The Narrowing of Charge Balance Function and Hadronization Time in Relativistic Heavy Ion Collisions

The widths of charge balance function in high energy hadron-hadron and relativistic heavy ion collisions are studied using the Monte Carlo generators PYTHIA and AMPT, respectively. The narrowing of balance function as the increase of multiplicity is found in both cases. The mean parton-freeze-out time of a heavy-ion-collision event is used as the characteristic hadronization time of the event. It turns out that for a fixed multiplicity interval the width of balance function is consistent with being independent of hadronization time.Comment: 4 pages, 7 figure

Do Executives Have Fixed Effects on Firm-Level Stock Price Crash Risk?

This paper investigates whether individual CEOs and CFOs have “styles” (i.e. manager’s fixed effects) in withholding corporate bad news, which is captured using firm-level future stock price crash risks. Tracking managers that move across firms and employing a manager fixed-effect model, I find that both CFOs and CEOs have fixed effects on firm-level stock price crash risks in the future using multiple crash risk measures adopted from previous studies (e.g. Kim et al.(2011a,b)). In addition, I find that CFOs’ managerial ability is positively associated with one crash risk measure. Lastly, I find systematic differences in CEO vis-à-vis CFO’s preferences in exploiting voluntary disclosure, earnings management, tax avoidance and other channels to withhold bad (good) news which generates crash risks. And these preferences vary in accordance with managerial ability, age cohort group and gender of managers

Research on user participation behavior of mobile short video APPs: Taking Xiaohongshu as an example

Short video apps for mobile devices are rising in popularity. Using Xiaohongshu as an example, this work carefully studies the user participation behavior of mobile short video Apps and contributes to the body of knowledge in the field of pertinent theoretical research. This study equips creators of short videos with the knowledge they require to improve user experience and content marketing on a more objective basis, as well as to enable app upgrading and optimization. The UTAUT theoretical model is used in this paper to develop hypotheses, which are then tested using survey data. Finally, the theoretical model and hypothesis are validated using multiple regression analysis and hierarchical regression analysis. The significant study results are as follows: Users\u27 behavior is significantly influenced by social value, perceived entertainment value, individual innovation, facilitating conditions, and privacy security when using communities; by social value, individual innovation, facilitating conditions, and privacy security when participating in communities; and by social value, facilitating conditions, and privacy security when contributing to communities. Finally, it makes some suggestions for the long-term expansion of mobile short video apps based on the testing results

A Two-Stage Real-time Prediction Method for Multiplayer Shooting E-Sports

E-sports is an industry with a huge base and the number of people who pay attention to it continues to rise. The research results of E-sports prediction play an important role in many aspects. In the past game prediction algorithms, there are mainly three kinds: neural network algorithm, AdaBoost algorithm based on Naïve Bayesian (NB) classifier and decision tree algorithm. These three algorithms have their own advantages and disadvantages, but they cannot predict the match ranking in real time. Therefore, we propose a real-time prediction algorithm based on random forest model. This method is divided into two stages. In the first stage, the weights are trained to obtain the optimal model for the second stage. In the second stage, each influencing factor in the data set is corresponded to and transformed with the data items in the competition log. The accuracy of the prediction results and its change trend with time are observed. Finally, the model is evaluated. The results show that the accuracy of real-time prediction reaches 92.29%, which makes up for the shortage of real-time in traditional prediction algorithm

Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the base of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution route with and without carbon emissions cost are constructed, and fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, the path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit

Analyzing opinion conflicts in an online group discussion: From the perspective of majority and minority influence

Online community and groups often experience heated discussion. This paper examines a WeChat group discussion from the perspective of majority and minority influence to explore the evolvement of the discussion and the be-haviors of group members. Content analysis of 515 messages suggests that opin- ion conflicts between majority and minority evoke discussion engagement and knowledge exchange. There are different patterns of knowledge construction expressions between majority and minority groups. The majority prefer egocentric expression, while the minority prefer allocentric expression. Majority opinion holders have different conflict handling styles compared to minority opinion holders, who are more likely to avoid. Minority group is under great pressure in social interaction, they are easier to receive unfair comments and personal attacks

Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications

The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms. Existing approaches often address these problems in isolation, lacking a unified perspective that can reveal commonalities and enable effective solutions. Therefore, in this work, we proposed a new framework, named Learning with Constraint Learning (LwCL), that can holistically examine challenges and provide a unified methodology to tackle all the above-mentioned complex learning and vision problems. Specifically, LwCL is designed as a general hierarchical optimization model that captures the essence of these diverse learning and vision problems. Furthermore, we develop a gradient-response based fast solution strategy to overcome optimization challenges of the LwCL framework. Our proposed framework efficiently addresses a wide range of applications in learning and vision, encompassing three categories and nine different problem types. Extensive experiments on synthetic tasks and real-world applications verify the effectiveness of our approach. The LwCL framework offers a comprehensive solution for tackling complex machine learning and computer vision problems, bridging the gap between theory and practice