bluuebunny/crossref_metadata_2025 huggingface dataset:
The Economic Reasons Behind the Policy Game Between China and the United States in Southeast Asia: An Analysis from the Perspectives of Trade and Trade Structure
Authors: Chen Xinyi
Published: December 2024

The United States is the most influential country in Southeast Asia, while China, though starting later, is rapidly expanding its influence. In 2009, Chinas trade volume with ASEAN surpassed that of the United States, and since then, the U.S. has continuously introduced various policies in the Asia-Pacific region, attempting to curb Chinas growing influence. Changes in economic power are the main factors behind the policy game between China and the United States. This paper analyzes the economic reasons behind the geopolitical policy game between China and the United States in the Asia-Pacific region by comparing the changes in trade volume and trade structure between China, the U.S., and ASEAN.
A Deep Comparative Study on the Cultural Tourism Image Construction of Bashu and Southeast Asia, and an Exploration of Development Strategies for the Bashu Region
Authors: Guo Li , Liu Chang
Published: December 2024

Cultural tourism, characterized by its unique historical depth, cultural allure, rich connotations, and immersive experiences, has become a major driving force in attracting tourists. As regions with abundant cultural and tourism resources, both the Bashu area and Southeast Asia have demonstrated distinct features and strategies in constructing their cultural tourism images. This paper provides a comparative analysis of multiple aspects involved in the tourism image construction of the Bashu and Southeast Asian regions. Under the framework of cultural and tourism integration, it explores the intrinsic value of cultural tourism in both areas, investigates the similarities and differences in their cultural tourism images, and offers suggestions for building a culturally rich and regionally distinctive tourism image for the Bashu region.
Changes in Employment Structure and Response Strategies in the Context of the Digital Economy
Authors: Duan Rongrong , Li Zongyi , Li Minheng
Published: December 2024

The rapid development of the digital economy has profoundly impacted employment structures. This paper focuses on analyzing the changing trends in employment structures within the context of the digital economy, including the emergence of new digital occupations, the digital transformation of traditional jobs, and the increase or decrease in employment opportunities. Through case studies of Beijing and Yunnan, strategies to address these employment structure changes are summarized. These strategies include enhancing digital skills training, improving employment policies, and promoting entrepreneurship and innovation to alleviate employment pressures, optimize the allocation of labor resources, and foster stable economic and social development.
Quantitative Methodology for Industrial Activities and Water Resources: An Analysis on Healthcare, Materials, and Consumer Discretionary Sectors and Implications for Chula Vista
Authors: Jing Sanja
Published: December 2024

This study investigates the relationship of industrial activities and water resources evaluation, focusing on the healthcare, materials, and consumer discretionary sectors. Utilizing the ENCORE data, I analyzed the dependencies and impacts of these sectors on water resources through 7 indicative endpoints, revealing significant variations in water usage and pollution. The healthcare sector demonstrated a critical reliance on groundwater and surface water, while the materials and consumer discretionary sectors showed similar patterns with opportunities for climate regulation. To further investigate the interactions between water resources and local socioeconomic and industrial development, I conducted a case study of Chula Vista. This analysis demonstrates how these industrial dynamics affect local communities, highlighting the socioeconomic implications of water-related challenges. The analysis leveraged spatial data, census information, and employment statistics to demonstrate how industrial activities influence water resources and community well-being. Overall, the findings from this study emphasize the need for targeted water management strategies and integration of local socioeconomic factors to resource and environmental management and sustainable practices. Policymakers and local leaders can leverage these insights to develop long-term policies that hold businesses accountable, ensuring resilient frameworks that support community success and uphold sustainable values.
Market Status and Marketing Strategy of the Canva App in China
Authors: Lin Tsz Lap
Published: December 2024

This study examines the marketing strategy of the Canva app in the Chinese market, with a special focus on its localized cultural adaptation and market segmentation and positioning. This thesis focuses on how Canva has established and maintained its competitive advantage in the Chinese market through its unique marketing strategies, including market segmentation, targeting and product localization. The study also analyzes the specific measures taken by Canva in facing local Chinese competitors and examines the effectiveness of these strategies. The research found that Canva has achieved a significant market position in China's online design platform market through its user-friendly interface, reasonable pricing strategy, and powerful collaboration features. Meanwhile, Canva's localization adjustments, such as templates adapted to the needs of China's social software and its strategic partnership with Visual China, have further strengthened its market competitiveness. However, Canva still faces challenges in attracting professional users who need highly customized design tools. This study not only provides a case study for academics on the operation of international brands in the Chinese market, enriching the research literature on globalization and cross-cultural management, but also provides valuable references for business practices, especially in terms of technological innovations and global adaptation of service models. Through an in-depth analysis of Canva, the study provides new perspectives for understanding how technology products meet local needs in different market environments.
Reexamining ESG from a Genealogical Perspective: The Similar Logic of Sustainability Across Different Periods
Authors: Liu Shangkun
Published: December 2024

As a current research hotspot, ESG (Environmental, Social, and Governance) has been widely applied in fields such as investment and management. However, defining its connotation and exploring sustainability on a social level remain under-researched, leading to numerous issues and controversies. This paper analyzes the social sustainability of ESG from three aspects to redefine ESG, enabling it to serve practical needs more effectively. The first section outlines the prevailing concepts of ESG, reviews its development trajectory, and redefines its connotations. The second section revisits the evolution of workers' welfare in Britain from the Industrial Revolution to the present, focusing on the intrinsic logic of sustainability to explore the historical roots of ESGs sustainability. The third section provides a brief analysis of the historical inevitability of ESG within the context of modern development trends. This paper aims to contribute to future ESG research.
Analysis of Corporate Sustainable Development Strategy and Social Responsibility Practice Effectiveness Based on ESG Principles A Case Study of Sinopec
Authors: Chen Yueyao
Published: December 2024

As global attention to environmental protection, social equity, and sound corporate governance continues to grow, ESG presents both challenges and opportunities. This study aims to explore how integrating ESG (Environmental, Social, and Governance) principles can enhance corporate sustainable development strategies and analyze the effectiveness of social responsibility practices. The study conducts a theoretical analysis of corporate social responsibility practice effectiveness and uses a case study approach to examine Sinopecs sustainable development strategy and social responsibility practices in depth, evaluating the role of ESG principles in promoting corporate sustainable development strategies and improving the effectiveness of social responsibility practices. The research reveals that integrating ESG principles into corporate sustainable development strategies can significantly enhance a companys long-term competitiveness and market adaptability.
The Evolution and Recession of the Zhejiang Village in Beijing
Authors: Chen Rui
Published: December 2024

This paper investigates the evolution of Zhejiang village in Beijing, highlighting its rise, transformation, and decline. The paper recollects the origin of the village, organizes and explains the aggregate development of the region since the year 1984. The paper further discusses the prime of the Zhejiang village, and the eventual fall in the mid 2010s. This paper analyzes the reason of the fall from three perspective, explaining the effect of government projects and policies, the advancement of e-commercial and automation of production, and the social dilemmas and challenges faced by its members due to the change in environment from migration. Ultimately, this paper emphasizes the need for inclusive policy-making that supports the sustainable development and integration of migrant communities, drawing valuable lessons from the experience of Zhejiang village.
The Impact of ESG Report Transparency on Investor Behavior
Authors: Zheng Ziqing
Published: December 2024

This study compares investor behavior in the context of comprehensive and selective ESG disclosure. The research underscores how openness promotes corporate performance, although it does not address the psychology and decision-making processes of investors. Additionally, it examines the responses of both institutional and individual investors to ESG disclosures, highlighting the potential influence of behavioral features on investment decisions an area previously underexplored. The study further examines how ESG openness affects investor behavior across sectors, including both high-risk and low-risk companies, a focus largely neglected in earlier research. Employing quantitative empirical methods and case studies, the research examines how ESG transparency in corporate reporting affects investor behavior. Transparency will be assessed using data from MSCI and Sustainalytics, alongside investor behavior metrics such as trading volumes, stock price volatility, and confidence indexes obtained from Bloomberg and Thomson Reuters. The study posits that enhanced ESG transparency is likely to increase investor trust, improve decision-making, and ultimately bolster corporate value. This paper seeks to provide valuable insights for business practitioners and policymakers regarding the significance of ESG reporting in fostering transparency and promoting sustainable investment practices.
The Impact of Fiscal Decentralization on Infrastructure Development: Based on a Dual Perspective of Quantity and Quality
Authors: Zhao Zixi
Published: December 2024

Currently, the focus of infrastructure development must shift from the pursuit of high growth to high-quality development. As a public good, comprehensive infrastructure development is inextricably linked to the government's financial contribution. This paper uses a provincial panel data set comprising 30 provinces (Tibet is excluded due to incomplete data) from 2005 to 2021, measures fiscal decentralization indicators based on two perspectives: revenue decentralization and expenditure decentralization, and examines the impacts on the quantity and quality of infrastructure respectively. Furthermore, the mechanism is investigated from the perspective of fiscal pressure, and a series of endogeneity, robustness, and heterogeneity are examined. The results demonstrate that fiscal decentralization exerts a positive influence on the quantity of infrastructure, yet a negative impact on its quality. This conclusion remains robust following a series of robustness tests and endogeneity analyses. The mechanism of fiscal decentralization, as evidenced by the significant alleviation of fiscal pressure on the quantity of infrastructure, does not yield a similar effect on the quality of infrastructure. This results in disparate inputs from local governments. Additionally, quantile regressions demonstrate that the varying effects of fiscal decentralization on the quantity and quality of infrastructure are particularly pronounced at low to medium levels of quantity. This paper provides theoretical evidence for promoting fiscal system reform, which is conducive to enhancing the effectiveness of local government governance and playing a strategic, fundamental, and pioneering role in the overall national development of infrastructure.
The Future Trend of Consumption Downgrade and Real Estate Bubble Economy in First-tier Cities
Authors: Zhou Kexin
Published: December 2024

This paper explores the trend of consumption downgrading and the real estate bubble economy in first-tier cities. The study is set against the backdrop of slowing economic growth and increasing uncertainty in household income, which are driving residents in these cities to shift their consumption habits towards more affordable goods. The research methods invovle examining the definition, causes, consequences, and effects of the real estate bubble economy, analyzing policy responses and preventive measures, and investigating the definition and characteristics of the consumption downgrading phenomenon, along with its impact on real estate demand and investment. The study concludes that the consumption downgrading trend has a complex and multifaceted impact on the real estate investment landscape in first-tier cities, altering the markets supply and demand dynamics and inflencing the strategic investment decisions of developers and investors.
Female Leadership, Philanthropy, and Corporate Management: A Study Based on Listed A-Share Companies
Authors: Yang Haoyue
Published: December 2024

This paper investigates the impact of female leadership and their involvement in philanthropic activities on corporate management, utilizing data from listed A-share companies. The study reveals that from 2006 to 2021, the proportion of female leaders in listed companies steadily increased, with particularly significant growth in female supervisory positions. Further analysis shows that companies with female directors and executives tend to have a higher proportion of charitable donations compared to those without female leadership. Moreover, companies with higher charitable donation ratios also exhibit correspondingly higher returns on assets. This finding not only highlights the positive role of female leaders in promoting philanthropy but also provides empirical evidence for the synergy between corporate social responsibility and economic benefits. The study also notes that despite the numerous opportunities for female-led philanthropy, such as policy support, improved market conditions, and heightened awareness of social services, challenges remain, including the relatively low number of female leaders and limited societal recognition. The value of this research lies in emphasizing the potential and importance of female leadership in philanthropy, providing theoretical and practical references for the sustainable development of female-driven philanthropy.
An Analysis of Teslas Development and Profitability
Authors: Zhang Zeyang
Published: December 2024

Tesla, which is the most famous electric car manufacturer in 2023, has developed a strong following in relatively short existence due in part to its more advanced technology as well as its forward-looking vision. Tesla is presented in the prominent markets of the United States, Europe, Asia and Canada and it is at the leading position of new energy vehicles markets. In current situation, Tesla seems to face several problems: quality, competition and production. Based on Tesla's financial statements from 2020 to 2023, this paper selects several key indicators such as earnings per share (EPS), net profit margin and return on equity (ROE) to do a horizontal analysis about Teslas profitability, so as to to judge new energy vehicle markets future trend. The study reveals that as the electric vehicle market is close to saturation, the main competition in the electric vehicle industry now lies in cost and quality issues. Tesla can vigorously research battery technology so as to reduce production costs and stabilize its market share.
Research on the Revenue of Basketball Celebrity Endorsement Brands Based on Regression Model
Authors: Li Yi
Published: December 2024

Nowadays, the influence of sports brands is increasing, and many sports brands choose to sign well-known sports stars to enhance their brand influence. Chinese sports brands are also like this, increasing brand awareness by signing a large number of star players, and many of these brands choose to sign NBA stars as spokespersons. But are the profits brought by different levels of basketball stars different? This article will analyze and discuss this. This article uses regression models to analyze the basketball stars at different levels, in order to determine which category of basketball stars can bring the greatest income to the brand. Firstly, this article collected sales data for five players' respective brands: Aaron Gordon, Nikola Jokic, Klay Thompson, Keldon Johnson, and Austin Reaves. And preprocess the five sets of data using Visual Studio Code, integrate the five sets of data and export them to Excel. Then, the data was imported using R studio, analyzed using R language, and four regression models were created. The next step is to determine whether different factors bring good benefits through the significance of their respective factors in each regression model. Finally, by comparing the five players, it can be inferred which level of star is more suitable for brand endorsement and brings the greatest benefits.
Analysis of the Role of Renewable Energy Investment in China's Economic Growth
Authors: Weng Guangpeng
Published: December 2024

Following the Industrial Revolution, global dependence on non-renewable energy has led to significant environmental challenges and resource scarcity. Recognizing the need for a sustainable energy system, China has committed to investing in renewable energy, evidenced by the substantial allocation outlined in its 13th Five-Year Plan. This study examines the multifaceted impacts of renewable energy investment on economic growth, considering both short-term inhibitory effects due to initial infrastructure costs and long-term benefits related to economic efficiency and environmental sustainability. Additionally, this paper explores the critical role of renewable energy investment in fostering economic growth in China, particularly in the country's transition from traditional energy sources. The analysis also incorporates cases from various countries at different levels of development, enabling a comparative understanding of China's renewable energy landscape. Limitations of the current research, including the need for more comprehensive data on regional disparities and socio-economic effects, are acknowledged. Future research directions are suggested to enhance understanding of the socio-economic implications of renewable energy investment across diverse demographics and to evaluate the effectiveness of policy interventions aimed at promoting sustainable energy development in developing nations.
An Assessment of Policy Impacts on the UK Economy
Authors: Chen Tao
Published: December 2024

In the contemporary global economic landscape, the United Kingdom's role as a leading economic powerhouse necessitates constant adaptation and strategic planning to maintain its competitive edge. Against this backdrop, the study delves into the impact of three pivotal policies enacted by the UK government on its economic growth trajectory. Rooted in the recognition that policy interventions are critical drivers of national economic development, this research endeavors to unpack the intricacies of how these policies shape the nation's economic fabric. With a keen focus on the educational reform, the National Infrastructure Strategy, and the Green Economy Transition Plan, the study meticulously examines their intended goals and actual outcomes, drawing from a robust blend of qualitative and quantitative data sources. This study delves into the impact of three key policies enacted by the United Kingdom on its economic growth, and focuses on analyzing the educational reform, the National Infrastructure Strategy, and the Green Economy Transition Plan, examining their anticipated outcomes and actual effects post-implementation.
Analysis of Brand Promotion Strategy in Short Video Platform Marketing - Taking DJI as an Example
Authors: Qiao Simeng
Published: December 2024

Since TikTok exploded in the world, various new forms of business born by the platform have emerged, with e-commerce live marketing being just one example. With the application of e-commerce live broadcasting in all walks of life, different live content and types continue to emerge, full-time carrier broadcast and key opinion leaders with their huge exposure and fan groups, can make the brand's publicity achieve outstanding results. With the continuous improvement of users' attention to e-commerce live broadcasting, more scene-based short video content has been applied to marketing strategies, and instant consumption has become a popular trend. This paper aims to analyze the performance of DJI's digital products in live video broadcasting, goods distribution, and short video data performance over the past six months, visualizing the data for display and analysis, and then use the sales prediction model to predict the sales of goods after 10 days; moreover, it summarizes the marketing performance characteristics of the new frontier based on six months of data. This study provides reference and guidance for brands to carry out e-commerce live broadcasting.
Research on Capital Asset Pricing Models: Literature Review and Possible Optimization
Authors: Liang Yiran
Published: December 2024

The Capital Asset Pricing Model (CAPM) assists investors in understanding the relationship between asset returns and risks, so as to provide the basis for investment decisions. Developed based on portfolio theory and capital market theory, CAPM mainly studies the relationship between the expected return rate of assets and risk assets in the security market, and how the equilibrium price is formed. This paper aims to discuss the basic principle and theoretical basis of CAPM, its use methods and specific functions, as well as its advantages and disadvantages in the process of use, and summarizes its optimization possibilities in terms of functions. The paper reviews and outlines the theoretical framework and practical techniques of CAPM, alongside the latest findings in research. Then, this paper extends the topic to the advantages and disadvantages of the functions found so far in the research and use process, finds effective frontier areas combined with related research articles, and proposes some possible optimization methods. In addition, in view of the different situations, this paper introduces the concept of CAPM.
Exploring the Impact and Decisions of Loss Aversion Psychology in the Real Estate Field and Energy Conservation
Authors: Lin Junjie
Published: December 2024

The well-known phenomenon of loss aversion, which refers to the tendency for individuals to strongly prefer avoiding losses over acquiring gains, has long been recognized as a significant factor influencing decision-making in various aspects of life. This innate fear of experiencing losses can often lead to irrational behavior and unwise choices. One area where loss aversion plays a crucial role is in the realm of personal finance. Individuals who have a strong aversion to losses may exhibit cautious behavior by increasing their savings as a precautionary measure against uncertain future events. From real estate and various national policies to daily life choices, these choices can change people's aversion to losses. Therefore, the primary focus of this article is to explore the decision-making processes employed by real estate developers and customers under the influence of loss aversion mentality, as well as people's choices for energy conservation driven by loss aversion mentality. Through data analysis, this article concludes that if consumers reasonably control and utilize their loss aversion psychological effects, they will not suffer any economic losses.
The Analysis of the Well-suited Personality of MBTI for Working Conditions
Authors: Lyu Runzhi
Published: December 2024

People often make judgements about other's ability based on a single letter in the MBTI type, but few have explored the differences between workers with various MBTI personalities and how these differences manifest in their work. The Myers-Briggs Type Indicator (MBTI) is a widely used personality assessment tool globally, helping guide talent allocation and optimize team dynamics. Developed by Isabel Briggs Myers and Katherine Cook Briggs, the MBTI categorizes personalities into four primary types: Analysts (represented by purple), Diplomats (represented by green), Explorers (represented by yellow), and Sentinels (represented by blue). This paper summarizes the personality characteristics and work performance of different color personalities through a literature review, case analysis, and online data investigation. Each type is associated with distinct traits: Analysts are intuitive and thinking, Diplomats are intuitive and feeling, Explorers are observant and prospecting, and Sentinels are observant and judging. These categories help in understanding individual preferences and improving teamwork. The study highlights that certain MBTI types, particularly INTJ, ESTJ, and ENTJ, exhibit strong leadership qualities, making them well-suited for project management roles. Understanding these types can enhance managerial strategies, motivation, and overall team effectiveness. Individuals with purple and blue personality tend to be more organized, making them better suitable for rigorous and serious tasks, while those with yellow and green personalities are more creative and ideal for innovative work.
The Integration of Smart Technology and Traditional Culture Innovation and Sustainable Development in Modern Coffee Shops
Authors: Wang Yixue
Published: December 2024

This study examines the integration of intelligent technology with traditional cultural elements in coffee shops, particularly within the rapidly evolving markets of Jiangsu and Shanghai. The research aims to explore how these fusion drives innovation and sustainable development in the coffee industry. Through a systematic analysis of sales data, consumer satisfaction surveys, and in-depth interviews, the study assesses the effects of combining smart technologiessuch as AI and big datawith cultural aspects like Chinese tea rituals and regional art. The findings reveal that this integration significantly enhances operational efficiency, enriches the consumer experience, and increases customer loyalty. Furthermore, it contributes to market expansion by attracting a diverse consumer base that values both modern convenience and cultural authenticity. The study highlights the strategic advantages of blending technology with culture in creating unique, competitive offerings in the global coffee market. By providing empirical insights, this research not only enriches the academic literature on cultural and technological integration in retail environments but also offers practical guidance for industry stakeholders aiming to leverage these elements for sustained business growth and innovation in a competitive global marketplace.
An Analysis of the Impact of Supply Chain Digital Transformation on Enterprises
Authors: Yu Yifan
Published: December 2024

The innovation goals of enterprises are closely tied to their impact on the supply chain. As technology advances, businesses are increasingly prioritizing digital transformation as a key strategy. This paper aims to examine the impact of supply chain digital transformation on enterprises. The paper conducts case studies of three successful digital transformation instances in enterprises and assesses the impact of these transformations on the supply chain through qualitative analysis. Additionally, this paper summarizes the potential challenges of enterprise digital transformation through a literature review. The research indicates that digital transformation significantly enhances enterprise supply chains by enabling comprehensive monitoring and improving the speed and efficiency of responding to real-time market demand fluctuations. Furthermore, digital transformation fosters information sharing within the supply chain, reducing information asymmetry and providing a sound basis for decision-making within enterprises. Moreover, data-driven decisions optimize logistics efficiency. In conducting a literature review, this paper identifies two key reasons for the failure of enterprise digital transformation. The first factor is the lack of unified internal opinions and imperfect planning, while the second factor is the absence of essential personnel and equipment.
Application of LSTM Models in Forecasting Brent Crude Oil Prices During the Green Energy Transition
Authors: Zhao Yuwen , Hu Baojun , Wang Sizhe
Published: December 2024

Crude oil, as a vital strategic resource in the global energy and environmental landscape, significantly influences the global economy, financial markets, and progress toward low-carbon development. In recent years, the crude oil market has become increasingly complex due to the accelerating transition to green energy and low-carbon policies worldwide. Crude oil prices are shaped by traditional factors such as supply-demand dynamics, geopolitical tensions, and technological advancements, as well as emerging challenges like shifts in energy policies, carbon emissions regulations, and innovations in renewable energy technologies. This multifaceted interplay of drivers underscores the critical importance of crude oil price forecasting for economic planning, energy policy, and financial market stability. This study employs spot price data for European Brent crude oil, sourced from the U.S. Energy Information Administration, to develop a deep learning model comprising three LSTM layers for short-term price prediction. The findings demonstrate that the LSTM model effectively captures overall pricing trends, though it exhibits minor discrepancies during periods of significant price volatility. This research not only validates the LSTM model's utility in forecasting within energy markets but also offers valuable insights and data to support policymakers and investors navigating the uncertainties of crude oil price fluctuations.
Analysis of Japans Monetary Policy on Price Stability: A Vector Error Correction Model Approach
Authors: Zhang Qinghan
Published: December 2024

In response to prolonged deflation and economic stagnation, Bank of Japan implemented non-traditional easing monetary policies, including Quantitative Easing (QE) adopted in 2001 and Quantitative and Qualitative Easing (QQE) adopted in 2013. This paper aims to uncover the effectiveness of these policies using a Vector Error Correction Model with Johansen normalization restrictions. By analyzing the impact on Consumer Price Index, M2 Money Supply, and the Japanese yen to US dollar exchange rate from year 2000 to 2024, this research studies the short-term dynamics of Japanese economy and the long-term relationships among these variables. The results indicate that QQE was more effective than QE in stabilizing inflation and influencing the money supply, contributing to Japans economic recovery in 2024. Moreover, the research shows that the impact on exchange rate was complex, reflecting the interplay between Japan's unique deflationary environment and global financial conditions. The findings of this research suggest that the Bank of Japan may need to conduct a more aggressive and diversified measures to effectively manage inflation and stabilize the economy.
Environmental Regulations and Left-behind Children
Authors: Wang Qirui
Published: December 2024

Environmental regulation is the enactment of a series of laws and regulations by the government that require companies or individuals to reduce emissions of hazardous substances and greenhouse gases. This study finds that strong implementation of environmental regulatory policies may increase the number of children left behind. The intrinsic mechanism may be that the policy forces enterprises to adjust in their business strategies, which increases the uncertainty of the life and employment of migrant workers and leads to their tendency to keep their children in their hometowns. This study utilizes data from the China Industrial Enterprise Database, China Urban Statistical Yearbook, China Family Tracking Survey (CFPS), China Migrant Population Dynamics Survey (CMDS), and China Labor Dynamics Survey (CLDS). The study employs regression models to conduct econometric analyses, illustrating the positive correlation between the effectiveness of urban environmental regulation policies and the prevalence of left-behind children. It is hoped that more thoughts will be brought to the left-behind children issue.
Study on the Social and Economic Impact of Chinas One-Child Policy
Authors: Qing Yikun
Published: December 2024

China implemented the one-child policy in 1979 to control population growth by limiting most couples to one child. This policy has been strictly implemented for over thirty years. However, it is one of the most controversial and radical social policies in modern history. Through an analysis of existing literature, this paper shows that the implementation of the one-child policy was historically necessary because it could better stabilize the economic and human social development at the time, but it also had significant social impacts on Chinese society. These impacts include changes in family structure, gender norms, and social and economic inequality between urban and rural areas. At the same time, the policy also had a significant impact on the well-being of individuals. Because the one-child policy has complex and far-reaching social impacts on Chinese society, understanding these impacts is crucial to developing policies that promote the well-being and sustainability of individuals, families, and communities.
An Analysis of Inequality in Educational Resources in the United States
Authors: Lian Jiayuman
Published: December 2024

Educational resources have always been a much-discussed issue, as inequity not only directly affects the learning opportunities and future development of each student, but also hinders sustainable social and economic development. This article focuses on inequalities in educational resources in the United States, primarily related to differences in extracurricular programs. These disparities are often the result of socioeconomic factors, geographic location, and school type (public or private), leading to significant differences in students' educational experiences. The analysis illustrates the profound effects of these inequalities on student achievement, future development, mental health, and social skills. The paper finds that in underfunded schools, students are limited in many ways by their lack of access to diverse extracurricular programs. These disparities exacerbate social inequalities and impede social mobility. To address these issues, the paper recommends that the government could increase funding for schools in disadvantaged areas, ensure equitable distribution of resources, and promote interest-based curricula. The paper also recommends strengthening professional development for teachers and utilizing community resources to narrow inequalities in educational resources.
The Future of Education: Applications of Virtual Reality and the Metaverse
Authors: Zhang Chenrui
Published: December 2024

The use of virtual reality and metaverse in education is gradually changing the traditional teaching model. Virtual reality technology enables students to learn in simulated laboratories, historical scenarios, or complex scientific models by providing immersive virtual environments. This interactivity and immersion significantly improve the intuitiveness and effectiveness of learning. Virtual reality not only enhances students' understanding of abstract concepts, but also provides a safe and controlled environment for hands-on practice. On the other hand, Metaverse combines technologies such as VR, Augmented Reality, and Blockchain to create an integrated virtual platform that enables educational resources and social interactions to be shared globally across geographical boundaries. In the metaverse, educational institutions can build dynamic virtual campuses that facilitate collaborative learning and resource sharing on a global scale, breaking the time and space constraints of the traditional classroom. However, the application of these technologies also faces challenges, including issues such as the technological divide, equipment costs, data privacy, and mental health. Therefore, this paper will investigate the practical uses of virtual reality and the metaverse within the educational sector, assess the innovative prospects and obstacles they present, and offer recommendations for the future advancement of education.
Machine Learning-Based Research on the Adaptability of Adolescents to Online Education
Authors: Wang Mingwei , Shen Xulingwei , Liu Sitong
Published: December 2024

With the rapid advancement of internet technology, the adaptability of adolescents to online learning has emerged as a focal point of interest within the educational sphere. However, the academic community's efforts to develop predictive models for adolescent online learning adaptability require further refinement and expansion. Utilizing data from the "Chinese Adolescent Online Education Survey" spanning the years 2014 to 2016, this study implements five machine learning algorithmslogistic regression, K-nearest neighbors, random forest, XGBoost, and CatBoostto analyze the factors influencing adolescent online learning adaptability and to determine the model best suited for prediction. The research reveals that the duration of courses, the financial status of the family, and age are the primary factors affecting students' adaptability in online learning environments. Additionally, age significantly impacts students' adaptive capacities. Among the predictive models, the random forest, XGBoost, and CatBoost algorithms demonstrate superior forecasting capabilities, with the random forest model being particularly adept at capturing the characteristics of students' adaptability.
Fine-Tuning Methods of Multimodal Pretraining Models for Emotion Recognition
Authors: Lin Zekai
Published: December 2024

With the continuous progress of artificial intelligence, single-modal models are increasingly falling short of meeting the requirements of complex tasks. As a result, there has been a burgeoning interest in multimodal pretraining models. Traditional single-modal models, like those in natural language processing that concentrate solely on text or computer vision models that focus only on images, have limitations when dealing with multimodal data. They are unable to fully exploit the complementary and associative information among different modalities. The development of multimodal models has been driven by significant growth in computational power, the availability of large-scale multimodal datasets, and breakthroughs in deep learning technology. These advancements empower models to learn feature representations from diverse modalities and establish cross-modal associations and fusion. This paper centers on the current research scenario of multimodal pretraining models in the field of emotion recognition. It analyzes mainstream model architectures and methodologies, as well as their efficacy in identifying various emotional expressions. Through a comparison of existing fine-tuning techniques, this study explores strategies for attaining optimal performance in multimodal emotion recognition. Additionally, this paper discusses the principal challenges that current multimodal pretraining models face in emotion recognition, such as data scarcity and model interpretability. It also contemplates potential future research directions. This study aims to provide a comprehensive reference for researchers in the field, thereby facilitating further advancements and applications of multimodal emotion recognition.
Efficient Credit Card Fraud Detection Based on Binary Logistic Regression
Authors: Chen Junfan , Qian Hanyang , Yao Weijian
Published: December 2024

With the rapid increase in credit card usage, instances of credit card fraud are also on the rise. The aim of this paper is to design a credit card fraud detection model using binary logistic regression. By using effective detection techniques, the model increases detection accuracy, safeguarding consumer interests and preserving financial market stability. The findings demonstrate that the binary logistic regression model developed for this investigation has a 93.9% accuracy rate in identifying credit card fraud. Important metrics like recall rate and accuracy rate performed exceptionally well, reaching 93.1% and 94.5%, respectively. The model significantly lowers false positives and incorrect assessments in addition to being very good at spotting fraudulent transactions. In addition to offering a reference for resolving other financial fraud detection issues, the paper presents a new method of credit card fraud detection. By improving the model and incorporating additional data characteristics, its performance and applicability can be further enhanced to provide financial institutions with stronger support against future fraud threats.
Joint Spatial-Spectral Convolutional Neural Network Enhanced with Attention Mechanism for Optimized Hyperspectral Image Classification
Authors: Zhao Xurui
Published: December 2024

Hyperspectral imagery (HSI) classification is essential for remote sensing analysis, utilizing various image bands. Convolutional Neural Networks (CNNs) are prevalent in deep learning for visual data processing, with recent applications in HSI classification primarily employing 2D and 3D CNNs. However, 3D CNNs demand significant computational resources due to their complexity. This paper introduces a two-branch spatial-spectral joint convolutional neural network (SSDB) leveraging an attention mechanism for HSI classification. SSDB effectively extracts spectral and spatial information while reducing model complexity, resulting in a lightweight alternative to 3D CNNs. In this comprehensive hyperspectral image (HSI) classification experiments utilizing the Indian Pines, Pavia University, and Salinas Scene datasets, we benchmarked our findings against leading-edge handcrafted and end-to-end deep learning methodologies, demonstrating exceptionally commendable performance with the SSDB approach.
Review of Varied Types of Rescue Robotics with their Functions and Shortcomings
Authors: Guo Yujia
Published: December 2024

It is an undoubtful phenomenon that earthquakes are still affecting humans lives and causing a dramatic amount of death. Therefore, with the popularity of rescue robotics in recent years, varied types of rescue robotics appear to assist humans in the rescue process. Using the literature reading and review method, 4 types of rescue robotics will be discussed with their functions examples and future improving aspects. The research robotics mentioned are analyzing robotics, locating robotics, rescuing robotics, and medical robotics, which can primitively use the related data to analyze the happening of earthquakes as well as the environmental situation after earthquakes. Next, the system will locate the injured and dispatch them to the rescue center. With diverse assistance provided by rescuing robotics, the operation of rescue will be much more efficient. Eventually, some simple diagnoses and surgeries will be performed through medical robotics, increasing the possibility for the survival of the wounded. While the rescue robotics have gained the basic capability to act, more improvement in precision and multifunction will be given to them.
Educational Degree Design: The Economic Rationales
Authors: Winter Stefan , Kistner Melissa , Maffia Deborah , Matz Robin Maximilian , Thevanesan Elka
Published: December 2024

Within the economics of education, degrees are treated as given empirical phenomena. There are neither scientific definitions of degrees nor theories of degree design. So far, the economics of degrees are not well understood. This paper aims to improve this understanding. It suggests that especially the economics of information, the real options approach of investment theory, agency as well as transaction cost theories can help to understand the design features of degrees, their inner complexities, and their hierarchical embeddedness. A deeper understanding of the rationales behind degrees allows for new approaches to optimize degrees.
The University of the Highlands and Islands project
Authors: Adamson Veronica , Plenderleith Jane
Published: December 2024

The Highlands and Islands of Scotland are a remote and rugged expanse of land on the periphery of Britain and Europe. The region has Jong struggled against the adversity of isolation - geographic, economic and social - and has endured significant economic decline and depopulation. The area, from Shetland to the Mull of Kintyre, from the Western Isles to the foothills of the Cairngorms, comprises nearly twenty per cent of the land mass of Britain. It has less than one per cent of the UK population (fewer thar1 half a million inhabitants) and as such is one of the least densely populated areas of Europe. With the exception of the conurbation of Inverness. which is one of Europe's fastest-growing towns and currently bidding for city status, most of the inhabitants live in small arid widely dispersed towns, villages and crofting communities. At the 1991 census there were ninety-three inhabited islands in archipelagos of Shetland and Orkney, and the Inner and Outer Hebrides.
An Investigation and Design of Networked Learning in Inner-City Leeds
Authors: Barker Tim , Pilkington Rachel
Published: December 2024

A pedagogical framework for the implementation of a World Wide Web based course at an inner city Leeds school is described together with a detailed study of students own summarisation of course notes contained therein. Results of this study lead to some recommendations, principally that an artificial Learning Companion would help with the summarisation process, a feature which is lacking with the present support tool. Finally, an overview of a preliminary design for such a Learning Companion is presented as future work.
On-Line Learning
Authors: Davis Mike , Denning Kate
Published: December 2024

The aim of this paper is to look at the social and learning dynamics of computer mediated communication in postgraduate education. The research was conducted within the tradition of ethnographic action research and draws upon insights from the theory and practice of Adult Education, and from Social Anthropology.
Negotiating Practice
Authors: Foster Jonathan , Bowskill Nicholas , Lally Vic , McConnell David
Published: December 2024

The following paper analyses data which has emerged from a strand of the TLTP3 Computer Based Collaborative Group Work (CBCGW) project focusing on the topic of ‘institutional readiness for networked learning’. The purpose or the strand to date has been to survey and evaluate the readiness of a traditional, research-led university for implementing networked learning, specifically, networked collaborative learning. As such the survey has been concerned with a potential change process. One aspect of a potential change process is a concern with the meaning of such a process for the participants concerned (Fullan, I 99 I; Wenger, I 998). Drawing on the theoretical framework of communities-of-practice and on data from interviews with university staff the paper explores the economies of meaning at the University and the implications for the university's capacity to learn about networked learning.