• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Korean Journal of Ecology and Environment
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• v.51 no.4
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• pp.311-321
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• 2018

The purpose of this study is to investigate effect of stream channel naturalness on ecological health by using environmental factors and Fish Assessment Index (FAI) in the Han River of South Korea. These samples and data have been released from the research project titled Stream/River Ecosystem Survey and Health Assessment, which was conducted in 444 sites in the Han river watershed from 2008 to 2016. All samples were classified into five groups according to a degree of morphological changes of stream. Water chemistry analyses indicated a decline in water quality by decreasing stream channel naturalness, it is assumed that channelized stream was vulnerable to aquatic pollution compared to the natural meandering stream. In the result of frequency of dominant species, sensitive species and insectivore such as Zacco koreanus, Rhynchocypris kumgangensis and Pungtungia herzi were frequently dominated in the natural meandering stream while tolerant species and omnivores such as Carassius auratus and Cyprinus carpio were more dominated in the channelized streams. The FAI in the channelized stream shows decline to average of $46{\pm}25$ compared with that of the natural meandering stream ($80{\pm}20$). The decrease in FAI was highly influenced by changes in matrixes of fish assemblage structure such as number of sensitive species (M3), portion of omnivores (M5) and insectivores (M6). Moreover, annual average FAIs from 2008 to 2016 were significantly correlated with water chemistry, especially TN, TP and BOD ($r^2=0.59$, p<0.0001). Taken together, all the results suggest that the stream channelization could negatively impact on the water quality and fish assemblage structure, leading to degradation in aquatic ecosystem health.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.942-953
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• 2019

The objective of this study was to evaluate the effects of L-arginine supplementation on muscle damage and fatigue indices and athletic performance improvement of canoe athletes after conducting a high-intensity training program. To achieve the objective, this study applied a high-intensity training program to seven high school canoe athletes. The high-intensity training program is composed of aerobic exercise sessions (twice per week; Tuesday and Thursday), anaerobic exercise sessions (three times per week; Monday, Wednesday, and Friday), and flexibility exercise sessions (five times per week). During the 6 week high-intensity training program, drug ingestion (L-arginine or placebo) was conducted in the first two weeks, wash out (two weeks) followed it, and drug ingestion (L-arginine or placebo) was carried out again in the last two weeks. The crossover design was used for the experiment so all study subjects were assigned to either the L-arginine intake group (the treatment group) or the placebo group (the control group). Each subject ingested 3g per day. This study confirmed the significant effects of L-arginine supplementation on muscle damage indices, fatigue indices, and antioxidants using blood samples. Additionally, FMD was analyzed to evaluate vascular endothelial cell functions and canoe performance was examined using the canoe ergometer. The results of this study showed that L-arginine intake did not have direct effects on the levels of ammonia, IP, and CK. The level of LDH decreased significantly more in the ARG group than in the PLA group due to L-arginine supplementation. Moreover, L-arginine supplementation did not change total NO, d-ROMs, BAP, and FMD significantly. Lastly, the results of the 500m canoe ergometer, which was conducted to evaluate the canoe performance, revealed that L-arginine did not have direct effects on total time, stroke distance, and mean velocity. However, L-arginine supplementation significantly improved muscle damage indices, fatigue indices, antioxidants, FMD, and canoe performance. Therefore, it is believed that additional studies are needed for examining the potential effects of L-arginine supplementation athletic performance enhancement.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.469-483
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• 2020

The results of long-term groundwater level and quality monitoring can be used not only as the basic data for evaluating the impact of various disasters including climate change and establishing responses, but also as key data for predicting and managing geological disasters such as earthquakes. Some countries use groundwater level and quality monitoring for researches to predict earthquakes and to assess the impacts of the earthquake disaster. However, a few cases in Korea report on individual groundwater quality factors (i.e., dissolved ions) observed before and after the earthquakes, being different from other countries. To establish the abnormality criteria for groundwater quality in Pohang, groundwater samples were collected and analyzed five times from 14 agricultural or private wells existing in Shingwang-myeon and Heunghae-eup. As a result of the analysis, it was found that Ca2+ was the dominant cation in Shingwang-myeon, while Na+ was the dominant cation in Heunghae-eup. The elevated NO3- concentration in Shingwang-myeon is contributed to the agricultural activity in the area. A high concentration of Fe was detected in a well on Heunghae-eup; the concentration exceeded the drinking water standard by nearly 100 times. Relatively higher dissolved ions were observed in the groundwater of Heunghae-eup, and it is considered as the result of the flow velocity difference and water-rock reaction accompanying the difference in bedrock and sediment characteristics. The groundwater of Shingwang-myeon appeared to be most affected by the weathering of granite and silicates, while that of Heunghae-eup was mainly affected by the weathering of silicates and carbonate. The background concentrations (baselines) of groundwater Shingwang-myeon and Heunghae-eup was identified through the survey; however, the continuous monitoring is required to monitor the possible changes and the repeatability of seasonal variation.

• 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 the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.80-92
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• 2021

Mount Tai, with an elevation of 1,532 meters, has a reputation as 'The Most Revered of the Five Sacred Mountains(五嶽獨尊)', despite not being the highest mountain in China. The literati of the past dynasties created a multitude of works based on the landscape of Mount Tai. Traditional literature is a part of national culture that directly reflects the national characteristics and styles, and is an important part of humanities, which can be linked to landscapes. The purpose of this study is to investigate the landscape elements and characteristics of Mount Tai by analyzing the landscape types and elements and the Kernel Density, Mean Center and Standard Deviational Ellipse of the landscape elements appearing in the representative poems of traditional literature. The research results of this study are summarized as follows. First, Mount Tai is a scenic spot dominated by human activities, different from the natural landscape of prior research related to scenic spots. Second, among the landscape elements of Mount Tai, the importance of "sunrise", "cyan", "towering" and "majestic", "Divine Dragon" is confirmed, symbolizing the hope, brightness, vitality, national stability and prosperity represented by Mount Tai, which can explain the leadership position of Mount Tai. Third, it can be found from the poems about Mount Tai that various landscape elements were embodied in belief (the behavior of gods or emperors) in the Pre-Qin, Sui and Tang dynasties, while in modern times, landscape elements are shown by action (climbing and looking far into distance), so it can be said that the landscape elements have changed from belief landscapes to experience landscapes. Fourth, the spatial distribution of landscape elements in the past dynasties was widely distributed in the Daiding(岱頂). Approaching the modern times, the mean center moved from south outside of Mount Tai to the summit of Mount Tai, and the spatial distribution changed from a widely scattered distribution to narrow linear distribution centered on Mount Tai. The present study is of great significance to provide key factors or spaces for future landscape protection and restoration of Mount Tai.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.67-75
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• 2022

The aim of this study is to establish a new QC method that can simultaneously evaluate the resolution of the x/y plane and the z-axis by producing a phantom that can reflect exposure and reconstruction parameter of MDCT system. It was used with Aquilion ONE(Cannon Medical System, Otawara, Japan), and the examination was scanned using of 120 kV, 260 mA, and the D-FOV of 300 mm². It produced new SSP phantom modules in which two aluminum plates inclined at 45° to a vertical axis and a transverse axis to evaluate high contrast resolution of x/y plane and z axis. And it changed factors such as the algorithm, distance from gantry iso-center. All images were reconstructed in five steps from 0.6 mm to 10.0 mm slice thickness to measure resolution of x/y plane and z-axis. The image data measured FWHM and FWTM using Profile tool of Aquarius iNtusion Edition ver. 4.4.13 P6 software(Terarecon, California, USA), and analysed SPQI and signal intensity by ImageJ program(v1.53n, National Institutes of Health, USA). It decreased by 4.09~11.99%, 4.12~35.52%, and 4.70~37.64% in slice thickness of 2.5 mm, 5.0 mm, and 10.0 mm for evaluating the high contrast resolution of x/y plane according to distance from gantry iso-center. Therefore, the high contrast resolution of the x/y plane decreased when the distance from the iso-center increased or the slice thickness increased. Additionally, the slice thicknesses of 2.5 mm, 5.0 mm, and 10.0 mm with a high algorithm increased 74.83, 15.18 and 81.25%. The FWHM was almost constant on the measured SSP graph for evaluating the accuracy of slice thickness which represents the resolution of x/y plane and z-axis, but it was measured to be higher than the nominal slice thickness set by user. The FWHM and FWTM of z-axis with axial scan mode tended to increase significantly as the distance increased from gantry iso-center than the helical mode. Particularly, the thinner slice thickness that increased error range compare with the nominal slice thickness. The SPQI increased with thick slice thickness, and that was closer to 90% in the helical scan than the axial scan. In conclusion, by producing a phantom suitable for MDCT detectors and capable of quantitative resolution evaluation, it can be used as a specific method in the management of research quality and management of outdated equipment. Thus, it is expected to contribute greatly to the discrimination of lesions in the field of CT imaging.

• Journal of Life Science
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• v.32 no.11
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• pp.833-840
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• 2022

Chronic inflammation has been shown to be closely associated with tumor development and progression. Nuclear factor kappa B (NF-κB) is composed of a family of five transcription factors. NF-κB signaling plays a crucial role in the inflammatory response and is often found to be dysregulated in various types of cancer, making it an attractive target in cancer therapeutics. In this study, CDC-like kinase 3 (CLK3) was identified as a novel kinase that regulates the NF-κB signaling pathway. Our data demonstrate that CLK3 inhibits the canonical and non-canonical NF-κB pathways. Luciferase assays following the transient or stable expression of CLK3 indicated that this kinase inhibited NF-κB activation mediated by Tumor necrosis factor-alpha (TNFα) and Phorbol 12-myristate 13-acetate (PMA), which are known to activate NF-κB signaling via the canonical pathway. Consistent with data on the ectopic expression of CLK3, CLK3 knockdown using shRNA constructs increased NF-κB activity 1.5-fold upon stimulation with TNFα in HEK293 cells compared with the control cells. Additionally, overexpression of CLK3 suppressed the activation of this signaling pathway induced by NF-κB-inducing kinase (NIK) or CD40, which are well-established activators of the non-canonical pathway. To further examine the negative impact of CLK3 on NF-κB signaling, we performed Western blotting following the TNFα treatment to directly identify the molecular components of the NF-κB pathway that are affected by this kinase. Our results revealed that CLK3 mitigated the phosphorylation/activation of transforming growth factor-α-activated kinase 1 (TAK1), inhibitor of NF-κB kinase alpha/beta (IKKα/α), NF-κB p65 (RelA), NF-κB inhibitor alpha (IκBα), and Extracellular signal-regulated kinase 1/2-Mitogen-activated protein kinase (ERK1/2-MAPK), suggesting that CLK3 inhibits both the NF-κB and MAPK signaling activated by TNFα exposure. Further studies are required to elucidate the mechanism by which CLK3 inhibits the canonical and non-canonical NF-κB pathways. Collectively, these findings reveal CLK3 as a novel negative regulator of NF-κB signaling.

• Journal of Life Science
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• v.32 no.6
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• pp.447-454
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• 2022

In this study, we confirmed the effect of HK shiitake mushroom mycelium (HKSMM) on immune enhancement in Balb/c mice. Experimental animals were divided into five groups: negative control (NC), positive control (PC; 1,000 mg/100 g; AHCC), T1 (500 mg/100 g; HKSMM), T2 (1,000 mg/100 g; HKSMM), and T3 (2,000 mg/100 g; HKSMM), and dissection was performed at four and six weeks. COX-2 and iNOS concentrations were significantly lower in the six-week experimental group than in the control group, and the NO results were also similar. Results of the confirmation of the factors related to the NF-κB (p-p65 and p-IκBα) and MAPK (pERK, pJNK, and p38) signaling pathways revealed that the HKSMM-fed experimental group significantly decreased compared with the control group. A comparative analysis of the number and size of white pulp in the spleen tissue showed that those of the experimental group were significantly higher than those of the control group in a concentration-dependent manner. These results suggest that HKSMM has both immune-enhancing and anti-inflammatory effects in Balb/c mice, indicating that it can be used as a health functional food ingredient.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.221-229
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• 2022

This study was aimed to investigate spatial and vertical characteristics of greenhouse environments according to the location of the environmental sensors, and to investigate the correlations between temperature, light intensity, and carbon dioxide (CO₂) concentration according to the type of greenhouse. Temperature, relative humidity (RH), CO₂, and light sensors were installed in the four-different vertical positions of the whole canopy as well as ground and roof space at the five spatial locations of the Venlo greenhouse. Also, correlations between temperature, light intensity, and CO₂ concentration in Venlo and semi-closed greenhouses were analyzed using the Curve Expert Professional program. The deviations among the spatial locations were larger in the CO₂ concentration than other environmental factors in the Venlo greenhouse. The average CO₂ concentration ranged from 465 to 761 µmol·mol^-1 with the highest value (646 µmol·mol^-1) at the Middle End (4ME) close to the main pipe (50Ø) of the liquefied CO₂ gas supply and lowest (436 µmol·mol^-1) at the Left Middle (5LM). The deviation among the vertical positions was greater in temperature and relative humidity than other environments. The time zone with the largest deviation in average temperature was 2 p.m. with the highest temperature (26.51℃) at the Upper Air (UA) and the lowest temperature (25.62℃) at the Lower Canopy (LC). The time zone with the largest deviation in average RH was 1 p.m. with the highest RH (76.90%) at the LC and the lowest RH (71.74%) at the UA. The highest average CO₂ concentration at each hour was Roof Air (RF) and Ground (GD). The coefficient of correlations between temperature, light intensity, and CO₂ concentration were 0.07 for semi-closed greenhouse and 0.66 for Venlo greenhouse. All the results indicate that while the CO₂ concentration in the greenhouse needs to be analyzed in the spatial locations, temperature and humidity needs to be analyzed in the vertical positions of canopy. The target CO₂ fertilization concentration for the semi-closed greenhouse with low ventilation rate should be different from that of general greenhouses.