bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Religious Belief Culture on Legislation: In Regions of EU and Fujian
Authors: Li Zonglun
Published: September 2024

The interplay between cultural beliefs and legislation is a critical area of study, particularly in regions where religion significantly influences societal norms and values. This paper explores the influence of religious beliefs on legislation by examining key theoretical perspectives and empirical case studies. It begins with an analysis of the ideas of prominent philosophers--Georg Wilhelm Friedrich Hegel, Karl Marx, and Max Weber--who provide distinct viewpoints on the role of religion in shaping law. This paper then delves into two different empirical studies: the influence of Christian animal welfare on European Union (EU) legislation and the Mazu Mediation Room in Fujian, China. Understanding how deeply the cultural religious principles shape legal frameworks provides foundations considering human rights and social justice, and how religious culture cooperates with the enforcement of law. By studying and comparing these contexts, this paper focuses on analyzing these religious influences on legislation, investigating their impacts, and suggesting the future development of laws in religious regions. Ultimately, the paper wants to contribute to a broader sense of the relationship between religion and legislation, discovering the need for a legislative approach that respects local religious culture while upholding universal human rights and social justice.
The Identification of Bundle Sales Between the US and China
Authors: Qi Meng
Published: September 2024

As a common monopoly means in the market in today's market, bundle sales is banned by many countries in the anti-monopoly law, but each country has different ways of identifying it. United States as the first country to establish an anti-monopoly law, it has undergone a process of shifting from structuralism to behaviorism, especially in the "Microsoft case", from the different determinations of bundle sales in the two judgments, it can be seen that the United States anti-monopoly law has gradually relaxed the sanctions on the monopoly of large enterprises, Instead, they hope to make use the technological advantages of large enterprises to complete its own innovation and development, and thus United States pay more attention to this component of technical talents in the consumer group. As far as China is concerned, through the determination of bundle sales in the "Qihoo case" and the "Huawei case", it is not difficult to see that China, as a rising star in the anti-monopoly law, has consistently implemented behaviorism, so that China's small and medium-sized enterprises can have more room to survive, Therefore, the rights and interests of its ordinary consumers will also be relatively better protected.
Inspiration from the Deferred Prosecution Agreement: Based on Its Comparison with Chinas Compliance Non-prosecution System
Authors: Su Fengyi
Published: September 2024

Since the break out of the financial crisis in 2008, the US has attached more importance to economic surveillance. In order to avoid and fight against crimes which take place in big enterprises effectively, meanwhile, release the burden that the prosecution process might bring to companies, the legislature has come up with a new system called deferred prosecution and promoted its application in real practice. In recent years, the legislation has been widely used in many cases to tackle with the corruption problem, especially those happen in MNEs, who is powerful enough to play an essential role in global trade and economic growth, and has proved to be a success for it creates a balance between the benefit of MNEs and that of the country, purifying the market atmosphere and optimizing the market order. Similarly, China has a relevant system named Compliance Non-prosecution. However, there are some differences between two systems, also, application is still strictly limited in China. This essay aims at identifying the contrast of two systems and analysis the essence behind the differences, using cases from both China and the US as an example.
Shaping the Future of Learning, Teaching, and School Leadership Management
Authors: Li Yanan
Published: September 2024

This paper is about transformative trends in education, exploring learning, teaching methodologies, and management of school leadership, which will redefine education in the next ten years and beyond. The concept of personalized learning, the integration of technology in study activities, and the reform of leadership from the traditional, routine-oriented style into the more open, adaptive one are the critical issues of the research currently being conducted. These changes referring to personal learning differences, lifelong learning, and collaborative environment are meant to make them more evolved or advanced. Through the analysis, teachers being purely instructors changed to facilitators of learning, cultural responsiveness to teaching was emphasized, and the impossibility of attaining good outcomes without digitalization was brought into view. On the other hand, transformational leadership is especially relevant for change management with respect to community participation and data-based decision-making. This view of all angles on the future shows how the education systems that tend to be more flexible and diverse, as well as the ones that are better suited to meet all the diverse learning needs of all students, are the ones to thrive in this new global paradigm.
Analysis and Research on the Turning Points in the Development of Chinese and Western Civilizations
Authors: Liu Mingyuan
Published: September 2024

The 17th century was a significant stage in global history, with the West ushering in the Scientific Revolution and the rise of Enlightenment thinking, and China experiencing the transformations of the Ming and Qing dynasties. These advances have had a profound impact on the development of contemporary society. However, most of the existing studies focus on a single civilization, and there are fewer comprehensive studies on the interaction between Chinese and Western civilizations. The study examines the development of civilisation in ancient China and the West (Europe) through the analysis of existing historical data on China and the West and compares the performance of civilisation in China in the 17th century with that of the West during the same period through a horizontal comparison of the historical processes of the Middle Ages, including the change of polity, the development of the economic system, and the way of education. The results show that China's level of civilisation was higher than that of the West before the 17th century, but the West's civilisation gradually surpassed China's after that. Despite the different paths and speeds of development between China and the West, the turning point in the 17th century had a profound impact on the course of world history (turning point: the 17th century, when China's civilisational development gradually lagged behind that of the West, i.e. the West's entry into a capitalist society).
Exploring the Impact of Gamification on STEM Learning in Education: The Mediating Role of Facilitating Focus
Authors: Su Wanting
Published: September 2024

This study aims to examine the effect of gamification on STEM Learning in education, the mediating role of promotion focus, and the moderating role of teacher guidance. A sample of 385 college students majoring in computer science across different colleges in Jiangsu Province who experienced gamification in their curriculum was surveyed. The data were collected using questionnaires designed to measure gamification in education, STEM Learning outcomes, the level of promotion focus, and the degree of teacher guidance. Confirmatory factor analysis showed good discriminant validity (^2 = 596.165, df = 260, ^2/df = 2.293, RMSEA = 0.056, CFI = 0.922, IFI = 0.924). Positive correlations were found between Gamification Engagement, Facilitative Focus, and STEM Learning in Education (Self-report & Teacher Evaluation). Hypothesis testing indicated gamification's direct effect on STEM Learning (=0.814, p<0.01), mediation by promotion focus (effect value 0.072, 95% CI [0.002, 0.155]), and moderation by teacher guidance (effect value 0.130, 95% CI [0.074, 0.156]). Gamification directly enhances STEM Learning and can be further optimized through teacher-guided promotion focus. To maximize gamification's impact, teachers should balance it with traditional methods and stimulate positive learning motivation among students. Thus, incorporating gamification, reinforcing teacher guidance, and nurturing a promotion-focused learning environment are key for optimal educational outcomes.
Opioid-Induced Neuroplasticity: Insights from Animal Models
Authors: Yang Zhaolin
Published: September 2024

Synaptic plasticity is defined as the modification of the transmission of synapses. It has been proven to be strongly associated with learning. Thus, drug-evoked synaptic plasticity in brain reward circuits can establish persistent learning of addictive drugs, reflecting the neural basis underlying addiction. The mesolimbic dopamine pathway has been widely indicated to be strongly associated with opioid use disorder (OUD) and other drug addictions. The paper focuses on discussing the drug-evoked neural plasticity underlying two important stages called intoxication and withdrawal which are critical for addiction and reinstatement of drug use respectively. The paper first explores the neural basis of OUD, emphasizing drug-induced plasticity at glutamate and Gamma-aminobutyric acid (GABA) synapses on neurons of key substrates in the pathway and how they influence mesolimbic dopamine (DA) neuron transmission. Then, the review discussed withdrawal-induced neuroplasticity and reorganization of associated neuron circuits, which explain deficits led by withdrawal from opioid administration. An overall understanding of drug-evoked synaptic plasticity in key brain circuits in the development of addiction helps find possible therapeutic methods to prevent the initiation of OUD and reinstatement.
Academic Procrastination of High School Students as a Product of Low Self-Esteem: A Mediation Role of General Self-Efficacy
Authors: Liu Jiaye
Published: September 2024

Academic procrastination is a common behavior among high school students. This study aims to evaluate the direct and indirect effects of self-esteem on academic procrastination among high school students based on their general self-efficacy. This study used the Rosenberg Self-Esteem Scale, General Self-Efficacy Scale, and Middle School Student Academic Procrastination Scale as the main measurement tools. 216 high school students in Hefei were selected as the research subjects, and descriptive statistics, reliability and validity analysis, correlation analysis, multiple linear regression analysis, and mediation effect analysis were conducted using SPSS 21.0. The following research conclusion is drawn: First, there is a significant positive correlation between self-esteem and general self-efficacy. Second, there is a significant negative correlation between self-esteem and academic procrastination. Third, general self-efficacy mediates the relationship between self-esteem and academic procrastination.
Research on Cultural Consumption from the Perspective of Mental Accounting
Authors: Fang Jefferson Tianyu
Published: September 2024

Behavioral economics emerged in the late 1970s and early 1980s. Compared to traditional economics, its theory is based on bounded rationality and combines with disciplines like psychology to address the limitations of traditional economic theory, providing more convincing explanations for market phenomena. Behavioral economics covers a wide range of topics, and this paper focuses on the perspective of mental accounting to study cultural consumption. Cultural consumption is the endogenous driving force for the development of the cultural industry. The psychological factors behind cultural consumption are key to influencing consumption decisions. Adopting the mental accounting theory from behavioral economics, residents' cultural consumption mental account consist of three main categories: economic capital account, cultural capital account, and social capital account, as well as six sub-accounts: transaction cost account, sunk cost account, leisure and entertainment account, aesthetic value account, social interaction account, and identity differentiation account. These accounts can effectively explain the influence mechanism between utility changes and cultural consumption behavior decisions. Based on the connotation and mechanism of the cultural consumption mental account theory, formulating various bundled sales strategies, reducing the sunk costs of cultural consumption, and promoting the high-quality development of the cultural industry are important paths to expand residents' cultural consumption. By integrating insights from behavioral economics, this approach can provide more effective solutions to boost cultural consumption and drive the sustainable development of the cultural industry.
The Benefits of Incorporating Phonology and Morphology in English Education
Authors: Yu Zongxuan
Published: September 2024

English teaching methods have undergone significant changes and improvements over the past four decades. However, teachers continue to encounter challenges when delivering lectures in the classroom. While numerous teaching approaches have been proposed, there has been limited focus on the areas of phonology and morphology. Nonetheless, phonology and morphology have demonstrated their effectiveness in teaching English as a second language. Integrating these linguistic elements into the teaching process can greatly enhance its efficiency and provide inspiration to new learners. The paper analyzes how these two fundamental linguistic components can be effectively integrated into our current English teaching model. Based on the studies completed by former researchers, the results indicate that they can serve as valuable tools and crucial building blocks for both teachers and students alike. This paper may help envision a future where the current English teaching model is reshaped, and teaching becomes more efficient, inspiring a new generation of English learners.
Structural and Coherence Analysis Towards The Renunciation
Authors: Le Tian
Published: September 2024

While many researchers have paid attention to Rabindranath Tagore's poems, few have analyzed his short stories, still less on a specific story, like The Renunciation. The fiction critiques the caste system of Hinduism and portrays a unique female character. This paper proposes that structural and coherence analysis can assist in a better appreciation of the story. The disruption of the timeline emphasizes the importance of a series of events occurred in the past. Coherence relations within them are established and analyzed so that the caste system's role as the origin of perpetuating tragedy among Hindus is explicitly revealed. The blank in the timeline along with the nightingale which appears throughout the novel indicates the plight of women in Hindu society. Analyses in this paper provide argumentation for views proposed in previous studies, namely Tagore's firm stance against the caste system and his empathy for suffering women, from a new angle. It also demonstrates the feasibility of linguistic theories appliance to the field of literary criticism.
The Impact of Meritocracy on Higher Education: A Perspective from College Admissions Policies in Mainland China
Authors: Du Siyu
Published: September 2024

This study examines the impact of meritocracy on higher education in mainland China, focusing on university admission policies. Since the reform and opening-up period, Chinas economic and educational advancements have increasingly emphasized elitism, particularly in allocating university admission slots through mechanisms like the Gaokao. The analysis, conducted from historical and contemporary perspectives, reveals that while elitism has optimized the distribution of educational resources, it has also deepened social inequalities. Despite their intent to promote fairness, policies such as the provincial quota system and independent admissions have widened regional and urban-rural disparities in educational resources. Furthermore, government initiatives like targeted poverty alleviation and regional preferential policies have had limited success in addressing these disparities. The study concludes that while elitism has centralized high-quality educational resources, it has hindered upward social mobility, especially for students from rural and disadvantaged backgrounds, highlighting the need for comprehensive reforms to achieve educational equity and social justice.
The Role of Motivation in Decision-Making and Underlying Neural Mechanism
Authors: Wei Jiale
Published: September 2024

This paper presents crucial findings on how motivation, both internal and external, significantly influences decision-making processes at cognitive and neural levels. It highlights the role of reward anticipation and uncertainty in shaping decision-making behavior, particularly focusing on brain regions like the prefrontal cortex, amygdala, and dopaminergic pathways, which govern strategic and risk-related decisions. Studies involving psychiatric conditions, such as schizophrenia, and healthy individuals using fMRI reveal that increased motivation enhances decision-making strategies by activating frontal and parietal brain regions, while certain areas like the Orbitofrontal Cortex (OFC) show less involvement. Additionally, the essay explores the role of insight-driven internal motivation, showing that moments of sudden clarity (AHA! experiences) can elevate risk-taking behavior by stimulating the nucleus accumbens (NAcc), a key region involved in reward processing. The research also delves into strategic decision-making theories, emphasizing the balance between logical reasoning and instinctive, politically influenced decision-making processes within organizations. The essay concludes by underscoring the need for further research into the neural mechanisms linking motivation and decision-making, particularly the impact of neurotransmitter systems and individual differences in neural responsiveness to rewards. This deeper understanding could lead to enhanced strategies for improving decision-making outcomes across various fields, including healthcare, management, and personal decision-making, offering insights into the complex interplay between motivation, cognition, and neural activity.
The Effectiveness of Ketamine Treatment on Depression
Authors: Liu Yichen
Published: September 2024

As the diagnosis rate of depression continues to rise, effective treatment options for this condition have become one of the most researched areas in the field. Earlier antidepressants were primarily developed based on mechanisms that involve regulating imbalanced neurotransmitters. For example, the widely known Selective Serotonin Reuptake Inhibitors (SSRIs) and various tricyclic antidepressants work by adjusting the concentration of different neurotransmitters, such as dopamine and norepinephrine, in the synaptic cleft to alleviate depressive symptoms. Additionally, many therapeutic approaches have been developed and applied based on the widely accepted hormone hypothesis and neurotrophic factor hypothesis. Ketamine, a drug traditionally used as an anesthetic, has recently been discovered to possess antidepressant properties. Its efficacy and underlying neuroscientific mechanisms are currently under investigation. Unlike earlier antidepressants, Ketamine produces significant antidepressant effects within a short time after administration. Multiple studies have suggested that Ketamine's rapid and effective therapeutic results may be based on entirely new antidepressant mechanisms and targets that have yet to be fully understood. This provides a new solution for the treatment of treatment-resistant depression and offers new hope to patients who have tried various treatments without success. In this literature review, Ketamine's therapeutic effects and its underlying mechanisms will be thoroughly examined. The content will be divided into three sections: significant antidepressant effects, underlying principles and mechanisms, and factors relevant to the improvement of Ketamine therapy.
The Social Integration Challenges for the Population with Rare Diseases
Authors: Fu Yueanshu
Published: September 2024

Rare illnesses affect people of all ages across the world. Patients, families, and caregivers have many unmet medical and social requirements, resulting in significant mental and financial hardships. Efforts to enhance diagnostic skills and create medicines for an estimated 7000 uncommon illnesses have been very successful. In the United States, a rare illness or condition affects less than 200,000 persons. In the European Union (EU), a rare illness is defined as one that affects fewer than five persons in every 10,000. However, no effective medicines exist for 90% of uncommon disorders. There is a need to increase awareness, advocacy, and outreach to everyone, especially those with low incomes, low literacy, minority ethnic status, and living in underserved and marginalized communities in urban and rural regions, as well as developing countries throughout the globe. The acceptance of patients as research partners complements greater research focus and large regulatory activities, which contribute to faster review and approval programs for goods for severe or life-threatening disorders. The social challenges and lack of recognition of these illnesses cause the patients and their families facing the server difficulties when they try to get treatments, job opportunities, or engage in society. This paper aims to analyze the problem with various research and resources, which seeks to raise awareness of the rare disease population and bring more benefits and help to them.
The Impact of Social Media on Mental Health in 21st Century's China
Authors: Shen Minglai
Published: September 2024

The widespread use of social media in the 21st century has raised significant concern about its potential impact on mental health across various demographics, especially in China, which is one of the largest social media markets in the world. Its platforms such as Wechat, Weibo, and Douyin enjoy immerse popularity. With millions of users actively engaging on these platforms in their daily life. Among the various groups affected by this phenomenon, college students constitute a particularly impressionable demographic. This age group is already navigating the complex difficulties of transition to adulthood stage, including academic pressure, social anxiety, and identity formation. The rising of social media has brought new challenges to these dimensions. According to the unique Chinese culture and background, it is essential to understand how these factors shape the experience of Chinese college students on social media. This research aims to delve into the diversified influences of social media on mental health within this demographic.
Research on the Innovative Development of Traditional Cultural and Creative Industries in Henan Province
Authors: Yu Hao
Published: September 2024

China is celebrated as a cradle of civilization, particularly Henan Province, which is the heartland of Chinese heritage. This region's traditional culture is the cornerstone of our national identity and underpins our cultural confidence, essential for sustainable, high-quality development. In today's rapidly evolving world, the demands for preserving and propagating traditional culture have escalated. Addressing the challenge of how to innovatively transform and develop traditional culture is crucial. The cultural and creative industries, a burgeoning economic sector, offer substantial potential. Through the development of cultural and creative industries, it can spur local economic growth, create jobs, enhance incomes, and promote cultural diversity and inclusivity, thereby igniting innovative and creative energies across society. This study uses a SWOT analysis to explore the current landscape of Henan's traditional cultural and creative industries. It pinpoints the constraints hindering their growth and suggests strategies for fostering innovative and sustainable development in this sector.
Environmental monitoring system design based on STM32 platform
Authors: Tie Yuhe , Chen Peiming
Published: September 2024

This study addresses the current societal demand for environmental monitoring by designing an environmental monitoring system based on the STM32 platform. This system assesses and monitors environmental conditions in real-time by tracking parameters such as CO, PM2.5, temperature, humidity, and light intensity. It holds significant value in preventing air pollution and improving indoor air quality. The system employs four types of sensors: the DHT11 digital temperature and humidity sensor, the BH1750FV light sensor, the GP2Y1010AUOF optical dust sensor, and the MQ-7 CO sensor to collect environmental data, which is then processed by the STM32F103C8T6 controller. This system is characterized by its real-time capabilities, high precision, and low power consumption, making it highly practical and valuable for widespread application. The paper provides a detailed discussion of sensor selection, measurement algorithms, and system design and implementation, offering valuable insights for research and applications in related fields.
Python-enabled dynamic analysis of earth-moon tidal forces
Authors: Liu Zhi
Published: September 2024

This study investigates the origin of tidal forces within the Earth-Moon system, formulating the corresponding dynamic equations and solving them using Python for computational modeling. This allows for the prediction of tidal intensity at specific locations and times. The methodologies and Python algorithms developed quantify the impact of Earths tidal forces on the Moon, analyzing the deformation and thermal effects caused by tidal force variations across different distances. This framework also elucidates the mechanisms behind Ios volcanic activity. The computational approach, characterized by accuracy and precision, enhances the interpretability of the results through data visualization. This study lays a foundational basis for extending the analysis to other celestial bodies under diverse conditions, contributing to the broader field of celestial mechanics.
Applications of Dirac equation in curved spacetime
Authors: Gu Haoran
Published: September 2024

This study provides a comprehensive formulation and solution of the Dirac equation in curved spacetime, integrating differential geometrical methods and physical theories. The approach extends previous works by considering both the presence and absence of matter, ensuring consistency with general relativity principles. Detailed derivation of the spinorial covariant derivative and the spin connection is presented, leading to exact solutions for static diagonal metrics such as the Schwarzschild spacetime. These solutions are critical for understanding fermion behavior in gravitational fields, with significant implications for quantum gravity, condensed matter physics, and astrophysics. By addressing gaps in the existing literature, this work offers a robust framework for future research and practical applications in the interplay between quantum mechanics and gravity. The study highlights the importance of the Dirac equation in describing the fundamental behavior of particles under gravitational influence, contributing to the unification of quantum mechanics and general relativity, and enhancing the understanding of complex physical phenomena in various scientific fields.
Research on regional difference of economic development level in Zhejiang Province based on factor analysis
Authors: Zhang Ji , Feng Yuao
Published: September 2024

Regional economic disparities are widespread, especially in China. This is reflected not only in the differences between the four regions, but also in the differences between provinces. At the same time, the regional economic differences within each province are also very obvious. This paper studies the gap between the economic development levels in Zhejiang Province, which contributes to the balanced development and sustainable development of the overall economy of Zhejiang Province, in order to intuitively see the gap between the cities. This paper uses SPSS to reduce the dimension of factor analysis and calculate the comprehensive score through 10 indicators reflecting the level of economic development in 11 cities. Finally, this paper finds that the economic development of each city in Zhejiang Province has great potential. But at the same time, it exposes the imbalance of economic development in the province. The economic development level of Hangzhou, Ningbo and Zhoushan is far higher than that of other cities in the province, and the economic development level of some cities is general. The development level of some cities is poor. According to the research results, the causes and problems of the current situation of economic development are analyzed. Reasonable suggestions are put forward to further promote the coordinated development of the economy.
Smoke Detection and Recognition Based on Flame Feature Extraction
Authors: Li Xinyue
Published: September 2024

The sensor-based fire monitoring scheme requires the installation of temperature and smoke detectors near the monitoring target. Meanwhile, the pictorial fire detection technology is capable of capturing the location and development degree of the fire instantly, enabling rapid detection. Aiming at the issue that the traditional fire monitoring system is prone to failure in large-space indoor and open outdoor environments, a digital image fire monitoring algorithm based on the characteristics of fire flames and smoke is proposed. In the early stage of a fire, smoke is frequently accompanied by its generation and can spread over a large area in the space and is not easily blocked. Therefore, the detection of smoke can realize the early warning of fires and conduct fire rescue work in a targeted manner. In this paper, the background subtraction method is initially employed to extract the moving object, and then the required flame model features are extracted through image preprocessing, and the fire image is identified through multi-feature fusion. The experiment indicates that the flame identification method proposed in this paper can accurately identify the flame under various circumstances and provides a favorable judgment basis for fire alarms.
Stock prediction based on HMM and LSTM model and model selection using SVM
Authors: Deng Moxun
Published: September 2024

Su G and Deng F, 2006 Model Selection for Support Vector Regression, Science and Technology Bulletin, 22(2), 5.Su G and Deng F, 2006 Model Selection for Support Vector Regression, Science and Technology Bulletin, 22(2), 5.Su G and Deng F, 2006 Model Selection for Support Vector Regression, Science and Technology Bulletin, 22(2), 5.
Probing the Strong-field QED Based on Ultra Intense Laser
Authors: Zhou Zikai
Published: September 2024

Contemporarily, with the rapid development of laser techniques, the laser plasma interaction is entering the quantum electrodynamics regime. This study provides a comprehensive analysis of recent advances in the study of strong-field quantum electrodynamics (SF-QED) effects in recent years, especially since 2020. The research details the remarkable developments in laser technology, especially the chirped pulse amplification (CPA) technique, WHICH emphasizes the study of ultrahigh-intensity lasers interacting with electron beams, reaching laser intensities in excess of 10^23 W/cm^2. The paper also explores the theoretical framework of QED, focusing on particle generation, spin-related events and their importance in applications such as vacuum birefringence and photon angular momentum effects, and observes key QED effects like nonlinear Compton scattering or multiphoton B-W pair generation. In addition, important QED applications including vacuum birefringence and angular momentum photon effects are discussed. Finally, it is summarized that although the theoretical part of QED is well established and has strong theoretical support, experimental studies are still being followed up. The current strong-field QED experiments have limitations, and looking ahead, with the progress of laser science, future breakthroughs in laser intensity and experimental methods will further promote the development of QED research.
Forecasting Gold Futures Prices: An Empirical Analysis Using the Vector Autoregression Model
Authors: Wang Zihan
Published: September 2024

This study presents an empirical examination of the Vector Autoregression (VAR) model's efficacy in predicting gold futures prices, a critical analysis for investors and financial analysts amidst global economic fluctuations. The research leverages a dataset spanning from January 4, 2010, to June 7, 2024, derived from Investing.com, encompassing a multitude of financial indicators such as gold and silver futures, the US Dollar Index, and Brent Crude Oil futures.The methodology includes a comprehensive data preparation phase, ensuring the stationarity of time series data through Augmented Dickey-Fuller (ADF) testing and cointegration analysis. The VAR model is meticulously estimated, identifying the lag order that optimizes model performance while mitigating overfitting risks.The empirical findings underscore the VAR model's high accuracy in forecasting gold futures prices, with significant influences attributed to silver futures prices, the US Dollar Index, and Brent Crude Oil futures. Despite minor discrepancies in capturing short-term price volatility, the model adeptly reflects overall market trends.The study acknowledges limitations, such as the potential exclusion of influential variables and the scope of data timeframe, suggesting future research should expand variable inclusion and integrate advanced prediction techniques. The paper concludes that the VAR model is a robust analytical instrument for predicting gold futures prices, offering substantial support for strategic financial decision-making.
A comparative study between WGAN-GP and WGAN-CP for image generation
Authors: Gao Jiajun
Published: September 2024

Image generation allows the creation of visual content in a convenient manner. It is critical for enhancing digital experiences, from video games to virtual reality, enabling more engaging and immersive experiences. In current technologies, Generative Adversarial Networks (GANs) have achieved significant success but face challenges like training instability and mode collapse. By utilizing the Wasserstein distance, Wasserstein GAN (WGAN) enhances conventional GANs; however, its weight clipping method may not be ideal. In this study, WGAN with gradient penalty (WGAN-GP) and WGAN with weight clipping (WGAN-CP) are compared, which aims to enhance stability by better enforcing the Lipschitz constraint. For comparison, these approaches are validated using Fashion Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR)-10 datasets. Experimental results show WGAN-GP produces higher quality images and more stable training than WGAN-CP. However, WGAN-GP also requires longer training times and computational burden. The findings highlight a trade-off between training efficiency and output quality, guiding the choice of technique based on specific application needs.
An exploration of KANs and CKANs for more efficient deep learning architecture
Authors: Zhou Shuhui
Published: September 2024

Deep learning has revolutionized the field of machine learning with its ability to discern complex patterns from voluminous data. Despite the success of Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), there is an ongoing quest for architectures that offer higher expressiveness with fewer parameters. This paper focuses on the Kolmogorov-Arnold Networks (KANs) and Convolutional Kolmogorov-Arnold Networks (CKANs), which integrate learnable spline functions for enhanced expressiveness and efficiency. This study designs a range of networks to compare KANs with MLPs and CKANs with classical CNNs on the CIFAR-10 dataset. Moreover, this study evaluates the models based on several metrics, including accuracy, precision, recall, F1 score, and parameter count. Based on the experimental results, networks with KANs and CKANs demonstrated improved accuracy with a reduced parameter footprint, indicating the potential of KAN-based models in capturing complex patterns. In conclusion, integrating KANs into CNNs and MLPs is a promising approach for developing more efficient and interpretable models, offering a path to advance deep learning architectures.
Leveraging LSTM and NEAT for enhanced performance in multi-agent evolutionary games
Authors: Gao Ruixiang
Published: September 2024

Nowadays, the application of deep learning in the field of evolutionary games has become a very popular topic. Humans use artificial intelligence as a powerful tool to predict the decision-making of multiple agents and to analyze it thoroughly, which can significantly reduce people's workload. Two of the most typical situations in game theory are the Iterated Prisoner's Dilemma (IPD) and the Iterated Snowdrift (ISD) games. In this paper, the Neuro Evolution of Augmenting Topologies (NEAT) algorithm is employed to perform population evolution in these two scenarios, and the Long Short-Term Memory (LSTM) model is utilized to predict the behavior of the population. The unique structure of the LSTM model contributes to its excellent predictive performance in forecasting the behavior of populations. Furthermore, this paper also investigates the changes in population intelligence and the frequency of cooperative behaviors during the process of population evolution, in order to explore the trends and specific proportions of different strategies as the population evolves.
Optimizing online advertising with muti-armed bandit algorithms
Authors: Jiang Chenyan
Published: September 2024

The rapid digitalization of the global economy has significantly transformed the landscape of advertising, necessitating more sophisticated and adaptive strategies to reach and engage with consumers effectively. This paper explores the application of multi-armed bandit (MAB) algorithms as a powerful tool for optimizing online advertising processes. We examine how MAB algorithms can enhance various stages of the advertising cycle, from audience segmentation and creative development to bidding strategies and real-time optimization. Through an analysis of existing literature and practical applications, we demonstrate the potential of MAB algorithms to balance the trade-offs between exploration and exploitation, enabling advertisers to maximize click-through rates, conversion rates, and return on investment. Furthermore, we address specific challenges such as the cold-start problem and the optimization of search advertising, proposing innovative solutions that leverage the adaptive capabilities of MAB algorithms. Our findings suggest that integrating MAB algorithms into online advertising strategies can significantly improve targeting accuracy, user engagement, and overall advertising performance. We conclude by discussing the implications of these findings and suggesting directions for future research to further enhance the application of MAB algorithms in the evolving digital advertising landscape.
Enhancing WGAN performance by architectural and optimizer variations for image generation
Authors: Wang Liuding
Published: September 2024

Generative model has opened up the area of image generation and has become a hot topic in recent years. Among the most famous generative models, Generative Adversarial Network (GAN) is outstanding among them, offering extensive avenues for exploration. The Wasserstein GAN (WGAN), as one of the GANs, introduces an innovative framework for training GANs based on the Earth Movers (Wasserstein) distance, providing a steadier training process. The experiment tried various modifications to WGAN, including changing the optimizers and the network architecture. Specifically, this work tried replacing the original Root Mean Square Prop (RMSprop) with another optimizers. Also, this work tried to add residual blocks to the network structure. These modifications provided interesting results, providing supplementary validation of the original WGAN structure, and providing some possibilities of optimization. According to the results, it could be found that the results of some modifications are very positive. However, some of the changes presented very unsatisfactory results, which gave us some insight.
Comparative Study of Multi-Armed Bandit Algorithms in Clinical Trials
Authors: Huang Wuyue , Wang Wenling , Wu Yudong , Xi Chuheng
Published: September 2024

In recent years, with the rapid development of the information age, the influence of Multi Armed Bandit Algorithms (MAB) models in clinical trials for disease prevention has been increasing. In this study, based on Python programming language, Multi-Armed Bandit Algorithms (MAB) algorithm, Upper Confidence Bound (UCB) algorithm, Adaptive Epsilon-Greedy Algorithm, and Thompson Sampling (TS) algorithms to validate the idea of preventing, controlling and predicting the occurrence of diseases. The results show that the MAB model can effectively solve various decision-making problems in clinical trials, improve the efficiency of access to medical care, save doctors diagnosis time, and at the same time achieve the prevention and treatment of diseases while minimising patients pain. This study is dedicated to proposing a more effective decision-making method and verifies that the method has a wide range of applications and great potential for development today.
Review on sensor technology in robot positioning and control solution
Authors: Yang Zhenyuan
Published: September 2024

A sensor is a device that receives signals and responds to the signals or stimuli. The output of the sensors can be the voltage or current. The input of the sensors is very complex. For different types of sensors, there are different types of input. All sensors are categorized based on their applications and principles. In this article, there are mainly two major categories of sensors in the field of engineering included. One is the localization. The localization mainly includes visual sensors, infrared sensors, lidar and ultrasonic sensors. This paper discusses the application scenarios as well as the advantages and disadvantages of each kind of sensor. The SLAM control algorithm for robotics based on sensors will also be addressed. Another focus of this paper is robot control. It only includes a major type of sensor which is the inertial sensor. For this part, the article mainly discusses the applications and advantages & disadvantages of the gyroscope sensor. Also, the prevailing robotics control algorithm- PID control is illustrated. In a more complex environment in engineering, a single sensor may not meet the requirements of the engineers. So, the combination use of the sensors appears. This article also explores the use of machine learning for intelligent sensor design.
Study of the characteristics and application scenarios of three SLAM algorithms based on comparative methods
Authors: Wang Qichen
Published: September 2024

This paper compares three Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms are the core technology for autonomous navigation and environmental perception of mobile robots. SLAM algorithms are used by mobile robots to perceive the surrounding environment, build up an environment map and position themselves in real-time in an unknown environment. This article first systematically reviews the basic principles of each algorithm based on experiments and studies that have been completed by previous researchers and illustrates their respective unique mechanisms for processing sensor data, map construction, and localization. Subsequently, this paper analyzes the performance differences and characteristics of the three algorithms in practical applications in terms of robustness in complex environments, consumption of computing resources, and accuracy for generated maps. Finally, based on the advantages and disadvantages of each analyzed algorithm, this article summarizes the most suitable and unsuitable usage scenarios of different algorithms in specific situations. Moreover, this article puts forward specific algorithm selection suggestions for different scenarios to help engineers and researchers make more appropriate decisions in actual projects.
Review on Intelligent Driving Schemes Based on Different Sensors
Authors: Li Yihui
Published: September 2024

With the progress of science and technology, more and more cars have the function of automatic driving. After reviewing a series of articles, it is found that there are still many problems in the research on automatic driving. These include problems with sensors, cameras, navigation systems, signals and so on. The primary focus of this article is the automobile's sensor system, which includes body-sensing sensors, radar sensors, vision sensors, and GPS systems. However, there will be numerous issues if these sensors are used alone. It can be seen that many researchers have done the application of multiple sensors and achieved good results. Thus, in order to enable the widespread adoption of automated driving in the future, it is advised to integrate two or three sensors and conduct testing through real-world applications. This can not only reduce the occurrence of accidents but also promote the broader development of autonomous driving.
Exploring the impact of width in convolutional neural network-based architectures for sentiment analysis
Authors: Xiao Chengyu
Published: September 2024

Born from the machine learning (ML) subfield of neural networks (NN), deep learning (DL) has many advantages over other ML algorithms and has become more significant today. As one of the most essential model architectures of DL, the convolutional neural network (CNN) has attracted the attention of many researchers, especially in recent years. Meanwhile, sentiment analysis has become more renowned since the rapid development of various online platforms like blogs, social networks, etc. To study these two heated topics together, this article selects a particular CNN model designed for sentiment analysis and explores its widths potential influence on the result. During the experiment, four CNN models are created based on the same structure but with increasing width. By forwarding the pre-processed datasets to the four models and comparing their performances from different perspectives using different metrics, its concluded that the more expansive the model's width, the better it performs in the training, validation, and testing sections.