Tao Zhang

This study mainly explores how green institutional investors affect corporate ESG performance and conducts empirical analysis using Chinese A-share listed companies from 2014 to 2021 as research samples. The research results show that green institutional investors can promote corporate ESG performance by alleviating financing constraints, enhancing green management capabilities, and improving corporate governance levels. This promoting effect is more pronounced in non-state-owned enterprises, high-tech enterprises, and enterprises with high analyst attention. Further research has found that green institutional investors have a more prominent promoting effect on the performance of S and G dimensions, while the promoting effect on the performance of E dimension is relatively small. Meanwhile, insufficient market competition and environmental uncertainty will weaken the promoting effect of green institutional investors on corporate ESG performance. The research conclusion clearly presents the intrinsic relationship between green institutional investors and corporate ESG performance, providing new theoretical insights for promoting sustainable development of enterprises.

Perceived trust is a key factor affecting the behavior to donate online. In order to further explore the factors and influencing mechanisms that affect the success of medical crowdfunding projects, this paper, combined with the Stimulus-Organism-Response theory, introduces the mediating variable of social presence and perceived differences in trust, and constructs a model of online users’ donation behavior to medical crowdfunding projects. We collected 437 valid samples through a questionnaire survey, and processed the data with SPSS and Amos software to test and analyze the theoretical model. The research results showed that project description and user participation have a significant positive effect on social presence; project transparency and patient identity have a significant negative effect on perceived differences in trust; social presence has a positive effect on donation behavior, while the perceived difference in trust has a negative effect; social presence and perceived differences in trust play a mediating role respectively; there is no significant effect of patient status on social presence. This study further expands the application of social presence and perceived differences in trust in medical crowdfunding, and provides a theoretical basis for the success of medical crowdfunding projects.

Hybrid renewable energy system arises regularly in real life. By optimizing the capacity and running status of the microgrid, HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems.

From the 1870s to the 1950s, African Americans frequently referred to the Chinese sage Confucius when demanding justice from American white society. This hitherto unnoticed strategy emphasized Confucian morality to undermine the theoretical basis of American racism. It exposed the hypocrisy of white society on racial relations and illuminated a different path for blacks’ racial advancement. In the process, blacks added an American racial color to Confucius’ image while portraying themselves as cosmopolitan and rational fighters for racial fairness. Chinese culture is therefore not only widely known in America but has an important role to play in its interracial relations.

The article explores two different articulations of the attitudes of young Chinese netizens towards the state: the neo-nationalist web community, ‘Anti-CNN.com’/‘April Youth’, and the online ‘machinema’ film, ‘Online Gaming Addicts’ War’. Both of these online practices are associated with the post-’80 s generation, which I argue is a key constituency in contemporary Chinese internet discourse. Through these case studies, the article explores the viability of recent attempts to apply Foucauldian theories of governmentality to the case of China. It identifies a determining factor here in the recurring tendency of the Party-state, whilst generally attempting to embrace more sophisticated forms of governance, to default towards crude techniques and technologies of prohibition in its regulatory stance towards the internet. Whilst this stance is likely to be unsustainable in view of the dynamics of global media culture, the article argues that this does not undermine claims over the existence of a distinctive Chinese form of governmentality. Rather, the Chinese case should strengthen doubts over the view of some that governmentality is incompatible with the state’s deployment of violence.

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river.

Model predictive control technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method.