bluuebunny/crossref_metadata_2025 huggingface dataset:
Wild Life World Game Development, Design and Evaluation Optimization
Authors: Zhang Ruixi
Published: January 2024

VR is a kind of computer technology that combines virtual and reality gradually developed and mature in the 20th century. VR uses computers to create a three-dimensional multi-sensory and realistic virtual environment, which gives players immersive feelings during the game. Due to the disaster that many wild animals are facing the extinction of this species, Therefore, virtual reality technology is used to create a 3d stand-alone mobile game to warn and improve players' attention to the current situation of African wildlife. Therefore, this paper will introduce and analyze the concept, creation process and gameplay of the game wild life world developed in this study. In this study, we choose Unity as the game engine of wild life world, which can operate and operate the game more easily. In this study, Houdini is used to produce 3d modeling, and Houdini's rendering function can greatly improve the operation efficiency of the game. In order to enable players to have a more in-depth and immersive experience, this study also used dolby access to add panoramic sound effects to the game, and finally conducted game evaluation and optimization, so that players could summarize and evaluate the game experience.
Color-Based Object Tracking Using Forward and Inverse Kinematics
Authors: Li Haoyang , Li Yangping , Zhang Beichen
Published: January 2024

Nowadays, the current surge in diverse applications of robotic arms makes comprehending these concepts of paramount importance. Various studies have been conducted on the topic of robotic arms. However, little attention was paid to the coupling relationship between object tracking and kinematics. Therefore, this research paper delves into the intricacies of robotic arm dynamics, with a specific focus on the topics of Forward and Inverse Kinematics, alongside object tracking facilitated by color-based methodologies to deepen foundational insights into robotic arm functionality. Controlling the motion and movement of robotic arms is the initial step. Leveraging the capabilities of cutting-edge technologies such as OpenCV, Armpi, and Hiwonder, it becomes feasible to precisely rotate the servos to user-input degrees and accurately position the arm based on three-dimensional Cartesian coordinates. Additionally, cameras equipped in the system enable photographing and recognizing target objects. By analyzing various positions of the target object, the robotic arm can dynamically adjust multiple servos to maintain the object's relative position. This tracking mechanism relies on the Proportional-Integral-Derivative algorithm, incorporating a minimum valid area parameter to mitigate interferences. Harnessing these algorithms empowers the robotic arm to discern objects based on camera-detected colors, and is affected slightly by different angles or distances, meaning the system's error margin consistently resides within a favorable interval. The approach exhibits practical utility in daily life scenarios, such as drug classification and recognition.
Literature Review: Machine Learning in Stock Predictions
Authors: Li Neal
Published: January 2024

Machine learning has revolutionized the field of stock prediction by offering a wide range of models capable of handling complex patterns and making accurate forecasts. Machine learning models vary widely in their application, uses, and effectiveness, and stocks vary as well in terms of volatility within the stock and also between stocks of different industries and at different market conditions. As such, the selection of the proper algorithmic tool to aid an investor is often difficult. This literature review paper provides an overview of ten popular machine learning models over two problem types (prediction and classification), namely Linear Regression, XGBoost, LSTM, ARIMA, GARCH, Random Forest, Logistic Regression, Adaboost, GRU, and CNN. By providing an exploration of these ten machine learning models, this literature review offers valuable insights into their underlying principles, applications and uses, results strengths, and limitations. This paper equally, by consequent, facilitates informed decision-making and encourages further research in the field of machine learning.
Financial Management Risk and Legal Risks Analysis: Take Pfizer as an Example
Authors: Li Peishan
Published: January 2024

With the continuous growth of the global biotechnology and vaccine industry, more and more companies are joining in. Risk assessment in the field of biopharmaceuticals has drawn the attention of the whole society. There are lots of factors such as market competition, government intervention, cost increases, product safety, and breaking laws and rules, resulting in an increase in the company's financial risk. Facing these challenges, this research is based on overall analysis and searching for company data, by adopting the company's financial risk management calculation method to compare the selected companies, such as market risk-return rate, market risk-free return rate, weighted average capital cost, and so on. This research has found that Pfizer has a higher risk compared to other companies but the challenges and opportunities coexist by raising legal awareness, improving management measures, and other means to reduce risks. Its future development prospects are still considerable. The following is a multidimensional financial risk analysis of Pfizer Company.
Sustainable Community Transformation and Planning Based on Community Layout and Low Impact Development Concept
Authors: Chen Qingsong
Published: January 2024

The Sustainable Cities and Communities emphasized in the United Nations Sustainable Development Goals are cities and residential communities that prioritize inclusivity, safety, and resilience against disaster risks. China's urbanization process has embarked on a trajectory of rapid development, characterized by substantial advancements in urban construction and continuous enhancements to the urban landscape and environment. Confronted with an immense urban population scale, the sustainable transformation of traditional communities has emerged as the primary focus for establishing resilient communities. This paper compares the disparities between communities established by the traditional community model and sustainable community concept, scrutinizes the limitations of traditional community models, and proposes strategies for the sustainable transformation of traditional communities through adopting low impact development concepts in rainwater management. For the "block" layout, this paper proposes enhancing the connectivity of green spaces and promoting diversity. For the "central" layout, it is recommended to optimize access roads, including pedestrian and vehicle routes, by integrating green spaces. In the case of an "intensive" layout, emphasis should be placed on improving resource utilization efficiency through multi-functional approaches. For the planning of new sustainable communities, this paper puts forward suggestions from the micro and macro planning levels.
Using Scanner and Algorithm to Improve the Accuracy of Fused Deposition Modeling Printer
Authors: Chen Yuxuan , Li Yezheng , Qiao Yisen
Published: January 2024

This essay explores the application of laser sensors and algorithms to enhance the accuracy of Fused Deposition Modeling (FDM) Three dimensions (3D) printers. Laser sensors, combined with advanced algorithms, provide a promising solution for improving the accuracy of FDM printers. The essay discusses the limitations of traditional FDM printers in achieving high precision and outlines the significance of accuracy improvement in 3D printing. It then explores the principles and functioning of laser sensors. By analyzing real-time sensor measurements, algorithms can identify errors, compensate for mechanical inaccuracies, and adjust the printer's motion to ensure precise printing. Additionally, the essay discusses the advantages and challenges of incorporating laser sensors and algorithms into FDM printers. The benefits include improved print quality, enhanced compatibility with complex geometries, and reduced dependency on manual calibration. However, challenges such as cost, sensor integration, and algorithm optimization must be addressed for specific adoption or further development. The integration of laser sensors with advanced algorithms offers a promising pathway for achieving high precision and detail in printed objects, ultimately advancing the capabilities and applications of FDM technology.
Chinese and Us Economic Policies During the Covid-19 Period: Analysis of Fiscal and Monetary Policies in the Two Countries
Authors: Zhao Mingcheng
Published: January 2024

During the ongoing COVID-19 pandemic, which is widely regarded as one of the most significant crises in human history, both China and the United States, being prominent global economies, implemented fiscal and monetary policies that were tailored to their own national contexts. Due to the substantial scale of their governmental structures and economic systems, conducting a comparative analysis of policy efficacy between these two nations has evolved into a multifaceted subject within the field of economics. The research aims to conduct a comparative analysis of the behavior and effectiveness of macro-stimulative fiscal policies in China and the United States, specifically focusing on their linkage with the monetary policies issued by the respective central banks. The primary objective is to examine how these policies contribute to the enhancement of economic vitality in both nations. In addition to analyzing the effectiveness of these policies, this study will investigate the adverse effects of these policies on the economies of the two countries.
Impacts of Climate Change on Arctic Biodiversity
Authors: Lyu Yanru
Published: January 2024

Climate change's impacts have recently gained a lot of attention. According to a United Nations report, human activity is the main factorof climate change, global warming in particular, for the past two centuries. In recent years, it’s been widely acknowledged that global warming has profoundly impacted not only human welfare but also other species on Earth. Global warming is associated with increased species extinctions and decreased biodiversity. Despite increased attention on this trending topic, how these climate shifts are affecting marine biodiversity and ecosystems are still poorly understood. A large amount of data on marine species in the Arctic region has been obtained during recent years. In this article, we review four recent studies on the interplay among biodiversity, ecosystems and climate change, and discuss challenges in data interpretation and possible strategies that are required to tackle biodiversity loss. More comprehensive research, analysis and understanding of the results of numerous studies are essential to protect biodiversity and ecosystems.
Comprehensive Analysis of Wireless Charging Technology in Electric Vehicles
Authors: Xu Chang
Published: January 2024

In this new era, transportation vehicles mainly uses non renewable primary energy - fossil fuels. In order to avoid energy shortages, electric vehicles using clean energy have emerged. New electric vehicles require fixed charging stations for charging, as traditional technologies such as fixed charging stations cannot be charged on the move and have longer charging cycles, acquire high equipment and environmental requirements, and require manual intervention, resulting in higher costs. Therefore, people urgently need new ways and mechanisms to optimize the charging process, reduce human intervention, and make the charging process safer, automated, and intelligent. Wireless charging WPT technology has just made up for many shortcomings of traditional charging stations, and can achieve high-speed and mobile charging. This article provides an overview of the technologies and principles applied in wireless charging of electric vehicles, as well as a comparison of different wireless charging technologies and their possible future development directions.
Effects of Metformin on Human Metabolic System, Gene Expression and Mechanism of Action
Authors: Qiao Yuyao
Published: January 2024

Metformin, a pivotal medication in type 2 diabetes management, has garnered attention due to its multifaceted effect on the human body. This research explores the diverse effects of metformin, encompassing its role in the metabolic system, molecular mechanisms of action, and influence on gene expression. Metformin's primary mode of action involves enhancing insulin sensitivity, regulating hepatic glucose production, and modulating various metabolic pathways. Beyond glucose control, it has shown promise in addressing conditions like polycystic ovary syndrome, cancer treatment, and even COVID-19 outcomes. At the molecular level, metformin's activation of adenosine monophosphate-activated protein kinase (AMPK) orchestrates a cascade of gene expression changes, contributing to its therapeutic outcomes. However, the variability in findings across studies underscores the intricate nature of Metformin's mechanisms. This research highlights the remarkable journey of metformin, from its herbal origins to its status as a globally recognized therapy, underscoring the importance of continuous research to refine its applications and improve patient care. Understanding metformin’s multifaceted effects provides a comprehensive perspective on its broader implications for human health and well-being.
Research on pricing and replenishment decision of vegetable and fruit commodities-Based on nonlinear programming model and ARIMA time series forecasting
Authors: Fu Yifan , Bao Xiwen
Published: January 2024

Due to the timeliness of vegetable and fruit sales, supermarkets generally supplement each product on a daily basis based on the past sales and demand of each product according to the specific situation. In order to make replenishment and pricing decisions for vegetables and fruits in commercial supermarkets, this paper considers cost, sales volume, supply and demand relationship and loss factors under profit maximization based on cost plus pricing method. Sales volume, cost plus pricing functions are defined, and seasonal ARMIA model is used for forecasting. The sales profit function and constraint conditions are established, and the nonlinear programming model is constructed to solve the problem to obtain the profit maximization scheme.
Social Evaluation of Female Preferences in Birds’ Songs and Songs Function in Mating
Authors: Jin Hanwen
Published: January 2024

The selection of a mate is influenced by bird songs, which are known for their complexity and diversity. This essay delves into the realm of female mate preferences within various avian species, presenting both supportive and contradictory evidence regarding the importance of larger song repertoires. It emphasizes the distinction between controlled laboratory settings and ecologically authentic field studies, revealing nuanced findings. Furthermore, it explores additional factors like individual variation, song complexity, cognitive abilities, and contextual adaptability to elucidate this mixed outcome, enriching our comprehension of avian communication dynamics. Male bird songs emerge as essential conduits for conveying vital information that enables females to make informed choices about potential mates. Through song types and tones, they transmit demographic information, social status, and physical condition, offering females a means of remote evaluation. The examination of distinct song behaviors, including duets, flight songs, and the dawn chorus, highlights their significance as indicators of partnership, strength, and commitment. The varied strategies employed by male birds and the selective decisions made by females underscore the profound role of avian songs in shaping relationships and ensuring the perpetuation of species. This marks a substantial contribution to our understanding of avian mate selection dynamics.
The Comparative Analysis of Maotai Profitability
Authors: Bai Xuening
Published: January 2024

Maotai is a well-known liquor brand in China, and its profitability has been the focus of market attention. However, there are few in-depth studies and analyses on Maotai's profitability. This paper focuses on Maotai's profitability from financial indicators, operating performance, and market environment. Maotai, as a leading enterprise in China's liquor industry, has always been at a high level of profitability, and analyzing the profitability of Maotai's liquor industry can play a demonstration role and drive the development of China's liquor industry. At the same time, it provides solutions to the existing problems of the Maotai liquor industry and provides specific reference for the future development of the Maotai liquor industry. As well as the development of the domestic economy and the expanding influence of the Maotai brand, the profitability of Maotai will be further improved.
Research on the Opportunities for Chinese Domestic Cosmetics to Advance into High-end Products
Authors: Yue Muyang
Published: January 2024

In recent years, with the rapid growth of China's economy, domestic cosmetics brands have emerged; With the improvement of domestic people's living standards and the improvement of market purchasing power, China has become the third largest luxury consumer in the world, after the United States and Japan. As the entry-level industry of luxury goods, high-end cosmetics still occupy the main market by European and American international brands. This paper will take China's domestic cosmetics brands as the research object. This paper will mainly use the methods of qualitative analysis, general analysis, and case analysis, and analyze the development status of the domestic cosmetics market from the overall market and individual manufacturers through data analysis and chart analysis. SWOT analysis was used to analyze the market environment of domestic cosmetics brands of the Revised Group from macro and micro market environment and summarized the advantages and disadvantages, opportunities and hidden dangers at the present stage. It focuses on analyzing how to position domestic cosmetics brands and discusses how to carry out brand marketing around the target market. Finally, it summarizes the main factors that promote the success of brand positioning. Brand development planning is the soul of a product, especially high-end cosmetics, which determines whether a brand can gain a competitive advantage in a complex environment to strive for a larger market share. In the face of fierce competition and continuous development of the cosmetics market, domestic cosmetics only take the brand road, shape the brand image, strengthen the brand characteristics, and establish a strong competitive advantage, enterprises can continue to develop.
Comparative Analysis of ESG Indicators in Enhancing Debt Default Prediction with Transformer-derived Attention Models
Authors: Zheng Zilin
Published: January 2024

This study explores the application of Transformer-derived algorithms, namely TabNet and Tab Transformer, in predicting corporate debt defaults and compares their performance with classical machine learning models. Concurrently, it assesses the efficacy of incorporating ESG indicators into the corporate debt default risk assessment framework. The research focuses on Chinese A-share listed companies from January 2018 to June 2023, comprising 23 companies with observed defaults and a control group of 846 companies with regular debt maturities. The findings indicate that although attention-based models like TabNet and Tab Transformer provide enhanced interpretability, their performance does not significantly surpass ensemble algorithms such as XGBoost. Attention-based models emphasize the importance of merging the advantages of deep learning with the interpretability of traditional algorithms, especially when dealing with vast, high-dimensional datasets. Additionally, the incorporation of ESG data did not yield a significant improvement in prediction outcomes. Potential reasons for the limited impact of ESG indicators on predictions, including data quality and the comprehensiveness of existing financial disclosures, are discussed. Given the limited sample size and constraints related to test data, future research directions suggest expanding the dataset and diversifying ESG data sources.
Maxwell’s Equation and Its Applications in Electromagnetism
Authors: Ye Jianyu
Published: January 2024

As people always admitted that James Clerk Maxwell is one of the greatest scientists in the world, but major people may do not know the source of this glory. This paper is dedicated to give a rough impression of Maxwell and his experiences. Especially, the author then focuses on the well-known Maxwell’ equations and attempt to illustrate them in simplest tune so that those people with weak knowledge bases but interested in Maxwell can recognize his imperishable contribution easily. This article reviews the history of electromagnetism and then focus on Maxwell himself, especially Maxwell’s equations. The author will introduce those equations separately so that readers can get a rough profile of Maxwell’s equations and applying them in different fields like antenna design, spectrum and wireless communication. These applications not only improve people’s living standard to a large extent, but also raise the productivity of the society. The work should be a beneficial for people in the field of electromagnetism.
Optimization Of Automatic Pricing and Replenishment Decision Based on Time Series and BP Neural Network Model
Authors: Sun Chang , Li Xiaohan , Lai Zhiheng
Published: January 2024

In major fresh food supermarkets, vegetable products often have a short shelf life and poor product quality, which affects their sales. Therefore, it is particularly important to make replenishment decisions for each vegetable category on the same day without knowing the specific individual product and purchase price. This article focuses on the problem of automatic pricing and replenishment decision-making for vegetable products. Based on the fact that vegetable products have a relatively short shelf life, the automatic pricing and replenishment strategies for vegetable products are optimized, indirectly proving the correlation between certain individual products. A mathematical model is constructed using time series and BP neural network models to obtain corresponding replenishment strategies, Finally, provide recommendations on automatic pricing and replenishment decisions for vegetable products. The "cost plus pricing" method involved in this question plays a crucial role in the pricing of vegetables. At the same time, from the perspectives of the demand side and supply side, the limitations of sales time and sales space are also particularly important.
Research And Analysis of IPO underpricing In the Market with Chinese Characteristics
Authors: Liu Qingqiao , Su Hang , Yang Manni
Published: January 2024

This paper mainly introduces and popularizes science about IPO underpricing and discusses the characteristics and future development direction of China's securities market. This paper introduces the opinions of domestic and foreign scholars on the IPO underpricing phenomenon from different angles. Finally, through the analysis and summary of resources integration, this paper discusses how to deal with the new strategy of high underpricing rate, and further expounds the future direction of national efforts in the stock exchange market. This paper finds that there is a significant IPO price suppression phenomenon in China's A-share market, which is the IPO issue price of the stock is significantly lower than its listing price. This paper points out the negative impact of IPO price suppression on the effective operation of the securities market, and deeply analyzes the reasons for the existence of IPO price suppression in China.
The Relative Mechanism of Tetrodotoxin: From Poison to Drug
Authors: Li Yule , Ou Xuanzhi
Published: January 2024

Tetrodotoxin (TTX), an exceptionally potent neurotoxin initially found in pufferfish ovaries in 1909, is renowned for its extreme toxicity to humans, surpassing cyanide. This research delves into TTX, from its action on sodium channels to its potential as a therapeutic agent. TTX primarily blocks voltage-gated sodium channels, halting nerve cell electrical signals, but its binding varies among channel subtypes. TTX's role in pain, especially cancer-related pain, is intriguing, but its medical use is in the early stages and needs more research. TTX poisoning treatment is limited to emesis, activated charcoal, sodium bicarbonate, fluid replacement, and respiratory support. This research will conclude by highlighting TTX's potential in future research, such as antitoxin development and medical applications. TTX, once feared, may become a valuable medical tool, offering hope and healing. A deep understanding of the mechanism of action of TTX is beneficial for the development of corresponding applications in different medical fields.
A study of multibeam line measurement based on analytic geometry and genetic algorithm
Authors: Zhou Qianyun
Published: January 2024

This paper focuses on the multibeam bathymetry technology for seabed terrain detection, and utilizes the geometric model, genetic algorithm to study the multibeam bathymetry line according to the actual situation of multibeam bathymetry coverage width, measurement length and overlap rate. Firstly, this paper uses the geometric analysis of the coverage width in relation to the slope angle  and seawater depth , establishes the multibeam bathymetry coverage width and overlap rate model, and obtains the results of the coverage width and overlap rate in the specified cases by using python. Secondly, on this basis, a new condition of line direction angle  is introduced to establish a multibeam bathymetry coverage width model based on line direction, and the coverage widths under different line directions are obtained by using pyhon, and the analytical results show that the coverage widths and seawater depths are positively correlated with each other under the main oblique measurement direction. Finally, considering the influence of the minimum coverage area, the range of overlap rate and other constraints, the objective function of the shortest survey line length is established under the oblique measurement direction, and the shortest survey line program is obtained based on the genetic algorithm solution. The model constructed in this paper can provide a new idea in the field of nautical miles surveying and mapping.
Spatial and Temporal Characteristics Analysis and Demand Forecasting based on ARIMA Model - An Example of Yellow Taxi in New York
Authors: Wang Siyuan
Published: January 2024

With the development of technology and urbanization, cars have greatly facilitated people's travel, but they also bring problems such as road congestion, pollution and accidents, which restrict the healthy development of cities. How to predict traffic flow efficiently and accurately has become a hot topic. Taxi data is highly recommended in urban transportation research due to the characteristics of broad spatial coverage, long driving time and relatively complete data. In this paper, the data of yellow taxis in New York during March 2023 are used to present the demand changes by means of line chart thermal map, and divide the database of March into two parts, one is training set and the other one is test set, then simulation and analysis using ARIMA model. It reveals the changing relationship between taxi demand and time. By predicting the traffic flow, this study can alleviate the congestion of urban main roads and improve the quality of citizens’ life. At the same time, forecasting commuting demand can balance supply and demand, provide more efficient services for operating companies, taxi drivers and passengers, and provide decision-making basis for government planning.
NVIDIA Stock Price Prediction by Machine Learning
Authors: Tan Jiaqi
Published: January 2024

This study explores how to forecast NVIDIA stock values using machine learning models. The chosen prediction models are Long Short-Term Memory (LSTM), Random Forest, and Linear Regression. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score are only a few of the metrics used to assess the effectiveness of a model. The empirical findings showcase the remarkable prowess of the LSTM model in prognosticating NVIDIA stock prices, exhibiting a heightened level of accuracy and predictive acumen in contrast to Random Forest and Linear Regression counterparts. This investigation not only contributes a potent methodology to the realm of stock market prognostication but also holds promising prospects for prospective real-world implementations. Future implications include enhancing decision-making for investors and promoting financial stability through accurate market trend assessment.
The Analysis of Luckin Coffee's Accounting Scandal
Authors: He Jinpeng
Published: January 2024

In recent years, a company called Luckin Coffee in China got caught up in a big money scandal that shook both Chinese business circles and global financial markets. This study dives into what happened with Luckin Coffee, trying to figure out why it happened, how it impacted other companies, people who put their money in, and the whole market, and what can be done to stop such problems down the road. By going through different articles and reports, this study will thoroughly look into the backdrop and particulars of Luckin Coffee's financial mishap, and its repercussions both in China and on a global scale. The objective is to dig out the true causes of such financial wrongdoings and propose solutions for better oversight of company finances to prevent a repeat of such scenarios. Through an in-depth analysis of the financial fraud incident of Luckin Coffee, this research not only has important theoretical and practical significance for understanding and preventing financial fraud, but also provides valuable experience and lessons for financial regulatory agencies and enterprises, and promotes financial management and supervision.
Current Perspective on Sleep and Emotion Regulation
Authors: Yang Yiwei
Published: January 2024

This article explores the correlation between emotions and sleep. Sleep plays an essential role in regulating our emotions and cognitive processes. Extensive research demonstrates the intricate connection between sleep and emotional well-being. During sleep, the brain engages in vital activities that influence emotional memory consolidation, regulation, and cognitive functioning. Insufficient sleep or sleep deprivation has detrimental impacts on various brain functions related to emotions. These include impairments in working memory and attention, disruptions in dopamine function, and altered processing of aversive stimuli. Sleep deprivation can also influence different components of the emotion regulation process model. Furthermore, sleep abnormalities have been linked to several mental disorders, including post-traumatic stress disorder (PTSD) and major depression. Understanding the intricate relationship between sleep and emotions can provide valuable insights into developing effective interventions for individuals with sleep disorders or mental health conditions. Promoting healthy sleep habits and addressing sleep disturbances can improve emotional regulation and overall well-being. Additionally, exploring the reciprocal relationship between sleep and emotional processes can contribute to advancements in therapeutic interventions for mental health disorders, ultimately improving the quality of life for affected individuals.
Finite Element Analysis of Joint in Prefabricated Carbon Fiber Composite Truss
Authors: Gao Yuxuan
Published: January 2024

Carbon fiber composite truss, as an innovative structural form, has been widely used in the construction industry. However, the joint is serving as a crucial component of the truss structure construction. In order to improve the design level of the truss, this paper mainly studies the prefabricated carbon fiber composite truss joints. Firstly, the finite element analysis method is used to simulate the truss joint, and the stress nephogram under different geometric conditions is obtained. Then, the stress deformation mode and stress distribution mode are analyzed in depth. The calculation results are compared with similar steel pipe truss joints, and the accuracy of stress distribution is verified. Finally, the relationship between stress concentration area, stress concentration degree, maximum stress size and geometrical parameters of joint is revealed. Results in this paper can guide the design of this kind of truss structures, which can help develop more efficient and durable construction projects
Respiratory Diseases Caused by Air Pollution and Interventions
Authors: Wu Chunyan , Xie Ying , Zhu Wenxin
Published: January 2024

Air pollution become a concerning environmental problem nowadays, and the morbidity of respiratory diseases caused by air pollution increased rapidly in recent years. Interventions for air pollution and respiratory diseases related to air pollution is essential. The essay mainly discussed the background of air pollution and respiratory diseases, the harm to public health, interventions for air pollution and factors related to respiratory diseases. Through the comparison between interventions in China and western countries, it can be seen that most interventions are the same, for example setting standards for emissions, warning people risk factors of respiratory diseases and taking protective measures. The difference between the interventions is that Chinese residents turn to have less knowledge about the diseases and risk factors and they rely on authorities more, while western people’s individual efforts and personal willing play important roles in policies and actions, both the government and individuals are important in the control of air pollution and the protection of public health towards air pollution.
The Prediction of Carbon Dioxide Emissions from Past Data Based on Algorithms
Authors: Jin Linkai , Wu You , Zhang Enzo
Published: January 2024

Carbon dioxide is one of the most notorious greenhouse gases, which has been largely emitted especially in recent years. Therefore, it is crucial to predict the emission trends in order to propose corresponding measures to reduce it. Thanks to the invention and development of machine learning schemes, it is feasible to predict the trends accurately. With this in mind, this study explores the crucial task of predicting carbon dioxide (CO2) emissions based on past data. Leading authorities and organizations have relied on predictive models to anticipate future emission levels. As the implementation of models and algorithms becomes increasingly common, this project seeks to explore different approaches to this topic and summarizes their common discoveries, as well as discussions on possible variations. According to the analysis, the pros and cons of the different models are clarified. Overall, these results shed light on guiding further exploration of carbon dioxide (CO2) emissions predictions.
The Study and Challenge of Producing Sugar Substitutes via Microorganism Pathway
Authors: Liang Boren
Published: January 2024

Nowadays, the ratio of obesity increases continually because of excessive sugar intake with the improvement of living standards, especially for children and teenagers. The obesity, caused by excessive sugar intake, is a main risk to induce cardiovascular disease both in adults and children because of abnormal glucose tolerance, such as hypertension, atherosclerosis and coronary heart disease. Consequently, it’s important to find an effective method to limiting sugar intake, which are welcomed by sweet person, reducing onset of higher sugar intake associated diseases, contains optimistic business opportunities. Therefore, the research and development of sugar alternative products grew rapidly and soon occupied an important position in the market. In comparison with artificial sweeteners, erythritol, as a natural sugar substitutes (NSS), is widely used in good industry to add sweet tastes. But producing erythritol faces problems. One of the main problems is using erythritol is far more expensive by using traditional sugar, like sucrose and fructose. In order to solve this problem, scientists suggest to replace glucose by glycerol. At the same time, change the environment of bacterium or fungi greatly influence the efficiency. In this review, the author aims to summarize the production of erythritol, compare its advantages and limitations, and provide some references for its development as an important sugar substitute in future.
Comparative Analysis of NPV and IIRR in Financial Market Applications
Authors: Lin Zihan
Published: January 2024

This paper discusses the comparison between two popular investment rules: Net Present Value (NPV) and Internal Rate of Return (IRR). This study aims to determine which investment rule is more appropriate in financial market.  This study is more suitable for people who don’t know much about NPV and IRR but need to use them to evaluate something. This study analyses the advantages and limitations of both NPV and IRR, using factors such as cash flow, discount rate and project scale. In addition, this study also lists examples and practical applications of these investment rules to illustrate which investment rules are more suitable for which situations. By studying the advantages and disadvantages of NPV and IRR, this study aims to help people choose the most appropriate investment rule to make informed investment decisions in financial markets. This study has important practical significance for participants in financial markets to make project decisions.
Navigating The Integrated Circuit Industry: Definitions, Evolution, Innovations, And Challenges
Authors: Hu Han
Published: January 2024

Now, the integrated circuit (IC) industry is experiencing rapid growth, and some of its associated technologies play vital roles in our everyday lives. This paper aims to provide comprehensive insights into the entire IC industry by elaborating on various aspects. First and foremost, it elucidates the definition, development history, and current status of integrated circuits (ICs). Subsequently, it delves into intricate details regarding three state-of-the-art technologies linked to ICs, all of which are conveyed with their full names for clarity: Integrated Circuits in Silicon Germanium (SiGe) Bipolar Complementary Metal-Oxide-Semiconductor (BiCMOS) for Millimeter-Wave and Terahertz Bioanalyzers, 3D Packaging for Heterogeneous Integration, and, once again, 3D Packaging for Heterogeneous Integration. To conclude, this paper provides an in-depth exploration of the present challenges faced by the integrated circuit industry and the prospective hurdles that could impact the industry's future trajectory. By reading this paper, you can swiftly gain a comprehensive understanding of integrated circuits, covering aspects such as their definition, historical evolution, current status, cutting-edge technologies, and the prevailing challenges that demand attention.
Character Motion Synthesis Based on Deep Learning: A Survey
Authors: Chen Anjian
Published: January 2024

Character motion synthesis can be more cost-effective, flexible, and time-efficient compared to motion capture or traditional animation. As character motion synthesis regards multiple major industries, along with the development in deep learning techniques, character motion synthesis based on deep learning conspicuously receives substantial attention, resulting in numerous related studies that need to be analyzed and summarized. This paper presents an overview of character motion synthesis based on deep learning. Firstly, it epitomizes methods incorporating different types of neural networks, then encapsulates methods that did not utilize neural networks but simply deep learning, such as deep reinforcement learning, and lastly summarizes and evaluates the advantages and limitations of different deep learning methods on character motion synthesis.
A study on the correlation of cyclical fluctuations in vegetable commodity sales based on wavelet analysis modeling
Authors: Liang Yiling , Tang Chuhan
Published: January 2024

As the sales volume and time of vegetable commodities tend to have a certain correlation, in order to explore the distribution law of the sales volume of each category and single product of vegetables, this paper firstly handles the data to get the monthly sales volume of each vegetable category and single product of time series data, then uses Matlab's Wavelet Toolbox to carry out wavelet analysis,drawing wavelet variance diagrams, the main cycle trend graph to judge the cyclical pattern of each vegetable category and single product,and then use Origin to make wavelet coefficients real part contour graph to verify the law, and finally get the distribution law. This study can help superstores better grasp the sales situation and development trend of each vegetable category and single product.
Research On Supermarket Replenishment and Pricing Strategies Based On K-Means Clustering and ARIMA Time Series Model
Authors: Liu Qi , Cui Wenbin , Zhang Xinbo
Published: January 2024

At present, the shelf life of fresh supermarket vegetables is short, and the quality gradually declines with time. In order to maximize the benefits of supermarkets, this paper established a K-means clustering model, conducted cluster analysis of various categories, explored the correlation between different categories and different items of vegetables, and used ARIMA time series model to predict the pricing and replenishment volume of various commodities in supermarkets, which had important practical significance for supermarket decision-making.
Research on Risk Measurement in Chinese Stock Market - Based on GARCH-VaR Modeling
Authors: Zhang Zuming
Published: January 2024

Chinese stock investors, whether he is an individual investor or working for a investment company, are always worried about the risk of their asset, it is difficult to measure the risk without a functional model, thus, using a model to solve it is of the pivot. The initial aim of this paper is to measure and predict the risk of Chinese stock market using GARCH-VAR model. By analyzing historical stock index return data, this paper develops a dynamic model to capture the volatility and risk correlation of the stock market. The comprehensiveness and accuracy of the results are ensured by using recent data, including stock indices that broadly cover different industries and market capitalization. The findings suggest that significant volatility and risk correlations exist in the Chinese stock market. This paper provides an effective methodology to measure and predict the level of risk in the stock market, which provides investors and risk management organizations with valuable references and decision-making bases and is important for understanding and managing the risk in the Chinese stock market, which helps stock investors to make informed optimal decisions on risk management and asset allocation.
Does the Average Age of Executives Affect the ESG Performance of Enterprises?
Authors: Chen Yunzhi
Published: January 2024

In today's global business environment, the importance of ESG has been widely recognized globally. This article uses data from 4578 A-share listed companies from 2015 to 2021 to study the impact of the average age of executives on ESG performance. The results show that the average age of executives is significantly positively correlated with ESG performance. For the three specific aspects of ESG, as the average age of executives increases, the performance of environmental (E) and social (S) also shows a better trend, but the performance of corporate governance level (G) is worse. This study suggests that companies should attach importance to the diversity of their executive teams, establish continuous training mechanisms, and develop clear ESG strategies and goals. This study provides more accurate guidance for enterprise decision-making, ESG management, and executive selection, and helps companies select and cultivate executives more accurately to ensure that the executive team is aligned with the company's ESG goals.
The History of Fed Interest Rate Hikes and the Impact on Stock and Foreign Exchange Markets
Authors: Zhang Wenting
Published: January 2024

Following COVID-19, America uses policies to boost its economy, inadvertently pushing inflation from 2018 to 2022. This paper discusses the history of the Federal Reserve rate increase and its repercussions on financial areas, especially on the stock and foreign exchange markets. The main analysis centers on the impacts of the markets and behaviors of the answers. Initially, the Federal Reserve rate increase seemed economically beneficial. However, long-term effects may negatively impact both domestic and international economies to some degree. By examining historical data, this paper dissects the rationale behind such moves and their direct outcomes. This offers a better perspective on potential future interest rate influences. For investors, to be positive and need to adjust their investment strategies to adapt when they face the Federal Reserve rate increase. For policymakers, during the fluctuations in interest rates, they need to maintain the global equilibrium as far as possible, without excessive disruption or impact.
Current Status and Countermeasures of Health Surveillance for the Elderly --Based on the Status of Aging in China
Authors: Wang Yuqi
Published: January 2024

By 2024, more than 20% of China's population will be beyond the age of 60 due to rising life expectancy and the implementation of the one-child policy. Leading China to face substantial challenges in providing health surveillance for its rapidly aging population. The national health literacy level of China is 27.78% in 2022, the elderly population deserve a lower level due to low education level. Resources between rural and urban has a huge gap. The majority of primary care facilities are unable to handle the chronic illnesse`1s that affect senior individuals. Lifestyle has changed along the financial arise, chronic illness categories are changing. Addressing risk factors for contemporary diseases like diabetes requires a shift in the healthcare system. In addition, declining family sizes are restricting the availability of traditional familial care. Community services have grown, but their quality and coverage are still inconsistent, particularly in rural areas. Coordinated efforts are required to increase health literacy. Fairly distribute medical resources and experts, direct illness preventive initiatives, and create comprehensive community-based care models in order to improve health surveillance for China's elderly population. Resource imbalance can be addressed with targeted investments in geriatric training programs, primary care, and digital health technologies.
Innovative Therapies: Diverse Applications of Dance into Physiotherapy
Authors: Zhou Xuelian
Published: January 2024

While traditional physiotherapy focuses on functional restoration, this paper delves into the multiple applications of dance in physiotherapy, including its role in motor rehabilitation, neurological rehabilitation, pain management, cardiorespiratory rehabilitation, and emotional and mental health. This line of thinking emphasises not only the biomechanical structure of the body, but also the psychological, emotional and social dimensions. In addition, we present innovative physiotherapy approaches that combine dance and rehabilitation to provide patients with more comprehensive treatment options. Future research and practice will require a deeper understanding of the mechanisms of dance and physiotherapy and more extensive randomised controlled clinical trials to validate their effectiveness. It is also important to understand patients' perceptions of dance therapy to better meet their needs. Taken together, this paper highlights the potential of dance in the field of rehabilitation to provide promising therapeutic avenues for improving patients' quality of life.