bluuebunny/crossref_metadata_2025 huggingface dataset:
Adapting Trademark Law to the Digital Economy: Challenges, Reforms, and Solutions for Non-Traditional Marks
Authors: Young Jessica
Published: February 2025

The rise of the digital economy has expanded international trade while challenging traditional trademark law. This study examines how trademark law has adapted to issues like cybersquatting, keyword advertising, and non-traditional marks protection. Using a doctrinal approach, it analyses key legal frameworks including the Anticybersquatting Consumer Protection Act and the Uniform Domain-Name Dispute Resolution Policy, alongside cases like Google v. Louis Vuitton Malletier and the ongoing case of Sydney Nicole LLC v. Alyssa Sheil. By evaluating these legal sources, this study will highlight both their effectiveness and gaps in addressing digital trademark challenges, particularly in cross-border contexts. The findings reveals that while progress has been made, current frameworks remain reactive and territorially limited. This study advocates for more proactive, harmonised international measures and explores future research directions, including artificial intelligence, blockchain, and the evolving role of non-traditional marks in the digital marketplace.
Asymmetric Information: Insights Across Economic and Market Contexts
Authors: Liu Jiarui
Published: February 2025

Asymmetric information seeks to explain how uneven knowledge among different parties distorts markets, leading to inefficiency and even market failure. This essay examines asymmetric information as a key factor of influence in the market and explores some theoretical solutions, such as signaling and screening. The study demonstrates the benefits and limitations of these remedies through real-world examples, such as the quality gaps in the tourism industry and the reliance of e-commerce on product disclosures. Signaling helps the informed parties convey credibility, while screening assists the uninformed in obtaining crucial information. However, these mechanisms are not perfect and can have their own shortcomings. While signaling and screening mitigate the effects of asymmetric information, neither can fully resolve its consequences. The study also underscores that the problem of achieving efficient market outcomes persists due to asymmetric information.
A Study on the Impact of Anchoring Effect on Investor Behavior in Financial Markets
Authors: Dong Yujiao
Published: February 2025

The anchoring effect refers to the cognitive bias where initial information serves as a reference point, greatly influencing individuals judgments and decisions in conditions of uncertainty. Since Tversky and Kehneman introduced the anchoring and adjustment heuristic, the behavioral biases of a large number of investors in the financial market have aroused widespread attention to the role of the anchoring effect in investment decisions. This paper aims to investigate the impact of the anchoring effect on investor behavior in the financial market. Through literature review and theoretical analysis methods, the specific manifestations of the anchoring effect in price anchor formation, asset category selection and emotional influence are studied. The results show that the anchoring effect is widely present in investor decision-making, and its mechanism of action makes it difficult for investors to make objective decisions when facing market fluctuations, and they are often affected by the initial anchor point and induce irrational behavior. To this end, this paper proposes coping strategies such as multi-source information integration and dynamic evaluation to reduce the impact of anchoring bias and provide support for investors rational decision-making.
Brand Co-branding Marketing Strategy Analysis --Taking Luckin and Moutai Co-branding as an Example
Authors: Lin Yitong
Published: February 2025

With the growing diversity of marketing tools, co-branding has become a key strategy for major brands. By collaborating and launching products featuring elements from both brands, companies leverage shared popularity to achieve synergy. Luckin Coffee, a leading player in the beverage industry, exemplifies successful co-branding. Striving to become Chinas largest coffee chain by 2023, Luckin focuses on offering high-quality, affordable, and convenient products. Its collaboration with Moutai showcases the power of cross-industry partnerships, combining strengths to attract a broader consumer base and boost purchase interest. This paper examines Luckin's co-branding with Moutai, analyzing the advantages from 4P perspectives and consumer psychology. It explores marketing strategies, consumer perceptions of co-branded products, and how to craft a co-branding approach that drives high attraction, traffic, and returns. The findings highlight key factors for successful co-branding and provide valuable insights for other brands looking to adopt this strategy. Luckin and Moutais collaboration serves as a benchmark, offering actionable guidance for businesses aiming to maximize the potential of co-branding initiatives.
A Review of the Impact of Artificial Intelligence on Consumer Profiling Based on Regression Statistics
Authors: Mei Xien
Published: February 2025

Using regression analysis and statistical methods, this study explores the impact of artificial intelligence (AI) on consumer portraits, focusing on its self-learning ability and adaptability to new environments. With the rapid development of AI technology, its transformative potential and application value in various fields of social economy continue to emerge. The results show that AI, powered by big data, can accurately characterize consumers, significantly improving the efficiency of personalized recommendation, precision marketing, and intelligent customer service. This plays a critical role in improving user satisfaction and market competitiveness of enterprises. However, the research also identifies limitations in AIs performance when the amount and quality of data are insufficient. In complex situations or value trade-offs, AI struggles to completely replace human judgment and ethical thinking ability. Overall, while AI holds great potential in consumer portraits, it is still necessary to further optimize the algorithm model, expand data sources, and strengthen the protection mechanism for data privacy and security. These improvements will lay the foundation for a wider range of cross-domain applications in the future.
Efficiency of Unmanned Storage in Intelligent Logistics: A Case Study of Jingdong Logistics Kunshan Sorting Center
Authors: Cao Boya
Published: February 2025

With the rapid development of Internet of Things technology, the number of parcels in circulation on the market has soared, and people's response speed requirements for commodity supply are getting higher and higher. Intelligent logistics unmanned sorting systems have proposed new solutions to speed up the response speed of the supply chain. Taking Jingdong Logistics Kunshan unmanned sorting center as an example, this paper introduces the workflow of the unmanned sorting center and explores the reasons for the high efficiency of automatic warehousing in Jingdong Logistics Kunshan unmanned sorting Center, involving automatic guided vehicle (AGV), radio frequency identification (RFID) and other technologies. It extends from the Kunshan unmanned sorting Center to the whole unmanned sorting field and puts forward optimization suggestions for the possible problems in the current unmanned sorting field. This study provides ideas for the technological transformation of relevant small and medium-sized logistics enterprises and provides suggestions for the development of the unmanned sorting field of intelligent logistics.
Research on the Impact of Digital Transformation on Internal Control of Enterprises
Authors: Li Mengxiao
Published: February 2025

In todays era, the rapid development of digital technologies, such as cloud computing and big data, has profoundly altered the operational environment and development models of enterprises. Digital transformation has become an inevitable path for the development of enterprises. From a macro perspective, the global economic landscape has been reshaped by the wave of the digital economy, with countries enacting policies to encourage corporate transformation. Chinas 14th Five-Year Plan also strongly supports this initiative. From a micro perspective, market competition is fierce, and consumer demands are diverse. Enterprises can only establish themselves in the market by leveraging digital transformation to improve efficiency, reduce costs, and optimize products and services. However, internal control in enterprises faces both challenges and opportunities in the process of digital transformation. Traditional internal control systems are struggling to adapt to rapid information flow, massive data growth, and other circumstances, revealing problems such as poor information circulation. This paper deeply investigates the impact of digital transformation on internal control, finding that prior to transformation, there were many inadequacies in internal control, while after transformation, the five key elements of internal control are significantly empowered. However, enterprises must remain vigilant about new risks, such as data security, and achieve the collaborative development of both to ensure sustainable growth.
The Impact of Corporate ESG Performance on Stock Prices in the Manufacturing Sector
Authors: Hou Jianshu
Published: February 2025

This paper focuses on manufacturing enterprises, exploring the impact and mechanism of ESG performance on stock prices. Based on data from 2015-2022, empirical analysis reveals the industry and enterprise heterogeneity in ESG performances impact on stock prices. The research finds that ESG performance has a more significant stock price-boosting effect for highly polluting enterprises, primarily due to the markets higher valuation of marginal environmental governance improvements in high-pollution companies. State-owned and foreign-funded enterprises excel in ESG practices, with significantly higher ESG scores compared to private enterprises. Foreign-funded enterprises, due to international operational requirements, demonstrate more concentrated ESG performance and higher stock price stability. Further mechanism analysis indicates that ESG performance significantly enhances corporate financial performance through multiple channels, including optimizing financing structure, reducing financial risks, and improving cash flow conditions, thereby positively influencing stock prices. Specifically, good ESG performance can lower asset-liability ratios, optimize cash outflow structures, and improve net operating cash flow, thus enhancing enterprise market competitiveness and stock price stability. This study provides theoretical support for corporate ESG practice strategic planning and offers important references for investor decision-making and policy formulation. The research results demonstrate that ESG performance is not only a crucial manifestation of corporate social responsibility but also a key factor in improving financial performance and market competitiveness.
ESH Company Profitability Study Based on the Dupont Financial Analysis
Authors: Liu Shutong
Published: February 2025

The Dupont financial analysis is a comprehensive financial analysis method. When analyzing the profitability of an enterprise, it can combine the operating results with the financial status, so it can not only analyze the historical situation and current situation of the enterprise, but also predict the future development status of the enterprise according to the analysis results, which can more accurately evaluate the profitability of the company. Based on the Dupont analysis framework system, this paper takes ESH Company as the main case object, analyzes and studies the company's profitability data by using the company's published financial reports from 2019 to 2023, and finds out the problems in the business activities or the deviations in the development decisions of the enterprise. And put forward corresponding improvement measures for the company to improve profitability. The analysis finds that enterprises can maintain profits and stable operating income while increasing financial leverage. But at the same time, ESH company also has some problems in profit, mainly due to the increase in operating costs caused by the sharp compression of corporate profits. In this regard, the author believes that enterprise managers should take a more prudent attitude to make strategic decisions, so as to avoid the compression of profit margins and the situation of the enterprise into the loss.
Institutional Investor Shareholding and Corporate Social Responsibility
Authors: Dong Xinning
Published: February 2025

Under the new normal of economic development, China's emphasis on corporate social responsibility continues to increase. Institutional investors are often seen as an important force that can improve corporate governance. Examining the influence of shareholding on the sustainable growth of firms aids in comprehending how to enhance corporate governance structures to foster sustainable development. This article focuses on Chinese A-share listed firms from 2010 to 2021 as the subjects of study. The two-way fixed effects method is employed to examine the impact of institutional investors' ownership on corporate social responsibility scores in China. The results show that institutional investors hold shares can enhance corporate social responsibility. By improving corporate governance, institutional investors help companies manage and respond more effectively to risks and opportunities associated with social responsibility. In this paper, the heterogeneity analysis is further carried out, and it is found that the shareholding of institutional investors shows a more distinct facilitative impact on state-owned enterprises social responsibility performance. This study has a guiding role in optimizing corporate governance structure and enhancing the level of CSR fulfillment.
Research on Empowering Supply Chain Management Innovation with Blockchain Technology: A Case Study of Dangerous Goods
Authors: Cheng Youran
Published: February 2025

Against the backdrop of the rapid development of modern logistics, the chemical logistics industry is one of the important sub-sectors of the modern logistics industry and is also developing rapidly. The country is a major producer and user of dangerous goods such as hazardous chemicals, explosives, and radioactive materials, and is also a major country in road transportation of dangerous goods. In recent years, enterprises and governments have gradually upgraded their awareness of the safety of hazardous chemical transportation, and have paid more and more attention to the safety and efficiency of hazardous chemical transportation. This paper selects Yonggang Haian Logistics Co., Ltd. (referred to as "Yonggang Haian") as the research object to study how traditional chemical transportation companies can better transform and upgrade in today's era of the Internet of Things, respond to market opportunities, and achieve a steady and upward development trend. Yonggang Haian has successfully achieved corporate transformation and promoted higher-quality development of the company by completing the "three transformations". This case study provides useful practical experience for traditional chemical transportation companies on how to adapt to modern development. With the continuous development of the chemical transportation industry, companies should continue to pay attention to market opportunities and development trends, continuously optimize resource actions and strategic layout, and achieve continuous innovation and steady development
Research on Tesla Automobile Marketing Strategy - Taking the Chinese Market as an Example
Authors: Huang Maoxiang
Published: February 2025

The global automotive industry has been rapidly evolving, driven by environmental concerns and technological advancements, with the new energy vehicle market witnessing significant growth. Tesla, renowned for its innovative technology and high-performance electric vehicles, entered the Chinese market on January 7, 2020.This study provides an overview of Tesla's marketing strategies for entry and development in the Chinese electric vehicle market. Entering in January 2020, Tesla was attracted by lower production costs and government subsidies. Despite intense competition from domestic brands and data protection issues, it has achieved remarkable success through strong sales and a significant market share. Tesla's market strategies include a product strategy emphasizing technology edge and a wide product range, a price strategy following a high-to-low pricing model, and a direct sales-based marketing channels strategy. Although challenges persist, this combination has enabled Tesla to thrive in the competitive Chinese EV market. Through trends and reputation, Teslas brand building in China has emphasized the unique value of its electric vehicles, leveraging both online and offline channels for promotion.
AI Adoption and Corporate ESG Performance: Evidence from Chinese Listed Companies
Authors: Jia Huiyu
Published: February 2025

With the rapid development of artificial intelligence technology and increasing attention to corporate environmental, social, and governance performance, this study investigates the impact of AI adoption on firms' ESG performance in the Chinese context. Based on the sample of Chinese A-share listed companies from 2009 to 2020, this paper uses the method of textual analysis based on annual reports to examine how AI implementation affects corporate ESG ratings. The results indicate that the extent of AI-related information disclosed in corporate annual reports is significantly and positively related to their ESG ratings. Further analysis shows that this positive relationship holds after controlling for firm characteristics and conducting various robustness tests using alternative measures of AI adoption. The research enriches the literature on the determinants of ESG performance by providing new evidence on the role played by AI technology in enhancing corporate sustainability, and offers practical implications for corporate managers and policymakers on how to use AI technology in improving ESG performance and achieving the goals of sustainable development.
Advancing Operational Efficiency in the Logistics Industry: The Role of Logistics Information Systems (LIS)
Authors: Gong Chenghan
Published: February 2025

Traditional enterprises generally lack systematic support services, affecting product traceability and data storage, and reducing the logistics industry's efficiency. Logistics information systems (LIS) are very important for the future development of the logistics industry, especially in distribution, tracking, and supply chain optimization. Applying the LIS system has greatly improved the distribution performance and customer satisfaction of the logistics industry. Furthermore, LIS plays an important role in improving customer satisfaction, internal communication efficiency, and synchronization processes. However, the adoption of LIS faces significant challenges, including data security and change management. Investing in robust network security measures, such as cryptography and multi-signature protocols, can address data security concerns. At the same time, organizations also need top-down effective communication and synchronized LIS. Reasonable application of LIS is the key to improving the efficiency of modern logistics. LIS developers should stay attuned to market trends and technological advancements, ensuring systems are prepared for future innovations.
Research on the Dual-driving Mechanism of Social Media Marketing on Enterprise Brand Building and Sales Growth in the Digital Age: From the Perspective of Operations Management
Authors: Xie Yiyun
Published: February 2025

In the digital age, social media marketing has become a crucial strategy for enterprise development. As the digital landscape continues to evolve, businesses are constantly seeking effective ways to stand out and connect with their target audiences. Leveraging its advantages such as wide coverage, low cost, and high interactivity, this paper deeply analyzes the dual-driving mechanism of social media marketing on enterprise brand building and sales growth from the operations management perspective. Through a combination of theoretical elaboration and case analysis, it reveals how social media marketing helps enterprises shape their brand image, enhance brand awareness and reputation, and thereby promote sales and profit growth through means such as precise positioning, content dissemination, and user interaction. This provides theoretical support and practical guidance for enterprises' operations management in the digital wave. The results show that social media marketing has a significant dual-driving effect on enterprise brand building and sales growth, providing a practical path for further expanding the influence of social media marketing and its innovative construction.
Risk Exposure A Case Study of Monte Carlo Simulation
Authors: Li Lingjun
Published: February 2025

This study considered putting the S&P 500 and DAX indices into a portfolio and evaluating the Value at Risk of the portfolio. The former showcases the US market, while the latter stands for the European markets. Data for the study is assumed to consist of weekly closing prices spanning from the year 2000 to 2023. The research further developed volatility and tail characteristics of returns by modeling them using the AR-TGARCH model with skewed t-distribution. Stationarity and linear dynamics were tested for logarithmic returns using an AR model. GARCH and TGARCH resolved volatility clustering and asymmetry. The paths had to match the original distribution using PIT and Copula methods. Monte Carlo simulation was thereafter used to compute VaR. The test gave a 99% VaR of -0.071813 and a 95% VaR of -0.035805. The results interpret possible maximum losses that can be read as 7.18% and 3.58%, respectively. This study adequately applies the TGARCH (1,1) model by setting a skewed t-distribution to extreme tails and escalating volatility in order to capture severe market conditions.
Trademark Infringement and Dilution Defense in Social Networking Platforms - A Perspective on Trademark Parody
Authors: Huang Yichen
Published: February 2025

By 2024, active global internet users reached 470 million, with social media emerging as a key commercial platform due to its fast response, wide dissemination, and low cost. However, these advantages also lead to issues like trademark parody, where users imitate well-known brands for entertainment, criticism, or competition, potentially misleading consumers and harming brand reputation. This study explores the legal framework for regulating such behavior and the validity of parody as a defense. It proposes a model to identify and assess trademark parody to qualitatively determine whether the creation belongs to parody and to distinguish between commercial and non-commercial parody. On the basis of commercial parody, the model reviews whether it meets the constituent elements of infringement and dilution, and finally summarizes. This model can effectively define the scope and effectiveness of the defense claim based on trademark parody, fully protect the legitimate rights and interests of trademark owners, and at the same time protect the freedom of expression and creative enthusiasm of creators, thereby providing strong support for the healthy development of social network platforms.
Research on the Impact of Investor Sentiment on Chinese Stock Pricing
Authors: Hao Boxi
Published: February 2025

Asset pricing has always been one of the core topics in finance research. Traditional asset pricing theory builds a multi-factor pricing model based on market and company fundamentals and verifies it through empirical data. However, financial markets often exhibit irrational characteristics, and market efficiency theory and investor rationality assumptions cannot fully explain abnormal phenomena in the market. Therefore, behavioral finance has gradually emerged, expanding the framework of asset pricing models by introducing investor behavior and emotional factors. This study aims to explore the impact of investor sentiment on Chinese stock market pricing. By analyzing the monthly data of the Chinese stock market, the traditional Fama-French three-factor model was constructed, and on this basis, the sentiment factor was added to propose a new four-factor model. Research results show that the sentiment factor (SENT) has a significant impact on the returns of small-cap stocks and low price-to-book ratio stocks, especially during periods of large mood swings. Changes in investor sentiment often trigger irrational trading, leading to stock price declines. Reverse fluctuations. Compared with the three-factor model, the four-factor model has a better fitting effect in explaining stock market returns, further proving the importance of emotional factors in the Chinese stock market. This study not only fills the gap in the existing literature on investor sentiment in the Chinese stock market but also provides a theoretical basis for localized adjustment of multi-factor pricing models.
Analysis of the Construction of China's Digital Inclusive Finance Supervision System
Authors: Yu Xiao
Published: February 2025

In the Chinese market, there are many small and medium-sized enterprises with financing needs. Due to existing risk factors and the high cost of physical business outlets, however, financial institutions are unable to serve all those in need. Nevertheless, the total demand of such groups is huge, making the need for finance by the SME(small and medium enterprise) community cannot be ignored. This thesis adopts the method of literature analysis and review to mainly study what kind of problems will be encountered in the process of using digital technology to realize financial inclusion in China, and how the regulators and policymakers should manage the risks and formulate relevant policies in light of China's national conditions, so as to enable financial institutions to develop digital financial in a viable and effective way, and ultimately achieve the goal of providing financial services to all groups in the social strata at a low cost. This thesis finds that while strengthening regulation of digitalization in the financial sector in order to reduce risks, it is important to realize that the current lax regulatory environment is one of the key reasons for the rapid development of digital finance in China and that policymakers should weigh the pros and cons and find a compromise in the policymaking process.
AI-Driven Agent-Based Modeling of Investor Behavior: Leveraging Reinforcement Learning and Neural Networks to Simulate Irrationality in Financial Markets
Authors: Hu Bowen
Published: February 2025

The paper presents how ABM, RL and NNs are combined to simulate the behaviour of investors in financial markets. ABM is an adaptable approach to modeling highly complex financial systems in which agents (investors) act and change on incomplete or distorted data. Reinforcement Learning optimizes agent behaviour through learning from their mistakes and making changes over time Neural Networks optimise the model by finding non-linearity in the market data to enhance the models predictive ability. The research in this paper examines how misguided investor decisions, such as overconfidence and herd behavior, cause market crashes, bubbles and crashes, even when agents have taken some lessons from their experiences. The results show that ABM, RL and NNs work together in way that provide valuable insights into the nature of financial markets, and reveal the promise and limitations of applying AI to predict complex and unpredictable markets. These results indicate that although Neural Networks predict more accurately when markets are stable, they are poorly effective when market is unstable with very high irrationality. These insights may be used to improve the regulation and policymaking of markets in practice.
Beat ChatGPT at Fortune-TellingAn Attempt to Optimize a Large Language Model
Authors: Cheng Steven , Han Mingyang , Lu Zonghong , Xu Weibo , Yu Jingyan
Published: February 2025

In the context of the growing popularity of fortune-telling in modern Chinese society, this paper attempts to construct and optimize a language model specifically for fortune-telling and compares its performance with mainstream models such as ChatGPT and Llama. Due to the antiquity and cultural particularity of Chinese fortune-telling systems such as Zhou Yi and Eight Characters, they face unique challenges. We generated a customized Chinese fortune-telling data set through ChatGPT, crawled the data on multiple websites, continuously optimized the instructions to build specialized GPTs, and improved its output by continuously optimizing the question design and design. We then evaluated and compared the model's responses in the fields of career, finance, and health, and the results showed that the optimized model outperformed ChatGPT in terms of accuracy and depth of prediction. The paper also highlights the importance of preserving Chinese cultural traditions through AI and explores the user-friendliness of GPT-based solutions when training custom models.
Sentiment Analysis of News Headlines and Stock Price Prediction
Authors: Meng Fanshuai , Yeerlan Amanzhuli , Zhang Zihan
Published: February 2025

Investing methods and decision-making of the stock market are driven by lots of factors, among all these factors, the significant impact of news should not be overlooked. This article combines machine learning algorithms to analyze sentiment analysis based on daily top 25 news titles and the up and down condition of stock price, using CountVectorizer and Term Frequency-Inverse Document Frequency (TF-IDF) to extract textual feature, then implementing Random Forest (RF) and Logistic Regression (LR) to train, test, and carry out prediction. From the results of the classification report, the performance and efficiency of each combination are analyzed and compared to draw conclusions. The results show that using TF-IDF instead of CountVectorizer can bring higher accuracy in this large and high precision text categorization task. Even if LR can more accurately recall news of stock price declines in binary classification problems, RF has the best performance in the balance of accuracy and classification.
Emotion Detection and Analysis Techniques Based on NLP
Authors: Yang Siyu
Published: February 2025

Emotion, as one of the key bridges connecting human society, serves as a central hub for human communication. With the continuous advancement of computer science, the field of artificial intelligence has developed many innovative branches and subfields to help computers understand human language, analyze the emotions and sentiments embedded within it, and make decisions and judgments. This technology has been applied across various domains, playing practical and significant roles, greatly benefiting human daily life and scientific exploration. Over the past two decades, natural language processing (NLP) has achieved remarkable progress, demonstrated extensive potential and contributed significantly to numerous areas. Emotions hold profound significance and impact on human life and social relationships. As an essential subfield of NLP, sentiment analysis and emotion detection have found broad applications in multiple domains. Despite the rapid development of sentiment analysis technologies, their applications still face many challenges. This paper explores the practical value and future prospects of sentiment analysis and emotion detection, reviews their currently employed methods and technological advancements, and focuses on analyzing existing issues while discussing their causes and potential solutions.
Advancements and Challenges of Multimodal Models in Medical Applications
Authors: He Zimeng
Published: February 2025

Multimodal models have demonstrated significant potential in the medical field integrating information from various modalities such as images and text to improve understanding and reasoning. This paper provides a comprehensive review of their applications, focusing on medical visual question answering (VQA), medical report generation, and surgical assistance systems. In VQA, multimodal models like MedFuseNet and XrayGPT enhance patient-doctor communication and assist in disease diagnosis. For medical report generation, models such as Medical-VLBERT and RadFM automate report writing, alleviating the workload of healthcare professionals while improving accuracy. In surgical assistance, models like Surgical-LVLM and PitVQA-Net support surgical localization, pathological analysis, and procedural annotations. Despite these advancements, challenges persist, including data scarcity, limited model interpretability, and difficulty adapting to dynamic medical scenarios. The lack of diverse and annotated datasets, particularly for rare diseases, hinders the models generalization capabilities. Furthermore, ensuring patient privacy and compliance with regulatory frameworks is critical for broader adoption. This review synthesizes recent developments, highlights challenges, and provides insights into the future of multimodal AI in healthcare. By advancing intelligent healthcare systems, multimodal models have the potential to transform clinical practices, improve diagnostic accuracy, and enhance patient outcomes.
An In-Depth Analysis of the Transition from Gasoline Vehicles to Electric Vehicles (EVs): Factors Influencing Consumer Choices and Market Dynamics
Authors: Li Hongyu
Published: February 2025

This paper examines the elements that affect consumers' decisions between gasoline and electric vehicles (EVs), examining the growing trend of EV adoption. It examines the distinctions between gasoline and electric vehicles with an emphasis on important factors including performance, environmental impact, maintenance costs, and convenience. The study highlights EVs' economic and environmental benefits, especially in lowering carbon emissions and halting climate change. It does, however, clearly recognize the drawbacks of EVs, including their greater initial price, inadequate infrastructure for charging, and range anxiety. The study also looks at how government regulations, cultural variations, and the growth of ethical consumption will all have a significant impact on how sustainable transportation develops in the future. To encourage wider adoption of EVs, the paper suggests enhancing consumer awareness, expanding charging infrastructure, and improving vehicle affordability. Although cultural distinctions across different areas are mentioned in passing, the study does not go into great detail into the particular circumstances of global markets, such as the different dynamics between industrialized and developing nations. Future studies could examine these geographical and cultural variations in greater detail, offering comprehensive evaluations of consumer behaviour as well as the effects of infrastructure development and policy on the global acceptance and uptake of electric vehicles.
Clinical applications of artificial intelligence based on cardiovascular disease
Authors: Li Jialin , Zhu Mengyuan
Published: February 2025

Artificial intelligence has played a significant role in the medical field of cardiovascular diseases, especially in cardiac imaging. Heart diseases affect the function of the heart, causing the heart to be unable to supply blood normally, thus affecting the functions of the body's organs and systems. This paper summarizes the applications of algorithms such as deep learning and machine learning in the clinical treatment of cardiovascular diseases and their differences from two aspects: medical imaging and heart signals. It also reviews how these algorithms assist doctors in making more accurate diagnoses in the early screening, data analysis, and postoperative detection of cardiovascular diseases. It studies the optimization of traditional algorithms and image reconstruction by artificial intelligence-based imaging detection methods, as well as the specific application ways of deep learning in frame selection, segmentation, and disease assessment. Meanwhile, it discusses the advantages of convolutional neural networks (CNN) and recurrent neural networks (RNN) in performing classification tasks in Electrocardiogram (ECG) and puts forward the possibility of using 3D computer simulation synthesis system data to improve the generalization ability of machine learning models. In addition, this paper also introduces the limitations and challenges in the clinical application of AI algorithms in this field as well as their future development, providing a new perspective for the further prevention and control of cardiovascular diseases. The computational methodologies expounded within this review constitute a formidable asset in the realm of medical discoveries, and their translation into the clinical domain holds the potential to precipitate promising advancements.
Next generation perovskite photodetectors : nano-trend and their application
Authors: Wu Rongrong
Published: February 2025

This paper aims to the synthesis methods of four kinds of perovskite nanostructures and their properties, advantages and disadvantages under different nanostructures. Three-dimensional CsPbX3 perovskite quantum dots were obtained by high temperature hot injection method, and CsPbBr3 nanorods were obtained by ion exchange to form intermediate products. Two-dimensional MAPbI3 nanorods were obtained by physical vapor deposition and chemical vapor deposition, and CsPbBr3 nanoribbons were obtained by interfacial phase transformation method. Finally, it is concluded that in terms of synthesis difficulty, nanorods, nanofilms, nanoribbons and quantum dots are in order from difficult to easy. In terms of flexibility, from high to low, nanosheets, nanoribbons, quantum dots, and nanorods are in order. In terms of optical properties, quantum dots and nanoribbons have better fluorescence properties. The nanorods have better luminous stability, the nanosheets have better thermal and chemical stability, and the nanoribbons have better water stability. Continued innovation in materials and manufacturing will ensure their future role in renewable energy solutions.
Emotion Classification through Song Lyrics in Multi-Languages with Bert
Authors: Hong Yiyang , Xue Yuqi
Published: February 2025

This research explores emotion classification in song lyrics using BERT models for multi-language datasets, focusing on English and Chinese lyrics. The study emphasizes the application of music therapy techniques by utilizing song lyrics to assist clients in expressing and processing emotions. Sentiment analysis is conducted through a combination of CNN, LSTM, and GRU models, with a GRU + CNN hybrid model demonstrating enhanced performance in multilingual contexts. A comprehensive preprocessing process, including translation processing and tokenization, enables the effective analysis of both English and Chinese lyrics. Experimental results indicate that the GRU + CNN model outperforms traditional models, particularly in cross-lingual sentiment analysis, achieving significant improvements in accuracy and emotional classification.
A Study on ChatGPT-Based Code Translation from Python to Java
Authors: Gao Xiling
Published: February 2025

Programming language translation is essential in modern software development, facilitating cross-platform compatibility and the adaptation of legacy systems. This study examines the performance of large language models (LLMs), such as ChatGPT, in Python-to-Java code translation. Using a dataset of ten diverse algorithmic problems and advanced prompt engineering techniques, we evaluate the models effectiveness in maintaining computational accuracy (CA) and preserving method correctness (PMC). Results indicate that LLMs perform well on standard tasks but encounter challenges in complex scenarios involving advanced data structures and recursion. These findings uncover the potential of LLMs in code translation while highlighting the need for improved prompt strategies and domain-specific fine-tuning for complex tasks.
Research on the causes of flood disasters in typical cities on the mainstream of the Beijiang River: Taking the “22.6” catastrophic flood of the Beijiang River as an example
Authors: Li Yan , Huang Yuwei , Zhang Zhaokun , Wu Qingzhou , Wu Yunjiang
Published: February 2025

In recent years, urban flood disasters in China have become increasingly serious. In mid-June 2022, the northern part of Guangdong Province was affected by continuous rainfall, and floods occurred in many rivers in the upper and middle reaches of the Beijiang River in the Pearl River Basin, causing serious floods in many cities, villages, and towns in the basin. Based on the rain and flood processes of these flood disasters and analyses of the specific disaster situations of three typical cities, this paper deeply analyzes the urban water system, vertical topography, etc. The main and secondary causes of flood disasters in the three cities are studied, and the deficiencies in the expansion of cities under different natural geographical conditions are explored through comparisons to address flood disasters. This work provides a basis for the cities to establish flood control systems that are integrated, systematic, and adapted to local conditions.
The problem of small-scale marine oil spills discharged by unattributable vessels: A review
Authors: Al-Kamzari Amran , Carrick Jayne , Gray Tim , Fitzsimmons Clare , Burgess J. Grant
Published: February 2025

Oil pollution is one of the most conspicuous forms of damage to the marine environment. It can be divided into two categories: large-scale and small-scale spills. Considerable research has been conducted on large-scale spills, including investigations into the damage caused, the methods of remediation employed, the compensation paid, and the reasons for a sharp decrease in the number of major oil spills during the last 20 years. However, very little attention has been paid to small-scale spills, leaving largely unresearched the reasons for their continued occurrence, the damage they cause, and the lack of compensation paid out for them. Because the number of large-scale marine oil spills has greatly reduced in recent years, there may be a perception that the problem of marine oil spills has been broadly brought under control. However, this would be to ignore the issue of small-scale spills, which have not decreased and are causing severe problems in coastal areas. The perpetrators of these small-scale spills are very difficult to identify, so they invariably escape detection and thereby avoid having to pay for the clean-up and damages costs caused by their actions. The main aim of this review paper is to draw attention to the lack of research into this unsatisfactory situation. The paper describes the scale and type of damage caused by small-scale spills; analyzes the problem of identifying unattributable polluters; considers ways of remediating oil spills; and evaluates current procedures for obtaining compensation for clean-up operations. Given the escalating frequency of these small-scale spills and their profound impact on marine ecosystems and vulnerable communities, it is imperative that researchers, policymakers, and stakeholders prioritize the development of effective strategies to address this pressing issue. The time to act is now, as the health of our coastlines and the livelihoods of countless individuals depend on our ability to confront the challenges posed by small-scale oil spills.
Development and Research of Systematic Course Based on Work Process
Authors: Hao Yu
Published: February 2025

Systematization of work processes is a curriculum development model, with the core concepts of work processes and systematization. Systematization of work processes can present work objects in a structured manner, achieving the organic unity of concrete behavior and abstract processes. This method helps students better understand and apply the knowledge they have learned, and lays a solid foundation for their future career development. By defining the concepts of workflow systematization, typical work tasks, action areas, learning areas, and learning contexts, the theoretical characteristics of course development balance, diversity of course objectives, relevance of course content, and course evaluation process were studied. Five basic steps for developing workflow systems were established, providing theoretical basis and practical guidance for the development and application of workflow systematization courses. These improvement effects have important practical significance for vocational college students to engage in safety related work in the future.
The Grotesques under the Perspective of Inferiority Theory: The Inferiority Complex in Winesburg, Ohio
Authors: Liu Siyuan
Published: February 2025

American modernist writer Sherwood Anderson's masterpiece Winesburg, Ohio shows the gradual distortion of the psychological process of the Winesburg people. Anderson’s grotesques lack of a sense of social belonging, and their attempts to establish love and friendship end in failure, which echoes with the theory of inferiority complex put forward by Austrian psychiatrist Alfred Adler. This paper uses Adler's theory of inferiority, takes the sense of inferiority as an entry point, analyzes the symptoms of the inferiority complex and the reasons for that in Winesburg, Ohio, and finally reveals the importance of the sense of cooperation and social emotion. Anatomy of the malformed psychology of the Winesburg inhabitants contributes to a further understanding of the modern consciousness of confusion, loneliness, and loss. Adler's theory of inferiority is also confirmed in the psychological interpretation of Winesburg, Ohio.
Huaichen Hu 's Chinese Teaching Thought and Practical Effect
Authors: Ding Gaokui
Published: February 2025

Huaichen Hu has made outstanding contributions to modern Chinese education. His Chinese teaching thought is influenced by regional culture and family origin. The graduation and entry into the Commercial Press of Nanyang Middle School is an important time node for the accumulation, exploration and maturity of Chinese teaching ideas. In many years of Chinese practice, he has established the concept of attaching importance to extracurricular reading, put forward the idea of writing teaching that pays equal attention to practicality and students ' subjectivity, and practiced the idea of ' giving consideration to both integrity and academicity ' in classroom teaching. Its research can provide enlightenment for the moment to ' talk about professional knowledge first, then discuss educational concepts ' and ' pay attention to self-education and do effective Chinese education '.
Reform and Practice of the Talent Training Model for Higher Vocational Marketing Majors Based on School-Enterprise Cooperation
Authors: Zheng Yiqi
Published: February 2025

Taking the school-enterprise cooperation between McDonald's and the Marketing major of Zhejiang Dongfang Vocational and Technical College as an example, this paper deeply explores the reform and practice of the talent training model for higher vocational marketing majors. It analyzes the corporate background and existing conditions of McDonald's, elaborates on the background, significance and origin of the project implementation, and studies the current situation and development trends of relevant technologies at home and abroad. It also introduces in detail the basic ideas, main contents, key technologies and innovation points of the research and development of the cooperation project. The aim is to provide useful references for the school-enterprise cooperation talent training of higher vocational marketing majors, promote the in-depth development of the integration of industry and education in vocational education, and improve the quality of talent training to meet the needs of society and enterprises.