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.

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.

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.