• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.277-284
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• 2015

This study aims to evaluate the impact of forest therapy on neuro-cognitive, psychosocial, and physiological aspect of adolescent internet addiction risk group. We have classified potential and high risk user group as internet addiction risk group according to the criteria of Korean Internet Addiction Proneness Scale(K Scale). Based on the results of k-scale from the adolescents in metropolitan area from May to July 2013, 25 people were selected as Internet addiction risk group. We have randomized 13 participants joining forest therapy camp and 12 participants not joining one, and analyzed the change of the two groups with Continuous Performance, Kimberly S. Young, Connor-Davidson Resilience, Relationship Change Scale, heart rate variability and cortisol. Statistically significant changes were observedd in neuro-cognitive, psychosocial, and physiological variables, Through this study, we can consider that the therapy healing may relieve the level of internet addiction and can be an alternative to control emotional stability and impulsive behavior.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Child Health Nursing Research
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• v.7 no.1
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• pp.118-140
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• 2001

The purpose of this study was to measure the effect of safety education program in preschool children for accident prevention and improve their health through more systematic method. Data were collected from 584 preschoolers(247 preschooler are assigned to experimental group and 337 preschoolers are assinged to control group) from 4 to 6 years old using APP paper test which consists of questions and drawings. To experimental group, safety education were done 4 times within the time of 30 minutes per 1 time using education books, drawings, OHP. The findings of this study are as follows: 1. There were significant difference in movement(χ²=18.732, p=.0000), behavioral character(χ²=27.785, p=.000), synthetic judgement(χ²=12.02, p=0.002). So, safety education program have effect on preschooler. 2. In the accident proneness on preschooler between experimental group and control group according to general characteristics, it proved significant difference in the case of accident prevention education were done, reasoning power(χ²=10.48, p=.005), movement speed(χ²=7.341, p=.025) and behavioral character(χ²=18.86, p=.000), in the case of housing pattern is private house(individual house, yard?), reasoning power(χ²=6.683, p=.035), movement speed(χ²=12.76, p= .002) and behavioral character(χ²=12.24, p=.002), in the case of housing pattern is mixed-type, movement speed(χ²=6.935, p= .031) and behavioral character(χ²=10.816, p=.004), in the case of housing pattern is over six stories, movement speed(χ²=7.543, p=.023), in the case of subjects' age is 4 years old, movement speed(χ²=16.5, p= .000) and behavioral character(χ²=12.18, p=.002), in the case of subjects' age is 5 years old, movement speed(χ²=7.519, p= .023), watchfulness(χ²=6.372, p=.041), behavioral character(χ²=14.74, p=0.001) and synthetic judgement(χ²=14.5, p=.001), in the case of subjects' sex is male, life safety(χ²=6.406, p=.041), movement speed(χ²=22.86, p= .000), behavioral character(χ²=13.72, p= .001) and synthetic judgement(χ²=13.82, p=.001), in the case of subjects' sex is female, reasoning power(χ²=12.57, p=.002) and behavioral character(χ²=13.16, p= .001), in the case of childrens have past accidental experience, traffic safety(χ²= 6.683, p=.035), in the case of childrens have no past accidental experience, reasoning power(χ²=8.384, p=.015), movement speed(χ²=20.6, p=.000), behavioral character(χ²=25.1, p=.000) and synthetic judgement(χ² =10.79, p=.005), in the case of children's order is first, reasoning power(χ²=11.15, p=.004), movement speed(χ²=11.92, p= .003) and behavioral character(χ²=7.003, p=.030), in the case of children's order is second, movement speed(χ²=6.694, p= .035), behavioral character(χ²=26.9, p= .000) and synthetic judgement(χ²=14.3, p= .001), in the case of nuclear family, reasoning power(χ²=8.777, p=.012), movement speed(χ²=19.0, p=.000), behavioral character (χ²=26.4, p=0.000) and synthetic judgement (χ²=9.999, p=.007), in the case of mothers' school career is under high school graduate, life safety(χ²=8.023, p=.018), movement speed(χ²=10.99, p=.004) and behavioral character(χ²=6.777, p=.034), in the case of mothers' school career is beyond college graduate, reasoning power(χ²=6.717, p= .035), movement speed(χ²=8.963, p=.011), behavioral character(χ²=25.03, p=.000) and synthetic judgement(χ²=15.19, p=.001), in the case of mothers' age ranged 31-34, movement speed(χ²=12.29, p=.002) and behavioral character(χ²=14.17, p=.001), in the case of mothers' age ranged 35-39, movement speed(χ²=9.859, p=.007), behavioral character(χ²=9.095, p=.011) and synthetic judgement(χ²=7.810, p=.020), in the case of mothers' age is over 40, life safety(χ² =5.593, p=.025), in the case of mothers' job is full-time, traffic safety(χ²=6.032, p=.049) and reasoning power(χ²=8.502, p= .014), in the case of mothers' job is part- time., movement speed(χ²=10.99, p=.004) and behavioral character(χ²=7.895, p= .019), in the case of mothers have no job, movement speed(χ²=6.410, p=.041), movement stability(χ²=6.879, p=.032), behavioral character(χ²=27.72, p=.000) and synthetic judgement(χ²=18.11, p=.000). The difference of accident proneness between experimental group and control group according to general characterists, it also showed that there were significant difference in behavioral character compared to other area.. From this findings, we can guess that safety education program change and guide preschoolers' behavioral character to desirable direction.

• Journal of Distribution Research
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• v.17 no.1
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• pp.65-86
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• 2012

The $20^{th}$ century was the era of manufacturer brands which built higher brand equity for consumers. Consumers moved from generic products of inconsistent quality produced by local factories in the $19^{th}$ century to branded products from global manufacturers and manufacturer brands reached consumers through distributors and retailers. Retailers were relatively small compared to their largest suppliers. However, sometime in the 1970s, things began to slowly change as retailers started to develop their own national chains and began international expansion, and consolidation of the retail industry from mom-and-pop stores to global players was well under way (Kumar and Steenkamp 2007, p.2) In South Korea, since the middle of the 1990s, the bulking up of retailers that started then has changed the balance of power between manufacturers and retailers. Retailer private labels, generally referred to as own labels, store brands, distributors own private-label, home brand or own label brand have also been performing strongly in every single local market (Bushman 1993; De Wulf et al. 2005). Private labels now account for one out of every five items sold every day in U.S. supermarkets, drug chains, and mass merchandisers (Kumar and Steenkamp 2007), and the market share in Western Europe is even larger (Euromonitor 2007). In the UK, grocery market share of private labels grew from 39% of sales in 2008 to 41% in 2010 (Marian 2010). Planet Retail (2007, p.1) recently concluded that "[PLs] are set for accelerated growth, with the majority of the world's leading grocers increasing their own label penetration." Private labels have gained wide attention both in the academic literature and popular business press and there is a glowing academic research to the perspective of manufacturers and retailers. Empirical research on private labels has mainly studies the factors explaining private labels market shares across product categories and/or retail chains (Dahr and Hoch 1997; Hoch and Banerji, 1993), factors influencing the private labels proneness of consumers (Baltas and Doyle 1998; Burton et al. 1998; Richardson et al. 1996) and factors how to react brand manufacturers towards PLs (Dunne and Narasimhan 1999; Hoch 1996; Quelch and Harding 1996; Verhoef et al. 2000). Nevertheless, empirical research on factors influencing the production in terms of a manufacturer-retailer is rather anecdotal than theory-based. The objective of this paper is to bridge the gap in these two types of research and explore the factors which influence on manufacturer's private label production based on two competing theories: S-C-P (Structure - Conduct - Performance) paradigm and resource-based theory. In order to do so, the authors used in-depth interview with marketing managers, reviewed retail press and research and presents the conceptual framework that integrates the major determinants of private labels production. From a manufacturer's perspective, supplying private labels often starts on a strategic basis. When a manufacturer engages in private labels, the manufacturer does not have to spend on advertising, retailer promotions or maintain a dedicated sales force. Moreover, if a manufacturer has weak marketing capabilities, the manufacturer can make use of retailer's marketing capability to produce private labels and lessen its marketing cost and increases its profit margin. Figure 1. is the theoretical framework based on a strategic market management perspective, integrated concept of both S-C-P paradigm and resource-based theory. The model includes one mediate variable, marketing capabilities, and the other moderate variable, competitive intensity. Manufacturer's national brand reputation, firm's marketing investment, and product portfolio, which are hypothesized to positively affected manufacturer's marketing capabilities. Then, marketing capabilities has negatively effected on private label production. Moderating effects of competitive intensity are hypothesized on the relationship between marketing capabilities and private label production. To verify the proposed research model and hypotheses, data were collected from 192 manufacturers (212 responses) who are producing private labels in South Korea. Cronbach's alpha test, explanatory / comfirmatory factor analysis, and correlation analysis were employed to validate hypotheses. The following results were drawing using structural equation modeling and all hypotheses are supported. Findings indicate that manufacturer's private label production is strongly related to its marketing capabilities. Consumer marketing capabilities, in turn, is directly connected with the 3 strategic factors (e.g., marketing investment, manufacturer's national brand reputation, and product portfolio). It is moderated by competitive intensity between marketing capabilities and private label production. In conclusion, this research may be the first study to investigate the reasons manufacturers engage in private labels based on two competing theoretic views, S-C-P paradigm and resource-based theory. The private label phenomenon has received growing attention by marketing scholars. In many industries, private labels represent formidable competition to manufacturer brands and manufacturers have a dilemma with selling to as well as competing with their retailers. The current study suggests key factors when manufacturers consider engaging in private label production.