• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.331-350
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• 2022

This study aims to understand topics of incivility related to COVID-19 from analyzing Twitter posts including COVID-19-related hate speech. To achieve the goal, a total of 63,802 tweets that were created between December 1st, 2019, and August 31st, 2021, covering three targets of hate speech including region and public facilities, groups of people, and religion were analyzed. Frequency analysis, dynamic topic modeling, and keyword co-occurrence network analysis were used to explore topics and keywords. 1) Results of frequency analysis revealed that hate against regions and public facilities showed a relatively increasing trend while hate against specific groups of people and religion showed a relatively decreasing trend. 2) Results of dynamic topic modeling analysis showed keywords of each of the three targets of hate speech. Keywords of the region and public facilities included "Daegu, Gyeongbuk local hate", "interregional hate", and "public facility hate"; groups of people included "China hate", "virus spreaders", and "outdoor activity sanctions"; and religion included "Shincheonji", "Christianity", "religious infection", "refusal of quarantine", and "places visited by confirmed cases". 3) Similarly, results of keyword co-occurrence network analysis revealed keywords of three targets: region and public facilities (Corona, Daegu, confirmed cases, Shincheonji, Gyeongbuk, region); specific groups of people (Coronavirus, Wuhan pneumonia, Wuhan, China, Chinese, People, Entry, Banned); and religion (Corona, Church, Daegu, confirmed cases, infection). This study attempted to grasp the public's anti-citizenship public opinion related to COVID-19 by identifying domestic COVID-19 hate targets and keywords using social media. In particular, it is meaningful to grasp public opinion on incivility topics and hate emotions expressed on social media using data mining techniques for hate-related to COVID-19, which has not been attempted in previous studies. In addition, the results of this study suggest practical implications in that they can be based on basic data for contributing to the establishment of systems and policies for cultural communication measures in preparation for the post-COVID-19 era.

• Journal of Life Science
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• v.32 no.3
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• pp.202-209
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• 2022

The green alga Chlamydomonas reinhardtii, a unicellular haploid eukaryote, has long been used by researchers and industries as a cell factory to produce high value-added microalgae substances using genetic modification. Microalga K01, presumed to be Chlamydomonas, was isolated from 12 freshwater samples from the Chungcheong and Jeolla regions to replace C. reinhardtii, an introduced species currently used in most basic and industrial research. The isolated K01 strain was identified as C. reinhardtii through morphological and phylogenetic studies of the 18S rDNA gene sequence (NCBI accession number KC166137). The growth and lipid content of the isolated C. reinhardtii K01 were compared with three wild and four mutant strains in TAP medium, and it was found that the K01 strain could produce 1.74×10⁷ cells/ml by the third day of culture. The growth rate of C. reinhardtii K01 was 1.5 times faster than UTEX2244, which showed the highest number of cells (1.20×10⁷ cells/ml) among the compared strains. The lipid content of the isolated C. reinhardtii K01 (20.67%) was similar to those of the wild strains, although the fatty acid oleate C18:1 was not detected in the isolated strain but was identified in the seven others. The cell density of the isolated strain increased to 0.87 g/l during a six-day culture in BG11 medium, where nitrate (NaNO₃) was introduced as a nitrogen source, while the seven acquired strains showed almost no cell proliferation.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.188-196
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• 2023

Among marine accidents caused by bad weather, visibility restrictions caused by sea fog occurrence cause accidents such as ship strand and ship bottom damage, and at the same time involve casualties caused by accidents, which continue to occur every year. In addition, low visibility at sea is emerging as a social problem such as causing considerable inconvenience to islanders in using transportation as passenger ships are collectively delayed and controlled even if there are local differences between regions. Moreover, such measures are becoming more problematic as they cannot objectively quantify them due to regional deviations or different criteria for judging observations from person to person. Currently, the VTS of each port controls the operation of the ship if the visibility distance is less than 1km, and in this case, there is a limit to the evaluation of objective data collection to the extent that the visibility of sea fog depends on the visibility meter or visual observation. The government is building a marine weather signal sign and sea fog observation networks for sea fog detection and prediction as part of solving these obstacles to marine traffic safety, but the system for observing locally occurring sea fog is in a very insufficient practical situation. Accordingly, this paper examines domestic and foreign policy trends to solve social problems caused by low visibility at sea and provides basic data on the need for government support to ensure maritime traffic safety due to sea fog by factually investigating and analyzing social problems. Also, this aims to establish a more stable maritime traffic operation system by blocking marine safety risks that may ultimately arise from sea fog in advance.

• Journal of the Korean Orthopaedic Association
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• v.54 no.2
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• pp.157-163
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• 2019

Purpose: To investigate the incidence and associated factors of delirium after orthopedic surgery. Materials and Methods: A total of 2,122 cases, who were older than 20 years and underwent orthopedic surgery at a single medical center during a one year period were included. Among them, 132 patients who were diagnosed with delirium after surgery under the Diagnostic and Statistical Manual of Mental Disorders-V criteria and medicated under the consultation of a psychiatrist were included in the study The differences in the incidence of delirium and several affecting factors were analyzed. Results: The overall incidence of delirium after surgery was 6.2% (132 in 2,122 cases). The mean age of the delirium group was 77.4 years (range, 54-92 years), which was higher than that of the non-delirium group (58.1 years). The percentage of women in the delirium group was 63.6% (84 in 132 cases), which was higher than that of the women in the non-delirium group (49.0%). The incidence of delirium after surgery was 9.3% (85 in 916 cases) due to trauma and 3.9% (47 in 1206 cases) due to disease. The incidence of postoperative delirium according to the surgical region was 29.2% (7 in 24 cases) in two or more regions, 13.7% (72 in 526 cases) in the hip, and 9.6% (14 in 146 cases) in the spine, 3.5% (20 in 577 cases) in the knee-lower leg, 2.5% (5 in 199 cases) in the foot-ankle, 2.4% (11 in 457 cases) in the shoulder-elbow, and 1.6% (3 in 189 cases) in the forearm-wrist-hand. Delirium occurred more rapidly in women and surgery due to disease, and the duration of delirium was longer in patients with dementia and major depressive disorders. Conclusion: The incidence of postoperative delirium was high in cases of surgery due to trauma and in cases of surgery in two or more sites. The incidence of postoperative delirium according to a single surgical region was higher in the order of the hip, spine, and knee. Active intervention is needed regarding the correctable risk factor.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.209-223
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• 2023

This study was conducted to evaluate the effectiveness of a polar literacy education program for elementary and middle school students, and to derive implications for new education to respond to climate change. We developed modular education programs based on the seven principles of polar literacy established by the Polar-ICE team. We divided them into two courses, one emphasizing science concepts and another emphasizing humanities and sociological issues. We then selected and structured detailed programs suitable for the two courses. These two courses were applied to 26 elementary and middle school students for approximately 69 hours in a Saturday science class hosted by the Department of Science Education at a university in Seoul. The 26 students were divided into three groups. Two groups completed the science education program for polar literacy and a humanities and social studies education program for polar literacy, respectively. The third group, the control group, received general science education unrelated to polar literacy. Before and after running the programs, all three groups responded to a polar literacy test and questionnaires that used vocabulary and presented scenes associated with polar regions. The test results were expressed using Wilcoxon signed ranks, which is a non-parametric test method, and improvements made upon completion of the program were analyzed. From a cognitive aspect, all three groups showed improvement after completing the program in the knowledge area; however, the experimental groups showed a greater degree of improvement than the control group, and there was a clear difference in the contents or materials explicitly covered. From an affective aspect, the difference between before and after the program was minor, but the group that focused on humanities and social issues showed a statistically significant improvement. Regarding changes in polar imagery, the two experimental groups tended to diverge from monotonous images to more diverse images compared to the control group. Based on the above results, we suggested methods to increase the effectiveness of polar literacy education programs, the importance of polar literacy as appropriate material for scientific thinking and earth system education, measures to improve attitudes related to the polar region, and the need to link to school curriculums.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.589-598
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• 2010

When the hydraulic structures, such as bridge and weir, are consecutively installed to a short section of a river with complicated cross section, analyzing the flow characteristics and the riverbed change modality of the river is very important. In the 250 m section of the Taehwa river near the Samho-bridge, which passes through Ulsan city, three bridges has been installed, and the tributary water is flowing into both up and downstream of the section. Due to these factors, when the flood occurs, the cross section of the river changes vastly by the water level change and scour. Even so, due to the fact that the Samho-bridge divides the section into two parts, the national river and the regional river, each part is being analyzed separately by the onedimensional model. In this study, the flow characteristics due to the bridge concentration and the tributary water inflow were jointly analyzed for both up and downstream by using the one-dimensional HEC-RAS model and the two-dimensional SMS model, such as RMA2. The riverbed change modality of the section was also investigated by using the SED2D model. The results showed that the water level difference between the HEC-RAS and RMA2 was 0.87 m when applied to the three consecutive bridges. The riverbed change simulation using SED2D showed that the maximum scour was 0.231 m and it occurred at the Samho-bridge, which located in the middle and has short pier distance. In conclusion, when planning the river maintenance for the regions with concentrated bridges or the sections with severe changes in cross-section and flow, estimating the flood elevation by two-dimensional model and establishing countermeasures for the scouring of the bridge are required. In addition, an integrated analysis on both the national river and the regional river is necessary.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.115-124
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• 2023

Currently, as the frequency of extreme weather events increases, the scale of damage increases when extreme weather events occur. This has been providing forecast information by investing a lot of time and resources to predict rainfall from the past. However, this information is difficult for non-experts to understand, and it does not include information on how much damage occurs when extreme weather events occur. Therefore, in this study, a risk matrix based on heavy rain damage rating was presented by using the impact forecasting standard through the creation of a risk matrix presented for the first time in the UK. First, through correlation analysis between rainfall data and damage data, variables necessary for risk matrix creation are selected, and PERCENTILE (25%, 75%, 90%, 95%) and JNBC (Jenks Natural Breaks Classification) techniques suggested in previous studies are used. Therefore, a rating standard according to rainfall and damage was calculated, and two rating standards were synthesized to present one standard. As a result of the analysis, in the case of the number of households affected by the disaster, PERCENTILE showed the highest distribution than JNBC in the Yeongsan River and Seomjin River basins where the most damage occurred, and similar results were shown in the Chungcheong-do area. Looking at the results of rainfall grading, JNBC's grade was higher than PERCENTILE's, and the highest grade was shown especially in Jeolla-do and Chungcheong-do. In addition, when comparing with the current status of heavy rain warnings in the affected area, it can be confirmed that JNBC is similar. In the risk matrix results, it was confirmed that JNBC replicated better than PERCENTILE in Sejong, Daejeon, Chungnam, Chungbuk, Gwangju, Jeonnam, and Jeonbuk regions, which suffered the most damage.

• Journal of Service Research and Studies
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• v.12 no.1
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• pp.25-35
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• 2022

Covid-19, of which the first patient in Korea occurred in January 2020, has affected various fields. Of these, the tourism sector might havebeen hit the hardest. In particular, since tourism-based industrial structure forms the basis of the region, Gangwon-province, and the tourism industry is the main source of income for small businesses and small enterprises, the damage is great. To check the situation and extent of such damage, targeting the Chuncheon region, where public access is the most convenient among the Gangwon regions, one-day tours are possible using public transportation from Seoul and the metropolitan area, with a general image that low expense tourism is recognized as possible, this study conducted empirical analysis through data analysis. For this, the general status of the region was checked based on the visitor data of Chuncheon city provided by the tourist information system, and to check the levels ofinterest in 2019, before Covid-19, and in 2020, after Covid-19, by comparing keywords collected from the web service sometrend of Vibe Company Inc., a company specializing in keyword collection, with SK Telecom's T-map search site data, which in parallel provides in-vehicle navigation service and communication service, this study analyzed the general regional image of Chuncheon-city. In addition, by comparing data from two years by developing a tourism popularity index applying keywords and T-map search site data, this study examined how much the Covid-19 situation affected the level of interest of visitors to the Chuncheon area leading to actual visits using a data analysis approach. According to the results of big data analysis applying the tourism popularity index after designing the data mart, this study confirmed that the effect of the Covid-19 situation on tourism popularity in Chuncheon-city, Gangwon-provincewas not significant, and confirmed the image of tourist destinations based on the regional characteristics of the region. It is hoped that the results of this research and analysis can be used as useful reference data for tourism economic policy making.