• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.19-41
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• 2016

Recently, the proliferation of mobile devices such as smartphones and tablet personal computers and the development of information communication technologies (ICT) have led to a big trend of a shift from single-channel shopping to multi-channel shopping. With the emergence of a "smart" group of consumers who want to shop in more reasonable and convenient ways, the boundaries apparently dividing online and offline shopping have collapsed and blurred more than ever before. Thus, there is now fierce competition between online and offline channels. Ever since the emergence of online shopping, a major type of multi-channel shopping has been "showrooming," where consumers visit offline stores to examine products before buying them online. However, because of the growing use of smart devices and the counterattack of offline retailers represented by omni-channel marketing strategies, one of the latest huge trends of shopping is "webrooming," where consumers visit online stores to examine products before buying them offline. This has become a threat to online retailers. In this situation, although it is very important to examine the influencing factors for switching from online shopping to webrooming, most prior studies have mainly focused on a single- or multi-channel shopping pattern. Therefore, this study thoroughly investigated the influencing factors on customers switching from online shopping to webrooming in terms of both the "search" and "purchase" processes through the application of a push-pull-mooring (PPM) framework. In order to test the research model, 280 individual samples were gathered from undergraduate and graduate students who had actual experience with webrooming. The results of the structural equation model (SEM) test revealed that the "pull" effect is strongest on the webrooming intention rather than the "push" or "mooring" effects. This proves a significant relationship between "attractiveness of webrooming" and "webrooming intention." In addition, the results showed that both the "perceived risk of online search" and "perceived risk of online purchase" significantly affect "distrust of online shopping." Similarly, both "perceived benefit of multi-channel search" and "perceived benefit of offline purchase" were found to have significant effects on "attractiveness of webrooming" were also found. Furthermore, the results indicated that "online purchase habit" is the only influencing factor that leads to "online shopping lock-in." The theoretical implications of the study are as follows. First, by examining the multi-channel shopping phenomenon from the perspective of "shopping switching" from online shopping to webrooming, this study complements the limits of the "channel switching" perspective, represented by multi-channel freeriding studies that merely focused on customers' channel switching behaviors from one to another. While extant studies with a channel switching perspective have focused on only one type of multi-channel shopping, where consumers just move from one particular channel to different channels, a study with a shopping switching perspective has the advantage of comprehensively investigating how consumers choose and navigate among diverse types of single- or multi-channel shopping alternatives. In this study, only limited shopping switching behavior from online shopping to webrooming was examined; however, the results should explain various phenomena in a more comprehensive manner from the perspective of shopping switching. Second, this study extends the scope of application of the push-pull-mooring framework, which is quite commonly used in marketing research to explain consumers' product switching behaviors. Through the application of this framework, it is hoped that more diverse shopping switching behaviors can be examined in future research. This study can serve a stepping stone for future studies. One of the most important practical implications of the study is that it may help single- and multi-channel retailers develop more specific customer strategies by revealing the influencing factors of webrooming intention from online shopping. For example, online single-channel retailers can ease the distrust of online shopping to prevent consumers from churning by reducing the perceived risk in terms of online search and purchase. On the other hand, offline retailers can develop specific strategies to increase the attractiveness of webrooming by letting customers perceive the benefits of multi-channel search or offline purchase. Although this study focused only on customers switching from online shopping to webrooming, the results can be expanded to various types of shopping switching behaviors embedded in single- and multi-channel shopping environments, such as showrooming and mobile shopping.

• Korean Journal of Agricultural Science
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• v.13 no.1
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• pp.113-122
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• 1986

In order to promote mechanization of corn harvesting in Korea, this study was conducted to find out the effect of moisture content on compressive properties such as force, deformation, energy and modulus of stiffness to the bioyield and the rupture point for Korean corn kernel. In this study, the loading positions of corn were flat, edge, longitude and the moisture contents were about 13, 17, 21, 25% in wet basis. The compression test was carreied out with flat plate by use of dynamic straingage for three varieties of Korean corn under quasi-static force when the loading rate was 1.125mm/min. The results of this study are summarized as follows; 1. When the moisture content of corn ranged from 12.5 to 24.5 percent, at flat position, the bioyied force was in the range of 13.63-26.73 kg and the maximum compressive strength was in the range of 21.55-47.65kg. Their values were reached minimum at about 17% and maximum at about 21% moisture content. The bioyield force was in the range of 13.58-6.70kg at edge position and the maximum compressive strength which was 16.42 to 7.82kg at edge position was lower than that which was 18.55-9.05kg at longitudinal position. 2. Deformation of corn varied from 0.43 to 1.37 mm at bioyield point and from 0.70 to 2.66mm at rupture point between 12.5 to 24.5% moisture content. As the moisture content increased, deformation was increased. 3. The moduli of resilience and toughness of corn ranged from 2.60 to 8.57kg. mm and from 6.41 to 34.36kg. mm when the moisture content ranged from 12.5 to 24.5 percent, respectively. As the moisture content increased, the modulus of toughness was increased at edge position and decreased at longitudinal position. And their values were equal each other at 22-23% moisture content. 4. The modulus of stiffness was decreased with increase in the moisture content. Its values ranged from 32.07 to 5.86 kg/mm at edge position and from 42.12 to 18.68kg/mm at flat position, respectively. Also, the values of Suweon 19 were higher than those of Buyeo. 5. It was considered that the compressive properties of corn at flat position were more important on the design data for corn harvesting and processing machinery than those of edge or longitudinal position. Also, grinding energy would be minimized when a corn was processed between about 12.5 to 17% moisture content and corn damage would be reduced when a corn was handled between about 19 to 24% moisture content in wet basis.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.1
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• pp.77-91
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• 1996

In order to elucidate the biological etiology and the effects of comorbidity on biological variables in tic disorders, plasma serotonin (5-hydroxlfryptamine, 5-HT) and 5-hydroxy- indoleacetic acid (5-HIAA) we.e measured in 87 tic disorders and 30 control subjects. The 87 tic disorder were composed of 45 Tourette's disorder(TS), 22 chronic motor tic disorders (CMT) and 20 transient tic disorders (TTD). Among these patients,43 patients were pure tic disorder (PT), 28 subject also had attention deficit hyperactivity disorder (T+ADHD) and 16 subjects had obsessive compulsive disorders (T+ OCD) as comorbid disorders. The results are summarized as follows : 1) Plasma 5-HT levels showed significant positive correlations with plasma 5-HIAA levels (Pennon r=0.77, p<0.05). 2) Plasma 5-HT and 5-HIAA levels showed no significant correlation with age in tic disorders. 3) Plasma 5-HIAA and 5-HT levels showed no significant correlations with age in control subjects. 4) There was significant difference in plasma 5-HT levels among TS, CMT, TTD and control groups (ANOVA F=34.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and ITD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TTD groups. 5) There was significant difference in plasma 5-HIAA levels among TS, CMT, TTD and control groups (ANOVA F=26.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and TTD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TID groups.f) There was significant difference in plasma 5-HT and 5-HIAA levels among PT, T+ADHD, T+OCD and contol groups (ANOVA 5-HT, F=37.59, df=3, 113, p<0.01, 5-HIAA, F=27.37, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed signiscant differences between control and PT, control and T+ADHD and control and T+OCB. But, post-hoc test showed no significant differences between PT and T+ADHD, PT and T+ OCD and T+ADHD and T+ OCD. These results show that decreased 5-HT and 5-HIAA levels may play a role in the genesis of tic disorders, but these findings have no significant correlations with the severity of tic disorders. And the comorbid disorders of tics may have minimal effects on the biochemical abnormalities. Future studies must be focused on the effects of serotonin agonists and antagonists on tic disorders and molecular biological methodology may enhance to elucidate the mechanisms of these abnormal findings.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.165-183
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• 2016

Over the past decade, there has been a rapid diffusion of technological devices and a rising number of various devices, resulting in an escalation of virtual reality technology. Technological market has rapidly been changed from smartphone to wearable devices based on virtual reality. Virtual reality can make users feel real situation through sensing interaction, voice, motion capture and so on. Facebook.com, Google, Samsung, LG, Sony and so on have investigated developing platform of virtual reality. the pricing of virtual reality devices also had decreased into 30% from their launched period. Thus market infrastructure in virtual reality have rapidly been developed to crease marketplace. However, most consumers recognize that virtual reality is not ease to purchase or use. That could not lead consumers to positive attitude for devices and purchase the related devices in the early market. Through previous studies related to virtual reality, there are few studies focusing on why the devices for virtual reality stayed in early stage in adoption & diffusion context in the market. Almost previous studies considered the reasons of hard adoption for innovative products in the viewpoints of Typology of Innovation Resistance, MIR(Management of Innovation Resistant), UTAUT & UTAUT2. However, product-based antecedents also important to increase user intention to purchase and use products in the technological market. In this study, we focus on user acceptance and resistance for increasing purchase and usage promotions of wearable devices related to virtual reality based on headgear products like Galaxy Gear. Especially, we added a variables like attitude confidence as a dimension for user resistance. The research questions of this study are follows. First, how attitude confidence and innovativeness resistance affect user intention to use? Second, What factors related to content and brand contexts can affect user intention to use? This research collected data from the participants who have experiences using virtual rality headgears aged between 20s to 50s located in South Korea. In order to collect data, this study used a pilot test and through making face-to-face interviews on three specialists, face validity and content validity were evaluated for the questionnaire validity. Cleansing the data, we dropped some outliers and data of irrelevant papers. Totally, 156 responses were used for testing the suggested hypotheses. Through collecting data, demographics and the relationships among variables were analyzed through conducting structural equation modeling by PLS. The data showed that the sex of respondents who have experience using social commerce sites (male=86(55.1%), female=70(44.9%). The ages of respondents are mostly from 20s (74.4%) to 30s (16.7%). 126 respondents (80.8%) have used virtual reality devices. The results of our model estimation are as follows. With the exception of Hypothesis 1 and 7, which deals with the two relationships between brand awareness to attitude confidence, and quality of content to perceived enjoyment, all of our hypotheses were supported. In compliance with our hypotheses, perceived ease of use (H2) and use innovativeness (H3) were supported with its positively influence for the attitude confidence. This finding indicates that the more ease of use and innovativeness for devices increased, the more users' attitude confidence increased. Perceived price (H4), enjoyment (H5), Quantity of contents (H6) significantly increase user resistance. However, perceived price positively affect user innovativeness resistance meanwhile perceived enjoyment and quantity of contents negatively affect user innovativeness resistance. In addition, aesthetic exterior (H6) was also positively associated with perceived price (p<0.01). Also projection quality (H8) can increase perceived enjoyment (p<0.05). Finally, attitude confidence (H10) increased user intention to use virtual reality devices. however user resistance (H11) negatively affect user intention to use virtual reality devices. The findings of this study show that attitude confidence and user innovativeness resistance differently influence customer intention for using virtual reality devices. There are two distinct characteristic of attitude confidence: perceived ease of use and user innovativeness. This study identified the antecedents of different roles of perceived price (aesthetic exterior) and perceived enjoyment (quality of contents & projection quality). The findings indicated that brand awareness and quality of contents for virtual reality is not formed within virtual reality market yet. Therefore, firms should developed brand awareness for their product in the virtual market to increase market share.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Life Science
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• v.16 no.3 s.76
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• pp.534-539
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• 2006

In the ozone disinfection unit process of a piston type batch reactor with continuous ozone analysis using a flow injection analysis (FIA) system, the CT values for 1 log inactivation of Cryptosporidium parvum by viability assays of DAPI/PI and excystation were $1.8{\sim}2.2\;mg/L{\cdot}min$ at $25^{\circ}C$ and $9.1mg/L{\cdot}min$ at $5^{\circ}C$, respectively. At the low temperature, ozone requirement rises $4{\sim}5$ times higher in order to achieve the same level of disinfection at room temperature. In a 40 L scale pilot plant with continuous flow and constant 5 minutes retention time, disinfection effects were evaluated using excystation, DAPI/PI, and cell infection method at the same time. About 0.2 log inactivation of Cryptosporidium by DAPI/PI and excystation assay, and 1.2 log inactivation by cell infectivity assay were estimated, respectively, at the CT value of about $8mg/L{\cdot}min$. The difference between DAPI/PI and excystation assay was not significant in evaluating CT values of Cryptosporidium by ozone in both experiment of the piston and the pilot reactors. However, there was significant difference between viability assay based on the intact cell wall structure and function and infectivity assay based on the developing oocysts to sporozoites and merozoites in the pilot study. The stage of development should be more sensitive to ozone oxidation than cell wall intactness of oocysts. The difference of CT values estimated by viability assay between two studies may partly come from underestimation of the residual ozone concentration due to the manual monitoring in the pilot study, or the difference of the reactor scale (50 mL vs 40 L) and types (batch vs continuous). Adequate If value to disinfect 1 and 2 log scale of Cryptosporidium in UV irradiation process was 25 $mWs/cm^2$ and 50 $mWs/cm^2$, respectively, at $25^{\circ}C$ by DAPI/PI. At $5^{\circ}C$, 40 $mWs/cm^2$ was required for disinfecting 1 log Cryptosporidium, and 80 $mWs/cm^2$ for disinfecting 2 log Cryptosporidium. It was thought that about 60% increase of If value requirement to compensate for the $20^{\circ}C$ decrease in temperature was due to the low voltage low output lamp letting weaker UV rays occur at lower temperatures.

• Journal of the korean academy of Pediatric Dentistry
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• v.26 no.2
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• pp.399-415
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• 1999

From bacteria to mammalian cells, one of the most important mediators of intracellular signal transduction mechanisms which regulate a variety of intracellular processes is free calcium. In salivary acinar cells, elevation of intracellular calcium concentration ($[Ca^{2+}]_i$) is essential for the salivary secretion induced by parasympathetic stimulation. However, in addition to $[Ca^{2+}]_i$, gap junctions which couple individual cells electrically and chemically have also been reported to regulate enzyme secretion in pancreatic acinar cells. Since the plasma membrane of salivary acinar cells has a high density of gap junctions, and these cells are electrically and chemically coupled with each other, gap junctions may modulate the secretory function of salivary glands. In this respect, I planned to investigate the role of gap junctions in the modulation of salivary secretion and $[Ca^{2+}]_i$, using mandibular salivary glands of rats. In order to measure the salivary flow rate, fluid was collected from the cannulated duct of the isolated perfused rat mandibular glands at 2 min intervals. $[Ca^{2+}]_i$, was measured from the cells loaded with fura-2 by spectrofluorometry. The results obtained were as follows: 1. CCh-induced salivary secretion was reversibly inhibited by 1 mM octanol, a gap junction blocker. 2. CCh-induced increase in $[Ca^{2+}]_i$, was also reversed by the application of 1 mM octanol. 3. Octanol did not block the initial increase in $[Ca^{2+}]_i$ caused by CCh, which suggested that the reduction of $[Ca^{2+}]_i$, caused by gap junction blockade was not resulted from the inhibition of $Ca^{2+}$ release from intracellular $Ca^{2+}$ stores. 4. Addition of octanol during stimulation with $1{\mu}M$ thapsigargin, a potent microsomal ATPase inhibitor, reduced $[Ca^{2+}]_i$, to the basal level. This suggested that inhibition of gap junction permeability closed plasma membrane $Ca^{2+}$ channels. 5. 2,5-di-tert-butyl-1,4-benzohydroquinone (TBQ) generated $[Ca^{2+}]_i$ oscillations resulting from periodic influx of $Ca^{2+}$ via plasma membrane. The TBQ-induced $[Ca^{2+}]_i$ oscillations were stopped by the application of 1mM octanol which implicated that gap junctions modulate the permeability of plasma membrane $Ca^{2+}$ channels. 6. Glycyrrhetinic acid, another well known gap junction blocker, also inhibited CCh-induced salivary secretion from rat mandibular glands. These results suggested that gap junctions play an important role in the modulation of fluid secretion from the rat mandibular glands and this was probably due to the inhibition of $Ca^{2+}$ influx through the plasma membrane $Ca^{2+}$ channels.