bluuebunny/crossref_metadata_2025 huggingface dataset:
The Harm of Nature and the Light of Man: Philosophical and Theological Reflections on Gratitude and Growth from Disaster
Authors: Ge Lvyueyue
Published: October 2024

This article explores in depth the multiple implications of natural disasters, especially in the context of scientific progress, human solidarity, and theology. Firstly, this paper reviews the catastrophic impact of Hurricane Katrina in 2005 and discusses the multi-dimensional effect of natural disasters on human society and its profound philosophical and theological significance. Despite the devastation and suffering caused by the disaster, Americans have shown a strong sense of gratitude. Then, from the dialectical point of view, the author analyzes the cosmological view of the coexistence of good and evil and emphasizes the positive role of natural disasters in promoting scientific and technological progress, promoting human unity, stimulating learning motivation, and demonstrating free will.Further, from the perspective of philosophy and theology, the paper expounds on the intrinsic value of pursuing knowledge and exercising natural disasters on free will. The learning process of natural laws deepens human understanding of the world. It strengthens human free will, making people more autonomous and independent in the face of good and evil choices.At the same time, from the theology perspective, this paper compares the different understandings of natural disasters and human suffering between atheism and theism. It reveals the respect for human autonomy and the essence of faith behind God’s non-intervention. In the end, the article concludes that natural disasters, while a source of suffering, are also catalysts for human growth and gratitude, encouraging us to look at disasters from a broader perspective and draw strength and wisdom from them.
Weather Prediction with Feature Selection and Random Forest
Authors: Dai Zhihao
Published: October 2024

Accurate weather prediction is crucial for various sectors, including agriculture, disaster management, and aviation. Traditional weather prediction relies heavily on numerical weather prediction (NWP) models that simulate atmospheric processes using mathematical equations. While these methods have been the backbone of forecasting for decades, they often require significant computational resources and may need help capturing localized weather events. In contrast, machine learning models can quickly analyze large datasets to identify patterns and relationships that traditional methods might miss. This paper employs a Random Forest model to investigate the impact of feature selection on weather prediction accuracy. This paper identifies those critical to model performance by systematically excluding individual features, offering valuable insights into how ML can enhance traditional forecasting techniques. The results underscore the importance of features such as “Present_Tmin” and “LDAPS_LH”, which significantly influence model performance as measured by Root Mean Square Error (RMSE). This study contributes to a better understanding of feature selection in developing accurate weather prediction models. It lays the groundwork for future research to improve prediction accuracy through advanced techniques and expanded datasets.
Genetic selection for lung adenocarcinoma: a statistical approach based on pathological genetic data
Authors: Wang Yuhao
Published: October 2024

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), with high morbidity and mortality worldwide, posing a serious threat to human health. Due to the complex molecular mechanism of lung adenocarcinoma, which involves a variety of genetic and environmental factors, this makes early diagnosis and treatment a major challenge. This research aims to identify the key pathogenic genes of lung adenocarcinoma to solve the challenges in early diagnosis and treatment. In this paper, three differential analysis methods, limma, DESeq2 and edgeR, were used to select 3315 differential genes. Then, combined with Lasso regression and XGboost algorithm, the pathological gene data were analyzed in depth, and through these advanced statistical and machine learning techniques, the genes related to lung adenocarcinoma were screened from a large number of gene expression data, and 17 characteristic genes of the highest importance were found GO enrichment analysis and KEGG enrichment analysis were performed on these characteristic genes to clarify the biological functions of the genes. Finally, 8 significant pathogenic genes were identified by Cox survival analysis. The expression levels of these genes are closely related to the prognosis of lung adenocarcinoma patients, providing a new perspective for understanding the disease mechanism. The conclusions of this study not only improve the understanding of the molecular mechanism of lung adenocarcinoma, but also provide potential biomarkers for clinical diagnosis and treatment. The discovery of these genes is expected to promote the development of early diagnosis technology and guide the formulation of personalized treatment plans, thereby improving the treatment efficacy and quality of life of lung adenocarcinoma patients. In the future, these genes may become key targets for the development of new drugs and therapeutic strategies.
The Investigation of the Application of RAG Technology in the Field of EHR
Authors: Xu Yifeng
Published: October 2024

Electronic Health Records (EHR) play a crucial role in contemporary medical information systems, capturing extensive data in multiple scenarios. This review explores the impact of Retrieval Augmented Generation (RAG) on managing EHR. By integrating retrieval and generation processes, RAG significantly enhances data handling, clinical decision support, and patient management. EHRs contain extensive data like patient histories and diagnostic information, which are becoming increasingly complex. The RAG improves the accuracy and richness of this data processing by using a retrieval module to extract relevant text fragments and a generation module to create precise outputs. Despite its potential, RAG faces challenges such as data inconsistency, privacy concerns, and the need for efficient training. Recent studies highlight the RAG’s effectiveness in summarizing clinical notes and enhancing prediction accuracy, suggesting a promising future in healthcare. However, ongoing research is necessary to optimize the RAG’s application and address these challenges, aiming to transform healthcare delivery effectively.
Research on the Path of the Transformation and Development ofCommercial Banks under the Background of Internet Finance --Taking Bank A as an Example
Authors: Tang Haoming
Published: October 2024

The booming rise of Internet finance, which relies on the leap of Internet technology and the deep integration of cutting-edge technologies such as big data, AI, and blockchain, has completely reshaped the landscape of financial services. It has not only transcended geographical boundaries but also streamlined cumbersome intermediary processes, promoting the democratization and popularization of financial services. Then, In the face of this transformation, Bank A, as a representative of the traditional financial system, is facing unprecedented challenges and opportunities. It is important that users’ preferences have shifted towards the pursuit of ultimate convenience, efficiency, and personalized services, forcing Bank A to re-examine its business model and operational strategy. In the tide of Internet finance, Bank A urgently needs to explore a development path that can not only maintain stable operations but also flexibly adapt to market demands, realizing digital and intelligent transformation to remain competitive in the fiercely competitive market.
Utilizing Paleoclimate Data to Analyze the Relationship Between Climate Change and Human Activities
Authors: Yang Siyuan , Xiao Xiaoyang , Lu Yichen , You Xuanyi , Jin Shuxian
Published: October 2024

Our research aims at finding the relationship between climatic cycle-changes and the human activities in ancient China. Most climatic changes show shifts in the precipitation volume and alternations between warm and cold periods. Through other research findings, we find the precipitation volume is closely connected with alternations of warm and cold periods. This report presents a linear comparison between the population of ancient China and the east Asia climate variables and chooses tree rings as the main indicator of the paleo-climate. Through the literature review and the data analysis, we find that (1) The dominant climate pattern in East Asia is East Asia Summer Monsoon system, causing centennial scaled climate periodicities, affecting the ecosystem and the society. (2) There are certain cycles of climate change, in which the warm period and the cold period have a certain influence on the population change. (3) The precipitation has a certain correlation with the population change. However, single temperature and precipitation cannot directly affect the population change, but also need to be combined with social and historical factors specific analysis.
The Impact of Character Creation and Customization on Self-Expression and Identity in RPGs
Authors: Shang Mingge
Published: October 2024

While a large of research investigates the importance of character creation and customization in role-playing games (RPGs) on players’ motivation and enjoyment to play, little is known about the impact of character creation and customization on players’ self-expression and identity exploration. Video games have been a venue for both developers and gamers to explore complex stories and ideas. Role-playing games (RPGs) are a type of video game in which players can take the role of a character in a fictional setting and interact with the game’s narrative. This paper is part of a larger research on the construction of individual identities using RPG character creation and customization. Building upon existing research on identity adaptation theory, queer theory, the proteus effect theory, and the performance of identity, this paper finds that players’ choices of character creation and customization contribute to opportunities for self-expression and individual agency. This paper also examines the character Creation and Customization preferences among different demographic groups and the association between in-game identity and real-life self-perception and social behaviors of players.
Research on the Impact Mechanism of Food Safety Governance Level on Residents’ Social Attitudes: Empirical research based on CGSS data
Authors: Pan Zhexi
Published: October 2024

Food safety is crucial for public health and the healthy and stable development of society. Therefore, exploring the impact of the government’s food safety governance on residents’ social attitudes is of significant importance. This paper primarily examines how the government’s level of food safety governance affects residents’ subjective well-being and perceptions of social justice. By establishing multiple linear regression models, we analyze the specific direction and extent of these influences. Additionally, this paper delves into the variations in these effects across different age groups and between urban and rural residents. The findings from this study offer a valuable and positive contribution to the field of food safety governance.
The Comprehensive Investigation for Covid-19 Trend Prediction Through Machine Learning and Deep Learning
Authors: Xun Jiayu
Published: October 2024

Corona Virus Disease (Covid-19) has surely been a challenging problem to solve for the past few years. Due to the diversity in the form of dataset, it is essential to obtain accurate predictive results of Covid-19 trends. This paper analyzes different artificial intelligence methods used in Covid-19 trend prediction, including several machine learning and deep learning methods. More specifically, this work investigates linear regression, random forest, and decision trees in terms of machine learning and delves into Artificial Neural Network (ANN) as well as Long Short-Term Memory (LSTM) for deep learning. By comparing various past works, the effectiveness of machine learning and deep learning methods is achieved by their hidden algorithms, such as the Multiple Linear Regression (MLR) model for linear regression analysis. Incorporation with other models or methods is applied in deep learning. For example, Ensemble Empirical Mode Decomposition (EEMD) is included in ANN structure to decrease the noises within the Covid-19 datasets. Furthermore, the paper also inquiries into potential improvement of some drawbacks in predictive results for Covid-19 trends by reviewing related works of expert system and transfer learning as well as domain adaptation. The machine learning and deep learning models could provide accurate predictive results as a reference for related organizations to consider or establish insightful policies.
The Impact of Trendy Diets on Nutritional Imbalance and Health Risks
Authors: Wu Zhouhua
Published: October 2024

In nowadays society people are focusing on their well-being and appearance more than ever. Social media has amplified almost every detail on daily occasions, however, such additional attention may not necessarily be beneficial for everyone. Influencers post pictures of their perfect physique and use them for more attention and followers. Among all the so-called ‘secrets’, people are most curious about what they eat and how they eat to look that stunning. Compared to tiring gym sessions and boring lectures about anatomy and physiology, a brand-new revolutionary recipe is far more appealing. The trendy recipes are beneficial to some people who have special needs such as those who are obese, type-II diabetes patients, people who suffer from cardiovascular diseases, etc. Such recipes greatly altered the proportions of the different essential nutrients to improve some symptoms that are occurring due to an imbalance of nutrition. However, such a diet may be beneficial to the human body in the short run by adjusting the daily calorie intake and controlling the categories of food in each sitting, in the long run, it would lead to another malfunction of the body due to nutrition imbalance.
Gamma wave frequency affect Microglial states
Authors: Shen Jiabao
Published: October 2024

Alzheimer’s disease (AD) is a chronic neurodegenerative disease, and its core symptoms are memory loss and cognitive impairment. Recent studies show that gamma wave oscillation plays an important role in regulating the protein level of starch and slowing down the progress of AD. This study focuses on the effect of gamma wave oscillation on the formation of amyloid spots in AD mouse model, and evaluates its possibility as a non-invasive treatment.
An Exploratory Study on Digital Transformation in Business Operations of China’s Food Industry
Authors: Zhu Puxian
Published: October 2024

In recent years, the changes that have taken place in the food industry in China are unprecedented. The integration of digital technologies reshapes traditional practices with higher efficiency and gives chances for sustainable development. This paper investigates the impact of digital technologies on business operations within this sector in areas like production efficiency, supply chain optimization, financial performance, marketing, and regulatory compliance. Among others, Internet of Things (IOT), Artificial Intelligence (AI), big data, cloud computing, and blockchain have entirely revolutionized traditional practices toward more precise operations, enhanced decision-making, and improved product safety and transparency. Empirical evidence highlights key benefits such as higher production precision, lower waste, smoother operations of the supply chain, and better financial performances. On the other hand, high costs of implementation, a lack of digital infrastructure, and a shortage of human resources are some of the major obstacles. These potential obstacles notwithstanding, the potential for innovation and growth is very immense. It delivers long-term cost reduction, efficiency improvement, and competitiveness in the global marketplace only for companies that make strategic investments in digital technologies and upskill their workforce. Evidence of support from the side of the government is given by the “Overall Layout Plan for the Construction of Digital China,” which involves enhanced digital infrastructure combined with digital talent cultivation. The following essay underlines the potential of digital technologies to transform China’s food industry, trying to explain how these developments can become important drivers for both sustainable growth and competitiveness.
Credit Anomaly Detection Method based on Bayesian Networks
Authors: Ran Xunwei
Published: October 2024

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.
Enterprise green supply chain management under the dual-carbon background: A case study of Geely Automobile
Authors: Su Yajing
Published: October 2024

With the severe challenge of global climate change and the increasing constraints of resources and environment, China has put forward the goals of “carbon peaking” and “carbon neutrality” (referred to as “dual carbon”), aiming to promote green economic transformation and high-quality development. Green supply chain management, as an important means to achieve the goal of “double carbon”, is gradually becoming a key path for the sustainable development of enterprises. this study takes Geely Automobile as an example, an in-depth analysis of its green supply chain management practice under the background of “dual carbon”, discusses its successful experience and existing problems, and puts forward suggestions for further optimization, to provide reference and reference for other Chinese enterprises, and promote the green transformation and sustainable development of the entire industry.
Research on the Joint Crossover Marketing of Traditional Liquor Moutai and Modern Brand Luckin
Authors: Yang Fujin
Published: October 2024

This study delves into the innovative cross-border cooperation model between Moutai, a venerable liquor manufacturer with a storied history in China, and Luckin, a dynamic and rapidly expanding coffee chain. By examining their market positioning, consumer preferences, and brand values, the research aims to propose novel collaborative initiatives that can benefit both brands. The empirical findings suggest that this partnership holds significant potential for complementary market advantages, thereby enhancing the brand influence of each entity. Moutai, known for its traditional craftsmanship and premium quality, brings a rich heritage and strong brand loyalty to the table. On the other hand, Luckin, with its modern, tech-driven approach and widespread consumer base, introduces a fresh and innovative edge. The synergy between these two distinct brands can lead to unique product offerings and marketing strategies that appeal to a broader audience. The conclusion of this study not only offers strategic insights for the joint ventures of Moutai and Luckin but also serves as a valuable reference for other enterprises exploring cross-industry collaborations. By leveraging their respective strengths, Moutai and Luckin can create a powerful alliance that not only boosts their individual market presence but also sets a precedent for successful brand partnerships in the ever-evolving marketplace.
Analysis of the Digital Transformation Path of Chinese E-commerce Companies
Authors: Li Han
Published: October 2024

Digital technology has revolutionized China’s e-commerce sector, changing company operations and client relationships. This article analyzes the impact of digital transformation on online purchasing patterns. This article concentrated on the important of cloud computing, AI, and big data have become essential to online retail tactics. Digitalization is fueling economic development and innovation in China as its digital economy grows and impacts GDP. Digital technologies enable e-commerce and other industrial sectors to interact easily, improving operational efficiency and market reach. Moving from isolated firm models to connected platforms is a sign of a more responsive and integrated economy. This integration fosters a value model of co-creation in which stakeholders (including companies) collaborate to develop product and service offerings, improving customer experience and operational productivity. The route to digital maturity is not without challenges. E-commerce companies struggle with inefficient logistics, costly digital transformation, and a dearth of competent digital workers, as this study shows. Companies must make dramatic organizational and technological adjustments to overcome these constraints and maximize digital technologies. The analysis concludes that China’s e-commerce market will depend on enterprises’ digital adoption. E-commerce enterprises must embrace ongoing learning and development and remove digital integration barriers to compete in today’s dynamic online industry. The study illuminates digital transformation and how it will impact e-commerce.
Research of Optical Fiber Sensing Technology in Bridge Detection
Authors: Liu Yeqi
Published: October 2024

Fiber optic sensing technology is a new type of detection technology that realizes information transmission through total reflection of light and photoelectric conversion. It has been used in the fields of medicine, military, communication, transportation, industrial control, scientific research, etc., especially in the field of bridges for decades.This paper summarizes the research status of optical fiber sensing technology and optical fiber in bridge detection, and reviews the application of optical fiber in bridge detection. First, this paper introduce the optical fiber technology, its working principle and application advantages. Secondly, the feasibility of optical fiber sensing technology in bridge detection is demonstrated through concrete application cases. It includes the detection of bridge structure and the detection of bridge material structure. Through the optical fiber sensing technology, this paper can detect the hidden dangers of the bridge in a timely and non-destructive manner Finally, this paper prospect the future development of optical fiber technology in the field of bridge detection.
Optimization of PID Control of a Snake Robot’s Winding Crawling Motion
Authors: Hu Junwei , Yao Yiran , Zhou Tianhan , Jiang Runzhe
Published: October 2024

This paper explores the application of PID control to the winding crawling motion of a snack robot. Snack robots, mimicking the movement of real life limbless reptile, are designed with ultiple connected segments equipped with actuators and sensors, enabling them to navigate complex environments. The study aims to optimize the ground crawling behavior and obstacle avoidance f the snake-like robot using MATLAB simulation software. Initially, the robot’s joints were modeled in SolidWorks, and their degrees of freedom were programmed in MATLAB’s Simulink to enable controlled rotation. The PID control system was added to manage the robot’s motion, ensuring smooth and accurate crawling.
Topology and Automorphism Structures in General Linear Group
Authors: Cao Yinchang
Published: October 2024

The general linear group, as a significant topic in algebra and topology, has a wide-ranging research background and application value. With the continuous advancement of mathematical research, the combination of topological structures and automorphism structures has become a key entry point for exploring the properties of the general linear group. This paper investigates the topological properties of general linear group GL (n, R) , specifically focusing on compactness, connectedness and the fundamental group, and the automorphism. The first part of the study is dedicated to a detailed analysis of these properties, providing new insights into the topological structural characteristics of GL (n, R)  . This paper scrutinized the homotopy type within it, giving proof to the fundamental group of GL (n, R) , which is showed to be isomorphism to a trivial group. In the second half, This paper introduce and examine the function φ (G A GL n G A G ) = { ( , )| • } ∈ =  , which is used to identify the automorphism group of a dense subgroup G of Rn
. Specifically, the automorphism group of Qn is investigated. This paper show that the automorphism group of it is isomorphic to the subgroup GL (n, Q)  of general linear group GL  (n, R) . Through this function, this paper offer an innovative approach to understanding the automorphisms within general linear groups, revealing the deep connections between algebraic and topological aspects of these groups.
Cox Regression Model with Time-varying Coefficients Applied to Survival Estimation of Heart Failure
Authors: Zhang Jiahong
Published: October 2024

This study focuses on survival analysis of heart failure patients. All the patients, who are over 40 years old, have NYHA classes III or IV stages of heart failure. Cox regression model with time-varying coefficients is applied to deal with these data, which is updated in 2015. Kaplan Meier curves are plotted to screen out categorical variables that violates PH assumptions. As for continuous variables violating PH assumption, Schoenfeld residual plot is drawn to realize time stratification. Therefore, for each time period, cox proportional hazard model can be fit to estimate mortality and find out the key factors promoting or inhibiting death event of heart failure. As a result, age, blood pressure, serum creatinine, and ejection fraction are the significant features of hazard of death led by heart failure, which is validated by Wald test, Likelihood ratio test, and Log-rank test. Discrimination ability is also characterized by ROC curves and AUC finally.
Diverse Attitudes Toward AI-Produced Music: A Case Study between Instrument Learners and Non-Instrument Learners in Yangtze River Delta Region
Authors: Jiang Yanxi
Published: October 2024

Recently, with the gradual popularization of AI technology in music, AI music generation technology, as a tool, has triggered thoughts about the transformation of traditional music composition while enhancing the innovation and efficiency of music creation. This study aims to explore the different attitudes toward AI-generated music among instrumental and non-instrumental learners in the Yangtze River Delta region of China, so as to increase the knowledge of AI music composition technology. This study uses a questionnaire method to collect quantitative data, with semi-structured interviews as a qualitative supplement. It is found that most people are aware of AI music technology and express acceptance. The style, uniqueness, and quality of AI-generated works, as well as the convergence of the technology on traditional platforms, and copyright issues, are topics of concern. The differential impact of whether or not one has learned a musical instrument on attitudes towards AI music centers on personal preference. The low price of the service was a significant influencing factor for those who embraced the improved AI technology. In addition, instrumental learners placed more emphasis on the professionalism, artistry, and emotionality of AI music, while non-instrumental learners placed more emphasis on the technological convenience and affordability of AI music. This study helps to demonstrate the influence of instrumental learning experiences on people’s attitudes towards AI music generation, and can provide inspiration for further exploration of music education research in cross-cultural contexts. Meanwhile, the emotional relationship and cognitive responses between humans and AI music can be revealed more.
A Comprehensive Review of Evolving Brand Image Strategies: From Universal Appeal to Targeted Resonance in Modern Marketing
Authors: Chen Yuxiu
Published: October 2024

In today’s fierce market competition, brand image building has become the object of attention of major corporations, more and more enterprises take brand image building as the key work of marketing, such as Amazon, and Disney. Therefore being fully aware of the model and internal mechanism of brand image sharping in enhancing brand market competitiveness, it can provide an effective reference for enterprises to improve the efficiency of brand image building. This paper is devoted to reviewing the history route of brand image building, and summarizing the typical cases, then summarize the business strategies of several different brands. Finally, we summarize the effective business strategy for brand image building, the first is high-quality products, good quality and cheap products always can attract more customers; the second is precise brand positioning, targeting customers to gain competitive advantages; the third is high-quality corporate culture, good cultural atmosphere can enrich the connotation of enterprises and the external competitiveness, to stand out from the homogenized products; the forth is great ethical image, enterprises though attend public welfare activities and other ways to create a positive brand image, then enhance customer’s loyalty.
The Importance of Credit Card Fraud Detection in the Digital Age
Authors: Fan Yuan
Published: October 2024

It is true that credit card transactions pave part of daily life in today’s digital economy. Also, it is undeniable that this assurance comes at a cost: it has inadvertently opened the floodgate on one of the most daunting challenges to financial safety-credit card fraud. Credit card fraud causes billions of dollars in losses annually, which is brought about by unauthorized transactions carried out using information related to stolen or cloned credit cards. As such, fraud is very difficult to trace and prevent because advanced technologies and strategies are involved.
The Survival and Medical Dilemma of Rare Disease Patients in the Greater Bay Area
Authors: Zhao Zichun
Published: October 2024

The population of rare disease patients in China faces many difficulties in disease diagnosis and treatment, medical security, economic burden, quality of life, social assistance, employment opportunities, mental health, and other aspects. It is of great significance to identify the common problems faced by this group and try to propose possible solutions, calling on all sectors of society to pay attention to the rare disease minority group, help them solve their survival and security difficulties, and enable more patients to return to society. This paper investigates and addresses the survival and security challenges faced by rare disease minority patients in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China. Through a survey of 223 rare disease patients in the Greater Bay Area, the study reveals that these patients face many challenges in diagnosis, treatment, drug access, and economic support. The conclusion emphasizes the necessity of improving diagnostic efficiency, accelerating drug development, and improving the medical insurance system. Thus, it further puts forward practical suggestions.
Optical Performance of Quantum Dots and Their Applications in Biomedical Imaging
Authors: Fan Xiongzhe , Yi Meng
Published: October 2024

Quantum dots are nanomaterials which have attracted significant focus over the past few years because of their singular optical features. They have shown great potential in the field of bioimaging. This study delves into the photonic properties of quantum dots which include good luminescence stability and broad excitation spectra. Besides, the emission wavelength can be precisely controlled. These properties make them an excellent choice for bio-imaging. The usage of quantum dots in live organism imaging, such as pinpointing cell boundaries, tracking intracellular molecular dynamics, and early disease diagnosis, indicate the immense potential of quantum dots in biomedical realm. Through modifications on the surface of quantum dots, targeted imaging can be realized, which greatly improves the specificity and sensitivity of imaging. Nonetheless, the biosafety and long-term in vivo behavior of quantum dots still need to be further investigated. Overall, the light-based attributes of quantum dots have brought breakthroughs in bio-imaging technology. In addiiton, it would foretell a bright future for quantum dots in the biomedical field.
Research on the Factors Affecting House Price using Multiple Linear Regression Model
Authors: Ye Jully
Published: October 2024

This article aims to identify the factors that affect the change in house prices. In this paper, the Multiple Linear Regression Model is used to analyze the factors with 1000 random samples from the USA which was collected from the 2nd of May in 2014 to the 10th of July in 2014. Based on the datasets, 13 variables (bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, sqft-above, sqft_basement, yr_built, yr_renovated, street, city) are selected as independent variables, and used in scatter diagram to show the correlation with the dependent variable price. Turns out that 3 variables have not shown a significant correlation with the price (yr_built, yr_renovate, city). To find which of the factors have the most significant impact on the change in house prices, VIF value is used to compare the collinearity of those 10 variables. Finally turns out that only 5 variables (bedrooms, view, condition, waterfront, sqft_lot) show the most significant effect on the price of housing. Overall, the main factors affecting the price of housing in the USA were the number of bedrooms and views next to the house, as well as the conditions needed for buying the house.
Machine Learning in Agriculture: KNN-Based Classification of Fruits and Vegetables
Authors: Yao Siyang
Published: October 2024

In modern agriculture and the food industry, it is essential to classify fruits and vegetables accurately and efficiently to meet growing consumer demand and reduce post-harvest losses. Traditional manual methods are often labor-intensive and error prone, highlighting the need for automated solutions. This paper discusses the application of the k Nearest Neighbor (KNN) algorithm to fruit and vegetable recognition using image processing technology. In this study, image data sets are used for feature extraction using Directional Gradient Histogram (HOG) and dimensionality reduction using Principal Component Analysis (PCA). The accuracy of KNN model on verification set and test set reaches 97%, which proves its validity. Confusion matrix analysis and F1 score evaluation further revealed the strengths and areas of improvement of the model, particularly in distinguishing visually similar categories. The results show that the integration of artificial intelligence (especially KNN) offers great potential for the automation of agricultural classification tasks. Future studies could combine more advanced models, such as CNNS, with larger datasets to improve accuracy and robustness.
Why Civilizations Collapse: The Cruciality of Social Solidarity and the Dangers of Its Absence
Authors: Liu Rui
Published: October 2024

This essay examines the complexities of civilizational collapse, proposing that it is not a singular event but a prolonged process driven by internal conflicts and external threats. It begins by defining the concept of collapse and uses historian Jared Diamond’s perspective to analyze how internal strife, such as loss of cultural identity, conflicting ideologies, and political division, can initiate the decline of a civilization due to a fragmentation of social solidarity. The essay then uses historical examples of the Islamic and Roman Empires to illustrate the importance of social solidarity for the survival of civilizations. It also discusses the internal challenges faced by modern civilization, including issues brought about by globalization, political polarization, East-West conflicts, global supply chain shortages, and how these factors can erode solidarity, posing a risk of collapse to the contemporary “world civilization.” The essay concludes by highlighting that internal conflicts and the absence of social solidarity are the primary causes of civilizational collapse, a theory that remains relevant for understanding the challenges faced by the global civilization today.
Research on Techniques for Enhancing the Speed of Low-Power Operational Amplifier
Authors: Wan Pengcheng
Published: October 2024

In the context of low utilization, this paper explores various techniques for enhancing the speed of operational amplifier (OA). The main text delves into three methods to gain equilibrium: fully differential operational amplifier (FDA), twostage operational transconductance amplifier (OTA), and a novel high-speed calculation system architecture employing a “rhombus crystal tube.” The FDA improves speed by minimizing noise and enhancing bandwidth and output voltage swing, without increasing power consumption. The design of the two-stage OTA combines the characteristics of a differential amplifier and a telescopic amplifier, optimizing gain and slew rate through Miller compensation and noise gain manipulation, thereby achieving high-speed performance. A novel high-speed operational amplifier structure employs diamond transistors to supply substantial current for capacitor charging, enhancing the slew rate and overall speed. Throughout the text, these methods are presented as a means to jointly promote high speed, low power consumption, and the handling of a significant number of transistors. To further increase the speed, the size of the microcrystalline tube is crucial. The research direction is outlined, and while the design equipment is in the foreground, there remains room for progress, especially in applications for large-scale electrical models. Future research aims to enhance the power efficiency of circuits, making them more practical and efficient. This study provides valuable insights into balancing power consumption and speed in advanced electronic devices.
To promote the cultivation of mathematical thinking skills and strategies in games for the senior class of kindergarten children
Authors: Li Shujing
Published: October 2024

With the advancement of quality education and an increase in research into preschool education, early childhood education is becoming more focused on individual physical and mental development, as well as the development of comprehensive quality education. Mathematical thinking ability represents a significant aspect of young children’s cognitive development. Given that young children’s brain development is not yet fixed, so their capacity for “plasticity” is considerable. The period between the ages of three and eight represents a critical phase in developing mathematical thinking ability in young children. Children undergo rapid growth in their mathematical understanding and reasoning abilities during this time. This paper will look at promoting the development of mathematical thinking and strategies in play for the senior class of kindergarten children, based on practice, in order to provide new strategies for preschool education. Through the research on the combination of games and mathematics education, we can provide better ideas and methods for the use of games in mathematics education, help the senior class of kindergarten children to develop their mathematical thinking skills, and help them to recognize, articulate, and generalize the quantitative relationships and spatial forms that exist in their daily lives so that they can be well-prepared for the next step of entering the primary school.
A Study of the Liberation of Physical Space in Edgar Allan Poe’s Novels
Authors: Wang Yue
Published: October 2024

In Edgar Allan Poe’s novels, deaths often occur at night, in narrow stairwells, and in dark, confined basements, hidden within the thickness of walls or beneath the ruins of collapsed buildings. By integrating Henry Lefebvre’s theories of social space and physical space with the plot of Poe’s stories, this paper conducts a textual analysis to interpret the author’s intentions in choosing specific social spaces. In Poe’s novels, the murderer’s use of space to commit murder and conceal the truth of death is a manifestation of the individual’s struggle for power within the society they inhabit and the deprivation of others’ power, reflecting the alienation of human nature. To counter such alienation, Poe regards the individual body as the subject of social space production in his novels. By focusing on the depiction of “resurrection from the dead” and other magical and romantic elements, and by emphasizing the liberation of bodily sensations—particularly the medium of sound—to reveal the truth of death, it is argued that Poe’s novels demonstrate the purpose of bodily resistance to the alienation of human nature through the liberation of physical space.
Eye-Tracking Analysis: Examining the Impact of Fan Culture and Entertainment News on Engagement with Social Justice Information
Authors: Fang Chenyu
Published: October 2024

Celebrity culture and idolatry are prevalent nowadays. The development of online social media facilitates the distribution of entertainment news, fan culture has become increasingly prevalent. On the other hand, the extreme fan culture brought negative impacts to people which affects their psychological and physical status. This also leads to a negative stereotype of fan culture, i.e. addicted to entertainment news and not concern themselves of public affairs. This study aims to investigate the impact of fan culture on individuals’ attention to public affairs. Social justice news refers to news that reports and reveals inequality, injustice and oppression in society. Public attention to social justice news can help promote fairness and justice, social progress and change. This study utilized eye-tracking to collect data to investigate the effect of idolatry on social justice news attention. In the experiment, subjects were randomly divided into two groups; Group A subjects would view images of news web pages containing entertainment news and social justice news, while Group B subjects’ material consisted of social justice news and other types of news. Subjects were asked if they are a fan of any celebrity after the trial. The entire browsing process was recorded by an eye-tracker. The eye-tracking data revealed that, generally, people paid less attention to social justice news than to entertainment news and other unrelated topics. This is evidenced by shorter total fixation duration (TFD), fewer fixation counts (FC), and longer times to first fixation (TFF) for social justice news. Analysis of the two categories of subjects’ gender and whether or not they were stargazers revealed that, contrary to the stereotypes commonly perceived by the general public, fans and females would pay more attention to social justice information, exhibiting more TFD and FC. The present study’s use in this area of research. The public to abandon stereotypes and view fans and females in a fair and rational way. At the same time, the media should increase the exposure and weight of social justice news and direct viewers to pay more attention to social news.
Exploring Current Media Funding Model: Strategies and Recommendations for Sustainability
Authors: Deng Haoyu
Published: October 2024

Media plays a vital role in the globe as it is perceived as the primary source of information which will make people more participated in the society. However, to ensure sustainability and development, funding is indispensable for establishing, operating, and maintaining media businesses. Through a literature review and the case study of press subsidies in Germany, this paper aims to delve into the historical and current states of media funding models, exploring challenges and future directions of media funding models, proposing recommendations for improving media funding. The result suggests that the current funding model has ensured the operation of the media industry while funding with more autonomy and democracy is capable of establishing a better news environment as well as a society with higher democratic resilience.
The Intersection of Anorexia Nervosa and Personality Disorders: A Comprehensive Review of Comorbidities, Influencing Factors, and Therapeutic Approaches
Authors: Chen Siyan
Published: October 2024

Complex eating disorders like anorexia nervosa (AN) and personality disorders (PDs), including obsessive-compulsive personality disorder (OCPD) and borderline personality disorder (BPD), often co-occur. The confluence of PDs and AN poses distinct difficulties in terms of diagnosis, prognosis, and therapy, complicating clinical management and producing less favorable results. To summarize the most recent findings on the co-occurrence of anorexia nervosa and personality disorders, this review will look at their prevalence, psychopathological relationships, effects on treatment results, and efficacy of different therapy modalities, with a thorough search conducted, concentrating on research published between 2000 and 2024, utilizing peer-reviewed publications from PubMed, PsycINFO, and Proquest. Past literature presents a substantial overlap between PDs and AN, and concomitant PDs have been linked to recurrence rates, treatment resistance, and more severe types of AN. The psychopathological relationships among these illnesses are complex, encompassing both common and unique etiological paths. Depending on the PD implicated, various therapy modalities, such as dialectical behavior therapy (DBT) and cognitive behavioral therapy (CBT), have been investigated with varying degrees of success. This review emphasizes the need for more specialized, integrated treatment approaches targeting AN and concomitant PDs and appeals that future research should concentrate on creating tailored therapies and early detection techniques to enhance therapeutic results. The results emphasize the importance of considering personality pathology while managing AN to improve treatment efficacy and promote long-term recovery.