If radioactive plumes are released outside due to loss of containment building integrity during a nuclear power plant accident, these materials might travel with the wind, affecting both the surrounding environment and neighboring countries. In China, most nuclear power plants are located on the eastern coast. Consequently, a radioactive plume generated during an accident could negatively impact even the western part of the Korean Peninsula due to westerly winds. To detect such problems early, respond quickly, and protect residents, a system that can monitor aerial radiation under normal conditions is needed. Additionally, a detection system that can operate in real-time in an emergencies conditions is required. The current method for aerial radiation measurement takes environmental radiation data from a monitoring post 1.5 m above the ground and converts it to altitude. To measure actual aerial radiation, an expansive area is surveyed by aircraft. However, this approach is both time-consuming and expensive. Thus, to monitor radioactive plumes influenced by environmental factors like wind, we need a radiation detector that can gauge both radioactivity and directionality. In this study, we developed a radiation detector capable of assessing both the radioactivity and directionality of a radioactive plume and conducted its performance evaluation. We miniaturized the radiation detector using a CZT (Cadmium Zinc Telluride) sensor, enabling its mounting on unmanned aerial vehicles like drones. It is configured with multi-channels to measure directionality of a radioactive plumes. For performance evaluation, we positioned two-channel CZT sensors at 90 degrees and measured the energy spectrum for angle and distance using a disk-type radioactive isotope. Using this method, we compared and analyzed the directionality performance of the multi-channel radiation detector. We also confirmed its capability to discern specific radioactivity information and nuclide types in actual radioactive plumes. Our future research direction involves mounting the multi-channel radiation detector on a drone. We aim to gather actual aerial radiation data from sensors positioned in various directions.

The Lepidoptera - moths, butterflies, and skippers, is one of the three most species-rich, studied, diverse, and widely distributed insect orders, with over 157,424 species worldwide (van Nieukerken et al., 2011). Moths and butterflies serve as valuable indicator species for monitoring climate change. Conversely, the distribution of lepidoptera is actively and interactively influenced by changes in climate and land cover (Warren et al., 2001). Over the last five years, there are many oriental moth species, such as genus Stictane, Siccia, Philenora, Ammatho, Asota, etc., have been newly recorded in Korea, since other new records are very likely yet to be discovered in this country and nearby.

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

As world become closer and many people travel around the world, infectious diseases are likely to be prevalent, Dengue, chikungunya, Zika virus would become endemic diseases in many places in the world as climate is more likely to be fit to mosquito vectors than before. As a matter of fact, Aedes albopictus mediating these diseases became endemic species in Japan and France recently. One of the things that we employ is that we need to apply the Internet of Things concept in disease vector control soon to increase the accuracy of vector species in time as they are present unexpectedly. Here in this presentation, many lines of current state-of-the-art technologies to control the disease vectors using big data informatics application and machine learning strategies. With this trend, this presentation will add future directions of integrated vector management with new research endeavors to monitor the population of disease vectors concisely and precisely, Application of internet of things (IoT) in disease vector management will be addressed in this presentation. This will change future research and development of apparatus to monitor and control disease vectors.

식물에게 있어 화분매개는 필수적인 요소 중 하나인데, 화분매개를 하는 식물 중 50%이상이 곤충에 의해 화분매개가 이루어지고 있다. 화분매개를 하는 곤충에 대한 조사는 주로 농업과 관련되어 있는 과수작물 주변의 화분매개곤충에 대해 조사가 되어있지만, 정작 양봉과 관련되어 있는 밀원식물 주변의 화분매개곤충은 조사된 바가 없다. 이에 연구진은 밀원식물 중 국내에서 가장 많은 양봉생산물을 만드는 아까시나무(Robinia pseudoacacia L.)의 개화시기에 맞춰서 화분매개곤충을 조사하였다. 조사지역은 총 6군데로, 백두대간을 중심으로 RCP 기후변화 시나리오에 의해 지정되었다. 조사 결과, 전체적으로 6목 60과 183종 1,555개체의 화분매개곤충이 채집되었다. 이중, 가장 많이 채집된 종은 노린재목의 애긴노린재(Nysius plebejus)로 약 21.30%가 채집되었다. 채집된 종을 군집분석한 결과, 강릉지역이 가장 안정적인 생태계를 유지하고 있으며, 완주지역이 가장 불완전한 생태계를 유지하고 있는 것으로 확인되었다.

In recent years, high-throughput next-generation sequencing (NGS) techniques have provided fascinating opportunities to understand the biology of non-model organisms, especially insect species. The decrease in sequencing costs and extensive sequencing services from NGS providers has brought many entomologists to be involved in genome sequencing. However, poor planning can lead to extremely fragmented genome assemblies which prevents high quality gene annotation and other desired analyses. Insect genomes can be problematic to assemble, due to combinations of high polymorphism, inability to breed for genome homozygosity, and small physical sizes limiting the quantity of DNA able to be isolated from a single individual. Given to the rapid development of host resistance to multiple classes of insecticides, it is indispensable to study the comprehensive genomic information of insects. Recent advances in sequencing technology and assembly strategies can able to fetch breakthroughs in deciphering the genetic information of insects. Here, we present the cost effective high throughput genome sequencing and assembly strategies for insect species in respects to taxonomy, evolutionary history, immune response, drug development, insect host-virus interactions and pest management etc.