곤충호텔은 곤충이 서식할 수 있도록 인위적으로 만든 구조 물로서 정원이나 텃밭 등 다양한 곳에서 활용되고 있다. 하지만 식생유형, 재료, 설치 방식 등에 따른 곤충 유입 효과에 대한 검증은 부족하며, 국가 또는 지역에 따른 곤충의 종류와 생육환 경이 다름에도 불구하고 곤충호텔에 대한 국내 연구는 전무한 실정이다. 따라서 본 연구에서는 국립수목원을 대상으로 구체적 인 식생유형 및 재료에 따른 유입 곤충 특성을 확인하고, 국내 실정에 맞는 효과적인 곤충호텔 설치 및 관리방안에 대한 기준 을 제시하고자 하였다. 조사구는 세 가지 식생유형(초지, 침엽수 림, 활엽수림)으로 구분하였으며, 각 식생의 조사구에 두 가지 재료(참나무, 잣나무)를 활용한 곤충호텔을 설치하였다. 조사는 2년동안(2022~2023) 진행되었으며, 매년 4~9월까지 주 1회 씩(총 48회) 곤충호텔의 유입 곤충을 직접 채집하였다. 곤충호 텔에서 채집된 곤충은 총 9목 46과 129종 3,057개체로, 2022 년에는 7목 34과 85종 1,750개체, 2023년에는 8목 35과 77종 1,307개체가 출현하였다. 연도별로 비교하면 1차년도에 유입 된 곤충의 개체수가 2차년도 보다 약 1.3배 많았고, 재료의 부식 에 따른 곤충의 구성도 달라졌다. 식생유형에 따라 구분하면 기간에 상관없이 활엽수림에서 유입 곤충이 가장 많았고, 침엽 수림에서 가장 적었다. 또한 재료에 따른 구분에서는 참나무가 잣나무보다 유입 곤충이 많았다. 참나무의 경우 유입 곤충의 연도별 차이가 적은 반면, 잣나무는 1년차에 비해 2년차에 약 2.3배 줄었다. 상관분석과 계층적 군집분석을 통한 곤충의 유입 특성은 식생유형보다는 재료의 영향이 큰 것으로 확인되었고, 식생유형만 비교했을 경우 초지에 비해 활엽수림과 침엽수림의 유사성이 높았다. 결론적으로 생물 다양성 증진을 목적으로 곤 충호텔을 설치하고자 한다면 초지나 활엽수림에 참나무 재료를 사용하는 것이 유리하며, 잣나무 재료를 이용한다면 1년 주기로 재료 교체가 필요할 것으로 판단된다. 하지만 특정 재료를 선호 하는 곤충의 기주특이성이 확인되었기 때문에 다양한 종류의 재료를 같이 사용하는 것도 하나의 방법이라고 사료된다. 향후 이를 기반으로 전시원에서 실질적으로 활용할 수 있는 곤충호텔 모델 개발로 확대해 나갈 예정이다.

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

본 연구는 주변 환경의 차이에 따른 화분매개곤충의 유입 특성을 파악하기 위하여 국립수목원 내 진화속을걷 는정원과 부추속전문전시원에 식재된 울릉산마늘의 화분매개곤충을 조사하였다. 2023년 5월 22일부터 6월 2일 까지 꽃이 70% 이상 개화하였을 때 포충망을 활용하여 8일간 곤충을 채집하였고, 각 전시원 별 식생(피도), 기후 (온도·습도·조도)를 조사하였다. 조사 결과 진화속을걷는정원에서 피도 60% 온도 26.4℃, 습도 31.5%, 조도 40953.6lx, 화분매개곤충 20과 450개체, 부추속전문전시원은 피도 90%, 온도 25.6℃, 습도 31.6%, 조도 6387lx, 화분매개곤충 15과 196개체로 나타났다. 온도와 조도가 상대적으로 높은 진화속을걷는정원이 채집된 곤충의 다양성과 방문 빈도가 높았다. 시간대별 곤충의 방문 빈도를 비교해본 결과 온도와 조도는 개체수가 증가할 때 같이 증가하는 경향을 보였으며, 습도는 반대의 경향을 보였다.

In 2022, research for native prokaryotic species in Korea reported 10 unrecorded bacterial strains affiliated to phyla Actinomycetota, Bacillota, and Pseudomonadota. The strains formed monophyletic clades with the most closely related species (with ≥98.7% sequence similarity) in the 16S rRNA gene sequencing. Among them, four species of the phylum Actinomycetota, two species of the phylum Bacillota, and four species of the phylum Pseudomonadota have not been reported in Korea, suggesting unrecorded species in Korea. Information on strains such as Gram staining reaction, colony and cell morphology, biochemical characteristics, and isolation sources were provided in the species description.

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.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

The goal of this study was to develop a curriculum based on practice and job competency, reflecting opinions on the required job competence of nail practitioners and professionals related to nail beauty. Through in-depth interviews with nail experts, the research focuses on developing nail beauty competency-based curriculum and curriculum profiles that reflect practitioners’ needs of job competence in the field. In-depth interviews with 11 field experts and surveys of 154 people were conducted to develop a competency-based curriculum for beginner nail hairdressers. The results of this study show that the existing 38 National Competency Standards (NCS) job competencies were reduced to 21 job competencies. In addition, based on the common opinions of experts who reflect the current trend, two tasks on “eyelashes” and “waxing” were added, and they were modified and supplemented with 23 core competencies. The development of a competency-based curriculum and educational programs for nail beauty was performed based on the requirements of the core competencies investigated and the development of a systematic map for the core competencies of beginner nail technicians and hairdressers. In conclusion, the need for professional education and training for nail hairdressers is growing, and it can be seen that a curriculum building multi-faceted abilities is needed for their qualifications as experts. This study found that it is necessary to develop interpersonal communication skills that include marketing elements other than practical skills such as personality and customer response methods in the nail beauty curriculum.

Two tree frogs, Dryophytes suweonensis and Dryophytes japonicus, inhabiting Korea, are morphologically similar and share the same habitats. Therefore, they are identified mainly through their calls, especially for males. Dryophytes suweonensis is registered as an endangered (IUCN: EN grade) and protected species in South Korea. Thus, it is necessary to develop a method to rapidly identify and discriminate the two species and establish efficient protection and restoration plans. We identified significant genetic variation between them by sequencing a maternallyinherited mitochondrial 12S ribosomal DNA region. Based on the sequence data, we designed a pair of primers containing 7 bp differences for high resolution melting (HRM) analysis to rapidly and accurately characterize their genotypes. The HRM analysis using genomic DNA showed that the melting peak for D. suweonensis was 76.4±0.06°C, whereas that of D. japonicus was 75.0±0.05°C. The differential melt curve plot further showed a distinct difference between them. We also carried out a pilot test for the application of HRM analysis based on immersing D. suweonensis in distilled water for 30 min to generate artificial environmental DNA (eDNA). The results showed 1.10-1.31°C differences in the melting peaks between the two tree frog samples. Therefore, this HRM analysis is rapid and accurate in identifying two tree frogs not only using their genomic DNA but also using highly non-invasive eDNA.

Functional dyspepsia (FD) is a gastrointestinal disorder with diverse symptoms but no structural or organic manifestations. Benachio-F® (herein named ‘BF-1’) is an over-the-counter liquid digestive formulated with multiple herbal extracts, which has been reported to improve symptoms of FD. A total two experiments were conducted. First, we examined whether BF-1 can modulate the progression of FD through two experimental rat models. A total of three doses (0.3x, 1x, 3x of the human equivalent dose) were used. In the gastric emptying model, both 1x (standard) or 3x (3-fold-concentrated) BF-1 enhanced gastric emptying was compared with that of vehicle-treated animals. In a feeding inhibition model induced by acute restraint stress, treatment with 1x or 3x BF-1 led to a similar degree of restoration in food intake that was comparable to that of acotiamide-treated animals. Among the constituents of BF, fennel is known for its choleretic effect. Thus, we next investigated whether a novel BF-based formula (named ‘BF-2’) that contains an increased amount of fennel extract (3.5-fold over BF-1), has greater potency in increasing bile flow. BF-2 showed a superior choleretic effect compared to BF-1. Furthermore, the postprandial concentration of serum secretin was higher in animals pretreated with BF-2 than in those pretreated with BF-1, suggesting that the increased choleretic effect of BF-2 is related to secretin production. Our results demonstrate that BF-1 can modulate the pathophysiological mechanisms of FD by exerting prokinetic and stress-relieving effects, and that BF-2 has a better choleretic effect than BF-1.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.