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 idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.

This study explores the integration of data mining, customer relationship management (CRM), and strategic management to enhance the understanding of customer behavior and drive revenue growth. The main goal is the use of application of data mining techniques in customer analytics, focusing on the Extended RFM (Recency, Frequency, Monetary Value and count day) model within the context of online retailing. The Extended RFM model enhances traditional RFM analysis by incorporating customer demographics and psychographics to segment customers more effectively based on their purchasing patterns. The study further investigates the integration of the BCG (Boston Consulting Group) matrix with the Extended RFM model to provide a strategic view of customer purchase behavior in product portfolio management. By analyzing online retail customer data, this research identifies distinct customer segments and their preferences, which can inform targeted marketing strategies and personalized customer experiences. The integration of the BCG matrix allows for a nuanced understanding of which segments are inclined to purchase from different categories such as “stars” or “cash cows,” enabling businesses to align marketing efforts with customer tendencies. The findings suggest that leveraging the Extended RFM model in conjunction with the BCG matrix can lead to increased customer satisfaction, loyalty, and informed decision-making for product development and resource allocation, thereby driving growth in the competitive online retail sector. The findings are expected to contribute to the field of Infrastructure Finance by providing actionable insights for firms to refine their strategic policies in CRM.

Purpose: This study aims to clarify the meaning of sport analysis, explore the contributions derived from sporting event analysts, and highlights the importance of responsible sport gambling. It also investigates how sustainable practices can be integrated into sports analysis to enhance social well-being. Design/methodology/approach: Secondary text data from government documents, news articles, and website information were extracted by searching keywords such as sports lottery and sports analysis in traditional Chinese, and then analyzed to establish the research framework and scope. Subsequently, 18 interviews were conducted with stakeholders to gain deeper insights. Findings: The content analyses reveals that sport analysis tends to be sport data science. Sporting event analysts may contribute to improving the performance of players or a team, enhancing spectator sports, and increasing sports lottery revenues. In the leisure aspect, the professionalism of sporting event analysts not only increases epistemic and entertainment values in spectator sports but also boosts engagement with sport lotteries. To ensure these enhancements remain beneficial, it is vital to emphasize responsible sport gambling and sustainable practices that protect vulnerable groups and promote long-term health benefits for those involved in sports. The integration of sustainable practices in sport analysis and the expertise of sporting event analysts can significantly advance economic and social development by generating funds through sport lottery industry for athlete programs, sports infrastructure, and educational initiatives, aligning with multiple Sustainable Development Goals. Additionally, the professionalism of these analysts may enhance public understanding and engagement of sports, promoting increased participation in sports, reducing healthcare costs, and contributing to the development of a healthier and more resilient society. Originality: Emphasizing responsible sports gambling is essential to the sustainability of sports lotteries and the role of sporting event analysts.

Developing “New Quality Productive Forces” (NQPFs) has been accepted as a new theory to accelerate the high-quality development in China. In current, China’s high-quality development mainly relies on the traction of the digital economy. In view of this, developing NQPFs in China’s digital economy sector requires locate and remove some obstacles, such as the insufficient utilization of data, inadequate algorithm regulation, the mismatched supply and demand of regional computing power and the immature market environment. As a solution, it is necessary to allocating data property rights in a market-oriented way, establishing a user-centered algorithm governance system, accelerating the establishment of the national integrated computing network, and maintaining fair competition to optimize the market environment.