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( Monika Hartmann ) 세종대학교 경제통합연구소 1993 Journal of Economic Integration Vol.8 No.2
There is a growing concern throughout Europe with the environmental effects of intensive farming practices. This new awareness has led to the demand for stricter environmental regulations. The theoretical part of the paper explores the possible effects of environmental regulations. The theoretical part of the paper explores the possible effects of environmental regulations and health standards on competitive advantage, trade and welfare. The empirical part of the paper is based on the SWOPSIM model TEPSIM, which encompasses factors of production, such as pesticides, fertilizer and land. Using this extended SWOPSIM framework the impact of alternative environmental policy scenarios on agricultural trade and economic welfare is simulated.
Monika Moravkova,Michal Slany,Jiri Lamka,Ivo Pavlik 대한수의학회 2013 JOURNAL OF VETERINARY SCIENCE Vol.14 No.1
IS901 RFLP analysis of 36 Mycobacterium avium subsp. avium (MAA) isolates from 15 pheasants (Phasianus colchicus) and two goshawks (Accipiter gentilis) from four pheasant farms was performed. Using this method, six different IS901 RFLP types (E, F, G, M, Q, and V) were identified. The distribution of IS901 RFLP profiles was tightly linked to individual flocks. Matching IS901 RFLP profiles observed in the present study indicate MAA transmission between pheasants and goshawks in the same locality. In two flocks, different pheasants within a flock as well as in various organs of five individual pheasants were found to have two distinct IS901 RFLP profiles.
Acquisition of the English Article System by Spanish and Korean Speakers
Monika Ekiert,Eun Sung Park 한국응용언어학회 2010 응용 언어학 Vol.26 No.2
This study examined the second language (L2) acquisitional patterns of the English article system by adult ESL learners with two contrasting first languages: Spanish, a language which has an article system, and Korean which does not have articles or article-like morphemes. Data were collected from 43 participants via means of a fill-in-the-article test consisting of 46 items. Results revealed that the different semantic uses of a, the, and zero article presented different levels of difficulty for the two groups of learners, and that they did not appear to be acquired at the same time. The results also indicated that both groups of learners seemed to follow roughly the same path of acquisition regardless of the differences in their first language. This study concludes with a discussion of different theoretical explanations regarding the observed development of the English article system.
Leveraging Big Data for Spark Deep Learning to Predict Rating
( Monika Mishra ),( Mingoo Kang ),( Jongwook Woo ) 한국인터넷정보학회 2020 인터넷정보학회논문지 Vol.21 No.6
The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users’ previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users’ ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.
Monika Tomczykowa,Katarzyna Leszczyńska,Micha1 Tomczyk,El_zbieta Tryniszewska,Danuta Kalemba 한국식품영양과학회 2011 Journal of medicinal food Vol.14 No.4
The chemical composition of the essential oil obtained from the roots of Bidens tripartita L. by hydrodistillation was investigated by gas chromatography–mass spectrometry. In total, 106 compounds were identified (97.1% of the total oil). The main components of the oil were α-pinene (15.0%), β-bisabolene (9.3%), p-cymene (6.0%), hexanal (5.7%), linalool (4.6%), p-cymene-9-ol (3.4%), β-elemene (2.6%), 2-pentylfuran (2.2%), and silphiperfol-6-ene (2.1%). The antibacterial and antifungal properties of the essential oil were evaluated against eight Gram-positive and 11 Gram-negative bacterial species and 10 fungal strains. The oil exhibited a strong antifungal activity.