• Journal of Gastric Cancer
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• v.5 no.1
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• pp.16-22
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• 2005

Purpose: In Korea, the number of laparoscopy-assisted distal gastrectomies for early gastric cancer patients has been increasing lately. Although minimally invasive surgery is more beneficial, no reported case of a totally laparoscopic distal gastrectomy has been reported because of difficulty with intracorporeal anastomosis. This study attempts, through our experiences, to determine the feasibility of a totally laparoscopic distal gastrectomy using an intracorporeal gastroduodenostomy in treating early gastric carcinoma. Materials and Methods: We investigated surgical results and clinicopatholgic characteristics of eight(8) patients with an early gastric carcinoma who underwent a totally laparoscopic distal gastrectomy at the Department of Surgery, Our Lady of Mercy Hospital, The Catholic University of Korea, between June 2004 and September 2004. The intracorporeal gastroduodenostomy was performed with a delta-shaped ananstomosis by using only laparoscopic linear staplers (Endocutter 45mm; Ethicon Endosurgery, OH, USA). Results: The operative time was $369.4\pm62.5$ minutes (range $275\∼465$ minutes), and the anastomotic time was 45.1\pm14.4$ minutes (range $32\∼70$ minutes). The anastomotic time was shortened as surgical experience was gained. The number of laparoscopic linear staplers for an operation was $7.1\pm0.6$. The number of lymph nodes harvested was $31.9\pm13.1$. There was 1 case of transfusion and no case of conversion to an open procedure. The time to the first flatus was 2.8$\pm$0.5 days, and the time to the first food intake was $4.1\pm0.8$ days. There were no early postoperative complications, and the postoperative hospital stay was $10.0\pm3.9$ days. Conclusion: A totally laparoscopic distal gastrectomy using an intracorporeal gastroduodenostomy with a delta-shaped anastomosis is technically feasible and can maximize the benefit of laparoscopic surgery for early gastric cancer.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• 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.

• Radiation Oncology Journal
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• v.16 no.1
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• pp.17-25
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• 1998

Purpose : Although local recurrence rates of stomach cancer after radiocal surgery have been reported in the range of $30-70\%$, the role of postoperative adjuvant therapy has not been established. We report the result of radiotherapy in resected stomach cancer with positive surgical margin to elucidate the role of postoperative radiotherapy. Materials and Methods : From June 1991 to August 1996, twenty five patients with positive surgical margins after radical gastrectomy were treated with postoperative radiotherapy and chemotherapy. Median dose of radiation was 55.8Gy and the range was 44.6-59.4Gy. Second cycle of chemotherapy was delivered concurrently with radiation and total number of six cycles were delivered. Twenty three had adenocarcinoma and the other two had leiornyosarcoma. The numbers of patients with stage I B, II, III A, III B, and IV were 1, 2, 11, 10 and 1 respectively. Positive margins at distal end of the stomach were in 17 patients and proximal in 5. The other three patients had positive margin at the sites of adjacent organ invasion Minimum and median follow-up periods were 12 months and 18 months, respectively, Results : Twenty-four of 25 patients received prescribed radiation dose and RTOG grade 3 toxicity of UGI tract was observed in 3, all of which were weight loss more than $15\%$ of their pretreatment weight. But hematemesis. melena, intestinal obstruction or grade 4 toxicity were not found. Locoregional failure within the radiation field was observed in 7 patients, and distant metastasis in 10 patients. Sites of locoregional recurrences involve anastomosis/remnant stomach in 3, tumor bed/duodenal stump in 3, regional lymph node in 1 patient Peritoneal seeding occurred in 6, liver metastases months and median disease free survival time was 26 months. Stages andradiation dose were not significant prognostic factors for locoregional in 2, and distant nodes in 2 patients. Four year disease specificsurvival rate was $40\%$ and disease free survival was $48\%$. Median survival was 35 failures. Conculsion : Although all patients in this study had positive surgical margins, locoregional failure rate was $28\%$, and 4 year disease specific survival rate was $40\%$. Considering small number of patients and relatively short follow-up period, it is not certain that postoperative radiotherapy lowered locoregional recurrences. but we could find a Possibility of the role of postoperative radiotherapy in Patients with high risk factors.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.26 no.1
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• pp.56-68
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• 1981

A maize line was selected in 1979 among 1000 Korean local maize lines collected in 1977. The selected maize line was characterized by having three to four tillers and eight to 10 ears on each individual plant. The line was assumed to have a great potential as a silage crop. The investigation was conducted as one of the serial studies on the Korean maize collected lines to provide basic information on the genetic variabilities of the multi-eared and tillered (MET) line and on other agronomic characters, prior to use the line as material for future breeding works for silage crop. The MET line and Suwon #19, single cross hybrid, as check variety were planted on May 1, 15 and 30, in three different levels of plant populations. The results obtained were summarized as follows: 1. The genetic variabilities of multi-ear and tillering habits were greater than environmental variabilities. 2. Total dry leaf weight of individual plant of MET line was also significantly higher than that of Suwon #19. 3. The mean number of tillers and ears bearing on the individual plant of MET line varied greatly with plant densities. The number of tillers and ears was on the average 2.9 and 7.0, respectively, when planted in 60cm. by 60cm. 4. The total dry matter and dried stem weight of the individual plant on MET line were comparable to those of Suwon #19. 5. The kernel weight from the individual plant of MET line was 5 to 40% less than that of Suwon #19, depending upon the plant densities. 6. The Kernel to stover ratio was higher for Suwon #19 than for the MET line. (41% to 35%). 7. The MET line had shown first tiller two weeks after planted on May 1. The second and third tillers appeared three to five days after the appearance of the first tiller. 8. The MET line was very specific in tillering habits. All the tillers were borne on the first few nodes of main stem below the soil surface. 9. The tillering habits of MET line were vigorous in the early part of the growing season, but less vigorous in the later part of the growing season. The number of efficient tillers bearing useable ears, was around two to three, when planted in 60cm. by 60cm. 10. The difference of plant height between main stem and first few tillers was around 10cm. 11. The ear size of MET line was around one-third of the major corn belt hybrids. The shape of ear of MET line was conical, with different diameter. 12. The kernel of the MET line was flinty with small soft starch patches on the endosperm part. 13. The 100 kernel weight was around 15gr., which is about one half of the major high yielding hybrids. 14. The ear height of MET line was comparatively higher than that of Suwon #19. 15. Significantly high and positive phenotypic correlation coefficients were obtained among major plant characters. 16. The growth rate of MET line was slower than that of Suwon #19. 17. MET line and Suwon #19 were both heavily infected with black streaked mosaic virus.

• Korean Journal of Breeding Science
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• v.51 no.4
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• pp.496-503
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• 2019

The soybean cultivar, 'Saegeum', has been developed for preparing soy-paste and tofu. The soybean cultivars 'Daepung' and 'SS98207-3SSD-168' were crossed in 2003 to obtain 'Saegeum'. Single seed descent method was used to advance the generation from F₃ to F₅, and the plant lines with promising traits were selected from F₆ to F₇ by pedigree method. The preliminary yield (PYT) and advanced yield trials (AYT) were conducted from 2009 to 2010, and the regional yield trial (RYT) was conducted in 12 regions between 2011 and 2013. The morphological characteristics of 'Saegeum' were as follows: determinate plant type, white flower, tawny pubescence color, and brown pod color. Flowering and maturity dates were August 2, XXXX and October 17, XXXX, respectively. Plant height, first pod height, number of nodes, number of branches, and number of pods were 79 cm, 18 cm, 16, 2.3, and 44, respectively. The seed characteristics of 'Saegeum' were as follows: yellow spherical shape, yellow hilum, and the 100-seed weight was 25.4 g. 'Saegeum' was resistant to bacterial pustule and SMV in the field test, and its lodging resistance was mildly strong, whereas its shattering resistance was excellent. The ability of this cultivar to be processed into tofu, soybean malt, and other fermented products was comparable with that of 'Daewonkong'. The yield of 'Saegeum' in the adaptable regions was 3.02 ton ha^-1. Thus, 'Saegeum' is adaptable to mechanized harvesting because of its high first pod height, as well as lodging and shattering resistance. (Registration number: 5929)

• Korean Journal of Agricultural Science
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• v.12 no.2
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• pp.206-241
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• 1985

Attempts to search infection period, infection speed in the tissue of neck blast of rice plant, location of inoculum source and effects of several conditions about the leaf sheath of rice plants for neck blast incidence have been made. 1. The most infectious period for neck blast incidence was the booting stage just before heading date, and most of necks have been infected during the booting stage and on heading date. But $Indica{\times}Japonica$ hybrid varieties had shown always high possibility for infection after booting stage. 2. Incubation period for neck blast of rice plants under natural conditions had rather a long period ranging from 10 to 22 days. Under artificial inoculation condition incubation period in the young panicle was shorter than in the old panicle. Panicles that emerged from the sheath of flag leaf had long incubation period, with a low infection rate and they also shown slow infection speed in the tissue. 3. Considering the incubation period of neck blast of rice plant, we assumed that the most effective application periods of chemicals are 5-10 days for immediate effective chemicals and 10-15 days for slow effective chemicals before heading. 4. Infiltration of conidia into the leaf sheath of rice plant carried out by saturation effect with water through the suture of the upper three leaves. The number of conidia observed in the leaf sheath during the booting stage were higher than those in the leaf sheath during other stages. Ligule had protected to infiltrate of conidia into the leaf sheath. 5. When conidia were infiltrated into the leaf sheath, the highest number of attached conidia was observed on the panicle base and panicle axis with hairs and degenerated panicle, which seemed to promote the infection of neck blast. 6. The lowest spore concentration for neck blast incidence was variable with rice varietal groups. $Indica{\times}Japonica$ hybrid varieties were infected easily compared to the Japonica type varieties, especially. The number of spores for neck blast incidence in $Indica{\times}Japonica$ hybrid varieties was less than 100 and disease index was higher also in $Indica{\times}Japonica$ hybrid than in Japonica type varieties. 7. Nitrogen content and silicate content were related with blast incidence in necks of rice plants in the different growing stage changed during growing period. Nitrogen content increased from booting stage to heading date and then decreased gradually as time passes. Silicate content increased from booting stage after heading with time. Change of these content promoted to increase neck blast infection. 8. Conidia moved to rice plant by ascending and desending dispersal and then attached on the rice plant. Conidia transfered horizontally was found very negligible. So we presumed that infection rate of neck blast was very low after emergence of panicle base from the leaf sheath. Also ascending air current by temperature difference between upper and lower side of rice plant seemed to increase the liberation of spores. 9. Conidial number of the blast fungus collected just before and after heading date was closely related with neck blast incidence. Lesions on three leaves from the top were closely related with neck blast incidence, because they had high potential for conidia formation of rice blast fungus and they were direct inoculum sources for neck blast. 10. The condition inside the leaf sheath was very favorable for the incidence of neck blast and the neck blast incidence in the leaf sheath increased as the level of fertilizer applied increased. Therefore, the infection rate of neck blast on the all panicle parts such as panicle base, panicle branches, spikelets, nodes, and internodes inside the leaf sheath didn't show differences due to varietal resistance or fertilizers applied. 11. Except for others among dominant species of fungi in the leaf sheath, only Gerlachia oryzae appeared to promote incidence of neck blast. It was assumed that days for heading of varieties were related with neck blast incidence.