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According to the market competition theory, a firm’s decision-making is influenced by the behaviors or strategies of its competitors. The repercussions of competition include market-stealing and spillover effects. Relatively few studies in the reinsurance literature discuss the effect of competitors on an insurer’s decision-making. This study aims to fill a gap in the reinsurance literature by comparing insurers' reinsurance demand to their competitors' reinsurance purchases.

This study uses unbalanced panel data for the US property-liability insurance industry from 2006 to 2017 to determine the impact of competitors' reinsurance purchases on insurers' reinsurance demand. This study employs the Mixed Effect Model and the Quantile Regression to test the proposed hypotheses.

The evidence suggests that the affiliated reinsurance purchases of competitors have a positive and substantial effect on the affiliated reinsurance demand of insurers, crediting mimicking the reinsurance strategy. Interestingly, the market-stealing effect is supported while the non-affiliated reinsurance metric is used. Remarkably, given insurers with low non-affiliated reinsurance purchases, the finding sustains the mimicking reinsurance strategy. Nevertheless, the market-stealing effect remains a concern for insurers with a high non-affiliated reinsurance purchase.

The new findings concerning competitor effects analysis fill a void in the reinsurance literature. Risk diversification, capital substitution, and real services demand may play a crucial role in determining the market-stealing effect, leading to a decrease in market share. Insurers can mitigate the market-stealing effect of competitors by accessing expertise and capital substitution through non-affiliated reinsurance purchases.

This paper aims to investigate the application of 3D printing technology, particularly using sand-type materials, in the creation of artificial rock models for rock mechanics experimentation.

Using a comprehensive analysis, this research explores the utilization of 3D printing technology in rock mechanics. Sand-type materials are specifically investigated for their ability to replicate natural rock characteristics. The methodology involves a review of recent achievements and experimentation in this field.

The study reveals that sand-type 3D printing materials demonstrate comparable properties to natural rocks, including brittle characteristics, surface roughness, microstructural features and crack propagation patterns.

While the research establishes the viability of sand-type 3D printing materials, it acknowledges limitations such as the need for further exploration and validation. Generalizability may be constrained, warranting additional research to address these limitations.

This research contributes insights into the potential application of sand-type 3D printing materials in indoor rock physics experiments. The findings may guide future endeavors in fabricating rock specimens with consistent structures for practical rock mechanics applications.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.