• Title/Summary/Keyword: the theological trend

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Effect of Chlorine Treatment on the Rheological Properties of Soft Wheat Flour (박력분의 리올로지 특성에 대한 염소처리의 영향)

  • Han, Myung-Kyoo;Chang, Young-Sang;Shin, Hyo-Sun
    • Applied Biological Chemistry
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    • v.32 no.4
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    • pp.327-331
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    • 1989
  • In this study the Theological properties between C1-treated soft wheat flour and untreated soft wheat flour was determined. Chlorine treatment lowered pH of the flour in a linear fashion. Water absorption and dough stability was high in proportion to the increase of treatment level but mechanical tolerance index was reduced by each increment of chlorine. The valorimeter value did not exhibit reproducible trend on treatment of chlorine. In general, resistance(BU), resistance to extension and maximum viscosity(BU) were highest in control group; lowest in 1 oz./cwt. flour and tended to rise in 2 oz./cwt. flour when it fermented in chamber for 90 min and 135 min. The maximum viscosity was highest (1,160BU) in 4 oz./cwt. flour and temperature at maximum viscosity tended to rise gradually in proportion to the increase of treated level.

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Domestic Research Trend of AI Education Program: A Scoping Review (국내 AI 교육 프로그램 연구동향 분석: 주제범위 문헌고찰 방법론을 적용하여)

  • Han, Jeongyun;Huh, Sun Young
    • Journal of The Korean Association of Information Education
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    • v.25 no.6
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    • pp.879-890
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    • 2021
  • AI education is being emphasized nationwide as a literacy education. At this point, it is necessary to identify critical issues and suggest the direction of future research by examining domestic AI education research trends. To this end, the study applied the scoping review method. A total of 29 AI educational studies from 2017 to 2020 in South Korea were analyzed. As a result, it was confirmed that the number of studies increased rapidly in 2020, and a large proportion of studies targeted elementary school students. In addition, the study found that AI principles were treated as contents at a high rate, both cognitive and affective aspects were frequently reported as a learning outcome, and various practice environments were used relatively evenly. Based on the results, the direction of future research was discussed and suggested.

Trends in Patents for Numerical Analysis-Based Financial Instruments Valuation Systems (수치해석 기반 금융상품 가치평가 시스템 특허 동향)

  • Moonseong Kim
    • Journal of Internet Computing and Services
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    • v.24 no.6
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    • pp.41-47
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    • 2023
  • Financial instruments valuation continues to evolve due to various technological changes. Recently, there has been increased interest in valuation using machine learning and artificial intelligence, enabling the financial market to swiftly adapt to changes. This technological advancement caters to the demand for real-time data processing and facilitates accurate and effective valuation, considering the diverse nature of the financial market. Numerical analysis techniques serve as crucial decision-making tools among financial institutions and investors, acknowledged as essential for performance prediction and risk management in investments. This paper analyzes Korean patent trends of numerical analysis-based financial systems, considering the diverse shifts in the financial market and asset data to provide accurate predictions. This study could shed light on the advancement of financial technology and serves as a gauge for technological standards within the financial market.

Data Efficient Image Classification for Retinal Disease Diagnosis (데이터 효율적 이미지 분류를 통한 안질환 진단)

  • Honggu Kang;Huigyu Yang;Moonseong Kim;Hyunseung Choo
    • Journal of Internet Computing and Services
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    • v.25 no.3
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    • pp.19-25
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    • 2024
  • The worldwide aging population trend is causing an increase in the incidence of major retinal diseases that can lead to blindness, including glaucoma, cataract, and macular degeneration. In the field of ophthalmology, there is a focused interest in diagnosing diseases that are difficult to prevent in order to reduce the rate of blindness. This study proposes a deep learning approach to accurately diagnose ocular diseases in fundus photographs using less data than traditional methods. For this, Convolutional Neural Network (CNN) models capable of effective learning with limited data were selected to classify Conventional Fundus Images (CFI) from various ocular disease patients. The chosen CNN models demonstrated exceptional performance, achieving high Accuracy, Precision, Recall, and F1-score values. This approach reduces manual analysis by ophthalmologists, shortens consultation times, and provides consistent diagnostic results, making it an efficient and accurate diagnostic tool in the medical field.