• 융합경영연구
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• 제10권5호
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• pp.1-6
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• 2022

Purpose: To overcome the question that depends too much on expert's subjective judgment in traditional risk identification, this paper structure the multilevel generalized fuzzy comprehensive evaluation mathematics model of the risk identification of project, to research the risk identification of the project. Research design, data and methodology: This paper constructs the multilevel generalized fuzzy comprehensive evaluation mathematics model. Through iterative algorithm of AHP analysis, make sure the important degree of the sub project in risk analysis, then combine expert's subjective judgment with objective quantitative analysis, and distinguish the risk through identification models. Meanwhile, the concrete method of multilevel generalized fuzzy comprehensive evaluation is probed. Using the index weights to analyse project risks is discussed in detail. Results: The improved fuzzy comprehensive evaluation algorithm is proposed in the paper, at first the method of fuzzy sets core is used to optimize the fuzzy relation matrix. It improves the capability of the algorithm. Then, the method of entropy weight is used to establish weight vectors. This makes the computation process fair and open. And thereby, the uncertainty of the evaluation result brought by the subjectivity can be avoided effectively and the evaluation result becomes more objective and more reasonable. Conclusions: In this paper, we use an improved fuzzy comprehensive evaluation method to evaluate a railroad engineering project risk. It can give a more reliable result for a reference of decision making.

• The Journal of Asian Finance, Economics and Business
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• 제9권8호
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• pp.99-108
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• 2022

This study aims to examine the influence of capital structure on the business efficiency of joint stock commercial banks listed on the Vietnamese stock market. The article uses data collected from the financial statements of 15 prominent joint-stock commercial banks out of 27 joint-stock commercial banks listed in Vietnam from 2011 to 2021. The research uses E-view software in quantitative analysis to build regression models to determine the relationship and the impact of capital structure factors on the business efficiency of listed joint stock commercial banks. Research results show that ROA is affected by 2 variables of capital structure. It is the sum of customer deposits to total assets and total liabilities to total equity. Total debt to total equity and total customer deposits to total assets both have a negative effect on ROA. For the regression results of ROA with all control variables, the control variables have a positive relationship with the dependent variable. The article has provided recommendations based on the research findings to determine the proper capital structure. Managers must solve the outstanding amount of mobilized capital in previous years, combined with the bad debt handling activities that have arisen.

• 대한임베디드공학회논문지
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• 제17권5호
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• pp.273-280
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• 2022

Along with the advancement of deep learning technology, securing high-quality dataset for verification of developed technology is emerging as an important issue, and developing robust deep learning models to the domestic road environment is focused by many research groups. Especially, unlike expressways and automobile-only roads, in the complex city driving environment, various dynamic objects such as motorbikes, electric kickboards, large buses/truck, freight cars, pedestrians, and traffic lights are mixed in city road. In this paper, we built our dataset through multi camera-based processing (collection, refinement, and annotation) including the various objects in the city road and estimated quality and validity of our dataset by using YOLO-based model in object detection. Then, quantitative evaluation of our dataset is performed by comparing with the public dataset and qualitative evaluation of it is performed by comparing with experiment results using open platform. We generated our 2D dataset based on annotation rules of KITTI/COCO dataset, and compared the performance with the public dataset using the evaluation rules of KITTI/COCO dataset. As a result of comparison with public dataset, our dataset shows about 3 to 53% higher performance and thus the effectiveness of our dataset was validated.

• Journal of Animal Science and Technology
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• 제64권3호
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• pp.588-598
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• 2022

Despite vaccination, equine influenza virus (EIV) and equine herpesvirus (EHV) infections still cause highly contagious respiratory diseases in horses. Recently, concurrent vaccination with EIV and EHV was suggested as a new approach; however, there have been no reports of concurrent vaccination with recombinant canarypox EIV and inactivated EHV vaccines. In this study, we aimed to compare the EIV-specific immune responses induced by concurrent administrations of a recombinant canarypox EIV vaccine and an inactivated bivalent EHV vaccine with those induced by a single recombinant canarypox EIV vaccine in experimental horse and mouse models. Serum and peripheral blood mononuclear cells (PBMCs) were collected from immunized animals after vaccination. EIV-specific serum antibody levels, serum hemagglutinin inhibition (HI) titers, and interferon-gamma (IFN-γ) levels were measured by enzyme-linked immunosorbent assay, HI assay, and quantitative polymerase chain reaction, respectively. Concurrent EIV and EHV vaccine administration significantly increased IFN-γ production, without compromising humoral responses. Our data demonstrate that concurrent vaccination with EIV and EHV vaccines can enhance EIV-specific cellular responses in horses.

• Journal of Microbiology and Biotechnology
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• 제33권2호
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• pp.251-259
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• 2023

Immobilizing microalgae cells in a hyphal matrix can simplify harvest while producing novel mycoalgae products with potential food, feed, biomaterial, and renewable energy applications; however, limited quantitative information to describe the process and its applicability under various conditions leads to difficulties in comparing across studies and scaling-up. Here, we demonstrate the immobilization of both active and heat-deactivated marine diatom Phaeodactylum tricornutum (UTEX 466) using different loadings of fungal pellets (Aspergillus sp.) and model the process through kinetics and equilibrium models. Active P. tricornutum cells were not required for the fungal-assisted immobilization process and the fungal isolate was able to immobilize more than its original mass of microalgae. The Freundlich isotherm model adequately described the equilibrium immobilization characteristics and indicated increased normalized algae immobilization (g algae removed/g fungi loaded) under low fungal pellet loadings. The kinetics of algae immobilization by the fungal pellets were found to be adequately modeled using both a pseudo-second order model and a model previously developed for fungal-assisted algae immobilization. These results provide new insights into the behavior and potential applications of fungal-assisted algae immobilization.

• Journal of Microbiology and Biotechnology
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• 제33권1호
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• pp.43-50
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• 2023

Fungal keratitis is a refractory kind of keratopathy. We attempted to investigate the antiinflammatory role of thymol on Aspergillus fumigatus (A. fumigatus) keratitis. Wound healing and fluorescein staining of the cornea were applied to verify thymol's safety. Mice models of A. fumigatus keratitis underwent subconjunctival injection of thymol. The anti-inflammatory roles of thymol were verified by hematoxylin-eosin (HE) staining, slit lamp observation, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blotting. In contrast with the DMSO group, more transparent corneas and less inflammatory cells infiltration were detected in mice treated with 50 ㎍/ml thymol. Thymol downregulated the synthesis of TLR4, MyD88, NF-kB, IL-1β, NLRP3, caspase 1, caspase 8, GSDMD, RIPK3 and MLKL. In summary, we proved that thymol played a protective part in A. fumigatus keratitis by cutting down inflammatory cells aggregation, downregulating the TLR4/ MyD88/ NF-kB/ IL-1β signal expression and reducing necroptosis and pyroptosis.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• 한국발생생물학회지:발생과생식
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• 제26권4호
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• pp.145-153
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• 2022

The gut microbiota is involved in the maintenance of physiological homeostasis and is now recognized as a regulator of many diseases. Although germ-free mouse models are the standard for microbiome studies, mice with antibiotic-induced sterile intestines are often chosen as a fast and inexpensive alternative. Pathophysiological changes in the gut microbiome have been demonstrated, but there are no reports so far on how such alterations affect the bacterial composition of the uterus. Here we examined changes in uterine microbiota as a result of gut microbiome disruption in an antibiotics-based sterile-uterus mouse model. Sterility was induced in 6-week-old female mice by administration of a combination of antibiotics, and amplicons of a bacteria marker gene (16S rRNA) were sequenced to decipher bacterial community structures in the uterus. At the phylum-level, Proteobacteria, Firmicutes, and Actinobacteria were found to be dominant, while Ralstonia, Escherichia, and Prauserella were the major genera. Quantitative comparisons of the microbial contents of an antibiotic-fed and a control group revealed that the treatment resulted in the reduction of bacterial population density. Although there was no significant difference in bacterial community structures between the two animal groups, β-diversity analysis showed a converged profile of uterus microbiotain the germ-free model. These findings suggest that the induction of sterility does not result in changes in the levels of specific taxa but in a reduction of individual variations in the mouse uterus microbiota, accompanied by a decrease in overall bacterial population density.

• 한국비블리아학회지
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• 제33권1호
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• pp.167-198
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• 2022

융합 과학 연구가 활성화되며 인문학에서도 디지털 인문학(Digital Humanities) 연구가 장려되고 있다. 이에 본 연구는 역사 데이터에 텍스트마이닝과 개체계량학 연구 방법을 적용한 시론(試論) 연구를 제안하고자 하였다. 선조실록(宣祖實錄)·선조수정실록(宣祖修正實錄), 난중잡록(亂中雜錄), 징비록(懲毖錄)을 활용하였으며, 사료(史料)에서 주제 변화와 공통 개체를 탐색하기 위해서 네트워크 분석과 DMR 토픽모델을 사용하였다. 분석 결과를 통해서 텍스트 데이터에 대한 계량 분석의 활용 가능성 확인, 특정 주제의 시기적 변화, 인물 개체 간 미발견 관계를 제시함으로써 연구의 확장 가능성을 제안할 수 있었다.

• 한국인공지능학회지
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• 제11권2호
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.