bluuebunny/crossref_metadata_2025 huggingface dataset:
Projeto Quimafis: uma estratégia interdisciplinar para superar dificuldades de aprendizagem oriundas da Pandemia de Covid-19
Authors: Lopes Fabrícia Sales , Lavor Gledson Dimas , Carmo Elinéia Cordeiro do , Alencar Antonia Alves de , Cavalcante Francimary Bezerra , Gusmão Amanda de Sena , Amorim Célia Maria Freitas Guedes , Silva Benjamim Henrique de Lima e
Published: November 2024

A pandemia de 2020 provocou a perda de milhões de vidas e impactou negativamente em diversos setores da sociedade, incluindo a educação. No Brasil as escolas foram fechadas e adotaram o ensino remoto, que não é tão eficaz quanto as aulas presenciais. O Conselho Nacional de Educação aconselhou que estudantes do ensino básico não fossem retidos em suas séries para evitar grande índice de reprovação e atrasos no desenvolvimento escolar. Este cenário agravou as dificuldades de aprendizagem, exacerbou as desigualdades educacionais e diminuiu o estímulo para o estudo. Esses efeitos negativos destacaram-se nas disciplinas de ciências da natureza e na matemática, por já sofrerem historicamente considerável rejeição dos alunos. Dessa forma, um projeto de intervenção, chamado Quimafis: superando as barreiras da interdisciplinaridade, foi implementado em escolas da rede pública da região do município de Iguatu-CE com fins de suprimir as defasagens do processo de ensino-aprendizagem e tornar os estudantes do ensino básico motivados, interessados e engajados ao longo de todo o período letivo. O objetivo deste artigo é relatar e analisar as potencialidades educacionais observadas na execução do citado projeto. Utilizando abordagem metodológica qualitativa, observamos que as ações contribuíram para o aumento no interesse dos alunos da educação básica e proporcionaram uma experiência enriquecedora aos(as) licenciandos(as) em química participantes, contribuindo significativamente para o desenvolvimento e aprimoramento de suas práticas docentes.
Boron-Nitrogen Compounds in Luminescent Materials: A Review of Applications and Synthesis Methods
Authors: Lin Muyang , Liang Jiayan
Published: November 2024

Boron-nitrogen compounds (B-N) have emerged as a promising material due to their unique electronic structure and excellent luminescent properties, showing great potential in organic light-emitting diodes (OLED) and electroluminescent devices. This paper systematically reviews the molecular structure, optical, and electronic properties of B-N compounds, discusses their applications in luminescent materials, and explores synthesis methods, including Friedel-Crafts-type reactions, borohydride reduction reactions, and olefin metathesis. Additionally, the paper analyzes key factors in controlling the performance of luminescent materials, investigating the impact of synthesis conditions, doping, and the preparation of composite materials on luminescent efficiency and stability. Despite significant progress in B-N compound research, challenges remain in synthesis precision, luminescent efficiency, and industrial production. Future research should focus on improving targeted synthesis capabilities, optimizing electronic structure and luminescent performance, and exploring more efficient, cost-effective production processes. With continued innovation, B-N compounds are poised to become a foundation for the next generation of high-efficiency luminescent materials, driving advancements in optoelectronic technology.
Risk Factors Associated with Juvenile Delinquency: Implications for Intervention
Authors: Yang Ruolin
Published: November 2024

Juvenile delinquency is a critical social issue affecting communities globally. This paper examines risk factors associated with adolescent delinquency and implications for interventions. The key points in findings emphasize the importance of early externalizing behaviors, maternal mental health, peer relations and socio-economic status. Detecting children with significant externalizing behavior early is as essential as realizing that maternal depression plus victimization significantly impact those behaviors. Positive parent-child relationships reduce delinquency risks — which has been evidenced by studies conducted in Saint Lucia and Chile on effective family supervision. However, this varies due to different cultures and environments. Differences were observed among India, Australia, and the US. Influence from peers can also be very significant. Having deviant peer associations increases likelihood of delinquency, while having adult social support or religious beliefs decreases it considerably — along with having positive future aspirations. In the future, long-term and multidimensional intervention studies are needed, to better understand juvenile delinquency and, therefore, prevent it more effectively.
Progress of silicon and graphene composites in lithium-ion battery anodes
Authors: Wang Chunpeng
Published: November 2024

As society progresses, the need for high-power, long-life energy supply devices gradually increases. Lithium-ion batteries (LIBs) with large energy reserves and high power have recently become the preferred choice. The anode of LIBs plays a crucial role in battery performance, safety, and cycle life. Typically, graphite materials are used for LIBs anodes. Still, they have drawbacks such as low first-time Coulombic efficiency, capacity degradation, safety issues, short cycle life, insufficient gram capacity, and purity and side reactions, which limit the application of LIBs. Therefore, new harmful electrode materials are in demand. Graphene and silicon are both environmentally friendly and sustainable materials. They both have the advantages of high theoretical capacity and high energy density, which are critical materials for developing LIBs. Combining the two can exert a synergistic effect and jointly improve the cycle stability of the battery. In this paper, we review the preparation methods of graphene and silicon composites in recent years, including mechanical ball milling and hydrothermal and thermal reduction methods. In addition, graphene and silicon composites have been explored for LIBs applications.
Preface: 2nd International Conference on Researches in Educational Technology and Psychological Sciences (RETPS 2024)
Authors: Mansfield Lilith , Ke Yuxi
Published: November 2024

This is the proceedings for 2024 2nd International Conference on Researches in Educational Technology and Psychological Sciences (RETPS 2024). This conference was held on October 12-13, 2024 in Houston, USA, and it was a very successful joint conference by four countries namely China, United States, Canada, and New Zealand. Papers from educational technology, public management, psychology, humanities, and social science were well represented in this volume and we hope that these contributions will continue to play a large part in future RETPS conferences.
RETPS 2024 also fosters cooperation among organizations and researchers involved in the merging fields and provides in-depth technical presentations with ample opportunities for one-on-one discussions with the presenters.
We wish to thank each and every member of the proceeding editors and local technical committee. Many people contributed to the success of the conference, and the organisers specially would like to thank the reviewer team. The local organisers and conference office did a splendid job, and we all enjoyed an excellent conference atmosphere. They were very patient and committed in making this conference a success.
Organizing Committee of RETPS
Houston, USA
Telemedicine and remote monitoring for people with Parkinson’s disease
Authors: van den Bergh Robin
Published: November 2024

This study aims to advance telemedicine, particularly remote monitoring, for individuals with Parkinson's disease. It contains three areas of investigation: maintaining access to care, capturing objective data at home, and providing proactive care. The first contribution emphasizes the critical role of telemedicine in ensuring continued access to healthcare, especially highlighted during the COVID-19 pandemic. Remote consultations and treatments facilitate care delivery, although they may reduce important personal interaction. The second contribution focuses on remote monitoring technologies that gather detailed objective information about people with Parkinson’s disease daily functioning. Such data can be useful for healthcare professionals, particularly physiotherapists, and people with Parkinson’s disease to gain insight in functioning at home, for example fall incidents and physical activity. The usability of these systems varies among users, influenced by factors such as cognitive abilities and digital literacy. The third contribution explores the potential of telemedicine to deliver proactive care by monitoring modifiable risk factors related to common complications of Parkinson’s disease. By identifying important causal risk factors for these complications, this thesis aims to support prevention efforts utilizing telemedicine tools. Overall, this study investigated how telemedicine and remote monitoring can enhance care for people with Parkinson’s disease.
The Evolution of Women's Roles and Status in Chinese Modern-Themed Films and Television Shows Between 2014 and 2024
Authors: Wu Shuang , Yin Xinwei
Published: November 2024

Abstract: This paper devotes to study the changes in the role status and positioning of female characters in the context of modern Chinese film and television shows in the last ten years. This paper is based on the literature research method, case study, and comparative analysis method. Papers in related fields are studied to analyze the differences in the status and positioning of female characters in film and television works in various periods. In conclusion, this research finds that the evolution of female roles in modern Chinese film and television is a testament to the awareness and appreciation of gender diversity and female empowerment. As society continues to develop, future productions will embrace and celebrate the richness of women's careers and family lives. The film and television industry will continue to challenge and redefine traditional gender roles. The industry's response to audience feedback and the need for more realistic representations of women will elicit further innovation and diversity in the representation of female characters on screen.
Research on the Application of Smart Home in Interior Design
Authors: Yu Jinwei
Published: November 2024

Abstract: With the rapid development of science and technology, the smart home in the field of interior design came to prominence. This paper discusses the application of smart home in interior design in the current environment. Smart home integration of advanced technology, covering intelligent lighting, home appliances, security, and environmental control systems. It provides designers with new creative ideas, challenges the traditional design mode, and promotes the perfect combination of technology, and art to meet people's pursuit of a high quality of life. For the residents, a smart home brings a new life experience, providing a comfortable, convenient, safe and personalized living environment. Therefore, this paper combines the new concepts, methods, and elements brought by smart home to interior design, analyses how smart home injects new vitality and innovative elements into interior design, and finds that the smart home opens up new dimensions, enriches the functional connotation, provides aesthetic inspiration, and changes the design process and method for interior design, that smart home is of great significance in promoting interior design innovation and change.
A Comparison of the Extensibility of Marriage in Ancient Greek and Chinese Taoist Traditions
Authors: JIN PEACE
Published: November 2024

Abstract: In ancient societies, people experienced different wedding rituals under Chinese Taoist culture and in ancient Greece. Women of various social backgrounds faced distinctive treatment. Comparing two kinds of cultural settings, this paper will analyze how these distinct married conventions manifest the past broader societal perspectives and morality towards power dynamics, involving rules, status, and rituals for couples. In addition, this research purposely focuses on their societal structures and gender roles, promoting gender equality and diminishing patriarchal for future development. In ancient Greek society, particularly in Athens and Sparta, this paper reveals that marriage was an important process that reflected family ties and societal expectations, leading to limited rights for women. In contrast, Chinese Taoism approached marriage more practically, this culture emphasized the balance of yin and yang and announced a more egalitarian perspective with some limitations. Utilizing a comparative analysis method, the study gathers insights into the marriage customs of both civilizations. This investigation illuminates the evolution of marital norms and encourages contemporary discussions about relationships and gender roles.
Exploring the Creation of Zibos Viral City Image
Authors: Yuan Jiulong
Published: November 2024

Abstract: Recently, the city of Zibo, known as an internet-famous city, has gained massive popularity on social media platforms, attracting a surge of tourists to visit and enjoy its food. Discussions about Zibo have dominated major social platforms, with data consistently ranking high as users spontaneously follow the trending topic, bringing undeniable economic benefits to the city. The development of Zibo-style traffic has exceeded expectations, with high levels of interest across platforms and an unusually long-lasting buzzsomething rare in previous tourism promotions. Zibo has crafted a model for building a viral city image that aligns with its own growth. This model reflects the characteristics of current online communication trends and offers insights into the future direction of internet dissemination. It serves as an example for other regions to learn from when using internet communication to promote tourism and stimulate economic growth.
The Analysis of the Phenomenon of Pan-Entertainment in the Era of New Media
Authors: Wang Yueying
Published: November 2024

Abstract: Digital media, such as computers and cellular phones, have undergone a period of accelerated development since the second half of the 19th century, with the general public becoming increasingly immersed in the new media age. In the digital age, there has been a significant increase in the variety, scope and speed of mass communication, facilitated by a wider variety of technologies. The accessibility of information has also increased, especially compared to the era of traditional media. Entertainment as a component of communication has proliferated in many ways and has become a ubiquitous aspect of public life. However, in the context of contemporary society, the prevalence of pan-entertainment has given rise to the phenomenon of pan-entertainment, whereby almost every aspect of life is regarded as forms of entertainment, deserving social attention. This paper discusses the phenomenon of pan-entertainment in the context of the new media era, combining theory and case study. Using the example of comedian Li Haoshi (HOUSE) and his fans, the paper explores the role of media and capital, technological development, and social demand in the emergence of this phenomenon. Finally, the paper argues that while entertainment is positive, the public must be alert to its potential harm to aesthetics, spirituality and values. To this end, the paper suggests measures at the educational, law and platform levels.
Exploration and Practice of Digital Technology and Virtual Reality Technology in the Creation of Public Art with Chinese Characters: Take Qingshan Hongfang as an Example
Authors: Peng Zijun
Published: November 2024

Abstract: In the modern context of rapid development of artificial intelligence, it provides a new direction for the development of public art in the new situation. The digital reconstruction of modern and contemporary public art has provided new experiences in terms of interaction and emotion. The popularization of interactive devices and virtual reality technologies has made the forms of public art more diverse and the audience's experience more personalized. As the carrier of Chinese civilization, Chinese characters have their special cultural significance and resonance. Translating Chinese characters through public art is a good form of expression. Currently, there is a lack of public art works with cultural identity in urban public spaces in China. This article will use a combination of quantitative and qualitative methods to analyze the public art and Chinese character design cases of the Red Steel City Red House architectural complex in Qingshan District by sorting out relevant concepts and combining case study methods. Ultimately exploring the combination of digital technology and virtual reality, incorporating Chinese characters as a creative element in contemporary art and design. Intended to provide reference and inspiration for art creators and related practitioners.
AI and Ethics: Moral Considerations of Automated Rumor Detection Technology
Authors: Deng Yifei
Published: November 2024

Abstract: As digital society continues to develop, social media and information distribution have experienced significant changes, leading to challenges with accessing credible information and identifying misinformation. In response, the use of AI automation technology has been advocated as a solution to identifying fake news and improving information credibility. However, implementing this technology raises ethical concerns related to data privacy protection, transparency in information monitoring, and maintaining a balance between free speech and censorship. This paper examines ethics associated with using AI automation techniques for identifying misinformation, and proposing relevant laws and ethical guidelines to address these concerns. Through a literature review and case analysis, this study provides guidance for the ethical implementation of AI automation techniques for identifying misinformation. Our research aims to contribute to the development of sound ethical practices in the use of AI automation techniques in misinformation identification. Furthermore, this study highlights the need for an integrated approach in AI automation technology implementation and the significant role of transparency and accountability in mitigating ethical concerns. Additionally, this paper explores the potential benefits and pitfalls associated with using AI automation techniques to identify misinformation. While such technology can improve information credibility and protect the democratic process, it also poses risks to the quality of information and public trust in technology. In conclusion, this study underscores the importance of ethical considerations in implementing AI automation techniques for identifying misinformation and provides guidance for policymakers and practitioners working in the field of AI and information governance.
The Chinese Online Youth Subculture of the "Electronic Wooden Fish" A "Lying Flat" or A "Self-salvation ?"
Authors: Liao Ziqi
Published: November 2024

Abstract: With the rapid development of the Internet, many products have undergone digitalization. The electronic wooden fish, for example, represents a virtual rendition of a religious instrument. In contemporary times, many Chinese youths encounter difficulties in coping with the intense competition prevalent in school entrance examinations and job markets. Also, their limited social recognition inhibits them from fully expressing their opinions in reality. Consequently, resorting to the electronic wooden fish serves as an outlet for these individuals. The youth tend to use the Internet as a means to alleviate stress, thereby giving rise to an online subculture among them. This article delves into the causes and effects of knocking on the virtual wooden fish. By collecting data from three social media platforms in China and conducting thorough analysis, it concludes by identifying three motivations behind knocking on the electronic wooden fish: seeking spiritual merits, relaxation purposes, and sharing experiences with others. These incentives can be explained through various aspects such as cultural symbolism within Chinas context, serving as a tool for decompression, and contributing to self-identity formation among users. Ultimately, this article argues that embracing the subculture surrounding Electronic wooden fish signifies Chinese youths pursuit of self-salvation, rather than lying flat.
Si Maqian's Sense of Revenge from the Biography of Wu Zixu
Authors: Zhang Shu
Published: November 2024

Abstract: In literary history, Si Maqian often appears as the image of a loyal and patriotic character who endures humiliation. The positive tendencies in his personal emotions are infinitely magnified under human control, but the negatively inclined emotions are always shunned because they do not conform to the Confucian laws used by the king for ruling. If we look at Si Maqian as a complete human being, it is impossible for him not to resent the fact that he was alienated by the king and tortured in the palace. This paper mainly discusses Si Maqian's sense of revenge as revealed in the Biography of Wu Zixu, and this study has made the character of Si Maqian more vivid and concrete, taking him away from the saintly state of melancholic concern for his country. It provides a more choice and reference for the future analysis of Si Maqian's authorship; it provides more possibilities for the future analysis of the cultural environment of the Han Dynasty.
A Study on the Influence of Beaconism on Immigration Propensity among Youth and Middle-aged Chinese Individuals
Authors: Gan Zihe
Published: November 2024

Abstract: In 21st-century China, the phenomenon of migration is becoming increasingly common. Some people migrate to improve their standard of living, while others are inspired by the call of Beaconism to go to other countries in search of an imagined utopia. This study investigates the spread of Beaconism on the Chinese internet and its influence on the immigration propensity of young and middle-aged Chinese individuals. Through literature analysis and case studies, the paper elucidates the connotation and characteristics of Beaconism, as well as its dissemination mechanism on platforms such as immigration intermediaries and social media. The findings indicate that Beaconism has a significant impact on the immigration motives, destination choices, and value systems of the young and middle-aged groups, characterized by a blind pursuit of Western civilization and a neglect of traditional culture. This research provides a new perspective for understanding the immigration phenomenon in China and offers prospects for future research.
Construction and performance analysis of DC servo system based on operational amplifier
Authors: Tang Tianyu
Published: November 2024

Abstract. The motivation for researching modelling the severe motor system is its frequent use in applications that demand accurate control of position, speed, or force. Examples include robots and CNC machines, extensively employed and advanced in modern manufacturing processes. The application field of this technology is expanding significantly due to the increasing demand for automation in modern times. In order to achieve seamless integration of the system, using Op-Amp as the central element for signal processing and control is crucial. This research aims to determine the methodology for utilising an operational amplifier (Op-Amp) to construct a model for a direct current (DC) servo motor. Additionally, the research aims to develop a control system and do circuit board simulations.The outcome of this research is to implement theoretical principles into a design model, identify the most appropriate system for this model and develop the corresponding circuit board. This study aims to incorporate a control system to regulate server motors, effectively enhancing the automation process.
Research on real-time 3D imaging technology based on CMOS sensors
Authors: Zhu Qiwen
Published: November 2024

Abstract. With the continuous progress of science and technology, 3D imaging technology has been widely used in many fields, such as medical imaging, virtual reality and protection of cultural heritage. It can provide more information than traditional 2D images, greatly improving the accuracy and reliability of various applications. CMOS (Complementary Metal Oxide Semiconductor) sensors, as efficient and low-cost image sensors, have been widely used in image imaging and processing technologies in recent years. However, how to realize real-time and high-precision 3D imaging with CMOS sensors is still a technical challenge to be solved. Based on this background, this paper will focus on real-time 3D imaging techniques based on CMOS sensors through three topics. This paper will first introduce how CMOS sensors work, followed by the introduction of real-time 3D imaging technology, and finally the real-time 3D imaging technology based on CMOS sensors. The research in this paper will be valuable for the research and application of real-time 3D imaging technology based on CMOS sensors.
Deep learning-based real-time ray tracing technology in games
Authors: Peng Siqi
Published: November 2024

Abstract. In recent years, deep learning-based techniques have revolutionized real-time ray tracing for gaming, significantly enhancing visual fidelity and rendering performance. This paper reviews various state-of-the-art methods, including the use of Generative Adversarial Networks (GANs) for realistic shading, the use of neural temporal adaptive sampling, the use of subpixel sampling reconstruction, and the use of neural scene representation. Key findings highlight improvements in perceived realism, temporal stability, image fidelity, and computational efficiency. Techniques such as neural intersection functions and spatiotemporal reservoir resampling further optimize rendering speed and memory usage. Additionally, approaches like adaptive sampling and neural denoising using layer embeddings contribute to reduced noise and enhanced image clarity. Collectively, these advancements make real-time ray tracing more feasible for high-fidelity gaming applications, offering enhanced graphics without compromising performance. These improvements are significant. My analysis underscores the critical role of deep learning in overcoming traditional ray tracing challenges, paving the way for more immersive and responsive gaming experiences. Furthermore, these innovations suggest a promising future for integrating advanced ray tracing techniques into a broader range of interactive media, ensuring both visual excellence and operational efficiency.
Optimizing inverted pendulum control: Integrating neural network adaptability
Authors: Xie Yusen , Mi Yingjie
Published: November 2024

This study explores the implementation and efficacy of a neural network controller for an inverted pendulum system, contrasting it with traditional state feedback control. Initially, state feedback control exhibited limitations in managing complex system dynamics. Subsequently, a neural network controller was developed, trained using datasets from both uncontrolled and refined state space models. The refined model yielded lower training loss and superior control performance. This research demonstrates the neural network controllers enhanced adaptability and precision, offering significant improvements over traditional methods in controlling dynamic systems like inverted pendulums.
Research on target detection and recognition system of autonomous driving based on deep learning
Authors: Ye Haotian
Published: November 2024

Abstract. Autonomous driving technology relies heavily on advanced target detection and recognition systems to ensure safety and efficiency. This essay explores how deep learning has revolutionized these systems in four key areas: lane detection, detection of obscured vehicle parts, blind spot detection, and multi-target detection. Traditional methods often struggle under challenging conditions, but deep learning approaches, including Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs), offer superior performance by analyzing complex features from raw data. Techniques like the Detection of Incomplete Vehicles using Deep Learning and Image Inpainting (DIDA) enhance safety by reconstructing obscured vehicle parts. In blind spot detection, models such as Sep-Res-SE blocks provide an effective, cost-efficient alternative to radar systems. The Adaptive Perceive SSD (AP-SSD) framework improves multi-target detection accuracy and real-time tracking by incorporating advanced feature extraction and temporal analysis. The essay concludes with future research directions aimed at refining real-time capabilities, expanding datasets, and exploring collaborative learning to further enhance autonomous driving technology.
Recent advances in LDO chip research
Authors: Lai Jiaqi
Published: November 2024

Abstract. With the rapid development of integrated circuit technology and the wide application of Internet of things devices, the demand for power management chips is increasing. Low-dropout regulators (LDO) play an important role in modern electronic devices. It provides a stable low output voltage while maintaining low power consumption. Recently some progress has been made in the research of LDO chips, focusing on improving power efficiency, reducing static current, and enhancing transient response. The study shows that the static current has been significantly reduced by using the subthreshold MOS tube and dynamic current bias technology. In terms of power supply rejection ratio (PSRR), by improving the circuit design, the PSRR of LDO can be maintained above -80 dB under various conditions. In addition, improvements to the transient response result in significantly optimized overshoot and recovery times of the output voltage, ensuring voltage stability when the load changes rapidly. These advances enable LDO chips to meet the demanding requirements of high-performance and Internet of things device applications. Future challenges include further reducing static current, improving PSRR performance in high-frequency environments, and effectively addressing thermal management issues.
Machine learning-based EEG analysis for enhanced schizophrenia classification and diagnosis
Authors: Liu Xiaofan
Published: November 2024

Abstract. Schizophrenia can have a significant impact on patients' lives, studies, and work. Patients may experience delusions, hallucinations, disorganized thinking, and abnormal behavior, among other symptoms. Therefore, research related to schizophrenia is of great importance. This paper proposes the design of a classifier based on machine learning for diagnosing schizophrenia. The classifier extracts N100 features, P300 features, and power features across different frequency bands from both time-domain and frequency-domain characteristics from Electroencephalography (EEG) signals. Given the small dataset, a Support Vector Machine (SVM) machine learning algorithm was chosen to process and classify the selected features. To address the limitations of SVM in handling high-dimensional data and nonlinear problems, this study introduces a comprehensive improvement method based on Bayesian optimization, Recursive Feature Elimination (RFE), and data augmentation. Bayesian optimization was used to find the best combination of hyperparameters for the model, thereby improving its performance; RFE was employed to assess feature importance and remove the least important features, enhancing the model's training efficiency and generalization capability. Additionally, data augmentation was included to increase the sample size and introduce diversity, thereby improving the model's robustness. The study found that these methods effectively improved classification accuracy and generalization ability.
Analysis and optimization of the efficient press shop operation
Authors: Zhang Jiahang
Published: November 2024

Abstract. In the last decade, stamping and forming manufacturing methods have been widely used in the manufacturing industry, and the stamping and forming industry has gradually shifted from the competition of scale and quantity to the competition of high quality, cost-effectiveness, and multi-category. Therefore, it is of far-reaching significance to study the efficient operation method of the press shop. In this paper, the efficient operation modeling method, digital simulation platform, and production line improvement of the press shop are reviewed. According to analysis, it can be concluded that the dynamic data-driven modeling method is perfect, but the theoretical basis involved in the practical application process is too complicated, therefore, press shop managers may be required to have a certain degree of theoretical basis so that the method can be effectively realized. Besides, the virtual-real combination of digital simulation platforms is easy to operate in the press shop, but the model assumptions analyzed in this paper are too idealized, and potential unresolved problems may arise in the practical application of the press shop with a larger scope and wider coverage. Finally, the production-based transformation of the shop floor production line is a more suitable management method for older factories that are ready for upgrading.
Intelligent agricultural mechanization: A new era engine for agricultural development
Authors: Wei Xiang
Published: November 2024

Abstract. In recent years, the rapid advancement of technology and the increasing demand for efficient agricultural practices have propelled significant progress in the field of intelligent agricultural mechanization. This paper aims to comprehensively explore this important topic by analyzing the key role of intelligent agricultural mechanization in modern agriculture, examining its technological applications, and identifying prevailing challenges. Intelligent agricultural mechanization involves the integration of cutting-edge technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) into agricultural machinery and processes, revolutionizing traditional farming methods. Through an in-depth study of relevant literature, it elucidates how intelligent agricultural mechanization can significantly enhance agricultural production efficiency by minimizing resource waste, reducing labor costs, and increasing crop yields. Furthermore, the paper explores the broader implications of these advancements for sustainable development. The research results presented herein seek to provide robust theoretical support and practical guidance for advancing agricultural modernization. In this paper, future development trends are also discussed, including the potential for further innovations and the role of policy and regulatory frameworks in facilitating the adoption of intelligent agricultural mechanization.
A study of advances in machine learning-based electronic design automation
Authors: Li Jiulin
Published: November 2024

Abstract. The electronic design automation (EDA) is a convenient tool for designing integrated circuits (IC), which is employed extensively both in academic and engineering. The design of integrated circuits is conducted in accordance with a defined design flow, commonly referred to as the chip design flow, which can be divided into two distinct parts, the front-end design and the back-end design. Following a long period of evolution, the chip design flow of EDA has been gradually improved. Besides, it achieved some accomplishments during this period. However, with the growing demand of ICs, especially Very Large-Scale Integration Circuit (VLSI), the existing EDA is not adequate for the requirement. In addition, the EDA technology has been so developed that it is relatively less flexible. In this context, concerns about the future of EDA have recently emerged. In response to this challenge, research has mentioned that machine learning methods (ML methods) can improve the functionality of EDA. The ML method covers most of steps of EDAs design flow, especially back-end design. The machine learning-based electronic design automation is still in its infancy, which is presented with a multitude of challenges. Therefore, the paper explores the development of EDA by reviewing and organizing the related literature, and summarizes the application of ML methods in EDA, thereby providing the future development trend of ML-based EDA.
Artificial intelligence in the Internet of Things: Integrating and optimizing AI algorithms for real-time data processing and decision-making
Authors: Yuan Pan
Published: November 2024

Abstract. The introduction of Artificial Intelligence (AI) will provide great opportunities and pose significant challenges in Internet of Things (IoT) systems. IoT devices can create huge amount of data in real-time. Meanwhile, it is crucial to handle immediate processing and decision-making in IoT applications. However, IoT devices are usually constrained by limited computation power, memory, and energy compared to traditional devices, making the deployment of efficient AI algorithms a challenging task. In this paper, we present different algorithmic strategies to overcome the above problems, including model compression, quantization, and hardware accelerators. On the other hand, we also focus on the role of decentralization and edge computing architectures to increase the scalability and performance of AI-IoT systems. Finally, this paper also reviews energy-efficient AI algorithms and latency reduction methods to make the decision process real-time for various IoT applications.
Research on autonomous obstacle avoidance and automatic obstacle avoidance of minimally invasive surgical robots
Authors: Luo Congwen
Published: November 2024

Abstract. With the development of modern society, people pay more and more attention to health and safety, especially to the development of medical techniques. The demand for higher medical standards has led to minimally invasive surgery becoming a better choice for patients. Minimally invasive surgeries involve less trauma, lower blood loss, quicker recovery, and a lower risk of infection. However, compared to traditional surgeries, these surgeries demand higher precision and skill from the surgeons, making surgical robots the preferred tool for most minimally invasive operations. Surgical robots can eliminate the surgeon's hand tremors during the operation, enhance the precision of surgical operations, and offer greater flexibility with their small end size inside human organs. Despite these advantages, minimally invasive surgical robots can not avoid obstacles and plan the path of human organs and cavities in real-time. This paper reviews the dynamic path planning and real-time obstacle avoidance of modern minimally invasive surgical robots, including their development progress and research directions. It also introduces in detail the mainstream research methods of path planning and real-time obstacle avoidance in modern research. Finally, the current technical challenges and the prediction of the future research direction are pointed out and the reference suggestions are provided.
Technological State and Optimization Analysis of High-Rise Glass Curtain Wall Cleaning Robots
Authors: Zhang Kai
Published: November 2024

Abstract. With the proliferation of high-rise buildings in urban areas, glass curtain walls are widely used due to their excellent light transmittance and modern appearance. However, these glass curtain walls are prone to accumulating dust, rain stains, and other dirt during use, affecting their aesthetics and transparency, thus requiring regular cleaning. Traditional manual cleaning methods are inefficient and pose significant safety risks. Consequently, high-rise glass curtain wall cleaning robot technology has become a research hotspot. This paper reviews the current research status of high-rise glass curtain wall cleaning robots, which provides a detailed analysis of four main adhesion methods (magnetic adhesion, thrust adhesion, vacuum negative pressure adhesion, and bionic adhesion), four main motion modes (legged, wheeled, tracked, and external assistance), and two main cleaning methods (physical and chemical). For path planning, this paper explores the application status and challenges of regular scanning, random walking, sensor feedback dynamic planning, and bionic path planning. Through the evaluation of the advantages and disadvantages of existing technologies, this paper identifies the main issues faced by each technology and proposes optimization strategies for adhesion technology, motion modes, cleaning efficiency, and path planning. Future directions include improvements in intelligence and automation, multifunctional integration, energy efficiency enhancement, and human-machine collaboration. This paper aims to provide a comprehensive technological review and constructive suggestions to advance high-rise glass curtain wall cleaning robot technology.
Advances in Brain-Computer Interface Technology for Depression Diagnosis: A Focus on EEG, MEG, and fNIRS
Authors: Tai Yang
Published: November 2024

Abstract. With the acceleration of the pace of modern social life, the psychological pressure on individuals has also increased. Consequently, a series of problems brought about by depression are increasingly recognized, such as its impact on mental health, social functioning, and overall quality of life, and the demand for accurate depression diagnosis has gradually increased. This has led to the exploration of innovative Brain-Computer Interface (BCI)-based diagnostic methods that leverage brain-machine interface technology. The application of related technologies for depression diagnosis is introduced from the aspects of Electroencephalography (EEG), Magnetoencephalography (MEG), and Near-infrared (NIR) imaging technology. These three technologies represent emerging approaches in depression diagnosis, adopting concepts that differ from traditional diagnostic methods. During the diagnostic process, they rely on electrocardiac and brain magnetic field patterns to provide important information related to brain activity. These methods enable non-invasive monitoring of cerebral blood oxygen levels, which is crucial for understanding the physiological underpinnings of depressive symptoms.
Overcurrent system suppression measures for HVDC transmission system
Authors: Ding Ruiyao
Published: November 2024

Abstract. LCC-HVDC technology, an essential Commutated Converter based on High Voltage Direct Current, is extensively employed in power system. However, because DC side overcurrent has the potential to seriously affect both system stability and equipment dependability, it has long been the focus of LCC-HVDC systems. This research presents an effective overcurrent suppression technique for the LCC-HVDC side overcurrent. This work first explains the damage caused by DC side overcurrent. A defect or abnormal situation in an LCC-HVDC system can result in DC side overcurrent, which can have a number of negative effects, including equipment damage, power grid instability, and system collapse. Therefore, it is imperative to address the DC side overcurrent issue in order to ensure the system's reliability and safe functioning. The overcurrent suppression method is then presented in this study. This research then presents an overcurrent suppression technique. This approach is predicated on a thorough examination of the properties of DC side pass current in conjunction with power electronic device regulation. First, the primary cause of the DC side pass current is identified by examining its source. Next, a control algorithm is created that has real-time monitoring and suppression capabilities for the DC side overcurrent. The method is able to precisely detect the overcurrent and promptly implement the necessary control measures.This study contrasts simulated trials carried out in the actual LCC-HVDC system with the conventional overcurrent suppression technique. The results show that the proposed overcurrent suppression strategy improves stability and reaction speed while effectively suppressing the DC side overcurrent. Furthermore, the method exhibits strong resilience and flexibility across a range of malfunction scenarios and operational contexts.
A Study of High Transient Response Low Dropout Linear Regulator Chips
Authors: Song Meizheng , Zhang Qinyan
Published: November 2024

Abstract. With the rapid development of the microelectronics industry in the world, electronic products are being updated more and more quickly. This has created a growing large market demand for power management chips. Low drop-out linear regulators have become one of the most widely used types of power management chips due to their advantages of high stability, low drop-out voltage, and fast response. This paper is a data collection study and comparison of improvement methods based on high transient response low drop-out linear regulators. It describes the basic principles of LDOs as well as the principles, advantages and disadvantages of different improvement methods. It was found that the method of shortening the overshoot and undershoot durations by constructing transient enhancement circuits was the best in terms of performance among the following research methods. The stability of capacitor-less LDO is harder to control. Using compensation circuits to improve stability will increase circuit complexity. The capacitor-coupled current mirror method has a longer startup time, because it takes some time for it to reach homeostasis. The high complexity and energy consumption of the dual feedback loop structure makes the circuit costly.
Curiosity-Driven Multi-Level Intrinsic Reward DQN for Enhanced Exploration in Reinforcement Learning
Authors: Cao Zheyuan , Jiang Jiyu , Liu Hengyan
Published: November 2024

Abstract. Reinforcement learning (RL) has shown great potential in solving complex decision-making tasks. However, efficient exploration remains a significant challenge, particularly in environments with sparse or deceptive rewards. This paper introduces DQN-Mult-Cur, a novel reinforcement learning algorithm that enhances exploration by integrating curiosity-driven and multi-level intrinsic rewards within the Deep Q-Network (DQN) framework. The proposed method addresses the limitations of conventional exploration strategies by incentivizing agents to explore novel and meaningful states, thereby improving learning efficiency and performance. Extensive experiments across three standard environmentsCartPole-v1, MountainCar-v0 and Acrobot-v1demonstrate that DQN-Mult-Cur outperforms traditional DQN variants, achieving faster convergence, higher rewards, and greater stability. An ablation study further highlights the importance of each intrinsic reward component, confirming the robustness of the proposed approach. The results suggest that DQN-Mult-Cur offers a comprehensive solution to the exploration-exploitation trade-off in reinforcement learning, making it applicable to a wide range of challenging environments.