Based on the panel data of 277 cities in China from 2011 to 2018, this paper constructs the digital economy index and the green economy efficiency index. The research found the following: first, the digital economy has significantly improved the efficiency of the green economy in the region. Second, the digital economy has a greater impact on the efficiency of the green economy in the eastern region and large cities than in the central and western regions and small cities. Third, technological innovation is an important way for digital economy to improve the efficiency level of green economy.

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In a 2×2 crossover trial for establishing average bioequivalence (ABE) of a generic agent and a currently marketed drug, the recommended approach to hypothesis testing is the two one-sided test (TOST) procedure, which depends, among other things, on the estimated within-subject variability. The power of this procedure, and therefore the sample size required to achieve a minimum power, depends on having a good estimate of this variability. When there is uncertainty, it is advisable to plan the design in two stages, with an interim sample size reestimation after the first stage, using an interim estimate of the within-subject variability. One method and 3 variations of doing this were proposed by Potvin et al. Using simulation, the operating characteristics, including the empirical type I error rate, of the 4 variations (called Methods A, B, C, and D) were assessed by Potvin et al and Methods B and C were recommended. However, none of these 4 variations formally controls the type I error rate of falsely claiming ABE, even though the amount of inflation produced by Method C was considered acceptable. A major disadvantage of assessing type I error rate inflation using simulation is that unless all possible scenarios for the intended design and analysis are investigated, it is impossible to be sure that the type I error rate is controlled. Here, we propose an alternative, principled method of sample size reestimation that is guaranteed to control the type I error rate at any given significance level. This method uses a new version of the inverse-normal combination of p-values test, in conjunction with standard group sequential techniques, that is more robust to large deviations in initial assumptions regarding the variability of the pharmacokinetic endpoints. The sample size reestimation step is based on significance levels and power requirements that are conditional on the first-stage results. This necessitates a discussion and exploitation of the peculiar properties of the power curve of the TOST testing procedure. We illustrate our approach with an example based on a real ABE study and compare the operating characteristics of our proposed method with those of Method B of Povin et al.

Recently, the Chinese government decided to support the integrated development of the Yangtze River Delta (YRD) in a national strategic way. On this background, this paper investigates the regional integration in the technology transfer system of the YRD based on patent transfer from three levels: overall, technology supply chain, and technology sales chain. It also uses the modularity maximization method to detect the community structure of the inter-city patent transfer network in China. The results show that regional integration of the technology transfer system of the YRD at both overall level and technology supply chain level had not been realized up to 2015, but had been achieved at the technical sales chain level. Technology flow in the YRD was increasingly moving across the border, and the intra-region technology transfer network was increasingly unable to meet the needs of technological development of the cities in the YRD. This paper has several limitations concerning the representativeness of patent data, the manifestation of patent data in technological transfer and international comparison.

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