• Korean Journal of Agricultural Science
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• 제16권1호
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• pp.84-101
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• 1989

An accurate measurement of size, surface area and volume of agricultural products is essential in many engineering operations such as handling and sorting, and in heat transfer studies on heating and cooling processes. Little information is available on these properties due to their irregular shape, and moreover very little information on the rough rice, soybean, barley, and wheat has been published. Physical dimensions of grain, such as length, width, thickness, surface area, and volume vary according to the variety, environmental conditions, temperature, and moisture content. Especially, recent research has emphasized on the variation of these properties with the important factors such as moisture content. The objectives of this study were to determine physical dimensions such as length, width and thickness, surface area and volume of the rough rice, soybean, barley, and wheat as a function of moisture content, to investigate the effect of moisture content on the properties, and to develop exponential equations to predict the surface area and the volume of the grains as a function of physical dimensions. The varieties of the rough rice used in this study were Akibare, Milyang 15, Seomjin, Samkang, Chilseong, and Yongmun, as a soybean sample Jangyeobkong and Hwangkeumkong, as a barley sample Olbori and Salbori, and as a wheat sample Eunpa and Guru were selected, respectively. The physical properties of the grain samples were determined at four levels of moisture content and ten or fifteen replications were run at each moisture content level and each variety. The results of this study are summarized as follows; 1. In comparison of the surface area and the volume of the 0.0375m diameter-sphere measured in this study with the calculated values by the formula the percent error between them showed least values of 0.65% and 0.77% at the rotational degree interval of 15 degree respectively. 2. The statistical test(t-test) results of the physical properties between the types of rough rice, and between the varieties of soybean and wheat indicated that there were significant difference at the 5% level between them. 3. The physical dimensions varied linearly with the moisture content, and the ratios of length to thickness (L/T) and of width to thickness (W/T) in rough rice decreased with increase of moisture content, while increased in soybean, but uniform tendency of the ratios in barley and wheat was not shown. In all of the sample grains except Olbori, sphericity decreased with increase of moisture content. 4. Over the experimental moisture levels, the surface area and the volume were in the ranges of about $45{\sim}51{\times}10^{-6}m^2$, $25{\sim}30{\times}10^{-9}m^3$ for Japonica-type rough rice, about $42{\sim}47{\times}10^{-6}m^2$, $21{\sim}26{\times}10^{-9}m^3$ for Indica${\times}$Japonica type rough rice, about $188{\sim}200{\times}10^{-6}m^2$, $277{\sim}300{\times}10^{-9}m^3$ for Jangyeobkong, about $180{\sim}201{\times}10^{-6}m^2$, $190{\sim}253{\times}10^{-9}m^3$ for Hwangkeumkong, about $60{\sim}69{\times}10^{-6}m^2$, $36{\sim}45{\times}10^{-9}m^3$ for Covered barley, about $47{\sim}60{\times}10^{-6}m^2$, $22{\sim}28{\times}10^{-9}m^3$ for Naked barley, about $51{\sim}20{\times}10^{-6}m^2$, $23{\sim}31{\times}10^{-9}m^3$ for Eunpamill, and about $57{\sim}69{\times}10^{-6}m^2$, $27{\sim}34{\times}10^{-9}m^3$ for Gurumill, respectively. 5. The increasing rate of surface area and volume with increase of moisture content was higher in soybean than other sample grains, and that of Japonica-type was slightly higher than Indica${\times}$Japonica type in rough rice. 6. The regression equations of physical dimensions, surface area and volume were developed as a function of moisture content, the exponential equations of surface area and volume were also developed as a function of physical dimensions, and the regression equations of surface area were also developed as a function of volume in all grain samples.

• Journal of Global Scholars of Marketing Science
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• 제17권2호
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• pp.1-29
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• 2007

The flow theory becomes one of the most important frameworks in the internet research arena. Hoffman and Novak proposed a hierarchical flow model showing the antecedents and outcomes of flow and the relationship among these variables in the hyper-media computer circumstances (Hoffman and Novak 1996). This model was further tested after their initial research (Novak, Hoffman, and Yung 2000). At their paper, Hoffman and Novak explained that the balance of challenge and skill leads to flow which means the positive optimal state of mind (Hoffman and Novak 1996). An imbalance between challenge and skill, leads to negative states of mind like anxiety, boredom, apathy (Csikszentmihalyi and Csikszentmihalyi 1988). Almost all research on the flow 4-channel model have been focusingon flow, the positive state of mind (Ellis, Voelkl, and Morris 1994 Mathwick and Rigdon 2004). However, it also needs to examine the formation of the negative states of minds and their outcomes. Flow researchers explain play or playfulness as antecedents or the early state of flow. However, play has been regarded as a distinct concept from flow in the flow literatures (Hoffman and Novak 1996; Novak, Hoffman, and Yung 2000). Mathwick and Rigdon discovered the influences of challenge and skill on play; they also observed the influence of play on web-loyalty and brand loyalty (Mathwick and Rigdon 2004). Unfortunately, they did not go so far as to test the influences of play on state of mind. This study focuses on the relationships between state of mind in the flow 4-channel model and play. Early research has attempted to hypothetically explain state of mind in flow theory, but has not been tested except flow until now. Also the importance of play has been emphasized in the flow theory, but has not been tested in the flow 4-channel model context. This researcher attempts to analyze the relationships among state of mind, skill of play, challenge, state of mind and web loyalty. For this objective, I developed a measure for state of mind and defined the concept of play as a trait. Then, the influences of challenge and skill on the state of mind and play under on-line shopping conditions were tested. Also the influences of play on state of mind were tested and those of flow and play on web loyalty were highlighted. 294 undergraduate students participated in this research survey. They were asked to respond about their perceptions of challenge, skill, state of mind, play, and web-loyalty to on-line shopping mall. Respondents were restricted to students who bought products on-line in a month. In case of buying products at two or more on-line shopping malls, they asked to respond about the shopping mall where they bought the most important one. Construct validity, discriminant validity, and convergent validity were used to check the measurement validations. Also, Cronbach's alpha was used to check scale reliability. A series of exploratory factor analyses was conducted. This researcher conducted confirmatory factor analyses to assess the validity of measurements. All items loaded significantly on their respective constructs. Also, all reliabilities were greater than.70. Chi-square difference tests and goodness of fit tests supported discriminant and convergent validity. The results of clustering and ANOVA showed that high challenge and high skill leaded to flow, low challenge and high skill leaded to boredom, and low challenge and low skill leaded to apathy. But, it was different from my expectation that high challenge and low skill didnot lead to anxiety but leaded to apathy. The results also showed that high challenge and high skill, and high challenge and low skill leaded to the highest play. Low challenge leaded to low play. 4 Structural Equation Models were built by flow, anxiety, boredom, apathy for analyzing not only the impact of play on state of mind and web-loyalty, but also that of state of mind on web-loyalty. According the analyses results of these models, play impacted flow and web-loyalty positively, but impacted anxiety, boredom, and apathy negatively. Results also showed that flow impacted web-loyalty positively, but anxiety, boredom, and apathy impacted web-loyalty negatively. The interpretations and implications of the test results of the hypotheses are as follows. First, respondents belonging to different clusters based on challenge and skill level experienced different states of mind such as flow, anxiety, boredom, apathy. The low challenge and low skill group felt the highest anxiety and apathy. It could be interpreted that this group feeling high anxiety or fear, then avoided attempts to shop on-line. Second, it was found that higher challenge leads to higher levels of play. Test results show that the play level of the high challenge and low skill group (anxiety group) was higher than that of the high challenge and high skill group (flow group). However, this was not significant. Third, play positively impacted flow and negatively impacted boredom. The negative impacts on anxiety and apathy were not significant. This means that the combination of challenge and skill creates different results. Forth, play and flow positively impacted web-loyalty, but anxiety, boredom, apathy had negative impacts. The effect of play on web-loyalty was stronger in case of anxiety, boredom, apathy group than fl ow group. These results show that challenge and skill influences state of mind and play. Results also demonstrate how play and flow influence web-loyalty. It implies that state of mind and play should be the core marketing variables in internet marketing. The flow theory has been focusing on flow and on the positive outcomes of flow experiences. But, this research shows that lots of consumers experience the negative state of mind rather than flow state in the internet shopping circumstance. Results show that the negative state of mind leads to low or negative web-loyalty. Play can have an important role with the web-loyalty when consumers have the negative state of mind. Results of structural equation model analyses show that play influences web-loyalty positively, even though consumers may be in the negative state of mind. This research found the impacts of challenge and skill on state of mind in the flow 4-channel model, not only flow but also anxiety, boredom, apathy. Also, it highlighted the role of play in the flow 4-channel model context and impacts on web-loyalty. However, tests show a few different results from hypothetical expectations such as the highest anxiety level of apathy group and insignificant impacts of play on anxiety and apathy. Further research needs to replicate this research and/or to compare 3-channel model with 4-channel model.

• Korean Journal of Soil Science and Fertilizer
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• 제10권3호
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• pp.171-188
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• 1977

The response and effect on main upland crops to potassium were discussed and summarized as follows. 1. Adequate average amounts of potash per 10a were 32kg for forage crop; 22.5kg for vegetable crops; 17.3kg for fruit trees; 13.3kg for potatoes; and 6.5kg for cereal crops. Demand of potassium fertilizer in the future will be increased by expanding the acreage of forage crops, vegetable crops and fruit trees. 2. On the average, optimum potash rates on barley, wheat, soybean, corn, white potato and sweet potato were 6.5, 6.9, 4.5, 8.1, 8.9, and 17.7kg per 10a respectively. Yield increaments per 1kg of potash per 10a were 4-5kgs on the average for cereal crops, 68kg for white potato, and 24kg for sweet potato. 3. According to the soil testing data, the exchangeable potassium in the coastal area was higher than that in the inland area and medium in the mountainous area. The exchangeable potassium per province in decreasing order is Jeju>Jeonnam>Kangweon>Kyongnam. Barley : 4. The response of barley to an adequate rate of potassium seemed to be affected more by differences in climatic conditions than to the nature of the soil. 5. The response and the adequate rate of potassium in the southern area, where the temperature is higher, were low because of more release of potassium from the soil. However, the adequate rate of phosphorus was increased due to the fixation of applied phosphorus into the soil in high temperature regions. The more nitrogen application would be required in the southern area due to its high precipitation. 6. The average response of barley to potassium was lower in the southern provinces than northern provinces. Kyongsangpukdo, a southern province, showed a relatively higher response because of the low exchangeable potassium content in the soil and the low-temperature environment in most of cultivation area. 7. Large annual variations in the response to and adequate rates of potassium on barley were noticed. In a cold year, the response of barley to potassium was 2 to 3 times higher than in a normal year. And in the year affected by moisture and drought damage, the responses to potassium was low but adequate rates was higher than cold year. 8. The content of exchangeable potassium in the soil parent materials, in increasing order was Crystalline Schist, Granite, Sedimentary and Basalt. The response of barley to potash occurred in the opposite order with the smallest response being in Crystalline Schist soil. There was a negative correlation between the response and exchangeable potassium contents but there was nearly no difference in the adequate rates of potassium. 9. Exchangeable potassium according to the mode of soil deposition was Alluvium>Residium>Old alluvium>Valley alluvium. The highest response to potash was obtained in Valley alluvium while the other s showed only small differences in responses. 10. Response and adequate rates of potassium seemed to be affected greatly by differences in soil texture. The response to potassium was higher in Sandy loam and Loam soils but the optimum rate of potassium was higher in Clay and Clay loam. Especially when excess amount of potassium was applied in Sandy loam and Loam soils the yield was decreased. 11. The application of potassium retarded the heading date by 1.7 days and increased the length of culm. the number of spikelet per plant, the 1,000 grain weight and the ratio of grain weight to straw. Soybean : 12. Average response of soybean to potassium was the lowest among other cereal crops but 28kg of grain yield was incrased by applying potash at 8kg/10a in newly reclaimed soils. 13. The response in the parent materials soil was in the order of Basalt (Jeju)>Sedimentay>Granite>Lime stone but this response has very wide variations year to year. Corn : 14. The response of corn to potassium decreased in soils where the exchangeable potassium content was high. However, the optimum rate of applied potassium was increased as the soil potassium content was increased because corn production is proportional to the content of soil potassium. 15. An interaction between the response to potassium and the level of phosphorus was noted. A higher response to potassium and higher rates of applied potassium was observed in soils contained optimum level of phosphorus. Potatoes : 16. White potato had a higher requirement for nitrogen than for potassium, which may imply that potato seems to have a higher capability of soil potassium uptake. 17. The yield of white potato was higher in Sandy loam than in Clay loam soil. Potato yields were also higher in soils where the exchangeable potassium content was high even in the same soil texture. However, the response to applied potassium was higher in Clay loam soils than in Sandy loam soils and in paddy soil than in upland soil. 18. The requirement for nitrogen and phosphorus by sweet potato was relatively low. The sweet potato yield is relatively high even under unfavorable soil conditions. A characteristics of sweet potatoes is to require higher level of potassium and to show significant responses to potassium. 19. The response of sweet potato to potassium varied according to soil texture. Higher yields were obtained in Sandy soil, which has a low exchangeable potassium content, by applying sufficient potassium. 20. When the optimum rate of potassium was applied, the yields of sweet potato in newly reclaimed soil were comparable to that in older upland soils.