bluuebunny/crossref_metadata_2025 huggingface dataset:
LETRAMENTO EM SAÚDE POR MÍDIA SOCIAL DURANTE A PANDEMIA
Authors: Cesar Flaviane Cristina Rocha , Mendes Mariana Carla , Costa Christina Souto Cavalcante , Souza Thassara Felipe de , Alves Angela Gilda , Barbosa Maria Alves , Moraes Katarinne Lima , Oliveira Lizete Malagoni de Almeida Cavalcante
Published: May 2023

Este relato de experiência, tem por objetivo descrever a elaboração e veiculação de ações de extensão para prevenção de COVID-19 em mídia social, elaboradas de acordo com os pressupostos do letramento em saúde. A extensão universitária por meio de mídia social permitiu desenvolver letramento científico com acesso e compreensão de informações relevantes para a prática baseada em evidências entre estudantes de medicina. O debate online permitiu construir processo de avaliação formativa no qual os estudantes e docentes vivenciaram a interação e aplicação de conceitos teóricos em ambiente virtual de ensino em tempo real. Por outro lado, os materiais educativos construídos e palestras online possibilitaram oportunidade de prática de letramento em saúde para os estudantes de medicina, bem como a construção de repositório de materiais educativos em mídia social para consulta e divulgação na comunidade. O uso de tecnologia de informação e comunicação para o ensino em saúde é uma alternativa de baixo custo e com ampla capacidade de divulgação na comunidade, sendo uma ferramenta potencial para atividades de extensão em situações de pandemia ou contextos que envolvam impossibilidade de acesso presencial ao público-alvo. Os principais desafios no uso dessas tecnologias são a necessidade de capacitação de professores e estudantes para uso de softwares e adequação de materiais acadêmicos a linguagem adequada ao nível de compreensão do público-alvo.
Agência Experimental Toca: comunicação, experimentação e vivências na formação discente da UFMT
Authors: Ferreira Dôuglas Aparecido , Machado Mylena Leite
Published: May 2023

Este artigo tem o objetivo de relatar as experiências práticas e educacionais ocorridas na TOCA - Agência Experimental de Comunicação, um projeto de extensão da Universidade Federal de Mato Grosso. Para isso, se discute a importância da agência experimental como laboratório de aprendizado, sua contribuição para a formação profissional e cidadã dos participantes e seus impactos na transformação da comunidade interna e externa da universidade. Também, apresenta-se três casos de produções elaboradas pelos discentes, destacando os processos envolvidos e as exigências técnicas e teóricas desenvolvidas em cada um deles. Conclui-se que na TOCA os alunos têm a oportunidade de aprofundar os conceitos discutidos em sala de aula, ao mesmo tempo em que refletem sobre as questões emergentes da sociedade.
Fatores internos e seus impactos no desempenho dos países nas Cadeias Globais de Valor
Authors: Ferreira Carolina Rodrigues Corrêa , Moraes Victor Ramon Oliveira
Published: May 2023

Em virtude do notável e rápido crescimento dos fluxos de comércio internacional de bens intermediários, advindo da fragmentação produtiva, faz-se necessário que se compreenda os impactos e as potencialidades da inserção dos países nas Cadeias Globais de valor, bem como as características domésticas que permitem uma maior agregação de valor nesse cenário. Assim sendo, o presente trabalho tem como objetivo avaliar quais variáveis são determinantes para uma maior captação de valor adicionado doméstico nas exportações de bens manufaturados. Para tanto, estimou-se uma regressão não linear com dados em painel através do Poisson Pseudo Maximum Likehood (PPML). Os resultados apontam que países que buscam uma maior agregação de valor em suas exportações devem apresentar maior liberdade comercial, mobilidade de capitais, manter um ambiente favorável para a captação de investimentos estrangeiros e investir em infraestrutura logística.
The Challenge of Diversity in Nursing Leadership: The Need to Avoid Misinformation and False Facts
Authors:   Department of Sociology and Anthropology, University of Guelph, Guelph, Canada, McDonald Lynn
Published: May 2023

This article probes two recent (2022-2023) instances of false narratives in nursing, both with the laudable goal of promoting diversity in nursing leadership, but which presentations are flawed by false facts. The first example is the installation of portraits of Florence Nightingale, the founder of nursing, and Mary Seacole, the Crimean War businesswoman and volunteer, at Toronto’s University Health Network and Princess Margaret Cancer Centre. The second is a 2023 article by a nursing academic, Jennifer Woo, urging decolonization of “the history of nursing by magnifying the contributions of nurses of colour”. It includes mention of a number of leading nurses of colour, however with an enormous number of false claims for Mary Seacole. Brief mention is also made of Rappaport’s 2022 revised book on Seacole calling her “a Black Cultural Icon and Humanitarian”. This article goes on to present two leading diversity nurses well worthy of celebration: The Nigerian Kofoworola Abeni Pratt, the first Black nurse in Britain’s National Health Service, who led in Nigerian nurses assuming leadership roles from white, British, expatriate nurses. The other is the redoubtable Mary Elizabeth Carnegie (1916-2008), the African-American nurse who led in the racial integration of nursing in the United States. Consideration is given as well to how such major mistakes are made in the nursing literature, with a recommendation for much more critical reading of sources.
A Literature Review on the Ways of Minor Shareholders' Participation in Corporate Governance
Authors: Zhang Yadu , Zhang Weiran , Zhang Zimu
Published: May 2023

This paper summarizes the research on the ways of minor shareholders' participation in corporate governance. The ways of minor shareholders' participation in corporate governance include voting with hands, voting with feet and voting with mouths. In this paper, the case analysis of Gree Electric Appliances' general meeting of directors election shows that the activism of minor shareholders can play a better role in governance. However, controlling shareholders may take passive means such as related transactions to seize the interests of minor shareholders. Finally, this paper puts forward some suggestions to strengthen the protection of minor shareholders' rights and comments on the policy enlightenment.
Research on the Contribution of Regional Import and Export Influential Factors Based on RBF Neural Network
Authors: Xu Yifan , Chen Mohan , Zhao Jinyanxi
Published: May 2023

Since the reform and opening up, the proportion of imports and exports in all provinces in China has increased to a great extent, and the total import and export volume is an important condition for reflecting the economic level of a region, and it is also one of the important influencing factors affecting economic growth. In the context of economic development, by analyzing what are the factors affecting import and export, the contribution of import and export to economic growth and the reasons for promoting economic development can be analyzed. Based on this, this paper selects Zhejiang Province as the research object, uses the data from 1990 to 2020, and uses the RBF neural network algorithm to analyze this series of data. In this paper, multiple secondary indicators are selected from the five perspectives of population, industrial structure, economic development level, currency, and science and technology, and RBF training is carried out, and it is found that when the model parameter is 19, the model accuracy converges and the algorithm reaches the optimal value. According to the algorithm results, we conclude that the methods to increase the total import and export volume of the region include implementing an expansionary fiscal policy and increasing the total budget expenditure; Increase research and development funding, such as university research funding and talent subsidies; Increase industrial output value, such as providing assistance and subsidies to industrial enterprises for export orientation.
Analysis of the synergistic benefits of regional green finance and the regional economy based on the CRITIC model
Authors: Yin Mingxiang , Yang Xiaokang , Xiao Bo
Published: May 2023

Green finance, which is a form of fusion of the financial and green industries, has a big influence on the economy, and it is vital to investigate both its impact mechanism and its synergistic mechanism. To analyse the synergistic benefits of both, this paper first collects evaluation index data related to green finance and the regional economy in the Xinjiang region of China from 2011 to 2020. Then, it calculates the index weights using the Criteria Importance Through Intercriteria Correlation (CRITIC) method. The findings indicate that although there is a positive trend between the two, the synergy between the regional green finance and economy of Xinjiang is generally at a low stage, and the development of the orderliness of the regional green finance and economy subsystems is unstable. However, the regional economy is less volatile than the regional green finance and has a good development trend.
Coupled coordination of scientific and technological innovation capability in the Pearl River Delta under the background of green Economy
Authors: Chen Wenhui , Cai Qianyao , Kuang Yiwei
Published: May 2023

Science and technology innovation has been recognized as the driving force behind productivity, and understanding the coordination mechanism between science and technology innovation inputs, outputs, and the environment is of great practical significance in promoting the development of the green economy. This study aims to develop a green economy index measurement system and a science and technology innovation level measurement system to measure the green economy, science and technology innovation input, output, and environment. Additionally, this study examines the coordination between inputs, outputs, and the environment for scientific and technological innovation, conducts a regional convergence analysis, and investigates the impact mechanisms of science and technology innovation subsystems on the green economy using a panel regression model.The combination weight method and comprehensive index method are employed to measure the green economy, science and technology innovation input, output, and environment. The coupling coordination model is then used to examine the coordination between inputs, outputs, and the environment for scientific and technological innovation. Regional convergence analysis is conducted on the coupling coordination of science technology and innovation, and a panel regression model is used to investigate the impact mechanisms of science and technology innovation subsystems on the green economy, study found that the green economy and science technology innovation level have improved during the period of 2010-2020, and the variability of the green economy index between regions has been decreasing year by year. The coupling and coordination between the inputs and outputs of science and technology innovation and the environment have also achieved a significant leap forward, and the variability among regions has shown a decreasing trend. Science and technology innovation input, output, and environment all contribute significantly to the growth of the green economy.The findings of this study suggest that the coupling and coordination between science and technology innovation inputs, outputs, and the environment is crucial in promoting the development of the green economy. The results of this study also provide relevant recommendations for the implementation of the strategy of scientific and technological innovation.
Impact analysis of intra-firm factors on share prices of Chinese construction SMEs based on the XGBoost algorithm
Authors: Liu Xinyi , Xiong Yeqin
Published: May 2023

At this stage, small and medium-sized construction enterprises (construction SMEs) are in a difficult position due to fierce competition and severe homogenization. However, current studies on China's construction industry are mainly from the macro perspective of regions and policies. There is a lack of studies that can guide business managers to improve enterprises' performance. This paper constructs a 5-dimensional and 18-indicator system of factors influencing the share price of construction SMEs from an intra-firm micro perspective. Based on this, several XGBoost models were trained, and the highest accuracy was selected as the criterion for feature importance ranking using  and RMSE to output the importance ranking of internal factors influencing the share price of Chinese construction SMEs. The results show that the most critical intra-firm factors for the share price of Chinese construction SMEs are the growth capacity of capital, especially the growth ability of shareholders' equity, and the long-term and short-term solvency. This study provides an actionable starting point for Chinese construction SME managers. It uses the XGBoost algorithm, which has higher predictive accuracy than the linear regression commonly used in existing studies, to obtain more reliable factor importance rankings.
LM jump detection-high frequency grid trading model based on LSTM prediction
Authors: Sun Haowei , Li Wei , Ou Chenghuan
Published: May 2023

In the computer era, high frequency trading is one of the hottest topics in the financial investment market. In order to develop a high-frequency trading strategy model with specific validity for different types of stocks, this paper proposes a LM jump detection-grid trading model based on LSTM prediction to construct a trading strategy for three types of stocks: blue chips, volatile stocks and stable stocks. The proposed model is a combination of a black-box model and a white-box model, which not only has the powerful learning capability of LSTM Model and but also the excellent scalability of LM Jump Detection Model and Grid Trading Model. These allow the model to have greater advantages in terms of trading strategies and the convenience of optimizing anomaly detection.
The Impact of Managerial Power on Financing Constraints
Authors: Pei Xinran , Lin Zhile , Ye Ye
Published: May 2023

With the increasing scale of enterprises, the demand for funds is also increasing, which makes enterprises must meet their own operation and management needs through financing. Based on the data of China's A-share listed companies from 2013 to 2020, this paper examines the impact of management power on corporate financing constraints. It is found that management power has a significant negative effect on financing constraints. The greater the management power, the smaller the financing constraints. This result is still valid after various stability tests. The marketization process has a negative moderating effect on the relationship between managerial power and financing constraints. The heterogeneity test finds that the management power of enterprises that do not choose the Big Four as audit institutions has a more obvious effect on financing constraints than those that choose the Big Four as audit institutions. The management power of enterprises with high equity balance has a more significant impact on financing constraints than those with low equity balance. The results of this study can provide suggestions for reducing corporate financing constraints.
A study on global national green GDP accounting based on the System of Integrated Environmental-Economic Accounting
Authors: Huang Jiayu , Yu Shaobo
Published: May 2023

In recent years, green GDP has been widely used but not precisely defined, and the measurement methods are mostly vague and confusing. In order to accurately account for global green GDP, this paper adopts the latest international standard system, the system of integrated Environmental-Economic Accounting: a central framework (SEEA-2012), to construct a model of global GGDP accounting system. Based on SEEA-2012, the global green GDP accounting system is established, which takes into account the negative impacts caused by the depletion of natural resources and losses due to environmental pollution, as well as the positive impacts caused by the improvement of resources and environment on the basis of the traditional GDP. Finally, the values of 26 countries are specifically substituted into the accounting model to obtain the exact values of green GDP for 26 countries worldwide.
Has the development of digital finance driven population agglomeration?
Authors: Zhao Dingjia
Published: May 2023

Digital finance is considered an important driving force for improving the level of population agglomeration. This article matches the data of Peking University's digital inclusive finance with information on regional population agglomeration to study the relationship between the development of digital finance and population agglomeration. The regression results of the instrumental variable method show that the development of digital finance can promote population agglomeration at a significant level of 1%, and a series of robustness tests further support this finding. In addition, this article finds that the breadth of coverage in the digital finance index has a greater impact on population agglomeration, and the heterogeneity analysis of regions shows that the promotion of digital finance development on population agglomeration is more evident in western regions. This has reference significance for promoting the even distribution of the national population.
Scientific management of subsistence materials during the COVID-19 outbreak in Changchun
Authors: Hu Huaiyu , Fan Lijun , Wang Qian
Published: May 2023

In this paper, we firstly constructed an improved SEIR model to predict the development of the epidemic in Changchun without control measures and compared it with the actual situation; meanwhile, we introduced regenerative numbers to calculate 0R using realistic data and observed the interval of 0R before and after the distribution of vegetable packs. Secondly, the problem of planning multi-level logistics network planning for supply-demand balance is addressed for the design of preplanning for the orderly distribution of residents' living materials in the case of large-scale closure control. Meanwhile, according to the measurement standard of 400 grams of vegetables consumed per person per day, the material demand of each cell in the downstream of all networks is obtained. The real street route distance between each node is used as the weight of the edge in the ordered network graph, and then the transshipment volume of each midstream of the network is introduced, and similarly the transshipment volume of the midstream can be derived from the supply volume of supplies that need to be guaranteed to be provided in the upstream of the network. After that, large trucks (10T load) and small trucks (4T load) are used to transport materials respectively for comparison and analysis. In actual material distribution, trucks with corresponding load should be selected reasonably according to the scale of demand.
Preface: 2nd International Conference on Applied Mathematics, Modeling Simulation and Automatic Control (AMMSAC 2023)
Authors: Haggai Emmanuel , Han Yingke
Published: May 2023

It is our honor to present the proceedings of the 2023 2nd International Conference on Applied Mathematics, Modeling Simulation and Automatic Control (AMMSAC 2023). This conference was successfully held in San Diego, USA during April 29-30, 2023.

AMMSAC 2023 aims to provide an ideal academic platform for researchers to present the latest research findings and describe emerging technologies, directions and developments in applied mathematics, modeling simulation, computer science, intelligent manufacturing, and automatic control. As these goals require the cooperation of people from different background, we hope that our research will help to achieve a better and more sustainable future for all.

During the conference, we were pleased to be joined by four knowledgeable keynote speakers. More than 80 participants were benefited through each of the 30-35 minutes keynote speech. The parallel sessions were arranged and carefully monitored by the organizing team.

We truly believe that AMMSAC 2023 is a great platform for researchers, academician, industry players and student to brainstorm and exchange research idea, communicate their new research findings and eventually leading the world to meet the sustainable goals.

The AMMSAC Scientific Committee

San Diego, USA
A Need For Speed: Enhancing F1 Race Cars with a Novel Computational Fluid Dynamics and Machine Learning Method
Authors: Cheng Ken
Published: May 2023

Formula One (F1) engineers dedicate their lives to finding tenths of a second in car performance. But, the technical complexities of F1 cars must now also make inroads to increasing sustainability because fuel consumption in F1 is a major problem. Furthermore, F1 teams with lower budgets now need to find ways to be more efficient in aerodynamic testing to match the richer teams’ expensive equipment. This would improve the competitiveness of F1 and please 100 million fans around the world. Computer-aided design to model Formula One race cars for aerodynamic testing, computational fluid dynamics to test 90 different airfoils and simulation setups as well as the finalized model car against the control car, and machine learning to quickly predict the results of other simulations I have not conducted yet with high accuracy rates in a very little amount of time.
A Study on the Optimal Investment Strategy Portfolio of Gold and Bitcoin Assets Based on Grey Prediction and Programming Models
Authors: Li Xiuzhi
Published: May 2023

Nowadays,Gold and Bitcoin are popular traded products in trading market. In order to build a trading portfolio that maximizes returns, this paper selects a gray prediction model at first, to predict the price of the traded products on the 6th day based on the historical data of the first 5 days of the trading day, and then conducts rolling analysis by using the historical data of each trading day. By following the principle of currency value maximization trading strategy, this paper selects the objective planning model and establishes the objective function representing the currency value and the constraint function of daily trading volume. Finally, the programming model is solved according to the results of the gray prediction model, and obtain the change in the value of the currency held in five years.
High-Frequency Time Series Data Regression Based on the Functional Bayesian Model Averaging
Authors: Cui Jiarui , Yang Shuo , Chen Dinghui
Published: May 2023

Regression analysis of high-frequency time series data has been an important problem in the field of statistical learning. In this paper, we propose a novel approach based on multiple basis function expansions and Bayesian model averaging when the predictor variable is a high-frequency time series and the response variable is continuous scalar data. On the one hand, the proposed method avoids the curse of dimensionality and extracts the functional information of the original sequence by transforming the discrete data into continuous functions in a function space consisting of some fixed basis functions. On the other hand, for the choice of basis functions, this paper proposes to adaptively determine the optimal solution using Bayesian model averaging, effectively balancing the variance and bias of the predictive model. The real data analysis shows that the proposed method has smaller mean squared error and absolute error, and is robust compared to other methods. Finally, the proposed method can be further extended to time series data applications such as weather forecasting and stock price prediction.
Dynamic Model Based Research on Intelligent Vehicle Steering and Obstacle Avoidance
Authors: Huang Chihan
Published: May 2023

In order to improve road safety, promote the development of autonomous driving, and realize the stability of unmanned vehicles during steering and obstacle avoidance, this paper establishes a lateral dynamic model and a cyclotron curve model based on the 3-degree-freedom vehicle dynamics model. Taking no collision between cars and timely obstacle avoidance as the optimization goal, this paper discretizes the model, a prediction model is established, a soft constraint solver is designed, and a relaxation factor is added to the performance index function. Based on the model prediction and control principle, the obstacle avoidance trajectory planning and tracking of unmanned vehicles is integrated and controlled, and the solution is converted into a quadratic programming problem according to the constraints and rolling optimization characteristics. Finally Matlab/Prescan joint simulation is used to verify the steering and obstacle avoidance quality of the model. The results show that the model has excellent safety, stability and comfort in non-highspeed situations.
Analysis of Factors Affecting the CSI300 Index Based on KPCA and Various Machine Learning Algorithms
Authors: Wang Ziyue , Chen Hongyue , Xu Zile
Published: May 2023

This paper utilises Kernel Principal Component Analysis (KPCA) and various machine learning algorithms to analyze the importance of factors affecting the Chinese Securities Index 300 (CSI300). Based on previous research, this paper constructs an indicator system consisting of 4 secondary and 21 tertiary indicators affecting the CSI300. The data is then reduced through KPCA and processed by various machine learning algorithms, including LightGBM, XGBoost, SVM, and Random Forest, to compare their predictive ability and feature importance. The results indicate that: (1) Under appropriate model parameter settings, the LightGBM model performs the best, while the other algorithms, such as the XGBoost, SVM, and Random Forest models, perform worse and with greater variability than the former. (2) This paper identifies the most significant indicator factors that affect the CSI300 index, such as closing price, price-to-book ratio, and turnover rate. Conversely, some factors, such as the buy-to-sell ratio, exhibit lower importance. These research findings have certain reference and guiding significance for improving the accuracy and reliability of stock market forecasting and practical and theoretical research in financial markets.
A Study on the Influencing Factors of Fertility Rate in China
Authors: Quan Yuexin
Published: May 2023

Over the past 40 years of reform and opening up, the family planning policy, as a basic state policy of China, has had a very profound impact on the development of the Chinese economy. Due to the dropping fertility rate in recent years, the birth rate has once again been under intense public scrutiny. To comprehend the reasons for the reduction in fertility, we must comprehend the variables that affect the fertility rate. It was discovered that the fundamental demographic position of the nation is influenced by the economy, education, and the state's fertility policy. By developing regression equations and examining the generated mathematical models, this research comes to the conclusion that the rates of female illiteracy, the elderly dependency ratio, the juvenile dependency ratio, and elderly dependency have a major impact on fertility rates. This study will assist the government in determining what can be done to raise the rate of fertility.
Research on the Social Impact of Aging Problem
Authors: Wu Hongrui
Published: May 2023

This essay mainly talk about the more and more serious aging population problem will make a big difference to the social and people’s lives. Aging population becomes hotter and hotter in the whole country and even the whole world, and it matters to human beings. Due to the high pressure of the competition nowadays, young people would not like to get marry or even have babies, so it accelerates the aging process. Through finding some data and get the trend of the increasing, this essay works out some reasons and consequence to seek a conclusion. To analyze the trend and the number of old people in China, this paper selects some data from different database and manage to get some reasons and consequence. Producers have lower output and the economy depression occurs while more aging population and purchasing power decrease. In the essay, analysis by the data and the logic will prove the points and consequences. This work is mainly to check the aging rate and to survey the serious problem of the aging population brought and to find some ways to solve it.
Red Wine Quality Analysis based on Machine Learning Techniques
Authors: Dong Jianhong
Published: May 2023

The red wine industry is growing at a tremendous speed as more and more people start to drink wine. Therefore, the industry is becoming competitive and wine companies need to make better quality wines to stand out. This paper used machine learning techniques to analyze 1599 wine samples each with 11 input variables in order to find the variables that have the most impact on wine's general quality.  The linear regression model used in the paper shows the most influential variables on quality are alcohol and acid. In addition, a heat map was adopted to show all the correlation between the variables. To go deeper, box plot and 3D scatter plot were used to support the finding through linear regression model and have a more detailed conclusion on the variables that have the most impact on quality. These results shed light on what are the most influential variables on wine’s quality.
Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost Models
Authors: Yang Tian
Published: May 2023

The technique of estimating future sales levels for a good or service is known as sales forecasting. The corresponding forecasting methods range from initially qualitative analysis to later time series methods, regression analysis and econometric models, as well as machine learning methods that have emerged in recent decades. This paper compares the different performances of OLS, Random Forest and XGBoost machine learning models in predicting the sales of Walmart stores. According to the analysis, XGBoost model has the best sales forecasting ability. In the case of logarithmic sales, R2 of the XGBoost model is as high as 0.984, while MSE and MAE are only 0.065 and 0.124, respectively. The XGBoost model is therefore an option when making sales forecasts. These results compare different types of models, find out the best prediction model, and provide suggestions for future prediction model selection.
Comparison of Real Estate Price Prediction Based on LSTM and LGBM
Authors: Shi Songyi
Published: May 2023

Housing price prediction is one of the widely discussed topics, and the application of machine learning and deep learning models to housing price prediction is a research hotspot. Exploring which models are suitable for predicting real housing price datasets has significant implications for guiding government and homebuyers' decision-making. In this study, the LSTM and LightGBM models were selected as research objects, and the suitability of the models was explored and compared using the second-hand housing price dataset in Beijing. Based on the analysis, in the task of pure time series prediction based on historical housing prices, the LSTM model had a better fit (R2=0.91), while in the task of housing price prediction based on multiple influencing factors, the LightGBM model had better comprehensive evaluation index results (R2=0.53). Both models can be used to predict pure time series, but the LSTM model is not suitable for predicting multi-factor input models that are not time series.
Important Evaluation Model of University Mathematics Curriculum
Authors: Feng Xinrui
Published: May 2023

Mathematics is the foundation of all subjects, and every great progress of human beings has the strong support of mathematics. Mathematics has shown an extremely important role in university education and the importance of mathematics is increasing. With the differentiation of objective environment, the personalization of individual disciplines, the subjective learning attitude, and the interactive influence between various disciplines, a lot of precise statistical requirements are put forward. In this context, the study of the importance model of university mathematics curriculum is helpful for college students to better study and plan, and also provides a reference for other industries such as education. This paper combines the performance data of mathematics students of each major and each class. It also combines the relevant characteristics of college students themselves, and puts forward suggestions on the connection of middle school and university mathematics and the subsequent study.
The Contour Integral Approach to Several Improper Integrals with Hyperbolic Functions
Authors: Liu Peiyao , Ning Yuzhe , Tang Qianhui , Zhang Enhe
Published: May 2023

In the field of Complex Analysis, it is acknowledged that Cauchy’s Residue Theorem plays an essential role, which allows the calculation of complex integrals by adding up the residues of singularities in the complex plane. Many mathematicians have developed various theorems out of Cauchy’s Residue Theorem and have solved numerous problems using Cauchy’s Residue Theorem, but there are still a lot more studies needed. Thus, this paper focuses on examining Residue Theorem deeply by introducing singularity point and residue, combining Laurent series and complex integral, then deducting Cauchy’s Residue Theorem. This paper then concentrates on solving four unique complex integrals to illustrate Cauchy’s Residue Theorem by analyzing the graph of integration, reformatting the integrals, applying theorems or tricks, integrating the reformatted integrals, and simplifying the results. As a result, this paper not only presented a deeper analysis of the deduction of Cauchy’s Residue Theorem, but also presented the solutions towards four previously unsolved complex integrals. The deduction of Cauchy’s Residue Theorem and the four complex analysis problems have important applications for dealing with integrals associated with hyperbolic functions and lead to future research in other areas of mathematics and physics.
Solutions of Gaussian and Gauss-like integrals in Real and Complex Fields
Authors: Huang Xiaoyu
Published: May 2023

The definite integrals play a vital role in many branches of natural science. Among them, the Gaussian integrals and Gaussian-like integrals are widely used in many fields, especially in statistics. Many times, the range of integration extends to the complex field. When people need to calculate these more complex integrals, they need some simpler methods to help them quickly calculate the integral value. This article describes how to solve Gaussian integrals and Gaussian-like integrals in real and complex domains. When calculating some complex integrals, integral transformations can be used, such as using Cauchy's remainder theorem to construct closed loops of sectors and parallelograms in complex fields. These methods greatly simplify some integration problems in the real number field. At the same time, some proven formulas such as Fresnel's theorem and the covariant theorem of the Γ function can also be used to solve some Gaussian integrals with trigonometric functions or higher order terms.
Variance Reduction Techniques Based on Binary Option Pricing
Authors: Ding Ying
Published: May 2023

Aiming at the binary option pricing model, control variates and antithetic variates methods are focused on comparing the reasons and influencing factors for the difference of variance reduction effect between them. By adjusting the independent variable of the control variates method and the range of binary options indication function, and by sensitivity analysis of four parameters of the pricing model, the study finds a way to improve the variance reduction ability. Through the detailed mathematical derivation of the antithetic variates method to confirm the law of the 4 parameters in the Black-Scholes model and the binary option price found by the former. And construct more accurate theoretical values to provide better direction for setting options prices in the real world.
Fundamental results in probability theory
Authors: Fang Zizhou , Tan Kaixi , Wang Ziyi
Published: May 2023

Probability theory is an area of mathematics that deals with the concept of likelihood. Probability theory is the mathematical foundation of statistical reasoning, and understanding how unpredictability impacts data is crucial for data scientists.  Gaussian (normal) distribution is the most widely used distribution. It has two parameters which are mean and variance and easy to interpret. Also, the central limit theorem tells us that sums of independent random variables make the least number of assumptions. In addition, Poisson, Laplace, Beta, Pareto, Dirichelt, Binomial and Gamma Distributions are useful in different areas. The multivariate Gaussian is the most widely used joint probability density function. Covariance and correlation are used to measure the degree between two random variable’s X and Y. Chebyshev Inequality defines a topological space, which includes a sequence of elements, and let the sequence be called . Strong Law of Large Numbers Theorem use in large number of random variable in pairwise independent identically distributed and Renewal Theory is and example in Strong Law of Large Numbers Theorem.
Traffic Accident Density Prediction Considering Injury Severity Based on Random Forest and GAM
Authors: Wang Yizhen , Cao Jinglin , Kou Liqi
Published: May 2023

Traffic accident density prediction is an significant topic of road safety. This study uses some data in the census tracts in California to develop traffic accident density models using two different modeling approaches. In this paper, the dependent variable was the density of traffic crash which is a measure of the relative distribution of traffic crashes. The random forest regression was used to predict crash density. But many machine learning models like this are like a black-box, which can’t find the causal relationship between features and targets. For many practical problems, it is often more important to explain why a phenomenon happens than improving the model’s predictability. As a result, differed from pure predictive study, the generalized additive model (GAM) was introduced which was an interpretable statistical model to achieve explanatory analysis. The GAM explores which independent variables will have an impact on response variables and the extent of their impact. Thus, the traffic accident density prediction of this practical problem has more credibility analysis.
Prediction of Heart Disease Based on Logistic Regression and Random Forest Models
Authors: Liu Xinyi , Su Siyuan , Wang Baoyi , Zhang Xuewei
Published: May 2023

Heart disease was a hot topic of research in the field of medicine in the 20th century. Heart disease is one of the more common diseases of the circulatory system. When they progress to a certain stage, they all have an impact on heart function. Severe heart disease can lead to heart failure, which can also affect a patient's quality of life and can be life-threatening. And using medical data to find appropriate ways to predict heart disease can also be effective in improving national health care. Our experiment is about predicting whether people have heart disease. This experiment was conducted to let people know for themselves if they have the disease and to treat it quickly. Some pattern recognition methods, such as logistic regression and random forest, were used in this study. Our attempt was to find the more important features using a large amount of experimental data, and then this study used different combinations of features and the two classification techniques mentioned above to build a model for predicting heart disease, in which we tried to identify the important features through a large number of experiments to improve the accuracy of cardiovascular disease prediction. The results of the heart disease model were related to the type of chest pain experienced and the maximum heart rate achieved by those with the highest odds of developing the disease. The prevalence of heart disease was higher in middle-aged and older adults, followed by a smaller proportion of younger people and those with genetic disorders. The number of men suffering from heart disease was also higher than that of women. The results of this experiment amply demonstrate the validity of our random forest model and logistic regression model in this dataset.
Markov Chain Models in Covid-19 Prediction: State-of-the-art and Future Perspectives
Authors: Ding Xiafei , Ma Yue , Tang Jiachen
Published: May 2023

Novel tuberculosis pneumonia, caused by COVID-19, has become the most serious epidemic in the world today. In times of rampant epidemics, many countries adopt policies that restrict civil liberties, and predictive models can provide advice on the best time to predict when restrictions will begin and end, as well as provide data to support other epidemic prevention policies. In this paper, the authors will use the existing literature as well as research findings to predict the prevalence of COVID-19. This paper firstly analyzes and optimizes the models developed in the literature, mainly involving Markov Chain models. The data summarized in the literature are also analyzed and integrated, and many studies combine models other than Markov Chain for combination. Finally, the evaluation methods for each model are summarized.
Multiple Regression: Methodology and Applications
Authors: Sun Yiming , Wang Xinyuan , Zhang Chi , Zuo Mingkai
Published: May 2023

Multiple regression is one of the most significant forms of regression and has a wide range of applications. The study of the implementation of multiple regression analysis in different settings contributes to the development of relevant theories and the improvement of models. In this paper, four different kinds of regressions are discussed individually by referring to different articles. The four kinds of regressions discussed are multivariable/multiple linear regression, multivariate multiple linear regression, multinomial logistic regression, and multivariate non-linear regression. As for multivariable/multiple linear regression, examples in the manufacturing industry and medical field show that it can be applied in more fields. Multivariate multiple linear regression is more accurate than multivariable/multiple linear regression and can be used with more than a variable. Multinomial logistic regression is relatively mature and accurate, and can help people well solve the problem with non-linearity and multiple independent variables. It does not require the variables to obey a multivariate normal distribution, and is more widely used as well. Multivariate non-linear regression, however, cannot be used properly without powerful professional knowledge. This paper investigates the theoretical development and model applications of multiple regression to demonstrate the flexibility and broadness of the adoption of multiple regression analysis.
Preface: 3rd International Conference on Environment Science and Advanced Energy Technologies (ESAET 2023)
Authors: Kennan Eleanore , Ding Shikai
Published: May 2023

2023 3rd International Conference on Environment Science and Advanced Energy Technologies (ESAET 2023) was held in Chongqing, China during April 08-09, 2023. ESAET 2023 is a comprehensive conference focused on various aspects of advances in environmental science and management, power engineering, ecological system, sustainable development, fuels and renewable energy, and advanced energy technologies.

The aim of ESAET 2023 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities. This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration.

We sincerely hope that ESAET 2023 will be a forum for excellent discussions that will put forward new ideas and promote collaborative researches. We would like to acknowledge the distinguished keynote speakers and all of those who supported ESAET 2023. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference.

With our warmest regards,

Conference Organizing Committee

Chongqing, China
Applications of CFD Simulations in Environmental Science
Authors: Qu Guanni
Published: May 2023

Computational Fluid Dynamics (CFD) Simulations are an essential tool in environmental engineering. Since its development, it has been widely utilized in different subject fields to compute numerical solutions to solve complicated fluid dynamics equations. To better understand the strengths and limitations of the application of Computational Fluid Dynamics Simulations, research papers using CFD are analyzed, and their particular methods are discussed. The simplification of the modeling process and the generation of mesh are likely to induce errors. The geometry of the model in the porous media shows that the choice of the model makes a huge difference on the simulation results. Thus, qualifying the discrepancies between the real on-site measurements and the simulation results is of great importance by verifying both the CFD modeling and other modeling methods used. The meshing should also be verified by comparing the velocity of the fluid in the medium mesh and that in the fine mesh to ensure the quality of the mesh.
Forest Assessment and Management Model Based on Carbon Sequestration
Authors: Zhang Renrui , Liu Guangjin
Published: May 2023

Proper forest management activities can sustainably increase forest carbon stocks to mitigate the effects of climate change. In this paper, we combine CBM-CFS3 model to calculate the amount of carbon sequestration and build a forest valuation model, and integrate various factors to obtain the way to maximize the amount of forest value per unit area. At the same time, this paper analyzes the global macroscopic distribution of forest value in the context of forests with typical characteristics and derives a solution for maximizing forest value under the planning of this model in the context of Jiangxi Forest. The model fully considers the ecological, social and economic values of forests to maximize the benefits of forests while sequestering carbon.
Plant Competition Model Based on Self-Inhibition and Drought
Authors: Xie Zhuoer , Xue Yu , Zhao Jiaxin
Published: May 2023

Continued global warming has led to frequent droughts in many parts of the world. Drought has a large impact on plant communities in ecosystems everywhere, and therefore the adaptation of plant communities to drought will be critical to their future development and survival. This paper focuses on two key questions: first, how plant community populations change under the influence of dry weather and how plants adapt to dry environments. Second, how the succession of dominant populations affects the biodiversity within that plant community and what the biomass is when the community state is stable. In this paper, a grassland community dynamics system model based on population numbers and environmental resources is established, while precipitation is selected as a key indicator to study the effects of dry weather. Finally, the differential equation for the change of plant population with dry weather is derived. Through analyzing the relationship between total biomass, critical available resources and number of species, the optimal number of species when the plant community is stable is obtained.