• Korean Journal of Soil Science and Fertilizer
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• v.16 no.1
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• pp.20-27
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• 1983

A series of laboratory experiment was conducted to find out the chemical composition, characterization of humic substances by physical and chemical methods and reaction of Na-pyrophosphate, $Ca(OH)_2$ and rice straw with albumin on the degradation of soil organic matter in the volcanic ask soils of the Jeju Island. Results obtained were summarized as follows: 1. The contents of organic matter, available silicon, active iron and aluminum concentration in volcanic ash the soils were remarkably higher but available phosphorous was comparatively lower than the mineral soils. In volcanic ash soil, the contents of potassium, calcium and magnessium were higher in upland soil than that of forest soil. The ratios of active $Al^{{+}{+}{+}}/Fe^{{+}{+}}$, C/P and $K/Ca^+$ Mg were apparently high in volcanic ash soils while that of $SiO_2$/O.M. was high in mineral soil. 2. The carbon/nitrogen ratio in humin, humic acid content in organic matter, and carbon contents of humin in total carbon of soil organic matter were apparently higher in the volcanic ash soils than in the mineral soils, The total nitrogen and fractions of acid or alkali soluble nitrogen were remarkably high in volcanic ash soils while mineralizable nitrogen ($NH_4$-N and $NO_3$) contents were high in mineral soils. 3. The values of K600, RF and log K were also higher in volcanic ash soils than those in mineral soils, and the absorbance in the visible range were high and color was dark in the soil of which humification was progressed Extracted humic acid from volcanic ash soil was less reactive to the oxidizing chemical reagent and was persistance to the acid or alkali hydrolysises. 4. The major oxygen-containing functional groups in humic substances of volcanic ash soils were phenolic-OH alcoholic-OH and carboxyl groups while those in mineral soil were methoxyl and carbonyl groups. 5. Absorption spectra of alkaline solution of humic acid ranged from 200 nm to maxima 500 nm. Visible spectra peaks of from humic substances in the visible region were recognized at 350, 420, 450 and 480 nm. Only one single absorbance peak was observed in the visible region at 362 nm for Heugag series and two absorbance Peak were also at 360 nm and 390 nm for Yeungrag series. 6. Evolution of carbon as $Co_2$ was increased with addition of Na-pyrophosphate in Namweon and Heugag series, and "priming effects" took place on the soil organic matter decomposition by addition of rice straw with albumin in Ido series.

• Molecules and Cells
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• v.21 no.1
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• pp.52-62
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• 2006

In previous reports we demonstrated that ginsenosides, active ingredients of Panax ginseng, affect some subsets of voltage-dependent $Ca^{2+}$ channels in neuronal cells expressed in Xenopus laevis oocytes. However, the major component(s) of ginseng that affect cloned $Ca^{2+}$ channel subtypes such as ${\alpha}_{1C}$(L)-, ${\alpha}_{1B}$(N)-, ${\alpha}_{1A}$(P/Q)-, ${\alpha}_{1E}$(R)- and ${\alpha}_{1G}$(T) have not been identified. Here, we used the two-microelectrode voltage clamp technique to characterize the effects of ginsenosides and ginsenoside metabolites on $Ba^{2+}$ currents ($I_{Ba}$) in Xenopus oocytes expressing five different $Ca^{2+}$ channel subtypes. Exposure to ginseng total saponins (GTS) induced voltage-dependent, dose-dependent and reversible inhibition of the five channel subtypes, with particularly strong inhibition of the ${\alpha}_{1G}$-type. Of the various ginsenosides, $Rb_1$, Rc, Re, Rf, $Rg_1$, $Rg_3$, and $Rh_2$, ginsenoside $Rg_3$ also inhibited all five channel subtypes and ginsenoside $Rh_2$ had most effect on the ${\alpha}_{1C}$- and ${\alpha}_{1E}$-type $Ca^{2+}$ channels. Compound K (CK), a protopanaxadiol ginsenoside metabolite, strongly inhibited only the ${\alpha}_{1G}$-type of $Ca^{2+}$ channel, whereas M4, a protopanaxatriol ginsenoside metabolite, had almost no effect on any of the channels. $Rg_3$, $Rh_2$, and CK shifted the steady-state activation curves but not the inactivation curves in the depolarizing direction in the ${\alpha}_{1B}$- and ${\alpha}_{1A}$-types. These results reveal that $Rg_3$, $Rh_2$ and CK are the major inhibitors of $Ca^{2+}$ channels in Panax ginseng, and that they show some $Ca^{2+}$ channel selectivity.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.