• Composites Research
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• v.34 no.3
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• pp.192-198
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• 2021

Due to enormous market growing of electric vehicles without combustion engine, reducing unwanted BSR (buzz, squeak, and rattle) noise is highly demanded for vehicle quality and performance. Particularly, innerbelt weatherstrips which not only block wind noise, rain, and dust from outside, but also reduce noise and vibration of door glass and vehicle are required to exhibit high damping properties for improved BSR performance of the vehicle. Thermoplastic elastomers (TPEs), which can be recycled and have lighter weight than thermoset elastomers, are receiving much attention for weatherstrip material, but TPEs exhibit low material damping and compression set causing frictional noise and vibration between the door glass and the weatherstrip. In this study, high damping EPDM (ethylene-propylene-diene monomer)/PP (polypropylene) thermoplastic vulcanizates (TPV) were investigated by varying EPDM/PP ratio and ENB (ethylidene norbornene) fraction in EPDM. Viscoelastic properties of TPV materials were characterized by assuming that the material damping is directly related to the viscoelasticity. The optimum material damping factor (tanδ peak 0.611) was achieved with low PP ratio (14 wt%) and high ENB fraction (8.9 wt%), which was increased by 140% compared to the reference (tanδ 0.254). The improved damping is believed due to high fraction of flexible EPDM chains and higher interfacial slippage area of EPDM particles generated by increasing ENB fraction in EPDM. The stick-slip test was conducted to characterize frictional noise and vibration of the TPV weatherstrip. With improved TPV material damping, the acceleration peak of frictional vibration decreased by about 57.9%. This finding can not only improve BSR performance of electric vehicles by designing material damping of weatherstrips but also contribute to various structural applications such as urban air mobility or aircrafts, which require lightweight and high damping properties.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.588-593
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• 2022

Terephthalaldehyde (TPA) is introduced as a cross liker to enhance electron transfer of hemin-based cathodic catalyst consisting of polyethyleneimine (PEI), carbon nanotube (CNT) for hydrogen peroxide reduction reaction (HPRR). In the cyclic voltammetry (CV) test with 10 mM H₂O₂ in phosphate buffer solution (pH 7.4), the current density for HPRR of the suggested catalyst (CNT/PEI/hemin/PEI/TPA) shows 0.2813 mA cm^-2 (at 0.2 V vs. Ag/AgCl), which is 2.43 and 1.87 times of non-cross-linked (CNT/PEI/hemin/PEI) and conventional cross liker (glutaraldehyde, GA) used catalyst (CNT/PEI/hemin/PEI/GA), respectively. In the case of onset potential for HPRR, that of CNT/PEI/hemin/PEI/TPA is observed at 0.544 V, while those of CNT/PEI/hemin/PEI and CNT/PEI/hemin/PEI/GA are 0.511 and 0.471 V, respectively. These results indicate that TPA plays a role in facilitating electron transfer between the electrodes and substrates due to the π-conjugated cross-linking bonds, whereas conventional GA cross-linker increases the overpotential by interrupting electron and mass transfer. Electrochemical impedance spectroscopy (EIS) results also display the same tendency. The charge transfer resistance (R_ct) of CNT/PEI/hemin/PEI/TPA decreases about 6.2% from that of CNT/PEI/hemin/PEI, while CNT/PEI/hemin/PEI/GA shows the highest R_ct. The polarization curve using each catalyst also supports the superiority of TPA cross liker. The maximum power density of CNT/PEI/hemin/PEI/TPA (36.34±1.41 μWcm^-2) is significantly higher than those of CNT/PEI/hemin/PEI (27.87±0.95 μWcm^-2) and CNT/PEI/hemin/PEI/GA (25.57±1.32 μWcm^-2), demonstrating again that the cathode using TPA has the best performance in HPRR.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.80-89
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• 2022

In order to solve the limitations of horizontal thermal environment improvement, this study compared the thermal environment of the indoor and outdoor of a building in summer according to the presence or absence of a green curtain, a vertical greening method. In the summer of 2021, the air temperature, relative humidity, wind speed, and shortwave and longwave radiation were measured at a central point inside a building and the grass field outside of the building to determine the human thermal sensation index, PET and UTCI. As a result, the green curtain showed an average 1.6℃ cooler air temperature during the daytime, but it did not have an effect at night. For relative humidity, it showed higher humidity indoors by an average of 5.6% and 1.0% during the daytime and at night, respectively. Wind speed was 1.4-1.8 ms^-1 and 1.4-1.5 ms^-1 higher outdoors on average during the daytime and at night, respectively, showing a high value outdoors regardless of whether a green curtain was installed. The green curtain showed an average indoor mean radiant temperature reduction effect of 4.7℃ during the daytime, but it did not have an effect at night. In PET and UTCI, the green curtain reduced the indoor PET by about a 1/3 level, an average of 2.1℃, and the indoor UTCI by about a 1/6 level, an average of 1.1℃, during the daytime. However, no effects appeared in PET and UTCI at night. For landscape planning, a green curtain can effectively modify the thermal environment during the daytime in summer.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.42-55
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• 2023

A methodology to predict the carbon performance of newly created urban greening plans is required as policies based on quantifying carbon performance are rapidly being introduced in the face of the climate crisis caused by global warming. This study developed a tree carbon calculator that can be used for carbon reduction designs in landscaping and attempted to verify its effectiveness in landscape design. For practical operability, MS Excel was selected as a format, and carbon absorption and storage by tree type and size were extracted from 93 representative species to reflect plant design characteristics. The database, including tree unit prices, was established to reflect cost limitations. A plantation experimental design to verify the performance of the tree carbon calculator was conducted by simulating the design of parks in the central region for four landscape design, and the causal relationship was analyzed by conducting semi-structured interviews before and after. As a result, carbon absorption and carbon storage in the design using the tree carbon calculator were about 17-82% and about 14-85% higher, respectively, compared to not using it. It was confirmed that the reason for the increase in carbon performance efficiency was that additional planting was actively carried out within a given budget, along with the replacement of excellent carbon performance species. Pre-interviews revealed that designers distrusted data and the burdens caused by new programs before using the arboreal carbon calculator but tended to change positively because of its usefulness and ease of use. In order to implement carbon reduction design in the landscaping field, it is necessary to develop it into a carbon calculator for trees and landscaping performance. This study is expected to present a useful direction for ntroducing carbon reduction designs based on quantitative data in landscape design.

• Journal of Environmental Impact Assessment
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• v.32 no.2
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• pp.94-111
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• 2023

In order to establish an effective management strategy for invasive species early detection and regular monitoring are required to assess their introduction or dispersal. Environmental DNA (eDNA) is actively applied to evaluate the fauna including the presence of invasive species as it has high detection sensitivity and can detect multiple species simultaneously. In Korea, the applicability evaluation of metabarcoding is being conducted mainly on fish, and research on other taxa is insufficient. Therefore, this study identified the feasibility of detecting invasive species in Korea using eDNA metabarcoding. In addition, to confirm the possibility of detection by taxa, the detection of target species was evaluated using four universal primers (MiFish, MiMammal, Mibird, Amp16S) designed for fish, mammals, birds, and amphibians. As a result, target species (Trachemys scripta, 3 sites; Cervus nippon, 3 sites; Micropterus salmoides, 7 sites; Rana catesbeiana, 4 sites) were detected in 17 of the total 55 sites. Even in the selection of dense sampling sites within the study area, there was a difference in the detection result by reflecting the ecological characteristics of the target species. A comparison of community structures (species richness, abundance and diversity) based on the presence of invasive species focused on M.salmoides and T.scripta, showed higher diversity at the point where invasive species were detected. Also, 1 to 4 more species were detected and abundance was also up to 1.7 times higher. The results of invasive species detection through metabarcoding and the comparison of community structures indicate that the accumulation of large amounts of monitoring data through eDNA can be efficiently utilized for multidimensional ecosystem evaluation. In addition, it suggested that eDNA can be used as major data for evaluation and prediction, such as tracking biological changes caused by artificial and natural factors and environmental impact assessment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.35-52
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• 2023

Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.

• Korean Journal of Animal Reproduction
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• v.26 no.3
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• pp.275-289
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• 2002

The objective of this study was to develop an in vitro assessment of sperm fertilizing capacity of bulls and investigate the factors influencing sperm function and characteristics of frozen-thawed bovine spermatozoa. in vitro fertilization (IVF), the evaluation of motility and normal morphology, HOST (hypoosmotic swelling test), Ca-ionophore induced acrosome reaction, luminol and lucigenin-dependent chemiluminescence for the measurement of reactive oxygen species (ROS), the measurement of malondialdehyde formation for the analysis of lipid peroxidation (LPO), and the evaluation of DNA fragmentation using the method of 747-mediated nick end labelling (TUNEL) by flow cytometry were performed in frozen-thawed bovine spermatozoa. Correlations between the rates of fertilization, blastocyst formation after IVF and the values of respective assays were investigated. 1. IVF rate and blastocyst formation rate averaged 64.4% and 34.3% for spermatozoa from high -fertility bull group and averaged 18.5% and 6.2% for spermatozoa from low-fertility bull group, respectively. There were significantly different between two bull groups. Sperm motility and percentage acrosome reaction averaged 79.0% and 66.2% for spermatozoa from high-fertility bull group and averaged 40.7% and 22.9% for spermatozoa from low-fertility bull group, respectivitely. There were not different between two bull groups. 2. Luminol depenent chemiluminescence, LPO and DNA fragementation averaged 6.4, 2.0 nmol and 2.6% from spermatozoa from high-fertility bull group and averaged 6.5, 3.1 nmol and 7.4% for spermatozoa from low-fertility bull group, respectively. There were significantly different between two bull groups. There was no significant difference in lucigenin dependent chemiluminescence between two bull groups. 3. Fertilization rate was positively correlated with motility and the rate of Ca-ionophore induced acrosome reaction, but negatively correlated with the frequency of luminol-dependent chemiluminescence, the rate of LPO, and the percentage of sperm with DNA fragmentation. There was no correlation between fertilization rate and the percentage of swollen spermatozoa, normal morphology, and the frequency of lucigenin-dependent chemiluminescence. 4. Blastocyst formation rate was positively correlated with the rate of Ca-ionophore induced acrosome reaction, but negatively correlated with the frequency of luminol-dependent chemiluminescence, the rate of LPO, and the percentage of sperm with DNA fragmentation. There was no correlation between blastocyst formation rate and motility, the percentage of swollen spermatozoa, normal morphology, and the frequency of lucigenin-dependent chemiluminescence. In conclusion, these data suggest that ROS significantly impact semen quality. The assays of this study may provide a basis fur improving in vitro assessment of sperm fertilizing capacity.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Sleep Medicine and Psychophysiology
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• v.17 no.1
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• pp.41-49
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• 2010

Unlike the case of adult obstructive sleep apnea syndrome (OSAS), there was no consistent finding on the changes of sleep architecture in childhood OSAS. Further understanding of the sleep electroencephalogram (EEG) should be needed. Non-linear analysis of EEG is particularly useful in giving us a new perspective and in understanding the brain system. The objective of the current study is to compare the sleep architecture and the scaling exponent (${\alpha}$) from detrended fluctuation analysis (DFA) on sleep EEG between OSAS and normal children. Fifteen normal children (8 boys/7 girls, 6.0${\pm}4.3$2.2 years old) and twelve OSAS children (10 boys/2 girls, 6.4${\pm}4.3$3.4 years old) were studied with polysomnography (PSG). Sleep-related variables and OSAS severity indices were obtained. Scaling exponent of DFA were calculated from the EEG channels (C3/A2, C4/A1, O1/A2, and O2/A1), and compared between normal and OSAS children. No difference in sleep architecture was found between OSAS and normal controls except stage 1 sleep (%) and REM sleep latency (min). Stage 1 sleep (%) was significantly higher and REM latency was longer in OSAS group (9.3${\pm}4.3$4.3%, 181.5${\pm}4.3$59.9 min) than in controls (5.6${\pm}4.3$2.8%, 133.5${\pm}4.3$42.0 min). Scaling exponent (${\alpha}$) showed that sleep EEG of OSAS children also followed the 'longrange temporal correlation' characteristics. Value of ${\alpha}$ increased as sleep stages increased from stage 1 to stage 4. Value of ${\alpha}$ from C3/A2, C4/A1, O1/A2, O2/A1 were significantly lower in OSAS than in control (1.36${\pm}4.3$0.05 vs. 1.41${\pm}4.3$0.04, 1.37${\pm}4.3$0.04 vs. 1.41${\pm}4.3$0.04, 1.37${\pm}4.3$0.05 vs. 1.41${\pm}4.3$0.05, and 1.36${\pm}4.3$0.07 vs. 1.41${\pm}4.3$0.05, p<0.05). Higher stage 1 sleep (%) in OSAS children was consistent finding with OSAS adults. Lower $'{\alpha}'$ in OSAS children suggests decrease of self-organized criticality or the decreased piling-up energy of brain system during sleep in OSAS children.