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.