Research shows that social welfare crowdfunding is in the rising stage of development in China, showing a vigorous development trend. Social welfare crowdfunding owns advantages such as accuracy and efficiency that traditional social welfare forms do not have, but problems including imperfect relevant policies and regulations, lack of supervision of social welfare crowdfunding platform, and low process transparency still exist. From the perspective of PEST‐SWOT model, this paper sums up the research trends and development status of social welfare crowdfunding, deconstructs the strengths, weaknesses, opportunities, and threats faced by social welfare crowdfunding from the aspects of politics, economy, society, and technology, and puts forward a feasible analysis of relevant development strategies to provide a reference for its future sustainable development.

Optimizing the layout of data centers is important for the rapid development of digital infrastructure, while also addressing the issues of energy consumption, environmental sustainability, and geographic resource distribution. Traditional strategies usually focus only on the distance to demand centers and ignore the energy and environmental costs of data centers in densely populated areas. In this paper, we propose a layout optimization model based on energy consumption constraints that combines integer linear programming with binary decision variables. The model combines energy efficiency, renewable resource availability, and regional characteristics to balance economic benefits and environmental impacts, consistent with the “East data, West computing” project. The experimental results showed that the energy efficient scenario consistently reduced costs, from ¥3.68 × 10⁸ to ¥3.08 × 10⁸ without energy constraints, and from ¥4.08 × 10⁸ to ¥3.47 × 10⁸ under energy consumption constraints. Additionally, energy constraints increased the number of required data centers from two to three. The results of the study emphasized the importance of strategic siting, especially in low electricity price areas, in order to optimize the layout and improve sustainability.

Quantitative investment has attracted much attention, along with the vigorous development of Fintech. Fundamentals are one of the most important reference factors for investment. Before quantitative trading, evaluation of fundamentals may have been more dependent on personal experience. While artificial intelligence evaluation models can provide good investment suggestions and select stocks with better fundamentals. From the four angles of solvency, growth ability, operation ability, and profitability, this research selects 13 financial indicators to build a fundamental evaluation system through correlation coefficient analysis. The corporate life cycle assessment indicator is innovatively added so that the fundamental improvement expectation is put into the evaluation system. Four different kinds of scoring methods are applied to obtain a more rational and comprehensive evaluation of indicators. Then, grey relational analysis is adopted to determine the initial weight to calculate the expected output. Finally, BP neural network (back propagation) is used for training and testing to realize weight optimization. It is concluded that the model is suitable for quantitative scoring of the fundamentals of listed companies and can effectively reflect their value of them.

Optimizing the layout of data centers is important for the rapid development of digital infrastructure, while also addressing the issues of energy consumption, environmental sustainability, and geographic resource distribution. Traditional strategies usually focus only on the distance to demand centers and ignore the energy and environmental costs of data centers in densely populated areas. In this paper, we propose a layout optimization model based on energy consumption constraints that combines integer linear programming with binary decision variables. The model combines energy efficiency, renewable resource availability, and regional characteristics to balance economic benefits and environmental impacts, consistent with the "East data, West computing" project. The experimental results showed that the energy efficient scenario consistently reduced costs, from ¥3.68×10^8 to ¥3.08×10^8 without energy constraints, and from ¥4.08×10^8 to ¥3.47×10^8 under energy consumption constraints. Additionally, energy constraints increased the number of required data centers from two to three. The results of the study emphasized the importance of strategic siting, especially in low electricity price areas, in order to optimize the layout and improve sustainability.