Modeling is one approach to better understanding the complex interaction in the abstracted and simplified forms. Here I present the interaction of honeybee, Apis mellifera and ectoparasitic mites of Varroa destructor and Tropilaelaps mercedesae. In the beginning, I provide the basic mechanism of ectoparasitic mite’s life cycle in association with the host insect life. Population behavior was analyzed as a single analytical population growth model. Then, since the carrying capacity of mite’s breeding resources are changing, I incorporated the damage function of mite to honeybee population. Incorporating the damage function into the honeybee-varroa interaction model provided more realistic behavior of both species. Simulation study showed that possible beneficial impact of hive-splitting on mite control. Also, varying the chemical spray timing and efficacies, the model simulation revealed that early spring acaricide treatment was essential for protection of honeybee from the varroa mites. At the last, this model has been expanded to include the other parasitic mite of Tropilaelaps mercedesae. For this, two species competition model was considered as well as incorporation of the host population behavior being added. Further discussion and call for collaboration were presented.

This essay discusses the enhancement of the KMV model to achieve greater accuracy in predicting default risk and mitigating the effects of asymmetric information in the financial market. Due to the existence of the problem of asymmetric information persists, with some market participants possessing more information than others. This imbalance disrupts the normal market operation, complicates financial regulation, and reduces market stability. Rating agencies have made efforts to disclose and predict default risks to provide more information to the market. Still, traditional models’ prediction accuracy has struggled to meet the market’s evolving demands. To address these challenges, this essay analyzes an improved model, the SIZE-PSO-KMV model. This model builds on the KMV model but introduces a differentiation between large and small firms. By doing so, it refines default risk predictions, thereby alleviating information asymmetry. Enhanced accuracy empowers financial regulators to make more informed decisions and helps prevent future financial crises. The SIZE-PSO-KMV model’s validity is established through rigorous testing, including a comparison with other KMV models and out-of-sample tests. The results demonstrate that this model significantly outperforms traditional KMV models in predicting default risk. Additionally, it adapts to the size of firms, acknowledging that large and small firms face distinct default risk profiles.

본 연구에서는 SWAT 모형과 HELP 모형을 이용하여 보청천 유역의 지하수 함양량을 산정하였다. SWAT 모형은 지표수 및 지하수 성분을 모두 고려할 수 있는 물순환 모형이지만, 토양층에 대한 침루과정의 물리적 해석이 미흡하다. 반면에 HELP 모형은 중간유출 및 지하수 유출성분을 모의하지 못하지만, 토양층에서의 비포화흐름을 고려하여 침루과정을 해석할 수 있다. 국내유역에서 함양량 산정을 위해 SWAT 모형은 여러 유역에서 성공적으로 적용되어 왔지만,