This paper provides a corresponding coping strategy for developing the insurance industry under extreme weather by establishing an insurance company underwriting model. An insurance model (ICU model) for assessing catastrophe risk is proposed based on the results of some international databases and disaster resilience studies. The ICP coefficient is obtained by multiplying the regional vulnerability index with the regional risk index, where our innovatively proposed ARIMA-LSTM coupling algorithm predicts the risk index. The inverse proportionality function of the ICU coefficient is constructed based on the fact that the risk of insurance companies is positively correlated with the regional risk (ICP coefficient) and negatively correlated with the regional purchasing power (CBP coefficient). The CBP coefficients were computed by K-means clustering, and the derived ICP coefficients were used to derive the ICU coefficients for each region. Finally, the coefficients were categorized into three intervals to give the insurance company’s coverage model.

Due to the low efficiency of traditional communications and many problems, the development of optical fiber communications is currently necessary. Among them, fiber laser is the core of fiber communication, and the higher the output band of a fiber laser, the more information it carries, and the information that can be transmitted increases accordingly. Therefore, the primary goal is to develop a fiber laser with high-band output. At present, research on high-band output fiber lasers is being carried out at home and abroad. Domestic research focuses on the selection of fiber media; foreign research focuses on random fiber lasers. This article adopts the domestic research route, based on the premise that vanadium ions can emit laser wavelengths that include the L+ band when performing energy level transitions. At the same time, vanadium-doped gallium lanthanum sulfide glass has good functions of absorbing pump light and emitting lasing light. Therefore, this article mainly discusses the design of vanadium-doped fiber lasers using vanadium-doped gallium sulfide as the gain medium under simulation conditions to achieve high-band output of fiber lasers.

In 1964, Peter Higgs proposed the Higgs mechanism, a theory explaining the generation mechanism of the property “mass“ for gauge bosons. After that, physicists proposed many theories and experiments to prove the existence of the Higgs boson. Then in 2013, the European Organization for Nuclear Research (CERN) discovered the Higgs boson, and in the next ten years, physicists did a large amount of research about the boson, the other possible kinds of Higgs boson, and the pairs of Higgs boson. However, it is hard to prove those theories through experiments mainly due to the large mass of the Higgs boson. In this paper, the author discusses the process of particles getting mass and the significance of the Higgs boson. The history of the Higgs boson and its impact on the world is summarized, and some probable predictions of the future research orientation are proposed by summarizing the past research papers and concluding from those articles. Overall, physicists can do more research on the interactions between the Higgs bosons, whose sensitivity can be used to discover those possible particles in the future.

This paper explores the transformative integration of econometrics and data science, a synergy poised to redefine empirical research within economics. By merging traditional econometric methods with advanced data science techniques, such as machine learning algorithms and big data analytics, this interdisciplinary approach enables a deeper, more nuanced understanding of complex economic phenomena. We delve into the theoretical foundations underlying this integration, highlighting how machine learning algorithms like random forests and neural networks complement conventional regression analysis, thereby enhancing model complexity and predictive accuracy. The paper further discusses methodological advancements, including handling high-dimensional data, incorporating unstructured data through natural language processing, and the evolution of model selection processes empowered by machine learning. Practical applications are thoroughly examined across three pivotal areas: economic forecasting and policy analysis, financial markets and risk management, and social economic analysis and public policy, showcasing the significant contributions of this convergence to economic forecasting, policy formulation, and the assessment of public interventions. This comprehensive exploration underscores the potential of combining econometrics and data science to offer more precise and actionable insights for policymakers, researchers, and practitioners in the field of economics.

This paper explores the foundations and applications of quantitative analysis in financial risk management. It examines the pivotal role of probability theory, statistical inference, and advanced algorithms in identifying, quantifying, and mitigating financial risks. Key concepts such as the Normal, Poisson, and Binomial distributions are discussed in the context of risk analysis, alongside statistical inference methods like hypothesis testing and confidence intervals. Furthermore, the paper investigates the application of portfolio optimization models, credit risk evaluation techniques, and market risk assessment methodologies in practical risk management scenarios. Additionally, it addresses the challenges posed by model risk, data quality, and regulatory compliance, emphasizing the need for rigorous validation, robust data governance, and ethical considerations in risk management practices. By integrating sophisticated quantitative techniques with real-world applications, financial institutions can enhance their ability to navigate the complexities of modern financial markets and achieve more effective risk management strategies.

Traffic accident is getting increasingly serious. Although previous researchers use a variety of methods to predict the traffic accident, there are numerous demerits that need to be improved. This article demonstrates 12 variables that impact the traffic accident with 679 samples of accidents in UK from 2012 to 2014. This paper first analyses the relevance between dependent and independent variables, and also two independent variables to show the correlation between each factor. By using the multiple linear regression, it is concluded that although some independent variables do not have relationship with the dependent variable ‘urban or rural area’, Accident Severity, Number of Casualties, Road Type, Speed limit, Junction Control show significant relationship with the dependent variable. The paper also considers the 95% confidence interval in order to compare the effective density of data. Overall, the prediction of traffic accident is based on a number of factors and a sizable sample of accidents to summarize the impact that traffic accidents bring.

In current years, the mental issues of university students have become progressively thoughtful, attracting widespread attention from society. Therefore, studying the elements that influence the mental issues of university students has become an urgent issue, but few scholars have done this work. Therefore, based on the multiple linear regression model, this article quantitatively studies several factors that affect the mental health status of college students, attempting to make a contribution to explaining the causes of mental issues among university students. The empirical study finds that stress level is currently the main factor affecting the mental health status of college students, and a significant positive correlation between this variable and the incidence of mental health problems has been found in the model. Therefore, schools and society must heed the psychological pressure of students, provide reasonable help and psychological counseling when necessary, and college students should also learn how to eliminate their own psychological pressure to prevent the occurrence of mental health problems caused by excessive pressure.

With the steady development of social economy, commercial housing, as an important real estate, occupies a large proportion in family assets. According to the “China Household Wealth Survey Report” (2018) compiled by the Social China Economic Trends Institute, household net worth accounts for 70% of household wealth, including housing prices in Beijing and Shanghai. In higher cities, the proportion is as high as 80%. This paper analyzes the transaction data of about 10,000 second-hand houses in Beijing, constructs a multiple regression model with SPSS software, and obtains the dependent variable (housing price per unit area). The dataset used in this paper is fetched from the Kaggle website (Housing Price in Beijing). The results show that the relationship between the elevator, the floor situation, the decoration method, the administrative division and other independent variables. Also, it is shown that the correlation between the two is significant, so the model can be used. This paper provides reference for the actual transaction of second-hand housing in Beijing.

In this study, we investigate the profound impacts of the sex determination pattern in the invasive sea lamprey on ecological dynamics and focuses on how the pattern influences the population based upon the interplay of system stability. In the initial segment, we employ the Lotka-Volterra model and system dynamics to study lamprey sex ratio’s correlation with ecosystem stability. Focusing on food impact on lamprey sex ratio, it can be delved that its sex determination maintains the prey population at a consistently low level, thereby affecting population stability. The following segment explores lamprey sex ratio’s evaluations utilizing system dynamics model based on Analytic Hierarchy Process (AHP). Cellular Automata (CA) is employed for cross-validations, revealing nuanced insights into the adaptive advantages and vulnerabilities of lamprey’s reproductive strategy, highlighting the resilience of lamprey populations under natural pressures. Our modeling, with visualizations and simulations, supports findings and highlights avenues for future research. This study contributes to the evaluations of bio sex ratio switching, contributing for the possible ecosystem conservation strategies.

In this paper, we investigate various areas of financial risk management, reveal theoretical foundations, model implementation, verification processes, and advanced technologies for increasing the risk for financial risk mitigation. From the basic overview of probability and statistical analysis, we investigate an important role in quantitative management of uncertainty in financial portfolios. Discussing, optimizing, and validating financial risk models, emphasizing the importance of data integrity in the model implementation. The discussion focuses on the integration of asset diversification, compliance, capital adequacy, machine learning and block chain technology. By discussing these factors, this paper provides a comprehensive overview of the current financial risk management field, emphasizing the importance of mathematical models such as var and CVaR, and the impact of technological changes to the practice of traditional risk management. Through this exploration, we are insisting on a balanced approach that combines classical theories and innovative technical solutions for the purpose of contributing to strategic decisions and supervisory management in financial risk management.

This article mainly studies the physical model design and code simulation of a thulium-doped fiber laser that outputs 1750nm laser. The optical fiber laser has been widely used due to its excellent performance compared with other solid-state laser devices. Among all types of fiber lasers, ytterbium-doped fiber lasers possess the highest output power, whereas they seem to be impractical when an output laser with a longer wavelength is required. However, Thulium-doped lasers have huge room for development to solve the problem, whose unique center wavelength characteristics provide them with huge utilization value. This article shows the electronic transition process a of thulium-doped laser and the power transmission diagram in the fiber. Its rate equation and power equation are also listed below. Through paper retrieval and data extraction, the values of laser parameters can be accurately confirmed, and its power curve can also be obtained through simulation. After analyzing the data obtained from the simulation, the working principle of the fiber laser was intuitively displayed, and the performance of the system was further evaluated. Last but not least, several limitations of this experiment will also be discussed, in order to explore the system more thoroughly.

Capacitive flexible stretch sensors, compared to resistive ones, offer better linearity and are thus more promising for human motion detection applications. Current capacitive sensors, however, face challenges in effectively enhancing their Gauge Factor (GF), limiting their sensitivity. This paper presents a capacitive stretch sensor utilizing a negative Poisson’s ratio structure made of high Shore hardness silicone as the framework and low Shore hardness silicone as the dielectric layer. Liquid metal composite material is used for the electrodes. Finite element simulation validated the sensor’s stretching effect. The sensor achieved a sensitivity of 2 pf/mm and a GF value of 2.19. Its efficacy is demonstrated through the measurement of finger joint movements, indicating broad application potential in human motion detection.

This article delves into the intricate fabric of the universe through the lens of symmetry breaking, the elusive search for magnetic monopoles, and the mathematical elegance of homotopy groups. Spontaneous symmetry breaking, pivotal in distinguishing fundamental forces, is illustrated by the Higgs mechanism within quantum field theory. The quest for magnetic monopoles, inspired by grand unified theories, challenges our understanding of electromagnetic symmetry, suggesting an exquisite balance between the electric and magnetic charges as per the Dirac quantization condition. Homotopy groups offer a profound mathematical framework to classify topological defects, enriching our understanding of the universe’s early conditions and the formation of cosmic structures. Despite extensive searches, direct evidence for magnetic monopoles remains elusive, propelling theoretical and experimental physics into new territories. This article synthesizes theoretical foundations, experimental endeavors, and the implications of these pursuits on our understanding of the cosmos.

This essay explores the evolution of option pricing models, tracing their development from the foundational Black-Scholes model to more advanced frameworks such as the Heston model and beyond. Beginning with an introduction to option pricing theory, the essay discusses the origins of the Black-Scholes model and its assumptions, as well as the challenges and limitations it faces. It then examines the extension of the Black-Scholes model, so-called the Black-Scholes-Merton model. It incorporates dividends, and lays the groundwork for further research into options pricing and financial derivatives. Then various stochastic volatility models emerge, and the essay chooses the Heston model as a typical example for analysis, highlighting its advantages and applications in option pricing. Furthermore, the essay compares the Heston model with other option pricing models, including the SABR model and Bates model. At the end of the essay, recent advances and future directions in option pricing are introduced and discussed. Through this comprehensive exploration, readers can gain a deeper understanding of the evolution of option pricing models and their significance in modern finance.

This article aims to identify the factors that have an impact on housing prices. The significance factors of 241 samples from the United States from 2003 to 2022 were analyzed using multiple linear regression method. Based on a hypothesis, the selected 5 variables are indeed related to housing prices. This article also collected a lot of other data related to the housing price index for multivariate judgment analysis, and used exploratory factors to test the research significance of each variable. To test the effectiveness of this operation, the study compared the VIF values and significance of these variables. The conclusion is that correlation analysis has been used to test the relationship between DATE and five variables: income, housing subsidies, unemployment rate, unsold or sold houses and total houses, as well as the magnitude of the impact of these factors on the housing price index. Overall, the volatility of the US housing price index can be considered based on the degree to which these factors affect it.

Dark matter differs from other common substances in physics. It is a rather abstract substance because it cannot be seen or touched. In astrophysics, most scientists believe that dark matter exists because many phenomena would likely be unexplainable without invoking dark matter. However, in quantum mechanics and particle physics, most scientists are convinced that dark matter does not exist, as in these fields, scientists rely on empirical evidence and are less inclined towards hypotheses and conjectures. Therefore, the existence of dark matter remains a mystery in the scientific community. The primary method of studying dark matter is to compare measured values with observed values and explore the reasons behind these differences when discrepancies arise. In this regard, this paper investigates the reasons for discrepancies from three perspectives: rotation curves, gravitational curves, and galaxy distribution. In conclusion, although the existence of dark matter is not certain, there is evidence supporting its existence.

This paper delves into the issue of soaring housing prices in China’s real estate sector, with a particular focus on Beijing. Employing a multiple linear regression model, it conducts an empirical analysis to identify the factors influencing housing prices. Furthermore, it synthesizes scholarly perspectives to offer a comprehensive discussion on these influencing factors. The research highlights Beijing’s real estate development, residential investment, and residential sales area as pivotal determinants of housing prices. Building upon these findings, the study advocates for the implementation of effective measures such as land policy regulation and restrictions on real estate transactions to guide the real estate market development prudently and maintain housing prices within a rational range. Results show that real estate development residential investment has a significant positive impact on housing prices, while residential sales area has a significant negative impact. Ultimately, the research endeavors to furnish a scientific foundation for policy formulation and assess its impact, thereby fostering the healthy growth of the real estate market, ensuring stable economic progress, and enhancing the well-being of people.

The U.S. market of music is the largest market in the globe with its huge influence spreading around, which enables it to be the dominant of the world music industry. The article is produced due to the prevailing music market in U.S. that has phenomenally influence around the world. Therefore, this article takes Taylor Swift as an example thanks to the significant influence power to calculate whether some factors such as acousticness that might directly affect the popularity of singer’s songs for the purpose of formulating some market strategies by corresponding suggestions and advice. The research methods include 3 common mathematical model: linear regression, decision tree and random forest, which indicates that the year of release has the most contribution to the prediction with the value of importance of 0.549929. However other factors seem to have less relativity given the small values for the following 2 factors folklore and reputation with values of importance both below 0.2.

This research introduces a novel method for forecasting cardiovascular diseases using an advanced combination of K-means++ clustering, Principal Component Analysis (PCA), and Logistic Regression techniques. Given the global impact of cardiovascular diseases as a primary cause of death, this research utilizes a comprehensive dataset to tackle the prediction challenges associated with CVDs. Initially employing K-means++ for enhanced data quality, followed by PCA for dimensionality reduction, the study applies Logistic Regression for outcome prediction, achieving remarkable accuracy, specificity, and sensitivity. This methodological rigor offers a promising avenue for early and accurate CVDs detection, significantly outperforming traditional predictive models. By refining data through these steps, the study ensures the predictive model is built on a solid foundation, enhancing the reliability and generalizability of the predictions. The integration of these advanced analytical techniques marks a step forward in the pursuit of effective cardiovascular disease management, highlighting the importance of data preprocessing in predictive modeling.

This report aims to analyze the relationship between global oil prices and various economic indexes by using linear regression and ARIMAX models. This study will predict global oil prices accurately and establish a reasonable system for regulating oil prices. The research uses the statistical approach to predict oil prices based on historical data (including independent variables and dependent variable). The study uses monthly average data of WTI crude oil prices from January 2000 to March 2023 and contains the analysis of various economic indicators such as Consumer Price Index (CPI), Personal Consumption Expenditures (PCE), Employment, Population, and Oil Price. The findings indicate that the linear regression model can explain about 40.89% of the variation in log oil price, with significant negative effects of log_PCE, log_EMPLOYMENT, and log_POPULATION, and a significant positive effect of CPI on log_price. However, there exists the probability that some other factors have impact on oil prices. In this study, the author employ the ARIMAX model with ARIMA(4,1,1) errors, which can describe a relatively good fit and small errors in training set measures. Overall, while the linear regression model partially explains the variability in global oil prices, further analysis on residuals is necessary. The study concludes that the ARIMAX model provides a better approach to capture the time-series nature of the data.