In this article, we demonstrate the investigation process of the determination of muon rate. During our investigation, we used four Cosmic Watches to trace variation of various physical quantities that would help with our further discovery. After cleaning the data, plotting, and calculating the number of muon and the actual working time of the detectors, we were able to determine muon rate, which are 0.089±0.004 Hz and 0.082±0.005 Hz respectively for the left two and the right two Cosmic Watches. Then, two of the four Cosmic Watches were placed at a larger distance between each other, the new rates obtained are 0.109±0.005 Hz and 0.102±0.005 Hz accordingly for the left and right two detectors. The specific setup of the Cosmic Watches is included later in this paper. By doing this, we discovered the inverse relationship between the rate of muon rate and the distance between the detectors, which may indicate the direction of cosmic ray pathways.

Elliptic flow (v_2) and triangular flow (v_3) provide critical insights into the quark-gluon plasma (QGP) formed in heavy-ion collisions. Using data from the sPHENIX detector, this study examines the hydrodynamic properties of QGP. The analysis involves plotting distributions of transverse momentum (p_T), pseudorapidity (η), azimuthal angle (ϕ), and charge, calculating delta phi (Δϕ) distributions, fitting harmonic functions, and conducting event mixing to reduce statistical uncertainties. This paper presents a detailed method and findings of v_2 and v_3 as functions of p_T, validated against theoretical models.

This thesis presents a comprehensive review of fuel cell technology, with a specific emphasis at the latest trends and programs of oxygen reduction response (ORR) catalysts. The examine starts offevolved with the aid of using evaluating the benefits and drawbacks of hydrogen fuel cells with different alternative power sources, which include sun power and secondary batteries, highlighting the ability of fuel cells to update traditional inner combustion engines because of their high power density and environmental benefits. The thesis then explores the essential running ideas of fuel cells and examines their operational mechanisms in numerous chemical environments. In the context of ORR catalysts, the assessment evaluates quite a number catalysts, which include precious metal catalysts, non-precious metal catalysts, and single-atom catalysts, that specialize in techniques to decorate their activity and stability. Finally, the thesis discusses the demanding situations and destiny possibilities for the industrial application of ORR catalysts in hydrogen fuel cells.

In order to compare and analyze the influence of different structural parameters on the muffler performance of multi-section expansion chamber muffler, this paper explores the main factors affecting the noise suppression performance through the transmission loss formula, and it establishes the model of two cylindrical expansion chamber silencer. The muffler expansion chamber is analyzed by changing the section number of the muffler expansion chamber, the cross-section diameter of the front and rear expansion chamber, the length of the front and rear expansion chamber, the length of connecting tube, the arrangement order of expansion chamber with different cross-section area and the arrangement order of expansion chamber with different length. The simulation results show that the muffler volume can be effectively increased by increasing the number of expansion chamber and the cross-section diameter of expansion chamber. The length of the expansion chamber does not show the peak value of the transmission loss curve, which only affects the span of the transfer loss curve; the order of the expansion chamber with different diameters and the arrangement order of the expansion chamber with different lengths have little influence on the transmission loss peak value of the muffler; the change of the length of the connecting tube affects the span of the transmission loss curve, but the peak value of the transfer loss curve is not affected. This study can be used as a reference for the design of a multi-section expansion chamber silencer.

Using the simulation data of different calorimeters, we investigate the use of machine learning and python algorithms for the simulation and reconstruction of particle flow energy in high-energy physics. We train models based on simulated pixel value image instead of common numerical data. We define two models that use different Regressor algorithms. Multi-layer Perceptron (MLP) Regressor offer stable and accurate prediction under less affection situation. Convolutional Neutral Networks (CNN) Regressor provided better and stronger modeling and reconstruction ability; however, it could cause overfitting results in certain situations. Furthermore, we optimize our models with the use of PyTorch. These models can serve as fast method for particle flow reconstruction in future studies and experiments.

In 2023, the Central Committee of the Communist Party of China and The State Council issued the Overall Layout Plan for the Construction of Digital China, emphasizing the construction of a smart water conservancy system with digital twin basins as the core. The Guangxi Zhuang Autonomous Region government has clearly proposed to actively build a "digital twin Pinglu Canal", and attaches great importance to the construction of digital twin platform application services during the construction of the Western land-Sea New Passage (Pinglu) canal. Aiming at the problems such as unsatisfactory early warning effect, inaccurate data and imprecise model of existing smart water conservancy technology, this paper proposes a digital twin system for disaster early warning based on physical level and numerical simulation technology, establishes a visual digital water conservancy model and digital twin smart water conservancy cloud platform, and combines multi-physics field and multi-dimensional coupling algorithm to respond to technical pain points. To achieve scientific and accurate monitoring and intelligent management, and contribute to the construction of digitally empowered Pinglu Canal.

To help advance the development of heat dissipation technologies, the research studies the effect of pore diameter on heat conduction within porous media. With the help of Ansys software, a cuboid with varying pore sizes is modelled to investigate its influence on effective thermal conductivity. Through analysing the data exported, an overall trend is derived: for a fixed number of nodes in a piece of porous material, the efficiency of heat conduction tends to be higher with the increase of pore sizes. The study is especially relevant for the design of radiators with porous plates, commonly used in cooling systems. Larger pores tend to facilitate more gas-phase conduction, which, when combined with solid-phase conduction, optimizes overall thermal conductivity. The results of the study can potentially advance the development of heat dissipation technologies.

Accompanied by the development of artificial intelligence industry, to play the data value of web crawler technology has been the focus of research in the field of computer network and data science, and the data mining technology based on artificial neural network intelligent algorithm is widely used. In view of this, this paper takes the BP neural network data mining technology, which has excellent nonlinear mapping capability, parallel processing capability and fault tolerance and is widely used, as the basis, and integrates the methods and ideas of data mining into the mining of data laws in the field of KOL identification of key opinion leaders, with a view to finding valuable intrinsic laws and relationships between the mining of web crawler technology and the identification of KOL features. The research content of this paper mainly includes two aspects of research work, the design of high-performance data mining technology and the actual work in the field of KOL recognition. On the one hand, this paper comprehensively describes the basic theory and methods of data mining, and focuses on the in-depth analysis and elaboration of BP neural network-based data mining technology on the basis of understanding and analyzing a variety of data mining technologies. On the other hand, this paper aims to solve the problem of bias in the prediction of traditional KOL model and design the experimental method of KOL recognition by BP neural network algorithm.

As is well known, with the continuous development of artificial intelligence technology and the increasing accessibility of data, various social platforms are committed to using intelligent recommendation algorithms to cater to user preferences. Some platforms even exaggerate facts and push sensational, valueless information to users, leading to the "Screaming Effect" and the "Echo Chamber Effect." The prolonged existence of these effects may result in "Information Cocoon," which is detrimental to the healthy development of individuals and society. To address this issue, different topics can have varying trajectories as online comments ferment, potentially reaching a neutral consensus or resulting in polarization. As a mainstream social platform in China, Weibo users serve as nodes for the dissemination of public opinion information, and the characteristics of information release, reception, forwarding, and commenting all influence the effectiveness of communication. First, we selected the topics "COVID-19" and "IG Electronic Sports Club" as our research subjects, identifying a range of topic popularity through Baidu Index, followed by data collection. Second, we used perplexity and coherence to determine the optimal number of topics for LDA, analyzing the changes in topic feature characteristics over different time periods. Through correlation analysis, we reached the following conclusions: Higher Weibo levels correlate with better information dissemination effectiveness; the quantity of information published on Weibo negatively impacts the forwarding and commenting behaviors; and as events progress, the enthusiasm for information dissemination on Weibo declines, with daily dissemination gradually decreasing.

Spotify, one of the largest music streaming service providers, boasts a vast user base and an extensive music catalog. To gain a deeper understanding of Spotify users' music preferences and behavioral patterns, this paper conducts a thorough analysis and explores the correlation between music features and user behavior. It collects Spotify users' music listening data and extracts various music features such as energy, danceability, valence, and beats per minute (BPM). These features not only reflect the musical style and rhythm but also potentially correlate with users' preferences and listening habits. Then we employ two machine learning models, Decision Trees and Random Forests, to model and analyze Spotify users' behavior data. Through these models, we can accurately identify potential relationships between music features and user behavior. The experimental results indicate that music features such as energy, danceability, valence, and BPM have a significant impact on users' music selection and preferences. By analyzing the most popular songs on Spotify over different time periods, we discover that songs with high energy, danceability, and valence tend to be more popular among users. This finding not only validates the correlation between music features and user behavior but also reveals the user preferences and market demands behind music trends. For music streaming service providers, understanding users' music preferences and behavioral patterns is crucial. By deeply analyzing user data, music streaming services can better satisfy users' needs, enhance user experience, and stand out in the fiercely competitive market. These findings have important practical implications for music streaming service providers and provide new insights and methodologies for the research and development of the music industry.