• Title/Summary/Keyword: A-type $K^+$ channel

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Mammalian Reproduction and Pheromones (포유동물의 생식과 페로몬)

  • Lee, Sung-Ho
    • Development and Reproduction
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    • v.10 no.3
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    • pp.159-168
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    • 2006
  • Rodents and many other mammals have two chemosensory systems that mediate responses to pheromones, the main and accessory olfactory system, MOS and AOS, respectively. The chemosensory neurons associated with the MOS are located in the main olfactory epithelium, while those associated with the AOS are located in the vomeronasal organ(VNO). Pheromonal odorants access the lumen of the VNO via canals in the roof of the mouth, and are largely thought to be nonvolatile. The main pheromone receptor proteins consist of two superfamilies, V1Rs and V2Rs, that are structurally distinct and unrelated to the olfactory receptors expressed in the main olfactory epithelium. These two type of receptors are seven transmembrane domain G-protein coupled proteins(V1R with $G_{{\alpha}i2}$, V2R with $G_{0\;{\alpha}}$). V2Rs are co-expressed with nonclassical MHC Ib genes(M10 and other 8 M1 family proteins). Other important molecular component of VNO neuron is a TrpC2, a cation channel protein of transient receptor potential(TRP) family and thought to have a crucial role in signal transduction. There are four types of pheromones in mammalian chemical communication - primers, signalers, modulators and releasers. Responses to these chemosignals can vary substantially within and between individuals. This variability can stem from the modulating effects of steroid hormones and/or non-steroid factors such as neurotransmitters on olfactory processing. Such modulation frequently augments or facilitates the effects that prevailing social and environmental conditions have on the reproductive axis. The best example is the pregnancy block effect(Bruce effect), caused by testosterone-dependent major urinary proteins(MUPs) in male mouse urine. Intriguingly, mouse GnRH neurons receive pheromone signals from both odor and pheromone relays in the brain and may also receive common odor signals. Though it is quite controversial, recent studies reveal a complex interplay between reproduction and other functions in which GnRH neurons appear to integrate information from multiple sources and modulate a variety of brain functions.

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Spatial effect on the diffusion of discount stores (대형할인점 확산에 대한 공간적 영향)

  • Joo, Young-Jin;Kim, Mi-Ae
    • Journal of Distribution Research
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    • v.15 no.4
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    • pp.61-85
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    • 2010
  • Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center. $$\array{{S_{i,t}=(p_i+q_i{\frac{Y_{i,t-1}}{m_i}})(m_i-Y_{i,t-1})\;i{\in}\{1,{\cdots},I\}\;(1a)}\\{S_{j,t}=(p_j+q_j{\frac{Y_{j,t-1}}{m_i}}+{\sum\limits_{i=1}^I}{\gamma}_{ij}{\frac{Y_{i,t-1}}{m_i}})(m_j-Y_{j,t-1})\;i{\in}\{1,{\cdots},I\},\;j{\in}\{I+1,{\cdots},I+J\}\;(1b)}}$$ We rise two research questions. (1) The proposed spatial diffusion model is more effective than the Bass model to describe the diffusion of discount stores. (2) The more similar retail environment of diffusing center with that of the vicinity of the contiguous market is, the larger spatial effect of diffusing center on diffusion of the vicinity of the contiguous market is. To examine above two questions, we adopt the Bass model to estimate diffusion of discount store first. Next spatial diffusion model where spatial factor is added to the Bass model is used to estimate it. Finally by comparing Bass model with spatial diffusion model, we try to find out which model describes diffusion of discount store better. In addition, we investigate the relationship between similarity of retail environment(conceptual distance) and spatial factor impact with correlation analysis. Result and Implication: We suggest spatial diffusion model to describe diffusion of discount stores. To examine the proposed spatial diffusion model, 347 domestic discount stores are used and we divide nation into 5 districts, Seoul-Gyeongin(SG), Busan-Gyeongnam(BG), Daegu-Gyeongbuk(DG), Gwan- gju-Jeonla(GJ), Daejeon-Chungcheong(DC), and the result is shown

    . In a result of the Bass model(I), the estimates of innovation coefficient(p) and imitation coefficient(q) are 0.017 and 0.323 respectively. While the estimate of market potential is 384. A result of the Bass model(II) for each district shows the estimates of innovation coefficient(p) in SG is 0.019 and the lowest among 5 areas. This is because SG is the diffusion center. The estimates of imitation coefficient(q) in BG is 0.353 and the highest. The imitation coefficient in the vicinity of the diffusing center such as BG is higher than that in the diffusing center because much information flows through various paths more as diffusion is progressing. A result of the Bass model(II) shows the estimates of innovation coefficient(p) in SG is 0.019 and the lowest among 5 areas. This is because SG is the diffusion center. The estimates of imitation coefficient(q) in BG is 0.353 and the highest. The imitation coefficient in the vicinity of the diffusing center such as BG is higher than that in the diffusing center because much information flows through various paths more as diffusion is progressing. In a result of spatial diffusion model(IV), we can notice the changes between coefficients of the bass model and those of the spatial diffusion model. Except for GJ, the estimates of innovation and imitation coefficients in Model IV are lower than those in Model II. The changes of innovation and imitation coefficients are reflected to spatial coefficient(${\gamma}$). From spatial coefficient(${\gamma}$) we can infer that when the diffusion in the vicinity of the diffusing center occurs, the diffusion is influenced by one in the diffusing center. The difference between the Bass model(II) and the spatial diffusion model(IV) is statistically significant with the ${\chi}^2$-distributed likelihood ratio statistic is 16.598(p=0.0023). Which implies that the spatial diffusion model is more effective than the Bass model to describe diffusion of discount stores. So the research question (1) is supported. In addition, we found that there are statistically significant relationship between similarity of retail environment and spatial effect by using correlation analysis. So the research question (2) is also supported.

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  • Intelligent VOC Analyzing System Using Opinion Mining (오피니언 마이닝을 이용한 지능형 VOC 분석시스템)

    • Kim, Yoosin;Jeong, Seung Ryul
      • Journal of Intelligence and Information Systems
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      • v.19 no.3
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      • pp.113-125
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      • 2013
    • Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

    Studies on the Rice Yield Decreased by Ground Water Irrigation and Its Preventive Methods (지하수 관개에 의한 수도의 멸준양상과 그 방지책에 관한 연구)

    • 한욱동
      • Magazine of the Korean Society of Agricultural Engineers
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      • v.16 no.1
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      • pp.3225-3262
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      • 1974
    • The purposes of this thesis are to clarify experimentally the variation of ground water temperature in tube wells during the irrigation period of paddy rice, and the effect of ground water irrigation on the growth, grain yield and yield components of the rice plant, and, furthermore, when and why the plant is most liable to be damaged by ground water, and also to find out the effective ground water irrigation methods. The results obtained in this experiment are as follows; 1. The temperature of ground water in tube wells varies according to the location, year, and the depth of the well. The average temperatures of ground water in a tubewells, 6.3m, 8.0m deep are $14.5^{\circ}C$ and $13.1^{\circ}C$, respercively, during the irrigation period of paddy rice (From the middle of June to the end of September). In the former the temperature rises continuously from $12.3^{\circ}C$ to 16.4$^{\circ}C$ and in the latter from $12.4^{\circ}C$ to $13.8^{\circ}C$ during the same period. These temperatures are approximately the same value as the estimated temperatures. The temperature difference between the ground water and the surface water is approximately $11^{\circ}C$. 2. The results obtained from the analysis of the water quality of the "Seoho" reservoir and that of water from the tube well show that the pH values of the ground water and the surface water are 6.35 and 6.00, respectively, and inorganic components such as N, PO4, Na, Cl, SiO2 and Ca are contained more in the ground water than in the surface water while K, SO4, Fe and Mg are contained less in the ground water. 3. The response of growth, yield and yield components of paddy rice to ground water irrigation are as follows; (l) Using ground water irrigation during the watered rice nursery period(seeding date: 30 April, 1970), the chracteristics of a young rice plant, such as plant height, number of leaves, and number of tillers are inferior to those of young rice plants irrigated with surface water during the same period. (2) In cases where ground water and surface water are supplied separately by the gravity flow method, it is found that ground water irrigation to the rice plant delays the stage at which there is a maximum increase in the number of tillers by 6 days. (3) At the tillering stage of rice plant just after transplanting, the effect of ground water irrigation on the increase in the number of tillers is better, compared with the method of supplying surface water throughout the whole irrigation period. Conversely, the number of tillers is decreased by ground water irrigation at the reproductive stage. Plant height is extremely restrained by ground water irrigation. (4) Heading date is clearly delayed by the ground water irrigation when it is practised during the growth stages or at the reproductive stage only. (5) The heading date of rice plants is slightly delayed by irrigation with the gravity flow method as compared with the standing water method. (6) The response of yield and of yield components of rice to ground water irrigation are as follows: \circled1 When ground water irrigation is practised during the growth stages and the reproductive stage, the culm length of the rice plant is reduced by 11 percent and 8 percent, respectively, when compared with the surface water irrigation used throughout all the growth stages. \circled2 Panicle length is found to be the longest on the test plot in which ground water irrigation is practised at the tillering stage. A similar tendency as that seen in the culm length is observed on other test plots. \circled3 The number of panicles is found to be the least on the plot in which ground water irrigation is practised by the gravity flow method throughout all the growth stages of the rice plant. No significant difference is found between the other plots. \circled4 The number of spikelets per panicle at the various stages of rice growth at which_ surface or ground water is supplied by gravity flow method are as follows; surface water at all growth stages‥‥‥‥‥ 98.5. Ground water at all growth stages‥‥‥‥‥‥62.2 Ground water at the tillering stage‥‥‥‥‥ 82.6. Ground water at the reproductive stage ‥‥‥‥‥ 74.1. \circled5 Ripening percentage is about 70 percent on the test plot in which ground water irrigation is practised during all the growth stages and at the tillering stage only. However, when ground water irrigation is practised, at the reproductive stage, the ripening percentage is reduced to 50 percent. This means that 20 percent reduction in the ripening percentage by using ground water irrigation at the reproductive stage. \circled6 The weight of 1,000 kernels is found to show a similar tendency as in the case of ripening percentage i. e. the ground water irrigation during all the growth stages and at the reproductive stage results in a decreased weight of the 1,000 kernels. \circled7 The yield of brown rice from the various treatments are as follows; Gravity flow; Surface water at all growth stages‥‥‥‥‥‥514kg/10a. Ground water at all growth stages‥‥‥‥‥‥428kg/10a. Ground water at the reproductive stage‥‥‥‥‥‥430kg/10a. Standing water; Surface water at all growh stages‥‥‥‥‥‥556kg/10a. Ground water at all growth stages‥‥‥‥‥‥441kg/10a. Ground water at the reproductive stage‥‥‥‥‥‥450kg/10a. The above figures show that ground water irrigation by the gravity flow and by the standing water method during all the growth stages resulted in an 18 percent and a 21 percent decrease in the yield of brown rice, respectively, when compared with surface water irrigation. Also ground water irrigation by gravity flow and by standing water resulted in respective decreases in yield of 16 percent and 19 percent, compared with the surface irrigation method. 4. Results obtained from the experiments on the improvement of ground water irrigation efficiency to paddy rice are as follows; (1) When the standing water irrigation with surface water is practised, the daily average water temperature in a paddy field is 25.2$^{\circ}C$, but, when the gravity flow method is practised with the same irrigation water, the daily average water temperature is 24.5$^{\circ}C$. This means that the former is 0.7$^{\circ}C$ higher than the latter. On the other hand, when ground water is used, the daily water temperatures in a paddy field are respectively 21.$0^{\circ}C$ and 19.3$^{\circ}C$ by practising standing water and the gravity flow method. It can be seen that the former is approximately 1.$0^{\circ}C$ higher than the latter. (2) When the non-water-logged cultivation is practised, the yield of brown rice is 516.3kg/10a, while the yield of brown rice from ground water irrigation plot throughout the whole irrigation period and surface water irrigation plot are 446.3kg/10a and 556.4kg/10a, respectivelely. This means that there is no significant difference in yields between surface water irrigation practice and non-water-logged cultivation, and also means that non-water-logged cultivation results in a 12.6 percent increase in yield compared with the yield from the ground water irrigation plot. (3) The black and white coloring on the inside surface of the water warming ponds has no substantial effect on the temperature of the water. The average daily water temperatures of the various water warming ponds, having different depths, are expressed as Y=aX+b, while the daily average water temperatures at various depths in a water warming pond are expressed as Y=a(b)x (where Y: the daily average water temperature, a,b: constants depending on the type of water warming pond, X; water depth). As the depth of water warning pond is increased, the diurnal difference of the highest and the lowest water temperature is decreased, and also, the time at which the highest water temperature occurs, is delayed. (4) The degree of warming by using a polyethylene tube, 100m in length and 10cm in diameter, is 4~9$^{\circ}C$. Heat exchange rate of a polyethylene tube is 1.5 times higher than that or a water warming channel. The following equation expresses the water warming mechanism of a polyethylene tube where distance from the tube inlet, time in day and several climatic factors are given: {{{{ theta omega (dwt)= { a}_{0 } (1-e- { x} over { PHI v })+ { 2} atop { SUM from { { n}=1} { { a}_{n } } over { SQRT { 1+ {( n omega PHI) }^{2 } } } } LEFT { sin(n omega t+ { b}_{n }+ { tan}^{-1 }n omega PHI )-e- { x} over { PHI v }sin(n omega LEFT ( t- { x} over {v } RIGHT ) + { b}_{n }+ { tan}^{-1 }n omega PHI ) RIGHT } +e- { x} over { PHI v } theta i}}}}{{{{ { theta }_{$\infty$ }(t)= { { alpha theta }_{a }+ { theta }_{ w'} +(S- { B}_{s } ) { U}_{w } } over { beta } , PHI = { { cpDU}_{ omega } } over {4 beta } }}}} where $\theta$$\omega$; discharged water temperature($^{\circ}C$) $\theta$a; air temperature ($^{\circ}C$) $\theta$$\omega$';ponded water temperature($^{\circ}C$) s ; net solar radiation(ly/min) t ; time(tadian) x; tube length(cm) D; diameter(cm) ao,an,bn;constants determined from $\theta$$\omega$(t) varitation. cp; heat capacity of water(cal/$^{\circ}C$ ㎥) U,Ua; overall heat transfer coefficient(cal/$^{\circ}C$ $\textrm{cm}^2$ min-1) $\omega$;1 velocity of water in a polyethylene tube(cm/min) Bs ; heat exchange rate between water and soil(ly/min)

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