bluuebunny/crossref_metadata_2025 huggingface dataset:
Introduction to Integration and Techniques to Calculate Integrals
Authors: Xiang Jikang
Published: June 2024

Integrals are the powerful tools that provide deep insights into the behavior of functions and are essential for modelling and understanding the world of human being. Integration has widespread applications across various fields. Integrals are used to calculate accumulation processes and find the area under curves. This is crucial in physics by calculating displacement from velocity and in various engineering and economic applications by calculating total profit from a production function. Integrals are used in probability theory and statistics for calculating probabilities and expected values. In statistical applications, integration plays a significant role in calculating areas under probability distributions, which are used in hypothesis testing and confidence intervals. Solving a complex integral is too tough if just use some fundamental formulas. The techniques of integration include substitutions, integral by parts, integration using Trigonometric Identities, fixed formulas of integration. This essay is going to introduce some of the most important methods of integration.
Music Production Based on Brainwave and Acoustic Theory
Authors: Zhang Xiai
Published: June 2024

The fusion of music production and cognitive science has carved a transformative path in an era characterized by tremendous technical advancement and an ever-deepening understanding of the human brain. This essay investigates this ground-breaking nexus in an effort to unravel the complex interplay between brainwave patterns and acoustic components in music and its potential to radically alter musical creativity, production, and emotional resonance. The neurological underpinnings of music's psychological effects have been revealed by recent studies looking into the relationships between brainwave patterns and musical structures. Composers and producers can create songs that are artistically compelling and scientifically informed, capable of evoking desired emotional and cognitive reactions in listeners, by merging brainwave data with acoustic theory. This method also has the potential to be therapeutic, improving emotional wellbeing through cognitive rehabilitation and stress reduction. This research aims to broaden the understanding of the art of music and increase its ability to engage people on deeper cognitive and emotional levels as technology and methodology develop. Beyond the domain of traditional rhythms and notes, it explores the world of brainwaves, enhancing knowledge of the human condition through the transforming power of music.
Exploring momentum in tennis using LightGBM-MLR fusion modeling
Authors: Liu Hongwei , Jing Qi , Wang Xufeng
Published: June 2024

Tennis Performance Prediction is a highly regarded study published daily in the 《Journal of Sports Science》. Its simplicity of prediction methodology and depth of insight have made it widely popular. In this paper we identify the factors influencing momentum in tennis, materialize the vague concept of momentum, and based on the results obtained we advise players on how to improve their performance. Initially, we meticulously scrubbed the raw data and narrowed down eight critical factors influencing player performance. Leveraging the gradient boosting tree, we formulated a LightGBM-MLR Evaluation Model to ascertain the significance of these eight factors. Post normalization, we employed a Multiple Linear Regression Model to compute a comprehensive player performance score. We then evaluated the model's accuracy, analyzed the outcomes to offer targeted recommendations for player performance enhancement, and compared the fit of five machine learning models, ultimately concluding that LightGBM offered the most optimal fit. This research has helped to improve player performance and prediction of game trends, and it can be extended to other sports.
Analysis of the Rationale for the Entry of Chinese New Energy Vehicle Companies into Europe: Evidence from Three Chinese Companies
Authors: Sang Ziyang
Published: June 2024

This paper is based on a study of three representative companies in China's new energy vehicle industry and analyses the rationality of Chinese new energy companies entering Europe through the macro context of China and Europe and the companies' own elements. The results show that even though Chinese new energy companies are still on the rise and have many shortcomings, entering Europe is a further development of the company's internationalisation and an important strategy for developing their own technology and competitiveness.
Analysis of Principle and Applications of Series Expansion
Authors: Mo Zifei , Wang Yiran , Wei Jia
Published: June 2024

As a matter of fact, the series expansion has been widely used in various fields since the proposed. The key reason is that it can be used for simplifying and approximation for complex structure, functions as well as situations. On this basis, it is able to offer an easy routine to calculate some of the values. With this in mind, this study mainly discussed the use of Taylor series expansion in chemistry and daily life, and obtained the freezing point reduction formula, which is not a small challenge for us. However, this expansion of Taylor's series is limited in many other areas of daily life and not all areas and similar problems can be solved. The significance of this paper is to help Taylor series in the freezing point, Taylor series to a certain extent to solve the problem of freezing point in mathematics. Overall, these results shed light on guiding further exploration of series expansion.
Exploring the Meandering Pathways of Random Walk: From Finance to Physics, Biology, and Beyond
Authors: Shi Juntao
Published: June 2024

As a matter of fact, the random walk has been implemented in various applications among different fields in recent years. With this in mind, this study explores the concept of random walk and its modern application in the three specific fields, i.e., finance, physics and biology. Based on an analysis of key articles and studies, the exploration identifies the main themes, including the efficient market hypothesis, Brownian Motion in physics as well as the genetic drift patterns. According to the analysis, this study also highlights gaps in the literature, suggesting the need for further empirical research in these areas. Understanding the intricacies of random walk theory is essential for fields in science, technology, as well as decision-making, and this study provides valuable insights into its evolution and current status. Overall, these results shed light on guiding further exploration of random walk as well as corresponding applications in different fields and subjects.
Evaluating the Achievement of Singapore's Environmental Policy Goals for Enhanced Sustainability and Livability through Big Data Analysis
Authors: Zhao Yuchen
Published: June 2024

In the face of rapid urbanization and increasing environmental concerns, the imperative of achieving sustainable development has become a global priority. Singapore, a pioneering advocate of innovative urban planning and governance, has emerged as a beacon of sustainability and livability through its strategic environmental policies. This research employs big data analysis to evaluate the extent to which Singapore's environmental policy goals have contributed to enhanced sustainability and livability. By curating and analyzing datasets spanning urban development, environmental quality, resource management, and societal well-being, this study quantitatively assesses policy outcomes. The analysis highlights both achievements and challenges within Singapore's policy framework. The research reveals an increase in population density, impacting resource allocation and urban development. Moreover, a rise in methane emissions and decreasing freshwater resources per capita present significant sustainability challenges. Conversely, efforts such as waste-to-energy initiatives and the active, beautiful, clean waters programme have contributed positively to emissions reduction and water resource enhancement.
The Bottom Line in a Post-Truth Perspective
Authors: Zhu Haiyan
Published: June 2024

In the environment of deepening globalization, the value of TV dramas in cultural dissemination, building national image and enhancing national cultural strength is becoming more and more prominent. As an important carrier of China's cultural communication, TV dramas have the important mission of spreading national culture, shaping national image and improving national cultural soft power. The article analyzes the drama "The Bottom Line" released in China from the perspective of post-truth, which mainly focuses on a series of legal issues arising from cyber violence to reflect human ethics.
Various Well-Designed Hydrogen Fuel Cells: Structure, Principle, and Advantages
Authors: Cao Yizhou
Published: June 2024

Nowadays, with the increasing use of fossil fuels, more and more environmental problems appear, like global warming and air quality degradation. In different industries that use fossil fuels as fuel, the harmful greenhouse gases emitted by car engines account for a large proportion. This paper will discuss the hydrogen fuel cells used for vehicles nowadays, which consist of three different kinds. These hydrogen fuel cells have similar structures, only different in some specific sectors. They have many advantages over traditional fossil fuels, like higher efficiency and lower carbon dioxide emissions. The basic structure, development process, principle, and catalysts needed for reaction will be introduced. Also, the advantages of each of the fuel cells will be discussed compared to others. The study of hydrogen cells in this paper can help understand its benefits. By promoting the development of hydrogen fuel cells, there will be more clean and efficient energy options in the future.
Research on position prediction of manned sightseeing submarine based on Kalman filter algorithm
Authors: Zheng Zhifei , Wang Zhiyang , Ma Tianyi
Published: June 2024

Aiming at the safety problem of manned sightseeing submarine, a position prediction method based on Kalman filter algorithm is proposed in this paper. First, the submarine topographic map and current distribution map of the submarine area were generated by ArcGIS software, and a continuous dynamic system model was established, and the influence of current on submarine movement was considered. Secondly, a preliminary algorithm model of the submarine is established, considering the motion state of the submarine in three-dimensional space, and the position of the submarine at different time points is calculated by kinematic formula. Finally, Kalman filter algorithm is used to optimize and predict the preliminary algorithm model, and the probability density map of submarine predicted position is generated by MATLAB programming, so as to realize the accurate prediction of submarine real-time position. The research results of this paper provide a strong support for improving the accuracy of submarine position prediction, and also provide a guarantee for the safe operation of submarines.
Research On Pricing and Replenishment of Vegetable Products Based on Time Series Prediction
Authors: Ma Xu , Ran Jian , Xiong Feng
Published: June 2024

Based on the commodity information, sales data, and recent loss rate of wholesale price commodities of vegetable categories provided by the superstore, this paper focuses on the relationship between vegetable categories and individual products and the prediction of sales volume in the next seven days. Firstly, the distribution pattern and interrelationship of the sales volume of each category and single product of vegetables were analyzed using data analysis and visualization techniques, and the specific size of the correlation of each category and single product was obtained. Secondly, the ARIMA model was used to predict the sales volume of vegetable categories, and the corresponding replenishment volume was obtained. Finally, through the above analysis, a corresponding replenishment strategy can be provided for the superstore to achieve the subsequent optimal revenue, and the superstore revenue planning model is also discussed and outlooked at the end of the paper.
A Comprehensive Look at Self-Referential Paradoxes and Their Evolution
Authors: Zhang Hanyu
Published: June 2024

An essential issue that has to be addressed is, what is Russell's Paradox, and why does it matter? The reader must first have a basic understanding of what Naive Set Theory is. In a nutshell, the Naive Set Theory was founded on the so-called Naive Comprehension Schema, which states that a set is a collection of items that meet a certain condition. there is an assumption being made here, and that assumption is that "set" refers to V, which is the collection of all other sets. As a result of the contradiction, we were motivated to reevaluate our understanding of what a "set" really entails. Mathematicians at the beginning of the 20th century were taken aback by this discovery, which led to the Third Mathematical Crisis. This study will focus on this crisis with possible solutions and predict some future outcomes.
Supermarket Sales Prediction Based on Xgboost Classifier Model
Authors: Yang Yuhao
Published: June 2024

As a matter of fact, it is necessary for the manager to have a better understanding of supermarket sales nowadays. With this in mind, it is significant to have a model to analyze the sales in supermarkets to help the manager. On this basis, in this article, the XGBClassifier model is used to do the prediction of supermarket sales. There are 750 training sets to fit the model and 250 testing sets to judge the performance. The accuracy of the model is 0.49 and the train score is about 62.67. The model is a normal way to do prediction, it is good to fit the data with big sets. This research is helpful for the people who want to manage a supermarket, it can give some important advice to improve the income of the supermarket. It will be more meaningful after training a big set of data because of higher precision.
Chronic Kidney Disease in Diabetes: Biomarkers for Early Detection and Management
Authors: Ma Xiaoqing , Wu Hongyan
Published: June 2024

Proteinuria and serum creatinine are commonly employed biomarkers for monitoring the advancement of diabetic nephropathy, but they may not comprehensively capture all types of lesions and are indicative of declining glomerular filtration rate only in the later stages. Recent research has identified biomarkers reflecting different aspects of diabetic kidney disease (DKD) that can provide information on the risk of DKD progression and associated poor prognosis. This review outlines several biomarkers, such as renal injury molecule-1, Neutrophil gelatinase-associated lipocalin, heat shock protein 72, soluble urokinase plasminogen activator receptor, angiopoetin-Like protein-4, monocyte chemoattractant protein-1, and liver-type fatty acid-binding protein. The research revealed that, following adjustingments for serum creatinine or estimated glomerular filtration rate and proteinuria, the concentrations of plasma and urine biomarkers remained elevated in individuals with diabetic kidney disease and were additionally correlated with the progression of DKD. These new biomarkers represent different biological pathways such as tubular injury, glomerular injury, tubular dysfunction, and inflammation, and can be used as liquid biopsies to better characterize the disease of diabetic nephropathy. Novel blood and urine biomarkers improve clinicians' ability to assess the prognosis of DKD progression and may improve understanding of the pathophysiology of DKD. However, for preclinical biomarkers associated with DKD outcomes, there was considerable heterogeneity between study cohorts and designs, limiting the comparison of prognostic performance between different studies. Larger clinical studies are needed to determine how these biomarkers could be used in the clinic to improve the clinical management of DKD.
Basic Concept About the Integral and Its Basic Applications
Authors: Ning Runze
Published: June 2024

Calculus is a breakthrough in human’s development and its development is keeping making progresses. This paper mainly talks about the integral, which is a crucial component of calculus. The appearance of integral is much earlier than another part of calculus, the differentiation. The integral helps people to calculate the area, length, and volume of some irregular and sophisticated figures or objects that cannot be simply computed with limited existing tools. The integral appears in many forms, including simpler form and more sophisticated one. The more sophisticated form of integral may comprise two or more compound functions, so that it cannot be calculated simply by the principle of integral. In order to overcome such challenges, mathematicians later developed many specific methods, which are corresponding to some complex forms of integral respectively, such as u-substitution, inverse substitution, integral by parts, improper integral, residue theorem, and Cauchy integral formula. Also, for the examples in this essay, several methods mentioned upon are applied, so that some compound functions including logarithm, exponent, trigonometric function, and infinity are all successfully computed. This essay briefly concludes the history and importance of calculus and the functions of differentiation and integral, detailly introduces the property and operation of integral, provides some examples of integral, and talked about the principle and applications of specific methods of finding integral used in examples is this essay.
Progress in the Diagnosis and Treatment of Veno-Occlusive Disease/Sinusoidal Obstruction Syndrome After Allogeneic Hematopoietic Stem Cell Transplantation
Authors: Bai Ziyi
Published: June 2024

In recent years, as the technology of haploidentical transplantation has become increasingly mature, allogeneic hematopoietic stem cell transplantation (allo-HSCT) has played an increasingly important role in the treatment of hematological diseases. However, veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) as a serious complication after allo-HSCT, has attracted much attention due to its extremely high mortality rate. This review aims to comprehensively explore the latest research progress of VOD/SOS after allo-HSCT, including epidemiological characteristics, pathogenesis, diagnostic criteria, prevention strategies and treatment regimens. First, the review summarizes the epidemiological features of VOD/SOS, pointing out that the incidence of the disease has shown a downward trend in recent years. In terms of pathogenesis, it emphasizes the core role of endothelial cell injury and the new progress in the aspects of intestinal microbiota and genetic susceptibility. Subsequently, the article reviews the updates of VOD/SOS diagnostic criteria in recent years, focusing on the quantification and clarification of the ambiguous parts in the traditional criteria, such as the optimal definition of hepatomegaly and ascites, the imaging characteristics of VOD/SOS, etc., making them more applicable in clinical practice, and further explores the new diagnostic technologies such as liver stiffness measurement and serological biomarkers. In terms of prevention, the article mainly evaluates the efficacy and safety of common prophylactic drugs for VOD/SOS. In terms of treatment, the article discusses the role of defibrotide in the treatment of VOD/SOS, and emphasizes the importance of supportive care and intensive monitoring. Finally, the review points out that although significant progress has been made in the diagnosis and treatment of VOD/SOS, further research is still needed to explore its complex pathophysiological mechanisms, to develop more specific and sensitive diagnostic criteria, and to devise more effective prevention and treatment strategies.
Study of Cathode Oxygen Reduction Reaction (ORR) Catalysts for Proton Exchange Membrane Fuel Cells (PEMFCs)
Authors: Li Xiaowen
Published: June 2024

With the increasing depletion of non-renewable energy sources, more and more attention is being paid to the development of sustainable energy sources. The paper outlines the fundamental ideas behind oxygen reduction reaction (ORR) catalysts and proton exchange membrane fuel cells (PEMFCs), discusses the various types of ORR catalysts, and examines the limitations of this technology. In this paper, three types of ORR catalysts, metal catalysts, non-precious metal catalysts, and non-metal catalysts, are introduced in turn, and the pros and cons of the three types of ORR catalysts are analysed. Among them, metal catalysts possess advantages such as high efficiency and controllability, but are expensive and difficult to commercialise widely. The second type of catalyst is usually made from more common and cheaper metals and is therefore less expensive to produce. However, the catalytic activity is lower, resulting in a slower reaction rate and therefore a higher amount of catalyst is required. Non-metallic catalysts are usually not dependent on rare metal resources and are therefore relatively low cost. In addition, non-metallic catalysts offer higher selectivity and controllability, but lower catalytic rates. This paper expects that with the growth of the hydrogen energy industry, proton exchange membrane fuel cells will gradually be widely used in the fields of transport and power supply, providing a cleaner and more efficient energy source for human beings.
Level of Urbanization, Social Security Satisfaction and Fertility Intentions
Authors: Zhang Guohai , Cheng Lei
Published: June 2024

Using the data from the China Social Situation Survey (CSS2021), we investigated the effect of social security satisfaction on fertility intention of people of childbearing age. Using OLS model and ordered probit model for regression, it is found that there is an inverted U-shaped curve relationship between social security satisfaction and fertility intention of people of childbearing age, which is "increasing first and then decreasing", and when social security satisfaction has not crossed the inflection point, it is in the stage of facilitating the influence on fertility intention; after interacting social security satisfaction with urbanization level, it has a certain inhibiting effect on fertility intention; after interacting social security satisfaction with urbanization level, it has a certain inhibiting effect on fertility intention. When social security satisfaction has not crossed the inflection point, it is in the promotion stage; after interacting social security satisfaction with urbanization level, it has a certain inhibiting effect on fertility intentions; after analyzing the heterogeneity, it is found that social security satisfaction has different degrees of influence on the fertility intentions of people of different ages and different regions, and the influence on the fertility intentions of the young group and rural residents is the greatest. Based on this, the government should prevent the spillover of the benefits of childbearing, reduce the cost of childbearing for the residents, build a birth-friendly society, and promote the synergistic and high-quality development of population and social security in China.
A Study on Maximising the Influence of Social Network Nodes Based on a Comprehensive Influence Hierarchy Model: Structural Analysis and Optimisation of Information Dissemination
Authors: Zhou Zhongbao
Published: June 2024

The work of network power is related to the construction of public opinion and the management of information dissemination, which is crucial to the development of industry and information technology. It has been a key issue to study how to maximise positive information influence in social network information dissemination. This study focuses on network structure and node influence distribution, and proposes a comprehensive influence level model (CILM) and an innovative node influence factor. The factor integrates the influence level and node degree attributes to provide a basis for node influence assessment. Further, the Neighbour Influence Traversal Algorithm (NITA) and Influence Cascade Transfer Algorithm (ICTA) are designed to optimize the node influence calculation through the network structure and information dissemination characteristics. Experimentally, it is proved that CILM and related algorithms are significantly better than other methods in terms of accuracy and running time, and provide an effective tool for the optimisation of information dissemination in social networks.
Analysis of influencing factors of tax-free consumption in Hainan Islands based on structural equation model
Authors: Fu Yifan , Zhang Da , Zhang Heyao , Xu Haotian
Published: June 2024

As an important part of the international tourism consumption center of Hainan Free Trade Port, the tax exemption policy of outlying islands has played an important role in driving tourism consumption in Hainan, driving local economic growth and promoting the return of high-end consumption. In this paper, we construct regression models and structural equation models to explore the factors that affect duty-free consumption in Hainan's outlying islands. Firstly, the regression model is used to conduct regression test on the influencing factors, and three important factors are screened out, such as whether the inventory of the duty-free points on the islands is sufficient, whether the facilities of the duty-free points on the islands are perfect, and the professionalism of the duty-free shop staff. Secondly, the structural equation model is used to analyze and modify the influencing factors in a deeper way, and finally it is determined that the four aspects of the perceived value of goods, policy, convenience and service have an important impact on the duty-free consumption of Hainan islands. The results of this study provide an important reference for the formulation and implementation of the tax exemption policy in Hainan's outlying islands, and also provide theoretical support for further exploring the tax-free consumption behavior in outlying islands.
The Application of Emotional Colours and Their Pattern Elements in Product Packaging
Authors: Liu Yuxuan , Yue Han
Published: June 2024

This study aims to explore that in contemporary product packaging design, emotional colour, as an important means of conveying emotions visually, can stimulate consumers' emotional resonance and enhance the attractiveness of product packaging. Through the analysis of the association of different colours in the consumer's psychology, combined with the design of pattern elements, the product packaging is more personalized and emotional characteristics. Reasonable use of emotional colour and pattern elements in product packaging design can effectively enhance product brand awareness and consumer purchase intention. In order to achieve the objective object of the product a transcendence and enhancement, so as to give the product itself a richer emotional value, enhance the competitiveness of the product market, for product promotion and marketing provides a new perspective and strategy.
Analysis of the Different Statistical Metrics in Machine Learning
Authors: Geng Shukun
Published: June 2024

The evaluation of machine learning models plays a pivotal role in ensuring their effectiveness across various domains. Metrics serve as vital tools for this purpose, quantifying model performance in tasks, e.g., classification, regression, and clustering. This study delves into the fundamental metrics used in machine learning, presenting their formulas and applications, including accuracy, precision, F1-Score, RMSE, and the Silhouette Score. The analysis underscores the importance of selecting metrics tailored to specific tasks, acknowledging the potential biases and interpretability challenges that may arise. While metrics provide invaluable insights, they also exhibit limitations, particularly in cases where trade-offs between metrics are inevitable. Looking to the future, this study envisions a landscape where multi-metric assessments, improved interpretability, domain-specific metrics, and explainable AI converge to address current limitations. These advancements promise more robust and transparent model evaluations, adapting to dynamic real-world applications. In summary, this exploration of metrics in machine learning highlights their crucial role in benchmarking model performance, fostering the development of reliable AI systems, and shaping transformative applications in diverse fields. Metrics not only aid in informed decision-making but also contribute to advancements in science, industry, and society.
Optimization design of heliostat field parameters based on gray Wolf algorithm
Authors: Wu Zejiang , Zhao Zhen , Tang Jia
Published: June 2024

Tower solar thermal power generation is the most promising way of light energy utilization in China at present, and it is also an important program to achieve the strategic goal of "carbon neutrality" and "carbon peak". In order to obtain the best solar optical efficiency and maximize the use of solar energy, how to design the relevant parameters of the heliostats field, the core equipment of tower solar solar thermal power generation, is extremely important. In this paper, the objective function, constraint conditions and decision variables are established based on the premise that the average annual output thermal power per unit mirror area is maximum and the rated average annual output thermal power of heliostat field is 60MW. Five specific times on the 21st day of each month are used to solve the relevant values, and several calculation models, including the sun position model and the optical efficiency model of heliostat field, are established. Then the grey Wolf algorithm is selected to optimize the model, and the decision variables are substituted into the grey Wolf optimization algorithm. When the Wolf pack updated the data every time, the decision variables were substituted into the DELSOL3 empirical layout, and finally the accurate and reliable heliostat field related layout parameters were obtained.
Evaluations of the Methods for Planet Searching and Detection
Authors: Deng Yueming
Published: June 2024

As a matter of fact, with the continuous progress of science and technology, it is increasingly important for people to study whether there are other planets in the universe. With this in mind, the aim of this paper is to analyze several commonly used planet search methods and summarize their advantages, disadvantages as well as scope of application. To be specific, by studying and investigating the radio velocity method, the transit method and the direct imaging method, it is found that the radial velocity method and the transit method are the most popular methods for discovering exoplanets. According to the analysis, the state-of-art observation results, current drawbacks as well as prospects are discussed. In reality, these studies are essential for obtaining the knowledge of planetary formation and planetary distribution in the Universe, and provide further impetus to human exploration of the Universe. Therefore, this study is of great scientific significance and applied value and these results shed light on guiding further exploration of planet searching.
From Set Theory to the Axiomatization of Set Theory
Authors: Jiang Jingying
Published: June 2024

Axiomatic set theory was created by German mathematician Zermelo as a strategy for addressing and resolving paradoxes in the field of mathematical study. By adopting this strategy, the axiomatic technique will be applied to set theory. The person argues that Cantor's failure to impose restrictions on the idea of a set is the cause of the dilemma. They also claim that Cantor's definition of a set is unclear. Both of these arguments are predicated on the idea that Cantor neglected to place limitations on the idea of a set. Zermelo hypothesized that the condensed version of the axioms would make it easier to define a set and elaborate on its properties, and he was correct in his prediction. The creation of an axiomatization for set theory is the first of this research's main goals. Second, the investigation of various set theory development methods in comparison to one another. Even though there are intriguing puzzles in the field of set theory that have not yet been solved, the axiomatization of set theory is widely regarded as a significant accomplishment in the field.
Paris House Rental Price Index Prediction-A Classical Statistical Model Approach
Authors: Zhang Xiaopeng
Published: June 2024

The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic real estate markets. The study also examines the impact of external factors like rent reference indices, house price indices, and unemployment rates on prediction accuracy. While these factors seem promising, the further analysis suggests they can introduce noise into predictions if not chosen carefully. In conclusion, this research contributes valuable insights for rental market stakeholders. The ARIMA model proves effective for rental price forecasting in Paris, emphasizing the importance of understanding intrinsic time series patterns. This study can guide decision-making for renters, landlords, and investors in Paris and similar urban areas, providing a better understanding of predictive modeling in housing markets.
Research on Vegetable Pricing and Replenishment Based on LSTM Time Series Prediction Method
Authors: Feng Xinyue , Xia Qihang
Published: June 2024

In the operation of fresh supermarkets, vegetable products exhibit significant price volatility compared to other commodities due to their perishability, freshness, and susceptibility to losses. This study utilizes historical sales and pricing data from supermarkets and employs the LSTM neural network forecasting model to conduct predictive analysis on the replenishment decisions and pricing strategies in fresh supermarkets. The results demonstrate that the proposed forecasting model achieves favorable predictive accuracy, revealing a certain correlation and regularity between the replenishment decisions in fresh supermarkets and historical time series data. This research offers positive economic benefits in guiding the variations of prices and replenishment quantities.
Analysis of signal integrity improvement in high frequency digital circuits
Authors: Jiang Hongtao
Published: June 2024

The purpose of this paper is to explore the characteristics and applications of high frequency digital signals. With the continuous development of modern communication technology, high frequency digital signals have become an essential and important part of the digital communication field. Firstly, this paper introduces the basic concepts of high-frequency digital signals, including the sampling, quantization and coding processes of digital signals. Secondly, this paper analyses the main characteristics of high frequency digital signals, including frequency, bandwidth, code rate and power. The transmission characteristics of high frequency digital signals are further discussed, including transmission distance, transmission bit error rate and interference immunity. Finally, the paper discusses the applications of high-frequency digital signals in the fields of communication, radar, measurement and control, and provides an outlook on the future development trend of high-frequency digital signal technology. In conclusion, the rapid development of high-frequency digital signals has supported the progress of digital communication technology, and also brought about great changes to the development of human society.
Analysis of Dark Matter Searching Based on Liquid Xenon: XENON, LUX-ZEPLIN and DARWIN
Authors: Tang Po Yu Boey
Published: June 2024

This paper delves into the ongoing quest to uncover the mysteries of dark matter, an endeavor that has captivated the scientific community for nearly a century. Focusing on the utilization of liquid xenon scintillators, specifically in the XENONnT and LZ experiments, it explores the remarkable properties of liquid xenon that make it a prime candidate for detecting Weakly Interacting Massive Particles (WIMPs), a leading dark matter candidate. These experiments have achieved significant milestones, placing stringent constraints on the WIMP-nucleon interaction cross section, yet face the challenge of the “neutrino fog” at lower energies, necessitating innovative solutions like advanced statistical methods and machine learning. The paper also highlights the promising future of dark matter detection through projects like DARWIN and the XLZD Consortium, which aim to construct next-generation liquid xenon detectors with increased target masses. These endeavors hold the potential to significantly enhance sensitivity, potentially unraveling the nature of dark matter. Furthermore, the versatility of liquid xenon detectors extends to the study of neutrinos, encompassing neutrinoless double-beta decays and solar pp neutrinos. These results shed light on guiding further exploration of dark matter searching.
Nurses Perceptions on Head and Neck Malignancies and Attitudes toward Cluster Oral Care Nursing in a selected Hospital in China
Authors: Zhang Fan , Agunod Cyrelle D.
Published: June 2024

As a key treatment for patients with head and neck tumors, radiotherapy has a significant clinical effect. However, the occurrence of oral mucositis after radiotherapy seriously affects the prognosis of patients. This aims to further improve the cluster nursing system and achieve the prevention and intervention effect of oral mucositis in patients with head and neck tumors and explores the respondent’s perception of head and neck malignancies and attitudes toward cluster oral care nursing. Results show that respondents have a more positive perception of head and neck malignancies and cluster oral care, believing that the cluster care system can effectively prevent oral mucositis, provide accurate oral care information, reduce the severity of oral mucositis, and improve the quality of life of patients.
Research on Natural Disaster Risk Assessment and Insurance Decision-Making Using EWM-TOPSIS and ARIMA Models
Authors: Zhou Ji , Bu Yi
Published: June 2024

This study aims to evaluate natural disaster risks and provide decision support for insurance companies by integrating the EWM-TOPSIS (Entropy Weighted Method combined with Technique for Order Preference by Similarity to Ideal Solution) and ARIMA (AutoRegressive Integrated Moving Average) models. Initially, through literature review and online data collection, we identified multiple factors associated with various natural disasters, including PPM (Parts Per Million), degrees Celsius, temperature, rainfall, altitude, frequency of natural disasters, and actual water barometric pressure. The entropy weight method was utilized to calculate the weights of these factors, which were then combined with the TOPSIS comprehensive evaluation method to determine the extent of disasters in different regions. Additionally, a random forest model was constructed to predict the underwriting risk for insurance companies, considering factors such as population density, income levels, and the strength of agricultural, industrial, and service sectors. Furthermore, the ARIMA model was employed for time series analysis of risk factors to forecast future risk trends, thereby identifying the optimal timing for insurance companies to assume risk. Finally, from the perspective of real estate developers, the study evaluated the potential benefits of constructing houses in different regions by combining the entropy weight method with the TOPSIS model. The findings indicate that Washington State has a high disaster severity index when considering the potential benefits of future real estate development, suggesting that real estate development in this area could yield higher returns. This study provides a scientific basis for risk management in the insurance industry and site selection decisions for real estate developers.
Brighten or Harm: Light Pollution Risk Assessment and Prevention
Authors: Li Han , Tian Yuxin , Xing Yang
Published: June 2024

Light pollution harms health and the environment, but global assessments only measure light pollution levels, lacking a comprehensive evaluation of its risk. The evaluation model was developed to assess direct and indirect influences with 11 factors, ensuring mutual independence through the KMO test, assigning weights via the AHP model, and applying it to 49 cities globally based on varying urban levels. Using the TOPSIS model, we calculated comprehensive scores for each city and applied K-Means clustering to categorize scores into different levels. Four distinct sites were chosen to validate the model's reliability. By utilizing pertinent data for each indicator, the established model was employed to assess the light pollution risk levels at these locations. Subsequent to this investigation, three intervention strategies were formulated as potential measures to mitigate light pollution, accompanied by a comprehensive analysis of their possible impacts. Finally, 49 cities were classified into five light pollution levels, affirming the practicality of the model. The analysis led to the identification of three intervention strategies: "Material Optimization," "Enhanced Artificial Application," and "Public Education and Awareness."
Correlates of Professional Self-Efficacy and Well-Being of Medical Surgical Nurses in China
Authors: Liang Ling , Cayanan Karen Cyril T.
Published: June 2024

The purpose of this study is to explore the relationship of professional self-efficacy and professional well-being of medical surgical nurses in selected hospitals in China. Professional well-being refers to an individual's overall satisfaction and happiness with his or her professional life. The study will be conducted in a tertiary hospital in Shandong Province. In this study, a descriptive creational research design will be used to explore the relationship between socio-demographic characteristics, general self-efficacy and overall well-being among medical surgical nurses in Shandong Province. The respondents of this study will be 126 medical surgical nurses from a Tertiary Hospital in Shandong Province, China. The researcher will describe and explore the socio-demographic characteristics, professional self-efficacy and professional well-being of medical surgical nurses in Shandong Province and analyze the correlation between the two variables.
Research on the Application of Artificial Intelligence Technology in the Field of Intelligent Transportation Systems
Authors: Yu Chenyu
Published: June 2024

This research report provides a comprehensive investigation into the application of artificial intelligence (AI) in the context of autonomous driving technology within Intelligent Transportation Systems (ITS). Firstly, it offers an analysis of the technical aspects of autonomous driving technology. It then focuses on the development and research status of Intelligent Transportation Systems (ITS), with a particular emphasis on vehicle-to-vehicle (V2X) communication as a major AI application in connecting vehicles. The goal is to explore and evaluate the current application of AI technology in connected vehicles. The research investigates existing literature, research directions, methodologies, and case studies to assess the progress, challenges, and potential future developments in this field. Through this study, one can gain a comprehensive understanding of the research progress and practical applications of AI in various domains within the ITS context. This helps uncover the strengths, challenges, and future directions of AI technology in the ITS field, providing valuable insights and guidance for further research and advancements.
Research on apple detection and maturity assessment based on computer vision technology
Authors: Zheng Junwen , Zhang Yuhui , Wang Ying
Published: June 2024

The study is based on computer vision technology for apple detection and maturity assessment. Firstly, the YOLOv7 algorithm is used for target detection of apples, and the number of apples in the image is counted to generate a distribution histogram of the number of apples. Then, the position of each apple is detected by the YOLOv7 algorithm and a 2D scatter plot of the geometric coordinates of the apples is drawn. Next, apple foreground was extracted by interactive ROI tagging and GrabCut algorithms, and a mathematical model was developed to assess the maturity of apples based on the histogram analysis of apple colours in HSV colour space model. In addition, the Faster R-CNN model is used to detect apples and estimate the quality of apples based on the 2D area of apples to generate a distribution histogram of apple quality. Finally, a convolutional neural network is used to build a fruit recognition model and draw a distribution histogram of apple image ID numbers. This study provides computer vision technical support for apple picking robots to improve apple production efficiency.
Characteristics of Traditional and Emerging Water Treatment Technologies and Their Comparison
Authors: Zhang Qiyue
Published: June 2024

With the growth of urbanization and industrialization in the world, excessive water pollution has led to water scarcity as one of the most important problems today. Therefore, how to purify water resources has become the main way to solve the freshwater problem, and the water treatment process is the main way to purify water resources. This article investigates the traditional technologies represented by biofilm and activated carbon technologies, and the emerging technologies represented by ozone oxidation, photocatalytic oxidation and membrane separation technologies, comparing and summarizing their principles, advantages and disadvantages, and application scope. The study finds that traditional technologies like activated carbon technology have the advantage of being inexpensive, low cost and technologically mature. However, they often produce secondary pollution and cannot be reused, leading to the fact that traditional technologies alone are not suitable for water treatment in today's society. As for emerging technologies such as photocatalytic oxidation, although they have solved the problems of secondary pollution and high efficiency of traditional technologies, they are also due to the high infrastructure cost to limit the pace of their promotion in water treatment plants. Therefore, by comparing and contrasting the advantages and disadvantages of these technologies and their scope of application, this article hopes that the technologies can be combined to realize the purpose of promoting them in water treatment plants, thus purifying water resources and alleviating the problem of water scarcity in today's world.