bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Public Policy and Public Administration Based on Social Research Methods
Authors: Zhao Zicheng
Published: September 2024

Public policy and public administration, as integral components of modern societal governance, have their efficacy directly impacting social stability and development. Social research methods, as crucial tools for the scientific inquiry of social phenomena, play an irreplaceable role in the formulation of public policies and the practice of public administration. Whether through quantitative or qualitative research methods, these approaches provide essential theoretical support and empirical evidence for policymakers and administrators. By systematically applying these research methods, one can significantly enhance the scientific basis of policy formulation and the efficiency of public administration, thereby advancing social equity and justice. In the context of globalization and information technology, a profound exploration of the application of social research methods in the public domain holds significant practical importance for improving governance capabilities.
The Role of Stress and Relevant Social Factors in Food Addiction
Authors: Fu Jingxuan
Published: September 2024

This study delves into the complex relationship between stress and eating behavior, with a particular focus on how stress affects food addiction. The study explores the role of stress perception, chronic stress, and social stress, highlighting their important impact on eating habits, especially on vulnerable groups such as women. The main findings suggest that stressed people tend to exhibit increased food intake, decreased emotional eating, and decreased intuitive eating, all of which increase the risk of obesity and related health problems. In addition, previous study also showed that chronic stress is a major factor associated with food addiction, with excessive workload, social stress, and long-term anxiety being important predictors. Recent research highlights the importance of stress management strategies, including cognitive behavioral therapies that target distorted beliefs. However, Previous studies also have... limitations, including a small sample size and heavy reliance on self-reported data, which may affect the generalizability and external validity of the findings. It is suggested that future studies address these limitations by expanding sample sizes, employing more objective and diverse measurement tools, and exploring the effectiveness of corresponding interventions in different populations. This review could help develop more effective treatment and prevention strategies for stress-related eating disorders.
Optimizing supply chain networks using mixed integer linear programming (MILP)
Authors: Li Xu , Ji Xiaoheng , Zeng Xiaolong
Published: September 2024

Mixed Integer Linear Programming (MILP) has emerged as a powerful tool for optimizing complex supply chain networks. This paper explores the theoretical foundations of MILP, including the integration of integer variables and advanced solution techniques such as branch-and-bound and branch-and-cut algorithms. Through detailed modeling of production planning, network design, and transportation logistics, MILP enables companies to achieve significant cost reductions and operational efficiencies. We present case studies from retail, manufacturing, and pharmaceutical sectors to illustrate the practical applications of MILP. These examples demonstrate how MILP optimization can lead to reductions in production and inventory costs, improved customer satisfaction, and enhanced service levels. The findings underscore the value of MILP in addressing the multifaceted challenges of modern supply chain management.
RETRACTED ARTICLE: Linear sections of determinantal varieties
Authors: Zhou Xiaoxi
Published: September 2024

Denote Mmn(k) to be the set of all m n matrices over k, and PMmn(k) to be its affine space. Let Dm,n,r PMmn(k) be the subvariety consisting of the projective classes of all m n matrices with rank less than or equal to r. We study the minimal integer k > 0 such that for any linear
subspace L of dimension k in PMmn(k), Dm,n,r L = . We get some partial results about this problem. Specially, when m = n = r + 1, we solve this problem for k = R, C and Q.
Cohesive self-dual Yang-Mills modules over four manifolds
Authors: Li Hao
Published: September 2024

This research is motivated by Atiyah, Hitchin, and Singers paper Self-duality in Four-Dimensional Riemannian Geometry , which introduced a relationship between self-dual Yang-Mills fields on smooth manifolds and holomorphic vector bundles on their twistor spaces. Here, self-duality is a specific structure in 4-dimensional manifolds and Yang-Mills fields are gauge fields that satisfy Yang-Mills equations in 4-dimensions and are corresponded to the holomorphic bundles on twistor spaces. In this paper, we extend the relationship from vector bundles to a generalization of the Yang-Mills fields. To achieve this purpose, we apply Atiyah, Hitchin, and Singers theorem to cohesive modules, which was originally introduced by Block in in studying coherent sheaves over complex manifolds and the relations between homomorphic torus and its dual non-commutative torus. We introduce the notion of cohesive self-dual Yang-Mills modules and show that the twistor correspondence actually induces the equivalence between the dg category of cohesive self-dual Yang Mills modules P_(A_SD ) and the dg category of holomorphic cohesive modules P_(A_Hol ) on the twistor spaces.
Multiqubit quantum dense coding
Authors: Zhao Ruoling
Published: September 2024

Multipartite quantum-information transmission has been a key task for practice purposes and applications in the past decades. In this paper, we present a novel protocol for the multiqubit super dense coding, by using the Greenberger-Horne-Zeilinger (GHZ) states. Our protocol involves measurements doable in the current technique of labs, and thus is possibly realizable and may be extendable to more complicated cases for many systems. In quantum physics, the way the object is measured will affect the code, and the information wont work like puzzles as the condition in general physics which means that, when the divided message is put together, the complete information will be obtained. It requires more complex work to get useful information, like the cooperation of code accepters. To compare with the way in general physics, it is more safe in quantum physics.
Assessment of influencing factors on the flight performance of J-series fighter jets based on the entropy weight-TOPSIS model
Authors: Gao Renhe
Published: September 2024

To enhance the scientific approach to fighter jet design and improve performance in areas such as supersonic cruise, long-range strike, and high maneuverability, this paper first collects, organizes, and thoroughly analyzes relevant data on factors affecting the flight performance of existing J-series fighter jets, including indicators such as engine thrust, aspect ratio, root-to-tip ratio, and wing shape. Using the entropy weight-TOPSIS evaluation analysis model, the performance factors influencing J-series fighter jets are quantitatively analyzed and ranked, avoiding subjective assumptions. Ultimately, the flight performance scores of eight J-series fighter jets under ten influencing factors are obtained. The results show that the top four scoring fighter jets are J-31 > J-20 > J-10 > J-15. This outcome provides directional guidance for the future development of fighter jets, indicating that by selecting appropriate engines, root-to-tip ratios, aspect ratios, and tail fin types, fighter jets can achieve better flight performance. This has significant implications for enhancing national defense capabilities, promoting military technological progress, and fostering economic development.
Classification of single-cell routine Pap smear images based on deep learning algorithms
Authors: Zhang Guanhao
Published: September 2024

In this study, we classified single-cell routine Pap smear images by applying deep learning algorithms such as AlexNet, VggNet, GoogleNet and MobileNet and compared their classification effects. The results show that the loss of all four models on both the training and test sets shows a trend of gradually decreasing and stabilising. Specifically, the loss of AlexNet gradually decreases from 0.637 to 0.212, VggNet from 0.777 to 0.278, GoogleNet from 1.77 to 0.31, and MobileNet from 0.809 to 0.267. At the same time, MobileNet exhibits the highest maximum and average accuracies which reached 93.9% and 88.3%, respectively, followed by GoogleNet model with 92.9% and 88.0%, AlexNet with 92% and 88.0%, and VggNet with 90.1% and 86.7%. The results show that MobileNet exhibits superior classification results in this task, which provides strong support for its potential application in the classification of single-cell routine Pap smear images. These findings are of great significance for further exploring the application of deep learning in the field of medical imaging and provide a useful reference for future related research.
Research on the improvement of image classification algorithm based on incremental learning
Authors: Yan Jundi
Published: September 2024

In the field of machine learning, incremental learning methods allow models to learn new tasks without forgetting the old ones, but the catastrophic forgetting problem often encountered in this process greatly limits their applications. In this paper, an improved algorithm EPASS (Enhanced Prototype Augmentation and Self-Supervision) is proposed for incremental learning in image classification tasks, which effectively alleviates the limitations of the traditional PASS algorithm and the EWC algorithm in terms of catastrophic forgetting, sensitivity of parameter selection, and computational cost. The EPASS algorithm improves the flexibility and adaptability of the model by automatically selecting which prototypes to keep for different tasks through a dynamic prototype storage strategy. Meanwhile, the EWC-based prototype update strategy allows prototypes to be updated as new data arrives, further improving the model's adaptability to new tasks while reducing computational cost. Experimental results on the CIFAR-100 dataset show that the EPASS algorithm outperforms the PASS algorithm to some extent in terms of accuracy at all stages. These results validate the effectiveness of the EPASS algorithm in mitigating the catastrophic forgetting problem and provide new directions for future incremental learning research.
Optically optimization of thin film light-emitting diodes based on transfer matrix
Authors: Lu Haoze
Published: September 2024

The evolution of white LEDs, both organic and inorganic, has marked a transformative era in lighting technology known as solid-state lighting. This revolution is driven by electroluminescence, where electron-hole pairs recombine to emit light. While OLEDs have achieved high internal quantum efficiency, limited out-coupling efficiency remains a challenge. Improving light out-coupling efficiency is crucial for enhancing LED performance. This paper employs the Transfer Matrix Method to analyze the optical properties of OLEDs and aims to enhance light coupling, efficiency, and overall impact of LED and OLED technologies.
Correlation between fishing yield and microplastic levels across nations
Authors: Wang Zilong
Published: September 2024

This research looks into the association between fishing and microplastic contamination across countries, with a particular focus on how the growth of microplastic levels can impact fishery production. Through the use of a mixed-effect model we were able to look through data from 100 nations during the period 1990-2021, where fishery yield data was extracted from The World Bank and microplastic concentration data was obtained from the National Centers for Environmental Information; population data served as a control variable in our analysis. We found that there is indeed a significant negative correlation between levels of microplastics and fishing yield: an increase by 1 piece of microplastic per cubic meter leads to decrease in fishing yield by anything between 65 and 100 metric tonnes (95% confidence interval). This relationship held true for about 93% of all coastal countries studied. To accommodate for differences amongst nations, we introduced random intercepts and slopes in our mixed-effect model which helped capture variations specific to each country while still identifying an overarching pattern. The research we are doing is on the connection between fish catches and microplastic pollution which takes place in the different countries of the world, where we focus more on how high microplastic levels influence fishery production. Having made use of a mixed-effect model, we have been able to look at data that represents 100 nations within the period of time between 1990 and 2021; The World Bank provided us with fishery yield data while microplastic concentration data came from the National Centers for Environmental Information. In addition to these variables, population data was used as a control variable. The summary of our analysis points towards a significant inverse relationship noted between microplastic levels and fishing yield: an increase by one piece of plastic results in a decline by somewhere between 65 to 100 metric tonnes (95% confidence interval). This generalization held true for about 93% coastal countries considered under this study. To capture specific variations among nations but also identify an overall trend line while dealing with inter-country variability, random intercepts and slope components were included as part of our mixed-effect model methodology.
An overview of the development and applications of information entropy
Authors: Huang Yiqi
Published: September 2024

With information entropy gradually taking the lead in modern information theory development, it begins to hold greater influence over multiple research areas as well as technology innovation. This paper aims to clarify peoples confusion with the development of entropy theory and provide a brief overview of the origin of entropy theory, including the original Shannons proposal, variants such as relative entropy and conditional entropy, and entropy concepts proposed by other scientists, such as Rnyi Tsallis entropy. The paper also includes the current application of entropy, studies hotspots, and predicts future entropy development trends. This research paper is able to add more coherence and consistency to information entropys development, helping more people to better understand the concept of entropy and its derivation. At the same time, with hotspots of entropy fields of study, this paper hopes to attract more people to devote themselves to studying entropy-related fields, and boost technological development.
Prediction of second-hand sailboat prices based on multivariate linear interpolation methods
Authors: Li Jingxuan , Ye Shuangkai , Li Xingyao
Published: September 2024

According to the requirements of the topic, our team has established a mathematical model based on a large amount of relevant data and conducted a quantitative analysis of factors such as the price of sailboats and their length, types, materials, etc. In the process of data analysis and model building, we mainly use interpolation method and error variance analysis, and display these results through charts and figures. In addition, we have used the obtained model to analyze and estimate the price of sailboats in Hong Kong.
Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models
Authors: Luo Lan
Published: September 2024

In the domain of finance and economics, ensuring the validity and dependability of forecasting models is crucial. In this paper, the White Noise Hypothesis Test is elucidated as an essential model validation tool for both the regression and ARIMA models. In the case of regression analysis, there is a need for residual independence and homoscedasticity to confirm that the estimates of the parameters are not biased, that is, the chances of the parameter estimated to be true is high. In the case of an ARIMA model, residuals are checked for the white noise characteristics, and so as to ensure that all the relevant patterns associated with the time series have been detected. The paper uses simulated stock market data and risk management scenarios to demonstrate how these tests enhance model reliability, since when white noise characteristics are ascertained, the residuals are confirmed random and independent. The importance of validating models is discussed in the context of economic forecasting and policy analysis, where the validated models are indispensable for anticipating a market upheaval or to make right economic decisions. Therefore, when white noise test is used to confirm that the model meets the white noise criteria, this improves the credibility of predictions and thus influences strategic decision making in the uncertain environment.
Factors influencing housing prices: A case study of Shanghai
Authors: Fang Xinning
Published: September 2024

Housing is a major consumer good for residents, and housing prices affect people's quality of life and happiness. This paper analyzes the factors influencing housing prices based on the housing price dataset for Shanghai in 2023-2024. Comparison is made via multiple linear regression model to discover the difference between with and without considering interaction terms, assessing the importance and accuracy of the two models' results, and optimizing the model. According to the analysis, living area, decoration type, and building usage are the factors most positively correlated with housing prices. The number of bathrooms, living rooms, and bedrooms also shows a significant positive correlation with housing prices. Additionally, building type and the construction age of the house are negatively correlated with housing prices, and this correlation is very significant. Finally, the housing area weakens the positive impact of the number of bathrooms and bedrooms on housing prices.
Research Progress on the Development of Fatigue Driving Detection Based on Deep Learning
Authors: Guan Zhibin
Published: September 2024

Inattentiveness, weariness, and drowsiness are prevalent causes of car accidents; however, timely warnings can prevent these dangerous situations. Advances in artificial intelligence (AI) and computer vision have made it feasible to continuously monitor a driver's condition and alert them when their attention starts to wane. AI technology can assess a driver's level of fatigue by analyzing various facial expressions such as yawning, the closing of eyes, and head movements. Additionally, it can collect data on vehicle behavior and the driver's biological signals. The primary metric for evaluating the effectiveness of these systems is detection accuracy, which indicates how reliably the system can identify signs of driver fatigue. This study provides a comprehensive review of recent techniques for detecting fatigue driving, with a strong focus on deep learning methodologies. It highlights the application and reliability of these advanced technologies in real-world scenarios. Furthermore, the study delves into the latest issues and challenges faced in this field, including limitations in current technology and potential obstacles to widespread adoption. Finally, it proposes suggestions and outlines future research directions to enhance the effectiveness and reliability of fatigue detection systems. This expanded analysis aims to contribute to the development of safer driving environments by leveraging cutting-edge AI and computer vision technologies.
Performance Comparison of ControlNet Models Based on PONY in Complex Human Pose Image Generation
Authors: Zeng Qinyu
Published: September 2024

Over the past two years, text-to-image diffusion models have advanced considerably. The PONY model, in particular, excels at generating high-quality anime character images from open-domain text descriptions. However, such text descriptions often lack the granularity needed for detailed control, especially in the context of complex human pose generation. To mitigate this limitation, recent research has introduced ControlNet to enhance the control capabilities of stable diffusion models. Nevertheless, the efficacy of a single model remains suboptimal for generating complex poses, highlighting the potential of combining multiple ControlNet models. This paper introduces the Depth+OpenPose methodology, a multi-ControlNet approach that enables simultaneous local control of depth maps and pose maps, in addition to other global controls. Distinct from single or other combined methods, Depth+OpenPose incorporates an additional conditional input. For addressing limb occlusion issues, depth maps provide positional relationships, while OpenPose captures facial expressions and hand poses, surpassing the performance of single models. Furthermore, Depth+OpenPose demonstrates superior speed and quality relative to other combinations. It is crucial to note that an excessive number of combinations can lead to too many conditional inputs, thereby reducing control efficacy. Through comprehensive quantitative and qualitative experimental comparisons, Depth+OpenPose proves its superiority in terms of speed, image quality, and versatility over existing methodologies.
Advances in Holographic Theory for Explaining Quantum Entanglement
Authors: Wang Jingjing
Published: September 2024

The non-locality in quantum entanglement, where particles exhibit instantaneous correlations over large distances, has long been a significant challenge in quantum mechanics. This paper explores the contributions of holographic theory in resolving the non-locality of quantum entanglement and the black hole information paradox. Through the ER=EPR conjecture, holographic theory proposes that quantum entanglement can be explained by tiny wormholes, offering a new geometric perspective to understand non-local effects. Moreover, holographic theory has made substantial progress in addressing the black hole information paradox, particularly through new concepts such as quantum extremal surfaces and replica wormholes. These theoretical approaches indicate that the entropy of black hole radiation follows the Page curve, supporting the idea of information preservation and resolving the paradox without violating the fundamental principles of quantum mechanics. Overall, holographic theory provides crucial theoretical tools and explanatory frameworks for quantum entanglement and black hole physics, advancing the understanding of the unification between quantum gravity and general relativity.
High school student GPA prediction by various linear regression models
Authors: Zhu Weijia
Published: September 2024

Academic performance (GPA) is a significant index for high school students in North America. The research aims to develop and validate the best predictive regression model to evaluate the GPA of high school students. In addition, the prediction numeric data of GPA can correspond to specific classification data of GPA (Grade Level) to calculate and compare the models accuracy for predicting Grade Level. The research subjects are high school students from different backgrounds in North America, including their background, study habits, participation in extracurricular activities, etc. The experiment explores the impact of different factors on student GPA and finds that the number of absences from lectures is a key factor in predicting student GPA. Multiple linear regression analysis is used as the main model in the experiment, which may be improved by the stepwise regression methods. The generalization ability of the model is evaluated through cross-validation (CV) methods. Also, the boosting or random forest model is used to be the comparing model for predicting GPA. The experimental result shows that the multiple linear regression model has high accuracy (84%) and reliability (R^2 value is 0.95) in predicting student GPA. The conclusion of the research emphasizes the importance of predicting student GPA in high school education and the potential for guiding educational practice through data analysis. Future work will consider introducing more subjects and variables, such as different subject learning abilities, mental health, and social support, to further improve the predictive accuracy of the model.
Research on housing prices prediction-take Boston as an example
Authors: Liu Boxuan
Published: September 2024

The problem of predicting Boston housing prices belongs to the field of artificial intelligence, specifically in the domain of regression problems, which is a crucial area of study in machine learning. This article aims to get an accurate prediction model of Boston housing prices by comparing four regression models based on the datasets derived from StabLib library. Multiple Linear Regression model, Random Forest Regression model, Extreme Gradient Boosting Regression model and Support Vector Machine Regression model are taken into consideration. Five evaluation index R-squared, adjusted R-squared, mean absolute error, mean squared error and root mean squared error are compared in terms of generalization ability of model. Eventually, Extreme Gradient Boosting Regression model was found to be the most effective model when predicting housing prices in Boston. The model has certain positive applications in real life, which can help government formulate real estate policies and people make wiser house purchasing strategies.
Research on application method of intelligent driving technology based on monocular vision sensor
Authors: Zhang Zeyu
Published: September 2024

With the development of driverless cars, intelligent driving technology is increasingly used in the automotive industry, monocular vision sensor plays an indispensable role in intelligent driving technology because of its simple structure, low cost and abundant information. This paper discusses and optimizes the application of the monocular vision sensor in intelligent driving. The basic principle and key technologies of the monocular vision sensor are described in detail. In the specific application of the monocular vision sensor, this paper focuses on the monocular vision sensor's depth learning network, multi-information fusion technology, improved target detection and tracking algorithm. Through in-depth research and analysis, a series of optimization strategies based on the monocular vision sensor, such as the FAST Region Convolutional Neural Network (FASTRCNN) vehicle target detection method and improved Scale-Invariant Feature Transform (SIFT) algorithm, are proposed. Finally, this paper summarizes the intelligent driving technology based on the monocular vision sensor and suggests that the monocular vision sensor will play a more important role in intelligent driving technology. Future research shall focus on improving the accuracy of the algorithm, such as the development of end-to-end convolutional neural network fusion methods, the proposed depth multi-modal sensor fusion network, and so on.
Analysis of customer purchasing behavior in E-commerce model
Authors: Ding Wenxuan
Published: September 2024

Because of globalization and information technology (IT), e-commerce (EC) has changed business practices and had an impact on established market systems. Global EC sales are still increasing, which emphasizes how crucial it is to comprehend consumer purchasing behavior. Using a Kaggle dataset named "Exploring E-commerce Trends," which contains product data including price, rating, stock amount, and sales, this paper examines consumer purchasing behavior. The paper used Multiple Linear Regression and the Random Forest methods. The quantity of stock, price, and the number of reviews were found to be important predictors of sales using the Random Forest ensemble learning technique. However, the model exhibited overfitting, performing well on the training set but poorly on the testing set. Multiple Linear Regression results showed minimal explanatory power for sales, indicating limitations in the model's effectiveness. While the Random Forest model identified key factors influencing sales, its overfitting and the ineffectiveness of the Multiple Linear Regression model suggest the need for more robust methods to predict customer purchasing behavior in e-commerce accurately. Future research should integrate additional variables and employ more sophisticated models to enhance prediction accuracy and business decision-making.
Influence and Inspiration of Asymmetric Information on Individual Investor Behavior
Authors: Yuan Ziya
Published: September 2024

Information asymmetry is a pervasive phenomenon in the financial market. For individual investors, this imbalance usually results in making them vulnerable in the investment decision-making processes, thus raising the challenges as well as risks that characterize their participation in the market. Unlike financial institutions that have access to extensive resources and research capabilities, the lack of reliable sources of data and knowledge about how to interpret and analyze available information add layer of complexity for individual investors to acquire full and sufficiently accurate information, which increases risks for them during investment activities compared with financial institutions. Without proper understanding of financial concepts and analysis techniques, they may struggle to make informed decisions in an environment characterized by asymmetric information. Thus, according to the current literature on information asymmetry and individual investor behavior, this paper sorts out and summarizes the viewpoints and conclusions of each paper. On this basis, this paper provides a series of suggestions for individual investors to have better insight of this financial phenomenon and make reasonable investment in the financial market environment with asymmetric information.
A Study of Stock Returns in China's A-share Market Based on the Fama-French Three-factor Model
Authors: Yang Zonglin
Published: September 2024

In recent years, the world's financial boom, all kinds of financial products are emerging, how to rationalize the pricing of financial assets, is a new test of asset pricing theory. The capital asset pricing model of the 1970s, although set up rigorously, many studies have shown that it is unable to fully explain the stock returns. In view of this, academics began to explore other variables affecting stock returns and gradually focused on multifactor models. In this regard, the Fama-French three-factor model stands out. In addition to focusing on the impact of the market premium factor on stock returns, it also simultaneously takes into account that the market capitalization factor and the book-to-market ratio factor also have a significant impact on stock returns. Although the explanatory power of the Fama-French three-factor model on stock returns has been verified in major developed capital markets abroad. However, when applying the three-factor model to the Chinese market, the views of various scholars are different. This paper examines the data of SSE A-shares for the past 10 years and tests the adaptability of the three-factor model in the Chinese market with monthly data, and finds that the market premium factor and the market capitalization factor are significantly effective in explaining stock returns, in contrast, the book-to-market ratio factor is relatively weak in its ability to explain stock returns.
The Role of AI and ML in Transforming Marketing Strategies: Insights from Recent Studies
Authors: Tang Zicheng
Published: September 2024

With the development of digital information technology, the application of AI and ML in marketing has always been a key research direction. In this paper, this review focuses on the applications of Predictive Analytics (P), Personalization (P), Advertising Optimization (A), and Customer Experience Enhancement (C) in the marketing mix, explores the latest applications and research results published in various journals in recent years, and summarizes the progress made in this field of machine learning. It is easy to understand that machine learning can help enterprise decision-makers to help determine decision-making guidelines, but it is controversial in terms of privacy due to the large amount of customer data it requires, and as the algorithm deepens, transparency and fairness agnosticism is also a major concern. Finally, this paper will provide research directions and suggestions for future research based on the overall advantages and disadvantages, which can be combined with human insight and multidisciplinary cooperation to further optimize the problem.
Analysis of Wal-Mart Stock Trend Based on CAPM Model
Authors: Kang Xuchang
Published: September 2024

The Capital Asset Pricing Model (CAPM) model is an important theoretical model in finance. This theory can be used to estimate the price of the target asset and predict its future development trend. As a famous brand, Wal-Mart is an indispensable backbone in the stock consumption sector and has attracted much attention from investors. This paper uses direct data and the CAPM model to conduct a case study on Wal-Mart's US stocks. The historical data of the S&P 500 is used as a sample to calculate the market return rate R(M), the one-year US Treasury bond rate is used as the risk-free rate R(f), and the historical rate of change of Wal-Mart's stock is used as R(i) to calculate its sensitivity to systematic risk . The author uses Excel to perform regression analysis on the relevant data. The results show that the model passes the F test and T test, and the model is established; the model R 2 is 0.9676, which proves that the model fits well. The of Wal-Mart stock is 0.9676. This data can prove that the stock is positively correlated with the changes in the S&P 500 market, that is, the stock is close to the market systematic risk. The greater the risk, the better the stock performs. When investors predict that the US stock market is on an upward trend, they can hold the stock.
Adolescent Cosmetic Consumption: Exploring Societal Influences, Safety Concerns, and Post-Pandemic Market Shifts
Authors: Bai Yuzhou
Published: September 2024

Adolescence is a formative period where identity and consumer behaviors are significantly influenced by societal norms and media portrayals. This research delves deeply into the engagement of adolescents with cosmetics, meticulously scrutinizing the persuasive power of advertisements and the profound implications of societal beauty standards on teenagers. By applying a critical lens, the study examines the presence of harmful substances in cosmetics that lack regulatory approval, uncovering potential threats to skin health and underscoring the necessity for stringent regulatory oversight and comprehensive consumer education. Furthermore, the study explores the disruptive effect of the COVID-19 pandemic on consumer patterns within the cosmetics sector. It illustrates a discernible pivot towards skincare and wellness-oriented products, reflecting a shift in adolescent preferences. The research concludes with evidence-based recommendations for the cosmetics industry to effectively navigate post-pandemic challenges. It advocates for a focus on safety, transparency, and alignment with the evolving preferences of a health-conscious adolescent demographic, emphasizing the importance of meeting the demands for safe and effective cosmetic products in a rapidly changing market landscape.
Analysis of Financial Performances for Monster Beverage: Comparison with KO, PEP and KDP
Authors: Wang Yifan
Published: September 2024

The soft drink market is a growth market with diverse products including carbonated drinks, energy drinks, sports drinks, ready-to-drink tea, etc., in which demand shifts by consumer behaviours, market innovations, as well as macro environment policies. This study analyzes the financial performance of Monster Beverage Corporation (MNST) in comparison with Coca-Cola (KO), PepsiCo (PEP), and Keurig Dr Pepper (KDP) by calculating financial metrics and making comparison analysis, and the aim is to find one stock which is worth value investing. Consequently, MNST is worth investing in on account of its outstanding financial metrics including its lower PEG ratio, higher revenue growth rate, and EPS growth rate, meanwhile, MNST stands out with its external factors such as innovation in flavour. The research fills the vacancy of value investing, especially for comparison analysis in the specific four soft drink companies, i.e., MNST, KO, PEP, and KDP. Though the research needs more predicting models, these results still can help investors make decisions in value investing.
Comparison of CAPM and DVM: Evidence from Costco and Walm
Authors: Hao Qianye
Published: September 2024

In the financial field, asset pricing models are crucial for investment decisions and enterprise value assessment. This article compares and studies two common asset pricing models, CAPM and DVM. There are differences in parameter estimation and assumptions between the two models. The CAPM offers a theoretical derivation of the relationship between required returns and systemic risk by precisely accounting for a firm's level of systemic risk relative to the entire stock market. However, the assumptions of this model are unrealistic, and the accuracy of variable values cannot be accurately grasped. The calculation process for DVM's conclusion is concise and applicable to most companies. Nevertheless, the input data used in the calculation process may not be accurate. At the same time, it ignores the growth of future returns, ignoring explicitly include risks, and is not applicable to companies that distribute zero dividends. This study is beneficial for providing investors and enterprises with a more comprehensive basis for selecting pricing models, and promoting the development of financial theory and practice.
Analysis of Financial Performances for Honeywell Based on Comparison with 3M, Emerson, and Johnson Controls
Authors: Li Yisi
Published: September 2024

Contemporarily, the advanced techniques of Iot has been widely implemented. Hence, this paper presents a comparative financial analysis for relevant industry of Honeywell International Inc., 3M Company, Emerson Electric Co., and Johnson Controls Inc., focusing on key financial metrics such as TTM P/E ratio, NTM P/E ratio, revenue growth rate, EPS growth rate, PEG, GP/A, gross profit margin, and net profit margin. The analysis reveals that Honeywell exhibits strong financial health with high profitability, stable growth, and efficient asset utilization, making it a favorable investment option. Johnson Controls, despite lower margins, shows potential for high returns, particularly due to its lower PEG ratio. Conversely, 3M and Emerson face financial challenges, with 3M showing negative growth rates and Emerson displaying a higher PEG ratio, indicating overvaluation relative to earnings growth. The study highlights the importance of leveraging IoT, AI, and machine learning technologies to drive future growth and efficiency in industrial automation. By providing a nuanced understanding of the financial health and growth prospects of these companies, these results offer valuable insights for investors and stakeholders in the industrial automation sector.
Copper Price Correlation Analysis and 2025 Copper Price Forecast
Authors: Meng Fanduo
Published: September 2024

As an important industrial metal, copper will play an increasingly important role in the future energy transition and technological progress. Therefore, it is helpful to study the fluctuations of copper prices to predict copper prices. In exploring the correlation between gold, silver and oil price changes and copper price volatility, the authors chose the OLS regression model to describe it. As a typical prediction model, the ARIMA model is highly favored by scholars from all over the world because of its accuracy and adaptability. This article will use the ARIMA model to forecast the 2025 copper price change. The study found a high correlation between the price changes of the three and the volatility of the copper price, with the silver and oil prices having a positive impact on the copper price and the gold price having an inverse impact on the copper price. The fitted model speculates that copper prices will continue to increase in 2025.
The Impact of Higher Education on Economic Growth: A Comparative Analysis of China, Germany, and the United States
Authors: Gu Siyi
Published: September 2024

The relationship between higher education and economic growth is a well-documented phenomenon critical to understanding modern economic dynamics. This study employs comparative analysis to investigate the relationship between educational attainment and GDP growth in China, Germany, and the United States. Higher education has a complex impact on economic development, including innovation, productivity, and labor market coordination. The study emphasizes the significant expenditure on education in these countries and the differences in impact. Germany's dual education system effectively blends vocational training with academic learning, lowering unemployment and improving output. China's rapid development of higher education has been critical to its shift to an innovation-driven economy. However, difficulties such as talent mismatches continue. The United States' higher education landscape is diversified, yet financial and access gaps remain, particularly in rural and low-income communities. This study emphasizes the necessity for implementing education policies tailored to address specific economic needs and regional disparities. Implementing models like the German dual education system and guaranteeing equal access to quality education can enhance countries' economic growth prospects. Future research should concentrate on the long-term impact of integrating vocational training into higher education and the impact of digital transformation on education and economic growth. The findings of this study provide a foundation for policymakers to develop inclusive education systems that promote sustainable economic development.
Analysis and comparison of sensor accuracy of autonomous vehicles
Authors: Chen Huiying
Published: September 2024

Autonomous driving is gradually becoming one of the major directions in the development of automotive technology nowadays. Environmental detection technology is indispensable for existing intelligent vehicles, especially when such cars are used in daily life, where many complex road environments cannot be helped by environmental detection technology. Environmental detection technology cannot be divorced from hardware and software support. This study will discuss the different sensors used in autonomous driving environment detection technology, to gain a deeper understanding of the characteristics, functions and applications of these sensors, and to discuss the advantages and limitations of each sensor. Then a comparison of the three widely used sensors in the world is conducted, in terms of the detection range and angle, the accuracy of road detection, and the stability of the detection. The three sensors are a camera, millimeter wave radar and laser radar. A more suitable and stable one from these three sensors will be chosen for in-depth consideration. On this basis, new ideas for the future development of existing sensors are provided, while the direction of improvement of existing sensors is summarized based on the results of existing analyses.
Integrating Traditional and Advanced Sensor Solutions in the Perception System of Industrial AGVs
Authors: Shi Gengjian
Published: September 2024

This paper examines the perception systems used in industrial Automated Guided Vehicles (AGVs), focusing on traditional and advanced sensor solutions. Traditional perception methods, such as track-based and magnetic tape guidance, offer reliability but are limited in flexibility. In contrast, radar, vision, and LiDAR sensors provide enhanced perception capabilities, enabling AGVs to navigate safely and efficiently in complex industrial environments. The study explores various sensors, including visible light, infrared, ultrasonic, LiDAR, magnetic strip sensors, Inertial Measurement Units (IMUs), tactile sensors, Ultrawideband (UWB) sensors, thermal sensors, and millimeter-wave (mmWave) sensors, highlighting their principles, advantages, and limitations. The integration of these sensors supports robust navigation and operational efficiency in diverse settings. The methodology involves reviewing existing literature and analyzing current technologies used in industrial AGVs. Results indicate that while traditional solutions are reliable, advanced sensor technologies significantly enhance AGV performance. The paper concludes that the future of AGV perception systems lies in the integration of advanced sensors with artificial intelligence and machine learning algorithms, promoting intelligent and adaptive industrial automation. Additionally, it underscores the necessity of developing robust sensor fusion techniques to harness the full potential of these advanced sensors.
Effect of basic periodontal treatment on CRP in elderly pregnant women with periodontal disease
Authors: Zai Liying
Published: September 2024

Periodontal disease is a chronic infectious disease, which is common and frequent disease in pregnant women. It is an infectious disease caused by a variety of microorganisms, and the physiological changes in pregnant women after pregnancy can promote the occurrence and development of periodontal disease. Women no less than 35 years old are at an increased risk of maternal and fetal complications in pregnancy, older pregnant women with periodontal disease are prone to preterm birth, fetal growth retardation, premature rupture of membranes, and the incidence of neonatal low birth weight is significantly higher than that in older women with healthy oral health, i.e., these risks are increased with age. CRP is an acute-phase protein produced by liver organs, which is produced by hepatocytes under the promotion of inflammatory factors, and serves as the most direct and sensitive marker of inflammatory response. Studies have shown that basic periodontal treatment relieve periodontal tissue inflammation in pregnant women with periodontitis, while significantly reducing their serum CRP concentration, thereby decreasing the occurrence of adverse pregnancy outcomes. The objective of this study is to investigate the effect of basic periodontal treatment on CRP in elderly pregnant women with periodontal disease.
Knowledge, Attitude and Practices Regarding Antibiotics usages among Medical Students at Zawia University
Authors: Reemah Almadah  , Hana Alazraq  , Shahrazad Gazeti
Published: September 2024

Antimicrobial resistance (AMR) is a serious threat to global public health. It increases morbidity and mortality, and is associated with high economic costs due to its health care burden. Medical students are particularly important since they will be the future leaders in clinic practice, responsible for antibiotic prescription and communication about antibiotic use with their patients. In this study we aimed to understand knowledge, attitude, and practice (KAP) with respect to antibiotic use among medical students in faculty of medicine, Zawia University. This is a cross-sectional design; it was undertaken in Faculty of Medicine during October and November: 2023. The questionnaire was administered to students in (III 3rd and IV 4th grades). All data were analyzed using Statistical Package for Minitab program. Majority of the respondents were female with ages of 22 years. Higher percentage of 4th-grade students (95.5%) correctly accepting the need to stop antibiotic use compared to 3rd-grade students (27.9%, p < 0.001). On other hand with a higher percentage of 4th-grade students reporting using non-prescribed antibiotics rarely compared to 3rd-grade students (46.3% for 4th grade and 69.8% for 3rd grade, p = 0.003). this study exposed an important awareness concerning the separate between medical students’ knowledge about antimicrobial resistance, and appalling attitudes and practices towards antibiotics use. We need for the re-evaluation of the educational curricula especially, teaching of clinical pharmacology courses in clinical years.