bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Steel and Concrete Composite Structure Bridge
Authors: Han Jiaxu
Published: July 2024

With the development of the transportation industry, bridge construction has become particularly important. Bridge materials can be divided into many types, and different materials have different properties. In view of the existing problems of bridge construction materials, this paper mainly studies the structure of steel and concrete composite bridge. By understanding the advantages of steel and concrete composite bridges, the stress characteristics and durability of different types are analyzed. The analysis results show that the steel and concrete composite structure bridge is beneficial to improve the bearing capacity and performance of the bridge structure. In addition, measures such as coating, weathering steel, high performance concrete and prestressing can be taken to improve the durability of steel and concrete composite structure bridges. It is shown that the steel and concrete composite structure can play the respective characteristics of the two materials, so the structure can be widely used in the future bridge construction.
Analysis and Study on Dynamic Elastic-plastic Time History of Damaged Steel Frame Structures
Authors: Li Donghan
Published: July 2024

Combining the moment-curvature relationship of the damaged section of the component, dynamic elastoplastic time-history analysis was conducted on the damaged steel frame structure. Using general finite element analysis software, the structural response of the damaged steel frame structure under the action of multiple seismic waves was studied, analyzing the influence of variations in different structural parameters on the seismic performance of the damaged steel frame structure.
Research on Dividend Payout of Chinese Real Estate Companies by Inflated Earnings
Authors: Liao Mengbo
Published: July 2024

This study examines the managerial incentives of earnings management of Chinese real estate companies with focuses on the high dividend payout and the effect of the “three red lines” policy which stipulates that several solvency and liquidity ratios should be higher than certain levels and then raise demand for increasing cash holding of real estate companies. The pressure from policy requirements may motivate managers to earnings management techniques related to discretionary accruals and real activities to inflate earnings for certain targets including dividend payments and debt-to-equity ratio. Accrual-based and real activities management are proxied by modified Jones’ model and Roychowdhury’s model respectively. Key findings are that managers of Chinese real estate companies tend to use discretionary accruals to inflate operating income and to manipulate real activities to increase cash holdings to meet the policy requirements and that dividend payments stickness still hold under the pressure from external stakeholders but may be supported by inflated income.
Pickup Strategy based on Table Tennis Pickup Robot
Authors: Huang Hongxue
Published: July 2024

Due to the injuries and inconvenience that picking balls caused, a number of ball pickup robot showed up in recent years. The existing ball pickup robots have a common problem that is they can’t complete the picking task at a fast speed for they are set to pick up all the balls. Put it another way, when facing multiple balls, their efficiency decreases to some extent. Different from their idea of picking all the balls, the new strategy in this paper aims to propose can balance efficiency and the degree of completion (picking enough balls) for the ball pick up robot. Taking table tennis as example, this paper first introduces the necessary hardware. And then it introduces the principle of detection, which is color recognition, and path planning, after which the collection structure is presented. Ultimately, the mode of data transmission is indicated. This paper offers a new way of thinking about ball pickup strategies for ball pickup robots, which contributes to the development of related robots.
Current Situation, Problems and Future Development of Sports Rehabilitation Industry: A Comparative Study of Sports Rehabilitation Industry in China and The United States
Authors: Wang Dezhen
Published: July 2024

With the continuous improvement of the living standard of Chinese residents, the continuous improvement of the urbanization construction, the continuous expansion of the scale of the sports industry, the base of the sports population is rising, and the demand for health is more and more large. This study makes a detailed comparative analysis of the status, predicament and development trend of the sports rehabilitation industry in China and the United States. Through the detailed definition of the industry and the elaboration of the scope of services, this paper analyzes the similarities and differences in rehabilitation methods, market status, policies and regulations of the two countries. China's sports rehabilitation field is facing dual challenges from the market and policy environment, while the United States is showing maturity in technological innovation and service models. The comparison between China and the United States reveals the differences in service concepts, technical capabilities and market potential, reflecting the development space and model optimization direction of China's sports rehabilitation industry. The findings of this paper not only provide ideas for the improvement of China's sports rehabilitation industry, but also have important practical significance for promoting international exchanges and cooperation and promoting the development of global sports rehabilitation industry.
Research on the Integration and Application of CNN and MRF in Geotechnics
Authors: Huang Tianle
Published: July 2024

Stratigraphic uncertainty can result in serious construction accidents or extra construction costs when the geotechnical information is limited. The Markov random field (MRF), based on the stochastic theory, provides engineers with a useful but time-consuming solution to predict the unknown stratum and relevant parameters under some priori knowledge. A new integrated model of convolutional neural network (CNN) and MRF has been proved to greatly improve the computation efficiency compared with the mentioned conventional method while keeping a relatively good accuracy, but it has not been widely used yet. In the background of rapid development of artificial intelligence (AI) and big data technology, this paper aims to introduce the above-mentioned stochastic geological modeling method to stimulate engineers to explore possible data-driven solutions to geotechnical problems. This paper firstly introduces the progress of stratigraphic uncertainty analysis, and then analyses the principles and applications in geotechnic fields of MRF and CNN. Consequently, the integrated model and a case study are introduced to prove its feasibility. Finally, the evaluation and foresight of the model is given.
Digital Economy Enabling China's High-Quality Economic Development: Impact Paths, Opportunities and Challenges
Authors: Cao Peilin
Published: July 2024

This paper concentrates on the topic of the digital economy that is enabling China's pursuit of high-quality economic growth, and provides a systematic review and analysis of the existing literature on the paths of influence, and the opportunities and challenges that may be faced in the process of influence. The academic community has conducted numerous studies on the correlation and mechanism of the two. However, a systematic literature review is lacking to sort out and summarize the relevant studies. This gap may hinder future research development in this field. The paper's findings demonstrate that the digital economy promotes economic development of high-quality from three perspectives: macro, medium, and micro. It concludes that the economy that is digital presents significant opportunities for poverty alleviation, sustainable development, entity integration, and enterprise management. However, it may also face challenges such as regional heterogeneity, lagging statistical methods, instability, and unsustainability. This systematic review will be useful for future scholars in the field to identify existing literature and research areas, and highlight the relative lack of research on the digital economy and China's economic development of high quality during the epidemic era. This suggests possibilities for future scholars to explore in depth.
Design of Six DOF Watering Robotic Arm
Authors: Yuan Junfeng
Published: July 2024

As an important automation device, the mechanical arm plays a pivotal role in enhancing efficiency and precision across various industries, including industrial production lines, medical surgeries, and service robot applications. Recognizing its versatility and significance, this study will work on design a robotic arm system. This research encompasses a holistic approach, addressing both the mechanical and electronic aspects of the mechanical arm system. Beginning with the mechanical design phase, advanced Computer-Aided Design (CAD) software is employed for meticulous modeling and simulation. This phase is crucial for ensuring the structural integrity, stability, and movement flexibility of the mechanical arm, thereby laying a solid foundation for subsequent stages. In parallel, the circuit control design aspect of the study is meticulously planned and executed. By designing the layout of Printed Circuit Boards (PCBs) with precision, we aim to optimize the integration of electronic components for seamless functionality. The adoption of Arduino microcontroller technology further enhances our ability to precisely control the movement and operation of the mechanical arm. Through the integration of mechanical and electronic design considerations, our aim is to develop a reliable and efficient robotic arm system capable of autonomously completing the flower-watering task with precision and accuracy. By leveraging cutting-edge technologies and innovative design principles, we aspire to contribute to the advancement of automation technology, with practical implications spanning diverse industries and applications.
Forecast the Building Insurance Program Based on Grey Predictions and TOPSIS
Authors: Wang Jianhui , Liu Weihang , Liu Huibo
Published: July 2024

The increasing frequency of extreme weather events around the world has had a significant impact on whether insurers continue to undertake projects related to buildings and the protection of architectural heritage. By building a grey forecasting model, this paper forecast the earnings of two national insurers, China, and the United States. By 2027, the earnings of the United States insurers will increase to 380 million dollars, and the earnings of Chinese insurers will increase to 106 million dollars. In addition, the technique for order preference by similarity to ideal solution (TOPSIS) model was used to calculate the degree of protection of the building, and the degree of protection of the building was divided into five levels according to the value of PT, and the protection suggestions were given respectively. The conclusions drawn in this article will help insurers whether to continue to underwrite in areas where extreme weather is frequent. At the same time, it provides new ideas for how to protect architectural relics in areas with frequent extreme weather.
A Numerical Investigation of Stress Distribution in Ship Deck Stiffener Panel Structures under Uniformly Distributed Load
Authors: Fu Tengyang , Xie Leyang , Xu Zhehao
Published: July 2024

Ship decks are commonly composed of stiffener panel structures, which consist of flat plates, longitudinal stiffeners, and transverse girders. These steel stiffener panels are subjected to significant loads when carrying cargo. This study aims to simulate the behaviour of stiffener panel structures under uniformly distributed load and identify the value of extreme stresses experienced by the structure and their corresponding locations. The results obtained provide valuable insights for optimizing and reinforcing the design of stiffener panel structures in the future. The research is divided into two main parts. Firstly, a finite element model of the stiffener panel structure was developed to obtain numerical simulation. Traditional holistic modelling of large deck structures can be time-consuming; hence, this part of the study focuses on proposing a simplified modelling approach while ensuring acceptable level of accuracy. The second part of the study involves utilizing existing theories, including Euler-Bernoulli beam theory and plate bending stress formulas introduced by Timoshenko and Roark, to calculate theoretical stress values at the critical locations determined in the first part. The calculated stress results were compared with the finite element analysis from the first part to validate the accuracy of the established model. The final results demonstrate excellent agreement between the outcomes obtained in the first and second parts of the study, confirming the validity and reliability of the approach used for stress analysis in stiffener plate structure.
Personal Financial Data Sharing Mechanisms within the Open Banking Framework
Authors: Ju Ye , Liu Haoran , Zhang Xinlin
Published: July 2024

The development model of open banking is a necessary condition for commercial banks to achieve stable development in the digital era, and it can make data as a resource coupled with finance deeply. However, in practice, due to the relevant rules of open banking personal financial data sharing , there is a gap in legislation, inadequate implementation of relevant regulatory systems, the urgent of improving data privacy protection and many other practical difficulties. Therefore, there is an urgent needs to build a system for China's open banking financial data sharing system, which can be designed by drawing on the relevant rules of other countries and combining with China's specific realities, contrapuntally, through the establishment of a classification and grading authorization mechanism, the improvement of the "common vote" mechanism, and the sharing of black and white lists of the target bidirectional co-ordination model and other system designs, to facilitate the digital transformation and upgrade of financial institutions, and ultimately foster the development of new trends centered around consumer-centric personal financial data protection and open banking.
Research on Existing Problems of Online Car-Hailing Platform from The Perspective of Consumers
Authors: Ma Yinuo
Published: July 2024

With the development of economy, the improvement of people's living standard, travel more frequently, travel time and travel destination more flexible and changeable, the demand for personalized travel services gradually increased, so there is the emergence of online car booking and platform. Due to the short development time of online car hailing in China, a large number of online car hailing companies are too hasty, and there are many problems in the process. If these problems are not solved, the service experience will be reduced from the users’ point of view. There is a growing body of research on the subject of ride-hailing platforms, but it still falls short in addressing existing issues such as driver quality, waiting times, and pricing. In view of these shortcomings, this paper studies the existing problems of online car-hailing platform to make up for the existing shortcomings. It can effectively guide enterprises to carry out practical activities. To sum up, this study has important theoretical and practical significance.
The Application of Positive Psychology in Primary and Secondary Classroom Teaching
Authors: Zhu Meie
Published: July 2024

Positive psychology is the applied science that reveals human strengths and promotes their skills. Its main function is to use scientific methods to discover the power of positive mental strengths, and use these mental strengths to help ordinary people maximize their own potential to achieve happiness. Applying the positive psychology to education, positive education came into being. The fundamental goal of positive education is to promote flourishing or positive mental health within the school community.By advocating the positive education method adopted by teacher Marva Collins, this paper tries to find the potential positive strength of students in daily work, enhance their positive experience, and finally cultivate their positive personality.
Research on Green and Low Carbon Prefabricated Buildings under the Background of Carbon Neutrality
Authors: Lv Zuli
Published: July 2024

China's building industry is developing rapidly, and the scale of building construction is expanding rapidly. However, at present, the development of the building industry has reached the stage of platform, and the sustainable development of traditional cast-in-place buildings has been unable to balance the relationship between the economic benefits of the building industry and environmental pollution. Prefabricated buildings have the characteristics of high standardization, high construction efficiency and environmental friendliness. In addition, with the development of the computer industry and intelligent technology, prefabricated buildings also have the ability to adapt to intelligent building software, which can improve the industrialization of the building industry and reduce the carbon emissions of the entire process. The Chinese government has issued a series of policies and regulations to encourage and guide the development of prefabricated buildings. With the development trend of prefabricated buildings, its research value is becoming more and more obvious. This paper mainly analyzes the development status of prefabricated buildings in China, summarizes the shortcomings of its development, and puts forward solutions.
Emerging Technologies in In-Situ Printed Wearable Sensors
Authors: Zhang Kuangzhi
Published: July 2024

Recent advancements in wearable sensors have significantly advanced the detection of physiological signals, both internally and externally to the body. The integration of innovative materials, such as flexible circuits, has allowed these sensors to achieve prolonged conformity with the monitored surfaces, thus enhancing the precision of signal gathering. In-situ bioprinting, a cutting-edge manufacturing technique, enables the direct fabrication of materials with diverse elastic properties onto specific surfaces. Sensors produced via in-situ bioprinting exhibit mechanical characteristics akin to human organs, improving their integration with the detection surface. Furthermore, the in-situ production process obviates the need for internal and external device transfer, substantially minimizing infection risks in clinical settings. The use of biocompatible inks also facilitates extended durations of wear, underscoring the profound potential of this technology. This article conducts a review of select studies to assess the current progress and technical hurdles in the domain of in-situ printed wearable sensors. It compiles the utilized printing materials, methodologies, and object tracking technologies, offering insights into the present and prospective advancements in in-situ printed wearable sensor technology.
Decision Making in the Internationalization of the Service Industry and the Applicability of Manufacturing Industry Internationalization Theories
Authors: Min Zekun
Published: July 2024

This study examines the decision-making process of service industry internationalization and the applicability of manufacturing industry internationalization theories through a literature review. With advancements in technology and relaxed regulations from the 1993 GATT Agreement, service companies are becoming key players in business internationalization. Service marketing research has focused on understanding the definition and characteristics of services, such as intangibility, simultaneity of production and consumption, perishability, and heterogeneity. Existing research primarily addresses why service companies seek overseas markets and whether manufacturing internationalization models apply to them. This study reviews both environmental and internal company factors influencing decision-making for overseas expansion and analyzes the applicability of manufacturing theories to service industries, suggesting the need for classification based on service characteristics.
Progress in Therapy of Lung Cancer
Authors: Xu Benchang
Published: July 2024

Lung cancer has become one of the most common cancers worldwide, and it is also the main cause of cancer-related deaths. It is estimated that over 22 million new cases and 1.79 million deaths are caused by lung cancer each year. The theory of "airway field of injury" has been used to explain the pathogenesis of lung cancer, but its specific molecular mechanism is not clear yet. With the completion of comprehensive genomic analysis of lung cancer, modern medicine has revealed its genetic heterogeneity and complexity, and identified many oncogenic driving genes and molecular pathogenesis of lung cancer. At present, common treatment methods for lung cancer include chemotherapy, radiotherapy, targeted therapy, immunotherapy, and gene therapy. Each treatment has a unique mechanism and targets, but also has different advantages and disadvantages. Corresponding to different lung cancer patients, it is necessary to comprehensively consider their physiological health and economic status, choosing appropriate treatment strategies.
How Perceived Soundscapes in Music Influence Our Emotional Health
Authors: Hu Yifan
Published: July 2024

This paper examines the influence of perceived soundscapes in music on emotional health. By exploring the relationship between different musical soundscapes and their emotional impacts, the study aims to provide a deeper understanding of how specific auditory environments can affect psychological well-being. Using a mixed-methods approach, including surveys, interviews, and physiological measurements, the research reveals significant findings on how various soundscape characteristics such as tempo, harmony, and rhythm contribute to emotional responses. The study’s results highlight the potential applications of soundscapes in music therapy and mental health practices, offering practical insights for music producers and mental health professionals. Future research directions are suggested to further explore the nuanced effects of soundscapes on emotional health.
Development of Silk Fibroin Dressings and Advances in Their Application in Wound Healing
Authors: Sun Chenghao
Published: July 2024

Wound healing is a complex process involving hemostasis, inflammation, proliferation, and remodeling phases. Medical wound dressings are commonly employed to promote wound healing, alleviate patient discomfort, and prevent further tissue inflammation or expedite wound closure. Among various wound dressings, silk fibroin dressings have garnered widespread attention due to their outstanding anti-inflammatory and hemostatic properties, as well as excellent biocompatibility. Therefore, this paper introduces the biological characteristics of silk fibroin, elucidates the current research status of silk fibroin-based wound dressings in various forms such as nanofibers, hydrogels, films, foams, and ointments, and discusses the challenges and future research directions in the field of silk fibroin-based dressings for wound repair, providing insights for further development and application of silk fibroin dressings.
The Effects of U.S. Tariff Increases on Chinese Automotive Parts Trade
Authors: Pan Wei
Published: July 2024

This paper examines the ramifications of the recent U.S. tariff increases on Chinese automotive parts and their broader implications for the Sino-American trade relationship. By analyzing trade data, industry responses, and economic theories, this study aims to provide a comprehensive understanding of the impacts on both the U.S. and Chinese automotive sectors. The analysis highlights the immediate economic effects, long-term strategic shifts, and potential future developments in the global automotive supply chain. This study further delves into the nuances of international trade dynamics, the historical context of U.S.-China trade relations, and the strategic adaptations by automotive companies in response to shifting economic policies.
An Analysis of The Change of Female Status from The Historical Dimension of Chinese and Western History
Authors: Wang Changlin
Published: July 2024

By analyzing the historical dimensions of both China and the West, the evolution of female status in both China and the West is largely driven by similar factors, including three basic ones:labour, reproduction and culture. In China, despite the emancipation of women since the founding of the People's Republic of China and the significant improvement in their status, women continue to suffer from stereotypical gender discrimination in the labour market, in childbearing, in family life and in other areas, due to the long-standing influence of the traditional idea of "men's superiority over women". By using gender analysis, this paper identifies the reason why these phenomenon are so easily overlooked, and points out that, besides technological factor, the human nature is also an important factor affecting female status. Finally, in the light of legal, educational and cultural considerations, it suggests that legislators should incorporate gender awareness in the process of the enactment of law, gender education should be included in the education reform, and female cultural confidence and identity should be re-constructed.
The Relationship Between Dietary Fiber Intake and the Risk of Type 2 Diabetes
Authors: Zhong Huiqian
Published: July 2024

This investigation explores the link between consuming wheat and the prevalence of Type 2 Diabetes (T2D) among the population of Weifang, China. It relies on preliminary data sourced from 35,509 participants enrolled in the China Kadoorie Biobank. The study categorizes wheat intake frequencies and correlates these with demographic and health-related characteristics of the participants. Findings indicate that individuals who consume wheat more frequently are typically younger, predominantly male, and possess higher educational and income levels compared to those who consume wheat less frequently. There appears to be a strong inverse relationship between how often wheat is eaten and the occurrence of T2D, hinting that a greater consumption of wheat could be linked to a reduced chance of coming down with this persistent ailment. The research underscores the critical role that nutritional elements play in both the control and deterrence of long-standing health conditions, particularly in the context of increasing global T2D rates. The findings powerfully suggest that wheat could be a key player in diet-based strategies to lower the likelihood of developing Type 2 Diabetes. This highlights the urgency for more comprehensive studies to corroborate these results and delve into the biological processes involved.
A Study on Vegetable Sales Forecasting Based on Seasonal Indices and LSTM Model
Authors: He Xudong , Huang Xincheng , Ding Siyin , Qian Weixing
Published: July 2024

Efficient food supply chain management is crucial for the perishable and seasonally affected vegetable sales field due to global population growth and urbanization acceleration. This trend has led to the widespread application of data analytics and machine learning technologies for optimizing pricing strategies and predicting sales trends. This study aims to explore the relationship between the total sales volume of a vegetable category and its cost-plus pricing using the Pearson correlation coefficient analysis method. Additionally, it seeks to establish a support vector regression (SVR) model and an improved long short-term memory network (LSTM) model based on seasonal index prediction. The projected purchase volume of mosaic and leafy vegetables for the next 7 days is 135.47, 135.65, 127.00, 125.26, 83.11, 105.29, and 157.34 kg. Finally, this dissertation formulated a purchase strategy for the upcoming week based on the predicted sales volume. We calculated the 7-day profit margins to be 0.80, 0.61, 0.62, 0.77, 0.63, 0.57, 0.63. This analysis offers a theoretical foundation for enhancing operational efficiency and profit levels in the vegetable sales sector, as well as practical guidance.
Printed Fonts vs. Calligraphic Fonts: A Comparative Study on Visual Perception
Authors: Ma Zongyu
Published: July 2024

This paper focuses on the visual perception of printed and calligraphic fonts, conducting a comparative study on their characteristics, existing issues, and optimization strategies. The purpose is to enhance people's aesthetic appreciation and cultural literacy of fonts, and to promote the development of font design and cultural undertakings. The study finds significant differences between printed and calligraphic fonts in terms of their characteristics, issues, and optimization strategies. These differences present various challenges in practical applications, necessitating a balance between respecting and protecting traditional culture, and focusing on innovation and development to meet the needs of modern society.
Training and Performance of Table Tennis Players and Their Influence on Self-efficacy
Authors: Li Wanbing
Published: July 2024

This study investigated several characteristics and educational experiences of table tennis players engaged in a training regimen at Beijing Sports University. The findings suggest that the program effectively catered to a wide variety of players, considering their age, sex, academic level, and training experience. This is consistent with the ideas of social cognitive theory, which highlights the significance of creating an inclusive learning environment. Players exhibited significant proficiency in the technical components of the sport, which aligns with the emphasis of social cognitive theory on the importance of mastery experiences in the process of learning and development. Players placed greater reliance on coach demonstrations rather than peer demonstrations, indicating a high regard for the coach's competency and impact as a credible role model. The participants' feelings of accomplishment and favorable emotional encounters following successful performances probably enhanced their commitment to the training program. Their aptitude for acquiring knowledge from demanding matches and their overall contentment with performance levels suggests that the program effectively fostered the development of crucial abilities and mentality. The results are consistent with the emphasis of social cognitive theory on the impact of self-efficacy beliefs on motivation, effort, and perseverance. The participants' ability to bounce back from setbacks most certainly played a role in their positive self-belief and performance. Players' age and training experience variations offer useful data for developing focused interventions. The relationship between training experience and performance highlights the significance of optimizing training load, time management, and individualization to enhance training efficacy. In summary, the study emphasizes the importance of establishing an all-encompassing, skill-focused learning atmosphere to facilitate the growth of athletes from various backgrounds.
Study on Optimal Training Methods for Teen-age 400-Meter Sprint Projects by Data Science
Authors: Fan Zhicheng
Published: July 2024

The research based on data science can provide more effective and personalized methods for the training of teen-age sprint events. This article analyzed the correlation between the consequent improvement in scores with gender, training methods, intensity, and cycles by collecting the key data of 400-meter running events. A multiple linear regression model was established to predict the running performance of teen-age athletes under different dependent variables, in order to provide references for the optimization of a teen-age training plan.
Chinese text sentiment classification method based on dual-channel attention capsule network
Authors: Liu Chunjia , Zhang Xinyu , Li Juan , Chen Jingwen , Li Chang
Published: July 2024

In recent years, text sentiment analysis has received close attention from researchers. The CNN-based neural network is frequently used in this field. Capsule network can overcome the problem of information loss caused by pooling layer in CNN-based neural network, but the capsule network cannot selectively focus on important words in the text. To solve this problem, this paper proposes a text sentiment analysis method based on the attention mechanism of the capsule network. First, use the cw2vec model and the SAT model to train word vectors and use them as the input of the two channels of the method, then combine the attention mechanism and the convolutional neural network to extract the characteristics of the two channels, and then input the capsule network to realize the emotion classification. The static routing mechanism is also used to reduce the complexity of the model, and it has a higher accuracy than the dynamic routing mechanism. We verified the effectiveness of the model on the Tan Songbo hotel review data set and we further crawls a large amount of public opinion data related to Meteorology and early warning from Weibo to form different fields. the model is also better than many sentiment analysis methods.
Research Progress of Long Span Steel Structures
Authors: Nan Yongji , Wu Ziwei , Xiang Jie , Li Hao
Published: July 2024

The structure of the studio has the characteristics of large span, large clearance height and large hanging load. Based on the mechanical advantages of steel structure, large-span steel structure has been widely used in this kind of building. Due to the geometric characteristics of the structure and the distribution characteristics of roof load, the influence of horizontal seismic action and vertical seismic action on the mechanical properties of the structure is difficult to ignore. In this paper, the seismic performance of large-span studio steel structure is deeply studied based on the actual engineering.
Research on Brand Development of Chinese Sports Events--Taking the Chinese Professional Basketball League CBA as an Example
Authors: Jiang Ruijin
Published: July 2024

From the current situation of the development of global sports events, the development of China's sports events brand has an unprecedented broad market and superior conditions, but in fact, the quality of the development of China's sports events is not very ideal. Under such circumstances, this paper conducts in-depth analysis and research on the current development status and future development strategies of Chinese sports event brands. The specific analysis object takes the Chinese Professional Basketball League (CBA) as an example, and explores the existing advantages and disadvantages of CBA League through horizontal and vertical comparison with the NBA League in the United States. At the same time, through the study and analysis of CBA League, I can reflect the overall trend and common difficulties of the development of China's current sports events, consolidate the advantages of Chinese sports events brands, analyze their disadvantages, and put forward personal ideas and suggestions on the correction of shortcomings. Finally, the conclusion is summed up, and the formula is flexibly applied to other league brands to achieve the all-round development of Chinese sports events.
Analysis of the Causes and Countermeasures of Financial Explosion--Taking Shanghai Electric Group Co., Ltd. as an Example
Authors: Wang Zijie
Published: July 2024

With the steady and rapid development of China's economy, the performance of enterprises shows an excellent growth trend. However, there are also negative news caused by a large number of corporate financial fraud and a significant decline in performance. This paper takes the financial explosion event of Shanghai Electric in 2021 as an analysis case and uses systematic theoretical knowledge to analyze the causes and consequences of the case in detail. We summarize the problems and put forward the safeguard measures. Finally, we provide corresponding suggestions to help enterprises avoid relevant financial risks and hope to further help the future development of the electrical equipment manufacturing industry.
An Analysis on Singapore's Business Cycle
Authors: Xie Zhaoting
Published: July 2024

Gross Domestic Product (GDP), pivotal in gauging a nation's economic health, has been extensively studied since its conceptualization in the 20th century. Nevertheless, while much attention has been given to its measurement accuracy and immediate components, detrending components and cyclical analyses of GDP constituents, particularly for export-oriented economies like Singapore, remain notably sparse. This investigation delves into this lacuna, utilizing a 21-year dataset from the International Financial Statistics (IFS) and employing detrending methodologies. By contrasting the cyclical influences of consumption and investment on Singapore's GDP from 2000 to 2021, our research unveils the nuanced interplay between these GDP facets. Moreover, through statistical tools, such as standard deviation and correlation, relationships and variations within the data are both visualized and quantified. Consequently, this study not only augments our comprehension of Singapore's economic trajectory but also underscores the importance of expanding academic discussions to encapsulate underrepresented export-heavy economies. This exploration heralds a call for a more inclusive and comprehensive approach in global economic research.
Empirical study on the performance of various machine learning models in predicting stock price movements as a binary classification task
Authors: Liu Keqian , Li Ang , Lin Xinran , Mao Zhuobin , Zhang Weiyang
Published: July 2024

This paper examines the accuracy of stock price rise-or-fall predictions of seven different machine learning algorithms, including support vector machines and random forests, for three industry types: securities, banks, and Internet companies. The purpose of the research is to explore the effects of different models in the stock market, so as to help people choose the optimal machine learning model in predicting different types of stocks. The study produced nine features based on the study by Patel et al for prediction. By collecting 9 types of stock data from companies in different industries, we performed necessary preprocessing on the data, fitted the model, tuned the parameters of the model and get the prediction result. Through the result, we found that the random forest algorithm has obvious advantages in binary classification prediction of stock prices. Linear discriminant analysis (LDA), Quadratic Discriminant Analysis (QDA) and logistic regression also have good fitting effects in this type of problem. K-Nearest Neighbor (KNN) and Naive Bayes algorithms exhibit poor prediction accuracy.
Four possible error conjectures with SPM in MATLAB
Authors: Ning Ke
Published: July 2024

Flanker tasks are mainly supposed to have activation signals on the prefrontal regions and anterior cingulate cortex (ACC) because this assignment requires immediate actions from participants after seeing unique signs. The SPM in MATLAB is used to process the data of activation signals of brains from flanker task data collection. This process has four steps: preprocessing of all the subjects, first-level analysis, second-level analysis, and Region of Interest (ROI) analysis. After all the steps, the final graph should show black spots representing the activation signal on the prefrontal and anterior cingulate cortex regions. However, the spotted area differs from the areas in the proper graph, indicating the errors during processing each subject in flanker tasks in this assignment. There are four conjectures of error steps after reviewing the wrong result
Maze and navigation algorithms in game development
Authors: Piao Jiachen , Hu Xinyuan , Zhou Qixuan
Published: July 2024

This paper introduces a small game that the authors plan on creating and discusses the code and algorithms implemented inside. The game is created using Pygame. Pygame is a set of python modules designed for writing 2D-games. The reason we use it is that Pygame is free and simple to operate for a new game designer. The game involves moving a character through a maze while eating coins along the path. The character is controlled using keyboard. The maze is randomly generated using various maze algorithms. Although the game is simple, the logics and algorithms included are useful for more complex games. This essay will introduce the maze algorithms and navigation algorithms needed for the game, as well as the code implemented.
Predicting drug-drug interactions using heterogeneous graph neural networks: HGNN-DDI
Authors: Liu Hongbo , Li Siyi , Yu Zheng
Published: July 2024

This research centers on predicting drug-drug interactions (DDIs) using a novel approach involving graph neural networks (GNNs) with integrated attention mechanisms. In this method, drugs and proteins are depicted as nodes within a heterogeneous graph. This graph is characterized by different types of edges symbolizing not only DDIs but also drug-protein interactions (DPIs) and protein-protein interactions (PPIs). To analyze the chemical structures of drugs, we employ a pretrained model named ChemBERTa, which utilizes simplified molecular input line entry system (SMILES) strings. The similarity between drug structures based on their SMILES strings is determined using the RDkit tool. Our model is designed to establish and link heterogeneous graph neural networks, taking into account the DPIs and PPIs as key input data. For the final prediction of interaction types between various drugs, we use the Multi-Layer Perception (MLP) technique. This model's primary objective is to enhance the accuracy of DDI predictions by factoring in additional data on both drug-protein and protein-protein interactions. The forecasted DDIs are presented with associated probabilities, offering valuable insights to healthcare professionals. These insights are crucial for assessing the potential risks and advantages of combining different drugs, particularly for patients with diseases at different stages of progression.
Predictive Models: Regression, Decision Trees, and Clustering
Authors: Huang Xiang
Published: July 2024

This paper explores three fundamental machine learning techniqueslinear regression, k-means clustering, and decision treesand their applications in predictive modeling. In the era of data proliferation, machine learning stands at the intersection of computer science and artificial intelligence, playing a pivotal role in algorithm and model development for enhanced predictions and decision-making. The study delves into the intricacies of these techniques, starting with a focus on linear regression, a supervised learning algorithm for establishing relationships between independent and dependent variables. The process involves data preparation, exploration, feature selection, model building, and evaluation. A practical example demonstrates the application of linear regression in analyzing the relationship between income and happiness. The exploration then extends to k-means clustering, an unsupervised learning algorithm used for grouping unlabeled datasets into distinct clusters. The iterative nature of k-means involves assigning data points to clusters based on centroid proximity, contributing to efficient data exploration. A graphical representation illustrates the step-by-step process of data point grouping and centroid recalibration. The advantages of k-means, including computational efficiency and simplicity, are discussed, along with considerations such as sensitivity to initialization and the manual specification of the number of clusters. The paper concludes with an examination of decision trees, versatile algorithms used for both classification and regression tasks. Decision trees construct hierarchical structures based on features, facilitating straightforward decision-making processes. A practical example illustrates how decision trees assess credit risk based on credit history and loan term. The strengths of decision trees, such as visual representation and non-linear pattern capture, are outlined, alongside considerations like overfitting. In summary, this paper provides insights into the strengths, limitations, and applications of linear regression, k-means clustering, and decision trees. These techniques offer valuable tools in data analysis and prediction, with their effectiveness dependent on specific problem domains and datasets. The study contributes to a comprehensive understanding of these machine learning methods and suggests future research directions, including exploring advanced variations and real-world applications.
The application progress of Convolutional Neural Networks (CNN) in lung nodule diagnosis
Authors: Wu Jingxuan , Yang Jiahao , Peng Guanlin
Published: July 2024

With the development of computers, machine learning continues to be widely used in various fields. And there are many application scenarios in the field of medicine. Among these, the broadest one is the field of medical image analysis. Medical image has the characteristics of huge data, excessive noise, and recognition difficulty. And the most difficult one is the analysis of lung medical images. Lung cancer has a higher incidence rate and mortality rate than other cancers. According to the National Cancer Center, about 127,070 people died from lung cancer in 2023, making it the highest death rate in the United States. Therefore, early detection of malignant pulmonary nodules has become crucial in the field of medical imaging. The medical imaging's inadequacies are most noticeable in the pictures of malignant pulmonary nodules, which are difficult for a doctor to identify with their naked eyes. However, pre-processing, segmentation difficulties, and poor fitting impact are the drawbacks of classical machine learning. As a result, we must create fresh approaches to these issues.
Comparative analysis of machine learning models in breast cancer diagnosis
Authors: Liu Peirui
Published: July 2024

This comprehensive article explores four prominent machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Neural Network (NN). It provides historical foundations, mathematical principles, and practical applications. The article includes a noteworthy experiment using the Breast Cancer Wisconsin (Diagnostic) Data Set to diagnose breast cancer. Notably, LR stands out with an impressive accuracy of 97%, the highest among the models. It demonstrates precision rates of 98% for benign cases and 97% for malignant cases, making it the top-performing model in both accuracy and precision. The integrated conclusion offers a comparative analysis of the models, highlighting their strengths and limitations for practical use in medical diagnostics. This article serves as a valuable resource for understanding and applying machine learning techniques, especially in the context of breast cancer diagnosis and prediction.
基于衣康酸酐与对苯乙烯磺酸钠合成的水溶性AIE共聚物并用于Hg2+离子检测
Authors: 陈 梦婷 , 郭 鸣明
Published: July 2024

光致发光材料对于科技和工业发展至关重要，本论文中，研究者通过衣康酸酐与对苯乙烯磺酸钠的自由基共聚反应合成了一种具有AIE效应的水溶性共聚物，利用1H NMR和FT-IR确认了其化学结构，并在水溶液中验证了其荧光发射特性及AIE效应。该聚合物在溶液中展现了优异的荧光特性，并对Hg2+离子有良好的荧光响应，被用于开发检测水体中Hg2+离子浓度的荧光探针。
基于回归模型的中国新能源汽车销售量影响因素探究
Authors: 毛 健升 , 任 晨男 , 高 焕琦 , 李 世洋 , 吴 鹏
Published: July 2024

本文旨在探究影响中国新能源汽车销售量的主要因素，并构建多元线性回归模型进行分析。通过文献综述和结构分析，确定政策、技术、市场和社会观念等因素对新能源汽车发展的影响。利用中国统计年鉴和数据库数据，分析了2014年至2022年新能源汽车销售量与多个影响因素的关系，并使用Origin软件进行可视化处理。通过MATLAB建立多元线性回归模型，得到回归方程并进行拟合优度检验，结果表明模型拟合效果良好。进一步进行权重分析和灵敏度分析，发现充电桩数量、石油价格、限牌地区等因素对新能源汽车销售量影响显著，且模型具有较强的稳健性。研究结果表明，充电桩的普及对新能源汽车销售量的影响最为重要，其次是石油价格、限牌地区、疫情等因素。政策支持力度和补贴力度对销售量的影响相对较小。该研究为中国新能源汽车产业发展提供了参考依据，并为相关政策的制定提供理论支持。