difficulties of private enterprises are also ubiquitous. The research on the impact of financing methods on technological innovation behavior of private enterprises is conducive for the government to launch more targeted financing support policies.I men private enterprises are becoming the mainbody of technological innovation, but the difficulties in financing is especially heavy in China. Based on the data of the World Bank survey on China’s enterprises in 2012, this paper studies the impact of different financing methods on technological innovation behavior of private enterprises. The results show that (1) internal financing can promote the technological innovation behavior of enterprises better than external financing can do and (2) among the various forms of external financing, bank loans have the most significant impact on the technological innovation behavior of private enterprises, followed by commercial credit

Unlocking the Potential of Deep Learning for Migratory Waterbirds Monitoring Using Surveillance Video

Estimates of migratory waterbirds population provide the essential scientific basis to guide the conservation of coastal wetlands, which are heavily modified and threatened by economic development. New equipment and technology have been increasingly introduced in protected areas to expand the monitoring efforts, among which video surveillance and other unmanned devices are widely used in coastal wetlands. However, the massive amount of video records brings the dual challenge of storage and analysis. Manual analysis methods are time-consuming and error-prone, representing a significant bottleneck to rapid data processing and dissemination and application of results. Recently, video processing with deep learning has emerged as a solution, but its ability to accurately identify and count waterbirds across habitat types (e.g., mudflat, saltmarsh, and open water) is untested in coastal environments. In this study, we developed a two-step automatic waterbird monitoring framework. The first step involves automatic video segmentation, selection, processing, and mosaicking video footages into panorama images covering the entire monitoring area, which are subjected to the second step of counting and density estimation using a depth density estimation network (DDE). We tested the effectiveness and performance of the framework in Tiaozini, Jiangsu Province, China, which is a restored wetland, providing key high-tide roosting ground for migratory waterbirds in the East Asian–Australasian flyway. The results showed that our approach achieved an accuracy of 85.59%, outperforming many other popular deep learning algorithms. Furthermore, the standard error of our model was very small (se = 0.0004), suggesting the high stability of the method. The framework is computing effective—it takes about one minute to process a theme covering the entire site using a high-performance desktop computer. These results demonstrate that our framework can extract ecologically meaningful data and information from video surveillance footages accurately to assist biodiversity monitoring, fulfilling the gap in the efficient use of existing monitoring equipment deployed in protected areas

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry.

Acknowledgements: This work was supported by the Medical Research Council, as part of United Kingdom Research and Innovation (UK Research and Innovation) (MC_UP_1201/22). For the purpose of open access, the Medical Research Council Laboratory of Molecular Biology has applied a CC BY public copyright license to any Author Accepted Manuscript version arising. This work was also partially funded by NARSAD Young Investigator Award (2020, BBRF) to J.R. and Ministry of Science and Technology (2022ZD0206700) and the Beijing Municipal Government of P.R.C. to R.L. We thank D. Friedmann for advice on Adipo-Clear, J. Kebschull and D. Friedmann for data sharing, and L. Luo, M. Hastings, A.M.J. Adams and J. Song for critique on the manuscript.Funder: Medical Research Council, as part of the United Kingdom Research and Innovation, MC_UP_1201/22Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested