bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Improving Amplifier Used in Brain-Computer Interface Technology
Authors: Cheng Xiaohan
Published: January 2024

With the rapid development of science and technology, the demand for human-computer interaction in many fields is increasing, and the requirements are getting higher and higher. As the most important part in human-computer interaction, the demand for Brain-Computer Interface (BCI) technology in medicine, games, military fields, etc. is increasing. Therefore, more and more scholars and institutions have begun to study BCI technology. In this paper, an experiment is designed to improve the performance of two-stage CMOS amplifiers, an important component of BCI technology. The experiment uses CoolSpice to build a basic model of the two-stage CMOS amplifier for simulation, replacing brain waves with a small signal, and adjusting the performance of the two-stage CMOS amplifier by adding a compensated capacitor and resistor. Through experimentation, it can be concluded that selecting an appropriate compensated capacitor can significantly improve the performance of the amplifier. Improving the performance of amplifiers can help improve the efficiency of BCI technology and help people collected the information emitted by brain waves more accurately in the future.
Optimal Placement Algorithm for Beehives with Pollination Probability Model
Authors: Zhu Jiaming , Xu Tianyuan , Zhang Jun
Published: January 2024

The beehive placement problem is an important optimization problem widely applied in industrial and logistics domains. This paper proposes pollination probability model and beehive covering model, converting beehive placement problem into spatial partition problem. We perform Voronoi diagram on the farmland based on triangulated beehive points set and define an energy function on each block, decreasing the value of the energy function by continuously moving the hive to the geometric center of each block, iterating until the maximum number of iterations is reached or the maximum moving distance is less than ε. After the iteration, the minimum pollination probability in each block will be calculated. If the probability of a block is less than ξ, the number of beehives will be increased and the algorithm will be re-run until all blocks satisfy the condition. Simply enter the shape of the farmland into the model to get the optimal hive placement, the model works with all common polygonal shapes and can be applied to other similar problems with little modification.
Analysis of the Principle for Exoplanet Searching and the State-of-art Detections
Authors: Weng Yuchen
Published: January 2024

In recent years, the exoplanet searching remain a hot topic among cosmology and astrophysics. In this case, this paper analyzes the methods and facilities used to detect exoplanets. To be specific, 7 main methods are discussed and transit method contributes most in the detection. However, some methods need restrict condition to happen and some has a high rate of misjudge. With regard to facilities, space telescopes are widely used and provide most data and images. Nevertheless, the quality of spectra is not enough so that some theory of early universe cannot be test yet. Previsou studies mainly focus on galaxies with high red shift which are old and faint, while in fact the information is enough to make prediction but not enough to confirm. In the future, those limitations will be improved by new facilities and new methods. On this basis, future research’s direction is clear and will help to improve methods and facilities. Overall, these results shed light on guiding further exploration of exoplanets searching.
Analysis of Star Formation Main Sequences based on IllustrisTNG Simulations
Authors: Hu Yongquan
Published: January 2024

As a matter of fact, N body simulations are widely used in cosmology and astrophysics. Thanks to the rapid development of computer resources, it is available to carry out galaxy simulations including magnetic evolution, for example, the project of IllustrisTNG and THESAN. In this case, based on the data collected from IllustrisTNG API, investigations into the star formation main sequence (SFMS) of dark matter (DM) halos are carried out by querying the accomplished simulation tasks. To be specific, multiple linear relation among the star formation rate (SFR) of DM halos, the stellar mass of DM halos and cosmological red shift are discovered. According to the analysis, a polynomial relation between the SFR and the stellar mass of DM halos is revealed, in which the coefficients are related to the redshift. Overall, these results shed light on guiding further exploration of star and galaxy formation based on analysis of the simulation results.
Application of Machine Learning in Heart Failure Prediction
Authors: Sheng Hongyi
Published: January 2024

Cardiovascular diseases (CVDs) are the primary cause of death worldwide, causing an estimated 17 million deaths each year, and are characterized by myocardial infarction and heart failure. Heart failure occurs when the heart is incapable of adequately pumping blood to satisfy the body's demands. It typically stems from conditions such as diabetes, hypertension, or other cardiac disorders. Early detection and management are crucial for individuals with cardiovascular disease or those at an elevated risk of developing cardiovascular issues due to factors such as hypertension, diabetes, hyperlipidemia, or pre-existing medical conditions. In this regard, utilizing a machine learning model can offer significant benefits. This research paper will utilize machine learning models to document the symptoms, physical attributes, and clinical laboratory test results of patients. Consequently, through the analysis of this patient information, biostatistical analyses can be conducted, enabling the detection of patterns and correlations that may elude medical practitioners. The research will benefit the research on cardiovascular diseases.
Analysis of Factors Affecting Fast-Moving Consumer Goods Franchising Based on MINISO
Authors: Wang Tian
Published: January 2024

In recent years, the offline retail industry has recovered under the impact of online shopping, and MINISO, as an emerging Fast-Moving Consumer Goods (FMCG) brand, has broken the price advantage of online shopping goods, while taking into account the advantages of online and offline. During the period of rapid development of MINISO, many franchisees have been attracted to join. For the franchisees, business circles, locations and store areas have become issues that need to be analyzed and selected. This thesis introduces the basic situation of MINISO firstly, analyzes the present situation of MINISO’s operation and management from four aspects: customer, cost, location and communication, analyzes the feasibility of franchising and the cost factors that franchisees can control. Then, regression analysis is used to establish a mathematical model, analyze the sales situation, find out the mathematical relationship between various factors, and provide theoretical support for the choice of various conditions. Based on the analysis of the factors affecting the franchise operation of MINISO, this thesis proposes a feasible plan for the decision to open retail stores. Provide a reference for the joining of FMCG brands such as MINISO.
Evolutions and Principles of Space and Radio Telescope
Authors: Song Su
Published: January 2024

Contemporarily, various types of telescopes have been proposed as tools for cosmology observation. This study discusses main types of telescopes, i.e., space and radio telescopes people used to observe universe and gives deep description and comparison between them. Radio telescope is the telescope which can receive radio waves from the outer space and can detect waves in different wavelengths besides visible light. It has a long history, as radio telescope is built on Earth and relatively easier for people to make it, but the drawback is that radio telescope may be disturbed by nature conditions easily, even a small change in the strength of wind or the thickness of atmosphere can make a huge difference to the result. While space telescope is different. The first space telescope is Hubble Space Telescope, which is launched in 1990, thus the history of space telescope is relatively short. Compared with radio telescope, space telescope has its own advantage: since it works in space, changing nature conditions in Earth will not affect its precision, and some cancan work without the impact of infrared radiation of the Earth. However, the repair and maintenance cost are problems as it is difficult for people to go to space and fix the broken parts regularly. This paper can help people have a better understanding of these types of telescopes which are used frequently in observation for the universe.
Analysis of Fundamental Metallicity Relation and Mass-metallicity Relation Evolution Based on IllustrisTNG
Authors: Zeng Chuhan
Published: January 2024

IllustrisTNG and the corresponding programming API function are used in this paper to analyze the relationship between different values and parameters (e.g., mass, density and SFR) and metallicity, FMR and visualize the essential relations such as relationship of metallicities and galaxies’ star formation rates (SFRs), gas fraction in galaxies with different redshifts and the stellar mass versus gas fraction at a given redshift. Based on the analysis, a path to a deeper understanding of the MZR properties and evolution is now paved by various results and conclusions. Based on the IllustrisTNG simulation, a major test of galaxy feedback models is the MZR, which simulates metal distribution and evolution. Based on IllustrisTNG simulations, MZR at redshifts 0 - 2 is generally in agreement with observations. A fundamental metallicity relation is supported by the model used and the conclusions presented in this article. In addition, this paper also focuses on the analysis of star formation rate associated with different values and parameters like redshifts with suitable example galaxies. For the relationship between the gas fraction and stellar mass, different z was fitted according to the relationship that Mgas/M* is proportional to M*γ, and for z=0, γ = −0.32. The fitting results agrees with the conclusion drawn, which states that as redshift increases, the relationship of gas fraction and stellar mass steepens.
Multi-beam Line Measurement Problem Based on Geometric Modeling and Genetic Algorithm
Authors: Xu Fulin , Zhao Shiyi
Published: January 2024

Multibeam bathymetry is a key technology to measure the depth of seawater by acoustic wave propagation. In this paper, several computational methods, such as geometric modeling, triangulation, multi-objective optimization, simulated annealing algorithm and genetic algorithm, are used to solve the actual multibeam bathymetry problem. First, the coverage width and the overlap rate between neighboring strips of multibeam bathymetry are calculated. Then, the scene is expanded from two-dimensional to three-dimensional, and the projection length is solved by using the sine theorem and the angular relationship. Again, a set of survey lines are designed so that they are as short as possible and can completely cover the entire sea area to be surveyed while meeting the overlap rate requirement between adjacent strips. Finally, the problem is abstracted into a multi-objective optimization problem by introducing relaxation variables, and a multi-objective genetic algorithm is used to find the optimal design solution. By establishing the mathematical model and applying the optimization algorithm, the researchers are able to better design the measurement wiring of the multibeam bathymetry system so that it can efficiently and accurately acquire the seabed topographic data, thus enhancing the level of the marine mapping technology.
Red wine Quality Prediction by Supervised Learning
Authors: Li Peini
Published: January 2024

In this essay, various machine learning techniques are carried out to model the red wine dataset, which contains 1599 samples and 11 physical features, and predict its quality. Data processing techniques including dropping duplicate values and removing outliers are first applied to normalize the features and bring them to a similar scale. Five algorithms, KNN, Multinominal Logistic Regression, Bagging, Random Forest, and Gradient Boosting are then applied. Cross-validation is performed to identify the best parameters for each model. Additionally, feature selection is also done in Logistic Regression based on the calculated p-values. All five models perform well, with test accuracies roughly around 60%, indicating that machine learning has enormous potential on red wine quality prediction to facilitate the whole industry. Among them, Logistic Regression and Random Forest have slightly higher accuracies. This paper highlights the importance of machine learning methods of Logistic Regression and Random Forest in red wine quality prediction.
Medical Image Segmentation Based on TransUnet and A2B
Authors: Liu Zijian , Liu Yufan
Published: January 2024

The objective of medical picture segmentation technology is to accurately delineate and partition regions affected by pathology, which is of great significance to medical diagnosis and treatment. Thanks to deep learning's quick development, prior efforts mainly utilize the convolutional neural networks equipped with powerful feature representation capability. In this paper, to make the segmentation performance a petter appearance, we introduce a new model in medical image segmentation technology, TransUNet, which combines Transformer and U-Net and uses labeled and unlabeled images for training. To enhance the feature quality and boost the accuracy and robustness of the segmentation results, we further introduce the A2B (Attention in Attention Block) attention mechanism in the TransUNet backbone network. At the same time, we use the CNN-Transformer hybrid model and conduct ablation experiments to verify the improvement effect of the performance indicators. The empirical findings show that TransUNet has accomplished success in tasks requiring medical picture segmentation and can help doctors and researchers more accurately identify and locate key structures.
The Application of Taylor formula in Limits and Approximation
Authors: Pan Levi Yiwei
Published: January 2024

In higher mathematics, the extremely significant content is Taylor’s formula. Simple polynomial functions can be translated from complex functions by this formula. The Taylor formula can be seen as a useful tool to analyze and study many problems in mathematics because of its ability to reduce complexity. The Taylor Formula is an essential mathematical process for solving some problems about limits and approximation and has a unique advantage in rough calculation. The Taylor formula is an important tool in calculus, as it can transform nonlinear problems into linear ones with high accuracy. The topic of this paper is researching the application of the Taylor formula in different fields. In this paper, it will talk about the introduction of the Taylor formula, the expansion of the Taylor formula, and its remainders. It also would talk about the application of the Taylor formula in limits and approximation. As a broad mathematical formulation, the Taylor formula can be used to approximate function singularities, non-analytic behavior, or to simplify complex functions. It can be used in a variety of disciplines, including engineering, physics, and mathematics.
Cauchy’s Integral Formula and Its Applications in Calculating Integrals
Authors: Xiao Yan
Published: January 2024

Cauchy's Integral Theorem is a crucial mathematical theorem that describes the behavior of a complex function on a complex plane. This paper reviews the proof of Cauchy's integral theorem and formula, and the deduction of Cauchy's integral like Cauchy's integral formula with the point in the numerator is on the contour integral. This paper also explores Cauchy's integral when the function contains a non-analytic point inside the contour integral. The way is splitting the entire integral into the sum of several contour integrals equivalently, putting the function f(z) in the numerator into Laurent Series, then simplifying each contour integral using different methods including the derivative of Cauchy’s Integral. The result is the contour integral around point  minus part of the Laurent Series of f(x) in the numerator. This paper shows a path to deal with Cauchy's integral when the function inside the contour integral is not holomorphic in the region.
A Study of The OCR Development History and Directions of Development
Authors: Wang Junmiao
Published: January 2024

Optical character recognition (OCR) is a well-established technology that enables the conversion of scanned images and documents into editable and searchable electronic text. This technology has numerous applications across a range of industries and has proven to be a crucial tool for digitizing books, documents, and records. One of the main benefits of OCR technology is its ability to automate data entry processes, saving time and reducing errors that can be introduced through manual data entry. Additionally, OCR technology is used to extract text from images, which can be used as input data for machine learning and artificial intelligence applications. To better understand the current state of OCR technology, this systematic literature review is conducted that collected various research articles on the topic. The results of this paper provide insights into the strengths and limitations of OCR technology and also offer directions for future research in the field. In terms of language support, the paper found that OCR technology is capable of supporting a wide range of languages, including English, Spanish, Chinese, and many others. However, the accuracy of OCR technology can vary greatly based on the language, with some languages being more challenging to recognize than others. With continued advancements in technology and the increasing need for digitization, OCR technology will continue to play a crucial role in the development of many industries.
Research on Design Management Process Based on BIM - Taking the New Phnom Penh Airport Project in Cambodia as an Example
Authors: Wu Lei , Wen Wen , Jiang Xiangyang , Wang Ran , Zhu Zhijie , Wang Hongyan , Kit Ang Chun , Jyh Yoong Mak
Published: January 2024

With the rapid development of international engineering quantity and scale, traditional project management methods have shortcomings such as relative separation of project design, construction, and operation, and loose connection of project management links. However, BIM technology can effectively integrate various stages of project management. This article combines the new Phnom Penh Airport project in Cambodia and establishes a BIM design management process based on existing management experience. By analyzing the changes in organizational management, team management, technical processes, and management points brought about by BIM, and addressing the problems in project practice, it summarizes and innovates the project design management process under DB mode, achieving a reasonable connection between design and construction management links.
Comparing third-generation wide bandgap semiconductor devices SiC MOSFET and GaN HEMT
Authors: Peng Keyi
Published: January 2024

Third-generation wide-bandgap semiconductor materials feature a wide bandgap, excellent breakdown electric field strength, high temperature resistance, and great radiation resistance. They can compensate for the drawbacks of conventional semiconductors and allow equipment to function normally even under severely demanding circumstances. Wide bandgap semiconductor materials therefore have a significant impact on the microelectronics industry. SiC MOSFET and GaN HEMT are examples of third-generation wide-bandgap semiconductor materials. The SiC MOSFET and GaN HEMT properties and uses are the focus of this essay. First, the internal structure, iv curve, threshold voltage, transconductance, and device characteristics of the respective SiC MOSFET and GaN HEMT are introduced. Then the two were compared for their electrical properties. Finally, by contrasting the application situations according to various features, it is possible to determine the various benefits and drawbacks of SiC MOSFET and GaN HEMT. The issues with materials and processes involved in creating wide bandgap semiconductor devices will be gradually resolved over time. In the future application field, it is crucial to select the appropriate device according to different advantages.
Research on Factors Influencing User Experience of Beijing Mobile Based on GBTD Classification Model
Authors: Xu Xiaojun
Published: January 2024

To improve the quality of user experience for Beijing Mobile, this article focuses on user evaluation, evaluates the influencing factors of user satisfaction, and determines its degree of impact. Firstly, this article preprocesses existing data and classifies it into scenarios. Secondly, descriptive statistical analysis is conducted on the data, and Outlier are studied. Finally, the GBTD classification model optimized by genetic algorithm is introduced and trained to determine the main factors of satisfaction and the importance percentage of each factor. The study found that the main factors affecting the satisfaction of Beijing Mobile's voice service are: "Total GPRS traffic", "MOU in the first three months", "ARPU in the current month", and "Network issues"; The main factors that affect the satisfaction of mobile internet business are: "Poor network signal/no signal", "redirect times", and "GPRS resource usage for the month".
Analysis of the Proton Therapy Controlling System and Application
Authors: Hong Zifei , Liu Zhuocheng , Tu Yutong
Published: January 2024

In recent years, the cancel therapy remains a key issue that needs to be addressed for human beings. As a matter of fact, the proton beam therapy (PBT) is a new kind of way to treat tumor by emitting proton beam. Different from conventional radiation therapy, the main principal is to cut the DNA of the tumor cells and prevent further development. In this case, this study will discuss the proton therapy controlling system as well as demonstrate the applications. Comparing to the X-rays, protons can align tumors more precisely and decrease the effect of other normal tissues, which also decrease the side effects. Nevertheless, the proton therapy is still be developed further. Many proton centers are being built and will begin operating in the future. This article popularizes the basic knowledge of proton therapy, from the basic principle, the applications to the limitations. These results shed light on guiding further exploration of proton therapy.
Analysis of Principle, State-of-art Detections and Applications of Gravitational Wave
Authors: Qiu Iok Sam
Published: January 2024

In recent years, with the development of stable and high-precision measurements, it is now available for human beings to observe gravitational waves based on interferometry. As a matter of fact, the first gravitational signal is obtained contemporarily based on the facility LIGO, which is actually a high-accuracy interferometer with an extremely large signal-noise ratio. On this basis and with this in mind, this study will detailly analyze the principle and detection results of the gravitational wave. To be specific, the state-of-the-art facilities LIGO as well as other advanced detection scenarios are estimated and evaluated with the introduction of the components, searching parameters as well as finding results based on the data retrieving. In addition, the obtained results are analyzed and the possible applications are also discussed. According to the analysis, the current limitations and prospects are proposed. Overall, these results shed light on guiding further exploration of gravitational wave detection.
The summation method and application of power series
Authors: Tian Xinyu
Published: January 2024

Summation, or the calculation of the sum of a series, is a fundamental operation in mathematics with widespread applications in various fields. It involves adding together a sequence of numbers or terms to obtain a single value. The ability to accurately determine the sum of a series is crucial for solving mathematical problems, analyzing data, evaluating functions, and making predictions. Power series are an integral part of mathematics and have found extensive applications in various fields. The study of power series summation, specifically the derivation and analysis of summation formulas, plays a crucial role in understanding the behavior and properties of these series. This paper aims to discuss the process of finding the sum function of power series by normalization method, definition method and the power series expansion, thus the power series establishes relations with several series which have common sum functions, and the advantages and limitations of each approach will be discussed, along with practical examples to illustrate the application of the sum function of power series in calculating π and integration.
The Research of Influence Factors that Possibly Lead to Alzheimer's Disease
Authors: Jin Yuhan , Li Chulei
Published: January 2024

In known studies it has been shown that the prevalence of Alzheimer's disease is related to factors such as heredity, aluminum intake, lifestyle, and gender, but there are still many factors that have not been considered and studied. Therefore, this study focused on the effect of other factors on the prevalence of Alzheimer's disease. The study used multiple linear regression models to process the data from the Alzheimer's Disease Prediction System, an online web-based system that publishes test data from 373 individuals, and visual graphs to analyze the independent variables. The study concluded that Alzheimer's disease was not significantly associated with age, but was significantly associated with gender, years of education, and socioeconomic status, and positively associated with the latter two. However, there was a significant relationship with gender, level of education and socioeconomic status, and a positive relationship with the latter two. These factors such as years of education and socioeconomic status have not appeared in previous studies, so this study provides a new perspective on Alzheimer's disease medical research and a new direction for predicting the disease.
Prediction and Recognition of Epileptic Seizures Based on Artificial Intelligence
Authors: Weng Zhiheng
Published: January 2024

Repetitive seizures caused by abnormal brain activity are the hallmark of the common neurological illness epilepsy, which has a serious impact on the health of those affected. The introduction of artificial intelligence (AI) in recent years has ushered in novel ways of predicting and recognizing epileptic seizures, transforming the discipline of neurology. In particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Support Vector Machines (SVMs), and Extreme Learning Machines (ELMs) are examined in this research as potential AI approaches for the prediction and identification of epileptic episodes. The underlying working logic of these AI approaches is investigated, emphasizing their distinct strengths and capacities. RNNs and LSTMs are well-known for their ability to describe temporal dependencies in sequential data, whereas SVMs excel in robust classification and ELMs excel at rapid training and real-time prediction. Real-world case studies show how these AI models may be used to analyze electroencephalogram (EEG) data, extract relevant features, and distinguish between seizure and non-seizure states. Furthermore, the essay explores the ramifications of AI in the medical profession, emphasizing the possibility of prompt intervention, improved patient care, and resource optimization. The difficulties and ethical concerns regarding AI in healthcare are also discussed. Finally, this piece highlights AI's promising role in predicting and identifying epileptic seizures, providing a glimpse into the future of personalized, data-driven healthcare. The combination of AI approaches opens the door to more accurate and timely seizure forecasts, increasing the quality of life for those living with epilepsy and revolutionizing the landscape of neurological healthcare.
The Research of Influence Factors that lead to Heart Disease
Authors: Xia David
Published: January 2024

Heart disease is one of the common diseases, and it is the disease with the highest mortality. Although there are many studies in the field of heart disease, including genetic aspects, environmental aspects and so on, most of them are single-factor studies. Therefore, this paper will establish a Probit regression model, based on 11 factors that may induce heart disease, and make a statistical analysis on the health status of 400,000 people collected in 2020. It is concluded that smoking, stroke, different walking, gender, age category, diabetes, Asthma, Kidney disease and skin cancer will have a significant positive impact on Heart Disease, and Physical Activity will have a significant negative impact on Heart Disease. However, Alcohol Drinking does not affect the Heart Disease. The results of this paper can provide a more comprehensive factor analysis direction for preventing heart disease, to do a good job of prevention, reduce the prevalence of diseases and reduce mortality.
Analysis of the Influencing Factors on Inbound Tourism
Authors: Shi Zhiyu
Published: January 2024

This paper analyzes and predicts the current inbound tourism from six aspects: travel population, travel reasons, tourism consumption, public transportation, social environment and time proportion. There are many other human factors that play a role, such as the capacity of scenic spots or the protection of natural resources. There will be this different impact. With the continuous development of the times, tourism has become one of the most important industries in the world. The income and impact it brought are very large. At present, the research on this aspect needs to be improved. The results show that in the past five years, the inbound foreign exchange income of cities has shown a growing trend, because it is affected by many factors, which can be used to predict the future development trend. The foreign exchange earnings of inbound tourism in Inner Mongolia have achieved a steady increase from 2010 to 2020.
The Application of Remote Sensing Technology in Returning Farmland to Forest
Authors: Jiang Keshu , Zhao Yuxuan
Published: January 2024

Since the 1980s, due to China's economic development, large tracts of forest trees have been felled, causing serious soil erosion and environmental pollution problems. But since the beginning of this century, China has started to convert farmland to forests and various vegetation restoration activities. The purpose of returning farmland to forest is to better protect the environment and prevent soil erosion. This paper introduces the use of remote sensing images to detect the implementation of the project of returning farmland to forest. Explain the method of sample selection and the steps of data collection. The plan on remote sensing monitoring of returning farmland to forest was introduced. At the same time, the help and continuous monitoring of remote sensing technology after returning farmland to forest was introduced. This article contains examples and analysis of scientists using GIS technology to assist in returning farmland to forests. Finally, it includes the prospect and expectation of GIS for the project of returning farmland to forest. And the future application of this technology in this project is prospected.
Acoustic Design of Elliptical Bells for Chime Sound Simulation
Authors: Zhang Leyu
Published: January 2024

This project seeks to unravel the opulent acoustical characteristics and sonic effects of ancient Chinese chime bells. Initially, distinctive features of the chime bells are encapsulated, highlighting the bifonic phenomenon and elucidating the role of fundamental and harmonic frequencies in chime bell sound production, while emphasizing the timbral diversity attributable to its multifrequency sound source. An exploration into the impacts of various factors on chime bell sound, such striking position, is conducted through experimentation with a bronze chime bell model, underscoring their significant influence. Employing experimental modal analysis methods, vibrational modes of the chime bell surface at disparate frequencies are revealed, laying a foundation for understanding the sound production principles. Furthermore, image processing technologies are utilized to construct a 3D model of the chime bell, and finite element modal analysis methods are applied to visualize its modes. The feasibility of using a circular ring to simulate chime bell sound is explored, identifying the elliptical ring as a more promising option and undergoing numerous rounds of iterative optimization. The theoretical research is brought to fruition by manufacturing a cast iron elliptical ring model for striking experiments, thereby validating the theoretical insights. In summation, this research provides a comprehensive and systematic exploration into the acoustical mechanisms and design principles of chime bells, offering invaluable insights into ancient Chinese music and cultural heritage. The multi-faceted and rich nature of chime bells as musical instruments is unveiled through the integrated application of various methods, deepening the understanding of their acoustical characteristics.
The State-of-art Searching Scenarios for Dark Matter Candidate: Evidence from Axion and WIMPs
Authors: Wu Weifan
Published: January 2024

In recent years, the dark matter still remains a mysterious in astrophysics and cosmology. So far, none of the candidate of dark matter is searched, even based on the state-of-art detection scenarios and facilities. With this in mind, this study comprehensively discusses dark matter's historical origins, research significance, and an overview of recent developments in this field. To be specific, the focus is placed on two candidate particles for dark matter: axions and Weakly Interacting Massive Particles (WIMPs). The ADMX project is discussed in detail concerning axions, emphasizing the utilization of susceptible detectors and instruments for large-scale searches across various mass ranges. Additionally, experiments such as XENON and LZ are examined to provide deeper insights into the existence of WIMPs. The article also addresses the limitations of current investigations and offers prospective directions for future research endeavors in dark matter. Overall, these results shed light on guiding further exploration of dark matter searching.
Proof and Application of the Mean Value Theorem
Authors: Wu Shuchen
Published: January 2024

In calculus, mean value theorem (MVT) connects a function's derivative and its rate of change over a certain interval. This paper delves into the mathematical intricacies of the MVT and its multifaceted applications. Through rigorous proofs and illustrative examples, this study establishes the MVT's fundamental role in calculus and its relevance in understanding the behavior of functions. The paper extends its exploration to encompass related theorems, including extreme value theorem, which connects function’s continuity and extrema, Intermediate Value Theorem, which states that the function value within an interval of a continuous function must be between the maximum and minimum values, local extreme value theorem, Rolle’s theorem, a specific situation of the theorem, and the integral MVT, an application in integral aspect of MVT, further enriching the comprehension of these pivotal concepts. These theorems provide powerful tools for understanding the properties of continuous functions, identifying critical points, and establishing relationships between function values and their derivatives. This paper highlights the significance of proving these theorems and solving mathematical problems as applications. Through a systematic exploration of the mathematical foundations, this paper contributes to a deeper comprehension of the core principles underlying calculus and their applied theorems in different contexts.
Prediction of ENSO Based on NCEP Reanalysis Datasets
Authors: Shi Mengli
Published: January 2024

In recent years, ENSO has been a climate phenomenon that has profound effects on global climate patterns and extreme events. Nino3.4 is a data representation of ENSO. In this study, CNN and LSTM were used to fit and foresee the data in the NCEP reanalysis data set. The findings demonstrate that while the LSTM model can also accurately forecast the changing tendency, it is relatively the effect of CNN is poor, and there is a situation where the simulation value is generally small, CNN does a good job of forecasting the Nino3.4 index, which reflects ENSO. When the index fluctuates significantly, the selection, however, has a tendency to be conservative. Additionally, this study's sample size was quite high, which led to more precise prediction results than in earlier research. To help the ENSO forecasting model forecast more precisely and timely, this study offers various references for its improvement of further study.
Principle and State-of-art Observation Scenarios of Black Holes
Authors: Zhong Zimu
Published: January 2024

Tracing back to the 17th century, the concept of black holes emerged as "dark stars," celestial bodies with gravitational pull surpassing the speed of light. Schwarzschild's solution to Einstein's equations in 1916 introduced the concept of a singularity and the Schwarzschild radius, defining black holes as objects compressed within this boundary. Two main classification methods for black holes based on mass and charge/angular momentum are discussed’. The study explores the principles of detection: gravitational waves, generated by events like black hole mergers, and gravitational lensing, where immense mass bends space and time, distorting light from distant objects. Facilities such as LIGO, Virgo, and KAGRA are introduced, showcasing their contributions in detecting gravitational waves. Optical telescopes like the Hubble Space Telescope and the Event Horizon Telescope play a vital role in visualizing black holes. Recent results include numerous successful black hole detections, testing the validity of General Relativity, and providing precise information on black hole masses and locations. While limitations in sensitivity and resolution persist, the future outlook is promising. Advancements in observatory technology, third-generation gravitational wave detectors, and multi-messenger astronomy collaborations will deepen our understanding of black holes and their role in the cosmos, fueling ongoing exploration of these enigmatic cosmic entities.
Impact of COVID-19 on Manufacturing Overhead Cost: A Case of an Electronic OEM Company in Guangdong, China
Authors: Hsu Juishan , Yu Zijie
Published: January 2024

This research analyzes the data of the manufacturing overhead costs (MOHC) of a plant in an electronic OEM company (located in Guangdong, China) during and after the COVID-19 pandemic. A paired t-test and a multiple variable regression analysis are conducted. MOHC and other indications are visualized by time sequence diagrams to facilitate the identification of the difference between mid- and post-pandemic. Through diving deeper into the effect of COVID-19 on the MOHC, Unit MOHC, and shipment, the overall influence of COVID-19 on the entire electronic OEM industry can be inferred. It is concluded that COVID-19 has influenced MOHC, Unit MOHC, shipment, and employee size, which may bring forth a paradigm-shifting experience on how people view the influence of COVID-19 on the entire electronic OEM industry and pave the way for further research afterward. This paper fills the research gap by focusing on one plant in one company, avoiding the difference in MOHC among different companies and plants.
Design and Optimization of Bismuth-Doped Fiber Amplifiers in the L+ Band (1600-1650nm)
Authors: Cao Chang , Lei Jingyao , Niu Tongxin
Published: January 2024

This study investigates the design and optimization of bismuth-doped fiber amplifiers in the L+ band (1600-1650nm). The L+ band, as a promising optical communication frequency range, demands high-performance fiber amplifiers. However, existing amplifier technologies exhibit limitations in the L+ band, leading researchers to explore bismuth-doped fiber amplifiers to overcome these constraints. This paper first introduces the working principles and relevant background knowledge of L+ band fiber amplifiers. Subsequently, a comprehensive investigation of the gain characteristics of bismuth-doped fiber amplifiers in the L+ band is conducted. Based on theoretical models, a series of optimization designs, including adjustments of bismuth doping concentration, fiber length, and pump power, are carried out to achieve optimal performance. The proposed optimization scheme demonstrates significant performance improvements through MATLAB simulations and a genetic optimization algorithm, achieving high gain and excellent characteristics in the L+ band.
Newton’s Methods: Constraints and Higher Order Convergence
Authors: Yang Haobo
Published: January 2024

Newton’s method is designed to approximate the roots of functions with iterative process. The basic idea of Newton’s method is repeatedly calculating the next approximation with a favorable initial guess which has a tangent line. Under some cases, such as indifferentiable functions, Newton’s method does not work. Since Newton’s method is not always desirable, it is varied to fit in different situations, and hence, different variants of Newton’s method prosper. One kind of variants aims to enhance the speed and the efficiency of Newton’s method, which is mainly discussed in this paper. Also, this kind of variants extends the applicability in different practical problems like chemicals and increase the order of convergence. This paper also discusses the efficiency of variants with efficiency index to normalize different methods and finds out real world problems need solutions with higher-order convergence and less computational cost. Nowadays, Newton’s method and its variants are still a powerful tool in diverse scientific applications.
Design and optimization of a full band fiber optic light source
Authors: Dong Xubo , Fang Jiaye , Wang Siqi
Published: January 2024

In this paper, we study and select the optical fiber materials, and fluorotellurite was finally chosen as the substrate material and co-doped with appropriate amounts of praseodymium ions (Pr3+) and erbium ions (Er3+), to achieve full-bandwidth luminescence of praseodymium and erbium co-doped fluorotellurite glasses pumped by a laser with a wavelength of 488 nm in the range of 1300 nm to 1680 nm, which includes all optical bands from O- to L-bands. In addition, the optical fiber amplifier’s working principle is described in terms of the four- and three-energy level systems for praseodymium and erbium ions, respectively, and the rate and power propagation equations in the steady state are established to analyze the influences of the fiber length, the doping concentration, and the pump power on the fiber-optic amplifier gain, which are optimized using a simulated annealing algorithm. A novel amplifier design strategy is presented in this work for the creation of full-band communication systems in order to satisfy the demands of upcoming high-speed, high-bandwidth data transmission requirements.
Types and the State-of-art Detections Scenarios for Black Holes
Authors: Li Chenxuan
Published: January 2024

As a matter of fact, the black holes remain a mysterious topic in cosmology and astrophysics. On this basis, plenty of scholars and research aim at detecting the features of black holes and the characteristics of gravitation for black holes in recent years. With this in mind, this study will discuss the state-of-art detection scenarios for black holes. To be specific, this study will first introduce the definition of black hole as well as the types and category for the black holes. Afterwards, the detection principle as well as schemes will be demonstrated. In detail, some of the detection facilities are introduced and demonstrated. Moreover, the state-of-art detection results will be analyzed and the current defects as well as drawbacks will be evaluated. According to the analysis, further suggestions to investigate black holes are put forward. Overall, these results shed light on guiding further exploration of black hole investigations and detections.
Study on Modern Hydrogen Carrier Technology
Authors: Lu Zihao
Published: January 2024

Hydrogen is a promising energy with high efficiency and zero pollution, but the low energy density makes it challenging to transport hydrogen efficiently and safely over long distances. Characteristics of liquid hydrogen bring difficulties to transportation system design. The low temperature and hydrogen embrittlement affect the design of the storage tank. The evaporation rate of liquid hydrogen makes it necessary to install an insulation system. The harsh working environment also affects the structure design. As large-scale transportation plays an important role in international energy trading, this article delves into the properties of liquid hydrogen. The storage and insulation technology are summarized. By comparing the characteristics of liquid hydrogen and LNG, according to the commonalities and differences between the two, the design of key systems of liquid hydrogen in storage, and container insulation technology has been analyzed. This article may provide a theoretical guide for the development of liquid hydrogen carriers.
Ordinary Differential Equation and Its Application
Authors: Zheng Bohong
Published: January 2024

Ordinary Differential Equations (ODEs) serve as a foundational mathematical framework with immense versatility and applicability across various domains. This essay delves into the multifaceted applications of ODEs in two distinct but interrelated fields: the domain of deep neural networks and the analysis of economic models. By establishing a bridge between theoretical foundations and real-world implementations, this study underscores the pivotal role of ODEs in shaping contemporary advancements. In the realm of deep neural networks, ODEs have revolutionized training methodologies. They enable dynamic architectures, where the network's behavior evolves continuously over time, improving its ability to capture complex patterns and adapt to changing data. This innovation has found use in computer vision, natural language processing, and reinforcement learning, leading to more robust and efficient AI systems. Simultaneously, ODEs play a crucial role in economic modeling. They facilitate the formulation of dynamic systems that depict the evolution of economic variables over time. These models help economists analyze complex economic phenomena, make predictions, and formulate informed policy decisions, ultimately contributing to the stability and growth of economies. This essay explores the mathematics underpinning these applications, emphasizing ODEs’ pivotal role in advancing both the realms of artificial intelligence and economic analysis, highlighting their significance in shaping modern technological and economic landscape.