bluuebunny/crossref_metadata_2025 huggingface dataset:
THE STUDY OF SOME APPLE GENOTYPES WITH RESISTANCE TO DISEASES CANDIDATE FOR OBTAINING NEW VARIETIES
Authors: Gavrilă Marian-Florin , Petre Gheorghe , Comănescu Daniel
Published: November 2024

The researches performed at Voineşti Research and Development Station for Fruit Growing in the apple breeding program for obtaining varieties with resistance to diseases highlights the biological characteristics of some existing genotypes in the competition microculture established in 2019. In the 11 apple genotypes taken in the study, compared to the 'Florina' variety taken as a reference, it is analysed the evolution of the flowering phenophases, the period of fruit ripening and storage, as well as the quality determined by biomass, dry matter content and firmness of the pulp during 2022-2024. Some of the apple genotypes in the competition microculture meet the requirements imposed for the approval of new varieties with resistance to diseases, which can meet the market requirements and the expansion in modern orchards from the established orchards. From the recording of the flowering phenophases, it is noted that they are advanced by about two weeks in the year 2024, compared to the years 2022 - 2023, due to the increase in temperatures in March, against a background of sufficient moisture, being a signal for the study of phenology in the face of climate change. The apple genotypes with disease resistance present fruits intended for fresh consumption, most with winter ripening, with a compact, consistent firmness and an appreciable dry matter content, characteristics that promote them for the approval of new varieties. Three of the apple genotypes were registered for homologation, one becoming the 'George' variety, being appropriate to the demands of the producer, sensitive to economic efficiency, with quality fruits, which will certainly meet the ever-increasing consumer demands.
La eficacia traslativa de la donación en el Código Civil a la luz de los antecedentes históricos y legislativos
Authors: Cardós Elena José María
Published: November 2024

Desde hace varias décadas, con base en el contenido del CC, la práctica totalidad de la doctrina sostiene que la perfección de la donación comporta la  transmisión de la titularidad de los bienes donados, sin necesidad de entrega o tradición. Dicha conclusión se alcanza a través de una interpretación literal, sistemática y lógica de la regulación de la donación en el CC. Sin embargo, la interpretación histórica de tales normas sido escasamente referida al abordar la eficacia traslativa de la donación, a pesar de su extraordinario interés. Este estudio pretende demostrar que una interpretación de la normativa reguladora de la donación en el CC a la luz de los antecedentes históricos y legislativos refuerza la conclusión de que la donación es un negocio dispositivo, con eficacia traslativa inmediata.
The Dual Nature of Trust in Participatory Sciences: An Investigation into Data Quality and Household Privacy Preferences
Authors: Lin Hunter Danielle , Johnson Valerie , Cooper Caren
Published: November 2024

There is a duality of trust in participatory science (citizen science) projects in which the data produced by volunteers must be trusted by the scientific community and participants must trust the scientists who lead projects. Facilitator organizations are third-party organizations that engage their members in participatory science to enrich their members’ experience at their organization. In Crowd the Tap, we engaged participants through facilitator organizations including high schools, faith communities, universities, and a corporate volunteer program. We used Kruskal Wallis tests and chi-square tests with Bonferroni post hoc tests to assess how data quality and privacy preferences differed across facilitator groups and amongst those who participated in the project independently (unfacilitated). Faith communities provided higher data quality while students provided lower data quality. Data quality in education settings differed based on the level of investment of the project in terms of both time and money as well as student age. We also found that demographic and household characteristics seemed more important in predicting privacy preferences than facilitation. Our results suggest that project leaders can support diverse participation by extending protection of participant privacy and investing in needed resources to support facilitators. They also suggest that education-oriented facilitators may need to prioritize data quality to ensure authentic learning opportunities. Ultimately our results reveal several tradeoffs that project leaders can weigh when deciding to work with facilitators.
Dimensions of Islamic Education in KH Zainudin, MZ's Thoughts on Life Partner Qualifications
Authors: Widoyo Agus Fatuh , Fikri Ahmad Kamalul
Published: November 2024

This qualitative research examines the content of Islamic educational values in KH Zainudin MZ's thoughts about finding a life partner to enter into marriage. Primary data is the content of KH Zainudin MZ's lecture material with the theme ‘Looking for a mate’ in a YouTube channel. While secondary sources are various relevant literature. Data collection techniques use media observation, and data analysis techniques use reduction, presentation, and verification. The results of the study state that there is a content of Islamic education values extracted from various criteria in finding a life partner, among others, the beauty criteria contains the value of character education to maintain physical quality and good behaviour, the propertycriteria contains the value of character education in the importance of maintaining hard work, and respecting financial differences, the descent criteria contains the value of character education in terms of the importance of Muslims to provide good examples to their children and families, the religious criteria contains the value of character education in the importance of maintaining the quality of religious life.
Área de Preservação Permanente (APP) urbana à luz do direito de propriedade pelo empreendedor
Authors: Figueiredo Shirley Cássia Oliveira Alves , Melo Daniele de Castro Pessoa de , Lins Eduardo Antonio Maia
Published: November 2024

O projeto de pesquisa investiga a preservação de uma Área de Proteção Permanente (APP) urbana localizada em Aldeia - Camaragibe/PE, onde foi construído um Centro de Treinamento esportivo as margens da área. O objetivo principal é compreender como o proprietário dono do empreendimento mantém as instalações regulares do ponto de vista ambiental, visando garantir sua função econômica e socioambiental. A pesquisa aborda a legislação ambiental federal com fulcro nos artigos sobre a APP em conjunto com os ditames jurídico de preservação com o Direito de propriedade, incluindo estudo sobre o impacto ambiental e aplicação de um Plano Gestor. O referencial teórico apresenta a importância e os conflitos que poderão surgir quando o empreendedor projeta a construção de um grande empreendimento as margens de uma APP urbana territorial. Destaca-se a necessidade de políticas públicas eficazes e instrumentos jurídicos que conciliem o desenvolvimento urbano com a proteção ambiental e a função social do empreendimento. A metodologia envolve a descrição do local de estudo e uma abordagem qualitativa para entender o tratamento dado à APP pelo proprietário a fim de manter as suas características naturais e de sustentabilidade do clube. Assim, com o fulcro de trazer medidas voltadas para a conscientização do empreendedor quanto a sua responsabilidade em atender os requisitos legais, gerando benefício para toda sociedade ao seu redor através do projeto social.
The impact of the "dual carbon" target on China's coal industry stock prices--Analysis based on the dual event study method
Authors: Feng Xinru , Zhang Kaili , Yi Ran
Published: November 2024

The proposal and implementation of the "dual carbon" target has a profound impact on the stock price of China's coal industry. 75 coal enterprises are selected as research objects, and based on the daily closing price data of each stock from 2020 to 2023, the coal industry stock index is constructed by using the Rathbair index method. The dual-event research method is used to empirically test the impact of the proposal and implementation of the "dual-carbon" target on the stock price of China's coal industry, and the transmission path and heterogeneity are analyzed. The results show that: (1) the proposal of the "dual carbon" target has no significant impact on the stock price of the coal industry, while the implementation of the "dual carbon" target policy has a significant negative impact on the stock price of the coal industry; (2) The implementation of the "dual carbon" target has a negative impact on both state-owned shares and non-state-owned shares, but state-owned shares are more obvious. (3) The implementation of the "double carbon" target has a greater impact on the GEM and is uncertain, and has a negative impact on the main board and the original small and medium-sized board. (4) The implementation of the "dual carbon" target has a negative impact on stock prices in different regions, and the fluctuation range is ranked as follows: central region > Western region > Eastern region > Northeast region. (5) The implementation of the "double carbon" target has a negative impact on both directly and indirectly participating enterprises, and the negative fluctuation of directly participating enterprises is more severe. Based on this, this paper puts forward suggestions from four aspects: the government, regulators, coal industry enterprises and investors, in order to jointly promote the implementation of the "dual carbon" goal and the transformation and upgrading of the coal industry.
Study on the Efficiency and Potential of China's Tire Export Trade to Ten ASEAN Countries under the Perspective of “One Belt, One Road”
Authors: Liu Yiyun
Published: November 2024

Tires are an important product category in China's export trade. Based on the stochastic frontier gravity model, this paper uses the panel data of China's tire export trade to the ten ASEAN countries from 2018 to 2022 to study the influencing factors of bilateral trade in tire exports, and to measure China's export trade efficiency, export potential and expandable space to the ten ASEAN countries. Based on these three indicators, we analyze the future prospects of China's tire product exports to the ten ASEAN countries. The results show that: (1) in the stochastic frontier model, GDP per capita, population, border variables and language variables of both sides of trade as natural factors have a significant positive impact on tire export trade. (2) In the trade inefficiency model, liner transportation connectivity, trade freedom and financial freedom of both sides have a significant positive impact on tire export trade. (3) China's overall tire export efficiency and potential to the ten ASEAN countries is high, but the country-specific differences are obvious, and the trade as a whole still has more room for improvement. (4) China's tire export potential market can be divided into saturated, expanding, developing and iceberg type, of which the expanding type includes Malaysia, Vietnam, Thailand and Brunei 4 countries, the developing type includes Cambodia, Indonesia and Singapore 3 countries, of which Malaysia and Singapore is the need to pay attention to the overseas market.
The Reform and Practice of College English Writing Teaching Mode by Integrating Information Technology
Authors: Wen Like
Published: November 2024

The integration of information technology (IT) into college English writing instruction has emerged as a transformational strategy in response to the limitations of traditional teaching methods. This paper explores the modifications and applications of a pedagogical approach that uses ICT to improve the effectiveness of college English writing instruction. Traditional approaches to teaching English writing often suffer from limitations such as lack of individual feedback, monolithic teaching methods, and inadequate engagement with digital tools that define modern communication. The study examines how the integration of ICTs into the curriculum can lead to a variety of instructional strategies, including digital tools, collaborative methods, and multimedia materials, that address different learning styles. The main focus areas are the challenges associated with traditional teaching methods, the specific benefits of ICT in teaching English writing, and practical strategies for better integration The paper builds on case studies and data-supported outcomes to demonstrate the success of ICT-enabled learning practices for improving students’ writing skills, critical thinking skills and engagement. The findings suggest that ICT not only modernizes English education but also integrates the demands of a digitally connected world. This integration opens up a more sustainable, inclusive, and competency-oriented approach to English writing education in colleges.
KNOWLEDGE AND ATTITUDES OF NURSES TOWARDS PALLIATIVE CARE FOR THE DYING PATIENT
Authors: KHAN K , SALEEM R , MURTAZA A , BANO S
Published: November 2024

Palliative care is crucial in managing elderly patients at the end of life, yet nurses’ knowledge and attitudes significantly influence the quality of care provided. Understanding these factors can highlight areas for improvement in training and practice. Objective: To assess the knowledge and attitudes of nurses towards palliative care for older dying patients. Methods: A cross-sectional study was conducted in the Nursing Department of Nishtar Hospital, Multan from September 2023 to September 2024. A total of 100 nurses involved with palliative care of elderly patients were selected for the study. Knowledge of nurses was evaluated by the Palliative Care Quiz for Nursing which contained 20 questions that could be answered by selecting true, false, or unknown options. The attitudes of nurses were assessed on a seven-item thanatophobia scale which could be answered on a Likert scale from 1 to 7 with 1 being strongly agree and 7 being strongly disagree. Results: The mean knowledge score was 12 ± 3.1 which showed a moderate understanding with a score range of 6-19. The median attitude score on the thanatophobia scale was 13 with a score range of 8 to 35. Age (r= -0.347, p=0.006) and experience (r= -0.286) were negatively associated with attitudes and these associations were significant. Total knowledge scores and total attitude scores were moderately and negatively associated indicating that good knowledge was correlated to positive attitudes (p=0.008). Conclusion: A moderate knowledge level and favorable attitudes of nurses were recorded towards palliative care. However, more experience and training can improve these scores.
Research on the Phenomenon of Hidden School Dropout of Rural Students in China
Authors: Hu Suyu
Published: November 2024

Since the implementation of compulsory education, the overall quality of the population residing in rural areas in China has improved markedly. However, a considerable number of students still fail to complete their studies, resulting in a hidden dropout group. This has emerged as a significant challenge in the context of rural education in China. This paper defines the concept of rural hidden dropout and studies the three aspects of the phenomenon of hidden school dropout of rural students in China. It examines the factors that lead to rural students ' hidden dropout, the negative effects of hidden school dropout, and the analysis of potential solutions to the problem of rural students' hidden dropout. The study identified four primary causes of hidden dropout in rural areas: economic factors, parental factors, limited educational resources, and the influence of the internet. It also demonstrated that hidden dropout in rural areas has a detrimental impact. In the following four aspects, respectively, which are individual, family, school, and society, measures can be taken to address the issue of hidden dropout in rural areas. These measures include establishing a comprehensive prevention mechanism, optimizing local teacher resources, and increasing the level of family involvement. The significance of this study lies in analyzing the reasons and impacts of hidden dropout in rural areas, thereby deriving corresponding countermeasures. This is beneficial for the development of rural education and improvement of overall educational level in China.
The Impact of Language Learning Strategy Use on English Performance of Asian College Students
Authors: Wang Cunxiang
Published: November 2024

Language learning strategies, which are numerous and complex, are essential tools for language learners to learn. Due to individual differences, many learners are at a loss when understanding and choosing language learning strategies. This paper aims to analyze some common language learning strategies through literature analysis to help language learners understand the impact of language learning strategy use on the English performance of Asian college students. This paper believes that English performance can become better with the aid of appropriate learning strategies. Besides affecting academic performance, some language learning strategies are able to enhance motivation or change attitudes to help learning. Language learners should choose appropriate learning strategies according to their abilities. This paper's findings may help college students in Asia when choosing language learning strategies.
Research on the Impact of SARS-CoV-2 on Heart Health
Authors: Wang Alec
Published: November 2024

Since the initial identification of SARS-CoV-2, its impact on heart health has been a subject of considerable interest. SARS-CoV-2 infection not only triggers respiratory symptoms but may also cause substantial damage to the heart and cardiovascular system, leading to conditions such as myocarditis, heart failure, and other cardiovascular events. Research indicates that these cardiac issues are particularly prominent in severe cases. This review examines the specific effects of SARS-CoV-2 infection on the heart, including changes in cardiac structure, Electrocardiogram (ECG) abnormalities, and variations in cardiac biomarkers such as troponin and B-type natriuretic peptide (BNP). It also explores the relationship between these changes and clinical outcomes in patients. In addition, by synthesizing current literature, the study reveals the multifaceted impact of SARS-CoV-2 on cardiac function and discusses its implications for clinical management. Notably, significant increases in biomarkers of myocardial injury, such as troponin and BNP, suggest possible cardiac damage or increased burden. The results highlight the importance of monitoring and protecting heart function in patients with SARS-CoV-2 infection. In short, the paper delves into the impact of SARS-CoV-2 on heart health and provides recommendations for further research and clinical practice to improve patient outcomes.
Impacts of genetics and geoscience on the behaviors of young people and how human behaviors change the geographic environment
Authors: Chen Siyan
Published: November 2024

Abstract. This review systematically summarized and discussed the impacts of genetics and geoscience on human behaviors, especially young people, and how human behaviors change the geographic environment. In the review, some basic methodologies of genetic epidemiology have been revisited. It also compared environmental influences with adaptive genetic influences across various adolescent behaviors as examples. Pathways for the occurrence of genetic risks arepresented in this review as well where issues not just in deviant behaviors but also in the health issues mentally and physically. While geographic environment has impacts on the behaviors of young people, human activities are one of the reasons for those changes as well. Summaries and insights exhibited in this review can not only shed light on future research of the impact of various human activity on the geographic environment, but also implies benevolent activities human can take for the environment that consequentially benefit the growth of our adolescents at both cognitive and body level.
The relationship between gut microbiota and Alzheimer's disorder
Authors: Li Tiankai
Published: November 2024

The pathology of Alzheimer's disorder: neuron degeneration is the main cause of Alzheimer's disorder. Several ways will stimulate the degeneration of cells. The links between gut microbiota and Alzheimer's disorder consist of three major pathways: the vagus nerve, blood circulation, and release of chemical molecules. This paper discusses not only the pathology of Alzheimers disorder but also explores the connections between gut microbiota and Alzheimers disorder, as well as potential treatments based on gut microbiota. Probiotics are mainly discussed in this paper to present possible treatments for Alzheimer's disorder. To conclude, this paper mainly discusses the link between Alzheimers disorder and gut microbiota. Moreover, the conclusion highlights unresolved issues and unknown aspects of the interaction between Alzheimers and gut microbiota, urging further research and increased attention from scientists.
The Impact of the US Subprime Mortgage Crisis
Authors: Bu Yukun
Published: November 2024

The subprime mortgage crisis has a huge impact on American society, it makes a lot of countries get into financial crisis too. A lot of countries economy goes downturn. It is due to the lack of supervision of the U.S. financial industry, and with the need for rapid economic recovery, led to the financial crisis in the later years, and the U.S. economy fell into negative growth. Subsequently, the U.S. strengthened the supervision of the financial market in order to restore the U.S. economy. The significance of this article is to analyze the economic impact of subprime mortgages on the United States and to study some wrong policies behind the prosperous U.S. economy. The policy which commended by this article are that the national economic development should not focus on the short-term, but on longterm development. Loans should be given to small and medium-sized enterprises to promote the progress of social and economic development.
Exploring Feature Detection: A Comparative Study of Classical and Deep Learning Methods Across Complex Scenes
Authors: Zhou Huichuan
Published: November 2024

With the swift advancements in computing technology and artificial intelligence, the field of image processing has undergone profound changes. As the key link of information extraction, image features play a central role in computer vision tasks. Conventional methods like SIFT and SURF are often utilized in vision problems owing to their robustness and invariance. In contrast, deep learning algorithms, such as Superpoint and D2-Net, show greater adaptability and robustness in complex environments. In this study, we comprehensively evaluate the performance of the classical algorithms SIFT and SURF, alongside the deep learning methods Superpoint and D2-Net, across various scenarios, including repetitive patterns, cluttered backgrounds, and strong illumination conditions The experimental results show that SIFT and SURF perform stably when dealing with simple environments, while Superpoint and D2-Net demonstrate stronger adaptability and robustness in complex scenes, especially in terms of matching efficiency, average matching distance and consistency of feature distribution. Through comprehensive analysis and experimental verification, this paper reveals the effectiveness and limitations of the algorithms in different environments, providing a scientific basis for the selection of algorithms in practical computer vision tasks.
Marine Life Identification System Based on Deep Learning
Authors: Wang Shengrong
Published: November 2024

Marine organisms are indispensable components of marine ecosystems, and the study and detection of these important organisms have gradually become a challenge. Therefore, the Marine Life Identification System (MLIS) has emerged. This system utilizes deep learning technology and pre-trains the existing YOLOv5 model with a large dataset to ensure rapid and accurate identification of marine organism photos uploaded by divers or anglers, and provides a service for biological information queries. This paper uses five metrics to evaluate the accuracy of the identification results. The key issues addressed by this study include how to ensure effective classification in complex environments, and how to improve identification accuracy and reduce misidentification. The system aims to enhance its adaptability and robustness by constructing multi-level models. It has been verified that the system can output identification objects that meet the given evaluation criteria thresholds. This paper not only contributes to the study of marine life by providing researchers with a wealth of real-time marine biological data, but also enhances people's awareness of marine ecological protection and promotes the rational use of marine resources.
Multimodal Brain Tumor Segmentation Based on Multi-Scale Feature Extraction Network
Authors: Chen Yunji
Published: November 2024

Brain tumors are prevalent and highly fatal conditions, making their precise detection crucial for effective treatment. Accurate brain tumor segmentation in magnetic resonance imaging (MRI) scans is often crucial for clinical diagnosis and developing more precise treatment strategies. MRI, a standard diagnostic tool for brain tumor identification, offers multiple modalities, each providing unique imaging characteristics. However, existing deep learning-based segmentation methods often struggle with varying tumor size, which can impact their effectiveness. To address these issues, we incorporate DeepLabv3+ into the task of brain tumor segmentation, leveraging its Atrous convolutional capabilities to capture comprehensive multi-scale features. By combining the multi-scale feature extraction strengths of DeepLabV3+ with the diverse imaging modalities of MRI, our approach mitigates the limitations associated with single-scale analysis, thereby enhancing diagnostic accuracy and supporting improved therapeutic outcomes. This article finds that this model achieves an accuracy of approximately 93.5% on the BraTS_2020 dataset.
Design and Analysis of Miller Compensated Two-Stage Operational Amplifier
Authors: Zou Yuheng
Published: November 2024

Abstract. With the development of integrated circuit technology, the size of electronic equipment continues to reduce, and speed continues to increase. Operational amplifiers are the key circuit in analog ICs, and the required performance of operational amplifiers is also increasingly high, research and design of high-performance operational amplifiers has become one of the key topics of today's research. A CMOS two-stage operational amplifier with high unity-gain bandwidth is analysed and designed with an appropriate Miller-compensation technique in this research to improve the frequency characteristics and stability. The design is based on 180nm CMOS process. Its performance is simulated and analysed in Cadence software environment. The performance parameters are shown by simulation results that the amplifier has a gain of 61.81dB, a GB (unity-gain bandwidth) of 907.42MHz and a phase margin of 75.02. The experimental results show that it achieves high unity-gain bandwidth while ensuring gain and stability. Through this study, the principles of two-stage operational amplifiers and their compensation as well as the design and simulation are shown in detail in both theoretical and experimental sections.
An Analysis of Current Situation and Prospect of Natural Language Processing Application in Discourse Analysis
Authors: Liu Tianshuo
Published: November 2024

Abstract. Natural Language Processing is a subfield of Artificial Intelligence and an interdisciplinary field of Linguistics and Computer Science. Previous research mainly focuses on the comparison and practical application of natural language processing technology, but the amount of research on its applications in Linguistics is limited. To fill this gap, this article focuses on the current application of technologies in discourse analysis, which is one of the most popular fields in Linguistics. The researcher will initially summarize the existing techniques and tools of Natural Language Processing used for pre-processing, such as Tokenization, Stemming, and Named Entity Recognition. Open-source tools such as NLTK and Spacy for pre-processing are also mentioned in the article. Subsequently, the researcher will introduce commonly used technologies like sentiment analysis and semantic analysis. This research concludes that discourse analysis can benefit many industries such as Medicine, Economics, Agriculture and Politics. However, this paper also analyzes the drawbacks and challenges that NLP technologies for discourse analysis face. Barriers such as Word Sense Disambiguation (WSD) and the context dependency of human language made it difficult for NLP technologies to be applied in Pragmatic analysis.
Depression Prediction Model based on NLP
Authors: Zhou Yuke
Published: November 2024

Abstract. With the acceleration of modern society's pace, depression has become a common mental health issue. This study aims to develop a depression detection model based on natural language processing (NLP) technology to automatically identify potential depression patients. First, collect data from the Weibo platform using web scraping techniques, and then use NLP tools such as XLNet for in-depth analysis of the text data. In the model evaluation phase, the paper uses key metrics such as accuracy (ACC) to assess the effectiveness of NLP techniques in predicting depression. Experimental results indicate that the XLNet model performed the best among all tested models, achieving an accuracy of 81%, thus confirming the feasibility of using NLP technology for depression prediction. The significance of this study lies in providing a new method for mental health monitoring, which aids in the early detection and intervention of depression, with important social value and application prospects.
Large Language Model Applied in Multi-agent SystemA Survey
Authors: Dong Kaiwen
Published: November 2024

Abstract. The application of large language models (LLMs) in single-agent systems within complex environments has proven successful, prompting a growing interest in their use within multi-agent systems (MAS). Despite the impressive capabilities of LLMs, it remains unclear how they can be optimally integrated and utilized to empower agents in MAS. Understanding how to effectively leverage the advantages of LLMs to enhance agent performance is crucial. This survey provides a comprehensive overview of the application of LLMs in MAS, focusing on their impact on agent cooperation, reasoning, and adaptive abilities. Finally, we discuss future directions and open questions in this evolving field.
Methods and Development of Chinese Word Tokenization
Authors: Fang Zhenghan
Published: November 2024

Abstract. Chinese word tokenization is an important task in natural language processing and has undergone significant evolution with artificial intelligence. This paper reviews the historical progression and contemporary methodologies for Chinese word segmentation. The paper examines the traditional character-based approaches, which rely on dictionaries and pattern matching, and transition into machine learning-based techniques that utilize statistical models and neural networks. A particular focus is given to the recent developments in deep learning, including the application of recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer models like BERT. The review also highlights innovative approaches such as memory networks and sub-character tokenization, which have shown promising results in improving segmentation accuracy and computational efficiency. Furthermore, the paper discusses the challenges faced in tokenization, such as handling out-of-vocabulary words and the integration of syntactic and semantic information. The paper concludes with insights on the future directions of Chinese word tokenization, emphasizing the potential of unsupervised learning and the need for more robust evaluation frameworks.
Deep Learning Based Music Recommendation Systems: A Review of Algorithms and Techniques
Authors: Liu Yixiang
Published: November 2024

Abstract. This paper provides a thorough examination of the utilisation of deep learning in music recommendation systems, which have transformed consumer discovery and engagement with music on streaming platforms. Scalability challenges and the cold-start problem are among the constraints that conventional recommendation methods, such as content-based filtering and collaborative filtering, encounter, which hinder their ability to deliver personalised recommendations that are effective. The processing of multi-modal and sequential data is significantly improved by deep learning methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders. This results in more precise and contextually pertinent music recommendations. Moreover, hybrid models that amalgamate deep learning with conventional techniques augment recommendation precision by synthesising user interaction data with acoustic characteristics. This paper examines essential performance metrics employed in the assessment of music recommendation systems, including precision, recall, F1-score, and Mean Reciprocal Rank (MRR). It also tackles issues including computational complexity, bias, and ethical dilemmas pertaining to data privacy.
A Review of 3D Human Pose Estimation
Authors: Zhai Lin
Published: November 2024

Abstract. Visual perception and human body recognition are fundamental capabilities required for effective and safe interactions between artificial intelligence (AI), computer vision, and humans in real-world scenarios. Recent groundbreaking developments in AI and computer vision have resulted in major advancements in human body recognition technology. However, research in human body recognition is still in the early stages of the product lifecycle. Identifying the three-dimensional locations of the joints in the human body from pictures or videos is known as 3D posture estimation. Although it is widely used in areas like human motion analysis and robotics, it continues to be a difficult task due to challenges such as depth ambiguity and the scarcity of robust datasets. Over the past decade, numerous methods have been developed, many of which are based on deep learning, significantly improving the performance of existing benchmarks. A comprehensive literature review of this field is crucial for future development. However, in nowadays,more and more such research has mainly concentrated on traditional techniques, requirement for a comprehensive examination of tools based on deep learning. This paper delivers a thorough overview of current deep learning-based 3D pose estimation algorithms, outlining their advantages and limitations while providing a detailed understanding of the field. It also explores commonly used benchmark datasets and methods for analyzing human poses in unlabeled field images, providing a thorough comparative analysis. Finally, insights are provided to aid in the design of future models and algorithms.
The Effect of Hyperparameters on the Model Convergence Rate of Cliff Walking Problem Based on Q-Learning
Authors: Zhu Jiahan
Published: November 2024

With the rapid development of AI, machine learning has become a hot topic. Among them, reinforcement learning is an important branch of machine learning. With the continuous efforts of scholars, various algorithms emerge in an endless stream. Q-Learning algorithm is a very classic reinforcement learning algorithm, which is the basis of many algorithms. Basically, the Q-table is updated by iteration, so that the agent can choose the best action in the corresponding situation, so as to get closer to the optimal solution. In essence, Q-Learning is sequential difference of different strategies. In the process of learning different strategies, there are two different strategies, goal strategy and behavior strategy. In order to balance the relationship between exploration and exploitation, the -greedy strategy is selected to maintain a certain exploratory property of the agent, and relevant hyperparameters such as learning rate (alpha) and discount factor (gamma) are set. However, the research on Q-Learning hyperparameters is not clear enough. In this paper, the author will study the influence of Q-Learning algorithm hyperparameters on its convergence speed under a relatively simple model.
The Application and Practice of Artificial Intelligence in the Entertainment Field
Authors: Tian Yuang
Published: November 2024

Artificial intelligence (AI) technology has witnessed unprecedented advancements and a gradual penetration into civilian applications. This paper aims to thoroughly investigate the application of AI in the entertainment industry, with a particular focus on the principles and cross-disciplinary implementations of 3D real-life scanning, AI for non-player characters (NPCs), and AI video generation. By synthesizing how these technologies streamline content creation processes, lower technical barriers, and inspire novel approaches to game design, we observe that AI is not only reshaping the ecosystem of the entertainment sector but also facilitating the entry of newcomers into game development. However, alongside the benefits, this study identifies several challenges and limitations associated with current AI technologies, such as accuracy, cost-effectiveness, and ethical concerns, which require attention and resolution in future research and practice. Through a detailed examination and synthesis of these phenomena, this research provides a reference for practitioners and suggests directions for subsequent studies.
Optimizing Digital Signatures for Enhanced Privacy Protection in Blockchain Systems
Authors: Chen Guangyuan
Published: November 2024

This paper delves into the optimization of digital signatures to bolster privacy protection within blockchain systems. With the proliferation of blockchain technology across various sectors, the imperative for robust privacy safeguards is accentuated. Digital signatures, a cornerstone of blockchain security, authenticate and uphold the integrity of transactions. Nonetheless, they may not consistently suffice in providing optimal privacy safeguards. This research scrutinizes the limitations inherent in current digital signature techniques and proposes innovative solutions to fortify privacy. It explores a spectrum of cryptographic algorithms and protocols aimed at enhancing the privacy capabilities of digital signatures. By optimizing these signatures, the research aims to offer users enhanced security, thereby fostering greater trust in blockchain applications. This abstract sets the stage for a comprehensive discussion on refining digital signatures to augment privacy in blockchain environments, highlighting the critical need for enhanced security measures as blockchain technology continues to evolve and integrate into mainstream applications.
ChatGPT: Technology Frontiers and Cybersecurity Challenges
Authors: Mao Xinbo , Xu Songjie , Yang Gang , Yang Yonghao
Published: November 2024

This paper examines the evolution and application of OpenAI's advanced conversational AI, ChatGPT, particularly within the domain of cybersecurity. With an architecture built on the Transformer model, ChatGPT demonstrates significant capabilities in language understanding and generation. It leverages vast datasets, ranging from social media posts to technical documents, ensuring the model adapts to diverse fields and maintains compliance with privacy and security regulations. The paper explores ChatGPT's role in network security, highlighting its proficiency in threat detection, vulnerability assessment, and incident response, essential as regulations like GDPR and CCPA become more stringent. Furthermore, the study addresses potential security risks associated with AI, such as phishing and misinformation, and discusses mitigation strategies through advanced training techniques like adversarial training and multi-task learning. A novel variational autoencoder (VAE)-based method, T-VAE, is introduced, offering enhanced generalization capabilities across different tasks and scenarios. The findings suggest that while ChatGPT has made significant strides in cybersecurity applications, continuous improvements in model robustness and adaptability are necessary to mitigate emerging threats and adapt to evolving digital landscapes.
Exploring the Application of Game Engine in Creating 2D Fighting Game
Authors: Chang Zhongyu
Published: November 2024

With the burgeoning growth of the digital entertainment industry, 2D side-scrolling fighting games, as a classic genre, are confronted with the dual challenges of innovation and technological renewal. To address these challenges, this study aims to delve into the development techniques and methodologies of 2D side-scrolling fighting games, with a particular focus on leveraging game engines to enhance the overall quality and player experience of the games. Utilizing a combination of literature review, case study, and empirical research, this study conducts a thorough assessment of current mainstream fighting game engines. By analyzing successful cases of 2D side-scrolling fighting games both domestically and internationally, this research endeavors to distill an effective development process and key technical points to guide game developers in making more informed decisions during the design and implementation phases. The findings of this study not only contribute to improving the playability and market competitiveness of games but also offer new perspectives and strategies for the sustainable development of the gaming industry.
The Applications of Non-Invasive Brain-Computer Interfaces in Rehabilitation and Medical Field
Authors: Jiang Lijiang
Published: November 2024

With the advancement of neuroscience and computer technology, Brain-Computer Interface (BCI) technology has emerged as an innovative mode of human-computer interaction, garnering increasing attention from researchers and clinicians alike. BCI technology collects signals directly from the brain and translates them into control commands, providing individuals with disabilities a novel means of communication and control. This article aims to review the recent applications of non-invasive BCI in the fields of medical rehabilitation and assistive device control, exploring its potential in stroke rehabilitation, spinal cord injury rehabilitation, and medical assistance. By summarizing the latest research progress of non-invasive BCI in the aforementioned areas, this article analyzes its advantages in practical applications and the challenges it faces. Finally, this paper concludes with a summary and outlook, highlighting that the research presented herein not only provides better rehabilitation information for patients but also drives technological progress and societal development in related fields.
Using LLM Model to Process Sensor-detected Images
Authors: Wang Shunqi
Published: November 2024

Modern digital Image processing was developed within the 1960s at Massachusetts Institute Of Technology. Image processing is still a popular topic in recent technology development as countless industries need to process the image in an efficient and economical way. In this paper, this paper will first briefly introduce the features of the large language model (LLM). Then, a discussion between the LLM and the sensor will be set up, introducing the feasibility that LLM can process the image effectively. In the next section, this study conducts an experiment on a LLM-based image-recognizing program to indicate language models are indeed competent in describing what happens in the image. The whole experiment flow will be go through detailedly and some experiment data is going to be presented. Lastly, a conclusion to the experiment will be made, including the usages of the program in the experiment, and the possible improvement of the experiment and the program in future.
A Review on Human Pose Estimation Based on Deep Learning
Authors: Chen Wenhao
Published: November 2024

With the rapid development of computer vision technology, deep learning-based human pose estimation has become a hot topic of research. This review outlines the progress made in this field in recent years, with a particular focus on the development of single-person and multi-person pose estimation. Single-person pose estimation primarily focuses on identifying and locating the joints of an individual, while multi-person pose estimation further extends to simultaneously recognizing the poses of multiple individuals. The article begins by introducing the basic concepts of pose estimation, then discusses in detail the application of deep learning models in single-person and multi-person pose estimation, as well as the advantages and disadvantages of the existing modules. In addition, the limitations of current models are analyzed at the end of the paper, and possible future optimization directions are explored. The aim of this article is to provide researchers with a comprehensive perspective to understand current technological trends and potential innovation points.
Pedestrian and Vehicle Detection Performance Analysis of Faster R-CNN under Extreme Weather Conditions
Authors: Wang Yuhan
Published: November 2024

Vehicle safety systems now have a significant problem in reliably and accurately detecting objects in adverse weather due to the ongoing advancements in autonomous driving technology. The main objective of this work is to assess how well the Faster R-CNN model performs when it comes to identifying cars and pedestrians in inclement weather, including rain, snow, and fog. In order to learn more about the accuracy and resilience of the model, the researchers trained and tested it using a variety of datasets, including a range of harsh weather conditions. The model's performance significantly degrades in poor visibility and small object identification, according to the testing data. This decline in effectiveness demonstrates how severely inclement weather affects detecting systems. The paper also addresses potential enhancements, such as parameter fine-tuning, model optimization, and the application of increased data augmentation methods to boost robustness. Expanding the dataset, enhancing image quality in adverse situations, and researching more sophisticated model architectures will be the main goals of future studies in order to increase detection accuracy and robustness in practical autonomous driving scenarios.
Research Advances in Facial Expression Recognition Technology
Authors: Jian Rui
Published: November 2024

As a key area in artificial intelligence and computer vision, facial expression analysis technology has been taking hold rapidly, yielding dazzling advances. This technology extracts even the subtlest facial movements to identify when a human is sad, happy, or angry. It has extensive application value in markets like mental diagnosis, secure monitoring, and intelligent interaction. The paper will provide a brief overview of the kernel technology of facial expressions recognition: geometric features, appearance features, and deep learning. And compared the experimental results of different experiments, as a goal, the experiments compare functions of several methods in a multi-dataset context, concentrating the primary problems in the tasks like dataset bias, real-time requirements, and different light levels and partial occlusions in various environments. Eventually, the paper forecasts the imminent growth trend in data fusion and deep learning improvement. This also indicates that emerging technologies, especially in terms of applications, will have the upper hand.