• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.68 no.4
• /
• pp.422-430
• /
• 2023

The nutrient-rich and climate-resilient finger millet (Eleusine coracana (L.) Gaertn.) is a relatively new crop on the agricultural landscape. The present study explores the agronomic characteristics and antioxidant activities of grains and cookies produced from 'Finger1ho,' which was the first finger millet variety developed in South Korea. With heightened calcium content (314 mg/100 g) and polyphenol levels, 'Finger1ho' exhibited superior radical scavenging activities compared to other millets. The investigation assessed the impact of whole finger millet flour at varying concentrations (0, 10, 30, 50, and 100%) on cookie properties. Increasing the substitution of finger millet flour in the cookie formulation resulted in a notable rise in calcium content, ranging from 1.8 times at 10% to an impressive 10.8 times at 100%, surpassing the levels found in conventional wheat cookies. Conversely, the sodium (Na) levels in finger millet cookies demonstrated minimal variations, presenting a potentially favorable aspect in addressing the high Na intake prevalent in the South Korean diet. Notably, the antioxidant activity, assessed through ABTS and DPPH radical scavenging assays, exhibited a significant elevation compared to the control. This increase in antioxidant activity was directly proportional to the quantity of finger millet incorporated (p<0.001), indicating the potential health benefits associated with higher levels of finger millet in the cookie formulation. This study highlights finger millet's potential as a beneficial ingredient, enhancing both consumer acceptability and the functional attributes of cookies. Notably, cookies with 10% to 50% added finger millet exhibited significantly superior quality characteristics compared to controls, suggesting promising avenues for health-functional cookie development.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.11 no.1
• /
• pp.1534-1548
• /
• 1969

This experiment was conducted, making use of the 'NONG-RIM6' arecommended variety of rice for the year of 1968. Main purposes of the experiment are to explore possibilities of; a) ways and means of saving irringation water and, b) overcoming drought at the same time so that an increased yield in rice could be resulted in. Specifically, it was tried to determine the effects of the Rotation irrigation method combined with differentiated thickness of lining upon the growth and yield of rice. Some of the major findings are summarized in the following. 1) The different thicknesses show a significant relationship with the weight of 1,000 grains. In the case of 9cm lined plot, the grain weight is 23.5grams, the heaviest. Next in order is 3cm lined plot, 6cm lined plot, control plot, and wheat straw lined-plot. 2) In rice yield, it is found that there is a considerably moderate significant relationship with both the different thickness of lining and the number of irrigation, as shown in the table. 3) There is little or no difference among different plots in terms of a) physical and chemical properties of soil, b) quality of irrigation water, c) climatic conditions, and rainfalls. 4) It is found that there is a significant relationship between differences in the method of rotation irrigation and the number of ears per hill. The plot irrigated at an interval of 7 days shows 17.4 ears and plot irrigated at an interval of 6 days, 16.3 5) In vinyl-treated plots, it is shown that both yield and component elements are greatest in the case of the plot ith whole of $3cm/m^2$ Next in order are the plot with a hole of $2cm/m^2$ the plot with a hole of $1cm/m^2$ In the case of the plot with no hole it is found that both yield and component elements are decreased as compared to the control plot. 6) The irrigation water reqirement is measured for the actual irrigation days of 72 which are the number subtracted the days of rainfall of 30 from the total irrigation days of 102. It is found that the irrigation water requirement for the uncontrol plot is 1,590mm as compared to 876mm(44.9% saved) for the 9cm-lined plot, 959mm(39.7% saved) for the 6cm-lined plot 1,010mm(36% saved) for the 3cm-lined plot and 1,082mm(32% saved) for the wheat straw lined plot. In the case of the Rotation irrigation method it is found that the water requirement for the plot irrigated at an interval of 8 days is 538mm(65% saved), as compared to 617mm(61.6% saved) for plot irrigated at an interval of 7 day 672mm(57.7% saved) for plot irrigated at an interval of 6day, 746mm(53.0% saved) for the plot irrigated at an interval of 5 days, 890mm 44.0% saved) for the plot irrigated at an interval of 4 days, and 975mm(38.6% saved) for the plot irrigated at an interval of 3 days. 7) The rate of evapotranspiration is found 2.8 around the end of month of July, as compared to 2.6 at the begining of August 3.4 around the end of August and 2.6 at the begining of August 3.4 around the end of August and 2.6 at the begining of September. 8) It is found that the saturation quantity of 30mm per day is decreased to 20mm per day though the use of vinyl covering. 9) The husking rate shows 75 per cent which is considered better.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.127-146
• /
• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.