• School Mathematics
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• v.16 no.4
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• pp.803-825
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• 2014

From the early stages of learning algebra, literal symbols are used to represent algebraic objects such as variables and parameters. The concept of parameters contains both indeterminacy and fixity resulting in confusion and errors in understanding. The purpose of this research is to compare the beginners of algebra and pre-service teachers who completed secondary mathematics education in terms of understanding this paradoxical nature of parameters. We recruited 35 middle school students in eight grade and 73 pre-service teachers enrolled in a undergraduate course at one university. Using them we conducted a survey on the perception of the nature of parameters asking if one considers parameters suggested in a problem as variables or constants. We analyzed the collected data using the mixed method of qualitative and quantitative approaches. From the analysis results, we identified several difficulties in understanding of parameters from both groups. Especially, our statistical analysis revealed that the proportions of subjects with limited understanding of the concept of parameters do not differ much in two groups. This suggests that learning algebra in secondary mathematics education does not improve the understanding of the nature of parameters significantly.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.23-31
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• 2022

This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.

• Korea journal of population studies
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• v.26 no.2
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• pp.33-62
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• 2003

The purpose of this study is to analyze the relative importance of three factor -socioeconomic development, public health development, egalitarian nature of socioeconomic development- affecting mortality declines. Infant mortality rate and life expectancy at birth are used as the mortality index, that is the dependent variables, while GNP is used as the indicator of socioeconomic development, primary school enrollment ratio of female as the indicator of egalitarian nature of socioeconomic development, population per hospital bed as the indicator of public health. The data of these variables are collected two time-periods -before 1970 and during 1970-1980- over 50 countries. The explanatory data analysis is used as the statistical technique. We can find whether the relationship between dependent variable and independent variables are linear or nonlinear, and which case is the influential case in our model. The main results of this study are followings. First, the association between infant mortality rates and four indices are not linear. The most important factor explaining the variation of infant mortality is GNP, while primary enrollment of female is the second and GINI is the third important factor. However, population per hospital bed does not have a significant effect on the infant mortality rates in this study. Second, life expectancy at birth is log-linearly related to GNP. Unlike infant mortality rates, the most important factor explaining the variation of life expectance at birth is women's education and the next important factor GNP, and then the third one GINI. But, still population per hospital bed is not significantly related to the variation of life expectance in this study.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.379-394
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• 2022

Shallow footings usually belonged to the category of thick plate structures. For accurate analysis of thick plates, the contribution of out-of-plane components of the stress tensor should be considered in the formulation. Most of the available shallow footing models are based on the classical plate theories, which usually neglect the effects of the out-of-plane stresses. In this study, a mixed-field plate finite element model (FEM) is developed for the analysis of shallow footings rested on soil foundations. In addition to displacement field variables, the out-of-plane components of the stress tensor are also assumed as a priori unknown variables. For modeling the interaction effect of the soil under and outside of the shallow footings, the modified Vlasov theory is used. The tensionless nature of the supporting soil foundation is taken into account by adopting an incremental, iterative procedure. The equality requirement of displacements at the interface between the shallow footing and soil is fulfilled using the penalty approach. For validation of the present mixed FEM, the obtained results are compared with the results of 3D FEM and previous results published in the literature. The comparisons show the present mixed FEM is an efficient and accurate tool for solving the problems of shallow footings rested on subsoil.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.1
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• pp.47-53
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• 2022

The escape behavior of prey fish to predator attack is directly linked to the survival of the fish. In this study, I explored the escape behavior of Medaka fish to bird attacks. To simulate the attack, I designed a model triangular-shaped bird to slide along a fishing line connected between rods at both ends of the tank. The triangular shape was set to 10×15 (S=1), 15×20 (S=2), and 20×25 cm (S=3) with base×height. The slope (θ) of the fishing line, which determines the attack speed of the model bird, was set to values of 15° (θ=1), 30° (θ=2), and 45° (θ=3). The escape behavior was characterized using five variables: escape speed (ν), escape acceleration (α), responsiveness (γ), branch length similarity entropy (ε), and alignment (ϕ). The experimental results showed when (S, θ)=(fixed, varied), the change in values of the five variables were not significant. Thus, the fish respond more sensitively to S than to θ In contrast, when (S, θ)=(varied, fixed), ν, α, and γ showed increasing trends but ε and ϕ did not change much. This indicates the nature of fish escape behavior irrespective of the threat is inherent in ε and ϕ. I found that fish escape behavior can be divided into two types for the five physical quantities. In particular, the analysis showed that the type was mainly determined by the size of the model bird.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• Iberoamérica
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• v.12 no.1
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• pp.407-439
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• 2010

Growth of researches addressing cross-culture in international business is exponential. This article reviews the extant researches around the national culture and describes the various conceptualization of culture through discussion of some of popular models of national culture. This article presented some of the most important issues in international business surrounding globalization, especially convergence and divergence of cultures and cultural changes. Global rapid changes in international business environment request the reconsideration of the assumption of cultural stability and the simple view of culture, which tends to examine the static influence of a few cultural factors in isolation form other cultural factors and contextual elements. This paper identifies a valid cultural grouping and proposes the following typology of the possible methodologies in international business; Ethnological description, Use of proxies, Direct values inference, and Indirect values inference. Rather than selecting a single methodology, it appears to be more appropriate to use multi-method in the cross-cultural international business research. It has been shown that cultural change is intertwined with socioeconomic-institutional variables, and that these variables may also add to determine culture contemporarily. This paper also explained the dynamics of culture as multi-level, multi-layer constructs. According to this model, we may understand how the dynamic nature of culture conveys the top-down-bottom-up processes where one cultural level affects changes in other level of culture.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• Safety and Health at Work
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• v.5 no.3
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• pp.147-151
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• 2014

Background: Agriculture ranks among the most dangerous trades worldwide. There is, however, still a lack of knowledge on nonfatal injuries in agriculture. The aim of this study was to describe the nature and occurrence of nonfatal injuries in farmers in two counties in central Norway. Methods: A questionnaire was sent to 7,004 farmers in Norway. We asked for information about the respondents and the farm, whether the farmer had had work-related injuries on the farm during the past 12 months, and details about the incidence and seriousness of the injury. Results: A total of 2,699 respondents gave a response rate of 42%. Of the respondents, 249 (9.2%) reported one or more work-related injuries. The most usual cause of injury involved an animal, and >75% of these happened inside the outbuilding. Among these, 17.5% had a consequence of sick leave or a more serious result. When all the accidents were analyzed by stepwise logistic regression, only the variables: works alone, has >3,500 stipulated working hours at the farm, and the type of production were statistical significant explanatory variables for having an injury. Conclusion: Incorporating safety aspects to a greater extend in the design and construction of outbuildings would make a substantial contribution to injury prevention in agriculture.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.