The Japanese encephalitis virus (JEV) is a zoonotic pathogen that affects the nervous systems of humans, pigs, and horses. It has been classified into five genotypes (G1-G5) based on molecular analysis of the pre-membrane or envelope gene. In the Republic of Korea, the predominant JEV genotype has recently shifted from G3 to G1 and G5, highlighting the need for a rapid and accurate diagnostic method. In this study, we designed specific common and differential primer sets for JEV G1, G3, and G5 to detect the JEV gene. Four specific primer sets for JEV G1, G3, and G5 were used to selectively amplify the target gene. The detection limits of the common primer set for JEV G1, G3, and G5 were 100, 0.1, and 10 TCID50/reaction, respectively. The detection limits of the three differential primer sets were 1, 0.1, and 1 TCID50/reaction, respectively. No cross-reactivity was observed with non-JEV reference viruses. We successfully developed a multiplex reverse transcription polymerase chain reaction (RT-PCR) assay to distinguish the three JEV genotypes. Our multiplex RT-PCR assay is highly sensitive and specific, providing a reliable tool for confirming JEV infection in suspected samples. Additionally, our assay can be applied to suspected mosquito samples and commercial veterinary biological products.

This study addresses the challenge of optimizing officer assignments by reflecting the mutual preferences of both officers and units. Current officer assignments primarily rely on subjective judgments made during assignment committee meetings, often resulting in officers being placed in undesired positions or locations. This misalignment leads to reduced morale, decreased work efficiency, and even resignation. This issue has become more important at a time when the attrition of junior officers is accelerating. To address this issue, we propose an optimal assignment model that incorporates both officer and unit preferences, aiming to balance organizational needs with personal preferences. Additionally, it discusses methods to improve the mathematical model by considering various demands for practical application in the field, such as minimizing dissatisfaction and addressing the occurrence of preferred or undesirable units. These refinements resolve practical issues such as preventing undesirable unit assignments and managing multiple optimal solutions. Through this approach, the study seeks to deliver a more balanced and satisfying assignment system for officers while enhancing organizational efficiency.

The family Gelechiidae (Lepidoptera: Gelechioidea) is known as one of the largest families in Microlepidoptera, encompassing about 600 genera and more than 5,000 described species worldwide. However, the genus Altenia Sattler, 1960 has been poorly studied in Korea, with only one known species. Here we introduce a new species, Altenia parascriptella sp. nov., from Korea, providing photos of adults and genitalia for both sexes, along with diagnostic characteristics.

이 연구에서는 Inception V3, SqueezeNet(local), VGG-16, Painters 및 DeepLoc의 다섯 가지 인공지능(AI) 모 델을 사용하여 차나무 잎의 병해를 분류하였다. 여덟 가지 이미지 카테고리를 사용하였는데, healthy, algal leaf spot, anthracnose, bird’s eye spot, brown blight, gray blight, red leaf spot, and white spot였다. 이 연구에서 사용한 소프트웨 어는 데이터 시각적 프로그래밍을 위한 파이썬 라이브러리로 작동하는 Orange3였다. 이는 데이터를 시각적으로 조작하여 분석하기 위한 워크플로를 생성하는 인터페이스를 통해 작동되었다. 각 AI 모델의 정확도로 최적의 AI 모 델을 선택하였다. 모든 모델은 Adam 최적화, ReLU 활성화 함수, 은닉 레이어에 100개의 뉴런, 신경망의 최대 반복 횟수가 200회, 그리고 0.0001 정규화를 사용하여 훈련되었다. Orange3 기능을 확장하기 위해 새로운 이미지 분석 Add-on을 설치하였다. 훈련 모델에서는 이미지 가져오기(import image), 이미지 임베딩(image embedding), 신경망 (neural network), 테스트 및 점수(test and score), 혼동 행렬(confusion matrix) 위젯이 사용되었으며, 예측에는 이미 지 가져오기(import image), 이미지 임베딩(image embedding), 예측(prediction) 및 이미지 뷰어(image viewer) 위젯 이 사용되었다. 다섯 AI 모델[Inception V3, SqueezeNet(로컬), VGG-16, Painters 및 DeepLoc]의 신경망 정밀도는 각 각 0.807, 0.901, 0.780, 0.800 및 0.771이었다. 결론적으로 SqueezeNet(local) 모델이 차나무 잎 이미지를 사용하여 차 병해 탐색을 위한 최적 AI 모델로 선택되었으며, 정확도와 혼동 행렬을 통해 뛰어난 성능을 보였다.

Nuclear facilities present the important task related to the migration and retention of radioactive contaminants such as cesium (Cs), strontium (Sr), and cobalt (Co) for unexpected events in various environmental conditions. The distribution coefficient (Kd) is important factor for understanding these contaminants mobility, influenced by environmental variables. This study focusses the prediction of Kd values for radionuclides within solid phase groups through the application of machine-learning models trained on experimental data and open source data from Japan atomic energy agency. Three machine-learning models, such as the convolutional neural network, artificial neural network, and random forest, were trained for prediction model of the distribution coefficient (Kd). Fourteen input variables drawn from the database and experimental data, including parameters such as initial concentration, solid-phase characteristics, and solution conditions, served as the basis for model training. To enhance model performance, these variables underwent preprocessing steps involving normalization and log transformation. The performances of the models were evaluated using the coefficient of determination. These results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. These models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and longterm risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

매년 국내로 비래해 오는 해충인 벼멸구는 그 기원이 중국 또는 중국 남부일 것으로 예상해왔으나, 이에 대한 유전학적 근거는 Mun et al. (1999)에 의해 제시된 세 가지 COI haplotype 비교가 유일하다. Mun et al. (1999)은 국내에 서 확인된 두 가지 haplotype 유형이 인도차이나반도 이남의 균일한 한 가지 haplotype 집단 유형과 중국에서 확인 된 또 다른 haplotype 집단 유형임을 근거로 국내 벼멸구의 기원을 중국으로 특정한 바 있다. 본 연구는 국내 및 동남아시아 5개국(부탄, 미얀마, 캄보디아, 라오스 및 태국)으로부터 직간접적으로 확보한 개체들을 대상으로 GBS (genotyping by sequencing) 및 NGS 기법을 통해 PCA를 포함한 다양한 집단유전학적 분석을 수행하였다. 그 결과 인도차이나반도의 벼멸구 집단은 크게 북부와 남부로 나뉘며, 국내 개체들은 북부에 비해 남부(캄보디 아, 태국)에 더 가깝다는 사실을 확인하였다. 따라서 벼멸구의 국내 비래는 중국으로부터의 기원 이전에 장마전 선이 형성될 무렵부터 인도차이나반도 남쪽의 고온다습한 서풍이 남남서풍으로 바뀌면서 중국 내륙을 거쳐 국내로 비래하는 경로를 따르는 것으로 보인다. 하지만 태안의 개체 중에는 인도차이나반도 집단들의 외군으로 확인되는 개체가 있었고, 이는 인도차이나반도 외의 샘플링되지 않은 다른 지역에서도 벼멸구가 국내로 비래할 수 있다는 가능성을 제시하였다. 따라서 국내로 유입되는 벼멸구의 유전적 기원을 확인하기 위해서는 인도차이 나반도 남쪽 지역에서 시작한 동아시아 여름 몬순의 바람이 한국으로 도착하는 경로에 위치한 다른 지역에서의 추가적인 샘플링 및 지속적인 관심과 추적이 필요할 것이다.

In the genus Acrobasis Zeller, 1839, there are 14 species known in Korea. In this study, we present Acrobasis rubrizonella (Ragonot, 1893) and Acrobasis subceltifoliella Yamanaka, 2006 from Korea for the first time. We hear provide diagnoses, descriptions, and images of the adults and genitalia of the two species.

As the importance of radioactive waste management has emerged, quality assurance management of radioactive waste has been legally mandated and the Korea Radioactive Waste Agency (KORAD) established the “Waste Acceptance Criteria for the 1st Phase Disposal Facility of the Wolsong Lowand Intermediate-Level Waste Disposal Center (WAC)”, the detailed guideline for radioactive waste acceptance. Accordingly, the Korea Atomic Energy Research Institute (KAERI) introduced a radioactive waste quality assurance management system and developed detailed procedures for performing the waste packaging and characterization methods suggested in the WAC. In this study, we reviewed the radioactive waste characterization method established by the KAERI to meet the WAC presented by the KORAD. In the WAC, the characterization items for the disposal of radioactive waste were divided into six major categories (general requirements, solidification and immobilization requirements, radiological, physical, chemical, and biological requirements), and each subcategories are shown in detail under the major classification. In order to satisfy the characterization criteria for each detailed item, KAERI divided the procedure into a characterization item performed during the packaging process of radioactive waste, a separate test item, and a characterization item performed after the packaging was completed. Based on the KAERI’s radioactive waste packaging procedure, the procedure for characterization of the above items is summarized as follows. First, during the radioactive waste packaging process, the characterization corresponding to the general requirements (waste type) is performed, such as checking the classification status of the contents and checking whether there are substances unsuitable for disposal, etc. Also, characterization corresponding to the physical requirements is performed by checking the void fraction in waste package and visual confirmation of particulate matter, substances containg free water, ect. In addition, chemical and biological requirements can be characterized by visually confirming that no hazardous chemicals (explosive, flammable, gaseous substances, perishables, infectious substances, etc.) are included during the packaging process, and by taking pictures at each packaging steps. Items for characterization using separate test samples include radiological, physical, and chemical requirements. The detailed items include identification of radionuclide and radioactivity concentration, particulate matter identification test, free water and chelate content measurement tests, etc. Characterization items performing after the packaging is completed include general requirements such as measuring the weight and height of packages and radiological requirements such as measurements of surface dose rate and contamination, etc. All of the above procedures are proceduralized and managed in the radioactive waste quality assurance procedure, and a report including the characterization results is prepared and submitted when requesting acceptance of radioactive waste. The characterization of KAERI’s radioactive waste has been systematically established and progressed under the quality assurance system. In the future, we plan to supplement various items that require further improvement, and through this, we can expect to improve the reliability of radioactive waste management and activate the final disposal of KAERI’s radioactive waste.

With rapid urbanization, the importance of urban warfare is increasing, and it is also required to reflect the characteristics of cities in wargame models. However, in the military's wargame models, the urbanization factor was calculated and used without theoretical basis. In this study, we investigate techniques for estimating the urbanization factor using Fractal dimension theory. The urbanization factor we propose can suggest a logical and valid representative value when used in conjunction with Agent Based Model and other methodologies.

As an alternative technology for the efficient disposal of spent nuclear fuel, various process flows can be selected based on the recovered and separated radioactive nuclide group. This is to examine the efficiency of the disposal area of spent nuclear fuel when various disposal technologies and several treatment processes are applied to spent nuclear fuel, compared to the deep geological disposal of burying the entire spent fuel in the ground. Above all, the biggest advantage of the optional treatment processes is that it can be applied to various disposal methods (deep borehole disposal, deep geological disposal) because it can process spent fuel in various sizes and separate into some groups according to the properties of radionuclides. These optional processes are not new technology and currently available as of today, and the level is classified based on the stepwise separation of high heat emission nuclides and long half-life nuclides. This is to increase the efficiency of the disposal of spent nuclear fuel by separating and managing high-risk radionuclides separately. Relatively various optional processes are possible depending on the level, and characteristic analysis is performed on wastes treated with alternative technologies. The mass balance for each option process is completed, and the amount of waste is also calculated accordingly. These are used as basic data for waste disposal area and economic evaluation. Besides it is easy to process spent fuel of various sizes suitable for deep geological disposal or deep borehole disposal technology when an optional treatment technology is applied to spent fuel. However, since this selective process is based on the process structure constructed in a broad framework, it is considered that additional follow-up studies are needed not only on detailed technology but also on the flow and amount of waste.