• Journal of Nutrition and Health
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• v.9 no.2
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• pp.59-67
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• 1976

A series of experiment was carried out to study the effect of commonly used dietary fat or oils on the growth, feed efficiency, nutrient utilizability, nitrogen retention and serum cholesterol of rats and chicks fed various fat or oils at the level of 10% during 12 weeks of experimentation. Fat and oils used in this experiment were also analyzed for the composition of some fatty acids. The main observations made are as follows: 1. All groups received fat or oils gained more body weight than unsupplemented control group except chicks fed fish oil and rapeseed oil although no statistical significance was found between treatments. It was found that body weight gain achieved by the rats fed soybean oil, rapeseed oil, animal fat or corn oil was much greater than other group and that achieved by the chicks fed corn oil and animal fat was greater than other vegetable oil groups, although no statistical significance was found among treatments. 2. Feed intake data indicated that corn oil group of both rats and chicks consumed considerably more feed than other groups. Whereas feed intake of fish oil groups was the lowest among the experimental animals indicating that fish oil might contain unfavorable compound that depresses the palatability. In feed efficiency, soybean oil group of rats and corn oil group of chicks were significantly better than other experimental groups. In general, addition of fat or oils in the diet improved feed effeciency of diet. 3. Nutrient utiIizabiIity and nitrogen retention data showed that fat in the experimental diet containing 10% fat or oils was absorbed better than crude fat in control diet. It was also found that there was no significant difference in nitrogen retention among treatment. 4. Liver fat content of rapeseed oil group was much higher than that of control group and other group. It was also noticed that feeding more polyunsaturated fatty acids resulted in higher content of Iiver fat. 5. Present data indicated that serum cholesterol content of rapeseed oil and sesame oil group of rat was the higher than that of control group. Serum cholesterol content of animal fat group of chicks was higher than other group. It was interesting to note that serum cholesterol content of chicken was higher than that of rats?regardless of the kind of oils received. 6. Analytical data revealed that fatty acid composition of vegetable oil was composed mainly of oleic acid and linoleic acid, whereas animal fat and fish oil were composed of saturated fatty acid such as, myristic and palmitic acid. It should be mentionted that the perilla oil contained a very large amount of linolenic acid (58.4%) comparing with that in order vegetable oils. Little arachidonic acid was detected in vegetable oil, whereas none in animal fat and. fish oil.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1041-1043
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• 1993

Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)＋dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of Food Hygiene and Safety
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• v.37 no.6
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• pp.373-384
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• 2022

Fluoxastrobin a fungicide developed from Strobilurus species mushroom extracts, can be used as an effective pesticide to control fungal diseases. In this study, we optimized the extraction and purification of fluoxastrobin according to its physical and chemical properties using the QuEChERS method and developed an LC-MS/MS-based analysis method. For extraction, we used acetonitrile as the extraction solvent, along with MgSO₄ and PSA. The limit of quantitation of fluoxastrobin was 0.01 mg/kg. We used 0.01, 0.1, and 0.5 mg/kg of five representative agricultural products and treated them with fluoxastrobin. The coefficients of determination (R²) of fluoxastrobin and fluoxastrobin Z isomer were > 0.998. The average recovery rates of fluoxastrobin (n=5) and fluoxastrobin Z isomer were 75.5-100.3% and 75.0-103.9%, respectively. The relative standard deviations (RSDs) were < 5.5% and < 4.3% for fluoxastrobin and fluoxastrobin Z isomer, respectively. We also performed an interlaboratory validation at Gwangju Regional Food and Drug Administration and compared the recovery rates and RSDs obtained for fluoxastrobin and fluoxastrobin Z isomer at the external lab with our results to validate our analysis method. In the external lab, the average recovery rates and RSDs of fluoxastrobin and fluoxastrobin Z isomer at each concentration were 79.5-100.5% and 78.8-104.7% and < 18.1% and < 10.2%, respectively. In all treatment groups, the concentrations were less than those described by the 'Codex Alimentarius Commission' and the 'Standard procedure for preparing test methods for food, etc.'. Therefore, fluoxastrobin is safe for use as a pesticide.

• Applied Biological Chemistry
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• v.29 no.1
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• pp.62-72
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• 1986

At present, the definition and chemical analysis method of available soil phosphorus for plants have not been standardized because of the complexity of crop and soil characteristics in Korea and many analysis methods have been suggested with different extraction conditions. Suitable analytical method of available soil P should be established by the trial of various methods based on crop nutrition and soil conditions. To establish the most suitable analysis method of available soiIP, a pot experiment with young maize was conducted over 44 different upland soils collected over the land of Korea. The amount of uptaken P by the plant was determined by ten different chemical methods for the available soil P. The results obtained were as follows: 1. Total phosphorus content in the sample soils ranged ranged $533{\sim}4917\;ppm$, and showed significant positive correlation with the content of organic matter. 2. The P content was relatively low in the acid sulfate soil and very high in the volcanic ash soil although both types of soil contained high level of orgic matter. 3. The amount of extractable P determined by ten different methods were varied more or less, and the ratios of the extractable P to the total soil P were in the range of $1{\sim}48%$. 4. The relative values to the amount of extractable soil P by different methods were in the order of $H_2O(5\;min.)\;1.0\;<\;H_2O(60min.)\;2.27\;<\;NH_4HCO_3\;5.57\;<\;NaHCO_3\;7.42\;<\;Double\;lactate\;9.71\;<\;Bray\;No.1\;12.53\;<\;Lancaster\;17.63\;<\;Nelson\;25.96\;<\;AcOH\;27.6\;<\;CAL-method\;50.27$ 5. The amount of extractable P determined by all of applied methods was very low in acid sulfate soil, volcanic ash soil and coarse textured soil. 6. Soil pH and total soil P generally showed significant positive correlation with the chemically extracted P, and soil organic matter was negatively correlated with the determined by Nelson-and CAL-method. Olsen method which showed significant correlation with exchangeable calcium seemed to be recommendable for calcareous soils. 7. Total amount of uptaken P by Young maize through continuos twice cropping was 4.05% of total soil P in average, and the uptake in the second cropping was twice as much as that of the first cropping. 8. Three determination methods, i.e. Soltanpour-, Double lactate and Bray No. 1-method seemed to be more suitable than Lancaster method which is widely practiced at present in Korea. However, further study should be carried out with other crops and soils to most adequate chemical method for determination of available soil P.

• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.7-16
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• 1985

To grasp canonical correlations, their related backgrounds in various growth factors of stem, the characteristics of stem by synthetical dispersion analysis, principal component analysis and canonical correlation analysis as optimum method were applied to Larix leptolepis. The results are as follows; 1) There were high or low correlation among all factors (height ($x_1$), clear height ($x_2$), form height ($x_3$), breast height diameter (D. B. H.: $x_4$), mid diameter ($x_5$), crown diameter ($x_6$) and stem volume ($x_7$)) except normal form factor ($x_8$). Especially stem volume showed high correlation with the D.B.H., height, mid diameter (cf. table 1). 3) (1) Canonical correlation coefficients and canonical variate between stem volume and composite variate of various height growth factors ($x_1$, $x_2$ and $x_3$) are ${\gamma}_{u1,v1}=0.82980^{**}$, $\{u_1=1.00000x_7\\v_1=1.08323x_1-0.04299x_2-0.07080x_3$. (2) Those of stem volume and composite variate of various diameter growth factors ($x_4$, $x_5$ and $x_6$) are ${\gamma}_{u1,v1}=0.98198^{**}$, $\{{u_1=1.00000x_7\\v_1=0.86433x_4+0.11996x_5+0.02917x_6$. (3) And canonical correlation between stem volume and composite variate of six factors including various heights and diameters are ${\gamma}_{u1,v1}=0.98700^{**}$, $\{^u_1=1.00000x_7\\v1=0.12948x_1+0.00291x_2+0.03076x_3+0.76707x_4+0.09107x_5+0.02576x_6$. All the cases showed the high canonical correlation. Height in the case of (1), D.B.H. in that of (2), and the D.B.H, and height in that of (3) respectively make an absolute contribution to the canonical correlation. Synthetical characteristics of each qualitative growth are largely affected by each factor. Especially in the case of (3) the influence by the D.B.H. is the most significant in the above six factors (cf. table 2). 3) Canonical correlation coefficient and canonical variate between composite variate of various height growth factors and that of the various diameter factors are ${\gamma}_{u1,v1}=0.78556^{**}$, $\{u_1=1.20569x_1-0.04444x_2-0.21696x_3\\v_1=1.09571x_4-0.14076x_5+0.05285x_6$. As shown in the above facts, only height and D.B.H. affected considerably to the canonical correlation. Thus, it was revealed that the synthetical characteristics of height growth was determined by height and those of the growth in thickness by D.B.H., respectively (cf. table 2). 4) Synthetical characteristics (1st-3rd principal component) derived from eight growth factors of stem, on the basis of 85% accumulated proportion aimed, are as follows; Ist principal component ($z_1$): $Z_1=0.40192x_1+0.23693x_2+0.37047x_3+0.41745x_4+0.41629x_5+0.33454x_60.42798x_7+0.04923x_8$, 2nd principal component ($z_2$): $z_2=-0.09306x_1-0.34707x_2+0.08372x_3-0.03239x_4+0.11152x_5+0.00012x_6+0.02407x_7+0.92185x_8$, 3rd principal component ($z_3$): $Z_3=0.19832x_1+0.68210x_2+0.35824x_3-0.22522x_4-0.20876x_5-0.42373x_6-0.15055x_7+0.26562x_8$. The first principal component ($z_1$) as a "size factor" showed the high information absorption power with 63.26% (proportion), and its principal component score is determined by stem volume, D.B.H., mid diameter and height, which have considerably high factor loading. The second principal component ($z_2$) is the "shape factor" which indicates cubic similarity of the stem and its score is formed under the absolute influence of normal form factor. The third principal component ($z_3$) is the "shape factor" which shows the degree of thickness and length of stem. These three principal components have the satisfactory information absorption power with 88.36% of the accumulated percentage. variance (cf. table 3). 5) Thus the principal component and canonical correlation analyses could be applied to the field of forest measurement, judgement of site qualities, management diagnoses for the forest management and the forest products industries, and the other fields which require the assessment of synthetical characteristics.