Excessive growth or improper disposal of construction waste can lead to negative consequences such as environmental destruction and waste of resources. The policy practice of construction waste reduction and resource utilization is facing challenging issues. Construction enterprises (also constructors of construction waste) and building material manufacturers (also recyclers of construction waste) play significant roles in the system of construction waste recycling and resource utilization. However, they are often absent or out of position in most cases. Therefore, this study constructs an evolutionary game model and conducts numerical simulation analysis, aiming to clarify the interactive relationship between their interests and government policy implementation, promote the formation of a cooperative system for construction waste management, and facilitate the achievement of ultimate governance objectives. The research results show that: (1) Current collaboration in construction waste management has fallen into a dilemma of relying solely on government efforts, resulting in inefficient or ineffective policy implementation. (2) The government can change the current situation and achieve better policy outcomes by taking measures such as increasing the income of recycled construction waste products, increasing fines for violations, and lowering industry entry barriers. (3) Different optimization measures vary in the speed at which they promote the evolutionary game system to evolve into a stable and ideal strategic combination. In comparison, increasing the market price of recycled products and increasing their sales volume are more effective optimization strategies. The process and conclusions of this study provide valuable reference and inspiration for the government to formulate construction waste management policies and optimize their policy implementation plans.

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center.

Mutual trust among social networks users encourages positive communications, so it is critical to study trust in the context of online social networks. In this study, we built a model to calculate trust of social media users. Data was collected from Qzone (Tencent Technology Co., Ltd.), a SN service (also known as QQ) in China. We identified 150 QQ users and 3 friends from each of the users; data of these users were collected by Python program. The relationship between trust and closeness was constructed using an ordinary least squares regression model, and the factors that influence trust between social network users were constructed using an endogenous switching regression model. We also conducted a two-stage least squares robustness analysis to confirm the results. We found that user trust and closeness are positively correlated. A user’s trust is positively related to three closeness indicators: comments, @s to QQ friends (a reminder nudge for attention), and messages. Increasing closeness in social networks has a positive effect on trust formation.