bluuebunny/crossref_metadata_2025 huggingface dataset:
The Research on the Investment Value of Healthcare Industry Based on Financial Analysis and Valuation Method: Case of CVS Health
Authors: Qin Junxi
Published: August 2024

This paper analyses the value of CVS Health's investment in the healthcare industry, comparing it to industry giants such as UnitedHealth Group, Cigna and Humana through financial analysis and valuation methods. The study reveals that CVS Health has adapted to market changes through expansion strategies and efficiency gains in the face of the challenges of an aging population and the COVID-19 pandemic. The article analyses CVS Health's net profit margins, gearing ratios, and operating income growth rates in detail, suggesting that it may be undervalued in the market and has high investment potential. The findings suggest that CVS Health has enhanced its market competitiveness and financial stability through effective cost management and market expansion strategies, which are important references for the healthy development of the healthcare industry and service model innovation.
Evaluating the Economic Dynamics of Immigration: A Comparative Analysis of Unemployment Rates and Policy Impacts in the USA, Canada, and Australia
Authors: Wang Ziqi
Published: August 2024

As globalization intensifies, immigration becomes a significant factor influencing economies globally. This paper presents a comparative analysis of immigration policies and their economic impacts in the USA, Canada, and Australia. Utilizing a blend of literature review and economic data analysis, the research investigates the relationship between immigration policies and key economic indicators such as unemployment rates and GDP growth. The study explores how diverse immigration policies in these countries, shaped by historical, cultural, and political contexts, influence their economic landscapes. It assesses the balance between welcoming immigrants and sustaining economic growth, highlighting the role of human capital in economic integration. The paper seeks to offer insights into how immigration policies can be optimized to promote economic prosperity while managing unemployment levels effectively. This concise analysis contributes to the ongoing dialogue on immigration policy, providing evidence-based recommendations for crafting more effective and harmonious immigration strategies in the face of global challenges.
Prediction of Diabetes Mellitus based on Logistic Regression and Random Forest
Authors: Qian Dingkang
Published: August 2024

Diabetes mellitus is a metabolic disease characterized by hyperglycemia caused by impaired insulin secretion and various degrees of peripheral insulin resistance. However, the primary mechanism is still unclear. The study analyzed medical data and used machine learning to build two predictive models to predict a patient's risk of heart disease. The dataset consisted of 3,000 clinical cases and 9 independent variables, such as high blood pressure, heart disease, and glycosylated hemoglobin. The target variable was whether the patient was diagnosed with diabetes mellitus. During this study, the logistic regression model initially had an accuracy of about 96% for the training set and then the fitted model was tested and the accuracy remained good at 94%. The prediction results of this logistic regression model were superior. In order to better predict the occurrence of heart disease, a second set of prediction models was developed in this study using Random Forest, with an accuracy of 100% in the training set, and then the fitted model was tested and the accuracy remained excellent at 96%. The two models used in this study provide a foundation for future testing of diabetes mellitus predictor variables and the discovery of even better predictive models.
Fraud Detection in Credit Risk Assessment Using Supervised Learning Algorithms
Authors: Xu Tianyi
Published: August 2024

Our study systematically evaluates the performance of various supervised learning algorithms in credit risk assessment and fraud detection, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Gradient Boosting Tree, and Neural Network. The results show that in credit risk assessment, the Gradient Boosting Tree performed best with an accuracy of 90.5% and a ROC-AUC of 0.84, followed by Random Forest and Neural Network, with accuracies of 89.2% and 88.8%, and ROC-AUCs of 0.82 and 0.81, respectively. In the fraud detection task, the Neural Network performed best with an accuracy of 97.5% and a ROC-AUC of 0.88, while Gradient Boosting Tree and Random Forest achieved accuracies of 97.1% and 96.3%, and ROC-AUCs of 0.87 and 0.85, respectively. Feature importance analysis indicates that repayment history, credit limit, bill amount, and repayment amount are key features in credit risk assessment, while transaction amount, transaction time, and location are crucial for fraud detection. Data preprocessing and feature engineering played critical roles in enhancing model performance. Further optimization of model hyperparameters and addressing data imbalance issues will help improve model performance. In conclusion, ensemble learning methods and Neural Networks exhibit significant advantages in credit risk assessment and fraud detection. By employing scientific data preprocessing and feature engineering, combined with advanced machine learning algorithms, financial institutions can significantly enhance their risk management effectiveness.
Assessing the Impact of Autonomous Media Organizations on Contemporary Society
Authors: Ba Yimeng
Published: August 2024

Nowadays, with the development of the internet and technology, alternative news media emerged in the media domain. While mainstream media is always being criticized, alternative news media showcases its importance because alternative news media can provide different perspectives and platforms for information. It analyzes the features, audience and differences with mainstream news media to reach the aim. The key aim of this essay is to study alternative news media and its contribution to diversifying news content. Incorporating the literature review method, it explores the alternative news media's function from many aspects. Specifically, includes positive and negative influence, influence on political beliefs and affiliations, the echo chamber effect and polarization caused by alternative news media. from these analyses, it can be concluded that alternative news media have a positive influence in providing diversity news content, it also enhances public conversation and fact-finding. Therefore, although with some negative influence, it is evident that alternative news media can promote news diversity, satisfy the needs of different audiences and bridge the gap caused by mainstream media.
Few-shot Font Generation based on SAE and Diffusion Model
Authors: Shi Yizuo , Yang Wenxia , Yuan Mengxu , Yi Zeping
Published: August 2024

Generating Chinese characters via few-shot font generation is an intriguing and important challenge in recent years, primarily due to the intricate and unique nature of Chinese fonts. However, the conventional GAN-based model for font generation has encountered issues such as unpredictable training and inaccurate generation. Simultaneously, in the realm of image generation, diffusion models have demonstrated remarkable success, even garnering application in AI painting commercials. Some studies have endeavored to integrate diffusion models into Few-shot Font Generation (FFG). In this paper, we present a straightforward, few-shot font generation framework utilizing a conditional diffusion model. We generate conditional embedding tokens using three encoders, which extract essential character information such as content and style. By combining these conditions into the diffusion process, we can effectively model these three pieces of information. Our model possesses three key features: i) Our model attains disentanglement of all encoders and the diffusion model. The content encoder focuses solely on extracting the content or the relative position of strokes, the style-coding provides only style features, and the diffusion model is limited to generating the target image without obscuring any content or style information. This enhances the model’s interpretability and makes the addition of new functionalities a simpler process. ii) For different fonts, our model requires fewer training steps due to the use of pre-training. We only train the style-coding on a small scale, bypassing the need for extensive training of the large-scale diffusion model. iii) Our model achieves two types of "Few-shot" training. The first type involves the same style but different characters, requiring only a few characters for training. The second type pertains to different styles, needing only a few style fonts for training. Experimental results reveal that our model outperforms previous few-font generation models in terms of quality, generation speed, and the scale of well-trained training datasets.
Research on the Development Trend of New Energy Vehicles in China Based on Pearson Correlation Analysis and Polynomial Fitting
Authors: Wang Jiahao , Wei Gonghao , He Xun
Published: August 2024

The purpose of this paper is to analyse multiple factors that influence the development of new energy vehicles in China and to predict the future development trend. First, this paper selects 12 representative factors, including total vehicle sales, new energy vehicle penetration, pollutant emissions, economic indicators, etc., and uses Pearson correlation analysis to verify their relationship with new energy vehicle sales. The results show that nine of these indicators are significantly correlated with new energy vehicle sales, which provides a basis for subsequent modelling. Next, establishes a quadratic polynomial model using the polynomial function fitting method to predict the sales volume of new energy electric vehicles in China in the next 10 years. The model evaluation shows a good fit, with little difference between the predicted and actual values and a coefficient of determination close to 1. Finally, combining the model prediction results and the analysis of the influencing factors, this paper describe the trend of the future development of China's new-energy electric vehicle industry, including the influences of governmental support, technological advancement, environmental protection awareness, market competition, and infrastructure construction, and look ahead to the development of new-energy automobile market.
Impact of the Russia-Ukraine Conflict on Russian House Prices
Authors: Yang Zhiyu
Published: August 2024

With the gradual improvement of Russia's economic form and the gradual tension in geopolitical relations, the contradictions between Russia and Ukraine have gradually intensified and evolved into conflicts. However, in the face of growing Russian-Ukrainian relations, increasing domestic tensions and increasing international tensions, the Russian economy and the housing prices are still growing steadily, so understanding domestic employment, average income and Russia's international cooperation is crucial for both domestic and foreign investors. As a consequence, this paper collects data on housing price, employment rate and per capita income in Russia, and based on the changes of housing price, employment rate and salary level in Russia under the war environment, Through the analysis of macroeconomic, capital flow, international cooperation and other aspects of Russia's future housing price changes to analyze and forecast, and put forward some regulation suggestions for the government and appropriate foreign investors to put forward some investment suggestions.
Can the Founding of Free Trade Zones Lead to Financial Employment Boom? --Based on Multi-period Double-difference model
Authors: Chen Yining , Wu Sunishan
Published: August 2024

The establishment of Free Trade Zones (FTZs) aims to enhance trade liberalization and financial openness, fostering economic growth and financial employment development. Inspired by panel data from 287 prefecture-level cities in China spanning from 2006 to 2019, the study explores the repercussions of FTZs on financial employment using a multi-period double-difference model. The research finds that FTZs have promoted the prosperity of financial employment in local and surrounding areas, the conclusion is upheld by a battery of robustness tests. Mechanism tests show that FTZs significantly promote financial employment prosperity from two perspectives: promoting overall industrial structure upgrading and enhancing the level of marketization. Heterogeneity analysis further reveals that FTZs have a more significant positive repercussions on the prosperity of financial employment in the middle and western regions, indicating that FTZ policies help balance regional development. This paper provides important references for policymakers to implement market reforms, adjust regional development strategies, and promote inter-sectoral cooperation, thereby facilitating the positive impact of free trade zone policies on financial employment.
disentanglement using pre-trained features
Authors: Liu Shugang , Zhao Yuhang
Published: August 2024

This article proposes using pre trained features to address the challenge of detecting depression through speech. Traditional raw audio has shown low accuracy and insufficient generalization performance in depression detection. We use pre trained models that have been developed to extract features, which can be used to extract general feature representations from speech data. During the pre training process, we further decouple the speakers, introducing prior information and providing a better starting point for training downstream models. The results indicate that we achieved the best performance when using the extracted features from the CONTANTVEC pre- trained model with speaker decoupling improvement.
Research on Application of Big Data on Financial Decision-Making
Authors: Chen Ao
Published: August 2024

In the context of the big data era, the data basis that enterprises rely on for financial decision-making includes not only financial data, but also non-financial data. Traditional financial decision-making methods cannot effectively process and analyze non-financial data. Therefore, it is necessary to big data technology is introduced into the financial decision-making process of enterprises to organize and analyze relevant data of enterprises in a timely and effective manner, providing corporate decision-makers with more scientific and effective decision-making basis. This article mainly uses two methods, literature research method and case analysis method, to conduct an in-depth study of the application process of big data technology in corporate financial decision-making. This article first explains the necessity of introducing big data technology into the corporate financial decision-making process. Then, the specific application path of big data technology in the enterprise's financial decision-making process is explained through the feasibility analysis, application principles and specific function realization of big data technology in the enterprise's financial decision-making process. This study found that the application of big data technology in the corporate financial decision-making process is mainly reflected in four aspects: first, data collection; second, data storage; third, financial analysis; fourth, decision support. Introduce big data technology into the corporate financial decision-making process, broaden the channels for data collection, enrich financial analysis and decision-making methods, and provide strong support for companies to formulate more accurate and effective financial decision-making plans.
A Study on Short-Term Traffic Flow Prediction Based on Random Forest Regression
Authors: Wu Jiong
Published: August 2024

Accurate and rapid prediction of short-term traffic flow is of great significance for improving the efficiency of traffic management as well as alleviating the pressure of urban traffic. In this paper, the traffic flow data are first analysed in depth in terms of spatial and temporal patterns. In the time dimension, through the daily traffic flow statistics line graphs of the four junctions, it is found that the traffic flow of the No.1 junction shows an obvious trend of increasing year by year, while the traffic flow of the No.2, No.3, and No.4 junctions grows more gently without any significant incremental pattern. In addition, the weekly traffic flow line graphs reveal a clear pattern: the traffic flow from Monday to Friday is relatively high, while the traffic flow on Saturdays and Sundays is relatively low. In the spatial dimension, the violin distribution plots of the traffic flow in each junction reveal that the traffic flow in each junction shows a skewed distribution. Therefore, this paper employs Quantile Transformer for data preprocessing to eliminate this skewness. Next, this paper explores the correlation of traffic flow between different junctions using Pearson's correlation coefficient, and the results show that the traffic flow between junctions is extremely correlated. In order to predict the traffic flow, this paper tries three models, Lasso, Ridge and Random Forest Regressor, respectively, and searches for the optimal hyperparameters of the above models through cross validation grid search. Finally, four evaluation metrics, namely, Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination R2, were used to comprehensively evaluate the prediction accuracy of the models. Eventually, it returned that the Random Forest Regressor model performed the best in terms of prediction results, with an MAE of 17.315, an MSE of 4.161, an RMSE of 2.458, and an R2 of 0.96.
Quantitative Research on Thermal Performance of Ready-To-Wear Apparel Based on Analytic Hierarchy Process Model
Authors: Li Zhuoye , Sun He
Published: August 2024

This work primarily focuses on quantifying the thermal insulation ability of fibers through modeling. The study establishes a comprehensive insulation evaluation index (CWI) with six variables derived from three dimensions: thermal conductivity, air content and structural properties, humidity, and permeability. The normalized weights of each variable are calculated using the Analytic Hierarchy Process model. The consistency ratio (CR) of pairwise comparison matrices is significantly less than 1, indicating good consistency in the pairwise comparison model. Additionally, the relationship between the thermal insulation performance of polyester fibers and their average length and diameter is considered. By analyzing how the parameters in the model are influenced by fiber length and diameter, adjustments are made to the model parameters. The conclusion is drawn that fibers with shorter average length and smaller diameter tend to have stronger thermal insulation performance. Finally, the microstructure of cotton and down feathers is studied to further refine the model parameters, and it is determined that down feathers exhibit the best thermal insulation performance, followed by polyester and cotton, with no significant difference between the latter two. The aim of this paper is to address the gap in previous research regarding the quantitative analysis of fiber thermal insulation capabilities. By accurately assessing the thermal insulation effects of fibers filled within garments, this study provides a scientific basis for clothing design and material selection.
A Comparative Study of House Price Prediction Using Linear Regression and Random Forest Models
Authors: Fu Yahan
Published: August 2024

The accurate prediction of house prices is crucial for stakeholders in the real estate sector, financial institutions, and urban planners. It not only informs investment decisions but also aids in policy formulation and market analysis. This research paper delves into the comparison of two prominent predictive analytics techniques-Linear Regression and Random Forest—to ascertain their effectiveness in forecasting house prices. Using a Kaggle dataset, this study analyzes key house price predictors such as building classification, living area size, construction year, and land area. The analysis shows Random Forest outperforms Linear Regression in accuracy, emphasizing the importance of building classification and living area in price prediction. Detailed visualizations, like feature importance graphs and scatter plots, offer clear insights into model performance. This research contributes significantly to real estate predictive analytics, offering insights to guide investment strategies and policy-making. It also opens avenues for exploring alternative machine learning approaches and socio-economic factors for a more comprehensive understanding of housing market dynamics.
A study on the effects of tree spatial distribution on the accuracy of canopy clumping index retrieved by remote sensing
Authors: Zhang Teng , Geng Jun , Xu Jingsong , Wang Yuping , Hong Hong
Published: August 2024

Clumping Index (CI) is a key parameter for estimating the leaf area index (LAI) in forest canopy and distinguishing between "sunlit foliage" and "shaded foliage" in forest canopy. It is of great significance for studying total primary productivity and evapotranspiration of forests. The method of canopy CI retrieved by multi-angle satellite data has been widely used. However, it is unclear whether the method of using the normalized difference between hotspot and darkspot (NDHD) to retrieve CI by remote sensing is suitable for forests with different tree spatial distributions (TSDs). In this study, the accuracy of canopy CI retrieved by remote sensing under different TSDs was investigated by using 1 km × 1 km within the Saihanba Forest Farm as the study area, and using the DART model to calculate the multi-angle gap fractions and reflectance under different TSDs. The results showed that: (1) The CI results of retrieval of the plots with clustered TSDs were generally larger than the CI results of validation, while the CI results of retrieval of the plots with regular TSDs were generally smaller than the CI results of validation. (2) The accuracies of the CI results of retrieval were small for plots with strong clustered TSDs or strong regular TSDs. The results of this study play an important role in understanding the influence of TSDs in canopy CI retrieved by remote sensing, as well as in assessing forest productivity and studying carbon cycling in terrestrial ecosystems.
Momentum Prediction Model Combining Markov Chain And MLP
Authors: Xing Anqi , Xu Jiaye , Chen Haoxin
Published: August 2024

In sports competitions, it is common for a certain player to dominate and score consecutively, which can be described as the effect of "momentum". To portray the abstract momentum to describe the change of athletes' state during the competition, and thus provide technical support for the subsequent training of athletes, a volume prediction model combining Markov chain and MLP is proposed. Firstly, the resultant variables are calculated by the entropy value method for momentum scoring, after that, technical indicators are considered, Markov features are calculated by the Markov chain model and added into the MLP model for momentum prediction, and finally, the important factors determining the momentum are analyzed by SHAP method. The experimental results show that the accuracy of predicting athletes' state reaches 98%, which can provide credible race reviews and guidance suggestions.
Problems and Countermeasures in Agricultural Futures Market
Authors: Hu Lingyue
Published: August 2024

Abstract. Recently, agricultural futures have grown rapidly, and is playing an integral role in the global economy, making an important contribution to global food security. However, agricultural futures also face several challenges and risks in many aspects. Firstly, agricultural futures prices are unstable and are largely influenced by climatic conditions, supply and demand, respectively; Secondly, the structure of agricultural futures varieties is unreasonable, the current futures varieties are mostly focused on grain and cash crops, and livestock and aquatic products are obviously neglected; Further, its market trading body is missing, and farmers find it difficult to access the futures market due to a lack of knowledge about the market; Finally, current agricultural products are still subject to regulatory risks. In response to these problems, the author suggests a series of measures to stabilize futures prices, optimize the structure of varieties, cultivate trading entities and strengthen market regulation. The paper may shed lights for both authorities and investors.
The Research On The Ownership Of Copyright Of AI-generated Content
Authors: Pan Zhe , Wang Shutong , Zhang Chenglin
Published: August 2024

Generative Artificial Intelligence (GAI) has significantly enriched society by producing a wide range of high-quality cultural products, offering aesthetic experiences comparable to traditional creations. However, the issue of ownership in legal practice remains ambiguous due to the involvement of multiple creators in the production process. To address this, the copyright granting process can be divided into initial authorization and subsequent rights transfer to delineate the rights of various parties involved. Current theories suggest that the commissioned works model effectively safeguards the interests of all parties by granting initial authorization to the hardware owner of the artificial intelligence while protecting the rights of the AI user through contracts. Moreover, lawmakers should enhance the current copyright law system, emphasize the importance of human involvement in generative artificial intelligence outputs, and establish collaborative mechanisms at the societal level to balance the interests of all parties involved. This approach will provide clarity and fairness in the ownership and distribution of rights associated with AI-generated products.
Impact of Macro Indicators on Investment in Manufacturing Companies
Authors: Ji Xianzhe , Luo Liuyi
Published: August 2024

The purpose of this study is to examine the impact of a variety of macroeconomic indicators on the investment decisions of manufacturing firms. Key macroeconomic indicators such as GDP growth rate, interest rate, consumer price index (CPI), purchasing managers' index (PMI), employment rate, industrial production index (PPI), money supply (M2), and imports are analyzed to assess their impacts on manufacturing firms' capital expenditures. A multiple linear regression model is used to empirically analyze the data from Chinese manufacturing firms. The results of the study show that the Consumer Price Index (CPI) and the Industrial Production Index (PPI) have a significant negative effect on the capital expenditures of manufacturing firms, while the increase in the money supply (M2) has a positive effect on capital expenditures. Changes in the GDP growth rate and the Purchasing Managers' Index (PMI) also affect the investment decisions of manufacturing firms. In particular, an increase in foreign trade imports promotes capital expenditures by export-oriented manufacturing firms. This paper analyzes in-depth the impact mechanism of macroeconomic indicators on the investment of manufacturing companies to provide a reference for the investment decisions of enterprises in different economic environments. At the same time, this study also provides an important basis for policy makers to help formulate and adjust economic policies to support the development of the manufacturing industry and stable economic growth. The results of the study have important theoretical and practical significance for understanding the impact of macroeconomic environment on the investment behavior of enterprises.
The Situation and Prediction of Air PM2.5 Pollution in Shanghai
Authors: Gu Jingheng
Published: August 2024

In the field of environmental science, fine particulate matter (PM2.5) has attracted much attention due to its significant impact on public health. These particles with a diameter of less than or equal to 2.5 microns can carry various harmful substances, penetrate the defense line of the respiratory tract into the deep lungs, and even enter the circulatory system, thereby increasing the risk of cardiovascular and cerebrovascular diseases, respiratory diseases, and cancer. With the acceleration of industrialization and the increase of urban activities, the pollution problem of PM2.5 is becoming increasingly severe, bringing huge challenges to public health management and environmental protection. Therefore, in-depth research on the sources, components, distribution characteristics, and human health impacts of PM2.5 not only has important value in environmental science research, but also has urgent practical significance for formulating effective prevention and control strategies and improving people's quality of life. The article will mainly discuss about these things that mentioned above.
The Problems and Countermeasures in China's A-Share Market
Authors: Zhou Ziyi
Published: August 2024

The Chinese domestic market, A-share, an integral part of the national economic boom is confronted with obstacles, which may threaten the long-term stability of the market. This paper explores three major hurdles: volatile market cycles, lack of transparency and abnormal retail investors’ strategy. These determinants build the unstable atmosphere framework for investors and businesspeople. The solutions are going to be a multi-prong approach. Stability through long-term focus, market efficiency, internationalization, and diversity are key to managing market volatility. Meanwhile, listing requirements, corporate governance, disclosure standards, regulatory framework, market access, reporting practices, audits, and oversight enhance market transparency. Thirdly, Investor education, information access, and independent research optimize retail investors' strategies. The market of A-share in China can continue growing as the main distinguished platform where all the parties enjoy a winning position. This essay focuses on the problems and challenges of the A-share market, explores the effective countermeasures, and contributes valuable references for investors and policymakers. Therefore, it provides not only for the healthy growth of the Chinese capital market but also has immense global implications for economic and financial stability.
The Impact of Urban Integrated Transportation Planning Policy on Economic Development Based on Big Data
Authors: Zhu Keying
Published: August 2024

With the accelerating process of urbanization, the scale of Chinese cities is larger and larger, the population capacity is more and more, the process of urban economic development is more and more rapid, and the comprehensive transportation system of cities is further expanded. Based on the availability of big data, this paper takes Chengdu City, Sichuan Province, as an example, to explore the impact of rail transit on promoting urban economic development under urban comprehensive transportation. Through the comparison of the land supply area and housing price data of the regions along the rail transit station and the non-regions along the rail transit station, it is found that the developed urban transportation can enhance the open development of the urban economy, optimize the investment environment and provide the investment market. Convenient and superior transportation conditions can accelerate the economic cycle within the city, meet people's requirements for high quality living standards, and promote the establishment and development of a harmonious society.
The Influential Factors on Brand Image
Authors: Chai Guanyu
Published: August 2024

This report examines Beijing Competitor Sports Science Technology Joint Stock Co., Ltd. (CPT), a Chinese domestic company, which is conducting early research on sports nutrition food. Despite having a significant customer base in China, the CPT excessively promotes products because of the consistent decline in gross margin from 2019 to 2021. This article aims to examine the reasons behind the substantial influence of brand image on the sales volume and profitability of a product. One significant factor is the influence of brand trust and product quality, which could potentially lead to customer purchasing behavior and their perception on brand image. The article argues that companies should give priority to product quality rather than excessive promotion. It further suggests that maintaining and improving consumer loyalty to strengthen the brand image will lead to increased sales. The paper also asserts that the quality of the product influences changes in purchase intentions. It suggests that firms might increase sales by understanding and fulfilling consumers' buying intentions.
Analysis of the Current Situation, Problems and Countermeasures of China's Foreign Exchange Market
Authors: Qin Yifei
Published: August 2024

The foreign exchange market is critical to national economies, trade, and financial security. The foreign exchange market provides trade liquidity and means of payment and is an important channel for capital flows and risk management, as well as a risk-transfer channel in the global economic system and has an important impact on central bank monetary policy. As the world's second-largest economy, China has attracted large amounts of overseas investment, bringing new opportunities to the global market. Against the backdrop of the new crown epidemic and geopolitics, China's economy has maintained a good momentum of development, providing impetus for world economic growth and boosting confidence in the global market. Based on the current situation of China's foreign exchange market in recent years, this paper analyses and concludes that there are currently cross-border capital flow restrictions, capital flow pressure, a single trading body, imperfect market supervision and other problems, and finally puts forward a feasible future development path and reform countermeasures for China's foreign exchange market in view of the above problems, which is of great significance for the healthy development of China's foreign exchange market.
E-Commerce Impact on Traditional Retail: Exploring the Transition to New Retail Approaches
Authors: Zhou Ziyan
Published: August 2024

The rise of e-commerce has greatly impacted the brick-and-mortar retail industry, and with the empowerment of digital technology, the new retail model has become the development trend of most enterprises. This paper analyzes the transformation of brick-and-mortar retail industry by using SWOT model and provides suggestions for the transformation of brick-and-mortar retail industry. Let the enterprise according to their own situation to develop a feasible transformation program, the use of policy support, in line with the consumer psychology of the implementation of the transformation, the flexible use of big data and other technologies to better position the changes in market demand, to achieve inventory stability, the reasonable ratio of import and export, drawing on the experience of e-commerce online sales, to create a more perfect service system and sound supply chain management, improve logistics technology, to provide consumers with a better shopping experience. In addition, enterprises in the transition process need to pay attention to the integration of online and offline to play the advantages of the new retail model, away from the homogenization of the competition, to achieve the purpose of enhancing competitiveness, so that enterprises are truly invincible. The rise of e-commerce has brought opportunities and challenges to the traditional retail industry, and the transformation of physical retail to new retail is the inevitable choice of the times.
Research on the Impact of Online Comments on Consumers' Willingness to Purchase Domestic Brands
Authors: Yin Mengran
Published: August 2024

With the rapid development of the Internet, the scale of online shopping has gradually increased, and with the expansion of the online shopping market and the impact of the epidemic, consumers' main reference information in online shopping has gradually turned to online reviews. Based on this background, this paper takes the SOR model as the basis, divides the dimensions of online reviews and uses them as independent variables, takes domestic brand identity as the mediator, and consumers' purchase intention as the result variable, and constructs the influence model of online reviews on consumers' purchase decisions of domestic brands. The results show that consumers' purchase intention of domestic brands is positively affected by the quality, quantity and professionalism of reviews, and domestic brand identity plays a partial role as an intermediary. However, the comment potency did not play a significant role.
Please Center Research on the Investment Value of Coca-Cola Based on SWOT, Financial, and Valuation Methods
Authors: Wang Yongji
Published: August 2024

Based on the financial report data, stock price changes, and stock data of the Coca-Cola Company over the years, this paper explores the operational capability, corporate strategy, and stock investment value of the Coca-Cola Company. In this paper, SWOT analysis, financial analysis, value analysis, and other methods are used. It is found that Coca-Cola has unique advantages. These include broad market recognition—more than 94 percent of the world's population recognizes the brand and more than 40 percent of the soft drinks market—as well as sophisticated marketing strategies and significant investment. More than 10 percent of annual revenue is spent on marketing efforts. But it also faces the challenge of the wave of health consciousness and diversification in the soft drinks industry in recent years. At the same time, its financial data show that its revenue capacity is growing steadily, and the stock has long-term investment value. In this paper, management and finance theories are used to supplement the lack of systematic research on the development of Coca-Cola and provide a reference for stock investors.
Research of Grassland Ecosystems and Communities Based on A Cyclical Model
Authors: Chen Jiaxu , Yuan Shaobo
Published: August 2024

In grassland ecosystems, different plants rely on and interact with each other, as well as with their geographic space, so that an inseparable and interdependent plant community is formed. To figure out the evolution of plant communities and the interactions between different plant species under dry conditions, a cyclical plant community model is established from two aspects that vary with time. One part of the cyclical model is a model of plant environmental adaptability based on the TOPSIS method and the Radial Basis Function Neural Network Algorithm. To quantify the adaptation of plants to the environment, the Plant Life-Form Adaptation Index (PLFAI) is introduced and the growth of different species is calculated in the model. The other part of the model introduces a model of plant species diversity and ecological niche to analyze the ecological niche width and spatial distribution characteristics of different plants over time, which is based on the Simpson diversity indices and Shannon-Wiener indexes. The niche concept emphasizes the spatial and temporal changes of species and is more concerned about how environmental factors affect the niche and how species adapt and respond to these changes. Also, small influences of human activities are incorporated into the model's scope by giving samples of White Gaussian Noise. Those contributory factors are negligible but necessary. These parts summarize the plant ecological environment impact model, which can be used to study the long-term survival of plant communities. By studying and using the models above, it is easy to conclude the evolution of grassland plant communities and the interactions among plants under drought conditions.
Legal Implications and Challenges of Blockchain Technology and Smart Contracts
Authors: Szabo Jenny , Bernard Charles , Philip Laurent
Published: August 2024

Blockchain technology and smart contracts have emerged as revolutionary tools with the potential to transform various industries by providing decentralized, transparent, and secure methods for recording transactions and automating contractual agreements. However, the adoption of these technologies also raises significant legal and regulatory challenges. This paper critically examines the legal implications and challenges associated with blockchain technology and smart contracts. Through an analysis of existing legal frameworks, case studies, and regulatory approaches across different jurisdictions, this study identifies key issues such as enforceability, jurisdiction, data privacy, and security. The paper also explores potential solutions and recommendations for addressing these challenges, aiming to contribute to the development of a robust legal framework that supports the responsible use of blockchain technology and smart contracts.
Preface: 4th International Conference on Environment Science and Advanced Energy Technologies (ESAET 2024)
Authors: Kennan Eleanore , Ding Shikai
Published: August 2024

2024 4th International Conference on Environment Science and Advanced Energy Technologies (ESAET 2024) was held in Sao Paulo, Brazil during June 15-16, 2024. ESAET 2024 is a comprehensive conference focused on various aspects of advances in environmental science and management, power engineering, ecological system, sustainable development, fuels and renewable energy, and advanced energy technologies.
The aim of ESAET 2024 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities. This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration.
We sincerely hope that ESAET 2024 will be a forum for excellent discussions that will put forward new ideas and promote collaborative researches. We would like to acknowledge the distinguished keynote speakers and all of those who supported ESAET 2024. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference.
With our warmest regards,
Conference Organizing Committee
Sao Paulo, Brazil
An Analysis of Textbook “ When English Rings a Bell” for Junior High School Grade Eight
Authors: Abdul Syahid  , Rahmania Ananda  , Amanda Salsabila  , Sumarni Sumarni , Florence Angelaila Fauzi
Published: August 2024

Textbooks are an important learning tool in the education system. It becomes more important when English is learned as a foreign language like in Indonesia. As a language learned other than the mother tongue, most likely English is only learned by Indonesian students in the classroom. As a result, English textbooks have the potential to be students' only access to language in the classroom. The purpose of this study was to find out whether the book "When English Rings a Bell" for the eighth grade of junior high school is by the criteria (Education National Standard Council) BSNP. In this study, researchers used methods of content analysis, presentation analysis, language analysis, and graphic analysis based on BSNP (National Education Standards Board) which focuses on language characteristics as communication by paying attention to text content. This research instrument is a document. Researchers will focus 13 chapters on grade VIII junior high school. Then a check is carried out using the suitability check of textbooks based on BSNP (Education National Standard Council). The result of this study, the quality of the "When English Rings a Bell" textbook for the eighth grade of Junior High School based on the Education National Standard Council (BSNP) is good. There are 4 elements, these are content eligibility, presentation eligibility, language eligibility, and graphic eligibility. But, for the content eligibility element side, not all items of its indicators are fulfilled clearly. Just for KD and KI there are not found in this textbook.
Analisis Tren K-pop terhadap Motivasi Belajar Bahasa Inggris di Kalangan Gen-Z
Authors: Rahma Arifatuzzahra  , Maila Syakhshiyatul Ma’rufah  , Annisa Sultan Alfiani  , Amalia Fajriyyatin Najichah
Published: August 2024

This research analyzes the impact of the K-pop trend on motivation to learn English among Gen-Z. The study identified that the importance of English for educational and career purposes encourages Gen-Z to learn it. However, there are factors that contribute to decreased motivation, such as uninteresting learning materials, social stigma, lack of confidence, and time constraints. In recent years, the K-pop phenomenon has provided an interesting and non-monotonous approach to learning English. Gen-Z is inspired to learn English through K-pop content on platforms such as YouTube, TikTok and Instagram. In addition, fandom interactions on social media require English as the language of communication. K-pop idols also actively interact with fans in English through YouTube channels and live streaming apps. The presence of English subtitles in K-pop content, the use of English lyrics in songs, and the use of English platforms by idol agencies further increase the motivation to learn English among Gen-Z. This research aims to provide a comprehensive analysis of the impact of K-pop trends on Gen-Z's motivation to learn English as well as the factors that contribute to this motivation. The results of this study are expected to deepen our understanding of the relationship between the K-pop trend and motivation to learn English, as well as facilitate the understanding of the relationship between the K-pop trend and motivation to learn English. and facilitate the development of effective language learning strategies
Strategi Pembelajaran Bahasa Indonesia Melalui Metode Mind Mapping dan Muatannya pada Profil Pelajar Pancasila
Authors: Nur Aini Indarwati  , Edy Suryanto
Published: August 2024

One of the problems faced by Indonesian language teachers is the lack of student interest and motivation to learn Indonesian. Mind mapping is a visual technique that uses diagrams to represent ideas and relationships between concepts. This research aims to find out how the application of mind mapping learning strategies in Indonesian language learning and also its content on the Pancasila Student Profile. This research is a descriptive qualitative research. This research was conducted in class XI F4 SMAN 5 Surakarta with the object of research on the Indonesian language learning process of explanatory text material using mind mapping strategy. Data were collected through interviews, observations and documentation. The data were analyzed inductively to produce grounded theory. The results of this study indicate that mind mapping can improve student understanding because students are looking for their own problems and materials on the material provided. There were also some difficulties in the learning process such as the teacher's difficulty in coordinating the class, time constraints and student involvement. The content of the Pancasila Student Profile in this learning process is critical, responsible, mutual cooperation and critical thinking.