• Journal of Life Science
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• v.34 no.3
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• pp.179-190
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• 2024

Cannabis sativa is a plant widely cultivated worldwide and has been used as a material for food, medicine, building materials and cosmetics. In this study, we assessed the functional effects of C. sativa stem and root extracts using network pharmacology and confirmed their novel functions. The components in stem and root ethanol extracts were identified by gas chromatography-mass spectrometry analysis, and networks between the components and proteins were constructed using the STICHI database. Functional annotation of the proteins was performed using the KEGG pathway. The effects of the extracts were confirmed in lysophosphatidylcholine-induced THP-1 cells using real-time PCR. A total of 21 and 32 components were identified in stem and root extracts, respectively, and 147 and 184 proteins were linked to stem and root components, respectively. KEGG pathway analysis showed that 69 pathways, including the MAPK signaling pathway, were commonly affected by the extracts. Further investigation using pathway networks revealed that terpenoid backbone biosynthesis was likely affected by the extracts, and the expression of the MVK and MVD genes, key proteins in terpenoid backbone biosynthesis, was decreased in LPC-induced THP-1 cells. Therefore, this study determined the diverse function of C. sativa extracts, providing information for predicting and researching the effects of C. sativa.

• Information Systems Review
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• v.24 no.4
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• pp.119-142
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• 2022

Organizations are attempting a digital transformation that converts physical business processing into virtual business processing. Through this digital transformation, organizations are overcoming time and space constraints and creating competitiveness. The digital transformation of this work environment has been accelerated as many organizations have implemented remote work due to the recent COVID-19 pandemic. This study focused on business process virtualizability, which is the result of the rapid digital transformation of the work environment. Business process virtualizability is the resulting quality, such as the suitability or excellence of business processing in a virtual environment. This research model is the effect of process, IT and organizational characteristics on business process virtualizability. As a result of the verification of people who have experienced remote work in a virtual environment, first, it was confirmed that, in terms of process characteristics, sensory requirements affect business process virtualizability, but relationship requirements, synchronism requirements, and identification and control requirements do not. Second, in terms of IT characteristics, it was confirmed that representation and reach affect business process virtualizability. Third, it was confirmed that, in terms of organizational characteristics, job autonomy affects business process virtualizability, but evaluation unfairness does not. This study found that representation and reach of IT had the most significant influence on business process virtualizability, job autonomy was next, and sensory requirements had the lowest influence. This presents practical implications for organizations to increase the success potential of business process virtualizability.

• Information Systems Review
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• v.18 no.4
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• pp.69-88
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• 2016

Nowadays, organizations have adopted Smart Work to efficiently manage tasks, such as electronic document approval, customer management, and site inspection, without spatial-temporal constraints. Smartphones, which are commonly used in Smart Work, enable individuals to perform their jobs anytime and anywhere, thus blurring the boundary between work and non-work. To solve the problem of blurred work/non-work boundaries, a construct of self-control and affective factors needs to be considered because business style is changed from command to autonomy in the Smart Work context. Moreover, employees can convey their emotions easily over smartphones. Recent marketing studies have analyzed consumers' behavior based on the combination of cognitive, affective, and behavioral components, and researchers of information systems are also interested in these factors. However, previous research has some limitations, such as not classifying factors into cognitive, affective, and behavioral as well as not covering all three factors. Therefore, we explore the roles of cognitive, affective, and behavioral components in emotional exhaustion and education performance, and conduct a survey on undergraduate and graduate students, who are the major users of smartphones. Findings show that when individuals improve their cognitive capability (self-control) and usage experience (smartphone communication and internet usage), they can decrease emotional exhaustion and increase education performance. In the role of affective capability, increasing education performance is partially accepted. These results imply that organizations should not focus on controlling the usage of smartphones but on promoting appropriate smartphone usage.

• 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.

• Cartoon and Animation Studies
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• s.47
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• pp.31-52
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• 2017

NCS education was created to realize a society in which skills and abilities are respected, such as transcending specifications, establishing recruitment systems, and developing and disseminating national incompetence standards. At the university level, special lectures and job training are being strengthened to raise industrial experts. Especially, in the field of animation, new technologies are rapidly emerging and demanding convergent talents with various fields. In order to meet these social demands, there is a limit to the existing one-class teaching method. In order to solve this problem, it is necessary to participate in a variety of specialized teachers. In other words, rather than solving problems of students' job training and job creation, It is aimed to solve jointly, Team teaching was suggested as a method for this. The expected effects that can be obtained through this are as follows. First, the field of animation is becoming more diverse and complex. The ability to use NCS job-related skills pools can be matched with professors from other departments to enable a wider range of professional instruction. Second, it is possible to use partial professorships in other departments by actively utilizing professors in the university. This leads to the strengthening of the capacity of teachers in universities. Third, it is possible to build a broader and more integrated educational system through cooperative teaching of professors in other departments. Finally, the advantages of special lectures and mentor support of college professors' pools are broader than those of field specialists. A variety of guidance for students can be made with responsible professors. In other words, time and space constraints can be avoided because the mentor is easily met and guided by the university.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.75-107
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• 2012

Previous researches on technological innovation have several limitations such as lack of general mechanism for technological innovation(inputs, throughputs and outputs of technological innovation), large company oriented studies, and ignoring importance of technology management capabilities. So, this study suggested a new model using resource-based theory and system theory, and empirically applied that to SMEs. Structural equation model analysis by using 223 SMEs in Daegu region provided a support for most of hypotheses. Research results showed that all of factors on technological innovation were significantly and positively related with each other: inputs(R&D leadership, innovation strategy, R&D investment, R&D human resource management, external network), throughputs(portfolio management, project management, technology commercialization) and output(technological innovation). In case of technological innovation inputs, R&D leadership influenced on innovation strategy positively and significantly. And R&D leadership and innovation strategy had positive and significant effects on R&D investment, R&D human resource management and external network. R&D human resource management and external network exerted positive and significant influences on technological innovation throughputs such as portfolio management and project management. But R&D investment did not significant impacts on technological innovation throughputs. Among technological innovation throughputs, both portfolio management and project management had positive and significant effect on technology commercialization. In addition, technology commercialization acted positively and significantly technological innovation output. This study suggests necessary of efforts to implement innovation strategy and manage R&D human resource effectively based on CEO's innovativeness and entrepreneurship. Also, if SMEs want to develop technology and commercialize it, they have to cooperate with external technology resources and informations. Research results revealed that proper level of R&D investment, internal and external communication, information sharing, and learning and cooperative culture were very important for improvement of technological innovation performance in SMEs. Especially, this research suggested that if SMEs manage technological innovation process effectively based on resource-based and system approaches, then they can overcome their resource limitations and gain high technological innovation performance. Also, useful policy support for technological innovation of central or regional government by this research model is important factor for SMEs' technological innovation performance.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.139-151
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• 2014

It is increasingly supported by scientific evidence that greenhouse gas caused by human activities is changing the global climate. In particular, the changing climate has affected human health, directly or indirectly, and its adverse impacts are estimated to increase in the future. In response, many countries have established and implemented a variety of mitigation and adaptation measures. However, it is significant to note that climate change will continue over the next few centuries and its impacts on human health should be tackled urgently. The purpose of this paper is to examine domestic policies and research in health sector in adaptation to climate change. It further aims to recommend future research directions for enhanced response to climate change in public health sector, by reviewing a series of adaptation policies in the selected countries and taking into account the general features of health adaptation policies. In this regard, this study first evaluates the current adaptation policies in public health sector by examining the National Climate Change Adaptation Master Plan(2011~2015) and Comprehensive Plan for Environment and Health(2011~2020) and reviewing research to date of the government and relevant institutions. For the literature review, two information service systems are used: namely, the National Science and Technology Information Service(NTIS) and the Policy Research Information Service & Management(PRISM). Secondly, a series of foreign adaptation policies are selected based on the global research priorities set by WHO (2009) and reviewed in order to draw implications for domestic research. Finally, the barriers or constraints in establishing and implementing health adaptation policies are analyzed qualitatively, considering the general characteristics of adaptation in the health sector to climate change, which include uncertainty, finance, technology, institutions, and public awareness. This study provides four major recommendations: to mainstream health sector in the field of adaptation policy and research; to integrate cross-sectoral adaptation measures with an aim to the improvement of health and well-being of the society; to enhance the adaptation measures based on evidence and cost-effectiveness analysis; and to facilitate systemization in health adaptation through setting the key players and the agenda.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.177-186
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• 2021

For disaster management and mitigation of earthquakes in Korea Peninsula, active fault investigation has been conducted for the past 5 years. In particular, investigation of sediment-covered active faults integrates geomorphological analysis on airborne LiDAR data, surface geological survey, and geophysical exploration, and unearths subsurface active faults by trench survey. However, the fault traces revealed by trench surveys are only available for investigation during a limited time and restored to the previous condition. Thus, the geological data describing the fault trench sites remain as the qualitative data in terms of research articles and reports. To extend the limitations due to temporal nature of geological studies, we utilized a terrestrial LiDAR to produce 3D point clouds for the fault trench sites and restored them in a digital space. The terrestrial LiDAR scanning was conducted at two trench sites located near the Yangsan Fault and acquired amplitude and reflectance from the surveyed area as well as color information by combining photogrammetry with the LiDAR system. The scanned data were merged to form the 3D point clouds having the average geometric error of 0.003 m, which exhibited the sufficient accuracy to restore the details of the surveyed trench sites. However, we found more post-processing on the scanned data would be necessary because the amplitudes and reflectances of the point clouds varied depending on the scan positions and the colors of the trench surfaces were captured differently depending on the light exposures available at the time. Such point clouds are pretty large in size and visualized through a limited set of softwares, which limits data sharing among researchers. As an alternative, we suggested Potree, an open-source web-based platform, to visualize the point clouds of the trench sites. In this study, as a result, we identified that terrestrial LiDAR data can be practical to increase reproducibility of geological field studies and easily accessible by researchers and students in Earth Sciences.

• (The)Study of the Eastern Classic
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• no.67
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• pp.297-325
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• 2017

In this paper, the ideological features of Mohism were examined through the analysis into the viewpoint of Mohism on justice and benefit and "universal love" based on it. Even before the viewpoint on justice and benefit became a main agenda in Confucianism, Mohism and the Hundred Schools of Thought, there had been discussions on it, and the relation between "justice" and "benefit" was generally understood as that of means and ends(本末) or that of the thing and its functions(體用). What succeeded to this tendency and set it as an individual's moral standard was the viewpoint of Confucianism including Confucius. Of course, the Confucian view was focused on the politicians or leaders of those times. Compared to which, Mohism represented the stance of their group members and pursued the interest of groups and the society rather than that of individuals. Accordingly, while Confucianism considered "justice" more important than "benefit", Mohism could understand both of them unificatively. The crucial reason why Mohism could be most active during the Warring States Period is that it had its metaphysical basis on "the disposition of Providence." Accompanying this, the viewpoint of Mohism on justice and benefit was internally reflected in its key arguments including "universal love." That is so-called "Jianxiangai, Jiaoxiangli", that is to say, "that loving each other is namely benefiting each other." On the other hand, the fact that the viewpoint of Mohism on justice and benefit, and furthermore, the ideological foundation of its ten main arguments including universal love was "the disposition of Providence" became a double-edged sword. It was because it could be easily accepted by the laborers, farmers, and craftsmen consisting of Mohism of those times, but it instead became the reason for falling into ruins since the establishment of the feudal empire of Qin and Han(秦漢). In the feudal empire, the ideology and activities of Mohism as an individual group couldn't be embraced. For example, the way to set "Heaven"(the heavenly king) above "the sovereign ruler" might be a decisive limit to the legitimacy and rationality of the regime. Moreover, the arguments by Mohism, such as "Jieyong", "Jiezang", "Feiyue" and others couldn't be taken easily by the privileged class. Therefore, Mohism couldn't do any activities as an academic school until Seojedongjeom(西勢東漸) during the Qing dynasty later, and it was different from Confucianism. In brief, ideas of Mohism including universal love ended up as an utopian idea historically, but the conception of sharing mutual interest along with mutual love and consideration with Confucianism from the position of the relatively disadvantaged in the society has a value worthy of being appreciated even today.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.