곤충생태원에서 진행되는 진로체험프로그램의 일환으로 ‘곤충 캠프, 파브르의 하루’ 프로그램을 개발하여 시범 적용하였다. 참가자는 전주시 지역에 거주하는 초등·중학생 16명이며, 야간시간(19:00-23:00)대에 활동이 이루어짐에 따라 사전에 보호자로부터 참여 동의를 획득하였다. 야외 활동은 각 개인별로 랜턴과 채집 도구를 지참하였으며, 4-5명씩 한 조를 이루어 담당 교사와 보조 교사가 동행하였다. 주된 활동은 야간등화, 통나무 무더 기, 야생의 서식지, 미리 설치한 곤충 트랩 속 곤충 찾기, 스탬프 미션활동 등으로 구성되었다. 실내활동으로는 곤충 동정하기, 곤충 관찰 수첩, 나만의 곤충도감 만들기 등을 진행하였다. 효과를 분석하기 위해서 프로그램이 진행되기 전과 끝난 후에 설문조사를 실시하였다. 분석 결과, 참여자의 평균 연령은 12.8세이며, 프로그램 참여를 통해 자아존중감의 향상(3.34점→3.61)과 진로인식 향상(3.40점→3.66)에 긍정적인 변화를 가져왔다(p<0.05). 야간에 이루어지는 활동으로 고려해야 할 사항이 많기는 하지만, 참여자의 재참여 의도가 90점으로 만족도가 높은 것으로 나타났기에, 향후 추가적인 연구 및 새로운 시도가 필요할 것으로 보인다.

Spent fuels (SFs) are stored in a storage pool after discharge from nuclear power plants. They can be transferred to for the further processes such as dry storage sites, processing plants, or disposal sites. One of important measures of SF is the burnup. Since the radioactivity of SF is strongly dependent on its burnup, the burnup of SF should be well estimated for the safe management, storage, and final disposal. Published papers about the methodology for the burnup estimation from the known activities of important radioactive sources are somewhat rare. In this study, we analyzed the dependency of the burnup on the important radiation source activities using ORIGEN-ARP, and suggested simple correlations that relate the burnup and the important source activities directly. A burnup estimation equation is suggested for PWR fuels relating burnup with total neutron source intensity (TNSI), initial enrichment, and cooling time. And three burnup estimation equations for major gamma sources, 137Cs, 134Cs, and 154Eu are also suggested.

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

This study investigated ethopabate (EPB) residues in edible tissues of broiler chickens given in drinking water and established the withdrawal time (WT) of EPB in poultry tissues. Twenty-four healthy Ross broiler chickens were orally administered with EPB at the concentration of 3.8 mg/L for 14 days (EPB-1, n=24) and 15.2 mg/L for 7 days (EPB-2, n=24) through drinking water, respectively. After the drug treatment, tissue samples were collected from six broiler chickens at 0, 1, 3, and 5 days, respectively. EPB residue concentrations in poultry tissues were determined using LC-MS/MS. Correlation coefficient values ranged from 0.9980 to 0.9998, and the limits of detection and quantification (LOQ) were 0.03~0.09 and 0.1~0.3 μg/kg, respectively. Mean recoveries in muscle, liver, kidney and skin/fat tissues were 95.9~109.8, 108.7~115.3, 89.9~96.6 and 86.7~96.8%, respectively, and coefficient of variations were less than 17.11%. At the end of the drug-administration period (0 day), EPB was detected at levels under the LOQ in all tissues from both the EPB-1 and EPB-2 groups. According to the results of EPB residue in Ross broiler tissues, withdrawal periods of both EPB-1 and EPB-2 in poultry tissues were established to 0 day. In conclusion, the developed analytical method is suitable for the detection of EPB in poultry tissues, and the estimated WT of EPB in poultry tissues will contribute to ensuring the safety of Ross broiler chickens.

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

On pig farms, the highest mortality rate is observed among nursing piglets. To reduce this mortality rate, farmers need to carefully observe the piglets to prevent accidents such as being crushed and to maintain a proper body temperature. However, observing a large number of pigs individually can be challenging for farmers. Therefore, our aim was to detect the behavior of piglets and sows in real-time using deep learning models, such as YOLOv4-CSP and YOLOv7-E6E, that allow for real-time object detection. YOLOv4-CSP reduces computational cost by partitioning feature maps and utilizing Cross-stage Hierarchy to remove redundant gradient calculation. YOLOv7-E6E analyzes and controls gradient paths such that the weights of each layer learn diverse features. We detected standing, sitting, and lying behaviors in sows and lactating and starving behaviors in piglets, which indicate nursing behavior and movement to colder areas away from the group. We optimized the model parameters for the best object detection and improved reliability by acquiring data through experts. We conducted object detection for the five different behaviors. The YOLOv4-CSP model achieved an accuracy of 0.63 and mAP of 0.662, whereas the YOLOv7-E6E model showed an accuracy of 0.65 and mAP of 0.637. Therefore, based on mAP, which includes both class and localization performance, YOLOv4-CSP showed the superior performance. Such research is anticipated to be effectively utilized for the behavioral analysis of fattening pigs and in preventing piglet crushing in the future.

As the capacity of renewable power generation facilities rapidly increases, the variability of electric power system and gas turbine power generation is also increasing. Therefore, problems may occur that require urgent repair while the gas turbine rotor is stopped. When the gas turbine rotor turning is stopped and then restarted, if the turning period is not appropriate, severe vibration may occur due to rotor bending. As a result of the experiment, it was confirmed that normal operation is possible when the gap data measured at the start of rotor turning after maintenance work is similar to the existing value. And the vibration value at the start of rotor turning was lower as the rotor temperature was lower or the stop period was shorter.

본 연구는 실내-외 우리가 있는 개방형 흑염소사에서 우리나라 5개월령 재래 흑염소를 대상으로 서열과 일주기 리듬이 하루 24시간 동안의 행동에 미치는 영향과 서열과 사료급여시간이 사료급여 후 30분 및 60분 동안의 행동에 미치는 영향을 조사하기 위해 수행하였다. 재래 흑염소의 행동은 CCTV를 사용해 녹화한 후 이들을 1분 단위로 측정하였고, 총 10개의 행동으로 분류하였다. 서열은 적대행동에 유의하게 영향을 미쳤으나 (p<0.05), 다른 행동에는 영향을 미치지 않았다. 일주기 리듬은 응립(p<0.05), 이동(p<0.01), 사료섭취(p<0.001), 적대행동(p<0.001), 비적대행동 (p<0.001), 사료탐색(p<0.05), 음수(p<0.001) 및 기타 행동(p<0.001)에 유의하게 영향을 미치는 반면 휴식(p>0.05)과 몸단장(p>0.05)에는 영향을 미치지 않았다. 서열과 일주기 리듬의 상호작용효과는 적대행동에만 유의하게 영향을 미쳤다(p<0.05). 서열은 사료급여 후 30분 동안 응립(p<0.05)과 사료섭취(p<0.05)에 유의하게 영향을 미쳤지만 다른 행동에는 영향을 미치지 않았다. 사료급여시간은 사료급여 후 60분 동안의 음수(p<0.05)에 유의하게 영향을 미쳤으나, 다른 행동에는 영향을 미치지 않았다. 서열과 사료급여시간의 상호작용효과는 모든 행동에 유의하게 영향을 미치지 않았다(p<0.05). 결론적으로, 서열, 일주기 리듬, 그리고 사료급여시간은 우리나라 5개월령 미성성숙한 재래 흑염소의 행동에 영향을 미치는 것으로 나타났다.

PURPOSES : The primary objective of this study is to analyze the relationship between the factors that affect traffic incident duration in the mainline, tunnel, and ramp segments of an expressway. In addition, this study derived the most suitable statistical prediction model based on various incident duration distributions. METHODS : South Korean expressway crash data for 11 years, from 2011 to 2021, were analyzed. The incident durations on the mainline, tunnel, and ramp segments were selected using the accelerated failure time model, which is a parametric survival analysis approach. RESULTS : The mainline segment showed that the incident duration increased during accidents, including guard pipe collisions, multivehicle collisions, and snowfall. In particular, collisions in a tunnel with shoulder facilities increase the incident duration, while decreasing the time in the ramp segment. CONCLUSIONS : The incident duration model for each segment type yielded the most accurate results when applying a log-logistic distribution.

본 실험은 마이크로웨이브 처리가 상추 유묘의 생육 변화와 이차대사산물 함량의 변화를 알아보고자 수행되었다. 파종 후 3주째 상추 유묘에 2.45GHz 주파수와 200W의 마이크로 웨이브를 0, 4, 8 및 12초 동안 처리하고, 4주간 식물공장에서 재배한 후 생육 및 성분 분석을 수행하였다. 지하부와 지상부 의 생체중과 건물중, 엽면적, 엽장 및 엽수는 마이크로웨이브 처리시간이 증가할수록 감소하였다. 4초 처리구와 비교하여 12초 처리구에서 chlorophyll a, chlorophyll b 및 총 carotenoids 의 함량이 증가되었으며 총 페놀 함량은 감소하였다. 무처리 구와 비교하여 8초 처리구에서 총 플라보노이드 함량이 감소 하였다. 이러한 결과들은 산화적 스트레스에 의해 이차대사 산물 함량이 변화된 것으로 사료된다. 총 플라보노이드 함량 을 제외한 이차대사산물 함량은 각 처리구에서 무처리구와 비 교하여 유의한 차이가 없었지만, 각 처리구 사이의 유의한 차 이는 200W와 2.45GHz의 마이크로웨이브 처리가 4주 후 상 추의 이차대사산물 함량에 영향을 줄 수 있다는 것을 시사한다.

Using Social Network Analysis methodology, specifically the Network of Similarity and Response Time Testing as a survey method, we measured and examined, based on conviction strength, the relationships between beliefs in various conspiracy theories. We employ Social Network Analysis (SNA) to uncover conspiracy thinking patterns. SNA facilitates the disclosure of interdependencies among variables and intricate direct and indirect relationships. The network of conspiracy convictions is mapped and scrutinized to discern the clustering of variables, which is achieved using greedy-modularity algorithms. Structural properties, such as nodal and subgroup density, are subsequently calculated to assess the quality of the clusters. A qualitative evaluation explores the semantic meanings underlying the observed patterns. Our analysis revealed strong correlations between the items, indicating that individuals who believe in one conspiracy theory are highly likely to believe in others. Furthermore, Response Time Testing allowed for measuring the level of people's conviction in these beliefs. We discuss the implications of these findings, suggesting that conspiracy theories may serve as a means for individuals to confirm their positions and feelings in society. This insight calls for a reassessment of strategies to address the spread and impact of conspiracy theories, focusing on understanding the psychological and social factors driving belief in multiple conspiracies and the strength of these convictions.

In recent years, advances in the digital and live streaming economy have led to exponential growth in the number of self-employed streamers who have become an integral part of the self-driven digital labor force. However, previous research on the impact of streamers' work time arrangements on their virtual gifts remains scarce. To fill this gap in the literature, using large-scale data from Kuaishou live streaming platform, we demonstrate that several features of streamers' work time have an important impact on their virtual gifts. Specifically, our results suggest that work time duration and timing improve streamers' virtual gifts; meanwhile, work time tempo has an adverse effect on streamers' earnings. Taken together, our results provide novel and actionable insights for millions of self-employed streamers, agencies, platforms, and policymakers.

The current research introduces a novel relationship between consumer attitudes toward time and consumer well-being, connected to organic food consumption based on the findings from 206 American consumers. Consumer motivations to seek nutritional information appears as a mediator impacting organic food purchase intentions. Practical and theoretical implications are discussed.

This study examined the combined effects of scarcity appeals and time remaining from the travel date on attitudes toward advertising and purchase intention. The results lead to a rationale for disparate advertising strategies of scarcity appeals considering the temporal distance of a consumer's hotel booking.

Electronic waste (E-waste) has become a long-standing global concern. People are purchasing new affordable and improved technologies long before the end-of-life (EOL)’s of their old devices, which is leading to overconsumption and growing volumes of e-waste. At present, official data indicates that 80% of the volume of e-waste is not formally treated globally. The complex nature of e-waste recycling processes is a significant challenge.