• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• 대한공업교육학회지
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• v.32 no.1
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• pp.134-152
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• 2007

This study deals with the development of computer program which can restrict students to access to the unallowable web sites during the Internet based class. Our suggested program can find the student's access list to the unallowable sites, display it on the teacher's computer screen. Through the limitation of the student's access, teacher can enhance the efficiency of class and fulfill his educational purpose for the class. The use of our results leads to the effective and safe utilization of the Internet as the teaching tools in the class. Meanwhile, the typical method is to turn off the LAN (Local Area Network) power in order to limit the student's access to the unallowable web sites. Our program has been developed on the Linux operating systems in the small network environment. The program includes following five functions: the translation function to change the domain name into the IP(Internet Protocol) address, the search function to find the active students' computers, the packet snoop to capture the ongoing packets and investigate their contents, the comparison function to compare the captured packet contents with the predefined access restriction IP address list, and the restriction function to limit the network access when the destination IP address is equal to the IP address in the access restriction list. Our program can capture all passing packets through the computer laboratory in real time and exactly. In addition, it provides teacher's computer screen with the all relation information of students' access to the unallowable sites. Thus, teacher can limit the student's unallowable access immediately. The proposed program can be applied to the small network of the elementary, junior and senior high school. Our research results make a contribution toward the effective class management and the efficient computer laboratory management. The related researches provides teacher with the packet observation and the access limitation for only one host, but our suggested program provides teacher with those for all active hosts.

• Journal of Society of Preventive Korean Medicine
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• v.4 no.2
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• pp.119-141
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• 2000

The current industrial health service is shifting to health improvement business with 1st primary prevention-focused service from secondary and tertiary prevention-focused business, and Oriental medicine can provide such primary prevention-focused service due to the characteristics of its science. In particular, the advanced concept of health improvement can match the science of health care of Oriental medicine. Notably, what is most important in health improvement is our lifestyle, This does not underestimate the socio-environmental factors, which have lessened their importance due to modernism. The approach of Oriental medicine weighs more individuals' lifestyle and health care through self-cultivation. This matches the new model of advanced health business. Oriental medicine is less systemized than Western medicine, but it can provide ample contents that enhance health. If we conceive health-improvement program based on the advantages provided by these two medical systems, this will influence workers to the benefit of their health. Also, health Program needs to define factors that determine individual lives, and to provide information and technologies essential to our lives. The Oriental medicine approach puts more stress on a subject's capabilities than it does on the effect his surrounding environment can have. This needs to be supported theoretically by not only defining the relations between an individual's health state and his lifestyle, but also identifying the degree to which an individual in the industrial work place practices health improvement lifestyle . This is the first step toward initiating health-improvement business . In order to do this, this researcher conducted a survey by taking random samplings from workers, and can draw the following conclusions from it. 1 The sampled group is categorized into', by sender, female 6.6%, and male 93.4%, with males dominant; by marriage status , unmarried 43.9% and married 55.6%, with both similar percentage, and, by age, below 30, 48.4%, between 30 and 39, 27.4%, between 40 and 49, 18.2%, and over 50, 6.0%. The group further is categorized into; by education, middle school or under 1.7%, high school 30.5%, and junior college or higher 65.8% with high school and higher dominant: and by income, below 1.7 million won 24.2%, below 2.4 million won 14.8%, and above 2.4 million 6.3% Still, the group by job is categorized into collegians with 23.9%, office worker with 10.3%, and professionals with 65.8% , and this group does not include workers engaged in production that are needed for this research, but mostly office workers . 2. The subjects selected for this survey show their degree of practicing health-improvement lifestyle at an average of 2.63, health management pattern at 2.64, and health-related awareness at 2.62 The sub-divisions of health-improvement lifestyle show social emotion (2.87), food (2.66). favorite food (2.59), and leisure activities (2.52), in this order for higher points. It further shows health awareness (2.47) and safety awareness (2.40), lower points than those in health management pattern . 3. In the area of using leisure time for health-improvement, males, older people, married, and people with higher income earn higher marks. And, in the area of food management, the older and married earn higher marks . In the area of favorite food management, females, lower-income bracket, and lower-educated show higher degree of practice , while in the area of social emotion management, the older. married, and higher-income bracket show higher marks. In addition, in the area of health awareness, the older, married, and people with higher-income show higher degree of practice. 4. To look at correlation by overall and divisional health-improvement practice degree , this researcher has analyzed the data using Person's correlation coefficient. The lifestyle shows significant correlation with its six sub-divisions, and use of leisure time, food, and health awareness all show significant correlation with their sub-divisions. And. the social emotion and safety awareness show significant correlation with all sub-divisions except favorite food management.

• Horticultural Science & Technology
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• v.28 no.4
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• pp.529-534
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• 2010

Occurrence of wilted symptom in watermelon plant ($Citrullus$ $lanatus$ L.) is known to be caused by physiological disorder. The symptom results in the loss of fruit production and thus the economical loss of watermelon growers. The incidence of symptom is often found from the middle of March to the end of May in the major watermelon crop production areas of Korea (i.e. Uiryeong, Gyeongnam (lat $37^{\circ}$56'64"N, long $126^{\circ}$99'97"E)). Despite of extensive information about the physiological disorder, little study has been conducted to understand a relationship between the wilted symptom and accompanying environment factors (e.g. temperature). This study aimed to investigate effects of environmental conditions amended by a forced-ventilation system on physiological characteristics of watermelon and incidence of the wilted symptom. Watermelon plants were grown from January to May, 2009 with either the forced-or natural-ventilation treatment in a greenhouse located in the Uiryeong. In the result, the forced-ventilation treatment decreased the air, leaf and root-zone temperature approximately $4.5^{\circ}C$, $5^{\circ}C$ and $3^{\circ}C$, respectively, compared to the natural-ventilation. The fruit growth rate was maximized twice during the entire growing period. The higher rate of fruit growth was observed under the natural-ventilation than the forced one. Maximization of the fruit growth rate (approximately 430 g per day) was first observed by 12 days after fruiting under the natural-ventilation treatment, while the second one (approximately 350 g per day) was observed by 24 days after fruiting. The wilted symptom started occurring by 22 days after fruiting under the natural-ventilation, whereas no incidence of the symptom was found under the forced-ventilation treatment. Interestingly, the forced-ventilation lowered the fruit growth rate (approximately 320 g per day) compared to the natural one. Maximization of the fruit growth rate under the forced-ventilation was found at 4 days later than that under the natural one. This result coincided with a slower plant growth under the forced-ventilation treatment. These results suggest that the forced-ventilation slows down extension growth of fruit and plant, which may be associated with lowering leaf temperature and saturation deficit. We suggest the hypothesis that the forced-ventilation may alleviate stress of the wilted symptom by avoiding extreme water evaporation from leaves due to high temperature and thus by reducing competition between leaves and fruits for water. More direct and detailed investigations are needed to confirm the effect of the forced ventilation.

• Journal of Korean Public Health Nursing
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• v.7 no.2
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• pp.53-66
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• 1993

By this time, a few of previous studies of factors related to separation from their jobs and job satisfaction only have dealt with the separation rate. the cause of separation and related factors that induce job satisfaction and incentive factors, the actualities of morale some suggestions for reduction of the separation rate. This study is attempted to determine factors that have effect on job satisfaction of national hospital nurses. and to proide information and materials for the development of the administration of nursing through the appreciation of factors influencing on job satisfaction between isolated ward nurses and general ward nurses working at national hospitals. 185 nurses of national hospitals responsed th this study, and were divided into two groups. Group 1: 57 nurses working at isolated wards for tuberculosis patients and Group 2 : 128 nurses at general wards. Relevant data were collected from August, 5, 1992 through August 20, 1992. The questionnaire consisted of 8 genalized items and 4 items concerning job satisfaction. The collected data were processed with SPSS, and the relationship among vaviables was analyzed by means of $X^2-test$, Pearson Correlation, Multiple Regression. The results of this study are as follows: 1. There is no significant difference between two groups in terms of generalized items. Age distributions show $44.3\%$ under the category of less than 34. and $55.7\%$ under the category more than 35, $19.3%$ was single and $74.6\%$ was married respectively. 2. $79.4\%$ of the nurses have the desire to have in-service education under the satisfactory physical environments such as welfare system, accommodating structures and facilities, instruments or management systems of the hospital, but under the category of unsatisfactory circumstances, $60.3\%$ have the intention of having in- service education. The concern in terms of in-service education shows statistically significant difference between two groups $(X^2=8.85,\;p<0. 05)$. This result accepts the hypothesis that good physical environments could intensify interests in service education. 3. The extent of satisfaction related to psychological environments is heightend according to good physical environments. In result, the hypothesis that the extent of satisfaction in terms of physical environments could raise satisfaction about psychological environment is accepted. 4. In the light of the extent of satisfaction about physical environments, $33.3\%$ of isolated ward nurses are satisfied with physical environments, but only $11.7\%$ of general ward nurses are satisfied. $(X^2=10.88,\; p<0.01)$. This result shows that the satisfaction degree about phusicalenvironments of isolated war nurses was higher than that of general ward nurses in spite of high physical and psychological risks due to exposure to infection. Consequently. the hypothesis was rejected that the satisfaction degree about physical environments would be lower in isolated ward nurses than in general ward nurses. 5. The fact that $87.7%$ of isolated ward nurses took interest in service education and $53.19\%$ of general ward nurses took interest in service education demonstrats that isolated ward nurse have more interest in service education than gerneral ward nurses. The result shows that the hypothesis is accepted that isolated ward nurses would have mor interests in service education than general ward nurses. 6. In the extent of satisfaction about physical environments such as morale human relationship promotion, there is no significant difference between two groups in terms of statistics. The hypothesis is rejected that satisfaction about psychologic environments would be high in isolated ward nurses than in general ward nurses. In conclusion. factors influencing on job satisfaction are considered to have effect on. another, and also interdisciplinary amelioration of factors accompanied with systematic inter cooperative investigation is necessary.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.72-78
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• 2009

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a $5{\times}5km$ grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

• Journal of Life Science
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• v.30 no.11
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• pp.931-938
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• 2020

Translationally controlled tumor protein (TCTP) is one of the most abundant proteins in various eukaryotic organisms. TCTPs play important roles in cell physiological processes in cancer, cell proliferation, gene regulation, and heat shock response. TCTP is also considered an important factor in the resistance to oxidative stress induced by dithiothreitol or hydrogen peroxide (H₂O₂). Arctic calanoid copepods have a variety of antioxidant defense systems to regulate the levels of potentially harmful reactive oxygen species generated by ultraviolet radiation in the Arctic marine ecosystem. However, information on the antioxidant activity of TCTP in the Arctic Calanus glacialis is still scarce. To understand the putative antioxidant function of the Arctic copepod C. glacialis TCTP (Cg-TCTP), its gene was cloned and sequenced. The Cg-TCTP comprised 522 bp and encoded a 174-amino acid putative protein with a calculated molecular weight of ~23 kDa. The recombinant Cg-TCTP (Cg-r TCTP) gene was overexpressed in Escherichia coli (BL21), and Cg-rTCTP-transformed cells were grown in the presence or absence of H₂O₂. Cg-rTCTP-transformed E. coli showed increased tolerance to high H₂O₂ concentrations. Therefore, TCTP may be an important antioxidant protein related to tolerance of the Arctic copepod C. glacialis to oxidative stress in the harsh environment of the Arctic Ocean.