• Journal of Science and Technology Studies
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• v.19 no.3
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• pp.51-117
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• 2019

On 15th November 2017, the coastal city of Pohang, located in the Southeastern part of South Korea was shaken by a magnitude 5.4 earthquake. The earthquake displaced more than 1,700 residents and caused more than $ 300 million dollars of economic loss. It was the second most damaging earthquake in the history of Korea. Soon after the earthquake, a group of scientists raised a possible link between the first Enhanced Geothermal System (EGS) project and the earthquake. At the same time, another group of scientists put forward a different hypothesis of the causation of the earthquake claiming that it was caused by the geological movements that were initiated by the Great Tohoku Earthquake in 2011. Since then, there were scientific debates between the two different groups of scientists. The scientific debate on the causation of the earthquake has been concluded temporarily by the Research Investigatory Committee on the Pohang Earthquake in 2019. The research committee concluded that the earthquake was caused by the Pohang EGS system: this means that the earthquake can be defined not as a natural earthquake, but as an artificially triggered earthquake. This article is to examine the Pohang earthquake can be defined as an Anthropocenic event. The newly suggested concept, the Anthropocene is a relatively novel term to classify the earthly strata and their relationship to geological time. The current geological period should be defined by human activities and man-made earthly environment. Although the term is basically related to geological classification, the Anthropocene has been widely debated amongst humanist and social science scholars. The current disastrous situation of our planet also implies with the Anthropocene. This paper is to discuss how to understand anthropogenic events. In particular, the paper pays attention to two different scholarly positions on the Anthropocene: Isabelle Stenger's Gaia theory and Barbara Herrnstein Smith's relativist theory. The former focuses on the earthly inevitable catastrophe of Anthropocene while the latter suggests to situate and contextualise anthropogenic events. On the basis of the theoretical positions, the article is to analyse how the Pohang earthquake can be located and situated.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.28-42
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• 2016

Objectives : The author tried to find out reasons why and how hysteria(and conversion disorder) patient numbers, which were so prevalent even a few decades ago, have decreased and the phenotype of symptoms have changed. Methods : The number of visiting patients diagnosed with conversion disorder and their phenotype of symptoms were investigated through chart reviews in a psychiatric department of a University hospital for the last 12 years. Additionally, the characteristics of conversion disorder patients visiting the emergency room for last 2 years were also reviewed. Those results were compared with previous research results even if it seemed to be an indirect comparisons. The research relied on Briquet P. and Charcot JM's established factors of the vicissitudes of hysteria(and conversion disorder) which has been the framework for more than one hundred and fifty years since hysteria has been investigated. Results : The author found decreased numbers and changes of the phenotype of the hysteria patients(and conversion disorder) over the last several decades. The decreased numbers and changes of the symptoms of those seemed to be partly due to several issues. These issues include the development of the diagnostic techniques to identify organic causes of hysteria, repeated changes to the symptom descriptions and diagnostic classification, changes of the brain nervous functions in response to negative emotions, and the influence of human evolution. Conclusions : The author proposed that the evolutionary brain discord reaction theory explains the causes of disappearance of and changes to symptoms of hysteria(conversion disorder). Most patients with hysteria(conversion disorder) have been diagnosed in the neurological department. For providing more appropriate treatment and minimizing physical disabilities to those patients, psychiatrists should have a major role in cooperating not only with primary care physicians but with neurologists. The term 'hysteria' which had been used long ago should be revived and used as a term to describe diseases such as somatic symptom disorder, functional neurological symptoms, somatization, and somatoform disorders, all of which represent almost the same vague concept as hysteria.

• Korean Journal of Heritage: History & Science
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• v.38
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• pp.129-156
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• 2005

Increasing discovery cases of underground storage facilities made of earth, wood, or stone are being reported from the recent excavation survey of the Baekje relics. Accordingly, the purpose of this study is to examine the structure and function of the underground storage facilities of Baekje following a classification made by the type and building method as follows: plask shape, wooden box shape, and stone box shape. The plask shape storage is the most representative underground storage of Baekje that has been found in numerous relics more than 600 sets around Hangang(Han River) and Geumgang(Geum River) from the Hansung period to Sabi period in Baekje Dynasty. It is a historical artefact as a part of the unique storage culture of Baekje around Hangang and Geumgang from the 3rd to 7th Century. Considering its structure and the example of Chinese one, it might had been used for a long-term storage of grains and various other items including earth wares. The storage facility in wooden box shape and stone box shape are found mostly in the relics Of Sabi period. Thus it might had taken some functions of the storage in traditional pouch shape which had decreased after the 6th Century. In particular, the wooden box shape and stone box shape storage required enormous labor force to build owing to their structure and building method. Thus, they were considered to had been used for official purposes in province fortress and citadel artefact. The wooden box shape storage facility is classified into flat rectangular type and square type based on the structure, and into Gagu type(架構式) and Juheol type(柱穴式) based on the building method. It might had been decided according to the geography and geological feature of the place where the storage was to be built. Considering the examples of Gwanbuk-ri relics and Weolpyong-dong relics, the wooden box shape storage facility might had been used for various items depending on the needs, including foods such as fruits and essential provisions at the military base. Considering the long-term food storage, the examples in Japan, and the functional characteristics of the underground storage facility, there is a possibility that the wooden and stone box shape storage facilities had been built so as to safely store important items in case of fire. This study is only a rudimentary examination for the storage facility in Baekje. Thus further studies are to be made specifically and comprehensively on the comparison with other regions, distribution pattern, discovered relics and artefacts, and functions.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Gastric Cancer
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• v.6 no.3
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• pp.154-160
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• 2006

Purpose: According to the 2nd English Edition of the Japanese Gastric Cancer Association (JGCA) in 1998, in case of distal gastric cancer, the 14v (superior mesenteric vein) lymph node (LN) is included in the N2 group. However, in Korea, a modified radical gastrectomy is performed, and a 14v LN dissection is not done as a routine procedure. Thus, we investigated the rate of metastatic 14v LNs, evaluated the necessity of dissection of the 14v LN, and searched for indications of 14v LN dissection. Materials and Methods: From April 2004 to August 2005, we enrolled the patients who were diagnosed as having advanced gastric cancer in the distal third portion of the stomach. We peformed a distal gastrectomy with D2 lymph node dissection as defined in the 2nd English edition of the JGCA classification. We calculated the positive rate of metastatic LNs of each station and analyzed the relationship between the positive rates of No.6 LNs and 14v LNs. We also compared the positive 14v LN group with the negative 14v LN group. Results: The total number of patients was 50, the mean age was 56 (range $30{\sim}80$) years, and sex ratio (Male/Female) was 1.63 : 1. In 47 (94%) cases, distal a gastrectomy with gastroduodenostomy was done, and in the remaining 3 (6%) cases, a distal gastrectomy with gastrojejunostomy was done. The most frequently metastatic LNs were nos. 3 and 6 (54%). The metastatic rate of the f4v LN was 10%, which was similar to that of LN no. 9. In the comparison of the 14v positive group with the 14v negative group, there were significant differences in the numbers of metastatic LNs (mean 25.4 vs 4.91, P<0.001) and the numbers of metastatic no. 6 LNs, (mean 6.8 vs 1.42, P<0.001), and if no. 6 LNs were metastatic, the possibility of metastasis to the 14v LN was 19.2%. In the 14v positive group, all cases were more than stage 3 by the UICC 6th edition. Conclusion: In cases of advanced cancer with metastasis to the no. 6 IN, there was a good chance of metastasis to the 14v LN. Thus, in the operative field, if the tumor is advanced to more than stage 3 by the UICC classification and the no. 6 LN is metastatic, a 14v LN dissection is necessary. However, the usefulness of a 14v LN dissection should be evaluated prospectively through an analysis of tumor recurrence and long-term survival.

• 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.

• Asia Marketing Journal
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• v.14 no.2
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• pp.157-176
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• 2012

Brand switching data frequently used in market structure analysis is adequate to analyze non- durable goods, because it can capture competition between specific two brands. But brand switching data sometimes can not be used to analyze goods like automobiles having long term duration because one of main assumptions that consumer preference toward brand attributes is not changed against time can be violated. Therefore a new type of data which can precisely capture competition among durable goods is needed. Another problem of using brand switching data collected from actual purchase behavior is short of explanation why consumers consider different set of brands. Considering above problems, main purpose of this study is to analyze market structure for durable goods with consideration set. The author uses exploratory approach and latent class clustering to identify market structure based on heterogeneous consideration set among consumers. Then the relationship between some factors and consideration set formation is analyzed. Some benefits and two demographic variables - age and income - are selected as factors based on consumer behavior theory. The author analyzed USA automotive market with top 11 brands using exploratory approach and latent class clustering. 2,500 respondents are randomly selected from the total sample and used for analysis. Six models concerning market structure are established to test. Model 1 means non-structured market and model 6 means market structure composed of six sub-markets. It is exploratory approach because any hypothetical market structure is not defined. The result showed that model 1 is insufficient to fit data. It implies that USA automotive market is a structured market. Model 3 with three market structures is significant and identified as the optimal market structure in USA automotive market. Three sub markets are named as USA brands, Asian Brands, and European Brands. And it implies that country of origin effect may exist in USA automotive market. Comparison between modal classification by derived market structures and probabilistic classification by research model was conducted to test how model 3 can correctly classify respondents. The model classify 97% of respondents exactly. The result of this study is different from those of previous research. Previous research used confirmatory approach. Car type and price were chosen as criteria for market structuring and car type-price structure was revealed as the optimal structure for USA automotive market. But this research used exploratory approach without hypothetical market structures. It is not concluded yet which approach is superior. For confirmatory approach, hypothetical market structures should be established exhaustively, because the optimal market structure is selected among hypothetical structures. On the other hand, exploratory approach has a potential problem that validity for derived optimal market structure is somewhat difficult to verify. There also exist market boundary difference between this research and previous research. While previous research analyzed seven car brands, this research analyzed eleven car brands. Both researches seemed to represent entire car market, because cumulative market shares for analyzed brands exceeds 50%. But market boundary difference might affect the different results. Though both researches showed different results, it is obvious that country of origin effect among brands should be considered as important criteria to analyze USA automotive market structure. This research tried to explain heterogeneity of consideration sets among consumers using benefits and two demographic factors, sex and income. Benefit works as a key variable for consumer decision process, and also works as an important criterion in market segmentation. Three factors - trust/safety, image/fun to drive, and economy - are identified among nine benefit related measure. Then the relationship between market structures and independent variables is analyzed using multinomial regression. Independent variables are three benefit factors and two demographic factors. The result showed that all independent variables can be used to explain why there exist different market structures in USA automotive market. For example, a male consumer who perceives all benefits important and has lower income tends to consider domestic brands more than European brands. And the result also showed benefits, sex, and income have an effect to consideration set formation. Though it is generally perceived that a consumer who has higher income is likely to purchase a high priced car, it is notable that American consumers perceived benefits of domestic brands much positive regardless of income. Male consumers especially showed higher loyalty for domestic brands. Managerial implications of this research are as follow. Though implication may be confined to the USA automotive market, the effect of sex on automotive buying behavior should be analyzed. The automotive market is traditionally conceived as male consumers oriented market. But the proportion of female consumers has grown over the years in the automotive market. It is natural outcome that Volvo and Hyundai motors recently developed new cars which are targeted for women market. Secondly, the model used in this research can be applied easier than that of previous researches. Exploratory approach has many advantages except difficulty to apply for practice, because it tends to accompany with complicated model and to require various types of data. The data needed for the model in this research are a few items such as purchased brands, consideration set, some benefits, and some demographic factors and easy to collect from consumers.

• Journal of Chest Surgery
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• v.36 no.9
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• pp.651-658
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• 2003

Since the first implanted in September 1997, the use of On-X prosthetic heart valve has been increasing around in the world. This study was designed to assess the feasibility, safety, and the postoperative hemodynamics with this new valve in clinical setting. Material and Method: The current study was carried out on 52 patients undergoing aortic valve replacement with this prosthesis between April 1999 to August 2002 at Chonnam National University Hospital to evaluate the surgical results. 52％ of the patients were male and the average age at implant was 50$\pm$13 years. The study followed the guidelines of the AATS/STS. Preoperatively, 32(61.5％) patients were in NYHA functional class III or IV and 2 patients had previous aortic valve surgery. Concomitant cardiac surgery was performed in 71.1％. The implanted valve sizes were 19 mm in 13 patients, 21 mm in 26, 23 mm in 10 and 25 mm in 3, respectively. Mean follow-up was 16.6$\pm$10.5 months (1∼39 months). Echocardiographic assessment was performed pre- and immediate postoperatively, as well as 3, 6, 12 months after surgery, evaluating pressure loss and regression of left ventricular hypertrophy. Result: Mean cardiopulmonary bypass time was 191$\pm$94.7 minutes with an aortic cross-clamp time of 142$\pm$51.7 minutes. There was no early and late mortality, Freedom from adverse events at 1 year in the study were as follows: thromboembolism, 95.6$\pm$6％; bleeding events, 90.2$\pm$4％; paravalvular leakage 92.3$\pm$4％; and overall valve-related morbidity at 1 year was 76.6$\pm$3％. There were no cases of valve thrombosis, prosthetic valve endocarditis and structural or non-structural failure. Left ventricular function at 12 months after surgery (EF=62.7$\pm$9.8％) revealed a statistically significant improvement compared to preoperative investigation (EF=55.8$\pm$15.9％, p=0.006). Left ventricular mass index was 247.3$\pm$122.3 g/$m^2$ on preoperative echocardiographic study, but regressed to 155.5$\pm$58.2 g/$m^2$ at postoperative 1 year (p=0.002). Over the follow-up period a further decrease of peak transvalvular gradients was observed in all patients: 62.5$\pm$38.0 mmHg on preoperative assessment, 18.2$\pm$6.8 mmHg at immediate postoperative period (p < 0.0001), 7.6$\pm$5.09 mmHg (p<0.0001) at 6 month, 18.0$\pm$10.8 mmHg (p<0.0001) at 1 year. Conclusion: The On-X prosthetic heart valve performs satisfactorily in the first 1 year period. Clinical outcome by examining NYHA functional classification revealed especially good results. Effective regression of left ventricular hypertrophy and statistically significant decrease of transvalvular gradient were observed over the first year, but longer-term follow-up of this patient group is needed to establish the expected rates for late valve-related events as well as the long-term clinical efficacy of this valve.