• Magazine of the Korean Society of Agricultural Engineers
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• v.19 no.1
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• pp.4279-4295
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• 1977

Two types of model were established in order to product the soil moisture content by which information on irrigation could be obtained. Model-I was to represent the soil moisture depletion and was established based on the concept of water balance in a given soil profile. Model-II was a mathematical model derived from the analysis of soil moisture variation curves which were drawn from the observed data. In establishing the Model-I, the method and procedure to estimate parameters for the determination of the variables such as evapotranspirations, effective rainfalls, and drainage amounts were discussed. Empirical equations representing soil moisture variation curves were derived from the observed data as the Model-II. The procedure for forecasting timing and amounts of irrigation under the given soil moisture content was discussed. The established models were checked by comparing the observed data with those predicted by the model. Obtained results are summarized as follows: 1. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as the equation(2). 2. Among the various empirical formulae for potential evapotranspiration (Etp), Penman's formula was best fit to the data observed with the evaporation pans and tanks in Suweon area. High degree of positive correlation between Penman's predicted data and observed data with a large evaporation pan was confirmed. and the regression enquation was Y=0.7436X+17.2918, where Y represents evaporation rate from large evaporation pan, in mm/10days, and X represents potential evapotranspiration rate estimated by use of Penman's formula. 3. Evapotranspiration, Et, could be estimated from the potential evapotranspiration, Etp, by introducing the consumptive use coefficient, Kc, which was repre sensed by the following relationship: Kc=Kco$.$Ka＋Ks‥‥‥(Eq. 6) where Kco : crop coefficient Ka : coefficient depending on the soil moisture content Ks : correction coefficient a. Crop coefficient. Kco. Crop coefficients of barley, bean, and wheat for each growth stage were found to be dependent on the crop. b. Coefficient depending on the soil moisture content, Ka. The values of Ka for clay loam, sandy loam, and loamy sand revealed a similar tendency to those of Pierce type. c. Correction coefficent, Ks. Following relationships were established to estimate Ks values: Ks=Kc-Kco$.$Ka, where Ks=0 if Kc,=Kco$.$K0$\geq$1.0, otherwise Ks=1-Kco$.$Ka 4. Effective rainfall, Re, was estimated by using following relationships : Re=D, if R-D$\geq$0, otherwise, Re=R 5. The difference between rainfall, R, and the soil moisture depletion D, was taken as drainage amount, Wd. {{{{D= SUM from { {i }=1} to n (Et-Re-I+Wd)}}}} if Wd=0, otherwise, {{{{D= SUM from { {i }=tf} to n (Et-Re-I+Wd)}}}} where tf=2∼3 days. 6. The curves and their corresponding empirical equations for the variation of soil moisture depending on the soil types, soil depths are shown on Fig. 8 (a,b.c,d). The general mathematical model on soil moisture variation depending on seasons, weather, and soil types were as follow: {{{{SMC= SUM ( { C}_{i }Exp( { - lambda }_{i } { t}_{i } )+ { Re}_{i } - { Excess}_{i } )}}}} where SMC : soil moisture content C : constant depending on an initial soil moisture content $\lambda$ : constant depending on season t : time Re : effective rainfall Excess : drainage and excess soil moisture other than drainage. The values of $\lambda$ are shown on Table 1. 7. The timing and amount of irrigation could be predicted by the equation (9-a) and (9-b,c), respectively. 8. Under the given conditions, the model for scheduling irrigation was completed. Fig. 9 show computer flow charts of the model. a. To estimate a potential evapotranspiration, Penman's equation was used if a complete observed meteorological data were available, and Jensen-Haise's equation was used if a forecasted meteorological data were available, However none of the observed or forecasted data were available, the equation (15) was used. b. As an input time data, a crop carlender was used, which was made based on the time when the growth stage of the crop shows it's maximum effective leaf coverage. 9. For the purpose of validation of the models, observed data of soil moiture content under various conditions from May, 1975 to July, 1975 were compared to the data predicted by Model-I and Model-II. Model-I shows the relative error of 4.6 to 14.3 percent which is an acceptable range of error in view of engineering purpose. Model-II shows 3 to 16.7 percent of relative error which is a little larger than the one from the Model-I. 10. Comparing two models, the followings are concluded: Model-I established on the theoretical background can predict with a satisfiable reliability far practical use provided that forecasted meteorological data are available. On the other hand, Model-II was superior to Model-I in it's simplicity, but it needs long period and wide scope of observed data to predict acceptable soil moisture content. Further studies are needed on the Model-II to make it acceptable in practical use.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.1
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• pp.33-42
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• 2000

Adsorption onto the surfaces of solid particles is a well known phenomenon that causes the retardation effect of heavy metals in soils. For adequate remediation of soil and groundwater contamination, it is important to investigate the mobility of heavy metals that largely depends on pH conditions in the soil water since adsorption of heavy metals is pH-dependent. In this study, we investigated the transport of Zn ion under various pH conditions in a sandy soil by conducting batch and column tests. The batch test was performed using the standard procedure of equilibrating fine fractions collected from the soil with eleven different initial $ZnCl_2$ concentrations, and analysis of Zn ion in the equilibrated solutions using ICP-AES. The column test consisted of monitoring the concentrations of soil solutions exiting the soil column with time known as a breakthrough curve (BTC). We injected respectively $ZnCl_2$ and KCl solutions with the concentration of 10 g/L as a tracer in a square pulse type under three different pH conditions (7.7, 5.8, 4.1) and monitored the flux concentration at the exit boundary using an EC meter and ICP-AES. The resident concentration was also monitored at the 10cm-depth by Time Domain Reflectometry (TDR). The results of batch test showed that ion exchange process between Zn and other cations (Ca, Mg) was predominant. The retardation coefficients obtained from adsorption isotherms (Linear, Freundlich, Langmuir) resulted in the various values ranging from 1.2 to 614.1. No retardation effect but ion exchange was found for the BTCs under all pH conditions. This can be explained by the absence of other cations to desorb Zn ion from soil exchange sites under the conditions of ETC experiment imposing blank water as leachate in steady-state flow. As pH decreased, the peak concentration of Zn increased due to the competition of Zn with hydrogen ions ($H^+$) and the concentrations of other cations decreased. The peak concentration of Zn was increased by 12.7 times as pH decreased from 7.7 to 4.1.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.301-306
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• 2018

Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.

• Journal of Korean Academy of Nursing
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• v.20 no.1
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• pp.88-117
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• 1990

The purposes of this study are to delineate a profile of the state of a stroke patient's adaptation at 3 months after hospitalization and to explore the relationship between the level of adaptation and the variables which influence the adaptation of hemiplegic patients. To these ends, theoretical framework was derived basically from the stress adaptation model. The basic assumption underlying the level of adaptation is influenced by the presenting focal, contextual and residual stimuli. This group of stimuli is further operationalized and represented by a perception of stress. which is the perceived effect of the disability and by the mediating variables such as sociodemographic factors as an external conditioning variables and perceived social support and hardiness personality characteristics as an internal intervening variables. The dependent varibales in this study is the level of physical, psychological and social adaptation and is hypothesized to be a function of the interaction between 3 sets of variables namely, the perceived disability effect, external conditioning variables and internal intevening varibles. A total of fourty three subjects from 3 general hospitals in Seoul were observed and interviewed with the aid of 7 structured instruments. The data were collected twice on each subject : first at the pre-discharge period arid at 3 months post-discharge from hospital for the second time. The study was carried out for the period from February to August, 1988. The instruments used for the study include 4 existing scales and 3 scales developed by the researcher for this study. They are : 1) The ADL dependency scale and the scale of the clinical physical functions for the assessment of physical adaptation. 2) the SDS(self report of depression) to measure the level of psychological adaptation. 3) The scale for the amount of social activities for the measurement of the level of social adaptation. 4) The scale for the perceived effect of disability for the measurement of the focal stimuli. 5) The health related hardiness scale and the perceived interpersonal support self evaluation list(ISEL) for the measurement of the hardiness personality character and the perceived social support. The data obtained were analyzed using percentage, oneway ANOVA, Pearson coefficients correlation and stepwise multiple regression. The findings provide valuable information about the present level of physical adaptation at 3 months after discharge. The patient revealed a decreased ADL dependency and lowered limitation of physical function as compared with pre - discharge state. Psycholcgically, the average degree of depression at follow up was within normal range of depression. Socially, the amount of social activities was very low. The one way ANOVA and the correlational analysis revealed the relationship between the 3 sets of variables and the adaptation level as follows : 1) The perceived disability effect was related to the degree of the depression and the amount of social activities but was not related to the physical adaptation. 2) Among the sociodemographic variables, sex and education were related to the difference of ADL dependency and the change of physical function. These factors indicate that women more than men and educated more than the less educated were found more independent. The education was also related to the degree of depression suggesting that the higher the educational level, the more well adapted the patients were both physically and psychologically. Age, marital status and job state were not found to be related to the patient's adaptation level. 3) Among the internal intervening variables, the health related hardiness characteristic was related to the differences of ADL dependency, physical functions and the social activities, indicating that the higher the hardiness character the higher the level of physical and social adaptation. 4) The perceived social support, another internal intervening variable, was related to the degree of depression and the social activities. This data suggest that the higher the perception of social support, the better adapted the patients were psychogically and socially. In summarizing the results of the correlational analysis, the level of physical adaptation was influenced by sex, the years of education and the hardiness character. The level of psychological adaptation was influenced by the years of education, the perceived disability effect and the perceived social support. And the level of social adaptation was influenced by the perceived disability effect, the hardiness character and the perceived social support. The stepwise multiple regression analysis shows findings as follows : 1) The most important factor to explain the difference of ADL dependency was sex, indicating females were more independent than males. 2) The most important factor to explain the difference of physical function and the degree of depression was the patient's education level. 3) The strongest explaining factor for the amount of social activities was perceived self esteem(one of the subconcepts of perceived social support). Thus the most important factors influencing the level of adaptation were found to be sex, education, the hardiness character and self esteem. From the above findings, the significance of this study can be delineated as follows : 1) Corroboration of the assumed relationship between the various variables and the adaptation level as suggested in the conceptual model. 2) Support for the feasibility of the cognitive approach for nursing intervention such as hardness character training, counselling and teaching for self-care in the chronic patients.

• Korean small business review
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• v.31 no.4
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• pp.67-93
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• 2009

The purpose of this paper is to explore the relations between financial constraints and dividend smoothing of innovative small and medium sized enterprises(SMEs) listed on Korea Securities Market and Kosdaq Market of Korea Exchange. The innovative SMEs is defined as the firms with high level of R&D intensity which is measured by (R&D investment/total sales) ratio, according to Chauvin and Hirschey (1993). The R&D investment plays an important role as the innovative driver that can increase the future growth opportunity and profitability of the firms. Therefore, the R&D investment have large, positive, and consistent influences on the market value of the firm. In this point of view, we expect that the innovative SMEs can adjust dividend payment faster than the noninnovative SMEs, on the ground of their future growth opportunity and profitability. And also, we expect that the financial unconstrained firms can adjust dividend payment faster than the financial constrained firms, on the ground of their financing ability of investment funds through the market accessibility. Aivazian et al.(2006) exert that the financial unconstrained firms with the high accessibility to capital market can adjust dividend payment faster than the financial constrained firms. We collect the sample firms among the total SMEs listed on Korea Securities Market and Kosdaq Market of Korea Exchange during the periods from January 1999 to December 2007 from the KIS Value Library database. The total number of firm-year observations of the total sample firms throughout the entire period is 5,544, the number of firm-year observations of the dividend firms is 2,919, and the number of firm-year observations of the non-dividend firms is 2,625. About 53%(or 2,919) of these total 5,544 observations involve firms that make a dividend payment. The dividend firms are divided into two groups according to the R&D intensity, such as the innovative SMEs with larger than median of R&D intensity and the noninnovative SMEs with smaller than median of R&D intensity. The number of firm-year observations of the innovative SMEs is 1,506, and the number of firm-year observations of the noninnovative SMEs is 1,413. Furthermore, the innovative SMEs are divided into two groups according to level of financial constraints, such as the financial unconstrained firms and the financial constrained firms. The number of firm-year observations of the former is 894, and the number of firm-year observations of the latter is 612. Although all available firm-year observations of the dividend firms are collected, deletions are made in the case of financial industries such as banks, securities company, insurance company, and other financial services company, because their capital structure and business style are widely different from the general manufacturing firms. The stock repurchase was involved in dividend payment because Grullon and Michaely (2002) examined the substitution hypothesis between dividends and stock repurchases. However, our data structure is an unbalanced panel data since there is no requirement that the firm-year observations data are all available for each firms during the entire periods from January 1999 to December 2007 from the KIS Value Library database. We firstly estimate the classic Lintner(1956) dividend adjustment model, where the decision to smooth dividend or to adopt a residual dividend policy depends on financial constraints measured by market accessibility. Lintner model indicates that firms maintain stable and long run target payout ratio, and that firms adjust partially the gap between current payout rato and target payout ratio each year. In the Lintner model, dependent variable is the current dividend per share(DPS_t), and independent variables are the past dividend per share(DPS_t-1) and the current earnings per share(EPS_t). We hypothesized that firms adjust partially the gap between the current dividend per share(DPS_t) and the target payout ratio(Ω) each year, when the past dividend per share(DPS_t-1) deviate from the target payout ratio(Ω). We secondly estimate the expansion model that extend the Lintner model by including the determinants suggested by the major theories of dividend, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory. In the expansion model, dependent variable is the current dividend per share(DPS_t), explanatory variables are the past dividend per share(DPS_t-1) and the current earnings per share(EPS_t), and control variables are the current capital expenditure ratio(CEA_t), the current leverage ratio(LEV_t), the current operating return on assets(ROA_t), the current business risk(RISK_t), the current trading volume turnover ratio(TURN_t), and the current dividend premium(DPREM_t). In these control variables, CEA_t, LEV_t, and ROA_t are the determinants suggested by the residual dividend theory and the agency theory, ROA_t and RISK_t are the determinants suggested by the dividend signaling theory, TURN_t is the determinant suggested by the transactions cost theory, and DPREM_t is the determinant suggested by the catering theory. Furthermore, we thirdly estimate the Lintner model and the expansion model by using the panel data of the financial unconstrained firms and the financial constrained firms, that are divided into two groups according to level of financial constraints. We expect that the financial unconstrained firms can adjust dividend payment faster than the financial constrained firms, because the former can finance more easily the investment funds through the market accessibility than the latter. We analyzed descriptive statistics such as mean, standard deviation, and median to delete the outliers from the panel data, conducted one way analysis of variance to check up the industry-specfic effects, and conducted difference test of firms characteristic variables between innovative SMEs and noninnovative SMEs as well as difference test of firms characteristic variables between financial unconstrained firms and financial constrained firms. We also conducted the correlation analysis and the variance inflation factors analysis to detect any multicollinearity among the independent variables. Both of the correlation coefficients and the variance inflation factors are roughly low to the extent that may be ignored the multicollinearity among the independent variables. Furthermore, we estimate both of the Lintner model and the expansion model using the panel regression analysis. We firstly test the time-specific effects and the firm-specific effects may be involved in our panel data through the Lagrange multiplier test that was proposed by Breusch and Pagan(1980), and secondly conduct Hausman test to prove that fixed effect model is fitter with our panel data than the random effect model. The main results of this study can be summarized as follows. The determinants suggested by the major theories of dividend, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory explain significantly the dividend policy of the innovative SMEs. Lintner model indicates that firms maintain stable and long run target payout ratio, and that firms adjust partially the gap between the current payout ratio and the target payout ratio each year. In the core variables of Lintner model, the past dividend per share has more effects to dividend smoothing than the current earnings per share. These results suggest that the innovative SMEs maintain stable and long run dividend policy which sustains the past dividend per share level without corporate special reasons. The main results show that dividend adjustment speed of the innovative SMEs is faster than that of the noninnovative SMEs. This means that the innovative SMEs with high level of R&D intensity can adjust dividend payment faster than the noninnovative SMEs, on the ground of their future growth opportunity and profitability. The other main results show that dividend adjustment speed of the financial unconstrained SMEs is faster than that of the financial constrained SMEs. This means that the financial unconstrained firms with high accessibility to capital market can adjust dividend payment faster than the financial constrained firms, on the ground of their financing ability of investment funds through the market accessibility. Futhermore, the other additional results show that dividend adjustment speed of the innovative SMEs classified by the Small and Medium Business Administration is faster than that of the unclassified SMEs. They are linked with various financial policies and services such as credit guaranteed service, policy fund for SMEs, venture investment fund, insurance program, and so on. In conclusion, the past dividend per share and the current earnings per share suggested by the Lintner model explain mainly dividend adjustment speed of the innovative SMEs, and also the financial constraints explain partially. Therefore, if managers can properly understand of the relations between financial constraints and dividend smoothing of innovative SMEs, they can maintain stable and long run dividend policy of the innovative SMEs through dividend smoothing. These are encouraging results for Korea government, that is, the Small and Medium Business Administration as it has implemented many policies to commit to the innovative SMEs. This paper may have a few limitations because it may be only early study about the relations between financial constraints and dividend smoothing of the innovative SMEs. Specifically, this paper may not adequately capture all of the subtle features of the innovative SMEs and the financial unconstrained SMEs. Therefore, we think that it is necessary to expand sample firms and control variables, and use more elaborate analysis methods in the future studies.