Bovine rotavirus (BRV) is one of the common gastrointestinal diseases that can occur in calves, causing severe diarrhea. This study was conducted to investigate the effects of oral administration of single-domain antibodies, single variable domain of heavy chain of HCAb (VHH), on the prevention and treatment of rotavirus-induced diarrhea in calves. Thirty calves were divided into five experimental groups (negative control, positive control, group A, group B and group C). Except for the negative control group, the experimental groups were inoculated with BRV on the 4th day after birth. The VHH supplementation groups were fed with a substitute milk containing different concentrations of VHH (1%, 2.5% and 7.5%). Changes in body weight, clinical symptoms, serum antibodies, and virus detection in feces were observed for a total of 10 days in each experimental group. The results showed a higher survival rate in the VHH supplementation groups compared to the positive control group. Clinical symptoms caused by rotavirus were alleviated in the VHH supplementation groups. The detection rate of rotavirus in feces also decreased. Gross findings indicated that the severity of disease in the VHH supplementation groups was milder than that in the positive control group. Therefore, VHH could be proposed as a useful alternative for the prevention and treatment of rotavirus-induced diarrhea in calves.

게임 엔진과 같은 3D공간에서 프로시저럴 모델링 애셋을 사용하여 도시를 구성하는 방식은 이전부터 존재하여 다양한 방법으로 제작되어 왔습니다. 초기 제작시간은 길지만 라이브러리가 구축됨에 따라, 크기와 모양이 다양한 건물을 보다 빠르고 다양하게 배치가 가능하다는 장점 을 바탕으로 사용되었습니다. 하지만, 앙곡과 안허리곡으로 이루어진 이중곡선을 가지며 결구 방식에 따라 모양이 변화하는 한옥의 경우에는 프로시저럴 기법으로 모델링 애셋을 제작하기 어려워 수동 모델링이나 스캔 데이터 형식으로만 존재하는 불편함이 존재하였습니다. 그렇기 에, 본 연구에서는 게임 엔진에서 사용이 가능한 가변적인 한옥 모델링에 대한 프로시저럴 기 법에 대해 소개하고자 합니다.

In the present study, a calorimeter was used to experimentally investigate the heating capacity and COP changes according to the pipe length of a variable capacity A/C system with long pipes. Cooling capacity, COP, and compressor discharge temperature were obtained by changing pipe lengths and loading duties at fixed indoor and outdoor temperatures. And the operation status and cycle change process of the A/C system were investigated using some experimental data and P-h diagrams. As the pipe length changes, the heat transfer within the cycle and the operating load of the compressor change, so the heating capacity and COP of the system change. At the same loading duty, as the pipe length increases, the heating capacity and COP decrease. As the loading duty increased, the heating capacity increased almost linearly, but the COP decreased. Since the long pipe experimental value for the compressor discharge temperature has a temperature deviation of up to 1 7℃(50m, L/D : 10/10) from the correlation equation, the optimal correlation equation must be derived through additional research.

In the present study, a calorimeter was used to experimentally investigate the cooling capacity and COP changes according to the pipe length of a variable capacity A/C system with long pipes. Cooling capacity, COP, and compressor discharge temperature were obtained by changing pipe length and loading duty. And the operation status and cycle change process of the A/C system were investigated using some experimental data and P-h diagrams. In long pipes, the pressure drop increases and the operating load on the compressor increases. Additionally, at the same loading duty, cooling capacity and COP decrease and the compressor discharge temperature increases. As loading duty increases, cooling capacity and compressor power consumption increase. Since the temperature deviation between the experimental value and the correlation equation for the discharge temperature of the long-pipe compressor shows a maximum of 10.5℃(50m, L/D : 20/0), the existing correlation equation needs to be modified.

In this study, the heating performance of a variable capacity A/C system was experimentally studied. A psychrometric calorimeter was used to obtain performance data of the A/C system using PWM(pluse width modulation) method and compare it with the compressor discharge temperature correlation equation. Heating capacity, COP, and compressor discharge temperature were obtained by changing indoor and outdoor temperatures, refrigerant amount, and loading duty. The following results were obtained by selecting 5 types of refrigerant amount, 3 types of outdoor temperature (fixed indoor temperature), and 2 types of loading duty. As the outdoor temperature increases, heating capacity and COP increase. Heating capacity was affected by both outdoor temperature and loading duty. However, COP was more influenced by outdoor temperature. The effect of increasing the amount of refrigerant on the performance of the A/C system was not significant. Additionally, the temperature deviation between the existing compressor discharge temperature correlation equation and the heating experiment data was about 5.1℃ at the maximum loading duty.

In this study, the cooling performance of a variable capacity A/C system was experimentally studied. A psychrometric calorimeter was used to obtain performance data of the A/C system using the pulse width modulation method and compare it with the compressor discharge temperature correlation equation. Cooling capacity, COP, and compressor discharge temperature were obtained by changing indoor and outdoor temperatures, refrigerant amount, and loading duty. The following results were obtained by selecting 5 types of refrigerant amount, 3 types of outdoor temperature (fixed indoor temperature), and 2 types of loading duty. As the outdoor temperature increased, cooling capacity and COP according to outdoor conditions decreased. And the higher the loading duty, the greater the cooling capacity, but the COP was minimal. The change in cooling capacity and COP due to the increase in refrigerant amount was not significant. Additionally, the change in compressor discharge temperature is more influenced by the outside temperature than by the loading duty.

PURPOSES : LED based Variable Message Signs(VMS) have been widely used to inform safety messages to the drivers in advance. Legibility Distance of VMS is the most important factor to provide the safety messages to drivers in timely and effective way. However, current National Standards on legibility distance design considers letter size only even there is a difficulty to read the signs at adverse weather conditions such as heavy fog. So, this study examined the legibility issue under fog by evaluating the legibility distance with two design factors such as letter size and luminance. METHODS : Two foggy weather conditions, intermediate and heavy fog, were simulated at real-road-scale Proving Ground. Legibility distance at daytime and nighttime was evaluated by test subjects. Subjects were asked to fill the legible distance on the test sheet and statistical significant was analysed at the lab. RESULTS : The legibility distance(LD) under fog was observed only 22 to 41% of LD observed under normal weather condition at daytime, and 26 to 45% at nighttime condition. Study results showed a consistent increase in LD with higher luminance even at same letter size and vice versa conditions, However, statically significant difference between groups was only revealed when both letter size and luminance level increased conditions. In order to apply the test results in terms of engineering benefits, LD results from significantly different groups was evaluated with relative to Stopping Sight Distance(SSD) within conceptual frame suggested in this study. CONCLUSIONS : From the study results, current National Standard on legibility distance design needs to consider letter size and luminance simultaneously to response the legibility issue in adverse weather conditions.

국제적인 선박 배출 가스에 대한 환경 규제가 점차 강화됨에 따라 전기추진 및 하이브리드 추진선박에 대한 관심이 증대되고 있으며, 이러한 선박에 적용하기 위한 다양한 솔루션이 개발 및 적용되고 있는데 특히 전력계통의 안정화, 시스템의 효율성을 높이기 위 한 방안으로 직류배전시스템이 적용되고 있다. 또한, 선박용 직류배전시스템에 대한 안전 및 성능에 대한 검증 및 시험의 중요성이 요구 되고 있다. 본 연구는 직류배전 테스트베드 구축 및 직류배전(가변속 발전)시스템에 대한 성능을 검증하고 연료소모량 분석한 결과로서 선박용 직류배전에 적용되고 있는 가변속 발전시스템을 적용하였으며, 발전기에서 출력되는 전력을 정류기를 통해 직류전력으로 변환하 여 시스템에 연계하고 이러한 장치들을 감시 및 제어하기 위한 시스템을 개발하였다. 이러한 직류배전 시스템을 적용한 시험을 통해 최 대 전압은 751.5V이고 최소전압은 731.4V가 계측되어 전압변동률은 2.7%로 3% 이내에서 안정적으로 전압이 공급되는 것을 확인하였고 부 하변동에 따라 가변속 발전시스템을 적용하였을 경우 연료소모량이 기존 정속발전시스템과 비교하여 구간에 따라 최대 20%이상 연료절 감이 되는 것을 시험을 통해 확인하였다.

The global power generation industry is becoming a key power generation industry with gas power generation and renewable energy solar power generation. This research aims to focus on solving two problems as a method to improve the solar light collection efficiency among fixed variable deformation methods. Maintaining the proper temperature of the water injection device through automatic temperature detection to solve efficiency degradation, and establishing an automatic operation system by finding the optimal angle for each season, are intended to derive a value that can represent the optimal power generation.

It is getting more intensified with the competition among participating companies for global market share in major industrial fields. The situation is accelerating especially within the top 5 market share, and these include electric vehicles, semiconductors, chemicals, and shipbuilding industries. The key to the advantage over the competition within a strategic group is which company leads the innovation in the field. On-the-ground innovation refers to job-based innovation. This paper aims to analyze job unit innovation in the structure of empowerment, LMX, and job crafting. Existing studies on job crafting have suggested a causal structure based on job design in the traditional sense, and there are not many scholars who study the causal structure using a job situational model. Therefore, this paper takes an approach from the perspective of the job situation. As a result of the study, LMX showed a moderating effect on the relationship between autonomy provision and job crafting. While, in the relationship between meaing-giving and cognitive crafting, there is no significant moderating effect shown on the relationship between autonomy provision and cognitive crafting. Therefore, the results of the analysis in this study suggest that the meaning of jobs and participation in decision-making should be managed in an integrated way in structural and design areas, not just qualitative factors such as empowerment and leadership.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.