• Title/Summary/Keyword: two-parameter model

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Phylogenetic Study of Ganoderma applanatum and Schizopora paradoxa Basd on 5S rRNA Sequences (5S rRNA 염기서열에 으한 잔나비걸상과 좀구멍버섯의 계통학적 연구)

  • Kim, Hak-Hyun;Jung, Hack-Sung
    • Korean Journal of Microbiology
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    • v.32 no.3
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    • pp.177-181
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    • 1994
  • The sequences of the cytoplasmic 5S rRNAs(EMBL accession number X73589 and X73890) from two polupores, Ganoderma applanatum and Schizopora paradoxa, were determined by the direct chemical method for sequencing RNA and compared to the sequences of 9 reported mushrooms. 5S rRNAs of Ganoderma applanatum and Schizopora paradoxa consisted of 118 bases and fit the secondary structure model of the 5S rRNAs of basidiomycetes proposed by Huysmans et al. Based on Kimura’s K_nuc values, the closest fungus to Ganoderma applanatum was Ceratobasidium cornigerum and the one to Schizopora paradoxa was Bjerkandera adusta. When the secondary structures of 5S rRNAs of 11 mushrooms were compared the base substitution occurred at helix regions more than at loop regions. When a phylogenetic tree was constructed using the Neighbor program of the PHYLIP package, it partially discriminated and separated the mushrooms of the Hymenomycetes by the order.

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Analysis of Within-Field Spatial Variation of Rice Growth and Yield in Relation to Soil Properties

  • Ahn Nguyen Tuan;Shin Jin Chul;Lee Byun-Woo
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.50 no.4
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    • pp.221-237
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    • 2005
  • For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. $6,600m^2$ in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of $10\times10m$. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay $(7.6\%)$ and the largest in Si $(21.4\%)$. The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to $38\%$, and that of rice yield ranged from 11 to $21\%$. CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as $76\%$ of yield variability, of which $21.6\%$ is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about $50\%$ of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.

Justice and Authenticity of Service Recovery : Effects on Customer Behavioral Intention (서비스 회복이 고객의 행동 의도에 미치는 영향에 관한 연구 : 서비스 회복의 공정성과 진정성을 중심으로)

  • Park, Eun-Ji;Kim, Chang-Gon;Kim, Myung-Soo;Han, Jang-hui
    • Journal of Distribution Science
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    • v.13 no.2
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    • pp.63-73
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    • 2015
  • Purpose - Satisfaction with service is evaluated according to customers' subjective judgment. The expected value of customer service and its evaluations depend on the customers' position. The customer recognizes two different forms of service levels. One is satisfaction and the other is dissatisfaction. Customers who are satisfied want to receive the service in future. However, those dissatisfied try to change the service. The service provider tries to improve the service. There are two different service cycles. One is the successful cycle and the other is the failure cycle. This study aimed to empirically determine the effects of the justice and authenticity of service recovery on customer behavioral intention through an integrated approach to cognitive justice and psychological authenticity. Research design, data, and methodology - Based on a literature review, justice of service recovery was categorized into three types: distributive, procedural, and interactive. Then, authenticity was added to obtain four independent variables, along with recovery satisfaction as a parameter. Behavioral intention, as an outcome variable, was divided into the repurchase intention and positive word-of-mouth. The model and hypotheses were created and measurement items were developed. A questionnaire survey of items concerning the service recovery experience at family restaurants was conducted on college students and residents in Gwangju from September 30 to October 31, 2013. A total of 400 copies of the questionnaire were sent out and 385 were returned. Respondents answered questions about the importance of, and satisfaction with service recovery on a 5-point Likert scale. Excluding 174 copies without service failure experiences and 7 inappropriate copies, 204 copies were analyzed using SPSS 21.0 for Windows and AMOS 20.0 to determine the reliability and validity of measurements. The hypotheses were tested through a goodness-of-fit analysis. Results - First, distributive justice positively affected recovery satisfaction. Second, procedural and interactive justice had no impact. Third, authenticity positively affected recovery satisfaction. Fourth, distributive justice had relatively stronger effects on recovery satisfaction than authenticity. Fifth, recovery satisfaction significantly affected repurchase intention and positive word-of-mouth and it proved effective in mediation. Finally, additional analysis was performed for descriptive statistics of the principal variables by various demographic characteristics and significant differences were found in gender, occupation, and so on. Conclusions - This study has academic significance as the fairness and authenticity of service recovery were investigated to reveal the effects on behavior. The findings could be applied to a wide range of service recovery strategies. However, there are some limitations. First, data was collected only from the residents of Gwangju and most respondents were aged 20-30. Future studies should target a wide range of areas and age groups. Second, because the questionnaire used in this study targets only convenience family restaurants, the results of this study cannot be generalized to all services companies. Future research should be done on a wide range of industries such as hotels, airlines, and hospitals, and perform a comparison between sectors.

Comparison of the fit of the coping pattern constructed by manual and CAD/CAM, depending on the margin of the abutment tooth (지대치 변연 형태에 따른 수작업과 CAD/CAM으로 제작한 coping 패턴의 적합도 비교)

  • Han, Min-soo;Kwon, Eun-Ja;Chio, Esther;Kim, Si-chul
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.10
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    • pp.6611-6617
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    • 2015
  • The purpose of this study is to compare the marginal and internal fit of metal and zirconia coping which is fabricated by manual and CAD/CAM(Computer Aided Design/Computer Aided Manufacturing). The model is prepared with Urethane material and two abutment teeth are fabricated with a knife and chamfer margin. Silicon replica technique is used to measure the marginal fit of manually fabricated and the CAD/CAM coping. Internal fitting level is measured with a microscope and the image is captured with a CCD camera. The distance between abutment teeth and coping is measured with a callibrated image analyzer software; marginal opening (MO), marginal gap (MG), internal gap (IG) at maximum curvature area, axial gap (AG), and occlusal gap (OG). Two-way ANOVA test is applied to compare fabrication technique and to analysis of abutment pattern. In addition, one-way ANOVA and Scheffe's test is used to analyze each parameter of the test. The result shows that the fit is < $120{\mu}m$ except OG of CAD/CAM and MO of knife margin. The CAD/CAM fabricated coping showed higher fit level at chamfer margin. However, knife margin showed better fitness compared to chamfer margin at MG. AG showed the minimum dimension with a constant result (< $38{\mu}m$).

On Developing The Intellingent contro System of a Robot Manupulator by Fussion of Fuzzy Logic and Neural Network (퍼지논리와 신경망 융합에 의한 로보트매니퓰레이터의 지능형제어 시스템 개발)

  • 김용호;전홍태
    • Journal of the Korean Institute of Intelligent Systems
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    • v.5 no.1
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    • pp.52-64
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    • 1995
  • Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

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Classification of Seismic Stations Based on the Simultaneous Inversion Result of the Ground-motion Model Parameters (지진동모델 파라미터 동시역산을 이용한 지진관측소 분류)

  • Yun, Kwan-Hee;Suh, Jung-Hee
    • Geophysics and Geophysical Exploration
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    • v.10 no.3
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    • pp.183-190
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    • 2007
  • The site effects of seismic stations were evaluated by conducting a simultaneous inversion of the stochastic point-source ground-motion model (STGM model; Boore, 2003) parameters based on the accumulated dataset of horizontal shear-wave Fourier spectra. A model parameter $K_0$ and frequency-dependent site amplification function A(f) were used to express the site effects. Once after a H/V ratio of the Fourier spectra was used as an initial estimate of A(f) for the inversion, the final A(f) which is considered to be the result of combined effect of the crustal amplification and loca lsite effects was calculated by averaging the log residuals at the site from the inversion and adding the mean log residual to the H/V ratio. The seismic stations were classified into five classes according to $logA_{1-10}^{max}$(f), the maximum level of the site amplification function in the range of 1 Hz < f < 10 Hz, i.e., A: $logA_{1-10}^{max}$(f) < 0.2, B: 0.2 $\leq$ $logA_{1-10}^{max}$(f) < 0.4, C: 0.4 $\leq$ $logA_{1-10}^{max}$(f) < 0.6, D: 0.6 $\leq$ $logA_{1-10}^{max}$(f) < 0.8, E: 0.8 $\leq$ $logA_{1-10}^{max}$(f). Implication of the classified result was supported by observing a shift of the dominant frequency of average A(f) for each classified stations as the class changes. Change of site classes after moving seismic stations to a better site condition was successfully described by the result of the station classification. In addition, the observed PGA (Peak Ground Acceleration)-values for two recent moderate earthquakes were well classified according to the proposed station classes.

Price Volatility, Seasonality and Day-of-the Week Effect for Aquacultural Fishes in Korean Fishery Markets (수산물 시장에서의 양식 어류 가격변동성.계절성.요일효과에 관한 연구 - 노량진수산시장의 넙치와 조피볼락을 중심으로 -)

  • Ko, Bong-Hyun
    • The Journal of Fisheries Business Administration
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    • v.40 no.2
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    • pp.49-70
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    • 2009
  • This study proviedes GARCH model(Bollerslev, 1986) to analyze the structural characteristics of price volatility in domestic aquacultural fish market of Korea. As a case study, flatfish and rock-fish are analyzed as major species with relatively high portion in an aspect of production volume among fish captured in Korea. For analyzing, this study uses daily market data (dating from Jan 1 2000 to June 30, 2008) published by the Noryangjin Fisheries Wholesale Market which is located in Seoul of Korea. This study performs normality test on trading volume and price volatility of flatfish and rock-fish as an advanced empirical approach. The normality test adopted is Jarque-Bera test statistic. As a result, first, a null hypothesis that "an empirical distribution follows normal distribution" was rejected in both fishes. The distribution of daily market data of them were not only biased toward positive(+) direction in terms of kurtosis and skewness, but also characterized by leptokurtic distribution with long right tail. Secondly, serial correlations were found in data on market trading volume and price volatility of two species during very long period. Thirdly, the results of unit root test and ARCH-LM test showed that all data of time series were very stationary and demonstrated effects of ARCH. These statistical characteristics can be explained as a reasonable ground for supporting the fitness of GARCH model in order to estimate conditional variances that reveal price volatility in empirical analysis. From empirical data analysis above, this study drew the following conclusions. First of all, from an empirical analysis on potential effects of seasonality and the day of week on price volatility of aquacultural fish, Monday effects were found in both species and Thursday and Friday effects were also found in flatfish. This indicates that Monday is effective in expanding price volatility of aquacultural fish market and also Monday has higher effects upon the price volatility of fish than other days of week have since it has more new information for weekend. Secondly, the empirical analysis led to a common conclusion that there was very high price volatility of flatfish and rock-fish. This points out that the persistency parameter($\lambda$), an index of possibility for current volatility to sustain similarly in the future, was higher than 0.8-equivalently nearly to 1-in both flatfish and rock-fish, which presents volatility clustering. Also, this study estimated and compared and model that hypothesized normal distributions in order to determine fitness of respective models. As a result, the fitness of GARCH(1, 1)-t model was better than model where the distribution of error term was hypothesized through-distribution due to characteristics of fat-tailed distribution, was also better than model, as described in the results of basic statistic analysis. In conclusion, this study has an important mean in that it was introduced firstly in Korea to investigate in price volatility of Korean aquacultural fishery products, although there was partially a limited of official statistic data. Therefore, it is expected that the results of this study will be useful as a reference material for making and assessing governmental policies. Also, it is looked forward that the results will be helpful to build a fishery business plan as and aspect of producer, and also to take timely measures to potential price fluctuations of fishery products in market. Hence, it is advisable that further studies related to such price volatility in fishery market will extend and evolve into a wider variety of articles and issues in near future.

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The Accuracy Evaluation of Digital Elevation Models for Forest Areas Produced Under Different Filtering Conditions of Airborne LiDAR Raw Data (항공 LiDAR 원자료 필터링 조건에 따른 산림지역 수치표고모형 정확도 평가)

  • Cho, Seungwan;Choi, Hyung Tae;Park, Joowon
    • Journal of agriculture & life science
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    • v.50 no.3
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    • pp.1-11
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    • 2016
  • With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

Customer Behavior Prediction of Binary Classification Model Using Unstructured Information and Convolution Neural Network: The Case of Online Storefront (비정형 정보와 CNN 기법을 활용한 이진 분류 모델의 고객 행태 예측: 전자상거래 사례를 중심으로)

  • Kim, Seungsoo;Kim, Jongwoo
    • Journal of Intelligence and Information Systems
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    • v.24 no.2
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    • pp.221-241
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    • 2018
  • Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

THEORETICAL STUDY ON OBSERVED COLOR-MAGNITUDE DIAGRAMS

  • Lee, See-Woo
    • Journal of The Korean Astronomical Society
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    • v.12 no.1
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    • pp.41-70
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    • 1979
  • From $B\ddot{o}hm$-Vitense's atmospheric model calculations, the relations, [$T_e$, (B-V)] and [B.C, (B-V)] with respect to heavy element abundance were obtained. Using these relations and evolutionary model calculations of Rood, and Sweigart and Gross, analytic expressions for some physical parameters relating to the C-M diagrams of globular clusters were derived, and they were applied to 21 globular clusters with observed transition periods of RR Lyrae variables. More than 20 different parameters were examined for each globular cluster. The derived ranges of some basic parameters are as follows; $Y=0.21{\sim}0.33,\;Z=1.5{\times}10^{-4}{\sim}4.5{\times}10^{-3},\;age,\;t=9.5{\sim}19{\times}10^9$ years, mass for red giants, $m_{RG}=0.74m_{\odot}{\sim}0.91m_{\odot}$, mass for RR Lyrae stars, $m_{RR}=0.59m_{\odot}{\sim}0.75m_{\odot}$, the visual magnitude difference between the turnoff point and the horizontal branch (HB), ${\Delta}V_{to}=3.1{\sim}3.4(<{\Delta}V_{to}>=3.32)$, the color of the blue edge of RR Lyrae gap, $(B-V)_{BE}=0.17{\sim}0.21=(<(B-V)_{BE}>=0.18),\;[\frac{m}{L}]_{RR}=-1.7{\sim}-1.9$, mass difference of $m_{RR}$ relative to $m_{RG},(m_{RG}-m_{RR})/m_{RG}=0.0{\sim}0.39$. It was found that the ranges of derived parameters agree reasonably well with the observed ones and those estimated by others. Some important results obtained herein can be summarized as follows; (i) There are considerable variations in the initial helium abundance and in age of globular clusters. (ii) The radial gradient of heavy element abundance does exist for globular clusters as shown by Janes for field stars and open clusters. (iii) The helium abundance seems to have been increased with age by massive star evolution after a considerable amount (Y>0.2) of helium had been attained by the Big-Bang nucleosynthesis, but there is not seen a radial gradient of helium abundance. (iv) A considerable amount of heavy elements ($Z{\sim}10{-3}$) might have been formed in the inner halo ($r_{GC}$<10 kpc) from the earliest galactic co1lapse, and then the heavy element abundance has been slowly enriched towards the galactic center and disk, establishing the radial gradient of heavy element abundance. (v) The final galactic disk formation might have taken much longer by about a half of the galactic age than the halo formation, supporting a slow, inhomogeneous co1lapse model of Larson. (vi) Of the three principal parameters controlling the morphology of C-M diagrams, it was found that the first parameter is heavy clement abundance, the second age and the third helium abundance. (vii) The globular clusters can be divided into three different groups, AI, BI and CII according to Z, Y an d age as well as Dickens' HB types. BI group clusters of HB types 4 and 5 like M 3 and NGC 7006 are the oldest and have the lowest helium abundance of the three groups. And also they appear in the inner halo. On the other hand, the youngest AI clusters have the highest Z and Y, and appear in the innermost halo region and in the disk. (viii) From the result of the clean separations of the clusters into three groups, a three dimensional classification with three parameters, Z, Y and age is prsented. (ix) The anomalous C-M diagrams can be expalined in terms of the three principal parameters. That is, the anomaly of NGC 362 and NGC 7006 is accounted for by the smaller age of the order of $1{\sim}2{\times}10^9$ years rather than by the helium abundance difference, compared with M 3. (x) The difference in two Oosterhoff types I and II can be explained in terms of the mean mass difference of RR Lyrae variables rather than in terms of the helium abundance difference as suggested by Stobie. The mean mass of the variables in Oosterhoff type I clusters is smaller by $0.074m_{\odot}$ which is exactly consistent with Rood's estimate. Since it was found that the mean mass of RR Lyrae stars increases with decreasing Z, the two Oosterhoff types can be explained substantially by the metal abundance difference; the type II has Z<$3.4{\times}10^{-4}$, and the type I has higher Z than the type II.

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