bluuebunny/crossref_metadata_2025 huggingface dataset:
Acute Myeloid Leukemia Overview With Focus On Small Molecules Treatment
Authors: Meng Sichong
Published: December 2024

Acute myeloid leukemia (AML) is the most prevalent subtype of acute leukemia in adults, posing crucial challenges in terms of prognosis, treatment, and relapse. This is particularly evident in elderly patients, who frequently present with comorbidities that complicate their clinical management. This paper examines small molecule therapies that have demonstrated promise in improving AML treatment outcomes, and explores the mechanisms and clinical advancements associated with targeted therapies, including IDH inhibitors, BCL-2 antagonists, FLT3 inhibitors, menin antagonists, and E-selectin inhibitors. Highlighting the potential of combination therapies, it discusses the challenge of treatment resistance, noting that innovative combinations can significantly improve therapeutic efficacy and patient response rates. The result indicates that IDH inhibitors can achieve response rates exceeding 40%, while FLT3 inhibitors have been associated with improved overall survival by approximately 30% when used in conjunction with standard chemotherapy regimens. This paper ultimately contributes to a deeper comprehension of the role of small molecule therapies in AML, thus, underscoring their therapeutic value to improve patient outcomes significantly and provide insights for future research directions in the field.
Leveraging ResNet for Disease Detection: Uncertainty Quantification and Sensitivity Analysis in Lung X-ray Image Segmentation
Authors: Shen Yiwei , Sun Weiyu
Published: December 2024

This paper focuses on the diagnosis of pneumonia through lung X-ray image analysis using machine learning techniques, particularly leveraging a Residual Network (ResNet) model. Traditional methods relying on radiologists are subjective and prone to misdiagnosis. By integrating uncertainty quantification and sensitivity analysis into the segmentation process, we aim to enhance the accuracy and reliability of pneumonia diagnosis. The ResNet model's architecture allows for deeper network training without the vanishing gradient problem, significantly improving segmentation accuracy. Uncertainty quantification provides confidence estimates for model predictions, while sensitivity analysis identifies key factors influencing segmentation accuracy, such as image quality and model parameters. Our results indicate that these methods and the advanced ResNet model significantly improve the diagnostic process, offering a robust approach to medical image analysis and aiding clinical decision-making. This work demonstrates the potential of advanced machine learning techniques, specifically ResNet, in medical diagnostics, aiming to optimise lung disease diagnostic processes.
ProteinBERT Algorithms: Applications in Antimicrobial Peptides Classification, Intrinsically Disordered Protein Prediction, and Toxicity Analysis
Authors: Li Xiaofeng
Published: December 2024

The burgeoning field of computational biology has been markedly enhanced by the integration of advanced machine learning models capable of tackling intricate protein-related challenges. ProteinBERT, a transformer-based deep learning algorithm, has emerged as a formidable tool in deciphering complex patterns within protein sequences. This study delves into ProteinBERT's robust application across three pivotal domains: antimicrobial peptide (AMP) classification, intrinsically disordered protein (IDP) prediction, and protein toxicity prediction. Leveraging domain-specific datasets alongside sophisticated evaluation metrics, ProteinBERT has shown superior performance, surpassing both traditional models and other contemporary deep learning approaches in these areas. The analysis reveals that ProteinBERT not only accurately classifies AMPs, effectively predicts IDP configurations, and reliably forecasts protein toxicity but also sets new benchmarks in the precision of computational predictions. This research underscores the significant capabilities of ProteinBERT and discusses prospective enhancements that could refine its utility in computational protein analysis, aiming to push the boundaries of current methodologies and foster innovations in protein research.
The development of sensory analysis techniques in the food industry and the research progress
Authors: Huang Kun
Published: December 2024

Sensory analysis plays a crucial role in the food industry, serving as an essential tool in product development, quality control, and consumer research. It involves evaluating the sensory attributes of food products-taste, aroma, texture, and appearance. Although human-based sensory evaluation is widely used and relatively developed, it is likely to be affected by subjective issues. Moreover, the result of sensory evaluation is difficult to standardize and quantify. To address this, instrumental analysis methods have been introduced to establish correlations between the chemical composition or physical properties of food and the outcomes of sensory evaluation. However, the data generated from instrumental analysis are often large and complex. To link these data with sensory evaluation results, chemometric methods are employed for analysis. With advancements in technology, more cutting-edge tools, such as electronic noses and electronic tongues, have been applied to sensory analysis. Due to their rapid assessment and ability to detect multiple indicators, these technologies are becoming increasingly popular in industrial applications.
The main methods and application prospects of treating cancer through the induction of ferroptosis
Authors: Cui Tiange
Published: December 2024

The highly deadly illness known as cancer is brought on by the malignant growth of cells, and it ranks among the top causes of death globally at the moment.In recent years, research on cancer treatment drugs has emerged one after another. However, due to the continuous mutation of cancer cells, they develop resistance to traditional anti-cancer drugs, making it even more difficult to achieve a complete cure for cancer.Recent experimental studies have shown that ferroptosis,a non-apoptotic cell death pathway triggered by iron, can effectively overcome issues such as drug resistance in cancer cells, thereby achieving effective treatment for cancer. It is currently a promising therapeutic approach, and many researchers have been investigating iron death induction treatment methods.And alarge number of research papers on this novel cancer treatment method known as ferroptosis have been published.This review summarizes and introduces two treatment methods based on ferroptosis that have been extensively researched, which called therapeutic method for inducing ferroptosis using targeted nanoparticles and Methods for combining immunity and ferroptosis.,and analyzing their development prospects as well as some issues currently faced in research and suggestions for future development directions.
The Impact of the Digital Economy on Informal Employment of Platform Economy Workers in Chinese Cities
Authors: Yang Jingcheng
Published: December 2024

In the context of the digital age, the digital economy is becoming a new driving force for global economic development. In China, in particular, it has had a significant impact on informal employment, especially platform economy workers. This study explores how the digital economy reshapes informal employment patterns and positively contributes to the urban labor market through a literature review and empirical analysis. The findings reveal that the digital economy not only creates new job opportunities and increases labor market flexibility but also fosters innovation and entrepreneurial activities. The paper further analyzes the "Digital Economy+" model, which integrates the digital and platform economies, demonstrating how this model provides diverse employment opportunities and enhances economic participation for urban informal workers. Additionally, the paper proposes strategies to help informal workers leverage digital technology to improve job quality and stability. The results aim to provide policymakers, educators, and informal workers with strategies and insights to navigate the digital economy era, while also acknowledging research limitations, such as the digital divide between urban and rural areas and the adaptability challenges faced by low-education groups. This study offers valuable perspectives for understanding the phenomenon of informal employment in the digital economy.
Empirical Analysis of SPX Options Volatility Arbitrage Strategies: Relative Differences in Historical and Implied Volatility
Authors: Lyu Yunqiang
Published: December 2024

Contemporarily, volatility arbitrage strategy is one of the common strategies in quantitative finance. This study evaluates the effectiveness of volatility arbitrage strategies within the SPX options market by analyzing the relative differences between daily historical volatility and daily implied volatility. Using the Black-Scholes-Merton (BSM) model and Newtons method, the research finds that the strategy performs particularly well during periods of heightened market volatility. Specifically, in the final 60 days of the study period, when implied volatility increased significantly, the strategy achieved a maximum single trade profit of $162.5, contributing to a total gain of $326.1. These findings highlight the strategy's potential for substantial profits and demonstrate its effectiveness in capitalizing on volatility discrepancies. The study enhances theoretical models by showing how increased volatility impacts arbitrage opportunities and fills a gap in the literature regarding the empirical application of these strategies, offering valuable insights for options traders and risk managers. However, limitations related to the BSM models assumptions and the omission of market shocks and macroeconomic factors suggest that future research should refine models, incorporate broader market factors, and use more comprehensive datasets to better capture real-world dynamics.
Research on Artificial Intelligence and Trade in Emerging Markets - A Global Value Chain Perspective
Authors: Lin Jialu
Published: December 2024

From the perspective of global value chain, this paper explores the correlation between AI and emerging market trade, and analyzes how AI technology affects the trade structure of emerging market countries and their changing position in global value chain. Based on regression analysis and panel data analysis methods, the study comprehensively assesses the specific impact of AI on emerging market trade in terms of production efficiency, product quality, and market entry strategies.This paper proposes that emerging market countries should further strengthen the R&D and application of AI technology, optimize their industrial structure, and actively participate in the innovation cooperation of GVCs. The findings provide an important reference for policymakers to promote emerging market countries to achieve higher quality development in global trade.
Using the SWOT Model to Explain the Core Competitiveness of College Students
Authors: Li Dongze
Published: December 2024

This paper is to explore the transformation of the world economic model after the epidemic and the changes in the country's targeted policies for college students' employment. The research significance of this paper is to explore the influence that college students' core competitiveness can have in the coming mid-21st century and summarize the efficiency and rationality of resource allocation. The theme of this paper is analysis of the influence and role of college students' core competitiveness in interviews and career competition by using the SWOT model. The results show that compared with college students in the last century and before 2010, college students who have entered the information age now have unique core competitiveness and effective application of information technology skills. This prospect planning that is not limited to specific industries is a very important core skill for college students in this era, and the SWOT model can effectively explain the opportunities and threats faced by college students at present, as well as their advantages and disadvantages. However, there is an inevitable lack of experience in the operation of practical problems by college students, and this common problem always exists regardless of which era. Moreover, the current era of free employment has resulted in not all talents being able to fully enter the market. Some college students will choose to engage in individualized network work. This rich and diverse employment situation has also created the current complex economic market structure.
The Impact of Population Aging on the French Economy
Authors: Yang Ziyue
Published: December 2024

In the face of the formidable challenge of population aging in the 21st century, France, as a typical aging society, is experiencing significant strains on its labor market structure and public finances. With the continually rising proportion of the elderly population, the French government has continuously adjusted the pension system by gradually increasing the statutory retirement age, aiming to alleviate the pressure on pension payments and maintain sustainable economic development. However, while this measure has somewhat slowed down the growth rate of financial burdens, it has been unable to fundamentally reverse the issues arising from aging, such as labor shortages and weakened economic growth momentum. In the future, France may need to adopt a more comprehensive strategy, including optimizing labor market policies, promoting employment and re-employment of the elderly, strengthening the construction of medical and health service systems, and so on, to comprehensively address the complex challenges of an aging society and ensure stable socio-economic development.
The Causes and Impacts of the Depreciation of the Japanese Yen
Authors: Ye Zifei
Published: December 2024

This paper analyzes the causes of the yens depreciation since the COVID-19 pandemic and examines its potential impacts on both the Japanese domestic economy and the international landscape. In the post-pandemic era, global productivity has significantly declined, leading to rising prices and ushering in an era of low growth, high inflation, and elevated interest rates. This has presented unprecedented challenges to the development of national economies. The paper first highlights Japans critical economic position in the post-pandemic context and then analyzes the causes of the yens depreciation from three perspectives: the divergence in U.S. and Japanese monetary policies, global economic conditions, and Japans domestic economic environment. Finally, the paper discusses the effects of the yens depreciation, noting that while it has contributed to an increase in net exports and an improvement in deflationary pressures, the persistent rise in domestic living costs, driven by higher commodity prices, remains a central challenge to Japans long-term economic growth. In conclusion, the depreciation of the yen amid global financial volatility highlights the intricate interplay between domestic economic policies and international financial markets. Japans experience underscores the complexities faced by advanced economies in a highly interconnected global economy, where shifts in major currencies and monetary policies can have far-reaching implications for economic stability and growth.
Implementation of Big Data Techniques in Banking: Evidence from Real-time Fraud Detection and Customer Segmentation
Authors: Gong Siyuan
Published: December 2024

In the constantly changing financial field, the combination of big data technology and the financial industry is very important to enhance the development of the financial industry. This research will investigate the application of big data in two key banking areas: real-time fraud detection and customer segmentation. In the case of JPMorgan Chase, this paper discusses how the bank used machine learning algorithms to monitor and analyze transaction data in real time, which greatly reduced the costs associated with fraud. At the same time, this study also studied Wells Fargo's utilization of big data tools in customer field, resulting in improved customer retention and cross-selling potential. The results of this study highlight the importance of big data in the banking industry, particularly in terms of operational efficiency, security and customer engagement. However, issues (e.g., privacy concerns and the need for expertise) remain important barriers. The study goes on to discuss the future possibilities of big data in banking, highlighting the potential for upcoming tools and schemes to further transform banking.
The Impact of E-Commerce on the Development Model of the Modern Economy
Authors: Cui Kaijun Jacky , Fan Xinyu
Published: December 2024

The rapid development of the Internet has profoundly reshaped human lifestyles, and electronic commerce, as a shining star in this wave, is reshaping the modern economic landscape at an unprecedented speed. It has not only significantly reduced consumer transaction costs but also revolutionized shopping experiences through convenient payment systems, intelligent recommendations, and immediate logistics. Additionally, electronic commerce has prominently enhanced transaction efficiency and volume, stimulating market vitality. With its unique advantages, electronic commerce has excelled in optimizing resource allocation, precisely matching supply and demand, and elevating the overall effectiveness of the economic system. At the same time, it has catalyzed profound changes in market structures, promoted the digital transformation of enterprises, and opened up new growth paths for small and medium-sized enterprises. In terms of fostering economic connectivity, electronic commerce has broken down geographical barriers, accelerated the process of regional economic integration, and injected new vitality into the global economy. This article will explore the profound impact of electronic commerce on economic growth, industrial structure, and regional economic integration from multiple dimensions, and offer suggestions for its future development, aiming to jointly create a new chapter in electronic commerce.
An Analysis of Yen Depreciation on Japans Economy
Authors: Tang Jiaqi
Published: December 2024

This study examines the pivotal role of the yen in Japan's economy, with a specific focus on its depreciation and the resulting effects on various aspects of its economy. The research identifies the primary drivers behind the yen's depreciation, including interest rate differentials, carry trade dynamics, and Japan's domestic monetary policies. Through a combination of qualitative and quantitative analyses, the study investigates the broader implications of a weakened yen, particularly its influence on import prices, consumer behavior, export performance, and foreign investment flows. The findings reveal that while the depreciation of the yen serves as a catalyst for boosting exports and attracting foreign investment, it concurrently escalates import costs, leading to higher consumer prices and altered savings behaviors. The study concludes with a set of policy recommendations aimed at optimizing the economic benefits of yen depreciation while mitigating its adverse effects, thus providing a balanced approach to sustaining Japan's economic stability.
Digital Marketing and Consumer Behaviour: The Role of Social Media in Decision-making
Authors: Pei Jinghan
Published: December 2024

Social media platforms provide brands with interactive tools to enhance consumer engagement, increase exposure, and build customer loyalty. Given the pervasive influence of social media, this paper investigates the crucial role of these platforms in shaping consumer purchasing decisions and their overall impact on digital marketing strategies. The research employs a qualitative approach, primarily utilizing case studies to examine the dynamics between social media activities and consumer behavior. By analyzing specific examples, the study identifies how user-generated content and brand interactions on social media significantly influence consumer perceptions and pre-purchase decision-making processes. Despite challenges such as privacy concerns, the synergy between social media and digital marketing enables brands to innovate and adapt, maintaining competitiveness in the digital age. Through this investigation, the paper underscores the necessity for brands to strategically leverage social media to foster deeper connections with their audience and drive purchasing decisions.
Analysis of Luckin's Financial Fraud
Authors: Yang Kangtian
Published: December 2024

With the rapid development of market economy and Internet technology, the number of listed companies has steadily increased. Luckin Coffee, one of the famous local coffee brand in China, not only challenges Starbuckss monopoly in the Chinese coffee market but also pioneers new directions for Chinas coffee industry. However, financial fraud has become increasingly prominent, causing significant losses to the country, securities market, and investors. This paper examines relevant theories of financial fraud using Luckin Coffee as a case study, providing a detailed review of its fraudulent activities from start to finish. The analysis reveals that Luckin Coffee primarily engaged in financial fraud by inflating online orders, exaggerating main business income and costs, and conducting complex related transactions. Furthermore, this paper conducts an in-depth analysis of three factors contributing to Luckin Coffee's financial fraud: financing pressure, market competition and balance of payments. Concrete measures and suggestions are proposed to prevent and control both internal and externa; financial fraud within listed companies. By delving into the Luckin Coffee's financial fraud case, this research aims to support optimization efforts in financial management practices while enhancing the soundness of related enterprises financial system. It also seeks to help these enterprises enhance their competitiveness in the market while offering inspiration for improvements within China's capital market system. Additionally, this paper also reveals the negative impact of financial fraud on investor confidence, market order and sustainable development of enterprises, and emphasizes the importance and urgency of combating financial fraud.
How ESG Factors Affect Corporate Valuation and Stock Investor Decisions
Authors: Song Ziyu
Published: December 2024

Recent studies indicate that ESG investing can positively influence overall economic health, leading to more substantial and sustainable returns for investors. The development of various data-driven scoring methods has improved the objectivity and efficiency of evaluating companies based on ESG principles, reducing investment risks and enhancing decision-making processes. Despite these advancements, challenges persist, including ongoing debates about the efficacy of mandatory ESG reporting, varied investor perceptions, and the quality of information disclosure. Regional analyses, such as evidence from China, suggest promising outcomes for ESG-focused portfolios, while the growing involvement of non-professional investors underscores the increasing importance of transparent and comprehensive ESG disclosures in shaping future investment landscapes.
Analysis of the Dynamic Influence of New Energy Automobile Market Expansion on Fuel Vehicle Market Share
Authors: Ye Yuqing
Published: December 2024

Global environmental issues, countries all focus on green and low-carbon development, industrial structure transformation (industrial transformation). The industry's awareness of low-carbon production has gradually increased. With the encouragement of the Chinese administration, new energy automobile have been introduced into the Chinese auto market on a large scale, and financial subsidies have been given to the research, production and promotion of new energy automobile, which promoted the development of new energy automobile and the fuel vehicless transformation. The government's support has improved the technology of new energy automoblile, which is more cost-effective than fuel vehicles and improves driving safety and comfort. In the data from 2017 to 2023, the share of new energy automoblile in the automotive industry continues to rise. The expansion of the new energy vehicle market will reduce the market share of fuel vehicles. The data on the share of Chinese cars shows that new energy automobile have strong competitiveness in the automotive industry.
China's Green Bond Market: Structural Characteristics, Formation Factors and Development Suggestions --Based on the Comparison of Chinese and the US Green Bond Markets Structure
Authors: Li Haolan , He Tiancheng , Liao Xihong , Tong Weizhen
Published: December 2024

The green projects are mainly focusing on solving the issues of pollution reduction, waste management, climate protection, etc. Green bond is a fixed-income instrument that helps eligible green projects to raise funds for sustainable development. In this article, we compare the structure of China and US green bond market, and analyze their difference from historical evolution perspective, the issuance standard and the market operation characteristics perspective. Based on the analysis above, we then discuss the advantages and disadvantages of Chinas green bond market. The advantages of Chinese green bond market structure are low risk and the close relationship between the banks and the enterprises, while the disadvantage is low financial efficiency. Finally, we conclude with recommendations for the future development and financial structural optimization of Chinas green bond markets in terms of rationalization, advancement and cascade.
Analysis of Economic Development of Moldova
Authors: Luo Yimeng
Published: December 2024

This comprehensive study delves into the multifaceted changes that Moldova has undergone over the past three decades, centering on its unique geographical setting, demographic shifts, and profound transformations in its economic structure. Moldova, a landlocked country blessed with fertile black soil, has long relied on agriculture as the lifeblood of its economy, significantly contributing to its GDP. Nevertheless, the country has grappled with economic growth constraints, including limited international trade opportunities and poor water quality, impacting the livelihoods of nearly half of its population. The research not only retraces Moldova's economic trajectory from a downturn spanning 1990 to 2000 to its peak of recovery in 2022 but also highlights the positive shifts in its economy a diversification away from sole reliance on agriculture towards a more inclusive mix of industries and services. Confronted with persistent challenges such as balance of payments issues, the intricacies of international relations, and the need to boost domestic production, particularly in the pharmaceutical sector, this study concludes by outlining a series of strategic recommendations aimed at fostering sustained economic growth and development in Moldova, illuminating a promising path forward for the country.
The Economic Impact of Air Pollution on Economic Growth
Authors: Wei Zonglan
Published: December 2024

Air pollution is a substantial risk to human health in the 21st century. The World Health Organization reports that merely 1 in 10 individuals reside in regions with air quality below recommended standards, resulting in 7 million deaths each year - one in eight fatalities worldwide. The article examines the complex relationship between air pollution and economic growth, emphasizing the effects of declining air quality on economic development. This study aims to fill this vacuum by examining the detrimental effects of air pollution on China's economic growth, particularly concerning PM2.5 and other pollutants. The findings suggest that pollution significantly hampers growth, causing losses in productivity, health, and economic activities across several sectors. Additionally, the study reviews air quality regulations in Europe, highlighting the economic benefits of stricter pollution controls. The report finishes by highlighting the necessity for more stringent policies to reduce pollution in order to foster sustainable economic growth.
Research on Education and Economic Mobility in Sub-Saharan Africa
Authors: Liu Yixuan
Published: December 2024

This study investigates the crucial roles of education in fostering economic mobility in the region of Sub-Saharan Africa, with a focus on three countries. The countries that are the focus of this study include Kenya, Nigeria, and South Africa. While education is becoming more accessible, there are still numerous problems: inequality in the quality of education between the cities and the villages, the gender gap, and the lack of opportunities for children from low-income families to receive proper education. This study looks at the effects of these challenges on the economic aspect and also aims to establish the role played by different levels of education, the primary, secondary, and vocational, in social mobility. Special attention is given to vocational and technical education since these systems are important in linking education to the labor market and enhancing the employment status of youths. Also, the study captures how specific strategies can be used to ensure that special attention is given to vulnerable groups like girls and families with low incomes to ensure that they get quality education. Thus, based on the mixed methods approach, this research identifies the ways to increase access to education, decrease inequalities, and increase the quality of education with the purpose of making education a more powerful weapon against poverty and for inclusive growth in the region.
NVIDIA's Market Strategy and Innovation: The Fusion of Technological Leadership and Brand Building
Authors: Song Yifan
Published: December 2024

With the rapid development of artificial intelligence (AI) and digital currencies, the global demand for computing power has surged sharply, creating an urgent need for advanced technological solutions. Graphics processing units (GPUs), as a key measure of a company's computing capability, present unprecedented opportunities for manufacturers to innovate and expand their market reach. However, these opportunities are accompanied by significant challenges, including intense competition and the need for continuous technological advancement. In this rapidly evolving landscape, standing out has become a pressing issue for manufacturers striving for success. This article focuses on NVIDIA, a leader in the GPU industry, analyzing its innovative strategies, effective marketing methods, and strategic partnerships. By examining how NVIDIA navigates the complexities of the competitive GPU market, the article explores the company's approach to maintaining its leading position while addressing various challenges and seizing new opportunities. Ultimately, this analysis sheds light on the essential factors that contribute to NVIDIA's sustained success in a dynamic technological environment.
Comparative Analysis of Machine Learning Algorithms for Sales Forecasting in the Russian Toy Retail Sector
Authors: Liu Hao
Published: December 2024

This research provides a detailed examination of various machine learning approaches for the sake of forecasting sales within the Russian toy retail industry. This sector is marked by fluctuating consumer preferences and a pressing need for precise inventory control, making accurate sales predictions vital. The study aims to determine which forecasting technique yields the most reliable results for this dynamic market. To achieve this, several predictive models were assessed, including Linear Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). The analysis involved a comprehensive process encompassing data preprocessing, exploratory data analysis (EDA), feature engineering, and rigorous model training, using a dataset sourced from Kaggle. The results highlight that Random Forest consistently delivers superior predictive performance compared to its counterparts. This model excels in balancing accuracy with generalization, making it particularly effective for sales forecasting in environments characterized by variability and uncertainty. The practical implications of these results are substantial, offering retailers a robust tool for refining their operational strategies, optimizing inventory management, and enhancing customer experience through improved forecasting accuracy.
User Experience Improvement Strategies of REDnote in Virtual Community Interaction
Authors: Xu Zhengying
Published: December 2024

This research has analyzed how REDnotes virtual community interaction model attracts long-term and high-engagement users and what shortcomings need to be improved, so as to give both itself and other social platforms some improvement suggestions for surviving in the fierce market competition. In the preparation phase of this study, I first read some previous scholarly research articles related to the topic of this study, and then listed the ideas and conclusions of them. In the process of researching and writing the paper, I summarized the main ideas of previous scholarly research articles and used them as references and support for some dissertations in the research. That could be seen in the analysis, although REDnote has had tens of thousands of long-term and high-engagement users, it still needs to make optimization in its content audit, KOL collaboration, and function design to obtain them and attract more new users, thus bringing more economic benefit and maintaining its position in this field.
A Comprehensive Evaluation of Emotion Recognition Techniques: Model and Data Analysis
Authors: Lyu Guoguo
Published: December 2024

This study evaluates three prominent techniques for emotion recognition: Convolutional Neural Networks (CNNs), the Emotions in Context (EMOTIC) dataset, and feature extraction methods like Principal Component Analysis (PCA) and Local Phase Quantization (LPQ). By analyzing these methods across datasets such as Fuyeor Language (FER) 2013 and EMOTIC, the research highlights CNNs' ability to classify emotions from Electroencephalogram (EEG) signals with high accuracy, enhanced by PCA's dimensionality reduction. The EMOTIC dataset's fine-grained emotional categories, combined with contextual data, improved emotion detection in real-world settings, particularly when facial expressions alone were insufficient. LPQ further enhanced texture analysis in challenging environments with variable lighting. While CNNs demonstrated strong performance, challenges like real-time processing and generalization across diverse datasets remain. Future work should focus on integrating audio and physiological data and incorporating temporal information to detect dynamic emotional changes. These developments will help only in creating a better and generalized emotion detection system for the respective areas like healthcare, virtual reality, and interaction with the virtual world.
The Application and Influence of E-commerce in International Trade -- Taking Amazon as an Example
Authors: Yuan Si
Published: December 2024

With the advancement of globalization and the rapid development of Internet technology, e-commerce plays an increasingly important role in international trade. Being one of the biggest online retailers in the world, Amazon's use and impact in global trade have garnered a lot of attention. This study primarily employs a methodology that integrates literature analysis with case analysis. First, by reading pertinent literature, one can gain an understanding of Amazon's use in international trade, including its big data and cloud computing technologies, worldwide supply chain network, diverse payment options, and cross-border e-commerce platform. Secondly, through the analysis of Amazon's application cases in international trade, such as: KIVA orange robot operation, the impact and role of e-commerce in international trade are deeply discussed. The study presented in this paper demonstrates that e-commerce is being used more and more in international trade, and that Amazon, one of the biggest e-commerce platforms globally, is having a major impact on this field of commerce. E-commerce plays a significant role in promoting trade facilitation, reducing costs, improving efficiency, and supporting the growth of medium-sized and small businesses in international trade. However, the digital divide and security issues remain as bottlenecks in the application and development of e-commerce in international trade. Therefore, it is crucial to strengthen the formulation and implementation of relevant policies and measures to promote the application and development of e-commerce in international trade.
Assessing the Accuracy of Implied Volatility and Historical Volatility in the Black Scholes Merton Model
Authors: Hong Peiwei
Published: December 2024

This paper demonstrates a comparative analysis of estimating option prices using the Black Scholes Merton model with implied volatility and volatility estimated by historical data. The call option prices were calculated using data from the company Electronic Arts (EA) collected from Yahoo Finance. The analysis suggests that using implied volatility in the Black Scholes model for pricing options yields more accurate estimation than using the volatility calculated from historical data, as attested by comparing the absolute error, error percentage, mean squared error, and financial profit or loss of the two methods. From the results, using implied volatility in the model generate a set of option prices that are closer to the market prices, with an average error percentage of 9.17% and mean squared error of 0.1359, which has significantly lower deviation than using historical volatility in the model, with an average error percentage of 42.46% and mean squared error of 2.4574. A delta-neutral hedging strategy was proposed based on the more accurate method of estimation, utilizing the total delta to reduce the risk of the portfolio. The findings highlight the advantage of implied volatility for option pricing in the Black Scholes model.
Digital Transformation and Corporate Leverage: A Panel Study of Capital Structure Dynamics
Authors: Ji Xuzhe
Published: December 2024

With the rapid development of the global digital economy (DE), Digital Transformation (DX) has become an important means for enterprises to enhance competitiveness and optimize capital structure. Based on annual report data from 5,639 listed companies in China from 1999 to 2023, this study examines the impact of DX on corporate capital structure. The research finds that DX significantly increases the level of the debt-to-asset ratio, primarily driven by increased investments in research and development and technological infrastructure during the DX process, enhanced market opportunity identification capabilities, and rising demand for technology talent, which collectively lead enterprises to rely more on debt financing. Fixed-effect regression analysis confirms the positive impact of DX on the corporate debt-to-asset ratio, and robustness tests validate the stability of the research results under different indicators of DX, providing practical references for enterprises on how to steadily promote DX and reasonably optimize capital structure in the context of the DE. The study indicates that although DX offers enterprises opportunities for innovation and growth, excessively high leverage may pose potential financial risks. Therefore, while advancing digital strategies, enterprises should reasonably control their debt levels to ensure financial stability.
Comparative Analysis of ARIMA, Random Forest, and LSTM Models for Mercedes-Benz Stock Price Prediction
Authors: Wang Wenjie
Published: December 2024

In the financial markets, accurate stock price forecasting is essential, especially for well-known companies like Mercedes-Benz. The effectiveness of three models in forecasting Mercedes-Benz stock prices is compared in this study: Random Forest (RF), Long Short-Term Memory (LSTM), and Autoregressive Integral Sliding Average (ARIMA). The analysis uses historical stock data from 2000 to 2023, applying feature engineering techniques such as simple moving averages to enhance model accuracy. Long-term dependencies are modeled using RF, complicated non-linear interactions are identified using ARIMA, and linear trends are found using LSTM. Mean Square Error (MSE) and Root Mean Square Error (RMSE) are used to evaluate the models. Results reveal that the RF model outperforms the others in short-term price forecasting, while the LSTM model excels in predicting long-term trends. The ARIMA model, though simple, struggles with volatility in turbulent market conditions. These results highlight the potential of cutting-edge machine learning models in financial forecasting and improve decision-making processes, providing insightful information for investors and financial experts.
Analysis of Fenty Beauty's Marketing Strategies from a Digital Marketing Perspective
Authors: Wu Xingyue
Published: December 2024

Fenty Beauty has emerged as a trailblazer in the competitive cosmetics industry through its innovative and inclusive digital marketing strategies, which have significantly enhanced consumer engagement and loyalty. This study investigates the effectiveness of Fenty Beautys digital marketing approach by examining its strategic use of social media platforms, influencer collaborations, and data-driven marketing techniques to reach a diverse global audience. Through these channels, Fenty Beauty has effectively engaged with consumers by leveraging social media to foster interaction, transparency, and authenticity in its brand communication. Furthermore, partnerships with key influencers and beauty experts have helped the brand extend its reach and solidify its position as a leader in inclusive beauty. By analyzing Fenty Beautys data-driven marketing strategies, this study highlights how the brand utilizes consumer insights to tailor personalized marketing campaigns that resonate with diverse demographic groups. Findings suggest that Fentys approach to digital marketing not only builds strong, inclusive communities but also reinforces its unique brand identity, providing a competitive edge in a crowded market. This study offers insights into how Fenty Beautys digital strategies have transformed consumer behavior, establishing the brand as a model for innovation and inclusivity within the beauty industry.
Study on Roles of Financial BP in Enterprise Budget Management --Take Company C as an Example
Authors: Yu Aixuan
Published: December 2024

Enterprise budget management is becoming more complex and challenging in today's global and digital business environment. As an emerging financial role, Financial Business Partner (Financial BP) is on its way to being an important part of enterprise budget management. This paper is intended to discuss the specific roles of Financial BP in budget management, analyze its impact on enterprise financial performance and business performance and provide targeted improvement suggestions. Based on the analysis of Company C's budget management, this paper employs the research method of case study to get the conclusion that Financial BP is of great significance to companies and can effectively improve the accuracy and efficiency of enterprise budget management. Effective operation of Financial BP can significantly improve the financial management level and enhance the market competitiveness of a company. By optimizing the working mechanism of Financial BP and strengthening relevant support, companies can achieve more efficient budget management and further promote the business development and achievement of strategic objectives.
Research on User Experience to Market Profitability: A Multidimensional Analysis of the Success of Honor of Kings
Authors: Yang Bingshun
Published: December 2024

"Honor of Kings," developed and operated by Tencent's TIMI Studio Group, is an extremely popular MOBA mobile game accessible on both Android and iOS platforms. Officially launched on November 26, 2015, the game centers around competitive PvP gameplay and offers various modes such as 1v1, 3v3, and 5v5. Players can participate in ranked matches and PvE challenges when certain conditions are met. On June 24, 2024, the international version was released to overseas markets, and by July 10, 2024, the game had surpassed 50 million registered users globally. This paper delves into the multifaceted reasons for its success by examining game psychology, human-computer interaction, cultural localization, and social engagement. By utilizing player data, game design analysis, and economic performance metrics, the study reveals how the games accessibility, cultural integration, and sophisticated interaction design contribute to its widespread appeal and commercial success. The research concludes that Honor of Kings effectively harnesses psychological and cultural elements to enhance user experience, resulting in significant market profitability.
A Strategic Guide to Cross-Border Acquisitions by Multinational Companies: An Analysis Based on Success and Failure Cases
Authors: Chen Yuchen
Published: December 2024

This article analyzes the factors for success and failure in cross-border acquisitions (CBAs) through the cases of Geely's acquisition of Volvo in Sweden and TCL's acquisition of Thomson in France. After Geely acquired Volvo achieved short-term profitability, market share growth, and long-term knowledge and technology integration. Its success includes reducing acquisition risks, the importance of top management's decision-making, innovation and development and strategic transformation after the acquisition, and precise market grasp. However, after TCL acquired Thomson, its performance was poor, facing issues such as reduced net profit and market losses. The reasons for failure include ineffective integration and innovation of resources, differences in the social regulatory environment between the two countries leading to management difficulties, and insufficient assessment of project rationality and risks. Finally, suggestions are provided for multinational companies conducting CBAs, emphasizing the importance of pre-acquisition assessment, teams and valuation during the acquisition, resource integration after the acquisition, and the crucial role of the decision-making process, which needs to be analyzed specifically in different situations.
The Impact of Digital Economy on Consumer Behavior
Authors: Wu Wendi
Published: December 2024

This paper examines the impact of big data technology on consumer behavior within the context of the digital economy. Leveraging big data analytics allows companies to better understand consumer needs, optimize products and services, and develop targeted marketing strategies. The study applies game theory and Nash equilibrium to explore the strategic interactions between firms and consumers, with a focus on privacy concerns and data utilization. The analysis highlights the balance between economic benefits for companies and the need for privacy protection for consumers. Through case studies, the paper demonstrates the advantages and risks associated with big data applications, emphasizing the importance of transparency, consumer control, and robust data security measures. The study concludes with policy recommendations for ensuring a balanced approach to data utilization, fostering innovation while safeguarding consumer rights.