Catastrophes, like earthquakes, bring sudden and severe damage, causing fatalities, injuries, and property loss. This often triggers a rapid increase in insurance claims. These claims can encompass various types, such as life insurance claims for deaths, health insurance claims for injuries, and general insurance claims for property damage. For insurers offering multiple types of coverage, this surge in claims can pose a risk of financial losses or bankruptcy. One option for insurers is to transfer some of these risks to reinsurance companies. Reinsurance companies will assess the potential losses due to a catastrophe event, then issue catastrophe reinsurance contracts to insurance companies. This study aims to construct a valuation model for catastrophe reinsurance contracts that can cover claim losses arising from two types of insurance products. Valuation in this study is done using the Fundamental Theorem of Asset Pricing, which is the expected present value of the number of claims that occur during the reinsurance coverage period. The number of catastrophe events during the reinsurance coverage period is assumed to follow a Poisson process. Each impact of a catastrophe event, such as the number of fatalities and injuries that cause claims, is represented as random variables, and modeled using Peaks Over Threshold (POT). This study uses Clayton, Gumbel, and Frank copulas to describe various dependence characteristics between random variables. The parameters of the POT model and copula are estimated using Inference Functions for Margins method. After estimating the model parameters, Monte Carlo simulations are performed to obtain numerical solutions for the expected value of catastrophe reinsurance based on the Fundamental Theorem of Asset Pricing. The expected reinsurance value based on Monte Carlo simulations using Indonesian earthquake data from 1979–2021 is Rp 10,296,819,838.

The effective drainage radius of coal seam is an important basis for the spacing of pre-drainage gas boreholes. To quickly and accurately determine the effective drainage radius, a new method was proposed. For the coal face where the desorbable gas content before mining has met the standard, the compliance of mine gas drainage rate was used as the basis to determine the effective drainage radius. The fluid-structure interaction model was constructed, numerical simulation of coal seam gas drainage was carried out by using COMSOL software, and the model was validated by combining the field test results. The results show that the new method has the advantage of short cycle. With the extension of drainage time, the increase of effective drainage radius gradually decreases, and finally reaches a relatively stable limit value, which conforms to the Langmuir function. The average error between numerical simulation and field test values of effective drainage radius is 4.9%, which proves that the model is reliable. This model can accurately predict the effective drainage radius under different coal seam gas contents and drainage times. The research results provide a new mean for determining the effective drainage radius of coal seam and the layout of gas drainage boreholes.

Infrared thermal imaging technology is another new branch for medical imaging after traditional medical imaging technologies such as X-ray, ultrasound and magnetic resonance (MRI). It has the advantages of noninvasive, nondestructive, simple and fast. Its application can radiate multiple clinical departments. This paper mainly expounds the principle, influencing factors of medical infrared thermography and its application in radiation protection and other medical fields.

While the rapid development of artificial intelligence has affected people's daily lives, it has also brought huge challenges to high school mathematics teaching, such as restructuring the classroom teaching structure, transforming the role of teachers, and selecting classroom teaching methods. Based on this, the article explores the application strategies of AI technology in improving knowledge introduction, improving mathematics classroom efficiency and stimulating students' learning interest, with a view to optimizing classroom teaching links, improving students' core discipline quality, and promoting the development of high school mathematics teaching informatization.

This study examined socio-economic factors affecting Micro, Small, and Medium Enterprises (MSME) e-commerce adoption, focusing on gender, income, and education. Using the 2022 National Socio-Economic Survey (Susenas) data, a logistic regression model was employed to analyze key determinants of e-commerce utilization. Additionally, an online survey of 550 MSMEs across 29 provinces was conducted to assess the impact of digitalization on business performance. In comparison, an offline study of 42 MSMEs with low digital adoption provided insights into the barriers hindering digital transformation. A natural experiment was conducted to evaluate the effectiveness of behavioral interventions in promoting the adoption of e-payments and e-commerce. The main contribution of this study lies in integrating large-scale national survey data with experimental approaches to provide a deeper understanding of digital adoption among MSMEs. Unlike previous studies focusing solely on socio-economic determinants, this research incorporated a digital nudging experiment to examine how targeted incentives influenced e-commerce participation. The findings revealed that digital transformation significantly enhanced MSME performance, particularly in turnover, product volume, customer base, and worker productivity. Socio-economic factors such as gender, household head status, and social media access significantly influenced digital adoption decisions. Behavioral nudging proved effective in increasing MSME participation in e-commerce. Although this study was limited to Susenas 2022 data and survey responses, it bridges a critical research gap by linking socio-economic factors with behavioral interventions in MSME digitalization. The findings offer key insights for policymakers in formulating evidence-based strategies to drive MSME digital transformation and e-commerce growth in Indonesia.

This paper aims to develop a holistic framework for the Maqasid al-Shariah in Responsible Investment (MSRI) index for selected publicly listed companies in the Malaysian capital market. To test the validity of the MSRI framework, a sample of 30 publicly listed companies from 2021 was selected using purposive sampling. The framework consists of eight themes with forty-five elements to evaluate companies based on their annual reports, sustainability reports, and public disclosures. The scores are classified into three categories: Shariah compliant, Shariah non-compliant, and Hajiyyat. Out of the 30 selected companies, the summary of MSRI scores concludes that twenty (20) companies were identified as Shariah compliant, while the remaining four (4) were classified as Shariah non-compliant, and six (6) as Hajiyyat. Overall, the results of the analyses show that the sustainability of the company and society has a higher percentage than the wealth preservation of companies. This research differs substantially from prior work by offering a novel approach that develops a holistic framework integrating Maqasid al-Shariah with elements of responsible investment. This study believes it can provide valuable guidance for formulating Islamic investment public policy for selected investment portfolios.