• Journal of Animal Science and Technology
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• v.48 no.1
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• pp.29-38
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• 2006

The objective of this study was to estimate genetic variances of growth curve parameters in Hanwoo cows. The data used in this study were records from 1,083 Hanwoo cows raised at Hanwoo Experiment Station, National Livestock Research Institute(NLRI). First evaluation model(Model I) fit year-season of birth and age of dam as fixed effects and second model(Model II) added age at the final weight as a linear covariate to Model I. Heritability estimates of A, b and k from Gompertz model were 0.22, 0.11 and 0.07 using modelⅠ and 0.28, 0.11 and 0.12 using modelⅡ. Those from Von Bertalanffy model were 0.22, 0.11 and 0.07 using modelⅠ, 0.28, 0.11 and 0.12 using modelⅡ. Heritability estimates of A, b and k from Logistic model were 0.14, 0.07 and 0.05 using modelⅠ, 0.18, 0.07 and 0.12 using modelⅡ. Heritability estimates of A from Gompertz model were higher than those from Von Bertalanffy model or Logistic model in both model Ⅰand model Ⅱ. Heritability estimates of b from Logistic model were higher than those from Gompertz model or Von Bertalanffy model in both modelⅠand model Ⅱ. Heritability estimates of birth weight, weaning weight, 3 month weight, 6 month weight, 9 month weight, 12 month weight, 18 month weight, 24 month weight, 36 month weight were after linear age adjustment 0.27, 0.11, 0.19, 0.14, 0.16, 0.23, 0.52 and 0.32, respectively. Heritability estimates of birth weight, weaning weight, 3 month weight, 6 month weight, 9 month weight and 24 month weight fit by Gompertz model were larger than those estimated from linearly adjusted data. Heritability estimates of 12 month weight, 18 month weight and 36 month weight fit by Von Bertalanffy model were larger than those estimated from linearly adjusted data. In the multitrait analyses for parameters from Gompertz model, genetic and phenotypic correlations between A and k parameters were －0.47 and －0.67 using modelⅠand －0.56 and －0.63 using model Ⅱ. Those between the A and b parameters were 0.69 and 0.34 using modelⅠand 0.72 and 0.37 using model Ⅱ. Those between the b and k parameters were －0.26 and 0.01 using modelⅠand －0.30 and 0.01 using model Ⅱ. In the multitrait analyses for parameters from Von Bertalanffy model, genetic and phenotypic correlations between A and k parameters were －0.49 and －0.67 suing model Ⅰ and －0.57 and －0.70 using modelⅡ. Those between the A and b parameters were 0.61 and 0.33 using modelⅠ and 0.60 and 0.30 using model Ⅱ. Those between the b and k parameters were －0.20 and 0.02 using modelⅠ and 0.16 and 0.00 using modelⅡ. In the multitrait analyses for parameters from Logistic model, genetic and phenotypic correlations between A and k parameters were －0.43 and －0.67 using model Ⅰ and －0.50 and －0.63 using modelⅡ. Those between the A and b parameters were 0.47 and 0.22 using modelⅠ and 0.38 and 0.24 using modelⅡ. Those between the b and k parameters were －0.09 and 0.02 using model Ⅰ and －0.02 and 0.13 using model Ⅱ.

• Korean Journal of Soil Science and Fertilizer
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• v.8 no.1
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• pp.1-17
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• 1975

Laboratory experiments on the phosphorus adsorption by soil were conducted to evaluate the parameters for determination of phosphorus adsorption capacity of soil, which serve as a basis for establishing the amount of phosphorus required to improve newly reclaimed soil and volcanic ash soil. The calculated Langmuir adsorption maxima varied from 6.2-32.9, 74.7-90.4 and 720-915mg p/100g soil for cultivated soils, non-cultivated soils, and volcanic ash soils respectively. The phosphorus absorption coefficient ranged from 116-179, 161-259 and 1,098-1,205mg p/100g soil for cultivated soils, non-cultivated soils, and volcanic ash soils respectively. The ratio of the phosphorus absorption coefficient to Langmuir adsorption maximum was low in soils of high phosphorus adsorption capacity (1.3-1.5) and high in soils of low phosphorus adsorption capacity (2.2-18.7). Changes in the amount of phosphurus adsorption induced by liming and preaddition of phosphorus were hadly detected by the phosphorus absorption coefficient, which is measured using a test solution with a relatively high phosphorus concentration. The Langmuir adsorption maximum was a more sensitive index of the phosphorus adsorption capacity. The Langmuir adsorption maxima of the non-cultivated soils, which were treated with an amount of calcium hydroxide equivalent to the exchangeable Al and incubated ($25-30^{\circ}C$) for 40 days at field capacity, were lower than the original soils. The change in the adorption maximum on incubation following the liming of soils was insignificant for other soils. The secondary adsorption maximum of soils, which received phosphorus equivalent to the Langmuir adsorption maximum of the limed soils incubated ($25-30^{\circ}C$) for 50 days at held capacity, was 74.5, 5.6 and 23.8% of the primary adsorption maximum for volcanic ash soils, non-cultivated soils, and cultivated soils respectively. The amount of phosphorus adsorbed by soils increased quadratically with the concentration of phosphorus solution added to the soils. The amount of phosphorus adsorbed by 5-g soil samples from 100ml of 100- and 1,000mg p/l solution for the mineral soils and volcanic ash soils respectively was found to be close to the Langmuir adsorption maximum. The amount of the phosphorus adsorbed at these concentrations is defined as a saturation adsorption maximum and proposed as a new parameter for the phosphorus adsorption capacity of the soil. The evaluation of the phosphorus adsorption capacity by the saturation adsorption maximum is regarded as a more practical method in that it obviates the need for the various concentrations used for the determination of the Langmuir adsorption maximum.

• Women's Health Nursing
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• v.2 no.2
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• pp.298-331
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• 1996

Recent improvements in living standard and development in medical care led to an increased interest in life expectancy and personal health, and also led to a more demand for higher quality of life. Thus, the problem of women's health draw a fresh interest nowadays. Since late-middle aged women experience various physical and socio-psychological changes and tend to have chronic illnesses, these women have to take initiatives for their health control by realizing their own responsibility. The basic elements for a healthy life of these women are understanding of their physical and psychological changes and acceptance of these changes. Health promoting behaviors of an individual or a group are actions toward increasing the level of well-being and self-actualization, and are affected by various variables. In Pender's health promoting model, variables are categorized into cognitive factors(individual perceptions), modifying factors, and variables affecting the likelihood for actions, and the model assumes the health promoting behaviors are affected by cognitive factors which are again affected by demographic factors. Since Pender's model was proposed based on a tool broad conceptual frame, many studies done afterwards have included only a limited number of variables of Pender's model. Furthermore, Pender's model did not precisely explain the possibilities of direct and indirect paths effects. The objectives of this study are to evaluate Pender's model and thus propose a model that explains health promoting behaviors among late-middle aged women in order to facilitate nursing intervention for this group of population. The hypothetical model was developed based on the Pender's health promoting model and the findings from past studies on women's health. Data were collected by self-reported questionnaires from 417 women living in Seoul, between July and November 1994. Questionnaires were developed based on instruments of Walker and others' health promotion lifestyle profile, Wallston and others' multidimensional health locus of control, Maoz's menopausal symptom check list and Speake and others' health self-rating scale. IN addition, items measuring self-efficacy were made by the present author based on past studies. In a pretest, the questionnaire items were reliable with Cronbach's alpha ranging from .786 to .934. The models for health promoting behaviors were tested by using structural equation modelling technique with LISREL 7.20. The results were summarized as follows : 1. The overall fit of the hypothetical model to the data was good (chi-square=4.42, df=5, p=.490, GFI=.995, AGFI=.962, RMSR=.024). 2. Paths of the model were modified by considering both its theoretical implication and statistical significance of the parameter estimates. Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data (chi-square =4.55, df=6, p=.602, GFI=.995, AGFI=.967, RMSR=.024). 3. The results of statistical testing were as follows : 1) Family function internal health locus of control, self-efficacy, and education level exerted significant effects on health promoting behaviors(${\gamma}_{43}$=.272, T=3.714; ${\beta}_[41}$=.211, T=2.797; ${\beta}_{42}$=.199, T=2.717; ${\gamma}_{41}$=.136, T=1.986). The effect of economic status, physical menopausal symptoms, and perceived health status on health promoting behavior were insignificant(${\gamma}_{42}$=.095, T=1.456; ${\gamma}_{44}$=.101, T=1.143; ${\gamma}_{43}$=.082, T=.967). 2) Family function had a significance direct effect on internal health locus of control (${\gamma}_{13}$=.307, T=3.784). The direct effect of education level on internal health locus of control was insignificant(${\gamma}_{11}$=-.006, T=-.081). 3) The directs effects of family functions & internal health locus of control on self-efficacy were significant(${\gamma}_{23}$=.208, T=2.607; ${\beta}_{21}$=.191, T=2.2693). But education level and economic status did not exert a significant effect on self-efficacy(${\gamma}_{21}$=.137, T=1.814; ${\beta}_{22}$=.137, T=1.814; ${\gamma}_{22}$=.112, T=1.499). 4) Education level had a direct and positive effect on perceived health status, but physical menopausal symptoms had a negative effect on perceived health status and these effects were all significant(${\gamma}_{31}$=.171, T=2.496; ${\gamma}_{34}$=.524, T=-7.120). Internal health locus and self-efficacy had an insignificant direct effect on perceived health status(${\beta}_{31}$=.028, T=.363; ${\beta}_{32}$=.041, T=.557). 5) All predictive variables of health promoting behaviors explained 51.8% of the total variance in the model. The above findings show that health promoting behaviors are explained by personal, environmental and perceptual factors : family function, internal health locus of control, self-efficacy, and education level had stronger effects on health promoting behaviors than predictors in the model. A significant effect of family function on health promoting behaviors reflects an important role of the Korean late-middle aged women in family relationships. Therefore, health professionals first need to have a proper evaluation of family function in order to reflect the family function style into nursing interventions and development of strategies. These interventions and strategies will enhance internal health locus of control and self-efficacy for promoting health behaviors. Possible strategies include management of health promoting programs, use of a health information booklets, and individual health counseling, which will enhance internal health locus of control and self-efficacy of the late-middle aged women by making them aware of health responsibilities and value for oneself. In this study, an insignificant effect of physical menopausal symptoms and perceived health status on health promoting behaviors implies that they are not motive factors for health promoting behaviors. Further analytic researches are required to clarify the influence of physical menopausal symptoms and perceived health status on health promoting behaviors with-middle aged women.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.94-100
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• 2010

Purpose: We can evaluate function of kidney by Glomerular Filtration Rate (GFR) test using $^{99m}Tc$-DTPA which is simple. This test is influenced by several parameter such as net syringe count, kidney depth, corrected kidney count, acquisition time and characters of gamma camera. In this study, we evaluated predose count according to matrix size in the GFR test using $^{99m}Tc$-DTPA. Materials and Methods: Gamma camera of Infinia in GE was used, and LEGP collimator, three types of matrix size ($64{\times}64$, $128{\times}128$, $256{\times}256$) and 1.0 of zoom factor were applied. We increased radioactivity concentration from 222 (6), 296 (8), 370 (10), 444 (12) up to 518 MBq (14 mCi) respectively and acquired images according to matrix size at 30 cm distance from detector. Lastly, we evaluated these values and then substituted them for GFR formula. Results: In $64{\times}64$, $128{\times}128$ and $256{\times}256$ of matrix size, counts per second was 26.8, 34.5, 41.5, 49.1 and 55.3 kcps, 25.3, 33.4, 41.0, 48.4 and 54.3 kcps and 25.5, 33.7, 40.8, 48.1 and 54.7 kcps respectively. Total counts for 5 second were 134, 172, 208, 245 and 276 kcounts from $64{\times}64$, 127, 172, 205, 242, 271 kcounts from $128{\times}128$, and 137, 168, 204, 240 and 273 kcounts from $256{\times}256$, and total counts for 60 seconds were 1,503, 1,866, 2,093, 2,280, 2,321 kcounts, 1,511, 1,994, 2,453, 2,890 and 3,244 kcounts, and 1,524, 2,011, 2,439, 2,869 and 3,268 kcounts respectively. It is different from 0 to 30.02 % of percentage difference in $64{\times}64$ of matrix size. But in $128{\times}128$ and $256{\times}256$, it is showed 0.60 and 0.69 % of maximum value each. GFR of percentage difference in $64{\times}64$ represented 6.77% of 222 MBq (6 mCi), 42.89 % of 518 MBq (14 mCi) at 60 seconds respectively. However it is represented 0.60 and 0.63 % each in $128{\times}128$ and $256{\times}256$. Conclusion: There was no big difference in total counts of percentage difference and GFR values acquiring from $128{\times}128$ and $256{\times}256$ of matrix size. But in $64{\times}64$ of matrix size when the total count exceeded 1,500 kcounts, the overflow phenomenon was appeared differently according to predose radioactivity of concentration and acquisition time. Therefore, we must optimize matrix size and net syringe count considering the total count of predose to get accurate GFR results.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.