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In the digital economy era, digital platforms are vital infrastructure for innovation subjects to perform digital innovation activities. Achieving efficient and smooth knowledge transfer between innovation subjects through digital platforms has become a novel research subject. This study aims to examine the knowledge transfer mechanism of digital platforms in the digital innovation ecosystem through modeling and simulation to offer a theoretical basis for digital innovation subjects to acquire digital value through knowledge-sharing and thus augment their competitive advantage.

This study explores the optimal symbiotic interaction rate between different users based on the classic susceptible-infected-removed (SIR) model. Additionally, it constructs a knowledge transfer mechanism model for digital platforms in the digital innovation ecosystem by combining the theories of communication dynamics and symbiosis. Finally, Matrix Laboratory (MATLAB) software is used for the model and numerical simulation.

The results demonstrate that (1) the evolutionary path of the symbiotic model is key to digital platforms' knowledge transfer in the digital innovation ecosystem. In the symbiotic model, the knowledge transfer path of digital platforms is “independent symbiosis—biased symbiosis (user benefit)—reciprocal symbiosis,” aligning with the overall interests of the digital innovation ecosystem. (2) Digital platforms' knowledge transfer effects within the digital innovation ecosystem show significant differences. The most effective knowledge transfer model for digital platforms is reciprocal symbiosis, whereas the least effective is parochial symbiosis (platform benefit). (3) The symbiotic rate has a significant positive impact on the evolutionary dynamics of knowledge transfer on digital platforms, especially in the reciprocal symbiosis model.

This study's results aid digital innovators in achieving efficient knowledge transfer through digital platforms and identify how symbiotic relationships affect the knowledge transfer process across the ecosystem. Accordingly, the authors propose targeted recommendations to promote the efficiency of knowledge transfer on digital platforms.

The purpose of this paper is to examine Chinese consumers’ attitudes toward genetically modified (GM) foods and the impact that consumers’ trust in different actors – GM scientists, non-GM scientists or individuals, the government and the media, has on their attitudes.

Consumers in Beijing were surveyed about their attitudes toward GM foods and their trust in different actors. The surveys were conducted from June to July of 2015. The sample size is 1,460 people. Given the potential endogeneity of trust variable, bivariate probit models are employed to estimate the impact of trust in different actors on consumers’ attitudes.

The results show that 55 percent of the Chinese consumers are opposed to GM foods and nearly 60 percent do not trust GM scientists. In total, 42 percent of Chinese consumers trust in the government and 39 percent trust the non-GM scientists or individuals. Around 35 percent of consumers believe the misinformation on GM technology that were provided by the media. Trust in the GM scientists and trust in the government have a significant positive impact on consumers’ acceptance of GM foods while trust in the non-GM scientists or individuals and believing the misinformation have a significant negative effect on the acceptance. Nearly 70 percent of Chinese consumers acquired information about GM food safety from the internet or via WeChat. Consumers who acquired GM technology information from the internet or via WeChat are less likely to embrace GM foods than those who obtain information from other sources.

Consumer trust plays a crucial role to accept biotech products in the market and it is crucial for producers, policy makers and consumers to have faith in new biotech products. The results of this study suggest that the government and GM scientists should make more effort to gain the trust and support of consumers, while the media should provide objective reports on GM products based on scientific evidence.

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

This paper is written as an attempt to employ the Chinese Social Science Citation Index (CSSCI) in the evaluation of Chinese humanities and social science research.

This paper uses statistics in the CSSCI (2000‐2004) to analyze the academic impact of researchers, papers and works, institutions and regions on Chinese humanities and social science research.

The authors identify 100 highly cited people, 50 highly cited papers, 50 highly cited works, 20 highly productive institutions and 20 highly cited institutions. Also provided is some regional information about Chinese humanities and social science research.

It is hoped that the CSSCI, as well as the analysis and evaluation based on it, will give researchers a better understanding of Chinese humanities and social science research.