• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Animal Bioscience
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• v.34 no.9
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• pp.1466-1478
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• 2021

Objective: Ruminants are completely dependent on their microbiota for rumen fermentation, feed digestion, and consequently, their metabolism for productivity. This study aimed to evaluate the rumen bacteria of lactating yaks with different milk protein yields, using high-throughput sequencing technology, in order to understand the influence of these bacteria on milk production. Methods: Yaks with similar high milk protein yield (high milk yield and high milk protein content, HH; n = 12) and low milk protein yield (low milk yield and low milk protein content, LL; n = 12) were randomly selected from 57 mid-lactation yaks. Ruminal contents were collected using an oral stomach tube from the 24 yaks selected. High-throughput sequencing of bacterial 16S rRNA gene was used. Results: Ruminal ammonia N, total volatile fatty acids, acetate, propionate, and isobutyrate concentrations were found to be higher in HH than LL yaks. Community richness (Chao 1 index) and diversity indices (Shannon index) of rumen microbiota were higher in LL than HH yaks. Relative abundances of the Bacteroidetes and Tenericutes phyla in the rumen fluid were significantly increased in HH than LL yaks, but significantly decreased for Firmicutes. Relative abundances of the Succiniclasticum, Butyrivibrio 2, Prevotella 1, and Prevotellaceae UCG-001 genera in the rumen fluid of HH yaks was significantly increased, but significantly decreased for Christensenellaceae R-7 group and Coprococcus 1. Principal coordinates analysis on unweighted UniFrac distances revealed that the bacterial community structure of rumen differed between yaks with high and low milk protein yields. Furthermore, rumen microbiota were functionally enriched in relation to transporters, ABC transporters, ribosome, and urine metabolism, and also significantly altered in HH and LL yaks. Conclusion: We observed significant differences in the composition, diversity, fermentation product concentrations, and function of ruminal microorganisms between yaks with high and low milk protein yields, suggesting the potential influence of rumen microbiota on milk protein yield in yaks. A deeper understanding of this process may allow future modulation of the rumen microbiome for improved agricultural yield through bacterial community design.

• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.169-175
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• 2022

In this study, envisioned a laboratory equipped with virus blocking equipment for chest X-ray examinations of respiratory or droplet-transmitted virus-infected patients, and the material with the least deterioration in X-ray output and image quality among the proven blocking materials that block viruses in the design process. and experimented to find the thickness. As a result, when 1 cm of acrylic was applied, the X-ray output was reduced by only about 3.27 % compared to the absence of the barrier material, the SNR was 40.7 and CNR was 30.9, which was the best. The SSIM index result was analyzed as 0.891, which was analyzed to be implemented as the most similar image compared to the original image. The barrier material applied in the research method was objective in that it used a product approved by the Ministry of Food and Drug Safety. the results of this study are expected to provide useful information when installing X-ray examination facilities for the diagnosis and treatment of respiratory-related virus-infected patients in the future.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.198-210
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• 2022

Recently, research on environmental sustainable fashion design has been conducted, but various studies on preference and purchase intention for environmental sustainable fashion products have not yet been conducted. Therefore, this paper studied the effect of the characteristics of environmental sustainable fashion products on purchase intention with preference as a mediating effect. This study focused on Generation MZ attending universities in Hubei Province, China, and a total of 350 online surveys were conducted from August 15, 2021 to August 20, 2021. Among them, 335 copies were used for the final analysis, excluding invalid questionnaires. SPSS26 and AMOS26 were used for analysis. First, the results of the analysis showed that environmental protection characteristics among the characteristics of environmental sustainable fashion products had a positive effect on preference. At the same time, environmental sustainability had a positive effect on purchase intention. Second, environmental protection and sustainability have a significant positive effect on preference. At the same time, environmental sustainability has a significant positive effect on purchase intention. Third, preference plays a significant mediating role in the relationship between the purchase intention of environmental protection.

• The Korean Journal of Franchise Management
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• v.13 no.1
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• pp.35-46
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• 2022

Purpose: With the rapid changes in the technical development and the trend of consumption trend, the convenience store industry is facing an unprecedented competitive situation in the consumption environment where the boundary between online and offline is broken due to the stagnation of offline distribution channels and the spread of online shopping. The biggest innovation strategy of the major convenience store brands in recent years are introducing the O2O (Online to Offline) platform and presenting new products and services beyond the boundaries of online and offline to transform themselves into Omni Channel stores. The study is designed to analyze the effect of innovativeness of convenience store as a stimulus in O2O platform which customers perceive on store loyalty, the final response to external stimuli, through customer engagement with convenience store brands. Specifically, the innovativeness of convenience stores was divided into types of core activities in corporate marketing and focused on innovations in services, products(proposals), promotions and experiences. Research design, data, and methodology: Various hypotheses have been developed to achieve this research purpose. The data were collected from 1,128 questionnaires the age between 15 and 60 who had experience using retail store apps and delivery apps and were analyzed using SPSS 22.0 and SmartPLS 3.3.7 program. Measurement model analysis was carried out to assess convergent and discriminant validity. Also, common method bias was tested using the values of VIF (variance inflation factor). The hypotheses were tested using structural equation modeling with SmartPLS 3.3.7 program. Results: First, service innovation has a positive effect on cognitive engagement. Second, product, promotion and experience innovation have a positive effect on cognitive and affective engagement. Third, cognitive influences affective engagement. Finally, both cognitive and affective engagement affect store loyalty, but affective engagement has a stronger effect on store loyalty than cognitive engagement. Conclusions: All four types of innovation and cognitive engagement have a positive effect on emotional engagement, which has a stronger effect on store loyalty than cognitive engagement. Thus, while innovation can build loyalty through emotional engagement, innovation strategies must be designed and pursued with caution in terms of impact through cognitive engagement may not achieve the planned goals.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.48-55
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• 2022

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In 'offset printing' mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called 'spot color' ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through 'Delta E' provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.48-48
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• 2010

Sound quality and NVH-issues(Noise, Vibration and Harshness) of vehicles has become very important for car manufacturers. It is interpreted as among the most relevant factors regarding perceived product quality, and is important in gaining market advantage. The general sound quality of vehicles was gradually improved over the years. However, today the development cycles in the automotive industry are constantly reduced to meet the customers' demands and to react quickly to market needs. In addition, new drive and fuel concepts, tightened ecological specifications, increase of vehicle classes and increasing diversification(increasing market for niche vehicles), etc. challenge the acoustic engineers trying to develop a pleasant, adequate, harmonious passenger cabin sound. Another aspect concerns the general pressure for reducing emission and fuel consumption, which lead to vehicle weight reductions through material changes also resulting in new noise and vibration conflicts. Furthermore, in the context of alternative powertrains and engine concepts, the new objective is to detect and implement the vehicle sound, tailored to suit the auditory expectations and needs of the target group. New questions must be answered: What are appropriate sounds for hybrid or electric vehicles? How are new vehicle sounds perceived and judged? How can customer-oriented, client-specific target sounds be determined? Which sounds are needed to fulfil the driving task, and so on? Thus, advanced methods and tools are necessary which cope with the increasing complexity of NVH-problems and conflicts and at the same time which cope with the growing expectations regarding the acoustical comfort. Moreover, it is exceedingly important to have already detailed and reliable information about NVH-issues in early design phases to guarantee high quality standards. This requires the use of sophisticated simulation techniques, which allow for the virtual construction and testing of subsystems and/or the whole car in early development stages. The virtual, testing is very important especially with respect to alternative drive concepts(hybrid cars, electric cars, hydrogen fuel cell cars), where complete new NVH-problems and challenges occur which have to be adequately managed right from the beginning. In this context, it is important to mention that the challenge is that all noise contributions from different sources lead to a harmonious, well-balanced overall sound. The optimization of single sources alone does not automatically result in an ideal overall vehicle sound. The paper highlights modern and innovative NVH measurement technologies as well as presents solutions of recent NVH tasks and challenges. Furthermore, future prospects and developments in the field of automotive acoustics are considered and discussed.

• Journal of Environmental Health Sciences
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• v.49 no.1
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• pp.37-47
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• 2023

Background: Paraben is a widely used substance with a preservative effect found in various materials such as food, medicine, personal care products, and cosmetics. Objectives: This study was conducted to identify the level of urinary paraben concentrations (i.e., methyl-, ethyl-, and propyl-) among Korean adults and to explore the factors related with the exposure levels. Methods: We analyzed the third period (2015~2017) of the Korean National Environmental Health Survey (KoNEHS). R statistical software (version 4.1.1) was used to estimate representative values for the whole population with weight variables to reflect sampling design. Whether urinary concentrations tended to increase as the level of paraben exposure-related characteristics increased was tested and P_trend was calculated using general linear models. Results: Urinary concentrations of all three parabens (i.e., methyl-, ethyl- and propyl-) were higher in women than in men (P_trend<0.0001, 0.008, and <0.0001), and the values of methylparaben and propylparaben tended to increase as the age of subjects increased (P_trend<0.0001, and <0.0001). Urinary concentrations of methylparaben and propylparaben were associated with intensity of exercise (P_trend<0.001, and 0.004), and that of propylparaben was higher in non-smokers (P_trend=0.01). In terms of paraben exposure-related variables, urinary concentrations of parabens (i.e., methyl-, ethyl- and propyl-) increased as the daily average frequency of teeth-brushing (P_trend<0.0001, 0.03 and 0.0001), the frequency of use of hair products (P_trend=0.005, 0.05 and 0.04), the frequency of use of makeup products (P_trend<0.001, 0.001 and <0.001), and the frequency of use of antibacterial products (P_trend=0.005, 0.02 and 0.02) increased. Conclusions: In our study, urinary concentrations of all three parabens are associated with gender, teethbrushing, hair products, make-up products, and antibacterial products. Methyl- and proyl-parabens were associated with age and intensity of exercise, and propyl-paraben was associated with smoking.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.575-576
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• 2020

본 논문은 근래에 식용곤충 식품에 대한 수요 및 국민적 관심이 증가하여 관련 산업이 급격히 성장하고 있는 가운데, 건강기능성 효과가 널리 알려진 흰점박이꽃무지 유충의 안정적인 생산량 확보를 위한 스마트 사육사를 제작하고 그 성능을 평가한 결과이다. 사육사는 L6m×W3m×H2.8m 크기로 제작하였으며, 안정적인 사육환경을 위하여 사육실과 공조실을 분리하여 설계하였다. 공시재료는 생후 15일이 경과된 흰점박이꽃 무지 유충 1령이며, 스마트 사육사 내 사육환경은 온도 25±2℃, 습도 65±5%로 제어하였다. 사육조사는 매주 1회, 유충의 체중, 길이, 두께를 측정하였으며, 스마트 사육사의 성능평가를 위해 일반 사육농가(전북 소재)와 비교·분석하였다. 사육 4주 후 조사 결과, 스마트 사육사에서 사육한 유충의 체중과 길이는 각각 평균 1.97g/마리와 3.75cm로, 일반농가의 1.58g/마리와 3.55cm에 비해 비교적 높은 것으로 나타났다. 하지만, 두께의 경우 2주 차까지 일반농가에서 대체로 높은 것으로 나타났으며, 이후 3~4주 차에서는 큰 차이를 보이지 않았다. 따라서 본 연구를 통해 개발한 흰점박이꽃무지 유충 스마트 사육사는 일반농가와 비교해 사육이 비교적 빠르고 생산량을 더 많이 확보할 수 있는 시스템으로 농가소득 증대에 유용할 것으로 판단되며, 장소 및 시간에 상관없이 생육환경 제어가 가능하여 개발된 시제품의 보급 확대가 필요하다.