Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

Reputational preference and other-regarding preference based rewarding mechanism promotes cooperation in spatial social dilemmas

To study the incentive mechanisms of cooperation, we propose a preference rewarding mechanism in the spatial prisoner’s dilemma game, which simultaneously considers reputational preference, other-regarding preference and the dynamic adjustment of vertex weight. The vertex weight of a player is adaptively adjusted according to the comparison result of his own reputation and the average reputation value of his immediate neighbors. Players are inclined to pay a personal cost to reward the cooperative neighbor with the greatest vertex weight. The vertex weight of a player is proportional to the preference rewards he can obtain from direct neighbors. We find that the preference rewarding mechanism significantly facilitates the evolution of cooperation, and the dynamic adjustment of vertex weight has powerful effect on the emergence of cooperative behavior. To validate multiple effects, strategy distribution and the average payoff and fitness of players are discussed in a microcosmic view.

Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment

Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural network models and semantic matching techniques.Experiments conducted on the Flickr8k and AraImg2k benchmark datasets,featuring images and descriptions in English and Arabic,showcase remarkable performance improvements over state-of-the-art methods.Our model,equipped with the Image&Cross-Language Semantic Matching module and the Target Language Domain Evaluation module,significantly enhances the semantic relevance of generated image descriptions.For English-to-Arabic and Arabic-to-English cross-language image descriptions,our approach achieves a CIDEr score for English and Arabic of 87.9%and 81.7%,respectively,emphasizing the substantial contributions of our methodology.Comparative analyses with previous works further affirm the superior performance of our approach,and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language.In summary,this study advances the field of cross-lingual image description,offering an effective solution for generating image captions across languages,with the potential to impact multilingual communication and accessibility.Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.

Analysis of the Choice of Return Mechanism of PPP Model in Rural Human Settlement Improvement under the Rural Revitalization Strat­egy:Research Based on the Perspective of Evolutionary Game

The improvement of rural human settlement environment is a significant direction of the rural revitalization strategy.Based on the finite rational evolutionary game theory,a cooperative behavior evolutionary game model of rural human settlement environment improvement PPP model with local government,social capital and rural residents as the main game players with the reward mechanism of Government Payment and one with the reward mechanism of Viability Gap Funding are constructed.Comparing the total project revenue of two reward mechanisms,the thesis will obtain the effects of choosing the reward mechanism of rural human settlement improvement PPP.Finally,available suggestions are made to the decision of the reward mechanism of PPP project about rural human settlement environment,thus promoting the application and development of PPP in rural environmental management and to promote sustainable improvement of rural habitat improvement.

Behaviours of Multi-Stakeholders under China’s Renewable Portfolio Standards:A Game Theory-Based Analysis

China has implemented both quantitative and policy incentives for renewable energy development since 2019 and is currently in the policy transition stage.The implementation of renewable portfolio standards(RPSs)is difficult due to the interests of multiple stakeholders,including power generation enterprises,power grid companies,power users,local governments,and the central government.Based on China’s RPS policy and power system reform documents,this research sorted out the core game decision problems of China’s renewable energy industry and established a conceptual game decision model of the renewable energy industry from the perspective of local governments,power generation enterprises and power grid companies.The results reveal that for local governments,the probability of meeting the earnings quota or punishments for not reaching quota completion are the major determinants for active participation in quota supervision.For power grid firms,the willingness to accept renewable electricity quotas depends on the additional cost of receiving renewable electricity and governmental incentives.It is reasonable,from the theoretical perspective,to implement the RPS policy on the power generation side.Electricity reform will help clarify the electricity price system and increase the transparency of the quota implementation process.Policy implications are suggested to achieve sustainable development of the renewable energy industry from price incentives and quantity delivery.

Usage of a Reward System for Dealing with Pediatric Dental Fear

Background： Pediatric dental fear, if left unchecked, can persist for a lifetime and adversely impact the physical and psychological health of a patient. In this study, a feasible nonmedical method for relieving pediatric dental fear was investigated. Methods： A randomized, single-blind, controlled trial model was applied. The juvenile patients experiencing dental fear, whose parents or guardian had signed an informed consent form, were randomly divided into two groups. Group A （n = 50） was the control group, while Group B （n = 50） was the reward group. Participants in Group A accepted routine treatment. Participants in Group B were told that they would obtain a gift as a reward for their good behavior if they were compliant during their dental treatments. The Chinese version of the Children＇s Fear Survey Schedule-Dental Subscale （CFSS-DS） was used to evaluate the level of dental fear of each patient both before and after each treatment. A contrast analysis and a correlation analysis of the results were used to assess the efficacy of the reward mechanism. Results： All participants in Group B, were obedient during the dental treatment, and they also successfully chose the present they wanted at the end of their dental treatment. Children at different ages showed different reward preferences. Significant difference in the fear scores of the participants in Group B before the treatment and after receiving the reward was found （independent samples t-test, t = 14.72, P 〈 0.001）. In Group A, 86% children＇s fear score did not undergo a noticeable change. Conclusions： A reward system is proved feasible to relieve pediatric dental fear, and the form of reward should meet the demand of patients.

Patent Propensity in Small Technologybased Firms:Evidence from Zhongguancun Science Park

Using a survey administered in Zhongguancun Science Park in Beijing, China, this paper investigates the impact of R ＆D personnel-related intellectual property management practices on the patent propensity of small technology-based firms. It is found that R＆D personnel- related management practices, including training and reward mechanisms, are effective in enhancing a firm ＇s willingness to patent. In particular, we find that reward mechanisms can negatively moderate the effect of size on a firm ＇s willingness to patent. One implication that emerged from the analysis is that a small firm can counteract its size disadvantage in patenting by introducing a well-developed reward mechanism.