Digital economy is a key breakthrough in the new round of the industrial revolution, and technological innovation is the key to economic growth and wealth creation by enterprises. The influence of the digital economy on the innovation of manufacturing enterprises is directly related to solving the problem of inadequate development. Therefore, it is of great theoretical value and practical significance to study the influence of the digital economy on enterprise technological innovation. Taking Chinese manufacturing companies during 2016–2020 as a sample, this paper measured the technological innovation level of enterprises by the quantity and quality of innovation output of manufacturing enterprises and measured the development degree of the digital economy by the four dimensions of digital economy infrastructure, digital industrialization, industrial digitalization, and digital governance. The influence of the digital economy on enterprise technological innovation was researched through the Moran index and panel regression model. Moran’s indexes were all greater than 0, and the data were concentrated in the first and third quadrants of the scatter chart, and the value dropped from 0.186 to 0.152, indicating that provincial digital economic development presents a phenomenon of aggregation in spatial distribution, which gradually decreases along the eastern coast to the western region and gradually moves from aggregation to dispersion. The regression coefficients are significant at least at the level of 10%, indicating that the digital economy can effectively promote the output quantity and quality of technological innovation in manufacturing enterprises. The digital economy has a double-promotion effect on technological innovation. In promoting the technological innovation of manufacturing enterprises, infrastructure, digital industrialization, and industrial digitization play a dual and positive role as well as digital governance. In addition, the digital economy and its four dimensions have a first-order and second-order time-lag effect, which has a double-promotion effect on enterprises’ technological innovation. In general, the digital economy can promote enterprise technological innovation.

Economic development plays an important role in regional employment quality. Taking provincial panel data in China during 2010–2019 as a sample, we constructed an employment quality and economic development quality indicator system. The system conducted a comprehensive evaluation model with entropy weight to measure provincial employment and economic development quality indexes. The system also applied fixed-effect panel regression models to research the influence of economic development quality on regional employment quality. The results showed that employment structure affected the regional employment quality index, followed by social security. There were regional differences in the employment quality index, among which the employment quality index in the eastern area was higher, and it was at its lowest in the western region. The sharing economy was a critical factor in economic development quality, which had significant regional heterogeneity. Economic growth had the most significant and positive influence on employment quality, followed by the sharing economy and economic structure. In addition, the consumer price index and the urban–rural gap substantially affected employment quality. However, education expenditure played a significant inhibitory role in employment quality. Economic development quality had the most significant effect on eastern employment quality, followed by the central region, and it had the least effect in the west.

Accounting for the information of input slack variables, as well as the effects of external environment and stochastic factors, a six‐stage DEA model was proposed based on four‐stage DEA model. It was employed to assess the financing efficiencies of 689 strategic emerging listed companies in 2015. By isolating the environmental and stochastic factors, the final efficiencies can reflect the actual financing level of these companies. The empirical results show that most financing efficiencies are still at a low level relatively. The scales of these strategic emerging companies are the main constraint on their development. And the special technical level also has an impact on these efficiencies. In addition, the efficiency difference among provinces in China gives another support to environmental influence on the strategic emerging industry. Therefore, a strategic emerging company should pay attention to expanding its scale of production and heighten its special technical level and it should improve its financing efficiencies with the help of local government power.

Distributed authentication has the property of high security compared with existing authentication schemes. However, its success ratio for authentication may be not high, especially in large-scale Mobile Ad- Hoc Networks (MANETs). In order to address this issue, we propose a hybrid authentication scheme which integrates the chained authentication with the distributed authentication. When a node can't find enough authentication nodes in its one-hop neighborhood to authenticate itself it requests its neighboring nodes to find a certificate chain to do so through the chained authentication. Hence, our scheme overcomes the drawbacks in terms of success ratio for authentication. Theoretical analysis and simulation studies show that, compared with the chained authentication schemes, our scheme is more scalable and it can increase the success ratio for authentication, without sacrificing the security requirement.

Based on the dynamic characteristics of enterprises’ competition and cooperation, this paper introduces the idea of ecology and synergy and constructs a dynamic coevolution model of financing allocation efficiency and financing niche based on improved Lotka‐Volterra model. The parameters in the model are difficult to be given by the least square method and the maximum likelihood estimation method; the accelerated genetic algorithm is proposed to solve the parameters in the dynamic coevolution model, which makes the parameter estimation more accurate and reasonable. Finally, the data of new energy vehicle listed companies in China from 2009 to 2017 are given to validate the proposed model, and the dynamic process of coevolution of new energy vehicle enterprises, capital allocation efficiency, and financing niche is described. The results show that the minimum financing niche breadth of industry and market determines the location of the equilibrium point. With capital allocation efficiency as the core, adjusting the financing niche through financing market, industrial policy, enterprise development, and other factors will help to improve the coevolution balance between industrial capital allocation efficiency and financing niche and promote the coordinated development of strategic emerging industries.

Compared with traditional manufacturing enterprises, intelligent manufacturing enterprises pay more attention to the investment of knowledge capital and technological capital. Taking 258 intelligent manufacturing listed companies in China from 2015 to 2020 as research samples, the paper selects the material capital, human capital, knowledge capital and technological capital of enterprises as the input variables of Cobb-Douglas production function. Considering that enterprises are often affected by spatial correlation, stochastic frontier panel model, spatial lag stochastic frontier panel model and dynamic spatial lag stochastic frontier panel model are constructed to measure capital allocation efficiencies of enterprises. The results show that all the factor capitals in the three models have a significant positive impact on enterprises’ performance, and the dual lag effect of time and space is significant. Moreover, it is more reasonable to use the dynamic spatial lag stochastic frontier panel model to estimate the parameters and measure capital allocation efficiencies. The development of intelligent manufacturing industry has significant space-time spillover effect among provinces. About 52.98% of intelligent manufacturing enterprises have high capital allocation efficiencies, but 12.04% still need to further optimize capital allocation. The gap between the actual performance of the sample enterprises and efficiency frontier is mainly due to technical ineffectiveness. From a regional perspective, the top ten enterprises with high capital allocation efficiencies are all in the eastern region, but the average of capital allocation efficiency is the highest in the western region, followed by the eastern and central regions.