bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Stock Return Based on Sentiment
Authors: Zhang Xianyin , Feng Haoran , Li Shuyu , Yang Yiqiao
Published: December 2024

In the rapidly evolving field of financial forecasting, the accurate prediction of stock returns remains a significant challenge. This paper aims to leverage Natural Language Processing algorithms (NLP) to develop a predictive model for stock returns. The research utilized return labels derived from stock price return and sentiment data extracted from StockTwits (a financial social media platform) comments from January 2020 to March 2022. A comparative analysis was conducted to assess the performance of traditional statistical models (Logistics Regression Model), supervised models (Random Forest , Gradient Boosting, XGBoost and Nave Bayes Model), and an ensemble model (Majority Vote Model) in the prediction tasks. The objective was to identify the most effective model, and to provide precise predictions for future stock returns. Our simulations show that (1) Sentiments can work as an effective proxy to predict stock return; (2) likes from users to comments is suitable for price prediction; (3) Logistic Regression didnt work well in prediction, even when used with other techniques; (4) Random Forest Model and Gradient Boosting Model outperform other simpler models, showing promising predictive results; (5) Ensemble model effectively diminishes the influences of potential model overfitting problems. These findings underline the potential of sentiment analytic models as a tool for more accurate financial forecasting.
The Impact of Digital Financial Inclusion on Corporate Financial Risk: An Empirical Study Based on Chinese A-Share Listed Companies
Authors: Zhang Yuhao
Published: December 2024

With the rapid advancement of digital technologies, digital financial inclusion has emerged as a crucial means to enhance the accessibility of financial services and promote economic development. However, while offering convenience to enterprises, digital financial inclusion may also introduce new financial risks. This research centers on Chinese A-share listed companies to investigate how digital financial inclusion influences corporate financial risk and the underlying mechanisms. Initially, the study revisits the definition and the current progress of digital financial inclusion. Subsequently, theoretical models and research hypotheses are proposed to examine its impact on enhancing corporate financing capacity, improving financial structures, and optimizing management processes, while also identifying potential risks such as higher leverage, increased financing risk, and operational challenges. Through empirical analysis, this study explores the diverse effects of digital financial inclusion on corporate financial risk, the moderating role of financial regulation, and evaluates the influence of regional marketization levels and disparities in traditional financial development. The results indicate that digital financial inclusion can reduce corporate financial risk under certain conditions, though its effectiveness is constrained by various factors. Finally, this study offers policy recommendations and corporate management strategies to provide valuable references for risk management in the digital finance era.
Understanding the Slowing of Chinas Economic Growth: An Empirical Examination of the Effects of Opening-up
Authors: Li Xuanyu
Published: December 2024

Understanding the factors behind the slowdown in Chinas economic growth since the early 21st century is crucial. This study uses a linear regression model to analyze the impact of Opening-up on economic growth, employing provincial-level panel data from the National Bureau of Statistics of China (NBS) for 1997-2023. Robustness is tested by including variables such as total imports and exports at the business units location, gross regional product, and total imports and exports at domestic destinations and sources in RMB. The study also examines heterogeneity across regions, time periods, trade types, and economic sectors. Key findings include: 1) A positive correlation between Opening-up and economic growth; 2) Variation in the impact of Opening-up based on region, time, trade type, and sector; 3) External shocks can weaken the benefits of Opening-up; 4) The economic slowdown is partly due to uneven regional development, external shocks, high reliance on imports, and insufficient industrial restructuring.
Research on Strategies to Improve Job Satisfaction of Grassroots Employees
Authors: Qin Siqi
Published: December 2024

With the rapid development of the social economy and the increasingly fierce market competition, enterprises must attach great importance to the job satisfaction of grass-roots employees if they want to gain a foothold in the market and achieve long-term development. As the cornerstone of the enterprise, the job satisfaction of grass-roots employees is directly related to the operation efficiency, product quality, and overall competitiveness of the enterprise. This paper aims to explore the promotion strategy of grass-roots employees' job satisfaction, combined with relevant theories and practical cases, and put forward specific improvement measures, to provide reference for enterprise management.This paper aims to explore the key factors that affect the job satisfaction of grassroots employees and propose corresponding improvement strategies. Through in-depth analysis of employee salaries, corporate culture, and other aspects, we have proposed a series of targeted measures to provide useful references for enterprises to improve employee satisfaction, promote employee development, and enhance overall performance.
Research on the Influence of Shadow Banking Development on Financial Risk
Authors: Wang Weiyan
Published: December 2024

The shadow banking sector is an important part of financial markets and has grown in size in recent years as financial markets have developed. This paper examines the impact of the shift of bank lending to this sector and how it affects the financial system. Taking China as an example, the paper focuses on the main problems it faces, the financial risks it poses, the consequences, and the direction and scope of regulation. In order to ensure the stable development of financial markets and to reduce and control risks, theoretical support and useful guidance are essential. This study attempts to explore the impact of the shift of bank lending to the shadow banking sector, which suggests that it may make the financial system more fragile, raise systemic risk, and make financial markets more volatile. This will pose new challenges for regulatory policy, and regulators will have to take action to mitigate the risks. This paper examines the shift of bank lending to this sector and its impact on financial risk. The paper further examines the main issues arising from the growth of shadow banking in China, aiming to determine the direction and concentration of regulation to ensure financial market stability. To this end, the results of the study demonstrate that the transfer of bank loans to shadow banking could heighten market volatility, increase systemic risk, and thus necessitate regulators to take measures to reduce risks, thus introducing fresh challenges to regulatory policies.
Exploring the 4 Dimensions of the CHANEL 5 Perfume Advertisement
Authors: Zhang Fan
Published: December 2024

Perfume has become an indispensable part of modern women's lives, and Chanel, as part of the perfume industry has an absolute say in the brand, Chanel No. 5 perfume is undoubtedly its most classic perfume, for Chanel to create a huge income. Moreover, its advertising condensed much ingenuity, many worthy of our study and research. This paper discusses the advertising of Chanel No. 5 perfume, analyzing how visual and sound effects in advertising promote consumers' desire to buy from the perspective of professional terms and "persuasion". This study aims to help people better understand how items are represented in daily advertisements and to make suggestions about how items will be represented in future advertisements. In this paper, it will use the literature research and case analysis to study this topic. It also explores the brand concept and advertising wisdom, while pointing out areas for improvement and future vision. The study fills a research gap by analyzing all aspects of advertising on a macro level.
Climate Finance and Green Investments
Authors: Wang Yingzan
Published: December 2024

This essay aims to discuss the importance of climate change regarding its impact on humanity and nature. It dives deep into several aspects and levels of global warming, seeking to dig through the problems and come up with viable solutions to each. It also uncovers projects, business investments and financing opportunities relating to the green-house effect, providing business and individuals with ideas and chances to seek advantage within these fields. Furthermore, this paper explores the relationship between financial mechanisms and environmental sustainability, how targeted funding can trigger the transition to a low-carbon economy. By analyzing the roles of public and private sectors, the paper highlights the importance of innovative financial instruments, like green bonds and climate funds. The essay concludes by reinstating the important role of climate finance in achieving international climate commitments and fostering sustainable economies as well as human capitals.
Research on the Impact of Aging on China's Macro-Economy
Authors: Han Liangyu
Published: December 2024

Since the beginning of the 21st century, China's aging problem has become serious. The proportion of the population over 65 years old in the total population is increasing year by year. The social age structure has changed greatly; on the other hand, China's economic growth rate, after an average of more than 10% in 2000-11, has also gradually slowed down from rapid growth to high-quality growth. This paper analyzes relevant literature, searches for relevant data, speculates on its relevant correlation, and uses qualitative research and other research methods to explore the impact of the accelerated aging rate on my country's macroeconomics from the perspectives of the labor market, savings and investment, and consumer demand. Aging problem to China's economy brings the labor scale, labor quality decline, savings rate, the government fiscal deficit challenges, but also brought the development of the pension industry, the second industry labor capital technology to the third industry transfer opportunities, this paper on the existing challenges and opportunities in the future and the use policy to improve fertility, legislation to improve the rights and interests of women and children, accelerate the development of network and electronic infrastructure construction, mining the elderly consumption potential and labor contribution, etc.
Analysis of the Application of Representativeness Bias in the Scenes of Clinical Practices and Operation Management
Authors: Wang Xiangyu
Published: December 2024

In this paper, the representativeness bias is introduced and explained. The purpose is to investigate how representativeness bias can be applied in real world examples, such as healthcare programs and business operation management. Three studies, in two different fields, completed by other researchers, are provided to elaborate the effect of representativeness bias by empirical approach and rational approach. In each field, the experiments would show how the representativeness bias made people choose a different choice, how it guided people to make sub-optimal decisions. Through the studies presented, the benefits, disadvantages, and consequences of the use of representativeness bias are all explained. Through the data collected, representativeness bias is used very frequently, and in some cases, it does lead people to the sub-optimal choice. Also, some steps that people can take to reduce the effect of representativeness bias will also be shown. This allows people to make more accurate judgments not just in specific fields, but also in daily life, and minor matters.
The Rafale Deal Turbulence A Discussion on Indias Monetary Policy and Its Impact
Authors: Tang Zichun
Published: December 2024

Starting from a discussion of the current turbulence surrounding the India-France Rafale Deal, this paper delves into the historically contextualized evolution of the Indian Rupee, tracing its trajectories of significant exchange rate volatility, primarily in the post-independence era. It examines the rupee's journey from its peg to the British pound to its current status, underpinned by momentous economic reforms and global economic crises. The focal point of the paper is the period surrounding the Rafale defense deal negotiations (2012-2016), during which the rupee faced exacerbated volatility spurred by a confluence of regulatory, economic, and political factors, including the implementation of GST, RERA, and a controversial demonetization initiative. The paper further articulates the ramifications of unregulated monetary policies, emphasizing their effect on inflationary dynamics via the mechanisms of money supply and demand responses to changing interest rates. An in-depth analysis of India's monetary policy reveals a complex interplay with inflation and bank credit, with implications for economic growth and inflation control. Additionally, fiscal policy coordination challenges at state and central levels are critiqued, particularly regarding their alignment with the Reserve Bank of India's monetary stance. In relation to sovereign debt management, the paper explores India's public debt and the Reserve Bank of India's strategies in managing debt-related risks amid economic and policy flux.
Research on Cost Control in Financial Management of Multinational Enterprises Taking Kimberly-Clark Corporation as an Example
Authors: Xu Ziyan
Published: December 2024

Known worldwide as a leading maker of paper towels and supplier of household and personal care products, Kimberly-Clark adopts a comprehensive cost control method. This method covers the entire business process, from product design, production, sales, to customer support, and also focuses on streamlining operational costs in different countries and regions. Importantly, the company skillfully manages currency fluctuations, reducing their influence on operational costs through flexible currency hedging tactics and strong risk management systems. In addition, Kimberly has achieved significant economic benefits by optimizing supply chain management, reducing procurement costs and transportation costs. Therefore, this research explores the cost reduction strategies used in the financial management of multinational companies, taking Kimberly-Clark as an example for a thorough analysis. The successful experience of Kimberly-Clark shows that by developing and implementing effective cost control strategies, companies can better deal with the difficulties of external economic environment and the cost pressure of internal operations.
Competition in Sharing Economy: A Literature Review
Authors: Liu Dong
Published: December 2024

The objective of this paper is to review the extant literature on competition within the sharing economy. In recent years, the rapid expansion of the sharing economy has produced both positive and negative effects, particularly on traditional industries. This study examines the advantages and disadvantages brought about by the sharing economy and the competitive pressures it imposes on traditional firms. Faced with these challenges, traditional companies must adopt strategies to enhance their competitive capabilities. Drawing on a broad range of literature, this paper also discusses comprehensive responses that may assist traditional industries in adapting to the rise of the sharing economy. Additionally, the study underscores the pivotal role of government regulation in moderating competition in the sharing economy. It explores the potential consequences of inadequate regulatory oversight and proposes actionable measures that governments can implement to address these competitive dynamics. The paper concludes by offering implications for future research and practical insights for both policy and industry stakeholders.
The Development and Current State of New Energy Vehicles: A Case Study of Xiaomi Brand
Authors: Sun Lingyu
Published: December 2024

With the promotion of sustainable development, the strong support of the government and the enhancement of people's environmental awareness, new energy vehicles have gradually entered the public's vision and successfully opened a new energy market. At the beginning, only a small number of people in the research and development of new energy vehicles, slowly more and more companies see that this industry is profitable, and the future trend of the industry is toward brilliance, so more and more companies enter the market. Marketing means must be an important factor in whether a company can occupy a large market share and successfully enter the mass unemployment, after all, only profitable companies can continue to develop new technologies and continue to go on in this industry. Based on PEST and SWOT models, this paper analyzes the representative Xiaomi trams in the industry. The analysis results show that Xiaomi has successfully attracted more young people who need to buy cars and achieved a certain sales volume by seizing the consumer psychology, reasonably using the Internet for marketing and other means, and making some differences and innovations in price and function. This paper will provide strategic reference for the management of small and medium-sized enterprises.
The Economic of Healthcare: Comparing Public and Private Healthcare Systems
Authors: Wang Duoying
Published: December 2024

Healthcare represents a significant aspect of the global economy, and various countries are adopting different models to manage their healthcare systems: the public and private systems. Economic analysis of these systems is therefore essential because these systems determine the quality and availability of healthcare services and influence economic stability and social justice within nations. This paper incorporates comparative UK, US, China, Canada and Switzerland cases to review the economic principles, the business management policies, social perspective of the private and public healthcare systems. The essay introduces the importance of public and private healthcare to the economy followed by the comparison and integrated analysis of the business management and the sociological dimensions of the private and public healthcare systems. The paper recommends policymakers to opt for a fair and harmonised model to ensure quality medical needs of all citizens. This can be possible by adopting regular and formulation of policies to cater for the dynamic needs of the population.
Research on Accounting Issues of Financial Asset Securitization in China
Authors: Yang Daimeng
Published: December 2024

In recent years, China's financial market has witnessed a growing maturity, with asset securitization gradually emerging as a pivotal approach for enterprises to reduce financing costs and improve the efficiency of capital utilization. However, asset securitization, as a very complex financial innovation tool, may also expose enterprises to greater risks. This study aims to explore the issues of financial asset securitization in terms of accounting treatment, providing an all-encompassing depiction of its definition, development, practice, and accounting treatment. By utilizing Chinas asset securitization market as an example case study, this research analyzes prospective challenges and novel aspects in terms of accounting and financial reporting that may arise in the future for financial asset securitization. In addition, this paper discusses cutting-edge developments in the field of asset securitization, such as the impact of technological advances on accounting treatment, and puts forward some suggestions for risk management and prevention of its accounting. An examination of the accounting treatment surrounding financial asset securitization aids in enhancing the transparency and accuracy within financial reporting systems, thereby fostering investor trust in the financial market while bolstering overall stability.
Research on the Design and Application of Climate Adaptation Bond Options (CABO): A New Type of Financial Derivative
Authors: Zhang Xinyu
Published: December 2024

In the face of unprecedented global climate challenges , including the increasing frequency of extreme weather events and the intensification of long-term climate patterns, the need to explore innovative financial mechanisms for promoting climate adaptation and mitigating the impacts of climate change has become particularly urgent. This study investigates Climate Adaptation Bond Options (CABO), a novel financial instrument, and aims to develop a comprehensive theoretical framework to assess its potential and value in the field of climate finance. CABO represents an innovative blend of traditional bond investments with the characteristics of financial derivatives, using option contracts to enable investors to flexibly hedge or speculate on the cash flows of climate adaptation bonds under specific future conditions. This mechanism not only provides investors with more sophisticated risk management tools but also enhances market interest and confidence in climate adaptation projects, leading to more efficient capital allocation. The research also explores the market acceptance of CABO, assessing awareness, interests, and potential concerns among investors, issuers, regulators, and the public. The findings suggest that while current market awareness of novel financial products like CABO remains at its early stages, the increasing severity of climate issues and growing risk awareness among investors indicate that CABO has the potential to emerge as a significant force driving the development of the climate finance market.
Sentiment Analysis and Strategic Considerations of KOL Weibo Comments
Authors: Ji Jiali , Zhao Wenshuo , Wei Zeshuai
Published: December 2024

Key Opinion Leaders (KOLs) possess significant commercial value in terms of focusing on user consensus. However, many companies that adopt a KOL-oriented marketing strategy fail to achieve the desired business outcomes in their market activities. At this stage, it is necessary to reference the experiences of relatively successful KOLs to address the shortcomings in their "precise efforts." This study utilizes web scraping tools and text mining techniques to conduct sentiment analysis on the Weibo content of Anchor A. The analysis ultimately reveals that Anchor As Weibo marketing lacks long-term, scientifically informed planning. The anchor does not have a clear understanding of their own role, nor do they appreciate the importance of Weibo marketing content. There is a failure to capture potential users and to deeply consider user satisfaction and emotions. This study aims to maximize the commercial value of KOLs in marketing activities through sentiment analysis of Weibo marketing content.
The Application of Discounted Cash Flow Model in Company Valuation Taking Gree Electric Appliances as an Example
Authors: Wang Yuhang
Published: December 2024

With the rapid development of the economy and the significant improvement of people's income level, the demand for investment is also becoming increasing strong. Choosing a scientific valuation model is beneficial to reduce investment risks for investors. Gree Electric Appliances is a well-known household appliance company in the world. Although its growth is currently sluggish, it has stable and sustainable operating capabilities and good operating conditions, can generate a large amount of cash flow, and has a high dividend yield. This paper uses the discounted cash flow model to value the company. Selected financial data of Greeley Electric for the years 2018 to 2022 and and the companys intrinsic value and share price are assessed based on its annual free cash flow. The expected research results indicate that the valuation model, as a judgmental tool, can help investors to assess the investment value and potential risks of Greeley, thus providing data support and expected reference for making investment decisions on Greeley's shares.
The Impact of Digital Transformation on Logistics Efficiency
Authors: Zhang Zixuan
Published: December 2024

The logistics business, like others, is undergoing dramatic transformations as a result of the worldwide digitization movement. The logistics industry in China, as a critical pillar of economic development, faces enormous potential for growth. Digital transformation has emerged as a fundamental driver for the logistics industry, with the potential to significantly improve logistics efficiency, cost, and user experience through technological and model changes. This article examines the digital transformation of the Chinese logistics business, using Jingdong Logistics as a sample case study. It investigates how Jingdong Logistics has significantly improved logistics efficiency through digital transformation, as well as an in-depth investigation of how it serves as an industry leader in this regard. The purpose of this study is to highlight Jingdong Logistics' digital transformation success stories, as well as provide useful references and inspiration for the digital transformation of the logistics industry and other industries. The number of firms participating in digital logistics has increased significantly across the country, and the market size has continued to grow, indicating the powerful significance of digital transformation in driving the logistics industry's expansion. Jingdong has significantly improved logistical efficiency by leveraging elements such as data-driven decision-making, process automation, and precision marketing, resulting in a more convenient and personalized shopping experience for customers. Jingdong not only focuses on improving its own operating efficiency, but it also actively provides digital procurement services to the real economy, supporting inclusive and shared growth in the logistics industry.
Causes and Effects of the Wealth Gap in the United States
Authors: Dai Shuo
Published: December 2024

The United States, as the world's largest economy, also stands out as one of the Western nations with the most pronounced wealth gap, long trapped in a vicious cycle where the rich continue to grow richer while the poor become poorer. This predicament profoundly impacts social equity and justice, simultaneously hindering the comprehensive and healthy development of the economy. Since the outbreak of the COVID-19 pandemic, despite the massive fiscal and monetary stimulus measures undertaken by the US government, these initiatives have failed to effectively benefit the underprivileged, instead offering billionaires opportunities to expand their wealth portfolios, thereby widening the chasm of the wealth gap even further. Unlike previous studies that focused solely on individual or specific categories of countries, this paper endeavors to comprehensively and deeply analyze the current state, underlying root causes, and widespread consequences of the wealth gap in the US. In addition, this paper puts forward targeted policy suggestions based on the unique national conditions of the US, including strengthening the implementation of anti-monopoly law, promoting the reform of electoral system, optimizing tax policy and so on. These policy recommendations aim to provide both theoretical foundations and practical guidance for alleviating the wealth gap, thereby promoting social equity and ensuring sustainable economic growth.
Prediction of Dirty Cryptocurrency Using Machine Learning Models
Authors: He Lina
Published: December 2024

Predicting the value of cryptocurrencies is essential due to their significant impact on the financial market and investment strategies. This paper evaluates the performance of three machine learning models: K-Nearest Neighbor (KNN), Linear Regression, and Random Forest Regression in predicting the value of various cryptocurrencies (Bitcoin, Ethereum, Bitcoin Cash, Litecoin, and Binance Coin (BNB)). Important metrics such as R-squared (R), accuracy, precision, recall, F1 score, Mean Squared Error (MSE), and Mean Absolute Error (MAE) are utilized alongside graphical comparisons of actual and predicted price categories. The analysis reveals that Random Forest Regression performs best for Bitcoin, while KNN is more suitable for Ethereum, BNB, and Litecoin. Linear Regression models tend to deviate more from actual prices. Both KNN and Random Forest Regression demonstrate a significantly closer relationship to real price categories. This study underscores the importance of model selection, highlighting the superior accuracy of KNN and random forest in predicting cryptocurrency prices. Future work could explore the integration of more advanced models and the impact of additional features on prediction accuracy.
Research on the Signal Denoising and Prediction Methods Based on Regression Models: Taking Three Types of Signal Waves as An Example
Authors: Chen Shu
Published: December 2024

Signal denoising and prediction have always been a focus in the fields of electronics and communication. Prior to this, Fourier transform was commonly used for denoising. More and more researchers are seeking other better signal processing methods. This paper provides a method of using regression models for this processing and uses Python code to generate relevant data and perform regressions processing. Then this paper makes a comparison between the regression model and Fourier transform for signal denoising and prediction performance. Finally, this paper provides prospects for optimizing and improving the experimental process, as well as exploring more applications of regression models in signal processing, based on the experimental results obtained. Through research and experiments, this paper has identified the advantages and disadvantages of different regression models for processing different signal waves, and verified the feasibility of regression models for processing signals, confirming the development prospects of machine models in the field of signal processing.
Research on the Current Status of Emotion Recognition and Response Strategies for Chatting Robots based on Deep Learning
Authors: Zhang Chengkai
Published: December 2024

With the rapid development of artificial intelligence technology, emotion recognition capability has become an important branch of the AI field. This study explores the current status of emotion recognition and response strategies for chatbots based on deep learning, focusing on analyzing the current state of emotion recognition and response strategies for chatbots. This research studies the problem through a questionnaire survey and literature research methods. The results show that chatbots still need to continuously improve their emotional algorithms to meet the growing needs of users, as well as the diversity and naturalness of various languages to provide users with a good experience.
Study of Turbofan Engine Components and Performance with Overheating Problems
Authors: Xu Zhenyang
Published: December 2024

The mainstream engines in the present world are classified into four types. Among them, the most prevalently used one is the turbofan engine. Compared with other engines, the turbofan engine features high thrust, high propulsive efficiency, low noise, and low fuel consumption. Moreover, its various components play an indispensable role in its operation. Based on the development history of the turbofan engine, this paper conducts an in-depth discussion on the components and working principle of the turbofan engine. Combined with its application in both the civil and military fields, the working performance of the turbofan engine is enhanced by improving common performance parameters, such as fan efficiency, fan flow, combustion chamber efficiency, and the excess air coefficient. This is aimed at enabling the engine to operate with higher efficiency and lower fuel consumption rate, which is a common feature of engines. The common high temperature problems in engines are studied and combined with the literature, it is concluded that new high temperature resistant materials such as ceramic matrix composites or spraying a thin thermal barrier coating on the turbine blades can be used to improve the resistance of engine components to high temperatures and increase the service life of the components.
Comparative Analysis of Expected Goals Models: Evaluating Predictive Accuracy and Feature Importance in European Soccer
Authors: Fu Siheng
Published: December 2024

Expected Goals (xG) is a widely used metric in soccer analytics that estimates the probability of a shot resulting in a goal based on various characteristics of the shot. This study compares the predictive accuracy and feature importance of two prominent xG models: Opta and Understat. Using data from the top five European leagues from the 2017-2018 to the 2023-2024 seasons, we evaluate the predictive accuracy of each model using L1 and L2 loss metrics. Our findings indicate that Understat outperforms Opta in terms of lower prediction errors in the Bundesliga, Premier League, and Serie A, while Opta yields more stable predictions in La Liga and Ligue 1. We further analyze the factors influencing xG predictions through feature importance techniques using Random Forest and XGBoost models, complemented by SHAP (SHapley Additive exPlanations) analysis. Results reveal that goal exposure angle, shooting angle, and shot distance are key features in predicting goal probability, with differences in how categorical variables are weighted between the models. The study concludes with a discussion of the strengths, limitations, and league-specific applications of both models, highlighting the need for standardized data collection practices and expanded contextual features to enhance xG model utility and accuracy.
Sentiment Analysis of Xiaohongshu Texts Based on the RoBERTa Model
Authors: Xie Yidi
Published: December 2024

This study investigates a novel sentiment analysis model designed for the Xiaohongshu platform, leveraging the RoBERTa model combined with BiLSTM and attention mechanisms. Xiaohongshu, a prominent social commerce platform in China, offers unique challenges for sentiment analysis due to its user-generated content, which is often informal and multi-dimensional. The RoBERTa-BiLSTM-Attention model is introduced to capture complex semantic nuances and enhance the accuracy of sentiment classification. A comparative experiment was conducted to evaluate this model's effectiveness against traditional methods, including Word2vec-LSTM, Word2vec-BiLSTM, BERT-BiLSTM, and BERT-BiLSTM-Attention. Results show that the RoBERTa-BiLSTM-Attention model outperforms these models in terms of accuracy and F1-score, demonstrating its potential to capture and classify sentiment in user-generated content on Xiaohongshu with improved robustness and depth.
Navigating the Landscape of Big Data Methods: A Survey through the Lens of Deep Learning
Authors: Dong Zexuan
Published: December 2024

The era of Big Data has ushered in an unprecedented deluge of information, characterized by its massive volume, diverse variety, high velocity, and inherent veracity challenges. Traditional data processing and analysis techniques often falter in the face of such complexities. Deep Learning, with its capacity to discern intricate patterns and representations from raw data, has emerged as a promising tool to navigate this landscape. This survey article provides a comprehensive overview of the methods employed in the realm of Big Data, systematically categorized based on the stage of data processing they address (preprocessing, storage, or processing & management), the type of learning involved (supervised, unsupervised, or reinforcement), the specific data characteristics they tackle (volume, variety, velocity, or veracity), and their application areas. We delve into the strengths and limitations of each method, highlighting their suitability for different Big Data scenarios. Furthermore, we explore the challenges and future trends in applying Deep Learning to Big Data, emphasizing the need for innovative solutions to harness its full potential. By offering a structured classification and insightful analysis, this survey serves as a valuable resource for researchers and practitioners seeking to understand and leverage the synergy between Deep Learning and Big Data.
Performance Comparison of Different Code Implementations of the KMP Algorithm
Authors: Tan Jiayi
Published: December 2024

This paper presents a performance comparison of diverse implementations of the KMP algorithm, a widely employed string matching technique for efficiently searching patterns in text. The study evaluates the time complexity, space complexity, and execution efficiency of different code versions. Key findings are derived from a review of relevant literature, focusing on advantages and challenges of various implementations. The experimental setup and performance metrics are described, comparing time and space usage across different implementations. The results are interpreted, discussing the significance of selecting the appropriate implementation for specific applications. The paper concludes with recommendations for future research and potential optimizations.
Deep Learning-Driven Order Execution Strategies in High-Frequency Trading: An Empirical Study on Enhancing Market Efficiency
Authors: Yang Yintian
Published: December 2024

This paper analyzes the efficacy of deep learning models, in particular reinforcement learning, in optimising the order execution for HFT. Typical execution models such as volume-weighted average price (VWAP) and time-weighted average price (TWAP) are typically unsuitable for HFT as they lack flexibility and are also latency. To solve these problems, our work utilizes a PPO reinforcement learning algorithm. Based on high-frequency tick data, the model continuously adapts to market conditions and enables performance enhancements in execution quality, speed and versatility under volatile markets. The experiment showed that the deep learning model performed better than the VWAP baseline, filling at 92%, market impact reduced by 15%, and running 43% faster. As this work shows, adaptive, deep learning based strategies not only enhance HFT execution, but also promote stability in the market, highlighting the possibilities of AI as a practical tool for trading.
A Summary and Discussion on the Current State of CVRP Research
Authors: Bao Yizhan
Published: December 2024

The Vehicle Routing Problem (VRP)is a fundamental combinatorial optimization problem in the field of transportation. Among its important variants, the Capacitated Vehicle Routing Problem (CVRP) focuses on optimizing delivery routes under vehicle capacity constraints to minimize transportation costs and enhance service quality. This paper systematically reviews the current state of CVRP research, encompassing the applications of exact algorithms and approximate algorithms, including genetic algorithms, ant colony optimization, and simulated annealing. It also explores improvements to the parent genetic algorithm (PGA) in CVRP, such as polymorphic mutation strategies and hybrid jump strategies. Finally, the paper addresses the challenges faced by CVRP in practical applications and proposes future research directions, including algorithm adaptability, exploration of hybrid algorithms, and efficient processing in big data environments. This study aims to provide a comprehensive reference framework for researchers in the CVRP field, promoting the further development and application of related technologies.
The Application of Artificial Intelligence and Machine Learning in Face Recognition Technology
Authors: Li Zhonghao
Published: December 2024

In the wake of the swift development of artificial intelligence (AI) and machine learning (ML) technologies, face recognition technology has emerged as a prominent research focus within the realm of biometrics. This paper delves into the most recent advancements of AI and ML algorithms with regard to enhancing the accuracy and speed of face recognition. To begin with, a comprehensive review of the development of face recognition technology is conducted. It traces the progression from traditional methods to the application of deep learning technology, while also summarizing the merits and limitations of the existing technology. Subsequently, the key technologies used in this paper are elaborated upon in meticulous These encompass the convolutional neural network (CNN), deep learning feature extraction, transfer learning, and the attention mechanism in face recognition, among others. These markedly augment the model's processing capabilities when dealing with complex scenes, varying lighting conditions, and occlusion situations. Furthermore, this paper undertakes an exploration of privacy protection and ethical concerns, It puts forward strategies aimed at bolstering data protection and privacy security without compromising the identification performance. Finally, the principal findings of this study are encapsulated, and future research directions are outlined. These include the further optimization of algorithms to curtail the consumption of computing resources, the development of more efficient data enhancement techniques to enhance model generalization, and exploration of a broader range of application scenarios, such as intelligent security, personalized services, and accessibility assistance systems. This study not only provides theoretical underpinning and practical guidance for the development of face recognition technology but also paves the way for promoting the extensive application of AI technology in social life.
Optimization of Quantitative Financial Trading Strategies Based on Machine Learning: Prediction and Decision Models for Stock and Derivatives Markets
Authors: Wang Zidu , Yang Yintian
Published: December 2024

Machine learning has become a cornerstone of quantitative finance, which allows for data-driven forecasting, strategy optimization, and trading-by-dot decision making. It analyzes the application of machine learning models such as recurrent neural networks (RNNs), long short term memory (LSTM) networks, and reinforcement learning (RL) in order to generate optimal predictive models and adaptive trading techniques. The research is based on historical stock and derivative market data and follows a rigorous process that includes data preprocessing, feature engineering and model training using random forests and gradient boosting. Our experimental evidence shows that LSTM performs better on long-term trends and ensemble models on short-term trends. The RL model significantly enhances risk adjusted returns, with higher Sharpe ratios and lower drawdowns than standard strategies, suggesting real-time market flexibility. The study highlights the positive effects of machine learning in improving trading performance, which will be applicable to other trading scenarios.
Overview of the Development of Wireless Communication Technology in Electronic Engineering
Authors: Guo Jiali
Published: December 2024

With the rapid development of the information society, wireless communication technology plays a crucial role in the field of electronic engineering. From the initial 2G to the current 5G, this technology has continuously evolved, driving the progress and development of human society. The rise of emerging technologies such as the Internet of Things (IoT) and artificial intelligence (AI) presents both challenges and opportunities for wireless communication. A thorough understanding of the evolution, application fields, and future trends of wireless communication technology is essential for grasping the pulse of technological advancement and driving innovation.However existing research on the development of wireless communication remains significantly limited. Therefore, this paper will provide a comprehensive summary of the characteristics of each generation of wireless communication technology. In addition, it will list several key application areas of this technology and, based on available materials and literature, identify the opportunities and challenges facing the wireless communication field, providing a reference for future research and development.
The Application of Intelligent Robots in the Medical Field
Authors: Zhang Runze
Published: December 2024

With the development of technology, the research and development of intelligent robots has extended into various fields. Among them, applications in the medical field are particularly prominent. The increasing demand for high-quality medical services and the continuous advancement of medical technology have also led to the continuous expansion of the global medical robot market. From 2010 to 2018, the global medical robot revenue grew from $1.361 billion to $2.8 billion, with a compound annual growth rate of 9.44%; the sales volume of medical robots grew from 932 units to 5,100 units, with a compound annual growth rate of 23.67%. In China, investment in medical robots has also seen substantial growth. From 2016 to 2019, the market size of China's medical robot industry grew annually at a rate of 28.82%. In 2020, the market size of medical service robots in China reached 5.94 billion yuan, and in 2021, the market size of medical robots in China reached 7.96 billion yuan. Therefore, this article studies the current application of intelligent robots in the medical field. This paper adopts the research method of theoretical analysis. The development of intelligent robots in the medical field has become increasingly mature, with the status and role of medical robots becoming increasingly important, functioning as an indispensable component of the sector.
Transtorno do espectro autista na Educação Infantil: superando mitos e estereótipos
Authors: Lima Thais de Oliveira , Batista Cleide Vitor Mussini
Published: December 2024

Este trabalho tem como objetivo debater a respeito de alguns mitos e visões estereotipadas que se criaram em relação ao Transtorno do Espectro Autista (TEA) ao longo do tempo. Propõe uma discussão balizada pela psicanálise em diálogo com as perspectivas atuais, enfatizando na Educação Infantil. A problemática é quais são as barreiras causadas pelos mitos e estereótipos em relação ao diagnóstico precoce do TEA? Foi realizada uma pesquisa qualitativa de literatura científica publicada nos últimos 11 anos em sites acadêmicos, na qual selecionamos uma entrevista realizada em 2023 com 23 profissionais da educação que trabalham em Centros de Educação Infantil, estabelecendo assim uma interlocução com os autores que estudaram a temática. Este estudo possibilitou uma reflexão sobre os equívocos de interpretação dos laudos de TEA durante o tempo, problematizando a proliferação de diagnósticos precoces das crianças, sem considerar o que estas têm a dizer sobre si. Destacamos o agravante de olhar a criança sob a perspectiva do adoecimento, principalmente os professores, e o DSM, criando para ela um cenário de incapacidade e de falsa inclusão no ambiente escolar. Precisamos discutir o aumento de casos de autismo, superando mitos e promovendo acolhimento, sem julgamentos, na áreas da Educação e da saúde.
Project Work in Networked Distance Education
Authors: Knudsen Morten , Helbo Jan , Jensen Lars Peter , Rokkjær Ole , Borch Ole , Østergaard Jørgen
Published: December 2024

Project organized problem based learning is a successful concept for on-campus education at Aalborg University. Recently it has been the basis in networked distance education as well. This paper describes the experiences of Internet-mediated project work in a new Master of Industrial Information Technology education. For a pilot project, each student group prepared a report, evaluating the collaborative work process, and these process reports combined with the teachers observations, comprise the background material. The main conclusions are, that the project work was a strong learning motivation, enhancing peer collaboration, and that networked communication and face-to-face meetings was an efficient combination.