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.

This comprehensive study delves into the intricate process of exfoliating and functionalizing boron nitride nanosheets (BNNSs) extracted from hexagonal boron nitride (h-BN), and meticulously explores their potential application within epoxy composites. The extensive research methodology encompasses a sequence of treatments involving hydrothermal and sonication processes aimed at augmenting the dispersion of BNNSs in solvents. Leveraging advanced analytical techniques such as Raman spectroscopy, X-ray diffraction, and FTIR spectroscopy, the study rigorously analyzes a spectrum of changes in the BNNS’s properties, including layer count variations, interlayer interactions, crystal structure modifications, and the introduction of functional groups. The research also rigorously evaluates the impact of integrating BNNSs, specifically glycidyl methacrylate (GMA)-functionalized BNNSs, on the thermal conductivity of epoxy composites. The conclusive findings exhibit notable enhancements in thermal properties, predominantly attributed to the enhanced dispersion of fillers and enhanced interactions within the epoxy matrix. This pioneering work illuminates the wide potential of functionalized BNNSs for significantly enhancing the thermal conductivity of epoxy composites, paving the way for advanced materials engineering and practical applications.

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.

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.

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.