Research on the Carbon Credit Exchange Strategy for Scrap Vehicles Based on Evolutionary Game Theory.

In this article, we construct a game model that uses government regulators and scrap vehicle owners as the main parties to investigate the carbon credit exchange strategy of scrap vehicles using evolutionary game theory. The results were validated using Matlab simulation analysis to reveal the dynamic evolution process of the strategy of both sides of the game. A sensitivity analysis of the key parameters was conducted to explore the influence of each parameter on the evolution process and the stabilization trends. The study shows that (1) The time for the game system to reach a steady state is inversely related to the size of the initial willingness of the parties to cooperate. (2) In the mixed steady-state scenario, when the overall return differential between the positive and negative regulatory verification by government departments is positive, the steady state is participation and positive scrapping. (3) When the probability of the government verifying and being successful in verifying the punishment of the owner's negative scrapping behavior increases, both parties of the game will eventually choose the strategy of participation and positive scrapping. When the cost of the government participation strategy and the cost of the government verification strategy increase, both sides of the game will eventually choose the strategy combination of no participation and positive scrapping. (4) When the owner's reward for cooperating with the strategy, the owner's cost of scrapping the vehicle, and the benefits of the owner's negative cooperation strategy change, they will not change the strategy stability results but will affect the time it takes for the game system to reach a stable state. This study has theoretical implications for government policies in the scrapping industry and how to guide vehicle owners to actively scrap their vehicles.

Combination predicting model of traffic congestion index in weekdays based on LightGBM-GRU.

Tree-based and deep learning methods can automatically generate useful features. Not only can it enhance the original feature representation, but it can also learn to generate new features. This paper develops a strategy based on Light Gradient Boosting Machine (LightGBM or LGB) and Gated Recurrent Unit (GRU) to generate features to improve the expression ability of limited features. Moreover, a SARIMA-GRU prediction model considering the weekly periodicity is introduced. First, LightGBM is used to learn features and enhance the original features representation; secondly, GRU neural network is used to generate features; finally, the result ensemble is used as the input for prediction. Moreover, the SARIMA-GRU model is constructed for predicting. The GRU prediction consequences are revised by the SARIMA model that a better prediction can be obtained. The experiment was carried out with the data collected by Ride-hailing in Chengdu, and four predicted indicators and two performance indexes are utilized to evaluate the model. The results validate that the model proposed has significant improvements in the accuracy and performance of each component.

Experimental measurement of the wake field in a plasma filament created by a single-color ultrafast laser pulse.

A laser plasma wake field in a single-color femtosecond laser filament determines the acceleration of ionized electrons, which affects the intensity and bandwidth of the emitted terahertz wave and is important for understanding the fundamental nonlinear process of THz generation. Since the THz wave generated by a laser wake field is extremely small and easily hidden by other THz generation mechanisms, no method exists to measure this wake field directly. In this paper, a simple and stable method for determining the amplitude of the laser plasma wake field is presented. Based on the cancellation of a positive laser plasma wake field and an external negative electric field, the "zero point" of the intensity of the generated THz wave at some frequency can be used to determine the exact amplitude of the corresponding laser plasma wake field. This finding opens an avenue toward the clarification of ultrafast electronic dynamic processes in laser-induced plasmas.

Molybdenum carbide as an efficient catalyst for low-temperature hydrogenation of dimethyl oxalate.

Silica-supported molybdenum carbide (Mo2C/SiO2) is found to be a highly active, selective and stable catalyst for the hydrogenation of dimethyl oxalate to ethanol at low temperatures (473 K). Moreover, the formation of ethanol over the Mo2C catalyst performs via the novel intermediate methyl acetate instead of ethylene glycol forming over the Cu catalyst.