• Title/Summary/Keyword: Embedding reliability

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The Effects of Franchise Firm's Green Leadership and Environmental Attractiveness on Environmental Marketing Strategy and Tactics, Environmental Performance (프랜차이즈 기업의 그린리더십과 환경매력도가 환경마케팅 전략과 전술 및 환경성과에 미치는 영향)

  • Kim, Kyu-Won;Seo, Min-Kyo;Lee, Jung-Un
    • The Korean Journal of Franchise Management
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    • v.8 no.1
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    • pp.19-30
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    • 2017
  • Purpose - As environmental issues, along with the growth of companies, are accelerating, social interests in eco-friendly management that requires corporate social role and responsibility are increasing. The eco-friendly management activity reflects the changes in environmental awareness of consumers. Therefore, the eco-friendly images of companies influence consumers, and the establishing of eco-friendly management strategy has become a very important factor in the greenmarket. In this regard, this study examined the impacts of green leadership and environmental attractiveness on strategic environmental marketing, tactical environmental marketing, and environmental performance towards the employees of franchisee headquarters. Research design, data, and methodology - The survey was conducted towards the 800 headquarters among 2,600 brands that are registered with the Fair Trade Commission of Korea by mail. Among the total of 162 questionnaires collected, 7 respondents were excluded for their incompletion, and thus 155 responses were used in this study. The data were analyzed with SPSS 21.0 and SmartPLS 3.0. Frequency analysis was carried out to understand the general characteristics of the subjects, and confirmatory factor analysis to measure the reliability and validity of the measurement. Correlation analysis was conducted to identify the correlation between constructs, and structural equation modeling to examine the structural relationships among the constructs. Result and Conclusions - First, green leadership had a positive impact on strategic environmental marketing, tactical environmental marketing, and environmental performance. Second, environmental attractiveness had a positive effect on strategic environmental marketing, tactical environmental marketing, and environmental performance. Finally, strategic environmental marketing and tactical environmental marketing had positive impacts on environmental performance. This study can be recognized for proposing new perspectives on eco-friendly management strategy for firms to be able to win competitive superiority and performance by embedding awareness of the importance of environmental market and suggesting practical implications on understanding of environmental attractiveness, strategies and tactics of environmental management, and environmental performance in the franchise industry.

Comparison of One-Tube Nested-PCR and PCR-Reverse Blot Hybridization Assays for Discrimination of Mycobacterium tuberculosis and Nontuberculous Mycobacterial Infection in FFPE tissues

  • Park, Sung-Bae;Park, Heechul;Bae, Jinyoung;Lee, Jiyoung;Kim, Ji-Hoi;Kang, Mi Ran;Lee, Dongsup;Park, Ji Young;Chang, Hee-Kyung;Kim, Sunghyun
    • Biomedical Science Letters
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    • v.25 no.4
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    • pp.426-430
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    • 2019
  • Currently, molecular diagnostic assays based on nucleic acid amplification tests have been shown to effectively detect mycobacterial infections in various types of specimen, however, variable sensitivity was shown in FFPE samples according to the kind of commercial kit used. The present study therefore used automated PCR-reverse blot hybridization assay (REBA) system, REBA Myco-ID HybREAD 480®, for the rapid identification of Mycobacterium species in various types of human tissue and compared the conventional one-tube nested-PCR assay for detecting Mycobacterium tuberculosis (MTB). In conventional nested-PCR tests, 25 samples (48%) were MTB positive and 27 samples (52%) were negative. In contrast, when conducted PCR-REBA assay, 11 samples (21%) were MTB positive, 20 samples (39%) were NTM positive, 8 samples (15%) were MTB-NTM double positive, and 13 samples (25%) were negative. To determine the accuracy and reliability of the two molecular diagnostic tests, the one-tube nested-PCR and PCR-REBA assays, were compared with histopathological diagnosis in discordant samples. When conducted nested-PCR assay, 10 samples (59%) were MTB positive and seven samples (41%) were negative. In contrast, when conducted PCR-REBA test, three samples (17%) were MTB positive, 10 samples (59%) were NTM positive and four samples (24%) were negative. In conclusion, the automated PCR-REBA system proved useful to identify Mycobacterium species more rapidly and with higher sensitivity and specificity than the conventional molecular assay, one-tube nested-PCR; it might therefore be the most suitable tool for identifying Mycobacterium species in various types of human tissue for precise and accurate diagnosis of mycobacterial infection.

Seismic Behavior Evaluation of Embedded Kagome Damping Device (콘크리트에 매립된 카고메 감쇠시스템의 내진거동평가)

  • Hur, Moo-Won;Lee, Sang-Hyun;Kim, Jong-Ho;Hwang, Jae-Seung
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.23 no.2
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    • pp.84-91
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    • 2019
  • Recently, there has been a tendency to improve seismic performance of building structure by installing a steel hysteretic damping device which is economically efficient and easy to install and maintain. However, for a reinforced concrete building, a set of complicated connecting hardware and braces to fix the steel hysteretic damping device yields deteriorated reliability in damping performance. Therefore, this study presents a method of directly embedding a Kagome damper, which was investigated in previous researches, into a concrete structure without additional connecting hardware. Moreover, in this study, a series of experiments conducted to provide a basis of the Kagome damper by confirming the seismic behavior for various embedded lengths. As a result, in a group of the embedded length of $1.0l_d$, the dampers were pulled out, while concrete breakout occurs. In a group of $2.0l_d$, neither pull-out nor concrete breakout occurred, while the dampers show stable behavior. Moreover, the buried length of $2.0l_d$ has 1.3 times better energy dissipation capacity. The system presented in this study can reduce the cost and period for installing, omitting making additional hardware.

Estimation on End Vertical Bearing Capacity of Double Steel-Concrete Composite Pile Using Numerical Analysis (수치해석을 이용한 이중 강-콘크리트 합성말뚝 연직지지력 평가)

  • Jeongsoo, Kim;Jeongmin, Goo;Moonok, Kim;Chungryul, Jeong;Yunwook, Choo
    • Journal of the Korean GEO-environmental Society
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    • v.23 no.12
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    • pp.5-15
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    • 2022
  • Conventionally, because evaluation methods of the bearing capacity for double steel pipe-concrete composite pile design have not been established, the conventional vertical bearing capacity equations for steel hollow pile are used. However, there are severe differences between the predictions from these equations, and the most conservative one among vertical bearing capacity predictions are conventionally adopted as a design value. Consequently, the current prediction method for vertical bearing capacity of composite pile prediction composite pile causes design reliability and economical feasibility to be low. This paper investigated mechanical behaviors of a new composite pile, with a cross-section composed of double steel pipes filled with concrete (DSCT), vertical bearing capacities were analyzed for several DSCT pile conditions. Axisymmetric finite element models for DSCT pile and surrounding ground were created and they were used to analyze effects on behaviors of DSCT pile pile by embedding depth, stiffness of plugging material at pile tip, height of plugging material at pile tip, and rockbed material. Additionally, results from conventional design prediction equations for vertical bearing capacity at steel hollow pile tip were compared with that from numerical results, and the use of the conventional equations for steel hollow pile was examined to apply to that for DSCT pile.

A Study on Market Size Estimation Method by Product Group Using Word2Vec Algorithm (Word2Vec을 활용한 제품군별 시장규모 추정 방법에 관한 연구)

  • Jung, Ye Lim;Kim, Ji Hui;Yoo, Hyoung Sun
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.1-21
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    • 2020
  • With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.