bluuebunny/crossref_metadata_2025 huggingface dataset:
From the Perspective of Originality: Analysis of the Legal Nature of Artificial Intelligence Products
Authors: Lu Jiaming , Wang Tianqi , Zhao Yahui
Published: August 2024

With the rapid proliferation of advanced artificial intelligence, such as GPT, the volume of AI-generated content on the internet has surged significantly. Consequently, the question of whether AI-generated content should be safeguarded by copyright law has emerged as a critical issue in both academic and practical domains. The determination of the eligibility of AI-generated content for copyright protection hinges on the assessment of its originality. Current research in the field of AI-generated content copyrightability has brought to light two distinct standards of originality: subjective and objective. However, a challenge arises when weighing the merits and drawbacks of these two standards. Given the inherent complexity of AI-generated content, a singular standard may not comprehensively and accurately encapsulate its attributes. Therefore, the most judicious approach at present appears to be a fusion of the subjective and objective standards, thereby embracing a more comprehensive framework that accounts for the nuanced nature of AI-generated content and its potential for originality under copyright law.
Early Diagnosis of Parkinson's Disease based on XGBoost Algorithm and Logistic Regression
Authors: He Jianbang , Zhang Jiyue
Published: August 2024

Parkinson’s disease is a common neurodegenerative disease that affects nearly millions of people in the world. Parkinson's disease is often misdiagnosed or missed because its early symptoms are similar to aging. Starting from the early dysarthria of Parkinson's disease, this paper selects 22 of 195 samples for XGBoost model. Meanwhile, in order to avoid multicollinearity, Pearson correlation coefficient is used to reduce dimension to obtain 12 sound indicators for logistics model prediction. In this paper, the two models were comprehensively compared by means of confusion matrix thermal map, AIC value and sensitivity, and it was found that the former was 95% accurate but the sensitivity was high and the model stability was poor, while the latter was 90% accurate and the model goodness of fit was good. Therefore, the method discussed in this paper provides a new idea for the early identification of Parkinson's disease and points out the direction for further research.
Predicting the Solar Activity Cycle Based on A LSTM-ARIMA Hybrid Model
Authors: Zhou Yusen
Published: August 2024

Solar activity has a profound impact on the Earth, and the development of effective models to accurately predict solar activity will help us to further our understanding of space weather changes caused by solar activity and their possible consequences.In this paper, data on the area of the solar magnetic field, the number of sunspots and the area of sunspot regions were collected and processed, and histograms and box plots of the distributions of the three types of data were obtained, and it was found that the former showed a normal distribution and the latter two showed a skewed distribution. Subsequently, the data were further processed using the sliding average method to obtain the smoothed trend time series and fluctuation time series of the above three groups of data.The use of a single common time series forecasting model to predict solar activity proves to be unreliable due to the uncertainty and non-linearity of the solar activity cycle and intensity. Therefore, in this paper, separate ARIMA and LSTM forecasting models are first constructed to predict the general and fluctuating trends of solar activity indicators, respectively. Then, a hybrid prediction model is built to integrate the prediction results of the two models through denormalization, weight adjustment and noise removal to obtain the final prediction results.This paper focuses on predicting the changes in the solar magnetic field area, the number of sunspots, and the sunspot area to further speculate on the beginning and end of solar activity and its intensity. Finally, this paper provides a comparative analysis, reliability validation, and parameterization of the established models. From the perspectives of sliding and rolling validation, this paper compares the stability and accuracy of the independent ARIMA prediction model and LSTM prediction model as well as the hybrid model in processing raw data. In terms of model parameter determination, this paper analyzes and determines the number of cycles, hidden layer grid size in the LSTM model and the autoregressive term, moving average term and difference order in the ARIMA model, respectively.
KNN model based financial revenue forecast of Guangzhou
Authors: Wu Ganzhou
Published: August 2024

It is mainly carried on the analysis by the Guangzhou 2013-2020 financial revenue situation, firstly, in Guangzhou The Bureau of statistics downloads the data and preprocesses the data to determine the main factors affecting Guangzhou's fiscal revenue, then the KNN model is established to forecast the fiscal revenue of Guangzhou in 2021. The numerical results show that the method is effective.
Financial Crisis Analysis and Prediction in Africa using Machine Learning and Statistical Learning Methods
Authors: Chen Junda
Published: August 2024

Africa has been prone to financial crises that hinder its development. Understanding the causes and predicting the financial crisis for Africa is important for investors and governments. This article aims to analyze those financial ratios that affect the crises and find the best machine learning model to predict possible financial crises in Africa. 11 different machine learning and statistical learning methods are applied to the African systemic crisis data for 13 African countries from 1860 to 2014 to find the most suitable model for financial crisis prediction. The financial ratios being considered are exchange rate, domestic debt, sovereign external debt, GDP, inflation rate, independence, currency crises and inflation crises. After visualizing various machine learning and statistical learning models, this essay concludes that support vector classifier with the “rbf” kernel is the best model after comparing the performance of those models with metrics that have the priority as follows: accuracy, f1 score, recall and precision. Finally, this article uses the support vector classifier to find that systemic crisis is the financial ratio that have the biggest effect on the African banking crises after shuffling the values of each ratio and analyzing the influence on the model's performance.
The Impact of Stock Price Fluctuations on the Financial Market
Authors: Zhang Yixin
Published: August 2024

This article thoroughly examines the multitude of factors influencing stock price fluctuations and the analytical methodologies employed to comprehend them. Macro-economic variables such as economic growth, inflation rates, and monetary policies exert direct influences on stock prices. In addition, investor sentiment, a subjective factor among market participants, significantly shapes market dynamics. Emotional fluctuations and their cumulative effects can lead to the significant market volatility, potentially compromising market stability. To provide the deeper insights into stock price movements, the article introduces advanced analytical models such as the ARMA-TGARCH-M and GARCH-SVM models. These frameworks mentioned enhance the precision and scientific rigor of analyzing stock price fluctuations, catering to both investors and researchers. By advocating for the adoption of more accurate analytical techniques, the article aims to foster the stable evolution of financial markets, facilitating informed decision-making, and promoting sustainable market development, thereby contributing to the advancement and stability of the financial market.
Correlation Between Chinese Carbon Market and New Energy Market——Evidence Based on Wavelet Analysis and Granger Causality Tests
Authors: Yu Bozhao
Published: August 2024

Based on the background of global energy conservation and emission reduction, this paper studies the correlation between Chinese carbon market and new energy market. Sample data are from August 2013 to January 2024. The empirical results of the morlet wavelet model and quantile Granger causality test show that the carbon market is ahead of the new energy market in the medium- and long-term dimensions, and that Guangdong carbon allowances and Beijing carbon allowances Granger affect the new energy market during bull and bear markets, respectively.And the impact of the carbon market on some segments of the new energy market has obvious similarities.The findings of this paper confirm that the carbon market contributes to the development of the new energy industry, which in turn has a positive significance for the transition to a green and low-carbon economy.
A Study on the Effectiveness of Integrating Virtual Reality Technology into Ideological and Political Education in Universities
Authors: Lu Yiqian , Zhang Fenfen
Published: August 2024

With the continuous development of education in China and the rapid advancement of information technology, virtual reality (VR) technology has gradually become an important tool in ideological and political education in universities. This paper mainly explores the effectiveness of embedding VR technology into ideological and political education in universities. The research is divided into three parts: firstly, the current application status of VR virtual reality technology in practical teaching of ideological and political courses is analyzed, and its existing achievements and challenges are elaborated; Secondly, the feasibility and design ideas of VR virtual reality technology in practical teaching of ideological and political courses were explored, and specific implementation plans and technical paths were proposed; Finally, a specific practical path for applying VR technology to ideological and political education practical teaching was proposed, and various innovative application cases were demonstrated. Through in-depth research on these aspects, this paper aims to provide theoretical support and practical guidance for enhancing the effectiveness and innovation of ideological and political education in universities.
Mitigating Financial Volatility: The Role of Duration in Interest Rate Risk Management
Authors: Zhang Ruilun
Published: August 2024

Duration is a resource that investors can use to lower interest rate risk. The purpose of this research is to analyze the bond market and to join bonds. An investor has many reasons to choose bonds as part of their portfolio and use Duration to value the risk. When choosing to bond, the way to measure whether it suits the portfolio is to balance risks. Therefore, it will be more suitable for investors to join more than two kinds of financial products in a portfolio. The method introduced in this passage is the Duration of the calculation, and the usage will be written clearly. When calculating the Duration, it is important to calculate the yield of maturity and be familiar with the process. It is also essential to list the face and present values in a table to clarify the data when calculating the Duration. Also, when calculating the Duration, it is crucial to look at the view in a portfolio and balance it in a higher position.
Research on Light Pollution Evaluation Using TOPSIS Model Based on Entropy Weight
Authors: Zhang Xiaopeng , Zhang Hua
Published: August 2024

Light is an indispensable lighting source in human life. From the initial fire light source to the later electric light, light provides human beings with the ability to see things in the dark, which greatly expands the time and scope of human activities. However, in recent years, light pollution has become a major problem for people. To quantify and evaluate the impact of light pollution, In order to solve this problem, this paper establishes a TOPSIS model based on entropy weight, and uses Arc GIS software to quantitatively analyze the visual light pollution map. Seven evaluation indexes are selected and quantified. Then, the weights of seven evaluation indexes are obtained by using the TOPSIS model based on entropy weight, and the light pollution evaluation of the selected cities is carried out. Finally, the comprehensive ranking of the light pollution degree of the five cities is obtained.Through this research, it is of great significance to protect human health, maintain ecological balance, promote astronomical observation and improve urban planning and lighting design.
The Development of African American Education and The Causes and Effects of Racial Educational Inequality
Authors: Zhu Zixuan
Published: August 2024

African Americans have been a highly visible race throughout history, and they have had a great impact on the United States throughout history. This race went from slavery to freedom, from segregation to the civil rights movement, and they gained legal equality. However, they continue to be treated unfairly in every aspect of society. Racial inequality continues to this day, divorced from the law, and relying only on ingrained ideas can create extremely visible and persistent inequality in many aspects of society. This also includes education, and the continuing inequality in education undoubtedly has a continuing negative impact on African Americans, thus becoming a central factor in the inequality and class solidification between the races. The thesis takes a look at educational inequality against African Americans in the United States, describes the history of African American education, focuses on racial educational inequality for African Americans, and then examines the multiple causes of present-day racial educational inequality and its continuing impact on racial discrimination.
Research on the trading strategy based on an All-Weather asset allocation model combined with HURST Exponent
Authors: Zhu Qiuyu
Published: August 2024

Investors are constantly seeking strategies that can provide stable returns across different economic conditions. The All-Weather investment strategy, pioneered by Bridgewater Associates, has gained significant attention for its ability to navigate various market environments effectively. This paper examines the All-Weather investment strategy and the application of the Hurst Exponent to equity investing. The All-Weather strategy aims to achieve stable returns on a portfolio across a variety of economic conditions by allocating assets to four different economic environments. This study details the components of the All-Weather strategy and how it hedges against different economic environment risks by allocating the same risk weights. In addition, the article explores the application of the Hurst Exponent to the equity component, analyzing how it can help investors identify stock trends and combine it with momentum indicators to make buy and sell decisions. By analyzing the All-Weather strategy and the Hurst Exponent, this article provides investors with a composite investment strategy that combines macroeconomic conditions and stock trend analysis.
Corporate Environmental Responsibility, Media Attention and Financial Performance
Authors: Zhang Tianyi
Published: August 2024

Taking listed companies of Chinese manufacturing enterprises as the research object, this paper utilizes the method of empirical analysis to deeply explore the relationship between environmental responsibility and financial performance of Chinese manufacturing enterprises. Enterprises' environmental management behaviors are exposed to the public through social media. Enterprises get media attention by fulfilling their environmental responsibilities, improve their business conditions, and ultimately enhance their corporate performance. The study enriches the related research on CER and financial performance, explores the impact of new media based on existing traditional media research, and makes policy recommendations on how to promote corporate environmental responsibility to enterprises, media, government and the public.
Stream, Influence, and Purchase: Dissecting live stream ing commerce's Stimulus on Markets and Consumer Mindsets
Authors: He Yanjin
Published: August 2024

As the market of online shopping continues to expand, people's requirements for online shopping become higher; The general introduction of goods on web pages has been unable to satisfy consumers. Therefore, the birth of live selling allows consumers to immediately see the physical goods to decide whether to buy. Consumers' psychology and behavior patterns play an important role in live stream commerceof goods, which can promote the sales of goods under the interaction with the sales models of merchants. Through case study, qualitative analysis and document review, this paper studies three important subjects of live stream commerceing: consumers, anchors and merchants. It is found that merchants and broadcasters will use consumers' psychological factors and behavior patterns to achieve sales. Consumers complete consumption under the influence of their own consumption intention and value perception; Anchors complete their sales tasks by establishing trust with consumers and setting off the tense atmosphere in the live stream commerce room. In light of the analysis of consumer psychology and behavior, merchants train anchors to improve the sales mechanism and increase sales.
Research on Denoising Diffusion Probabilistic Models
Authors: Jiang Yiyang
Published: August 2024

Diffusion models represent the latest state-of-the-art in the domain of deep generative models, boasting remarkable performance across a broad spectrum of applications. Despite the widespread success of diffusion models in various tasks, the original formulations of these models exhibit notable limitations. The article uses DDPM as an example, thoroughly and deeply exploring and deriving the mathematical principles of the model from two different perspectives. Additionally, this article explores the relationship between diffusion models and five other types of generative models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Autoregressive models, Normalizing flows, and Energy-based models. Concluding with open questions for future research, the paper offers insights into the prospective algorithmic and application-oriented developments of diffusion models. Diffusion models have become a powerful framework capable of competing with Generative Adversarial Networks (GANs) in most applications without resorting to adversarial training. For specific tasks, understanding why and when diffusion models are more effective than other networks, and comprehending the differences between diffusion models and other generative models, will help clarify why diffusion models can produce high-quality samples with high likelihood.
The Comparative Analysis of Retirement Systems between Japan and China
Authors: Li Zhuoheng , Zhang Yi
Published: August 2024

In a rapidly aging society, such an issue as the aging population deserves the attention and concern. This research in order to conduct a comparative analysis between China and Japan to compare their retirement system and policies to fill in the gap that not too much previous research has focused on regarding the differentiation of retirement systems among these two East-Asian countries--although they have a huge difference on their national status and the ways to manage their vast aging population numbers. In the end, this research intends to derive an answer that although both China and Japan focus on older adults’ caring issues, the way that these two countries choose to manage and deal with them are very different compared to each other, such as the differentiation of execution process, involving of the third-party companies, challenges bring by different cultural contexts and societal atmospheres. Overall, China’s decision-making process prefers to be centric and parental, whereas Japan is keen to decentralization and privatization.
Risks of Discrimination Violence and Unlawful Actions in LLM-Driven Robots
Authors: Zhou Ren
Published: August 2024

The integration of Large Language Models (LLMs) into robotics heralds significant advancements in human-robot interaction, enabling robots to perform complex tasks involving natural language understanding, common sense reasoning, and human modeling. However, despite their impressive capabilities, LLMs pose substantial ethical and safety concerns, particularly the risk of enacting discrimination, violence, and unlawful actions. This study conducts a comprehensive Human-Robot Interaction (HRI)-based evaluation of several highly-rated LLMs, focusing on their bias and safety criteria. Our findings reveal that LLMs exhibit significant biases across diverse demographic groups and frequently produce unsafe or unlawful responses when faced with unconstrained natural language inputs. These results underscore the urgent need for systematic risk assessments and robust ethical guidelines to ensure the responsible deployment of LLM-driven robots. We propose detailed strategies for mitigating these risks, including advanced bias detection techniques, robust safety mechanisms, and collaborative standards development. By addressing these critical issues, we aim to pave the way for the development of safer, fairer, and more reliable robotic systems.
Tennis Match Trend Prediction Based on LSTM
Authors: Jiang Yichen , Yuan Chuansen
Published: August 2024

The focus of this research is on the data processing, including data mining, data cleaning, feature extraction, feature engineering and other aspects. Whether the data can be used to fit the game trend well is the key to the final effect of the model. At the same time, there are many reasons (i.e., characteristics) that affect the trend of the game. The purpose of this study is to find out the most drastic variables and make corresponding tactical changes according to them. The model based on LSTM network constructed in this paper has a good ability to handle variable-length inputs, and uses the attention mechanism to enhance the "memory" of long-distance inputs, and uses the Dropout layer to avoid model overfitting to a certain extent. Finally, the random forest method was used to extract several variables with the highest sensitivity for the sensitivity analysis of the model.
Application of Machine Learning in Stock Price Analysis
Authors: Zhang Xi
Published: August 2024

With the advancement of technology, machine learning has emerged as a powerful tool for analyzing complex financial data, including stock prices. By leveraging algorithms capable of identifying patterns and trends, it offers insights into market behavior. This study explores the application of machine learning techniques in stock price analysis, aiming to enhance prediction accuracy and inform investment decisions. Through rigorous analysis, our research demonstrates that machine learning models can effectively capture the dynamic nature of stock markets, leading to improved forecasting capabilities. The results indicate a significant enhancement in prediction accuracy, suggesting that these techniques could significantly contribute to financial analysis. The significance of this study lies in its potential to revolutionize stock market analysis. By harnessing the predictive power of machine learning, investors can make more informed decisions, reduce risks, and enhance returns. This not only benefits individual investors but also contributes to the overall stability and efficiency of financial markets. Machine Learning, Stock Price Analysis, Prediction Accuracy, Financial Markets, Investment Decision-Making. The methods employed in this study include the utilization of various machine learning algorithms such as support vector machines and neural networks, as well as the application of statistical techniques for data analysis and validation.
The Impact of the Modification of US Trade Remedy System on Chinese Economy
Authors: Qian Shuhui
Published: August 2024

As one of the largest economies in the world, the trade policy of the United States has a profound impact on the global economic pattern. In recent years, the modification of the US trade remedy system has had far-reaching effects on China's export trade, high-tech sector, and job market. Therefore, this paper examines the recently revised trade remedy system in the United States, utilizing a comprehensive approach that includes comparative analysis, case studies, and literature review to assess the impact on China's economy. The focus is on understanding how changes in U.S. policy could affect Chinese trade dynamics, industrial sectors, and overall economic health. The paper argues that while the updated U.S. trade remedy system introduces challenges to China's economic landscape, it does not necessarily hinder China's potential for sustained growth and development. Furthermore, this situation presents an opportunity for China to accelerate its initiatives towards reform, opening-up, and modernization. By adapting to these changes, China can strengthen its economic resilience and further solidify its position in the global arena. This paper posits that through strategic adjustments and embracing innovation, China can navigate the complexities of international trade policies and continue on its path of economic prosperity.
Influences of the Different Analytical Modes in the Nonlinear Pushover Analysis based on the External Elevator Well
Authors: Shi Mo , Huang Xiaoxian , Li Yunxia , Choi Yeol
Published: August 2024

Elevators are essential vertical transportation tools in contemporary society, providing convenience and crucial utilities, especially for the disabled. However, many existing RC (Reinforced Concrete) buildings were not designed with elevators. Demolishing these existing RC buildings for new construction could cause significant environmental issues, as rebuilding increases carbon dioxide emissions and conflicts with sustainable development goals. Consequently, adding external elevators to existing RC buildings is a practical solution to satisfy modern requirements. Despite their benefits, external elevators may cause structural issues during seismic events, while this is a topic that has received limited research attention. Addressing this gap, this research evaluates the structural performance of an external elevator well in an actual project on Jinzhong Rd, Shanghai City, using nonlinear pushover analysis. The study employs five different analytical modes (Point, Multipoint Uniform, Multipoint Triangle, Line Uniform, and Line Triangle) to analyze the specific external elevator well. By examining these analytical modes, this research aims to provide valuable insights and serve as a significant reference for future engineering projects involving nonlinear pushover analysis of external elevators in existing RC buildings. The findings are expected to enrich the analytical approach to nonlinear pushover analysis, contributing to a beneficial understanding and improved seismic performance of external elevator structures.
Optimization Study of Space Utilization Rate of Lake Sheep Based on Spatial Planning Model
Authors: Zhao Mengqian , Zhang Tianci , Wu Wei
Published: August 2024

The article explores the issue of space utilization on a lake sheep farm and draws a series of conclusions through analysis and modeling. First, based on the allocation ratio of breeding rams to foundation ewes and the number of existing sheep pens, the number of breeding rams was determined to be 8, and the number of foundation ewes ranged from 386 to 392. Considering the limitation of 112 existing sheep pens, the enumeration method was used to solve the range of annualized number of sheep to be slaughtered to ensure not less than 1500 sheep, and the gap of the existing standard number of pens was determined to be 26 sheep. Next, on the basis of further optimizing the spatial planning model of lake sheep reproduction, the specific production plans of 112 standard sheep pens were enumerated, and the maximum value of the annualized number of sheep was determined. Finally, for the uncertainty factors of the time nodes such as the pregnancy period, a dynamic planning model was established, and the number of sheep pens allocated to the farm was calculated to be 115 with the objective of minimizing the expected loss. On this basis, the article proposed an optimal production planning scheme for space utilization in lake sheep farms to cope with various uncertainties in the breeding process, such as conception time, number of lambs, survival rate, etc., which provided specific guidance and reference for improving the turnover rate.
Study on Optical Efficiency of Heliostats Based on Different Models
Authors: Li Ziming , Ye Qingxin , Su Huijun
Published: August 2024

A tower-type solar power station comprised of heliostats and a collector stands as a pivotal solution for harnessing solar energy and transforming it into thermal energy, facilitating clean energy production. In the preliminary stages of constructing tower solar thermal power stations, optimizing the layout of heliostat fields emerges as a critical concern. This paper delves into the investigation of various parameters such as annual average optical efficiency, annual average thermal output, and annual average thermal output per unit mirror area of the heliostat field. Through the establishment of a mirror coordinate system, cosine efficiency model, atmospheric attenuation model, and truncation efficiency model based on predefined heliostat dimensions and positions, we meticulously calculate shadow shading efficiency, cosine efficiency, atmospheric attenuation efficiency, and truncation efficiency of the heliostat field. These analyses contribute significantly to the design and optimization of heliostat fields, enhancing the overall performance and efficiency of tower-type solar power stations.
The Enlightenment of the American Subprime Mortgage Crisis to the Development of China's Real Estate Industry
Authors: Peng Yue
Published: August 2024

This paper examines the evolution of China's present real estate market and the illumination of the American subprime mortgage crisis through an analysis of the latter. The article explains and analyzes the causes of the subprime crisis, and then discusses its impact on the American real estate industry at that time. Subsequently, through the analysis of the supply and demand of the Chinese real estate industry, it is believed that China's real estate sector has three related points to the American real estate industry during the subprime crisis. Firstly, the excessive confidence in the US market and the large population of some cities in China. Secondly, the financial derivatives and the management of them in the US market and the Chinese market. Thirdly, the excessive issuance of personal credit in the US market and the loose regulation of property companies in the Chinese market. At the end of the paper, based on the experience learned from the US subprime mortgage crisis and taking into account the position of the Chinese real estate market today, preventive measures and countermeasures are put forward to provide a reference for future risk prevention.
Exploring the Inheritance of Traditional Chinese Folk Culture under the Background of Integrated Era Media -- Taking Northeast Yanggeas an Example
Authors: Jiang Fuhang , Zou Deyu
Published: August 2024

Northeast Yanggehas a strong cultural and historical background, and is one of the outstanding representatives of Northeast folk art. Based on an overview of the development situation and practical problems of Northeast Yangko, combined with the background of new era folk culture, this article aims to promote the comprehensive development and inheritance of Northeast Yanggefrom three perspectives: strengthening the training of inheritors of Northeast Yangko, seizing the advantages of integrated media platforms to promote its popularization, and promoting the comprehensive development of Northeast Yanggein the new era. Exploring the inheritance and development of Northeast Yanggeart, in order to better protect this intangible cultural heritage.
Research on traffic light setting based on single objective nonlinear programming
Authors: Chen Yi , Liao Yilong , Wu Linjing , Liu Yuanbo
Published: August 2024

We establish a time-scheduling optimization model of single-objective nonlinear programming with the goal of minimizing parking length, and obtain the green flashing time which should be arranged and allocated theoretically. At the same time, field research and data recording are carried out in combination with the T-shaped traffic lights and intersections in Nanchong city. By studying the indefinite period timing strategy, a mathematical model is established from the perspective of mapping, and all the combinations of driving routes (the one-to-one combination of the main road and the compatible routes that can pass at the same time) are analyzed, the theoretical allocation time of cycle is solved and predicted, and the rationality of the model is verified according to the vehicle passing rate before and after optimization.
Research on the Influencing Factors of Depression and Anxiety Based on Anxiety dataset
Authors: Kong Yijie
Published: August 2024

This study delves into the complex relationship between depression and anxiety, aiming to elucidate their multifaceted nature and impact. Leveraging a robust dataset, it is conducted a comprehensive analysis to assess the significance of various factors associated with these mental health disorders. Through statistical techniques such as exploratory data analysis (EDA) and predictive modeling such as decision tree model, this paper explored predictors and underlying dynamics. The findings highlighted the importance of demographic, lifestyle, and psychological factors in understanding depression and anxiety. Notably, all variables examined showed significant deviations, emphasizing their relevance. Furthermore, the analysis revealed intricate interactions among factors, emphasizing the nuanced nature of these conditions. This study provides insights for healthcare policies and future research, stressing the need to consider less prominent factors and socio-cultural contexts. By comprehensively understanding the interplay of factors, the author aims to inform more effective interventions and policies in mental health.
Unveiling And Addressing the Causes of Educational Inequalities in China
Authors: Liu Tommy
Published: August 2024

This paper delves into the prevalence of educational inequality in China, conducting an in-depth analysis of existing academic articles, government reports, and policy documents. It explores the intricate mechanisms that underlie the disparities in education, particularly how geographic, socio-economic, and structural factors contribute to these gaps across regions and demographics. The study pays particular attention to the impact of family background, socio-economic status, and geographical location, identifying these as the main drivers of educational inequality in China. Through a rigorous and systematic review of the literature, the study reveals the nuanced and complex interplay of history, policy, and socio-economic dynamics on educational outcomes. The findings highlight the significant disparities, especially between urban and rural areas, where the hukou system and unequal access to educational resources exacerbate the issue. This comprehensive literature analysis not only sheds light on the complex landscape of educational inequality in China but also informs the discussion on potential policy interventions that can be implemented to promote a more equitable educational environment. The study's insights are crucial for policymakers, educators, and researchers alike in their efforts to address this pressing issue and ensure that all children in China have access to quality education, regardless of their background or location.
Problems and Solutions of China's Foreign Exchange Market
Authors: Luo Hongyi
Published: August 2024

As the largest foreign exchange reserve country in the world, the stability of China's foreign exchange market has an important impact on domestic economic development and global financial market. This study focuses on the main problems in China's foreign exchange market, including the volatility of foreign exchange reserves, the lack of free circulation of capital and the lack of foreign exchange risk management tools, and puts forward corresponding solutions. First, the volatility problem of foreign exchange reserves is mitigated by diversified allocation of foreign exchange types. Secondly, in view of the lack of free circulation of capital, this study proposes to gradually relax foreign exchange control and promote the opening of capital account, so as to promote the internationalization of RMB and the deepening of the capital market. In addition, improving foreign exchange derivatives and related management mechanisms is the key to solving the problem of insufficient foreign exchange risk management tools, which requires financial regulators, governments and market participants to work together to improve the risk management capabilities of enterprises and financial institutions by innovating financial products and optimizing the market environment. The implementation of this study will help to enhance the stability and international competitiveness of China's foreign exchange market and provide support for the sustainable and healthy development of the economy. At the same time, these measures will also promote the opening and internationalization of the domestic financial market and attract more foreign capital inflows, thus promoting the high-quality development of the Chinese economy.
Predicting The Position of a Submarine Based on A Dynamic Model
Authors: Pang Xitong , Cui Yongtao , Feng Jianming , He Mei
Published: August 2024

In deep-sea exploration projects, submarines have become one of the recreational activities for people and also serve as the primary means of scientific investigation in the deep ocean. To monitor the submarine's position and status in real-time, we have developed dynamic and Monte Carlo random walk models to predict its location over some time. Due to the complexity of the underwater environment, search and rescue equipment is necessary to locate the submarine in case of malfunction or loss of contact. Therefore, we employ the VIKOR evaluation ranking model to assess and rank various search and rescue devices, ultimately determining that carrying equipment such as the Teledyne BV5000 sonar, infrared thermographic camera, and deep-sea camera is most suitable. Through our model, we have a significant impact on future submarine diving missions, enabling accurate prediction of the submarine's position and selection of appropriate search and rescue equipment, thereby enhancing safety in underwater exploration and scientific research.
The Impact of Sino-U. S Trade Friction on the Export of Communication Equipment Manufacturing Industry
Authors: Yin Yingjian
Published: August 2024

In the context of global economic integration, Sino-U.S. trade friction has profoundly impacted the global trade pattern. Based on the communication equipment industry trade data released by UN Comtrade, this paper empirically analyzes the impact of Sino-U.S. trade friction on China's communication equipment industry and its future trends. It was found that Sino-U.S. trade friction has significantly slowed down the growth of China's world exports of communication equipment products, and exports to the United States have shown a downward trend. The Sino-U.S. trade friction has impacted China’s telecommunications equipment industry, yet it has also presented certain opportunities. Based on the analysis, the following recommendations are proposed: China and the United States should actively engage in peaceful negotiations to jointly foster a favorable international trade environment. The telecommunications equipment industry should expedite industrial upgrading and restructuring to achieve autonomy in high-end technologies. China should accelerate the development of a digital economy to enhance its international competitiveness.
Race momentum analysis prediction based on wavelet neural networks
Authors: Hu Jiale
Published: August 2024

Tennis is a popular sport around the world and 'momentum' plays a key role in determining the outcome of matches, as discussed in this report. Our team created a momentum model for tennis matches by analysing seven factors, including points scored, serve ends, and using an activity index and an error index as state scores and demerits. The model was based on a sample of the 2023 Wimbledon 1301 Championships and showed changes in athlete form through line graphs. Through Monte Carlo simulation, the experiment found that the momentum distribution of actual match results differed significantly from the simulated random distribution, indicating that momentum had a significant effect on match results. And the change in dominance in the middle of the match is predicted by a wavelet neural network. The innovative approach using wavelet neural networks also allows for more detailed analysis of momentum changes during a race, providing a new perspective on the dynamics of athletic performance.
Application of Bayesian Methods to Epidemics
Authors: Tian Tongfei
Published: August 2024

This template explains and demonstrates how to use Bayesian methods to estimate the epidemics. And this study will use the case COVID-19 in United State as an example. In this days, infectious diseases have had a huge impact on human society since ancient times. And in this article will find out how to use the parameter of the basic reproduction number, infection rate and recovery rate of the epidemics combined with Bayesian method and SIR model to estimate a linear regression and to figure out the connection in epidemics. This study will specifically explore how parameters estimated by Bayesian methods can be used to predict the spread trend and potential impact of the disease. This includes using Bayesian methods to update model parameters to adapt to new epidemic data, and how model predictions can be used to guide public health decisions and disease control measures. Moreover, helping people to predict the spread of the disease, and assess the potential scale of the disease outbreak and formulae effective intervention measure.
The Perspective of Originality: Research on the Legal Attribute of Content Generated by Artificial Intelligence
Authors: Han Muzhen , Wu Xinyu , Zhu Zhuobin
Published: August 2024

With the strong rise of generative AI represented by ChatGPT, the issue of copyrightability of AI-generated content has become the focus of controversy in both theoretical and practical circles. The pivotal aspect in determining the legal status of AI-generated content lies in establishing a standard for originality. The current discourse within the theoretical community regarding the copyrightability of such content often revolves around the contrasting viewpoints of the "Dialectics of Negation" and the "Dialectics of Affirmation." By refining the criteria for assessing the originality of AI-generated content and evaluating their attributes through objective measures like "independent creation" and "modicum of creativity", demarcating the boundaries of creative expression, streamlining the creative process, and driving forward the expansion of the artificial intelligence industry. This endeavor is essential for enhancing legal clarity, fostering innovation, and navigating the dynamic landscape where technology and creativity intersect.
Strategic Pathways for Shanghai Ruichen Environmental Protection Technology Co., Ltd.: A SWOT Analysis Approach in the Era of Carbon Neutrality
Authors: Zhang Zhehan
Published: August 2024

Since the 18th National Congress of the Communist Party of China. The construction of ecological civilisation has become a very important part of national construction, and the goal of "two-carbon" was proposed in 2020. In this context, small and medium-sized environmental protection enterprises began to develop rapidly, the main body of the environmental protection market further expanded, and the state also issued policies to support the development of environmental protection enterprises from the market and capital. Shanghai Ruichen Environmental Protection Technology Co., Ltd. is a high-tech company in the field of energy conservation and environmental protection. The company's main business is the research and development, design, production and sales of high-efficiency energy-saving equipment, including high-efficiency energy-saving centrifugal fans, high-efficiency energy-saving centrifugal pumps and other energy-saving products. It is a large environmental protection and energy-saving enterprise. This paper aims to analyze the corresponding solutions that Ruichen Environmental Protection Company should take in today's market environment through a SWOT analysis of the internal advantages, disadvantages, external opportunities and risks.
The Text Classification Method Based on BiLSTM and Multi-Scale CNN
Authors: He Bo , Yang Yongfen , Wang Lei , Zhou Jingxuan
Published: August 2024

Text classification is an important fundamental technique for efficiently managing the huge amount of textual information on the Internet. In the past few years, due to the unprecedented success of deep learning research, the task of text categorization has gradually become the center of gravity in the field of natural language processing, and the algorithms applied to it have also transitioned over time from traditional machine learning methods to neural network models based on deep learning, as well as the emerging attention mechanisms and pre-trained language models with good results. This paper outlines the traditional machine learning methods and deep learning methods involved in text classification, comprehensively analyzes the research progress and achievements of deep learning models for text classification, and compares the advantages and disadvantages of each. Then the evaluation metrics used for text classification and commonly used labeled datasets are introduced. Finally, the challenges faced by the text classification task and the difficulties to be further researched are summarized and outlooked to provide reference and support for future researchers in this field.
A Comparative Study of the Effectiveness of Machine Learning for Predicting Stock Prices in the New Energy Vehicle Industry
Authors: Yang Binrong
Published: August 2024

This paper investigates the effectiveness of machine learning models in predicting stock prices in the New Energy Vehicle (NEV) industry - a key part of the current transformation of the global transport system and electricity supply. This study applies five models, ARIMA, LSTM, GRU, CNN and TCN using the sample 20 NEV companies, to predict stock prices. And further use the risk metrics such as MSE, RMSE, R2, MAE and MAPE to measure the prediction performance. The results shows that the CNN model stands out for its superior accuracy in predicting stock movements, highlighting its potential as a valuable tool for investors and financial analysts in the fast-growing NEV market. The findings highlight the importance of utilizing advanced neural network algorithms to improve investment strategies and risk assessment in the face of market volatility and economic fluctuations.
A Review of Research on the Effects of Employee Workplace Micro-breaks on Job Performance
Authors: Zhang Guoyu
Published: August 2024

Although long hours and high work intensity ensure work progress to a certain extent, they tend to cause physical and mental fatigue, which ultimately affects work performance. Studies have shown that appropriate breaks between work can restore physical fitness, concentration and other bodily functions to maintain good working conditions. However, there are many organizational managers who believe that such micro-break activities are counterproductive, slowing down work progress and ultimately affecting productivity. Based on these two different attitudes, this paper decided to collect and summarize the recent literature on workplace micro-breaks and employee performance to investigate whether and through which pathways micro-breaks can improve employee performance.