• Korean Journal of Weed Science
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• v.30 no.3
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• pp.282-290
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• 2010

The objective of this research was to discover the best method for weed management in environment-friendly rice paddy fields through the study on the effect of weed control and injury levels of rice plants as affected by size, input time, and input amount of golden apple snail (GAS). The efficacy of weed control as affected by GAS when applied at 5, 10, and 15 days after transplanting (DAT) was 98, 89, and 58%, respectively. The efficacy of weed control had declined as late the input time of GAS. On the other hand, the efficacy of weed control as affected by rice bran followed by GAS treatment was higher than by GAS treatment alone. Weed species such as Persicaria hydropiper, Echinochloa crus-galli, Scirpus juncoides, and Monochoria vaginalis were not completely controlled by GAS when applied late. Input amount and time of adult GAS (70 days after hatching) for effective weed control were 3 kg $10a^{-1}$ at 5 DAT, 6-7 kg $10a^{-1}$ at 10 DAT, and 7 kg $10a^{-1}$ at 15 DAT. Input time and amount of young GAS (35 days after hatching) for effective weed control were 0 day after harrow (DAH) and 1 kg $10a^{-1}$, respectively. The young GAS when applied 0 DAH at 1 kg $10a^{-1}$ provided 100% control of P. hydropiper, E. crus-galli, S. juncoides, M. vaginalis, Ludwigia prostrata, Eleocharis kuroguwai, Sagittaria trifolia and Cyperus difformis. The rice foliar injury caused by adult (3 kg $10a^{-1}$) and young (1 kg $10a^{-1}$) GAS were 5-7% and 1% respectively. There was no significant difference in rice injury by size and input amount of GAS on plant height and number of tiller. These data indicate that the young GAS when applied 1 kg $10a^{-1}$ at 0 day after harrow was the best method for weed management in environment-friendly rice paddy fields.

• Food Science of Animal Resources
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• v.26 no.1
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• pp.37-42
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• 2006

The current study was conducted to investigate the effect of types of crate and season on transport condition and mortality, and ultimately to identify the best practice for reducing economic lose. Total loading weight stocking density, transport and lairage times and mortality were surveyed from the management data sheet of two companies for each first week of January, April, August and October, An average loading weight, length of transport and lairage times and mortality were 3.9 ton, 96 min, 478 min and 0.6%, respectively. Mortality after lairage was not significant between two types of crate. In addition, container type crate showed higher loading weight and stocking density than box type one. Spring and winter had significantly higher mortality with 0.7 and 0.8%, respectively, then summer and fell of 0.5%. An interaction between crate type and season on mortality showed that mortality for box type on was higher in spring and winter with 0.8 and 0.7%, respectively, compared to summer and fall of 0,3 and 0.4% respectively. In the case of container type crate, spring, fall and winter had greatly different death into with 0.7, 0.5 and 0.8%, respectively, while there was no difference between spring and summer, and between summer and winter, Mortality after transportation was similar between both crate type, with higher rate for spring and winder than other seasons. The result was likely related to death of exposure due to extended waiting time without heating facility.

• Korean Journal of Environmental Biology
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• v.38 no.4
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• pp.528-553
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• 2020

Upland fields are characterized by dry environments, a high degree of disturbance by farming practices such as double-cropping, and a high diversity of crops compared to other field types. This study focused on the floristic composition and characteristics of upland fields in South Korea. Flora surveys were conducted in 36 areas in nine provinces at two times (June and August) in 2015. The results showed that the vascular plants in the upland fields in South Korea included 532 taxa, containing 100 families, 322 genera, 483 species, nine subspecies, 37 varieties, one form, and two hybrids. Among the 100 families, Asteraceae was the most diverse in species (75 taxa), followed by Poaceae (68 taxa), Fabaceae (34 taxa), Polygonaceae (21 taxa), Rosaceae (19 taxa), and Liliaceae (17 taxa). Based on the occurrence frequency of each species, Acalypha australis L. (100%), and Artemisia indica Willd. (100%) were the highest, followed by Humulus scandens (Lour.) Merr., Rorippa palustris (L.) Besser, Conyza canadensis (L.) Cronquist, Erigeron annuus (L.) Pers., Lactuca indica L., Commelina communis L., Digitaria ciliaris (Retz.) Koeler, Echinochloa crus-galli(L.) P.Beauv., Cyperus microiria Steud., and Oxalis corniculata L. The biological type of upland fields in South Korea was determined to be Th-R₅-D₄-e type. Rare plants were found in 11 taxa: Taxus cuspidata Siebold & Zucc, Magnolia kobus DC, Clematis trichotoma Nakai, Aristolochina contorta Bunge, Buxus sinica (Rehder & E.H.Wilson) M.Cheng var. koreana (Nakai ex Rehder) Q.L.Wang, Melothria japonica (Thunb.) Maxim, Mitrasacme indica Wight, Lithospermum arvense L., Carpesium rosulatum Miq., Allium senescens L., and Pseudoraphis sordida (Thwaites) S.M.Phillips & S.L.Chen. Ninety-seven taxa contained naturalized plants composed of 24 families, 68 genera, 97 species, one variety, and one form. The urbanization and naturalization indices were 30.5% and 18.4%, respectively.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.