The current study examined the potential contribution of advanced Korean EFL learners’ writing abilities to their reading comprehension abilities. A total of 191 college students participated in this study and were tested on writing and reading comprehension abilities as well as other literacy-related measures including listening comprehension, textreading fluency, and knowledge of vocabulary to control for their effects. In order to account for different aspects of writing and reading comprehension abilities, multiple measures of reading and writing abilities were adopted. The Structural Equation Modeling (SEM) analyses demonstrated that the advanced Korean EFL learners’ English writing abilities had a significant effect on their reading comprehension abilities when other relevant literacy skills were controlled for. Furthermore, their writing abilities mediated the relationship between vocabulary knowledge and reading comprehension abilities. These results highlight the important pedagogical implications on the critical role of writing abilities in enhancing the reading comprehension abilities of L2 learners.

Maize is an important cereal crop widely grown in all of Nigeria’s agroecology based on its economic and food values. This has led to its optimum production with Nigeria ranking top 12th maize producer in the world. Presently, the fall armyworm (FAW), Spodoptera frugiperda is considered a potential threat to food security in Nigeria due to its devastating feeding pattern on maize and its wide host range. Its invasive and high dispersal nature enhances its spread from its first reported state (Oyo) in early 2016 to the entire 36 states of Nigeria by the year 2018. It accounts for averagely an estimated 34% or more yield reduction in Nigeria’s maize field, hence, a need for an efficient management approach. Although most farmers rely on the use of insecticide, it is found ineffective because of the inaccessible location of FAW in maize. This study was carried out to investigate the different management approaches adopted in Nigeria to control FAW and its impact on the insect pest population. Conventional measures such as planting improved seeds, avoidance of late planting, and polyculture to increase the natural enemy’s population have been used but with limited control of FAW. Maize lines with aflatoxin resistance or transgenic Bt-maize are found resistant to FAW. Botanicals such as neem extract lowered the percentage of leaf area damage, incidence of FAW larva infestation, and the number of larvae. It is difficult to curtail the menace of FAW through one control method, hence, the need for an integrated approach

본 연구의 목적은 학군사관후보생(ROTC)의 신체활동 참여습관, 체력수준, 학업성취능력의 관계를 조사하는 것이다. 본 연구의 참가자는 J 대학교에 재학중인 71명의 학군사관후보생을 선발하였 다. 신체활동 참여습관은 국제신체활동설문지(Global Physical Activity Questionnaire, GPAQ)를 이용하 여 조사하였고, 체력수준은 건강관련 체력평가로 측정하였다. 학업성취능력은 해당 학군단 장의 승인을 받은 대학성적자료(GPA)를 참가자 동의하에 수집하여 각각의 자료를 분석하였다. 신체활동 참여습관, 체력수준, 대학학점 간의 상관관계를 분석하였고, 체력수준별 학점을 비교하였다. 본 연구에서 신체 활동 참여습관과 학업성취능력 사이에 통계적으로 유의한 상관관계가 없는 것으로 조사되었다. 반면 주요 과목과 총체력점수, 심폐체력, 근지구력, 유연성, 신체조성 간에는 통계적으로 유의한 상관관계 가 나타났다. 체력 수준별로는 체력이 높은 그룹의 ROTC가 낮은 체력 그룹의 ROTC보다 통계적으 로 유의하게 높은 학업 성취능력을 보였다. 또한 낮은 체력수준의 ROTC 그룹은 높은 체력수준의 ROTC에 비해 학업성취능력이 다소 낮은 것으로 나타났다. 결론적으로 체력수준이 높은 ROTC는 그렇지 않은 사람에 비해 학습능력 향상에 긍정적인 영향을 미쳤다. 본 연구는 자율적이지만 체계적 인 체력훈련 프로그램을 제공하여 ROTC의 체력을 향상시키고 우수한 미래 장교를 양성할 것을 제 안한다.

Soybean is an important crop widely grown in the northern region of Nigeria for human and animal consumption. Commercial production of soybean in Nigeria has existed for eight decades. Current malnutrition and nutrient health problems in Nigeria warranted the introduction of high nutritive and affordable food sources such as soybean in the Nigerian diet. The high demand for soybean and its products has led to the rise in the cultivation of soybean in Nigeria. Although an increase in production has been recorded, the region of production has failed to achieve the potential yield per hectare. In improving soybean production in Nigeria, it is paramount to understand the factors that contribute to the process of production. This review paper provides information on factors affecting soybean production across the regions of production in Nigeria. The identified factors responsible for the yield gap include the impact of climate change on the agroecology shift, nutrient depletion and soil fertility, sowing date, the emergence of pests and diseases, and limited improved soybean cultivars. Adoption of improved soybean variety suitable in agroecology and sowing, at the appropriate sowing date by Nigerian farmers, can guarantee optimum soybean production. Further soybean breeding improvement studies are needed to provide more improved varieties with superior performance in Nigeria’s agroecology to achieve yield potential.

Recent progress has been made to establish intestinal organoids for an in vitro model as a potential alternative to an in vivo system in animals. We previously reported a reliable method for the isolation of intestinal crypts from the small intestine and robust three-dimensional (3D) expansion of intestinal organoids (basal-out) in adult bovines. The present study aimed to establish next-generation intestinal organoids for practical applications in disease modeling-based host-pathogen interactions and feed efficiency measurements. In this study, we developed a rapid and convenient method for the efficient generation of intestinal organoids through the modulation of the Wnt signaling pathway and continuous apical-out intestinal organoids. Remarkably, the intestinal epithelium only takes 3-4 days to undergo CHIR (1 µM) treatment as a Wnt activator, which is much shorter than that required for spontaneous differentiation (7 days). Subsequently, we successfully established an apical-out bovine intestinal organoid culture system through suspension culture without Matrigel matrix, indicating an apical-out membrane on the surface. Collectively, these results demonstrate the efficient generation and next-generation of bovine intestinal organoids and will facilitate their potential use for various purposes, such as disease modeling, in the field of animal biotechnology.

Receptor tyrosine kinase c-Kit, a marker found on interstitial cells of Cajal (ICCs), is expressed in Leydig cells, which are testicular interstitial cells. The expression of other ICC markers has not yet been reported. In this study, we investigated the expression of c-Kit and anoctamin 1 (ANO1), another ICC marker, in mouse testes. In addition, the relationship between c-Kit and ANO1 expression and Leydig cell function was investigated. We observed that c-Kit and ANO1 were predominantly expressed in mouse Leydig cells. The mRNA and protein of c-Kit and ANO1 were expressed in TM3, a mouse Leydig cell line. LH induced an increase in intracellular Ca2+ concentration, membrane depolarization, and testosterone secretion, whereas these signals were inhibited in the presence of c-Kit and ANO1 inhibitors. These results show that c-Kit and ANO1 are expressed in Leydig cells and are involved in testosterone secretion. Our findings suggest that Leydig cells may act as ICCs in testosterone secretion.

This study examines the word-initial stop variation in the speech of speakers who were born in North Kyungsang province and moved to Seoul after the age of 20 and that of speakers who were born in Seoul and moved to North Kyungsang province after the age of 20. Specifically, this study investigates whether Seoul Koreans moving to North Kyungsang have acquired the NKK-like word-initial tensification in any of the two contexts - wordlist and reading passage. Moreover, this study explores whether North Kyungsang Korean moving to Seoul have lost the NKK-like word-initial tensification in the two contexts. Finally, this study examines whether social factors, including gender, length of stay in a new region, attitude toward the first and second dialects, and awareness of the word-initial tensification affect the acquisition or loss of the second dialect feature. A major finding is that both mobile groups do not show the NKK-like word-initial tensification in wordlist and reading passage speech. With respect to the effect of the social factors, male Kyungsang Koreans in Seoul produce tensified stops more than female counterparts. Meanwhile, none of the social factors are significant in the speech of Seoul Koreans in North Kyungsang.

Korea Research Reactor 1&2 (KRR-1&2), Korea’s first research reactor, began dismantling in 1997. As of 2022, the demolition of general areas such as offices has been completed, and contaminated areas such as reactor rooms remain. On the other hand, construction waste generated in contaminated areas of nuclear facilities cannot be disposed of as general industrial waste. It is predicted that about 5,000 tons of construction waste will be generated if the contaminated area of KRR-1&2 is demolished. In this study, the application plan for the demolition of contaminated area of KRR-1&2 was reviewed through a review of laws and cases related to domestic and overseas disposal. The only method for disposing of construction waste in contaminated areas that can be applied in Korea is clearance in accordance with Nuclear Safety Commission Notice No. 2020-06. In addition, there has been no case of demolishing large-scale nuclear facilities in Korea. Therefore, there are limitations in domestic laws and standards to be applied to the dismantling of contaminated areas of KRR-1&2. The IAEA and the United States specify comprehensive matters such as optimization of radiation protection and minimization of waste products. The EU recommends demolition after decontamination by removing contaminated areas before demolition of buildings. It also presents three options for reuse, recycling, and disposal of buildings and building waste. In particular, in the case of Germany, detailed radioactivity measurement methods for deregulation of buildings and building waste are presented in accordance with the EU’s guidelines. As a result of synthesizing this, it is judged that the EU and Germany building clearance plan will be suitable for domestic application.

The International Atomic Energy Agency recommends the deep geological disposal system as one of the disposal methods for high-level radioactive waste (HLW), such as spent nuclear fuel. The deep geological disposal system disposes of HLW in a deep and stable geological formation to isolate the HLW from the human biosphere and restrict the inflow of radionuclides into the ecosystem. It mainly consists of an engineered barrier and a natural barrier. Safety evaluation using a numerical model has been performed primarily to evaluate the buffer’s long-term stability. However, although the gas generation rate input for long-term stability evaluation is the critical factor that has the most significant influence on the long-term hydraulic-mechanical behavior of the buffer, in-depth research and experimental data are lacking. In this study, the gas generation rate on the interface between the disposal canister and the buffer material, a component of the engineered barrier, was mainly studied. Gas can be generated between the disposal canister and the buffer material due to various causes such as anaerobic corrosion of the disposal canister metal, organic matter decomposition, radiation decomposition, and steam generation due to high temperature. The generation of gas in such a disposal environment increases the pore gas pressure in the buffer and causes internal cracks. The occurred cracks increase the intrinsic permeability of the buffer, which leads to a decrease in the primary performance of the buffer. For this reason, it is essential to apply the appropriate gas generation rate according to the disposal condition and buffer material for accurate long-term stability analysis. Therefore, the theoretical models regarding the estimation of gas generation were summarized through a literature study. The amount of gas generated was estimated according to the disposal environment and material of the disposal canister. It is expected that estimated values might be used to estimate the long-term stability analysis of buffer performance according to the disposal condition.

The bacterial soft-rot disease is one of the most critical diseases in vegetables such as Chinese cabbage. The researchers isolated two bacteria (Pseudomonas kribbensis and Pantoea vagans) from diseased tissue samples of Chinese cabbages and confirmed them as being the strains that cause soft-rot disease. Lactic-acid bacteria (LAB), were screened and used to control soft-rot disease bacteria. The researchers tested the treatments with hypochlorous acid water (HAW) and LAB supernatant to control soft-rot disease bacteria. The tests confirmed that treatments with the HAW (over 120 ppm) or LAB (Lactobacillus plantarum PL203) culture supernatants (0.5 mL) completely controlled both P. kribbensis and P. vagans.

The rapid development of computer vision and deep learning has enabled these technologies to be applied to the automated classification and counting of microscope images, thereby relieving of some burden from pathologists in terms of performing tedious microscopic examination for analysis of a large number of slides for pathological lesions. Recently, the use of these digital methods has expanded into the field of medical image analysis. In this study, the Inception-v3 deep learning model was used for classification of chondrocytes from knee joints of rats. Knee joints were extracted, fixed in neutral buffered formalin, decalcified, processed and embedded in paraffin, and hematoxylin and eosin (H&E) stained. The H&E stained slides were converted into whole slide imaging (WSI), and the images were cropped to 79 × 79 pixels. The images were divided into training (60.42%) and test (39.58%) sets (46,349 and 30,360 images, respectively). Then, images containing chondrocytes were classified by Inception-v3 and accuracy was calculated. We visualized the images containing chondrocytes in WSIs by adding colored dots to patches. When images of chondrocytes in knee joints were evaluated, the accuracy was within the range of 91.20 ± 8.43%. Therefore, it is considered that the Inception-v3 deep learning model was able to distinguish chondrocytes from non-chondrocytes in knee joints of rats with a relatively high accuracy. The above results taken together confirmed that this deep learning model could classify the chondrocytes and this promising approach will provide pathologists a fast and accurate analysis of diverse tissue structures.

This study investigated EFL college-level learners’ expectation on and their experience in an online English-medium instruction (EMI) course focusing on how participants interacted with their classmates and the instructor in their online class (zoom session) based on assumptions and rationales of Interaction Hypothesis and classroom interaction research. Analyses of questionnaire, observation, and interview data revealed that participants’ experience of interaction and their perception of interaction opportunities in the zoom session were significantly related to how they would evaluate the course-taking experience. It was also found that cognitive strategy such as participants’ preparation for each class rather than L2 confidence was more relevant to their level of satisfaction with the course. Results of analyses suggested that an online class could be more effective than a face-to-face class in terms of engaging EFL adult learners in an academic course offered in participants’ L2, English. Based on study results, suggestions on how to increase interaction opportunities in online EMI course are made.

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

This study explores multiple variables of an OTT service for discovering hidden relationship between rating and the other variables of each successful and failed content, respectively. In order to extract key variables that are strongly correlated to the rating across the contents, this work analyzes 170 Netflix original dramas and 419 movies. These contents are classified as success and failure by using the rating site IMDb, respectively. The correlation between the contents, which are classified via rating, and variables such as violence, lewdness and running time are analyzed to determine whether a certain variable appears or not in each successful and failure content. This study employs a regression analysis to discover correlations across the variables as a main analysis method. Since the correlation between independent variables should be low, check multicollinearity and select the variable. Cook's distance is used to detect and remove outliers. To improve the accuracy of the model, a variable selection based on AIC(Akaike Information Criterion) is performed. Finally, the basic assumptions of regression analysis are identified by residual diagnosis and Dubin Watson test. According to the whole analysis process, it is concluded that the more director awards exist and the less immatatable tend to be successful in movies. On the contrary, lower fear tend to be failure in movies. In case of dramas, there are close correlations between failure dramas and lower violence, higher fear, higher drugs.