bluuebunny/crossref_metadata_2025 huggingface dataset:
The Transformation of NATO-Russia Relations under the Context of NATO Expansion: From Post-Cold War to the 2014 Crimea Crisis
Authors: Zhang Zhenfeng
Published: July 2024

This paper will examine the transformation of NATO-Russia relations from the post-Cold War stage to the 2014 Crimea crisis. It will specify how NATO's several eastward expansions contributed to the shift of Russia's foreign policy and their bilateral relationship. The analysis will begin with the optimism the Russian side held after the Cold War and the establishment of bilateral cooperation acts such as the Basic Act and the Partnership for Peace. The paper will also analyze the color revolutions that occurred in Ukraine and Georgia, as well as the subsequent 2008 Russo-Georgian War, to examine how Russia's foreign policy has shifted during the 2000s. The paper will then shed light on the Ukrainian crisis in 2014, which ultimately ended the cooperation between Russia and NATO. Through case studies, this paper will examine the shift of Russias foreign policy in the context of NATO's eastward expansion, which has reshaped the geopolitical landscape of Eastern Europe in the past decades.
Analysis on the Modification of Article 5, Paragraph 2 of the WTO's SPS Agreement
Authors: Li Hongrui
Published: July 2024

Against the backdrop of the World Trade Organization's Agreement on Sanitary and Phytosanitary Measures (SPS Agreement), this study explores how health security measures affect international trade in the context of globalization, particularly in the context of global health crises such as COVID-19. Through an in-depth analysis of the provisions of the SPS Agreement and international trade data, as well as previous studies and case studies, the study made three key recommendations: enhancing transparency in risk assessment, promoting public participation in health policy-making, and facilitating the establishment of mechanisms for cross-border cooperation. These recommendations aim to ensure that member countries not only follow scientific principles when implementing sanitary and phytosanitary measures, but also reduce unnecessary barriers to trade and promote the sustainable development of global trade. The conclusions of the study highlight that these improvements can effectively alleviate the trade uncertainty that may be brought about by health security measures, thereby protecting public health and promoting the healthy growth of international trade.
English Writing Anxiety in Chinese High School Students: Causes and Consequences
Authors: Zhang Luning
Published: July 2024

Second language (L2) writing anxiety has been widely discussed by researchers, for writing anxiety has its specific causes and may affect peoples L2 writing performance. In the Chinese context, high school students undergo exam-orientated education and face the college entrance exams. They may have special causes and may suffer from specific consequences of English writing anxiety. The research is done in a normal high school in Zhejiang Province, focusing on the causes and consequences of English writing anxiety in Chinese high school students. The research used a questionnaire to collect data and analyze them in SPSS. Through exploratory factor analysis (efa), the research finds out that exams, fear of errors, and writing difficulties may be the main causes of Chinese high school students writing anxiety. The research also collected students recent exam grades and conducted co-relation analysis and regression analysis, which shows that English writing anxiety exerts negative impact on Chinese students English writing performance.
A Study on the Effects of Learning Motivation and Boredom on English Learning Outcomes of Chinese College Students
Authors: Li Hanming
Published: July 2024

In recent years, boredom has become a research hotspot in the field of second language acquisition. Whats more, the effects of boredom on learning outcomes and motivation on learning outcomes have been widely discussed by scholars. However, it is a rare study on the effects of motivation and boredom on English learning outcomes. There is a gap in the research. The aim of this study is to explore the relationship between the three projects. After that, this study will provide better strategies to achieve the best learning results for educators and learners. The results show that there is a significant correlation between boredom and learning motivation, and boredom also has an effect on learning outcomes. However, there is no correlation between learning motivation and learning outcomes. Based on this result, the educators and learners can reduce boredom to develop learning outcomes by many approaches, such as reward system, interest in learning and so on.
Review of Research on Motivation and EFL Students' Speaking Performance
Authors: Zhang Jinqi
Published: July 2024

In the context of global integration, English learning has become a more important topic, especially in developing speaking skills, which plays a vital role in cross-cultural communication. Motivation, as a psychological element that boosts individuals actions, has a significant impact on improving EFL students speaking competence. The study focuses on motivation and its influence on EFL students oral performance, aiming to explore how to improve students oral ability with motivational strategies effectively. In terms of methods, this study uses literature analysis, including literature review, theoretical analysis and empirical research. Based on the review of existing literature, the challenges posed by low motivation and self-regulation ability to EFL learners oral language development are identified. At the same time, this paper introduces the L2 motivational strategy, self-determination theory and Kellers ARCS motivation strategy theory and explores how these theories are applied in the real pedagogical context. The results show that intrinsic motivation positively correlates with students autonomous learning ability, teacher support autonomy and classroom participation. Moreover, self-determination motivation can effectively reduce spoken English anxiety and improve spoken English ability. Based on the result, this study points out the key role of motivational strategies in teaching and provides practical guidance for teachers to improve EFL students oral performance.
The Effect of L1 and L2 on L3 in Grammar Learning from the Perspective of Language Transfer ---University Students with Chinese as L1, English as L2, learning L3 Spanish Subjunctive Mood
Authors: Li Wenbo
Published: July 2024

Third language acquisition (TLA) research, as a novel area of study both domestically and internationally, has garnered significant attention from scholars worldwide in recent years. Previous studies on third language acquisition have extensively focused on interlingual transfer, but there has been limited attention given to grammatical transfer, especially related to the subjunctive mood. Additionally, Spanish has received less discussion than other languages. This present study aimed to investigate the language transfer of Chinese and English on learning Spanish subjunctive mood in a Spanish writing test conducted by 20 Chinese university students. The data were analyzed using frequency analysis and descriptive analysis in SPSS. The study revealed the following major findings: (1) Chinese transfer affects Spanish through changing verb form and the choice of tenses. (2) English transfer affects Spanish through the wrong verb form in the adverbial clause of purpose. Thus, the negative transfer from L2 to L3 is stronger than the one from L1 to L3.
The Influence of Interactive Technology of Contemporary Classroom Software on University Teaching Practice
Authors: Zeng Lihua
Published: July 2024

Contemporary classroom interactive software technology greatly impacts university practical education. This study adopts the research method of literature review and case analysis. Through extensive collection and analysis of domestic and foreign literature on classroom interactive software technology in higher education, it deeply understands its development history, functional characteristics, and current situation. Secondly, combined with specific teaching cases, this paper analyzes the use effect of these software technologies in the actual teaching process, the feedback of students, and the challenges faced and the basis. This article also explores how these interactive technologies will impact practical university education and how they have changed the learning environment. Through research, we also find that classroom interactive software technology is indeed helpful to improve teaching effect. Still, software technology needs to be used rationally, pay attention to balance, and ensure effective combination. Meanwhile, teachers also need to receive more professional training to ensure that they master relevant operational skills and teaching design principles.
Social Media Attention to Geopolitical Conflicts - An Analysis of Weibo Users' Comments on the Israeli-Palestinian Conflict
Authors: Cao Minghao
Published: July 2024

In the globalized information age, social media has become a primary channel for news and information dissemination, particularly during geopolitical conflicts. This study investigates public sentiment and discourse on the Israeli-Palestinian conflict on the Chinese social media platform Weibo. Utilizing content analysis, the researcher conducted sentiment statistics and word frequency analysis on Weibo comments to understand Chinese public attitudes toward this conflict. The research reveals a significant increase in negative emotions from 33.33% in October 2023 to 100% in April 2024, indicating growing public discontent and concern as the conflict intensified. Concurrently, positive emotions sharply declined from 47.62% to 0%, reflecting diminished hopes for a peaceful resolution. Neutral sentiments also fluctuated, initially at 19.05%, dropping to 0% by April 2024. Additionally, the study identifies a shift in keyword usage from "world peace" and "hope" to specific entities like "Israel" and "Hamas," and terms like "disaster." This highlights a change in public and media focus from peace initiatives to the humanitarian impact of the conflict. Understanding these dynamics is crucial for promoting a more inclusive gaming environment, challenging existing gender stereotypes, and fostering social stability. This research contributes to a deeper understanding of Chinese public opinion on international geopolitical issues and underscores the importance of social media in shaping public discourse.
Exploring the Dynamics of Bystander Behaviour in School Bullying: Empathy, Emergency Evaluation, and Victim-Bystander Relationships
Authors: Han Yuxuan
Published: July 2024

School bullying significantly impacts students' well-being and academic performance. This study investigates the relationships between empathy, emergency evaluation, and victim-bystander relationships, and the three types of bystander behaviours: perpetrator-helpers, outsiders, and defenders. A total of 230 Chinese students aged 12 to 20 participated in an online survey, collecting data on their bullying experiences, bystander roles, and knowledge of bullying. Statistical analyses, including chi-square tests and Pearson correlations used to examine these relationships. Findings indicate that friendships significantly increase the likelihood of bystanders becoming defenders, while friends of perpetrators are more likely to become perpetrator-helpers. Emergency evaluation is crucial; bystanders are more likely to help when they perceive the situation as an emergency. No statistically significant evidence was found linking empathy to helping behaviour, although there was a trend suggesting empathetic bystanders may be more inclined to help. Fostering friendships and improving students' ability to recognize and respond to emergencies can enhance bystander intervention. Educating students, teachers, and parents on these aspects is vital. Further research is needed to explore empathy's role in bystander behaviour comprehensively. Implementing programs to build social bonds, educate students about bullying, and train students in emergency evaluation can reduce bullying's incidence and impact. Enhanced support systems involving teachers and parents are also crucial for creating a supportive school environment.
Self-Efficacy in Core Linguistic Competencies of English Majors An Empirical Analysis at Zhejiang Wanli University
Authors: Cheng Ying
Published: July 2024

This study examines self-efficacy levels among English majors at Zhejiang Wanli University across essential language skills using Banduras self-efficacy theory. The research identifies key elements influencing self-efficacy and their relationship with specific language abilities and overall self-efficacy. By surveying 121 English majors through random sampling, four interconnected elements related to linguistic skills were identified. The results reveal a strong positive correlation between these competencies and overall self-efficacy, highlighting the importance of balanced language skill development. The findings stress the need for educational strategies that enhance confidence and proficiency in English acquisition by focusing on the comprehensive development of listening, speaking, reading, and writing skills. This comprehensive approach can significantly contribute to the overall academic performance and language proficiency of English majors, providing insights for educators to tailor their teaching methods effectively.
Perception of Industrial Heritage Tourism Destination Image Based on Web Text Analysis: A Case Study of Beijing Shougang Park
Authors: Tan Xin
Published: July 2024

With the transformation of China's economic structure, industrial heritage tourism has gradually become a new form of tourism. This paper uses Beijing Shougang Park as a case study. Utilizing Octopus software to collect reviews from Dazhong Dianping and Mafengwo, and employing ROST CM6 software, the paper analyzes tourists' image perceptions of Shougang Park based on the "Cognitive-Affective" model. The results indicate: Firstly,natural landscapes, cultural landscapes, and activity experiences are the basic cognitive images for tourists; Secondly, tourists generally have a positive emotional response to Shougang Park, with negative emotions primarily stemming from issues such as fees, holiday crowding, service attitude, and the park's large size; Thirdly, overall image perception mainly involves six aspects: tourism motivation, tourist attractions, tourism environment, tourism support system, tourism experience, and tourism evaluation. There is strong perception of natural and cultural landscapes, but weaker and more fragmented perception of historical culture. The study identifies problems in the scenic area and provides suggestions to offer effective practical guidance.
Application of Deep Learning Technology in Speech Recognition and Language Teaching
Authors: Li Luciano
Published: July 2024

In the process of learning a second language, modern Chinese learners are faced with many challenges due to the limitations of the learning environment and teaching conditions. The teachers are usually non-native speakers, and their language level is uneven, resulting in unsatisfactory teaching results. At the same time, the size of the group learning a second language continues to expand, and the requirement for teaching level is getting higher and higher. The topic of this paper is the application of deep learning to speech recognition and language teaching. The results and effects of deep learning in speech recognition and language teaching are summarized by combining the currently available information. It can be found that the current deep learning technology is more and more advanced, the accuracy rate of speech recognition technology has been greatly improved compared with the past, and the teaching effect has also made no small achievements. This technology can not only help learners better master English pronunciation, but also provide more efficient and effective learning experience.
Academic Procrastination in Preschool Children: The Role of Positive Discipline
Authors: Wang Zixuan
Published: July 2024

With the rapid development of society, the pressure of students' competition is huge. At the same time, families, schools, and society pay too much attention to students' performance, which also increases a lot of anxiety for children. In addition, some children use extremely high standards to strictly require themselves, which also brings them a lot of tension. As the age of learning moves down, preschool children, whether at home or in extracurricular institutions, also carry out many courses of study. Under such background and conditions, students begin to have bad psychology and learning habits and use procrastination to relieve stress and tension. But in the long run, this approach is not conducive to children's physical and mental development. This requires parents to correct their children's procrastination. However, it is precisely because some parents do not master reasonable educational methods to educate children and change children 's problems that the results run counter to expectations. On the basis of summarizing the causes and manifestations of academic procrastination, this paper gives the definition of positive discipline and puts forward reasonable suggestions and educational methods to parents. It aims to improve children 's problems and create a positive family environment suitable for children's growth.
The Relationship Between Self-control and Psychological Stress among College Students
Authors: Qiu Jieyi
Published: July 2024

The intensification of competition for further education and employment brought about by the development of the times has led to a sharp increase in psychological pressure on contemporary college students. The mental health of college students has become a social issue of common concern for parents and various groups in society. This article focuses on the relationship between self-control and psychological stress among college students. Through analysis of existing literature and research, it is found that college students with high levels of self-control are able to demonstrate stronger adaptability and lower psychological distress when facing academic, interpersonal, and economic pressures. This is mainly because individuals with higher self-control abilities have more disposable self-control resources compared to those with lower self-control abilities, so the resources allocated to psychological stress are more sufficient and can better control psychological stress. This study has important reference significance for alleviating psychological stress among college students and promoting the strengthening and improvement of student mental health education by the government and various social groups. In the future, further refinement can be made in the operationalization of the aforementioned variables to facilitate in-depth research on this topic.
Comparing the Impact of COVID-19 on Depression and Social Anxiety Disorder of Male and Female
Authors: Xia Anmi
Published: July 2024

This in-depth assessment of the literature aims to assess and compare how the COVID-19 pandemic has specifically impacted depression and social anxiety disorder in both males and females. By focusing on these two well-known mental health conditions, this essay seeks to lucubrate the complex interplay between gender dynamics and the pandemic's psychological effects. The study will carefully consider whether the various pandemic-related disruptions, such as extended social isolation, economic crises, and upheaval of daily routines, have had different effects on men and women in terms of the prevalence and severity of depression and social anxiety disorder. The study aims to highlight potential distinctions in the experience and expression of these mental health issues by examining the data via a gender-specific perspective. It will look into age-related variations, the impact of various social support networks, and the interaction within various societies in addition to gender disparities. The research intends to provide a thorough understanding of how people from varied gender origins manage their mental well-being in the face of the pandemic's difficulties by unraveling this complexity. By highlighting the gender-specific characteristics of depression and social anxiety disorder, the research hopes to advance our understanding of how the pandemic's effects affect all facets of people's lives.
The Impact of the Evaluation of Individual's Appearance from Social Media Platforms on Contemporary High School Student's Personality
Authors: Wang Pinyi , Chow Lok Lam Ronia , Shen Chener
Published: July 2024

The purpose of this study was to explore the effects of social media on self-perception and personality development of high school students, with an added focus on the positive correlation between social media usage and self-perception. While there are limitations to the data collection process, the analysis suggests that increased social media use is associated with increased pressure to adhere to appearance standards. High school students' self-evaluation of their own appearance, attention to peers' appearance, and media interactions with peers all contributed to changes in perception and personality. Understanding these dynamics is critical for educators, parents, and researchers when it comes to the impact of social media on adolescent development. Future studies should establish a target number for data collection to ensure more robust findings. This study provides insight into the complex relationship between social media, self-perception, and personality development among high school students.
Educational Equity Challenges in China: An In-Depth Investigation
Authors: Huang Xinrui
Published: July 2024

Analyzing the geographical distribution of educational institutions, changes in their numbers through time, and the consequences for equally accessible education, the article digs into the complex world of educational equality in China. A thorough effort is being made to provide education accessible across areas, as shown by the chart Regional Distribution of Educational Institutions (2021). Representing a dedication to supporting a variety of educational possibilities, Hebei leads with 14181 institutions, followed by Jiangsu with 7666 institutions. The dominance of big cities like Beijing and Tianjin, which together have 3082 institutions, emphasizes the diversity of educational options even more. The article identifies dynamic tendencies by analyzing the Changes in Number of Educational Institutions (2017-2021) data. A concerted effort has been made to improve educational infrastructure and accessibility at different academic levels, as seen by the rise of higher education institutions, high schools, and junior secondary schools. It is noteworthy that throughout five years, there were 125 more higher education institutions, 1030 more high schools, and 977 more junior secondary schools added. In contrast, the decrease in primary enrollment calls attention to the causes of this shift and its effects on education fundamentals. These developments demonstrate Chinas determined efforts to provide educational opportunities for people at all levels and locations. Continual growth in institutions, a sign of an endeavor to satisfy changing educational demands, has characterized the secondary and postsecondary education landscape. Additionally, the data raises concerns about the loss of elementary schools and the need to address prospective legislative changes or demographic trends. The article highlights Chinas comprehensive strategy for ensuring educational fairness and provides a glimpse of its development, difficulties, and educational objectives.
A new assembled experimental device for fluid mechanics
Authors: Runchen Zhao , Wenhao Shen , Tianqi Bu , Linhui Song , Guangjun Sun , Yanmei Jiao
Published: July 2024

Fluid mechanics is one of the important branches of basic mechanics education of science and technology in higher education, and its corresponding teaching experiments are numerous. However, there are great similarities among some components of most traditional hydrodynamics experimental devices. If an independent experimental device is designed for a specific experiment, it will result in a great waste of resources and space. Therefore, based on the full investigation on the similar components of some certain experimental devices, this study redesigned and integrated them, and innovatively proposed a new type of assembled hydrodynamics comprehensive experimental device. This experimental device can be utilized to carry out Reynolds experiment, Nozzle outflow experiment, Karman vortex street experiment, etc. based on different combinations. The corresponding results show that this assembled hydrodynamics comprehensive experimental device has the advantages of multiple functions, small occupied space, low cost, good stability, high corrosion resistance, strong expandability, and so on.
Machine learning based feedforward voltage control for buck converters in envelope tracking applications
Authors: Ying Ruixuan
Published: July 2024

Tracking the voltage envelope of 5G base station power supplies poses a significant long-term challenge. To achieve fast and accurate envelope tracking, an efficient and high-frequency power electronics circuit and an excellent voltage controller are essential. This paper presents a novel machine learning (ML) based feedforward control method for precise voltage reference tracking. The method is exemplified using a standard buck converter in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). A feedforward neural network (FNN) is trained to capture non-ideal and non-linear effects in real circuits. An automated data collection system, developed with MATLAB and Simulink, facilitates data acquisition and model training process. The trained FNN predicts the duty cycle for the buck converter to generate the targeted output voltage and output power. A simulation model is built to validate the effectiveness of the FNN controller in envelope tracking applications.
Laser Tweezers Raman Spectroscopy and its application in bio-chemical sensing
Authors: Luo Zipeng
Published: July 2024

Laser Tweezers Raman Spectroscopy (LTRS) technology combines Raman spectroscopy and optical tweezers technology, providing a new way to research in single cell analysis and microbial classification. Driven by the development of precision spectroscopy and deep learning techniques, LTRS plays an increasingly important role in biomedical research, clinical diagnosis and environmental research. For example, it can be used to detect cancer cells, analyse bacterial infections, and monitor harmful environmental substances. In this paper, we review the basic principles of LTRS technology through literature reading and case studies, explain how the technology combines the advantages of optical tweezers and Raman spectroscopy in order to provide a non-destructive, label-free and susceptible single-cell analysis method, summarize the latest development of LTRS technology in recent years, as well as its applications in the field of biochemical sensing, and finally discuss the problems it is currently facing and look forward to its future development direction. Finally, the current problems faced by LTRS are discussed, and the direction of its future development is anticipated.
Application of wireless energy delivery system in automobile charging
Authors: Lin Xinrui
Published: July 2024

With the popularity of electric vehicles, wireless charging technology has become a key area to address the convenience of charging electric vehicles. The aim of this study is to explore the principles of wireless energy transfer technology and its application in the field of electric vehicles. We first provide a background introduction to the field of electric vehicles, emphasising the importance of wireless charging technology. Subsequently, we delve into the charging and discharging characteristics of power batteries to better understand the need for energy transfer. Then, we carefully selected wireless charging methods suitable for electric vehicles and developed a corresponding wireless charging system architecture. In this paper, we also provide theoretical analyses of two major wireless energy transfer technologies, including electromagnetic coupled wireless energy transfer and resonant wireless energy transfer, as well as exploring the principles of RF wireless energy transfer technology. Finally, we propose an architecture for a static wireless charging system including five modules: pre-processing, transmission, reception, charging control and battery load. The contribution of this study is to provide an in-depth research and a comprehensive theoretical foundation for wireless charging technology in the field of electric vehicles, which helps to improve the charging efficiency and convenience of electric vehicles.
Optimization design model of heliostat field based on gravitational search algorithm
Authors: Lu Zhihua , Zhou Xinchu
Published: July 2024

In todays society, non-renewable resources are becoming increasingly precious, making the utilization and conversion of renewable resources more critical. Heliostat fields play a significant role in the actions taken by various countries to achieve carbon peaking and carbon neutrality. How can the installation and arrangement of heliostats maximize the annual average thermal power output per unit mirror area while achieving the rated power? This paper establishes an efficiency calculation model based on the flat-plate projection-Monte Carlo algorithm and an optimization design model of heliostat fields based on the gravitational search algorithm. The research progresses from shallow to deep, investigating methods to maximize the output thermal power under different constraints. First, it addresses the issue of maximizing the average thermal power output per unit mirror area under fixed heliostat field parameters and rated power conditions. Next, it solves the problem of maximizing the annual average thermal power output per unit mirror area under varying heliostat sizes and installation heights, with fixed rated power. Finally, it points out that the models established in this paper are applicable to complex real-world situations and can effectively improve the thermal efficiency of heliostat fields.
VoiceAD: A multilingual speech based classifier for early detection of Alzheimers disease
Authors: Wu Allison
Published: July 2024

Alzheimer's Disease (AD) causes memory decline beyond normal aging and is the most common cause of dementia. Early detection and intervention can delay the progression of AD and alleviate symptoms. However, high expense, insensitivity, and inconvenience are among various deterrents to traditional diagnosis methods. Dementia impacts more than 55 million people globally, with this number set to double every 20 years. Much of this increase will be in low- and middle-income countries which often lack the resources for early diagnosis. Recently speech has emerged as an effective digital biomarker for AD detection. However, most studies in this area are limited to English speech due to lack of suitable data in other languages. This study aims to construct a speech-based classifier (VoiceAD) that can be trained on English data and still maintain its performance on other languages. Acoustic features were extracted from English, Spanish, and Greek data from DementiaBank with the eGeMAPS v2.0 feature set. Subsequently, an Extra Trees model was employed for classification. VoiceADs performance for English and Greek surpassed that of the baseline model and model incorporating only demographic data while the Spanish performance was lower due to task effect. In addition, VoiceADs satisfactory generalizability and interpretability positions it as a promising candidate for clinical applications. It can be implemented as a free screening tool such as a mobile application to monitor AD risk. VoiceADs crosslingual applicability can its enable worldwide deployment, especially in settings where traditional methods of diagnosis are less attainable.
Research on group decision optimization and influence analysis in social network based on machine learning
Authors: Zhou Yanchen
Published: July 2024

With the rapid development of online social networks, the research on group decision-making in social networks has attracted extensive attention. Social networks facilitate interaction and behavior between individuals, businesses, and organizations. However, traditional group decision-making methods often ignore the social relationships between group members and fail to fully consider the impact of these relationships on subgroup division. In this work, we propose a novel model approach that combines graph neural networks and deep learning techniques to capture and analyze complex relational structures in social networks. The model uses node features and edge features to optimize the group decision-making process and effectively evaluate the influence between individuals through multi-layer network embedding and aggregation operations. Experimental analysis results show that the proposed method performs well in improving the accuracy and efficiency of decision-making, and significantly improves the quality of decision-making.
Comparative analysis of federated learning algorithms under extreme non-IID conditions for computer vision
Authors: Zheng Wilson
Published: July 2024

Data heterogeneity presents enormous challenges for Federated Learning (FL) where data is characterized by non-independent and identically distributed (non-IID) patterns across participating clients. Although a prominent issue, there is a lack of comprehensive studies regarding the efficacy of FL algorithms under different configurations of hyperparameters and models. Three state-of-the-art algorithms were chosen to be the focus of this paper FedAvg, FedProx, and SCAFFOLD. Experiments of the paper study varying degrees of data imbalance and number of local training steps to see their effects on model training results. The algorithms and hyperparameters were evaluated using ResNet50, AlexNet, and DenseNet121 on the CIFAR-10 dataset. Experimental results reveal that FedAvg and SCAFFOLD generally outperform FedProx, except for the latter excelling in the scenario of extreme non-IID data distributions in combination with minimal local training. It was also observed that models with complex architectures like ResNet50 are more susceptible to data imbalances, while simpler models such as AlexNet prove to be more robust. This study provides valuable insights to how different configurations of hyperparameters affect FL in non-IID scenarios, contributing to future development of more efficient and robust FL algorithms.
A review of federated learning algorithms in image classification
Authors: Qu Xingyi
Published: July 2024

Image classification is one of the most popular applications of machine learning. It has shown its potential in fields like healthcare, auto-driving and face recognition. Federated learning (FL) emerged in 2017, creating a major innovation to the field. The new structure brings new possibilities, but create new challenges such as data heterogeneity, privacy leakage and communication burdens in parameter updating. The paper solves the problem that there are relatively few papers providing a complete analysis relating to the application of FL in image classification. The paper contributes to describe the new challenges of FL in image classification and show the related reasons behind respectively, then analyze the current state-of-the-art algorithms designed for solving the challenges and improving image model performance by discussing the basic ideas and steps of algorithms and showing their pros and cons. The paper further more contributes to compare the performance of each model in accuracy and communication speed, and outline several possible directions for future advancement.
Communication security analysis of fully electronic interlocking systems
Authors: Hou Xiyan
Published: July 2024

Communication security in fully electronic interlocking systems is one of the key factors ensuring the safe and reliable operation of the entire system. EN50159 is an important standard in the European railway communication sector, aimed at ensuring the safety, reliability, and efficiency of railway transportation. According to the communication security standards of EN50159, there are six types of security risks in closed communication: data duplication, deletion, insertion, misordering, delay, and corruption. This paper analyzes and explains the aspects of communication security that need to be considered based on the safety communication standards of China's railway signaling system and the signal safety standards in EN50159, focusing primarily on communication security and reliability.
Applications of stochastic processes and reinforcement learning in strategic decision support and personalized ad recommendation: An AIGC study
Authors: Ma Qinxia
Published: July 2024

This paper investigates the integration of stochastic processes and reinforcement learning (RL) in strategic decision support systems (SDSS) and personalized advertisement recommendations. Stochastic processes offer a robust framework for modeling uncertainties and predicting future states across various domains, while RL facilitates dynamic optimization through continuous interaction with the environment. The combination of these technologies significantly enhances decision-making accuracy and efficiency, yielding substantial benefits in industries such as financial services, healthcare, logistics, retail, and manufacturing. By leveraging these advanced AI techniques, businesses can develop adaptive strategies that respond to real-time changes and optimize outcomes. This paper delves into the theoretical foundations of stochastic processes and RL, explores their practical implementations, and presents case studies that demonstrate their effectiveness. Furthermore, it addresses the computational complexity and ethical considerations related to these technologies, providing comprehensive insights into their potential and challenges. The findings highlight the transformative impact of integrating stochastic processes and RL in contemporary decision-making frameworks.
Decision tree C4.5 algorithm for generative AI technology ethics--Based on the results of the questionnaire
Authors: Xu Sihan
Published: July 2024

With the development of AI technology, generative AI has gradually entered the life of the public, for example, the explosion of CHAT-GPT has allowed more people to see the huge potential and obvious advantages of generative AI. However, in the process of generative AI operation, events that violate social responsibility and ethics often occur, which makes the research on the scientific and technological ethics of generative AI more urgent. In the past literature and research, many industry experts have analysed the impact of generative AI on specific industries, but everyone is or will be a user of generative AI, so we should pay attention to the study of the people's scientific and technological ethical issues of generative AI after putting aside the industry background, so this paper collects primary data by means of questionnaire surveys to find out the public's awareness of generative AI and their perception of generative AI. and attitudes towards generative AI, and using the decision tree C4.5 algorithm with Python as the tool, it is used to respond to people's awareness of generative AI and the public's perception of the relationship between the various factors of the ethical issues of
Research on investment project risk prediction and management based on machine learning
Authors: Diao Ziwen
Published: July 2024

In the era of digital economy, digital transformation is an inevitable choice in line with current economic development and national policy trends. Enterprises use new generation digital technologies such as big data, blockchain, cloud computing, artificial intelligence, and financial technology to apply these technologies to various production and business activities. The research on project risk management has gradually introduced the method of intelligent decision-making, using technologies such as big data and artificial intelligence to identify and analyze risks. We use learning models and ensemble learning techniques to predict and manage the risks of investment projects. Through long short-term memory networks (LSTMs), we are able to effectively extract spatiotemporal features from historical investment data to predict future risk dynamics. In order to further improve the accuracy of prediction and the robustness of the model, we introduced the Gradient Booster (GBM) ensemble learning method. These integrated technologies not only optimize the overall performance of the model, but also enhance the adaptability and forecast accuracy of various market changes. In the experimental analysis, we compare the performance of multiple models on real-world investment datasets, and the results show that the ensemble learning method has significant advantages in risk prediction accuracy.
Predicting financial enterprise stocks and economic data trends using machine learning time series analysis
Authors: Zheng Haotian , Wu Jiang , Song Runze , Guo Lingfeng , Xu Zeqiu
Published: July 2024

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.
An investigation and future prospects of artificial intelligence applications in natural disaster prediction
Authors: Chang Zexu , Yu Chenghao
Published: July 2024

The monitoring of natural hazards, such as those affecting the oceans, forests and geological formations, is important for safeguarding human life, protecting property, preserving the ecological balance and promoting technological progress. Research on the prediction and prevention of natural disasters has been conducted for decades in a wide range of areas, and this paper summarise recent research on the use of emerging computer technologies for such prediction, with the expectation that it will serve as a recommendation for future developments in the field. Effective prediction of such disasters depends on the ability to detect anomalous signals in a timely manner. Current methods that rely heavily on manual observation and data analysis are inefficient and prone to human error. Rapid advances in computer science and artificial intelligence technologies offer a promising solution, such as the use of deep learning algorithms or the Spark framework to improve model prediction accuracy and response timeliness. In recent years, computer technology has played a key role in improving these capabilities, contributing to better management of the nations natural resources, and enhancing emergency response strategies. By leveraging the capabilities of deep learning, machine learning and others in data processing and analysis, the field of natural disaster monitoring will continue to evolve towards more efficient and reliable methods.
Research on recurrent neural network recommendation algorithm based on time series
Authors: Qiu Danping
Published: July 2024

In the era of the Internet, all people's behaviors on the Internet will be stored in the server in the form of data, which leads to the image level growth of data today. In the context of big data, how to effectively analyze various existing data to obtain the necessary information is an urgent challenge that various industries need to overcome. Recommendation algorithms are one of them, mainly using existing data to recommend information of interest to users. In traditional recommendation algorithms, collaborative filtering recommendation algorithms encounter difficulties such as cold start and data sparsity. In order to better explore data features, deep learning algorithms have begun to be applied. Recurrent neural networks can not only learn input data but also perform self-learning, which can better extract features between data and improve the accuracy of recommendations. However, the information that users are interested in is greatly influenced by time, in order to improve the accuracy of recommendations. This study investigates the recursive neural network recommendation algorithm with added time series, and experiments have shown that this recommendation algorithm can indeed improve the accuracy of recommendations more accurately.
Sound feature analysis and gender recognition based on deep learning: A review
Authors: Zhu Chenxu
Published: July 2024

The application of deep learning in identifying sound features has become increasingly prevalent, greatly enhancing the performance of voice applications across various professional domains. This study focuses on deep learning techniques applied to sound feature analysis and gender recognition. It reviews methodologies, datasets, and case studies, emphasizing deep learning's crucial role in boosting efficiency and accuracy. Recent advancements highlight CNN-based architectures and novel models, demonstrating deep learning for voice systems for enhanced interaction and analysis. Challenges such as computational demands and limited data availability persist, but ongoing optimizations and multi-modal approaches promise future advancements in voice technology, enabling more intelligent and responsive interactions.
Application of artificial intelligence in cancer imaging diagnosis: A review
Authors: Chen Daochun
Published: July 2024

Medical imaging analysis is integral to modern clinical practice, providing crucial insights into internal anatomical structures essential for disease diagnosis and treatment planning. Traditional diagnostic methods often rely on subjective interpretation, leading to inconsistencies and delays. In recent years, artificial intelligence (AI) has revolutionized medical imaging by enhancing diagnostic accuracy and efficiency. AI technologies, particularly deep learning algorithms, process vast datasets to uncover patterns and anomalies, improving lesion detection and classification. This review explores the application of AI on cancer imaging diagnosis, highlighting advancements in image analysis, including lesion detection, segmentation, and feature extraction. It examines the integration of AI with omics technologies for comprehensive patient profiling and personalized treatment strategies. Moreover, the review discusses future directions and ethical considerations, underscoring AI's potential to reshape cancer diagnosis and improve patient outcomes